Soils underpin all nature-based systems and are therefore vital for Scotland’s communities and economy. From food security to transport disruption through events such as landslides, the climate resilience value of investing in healthy soils is recognised by the Climate Change Committee as a priority adaptation area for Scotland.

There are many risks threatening Scottish soils across different soil types and land covers. However, unlike air and water, there is no single overarching soil policy providing security and governance for Scottish soils. Soils are spread across multiple policy divisions, which results in a lack of cohesive leadership in tackling threats to soils.

The aim of this route map is to consolidate the challenges of managing soil systems to develop an overarching strategy for delivering improved soil security across Scottish landscapes.

Key points

There is increasing awareness of the important role soils play for our communities, economy and environment in terms of their ability to contribute to climate regulation, flood resilience, food security, support forestry and assist biodiversity. This is reflected in recent policy updates which have outlined objectives that directly relate to improvements in soil health and/or security, such as:

• The 3rd Scottish National Adaptation Plan 2024-2029
• Scotland’s National Peatlands Plan
• National Planning Framework 4
• UK Forestry Standard 5^th Edition
• The Vision for Agriculture and Agricultural Reform Programme (Agriculture and Rural Communities Act)
• Scottish Biodiversity Strategy to 2045

This route map acknowledges the challenges of addressing soil security in a policy context due to the absence of an overarching soil-specific policy. Currently actions to support soils sit across different policies, which focus on different environmental challenges at different scales. Nevertheless, this route map outlines opportunities to gain value and effectiveness through better coordination of existing activities and policy delivery.

Next steps

Objectives  

This route map recommends six objectives for Scotland to achieve our vision of ‘thriving soils for Scotland’s communities, economy and environment’: 

1. Lead – Inspire and collaborate to deliver the vision for Scottish soils 

2. Protect – Prevent further damage to soils 

3. Restore – Repair damaged soils 

4. Enhance – Strengthen soils for the future 

5. Evidence – Data, knowledge and wisdom relating to Scottish soils 

6. Mobilise – Communicate, engage and participate towards thriving soils in Scotland 

For further information, including recommendations, please read the report.

If you require the report in an alternative format, such as a Word document, please contact info@climatexchange.org.uk or 0131 651 4783. 

Image credit: Helga from Pixabay

Podcast and blog

Dr Sarah Buckingham gives an overview of the project and findings in episode 15 of our podcast: Evidence for climate policy in Scotland

A blog post summarising the podcast interview is also available on our website: Project snapshot: A soil route map for Scotland

Degraded peatlands are one of the largest sources of greenhouse gas emissions in Scotland. The Scottish Government has a budget of £250m to spend towards peatland restoration efforts through the Peatland ACTION (PA) programme up to 2030.

This research explored the evidence for peatland restoration costs in Scotland and examined emerging trends. It also investigated opportunities and challenges for contractors delivering peatland restoration services.

The researchers undertook a literature review, cost data analysis and contractor interviews.

Findings

• Observed peatland restoration costs per hectare vary significantly. Factors affecting cost include site characteristics, funding availability and environmental designation status.
• Approximately half of the variation in unit costs between sites could not be explained by the statistical analysis.
• There is some evidence that larger projects have lower unit costs.
• Supply of restoration services might be strengthened and value for money in peatland restoration increased through consideration of the following:
  • Include contingency costings as part of the tendering process, to address contractors’ cost risks.
  • Commit to long-term funding of a pipeline of restoration projects.
  • Ensure prompt payment upon project completion with provision for at least part payment if final inspection is delayed.
  • Simplify tendering procedures to stimulate supplier interest in peatland restoration work.
  • Continue with training support plus opportunities for knowledge exchange between funders and contractors.

For further details, please read the report.

If you require the report in an alternative format, such as a Word document, please contact info@climatexchange.org.uk or 0131 651 4783.

Image credit: Scottish Natural Heritage

Research completed January 2025

DOI: http://dx.doi.org/10.7488/era/5570

Executive summary

Aims

Degraded peatlands are one of the largest sources of greenhouse gas emissions in Scotland. The Scottish Government has a budget of £250m to spend towards peatland restoration efforts through the Peatland ACTION (PA) programme up to 2030.

This research explored the evidence for peatland restoration costs in Scotland and examined emerging trends. It also investigated opportunities and challenges for contractors delivering peatland restoration services. We reviewed existing literature and analysed cost data compiled by SRUC from the PA programme projects supported by NatureScot funding between 2018 and 2023. We also carried out interviews with contractors. Data from other PA delivery partners post 2021 was not examined in this project phase due to time constraints.

Key findings

• Observed peatland restoration costs per hectare vary significantly. This reflects a range of influencing factors, including:
  • project-specific factors (e.g. site characteristics, project length)
  • contractor-specific factors (e.g. firm size and history)
  • background commercial conditions (e.g. inflation, funding availability, tendering processes)
  • site location, baseline condition and environmental designation status.
• Approximately half of the variation in unit costs between sites could not be explained by the statistical analysis, often due to noise in the data, for example:
  • Differences in data recording on restoration processes, project characteristics and costs across projects within the study period
  • Wider economic factors such as regional variations in labour and material costs, poor transport networks and local competition for scarce resources (see the recent SRUC Rural and Islands Insights report for evidence of this at a local scale)
  • Limited local competition due to barriers to entry to the market.
• There is some evidence for economies of scale i.e. larger projects have lower unit costs. The extent of such economies of scales is difficult to determine due to other differences across projects.
• Statistically speaking, costs of restoration have not changed over time. The absence of such an observed time trend in restoration unit costs may simplify the use of unit costs as predicted by the model to future years.
• Interview data highlighted the impact of other factors, confirming the influence of complexities and uncertainties, both real and perceived, in the tendering process. These include:
  • perceived uncertainty in long-term commitment to government support for peatland restoration
  • challenging tendering processes
  • environmental and market conditions that add risk to a business engaged in restoration.
• This is largely independent of site characteristics but impairs value for money directly by increasing the overhead costs of tendering, and indirectly by constraining the pool of willing contractors.

Improving operational delivery of peatland restoration

• Estimates for restoration costs from our analysis could be useful for costings of large-scale policy programmes; the spatial approach to estimating variation in unit costs allows extrapolation at larger scale, although further work is needed to understand complex issues.
• Further research into the extent to which economies of scale are present would be helpful, as would steps to improve confidence in the accuracy of reported costs and associated site characteristics.
• Regional differences imply that uniform national benchmarking rates might be inappropriate, with large residual uncertainty of unit costs potentially increasing the risk of falsely rejecting projects that may deliver restoration cost-effectively.
• Using standardised costs to assess projects is also problematic because a large part of variation in costs remains unexplained. Either of the options below can improve this situation.
  • Give greater attention in the tendering process, in particular how that may be improved on both the demand and supply side. This would draw out true context-specific costs in a competitive market.
  • Seek greater transparency around individual cost elements for an individual project bid, including overhead charges and profit margins e.g. open-book tendering with agreed percentage markups.
• Supply of restoration services might be strengthened and value for money in peatland restoration increased through consideration of the following:
  • Include contingency costings as part of the tendering process, to address contractors’ cost risks regarding e.g. inflation spikes in key inputs (e.g. fuel) or unforeseen site complexities.
  • Commit to long-term funding of a pipeline of restoration projects. This will provide reassurance to existing and potential contractors that their investment in staff and machinery is merited.
  • Ensure prompt payment upon project completion with provision for at least part payment when final inspection is delayed due, for example, to weather conditions.
  • Simplify tendering procedures to stimulate supplier interest in peatland restoration work through rationalisation of information required, improved guidance and support for those tendering the work to provide better feedback.
  • Continue with (well received) training support plus opportunities for mutual knowledge exchange between funders and contractors. A specific area for training is in data collection for contractors.

Strengthening future analysis

Challenges and limitations of the analysis presented in this report could be addressed by:

• Exploring potential systemic differences across Peatland ACTION delivery partners by comparing the results derived from the NatureScot Peatland ACTION database with estimates generated by, for example the Cairngorms National Park and Forestry and Land Scotland.
• Confirming that the process of recording spatial location and recording of restoration area based on site outlines is standardised and consistently allows linking area and location with records of restoration costs and activities over time. Verification of reported area estimates through digitization in GIS can reveal important discrepancies. Re-recording of samples of outlines for restored areas, known as restoration footprint, on the ground should be considered for comparison.

Glossary / Abbreviations table

Abbreviations

Glossary

Background

A high proportion of Scottish peatlands are in a degraded state and the Scottish Government has been setting ambitious targets for peatland restoration^[1]. These reflect various overlapping policy objectives, notably reductions in greenhouse gas emissions (GHG) but also biodiversity enhancement and water management. Primarily via the Peatland ACTION (PA) programme supported by Scottish Government and administered by Scottish Natural Heritage (now NatureScot), Forestry and Land Scotland, and the National Park authorities, in excess of 52,000 hectares have been restored since 2012.

