Research completed December 2024

DOI: http://dx.doi.org/10.7488/era/5567

Executive summary

Project aims

Scottish public bodies need to make long-term investment and planning decisions. It is their responsibility to consider the risks affecting the outcomes of these decisions. These include risks from climate change, which are highly uncertain, difficult to communicate and require specific expertise.

For instance, public bodies need to be able to plan: where to build a new development considering the risk of coastal flooding; how much to invest in protecting a train line from heat damage or coastal change; or the expected increase in winter disruption to services in the coming decades.

Climate scenario analysis (or simply, scenario analysis) is a tool and process developed to help answer questions like these. It assesses the impact of different plausible future climate change scenarios on an organisation, project or strategy. Understanding the impact of climate change under each scenario can inform decisions.

This study reviewed policies, guidance and stakeholder insights, and examined practices and publicly available data. Based on our findings, we make recommendations for the development of a practical scenario analysis tool to help public bodies in climate adaptation planning. Many of the principles can also be applied to resilience and mitigation planning.

Findings

Stakeholders have told us that the main use of these recommendations will be to help with climate adaptation decisions.

We found a gap, as much of the guidance we reviewed focused on climate scenario analysis for financial reporting requirements^[1] and often focused on climate transition risks, making it less relevant for adaptation planning.

Scottish Government and other stakeholders relayed that long-term public-sector investment and planning decisions should be based on climate risk information and approaches that are:

• consistent across the public sector; e.g. they use the same scenarios, look at similar time horizons and use the same data to assess the same hazards.
• based on information that is up-to-date, accurate, useable and freely available
• consistent with climate risk information they are required to use for other purposes.

A data review indicated a relatively complex data landscape. Data availability varied significantly depending on the climate hazard. There is a lack of standardisation across data providers when it comes to scenarios, temporal and spatial resolution, and data format. These factors are a significant source of frustration for stakeholders.

Where our recommendations are different from existing regulations and guidance it is because they are intended to help public sector organisations make better long-term decisions to plan for adaptation.

Recommendations

We recommend that the scenario analysis decision tool covers each of the recommendations in Table 1.

Table : Summary of recommendations

These recommendations aim to support the development of a practical scenario analysis decision tool. This should then enable Scottish public bodies to spend more time on trying to understand how their organisation could respond to those scenarios and less time on identifying plausible scenarios to assess.

Glossary and abbreviations table

Terms defined in the glossary and abbreviations table are highlighted in bold throughout this report.

Introduction

Background

Climate change in Scotland

Scotland’s climate is changing due to the rise of global greenhouse gas emissions with further change expected over the coming decades (Scotland’s Environment, 2024). Average global temperatures are already 1.2°C above their preindustrial levels. Further warming up to 2°C or more is becoming increasingly likely, resulting in hotter, drier summers, wetter winters, more extreme weather events, and rising sea levels. Despite international efforts to mitigate further global warming, some of these changes are already ‘locked in’ until 2040 and are unavoidable (Watkiss, 2022). The most recent UK Climate Projections (UKCP18) suggest that Scotland will be exposed to more intense and frequent extreme weather events, such as heatwaves and storms, and long-term shifts in temperature, rainfall and sea level rise (Adaptation Scotland, 2021). These changes will significantly impact Scotland’s people, ecosystems, and economy.

Climate policy has also been responding to the changing climate today and future climate projections. Scotland’s third National Adaptation Plan (2024) sets out Scottish Government’s plans over 2024-2029 to adapt to climate change. Public bodies have a statutory duty to help deliver the Adaptation Plan (Scottish Government, 2011) and Scottish Government has committed to updating its corresponding statutory guidance.

Future climate in decision making

To successfully adapt to climate change, organisations must embed climate change considerations in their decision making over the short and long term. It is crucial for organisations to develop strategies and make decisions with the awareness that our climate is changing.

This is particularly important for public bodies, which often operate over longer time horizons and have a responsibility for decisions that often cannot easily be reversed (infrastructure planning, for example). It is also important they receive help to do this on a more consistent basis to improve the coherence of decision making.

Scenario analysis is a useful tool to help organisations consider climate change implications. It can be used to:

• Test the resilience of their current strategies and business plans to future changes in climate
• Understand the future potential impacts of climate change and actively prepare to adapt to these risks
• Explore and promote strategies to reduce their emissions and therefore mitigate future climate change

Aims

Adaptation measures can help reduce the risks associated with future climate change in Scotland. However, climate adaptation planning is not straightforward and faces uncertainties in both the magnitude of future change and timing. A single climate projection is likely to be inaccurate and therefore multiple versions of what could happen in the future need to be assessed to inform robust decision making. Climate scenario analysis addresses this challenge by providing a framework to better understand climate uncertainties by assessing the implications of different plausible climate futures.

As climate change has moved up the agenda over recent years, regulators in various jurisdictions have mandated climate related disclosures for public bodies, companies, and financial institutions. This has also included recommending scenario analysis to assess the resilience of strategies and portfolios to different climate futures and inform decision making (e.g. Taskforce for Climate-related Financial Disclosures (TCFD) in the UK and Corporate Sustainability Reporting Directive (CSRD) in the EU).

Regardless of purpose, conducting climate scenario analysis can feel complex and the choices which need to be made, for example, which scenarios to consider, can often be confusing. To support future-proofed plans and strategic decision making, the Scottish Government (2024) has committed to develop a climate scenario decision tool for the public sector. The tool will aim to provide guidance and support around the implementation of climate scenario analysis to drive robust and consistent analysis of future climate-related risks across the public sector in Scotland and enable cohesion in adaptation planning.

This report aims to provide advice to the Scottish Government on the development of guidance for climate scenario analysis. Specifically, it provides recommendations on the climate change emissions or temperature scenarios, timeframes, climate hazards and other important factors public sector bodies should consider as part of any climate scenario analysis. The report also sets out additional features and guidance required by a climate scenario decision tool for the public sector, informed by insights from stakeholder consultation and the wider literature.

The findings and recommendation of this report will guide the development of the Scottish Government’s climate scenario decision tool, supporting public bodies with climate scenario analysis and enabling climate adaptation planning and decision within Scotland informed by a robust understanding of future climate change.

Methodology

The research which forms the basis for the guidance and recommendations in this report was commissioned by CXC and conducted by GAD between March and September 2024. The research was largely based on information from three main sources which are described in Section 4 of this report. The research project was split into three phases.

Phase 1: A review of existing policy, guidance, and stakeholder practice on use of future climate scenarios and climate hazard data when making investment judgements, exploring the resilience of current plans, and developing adaptation strategies. 

We undertook a targeted desk-based review of current policy and guidance in relation to climate scenario analysis, consisting of:

• A scoping exercise to map out the volume of literature and collate policy papers and guidance published in the last five years.
• Identification of further key sources underpinning the literature published outside of the five-year timescale.
• A synthesis of the key recommendations and considerations of these for climate scenario analysis.  

The review focussed on guidance and policy applicable within Scotland and the UK, and the EU. This included TCFD scenario analysis recommendations and the Climate Change Committee’s (CCC) recommendations on global warming scenarios to consider in adaptation planning in Scotland.

For the review of current practice, we worked with ClimateXChange and Scottish Government to identify and prioritise relevant stakeholders to engage with. This included those in Scotland already using climate scenario analysis and future climate hazards data to inform their longer-term planning strategies.

Individual and group stakeholder engagement sessions were conducted over summer 2024 in person and virtually. Sessions sought to understand the purpose and aims of stakeholders’ climate scenario and hazard analyses and their experience of it. We examined what hazards and scenarios they had considered, how results had been used, pain points that they had encountered, and what decision-making support could further assist them. We captured stakeholders’ views via recording the meetings and using an online whiteboarding tool, Miro, where participants could record their ideas under question prompts. We also shared our key findings with stakeholders following the workshops to ensure we had accurately reflected and understood their views and comments. We engaged with a broad range of stakeholders including:

• Climate Change Committee
• Dynamic Coast
• Edinburgh City Council
• Forestry and Land Scotland
• Highlands and Islands Airports Limited
• Historic Environment Scotland
• Met Office
• NatureScot
• Network Rail
• Paul Watkiss Associates Limited
• Scottish Environmental Protection Agency (SEPA)
• Scottish Government
• Scottish Water
• Sniffer
• Transport Scotland
• University of Glasgow.

To supplement information gathered through stakeholder engagement we also examined current best practice in the private sector, specifically through the work of the Financial Reporting Council (FRC) thematic review of TCFD reports (FRC, 2022).

Phase 2: Identify common themes across existing guidance and stakeholder practice. 

We used qualitative content analysis methods to identify commonalities and differences in the policy and guidance and current practice. An analytical framework was developed to provide structured outputs of summarised qualitative data collated in Phase 1. The framework captured key guidance factors that feed into climate scenario analysis such as hazards to consider, scenario definitions, numbers of scenarios, timeframes, frequency of analysis and expected outputs.

This allowed themes in existing guidance to be easily identified whilst also providing a holistic view of the current policy and guidance landscape.

We also considered availability of data. As part of Phase 2, we conducted a rapid review of the latest publicly available physical climate hazard data. This included an assessment of potential data limitations and consideration of whether climatic tipping points are captured. This included an overview of the UKCP18 data from the Met Office.

Phase 3: Options and recommendations for setting national-level guidance to support accounting for future climate hazards in today’s decision making. 

Outputs from Phases 1 and 2 of the research have been critically assessed to determine the level of prescriptiveness that Scottish Government could take in setting out recommendations for assessing future climate-related risks for strategic planning and adaptation in the public sector.

The recommendations are based on considerations of the consistency required to establish shared planning assumptions across multiple public sector bodies, the needs of stakeholders in considering climate scenarios and hazards in Scotland, the complexity that may be introduced, potential user capability and associated costs. We actively consulted with Scottish Government and public sector stakeholders during this phase of the project to gain feedback and discuss their views.

Research limitations

As the research was conducted within fixed timelines and budget the level of detail may not meet the needs of all potential audiences, e.g. those requiring climate scenario details to support investigation of highly specific and unusual risks in their planning and decisions.

Indeed, due to the budget and timeline constraints, we carried out three stakeholder workshops as part of our research. With further workshops we could have potentially gathered wider and deeper views on climate scenario analysis from public bodies across Scotland. However, engagement during our workshops was very high and the insights we gained from participants were invaluable in shaping our recommendations.

Climate scenario analysis

There is inherent uncertainty in assessing the physical impacts of climate risks. This is due to the uncertain future trajectory of global emissions, and uncertainty around how the planet will respond to those levels of future emissions. The uncertainty at an organisational or project level is impossible to accurately quantify due to the combination and complexity of uncertain inputs.

Scenario analysis relies on defining plausible futures and analysing them to better understand the impacts of the risks being faced. No likelihood is placed on any single scenario. Instead, the relevance of the analysis relies on selecting a range of scenarios under which the risks most relevant to the organisation emerge.

Defining climate risks

Climate risks can be better understood by using the International Panel on Climate Change (IPCC) framework of hazard, exposure and vulnerability (Cardona et al., 2012). Each of these components should be considered when determining climate risk as part of climate scenario analysis.

Risk = hazard x exposure x vulnerability

Hazard: The possible future occurrence of physical climate events that may have adverse effects (damage and loss) on vulnerable or exposed people, assets, services, resources, infrastructure, or systems. Examples of climate hazards include heatwaves, sea level rise, floods, and storms.

Exposure: The presence of people, assets, services, resources, infrastructure and systems that could be adversely impacted by the hazard. Proximity to the hazard is an important consideration here. For example, buildings close to the coast will have a greater exposure to sea level rise than those further inland.

Vulnerability: The propensity of exposed aspects (people, assets, services, resources, infrastructure, systems) to suffer adverse events when impacted by climate hazards. Vulnerability relates to predisposition, susceptibility, fragility, weakness, deficiency, adaptive capacity etc. For example, elderly people are less able to regulate their core temperature compared to younger adults and therefore more vulnerable to overheating than younger people (Moreira Sousa, 2022).

Exposure and vulnerability are often thought of as one but can be distinguished – it is possible to be exposed to a climate hazard but not vulnerable to it, for example by living in a floodplain but having means to modify building structure to avoid potential loss. However, to be vulnerable to a climate hazard, you must be exposed to it.

Whilst hazard data can be relatively generic, information on exposure and vulnerability is normally specific to an organisation.

What are climate scenarios?

Climate scenarios are plausible future outcomes of climatic conditions and macro- and micro-economic development in response to climate change and the transition to a low carbon economy. They were brought into the public consciousness in large part by the IPCC. This is a United Nations body for assessing the science related to climate change whose purpose is to provide governments with scientific information that they can use to develop climate policies.

The IPCC define their scenarios by emissions pathways, also known as Representative Concentration Pathways (RCPs). Whilst these emissions pathways are widely used as different climate scenarios for scenario analysis, in recent years there has been a trend to focus on temperature increase scenarios, rather than emissions pathways. Temperature increase scenarios are also known as global warming levels.

Emissions-based pathway scenarios: These are different projections of atmospheric concentration of greenhouse gasses up to 2100. The RCPs correspond to different levels of total atmospheric “radiative forcing” (a direct measurement of the greenhouse effect) meaning that they each produce different degrees of future global temperature increase. There are ranges of temperature increases that could exist for each emissions pathway.

Global warming level scenarios: Global warming level scenarios don’t generally include a timeframe. Instead, they represent a world that has reached the stated average warming for the period (Met Office, 2023). The CCC looks at +2^oC and +4^oC temperature increase scenarios within their most recent Climate Change Risk Assessment (CCRA3) (CCC, 2021); as well as considering higher levels of warming and low-likelihood, high-impact events such as climate tipping points (Betts and Brown, 2021).

The Met Office provides the UKCP18 data which are based on regional climate model^[2] simulations. Data is available for different RCP scenarios but also global warming levels of 1.5^oC, 2^oC, 3^oC and 4^oC.

Climate scenario analysis in decision making

There is no single accepted definition of scenario analysis. Broadly it is a tool for assessing what could happen to different aspects of an organisation^[3] or project (costs, income, policy, asset values, liability, workforce etc) under different climate scenarios.

Scenario analysis is constantly evolving to better explore the impacts of climate change on the above listed aspects. As climate related risks and opportunities begin to become more commonly considered, analysis will become more sophisticated and likely produce outputs that better support decision making.

Scenario analysis is a tool to enhance critical strategic thinking. An initial single analysis is unlikely to capture all climate-related risks at the level of detail required. Scenario analysis should be an iterative process where the objectives and scope of each analysis are well defined and tailored to ensure the output of decision useful information is maximised.

Often there will be a trade-off between:

• Very well defined but near impossible to quantify narrative scenarios; and
• Scenarios that can be quantified, but in doing so need simplifying assumptions which may be unrealistic.

Scenario analysis is a valuable tool for assessing and understanding uncertainty. It can be used by organisations to:

• Challenge their current thinking. It is useful in testing if strategies and plans are resilient to plausible future changes in the climate
• Make better informed decisions by looking over the longer term
• Identify potential changes in the severity and frequency of climate-related risks. Additionally, completing scenario analysis may help organisations to identify new climate-related risks.

Limitations and challenges

Scenario analysis is difficult to carry out. For example, it is hard to know where to start and what scenarios are plausible. There is a need to recognise the limitations and challenges around data, skills, and uncertainty relating to timescales and quantifiability.

Data

Data can be hard to obtain and even when available it often has shortcomings like lack of coverage or uncertainty. This includes external data, like those covering the frequency and severity of climate hazards. It also includes lack of data held by the organisation itself on its exposure and vulnerability to climate risks.

Skills and risk awareness

A range of skills are needed to carry out scenario analysis. Few organisations will have access to all of those skills. Many address this by employing consultants or contractors, sometimes at great expense. The recommendations in this report will not eliminate this gap but aim to reduce this burden on public sector bodies.

One such skill is the ability to understand and communicate different types of uncertainty. Scenario analysis is a tool designed to help with this but also requires practitioners to have relevant skills in this area. Throughout the workshops, the importance of good communication of climate-related risks was a key theme. Participants noted the various challenges associated with ensuring communication with the public was transparent without causing climate anxiety.

Proportionality

Different organisations will be impacted by climate change in different ways, and it is the people who work at the organisation itself who will best understand the climate hazards that are most pertinent to their organisation.

Organisations should therefore take a proportionate approach to completing scenario analysis. When certain climate hazards are irrelevant for the organisation (for example, they have no exposure or are not vulnerable even where they are exposed), it is acceptable for these to be left out of climate scenario analysis. The organisation should satisfy itself that these hazards have been considered and agreed not to be investigated further. Stating this explicitly would be considered best practice and ensures transparency in any publications or disclosures.

Research findings

Recommendations presented in Section 5 are informed by three main sources of information:

• Policy and guidance: Review of documents including policy, scenario analysis guidance, and reviews of existing practices.
• Data: Rapid review of 21 commonly used data sources to understand data availability for different climate hazards.
• Stakeholder views and experience: Three workshops with stakeholders including Scottish Government, public sector organisations, and experts in climate risks.
  1. Policy and guidance review

We reviewed over 50 documents setting out policy, guidance and best practice examples of climate scenario analysis in Scotland and further afield. There are different legislations and regulations that bring climate reporting (such as that compliant with the TCFD (2017) recommendations including climate scenario analysis) into scope for various organisations and entities. We also considered any application guidance that went with the legislation and regulation.

Many of the sources considered covered more than just climate scenario analysis, and in contexts wider than just adaptation planning. Due to the focus of this review, greater consideration was made where sources spoke specifically about scenario analysis and in contexts relevant to adaptation planning. These sources are listed in Appendix C.

We identified nine factors that can be used to guide scenario analysis and that are frequently referred to within the policy and guidance literature. These were:

• Which climate hazards should be covered?
• What climate scenarios should be used?
• How many climate scenarios should be considered?
• Where data should be sought from?
• The scope of the climate scenario analysis (i.e. whether analysis should cover the entire organisation / project or only certain parts of it).
• Timeframes to be considered (i.e. how far into the future and at which specific time periods to look).
• Frequency of updates to analysis.
• Whether the analysis should be qualitative or quantitative.
• Materiality (including double materiality).

The accompanying spreadsheet to this report, Technical appendix – Review of current policy and guidance, sets out a framework which compares each climate scenario analysis factor to the guidance and policy reviewed. The framework also provides a cross comparison with insights from the stakeholder workshops and the recommendations given in Section 5.

Key findings from the review indicated:

• The current published guidance is primarily focused on scenario analysis based on requirements for financial reporting. The most obvious example of this is the recommendations of the TCFD (2017), but many other sources are also routed in this, including the requirements for pension schemes (The Occupational Pension Schemes (Climate Change Governance and Reporting) Regulations 2021) and companies (The Companies (Strategic Report) (Climate-related Financial Disclosure) Regulations 2022) in the UK.
• TCFD (2017) mainstreamed the categorisation of climate-related risks as either physical or transition. Although transition risks (risks associated with the transition to net zero) can impact organisations and projects, they are generally less relevant to adaptation planning which is predominantly focused on reducing vulnerability to physical climate hazards. Physical hazards can be divided into acute hazards (specific events, such as floods or storms) and chronic hazards (events that gradually evolve over time, such as average temperature increase or sea level rise). Considering both acute and chronic physical hazards is consistent with a range of guidance, including that from Defra (2023) and the CCC (2024).
• Due to the nature of sea level rise, including its lagged response to emissions of greenhouse gases, and the complex and dynamic nature of coastal change, alternative or additional scenario analysis guidance may be required for this climate hazard.
• Guidance related to financial reporting, often mentions considering a +2°C or lower or “Paris-aligned” scenario. Emissions or temperature scenarios below +2°C may be more appropriate for analysing transition risks rather than physical risks. The His Majesty’s Treasury Green Book (2020), CCC (2022), and Defra Adaptation Reporting Power (2023) all use scenarios based on global warming levels focussed on +2°C and +4°C by the end of the century.
• The more scenarios considered, the more analytical work and data gathering is required. Using fewer scenarios may allow organisations to consider each scenario in greater depth. However, multiple scenarios are needed to capture uncertainty associated with future climate change and allow for more robust decision making.
• Guidance is conflicted regarding the required scope of climate scenario analysis. For example, some sources state the full organisation should be covered (e.g. Defra, 2023), whilst others restrict scope, initially at least, to cover more significant areas of an organisation (e.g. Department for Business, Energy and Industrial Strategy, 2022).
• There is a lack of guidance on length of timescales to consider; analysis of reporting shows that many consider “long” timescales to be 10 years.
• From an adaptation perspective, it is important to focus on climate change impacts on the organisation, rather than the organisation’s impact on the climate. Considering both aspects is sometimes referred to as “double materiality” (Commission Delegated Regulation (EU) 2023/2772, 2023).
• Finally, published guidance makes clear that transparency around assumptions and limitations of analysis is vital.

Rapid data review

We reviewed 21 publicly available climate data sources commonly used to source data for climate scenario analysis (Appendix B). These included the providers of UK wide data (e.g. Met Office climate data portal and the UKCP18 user interface), global data (e.g. IPCC’s Interactive Atlas, Copernicus climate data store) and the Scotland focused data (e.g. Nature Scot GIS, Marine Scotland).

We found that there was a very wide variation in the data provided across this small sample of sources. Different data sources provided data on different climate hazards at different levels of spatial resolution and over different time projection periods. They also varied between using emissions-based (RCP) and temperature-base/global warming level scenarios. Climate projections based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which are used as the basis of the IPCC’s fifth assessment report (AR5), were the most readily available. This is despite updated CMIP6 model simulations being used for the more recent sixth assessment report (AR6) (IPCC, 2021), demonstrating the long time lag often experienced for climate data updates. This lack of standardisation across climate hazard data providers was a source of frustration for those we spoke to in our workshops.

Chart 1 indicates that data availability varies significantly depending on the climate hazard under investigation. Of the 21 data sources reviewed, the greatest data availability is for temperature-based hazards, such as chronic temperature change and extreme heat events, whilst there is very limited data available for more complex hazards such as soil movement and landslip.

Chart 1: Data availability by climate hazard (21 data providers reviewed)

ClimateXChange is conducting a geospatial climate hazard data review project which should improve the understanding of the data landscape for those carrying out scenario analysis.

Current practice and insight from stakeholders

Workshops on climate scenario analysis

We engaged with multiple Scottish public bodies and other relevant stakeholders (Appendix D) across two separate workshops to discuss their experience of climate scenario analysis.

Stakeholders shared their experience of completing (or advising others on completing) climate scenario analysis, their key challenges and what would have helped to alleviate them, the tools and resources they used along with limitations, and their ability to quantify the climate impacts on their organisation.

A summary of key findings from these workshops were:

• There was strong appetite across the stakeholders to learn more about how to conduct better scenario analysis. It was felt that nationally defined climate scenarios would help reduce conflict between parties using different data. 
• Scenario analysis is carried out for many purposes which lead to different needs for data and expertise. However, stakeholder primary use cases were to inform risk management strategies and plans and inform business decision making. Most organisations need to bring in outside expertise to help.
• Stakeholders agreed that quantification of analysis should be a clear aim, but the importance of qualitative analysis is also recognised. 
• Analysis should aim to increase the ability of organisations to make decisions under uncertainty. The impact of “doing nothing” should also be considered. 
• There are advantages and disadvantages to using emissions-based scenarios and global warming levels – each have their place. Emissions based scenarios may be more suitable for climate hazards which do not scale well with global mean temperature (CCC, 2024). This includes sea level rise where a long lag time exists between global temperature increase and the full sea level response.
• Stakeholders can often find it hard to obtain or understand climate data. Data availability can be limited as can the in-house capability to analyse it. Data does not always extend to the local level needed.
• More data on asset vulnerability to hazards is also needed so that risk can be fully assessed. 
• Secondary and indirect climate impacts are particularly difficult to quantify and more guidance in these areas would be welcome.
• Consideration of climate tipping points in adaptation planning is challenging due to large associated uncertainties in probability of occurrence, impact, and timing. It was noted that even when tipping points are breached, the impact may take many years to be felt. 
• Communication of the results of scenario analysis to users and the public was a key consideration for stakeholders and something that was often found to be challenging. Comparisons with other risks to communicate uncertainty may be helpful along with improvement of climate literacy beyond climate experts. 

Workshop on scenario analysis for coastal change

‘Compared to other factors, sea level only gets worse.’

Insight from a stakeholder at the coastal change workshop, June 2024.

In addition to the two workshops held on climate scenario analysis, we held a dedicated workshop with coastal change experts. This was to allow a better understanding of specific scenario analysis guidance that may be required for coastal hazards such as sea level rise, which has a significant lagged response time and that impacts highly complex coastal processes. CXC has also commissioned a piece of research on coastal change adaptation planning conducted by the University of Glasgow which will further contribute to improving future guidance on coastal change adaptation planning.

A summary of key findings from this workshop were:

• Sea level rise and coastal change can be considered to have a unique risk profile compared to other climate hazards. This is because 1) impact is always negative 2) timescales of impact are much longer and 3) the impacts are irreversible. Sea level rise is a chronic risk where the entire current baseline state is shifting.
• Sea level rise will affect erosion rates and wave heights. It will impact drainage systems, structures, natural features and ecosystems. The complex interaction between sea level rise and other systems and services needs to be better mapped.
• There was strong agreement that a precautionary approach was required for assessing the impacts of sea level rise and coastal change due to the permanent nature of the risk and the uncertainties associated with modelling, tipping points, and current understanding of dynamic processes.
• Due to the chronic nature of sea level rise, assessments need to look over long time periods. As in the climate scenario analysis workshops, stakeholders were very supportive of ensuring scenario analysis considered a long-term timeframe to ensure adaptation measures represented maximum cost-effectiveness.
• However, for adaptation planning, a focus on timescales may be a barrier to action as we are generally bad at long-term thinking. Instead, a focus on “what would the impact of a x meter rise in sea level be?” could be taken, analogous to the global warming level approach.
• Consideration should be given to where assets/infrastructure affected by sea level rise will need to be moved to.
• Coastal literacy is particularly poor among the public and within organisations. This prevents a comprehensive understanding of the associated risks. This has led to push back on modelled results in areas such as land use planning as there is an assumption that risks are being overstated.
• The stakeholders felt strongly that there should be better communication of the risks and uncertainties associated with impacts of sea level rise and coastal change. Scenarios should be communicated not as pessimistic but realistic given what is currently known and the associated uncertainties.
• The UK Met Office provide a range of datasets for the examining sea level rise under climate change and are in the process of updating these based on the more recent IPCC emissions scenarios.

Additional stakeholder insights

Stakeholder consultation as part of the 2024 SNAP statutory consultation process, indicated that there was strong support from organisations for any guidance to align scenarios with those recommended by the CCC (adapt to 2°C of warming, plan for the risks associated with 4°C of warming). Stakeholders also stated they would welcome guidance on the interpretation of data particularly relating to understanding climate data terminology (e.g. on emissions pathways and global warming levels).

Many stakeholders had previously considered flood risk, but the consideration of other hazards was less consistent. The Scottish Government (2023a) also confirmed this through the Business Insights and Conditions Survey. Over 60% businesses surveyed reported that they had not assessed for coastal erosion, increased flooding, temperature increases or water scarcity.

A scenario analysis decision tool has the potential to help ensure a range of hazards are considered in adaptation planning decisions, encouraging consistency and robustness. Whilst not all hazards will be material for all organisations, organisations should include all hazards that they deem to be material within their analysis.

Case Study: Climate Resilience Strategy (SP Energy Networks, 2021)

The Climate Resilience Strategy sets out how SP Energy Networks will maintain a safe and resilient network despite climate change. The analysis was done considering “four key climate change projection variables (temperature, precipitation, sea level rise, and wind speed/storminess) over three time periods (2030s, 2050s and 2100s) and two Representative Concentration Pathways (RCP) projection scenarios (RCP6.0 and RCP8.5)”.

Here, by considering chronic risks alongside acute ones, SP Energy Networks can ensure they understand interdependencies between different risks. For example, they note that “sea level and storm surge” could lead to an impact on their operations with sea level rise and coastal erosion increasing the exposure of their assets to storm surge events.

Recommendations

Our recommendations on the content of the decision tool are summarised below and more detail on these can be found throughout this section. There is also a section on our recommendations for the tool development (Section 5.10). These recommendations are designed to support adaptation planning so may not be suitable for scenario analysis carried out for other purposes such as financial reporting.

Table : Summary of recommendations

Hazards covered

Our research confirms that scenario analysis can consider both physical climate hazards and climate-related transition risks. However, as the decision tool will be designed to support adaptation planning, we recommend that the focus is on physical hazards only.

Transition risks should be considered separately by organisations, where they have the potential to have a significant impact on the organisation.

Scenario analysis should however cover different types of physical hazards, specifically it should cover both acute and chronic hazards (see Figure 1):

• Acute hazards are specific events such as floods or storms.
• Chronic events gradually evolve over time, such as average temperature increase or sea level rise.

Coastal change and sea level rise

While coastal change does pose specific threats, as indicated by the Scottish Government’s (2023b) Coastal Change Adaptation Plan Guidance and the Dynamic Coast (accessed 2024) project and explored further in the specific stakeholder workshop on this topic, we recommend considering it alongside other chronic risks as the first stage of the climate decision tool.

This will then enable stakeholders to get a better understanding of the shifting baseline in the future they are analysing, before assessing acute risks under that scenario. For example, sea level rise may bring with it a greater number and intensity of storm surges closer to shore. This is an important consideration for an organisation with infrastructure or physical assets that cannot be moved inland.

Chronic and acute hazards

Figure : Proposed structure of tool, considering chronic hazards before acute ones

Framing hazards as chronic and acute, with the sequencing set out as in Figure 1, should help tool users:

• Understand how baselines like current coastlines, precipitation levels and average temperatures are expected to change over time under different climate scenarios.
• Ensure that chronic physical risks are not overlooked, given their more gradual change which could lead to a progressive decline in service delivery rather than acute hazards that can cause more noticeable impacts and disruption.
• Consider connections and interactions between different physical risks, that in aggregate may provide a different risk profile than when considered independently.
• Ultimately provide a more holistic view, which will improve the standard of scenario analysis.

There will, however, be some challenges, specifically around data availability as revealed by the rapid data review.

An alternative would be to consider sea level rise and coastal change in a separate tool, noting some of the unique challenges posed by the risk. However, our recommendation encourages all risks to be considered in a single tool to help ensure interdependencies (and/or entire risks) are not missed.

Scenario prescription and definition

Prescribing the use of specific scenarios in the tool drives consistency across organisations and projects. This will enable better communication and comparisons supporting improved adaptation planning, particularly where several organisations are impacted by, or involved in, adaptation measures.

We recommend considering specific scenarios, aligned with other reporting frameworks we reviewed which organisations may be required/choose to comply with. This will further improve consistency (by allowing more consistency within organisations) and minimise additional work and costs for organisations.

We recommend considering both of the following scenarios:

• 2°C global warming level (above pre-industrial levels) by end of century.
• 4°C global warming level (above pre-industrial levels) by end of century.

When communicating the results of scenario analysis it is important to clearly articulate the rationale for choosing particular scenarios. Hence, we would recommend justifications for the choices of scenario are included in the tool, in particular, these could include:

• alignment with the updated CCC methodology (2024) and Defra’s Adaptation Reporting Power (2023).
• choosing a 2°C warming scenario allows organisations to assess their resilience against the lower end of plausible temperature outcomes by the end of the century.
• choosing a 4°C warming scenario allows organisations to assess their resilience to much higher physical risk, towards the upper end of plausible temperature outcomes by the end of the century.
• scenarios of 2°C and 4 °C gives a sensible range of likely futures based on current global efforts to reduce greenhouse gas emissions (CCC, 2020).

To enable a greater volume of the available climate data to be used we recommend that organisations can make use of emission pathways-based data, as well as data focused on global warming levels.

We have set out a table in Appendix F that can be used as a reference when comparing and contrasting emissions-based and temperature-based scenarios. In particular, the pathways best aligned to the scenarios prescribed above are:

Table : Global warming levels and equivalent RCPs and SSP-RCPs for prescribed scenarios

What are earth system tipping points?

Earth system tipping points are thresholds beyond which changes in a part of the climate system become self-perpetuating often leading to abrupt and irreversible changes that could have a profound impact on our planet (Armstrong et al., 2022).

Examples include melting of the major ice sheets or significant changes in the fundamental ocean circulation patterns.

GAD also recommends that earth system tipping points are excluded from the analysis at present due to the significant uncertainty and difficulty in robustly modelling their timing and impact. The tool should ensure this is explicitly stated so that users are aware of this limitation.

This guidance should be kept under review. Over time, as our understanding of tipping points develops, it may be reasonable to allow for them in the relevant scenarios. For this to be the case more data on their onset, the pace at which the impacts of tipping points occur, and the severity and extent of the potential impact will be needed. It is worth noting that CCRA3 includes some consideration of low likelihood, high impact risks (Watkiss and Betts, 2021).

Organisations may find it valuable to also consider a reasonable worst-case scenario. However, it is likely that this is more appropriate to do as part of emergency planning exercises, rather than scenario analysis for adaptation planning. Reasonable worst-case scenarios could include tipping points being breached and other thresholds being crossed beyond which the organisation may struggle to operate.

Number of scenarios

We recommend the use of at least two scenarios, in particular those described above being a +2°C and +4°C futures.

Considering two scenarios means that the scenario analysis meets the expectations of all the policy and guidance sources we reviewed in this project, detailed in Appendix C.

Climate data provider

Climate data is available from a large number of providers. While some of this data must be purchased, we recommend using publicly available data wherever possible as this increases transparency and reproducibility of the scenario analysis.

We recommend that the tool should point to primary sources of data for different hazards, informed by the ongoing climate data review by the Scottish Government.

Sources of data may be preferred based on a number of criteria:

Table : Criteria for climate data provider selection

Scope of scenario analysis

We believe the most significant factor in determining a suitable scope for the scenario analysis will be the context and purpose of the analysis. There are also advantages of considering a broader scope for the analysis to ensure that interconnected risks are understood and analysed appropriately.

For adaptation planning, we recommend that the entire organisation is included within the scope of the scenario analysis^[4]. There will be instances where certain hazards will be more material for certain areas of the organisation. However, including the entire organisation within the scenario analysis will ensure that adaptation measures are well considered and have less chance of creating unintended consequences to seemingly less-affected areas.

Depending on the nature of the organisation, or adaptation measure under consideration, it may be proportional to limit the scope of any analysis, in line with any specific guidance relevant to its use. The principle of proportionality was discussed further in Section 3.3.1.3.

The scope of scenario analysis should include a range of external factors which could affect an organisation such as energy supply, communications and transport systems.

‘Cross-boundary issues and wider interdependencies should also be considered with neighbouring bodies and wider stakeholders such as Network Rail, Transport Scotland, Scottish Water and SEPA.’

NHS Scotland Climate Emergency & Sustainability Strategy 2022-2026 (Scottish Government, 2022).

Timeframe of scenarios

A large volume of the scenario analysis literature reviewed refers to the use of short-, medium- and long-term timeframes. These are, however, seldom defined, leaving it up to the organisation to define timeframes relevant to them. This might, however, come at the expense of consistency which is needed when organisations are collaborating on adaptation planning.

There is also a risk that the timeframes considered are too short, preventing an organisation from taking a sufficiently long-term view for adaptation planning. To enable more consistency, we recommend that the timeframes are prescribed, and are aligned to those commonly cited, including mid-century and end-of-century.

GAD recommends the following timeframes:

Table : Timeframes for scenario analysis

We have left the most flexibility around the short-term timeframe recommendation. We think that this is most helpful for organisations who have different planning cycles. It will allow them to have a greater level of internal consistency between their adaptation and other business planning, which should lead to a greater incentive to integrate climate considerations into business-as-usual planning. However, by not defining this timeframe, there will be less consistency in scenario analysis between organisations. Given the longer-term nature of many climate risks, this is considered to be a reasonable compromise.

The recommendations for the medium- and long-term still offer some flexibility, as mid- and end-of-century, as opposed to specific years such as 2050 or 2100. This is intended to make it possible for organisations to use a range of data sources more easily, or to align with other work they are doing. We believe that this will give a sufficiently high level of consistency across organisations whilst not becoming too onerous.

Carrying out scenario analysis over a longer timeframe adds greater complexity. However, we believe there is good reason to consider timeframes to the end of the century for most adaptation decision making. Through the stakeholder consultation there was a clear steer to ensure scenario analysis covered sufficiently long timeframes.

There is a delay, often lasting decades, between global climate action and the resulting impact on temperature rise and other climate risks. This means that physical risk scenarios are often very similar to each other in the short to medium term. For example, up to 2050 a +2°C end of century warming scenario may be very similar to a +4°C end of century warming scenario. This should give opportunity to consolidate analysis for the earlier years to make it more efficient.

The long timeframe of the analysis can allow false comfort as it can show that the risks are unlikely to affect the organisation for many years. However, actions to address the risks can take a similar amount of time or longer and the longevity of a physical or infrastructure asset as well as the projected sustainability of the business or organisation are more often measured in decades rather than shorter term. The uncertainty inherent in the analysis should also be considered as it means the risks may appear sooner.

Frequency of updates to analysis

Understanding of our climate and how it is changing is constantly improving. Practice in scenario analysis is also improving. In this rapidly evolving field, it is therefore important to ensure that scenario analysis is not seen as a one-off activity, but as an iterative process. In this way, the results from one scenario analysis exercise can inform the input to the next.

As updating scenario analysis can be a significant undertaking, we recommend a pragmatic approach, updating scenario analysis every 3-5 years. This can be more frequent if, for example, there are significant developments in climate science or events mean that the assumptions used are no longer suitable. Scenarios, by design, should be plausible and hence new information may mean they need to be changed.

Events that could trigger an update to a scenario analysis include, but are not limited to, the following:

• New IPCC analysis or report, which has a significantly different future climate outlook affecting the hazard facing an organisation.
• New data with a greater spatial resolution is realised, allowing a more accurate assessment of an organisation’s exposure to a hazard.
• Assets moving from the planning to design to operational phases, affecting the organisation’s vulnerability as a result.

As with other factors, a proportionate approach should be taken, and organisations should consider the extent to which an update is needed. This will differ depending on what has changed since the last analysis. It may be appropriate for organisations to update scenario analysis at different frequencies to better align with internal planning and decision-making processes. This should be justified appropriately.

Qualitative versus quantitative analysis

What is the difference between qualitative and quantitative analysis?

Qualitative scenario analysis focuses on the identification of trends and on the overarching narratives of the scenarios, often providing insight into less quantifiable organisation characteristics. It can involve descriptions of plausible future worlds, describing their main characteristics, relationships between key driving forces, and the dynamics of their evolution (TCFD, 2020).

Quantitative scenario analysis refers to the presentation of quantified information within a scenario. Quantitative scenario analysis can take many forms, targeting various aspects of [an organisation’s] vulnerability to climate‑related risks (MIT, 2019).

Quantification is often useful for adaptation planning as it can form part of a cost benefit analysis or support business cases for different adaptation measures. Quantitative analysis can allow for an easier comparison of alternatives.

However, there is also value in qualitative exploratory analysis, particularly where climate data may be limited. Qualitative analysis can also be easier to communicate to a broader audience.

We recommend that organisations carry out quantitative analysis. We recommend the decision tool also suggests where qualitative analysis could be most helpful. This could be in the short term where it can be used alongside quantitative analysis results to supply a richer narrative.

Case Study: No Time To Lose: New Scenario Narratives for Action on Climate Change (Cliffe et al., 2023)

This report by the Universities Superannuation Scheme and University of Exeter focuses on the power of qualitative analysis, with four short-term scenario narratives defined by assumptions for a range of drivers.

The resulting analysis is colourful and highly descriptive. “In 2024, the world confronts the challenges of a “Super El Niño” event, exacerbated by human-induced climate change, resulting in powerful and prolonged weather phenomena. Southern Africa and India experience prolonged droughts exacerbating water scarcity and food insecurity, as changing rainfall patterns disrupt crop yields and livestock production. Record temperatures and prolonged droughts lead to ‘heatflation’ due to smaller harvests and higher prices.”

Qualitative analysis of this type can be a powerful communication tool, especially when quantitative analysis would require a considerable number of assumptions that may make communication challenging.

Good quality communication of the results of climate scenario analysis, whether analysis has been quantitative or qualitative is imperative. This includes communication to others involved with or affected by the analysis, disclosures or publications both inside and outside of the organisation. When communicating climate risks, organisations may find it helpful to compare these risks to others with which readers may be more familiar (Reisinger et al., 2020).

Inclusion of the impact of the organisation on the climate

We recommend that analysis should focus on the impact of climate on the organisation. Requirements to consider the impact of the organisation on the climate arise from other existing legislation and duties.

Additional recommendations for tool development

The decision tool could take many forms. Based on the literature and data review, insight from stakeholders and experience, we set out key recommendations for tool development below, split by tool content and tool features. We consider the design of both the content and features to be important to ensure public bodies use the tool. In producing the tool not all these features and content are needed at once. They can be released and updated in stages.

Tool content recommendations

The tool needs to include the recommendations summarised in Section 5. Additional content is needed to provide context, technical guidance, and links to useful resources. We recommend the following are considered:

• A step-by-step process for public bodies and/or types of adaptation decision-makers to follow to complete scenario analysis in their context.
• Technical guidance on assessment of risk, using the exposure / hazard / vulnerability framework (Cardona et al., 2012).
• Technical guidance on how to translate data between RCPs and global warming levels, see Appendix F.
• Material helping with communication of risk and uncertainty.
• Worked examples of analysis.
• Educational materials describing scenario analysis.
• A description of the limitations of the approach and data.

Tool features recommendations

The platform and format of the tool should be selected to provide the following features:

• Accessibility to public bodies including consideration of software requirements.
• Ability to roll out updates of the content effectively when new data becomes available.
• A clear guided pathway through the parts of the tool, potentially with interaction to allow users to make decisions based on their needs.

As well as this we recommend an awareness campaign and engagement or training programme to encourage use of the tool.

Examples of scenario analysis tools

Most examples available of scenario analysis tools are aimed at organisations in the private sector preparing climate-related disclosures. There are good examples of toolkits for scenario analysis from New Zealand’s Ministry for the Environment (no date) and for adaptation from Local Partnerships (no date). These examples take different approaches to different use cases, but both set out a clear process and link to further helpful resources.

Conclusions

This research has highlighted the importance of climate scenario analysis for effective adaptation planning, despite the lack of policy and guidance specific to this area. Our research findings were derived from a holistic review of policies, guidance and stakeholder insights, as well as an examination of current practices and publicly available data.

Our findings underscore the importance of considering a broad array of climate hazards, noting however this may be limited by data availability. The review also confirmed the importance of considering multiple scenarios across a variety of timeframes, including into the long term, to capture the uncertainty of future climate change.

Stakeholder engagement revealed a significant need for improved communication of climate risks and greater climate literacy. It also demonstrated clear support for a decision tool that can help standardise and streamline the scenario analysis process, making it more accessible and consistent across Scottish organisations.

Based on our research findings, we have made recommendations covering nine key factors that should be considered when undertaking scenario analysis.

The recommendations provided aim to guide the Scottish Government in developing a clear, practical decision tool for public bodies to use, which can make scenario analysis easier and more consistent. These include prescribed scenarios, consistent timeframes and a focus on quantitative analysis while recognising the value of qualitative insights. Additionally, we emphasise the need for iterative updates to scenario analysis to incorporate new data and evolving climate science.

We hope with this report that by defining some factors of the scenarios that organisations should consider within their scenario analysis, they will be able to spend more time on trying to understand how their organisation could respond to those scenarios and less time on identifying plausible scenarios to assess.

Ultimately, by implementing these recommendations and developing a robust decision tool, we hope public bodies in Scotland can enhance their climate resilience, ensuring that adaptation measures are well-informed, cost-effective and aligned with broader climate goals.

References

Adaptation Scotland (2021). Climate Projections for Scotland – Summary. Available at: climate-projections-scotland-summary-dec21.pdf

Armstrong McKay, D.I., Staal, A., Abrams, J.F., Winkelmann, R., Sakschewski, B., Loriani, S., Fetzer, I., Cornell, S.E., Rockström, J. and Lenton, T.M. (2022). Exceeding 1.5°C global warming could trigger multiple climate tipping points. Science, 377(6611). doi:10.1126/science.abn7950

Betts, R.A. and Brown, K. (2021). Introduction. In: The Third UK Climate Change Risk Assessment Technical Report [Betts, R.A., Haward, A.B. and Pearson, K.V. (eds.)]. Prepared for the Climate Change Committee, London. Available at: Introduction – UK Climate Risk

Cardona, O.D., van Aalst, M.K., Birkmann, J., Fordham, M., McGregor, G., Perez, R., Pulwarty, R.S., Schipper, E.L.F., and Sinh, B.T. (2012). Determinants of risk: exposure and vulnerability. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge: Cambridge University Press, pp. 65-108. Available at: 2 – Determinants of Risk: Exposure and Vulnerability (ipcc.ch)

CCC (2020). Why do we look at high warming levels when assessing UK climate risk? Available at: Why do we look at high warming levels when assessing UK climate risk? – Climate Change Committee

CCC (2021). Independent Assessment of UK Climate Risk – Advice to Government – For the UK’s third Climate Change Risk Assessment (CCRA3). Available at: Independent-Assessment-of-UK-Climate-Risk-Advice-to-Govt-for-CCRA3-CCC.pdf (theccc.org.uk)

CCC (2022). Is Scotland climate ready? 2022 Report to Scottish Parliament. Available at: Is Scotland climate ready? 2022 Report to Scottish Parliament (theccc.org.uk)

CCC (2023). Adapting to climate change – Progress in Scotland. Available at: Adapting to climate change – Progress in Scotland (theccc.org.uk)

CCC (2024). Proposed methodology for the Fourth Climate Change Risk Assessment – Independent Assessment (CCRA4-IA). Available at: Proposed methodology for the Fourth Climate Change Risk Assessment – Independent Assessment (CCRA4-IA) – Climate Change Committee (theccc.org.uk) [Accessed: September 2024]

CCC (no date). Introduction to the CCRA. Available at: Introduction to the CCRA – Climate Change Committee (theccc.org.uk)

Cliffe, M., Abrams, J.F., Barker, M., Branum-Burns, K., Campbell, R., Cordinale, M., Clark, M., Clark, N., Lalande, W., Laskey, T., Lenton, T.M., Matthews, L., Oliver, J., Picot, R., and Pilcher, S. (2023). No Time To Lose: New Scenario Narratives for Action on Climate Change. Available at: No-Time-To-Lose-New-Scenario-Narratives-for-Action-on-Climate-Change-Full-Report.pdf (greenfuturessolutions.com)

Climate Change (Scotland) Act 2009. Available at: Climate Change (Scotland) Act 2009.

Commission Delegated Regulation (EU) 2023/2772 of 31 July 2023 supplementing Directive 2013/34/EU of the European Parliament and of the Council as regards sustainability reporting standards (2023). Available at: Delegated regulation – EU – 2023/2772 – EN – EUR-Lex (europa.eu)

Defra (2023). Climate Adaptation Reporting – Fourth round guidance.

Department for Business, Energy and Industrial Strategy (2022). Mandatory climate-related financial disclosures by publicly quoted companies, large private companies and LLPs – Non-binding guidance. Available at: Mandatory climate-related financial disclosures by publicly quoted companies, large private companies and LLPs (publishing.service.gov.uk)

Dynamic Coast (accessed 2024). Available at: Dynamic Coast [Accessed: September 2024]

FRC (2022). CRR Thematic review of TCFD disclosures and climate in the financial statements. Available at: FRC TCFD disclosures and climate in the financial statements_July 2022

HM Treasury (2022, last updated 2024). The Green Book (2022). Available at: The Green Book (2022) – GOV.UK (www.gov.uk) [Accessed: September 2024]

IFRS (2023). IFRS S2 Climate-related Disclosures. Available at: ISSB-2023-A – Issued IFRS Standards

Lee, J.-Y., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J.P., Engelbrecht, F., Fischer, E., Fyfe, J.C., Jones, C., Maycock, A., Mutemi, J., Ndiaye, O., Panickal, S., and Zhou, T. (2021). Future Global Climate: Scenario-Based Projections and Near-term Information in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J.B.R., Maycock, T.K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B. (eds.)]. Cambridge: Cambridge University Press, pp. 553–672. doi:10.1017/9781009157896.006.

Local Partnerships (no date). Climate adaptation toolkit and risk generator. Available at: Climate adaptation toolkit and risk generator (localpartnerships.gov.uk) [Accessed: September 2024]

Met Office (2023). Making sense of climate change projections. Available at: Making sense of climate change projections | Official blog of the Met Office news team

MIT (2019). Climate-Related Financial Disclosures – The Use of Scenarios. Available at: Climate Finance Disclosures – Scenarios.pdf (mit.edu) [Accessed: September 2024]

Ministry for the Environment [New Zealand] (no date). Climate scenarios toolkit. Available at: Climate scenarios toolkit | Ministry for the Environment [Accessed: September 2024]

Moreira Sousa, A. (2022). Staying safe in extreme heat – UK Health Security Agency. Available at: Staying safe in extreme heat – UK Health Security Agency (blog.gov.uk)

Reisinger, A., Howden, M., Vera, C., Garschagen,M., Hurlbert, M., Kreibiehl, S., Mach, K.J., Mintenbeck, K., O’Neill, B., Pathak, M., Pedace, R., Pörtner, H., Poloczanska, E., Rojas Corradi, M., Sillmann, J., van Aalst, M., Viner, D., Jones, R., Ruane, A.C., and Ranasinghe, R. (2020) The Concept of Risk in the IPCC Sixth Assessment Report: A Summary of Cross-Working Group Discussions. Geneva: Intergovernmental Panel on Climate Change, pp15. Available at: Risk-guidance-FINAL_15Feb2021.pdf (ipcc.ch)

Scotland’s Environment (2024). Changing climate. Available at: Changing climate | Scotland’s environment web [Accessed: September 2024]

Scottish Government (2011). Public bodies climate change duties: putting them into practice, guidance required by part four of the Climate Change (Scotland) Act 2009. Available at: Public bodies climate change duties: putting them into practice, guidance required by part four of the Climate Change (Scotland) Act 2009 – gov.scot

Scottish Government (2022). NHS Scotland Climate Emergency & Sustainability Strategy 2022-2026. Available at: NHS Scotland Climate Emergency and Sustainability Strategy 2022-26 (www.gov.scot)

Scottish Government (2023a). BICS weighted Scotland estimates: data to wave 88. Available at: BICS weighted Scotland estimates: data to wave 88 – gov.scot (www.gov.scot)

Scottish Government (2023b). Coastal Change Adaptation Plan Guidance. Available at: Coastal Change Adaptation Plan Guidance

Scottish Government (2024). Scottish National Adaptation Plan 2024 – 2029 Actions today, for a climate resilient future. Available at: Scottish National Adaptation Plan (2024-2029) (www.gov.scot)

Seneviratne, S.I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S.M., Wehner, M., and Zhou, B. (2021) Weather and Climate Extreme Events in a Changing Climate Supplementary Material in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J.B.R., Maycock, T.K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B. (eds.)]. Available at: 11SM – Weather And Climate Extreme Events in a Changing Climate Supplementary Material (ipcc.ch)

SP Energy Networks (2021). Climate Resilience Strategy. Available at: Annex_4A.7-Climate_Resilience_Strategy.pdf (spenergynetworks.co.uk)

Sustainable Scotland Network (2023). Public Bodies Climate Change Reporting 2021/22 Analysis Report. Available at: Sustainable Scotland Network Analysis Report 2021 to 2022

Swinney, J (2024). Priorities for Scotland: First Minister’s statement. Available at: Priorities for Scotland: First Minister’s statement – 22 May 2024 – gov.scot (www.gov.scot)

TCFD (2017). Recommendations of the Task Force on Climate-related Financial Disclosures. Available at: FINAL-2017-TCFD-Report.pdf (bbhub.io)

TCFD (2020). Guidance on Scenario Analysis for Non-Financial Companies. Available at: 2020-TCFD_Guidance-Scenario-Analysis-Guidance.pdf (bbhub.io)

The Companies (Strategic Report) (Climate-related Financial Disclosure) Regulations 2022 (SI 2022/31). Available at: The Companies (Strategic Report) (Climate-related Financial Disclosure) Regulations 2022

The Occupational Pension Schemes (Climate Change Governance and Reporting) Regulations 2021 (SI 2021/839). Available at: The Occupational Pension Schemes (Climate Change Governance and Reporting) Regulations 2021

Watkiss, P. and Betts, R.A. (2021). Method. In: The Third UK Climate Change Risk Assessment Technical Report [Betts, R.A., Haward, A.B. and Pearson, K.V. (eds.)]. Prepared for the Climate Change Committee, London. Available at: Chapter 2: Method – UK Climate Risk

Watkiss, P (2022). The Costs of Adaptation, and the Economic Costs and Benefits of Adaptation in the UK. Available at: The Costs of Adaptation, and the Economic Costs and Benefits of Adaptation in the UK (theccc.org.uk)

Appendices

1. Limitations, reliance and liability

This report has been prepared by the Government Actuary’s Department (GAD) at the request of ClimateXChange on behalf of the Scottish Government.

The report has been prepared for the use of ClimateXChange and the Scottish Government and is published on ClimateXChange’s website. Therefore, we acknowledge that it will likely have a wider audience than the intended recipients. However, other than ClimateXChange and Scottish Government, no person or third party is entitled to place any reliance on the contents of this report, except to any extent stated herein. GAD has no liability to any person or third party for any action taken or for any failure to act, either in whole or in part, on the basis of this report.

In preparing this report, GAD has relied on publicly available data and other information as described in the report. Any checks that GAD has made on this information are limited to those described in the report, including any checks on the overall reasonableness and constancy of the data. These checks do not represent a full independent audit of the data supplied. In particular, GAD has relied on the general completeness and accuracy of the information supplied without independent verification.

GAD provides both actuarial and other advice. For clarity, this report provides research findings and recommendations and as a result is not subject to the Technical Actuarial Standard TAS 100 issued by the Financial Reporting Council (FRC) for actuarial work in the UK.

There is significant uncertainty involved when assessing climate risks. Care has been taken to ensure that, where material, this work has taken into consideration the latest climate change research and appropriate climate data.

The Institute and Faculty of Actuaries (IFoA), the regulatory body for GAD’s actuaries, has issued three climate change related risk alerts to members. These have all been considered when preparing this work.

Over time, as the global emissions pathway becomes clearer and there are advances in science and technology, our view of future climate risks will undoubtably change. Future developments may have a material impact on the results and conclusions contained in this work and care should be taken when referring back to this analysis after the date of issue.

One of the challenges for public sector organisations (and others) conducting scenario analysis is the range of potential approaches and assumptions that can be taken. Through preparing this guidance we have considered various other approaches in producing the final recommendations, some of which have been outlined in Section 4.

1. Data sources reviewed^[5]

IPCC WG1 Interactive Atlas

Climate Analytics Climate Impact Explorer

Climate Analytics Climate Risk Dashboard

Global Infrastructure Climate-Related Risk Analytics

Environment Agency Climate Impacts Tool

Met office Climate Data Portal

UK Climate Projections User Interface (UKCP18)

NatureScot GIS database

Marine Scotland

Copernicus Climate Data Store (extreme climate indices)

Copernicus Climate Data Store (changes in water levels)

Copernicus Climate Data Store (climate and energy indicators)

Copernicus Bioclimatic Indicators

World Resources Institute Aqueduct

UK Climate Risk Indicator Explorer

SEPA Flood Maps

SEPA Flood Risk Management Maps

World Bank Climate Knowledge Portal

Climate Central’s Coastal Risk Screening Tool

1. Policy and guidance review

Documents reviewed:

1. Stakeholder engagement
2. Organisations and individuals engaged with

We would like to thank the following organisations who contributed to our research and provided useful insights on their areas of expertise and experience of completing climate scenario analysis:

Climate Change Committee

Dynamic Coast

Edinburgh City Council

Forestry and Land Scotland

Highlands and Islands Airports Limited

Historic Environment Scotland

Met Office

NatureScot

Network Rail

Paul Watkiss Associates Limited

Scottish Environmental Protection Agency (SEPA)

Scottish Government

Scottish Water

Sniffer

Transport Scotland

University of Glasgow.

1. Climate risks and opportunities

Climate risks and opportunities are often broken down into risks related to the physical impacts of climate change and risks related to the transition to a lower-carbon economy. The TCFD (2017) further breaks down transition and physical climate risks as summarised below.

1. Physical risks

Acute:

• River and coastal flooding
• Surface water flooding
• Storm events – cyclone, hurricane etc
• Storm sea level surge

Chronic:

• Change in precipitation
• Rising mean temperatures
• Sea level rise and coastal change

1. Transition risks

Policy and legal:

• Increasing price of GHG emissions
• Enhanced emissions reporting requirements
• Regulation of products and services
• Exposure to litigation

Technology:

• Substitution with lower emitting products and services
• Unsuccessful investment in new technologies
• Costs to transition to lower emissions technologies

Market:

• Change in customer behaviour
• Uncertainty in market systems
• Increased cost of raw materials

Reputation:

• Change in customer preferences
• Stigmatisation of sector
• Increased stakeholder concern or negative stakeholder feedback

1. Emission-based scenarios and global warming levels

IPCC Coupled Model Intercomparison Project Phase 5 (CMIP5) – used for the IPCC’s 5^th assessment report and UKCP18 (Seneviratne et al., 2021):

IPCC Coupled Model Intercomparison Project Phase 6 (CMIP6) – used for the IPCC’s 6^th assessment report (Lee et al., 2021):

How to cite this publication:

Grace, E., Marcinko, C., Paterson, C., Stobbs, W. (2024) ‘Using future climate scenarios to support today’s decision making’ ClimateXChange. http://dx.doi.org/10.7488/era/5567

© The University of Edinburgh, 2024
Prepared by Government Actuary’s Department on behalf of ClimateXChange, The University of Edinburgh. All rights reserved.

While every effort is made to ensure the information in this report is accurate, no legal responsibility is accepted for any errors, omissions or misleading statements. The views expressed represent those of the author(s), and do not necessarily represent those of the host institutions or funders.

This work was supported by the Rural and Environment Science and Analytical Services Division of the Scottish Government (CoE – CXC).

ClimateXChange

Edinburgh Climate Change Institute

High School Yards

Edinburgh EH1 1LZ

+44 (0) 131 651 4783

info@climatexchange.org.uk

www.climatexchange.org.uk

1. 
   

   Usually these are to disclose information in line with ISSB (IFRS, 2023), or TCFD (2017) requirements. ↑

   
   
2. 
   

   Derived from their global HadGEM3 model. ↑

   
   
3. 
   

   Whilst the focus of this report is on public bodies, there are many aspects that will be applicable and useful to private sector organisations. Therefore, throughout the report, we refer to “organisations” to encompass both public bodies and private companies. ↑

   
   
4. 
   

   In some instances (for example Local Authorities) this will include the whole area where the organisation has influence. ↑

   
   
5. 
   

   Links correct at date of publication. ↑

   
   

Research completed: October 2023

DOI: http://dx.doi.org/10.7488/era/3991

Executive summary

This project was commissioned to inform the Scottish Government on the evidence and arguments for and against the inclusion of metered energy consumption data in Energy Performance Certificates (EPCs). Methods included a literature review and interviews with stakeholders in Scotland, the UK and Sweden.

We outline the potential opportunities for and barriers to using energy consumption data; the practicalities of obtaining and using energy consumption data; and the value of including such data, when considering the variables that affect actual energy usage.

Key findings

Metered energy consumption data could be used in EPCs in two ways to provide information to occupants or potential occupants:

• to provide more accurate information on building fabric performance, known as an asset rating
• to give a rating of how energy is used in a building when compared with similar buildings, known as an operational rating.

These two uses of metered consumption data – asset rating and operational rating – are not mutually exclusive and could both be included in EPCs. This could be developed as a dynamic, digital EPC.

Neither of these two uses could be implemented immediately as 57% of homes in Scotland do not yet have smart meters, which are the most reliable means of collecting metered energy consumption data. Particular difficulties include:

• A small proportion of homes will never have smart meter capability, including homes with unregulated heating fuels such as oil, LPG, or solid fuels.
• There is no process to access smart meter data to generate EPCs. The Smart Meter Energy Data Repository Programme is investigating the commercial feasibility of a repository that would enable this.

The most straightforward use for metered energy consumption data is to include the operational rating value on an EPC alongside a reference figure, such as a national average, modelled archetype, or historic consumption data for a property.

• Correcting energy consumption in a property for weather and normalising it by floor area would enable potential occupants to compare properties.
• An operational rating could be included as a part of the EPC or exist as a separate document.

EPCs should retain an asset rating that is based on standard assumptions of occupancy and use, to allow comparison between properties. This could be based on modelled or measured data.

For an accurate asset rating, metered energy consumption data can be used to calculate the heat transfer coefficient of buildings. This requires collecting internal temperature data, as well as metered energy consumption data. The latest smart meter in-home display units have inbuilt temperature sensors. The possibility of transmitting temperature readings alongside meter readings is being investigated by the Data Communications Company.

Accurate heat transfer coefficient figures can inform retrofit decisions. Further consideration is needed around the level of retrofit recommendations provided by EPCs and how these are used in policy decisions. Using metered energy consumption data to inform retrofit recommendations may be more suited to detailed retrofit plans such as renovation roadmaps.

Consumer consent will be needed to collect and process metered energy consumption data.

Recommendations

This report explores whether it is possible for metered energy consumption data to be used within EPCs and outlines two ways in which this data could be useful. In order to progress with either or both of these options, we recommend that the Scottish Government define the purpose and intended outcome of using metered energy consumption data within EPCs.

Our research has highlighted that further work is needed in this area to explore:

• The practicalities of collecting required data, including:
• Metered energy consumption data at the individual building level, rather than from aggregated datasets. This will require a standardised process for collecting consumer consent. Public sector bodies can obtain household-level data without the need for individual consent through the legal basis of ‘public task’. However, this is for aggregated data and there are no examples of data being used to provide insights into individual households, so further investigation is needed into the legal basis for this. Legal routes for this were not explored as part of this research.
• Processes for data collection, as these are mostly dependent on the rollout of smart meters. An alternative methodology will need to be developed for households using unregulated fuels, as their heating consumption will not be captured in smart meter data.
• Additional information from occupants, which can be used to contextualise energy consumption data when used for an operational rating. Examples of this kind of data include the number of occupants or typical heating regime. Further work is required to understand the minimum amount of contextual information to enable metered energy consumption data to be useful.
• Internal temperature data for the purpose of calculating a heat transfer coefficient as part of an asset rating. This would require the mass rollout of internal temperature sensors, which are already included in some in-home display devices. Internal temperature data could also be useful contextual data for an operational rating.
• Different formats that could be used to display consumption data when used for an operational rating. This should consider whether consumption data would work best as one of multiple ratings within the EPC or separately.
• For energy-generating homes, how total energy consumption, generation, export and cost can be displayed in a straight-forward manner.
• Any regulatory or practical barriers to inputting the heat transfer coefficient as a measured value in Standard Assessment Procedure calculations for the asset rating.
• The value of Display Energy Certificates for non-domestic public buildings in England and Wales, and whether there would be value in expanding their use in Scotland.

Glossary / Abbreviations table

Introduction

This research has been commissioned in response to calls on the Scottish Government to make use of metered energy consumption data within Scottish EPCs. A common criticism of EPCs is that they do not provide useful information to householders about the actual energy consumption and real-life performance of properties. As a result, EPCs can be perceived as unreliable and unhelpful.

Increasing evidence shows that there are significant and consistent gaps between properties’ actual energy consumption and the consumption modelled in EPCs (BEIS, 2021; Few et al., 2023; The Times, 2023). EPCs were not designed to predict actual consumption (see Section 3). This raises the question of whether the methodology or format would benefit from including metered consumption data. The installation of smart meters in an increasing number of Scotland’s homes presents an opportunity to collect this data. In this report, we explore how such data could be incorporated into EPCs to potentially improve their usefulness and reliability.

The question of using energy consumption data is complex – there are many ways it could be included, and each has different implications. This report sets out two key uses for energy consumption data: to inform an asset rating; and to inform an operational rating.

EPC Overview and Research Scope

Energy Performance Certificates (EPCs)

An EPC is a document that provides information about the energy efficiency of a building. Their introduction was driven by the European Union’s Energy Performance of Buildings Directive (EPBD). Article 11 of the EPBD states the original purpose of EPCs was “to make it possible for owners or tenants of the building or building unit to compare and assess its energy performance” (Directive 2010/31/EU, 2010). Article 2 specifies that EPCs are intended to show “the energy demand associated with a typical use of the building” (ibid.). This makes it clear that the original purpose of EPCs was to enable the comparison of building performance under ‘typical’ conditions.