In February 2020, the Scottish Government announced an increase in investment in peatland restoration of more than £250 million over 10 years, aiming to support the restoration of 250,000 hectares of degraded peat by 2030, as part of the Scottish Government’s Climate Change Plan for net zero. In the Update of the Climate Change Plan, the restoration target is upheld, and it is emphasised that “[t]o deliver on the 2032 emissions reduction envelope annual peatland restoration needs to be far higher than the current 20,000 hectare annual target”.^[2]

Scottish Government funding for peatland restoration is managed via the Peatland ACTION (PA) programme. This has five delivery partners: NatureScot, Forestry and Land Scotland, Cairngorms National Park Authority, Loch Lomond and The Trossachs National Park Authority and Scottish Water. This research examined only NatureScot projects. Harmonising data from all delivery partners was an initial ambition but considered out of scope within the time and budget available in the project. Nevertheless, cost data collated from NatureScot PA administered projects has wide coverage, geographically and in terms of restoration activities and accounts for c.70% of PA restoration.

Over 10,000 ha of Scottish peatlands were restored under PA in 2023/24, an increase in annual restoration area of 40% compared to the previous year. Despite this increase, meeting the policy ambition for peatland restoration will require significant upscaling of restoration efforts over coming years at times of continued pressure on public budgets. Value-for-money and scale of policy ambition imply a need for targeting restoration efforts where it is most cost effective, taking single (GHG emission reduction) or multiple social and environmental outcomes into account. Determining such cost-effective pathways, requires an in-depth understanding of the costs that currently underpin peatland restoration in Scotland. However, whilst variation in restoration costs across different projects are reported (Glenk et al., 2022), the causes of such variation have yet to be investigated systematically. Furthermore, despite the key role that contractors have in peatland restoration delivery (and therefore associated costs), their perceptions of the tendering and restoration process has not yet been sufficiently studied.

This report examines variation of costs of implementing restoration,^[3] factors affecting contractors ability and willingness to engage in restoration, and explores barriers to scaling restoration efforts related to costs and the supply of restoration services by contractors.

The project had three main aims:

1. Which factors affect restoration costs? (Section 4)

We take a broad perspective to offer an overview that considers environmental and site conditions, factors affecting bidding of contractors and actual restoration work. The synthesis is based on a rapid review of literature discussing bidding behaviour and cost of implementing nature restoration, combined with the joint expertise of the research team. Where possible, we discuss interactions between factors and how they have been evolving over time.

2. Which factors explain variation in restoration cost? (Section 5)

We provide a data driven quantification of relationships between restoration cost and environmental and site characteristics. The analysis draws on cost data collected via the NatureScot PA funded programme^[4], which is matched with spatial information on environmental and site characteristics for statistical analysis. This provides insight into any systematic variation of restoration cost to support restoration budgeting and planning.

3. What are the opportunities and challenges for contractors in engaging with restoration? (Section 6)

We draw on interviews with contractors of restoration services selected to represent a mix of size and geographical spread. Interview notes and transcripts were reviewed to provide perspectives on prospects and difficulties faced by contractors as crucial actors for scaling of restoration efforts.

Factors affecting restoration – an overview

A brief synthesis of related literature

To identify factors affecting restoration cost, we screened relevant literature related to costs of ecosystem restoration and nature-based solutions^[5]; and the factors affecting bidding behaviour of contractors.

Cost of conservation efforts, including ecosystem restoration

There is consensus in conservation literature that costs should play an important role for conservation planning, management and evaluation; they affect ‘value for money’ considerations. The efficiency of conservation spending is enhanced if funding is allocated based on considerations of cost-effectiveness, i.e., the benefit achieved relative to cost (e.g., Babcock et al., 1997; Naidoo et al., 2006; Perhans et al., 2008; Burkhalter et al., 2016; Rodewald et al., 2019; Field and Elphick, 2019). How benefits are measured is of relevance, too: counting benefits simply in terms of area or number of conservation units is associated with less efficient allocation of resources compared to measures that better reflected actual intended outcomes (e.g., biodiversity) (Engert and Laurance., 2019).

The efficiency gains of considering costs depend on the accuracy of cost predictions. This requires the development of cost projections that reflect the (spatial) variability in cost of conservation action (Burkhalter et al., 2016; Van Deynze et al., 2022), also allowing the identification of potential economies of scale (Cho et al., 2017; Armsworth et al., 2018).

Ecosystem restoration projects of all types are generally considered to be high cost, often requiring significant up-front capital investment (Sewell et al., 2016). However, costs of restoration vary greatly across contexts and locations (de Groot et al., 2013; Sewell et al., 2016; Van Deynze et al., 2022). Factors quoted to influence cost variation include the baseline level of ecosystem degradation, local infrastructure availability, type and scale of restoration, population pressure and density, the legal framework, existing land use and tenure arrangements, land value, labour costs and method of measurement (Sewell et al. 2016,). We found studies referring to complexity of restoration works, managing and protecting safe access to sites, access to labour and supplies, and other project characteristics including land cover, slope, elevation, number of sites in a project and distance between sites (Van Deynze et al., 2022).

More specific peatland restoration cost estimates for the UK and Scotland also show great variability. For example, costs per hectare vary greatly by restoration technique used (Artz et al 2018; Okumah et al., 2019; Glenk et al., 2020, 2021, 2022). A previous CXC study (Artz et al., 2019) investigated physical limitations to access to restoration sites. They focused on several factors – physical infrastructure (road network), snow days, rainfall, elevation, peat condition, drainage status and a NatureScot remoteness index. Further, Aitkenhead et al., (2021), in their mapping of peatland emission categories, provided evidence for strong regional variation in peatland conditions and levels of degradation. In an outline of a national peatland monitoring strategy, Artz et al. (2023) proposed features such as bare peat extent, topographic and hydrological connectivity, soil erosion levels, microclimatic proxies water table stabilisation such as rainfall or windspeed and changes to vegetation cover among others, as essential dimensions to monitor the potential success of restoration efforts. Previously, Artz et al., (2019) had also identified strong geographic divide in peatland conditions across Scotland and that high site fragmentation levels introduce substantial error into the estimation process.

Other studies confirm the relevance of factors including altitude and distance from roads (remoteness) (Okumah et al., 2019), and site condition (Glenk et al., 2020, 2021, 2022), pre-restoration site use and land-cover.

The conservation and restoration literature emphasises the importance of reporting cost elements (e.g. fixed & variable, capital, labour cost) instead of simply total cost (Cook et al., 2017; Artz et al., 2018). Knowledge of cost elements, ideally collected in a standardised way (Iacona et al., 2018; Artz et al., 2018), facilitates the transfer of cost estimates across sites and contexts, enhances their potential to enter decision support tools, and improves understanding of the relationship between cost and conservation outcome as spending increases or decreases (Cook et al., 2017). Lack of standardising how costs are accounted for adds to an already large variation in reported cost across projects (Sewell et al., 2016; Glenk et al., 2020).

Synthesis of papers investigating contractors’ decisions to bid

Peatland restoration is primarily undertaken by private-sector contractors who are invited to tender competitively for work. However, little research appears to have been undertaken specifically in relation to peatland contractors’ business models and factors influencing their decisions to bid for restoration projects. Nonetheless, some possible insights are offered by findings for other land-based sectors (e.g. forestry, landscaping, and civil engineering).^[6] Although the analogies are not perfect, they are sufficient to identify relevant types of issues.

Common factors identified in this broader literature fall into various risk categories: client-related, project-related, contractor-related, and other (Cohan, 2018; Oo et al., 2022; Olatunji et al., 2023). The latter relate to background market conditions and government policies which apply across all contractors and projects, for example, wage and price inflation or regulatory obligations. All other things being equal, uncertainty about relative costs and/or future regulatory requirements dampen contractors’ willingness to bid for projects and/or increase quoted bid prices (Oo et al., 2022; Binshakir et al., 2023; Olatunji et al., 2023).

Client-related factors include financial and organisational reputation plus willingness to foster longer-term relationships. For example, promptness in paying, openness of administrative processes, and degree of mutual trust. All other things being equal, a reliable client with simple(r) bidding processes and a willingness to share project information plus commit to a pipeline of work is more likely to receive bids, and at lower prices (Spencer, 1989; Oo et al., 2022; Binshakir et al., 2023; Olatunji et al., 2023).

Project-related factors essentially relate to the size and complexity of projects (and hence overlap with the site-specific factors noted above). For example, larger projects generally benefit from economies of scale and simpler projects have less risk of encountering unforeseen problems. Hence, all other things being equal, simpler and larger projects are more likely to attract bids, and at lower unit prices (Oo et al., 2022; Binshakir et al., 2023; Johansson et al., 2023; Kronholm et al., 2023; Olatunji et al., 2023).

Contractor-related factors relate to the capabilities and confidence of individual firms. For example, prior experience with similar projects, availability of relevant staff and machinery, and sufficient cash-flow. All other things being equal, a contractor is more likely to bid for a given project if they are familiar with the type of work required and either already have the necessary staff and machinery or are sufficiently confident to invest in additional capacity (e.g. perceive a good chance of follow-on work). Confidence to bid may also reflect the anticipated degree of competition from other contractors and perceived fairness of (client-related) bidding processes. For example, the likelihood of a rival bid by a competitor being viewed as strong and/or favoured may discourage bidding (Cohan, 2018; Spencer, 1989; Oo et al., 2022; Binshakir et al., 2023; Johansson et al., 2023; Kronholm et al., 2023; Olatunji et al., 2023).

Implications for costs

Given that all factors identified above are likely to vary across different projects, clients (e.g. funding bodies), contractors and time-periods, it would be expected that observed unit costs (e.g. per ha) will display significant variation. This is confirmed by previous analysis of peatland restoration costs across Scotland (Okumah et al., 2019; Glenk et al., 2020, 2021, 2022). For example, Glenk et al. (2022) report overall median costs of £1025/ha across 158 completed projects but with a standard deviation of £4328/ha, and also show that medians for different types of projects vary between £939/ha and £1778/ha.

Reported costs for other types of ecosystem restoration also show significant (>40%) variation. This is largely attributed to differences in project scales and complexity, including administrative processes, but also to a lack of standardisation in cost reporting. Econometric analysis of the determinants of cost variation typically struggle to explain all such variation (King and Bohlen, 1995; Keating et al., 2015; Knight et al., 2021; Van Deynze et al., 2022).

Likely factors affecting peatland restoration cost

The findings from the available literature are consistent with anecdotal evidence gleaned previously by members of the research team and of the Steering Group. As such, it is possible to hypothesise the types of factors likely to affect peatland restoration costs, to guide (but not dictate) issues to explore through statistical analysis of secondary data and through discussions with contractors.

We identified a wide overview of potential factors affecting restoration costs across sites and at a given point in time (Appendix Table A4.2). There are potential relationships between factors and restoration costs, for example, costs per hectare are likely to fall as project size increases and overhead cost elements can be spread more thinly. However, costs per hectare are likely to increase with severity of baseline degradation (e.g. proportion of site with eroded or bare peat) as the restoration effort required increases. Similarly, more remote sites and sites with more complex mosaics of features may also be relatively more expensive per hectare.

The issue is complex and factors may confound each other. For example, economies of scale effects may not be immediately apparent if larger sites also happen to be more remote and/or more degraded.

The statistical analysis relied on the cost data already collated by researchers of SRUC into a suitable database from PA NatureScot data, although inconsistencies in reporting over projects and the study period (2018-2023) presented challenges. Specific metrics for characterising projects may include various biophysical indicators (e.g. area, location, topography) as well as baseline condition and access conditions affecting which type and density of restoration techniques is cost-effective.

We understand that PA delivery partners differ in their approach to profiling projects for tendering with potential implications for a full analysis of reported cost. For example, the Cairngorms National Park Authority (CNPA) has a model to translate complexity into labour and machinery days necessary for restoration, providing options for adjustments of typical rates in the process. This approach makes intuitive sense given that many site-specific factors affecting cost are related to complexity (Appendix Table A4.2). However, pre-characterization of complexity of restoration via aerial photography is time consuming and may be challenging to apply at scale. This may change in the future, for example employing machine learning mapping tools to assess drainage and erosion features that provide indication for restoration complexity (Macfarlane et al., 2024).

In addition, background changes over time may affect all projects, including advances in restoration techniques (Appendix Table A4.3). For example, inflation increasing the costs of key inputs (e.g. fuel) but also, potentially, innovation and experience reducing unit costs. Dynamics of supply and demand for restoration services may affect unit cost of restoration and also change over time. For example, contractors of restoration services may become more experienced and thus efficient over time. However, whether this impacts on unit costs depends, among other things, also on the level of competition that contractors face.

In addition to the statistical analysis of reported cost data, more qualitative insights can be gained through interviews with contractors undertaking restoration activities on-the-ground. This offers an opportunity to confirm the relevance of factors identified for statistical analysis. It also offers an opportunity to explore other factors not included in the cost database.

For example, contractors’ willingness to bid and quoted prices for particular projects may be affected by their capacity and experience (e.g. number of diggers, work on similar sites previously), but also by alternative income-generating opportunities (e.g. other civil-engineering work). Moreover, it may also be affected by (perceived) complexity and fairness of tendering processes, including the (perceived) likelihood of bidding successfully (i.e., whether tendering is worth the effort).

Such issues can be explored through discussion with contractors using semi-structured interviews. Whilst a range of different types of contractors (e.g. varying by size, location and experience) can be interviewed, results should not be treated as statistically representative but rather as illustrative cases of the types of factors influencing contractors’ engagement with peatland restoration.

Conclusion

Peatland restoration costs are influenced by a range of factors, including:

• project-specific factors (e.g., site characteristics, project length),
• contractor-specific factors (e.g. firm size and history), and
• background commercial conditions (e.g. inflation, funding availability, tendering processes).

These factors vary across different projects, clients (e.g. funding bodies), contractors and time periods, leading to great variation in observed unit (e.g. per ha) costs. Lack of standardising how costs are accounted for further adds to this already large variation in reported cost across projects. Systematic analysis of the factors to identify variation and evidence collected directly from contractors are needed to gain in-depth understanding.

Explaining variation in restoration cost

In this section we use information entailed in the SRUC cost database, which is compiled from NatureScot Peatland ACTION grant application and final reporting forms (see Section 5.1). We combine data in the SRUC cost database with publicly available spatial data to determine how geography, climate, peat condition, land use and site designation (SSSI etc.) are associated with restoration costs. The main output of the work reported in this section is a statistical model which attempts to explain variation in the restoration cost per hectare across completed projects.

The model results can be used to understand systematic relationships between restoration costs and site characteristics (e.g. access, topography, land use) that vary spatially. Findings may provide answers to questions such as ‘typically, is restoring peatland under grassland or forested land more or less expensive?’; or ‘is there a trend for restoration to be more expensive in one region compared to another?’. Answers to such questions may provide insights on how peatland restoration in Scotland could be delivered more cost-effectively. The model may also be used for to derive estimates of costs associated with expanding restoration across Scotland, for example as part of a cost-benefit analysis. We also highlight gaps in knowledge and highlight areas for review and further research that could make this type of analysis more accurate.

Methodological approach: cost data analysis

The SRUC cost database (see Glenk et al., 2022 for an overview) contains detailed information on project costs and activities, and in its most recent form originates from 289 final project report forms of NatureScot PA administered restoration projects covering a period from April 2016 to March 2023. Due to issues with unreliable historic data contained in the forms (see 5.2.4), only 229 of the 289 final observations for a period between April 2017 to March 2023 were complete and sufficiently reliable to be used in the analysis. Full details of the methodology, including limitations of the SRUC database, are given in Appendix A5.

Cost of restoration of a particular peatland site is here defined as the sum of all expenses within the project implementation phase. This includes all the measure-related costs (labour, material, fuel, equipment/machinery), mobilisation costs, project management and monitoring costs (within implementation phase) and other necessary work not directly attributable to restoration measures, such as changes to access infrastructure, site boundaries/fences, location-specific biodiversity protection measures or livestock/wildlife management/exclusion. Cost estimates exclude costs associated with feasibility studies, bidding and grant application process, any pre-restoration site-specific expenses, post-restoration monitoring and maintenance or loss of income due to limited use of the site post-restoration. These non-implementation costs are excluded because they are not part of the contractor tendering process and relate to a different set of activities. In addition, many sites do not yet have a lengthy period of reporting of post-implementation costs.

A statistical model to infer the cost per hectare of a site in the SRUC cost database based on 37 explanatory variables was developed to determine which variables significantly impact on cost. Spatial variables were extracted from several maps based on the location of the restoration project under the assumption that the sites were perfect circles of an area equal to that reported in the SRUC cost database. Spatial variables used to infer cost include rainfall, peat condition, peat depth, pooled-biogeographical-zones. Various configurations of the model were tested (i.e., different explanatory variables, different units of measurement), but the model presented is the best in terms of statistical test performance (see Appendix A5 for more details). A full list of variables used in the model can be seen in the Appendix Table A5.1. and a more detailed description of the data extraction and statistical model can be found in Appendix A5.

Figure 5.1 displays the geographical distribution of projects considered in the analysis across what we refer to as ‘restoration zones’ (Appendix Table A5.4). It is important to note that Figure 5.1 is not a representative map of PA restoration activity. The eight restoration zones were created by pooling the original 21 ‘biogeographical zones’ for the ease of interpretation. The original biogeographical zones, also referred to as ‘Natural Heritage Zones’ represent discrete regions based on similarities in topography, climate and the composition of biological community. Sites within a restoration zone are expected to have similar environmental and geographical features and thus a similar foundation for peatland restoration.