Annex I also states that the energy performance of buildings can be evaluated using either the calculated (producing an asset rating) or actual energy consumption (producing an operational rating) (Directive 2010/31/EU, 2010). Methods based on measured energy consumption must separate out building performance from other factors, primarily occupancy. The variability of these other factors can be controlled when using calculated methods. However, calculated methods are often associated with inaccuracy (Crawley et al., 2019; Hardy and Glew, 2019) and pose the problem that what is built can be different from what was designed or modelled (the performance gap).

In practice, most EPC methodologies use a calculated approach, incorporating real building data from surveys or physical tests (Arcipowska et al., 2014). In Scotland, as in the rest of the UK, EPCs are produced using SAP, RdSAP and SBEM methodologies. SAP (Standard Assessment Procedure) is used to generate EPCs for both new and existing residential buildings. Full SAP is primarily used for new dwellings whereas RdSAP (Reduced Data SAP) is used for existing dwellings. RdSAP uses the same calculation as full SAP but with a simplified data collection process. This enables the calculation to take place where a complete data set for a property is unavailable, and for a lower cost than full SAP.

Existing SAP methodologies used to calculate the domestic asset rating use standard assumptions for occupancy, energy-use, and climate to ensure that the thermal performance can be compared under the same set of conditions. This asset rating is not reflective of how the building is used, for example due to the specific energy requirements of the occupants or the local climate.

SBEM (Standard Building Energy Model) is used to produce EPCs for non-domestic buildings. SBEM utilises a different calculation methodology to SAP. For the generation of an EPC, the SBEM calculation utilises standardised information for several factors to allow comparability between similar building types. Like SAP, SBEM requires a certain amount of standardisation to enable comparability between buildings for benchmarking purposes.

Research scope

This report considers whether metered energy consumption data can and should be used in the production of EPCs in Scotland. This brings with it questions around the suitability of EPCs for their various uses. However, the purpose of this report is not to assess whether EPCs (or SAP / RdSAP) are the most appropriate tool for the functions set out in Section 4. Additionally, this report does not detail the limitations of EPCs or SAP. There is an existing body of research which evidences these limitations, for example Jones Lang LaSalle (2012), Kelly et al. (2012), Jenkins et al. (2017), Hardy et al. (2019), and BEIS (2021).

The scope of this research is to consider whether it is possible to access and include metered energy consumption data on Scottish EPCs, and whether this would be a valuable addition. In some instances, we have suggested that the information provided by metered energy consumption data may be useful but would be better presented elsewhere and not as part of an EPC. The focus of the research is on domestic EPCs as tools for providing information to occupants, rather than EPCs as a policy tool or for benchmarking purposes.

The focus of this report is domestic EPCs. The use of metered energy consumption data for non-domestic EPCs is briefly explored in Section 10.

Functions of EPCs in Scotland

EPCs in Scotland are used for a range of purposes, including (but not limited to):

• Providing information to potential buyers and tenants on a building’s energy use, and estimated energy costs.
• Providing information to property owners on suggested retrofit measures.
• Serving as a policy tool to measure, regulate and set targets for the reduction of carbon emissions from housing.
• Facilitating housing stock analysis by landlords to plan and implement improvements.
• Supporting national housing stock analysis through the Scottish House Condition Survey (SHCS).
• Acting as a proxy indicator to support the identification of households in fuel poverty, for example for the targeting of fuel poverty prevention or alleviation services.

This report does not assess how well EPCs can perform each of these functions. The use of energy consumption data within EPCs will have implications for all of the above uses. Our research considers whether the use of energy consumption data could improve EPCs for the following specific purposes:

• Providing information on a building’s fabric performance.
• Providing an estimate of energy costs.
• Providing information on how buildings are actually used.
• Informing retrofit decisions.

The case for including energy consumption data

The arguments for using energy consumption data depend on the use-case of EPCs that is being considered. As outlined in Section 4, EPCs now serve a number of purposes for which they were not originally designed. This, along with issues such as inconsistencies between assessors, means that they are perceived as unreliable (Crawley et al., 2020; Kelly et al., 2012). A major driver for using energy consumption data is the premise that this will make EPCs more reliable for users, by reducing reliance on assumptions and assessor judgement.

Currently, EPCs can be of limited value to householders who may expect EPCs to provide information reflecting actual energy consumption. Similarly, for policy or housing stock management decisions, EPC asset ratings do not reflect the actual energy consumption of buildings. The need for policy decisions to be based on actual rather than modelled energy efficiency of buildings is also a key argument for the use of metered energy consumption data in EPCs (Baker & Mould, 2018; Lomas et al., 2019).

This report considers two key uses for energy consumption data in EPCs. It can be used to provide a more accurate asset rating or to provide an operational rating. An asset rating is a measure of building fabric performance and does not consider how a building is used. An operational rating based on energy consumption data can help understand how a building is used, which is not currently addressed by EPCs. This has the potential to provide information to householders on actual energy costs associated with a building, as well as supporting wider decarbonisation policy.

Reducing the performance gap

Improving the accuracy of EPCs through the use of energy consumption data is intended to reduce the performance gap. The performance gap refers to the difference between modelled energy performance (e.g. through SAP) and measured energy performance (Fitton et al., 2021). There are a significant number of variables which influence this gap. These include factors related to the building fabric, building use, and the accuracy of the model.

The term ‘performance gap’ usually refers to the discrepancy between designed and as-built fabric performance, particularly for new-builds. However, it is also used to refer to the difference between predicted energy usage and actual (metered) energy usage. When used in this way, the term is also incorporating the impact of occupancy factors.

Recent research found that even when other factors are accounted for (i.e. in households that meet EPC standard assumptions), EPCs overpredict energy use (Few et al., 2023). This suggests that the methodology and its underlying assumptions also contribute to the performance gap.

Improving the accuracy of asset ratings

Energy consumption data can provide a more accurate calculation of a building’s fabric performance. Utilising real-world data to calculate actual space heating demand could improve accuracy and therefore, increase consumer confidence in the reliability of the asset rating. A more accurate asset rating would enable more accurate predictions of annual energy cost. The cost metric would be predicted under standardised conditions, which would maintain the ability to make comparisons between buildings.

A programme of work by the International Energy Agency known as Annex 71 sought to test demand amongst industry stakeholders^[1] for a method to calculate HTC. Their survey results indicated a high level of demand for this across several different use-cases including energy certification (Fitton et al., 2021).

Providing an operational rating

Currently EPCs are based on a building fabric model, and do not consider how energy is used by occupants. Asset ratings alone are not sufficient to reduce energy demand. This requires measuring and achieving reductions in actual energy consumption in buildings (Few et al., 2023; Jones Lang LaSalle, 2012; The Times, 2023).

The use of energy consumption data can provide tailored information for consumers regarding the potential energy costs to occupy a specific property, i.e., a measure of the operational performance of the property. Research has shown that the ability to compare energy use with that of similar dwellings is perceived as beneficial to householders (Zuhaib et al., 2021). In order for comparisons between dwellings to be useful, some contextual information is needed to account for occupancy factors which impact energy use (Section 6).

The ways in which this contextual information could be collected and used are discussed in Section 9. However, some stakeholders (Richard Fitton, Professor of Building Performance; Alan Beal, Bacra; Thomas Levefre, Managing Director, Etude) were wary of using energy consumption data in this way, as we will never be able to fully account for or control all the variables that affect how energy is used in the home.

A significant benefit of introducing an operational rating is to provide more accurate cost saving figures to improve the energy efficiency improvement recommendations. Actual consumption data could also enable a better assessment of the impact of retrofit measures and whether they perform as intended.

There is evidence that householders would find it useful to see actual energy costs on an EPC. There are number of ways this information could be contextualised or compared. A study of five European countries (Zuhaib et al., 2022) found that the majority of householders who responded to their survey would like to see the energy costs of the previous occupier included in EPCs, as well as the energy cost of ‘similar’ households^[2]. However, the same study notes that energy consumption comparisons were was perceived as more useful when comparing against the previous year than with similar households. Year-on-year comparisons of energy use may be more appropriately provided by energy suppliers rather than on an EPC (see Section 7.2 for detail on dynamic EPCs).

Informing retrofit decisions

Another purpose of EPCs (as described in EBPD) is to provide improvement recommendations for householders. The Scottish Government’s latest consultation on EPCs states that EPCs are intended as a starting point for householders, but not to provide bespoke recommendations for retrofit (Scottish Government, 2023). However, the information currently provided to householders on an EPC could still be improved using energy consumption data, particularly in relation to predicted savings (Baker & Mould, 2018). Energy consumption data could be used to provide accurate predictions of savings from retrofit measures (Cozza et al., 2020).

Aside from informing individual householders, retrofit recommendations on EPCs and their associated predicted savings are also used to support the targeting of investment in retrofit. The scale of investment required for retrofit means that estimates of potential financial savings must be accurate. Laurent et al. (2013) argue that the economics of retrofit should not be evaluated using normative models. This is because all normative models (not just SAP) have been shown to overestimate potential savings and the cost effectiveness of retrofit measures. For these reasons, if the Scottish Government intends to continue to use EPC retrofit recommendations as a policy tool for directing funding, further investigation is needed into how energy consumption data could support this (Baker & Mould, 2018).

The use of energy consumption data in EPCs could better reflect the actual energy performance of building fabric (Section 8). This would provide a more realistic baseline asset rating on which to base recommended retrofit measures. However, the recommendations on an EPC would still be generated automatically by SAP based on general property characteristics. Metered energy consumption data could also play a role in measuring the impact of retrofit, as explained in Section 8.

Energy consumption data provides information on how a building is used. It can therefore be used to support the development of bespoke retrofit recommendations. However, such EPCs are not the tool for developing bespoke retrofit plans (Scottish Government, 2023). PAS 2035 or renovation roadmaps (Small-Warner & Sinclair, 2022) provide a more appropriate framework for this. This view was supported by interviewees (Kevin Gornall and Sam Mancey of DESNZ; Richard Atkins, Chartered Architect) who stated that retrofit plans should be delivered through the industry professionals and not through EPCs. An example of a tool being developed to support this is provided in Box. 1

Box 1: HTC-Up: Informing retrofit using metered energy consumption data

Chameleon Technology were recently awarded funding through the Green Home Finance Accelerator project from DESNZ to develop the HTC-Up project (Chameleon Technology, 2023). Using smart meter data alongside internal and external temperature data, a more accurate HTC figure can be generated which better reflects the actual thermal energy performance of a property. With this data, Chameleon Technology designs a programme for retrofit specific to the home. They direct householders to approved suppliers and installers, and also offer financing solutions if needed.

Validating models and assumptions

The Elmhurst Almanac (Elmhurst Energy, 2022) refers to the need to use the ‘Golden Triangle’ to inform decision-making. This refers to a building’s asset rating (predicted energy cost and consumption based on standard occupancy), occupancy rating (predicted energy consumption based on how the building is used), and actual energy consumption (smart meter data). In the Golden Triangle, smart meter data is used as a validation point for comparison with figures generated as part of the asset and occupancy ratings. This validation can help to identify issues with performance and where to focus improvements.

Metered consumption data could also be used to improve assumptions contained within SAP/RdSAP. For example, Hughes et al. (2016) showed that the difference between modelled and actual energy consumption could be reduced by using assumptions for internal temperature, number of heating hours, and the length of heating season, that are developed based on actual consumption data.

At a larger scale, metered energy consumption data could also be used to calibrate and improve the modelling used for EPCs (Thomson and Jenkins, 2023). Similar exercises have been undertaken to validate the PHPP model (Mitchell and Natarajan, 2020; Passipedia, n.d.). Using real energy consumption data for this purpose was explored as part of the X-tendo project (Zuhaib et al., 2021). The project findings suggest that real energy consumption data from large housing stock datasets can be used to improve models and for benchmarking performance levels. This particular use is not explored further in this report as it is out of scope. Our focus is on EPCs as a tool for providing information to building occupants.

Factors affecting metered energy consumption

Many variables impact on the energy use of a building. These can be broadly split into variables impacting the building fabric, system efficiency (e.g. heating) and those that impact how energy is used within the building. All of these are influenced by wider variables such as fluctuations in energy prices, deprivation levels, social and cultural norms, and changes in climatic conditions.

There is no consensus on the relative importance that can be attributed to either building characteristics or to consumption behaviour in terms of their impact on domestic energy consumption. The variables affecting household energy consumption are understudied (Fuerst et al., 2019) and strong conclusions about how to control or account for them cannot be drawn. Jones et al. (2015) found that 62 household level factors have been studied in the literature as potentially influencing domestic electricity use^[3], with varying significance.

In terms of occupancy factors, the review suggests that the number of occupants, the presence of teenagers, and level of household income and disposable income all have a significant impact on electricity consumption. Electrical appliances make a very significant contribution to a household’s electricity consumption (ibid.), however the review noted that only a few previous studies have analysed the effects of the ownership, use and power demand of appliances. The review also indicates that the following building fabric characteristics have a significant effect: dwelling age, number of rooms, number of bedrooms, and total floor area.

Building fabric

When considering the physical building characteristics alone, there is little consensus on the significance of physical building characteristics, other than floor area, that impact energy consumption. Research consistently suggests a significant positive correlation between floor area and consumption (ibid.), mostly associated with demand for space heating.

There is little consensus on the impact of dwelling age. Some studies reviewed by Jones et al. (2015) found newer dwellings have a higher electricity demand, attributed to high consumption appliances such as air conditioning. Other studies observed that newer homes had lower consumption due to efficient appliances and better insulation levels. Several studies also concluded there was no relationship, including a UK study by Hamilton et al. (2013).

Built-form type (such as terraced, detached, semi-detached) has also been investigated and a large number of studies concluded that electrical energy consumption increases with the degree of detachment of a building. However, it is not clear whether this relationship is explained by the building fabric or by occupancy factors. In general, the literature suggests that the influence of built-form type on electricity consumption is related to floor area. However, building occupancy is also a possible reason. For example, Wyatt (2013) attributed lower electricity consumption in bungalows to the fact they are normally occupied by elderly residents with comparatively lower energy consumption than the rest of the population. The review by Jones et al. (2015) suggests that there is a relationship between the level of detachment of dwellings and electricity consumption, but the effect could not be determined as either positive or negative.

Occupancy factors

A regression analysis of household energy consumption in England concluded that gas usage was largely determined by occupancy characteristics such as income and household composition, rather than physical characteristics of the building (Fuerst et al., 2019). This contrasts with the findings from other regression model studies across several countries which report that building characteristics have a greater effect on domestic energy consumption than occupancy characteristics (such as Santin et al., 2009, Estiri, 2014, Huebner et al., 2015).

Fuel poverty is another factor which impacts energy consumption. Levels of fuel poverty in Scotland are geographically uneven across the country, and are higher in rural areas (Changeworks, 2023). Fuel poverty is associated with coping mechanisms such as only heating one room – behaviours which would have a significant impact on energy use. It is well-recognised that households in homes with poor energy efficiency tend to ration energy, known as the ‘prebound effect’ (Sunikka-Blank and Glavin, 2012).

Any use of energy consumption data will need to be attuned to, for example, the difference between energy rationing and energy saving behaviours, and avoid approaches that inadvertently ‘reward’ underheating through favourable EPC ratings. For example, it would be problematic if a household with higher-than-standard heating regimes, such as for health reasons, received a more negative EPC rating. This highlights the importance of collecting internal temperature data (to measure heating outcomes), alongside consumption data (Section 8.1.1).

Regulated and unregulated energy use

The question of how and whether to include consumption data on EPCs largely relates to the purpose of doing so. Not all energy use is relevant to all audiences. The SAP calculations used for EPCs only consider regulated energy use, which includes energy used for heating and cooling, domestic hot water, mechanical ventilation, and fixed lighting. The total energy consumption of a property includes other uses (unregulated energy), such as appliances. This is primarily dependent on the occupants. Although unregulated energy generally accounts for a minority of the total energy consumption in most properties, it is also more likely to fluctuate more often. Factors that can impact this could be an occupant starting to work from home, an occupant moving out, or purchasing a new electrical appliance (Jones et al., 2015).

A householder may be interested in understanding the efficiency of their appliances, but this is less relevant to a building technician working to improve the building fabric or heating system. However, industry experts have suggested that SAP 11 should consider both regulated and unregulated energy use (BEIS, 2021). In part, this is to enable EPCs to better support Net Zero, which requires a reduction in all energy use – not just regulated energy. Another reason is that unregulated energy use is becoming a larger proportion of total energy use as buildings become more energy efficient and use less energy for heating.

Disaggregating energy use

Metered energy consumption data will account for both regulated and unregulated energy, and unless submetering is used it will be difficult to disaggregate these without relying on assumptions. This disaggregation issue was highlighted in the European X-tendo project (Hummel et al., 2022), where four countries tested a methodology for including energy consumption data on EPCs. Three of the countries encountered challenges around determining the energy consumption used for different purposes in the buildings. Metered data for the different energy uses was not available, so the consumption data for space heating and hot water were estimated based on energy bills. This was perceived as complex, time consuming, and inexact (ibid.).

In properties with natural gas heating, disaggregation is not a significant issue, as most of the metered gas consumption can be assumed to be used for heating. However, it poses a challenge in the increasing number of properties with electric heating. There is a risk that relying on assumptions of typical use will replicate the issues that the inclusion of metered data is trying to solve. In Sweden, the disaggregation of energy uses is carried out by the energy assessor based on their competence and judgement. Considering the existing inconsistencies identified among assessors in the generation of UK EPCs (Jenkins et al., 2017), it is likely this approach would introduce further inaccuracies in EPC output.

Box 2: An example scenario of the need to disaggregate energy use

A property with electric heating has recently had internal wall insulation installed. The household is interested in using an energy consumption metric to understand whether the wall insulation has resulted in the expected decrease in energy consumption. However, the same month they also bought an electric vehicle which they charge at home. Without disaggregating their electricity usage, they are unable to tell if their wall insulation is performing as predicted.

The use of sub-metering could help to alleviate these challenges. Chartered Architect Richard Atkins suggested that, in the future, smart meters will be fed into from a series of data points within the home (e.g., heating system, renewable generation assets, storage assets). However, Alan Beal of Bacra indicated that this granularity of metering is unlikely to be available for at least 10 years, and as noted in Section 8.1, regular smart meters are far from fully rolled out in Scotland.

Properties with energy generation

Further consideration is needed for properties with energy generating assets, which adds a layer of complexity to the question of how different aspects of household energy data can be displayed for different audiences.

MCS standards already require a generation meter, and smart meters record the amount of energy exported to the grid, so this data should already be available (Jon Stinson of Building Research Solutions), but it will need to be represented in a way that is legible to the relevant audiences. For example, David Allinson (Building Energy Research Group, University of Loughborough) suggested that consumers would want to see historic levels of energy generation displayed on an EPC.

Overall, the challenge is to design a methodology and an output that works for all properties in Scotland, from properties with no metered heating system and no smart meters, to those with complex systems that include various types of energy generation.

Considerations for using metered energy consumption data

Practicalities of data collection

The potential for using metered data to understand buildings’ energy performance is largely linked to smart meters, which provide accurate and frequent meter readings. The number of smart meters continues to increase. As of March 2023, 57% of all gas and electricity meters in the UK were smart (National Audit Office, 2023). However, in most of Scotland, the rates of domestic smart electricity meters were lower (43%), with rates below 10% in Na h-Eileanan Siar, the Orkney Islands, and the Shetland Islands (DESNZ, 2023). This has implications for the approaches reviewed in this report.

Accessing smart meter data

Aside from the rollout, the main challenge associated with accessing smart meter data relates to where the data is stored and how it can be shared. This also relates to General Data Protection Regulation (GDPR) (Section 7.3). Energy consumption data is considered personal data under current GDPR and requires the consumer’s consent to access it. Consumption data (and export profiles in homes with generation technologies) are stored on individual meters.

There are currently two ways that third parties can access smart meter data (Energy Systems Catapult, 2023), though both require explicit consent from the consumer:

1. Organisations (such as energy suppliers) can be integrated into the smart metering system. These organisations must lay out their approach to obtaining householder consent during the onboarding process. Work is underway within the DCC to make the on-boarding process easier and more streamlined.
2. Through a Consumer Access Device (CAD). This is a read-only monitor fitted to the home area network. These can only be fitted by registered users of the DCC’s systems.

DESNZ are currently exploring options for creating a central repository for smart meter data through their Smart Meter Energy Data Repository Programme. The aim of this is to explore the feasibility of creating a central repository which would support the innovation of services and products for the benefit of consumers and the wider network. This could include all types of smart meter data, either aggregated or at householder level. The primary focus of projects funded through this programme is to enable access to aggregated data sets.

Public sector bodies, or any organisation carrying out a specific task in the public interest, can access household metered energy data without the need for individual consent. This is through the legal basis of ‘public task’. However, currently this route is only used to access aggregated consumption data. There are no current examples of data being used to provide insights at the individual level. For example, metered gas consumption data is collected by DESNZ from individual households (through Xoserve^[4]) for the purpose of compiling subnational consumption statistics. In this instance, individual consent is not required from the householder, and data is presented in aggregate. Legal routes for accessing individual household consumption data under the basis of public task were not explored as part of this research. Further investigation is needed to understand the GDPR considerations.

Aggregated data sets could be used as a validation point to support the improvement of the existing SAP methodology (Section 5.5), though would have little benefit for the two approaches outlined in later sections of this report (improving the asset rating or calculating operational rating for individual EPCs). Our discussions with stakeholders indicate that the current focus of work is to enable access to aggregated smart meter data.

Matt James of the DCC explained that organisations seeking to access smart meter data via DCC must undertake a series of technical, security and administrative steps to on-board and integrate with the smart meter system.

Several policy initiatives, such as ‘Data for Good’ (Energy Systems Catapult, 2023) are making the case for improved, appropriate access to smart meter data for public benefit. An alternative access route to aggregated data is through the electrical Distribution Network Operators (DNOs). DNOs currently have access to anonymised half-hourly smart meter data, for the purpose of delivering an efficient network. By February 2024 DNOs will be obligated to report smart meter data as aggregated and anonymised open access data (interview with Matt James of the DCC). Phase 2 of the Smart Meter Energy Data: Public Interest Advisory Group Project is exploring how smart meter data collected by DNOs could be of value in delivering wider public policy objectives (Sustainability First & Centre for Sustainable Energy, 2021).

Properties without smart meters

For homes without smart meters there are sources of data for analogue (non-smart) meters. ElectraLink is responsible for operating the UK’s central energy data transfer function. They have access to metered electricity data, including from analogue meters, every time the meter is settled^[5]. ElectraLink estimates that 95% of UK households with analogue meters have at least annual electricity meter data available (interview with ElectraLink) which may be a useful source of energy consumption data for EPCs. Similar daa is collected for gas meters by Xoserve. However, infrequent meter readings from occupants can result in assumed energy use based on the suppliers’ algorithms. This would not be an accurate measure of energy consumption.

Different strategies would be needed to collect non-smart metered data for the different approaches explored in Sections 9 and 10. The SmartHTC approach (see Section 9) developed by Build Test Solutions overcomes this by being able to also work with just an opening and closing meter reading over a set period. In such cases the meter readings could be read by an energy assessor or surveyor, or could be supplied manually by the householder. The latter could introduce a risk of incorrect readings, deliberately or not (Zuhaib et al., 2021).

Alternatively, an assessor could take the manual meter readings, though this would add additional cost. As a workaround for homes undertaking retrofit monitoring without smart meters, JG Architects fit additional monitors to capture live energy data over a set time period. The representative from JG Architects suggested it is more valuable to capture time series energy use data than static meter readings. Time series data provides more detail about how the property is performing.

The risk from incorrect readings depends on how the data is used; it is more serious if the data is used as the input data on an EPC with policy implications, but less concerning if the data only serves the purpose of providing an additional metric for householders to better understand their energy usage. Given the large number of properties in Scotland without smart meters, this should be given significant consideration.

Properties heated with unregulated (unmetered) fuels

The stakeholders agreed that properties heated with unregulated fuels (such as oil, coal, wood, and biofuels) pose the most difficult challenge. As noted by Richard Fitton, Professor of Building Performance, these properties are out of scope of the smart meter rollout and at risk of being excluded from new approaches to EPCs that use metered data. Lomas et al. (2019) state that their proposed Domestic Operation Rating method (Section 9) will not work for homes using these types of fuels.

Different solutions could be implemented depending on the specific approach but would be associated with significant uncertainty and be difficult to implement. Build Test Solutions suggested an overnight test that uses direct electric heaters^[6]. This requires a property to be vacant for the 15-hour test period. It is also possible to add meters into LPG and oil supply feeds, which could be installed temporarily and then removed and reused. These are not generally fitted as standard. This does not overcome the issue of metering solid fuels.

Jon Stinson discussed that Building Research Solutions (BRS) has navigated this challenge by backtracking energy consumption from invoices, though noted that this is a time-consuming process. He also suggested a requirement for those using solid fuel to install some sort of heat meter (as with RHI, FIT and generation meters). This would still rely on some form of modelling and would also need an interface or programme through which people can submit their meter readings.

Alternatively, Richard Atkins, Chartered Architect, suggests instigating a requirement on coal and oil suppliers to keep a record and to provide this– though there would be no certainty of how the fuel is used in the property. Sam Mancey from DESNZ noted that for this data to be useful you would also need to know the length of time between refills to understand how long it takes to use a specific quantity.

Given the move toward ZDEH (Zero Direct Emissions Heating) systems, consideration should be given to whether it is proportionate to develop a system for assessing the metered energy consumption of properties using alternative fuels. An estimation based on an annual measure of fuel use may be more appropriate and proportionate (Lomas et al., 2019), although less accurate.

Dynamic EPCs

Most stakeholders supported proposals for dynamic EPCs. These will provide improved opportunities to utilise energy consumption data. Dynamic EPCs are live reports, and this will allow for some data inputs to be updated on a more regular basis than the required EPC timeline (currently 10 years but proposed to be 5 years). This could result in the inclusion of energy pricing or carbon emission factors.

Dynamic EPCs could also allow users to input their own contextual data (see 9.3) to tailor the reported consumption data to their own usage patterns. Stakeholders proposed a public EPC which contains building performance information, and a separate private element which allows users to input their occupancy data. A representative from Build Test Solutions suggested that if EPCs enabled householders to input their specific occupancy hours and set points, this would achieve an EPC much more closely aligned with actual consumption. This could overcome the challenges around collecting data on occupancy. Users can input this data if they would find the output useful, but otherwise a standard EPC for the building exists without the need for any occupancy data.

GDPR

Energy consumption data is considered as personal data under GDPR. GDPR is not a barrier to collecting and using energy consumption data for the purpose of EPCs, as exemplified by its use in Sweden and Germany. However, any process for collecting and processing energy consumption data will need to be GDPR compliant. Below are some of the key GDPR considerations for the use of metered energy consumption data at the individual household level.

Data ownership

Energy consumption data is owned by the person who consumed the energy (usually the energy bill payer). The stakeholders we consulted believed that householder consent would be required to access and use this data, and this was confirmed by the DCC. There was disagreement between the stakeholders we interviewed about the degree to which this poses a challenge for the use of energy consumption data.

The impact of GDPR on energy consumption data depends on how it is used and stored. For example, Build Test Solutions explained that they do not identify the individual or specific address associated with the energy consumption data they collect in order to calculate the heat transfer coefficient (Section 8), and they only hold location data at a partial postcode level. Kevin Gornall from DESNZ also noted that as part of the SMETER project (Section 8.1), there was a central database of metrics based on the metered data, but the metered data itself was not stored.

Data management

The stakeholders we interviewed agreed that the processing and management of personal energy data and consent poses a significant challenge. This is particularly true if live data is collected at scale, as mentioned in Section 7.2. The actors currently involved in energy consumption data management include energy utilities, DNOs, ‘Other Users’ (other registered users of the smart meter system), and the DCC.

Andrew Parkin at Elmhurst Energy highlighted the challenge of accessing energy consumption data which is decentralised and held by the energy utilities. Several stakeholders suggested that energy consumption data could be stored in a central repository. Householders could then have the option to consent to their energy data being used for different purposes. As indicated previously, work is being undertaken by DESNZ to explore the feasibility of this (Section 7.1.1).

Jon Stinson at Building Research Solutions pointed to the US Department of Energy (US DoE) as an example of how this could be done. He explained that the US DoE collates all energy data from utilities. Initially, this was done to enable academics to access these large data sets for research purposes. In this way, energy data is centralised, and there are fewer issues should the consumer change supplier or meters regularly.

Impact of tenancy type

There are also potential challenges associated with different tenancy types. Crawley et al. (2020) note that EPCs are often commissioned by a landlord, not the owner of the consumption data. In such cases the building owner would require the tenant to provide consent to access these data, adding a layer of complexity to the process.

Energy consumption data to improve the asset rating accuracy

Metered consumption data could be used to calculate a heat transfer coefficient (HTC), which is part of the calculation for EPC ratings. HTC is a common metric for the thermal performance of buildings. For the purposes of producing EPCs, HTC is predicted using SAP/RdSAP for domestic properties and SBEM for non-domestic properties. This is based on assumptions about the heat loss of various aspects of the building (walls, floor, roof, windows etc.) It is used as part of the calculations to estimate annual heating bills, CO₂ produced by the building, and the A-G asset rating (Fitton, 2020).

HTC can also be measured in-situ through a co-heating test. This is an intrusive and expensive test which measures the rate of heat loss over a certain period (usually one to three weeks) (Hollick, 2020) and must take place whilst the building is unoccupied.

Research is currently ongoing to investigate how metered energy consumption data could be used to calculate the HTC more accurately than the current predictions in RdSAP, and a more cost-effective way than the co-heating test.

Several stakeholders interviewed^[7] discussed the potential for energy consumption data to be used to calculate the HTC of individual properties. All were of the view that calculating an HTC using energy consumption data is more accurate than the HTC values predicted by RdSAP. However, some stakeholders did question the usefulness of this to householders. For example, the representative from the Climate Change Committee (CCC) suggested that this would be useful for improving building standards, but the information is unlikely to be something that householders want or need.

Current research

Several approaches are currently being developed and tested. The Smart Meter Enabled Thermal Efficiency Ratings (SMETER) Innovation Programme has undertaken field trials to test nine SMETER technologies. The trials took place in a non-representative sample of 30 homes (BEIS 2022). The accuracy of each SMETER technology was evaluated by comparison with the measured HTC^[8].

Build Test Solutions has developed the SmartHTC method, which is commercially available and has been applied to over 10,000 buildings at time of writing. . SmartHTC is a technology agnostic algorithm. It can either be delivered as an assessment service led by an assessor, or embedded into smart devices such as a smart meter IHD or a smart thermostat. The algorithm was used by the two best-performing HTC technologies in the SMETER research (BEIS, 2022). The IEA’s Annex 71 is also investigating methods for measuring HTC, including through smart meter data (Fitton et al., 2021).

Common to all these approaches is the need for three key pieces of information; metered consumption data (provided by smart meters for gas and electricity), internal temperature data and external temperature data.

Internal temperature data

Internal temperature is critical to collect. Senave et al. (2019) demonstrate that estimated internal temperatures can lead to errors in the HTC of up to 26.9% compared to internal temperature data from one room in the home. Ideally indoor temperatures should be measured in two locations. The literature points to the increasing popularity of “on-board devices” (Fitton, 2020) such as smart heating controls as a valuable source of internal temperature data. However, this is not currently a viable option in the context of producing EPCs. The majority of homes do not have this technology, and it is unclear how this data could be collected centrally.

Newer models of smart meter in-home displays (IHD) also have the capacity to record temperature data. For example, Chameleon’s IHD7 IHD which is already being deployed in the smart meter rollout. The UK Government is currently funding projects to explore whether smart meter infrastructure can be used for more than just energy data (DESNZ, 2023b). As part of this, Matt James explained that the DCC is involved in an ongoing pilot to investigate whether temperature and humidity data can be transmitted through the system, alongside meter readings.

Research has also explored whether it is possible to use smart meter data to estimate thermal performance without the need for temperature data. Chambers and Oreszczyn (2019) only used smart meter data and used the building’s location to make assumptions about local temperatures^[9]. Three of the SMETER trials also did not use internal sensors and demonstrated that it is possible to generate an HTC figure without collecting internal temperature data. However, these SMETER technologies were found to generate less accurate HTCs than those which also measured internal temperatures.