Results of cost data analysis

Descriptive data overview

After removing entries with obvious reporting errors (totalling 60 entries), the average cost per hectare (2020-£/ha) of restoration is £1,550/ha. However, there is a large variation in unit cost. To illustrate this: the unit cost at the 5^th percentile is £191/ha, while the unit cost at the 95^th percentile is £4,483/ha, Appendix Table A5.6.

Therefore, using an overall average cost per hectare to estimate costs of future restoration projects is not advised and further information about the site is required to infer variation in cost per hectare. The average restoration cost per hectare in each restoration zone shows that, all else equal, restoration in the Flow Country was least costly while restoration in the Central Belt was most expensive (Figure 5.2).

On average, 22% of sites were classified as ‘Near Natural Bog’ in the UK LULUCF Inventory (Appendix Table A5.5), and the largest area of restored peatland was classified as ‘Near Natural Bog’ at 32% of the area restored, for the sites considered in this study (Appendix Table A5.5). However, according to information provided by the NatureScot Peatland ACTION team only 3.8% of restored peat bog is near natural bog. It is likely that the ‘circle method’ (Appendix Figure A5.1) for calculating the area of restored peatland and/or the inaccuracy of the peat condition map used in the inventory may cause errors in our calculations.

To analyse the relationship between site area on costs per hectare, the sites were distributed equally to size-classes based on spatial area. The average unit costs for sites in the smallest area category were approximately three times as high as the ones in the largest area category, Table 5.2, pointing to the possibility of economies of scale (see Appendix A5.3 for an explanation and illustrative example related to peatland restoration).

These averages, however, need to be interpreted with caution due to the nature of calculation of costs per hectare (total site costs divided by total site area) and confounding factors, i.e., other factors co-vary (in our data) with size. The suggestion that decreased unit cost associated with larger site size in the data is due entirely to economies of scale could therefore be misleading. For example, a high proportion of larger sites are grassland sites rather than bare peat sites, meaning that their lower per ha costs may partly reflect their scale but may also partly reflect the relative ease of restoring grassland rather than restoring bare peat.

This was evident in the cost database, where we find that the largest sites (N=6 representing 17% of the restored area; site area >380ha) had none of the complex restoration activities such as mulching, stabilisation, felling and sphagnum transplanting (one notion of site-complexity). Therefore, it was difficult to determine if a large site was cheaper per hectare due to economies of scale, or because it required less complex restoration activities; both explanations are likely responsible for the observed decrease in cost per hectare with increased site area.

Statistical model results: drivers of spatial variation in cost per hectare

The results provide a good overview of the spatial drivers of restoration cost but may mask any interactions between variables. The statistical model (log-linear) helps us unpick all the variables that are driving cost for a site and determine features that are making sites more or less expensive (Appendix Table A5.7). The model explained 52.0% of the variation in cost per hectare amongst the 229 sites used in this study. After accounting for the number of variables (37) used in the model relative to the number observations (Adjusted R-squared), the explained variation was 42.4%, which compares favourably to other studies (Van Deynze et al., 2022). The unexplained part is attributed partly to noise in the reported data (e.g. errors in forms and in data entry) and to unobserved influences on costs – both of which reflect some of the limitations of the data collection process. However, it should be noted that it is unrealistic to expect 100% explanatory power on any statistical model: neither is the underlying relationship between different factors often known sufficiently to specify it perfectly in modelling terms nor are all possible data available to populate a perfect model.

Figure 5.3 displays all the variables considered as having an influence on cost per hectare, and the amount that they are predicted by the statistical model to change costs per hectare^[7]. Variables right of the red dashed line increase costs and those left of the dashed line decrease costs. Here we discuss variables which we are almost certain (‘significantly’) to affect cost per hectare according to the available data, i.e. those in green in Figure 5.3 as well as variables we initially expected to drive unit cost.

Year of funding

We expected that cost per hectare would vary across time (Appendix Table A4.3). However, the year in which the funding was granted is not statistically significantly explaining variation in costs. Since the costs are deflated, the data suggests that peatland restoration costs have changed over time in line with inflation. However, mostdata points were unreliable before 2017 and the reliability of data increased after 2019, which leaves only a six-year time period to be investigated here. This then limits conclusions in regards of time trends.

Nevertheless, those interested in time trends may inspect a descriptive analysis of area of restoration sites, restoration measures, land cover and regions over time for the study time period (2018-2023, Appendix 5.4).

Regions

For the pooled biogeographical zones, the lowest restoration unit costs, once all other factors such as forestry land use are controlled for, are reported for the Flow Country (which is used as a reference point in the statistical model and hence does not show up in Figure 5.3). Costs per hectare are significantly greater for sites in all other regions. Note that this applies after controlling for all other factors considered in the model. The restoration zones with the greatest restoration costs per hectare are:

1. The Isles: On average, log-cost per hectare is 2.1 times greater to restore a site in this region than in Flow Country. The high costs may reflect a mix of greater costs (e.g. fuel and haulage costs) on islands. Furthermore, the limited supply of contractor services on specific islands and their need to travel long distances and potentially transport the heavy machinery by ferry are potentially important factors.
2. Argyll: On average, log-cost per hectare is 1.4 times greater than for the Flow Country. The complexity of terrain and remoteness to some extent overlaps with The Isles, and thus similar challenges might be expected.
3. Central & Northern Highlands: log-cost per hectare restored is 1.3 times higher than in the Flow Country. The hilly terrain adds complexity due to more difficult access and environmental conditions in which the restoration needs to take place.

The availability of contractors in different restoration zones may also explain the regional differences (see Section 6 and factors related to demand and supply of contractors in Appendix Table A4.3.) ^[8].

Peatland condition classification

The proportion of peatland in certain condition categories affects restoration costs. In general, sites with lots of peat classified as ‘grassland’ are cheaper to restore, Figure 5.3. We hypothesize that this is because the land is more homogenous and because the grass is protecting the underlying peat from erosion. Therefore, it is more likely that the restoration activities required will be cheaper, such as drain blocking. It may also be that grassland areas have more favourable access conditions that reduce costs.

In contrast, sites with large proportions classified as ‘eroded bog’ increase the restoration cost. This is likely due to the complexity and raised cost of restoration activities to restore eroded bogs, e.g., hag reprofiling and sphagnum moss transplants. The proportion of the site with peat classified as ‘forest’ has the greatest positive effect on cost per hectare amongst Inventory peatland condition categories. We expect that this is due to the cost of felling, and the associated removal of stumps and possibly mulching, before restoration activities can begin. This finding is in line with earlier analysis presented in Glenk et al., (2022).

Site designation

Each site designation is self-reported and model results can be interpreted as the effect of a particular reported site designation, keeping all other designations the same. If a site reports SSSI designation, the log-costs per hectare are 80% higher than without it, Figure 5.3. This could be tied to careful operation on-site and risk of downtime through presence of important wildlife. The national scenic area (NSA) designation has the opposite effect on costs. If a site falls into this category, the log-costs per hectare are 69% lower. This effect might be the result of better access to scenic areas and overall better pre-restoration site conditions and management. Further work is required to understand the influence of this factor.

Site use

Like site designation, site use is a self-reported category, and each site could have several reported uses. The model results for each site use are interpreted as the effect of a particular reported site use, keeping all other reported site uses the same. Forestry reported as a site use dramatically increases restoration costs per hectare. On a hectare basis, sites that are used for grazing are cheaper to restore than those that are not used for this purpose, Figure 5.3. This is in line with ‘forest’ and ‘grassland’ peat condition categories discussed above (5.2.2.3). Although the effect is less certain (i.e., not significant), costs per hectare of sites self-reported as ‘field sports’ (i.e., shooting grouse) tend to be lower. We expect this is due to the good access on such sites.

Average rainfall

In general, sites with a greater average yearly rainfall rate are associated with lower cost per hectare. This could be due to various reasons, such as comparatively higher water tables that might imply healthier peatland and thus less complex restoration activities.

Statistical model results: summary

• The statistical model allows us to explain c.52% of the variation on per hectare peatland restoration costs.
• Site location within restoration zones and specific categories of peat condition, site use and site designation are significant predictors of variation of costs per hectare of peatland restoration.
• Of these factors, the geographical area that the site is in is the largest driver of cost per hectare with significantly greater values on the Isles, and significantly lower values for the Flow Country, after accounting for other factors.
• Forestry, both as a site use and a peat condition category, has a strong effect on overall costs due to complexity of activities related to forest removal^[9].
• High levels of peatland erosion are linked with greater per hectare restoration costs.
• Presence of floodplains/surface water on site, NSA designation and grazing, or peat covered by grassland all significantly reduce site restoration costs per hectare.
• On average, larger sites have lower unit costs (£/ha) than smaller sites. We attribute this to a combination of economies of scale and a tendency for larger sites to be associated with relatively less complex and thus cheaper restoration activities.

Main limitations of the analysis

While the explanatory power of our analysis lies within expectations for this type of study, it is important to note sources of ‘noise’ and data uncertainty. Apart from potential issues with data entry and collation into the SRUC cost database, a major source of uncertainty is related to large variation in detail and rigour of reporting of the restoration process via application and reporting forms. Several reports are missing crucial details making them invalid for further analysis. It is important to point out that such issues primarily arise for older sites in the SRUC cost database, and that reporting forms have been adapted several times over the study period to accommodate insights as the PA program evolved within NatureScot.

Each PA project that has been granted funding by NatureScot can be identified via a grant reference number. Thus, the sites that have been restored within the same restoration grant share the same reference number. However, throughout the duration of restoration, the definitions of sites often change, in part reflecting adjustments to initial restoration plans made throughout a project. Differences concern both the number of sites within a grant, and the area of identified sites can both increase or decrease based on what is currently considered feasible/priority. Therefore, the information detailed in project application forms can only be compared to final forms if these changes were sufficiently documented. Likewise, it was sometimes not possible to link past restoration grants to more recent grants on a specific area of peat. We recommend using the same grant reference codes for additional funding or encoding previous grant codes into new grant reference codes so that previous funding can easily be traced back to new funding for the same overall restoration area.

Inconsistencies of grant reference numbers and site IDs between the SRUC cost database and PA spatial data meant that it was impossible to easily link spatial site outlines to the cost data base. Consequently, we manually “triangulated” matches between sites in the SRUC cost database and sites in the spatial data for restoration from NatureScot PA, which was both time consuming and without guarantee of being free of error.

Due to unavailability of geospatial data for all sites in the SRUC cost database considered for analysis, we assumed that each site was a circle of the reported restoration area around a central point which reduces accuracy. According to NatureScot, spatial data has now a site ID field and the final report document has also this site ID field with cost associated, which should facilitate similar analysis of variation in restoration cost per hectare in the future. Furthermore, moving to digital reporting so that spatial information and cost data can be entered into the same data portal may reduce errors in site identification and matching of cost and spatial data. Due to a lack of a standardised methodology for the calculation of a total area of a restoration site, over time of study (2018-2023) and across restoration sites in the SRUC cost database, the account of area restored provided in the reporting form can be only treated as approximate.

Sites for which the reported areas were missing, unclear or otherwise impossible to work with were removed from the analysis. The format in which the type, unit and (unit or total) cost of restoration measures is reported also varies as reporting forms were updated over the years; and depended on preferences and reporting efforts invested by grantees. For example, the installation of wave dams has been reported either as the total number of individual dams, the total length of all the drains that were dammed, or the total area covered by the specific type of dams. Wave dams also feature only in later editions of application and reporting forms. Such issues with reporting complicate measure-specific analysis of restoration cost. For example, differences in units in which measures are reported make judgment on measure intensity in a restoration site challenging if not impossible. A more technical description of the limitations in the analysis can be found in the Appendix A5.1.5. An account of challenges regarding information used for collating an earlier version of the SRUC cost database is also included in Glenk et al. (2022).

Conclusions

• For costings of large-scale policy programmes, and in the absence of more robust alternatives, our model might be used to provide upper and lower bounds for restoration costs. The use of mostly spatially explicit variables in the statistical model facilitates extrapolation at larger scale. Accepting important caveats regarding the analysis (related e.g. to consistency of recording of cost within SRUC cost database and the proximate approach to deriving spatial variables from reported area), information on variation in unit cost could be combined with spatially explicit restoration pathways to derive baseline estimates of expected costs of large-scale policy implementation and related uncertainty. Such estimates could for example be combined with benefit estimates of peatland restoration in a cost-benefit analysis.
• Statistically speaking, costs of restoration have not changed over time. The absence of such an observed time trend in restoration unit costs may simplify the use of unit costs as predicted by the model to future years.
• Prior to extrapolation of unit cost estimates for large scale policy appraisal, further research is needed to assess the extent to which economies of scale are present. This could be combined with further efforts to improve confidence in the accuracy of reported costs and associated site characteristics.
• Because of the great degree of variation and the relatively large degree of unexplained variation in unit costs, the statistical model should not be used for appraisal of individual projects (as opposed to large scale policy programmes). However, there are potential implications of the unexplained variability for the practice of using standardised costs to assessing projects and benchmarking. Given the large degree of unexplained variability, greater flexibility in appraisals of cost should be offered. In this regard, for example, our model points to a need for accommodating for larger costs on the Isles.
• There has been great progress in harmonising cost and area reporting for projects, especially since 2019. Based on challenges in linking the SRUC cost database with spatial data on NatureScot Peatland ACTION administered projects for the study period, a review of the methodology for recording of the following data may prove useful. This recognises that much of the points below may already be in hand:
  • Costs: clear, separate categories for measure-related expenses and project management; costs identifiable at a site level and over time.
  • Site outlines: precise recording of site location and dimensions. Guidance for recording outlines and areas (e.g. distance buffers around areas where restoration measures are implemented) to record area impacted by restoration has been developed. It might be worth to review that guidance is implemented consistently and enforced for all projects by Peatland ACTION delivery partners.
  • Applied measures: unified accounting of units (i.e. length vs. number of dams).
  • Common and unified project and site identification: ensure that the system in place allows tracking of sites throughout project lifetime and beyond.
• Also, compare the statistical results derived from NatureScot Peatland ACTION projects within the SRUC cost database to the estimates generated for projects administered by other delivery partners.
  • For example, CNPA uses a bottom-up approach that classifies peatland restoration needs and associated costs by complexity mapping based on aerial photography. A more detailed analysis of costs of delivery by Forestry and Land Scotland could provide additional insights into the economics of forest to bog restoration.
• Verify reported area estimates in spatial data provided by NatureScot Peatland ACTION. Re-recording of site outlines (area restored/restoration footprint) on the ground should be considered and could be incentivised and/or organised via Peatland ACTION officers.

Opportunities and challenges for contractors delivering peatland restoration services

The rapid literature review (see 4.1.2) points to a knowledge gap about service providers implementing nature-based solutions. Our research partly addresses this gap with a focus on contractors of peatland restoration and their views and perceptions regarding business models, factors influencing decisions to tender and costing within tenders, and barriers and opportunities to scale business operations in the peatland restoration domain.

Methodological approach: contractor views

Eight interviews were conducted with contractors providing peatland restoration services in Scotland, primarily funded through NatureScot as the PA delivery partner (Table 6.1). Here, we define contractors as the company or individual enacting the peatland restoration. Details of the approach are given in Appendix B6, including the interview protocol (Table B6.2).

Interview notes and transcripts were reviewed to identify commonalities and points of difference in contractor perspectives of the tender process and wider factors affecting the industry. Findings are presented here around nine main themes: factors affecting tendering, alterations to tendering, costs, importance of business diversity to create resilience, consistency of funding and workflow, geographical area of work, recruitment and skills, training and increasing the restoration area.

Table 6.1: Study participant overview. To maintain anonymity, we remove identifiers and randomise order of appearance in this table

Results of interview analysis

Factors affecting tendering

A wide range of considerations affecting the decision to tender were mentioned by participants, including

• Ease of tendering, which determined whether contractors would tender or not. This applied mainly to smaller contractors
• Current workload
• Capacity, although this is increased by machinery hire or sub-contracting
• The accessibility of site
• Whether the operations matched their machinery portfolio
• One large contractor does their own formal value for money assessment to decide whether it is worth tendering

Experience of the tendering process was commonly raised as an important factor affecting the decision to tender, in line with findings from the literature (Section 4.1.2). Contractors further highlighted a number of issues with the tendering process that were leading to frustration and could pose a barrier to expanding the industry. Decision makers in administrations involved in implementing peatland restoration have some control over shaping the tendering process, thus offering potential for operational adjustments.

• Transparency of the process: Contractors highlighted a need for substantiated and clear feedback.
• Timeframes: knowing what is happening when and sufficiently in advance.
• Content of tenders was too involved.
• Public contracts tendering was perceived by smaller contractors as onerous and not always concomitant to the scale of project.
• Tendering is a non-productive aspect of a business that does not favour micro and small businesses. Several contractors perceived that the complexity of tendering is a barrier to smaller contractors entering the industry.

The time spent on tendering ranged from one to five days. Most contractors indicated that they spent several days working on each tender highlighting that tendering is a significant cost to be absorbed by businesses. Where contractors were very keen on a project, they would visit the site, therefore increasing their investment in, and commitment to, the site.

Tendering success was highly variable with smaller contractors often doing jobs not requiring a full tender process. Several contractors reported low success rates with a perception of time being wasted. One large contractor reported that their success rate was around one third. Two further (well experienced) contractors related that they had not won any “Peatland ACTION” work in the last year although they did work for SSE and FLS and had won PA contracts in the past. Contrastingly, one island-based contractor related that their success rate was near 100%. For those reporting low levels of success, this was understandably leading to frustration.