An interim solution, suggested by Baker and Mould (2018), is that until in-home sensing equipment is mainstream, homeowners and landlords could be incentivised to record this data voluntarily for inclusion in domestic EPCs. For their SmartHTC method, if internal temperature data cannot be collected via existing devices such as smart thermostats, Build Test Solutions send several low-cost temperature sensors to householders to collect temperature data over a period of 3 weeks.

External temperature data

External temperature is a key factor influencing the amount of energy used in a building. Whilst some smart heating controls do have external temperature sensors (for weather compensation), most studies and trials to date have relied on data from nearby weather stations and online tools. Stakeholders we spoke to commented that, generally, external weather data is readily available, detailed, and reliable (Richard Fitton, Professor of Building Performance and Build Test Solutions).

Potential applications

As an input to EPC calculations

The HTC is not weighted or normalised in any way. It does not account for the size, shape or age of a building. In general, the HTC is higher for larger homes (Fitton, 2020), and therefore does not allow buildings to be compared. For this reason, the majority of stakeholders interviewed for this research felt that the HTC figure should not be presented on EPC certificates and instead should be used in the calculation of EPC metrics.

As a standalone figure on EPCs

In contrast to the above, the IEA Annex 71 report recommends that the raw HTC figure is reported on EPCs. The report authors compare the HTC to the miles per gallon (MPG) metric used for vehicles. The MPG metric is widely understood by consumers and is not normalised for size (the cylinder capacity of the engine). Similarly, they propose the HTC value could become a recognised and well-understood metric. This would require householders to be provided with a bespoke annual heating degree day (HDD) figure, in the same way that motorists are usually aware of their annual mileage.

We did not find that this view was widely reflected amongst stakeholders that we interviewed, though David Allinson also used MPG as an analogy. He noted that when looking a purchasing a vehicle, we would not expect to know or predict exactly how much a particular vehicle would cost to run and that MPG is a useful metric to understand the relative fuel efficiency of a vehicle. He suggests that in the same way we should not look at an EPC and expect to know exactly how much a property will cost to run, though we could be using HTC figures in a more useful way. Richard Fitton suggested that if the HTC value is included on EPCs it should be normalised by floor space (m²) to become the ‘heat loss parameter’ or better still by volume (m³) to account for high ceilings.

The performance gap

The HTC can be used to identify where new buildings or retrofitted buildings are not performing in line with modelled predictions (Fitton, 2020). As outlined in Section 5, this is not uncommon.

In relation to new builds, Kevin Gornall from DESNZ suggested that one of the most promising applications for in-use HTC is to identify issues with building fabric. He suggested that if the modelled HTC derived through SAP is vastly different to the measured in-use HTC figure, then it may point to construction problems which needs to be addressed. This can prompt further investigation help to identify issues that would usually go unnoticed.

HTC readings can also be an effective tool for monitoring the impacts of retrofit. For example, Elmhurst suggests that their Measured Energy Performance (MEP) tool^[10] is most effective as a tool for evaluating the impacts of retrofit projects. Calculating the HTC pre- and post-installation can provide a more accurate assessment of the impacts that retrofit measures have had on the thermal performance of the property. MEP can also be used as a part of meeting the PAS 2035 requirements for monitoring and evaluation (Elmhurst, 2021).

Challenges to this approach

As outlined in Section 7 there are a number of challenges around relying on smart meter data.Technologies to measure and transmit internal temperature data are also not widely available in most homes. Both interviewees from DESNZ, Jon Stinson from BRS and a representative from Build Test Solutions all discussed the use of a co-heating test as an alternative method for homes without smart meters. This is not a practical or cost-effective solution for generating EPCs at scale. Overnight HTC tests or temporary meters are likely to be the most practicalsolutions for homes with unmetered fuels. Additionally, the SmartHTC algorithm can be used with only opening and closing meter readings for non-smart meters.

A representative of Build Test Solutions stated that another challenge is accounting for electrical loads outside the building envelope such as electric cars, outdoor offices or hot tubs. Ideally, these should be metered separately.

Annex 71 (Fitton et al., 2021) highlights that the regulatory energy models in the UK do not allow for the HTC to be directly entered as a measured value. Multiple stakeholders confirmed that this is technically possible to overwrite the HTC value in SAP. Therefore, further investigation is required as to whether there are regulatory or practical barriers to doing this.

Energy consumption data for operational performance

Metered energy consumption data can be used to produce an operational rating which is more closely aligned with actual energy use and gives an indication of how a building is used. This type of metric will include the impact of occupant behaviour. The influence of occupant behaviour makes this approach less suitable for comparison between buildings. However, this can also be an advantage, especially when combined with a good benchmark. Comparison against a benchmark can be used to encourage both building energy performance and user behaviour change (Zuhaib et al., 2021).

The most straightforward use for metered energy consumption data is to include the value on an EPC alongside a reference figure. The reference figure could be historical energy consumption data for that property (Zuhaib et al., 2021). This would not allow for comparison against other buildings unless the data is normalised to account for factors such as size and occupancy.

Current examples

Display Energy Certificates

Display Energy Certificates (DEC) for public non-domestic buildings^[11] are an example of an operational rating (section 10). Energy consumption is compared to a benchmark for similar types of buildings (Lomas et al., 2019).

Measured Energy Performance Indicator (MEPI)

The X-tendo project (Verheyen et al., 2019; Zuhaib et al., 2021) developed the Measured Energy Performance Indicator (MEPI) to be compatible with EPCs. It proposes that real energy consumption data is used to generate an ‘energy use indicator’ on EPCs. To enable comparison between buildings, this figure is weather-corrected and normalised for building size and primary energy factors^[12]. This method relies on sub-metering to disaggregate consumption for heating and hot water. Sub-metering is not widely used in domestic buildings in Scotland.

This method has undergone testing in four European countries. This revealed that further corrections are needed to be able to make useful comparisons, for example the number of hours the heating system is used. The method contains an optional module to correct for indoor temperature.

EPCs in Sweden

A representative from Boverket explained that EPCs in Sweden are based on real energy consumption data, which is disaggregated by the energy assessor to only consider energy used for heating, cooling, domestic hot water, and fixed lighting, and then corrected to reflect typical use. This results in an operational rating than enables comparisons between buildings. A challenge of this approach is that it requires the energy assessor to make assumptions about a building’s energy use, since disaggregated metered data rarely exists for each of the different energy uses.

Domestic Operational Rating (DOR)

Researchers from Loughborough University and De Montfort University have proposed and tested a DOR scheme for assessing the energy performance of occupied dwellings (Lomas et al., 2019). They propose this scheme as separate and complementary to existing SAP methodology, similar to DECs for non-domestic buildings.

The DOR uses metered energy consumption data alongside the existing survey data for a property collected for an EPC. For example, a key piece of information needed to normalise the energy consumption figure is total usable floor area (Lomas and Allinson, 2019). The proposed DOR scheme provides three operational ratings for energy demand (DORED), GHG emissions (DORGG) and energy costs (DOREC). These are intended to correspond with current metrics on an EPC. The energy cost metric is derived from the energy demand figure. It could be based either on a nationally standardised fuel cost (similar to SAP look-up tables) or on the actual fuel prices paid by each household.

The authors also explore the idea that a DOR certificate could be used to convey additional energy-related behaviour and advice to households. It could also have particular relevance for identifying homes in fuel poverty or residents that are under-heating their homes. Another key benefit of DOR is that it accounts for all energy used (regulated and unregulated).

David Allinson (Building Energy Research Group, University of Loughborough) suggests that moving towards DOR with normalised data to account for anomalies (e.g., a particularly cold winter), would allow people to compare with other people in the neighbourhood or the same property type.

Enabling comparison

Normalisation of data

Experts have proposed different methods which use different degrees of correction or normalisation. In its purest form, annual metered data could be included as-is. With no correction, this would result in a worse score during colder years where the heating requirements are higher. Conversely, recommendations for a new heating system based on a particularly mild winter where the heating demand of the property was lower than usual, or energy savings measured between non-typical years would be misleading.

There is consensus in the reviewed literature that a metric of this type should be normalised at least by floor area (Baker and Mould, 2018; Lomas et al., 2019). In France, EPCs for pre-1948 buildings were previously calculated based on an average of three years of metered data corrected by floor area (Crawley et al., 2020). However, this option was removed as part of recent EPC reforms due to issues related to buildings with irregular occupancy (Rosemont International, 2021; Thomson and Jenkins, 2023).

Weather-correction

The DOR uses weather-correction to enable the comparison of ratings between homes in different locations across the country. The metered daily gas and electricity consumption of homes is corrected based on the number of heating degree-days. An alternative to weather-correcting the energy demand data is to instead correct the benchmark that the energy is compared to (see below).

Corrections for standard user behaviour have also been proposed (Zuhaib et al., 2021). The latter is possible if occupancy profile data is available, but the authors note that this is hard to obtain.

Benchmarks

The DOR proposes that weather-corrected and normalised energy demand is compared against a benchmark of the average energy demand for the UK. Selecting an appropriate benchmark requires careful consideration (Lomas et al., 2019).

Jon Stinson of BRS also recommended inclusion of an average energy use figure across the previous three years, normalised with internal and external temperature data. He suggests that this could be a rolling figure, updated annually, linked to a dynamic EPC.

Non-domestic DECs use a building-specific benchmark corrected to account for the duration of occupancy and weather conditions. However, this approach is less appropriate for domestic buildings, since the proportion of energy that is used for space heating (and therefore should be weather corrected) varies significantly (Lomas et al., 2019).

Contextual occupancy data

If energy consumption data is provided on EPCs then some level of contextual data about the occupants is also required. For example, a potential tenant or buyer would need to know some details of the previous occupant(s) to understand the relevance of their energy usage.

Three stakeholders (from Build Test Solutions; Thomas Lefevre of Etude; Alan Beal of Bacra and Richard Fitton, Professor of Building Performance) were wary of using energy consumption data in isolation as it is difficult to account for all variables and to collect this data from occupants.

Several stakeholders (Kevin Gornall, DESNZ; Barbara Lantschner, JG architects; and a representative of the CCC) suggested that a small number of key questions regarding in-use occupancy information could be sufficient to generate an output which is accurate enough for the purposes of an EPC. Key information identified included:

• Occupancy (number of people in the household)
• Heating regime (hours of heating and preferred temperatures)
• Energy behaviours (information on unregulated energy use, e.g., large appliances)

Kevin Gornall from DESNZ suggested that in future there could be the option for occupants to answer several survey questions surrounding how they use energy in the home at the point of assessment. This information alongside internal temperatures and patterns of energy consumption could replace the occupancy assumptions used within SAP to generate more tailored outputs. His view was that the existing SAP model can generate accurate outputs providing that accurate information is fed in, and the key is to provide an open version of SAP where assumptions can be altered.

A similar exercise has been done with EPCs before, through the Green Deal Occupancy Assessment. This used standard EPC inputs and amended these with data from a series of additional questions. For example, standardised occupancy patterns were amended to reflect the household.

A representative of Build Test Solutions suggested that metered data could be used to achieve a more accurate baseline asset rating (see Section 8), with further occupational data added as a separate metric to achieve an output much more closely aligned with the total energy consumption.

As highlighted in Section 8.1.1, and by Jon Stinson of BRS, internal temperature data could be used to understand heating outcomes to contextualise the energy consumption data.

Alternatively, the DOR is designed so that it does not require any contextual data from occupants. Metered consumption data is normalised and compared to a national benchmark (Lomas et al., 2019). The authors note that not accounting for number of occupants may result in a poorer DOR for homes occupied by more people. They note privacy concerns over collecting this information, and the practicalities of defining occupant numbers, particularly in HMO properties (ibid.).

Presenting the data

An operational rating could be presented on an EPC alongside the asset rating. However, Lomas et al. (2019) suggest that the DOR is provided on a separate certificate. This would be similar to DECs for non-domestic buildings^[13]. The move to dynamic EPCs will have implications for how an operational rating can be displayed (Section 7.2).

In contrast, Baker and Mould (2018) suggest that consumption data should replace the existing modelled SAP methodology rather than complement it, with all EPCs being based on an operational rating.

It is possible to use asset ratings and operational ratings to produce two different kinds of EPCs. This is the case in Germany, where EPCs can take the form of either a demand certificate, which provides an asset rating, or a consumption certificate, which provides an operational rating (Lomas et al., 2019). While the resulting energy certificates differ, they are both considered to be EPCs that fulfil the requirements of EPBD. It should be noted that in Germany, the operational rating based EPCs are only available for buildings with more than five flats, since including multiple households approximates normalisation for different occupant behaviours. This would not be possible in Scotland where EPCs are produced for individual dwellings rather than buildings.

Challenges to this approach

One challenge to developing an operational rating is determining whether and how much contextual data to collect from occupants. Additionally, Lomas et al. (2019) state that it is desirable for a DOR to disaggregate energy used for space heating, domestic hot water, and electrical energy use. Sub-metering is not widely used in domestic properties (see Section 6.3.1), so this will be challenging.

Non-domestic EPCs

The most obvious use for metered energy consumption data in non-domestic EPCs in Scotland is to extend the use of DECs. This was suggested as the best way to use metered consumption data for non-domestic buildings by Joshua Wakeling of Elmhurst Energy. The operational rating on a DEC is based on meter readings for 12 months of energy consumption and compared to a benchmark. The operational rating is a numerical indicator and is also illustrated on an A-G scale.

Additionally, Joshua Wakeling (Elmhurst Energy) noted the need for more investment in improving the DEC methodology and to better understand occupancy assessment. The DEC methodology has not been updated for over 10 years (Elmhurst Energy, 2022).

The considerations around different types of energy use, as discussed in Section 7, are also relevant to non-domestic buildings. An analysis by Jones Lang LaSalle (2012) of 200 non-domestic buildings in the UK found little or no correlation between EPC ratings and actual energy performance. This significant performance gap has been attributed to a combination of uncertainty in the modelling, occupant behaviour, and poor operational practices (van Dronkelaar, 2015).

Jon Stinson of BRS has found that accessing metered data is more straightforward for non-domestic buildings than for domestic. Many occupants of non-domestic buildings will already have processes in place to collate energy consumption data, and larger buildings tend to have sub-metering arrangements as well as Building Energy Management Systems (BeMS). However, Joshua Wakeling of Elmhurst Energy noted that in England and Wales the deployment of DECs to private sector buildings has been hampered by a reluctance to share energy data.

Stakeholders discussed the use of metered energy consumption data for the purpose of an operational rating, but not for an asset rating. The comparison of HTC figures is not as important for non-domestic buildings as it is for domestic buildings. This is because building fabric has a comparably lower impact on heat loss than ventilation and air-conditioning systems (Jon Stinson, BRS).

Conclusions and recommendations

This report has explored two ways in which metered energy consumption data can be used in EPCs and the factors that need to be considered to enable this. Metered energy consumption data can provide more accurate information on building fabric performance (asset rating) and give an operational rating of how energy is used in a building.

A more accurate asset rating can be generated by using metered energy consumption data to calculate the HTC (heat transfer coefficient) in properties. Although various methods have been tested in recent years, they are not yet sufficiently developed for widespread roll out in EPCs. This approach requires collecting internal temperature data and is limited in properties without smart meters. Further work is required within the industry to enable the reliable collection of internal temperature data and consumption data across properties with different meters and fuel types.

Accurate HTC figures calculated using energy consumption data will also have value for informing retrofit decisions. This is currently being explored through projects such as Chameleon’s HTC-Up project. The use of energy consumption data in EPCs will provide a more realistic baseline asset rating on which to base recommended retrofit measures. However, the recommendations on an EPC would still be generated automatically by SAP.

Metered energy consumption data can be used to produce an operational rating to give an indication of how a building is used. A wide range of different approaches have been explored in the literature. The most straightforward use for metered energy consumption data is to include the value on an EPC alongside a reference figure. Another option is a DOR showing the energy consumption of a property, corrected by weather and floor area. This rating could be included as a part of the EPC or exist as separate document.

Using energy consumption to provide an operational rating has the challenge that different energy uses are not yet disaggregated. As a result, it can be difficult to determine what causes increases or decreases in energy consumption. Sub-metering has been suggested as a potential solution, though this technology is not commonplace in Scottish homes at present. The X-tendo project also proposes a method to achieve an operational rating but requires further normalisation of the data to account for different energy uses.

This operational rating could be included as part of existing EPCs or could be presented separately to provide additional information as to how efficiently energy is used in the home. Generation of an operational rating has the potential to be incorporated as part of dynamic, digital EPCs where data can be updated and adjusted without the need for a new EPC to be created. This format could enable occupancy-related data to be separate from the public asset rating.

Energy consumption data could be used in both or either of the two ways outlined above. EPCs should retain an asset metric (whether based on modelled or measured data) that is based on standard occupancy assumptions to allow comparison between properties regardless of who occupies them. This should not be replaced with an energy use metric, which contains occupancy variables that cannot be fully accounted for. Such a metric could be useful in addition to a standardised metric for comparison. It was suggested that metered data could be used to achieve a more accurate baseline asset rating, with further occupational data added as a separate metric to achieve an output much more closely aligned with the total energy consumption.

In both cases, consumer consent will be needed to collect and process metered energy consumption data and further consideration must be given as to how this can be facilitated.

Recommendations

This research has highlighted that further work is needed in this area to explore:

• The practicalities of collecting required data. This will include:
• Metered energy consumption data at the individual building level, rather than from aggregated datasets. This will require a standardised process for collecting consumer consent. Currently, public sector bodies can obtain household-level data without the need for individual consent through the legal basis of public task’. However, this is for aggregated data and there are no current examples of data being used to provide insights at the individual household level. Further investigation is needed into the legal basis of public task for collection of metered data for reporting at the household level. Legal routes for this were not explored as part of this research.
• Processes for data collection, as these are mostly dependent on the rollout of smart meters. An alternative methodology will need to be developed for households using unregulated fuels, as their heating consumption will not be captured in smart meter data.
• Additional information from occupants which can be used to contextualise energy consumption data when used for an operational rating. Examples of this kind of data include the number of occupants or typical heating regime. Further work is required to understand the minimum amount of contextual information to enable metered energy consumption data to be useful.
• Internal temperature data for the purpose of calculating HTC as part of an asset rating. This would require the mass rollout of internal temperature sensors, which are already included in some IHD (in-home display) devices. Internal temperature data could also be useful contextual data for an operational rating.
• Different formats that could be used to display consumption data when used for an operational rating. This should consider whether consumption data would work best as one of multiple ratings within the EPC or separately.
• For energy-generating homes, how total energy consumption, generation, export, and cost can be displayed in a straight-forward manner.
• Whether there are regulatory or practical barriers to inputting the HTC as a measured value in SAP calculations for the asset rating.
• The value of Display Energy Certificates for non-domestic public buildings in England and Wales, and whether there would be value in expanding their use in Scotland.

References

Appendix: Research methodology

Desk research

This report was informed by desk research in the form of a literature review of academic articles and grey literature such as reports, statements, policy literature, and consultations.

An initial literature search was carried out using the search terms listed in table 1. The list expanded throughout the research process as key terms and concepts were identified. Further sources were identified from relevant sources cited in included literature. Literature from the past five years was prioritised, though some older works also informed the research. Through the search, 51 relevant pieces of literature were identified.

Stakeholder interviews

Fourteen interviews were carried out with stakeholders in Scotland, the UK, and Sweden. These were semi-structured, 30–45-minute interviews undertaken in July and August 2023.

Interviews were held with the following stakeholders:

• A representative from Boverket, the Swedish National Board of Housing, Building and Planning.
• Richard Fitton, Professor of Building Performance, University of Salford.
• A representative from the Climate Change Committee.
• David Allinson, Building Energy Research Group, School of Architecture, University of Loughborough.
• Richard Atkins, Chartered Architect.
• Jon Stinson, Managing and Technical Director, Building Research Solutions.
• Thomas Levefre, Managing Director, Etude.
• Alan Beal, Bacra.
• Barbara Lantschner, Building Performance Specialist, John Gilbert Architects.
• A representative from Build Test Solutions.
• Sam Mancey, SMETER Implementation Team, DESNZ.
• Kevin Gornall, SMETER Implementation Team, DESNZ.
• Andrew Parkin, Director of Technical Development, Elmhurst Energy
• Joshua Wakeling, Director of Operations, Elmhurst Energy.
• Matt James from the Data Communications Company.

Qualitative analysis

The literature and interviews were analysed in NVivo using inductive coding. This allowed key concept (e.g. performance gap) and categories (e.g. asset vs operational ratings) to emerge throughout the analysis process. Findings from the interviews and the evidence review were analysed using the same coding structure. This approach also facilitated the identification of research gaps.

© The University of Edinburgh, 2023
Prepared by Changeworks on behalf of ClimateXChange, The University of Edinburgh. All rights reserved.

While every effort is made to ensure the information in this report is accurate, no legal responsibility is accepted for any errors, omissions or misleading statements. The views expressed represent those of the author(s), and do not necessarily represent those of the host institutions or funders.

1. 
   

   Survey respondents included engineers, architects, product manufacturers, social housing providers, policy makers and researchers. ↑

   
   
2. 
   

   The term ‘similar households’ was not defined in the study. Because of the variance of occupancy influence on energy use, this could be interpreted as similar age or number of occupants, heating pattern, income, or other factors. ↑

   
   
3. 
   

   For most studies included in the review the electricity use of dwellings may include electric space heating, electric water heating and electric space cooling. Not all studies explicitly stated whether these were included which makes it difficult to draw clear conclusions. ↑

   
   
4. 
   

   Xoserve is the Central Data Service Provider for Britain’s gas market. ↑

   
   
5. 
   

   Meters are ‘settled’ each time a meter reading is provided from the consumer. ↑

   
   
6. 
   

   Examples of these tests include QUB and Veritherm. ↑

   
   
7. 
   

   including a representative of Build Test Solutions, a representative of the Climate Change Committee, Sam Mancey and Kevin Gornall of the SMETER Implementation Team at DESNZ, Jon Stinson from Building Research Solutions, and Thomas Lefevre from Etude. ↑

   
   
8. 
   

   Determined using the QUB test, which is an alternative to the co-heating test and can estimate the HTC within a day. ↑

   
   
9. 
   

   Note that this study calculated Heating Power Loss Coefficient (HPLC) rather than HTC. The difference is that HPLC incorporates thermal losses from the heating system as well as the building fabric. ↑

   
   
10. 
    

    This tool uses four temperature and humidity monitors throughout the home to record internal data for a three-week period. Measured energy use during this period is also taken to calculate the HTC figure. ↑

    
    
11. 
    

    Public buildings in England and Wales over 250 m² must have a DEC. In Scotland, public buildings are required to have an EPC rather than DEC. ↑

    
    
12. 
    

    The amount of primary energy used to generate a unit of electricity or a unit of useable thermal energy in a building. ↑

    
    
13. 
    

    Public buildings in England and Wales over 250 m² must have a DEC. In Scotland, public buildings are required to have an EPC rather than DEC. ↑

    
    

Research completed in December 2023

DOI: http://dx.doi.org/10.7488/era/5478

Executive summary

Background

The generation of energy from organic matter, such as plants, is called bioenergy. The Update to the Climate Change Plan (CCPu) identifies the significant role that bioenergy could play in delivering Scotland’s legally binding commitment to achieve net zero by 2045. This could be achieved whilst also supporting a green economic recovery from the effects of the Covid-19 pandemic and a just transition that creates jobs and supports people and rural communities.

To meet this expanded role for bioenergy in Scotland, a scaling up of domestic biomass production would be required. The UK Climate Change Committee (CCC) highlighted the opportunity for domestic production as a key pillar for delivering the CCPu ambition.

This research examines the economic potential of perennial energy crops (PECs) for farmers and land-managers, as well as the wider economic implications. The three PECs considered are miscanthus, short rotation coppice (SRC) and short rotation forestry (SRF).

Key findings

Profitability of perennial energy crops

• PECs have the potential to generate income for farmers and land-managers in Scotland.
• Comparison of gross margins shows income from PECs is likely to be lower than from other typical farm enterprises on suitable land, such as lowland cattle and sheep and ‘mixed agriculture’. This is assessed on the basis of yearly average gross margins over the lifetime of the PEC in comparison to equivalent gross margins for other farm enterprises.
• Income (gross margin calculation) from PECs compared very favourably in the analysis compared to the farming type known as ‘general cropping: forage’. This is growing crops for animal consumption, usually on lower quality land, and it typically makes a significant loss.
• There is a need for greater confidence that PECs will deliver good economic returns in order for them to be viewed as an attractive, economically viable option by farmers and land managers in Scotland. High upfront establishment costs for perennial energy crops and low revenue potential are both likely to hinder uptake.
• Miscanthus showed the highest average gross margin of the three crops studied, at £382 per hectare per year. However, there are some potentially limiting factors:
  • There is uncertainty about achievable yields in the Scottish climate and on the grades of land above category 4.1 in the Land Capability for Agriculture in Scotland.
  • There is limited theoretical growing area in Scotland, which is much lower than for SRF or SRC.
• SRF and SRC showed lower profitability for farmers: £80 and £87 per hectare per year over their lifetime respectively for ‘SRF: broadleaved’ and SRC. However, there is more suitable land for growing these.
• SRF conifer would see a negative gross margin, given that the production costs outweigh the value of the crop sold.

Potential opportunities

• PECs could help diversify a business, creating additional income, without adding significant additional labour requirements or ongoing input costs because minimal management time and inputs are required once crops are established.

Potential barriers

• Cash flow could pose a barrier to uptake. The distribution of costs and income year-on-year for PECs is significantly different to typical farming activities which have an annual profit cycle. PECs need investment in site preparation and planting upfront, but income only arrives after first harvest several years later. This is 2-3 years for miscanthus with subsequent annual harvests, 6 years for SRC with periodical harvests thereafter, and 15 years for SRF first and only harvest.
• Farmers and land managers may view PECs as a risky proposition due to uncertainty about market demand and achievable crop sale prices, combined with the need for upfront investment to establish production.
• Other potential barriers to uptake include: farmer and land-manager unfamiliarity with PEC production, low appetite for risk, need for new skills, access to equipment and services, concerns about community perception of land-use change, and impacts on other agricultural production, e.g. available animal feed.

Enhancing economic potential and production

Potential approaches to improve the economic potential of PECs in Scotland include:

• Financial incentives, such as government specific subsidies under future agricultural support or other market-focused incentives.
• Risk reduction strategies, such as secure, attractively priced contracts with end markets, alongside expansion of the market.
• Innovations to allow processing at the farm and to improve transportability of crops could also help to increase the economically viable travel distance.
• Improving access to skills and knowledge to produce PECs could also remove a barrier to uptake, if economic prospects are improved.

Implications for wider Scottish economy

• Future demand for PECs to support the Scottish Government’s climate ambition is likely to require increased production, and previous research suggests 38,000 hectares could be feasibly planted by 2032 and 90,000 by 2045.
• We modelled two demand scenarios to illustrate the potential range in results if land was transitioned to growing PECs:
• Scenario 1: conversion of approximately 38,000 hectares. This would result in an economic gain in terms of increased gross margin of around £9.6 million. This would however result in a shortfall in non-PEC agricultural yield (crops, stock-feeding crops and grass) of between 537,600 and 700,000 tonnes.
• Scenario 2: conversion of approximately 90,000 hectares. This would result in an economic loss of around £9.5 million per year, based on gross margin, and a shortfall in non-PEC agricultural yield (crops, stock-feeding crops and grass) of between 708,200 and 1.6 million tonnes. The financial loss is because under this scenario more economically advantageous land is transitioned to PECs and the PECs perform less well economically.

Economically viable production locations

• Economically viable production locations for PECs are influenced by multiple factors including proximity to markets and local access to services and facilities for crop management, such as harvesting contractors, to avoid incurring excessive costs.
• The research identified suitable growing regions (some SRC/miscanthus and most for SRF) within an economically viable transport distance to existing biomass plants and potential sites for Bioenergy with Carbon Capture and Storage (BECCS) near the proposed east coast carbon capture and storage feeder pipeline (assumed 50-100 km).
• Economic viability may be a barrier to SRF production increases even if suitable land is available, given that it is economically uncompetitive against other land use options.

Potential further steps

Key debates and areas for further research include:

• Considering more in-depth ‘whole farm’ economic analysis. This study focused on gross margin comparison, which is useful for comparing specific crops and farm enterprises, but has limitations in terms of how well it allows assessment of integration of energy crops into a whole farm business. This will vary farm to farm but could be explored through farm case studies. This could include considering a wider range of costs for farmers and that after initial set up the PECs would require less workload.
• Comparing the economic and environmental potential of using land for energy crops with utilising the same land for other renewable energy options, such as using the land for solar panels alongside grazing.
• Exploring the potential role for on-farm use of perennial energy crops.
• Considering future biomass markets, including how future Greenhouse Gas Removal (GGR) schemes, global demand and demand from biotechnology sector may impact it.
• Identifying how to make domestic biomass from energy crops a more attractive option than imports and a more profitable use of land, and on what basis this can be justified. For example, taking account of full LCA and rewarding greatest emission saving.
• Considering in more detail the role of PECs in the context of how the agriculture sector is changing and how it may have to change to reduce GHG emissions.
• Considering the value, including the financial value, of other benefits of energy crops, such as flood mitigation or animal shelter, relative to existing or potential alternative land-uses.
• Exploring how PECs support/interact with tier 2 or 3 objectives of the ARP.
• Considering the impact of subsidies.

The most economically and environmentally advantageous approach is likely to be site-specific and determined by local circumstances. Making judgements about the best use of land is complex for policy makers, farmers and land managers alike. Guidance on this decision-making is likely to be needed.

Abbreviations table

Introduction

This evidence assessment focuses on examining the economic potential of perennial energy crops (PECs) for farmers and land-managers in Scotland, along with considering the wider economic implications for Scotland. The assessment builds upon recent ClimateXChange reports which demonstrated that there are significant opportunities for the expansion of perennial energy crop cultivation in Scotland (Martin et al, 2020) and that increased supply of biomass for energy generation from such crops will be needed to meet forecast future demand in the context of Scotland’s climate mitigation plan goals (Meek et al, 2022). However, in scaling up domestic biomass production it is important to consider how the economics intersect with other relevant issues including biodiversity, land-use, water management and a ‘just transition’. This report aims to consider these issues, alongside the economics and support the Scottish Government’s development of policy in relation to perennial energy crop production.

The policy context for energy crops in Scotland

The Scottish Government’s Update to the Climate Change Plan (CCPu) forecast a role for Negative Emissions Technologies (NETs) , including bioenergy with carbon capture and storage (BECCS), to remove carbon dioxide from the atmosphere to compensate for residual emissions. ). The UK Climate Change Committee (CCC) acknowledges Scotland’s opportunity to scale up domestic biomass production to meet this aim, recommending careful consideration of impacts on land-use and agriculture. In line with Scottish Government’s Vision for Agriculture, and set out in the Scottish Agricultural Bill, future subsidy support which will replace Common Agricultural Policy, will be split across unconditional support and support targeted to sustainable food production and environmental outcomes, including low carbon farming and biodiversity. Scotland’s draft ‘Energy Strategy and Just Transition Plan’ aims to use bioenergy where it can best support Scotland’s Net Zero Journey, and aligns with and supports Scotland’s goals for protecting and restoring nature. Considering the role for production of PECs in the evolving Scottish policy landscape will be critical.

Alongside this the UK Government has also published a new biomass strategy, which aims to support sector growth and strengthen biomass sustainability. The strategy acknowledges that bioenergy policy involves a mix of reserved and non-reserved powers, and so as the Scottish Government develops its Bioenergy Policy Statement, Scotland has an opportunity to build on UK policies and develop policies appropriate for Scotland. Further policy information is included in Appendix A.

Introduction to perennial energy crops for Scotland:

Previous research for CXC identified that perennial energy crops (PEC) present opportunities for scaling up biomass production in Scotland, with short-rotation coppice (SRC), short-rotation forestry (SRF) and energy grass Miscanthus, showing most potential (Martin et al, 2020). Details of each crop can be found in Appendix B. PECs support climate mitigation by providing a renewable energy source; displacing fossil fuel use; helping to reverse soil carbon loss, and acting as a carbon sink. When used for energy generation and combined with carbon capture and storage (CCS), such crops have the potential to generate negative emissions and contribute towards Scotland’s net zero ambitions. PECs can also bring additional benefits, such as flood mitigation (see Section 4 below for further details).

Figure 3.2: Schematic diagram of bioenergy with carbon capture and storage ^[1]

Currently, Scotland grows only a small area of PECs – about 250 ha (Martin et al, 2020). Previous geo-spatial mapping work for Scotland (Martin et al, 2020) has shown theoretical potential for approximately 900 kha of land, to be suitable for PECs (913kha SRF, 219 kha SRC and 52kha Miscanthus – with some overlap between suitable areas) mainly in the east and the lowlands. This analysis considered topography, soil type, climatic variables and suitable land capability classes^[2] to identify these theoretically feasible growing areas. Future demand for PECs to support the Scottish Government’s climate ambition is likely to require increased production of such crops.

Markets for PEC Biomass in Scotland

Research^[3] has identified the following potential uses of biomass via ‘Negative Emissions Technologies’:

• BECCS Power – bioenergy with carbon capture and storage (BECCS) for electricity in a power station
• BECCS hydrogen – either via gasification of biomass or steam methane reforming of biomethane, with carbon capture and storage
• BECCS in industry (for heat and other industrial processes)
• BECCS Biomethane – processing of biomass via Anaerobic Digestion (AD), gasification or pyrolysis, with carbon capture and storage
• Biochar – pyrolysis of biomass, with carbon capture and storage

PEC biomass can also be used in combined heat and power plants and biomass boilers at a variety of scales. The market for the biomass produced from PECs is relatively immature in Scotland. There are several biomass energy plants ranging in size from large scale industrial units and power stations to small units supplying individual farms. These mostly utilise wood from local forests, waste wood from Scotland sawmills and other industries so the market for further PEC biomass is currently limited^[4]. Scotland’s largest wood-fuelled power station, is located in Markinch, with 55MW capacity utilising mostly recovered wood, some virgin wood chip. The next biggest is Steven’s Croft, in Lockerbie which generates 44MW of electricity and 6MW of heat which initially planned to source fuel from local forests (60%), SRC willow (20%) grown within a 60-mile radius (and requiring around 4,000 hectares land) and recycled wood fibre (20%) (Warren et al., 2016), but the latest data suggests it mostly uses a mix of wood and waste wood^[5]. BECCS plants are not expected to deploy in Scotland until 2030.

Evaluating economic potential of PEC in Scotland

To understand the real potential, it is critical to consider not just the overall economic viability of PECs, but also how the demand for land for PECs can be balanced against, or integrated with other uses such as food and fodder production and biodiversity, and the skills, knowledge and attitudes of the farmers or land managers.

The economic potential of energy crops

A Rapid Evidence Assessment (REA) seeking evidence of the economic potential of energy crops in Scotland was undertaken and identified peer-reviewed and grey literature. The methodology can be found in Appendix C. The review focused on Miscanthus, SRC and SRF to specifically identify:

• The positive and negative economic potential of energy crops.
• Other (non-economic) opportunities and barriers to deployment.
• Further economic potential (e.g., in relation to employment; technologies; wider decarbonisation, just transition).

Key insights are presented here, along with relevant insights from the stakeholder research. For full details of information found in the literature review and references to information sources (please see Appendix d), for details of stakeholder interviews conducted see Appendix G.

Key findings of the rapid evidence assessment and stakeholder engagement 

Evidence of positive economic potential

• There is evidence that PECs can be profitable, but there are limited studies directly applicable to Scotland and to the current economic climate (Appendix D: 15.1)
• Economic performance of biomass production is influenced by production costs, crop yields, crop price and end-use/market opportunities. (Appendix D: 15.1)
• Several studies comparing energy crops reported a high return per hectare for miscanthus primarily due to low maintenance cost along with the low requirement for field operations. (Appendix D: 15.1)
• The tree species chosen for SRF influences plantation establishment costs and therefore profitability as costs vary between species. Initial indications from trials underway in Scotland suggest hybrid apsen to have most potential, with common alder, silver birch and Sitka spruce having potential at some sites. (Appendix D: 15.1)

Evidence of negative economic impacts

• The most prominent evidence of negative economic impacts in the literature was the high upfront cost to establish PECs, lack of established markets, and the uncertainty over the stability of the long-term market. (Appendix D: 15.2)
• Profitability and economic considerations for farmers are dominated by high establishment costs, uncertainties about the market, a delayed period of revenue, and biomass yield. (Appendix D: 15.2)

Economic potential of PECs in comparison to other land uses

• The literature review did not provide clear evidence of how the three key PECs compare economically to other crops, annual crops and agricultural land-uses – some studies showed favourable comparison and others did not. Limited insights can be gained from the literature given the recent economic changes affecting agricultural costs and market prices (Appendix D: 15.3)

Influences on decisions to plant PECs

• One of the main factors affecting the uptake of PEC is economic profitability (Appendix D: 15.4)
• Appetite for and perception of financial risk, skills, attitudes and access to markets can influence farmer and land-manager decisions. (Appendix D: 15.4)
• Even where PECs, or energy crops in general, can deliver positive economic results for farmers and land managers, this on its own is not always sufficient to convince them to start growing PECs. (Appendix D: 15.4)

Other features of PEC production that influence economic potential

• Producing PECs has specific economic implications for growers which influence their economic potential and attractiveness. These include lack of flexibility of land-use, reduced market responsiveness, and opportunities for diversification alongside current farming enterprises. (Appendix D: 15.5)
• To view PECs as economically worthwhile, farmers need confidence that they can achieve an acceptable and secure market price into the future. As farms typically operate in a risk-averse manner, reduced risk is an important factor in farmer decision-making for PECs. (Appendix D: 15.5)
• The way PECs are deployed on farms influences their economic potential. Integration of PECs alongside other enterprises and on land which is not performing well could be advantageous. (Appendix D: 15.5)

Opportunities to improve economic potential

• Cultivation techniques, crop variety choice and other technological developments can influence economic potential of PECs in Scotland and have potential to improve profitability for farmers and land managers in future. (Appendix D: 15.6)
• There are factors which can negatively affect the economics of PEC production, which if addressed are potential opportunities to improve economic performance. (Appendix D: 15.6)
• Gaps in the crop (i.e. patches where it didn’t grow) was a key factor reducing profitability of miscanthus in the UK.
• Ensuring access and enabling harvesting equipment is essential for economics of SRF to be viable
• For SRF effective plantation establishment is important for the economics and general success of a SRF plantation
• Single species monocultures can offer greatest economic return by providing higher yields per hectare
• Highest yield are achieved on fertile soil or under intensive management systems, including weed control, fertilizer application and irrigation

Evidence of potential for wider economy

• There was limited research addressing the potential contribution to the wider Scottish economy and a just transition, but some opportunities and challenges can be inferred. These include sales for local energy generation and other industrial uses, employment opportunities in contract services, along with potential payments for environmental outcomes. (Appendix D: 15.7)

Evidence of non-economic opportunities

• Non-economic opportunities and benefits of PECs were identified including several relating to positive environmental outcomes such as reduced agro-chemical use, reduced soil and water pollution, carbon sequestration, and biodiversity benefits. (Appendix D: 15.8)
• The opportunities for environmental improvements resulting from PECs vary depending on planting, prior land-use and landscape context. (Appendix D: 15.8)

Challenges and deployment barriers

• Non-economic challenges facing the production of PECs in Scotland, relate to skills, land-use commitment, compatibility with current culture and habits, farm businesses, perceived land suitability and environmental concerns. (Appendix D: 15.9)
• Deployment barriers include the need for farmers to commit land for a long period of time, land quality, knowledge, profitability, time to financial return and social resistance relating to whether land should be used for energy or food production. In addition for SRC and SRF, converting land once planted is challenging, and additionally for SRF conversion be restricted by regulations as land will no longer be classed as agricultural. (Appendix D: 15.9)
• Lack of access to specialist skills and to specialist contractors and machinery was identified as a barrier to deployment. While there is interest amongst farmers in diversification, appetite for change is tempered by concern about moving into unfamiliar activities which require new skills.
• Culture and attitudes can be a barrier to PEC deployment. (Appendix D: 15.9)
• There are concerns about the impact on biodiversity from PECs. (Appendix D: 15.9)
• Energy generation from biomass is a potential source of direct and indirect emissions and limiting these emissions would need consideration. (Appendix D: 15.9)

Other relevant crops and planting regimes

• Hemp has the potential to provide high yields or returns with little or no pesticides and insecticides, significant potential in carbon sequestration, fits well into crop rotations with food and feed crops and helps improve soil structure and soil-borne pests. Constraints on producing hemp in Scotland includes the current lack of market as there are no large processing facilities in or near Scotland, strict regulations on growing hemp including the need to obtain a costly license, and some reports of low profitability according to Scottish growers. (Appendix D: 15.10)
• Specific studies focused on Scotland to show how PECs could be grown in agroforestry systems were not found, but provided the design of agroforestry systems can allow for economically efficient planting, management and harvesting it could provide an advantageous model. (Appendix D: 15.10)

Key evidence gaps

The research found some uncertainties – due to lack of Scottish specific data and in relation to climate impacts on PECs – which are described in the relevant sections above, and also some key gaps in the evidence which are summarised here.

Lack of Scottish data and research leading to economic uncertainty

Research related to the production and economic potential of energy crops in Scotland is limited. SRC is currently grown, but only at a small scale, and miscanthus still requires further trials and research before implementing at a commercial scale. SRF trials are currently underway in Scotland with findings slowly emerging as plantations reach maturity (Parratt, 2017). There is therefore uncertainty regarding the economic potential in Scotland.

The literature is inconclusive regarding the financial performance of PEC production. Conflicting results are found across studies, for example, a study in Ireland found miscanthus production to be an economically viable option (Zimmermann et al., 2014), yet in France, Miscanthus was found to be less profitable compared to conventional cropping systems (Glithero et al., 2013). Research by Warren (2014) reported that the soils and climate across Scotland offer significant biophysical potential, especially for SRC willow cultivation, which can also achieve good growth rates. However, with such limited data on Scotland and in light of the less favourable climate than found in locations of many studies there is uncertainty about the economic viability in Scotland.

Climate change

The effects of climate change on PECs are to some extent unknown. Research suggests that SRC willow yield may reduce as a result of rising temperatures, while miscanthus performs favourably (Alexander et al., 2014). However, as the temperature rises, this may change the habitat suitability, further research is required to establish the suitability and risks that a changing climate may have on seed development in miscanthus throughout the UK (Martin et al., 2020). We did not find research which commented on how extreme weather such as storms, flooding and drought would affect PECs. Some research suggests that water-logged soils hinder growth of PECs (Martin et al, 2020), but a recent technical webinar from Biomass Connect suggested that willow SRC is not negatively affected by water-logging, and can help improve water management when established.

Active debates within the sector

It is evident from the literature and stakeholder interviews that there are some topics with differing views including what types of land are most suitable for PEC growing considering the wider land-use debates, and likely impacts on biodiversity.

Land use and use of unproductive areas of land

In Scotland, there is competition for land to deliver food, materials, environmental services (such as carbon sequestration), leisure opportunities and more (Martin et al., 2020; Scottish Government, 2021). Scotland has the potential to produce 9.25TWh/yr and 1.75Modt/yr for SRC (Martin et al., 2020) such as SRC willow, however amongst the farming community there is social resistance relating to land being used for energy instead of food production (Anejionu and Woods 2019). The Scottish Government’s Land-use Strategy (Scottish Government, 2021) highlights the complexity of balancing the need for land to support the move to net zero with other essential activities such as food production, and that whilst land-use decisions are often determined by the land suitability, much land is suited to multiple different uses. In these cases multiple factors need to be considered as to whether PECs are a suitable use for the land.

Literature identifies that using ‘marginal’ land, for energy crop production could be a solution to this land use debate. However, there are several challenges in understanding whether this ‘solution’ could usefully apply in Scotland. Ranacher et al., 2021 found there is a gap in the available literature regarding farmers’ willingness to adopt short rotation plantations on marginal lands. There is also no agreed definition in the literature of what comprises ‘marginal’ land, so it is unclear how this would apply in the Scottish context. Much discussion in research focuses on cropland, yet in Scotland grasslands including rough grasslands, which may be viewed as ‘marginal’ from some perspectives, are a critical part of the Scottish rural economy and environment and so a more indepth analysis of the potential social, environmental and economic implications of PECs on grasslands is needed. Additionally, not all literature agrees on whether PECS will successfully grow on marginal land.

Biodiversity & ecosystem services

Converting land to energy production in Scotland will have direct impacts on biodiversity, wildlife, and landscape connectivity, yet the exact nature of these is unclear from the literature. Research shows that bioenergy crop choice and location influence biodiversity outcomes – choosing appropriate bioenergy crops in the right location is vital for the protection of biodiversity and ecosystems and to prevent damage to the surrounding ecosystem.^[6] Contradictory evidence has been found throughout the REA on the effects of converting land for energy crop production in Scotland. Existing sustainability criteria for the use of biomass to produce heat or electricity require that PECs are not grown on land of high biodiversity value^[7]. Beyond application of these criteria, the research could create uncertainty about how to select the right crops for the right locations in Scotland to ensure good outcomes for biodiversity and ecosystem services. Extrapolation of potential biodiversity effects from conversion of ‘marginal’ land has low confidence (Holland, et al., 2015) (Vanbeverena & Ceulemansa, 2019), and application of this research to the Scottish context with different land-use types is therefore very difficult.

The impact on biodiversity from SRC, SRF and Miscanthus differ depending on location, previous land use and crop type and management (e.g., cultivations, pesticide, and fertiliser use). The replacement of any semi-natural habitat by a dedicated bioenergy crop is likely to result in significant biodiversity losses due to creating a monoculture habitat (Martin et al., 2020). Significant areas of land classified as ‘Less Favoured Areas’ (LFA) in Scotland which were identified as potential PEC growing areas could be described as semi-natural – and seen as ‘marginal’ – but there is a risk of biodiversity loss if this is converted to PEC.

The REA identified a conflict in opinion as to whether PECs provide a biodiversity gain or loss. Firstly, factors such as reduced ground disturbance, increased diversity of nectar and pollen sources, and the potential to provide over wintering sites which are associated with energy crop production will benefit pollinating species. Conversely the monoculture nature of energy crops is likely to be detrimental to pollinator species as landscape homogenisation is widely accepted to be a driver for the current loss of pollinating species (Martin et al., 2020). Holland et al. (2015) identified ecosystem services such as hazard regulation, disease and pest control, water, and soil quality may benefit from the conversion of arable land to energy crop production, and that the transition of marginal land^[8][9] to bioenergy crops will likely deliver benefits for some ecosystem services while remaining broadly neutral for others. On the other hand, conversion of forest to energy crops will likely have a negative impact due to the increased disturbance associated with the management cycle.

Estimating economic potential

This research looked at perennial energy crops (PECs), SRF, SRC and Miscanthus and included two core economic analyses:

• Farm-scale economic analysis and comparison with typical land-use options:
• A farm scale economic analysis of the net economic benefit for a farmer or land-manager from producing and selling the Miscanthus, SRC and SRF.
• A comparison of this net economic benefit for a farmer or land-manager with typical existing land-uses.
• Assessment of wider economic implications: drawing on geo-spatial data about existing farming and land-use types, the study analysed what the economics implications would be for the wider Scottish economy of a transition to growing more energy crops.

Farm Scale Economic Analysis

Methodology overview

• For the farm-scale economic analysis high, medium and low-cost scenarios were developed for the production costs for: Miscanthus; short rotation coppice: willow; short rotation forestry: conifer; and short rotation forestry: broadleaved. The higher scenario includes high output/high price minus low costs, the medium scenario scenario includes medium output/medium price minus medium costs and the low scenario includes low output/low price minus high costs.
• The following production costs were included; pre-planting/land preparation, planting, post-planting, harvesting and storage and reversion.
• Capital investment costs were not included: where specialist equipment would be needed, which a farmer would not typically have on a farm, such as cutting equipment for SRF, we have assumed services of a specialist contractor would be utilised and this cost has been included within the production costs.
• Estimates of likely income from PEC sales were combined with costs to create a ‘gross margin^[10]’ (income minus costs) for each bioenergy crop. Because the PECs all have a long lifespan, time series charts are used to show the income minus costs over the lifetime of the crop. The results of which can be found in section 5.1.3. Depending on the crop, the yield changes over the lifespan of the rotation, for example due to lower yields in early years after establishment and harvest only occurring in some years. Details on the yields during rotation can be found in Appendix D. For Miscanthus and SRF a low, medium and high price presented, whereas for SRC a single price is used due to limited data. Prices used in the analysis are in Appendix D.
• To compare to the economics of current land use, three farm types were used these were lowland cattle and sheep; mixed farming^[11]; and general cropping – forage. These were selected because they are feasible on the land capability of grades; 4.1, 4.2, 5.1, 5.2, 5.3 and 6.1, – typically suitable for mixed agriculture, improved grassland and high-quality rough grazing, and also the land capability grades assumed suitable for PECs . To calculate the gross margins for the farm types used in the analysis the latest data from the ‘Scottish farm business income: annual estimates 2020-2021’ were used^[12].
• Subsidies are not included in this analysis.
• Total average output in the farm business survey^[13] includes the output categories; total crop output, total livestock output and miscellaneous output. For the ‘general cropping – forage’ category census data is used and output represents the estimated farm-gate worth (£s) of crops and animals without taking account of the costs incurred in production.

A more detailed description the methodology used, assumptions and data sources is included in Appendix E.

Limitations with the methodology

The calculations for the farm types used in the analysis are based on data from the Scotland Farm Business Income Survey, therefore the estimates are based on averages and so any other factors that might influence the costs and output for example climate, soil type will not be accounted for. This is the same for the costs and output estimates for the bioenergy crops. We have not allocated an economic value to any additional benefits a farmer may gain for the other farm enterprises, such as shelter for livestock on adjacent land.

It should also be noted that this study focused on gross margin comparison, which is useful for comparing specific crops and farm enterprises, but has limitations in terms of how well it allows assessment of integration of energy crops into a whole farm business. This will vary farm to farm and would require more in depth ‘whole farm’ economic analysis to be fully understood.

Results of Farm Scale Economic Analysis

Figure 5‑1 shows what land managers could earn on average in a year if costs and yield were spread equally over the lifecycle of the bioenergy crop as well as for farm types (for full details on the method please see Appendix E). There are gross margins for a low, medium and high scenario for each of the bioenergy crops and for the farm types (except for general cropping, forage^[14]). The low, medium and high scenario for lowland cattle and sheep and mixed farming includes the lower (25%), average and upper (25%) of data from the farm business income data respectively, average data from 6 years 2016-17 to 2021-22 uprate to reflect 2023 prices^[15].

Figure 5-1 ‑Yearly average gross margins for each of the PECs over the lifetime of the PEC and for each farm type £/ha (2023 prices)

If costs and income were spread equally over the lifetime of the crop, the medium scenario suggests:

• Miscanthus produces a positive average annual gross margin of £382 per hectare, SRC £87 per hectare and SRF broadleaved £80 per hectare.
• SRF conifer would see a negative gross margin i.e., the production costs outweigh the value of the crop sold. The planting and the ground preparation costs are the main drivers behind this negative gross margin (see Appendix D for more detailed costs).
• Mixed farming and and lowland cattle and sheep farms both show a greater average annual gross margin than all of the PECs examined.
• The average gross margin per year for general cropping, forage is negative at around £990 per hectare, significantly lower than for all of the PECs. Based on these average annual gross margins, growing PECs in lowland cattle and sheep and mixed farming would reduce financial returns in the farm. Whereas, growing PECs in farms in the general cropping forage category could improve their financial returns.

Figure 5‑2, Figure 5-3, Figure 5-4, Figure 5-5 shows the low, medium and high scenario gross margins (output minus variable costs) over time of each of the PECs: Miscanthus, SRC, SRF broadleaved (silver birch) and SRF conifer (Sitka spruce). The higher scenario includes high output/high price minus low costs, the medium scenario includes the medium output/medium price minus medium costs and the lower scenario includes low output/low price minus high costs.

Costs included in the calculations included:

• Site preparation / land preparation (including from different prior land-uses where data is available)
• Establishment / planting
• Crop management costs e.g., during initial growth
• Harvesting
• Reversion (where relevant)

Detailed breakdowns of these costs for the PECs are included in Appendix E.

Figure 5-2 Gross margins for Miscanthus (£/ha) (2023 prices)

• Miscanthus shows an initial negative gross margin in the first two years during the site preparation and plant stages, but then picks up in the following years with harvesting driving the positive gross margins in the following years. The gross margin drops slightly in the year 21 when the costs of reversion take place.

Figure 5-3 Gross margins for short rotation coppice (£/ha) (2023 prices)

• Short rotation coppice shows a negative gross margin for the first 3 years, in part driven by the pre-planting/land preparation costs in years -1 and 0. Gross margin is then positive in the years 3, 6, 9, 12, 15 and 18 reflecting when harvesting takes place.

Figure 5-4 Gross margins for short rotation forestry – Sitka Spruce (£/ha) (2023 prices)

Figure 5-5 Gross margins for short rotation forestry – Silver Birch (£/ha) (2023 prices)

• Short rotation forestry for silver birch and Sitka spruce shows a negative gross margin except for the year 15 when harvesting takes place.

Linking back to Figure 5-1 with the lowland cattle and sheep category on average earning £433 per hectare per year, the mixed farming category £597 per hectare per year and the general forage making a loss of £990 per ha per year the results show;

• Miscanthus, initially has a lower gross margin than all the other farm types, however, after the first few years, land managers would be better off planting Miscanthus.
• SRC, produces a better gross margin than general cropping-forage after the first few years but is outperformed by all other categories when the yield is harvested in years five, eight, 11, 14, 17, 20 and 23.
• SRF, again outperforms general cropping- forage, but has a lower gross margin than the other farm types, except for when harvest takes place in year 18.

Assessment of implications for Scotland’s rural economy

To consider the potential implications of growing more PECs, the results from the farm-scale economic analysis (Section 5.1) were extrapolated across Scottish regions, to consider a transition of approximately 40,000 to 90,000 hectares of suitable land to grow PECs – the area judged to be feasible by 2032 and 2045 respectively (see below for the source of these estimates).

Key findings:

This transition of land in mixed holdings and non-LFA cattle and sheep to PECs would create a shortfall of non-energy crops and and reduced income across the Scottish rural economy due. Because PECs would be more profitable than ‘general cropping: forage’ land-use, there would be an economic gain from transition, but loss of production of animal feed, which may have knock-on implications for livestock production costs (which have not been quantified here).

This research found that, if land to match the level of demand as set out in these scenarios, was utilised for perennial energy crops it would create:

• a gain in gross margin^[16] of around £9.6 million (scenario 1) or a loss of around £9.5 million (scenario 2) per year across the regions.^[17]
• a shortfall in agricultural yields (of farm outputs generated by existing land-use activity, which would not be available when the activity ceased to be replaced with PECs) across the regions between 537,600 tonnes (scenario 1) and 708,200 tonnes (scenario 2).

Our analysis which forms the basis of this assessment is set out below – with details of each scenario (approximately 40,000 and 90,000 hectares).

Limitations:

This assessment does not consider potential loss or additions to the economy due to changes in associated services. Some additional contracting employment for PECs servicing is likely based on the research, but this, and any potential shortfall in other employment from reducing other farm enterprises have not been assessed.

It should also be noted that the findings relate solely to gross margin comparison. Actual farm income – whole farm business income – is very different, comprising multiple farm enterprises (livestock, crops, diversifications) and may be supplemented with off-farm income. For the farm types considered here typical farm income levels are shown in Table 5-1 below (note General Cropping, Forage is not a type assessed in the Scottish Farm Business Income Survey so data is not available). Assessment of implications for PECs on the overall farm costs and income has not been fully assessed here and may reveal additional positive and negative economic implications of PECs.

Table 5-1: Annual Farm Business Income (£) (average of 6 years 2016-17 to 2021-2022)

Method and results

For each of non-LFA cattle & sheep, mixed holdings, general cropping; forage, areas that would be suitable to grow PECs have been identified (see Table 5-1). (See Appendix E for further details on how these areas were selected.) This was done by using the GIS mapping done in previous work for CxC (Martin et al,2020) which identified land suitable for PECs to identify the percentage of land in region which was suitable for PECs. This percentage was then applied to the land area estimated to be in each farm type in the region, to derive the land are potentially suitable for PECs by farm type. There is some overlap between the types of land suitable for each of the three types of PECs so the areas in the table cannot be summed to give a total area.

Table 5-1 Potential land suitable for each bioenergy crop on different farm types (hectares)

A previous CXC study (Meek et al, 2022) indicated that, bearing in mind land suitability, an estimated total of approximately 27,000 ha PECs^[18] could be planted by 2030, 38,000 by 2032 and 90,250 hectares by 2045. Using these estimates and the potential land that can grow bioenergy crops two illustrative scenarios have been created to estimate the potential economic gain/loss of growing bioenergy crops at the Scottish level.

Scenario 1:

From the results presented in section 5.1 it was financially beneficial to grow bioenergy crops on general cropping, forage land. Furthermore, of all the PECs, growing miscanthus was the most financially beneficial. Therefore, the first scenario assumes that two-thirds (66%) of the general cropping, forage land suitable for SRF and for SRC will be converted and 100% for Miscanthus. Only 66% of land for SRF and SRC are assumed to be converted to avoid double counting due to the likelihood that some areas identified are suitable for both PECs and thus appear in both estimates of suitable areas. Although the results in section 5.1 show that growing bioenergy crops on both non-LFA cattle and sheep and mixed holdings would not be financially beneficial, the loss was less on non-LFA cattle and sheep land. Therefore, to get to the 38,000 hectares, it was assumed that 15% of the land suitable for both SRF and SRC in non-LFA cattle and sheep holdings will be converted and 30% for Miscanthus (see Table 5-2). Overall this means that about 20% of total land in Non-LFA Cattle and Sheep^[19] and 1.1% of land in general cropping, forage are converted to PECs.

Table 5-2 Land that is converted for each bioenergy crop for each farm type in scenario 1 (hectares)

Results: scenario 1

Figure 5-6, shows that, for Scenario 1 there would be an economic gain for converting land used for general cropping and forage to PECs. This is because PECs have a positive, albeit small gross margin, compared to the large negative gross margin for general cropping and forage. The total gain in gross margins across the region is around £16.6 million, of which almost half occurs in Grampian.

Figure 5-6 Change in gross margin for converting General Cropping, Forage land to Miscanthus, SRC and SRF (Scenario 1)

Figure 5-7, shows there would be potential economic loss for converting non-LFA cattle and sheep land to Miscanthus, SRC and SRF in table 5-2 (scenario 1) with Grampian showing a loss of a total of about £1.8 million. SRF showed the greatest loss in the majority of the regions, as it has the lowest gross margin of all the PECs but has more land suitable for it. Miscanthus showed the smallest loss across all regions. The total loss in gross margins across regions is just under £7 million. This loss is lower than the gain in gross margin from growing PECs on general cropping and forage farms, suggesting that achieving 38,000 ha of PECs could give a net increase in gross margins across the two farm categories of £9.6 million.

Figure 5-6 Change in gross margin from converting Non-LFA Cattle and Sheep land to Miscanthus, SRC and SRF (Scenario 1)

Figure 5-8 shows the reduction in production (crops, stock-feeding crops and grass from grazing land) that could occur when converting the land shown in Table 5-2 to PECs. From converting land to PECs, there is an estimated yield loss of 537,600 tonnes: 263,000 tonnes for crops replaced by with SRF, 85,300 tonnes for crops replaced by Miscanthus and 189,000 tonnes for crops replaced with SRC.

Figure 5-8 Reduction in production (barley, stock-feeding crops and grass) resulting from converting land to PECs (thousand tonnes) (Scenario 1)

Scenario 2:

For the second scenario to get to around 90,000 hectares of land, it was assumed that more of the suitable general cropping and forage land was converted to SRF and SRC (66%), and more of the non-LFA cattle and sheep land (30% of land suitable for SRF and SRC and 60% of land suitable for Miscanthus). It was assumed that a small percentage of the suitable land on mixed holdings was converted (50% of land suitable for SRF and SRC and 50% of land suitable for Miscanthus). Overall, this means that about 40% of the total land in non-LFA cattle and sheep farms, around 9% of total mixed holdings and 1.3% of total general cropping /forage land are converted to PECs.

Table 5-3 Land that is converted for each bioenergy crop for each farm type in scenario two (hectares) (Scenario 2)

Results: scenario 2

Figure 5-9, show the results of the conversion rates set out in table 5-3 (scenario 2). The only farm type which shows an increase in gross margin for conversion to PECs is general cropping and forage (due to its current large negative gross margin). Conversions on the other farm types (necessary to meet the target planting area of around 90,000 ha) give a loss in gross margins. Overall, the increase in income in general and cropping farms of £18.6 million is not enough to offset losses in the other two farm types, (£13.9 million in non-LFA cattle and sheep farms and £14. 2 million on mixed holdings) meaning there is a net loss in gross margin of £9.5 million.

Figure 5-9 Change in gross margins from converting Non-LFA Cattle and Sheep land to Miscanthus, SRC and SRF (Scenario 2)

Figure 5-10 shows the crop production that could potentially be lost from converting the land shown in table 5-3 (scenario 2) to PECs. This based on loss of stock feeding crops (barley, maize and lupin) and grass silage and hay produced on each farm type. From converting land to PECs, there is estimated yield loss of 708,200 tonnes for replacing with SRF, 248,100 tonnes for replacing with Miscanthus and 523,900 tonnes for replacing with SRC.

Figure 5-10 Reduction in crop production (barley, stock-feeding crops and grass) resulting from converting land to PECs (thousand tonnes) (Scenario 2)

Preferred locations: considerations

Preferred locations for economically viable production of PECs are influenced by multiple factors including proximity to markets (current biomass energy plants and potential future BECCS plants) and local enough access to services and facilities for crop management (e.g. harvesting contractors) to avoid excessive costs. We assessed preferred locations for economically viable energy crops in Scotland considering the locations of end markets in relation to viable growing areas for PECs.^[20]. Insights from our rapid evidence assessment and stakeholder consultation were also considered, for example comments on economically viable transport distance.

Our analysis showed economically viable areas for PEC production bearing in mind future anticipated demand resulting from Scotland’s net zero ambitions, but only SRF could provide quantity needed, due to lack of availability of suitable land for SRC and miscanthus. As SRF is economically uncompetitive against current land-use, this suggests economic viability may be a barrier to PEC production increases even if suitable land within economically viable distance of end markets is available.

Proximity to users of biomass for energy

Biomass energy crops are bulky to transport and so haulage cost from the location where they are grown to where they are used is a factor which determines which growing locations are economically viable – a crop grown too far from its end destination will be prohibitively expensive to transport. It has been difficult to identify a specific economically viable distance in the available research. Stakeholder comments suggest that whilst 100km is a typical maximum distance to haul wood to a sawmill, a significantly lower distance is economic for biomass crops, as their value is lower than wood which will become sawn timber. In our economic analysis transport costs pre-farm gate e.g. for delivery of planting material are included, but haulage of the bioenergy crops to biomass plants has not been included in the costs as this will depend on the distance and whether the price paid to the farmer is at the farm gate or at delivery to the bioenergy plant. For the purposes of the analysis here, we assume a maximum viable distance of 100km, and consider a shorter 50km distance to reflect stakeholder feedback.

Proximity to existing biomass plants:

Biomass plants in Scotland were identified from DESNZ’ Renewable Energy Planning database which lists both existing and planned plants^[21]. Existing sites vary in scale and use – some are generating power for the grid, others are located on industrial sites such as distilleries, sawmills and papermills supplying heat and power for the industry, whilst others are small supplying e.g. a hotel. Eight sites were selected from the list as being most likely to consider using PECs as a fuel (See Appendix J). Plant which are located on sites where there is already a ready supply of fuel (e.g. sawmills, paper and pulp) were excluded as were very small sites and sites which were not yet operational or under construction.

A buffer of 50km and 100km from these biomass plants has been applied in Figure 6-1, to show the potential geographical areas which could supply biomass markets in Scotland.

Figure 6-1: Biomass plant locations

Proximity to future BECCS facilities:

CCC^[22] highlights that Scotland has very good potential for deploying Bioenergy with Carbon Capture and Storage (BECCS) due to its access to a potential CO₂ storage site in the North Sea, along with its ability to produce domestic BECCS feedstocks. A pilot facility, the Acorn Transport and Storage Facility in Aberdeenshire looks set for further investment after the UK government announced in March 2023 that it considers this site to be one of the two best placed to deliver its objective of capturing 20-30 megatonnes of CO₂ across the UK economy by 2030^[23]. The proposed access points to this facility are via a feeder pipeline along Scotland’s east coast which starts at Bathgate and ends at St Fergus, with two injection points at Kirriemuir and Garlogie. Large scale BECCS plants for electricity, biomass gasification for hydrogen, or biofuel production^[24] may be located in proximity to these access points to benefit from easy access to the pipeline. This study assesses how much land suitable for growing bioenergy crops is within 50km and 100km of these access points. This mapping is presented in Figure 6-2.