“Do I want to put good money and time toward chasing peatland action work? Right now we will dabble where we think it’s appropriate, but I’d rather put time and effort into chasing work that will actually go somewhere.” (A4)

Contractors generally regarded the tendering process as overly complex and inefficient, requiring a level of information which could be out of proportion to the value of contracts. A particular problem raised was a lack of standardisation in both the information requested and the format required between different organisations, which increased the amount of time required to respond to each. Even those contractors who had built capacity in tendering through dedicated staff perceived that the tendering process was unnecessarily complex; one highlighted that lack of standardisation was a problem as it increased the risk that key information would be missed; another considered that complexity was a barrier to smaller contractors wishing to enter the industry . Adding to frustration around low tendering success, some contractors perceived that there was insufficient feedback provided on why tenders had been unsuccessful. While feedback on relative pricing was provided, other factors used to discriminate between tenders were rarely communicated.

“You don’t even get feedback that you can work off because everybody just goes, [the winning bidder’s] technical submission was better, and you go well, what was better about it? And they go, I’ll need to get back to you. It’s not like there’s a matrix and they go well, here’s where the other person’s scored higher.” (A5)

A perceived lack of transparency in how tenders were awarded was a key concern for one contractor in particular who considered that tendering had become “closed book” and that “it seems to be a small handful of main players who will all the contracts”. Providing an example of where a contract had been awarded to a company closely connected to the commissioning organisation they also voiced concern that contracts appeared to be being awarded without being listed on Public Contracts Scotland (PCS).^[10] These points were raised as breaches in what they considered should be a fair and transparent process to ensure fair allocation of public funds.

Contractors further related that the planning and timeframes for tenders were too often uncertain, which could lead to a “feast or famine” outcome. It was further highlighted that the current funding year had been particularly unusual.

“Due to the way in which projects are being assessed and funded by Scottish Government and Peatland Action, there has been a glut of tenders recently, so I’ve probably done in the space of two months, probably submitted about 24 jobs. And you know never in the history of my working life [have I] ever seen anything quite like it, you know, in terms of a glut of workload, of a single thing.” (A8)

Some contractors also indicated that they had begun bidding strategically to account for the risk that projects ultimately would not go ahead due to funding constraints. One larger contractor related that they ran their own value for money assessment to determine whether it was worth tendering. Another mid-sized contractor similarly indicated that they were starting to consider expected cost per hectare as a factor in their decision to bid for work.

Contractor views on alterations to tendering

Framework agreements were discussed as a potential means to reduce the volume of information in tender submissions. Although easier for the commissioning organisation as they only deal with one contractor, it was considered that the approach favours contractors who have the resources to tender well. One participant raised concern that this would lead to the dominance of larger contractors, leaving the smaller, less lucrative and active part of the contract to be subcontracted to smaller contractors. Although the framework provides a simplified approach, they considered that work could be done at lower cost by directly contracting smaller contractors.

A common view amongst contractors was that restoration work should support the local economy.

“I think it’s only right if the Lewis people get the Lewis work and the Skye people get the Skye work providing they’re doing it at competitive rates” (A7)

Linked to this, one mid-sized contractor questioned whether smaller contracts could be tendered on a different basis, and offered to local contractors first as a means of developing local capacity.

“I know when we were starting up these small jobs were great for us and we even picked up a lot of like ten, twenty grand AECS schemes and they were brilliant for us and they helped us get our feet and learning how to tender for bigger work.” (A4)

A similar view was given by a larger contractor who questioned whether it may be possible to differentiate tenders and make it easier for smaller contractors to bid for the smaller jobs and allow the larger contractors to take larger jobs.

Wishing to highlight a positive example, one contractor pointed to Bidwells as an example of an efficient tender process that was easy to understand and provided a good mapping system. Another contractor similarly praised Bidwells’ efforts to streamline the tender process by maintaining key contractor information on file, reducing the volume of information that must be submitted with each tender.

Risk factors and costs

The key risk factors affecting cost quoted by participants were:

• Difficulty and distance of site access: distance and accessibility affect costs in terms of additional travel time, machinery breakages and increased risk.
• Winter risk, flooding and snow restrict access to sites, potentially stranding machines or requiring premature mobilisation from sites.
• Activities: damming and ditch blocking were assessed as relatively straightforward to estimate, whereas hag- reprofiling was considered to be more variable.
• Contractors further referred to rising costs of machinery, and wages as future drivers of costs.

Importance of business diversity to create resilience

To survive in what potentially is an uncertain environment of peatland restoration and funding, most businesses had a reasonably diversified business model, not relying too heavily on peatland restoration. Two contractors indicated that they were quite specialised, with peatland restoration accounting for more than 80% of their turnover. In some cases, they reviewed their exposure to risk and considered reducing reliance on peatland restoration. Reducing the exposure to risk from peatland contracts included working with utilities, civil engineering (dualling of the A9), estate access, hydro-schemes, footpaths, fencing, dykeing and tree planting. Many of these alternatives are easier to implement, provide more certain longer-term work, reduced risk, with less travelling and reduced ongoing costs.

Consistency of funding and workflow

Consistency of commitment to funding was important for all the contractors. Prior blips in funding reduced confidence in the industry and ultimately the amount of time committed to peatland restoration. Planning, timelines and long-term contracts could all be improved to provide a more continuous flow of work. Multiyear funding was appreciated but it was felt this needed to be more co-ordinated to create a rolling programme of work for both large and small sites.

Although progress has been made in some areas with more summer restoration, the summer gap and down time reduces the amount of restoration completed. Some contractors considered the summer gap as positive, as it gave operators a break and change of scene to alleviate the monotony of peatland restoration.

Improving the diversity of funding was considered a good idea to reduce reliance on Government funding. If Government funding was to be reduced in the future it was suggested to apply gradual tapering rather than the sudden drop that many contractors experienced when the renewable obligation was suddenly stopped for windfarm construction.

Although a few years away, an early indication of the Scottish Governments long term strategy for funding restoration post 2030 would be appreciated to signal long-term commitment to the sector.

Geographical area of work

Most contractors were willing to travel, with some Scotland based contractors working in Ireland and England. The reasons for the travel were partially to diversify the business and provide new experiences for the business and operators. Most businesses preferred to work in their local area, but inevitably not all operatives could find housing near the business base and had to travel anyway. In some cases, contractors may drive up to an hour from their home base, followed by another ½ hour transiting to the sites via an access track. Finding suitable accommodation for staff is an issue in some cases.

Recruitment and skills

Contractors highlighted the importance of rural skills for working on peatlands efficiently. A key requirement voiced by contractors was the ability to ‘read the landscape and the conditions’. Technical skills in operating diggers and machinery were important, but not as critical as knowing how to move the machine on soft ground which was harder to come by and essential to avoid accidents and bogging. Ideal candidates for recruitment were those with hill experience; “farm kids” (A1) or “ex shepherds, stalkers and gamekeepers [who have] been on the hill most of their lives” (A3).

Fortunately, in terms of operator skills it was considered that due to the video gaming industry there were plenty of competent young people who could quickly learn how to operate diggers, and this aspect is not a problem for the businesses. The key issue requiring training was once on site and reading the landscape, which requires time and perseverance.

Retention of staff, particularly younger members present problems with staff leaving for less repetitive jobs or easier working conditions in civil engineering. Businesses try to combat this by offering variability of work and location, or through benefits such as a four- day week.

It was acknowledged that a wider range of skills is now requested of operators, principally mapping and GIS skills. In the case of smaller businesses this presented problems adding to workloads and need for upskilling. One large contractor questioned whether placing additional demands on operators was the most effective way to monitor work, believing that measurement could be undertaken more efficiently by a dedicated third party. Other (typically mid-sized and larger) contractors indicated that they had invested in IT and mapping capabilities.

Peatland ACTION funded training and apprenticeships were being used and appreciated.

Increasing and using restoration capacity

In circumstances where contractors perceived there to be a funding cut, they were not considering increasing their capacity. It was accepted that over time the amount of available work would increase. Using current capacity more efficiently was the approach being taken. Most contractors did not see evidence of additional work coming forward.^[11] The issue for increasing the area restored was not related to capacity.

Current capacity is underutilised due to:

• Uncertainty of funding, leading to contractors looking for other work to reduce risk.
• Poor work stream planning that leads to uncertainty and reduces contractor ability to plan and expand operations.
• A hiatus in contract confirmation after end of March which leads to bunching of contracts, reducing capability to complete within a given timescale.
• Summer working with long daylight would utilise the current capacity to restore substantially more hectares. Breeding birds are the main factor reducing or stopping restoration through the summer. Generally, restrictions on estates regards stalking seasons is now less of a problem as it is understood that by good planning both operations can coexist.
• Some contractors were aware that the Peatland Code and collection of information was delaying contracts and made planning more difficult.

Conclusions

Combining the results from the data analysis and the interviews, we can draw the following conclusions on the questions posed by this research.

The interviews with contractors offer insight into the industry’s views and perceptions regarding the tendering process and further engagement with peatland restoration as a business opportunity. Below is a synthesis of findings and options that may help address identified issues.