Kirriemuir

St. Fergus

Garlogie

Bathgate

Figure 6-2 Feeder pipeline locations and nearby land suitable for PECs

Table 6-1 shows the total potential PEC growing areas with these distances.

A previous CXC study (Meek et al, 2022) indicated that, bearing in mind land suitability, an estimated total of approximately 27,000 ha PECs^[25] could be planted by 2030 and 38,000 by 2032; With this area of land, depending on the yields obtained for PECs and the efficiency of the power plant, PECs could provide feedstock for a BECCs power plant producing between 60 and 80 MWe. The data in Table 6-1 suggests that this land is available, within 50km of all proposed access points along the east coast feeder pipeline for SRC and Miscanthus, but this would require a large portion of the suitable land to be used. A larger land area which is suitable for growing SRF is available.

Table 6-1: Total potential PEC growing areas within 50km and 100km of potential BECCS sites, and existing biomass plant locations.

Access to service and facilities for crop management, harvesting and processing.

Access to services and facilities for crop management harvesting and processing, such as local contractors with suitable equipment has been identified in the research and by stakeholders as a factor which would influence the suitability of growing areas for PACs. The evidence review did not provide information on the availability and access to these services in Scotland, or the speed with which services could develop if a growth in production were planned. Easy access should not be assumed, particularly given the shortage of forestry skills in Scotland and constraint on travel distance which influence the economic viability – access issues would need to be addressed before an area could be suitable for economically viable PEC growing.

Other location considerations

As is evident from the REA, biodiversity and other ecosystem services can impacted by PEC cultivation. Choice of crop, cultivation regime and location need to be carefully considered to optimise environmental benefits and avoid negative impacts. The impact is highly situation specific and could not be assessed in detail within scope of this research but should be considered carefully when selecting locations.

SWOT & PESTLE Analysis

This section provides analysis of the strengths and weaknesses of these crops, and the factors supporting or hindering uptake, drawing together the research findings. A PESTLE analysis was also carried to understand the potential enabling and preventative factors which could influence the economic viability of energy crops in Scotland. Further detailed SWOT and PESTLE analyses are available in Appendix I.

SWOT Analysis of energy crop economic potential

Table 7-1 presents SWOT analysis common to PECs assessed in this research. Further discussion of variations between Miscanthus, SRC and SRF is included in Section 9.

Table 7-1: Summary of strengths, weaknesses, opportunities and threats for PEC in Scotland.

PESTLE Analysis

The PESTLE analysis considersthe political, economic, social technical, legal and environmental factors which currently enable or prevent energy crops becoming an economically viable prospect for farmers in Scotland. The summary PESTLE is set out in Table 7-2 below, discussion of the results follows in Section 9.

Table 7.2: Summary of PESTLE analysis for growing PECs in Scotland.

Discussion

The research and analysis show multiple positive and negative features of the PECs. The implications of these for economic viability of PECs in Scotland is discussed here.

Economic potential to farmers and land managers

Economic potential of PECs in Scotland for farmer and land managers

Overall, the economic analysis showed Miscanthus could be most profitable over the life cycle, but though SRC and SRF broadleaves appear to achieve lower profitability, there are larger areas suited to these crops and less uncertainty about their suitability to the Scottish climate.

Achievable biomass yields, which significantly influences economic viability, is still subject to some uncertainty as commercial growing and trials in Scotland are limited, particularly for SRF and Miscanthus. The analysis shows a significant difference between high, medium and low costs and income from the three PECs considered. It could be reasonably assumed that this level of uncertainty may lead to farmers and land-managers having low confidence to plant the crops. Forthcoming results of Scottish research trials and developments may improve confidence, for example Miscanthus varieties more suitable to Scotland’s climate are in development (see Appendix H) which could extend the range or improve yields in Scotland.

Equipment needs, and therefore costs and economic potential, vary for the different PECs:

Miscanthus can be harvested by typical harvesting equipment which an arable or mixed farmer would either own, or have easy access to via local contractors; whereas for SRC and SRF the equipment needs are more specialist, so requires significant investment or access to local contractors, which is currently constrained in Scotland.

The PESTLE analysis shows that, of the factors which are likely to prevent farmers and land-managers from currently viewing PECs as an attractive proposition and hinder the uptake across Scotland. The most important, are:

• the low or negative income from the crops,
• upfront investment requirement, and
• uncertain market for the crops.

Stakeholder feedback suggested some approaches which may addressing these issues:

• Financial support for farmers, land-managers and other necessary parts of the sector including to enable adoption of forthcoming innovations aimed at improving yields and cutting costs, such as new harvesting techniques and mobile machinery for processing materials on farms.
• Fixed price and long-term contracts for future crops, at prices higher than production costs. However, given imported biomass and fossil fuels appear to be available at lower cost it is unlikely that end-users will find it feasible to offer attractively priced contracts.
• Greater clarity over the likely environmental impacts in Scottish context – both local impacts such as on biodiversity and wider impacts for example indirect land-use change from competition for food / animal feed crops – and how to design of PEC planting in Scotland to maximise positive environmental benefits.

Locational and temporal issues

In terms of suitable and preferred locations for energy crop production in Scotland, as described in Section 6, the proximity to biomass markets (such as power plants) is a key determining factor. The research has shown that there are suitable growing regions, primarily for SRC and SRF within 50km to 100km of existing biomass plants, or potential sites for BECCS plants close to the proposed east coast feeder pipeline, which are likely to be the dominant market demand in a future, more mature biomass market aligned to the Scottish Governments climate ambition. There is some uncertainty about the economically viable distance to transport energy crops, with stakeholders suggesting it would be significantly less than the typical 100km for sawmill quality wood. The number of viable production areas with 50km of potential sites is lower, but they are most advantageous due to lower transport costs (and GHG emissions).

The study did not explore in detail the potential for on-farm use of biomass, but stakeholder consultation suggested this may be an economically viable alternative, particularly for farms not located close to a suitable biomass plant, and given the context of high energy costs. On farm use of PECs is not a negative emissions technology, as it is not feasible to apply carbon capture to small scale plants, but it would contribute to decarbonising agriculture if it replaced fossil fuel use for power and heating in farm buildings.

Looking ahead, if demand for biomass grows in Scotand, UK and elsewhere as countries expand BECCS capacity the market prices for PECs may change. Input costs can also vary significantly. It is beyond scope of this research to deliver a full analysis of future scenarios for the market, or local market dynamics related to specific BECCS sites, but it is clear from the range of profitability demonstrated in Section 5.1, that a range of scenarios should be planned for.

Interactions between PECs and adjacent land-use and wider landscapes and ecology was shown to be an important location factor to consider. Impacts could be beneficial, such as shading / shelter for livestock and to reduce wind exposure for adjacent crops, or could be negative depending on local landscape features, for example reduced yields in adjacent crops due to shading. Positive potential biodiversity impacts have been suggested by some stakeholders, such as habitat for birds, mammals and beneficial insects if edges between PEC and other land-use is maximised, but there was also concern about negative consequences of land-use change and monoculture PECs on biodiversity. Water management benefits also vary across the crops, and the lifecycle of the crops. The implication of the research is that the effective integration of PECs into natural landscapes and farming systems in Scotland to deliver maximum additional environmental benefits will require careful design in relation to the specific local environmental context.

A key issue for economic viability of PECs is the distribution of costs and income over time. Poor cashflow for farmers and land-managers is typical for PECs, because initial costs of establishment are not recouped until harvest after several years. The time from establishment to first harvest varies so the time where a farmer/ land-manager would likely experience cash flow challenges would also vary. The shortest time to first harvest was for Miscanthus, at around 2-3 years for full harvest with potentially a small harvest in the first year, SRC is typically 3 years for first harvest, and 6 years to first full harvest, and for SRF there is typically around 15 years till first harvest. Sequential planting can help create a more regular income because a portion of the crop would be ready for harvest each year. For SRC/ SRF this can be feasible if the harvesting equipment is already available on the farm, or the yearly harvest would be enough to warrant a visit from a contractor. For Miscanthus, there is an annual harvest once established so sequential planting of a portion of land intended for Miscanthus each year would allow for some of initial income to be used for subsequent planting reducing the size of initial outlay whilst increasing the area allocated to the crop over time.

Income diversification

Stakeholder comments suggest that the current levels of interest from farmers in diversification, including into crops with lower input costs and stable income, could be a significant enabler to the uptake of PECs. However, the economic analysis suggests that this would only be the case, if the core barriers around profitability, cashflow and financial risk were addressed.

Other factors influencing PEC uptake

The research found that farmer and land manager attitudes, habits, skills and perceptions, as well as those of the wider community are likely to be influential, alongside the economics, in determining the degree to which energy crops are adopted in Scotland. Low appetite for financial risk is a key preventative factor, with most farmers looking to reduce their exposure to risk and so only likely to be interested in energy crops if they are perceived as a low risk strategy in their own right, or a beneficial diversification of income as part of a wider business risk reduction strategy. The research suggests that, without clearer evidence of favourable market, price and productivity the current perception of these crops as relatively risky is unlikely to change. Concerns about competition with other crops, sustainability credentials, and public perception of the ‘morality’ of energy crops is also likely to influence farmers and land-manager attitudes. Alongside these factors, it was highlighted during the research that farmers often have a strong preference for their current farming enterprises and so may be reluctant to adopt new crops even if they appear financially advantageous and that a significantly higher financial return may be needed to persuade a shift to energy crops in these circumstances.

State of the evidence base and identification of any key gaps

The key gaps and debates in the literature were described in Section 4, and limitations in economic analysis in Section 5. The research shows a need for more robust evidence on potential yields, production costs and environmental impacts specifically for Scotland.

Quantification of potential wider farm benefits, such as shelter for livestock, and estimation of economic value of these benefits to farmers was not identified through this research, but could help create a fuller picture of the economic potential of energy crops for Scotland.

We found limited research on the risks for crop failure / poor productivity from pest, diseases, extreme weather which hampers full assessment of the financial risk exposure of farmers and land-managers associated with planting PECs.

This study has not included a detailed comparison of PECs for NETs with annual bioenergy crops and other bioenergy technologies, such as anaerobic digestion or smaller scale use of PECs on farms for direct energy generation. The REA and stakeholder feedback indicated potential for farmers to benefit from energy security and reduce energy costs if they were to utilise energy crops for their own energy generation. This study has not modelled the current economics of investment in relevant plant and ongoing cost: benefit of this scenario. This research would be potentially beneficial to understand how local small-scale use compares to larger scale use in NETs, and therefore fully understand the relative economic potential of PECs in Scotland.

The research found debates and discussions about how land should be used to fulfil societies various material needs (food, fuel, fibre etc.) and provide space for biodiversity and deliver other ecosystem services. To inform this debate various additional factors, beyond the scope of this research are relevant including the relative benefits of using land for PECs vs other types of renewable energy such as wind and solar energy. Stakeholders highlighted that solar, for example compares, well and there is growing interest in agrivoltaics – solar voltaic panels within agricultural land that may still retain some of its agricultural use such as livestock grazing.

Conclusions

Perennial energy crops have the potential to generate income for farmers and land-managers in Scotland.

• However, income is likely to be lower than they could earn from other farm enterprises, such as lowland cattle and sheep and ‘mixed agriculture’, that are typical on the types of land which may be suitable.
• The exception is where PEC profitability is compared to ‘general cropping: forage’ farming type (growing crops for animal consumption, usually on lower quality land) this activity typically makes a significant loss, so PECs compared very favourably in the analysis.
• for PECs to be viewed as an attractive, economically viable option by farmers and land managers there is a need for greater confidence that it will deliver good economic returns. The high upfront establishment costs for perennial energy crops and low revenue potential are both likely to hinder uptake.

Profitability of perennial energy crops based on gross margin calculations

If costs and income were spread equally over the lifetime of the crop and compared, PECs are less profitable than current farming enterprises, except for ‘general cropping: forage’ which is not typically making a profit.

• Of the three crops studied, Miscanthus showed the highest average gross margin at £382 per hectare per year but there are some potentially limiting factors:
  • uncertainty about achievable yields in the Scottish climate and on the grades of land above category 4.1 in the Land Capability for Agriculture in Scotland. If yields were lower, then profit may be lower.
  • Limited theoretical growing area in Scotland – much lower than for SRF or SRC based on analysis of land quality and characteristics and Scotland’s climate.
• SRF and SRC showed lower profitability for farmers: £80 and £87 per hectare per year over their lifetime respectively for SRF: broadleaved and SRC, making them less attractive but there is more suitable land for growing these. SRF conifer would see a negative gross margin i.e., the production costs outweigh the value of the crop sold.

Potential opportunities

• The research also identified some potential positive attributes of PECs which might encourage uptake – PECs could help diversify a business, creating additional income, without adding significant additional labour requirements or ongoing input costs – minimal management time and inputs are required once crops are established.

Potential barriers

• Cash flow could pose a problem – the distribution of costs and income year-on-year for PECs is significantly different to typical farming activities which have an annual profit cycle. PECs need investment in site preparation and planting upfront, but income only arrives after first harvest several years later (2-3 years Miscanthus, 6 for SRC, 15 for SRF) and then only periodically after that.
• Coupled with uncertainty about market demand and achievable crop sale prices, the need for upfront investment to establish PEC production, means farmers and land managers may view them as a risky proposition and be reluctant to grow them.
• We identified other potential barriers to uptake, including farmer and land-manager unfamiliarity with PEC production, low appetite for risk, need for new skills, access to equipment and services, and concerns about community perception of land-use change and impacts on other agricultural production, e.g. available animal feed.

Enhancing economic potential and production of PECs

Potential approaches to improve economic potential in Scotland include:

• financial incentives, such as government specific subsidies under future agricultural support,
• risk reduction strategies such as secure, attractively-priced contracts with end markets, alongside expansion of the market.
• Innovations to allow processing at the farm and to improve transportability of crops could also help to increase the economically viable travel distance.

Implications for wider Scottish economy:

• Previous research suggests 38000 could be feasibly planted by 2032 (scenario one) and 90,000 by 2045 (scenario two).
• We found that, if land to match this level of demand, was utilised for perennial energy crops (using the scenarios as defined in section 5.2), it would create a gain in gross margin of around £9.6 million (scenario 1) or a loss of around £9.5 million (scenario two) across the regions.

Economically viable production locations:

Economically viable production locations for PECs are influenced by multiple factors including proximity to markets (current biomass energy plants and potential future BECCS plants) and local enough access to services and facilities for crop management (e.g. harvesting contractors) to avoid excessive costs.

• We identified suitable growing regions (some SRC/Miscanthus and most for SRF) within an economically viable transport distance to existing biomass plants and potential sites for BECCS near the proposed east coast carbon capture and storage feeder pipeline (assumed 50-100 km).
• As SRF is economically uncompetitive against current land-use, this suggests economic viability may be a barrier to PEC production increases even if suitable land is available.

Potential further steps

Key debates and areas for further research include:

• Considering more in-depth ‘whole farm’ economic analysis.  This study focused on gross margin comparison, which is useful for comparing specific crops and farm enterprises, but has limitations in terms of how well it allows assessment of integration of energy crops into a whole farm business. This will vary farm to farm but could be explored through farm case studies. This could include considering a wider range of costs for farmers and that after initial set up the PECs would require less workload.
• Comparing, the economic and environmental potential of using land for energy crops with utilising the same land for other renewable energy options (for example using the land for solar panels alongside grazing) and
• Potential role for on-farm use of perennial energy crops.
• Considering future biomass markets, including how future Greenhouse Gas Removal (GGR) schemes, global demand and demand from biotechnology sector may impact it.
• Identifying how to make domestic biomass from energy crops a more attractive option than imports and a more profitable use of land, and on what basis this can be justified. For example, taking account of full LCA and rewarding greatest emission saving.
• Considering in more detail the role of PECs in the context of how the agriculture sector is changing and how it may have to change to reduce GHG emissions.
• Considering the value, including the financial value, of other benefits of energy crops, such as flood mitigation or animal shelter, relative to existing or potential alternative land-uses.
• Exploring how PECs support/interact with tier 2 or 3 objectives of the ARP.
• Considering the impact of subsidies.

References

Alexander, P., D. Moran, et al. (2014). “Estimating UK perennial energy crop supply using farm-scale models with spatially disaggregated data.” Global Change Biology Bioenergy 6(2): 142-155.

Alexander, P., Moran, D., Rounsevell, M.D., Hillier, J. and Smith, P., 2014. Cost and potential of carbon abatement from the UK perennial energy crop market. GCB Bioenergy, 6(2), pp.156-168.

Alexander, P., Moran, D. and Rounsevell, M.D., 2015. Evaluating potential policies for the UK perennial energy crop market to achieve carbon abatement and deliver a source of low carbon electricity. Biomass and Bioenergy, 82, pp.3-12.

Anejionu, O.C. and Woods, J., 2019. Preliminary farm-level estimation of 20-year impact of introduction of energy crops in conventional farms in the UK. Renewable and Sustainable Energy Reviews, 116, p.109407.

Berkley, N. A. J., Hanley, M. E., Boden, R., Owen, R. S., Holmes, J. H., Critchley, R. D., … Parmesan, C. (2018). Influence of bioenergy crops on pollinator activity varies with crop type and distance. GCB Bioenergy, 10(12), 960–971. https://doi.org/10.1111/gcbb.12565

Bocquého, G., 2017. Effects of liquidity constraints, risk and related time effects on the adoption of perennial energy crops. Handbook of Bioenergy Economics and Policy: Volume II: Modeling Land Use and Greenhouse Gas Implications, pp.373-399.

Bourke, D., Stanley, D., O’Rourke, E., Thompson, R., Carnus, T., Dauber, J., … Stout, J. (2014). Response of farmland biodiversity to the introduction of bioenergy crops: effects of local factors and surrounding landscape context. GCB Bioenergy, 6(3), 275–289. https://doi.org/10.1111/gcbb.12089

Brown, C., Bakam, I., Smith, P. and Matthews, R., 2016. An agent‐based modelling approach to evaluate factors influencing bioenergy crop adoption in north‐east Scotland. Global Change Biology Bioenergy, 8(1), pp.226-244.

Busch, G., 2017. A spatial explicit scenario method to support participative regional land-use decisions regarding economic and ecological options of short rotation coppice (SRC) for renewable energy production on arable land: case study application for the Göttingen district, Germany. Energy, Sustainability and Society, 7, pp.1-23.

Dandy, N., 2010. Stakeholder Perceptions of Short-rotation Forestry for energy.

Davies, I., 2020. Miscanthus: Can it tackle climate change and turn a profit? Farmers Weekly. 18 March 2020. [Date accessed 9 August 2023].

Dogbe, W. and Revoredo-Giha, C., 2022 Current and Potential Market Opportunities for Hempseed and Fibre in Scotland.Donnison, I. S. and M. D. Fraser (2016). “Diversification and use of bioenergy to maintain future grasslands.” Food and Energy Security 5(2): 67-75.

Glithero, N.J., Wilson, P. and Ramsden, S.J., 2013. Prospects for arable farm uptake of Short Rotation Coppice willow and Miscanthus in England. Applied energy, 107, pp.209-218.

Griffiths, N.A., Rau, B.M., Vaché, K.B., Starr, G., Bitew, M.M., Aubrey, D.P., Martin, J.A., Benton, E. and Jackson, C.R., 2019. Environmental effects of short‐rotation woody crops for bioenergy: What is and isn’t known. GCB Bioenergy, 11(4), pp.554-572.

Hastings, A., Mos, M., Yesufu, J.A., McCalmont, J., Schwarz, K., Shafei, R., Ashman, C., Nunn, C., Schuele, H., Cosentino, S. and Scalici, G., 2017. Economic and environmental assessment of seed and rhizome propagated Miscanthus in the UK. Frontiers in Plant Science, 8, p.1058.

Haszeldine, R., Cavanagh, A., Scott, V., Sohi, S., & Masek, O. (2019). Greenhouse Gas Removal Technologies – approaches and implementation pathways in Scotland. University of Edinburgh & Heriot Watt University 2019 on behalf of ClimateXChange. https://www.climatexchange.org.uk/media/3749/greenhouse-gas-removal-technologies.pdf

Holland, R. A., Eigenbrod, F., Muggeridge, A., Brown, G., Clarke, D., & Taylor, G. (2015). A synthesis of the ecosystem services impact of second generation bioenergy crop production. Renewable and Sustainable Energy Reviews, 46, 30-40.

Hudiburg, T.W., Davis, S.C., Parton, W. and Delucia, E.H., 2015. Bioenergy crop greenhouse gas mitigation potential under a range of management practices. Gcb Bioenergy, 7(2), pp.366-374

Kralik, T., Vavrova, K., Knapek, J. and Weger, J., 2022. Agroforestry systems as new strategy for bioenergy—Case example of Czech Republic. Energy Reports, 8, pp.519-525.

Leslie, Andrew, Mencuccini, Maurizio, Perks, Mike and Wilson, Edward (2019) A

review of the suitability of eucalypts for short rotation forestry for energy in the

UK. New Forests, 51 (1). pp. 1-19.

Liu, C.L.C., Kuchma, O. and Krutovsky, K.V., 2018. Mixed-species versus monocultures in plantation forestry: Development, benefits, ecosystem services and perspectives for the future. Global Ecology and conservation, 15, p.e00419

Low Carbon Contracts Company, 2022. Fuel Measurement and Sampling (FMS) Guidance. Available at https://lcc-web-production-eu-west-2-files20230703161747904200000001.s3.amazonaws.com/documents/FMS_Guidance_-_Version_2_February_2022.pdf

Martin, G., Ingvorsen, L., Willcocks, J., Wiltshire, J., Bates, J., Jenkins, B., Priestley, T., McKay, H. and Croxten, S., 2020. Evidence review: Perennial energy crops and their potential in Scotland.

McCalmont, J.P., Hastings, A., McNamara, N.P., Richter, G.M., Robson, P., Donnison, I.S. and Clifton‐Brown, J., 2017. Environmental costs and benefits of growing Miscanthus for bioenergy in the UK. Gcb Bioenergy, 9(3), pp.489-507.

Meek, D. Jenevezian, A. , Leishman, R., Odeh, N., and Bates, J. Ricardo Energy & Environment, 2022, Comparing Scottish bioenergy Supply and Demand in the context of Net-Zero targets.

Mola-Yudego, B., I. Dimitriou, et al. (2014). “A conceptual framework for the introduction of energy crops.” Renewable Energy 72: 29-38.

Morris, J. and Day, G., 2023. The Potential of Agroforestry for Bioenergy in the UK.

Ofgem, 2018. Renewables Obligation: Sustainability Criteria. Available at https://www.ofgem.gov.uk/sites/default/files/docs/2018/04/ro_sustainability_criteria.pdf

Ofgem, 2021. Sustainability Self-Reporting Guidance. Available at https://www.ofgem.gov.uk/sites/default/files/docs/2021/04/sustainability_self-reporting_guidance_final_2021.pdf

Olba-Zięty, E., Stolarski, M.J. and Krzyżaniak, M., 2021. Economic evaluation of the production of perennial crops for energy purposes—A review. Energies, 14(21), p.7147.

Ostwald, M., Jonsson, A., Wibeck, V. and Asplund, T., 2013. Mapping energy crop cultivation and identifying motivational factors among Swedish farmers. Biomass and Bioenergy, 50, pp.25-34.

Parratt, M. 2017, Short Rotation Forestry Trials in Scotland 2017 Report, Forest Research

Perrin, A., Wohlfahrt, J., Morandi, F., Østergård, H., Flatberg, T., De La Rua, C., Bjørkvoll, T. and Gabrielle, B., 2017. Integrated design and sustainable assessment of innovative biomass supply chains: A case-study on Miscanthus in France. Applied Energy, 204, pp.66-77.

Petrenko, C. and Searle, S., 2016. Assessing the profitability of growing dedicated energy versus food crops in four European countries. Proceedings of the Working paper, 14.

Ranacher, L., Pollakova, B., Schwarzbauer, P., Liebal, S., Weber, N. and Hesser, F., 2021. Farmers’ Willingness to Adopt Short Rotation Plantations on Marginal Lands: Qualitative Study About Incentives and Barriers in Slovakia. BioEnergy Research, 14, pp.357-373.

Scottish Government, 2021, Scotland’s Third Land-use Strategy 2021-2026

Schiberna, E., Borovics, A. and Benke, A., 2021. Economic modelling of poplar short rotation coppice plantations in Hungary. Forests, 12(5), p.623.

Shepherd, A., Clifton‐Brown, J., Kam, J., Buckby, S. and Hastings, A., 2020. Commercial experience with Miscanthus crops: Establishment, yields and environmental observations. GCB Bioenergy, 12(7), pp.510-523.

Shepherd, A., Littleton, E., Clifton‐Brown, J., Martin, M. and Hastings, A., 2020a. Projections of global and UK bioenergy potential from Miscanthus× giganteus—Feedstock yield, carbon cycling and electricity generation in the 21st century. GCB Bioenergy, 12(4), pp.287-305.

Spackman, P., 2012. Energy crops need support to fulfill potential. Farmers Weekly. 8 June 2012. [Date accessed 9 August 2023].

Thornley, P., 2006. Increasing biomass based power generation in the UK. Energy Policy, 34(15), pp.2087-2099.

Tullus, H., Tullus, A. and Rytter, L., 2013. Short-rotation forestry for supplying biomass for energy production. Forest bioenergy production: management, carbon sequestration and adaptation, pp.39-56.

Vanbeverena, S., & Ceulemansa, R. (2019). Biodiversity in short-rotation coppice. Renewable and Sustainable Energy Reviews, 111, 34-43.

Walle, I.V., Van Camp, N., Van de Casteele, L., Verheyen, K. and Lemeur, R., 2007. Short-rotation forestry of birch, maple, poplar and willow in Flanders (Belgium) I—Biomass production after 4 years of tree growth. Biomass and bioenergy, 31(5), pp.267-275.

Warren, C. R., 2014. “Scales of disconnection: mismatches shaping the geographies of emerging energy landscapes.” Moravian Geographical Reports 22(2): 7-14.

Warren, C.R., Burton, R., Buchanan, O. and Birnie, R.V., 2016. Limited adoption of short rotation coppice: The role of farmers’ socio-cultural identity in influencing practice. Journal of Rural Studies, 45, pp.175-183.

Whittaker, C., Hunt, J., Misselbrook, T. and Shield, I., 2016. How well does Miscanthus ensile for use in an anaerobic digestion plant? Biomass and Bioenergy, 88, pp.24-34.

Witzel, C.P. and Finger, R., 2016. Economic evaluation of Miscanthus production–A review. Renewable and Sustainable Energy Reviews, 53, pp.681-696.

Winkler, B., Mangold, A., von Cossel, M., Clifton-Brown, J., Pogrzeba, M., Lewandowski, I., Iqbal, Y. and Kiesel, A., 2020. Implementing Miscanthus into farming systems: A review of agronomic practices, capital and labour demand. Renewable and Sustainable Energy Reviews, 132, p.110053.

Zhang, B., Hastings, A., Clifton‐Brown, J.C., Jiang, D. and Faaij, A.P., 2020. Spatiotemporal assessment of farm‐gate production costs and economic potential of Miscanthus× giganteus, Panicum virgatum L., and Jatropha grown on marginal land in China. GCB Bioenergy, 12(5), pp.310-327.

Zimmermann, J., Styles, D., Hastings, A., Dauber, J. and Jones, M.B., 2014. Assessing the impact of within crop heterogeneity (‘patchiness’) in young Miscanthus× giganteus fields on economic feasibility and soil carbon sequestration. Gcb Bioenergy, 6(5), pp.566-576.

Appendix A: Policy Context for Energy Crops in Scotland

Climate Change Policy

The Update to the Climate Change Plan (CCPu)^[27], published by the Scottish Government in December 2020, whilst focused on reducing emissions, identifies the need to also remove carbon dioxide from the atmosphere to compensate for residual emissions. It foresees a role for technologies to achieve a net reduction in emissions – often referred to as Negative Emissions technologies (NETs). It identifies several NETs pathways with potential in Scotland, including bioenergy with carbon capture and storage (BECCS). Climate Committee’s (CCC) 6^th Carbon Budget sets out that achieving the required scale of BECCS will necessitate a significant increase in the domestic production of biomass feedstocks^[28].

The CCC’s 2022 review of Scotland’s progress^[29] highlighted that Scotland’s planned deployment of NETs was ambitious, comprising two thirds of UK government overall ambition for 2030, but also notes the advantage of Scotland’s large land area and potential to draw on substantial biomass stocks. It recommends consideration of the impacts and interactions that increased domestic biomass production could have on land use and agriculture. The Scottish Government has acknowledged that these targets can’t be met – the NETs feasibility study gives more realistic targets^[30]. Failure to meet NETs targets for Scotland implies deeper emissions reductions in harder-to-decarbonise sectors, such as aviation and agriculture, and so it is critical to consider how farmers and land-managers can deliver the necessary biomass feedstocks. The CCPu includes a proposal to develop rural support policy to enable, encourage planting of biomass crops within broader measures on sustainable, low carbon farming^[31]. The CCC recommends maintenance and enhancement of support for agroforestry^[32], and a target of 5% trees on farmland by 2035.

Agricultural policy

Scottish Government’s Vision for Agriculture recognises the essential role agriculture has in delivering sustainable food production, climate adaptation and mitigation, biodiversity recovery and nature restoration and proposes that future subsidy support for agriculture will be split across unconditional support and support targeted to environmental outcomes, including low carbon farming and biodiversity The new Scottish Agriculture Bill as introduced to parliament on 28th September 2023 provides a replacement for the Common Agricultural Policy (CAP) and has been drafted to provide the required powers and framework to deliver the Vision for Agriculture. The bill would require Scottish Ministers to prepare a five-year Rural Support Plan for farming, forestry, and rural development. The Agricultural Reform Route Map (ruralpayments.org) sets out the milestones and timescales for change. The Agriculture Bill and Rural Support Plan will have implications for how economically viable it may be in future for farmers and land-managers to grow energy crops. Whilst the details are yet to be confirmed, it is clear that any expansion of perennial energy crops will need to take these policy developments into account.

Other key policies:

Principles of ‘just transition’ are defined in legislation^[33] and Scotland’s draft ‘Energy Strategy and Just Transition Plan’^[34] was published in January 2023. It describes Scotland’s aim to use bioenergy where it can best support Scotland’s Net Zero Journey, and aligns with and supports Scotland’s goals for protecting and restoring nature. It contains a commitment to review the potential to scale up domestic biomass supply chains. Bioenergy crops, if economically viable, could offer the agricultural sector a new income stream and support the rural economy, which would be consistent with the draft plan. The draft plan also includes a proposal to develop a strategic framework for the most appropriate use of finite bio-resources (published in a Bioenergy Action Plan), acknowledging the potential for competing demands on land and natural resources. CCPu also acknowledges the need for open a discussion on optimum land uses beyond just farming and food production to multi-faceted land use including forestry, peatland restoration and management and biomass production.

UK biomass policy context

The UK Government’s Department for Energy Security and Net Zero (DESNZ) published a Biomass Strategy in August 2023 which set out the Government’s view that well-regulated BECCS can deliver negative emissions and ensure positive outcomes for people, the environment, and the climate. It commits the UK Government to strengthen sustainability criteria and verification processes for biomass, acknowledging challenges with international supply chains, and creating a cross-sector sustainability framework for biomass (subject to consultation). The focus will be on addressing greenhouse gas emissions, indirect land-use change, and potentially soil carbon changes. The strategy anticipates a key role for both domestic and imported biomass use across the economy, on a limited timescale. It also sets out how the government is actively developing demand side policies to support emerging technologies such as BECCS and Greenhouse Gas Reduction (GGR) business models, for example the potential for a ‘Contracts for Difference’ (CfD)^[35] approach. The strategy acknowledges that bioenergy policy involves a mix of reserved and non-reserved powers, and so as the Scottish Government develops its draft Bioenergy Policy Statement, Scotland has an opportunity to build on UK policies and develop policies appropriate for Scotland.

Appendix B: Introduction to Perennial Energy Crops

Introduction to Miscanthus

Miscanthus is a tall perennial grass with woody canes like bamboo, of East Asian Origin. The most common variety of Miscanthus grown is the sterile hybrid Miscanthus x giganteus (M. giganteus). Miscanthus is a renewable source of fibre which has a wide potential range of uses as biomass or fibre. Whilst Miscanthus can be grown in parts of Scotland, it is not currently grown at commercial scale and further trials are required to verify its potential future contribution (Meek et al., 2022). Nonetheless, Martin, et al 2020 found 51,800ha of land is theoretically suitable in Scotland to grow Miscanthus which could produce 2.59TWh/yr and 0.52Modt/yr.

To grow, the crop must be established by planting pieces of rhizome (underground plant stem capable of producing the shoot and root systems) which have been collected from fields where Miscanthus is already established^[36]. Prior to planting, site preparation may typically involve breaking up compacted soil, removing weeds (using herbicides), ploughing to 30cm depth, then further levelling and soil cultivation to create a fine level soil to around 15cm^[37]. Equipment which is typically available on an arable farm can be used for this site preparation and planting. Planting using specialist equipment achieves best results, but a potato planter could alternatively be used^[38]. Biodegradable plastic film to prevent frost damage and retain moisture and fencing to prevent rabbits damage can improve success of crop establishment. Once planted, some gap filling might be needed (done manually) and chemical weed control in the first year or so. Fertilisers are not usually needed. After the first year the material is cut back and left in the field. In year 2, depending on the growth rate, there will be a small harvest, or another cut back. Once established a Miscanthus crop is harvested annually, usually in early spring when moisture content is lower, and can be productive for around 15 years. The material is baled, or sometime chipped, to enable easier transport and storage. Sometimes drying is required in storage (natural or mechanical ventilation). At the end of the crop lifetime, to revert the land to other uses, a herbicide is often used to kill Miscanthus shoots and rhizome, followed by ploughing.

Introduction to short rotation coppice^[39]

Short Rotation Coppice (SRC) commonly consists of high-yielding varieties of either poplar or willow, densely planted on a piece of land. The solid, woody biomass provides a source of biofuel that is either used alone or combined with other fuels to power district heating systems and electric power generation stations^[40],^[41]. It was noted previously by Martin et al. (2020), that the production of energy crops in Scotland has in the past been limited, with only SRC currently grown at small commercial scale (250ha). There is greater potential for further SRC cultivation in Scotland provided that suitable land area is available.

Most types of land, except for heavy clay soils and water-logged land, are suitable for SRC. The initial steps to establishment include removing weeds using herbicide, ploughing to 30cm and further cultivation to 15cm. Rods or cuttings are planted with a specialist planter. Gap filling and protection using rabbit or deer fencing may also be needed. During the first year weed control using herbicides and control of plant diseases using pesticides may be needed. Once established, SRC plantations are typically harvested at 3-year intervals using a forage harvester with a specific cutting system, then chipped and stored outside on a concrete base or in the field. Plantations typically remain productive for 15-25 years^[42]. After this, a new planting can be established, or the field reverted through a process that involves stump grinding and the application of herbicides to prevent regrowth.

Introduction to short rotation forestry

Short rotation forestry (SRF) involves planting relatively fast-growing tree species and harvesting them for biomass after around 15-20 years, which is much quicker than conventional forestry. Species can be coniferous (e.g., Sitka spruce, Douglas fir) or broadleaved (e.g., aspen, poplar, silver birch, downy birch, sycamore). SRF is not currently operated commercially in Scotland although there are some trial plots. Nonetheless 912,600 ha of suitable land is theoretically currently suitable for planting of SRF in Scotland (Martin et al., 2020). Limited, recent literature material and evidence was found in the REA relating to the economic potential of SRF in and around the UK.

Process steps are like conventional forestry: the plantation is grown from seedlings or cuttings, or sometimes direct seeding, into land prepared through steps such as drainage, ploughing, and fencing. Some weed control or replacement planting may be needed initially, but after this limited maintenance is required. All the trees in a growing area are harvested at the same time using specialist cutting equipment, then either cut into lengths and stacked to air dry ready for collection or chipped on site. With SRC the shorter rotation, and the higher planting density, reduces the potential for co-production of logs for sawmill timber^[43]. After harvest the site can be cleared, using machinery and herbicides as per SRC and then replanted or reverted to other land-use. Alternatively new stems can be allowed to regrow for coppicing, or a single good stem selected to continue growing for harvest after 15-20 years. Broadleaved varieties tend to produce higher wood density which is advantageous for use as bioenergy.

Appendix C: Methodology to Rapid Evidence Review

• The Rapid Evidence Assessment (REA) methodology used for this project aligns with NERC methodology^[44] and comprised of the following steps.
• Define the search strategy protocol, identify key search words or terms, define inclusion/exclusion criteria. A list of key words, terms and search strings was created and reviewed by Ricardo’s bioenergy and agriculture technical experts and the project steering group to direct the REA review to the most relevant sources. This list was and divided into six relevant categories ‘Energy Crops’; ‘Economic potential’; ‘Farm business and agronomic considerations’; ‘Preferred/feasible locations’; ‘Agricultural & land-use options’; ‘Other considerations e.g., just transition, decarbonisation’ to ensure that all appropriate aspects of the economic potential of energy crops were identified which supported the focus the review. Any literature that is considered out of scope based on our list of assumptions was excluded from the search. We also excluded literature that is older than 10 years, unless it was from a credible source and was the only piece of evidence available (particularly for data).
• Searching for evidence and recording findings. Literature was searched using Google Scholar and Science Direct, utilising our accounts with Science Direct and Research Gate to access restricted pdfs where required. Grey literature, such as farming press and industry reports were used to provide examples and case studies of the economic potential of energy crops. In addition to the search engines, two existing evidence reviews, prepared by Ricardo were used to sources relevant literature: ‘Evidence review: Perennial energy crops and their potential in Scotland’ and ‘Evidence review: Increasing Sustainable Bioenergy Feedstocks Feasibility Study’. Academic paper ‘Greenhouse Gas Removal Technologies –approaches and implementation pathways in Scotland’ (Haszeldine et al, 2019) was also provided to us to supplement our evidence base. For each individual search a unique search reference was assigned, the date, search string used, total number of results found, and the total number of relevant papers found were recorded. Our search strings can be found in the table below.

TableA‑2: Search strings used for REA

All results were recorded in an excel spreadsheet with information extracted on the following:

• Country
• Type of energy crop (SRC, SFC or Miscanthus)
• Additional information on crop type
• Scale of deployment
• Positive economic potential
• Negative economic potential
• Issues/barriers of deployment (non-economic uptake considerations)
• Temporal considerations (e.g., agronomic/climatic conditions)
• Further economic potential (e.g., decarbonisation of agricultural practices and creation of new jobs)

A RAG (red, amber, green) rating was assigned to each source, based on the g criteria:

• Screening. Sources of evidence was then screened initially by title and then accepted papers were then screened again using the summary or abstract. Literature was screened for information on the following inclusion criteria:
• SRC, SRF, Miscanthus (and hemp / alternatives if strong evidence to show economic viability)
• Economic potential (positive and negative) of energy crops – qualitative and quantitative information
• How farmers / land-managers are making decisions about which enterprises and land-uses to adopt and research which provides evidence of likely preferences and decision-making influences.
• Agronomic or other considerations which would influence viability / adoption by farmers / land-managers.
• Extract and appraise the evidence. The screening provided an organised list of papers which enabled evidence to be extracted directly from the literature into the report. Literature extracted also guided the internal workshop and supported information included in the SWOT and PESTLE tables.

Appendix D Evidence of positive economic potential

We found some evidence in literature that PECs can be profitable for farmers and land managers, but limited studies directly applicable to Scotland and to the current economic climate. The price of fuels and other agricultural inputs have been subject to significant rises and fluctuations since most studies were undertaken and studies were mostly in locations with different growing conditions to Scotland. Economic performance of biomass production is influenced by production costs, crop yields, crop price and end-use/market opportunities (Olba-Zięty et al., 2021).

Several studies comparing energy crops reported a high return per hectare for miscanthus, (Martin et al., 2020, Zhang et al, 2020). One reason for this is that miscanthus can produce high outputs from low inputs which is economically significant for farmers (Donnison and Fraser 2016), particularly in the current context of high agricultural input costs. Miscanthus is attractive as it requires few farm operations, has low labour needs, crop management is straightforward and existing farming machinery and skills can be utilised in its production (Shepherd et al., 2020a and Glithero et al 2013) thus improving its economic potential in comparison to annual crops (such as cereals) used for energy. Growers invest in miscanthus due to this low maintenance cost along with the low requirement for field operations (Shepherd et al., 2020). However, Mola-Yudego et al., (2014) in a Swedish study found SRC willow had the lowest production costs overall, compared with other energy crops (miscanthus, reed canary grass and triticale). The production costs, and therefore profit, will vary depending on equipment available on farm (Ostwald et al,2013a).

The tree species chosen for SRF influences plantation establishment costs and therefore enterprise profitability – costs vary between species: Hybrid Aspen requires a costly micro-propagation technique, and so is more costly to establish than Poplar (Tullus et al., 2013). The literature did not provide detailed information on how well-suited different species are to the Scottish climate and the expected yields of biomass in Scotland. Initial indications from trials currently underway in Scotland (Parratt, M, 2017) suggest Hybrid Apsen appears to have most potential, with common alder, silver birch and Sitka spruce having potential at some sites, but full assessment of biomass is not complete and economics are not assessed.

A farming press example of a grower for Terravesta, the major purchaser of Miscanthus in England (Davies, in Farmers Weekly, 2020), reported that for Miscanthus, an average net profit of £530.85/ha over a 15-year period based on a mature yield of 14/t/ha was achievable. Stakeholders interviewed for this study indicated that Miscanthus is still economically viable under this growing model in England, despite current economic conditions, but questioned whether this yield, which would be a key determinant of profit, is feasible in Scottish growing conditions.

Evidence for negative economic impacts

The most prominent evidence of negative economic impacts in the literature was the high upfront cost to establish PECs, lack of established markets, and the uncertainty over the stability of the long-term market (Martin et al., 2020 and Witzel and Finger 2016). Profitability and economic considerations for farmers are dominated by these costs, market dynamics and biomass yield (Zimmermann et al., 2014).

High establishment costs and uncertainties about the market, mean that farmers may perceive PECs as financially risky and are discouraged from growing them (Witzel and Finger 2016, Zimmermann et al., 2014, Hastings et al., 2017). Previous farm-scale modelling was conducted to improve the understanding of the potential economic PEC supply across the UK. The results concluded that without increases in market prices, SRC willow would likely only provide a small proportion of the UK’s PEC target (Alexander et al., 2014). Similar studies were not found for SRF and Miscanthus, and the economics will have changed since this study making it difficult to understand from the literature if this is still the case but it is clear market access and price is a key issue.

In relation to Scotland specifically, the research found that high initial capital investment and a delayed period of revenue are major factors that negatively influence economic potential of PECs. Farmers receive no income from crop sales in the first years after establishment of PECs leading to poor cash flow, which can be an obstacle preventing farmer uptake (Bocquého, 2017). This period before first crop sales varies: typically 2-3 years for miscanthus production (Martin et al. (2020), around 4 years for SRC (Warren, 2016), and 10-20 years for SRF (Martin et al., 2020, Tullus et al., 2013), meaning a farmer may be waiting several years before the crop breaks even, for example miscanthus typically breaks even after between 4 and 11 years (Martin et al 2020).

Economic potential of PECs, in comparison to other crops

The literature review did not provide clear evidence of how the three key PECs being studied here compare economically to other crops, annual crops and agricultural land-uses – some studies showed favourable comparison and others did not. Key studies are highlighted below, but limited insights can be gained on this question from the literature given the recent economic changes affecting agricultural costs and market prices. See Section 5 for a comparative analysis reflecting current economic situation. Petrenko and Searle (2016) found the profitability of miscanthus and SRC to be competitive, with oats in the south of England, and with oats and rye in Southern Germany and, but could not compete with wheat in Europe generally or typical arable rotations in France (Glithero et al., 2013). Lower input costs may mean that PECs are more competitive now, than arable crops which typically require high levels of expensive inputs (such as fuel, pesticides and fertiliser), but literature does not confirm this. Glithero et al (2013) showed miscanthus to have lower biomass production costs (calculated as cost per gigajoule of energy) in comparison to straw-based crops in England. Busch (2017), in Germany, found SRC to be financially superior when compared to three different crop rotation systems consisting of oilseed rape, wheat, barley, and maize crops, concluding that SRC can compete against annual crops provided proper site selection and a suitable market (in this case, wood chip production). Mola-Yudego et al., (2014) highlight research in Northern Ireland which showed similar gross margin to grain production, assuming average yields in both cases.

We did not find research which compared energy crop economics with livestock farming systems economics.

Influences on farmer and land-manager decisions on planting PECs

One of the main factors affecting the uptake of PEC is economic profitability (Olba-Zięty,2021). Appetite for and perception of financial risk, skills, attitudes and access to markets can also influence farmer and land-manager decisions about planting PECs. Evidence from the literature, and our research interviews with stakeholders suggests that even where PECs, or energy crops in general, can deliver positive economic results for farmers and land managers, this on its own is not always sufficient to convince them to start growing PECs. A choice-experiment study in Sweden, found that lower production costs can enable farmers to achieve higher profit from energy crops, in comparison the traditional crops, but that further compensation of up to 215 Euro per hectare would be needed to persuade a farmer to switch to SRC (Ostwald et al,2013a).

A study by Warren (2014) on farmers’ attitudes to PECs in south-west Scotland found that farmers perceived growing SRC to be ‘financially risky’. SRC production was associated with uncertain returns on harvested wood as prices can be volatile. A lack of access to local markets was also highlighted as a potential barrier to current market adoption by producers (Alexander et al., 2014).

Other economic features of PEC production which influence economic potential for farmers and land-managers in Scotland

Producing PECs has specific economic implications for growers which influence their economic potential and attractiveness. These include challenges: lack of flexibility of land-use, reduced market responsiveness; and opportunities for diversification alongside current farming enterprises.

Unlike with annual arable crops, miscanthus producers can’t maximise profitability by changing crop each year to react to market prices (Hastings et al. (2017). The implication of this, which was highlighted during stakeholder interviews, is that to view PECs as economically worthwhile, farmers need confidence that they can achieve an acceptable and secure market price into the future. Long term production contracts between private biomass processors/plants and farmers are an important consideration in managing financial risk for producers (Bocquého, 2017). Stakeholders highlighted that joined up contracts including harvesting and haulage services, currently being used for some crops, can also help reduce risk and simplify the economics for producers.

The literature review suggested that the way PECs are deployed on farms influences their economic potential. Integration of PECs alongside other enterprises and on land which is not performing well could be advantageous. Glithero et al., (2013) reported that when integrated as a diversification enterprise on-farm miscanthus can be highly competitive. Less productive land, for example poor agricultural land with insufficient returns for food crop, is suitable for miscanthus (Shepherd et al., 2020a), which implies it could provide an economic benefit if deployed on this type of land within a farm.

Brown et al., (2016) report that introducing SRC into traditional cropping systems allows producers to diversify their farming operation, which in turn enhances income, improves income security and reduces risk. Alexander and Moran., 2013, similarly found a portfolio of crops including conventional crops, alongside Miscanthus has been found to achieve a more stable income for farmers, and furthermore conclude that, as farms typically operate in a risk-averse manner, reduced risk is an important factor in farmer decision-making for PECs.

The economic potential of SRC is largely dependent on the establishment of strong markets and demand driven by power companies (Brown, 2016). In the UK, it is generally found that further development of energy cropping only occurs once a plant has been built and several farmers adopt SRC practices to supply crops for that plant (Alexander et al., 2015).

Opportunities to improve economic potential of PECs in Scotland

Cultivation techniques, crop variety choice and other technological developments can influence economic potential of PECs in Scotland and have potential to improve profitability for farmers and land managers in future. For example, the use of plastic mulch film to reduce establishment time can improve crop economics (Hastings et al. 2017). Introduction of new and seed propagated hybrids of Miscanthus alongside agronomic developments have been projected to significantly reduce the cost of Miscanthus production. Mobile briquetting of Miscanthus can also increase the economic potential of Miscanthus (Perrin et al., 2017). Through the Biomass Innovation Fund, £32 million of research funding was awarded to innovation projects across the UK to deliver ‘commercially viable innovations in biomass production. Several innovations have potential to improve yields and reduce production costs for Miscanthus in Scotland, including efficient and mobile harvesting equipment and development of new cultivars more suited to colder climates (see Appendix F).

The literature review and stakeholder interviews both highlighted some factors which can negatively affect the economics of PEC production, which if addressed are potential opportunities to improve economic performance. Gaps in the crop (patchiness) was a key factor reducing profitability of miscanthus in the UK, resulting in longer payback periods. Tackling this by addressing issues such as planting technique, bad rhizome quality, poor overwintering, or variations in the soil quality helps maximise crop yield and improve farmer income (Zimmermann et al., 2014). Ensuring access for harvesting equipment is essential for economics of SRF to be viable – ensuring areas planted are on slopes not more than around 20 degrees is important to ensure the economic benefits of mechanised harvesting can be accessed (Martin et al 2020). For SRF effective plantation establishment is important for the economics and general success of a SRF plantation, yet our research did not find clear consensus on how to achieve this: Tullus et al., 2013 found low planting density was preferred amongst producers to minimize establishment costs, although impact on yield is uncertain in the literature. Research also found that single species monocultures can offer greatest economic return by providing higher yields per hectare (Liu et al., 2018), highest yield are achieved on fertile soil (Tullus et al., 2013) or under intensive management systems, including weed control, fertilizer application and irrigation (Walle et al., 2007).

Evidence of potential for Scotland’s wider economy

There was limited research addressing the potential contribution to the wider Scottish economy and a just transition, but some opportunities and challenges can be inferred. These include sales for local energy generation and other industrial uses, employment opportunities in contract services, along with potential payments for environmental outcomes. The requirement for contractors and local services during annual Miscanthus harvesting presents employment opportunities (Martin et al., 2020), as does SRF planting and harvesting (Liu et al., 2018). Depending on the existing farm enterprises, and choice of PEC, the workload for PECs may fall at a different time of year to other peaks in labour demand, helping to spread labour requirement through the year and reduce overall labour requirement. This could make farming more economically viable on farms which rely on family labour or very small workforces and reduce seasonal labour demands.

In addition to being used as BECCS feedstock, PECs have other potential uses and markets. Miscanthus can be sold for animal bedding, thatching, paper production, horticulture, construction materials^[45], and biodegradable plastics (Anejionu and Woods 2019). There has been research on using Miscanthus as a feedstock for fermentation to transport fuels or through anaerobic digestion (AD) to biogas (Witzel and Finger, 2016). Miscanthus for AD has been found to be uneconomical according to Whittaker et al.(2016). Our stakeholder interviews confirmed that farmers would benefit more from growing feedstocks tailored to AD if this is their desired market, yet Winkler et al. (2020) reported significant potential for additional income from biogas production.

SRF and SRC, (when processed into woodchips) can provide a fuel source for biomass boilers and CHP units on-farm and for local domestic or other use^[46] (Spackman, 2012, Ranacher et al., 2021). This can be an alternative market to diversify income sources and also potentially save farmers money on their own energy bills. The literature did not provide details on the economic implications of this but the stakeholder interviews flagged that farmers are currently interested in exploring opportunities to cut energy bills. Miscanthus was also identified to be used in small scale CHP plants on-farms for heating buildings and for domestic uses such as wood burners^[47].

Beyond selling the biomass from PECs as a product, the literature reviewed suggested the potential of PECs to deliver environmental and ecological benefits which could potentially be monetised. SRC and SRF are currently not eligible for carbon credits, and it is unlikely that PECs can provide evidenced carbon storage in biomass or soils in order to qualify under other certification schemes. There may be opportunities to gain economic benefit from flood protection and biodiversity benefits that some PECs can deliver – the research has not identified significant information on this.

Evidence of non-economic opportunities

Non-economic opportunities and benefits of PECs were identified during the research, including several relating to positive environmental outcomes such as reduced agro-chemical use and biodiversity. All three PECs investigated require less chemical inputs, and reduce soil and water pollution (McCalmont et al., 2017). They also sequester carbon, for example miscanthus has a carbon mitigation potential of 4.0–5.3 Mg C ha-1 yr-1 (Zimmermann et al., 2014). Conversion of agricultural land to SRC leads to a reduction in management intensity of the land, resulting in potential soil benefits (Schiberna et al., 2021). The impacts of SRF may be positive or negative depending on what the land was previously used for. Soil compaction and disturbance caused by the harvest of SRF can lead to erosion and a loss in soil organic matter (Martin et al., 2020). Impacts may be neutral or possibly negative if conversion of land is from pasture or native forest to SRF (Griffiths et al., 2019). However, if displacing arable production, SRF has been reported to improve soil stability (Martin et al., 2020) with the potential to have positive effects on carbon soil organic carbon, water retention and erosion rates (Griffiths et al., 2019). SRF can also help flood alleviation as a SRF plantation would slow the rate of water flow (Martin et al., 2020).

The opportunities for biodiversity improvements resulting from PECs vary depending on planting, prior land-use and landscape context. Miscanthus has been reported to have positive effects on biodiversity (Bourke et al 2014 and Berkley et al 2018) in comparison to arable cropping systems. Shepherd et al., 2020 found an abundance of wildlife in UK miscanthus fields which, apart from at harvest time is left undisturbed. However, the effects on biodiversity of large-scale plantations are unknown (Bourke et al 2014). The introduction of SRC sites within arable cropping systems has in some cases been found to enhance the presence of some pollinators (hoverflies, bumblebees and butterflies), which can benefit crop production. However, it should be noted that these benefits are highly context dependent (Berkley et al., 2018). Opportunities to increase bird populations and diversity is thought to increase if native species of SRF are introduced (Martin et al., 2020).

Challenges and deployment barriers

The research identified several non-economic challenges facing the production of PECs in Scotland, relating to skills, land-use commitment, compatibility with current culture and habits, farm businesses, perceived land suitability and environmental concerns. Deployment barriers for Miscanthus include the need for farmers to commit land for a long period of time, land quality, knowledge (Glithero et al 2013), profitability, time to financial return and social resistance relating to whether land should be used for energy or food production (Anejionu and Woods 2019). These barriers also apply largely to SRC and SRF: land committed towards SRC and SRF will be in production for several years and conversion back to arable and the removal of tree roots is challenging (Warren 2016). Additionally for SRF land conversion may be deemed irreversible as reversion to farming use may be prohibited by government regulations once SRF is planted, and the land will no longer be classed as agricultural.

Lack of access to specialist skills (including a shortage of trained foresters^[48]) and to specialist contractors and machinery (e.g., for SRF mechanised planting machines was also identified as a barrier to deployment. The most likely cause of this is limited demand and a ‘lack of off the shelf machinery’^[49]. Whilst this could be seen as an opportunity for development of new infrastructure and employment opportunities, it could currently also be seen as a practical constraint for many producers. The establishment of SRC requires new skills and different machinery compared to conventional cropping, this unfamiliarity and technical lack of knowledge prohibits adoption by producers (Warren, 2014). Stakeholders who we interviewed suggested that there is increased interest amongst farmers in diversification, but that appetite for change was tempered by concern about moving into unfamiliar activities which require new skills.

Culture and attitudes can be a barrier to PEC deployment. Warren et al. (2016) found Scottish farmers opposed SRC (willow) production because they considered it was not suitable for their current farming business or the land. Whilst fertile land is best for SRF production, a study conducted by Walle et al., 2007 found that farmers willing to introduce SRF, are not willing to do so on their ‘best agricultural soils’. Ranacher et al., 2021 found there is a gap in the available literature regarding farmers’ willingness to adopt short rotation plantations on less productive land. Another potential barrier which may prejudice farmers against SRC cultivation is the cultural separation of forestry and farming in Scotland – SRC has historically been viewed as a threat towards the socio-cultural identity of Scottish agriculture (Warren, 2014). In addition, an Environmental Impact Assessment – something which farmers may not be familiar with and is likely to incur costs – may be required^[50] if converting agricultural land to forestry for SRF or SRC (Martin et al., 2020).

Concerns about biodiversity identified included, concern about SRF reducing the habitat for ground feeding birds and other ‘open land’ wildlife (Martin et al., 2020).The winterhardiness of miscanthus is considered a constraint for this crop in Scotland (Martin et al., 2020), and according to stakeholder may reduce achievable yields.

From a biofuel perspective, as with all PECs, it has been noted in the literature that energy generation from biomass is a potential source of direct and indirect emissions, despite carbon being captured during crop growth. Production, transport and processing are potential sources of direct emissions (Alexander et al., 2015). Considerations to limit such emissions, for example distance from farm to biomass plant, must therefore be taken into account. Indirect emissions related to land use change are more varied in the literature.It has been noted that the establishment of SRC on peat/high organic soils, found in the upland areas of Scotland, can potentially harm soil organic carbon (SOC) levels (Martin, 2020) . Existing sustainability criteria for the use of biomass to produce heat or electricity require that PECs are not grown on land that was peatland in January 2008, or of high biodiversity value, and that any change in SOC from cultivation of PECs is taken into account when checking that the electricity or heat produced meets the relevant GHG saving criteria (see e.g. Ofgem, 2018 and Ofgem, 2021, Low Carbon Contracts Company, 2022).

Other relevant crops and planting regimes

Aside from Miscanthus, SRC and SRF there are other potential energy crops – both perennial and annual crops – which can be used for bioenergy and which are potentially suitable for Scotland. The literature reviewed above mostly considered planting of PECs as replacement for arable crops . There is also literature to suggest integrating PECs alongside existing land-use may be feasible and potentially relevant for Scotland. These alternative crops and planting regimes are considered here. Note that relatively limited research was carried outon these as the PECs above were the core focus of this study.

Hemp

Hemp was once widely grown in Scotland and suits both the climate and growing conditions in the main agronomic areas especially parts of the Borders, East Lothian, Fife, Angus, Moray and the Black Isle. Hemp has a significant potential in carbon sequestration and there is evidence to demonstrate its suitability as a feedstock for bioenergy production therefore, bringing a new ‘cash-crop’ to Scotland which would also offer new job opportunities^[51]. Dogbe and Revoredo-Giha., (2022) found through a farmer’s survey, that farmers identify diversification benefits i.e. planting hemp ‘as a safety net’ as a reason for producing hemp in Scotland. Biomass Connect technical article (2023), considering the UK as a whole, found hemp to have greater versatility and profitability than other biomass crops like Miscanthus, willow and poplar and high biomass yield (12-15t/ha of air-dried biomass). They also reported it to be an above-average energy crop for some biochemical-based biofuel production (in comparison to other similar yielding bioenergy crops)^[52]. Hemp can also be used in bio-based building materials such as Hempcrete and textiles ^[53].

Hemp has the potential to provide high yields or returns with little or no pesticides and insecticides (Dogbe and Revoredo-Giha., 2022). It fits well into crop rotations with food and feed crops and helps improve soil structure and soil-borne pests. Constraints on producing hemp in Scotland includes the current lack of market as there are no large processing facilities in or near Scotland, strict regulations on growing hemp including, the need to obtain a costly license, and some reports of low profitability according to Scottish growers^[54].

PECs in agroforestry systems,

Agroforestry is the planting of trees on farmland, alongside cropland or pastureland, usually in strips, clusters or scattered individual trees, that can be grazed or cultivated in between. The REA did not find specific studies focused on Scotland to show how PECs could be grown in agroforestry systems, but provided the design of agroforestry systems can allow for economically efficient planting, management and harvesting (i.e. still allow for machinery access), it could provide an advantageous model. Kralik et al., 2022^[55] conducted a study to address the economic efficiency of agroforestry systems using SRC in comparison to conventional 4-year arable rotation, in Czechia. The results of this paper showed that the agroforestry system generate similar income and profits as the conventional annual crops when cultivating on appropriate sites and practicing good farming principles.

In terms of the scale of production which could be delivered through agroforestry, for the UK in general, Morris and Day (2023) estimated that 20% of UK farmland could transition to agroforestry by 2060. Utilising the aforementioned land area and yield data, the study observed three UK scenarios for SRC Willow. One scenario found where 30% of the yield arising from SRC Willow was used for bioenergy purpose and this would equate to 1.2 million tonnes of domestic wood fuel and therefore contribute significantly towards bioenergy needs and net zero.

Appendix E Methodology for economic analysis

Farm scale economic analysis

Calculating the gross margins for bioenergy crops

Step 1: Calculating the costs for the activities for the different types of bioenergy crops

Miscanthus, willow short rotation coppice (SRC), and short rotation forestry are the energy crops for which there is information that lets us build a baseline model that takes into consideration the different costs involved in the production process of these crops. We conducted an extensive literature review of the growing cycle for different crops, identifying the different steps for growing each of the crops and identifying the costs to undertake those actions. The costs used in our analysis are based on the costs that were used in the Sustainable Bioenergy Feedstocks Feasibility Study report for the Department for Business, Energy and Industrial Strategy (BEIS) published in 2021. This report carried out an extensive review of the available information for different types of bioenergy crops. Information was obtained through a literature review, which was supplemented by interviews with a range of key stakeholders, and expert insight from the project team. In addition, insights were gained through a review of development of SRC in Sweden, which has the largest planted area of SRC in the EU. A list of organisations consulted during the stakeholder analysis is given in appendix 2 of the Feedstocks Innovation Study report.

The three scenarios identified in the Feedstocks Innovation Study (low, medium and high-cost scenarios) were used in the analysis. This allows for some variation in factors that affect costs in agriculture and establish hypothetical scenarios that capture different combinations of costs. In the following sections, an overview of the actions and the costs are included for each of the three bioenergy crops;

• Site preparation / land preparation (including from different prior land-uses where data is available)
• Establishment / planting
• Crop management costs e.g., during initial growth
• Harvesting
• Reversion (where relevant)

For information on the assumptions on the costs please see the Feedstock Innovation Study.

Miscanthus

For Miscanthus, the cost of production is made up from a number of elements that will be grouped in four phases. The phases for growing Miscanthus are:

• Site preparation
• Planting
• Harvesting
• Reversion

Figure B‑1 shows an example timeline of the Miscanthus growth cycle.

Figure B‑2 Growing cycle for Miscanthus

Table B‑1 shows all the input costs for Miscanthus used in this study taken from the Feedstocks Innovation Study adjusted to 2023 prices using the latest GDP deflators^[56]. As well as adjusting for inflation, fertiliser costs have been increased using the latest data from AHDB on fertiliser prices^[57]. Using this data, costs for fertilisers were adjusted by comparing the average annual increase in fertilisers from 2019 to 2023.

Table B‑1 Input costs for Miscanthus (2023 prices)

The broad action category: site preparation category includes costs of establishment. The establishment phase involves preparing the soil for the new crops, acquiring all the plant material, weed control, and planting the crops. In the production phase, the crops are matured and harvested throughout the years. This is the longest phase as it repeats for every harvest and includes all processes related to harvesting and regrowing the crop. The third phase will be reversion, when the plant material is removed, and the field is made available for a new crop (see Figure 13‑1).

There are variabilities and uncertainties related to estimating the production costs for each crop. These may arise for a variety of reasons such as:

• Differences in soil type and/or condition
• Differences in climate
• Differences in farming practices across different companies/farms
• Differences in end-product requirements/specifications.

In the establishment phase, the first lifecycle stage of Miscanthus, the field is taken care of and prepared for plantation. In our model, we have done this in year -1, with year 0 being the reference year for the plantation of the crops. In year -1, the land is prepared for the plantation of the crops in year 0. Several factors affect the cost of planting such as the site, soil type, and drainage. We have incorporated this variance into our model by modelling for different cost scenarios to reflect different possible cost combinations.

In the high-end cost scenario, we have included a possible pest-control component, such as rabbit-fencing to protect the crops. If needed, the pest control section could possibly be a major cost factor.

A couple of years after planting the Miscanthus crops, the first harvest happens. This first harvest marks the beginning of the production phase, which happens every year for the next 18 years. In the production phase, all steps related to harvesting the Miscanthus yield take place. These include mowing/cutting the plant, baling the harvest, and loading it to be further processed or sold. A margin for miscellaneous costs has also been included in the high-cost scenario. At the end of the crop’s life cycle, the reversion process happens to make the land suitable for other crops.