• Confidence in future funding is critical for contractors working in the industry. Unexpected reductions in funding reduce contractor confidence and may deter investment. Therefore, funding should ideally be consistent within years, based on a long-term commitment to peatland restoration post 2030 that reflects the importance of restoration to address the twin climate and biodiversity crises. Interest and trust between funder and contractors may also be strengthened if information on how peatland restoration is funded post 2030 involved contractors at a very early stage.
• The tender process and its transparency were factors that concerned all contractors. Current tendering processes were considered to favour larger contractors with specific staff to respond to tenders. The amount of information required, whether the information was used and the ability to receive meaningful feedback were all factors affecting contractors’ willingness to tender. A review of tenders and information required and how that is achieved would encourage a wider range of contractors to engage and tender. Such a review may focus on simplification and proportionality. Consideration might be given to whether basic tendering information could be submitted on an annual (rather than project) basis to stop repetition of effort. A review might also include guidelines for providing substantiated post tender feedback, as several respondents were unclear on how to improve future tenders. Improved feedback could lead to less contractor comeback and a greater willingness to tender.
• Underlying the contractor conversations was that they seek to provide good value for money whilst making a profit in a highly variable environment. All the contractors interviewed valued their reputation and wanted to produce quality restoration. Clearly, tendering requires a balance between bureaucracy and accountability. However, a degree of pragmatism is necessary in light of the urgency for action to counter the twin climate and biodiversity crises. Consequently, the amount of information required as part of the tendering process should ideally be concomitant to the scale of work.
• Access to sites was seen as a key factor influencing the decision to tender (and also the cost of restoration). Poor and long-distance access increases both costs and risk. The purchase of specialised machinery to carry crews to the work site is required and the additional transit time reduces the length of the working day. In addition, poor and rough access results in machinery breakage and costly down time. To improve access conditions, in future any access granted under planning permission could allow for neighbours to use the access for the purposes of land management. There are cases of adjacent road standard tracks to sites that could not be used as they were on neighbouring land. Further considerations might include improving affordable rural housing to increase rural workers and reduce unsustainable travelling.
• Concerns were raised about consistency of funding and projects across the year. Peatland restoration generally has a short window of operation in the autumn, winter and early spring. This is further shortened due to heavy snow. Historical and current precedents of cuts in funding have made contractors very wary. Contractors suggested that diversified funding may help this situation. In response to this, options to assist contractors should be explored to identify and pursue diversified funding sources to reduce risk and increase contractor confidence.
• One opportunity to diversify funding sources lies in improved coordination of environmental projects. Currently there appears little or no coordination of environmental projects. With coordination, peatland restoration contracts could seamlessly run into river restoration contracts. Likewise, Scottish Water have many long-term infrastructure projects that could fill gaps in contractor work. Thus, a more continuous flow of conservation work could be achieved through improved planning and coordination of work across the land-based sector to better integrate peatland restoration contracts with, for example, river restoration and Scottish Water projects.
• Anecdotal evidence suggests that birds are less disturbed by consistent on-site presence than is recorded in scientific literature. A review of bird disturbance policy based on scientific evidence may thus help reducing down time and reducing uncertainty when tendering. To further reduce perceived uncertainty for contractors, low altitude contracts might be retained to cover periods of long-lasting snow.
• To stimulate investment, there is potential for interest free government backed loans for startups/early growth businesses. Consistency of projects would enable more assured payback of finance. In this regard, it might be worth to explore suitability of existing schemes and further opportunities to ease access to interest free government backed loans for startups/early growth.
• Training and apprenticeships for delivery of restoration works are of high value to individuals and businesses interested in entering the market, and should continue to be financially supported.

Conclusions

The research findings presented in this report reflect a rapid synthesis of the literature and our research team’s own expertise plus statistical analysis of cost data compiled from NatureScot administered Peatland ACTION (PA) projects and qualitive interviews with peatland restoration contractors. We have identified a multitude of factors affecting peatland restoration costs and contractors’ decisions to tender for restoration work.

Whilst information on peatland restoration costs is available for NatureScot projects funded through the PA programme, the causes of apparent variation in costs have not yet been analysed systematically. Our statistical model, combining cost data with project site characteristics, is able to explain c.52% of observed variation. This is in-line with attempts to model cost variation in analogous sectors (e.g. other ecosystem restoration, landscaping).

Our analysis does not identify a time trend, but highlights that there are regional differences in cost, with higher costs to be expected for the Isles. Site features indicating greater complexity of restoration action, such as forest land cover and high levels of erosion, are associated with greater restoration cost. While restoration cost per hectare decreases as size of restored sites increases, our data does not allow us to fully and causally attribute this effect to economies of scale alone. This requires further investigation. 

Overall, our analysis points to a need to recognise that there is large degree of unexplained variation in unit costs while unit costs vary considerably across sites in our data. This has implications for the relevance of standardisation in assessing projects and developing benchmarking of costings. For example, regional differences imply that uniform national rates might be inappropriate, while large residual uncertainty regarding unit costs would increase the risk of falsely rejecting projects that in fact deliver restoration cost-effectively.

However, although unexplained variation in costs may reflect genuine unobserved causes, our analysis was also hampered by several potential data imperfections. For example, the precise shape and size of individual projects is subject to some uncertainty, which may lead to errors in characterising sites. Equally, across the study period (2018-2023), categorisation of different types of cost is not necessarily consistent across all projects nor are different phases of the same project necessarily recorded consistently across different funding periods. Efforts to improve data quality have already been instigated. Nevertheless, it might be worth to clarify inconsistencies in older data, and confirm that harmonised data collection (site specific data on activities, cost, location, area, consistently recorded over time) is in place to improve the accuracy of future analysis. 

Contractors are service providers who implement restoration work on the ground. The quality of their work is therefore key to restoration success. Despite their important role in the restoration process, there is a paucity of literature on motivations and barriers to contractors to tender for and enter ecosystem restoration work (including peatland restoration), and on factors that affect costs and long-term viability of restoration work to businesses. We interviewed contractors of different size and varying geographical range of operation. We identify recommendations that will affect cost and quality of delivery and thus enhance value for money of peatland restoration delivery in Scotland.

Specifically, we point to a need for a streamlined tendering process that is simplified and proportionate to scale of work, and that provides meaningful post-tender feedback. Fostering reliable and strong relationships with contractors is important, as is mitigation of short-term (e.g. mitigating risk of interruptions to work) and longer-term (e.g. related to funding situation) business risks. Cash flow availability might be improved through more efficient processing of payments to contractors, although delays may be caused by agents and not the funding institutions (PA delivery partners). Business risk may also be reduced through offering opportunities to diversify funding sources, for example via improved planning and coordination of work across the land-based sector. Training opportunities are appreciated, but barriers to entering peatland restoration as a service provider would benefit from enhanced support for start-up, both in terms of e.g. interest free capital provision and tailored advisory support.

All of the above aspects affect costs and quality and thus value for money of peatland restoration delivery. A revision of the modus to deliver peatland restoration using public funds across Scotland should be embedded in a long-term commitment to peatland restoration post 2030 to attract investment and offer business perspective. Such a commitment to consistency of funding is needed to reflect the importance of peatland restoration to a world experiencing twin climate and biodiversity crises.

Acknowledgements

We like to thank the study participants for offering their time and valuable insights. We also thank the members of the project steering group for input throughout the project. Further, we would like to acknowledge the Peatland ACTION Data & Evidence team and the Peatland ACTION Funding team at NatureScot for their active support of this work. The collation and preparation of peatland restoration cost data for use in the analysis presented in Section 5 of this report was support of the Scottish Government, as part of the Environment, Natural Resources and Agriculture (ENRA) Strategic Research Programme 2022-2027, project JHI-D3-2 CentrePeat; and the project Wet Horizons (Horizon Europe GAP-101056848).

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Appendices

Appendix A4 Factors affecting restoration – review and synthesis

Table A4.1: Web of Science Search Terms. Results were supplemented by forward and backward tracing of citations plus the research team’s prior knowledge of relevant references.

Table A4.2: Potential factors affecting cost per hectare of peatland restoration across sites and at a given point in time.

Table A4.3: Factors affecting cost per hectare of peatland restoration over time

Appendix A5 Explaining variation in restoration costs

Appendix A5.1 Methodological approach (detailed) including data preparation and assumptions

Appendix A5.1.1 Factors included in analysis and spatial data sources

The analysis builds on the evidence review in Section 4 and previous work on understanding variation in site-specific restoration costs. For this study, the publicly available spatial data identified as potential predictors of variation in peatland restoration costs come from several sources listed in Appendix Table A5.1.

It is necessary to know location and dimensions (shape) of restored sites to be able to assign spatially explicit data to them. However, due to difficulty to reliably match many of the cost database sites with their Peatland ACTION polygon counterparts (5.2.4 ‘Main Limitations’), the site shape needed to be assumed. As all the sites selected for this analysis reported a UK National Grid location, representing a centroid for each site, and a restored site area (in hectares) was reported, we assumed that all sites were a circle of “restored area” centred at the grid location. This circle was then overlayed with the relevant spatial data and the data extracted. For example, to add the average number of ‘snow days’ expected on a site, we overlay the site circles on the HADUK grid of climate observations and extract the average snow days associated with the site. See Appendix A5.1.4 ‘Merging cost database with external data’ for full details of the methodology.