SRC: In this study, we have considered short-rotation coppice such as poplar and willow, two species which can be used for energy generation. Similar to Miscanthus, we have considered different costing phases that are involved in the process of growing SRC. However, given the differences there are between growing these crops and Miscanthus, the processes will be different, meaning that costs will also differ from Miscanthus. We have considered the following phases in the SRC production process:

• Pre-planting/land preparation
• Planting
• Post-planting
• Harvesting
• Reversion

The same as Miscanthus, the costs have been taken from the Feedstocks Innovation Study adjusted for inflation and the fertiliser costs adjusted as explained in the Miscanthus method section (see Figure B‑2).

Figure B‑2 Growing cycle for SRC

Table B‑2 Range of production costs for SRC (2023 prices)

In the pre-planting stage, the land is prepared for growing the SRC crop. Similar to Miscanthus, in the land preparation stage different steps to prepare the land such as soil sampling and testing, ploughing, and power harrow take place. We have modelled these to happen in year -1, with year 0 being the year in which planting takes place. Heavier or more compacted soils will require additional ploughing and sub-soiling compared to lighter costs. Multiple herbicide applications may be needed depending on the specific circumstances. A rabbit fence or other forms of pest control might be needed.

In the planting phase, costs for the plant material and other costs involved in the planting process (such as labour costs and fuel costs) are taken into consideration as well as the costs for soil fertilisation and herbicide application. Fertiliser will be applied either by the farmer or a contractor after planting in and around the plants. Fertiliser could be a purchased product or sewage sludge (if permitted) which comes at zero cost.

In the post-planting phase, the farmer maintains the plants to ensure the plants are healthy and the soil usage is being optimised. At the end of third year when the leaves have fallen, the farmer will apply herbicide and cut back the crop to encourage the plant to grow more stems and fill any gaps in the crop with new, larger size rods which can compete with the already established plants which have just been cut back. In this phase, the farmer also cuts the emerging shoots to encourage more shoots per plant.

Once the plants are ready for harvest, the harvesting process begins. We have combined all the different costs (machinery, labour, fuel, handling, storage, etc) into a single category as there would be too much granularity if we considered them separately. After each harvest, the application of fertiliser and weed/spraying takes place. We have also allowed for possible miscellaneous costs which could affect the final cost of this process.

Short Rotation Forestry (SRF)

Two scenarios have been defined for SRF:

• SRF conifer scenario
• SRF broadleaved scenario

As with Miscanthus and SRC the costs for SRF have been taken from the Sustainable Bioenergy Feedstocks Feasibility Study report for the Department for Business, Energy and Industrial Strategy (BEIS) published in 2021. The costs have been adjusted for inflation to 2023 prices using the latest GDP deflators^[58].

A low, medium and high scenario for both SRF broadleaved and SRF conifer are included.

For the SRF broadleaved scenario, the costs are based on fast growing native broadleaves on medium quality land in lowlands, grown without thinning on a 15- to 20-year rotation and harvested conventionally as pole length or shortwood. The lower cost outcome uses fast growing poplar on farmland, whereas the medium and higher cost outcomes use birch in forest conditions. For more information on the costs please see the Feasibility Study. Details on the costs can be found in Table 13‑4. For the SRF conifer scenario, the costs are on the basis on a fast-growing conifer species (e.g., Sitka Spruce) on medium quality land, grown without thinning on a 15 to 20-year rotation and harvested conventionally as pole length or shortwood. The lower cost outcome assumes new planting, whereas the medium and higher cost outcome assume restocking in forest conditions. For all costs, please see Table B‑5.

Table B‑3 Range of production costs for broadleaved short rotation (2023 prices)

Table B4 Range of production costs for conifer short rotation (2023 prices)

Step 2: Calculating the output (yield and price)

Miscanthus

Data for yields in Scotland were obtained from the Scottish farm management handbook. Similar to what has been done in the costing section, different scenarios have been considered in order to account for possible variance in yields. 12 ODT, 14 ODT and 15 ODT were used for the low, medium and high scenario, respectively. ODT/ha stands for Oven dry tonne per hectare and corresponds to the total amount of above-ground living organic matter produced in a single hectare. Harvesting takes place in year 3 and is harvested on annual basis. Pricing data for Miscanthus was obtained from the John Nix pocketbook, £95, £97, £98 £/odt for the lower, medium and higher scenario, respectively (adjusted from 2021 to 2023 prices using the latest GDP deflators). This value is taken from the value that is offered to farmers from Terravesta. There are penalties if the crop is out of specification and bonuses available of £2/tonne if bales have been stored in a barn.

SRC

SRC is harvested with 2–3-year intervals and similar to Miscanthus, yields can vary for a wide range of reasons such as site conditions, type of planting method, years since planting, crop type, orography, and weather conditions. The yields used in the analysis come from the official statistics published by Defra which looks at Plant biomass: Miscanthus, short rotation coppice and straw^[59]. These are 24, 35, 45 odt/ha, respectively. In the analysis, fluctuations in the yield of SRC have been included (Table ‑6).

Table B5 SRC rotation used in analysis if assuming fluctuations take place

For the price of SRC, the value used in the latest John Nixs Pocketbook (2022) has been used. Adjusted to 2023 prices this is £59 per odt. This figure is based on what a grower in Cumbria could get.

SRF

SRF is harvested at 15-year intervals for both conifer (sikca spruce) and broadleaved (silver birch). The yield estimates were taken from the Feedstock Innovation Study. The price for both types of SRF were taken from a stakeholder from Scottish Forestry, which estimated that the payment for SRF that had been stacked and cut would be between £50 to £64.

Step 3: Calculating the gross margin

To calculate the gross margins for the bioenergy crops, firstly the costs were placed over the lifetime of the crop. For example, clearance and ploughing costs for Miscanthus were included in the first year (-1). The accompanying spreadsheet shows how all the costs are spread over the lifecycle of the crop. The costs were then taken away from the output estimates to calculate the gross margins over the lifecycle of the crop.

To calculate the gross margins for all the farm types used in the analysis the latest data from the Scotland farm business survey^[60] was used using data from the years 2016 to 2022. An average over these years was used to take account of variability in agricultural costs and outputs. To get to the £ per hectare value, using the time series data from 2016, total average output for each of the farm types was divided by the average size of the farm. For variable costs, total average inputs – other fixed costs were taken away from the total average inputs to get to the variable costs. This was then converted to per hectare values. For the general cropping, forage category data was taken from the latest census^[61] for the output data and the costs were taken from the farm management handbook^[62].

Table C: Breakdown of costs and outputs used for gross margin calculations (average data from 6 years from 2016-17 to 2021-22 from Scottish Farm Business Income Survey)

Table D: General cropping – forage gross margin calculation data

Gross margin calculation: Average total cost per annum – forage output = gross margin

Figure A: Excerpt from Scottish Farm Mangement Handbook showing data used in the calculations in Table D above.

Comparing bioenergy crops to existing land-use economics: three scenarios

Bioenergy energy crop scenarios

For the low scenario, high costs were compared with lower output. For the medium scenario, medium costs were compared with medium output. For the high scenario, low costs were compared with high output.

Farm scenarios

For the different farm income scenarios, the farm business income definitions were used from the Scotland farm business survey. For low this uses the lower 25% percentile for that farm category, for medium the average percentile was used and for the higher, the upper 25% percentile was used.

Yearly average gross margins for each of the bioenergy crops and farm types

To calculate the yearly average gross margins for each of the bioenergy crop and the farm type scenarios a discount rate was applied to future years. The discount rate applied is the standard discount rate recommended by the green book^[65]. The Green Book recommends that costs and benefits occurring in the first 30 years of a programme, project or policy be discounted at an annual rate of 3.5%, and recommends a schedule of declining discount rates thereafter. A discount rate is applied as it is assumed that people prefer to receive financial outputs now rather then in the future.

Assessment of implications for Scotland’s rural economy

Using the geo-spatial mapping data from the previous project, which identified land that was theoretically suitable for PEC production considering land capability, slope, and climate (Martin et al, 2020), percentages of the land that could be converted to bioenergy crops were derived for each of the regions. This percentage was then applied to the land area estimated to be in each farm type in the region, to derive the land are potentially suitable for PECs by farm type. The land area in each farm type in each region was estimated by combining data on crop areas in each region with estimates of the percentge of crop area at the Scottish level which occurs in each each farm type.

A previous CXC study (Meek et al, 2022) indicated that, bearing in mind land suitability, an estimated total of approximately 27,000 ha PECs could be planted by 2030, 38,000 by 2032 and 90,250 hectares by 2045. Two scenarios were then constructed to see what land transitions could meet these areas of PECS. Using information on the gross margins for the three farm types of interest and the gross margins for the PECs, the economic impact of each land use change can be ranked.

Table E Change in gross margin (£/ha) in transitioning to PECs

These rankings were used to guide how much of the potential land suitable for PECs in each farm type was assumed to be converted, with more land converted for more economically beneficial transitions. Care was also taken, particularly in Scenario 2, where high levels of trnaition are needed to meet the higher PEC target area, that levels of overall change were not too high. This resulted in the assumed changes shown in the Tables below

Table F Assumed changes in land use Scenario 1

Table G Assumed changes in land use Scenario 2

The Potential change in farm income due to change in gross margin was calculated by multiplying the change in gross margin from each transition in Tables E, with the areas in transition in Tables F and G. This was done on a regional basis.

The estimated shortfall in crop production from a shift to PECs, was calculated by using data on the areas of crop land in each farm type and the areas converted to PECs to calculate lost areas of crop production. These were then multiplied by typical crop yields^[66]. This was all done at a regional level. Estimate the change in livestock production that might come from the shift to PECs would require a more detailed analysis than was possible in this study.

Appendix F: Mapping outputs from 2020 project

A previous CXC Project (Martin et al, 2020) used geo-spatial mapping to identify suitable areas of land in Scotland for growing PECs. The project focused on land capability of grades; 4.1, 4.2, 5.1, 5.2, 5.3 and 6.1, which are typically suitable for mixed agriculture, improved grassland and high-quality rough grazing ^[67], and assessed what area of these grades where suitable for SRC and Miscanthus growth which limited the potential production area. For SRF the assessment also included land capability for agriculture grades F1, F2, F3, F4 and F5.

Figure C-1: Distribution of suitable land available for Short Rotation Forestry

Figure C-2: Distribution of suitable land available for Short Rotation Coppice

Figure C-3: Distribution of suitable land available for Miscanthus

Data attributions

The data used in the bioenergy crop growth analysis was downloaded from multiple sources. In order to comply with their licences, as well as to acknowledge the use of the data, attributions for each data source is provided in Table C-1. In all cases these attributions are those directly required by the data licence or metadata.

Table C-1: Data attributions

AppendixG: Methology for geospatial analysis of agricultural land use change

Geospatial analysis

To calculate the current land area available for change to bioenergy cropping, based on the locations from the previous CXC project, geospatial analysis was completed. The percentage of the total land area suitable for bioenergy growth in each agricultural region was calculated and applied to the total hectarage of the the agricultural land used within the land capability categories. This was then divided into three main farm types: Non-LFA cattle and sheep, Mixed holdings, General cropping – forage. This presented a total hectarage by agricultural region and farm type that could be converted to SRC, Miscanthus and SRF. This data was used in economic calculations to present the change in economic potential for the three farm types under a land use change to bioenergy crops. Details of sources used are presented in Table D-1.

Table D‑1 Data sources and usage

Appendix H: Stakeholder engagement methodology and key findings

In addition to the rapid evidence assessment and economic analysis, we conducted stakeholder engagement with a robust representative sample of stakeholders from across the Scottish agricultural network to provide input into the project. The engagement was conducted in two stages: 

1. Topic expert research interviews: eight semi-structured interviews of approx. one hr were carried out as part of the evidence gathering process. Interviewees were sent a briefing of key areas of enquiry prior to their interview to aid their preparation. Ricardo recorded each discussion as meeting recording, transcript and attendee notes.

1. Stakeholder workshop: Stakeholder input was sought to scrutinise findings and ensure the SWOT and PESTLE are as complete and robust as possible.  This engagement was delivered through a one hour structured on-line meeting held on the 16^th October 2023 with a combination of stakeholders who had already contributed to individual interviews and representatives of wider organisation and businesses. Initial finding were presented by the project team and comment on accuracy, completeness and additional considerations sought throughout. 
   Following the meeting, the presentation and list of questions (below) was sent to all attendees with an invitation for follow up comment. 

Insights were gained into:

• What influences farmer and land-manager decisions on energy cropping.
• Wider concerns or questions about potential implications.
• Benefits and disadvantages of energy crops.
• Opportunities to drive greater uptake.
• Insights in economic aspects and state of knowledge on this for Scotland in particular.

Feedback reflected some of the points of discussion and debate that were identified in the REA such as questions over what land is suitable and how best to use land given Scotland’s climate targets and other priorities, and debate over yields, prices and how to ensure wider environmental benefits from energy crops, and to what extent this is possible in Scotland.

The insights from this stakeholder engagement have been integrated into Section 4 Evidence Base and Section 7 SWOT & PESTLE analysis.

Summary of questions posed to stakeholders during the engagement element of the project: 

General: 

• Do you think there are opportunities for farmers and land managers in Scotland to benefit from producing perennial energy crops? 
• If so, which crops, locations and circumstances do you think could be most economically viable, and why?  
• How could we improve our costings and economic assumptions to make them more reflective of the reality of the Scottish context? 
• What economic and other considerations would most influence farmers’ and land-managers’ decision to start producing energy crops? 
• What are the most significant potential benefits and challenges at a wider economy scale?  

 Economic analysis at farm scale 

• How could we improve our costings and economic assumptions to make them more reflective of the reality of the Scottish context? 
• Would you suggest any adjustments to our costs?   
• Would you suggest any adjustment to our yield or prices? 
• Are the rotation lengths appropriate?  

 Preferred locations 

• How is best to select preferred biomass locations? E.g. based on areas in proximity to market usage? Or based on land with best production potential? 
• Are there any existing or proposed large-scale biomass plants in Scotland? 
• What is a maximum travel distance from farm to plant? 
• Are there any key biomass planting / harvesting contractors in Scotland? If so, where? 

Output of Stakeholder Engagement 

The output of the stakeholder interviews included suggestions for data and information sources to support the economic analysis. Stakeholders also provided commentary on the opportunities and challenges of perennial energy crop production in Scotland; this is summarized below: 

 Individual stakeholder interviews:

Stakeholder online workshop attendees:

Appendix I: Biomass Feedstock Innovation Funding in the UK

Appendix J: SWOT and PESTLE Analysis: Detailed Results

The SWOT analysis assessed the current economic potential for perennial energy crops for farmers and land-managers in Scotland, looking at strengths, weaknesses, opportunities, and threats (SWOT) to provide a simplified picture and more clarity of what would be needed in order for these crops to be an attractive proposition economically, whilst also considering the other factors which farmers and land-managers would be likely to consider alongside the economics. The SWOT tables below are grouped according to the following categorisations:

• Perennial energy grasses (primarily Miscanthus);
• Short rotation coppice (primarily Willow);
• Short rotation forestry (including broadleaved; conifer)

Table G1. SWOT table covering Perennial energy grasses, focused on Miscanthus.

Table G2. SWOT table covering Short Rotation Coppice

Table G3. SWOT table covering Short Rotation Forestry

PESTLE Analysis of economic potential of energy crops in Scotland

Energy crops are subject to a range of enabling and preventative factors which would influence the benefits and potential uptake of the crops in Scotland. A political, economic, social, technical, legal, and environmental (PESTLE) analysis was therefore undertaken to assess the potential to…increase economic viability and uptake of energy crops in Scotland This assessment was produced following the SWOT analysis to incorporate the strengths and opportunities of each energy crops (and more generally) identified in the SWOT.

Table G4. Summary PESTLE Analysis: enabling and preventative factors for economically viable energy crops in Scotland

The combination of high production costs, particularly the upfront investments uncertain policies and uncertain market prices for future harvests discourage farmers from growing SRC plants. (Zięty et al, 2022)

Appendix K: Biomass plants included for proximity analysis

How to cite this publication: Dowson, F., Leake, A., Harpham, L., Willcocks, J., Peters, E., David, T., Bates, T., Wood, C. (2024). ‘Economic potential of energy crops in Scotland’, ClimateXChange. http://dx.doi.org/10.7488/era/5478

© The University of Edinburgh, 2024
Prepared by Ricardo plc on behalf of ClimateXChange, The University of Edinburgh. All rights reserved.

While every effort is made to ensure the information in this report is accurate, no legal responsibility is accepted for any errors, omissions or misleading statements. The views expressed represent those of the author(s), and do not necessarily represent those of the host institutions or funders.

ClimateXChange

Edinburgh Climate Change Institute

High School Yards

Edinburgh EH1 1LZ

+44 (0) 131 651 4783

info@climatexchange.org.uk

www.climatexchange.org.uk

1. 
   

   IEA, 2017 IEA Technology Roadmap: Delivering Sustainable Bioenergy, Unlocking the potential of bioenergy with carbon capture and utilisation or storage (BECCUS) – Analysis – IEA, License: CC 4.0 ↑

   
   
2. 
   

   Land Capability for Agriculture in Scotland | Exploring Scotland | The James Hutton Institute – the study identified the capability classes for agriculture 4.1 to 6.1 and classes for forestry F1 to F5. ↑

   
   
3. 
   

   Williams et al (for Ricardo), 2023, Report for the Scottish Government: Negative Emissions Technologies (NETS): Feasibility Study: Negative Emissions Technologies (NETS): Feasibility Study – gov.scot (www.gov.scot) ↑

   
   
4. 
   

   Stakeholder interview ↑

   
   
5. 
   

   ↑

   
   
6. 
   

   Bioenergy Crops Better For Biodiversity Than Food-Based Agriculture | University of Southampton ↑

   
   
7. 
   

   Defined as land which was primary forest, designated for nature protection, highly biodiverse grassland (except where harvesting is necessary to maintain grassland status), peatland, continuously forested, wetland in or after 2008. ↑

   
   
8. 
   

   Based on a meta-analysis of 45 studies on transition to energy crops from ‘marginal’ land. ↑

   
   
9. 
   

   Definition of marginal land may not be applicable to Scotland. ↑

   
   
10. 
    

    Gross margin in agricultural costings is typically defined as ‘Output from the enterprise less the Variable Costs, including the allocated variable costs of grass and other forage’ ↑

    
    
11. 
    

    Defined in the Scottish Farm business income survey as “Farms with no enterprise contributing more than two-thirds of their total standard output” – typically including livestock and crops, including animal fodder. An average income ↑

    
    
12. 
    

    Scottish farm business income: annual estimates 2020-2021 – gov.scot (www.gov.scot) – note that the mixed farming data is an average across farms that meet the definition above. ↑

    
    
13. 
    

    Scottish Agricultural Census: results – gov.scot (www.gov.scot) ↑

    
    
14. 
    

    The general cropping, forage category has only one scenario due to the data coming from the Scottish Government Census data which doesn’t provide a low, medium and high scenario and the cost data coming form the Farm Management Handbook 2023/2024 ↑

    
    
15. 
    

    Scottish farm business income: annual estimates 2021-2022 – gov.scot (www.gov.scot) ↑

    
    
16. 
    

    Gross margin is farm income from a specific production enterprise, e,g, crop or livestock minus costs directly associated with production of that output, but excluding ‘fixed costs’ such as costs associated with farm buildings, general labour and finance costs. Further detail available in: Appendix E Methodology for economic analysis. ↑

    
    
17. 
    

    The transition of a large land area – scenario 2 – to PECs creates a loss because of the assumptions within our study – we assumed that land which is more economically advantageous for PECs would be converted preferentially, so a larger portion of land transitioned in scenario 1 would make a profit from the transition to PECs, whereas in scenario 2 a large area of land which would make a loss from the transition was included, and so resulted in a total loss on balance. ↑

    
    
18. 
    

    This study focused mostly on Miscanthus and SRC, but has been used as a best estimate here to give some basis for understanding how potential demand for bioenergy crops could evolve in future to meet Scottish Government NETs ambition. ↑

    
    
19. 
    

    This refers to the percentage of all Non-LFA Cattle and Sheep land in Scotland – suitable and not suitable for PECs. ↑

    
    
20. 
    

    Methodology and maps of potential production areas of the three crops produced within the previous project are in Appendix F. ↑

    
    
21. 
    

    https://www.gov.uk/government/publications/renewable-energy-planning-database-monthly-extract. The database only includes plants generating electricity so large biomass boilers are not captured. ↑

    
    
22. 
    

    Scottish Emissions Targets – first five-yearly review (theccc.org.uk) ↑

    
    
23. 
    

    Green growth for Scotland with multi billion pound investment – GOV.UK (www.gov.uk) ↑

    
    
24. 
    

    These three types of BECCS (Bioenergy with Carbon Capture and Storage) were identified in CCPu, along with BECCS in industry, as potential options for Scotland. ↑

    
    
25. 
    

    This study focused mostly on Miscanthus and SRC, but has been used as a best estimate here to give some basis for understanding how potential demand for bioenergy crops could evolve in future to meet Scottish Government NETs ambition. ↑

    
    
26. 
    

    Based on stakeholder comments. ↑

    
    
27. 
    

    Securing a green recovery on a path to net zero: climate change plan 2018–2032 – update – gov.scot (www.gov.scot) ↑

    
    
28. 
    

    The-Sixth-Carbon-Budget-The-UKs-path-to-Net-Zero.pdf (theccc.org.uk) ↑

    
    
29. 
    

    https://www.theccc.org.uk/publication/scottish-emission-targets-progress-in-reducing-emissions-in-scotland-2022-report-to-parliament/ ↑

    
    
30. 
    

    Supporting documents – Negative Emissions Technologies (NETS): Feasibility Study – gov.scot (www.gov.scot) ↑

    
    
31. 
    

    Update to the Climate Change Plan 2018 – 2032: Securing a Green Recovery on a Path to Net Zero (www.gov.scot) p. 193 ↑

    
    
32. 
    

    Agroforestry is the practice of planting trees, usually to produce a crop of food or wood products, on farmland in combination with arable or livestock farming, often in small patches or strips with fields. ↑

    
    
33. 
    

    The just transition principles are defined in the Scottish legislation as:

    
    

    ‘the importance of taking action to reduce net Scottish emissions of greenhouse gases in a way which:

    
    

    a) supports environmentally and socially sustainable jobs,

    
    

    b) supports low-carbon investment and infrastructure,

    
    

    c) develops and maintains social consensus through engagement with workers, trade unions, communities, non-governmental organisations, representatives of the interests of business and industry and such other persons as the Scottish Ministers consider appropriate,

    
    

    d) creates decent, fair and high-value work in a way which does not negatively affect the current workforce and overall economy,

    
    

    e) contributes to resource efficient and sustainable economic approaches which help to address inequality and poverty.’ ↑

    
    
34. 
    

    Draft Energy Strategy and Just Transition Plan (www.gov.scot) ↑

    
    
35. 
    

    A Contract for Difference (CfD) is a private law contract between a low carbon electricity generator and the Low Carbon Contracts Company (LCCC), a government-owned company. Contracts for Difference – GOV.UK (www.gov.uk) ↑

    
    
36. 
    

    Teagasc- Miscanthus Energy Crop Miscanthus Energy Crop – Teagasc | Agriculture and Food Development Authority ↑

    
    
37. 
    

    Sustainable Bioenergy Feedstocks Feasibility Study report for the Department for Business, Energy and Industrial Strategy (BEIS) published in 2021 ↑

    
    
38. 
    

    Miscanthus Growers’ Handbook (forestresearch.gov.uk) ↑

    
    
39. 
    

    Sustainable Bioenergy Feedstocks Feasibility Study report for the Department for Business, Energy and Industrial Strategy (BEIS) published in 2021 ↑

    
    
40. 
    

    Short rotation coppice (SRC) – Crops4energy ↑

    
    
41. 
    

    Short rotation coppice establishment – Forest research ↑

    
    
42. 
    

    As above ↑

    
    
43. 
    

    Feedstocks innovation study task 1 report ↑

    
    
44. 
    

    https://nora.nerc.ac.uk/id/eprint/512448/1/N512448CR.pdf ↑

    
    
45. 
    

    Teagasc Miscanthus best practice guidelines Miscanthus_Best_Practice_Guidelines.pdf (teagasc.ie) ↑

    
    
46. 
    

    Energy crops need support to fulfil potential – Farmers Weekly ↑

    
    
47. 
    

    DEFRA Area of crops grown for bioenergy in England and the UK Area of crops grown for bioenergy in England and the UK: 2008-2014 – GOV.UK (www.gov.uk) ↑

    
    
48. 
    

    Forestry sector workforce ‘chronically under-resourced’ | The Scottish Farmer ↑

    
    
49. 
    

    Forest Research -Short Rotation Forestry Establishment Microsoft Word – TD Project Report FCS SRF DI SRMast v AJH.doc (forestry.gov.scot) ↑

    
    
50. 
    

    Dependent on size of planting area and location in relation to National Scenic Areas and other sensitive areas – latest guidance available from Forestry Scotland. Scottish Forestry – Environmental Impact Assessments ↑

    
    
51. 
    

    Hemp Project | The Rowett Institute | The University of Aberdeen (abdn.ac.uk) ↑

    
    
52. 
    

    Hemp-as-Biomass-Crop-1.pdf (biomassconnect.org) ↑

    
    
53. 
    

    HEMP-30 catalysing a step change in the production – phase 1 report (publishing.service.gov.uk) ↑

    
    
54. 
    

    Carbon-busting hemp could help transform Scottish agriculture to zero emissions (theconversation.com) ↑

    
    
55. 
    

    Agroforestry systems as new strategy for bioenergy — Case example of Czech Republic – ScienceDirect ↑

    
    
56. 
    

    GDP deflators at market prices, and money GDP March 2023 (Quarterly National Accounts) – GOV.UK (www.gov.uk) ↑

    
    
57. 
    

    GB fertiliser prices | AHDB ↑

    
    
58. 
    

    GDP deflators at market prices, and money GDP March 2023 (Quarterly National Accounts) – GOV.UK (www.gov.uk) ↑

    
    
59. 
    

    Section 2: Plant biomass: Miscanthus, short rotation coppice and straw – GOV.UK (www.gov.uk) ↑

    
    
60. 
    

    Scottish farm business income: annual estimates 2021-2022 – gov.scot (www.gov.scot) ↑

    
    
61. 
    

    Scottish Agricultural Census: results – gov.scot (www.gov.scot) ↑

    
    
62. 
    

    fas.scot/downloads/farm-management-handbook-2022-23/ ↑

    
    
63. 
    

    Source: Scottish Farm Management Handbook 2022-23 ↑

    
    
64. 
    

    Source: Final Results of the June 2021 Agricultural Census: Table 12 ↑

    
    
65. 
    

    Green Book supplementary guidance: discounting – GOV.UK (www.gov.uk) ↑

    
    
66. 
    

    June Agricultural Census (ruralpayments.org) ↑

    
    
67. 
    

    The James Hutton Institute, N.D., Land Capability for Agriculture in Scotland. https://www.hutton.ac.uk/sites/default/files/files/soils/lca_leaflet_hutton.pdf ↑

    
    
68. 
    

    https://www.gov.uk/government/publications/biomass-feedstocks-innovation-programme-successful-projects ↑

    
    
69. 
    

    https://www.gov.uk/government/publications/biomass-feedstocks-innovation-programme-successful-projects/biomass-feedstocks-innovation-programme-phase-2-successful-projects ↑

    
    

Research completed: July 2024

DOI: http://dx.doi.org/10.7488/era/5506

Executive summary

The Scottish Government’s Carbon Calculator for wind farms on Scottish peatlands was developed in 2008, to calculate the impact of wind farm development on peatland carbon stocks in Scotland and thereby support decision making. Electricity generation emission factors are updated annually, but no major revisions have been made to the Carbon Calculator since 2014.

Aims

The increased focus on the transition to net zero might affect the suitability of the Carbon Calculator for future use. This research conducted a detailed review of the latest spreadsheet version of the Carbon Calculator (v2.14), which mirrors the web version (v1.8.1). It provides an evidence base for future considerations and recommendations.

This review has initiated further discussions and highlighted the need for ongoing engagement, which will be instrumental in the development of the Carbon Calculator.

Key findings

Based on the findings of a technical assessment, evidence review and quality control mechanisms, we recommend that when considered against recent policy updates and advancements in science, the Carbon Calculator, in its current form, should be updated. Each area of the Carbon Calculator was assessed for scientific accuracy and data availability:

• The ‘payback time and CO₂ emissions’ are not relevant/consistent with the findings of the technical assessment and literature review. It is important to consider whether emissions due to turbine life and back up are required, given new planning policy and the applicability of whole lifecycle carbon assessments.
• For all peat-related areas of the Carbon Calculator, as well as the forestry area, accuracy is lacking in one or more methodologies, use of emission factors and assumptions.
• While some data are accessible to users, it is not clear if they are able to accurately obtain some of that data – in particular, for variables that drive the results (the water table depth and extent of drainage), which could affect the accuracy of outputs.

In addition to the technical assessment, the research has triggered the need to examine the wider planning and consenting context through the following questions:

Does the calculator need to consider the lifecycle emissions of the wind farm, or could the focus be purely on the impact of development on peat?

Well established methods and tools are available to undertake Whole Life Carbon Assessments (e.g. PAS2080), including forthcoming offshore wind carbon footprinting guidance. This aspect of the Carbon Calculator might not be necessary as it replicates these approaches. Instead, it may be more beneficial to concentrate efforts on analysing the specific impacts of development on peatlands/habitat carbon emissions.

Is the output of the Carbon Calculator useful as a decision-making tool?

Since the inception of the Carbon Calculator, it has become clearer that improving and restoring biodiversity is important to tackling climate change. This progress is reflected the National Planning Framework 4’s mitigation hierarchy.

As the UK transitions to net zero, the current ‘carbon payback’ approach becomes less relevant, as it compares development emissions to the counterfactual of electricity generated by fossil fuels. The focus should shift to evaluating the impact of the developments on the natural environment, specifically, whether it improves the environment and sequesters CO₂ effectively.

To better assess the development’s impact on peatland carbon emissions, the timeline for achieving ‘carbon payback’ or ‘carbon neutrality’ should consider land-based emissions. For example, ‘payback time’ could be defined as the period needed to restore peatland to a ‘near pristine’ condition from a reported baseline, compared to the site’s baseline emissions without development and counterfactual scenarios for non-peaty sites, and Scotland’s widespread peatland restoration efforts.

Should the Carbon Calculator incorporate other land use types?

This would offer a more comprehensive view of the carbon impact on other land use types, as compared to the carbon impact on peatland. This aspect should be evaluated considering Scotland’s evolving biodiversity net gain requirements, current Peatland Management Plans (PMP), Habitat Management Plans (HMP), and their anticipated updates.

Are the quality controls sufficient?

There are no in-built quality control mechanisms within the Carbon Calculator. Due to its complexity and skillsets required to review the data outputs, the Carbon Calculator is not used as a decision-making tool in the capacity it is intended. Additional quality controls would be beneficial.

The future of the Carbon Calculator

In addition to the technical review, the report also considers the future of the Carbon Calculator in terms of a review of incorporating high-resolution spatial data (HRSD) and/or peatland condition categories (from the Peatland Carbon Code), and applicability of the Carbon Calculator to other developments.

Integrating HRSD into the Carbon Calculator would enable an understanding of land cover types, providing proxies for peat condition and water table depth. This could reduce the need for manual site surveying for data collection and enable wider evaluation of the site.

We recommend that the integration of HRSD is explored for future versions of the Carbon Calculator, to ascertain the level of accuracy these enhancements could bring (i.e. through reduced manual inputs and/or quality controls). This can be done in conjunction with the findings from Scottish Government’s exploration of a national LiDAR mapping scheme.

The Peatland Code’s emission calculator provides emission factors to calculate the average net emissions from peatland in various conditions, based on the UK inventory. Whilst not Scotland-specific, integration of the peatland condition categories could provide a recognised approach to quantifying the benefits of peatland restoration activities.

There is potential for the Carbon Calculator to be adapted and applied to grid infrastructure and other development types on peatland and carbon rich soils, even though it is currently employed solely for wind farm developments. There are no concerns on the Carbon Calculator’s ability to be used on projects of all sizes. However, to be applied to different infrastructure types, consideration would need to be given to their unique spatial aspects, e.g. the effects of shading and effect of excess heat for solar farms. Further research is needed to understand the implications of other infrastructure developments on peatland and carbon rich soils prior to extending the applicability of the Carbon Calculator.

Glossary / Abbreviations