Appendix A5.1.2 Data modifications

For peatland conditions, land cover classes and biogeographical zones the variables taken from the original data sources were pooled into more general categories to increase the model’s ease of interpretation. For example, all land cover classes associated with forest were classified as one ‘forest’ category in the model, see Appendix table A5.2-A5.4 for more details.

For the costs to be comparable across all the sites, the total cost figure per site was divided by the total site area to arrive at a cost per hectare estimate. The costs have been deflated to 2020 levels using consumer price index (CPI) values from the Office of National Statistics.

Appendix A5.1.3 Multi-linear regression

We developed a multi-linear model which estimates cost per hectare of the final restored area, C, based on the spatial variables described in Table A5.1. The distribution of Cost was right-skewed due to the existence of some notably expensive sites (see Figure A5.2). In such cases it is recommended to transform the dependent variable, for example by taking its natural logarithm. We thus develop a model to predict the natural logarithm of cost per hectare C of the restoration project:

where the variables are continuous, for example ‘Average annual rainfall’ and the variables are dummy variables that take a value of one (else zero) if a condition applies, e.g. if Site Region ‘Argyll’ is associated with the site. Appendix Table A5.1 shows the list of continuous variables and dummy variables considered as well as their sources. Note that not all of the variables were included in the final statistical model (Table A5.7). Since the ‘Biogeographical zones’ are unique and cover every site (every site is in exactly one zone), we can remove one of these dummy variables from the regression and not lose any information. We choose to remove the ‘Flow Country’ and thus analysis of these results is relative to the cost of restoring sites in the Flow Country. Many prospective variables to be used in the log-linear model were likely to co-vary. To ensure there was acceptable levels of multi co-linearity in the variables used in the regression we ensured the variance inflation factors for each variable were less than 5, see Appendix Table A5.8 for the variance inflation factors of the variables used in the model. To account for the fact that multiple observations (sites) can be associated with the same grant, clustered errors for all observations derived from the same grant were calculated.

In the results, we present the coefficients associated with the variables () and dummy variables () on a graph as ‘log Cost multipliers’, along with the 25% confidence interval as error bars (Figure 5.3). For continuous variables this can be interpreted as: For every one-unit change in the variable, by what factor would you expect the log cost per hectare to change. For dummy variables, this can be interpreted as the site having this property will cause this multiplication of the log cost per hectare. Since log is monotonic, we can translate this to how the variable multiplies cost. Each variable has different units and scales, so it is difficult to compare one multiplier to another. A statistically normalised version of the plot can be seen in Figure A5.3, where magnitudes between multipliers can be compared.

Table A5.1: List of variables and dummy variables used in the linear regression to estimate log cost per hectare. If the class of variables are dummy (i.e. binary) then this is indicated in the class column.

Appendix A5.1.4 Merging cost database with external data

The process of merging the SRUC cost database of NatureScot PA administered projects with other spatial data involved the following steps:  

1. The site grid references in the cost database were converted to Easting-Northing coordinates (using standard UK coordinate reference system EPSG:27700) and converted to a GIS point shapefile (using QGIS software package version 3.16). 
2. The circular polygon shapefiles with the centre point being the actual site centroids with a total area corresponding to the reported restored area (in NatureScot PA final reporting forms) were created within the GIS framework.  
3. The maps containing spatial environmental information were overlaid over the circular polygon layer and cropped into the shape of the sites.  
4. For the microclimatic variables (snow days, temperature, wind speed), topography (elevation, slope, ruggedness) and remoteness, an average value per site was calculated (for raster maps that means the total value of each variable for all raster cells in each site divided by the number of cells). For land cover categories, firstly the raster picture was converted into a vector polygon shapefile by smoothing the cell edges with a fineness down to 15 meters. A total area of each category per site vas calculated and recorded as a separate variable (for all the land cover types that a specific site did not contain the variable values were zero). The areas of each category were divided by the total site area to arrive to a ratio of the site that has the particular land cover. The total length of outlines of individual land cover features was calculated to account for terrain heterogeneity (assuming that the more patchy the site is the longer the outline of the individual features). Similarly for the peatland condition map, a total area of each site that is peatland was calculated, individual peatland condition categories were recorded and ratios per site calculated. The bare peat ratio and floodplain/surface water area ratio were calculated as a ratio of the peatland per site rather than the total area of the site. Similarly, average peat depth was considered only for peatland area of each site.  Finally, sites were assigned to a biogeographical region based on the centroids’ precise location. 
5. The data was downloaded from the GIS software into a spreadsheet and merged back into the cost database using a unique site identifier (concatenated from a unique site ID and a report type). The further steps of analysis/ model and figure construction were completed in Excel, STATA and Python packages, respectively.  

Table A5.2: Inventory peatland condition classes pooled into larger categories  

Table A5.3: Land cover classes pooled into larger categories

Table A5.4: Biogeographical zones pooled into larger Restoration zones

Table A5.5: Percentage of total area of restored sites falling into each: a) peatland condition category as defined by the Inventory peat condition map; b) Site designation, and c) Land use as reported on the final report forms for NatureScot Peatland Action.

Appendix A5.1.5 Main limitations

A major source of uncertainty is related to large variation in detail and rigor of reporting of the restoration process via application and reporting forms. Several reports are missing crucial details that make them invalid for further analysis limiting the power of studies such as this.

Each project that has been granted funding by NatureScot can be identified via a grant reference number. Thus, the sites that have been restored within the same restoration grant share the same reference number. However, throughout the duration of projects, the definitions of sites often change. This includes both the number of sites within a grant, and the area of identified sites can both increase or decrease based on what is currently considered feasible/priority. Therefore, the information entailed in project application forms can only be compared to final forms if these changes were sufficiently documented.

For deriving site area and overlay with GIS information, the circular site outline approach was chosen due to difficulty to reliably link a substantial number of the sites from the cost database with spatial data from Peatland ACTION that contains both centroids and site outlines. The grant reference numbers are often inconsistent between cost database and spatial information, and the number, area, account of applied measures and grant amounts often do not match between the information sources. Consequently, we had to manually “triangulate” matches between sites in the cost database and sites in the spatial data from Peatland ACTION, which was both time consuming and without guarantee of being free of error.

Due to a lack of a unified methodology for calculation of a total area of a restoration site, over time and across sites in the database, the account of area restored provided in the reporting form can be only treated as approximate. Sites for which the reported areas were missing, unclear or otherwise impossible to work with were removed from the analysis. As mentioned above, the site areas were in some cases also pooled together within the same project, and thus arriving at a reliable area estimate for the individual sites was difficult.

The format in which the type, unit and (unit or total) cost of restoration measures is reported also varies as application and reporting forms were updated over the years, and depending on reporting efforts invested by grantees. For example, the installation of wave dams has been reported either as the total number of individual dams, the total length of all the dams combined, or the total area covered by the specific type of dams. Wave dams also feature only in later editions of application and reporting forms. Such issues with reporting complicate measure-specific analysis of restoration cost. For example, differences in units in which measures are reported make judgment on measure intensity in a restoration site challenging if not impossible.

Figure A5.1: An example of populating the circular polygons with the cropped spatial features (In this case different colours represent individual land cover classes).  

Appendix A5.2 Supplementary results

Figure A5.2: Distribution of costs considered in the analysis after deflation to 2020 levels.  

Table A5.6: Descriptive statistics of explanatory variable data and cost per hectare of sites, N=229.

In Figure A5.3, we plot the same figure as Figure 5.3 in the main text, but we divide the multiplier by the standard deviation of the variable so that the magnitude of the multipliers can be compared between variables.

Figure A5.3: Normalised Log of the Cost per hectare multipliers (i.e. coefficients in the regression) according to the multi-linear model. For continuous variables, (e.g. average rainfall) this can be interpreted as for every one standard deviation, the log of the cost per hectare increases by the multiplier represented by the dot. For dummy (binary) variables (e.g. region), can be interpreted as the site having that property will increase the log cost per hectare by the multiplier. Positive log of the cost multipliers (right of the red line) implies increasing the variable increases the cost and vice-versa for negative log cost multipliers. If the entry is green, then the multiplier is significant (p<0.05). In this case magnitude of multipliers can be compared.

Table A5.7: Ordinary least squared regression of log of the cost per hectare.

Table A5.8: Variance inflation factors (VIF) of the variables used in the log-linear model demonstrating the level of multi-collinearity. Variables were only included in the main model if the VIF<5.

Appendix A5.3 Additional information on economies of scale in peatland restoration with illustrative examples

Economies of scale arise at least partly from a contractor being able to spread fixed overhead costs for a project across a larger area. The literature review and interviews with contactors suggest that two main overhead costs are relevant: project tendering costs (i.e. the time and effort expended on submitting a bid) and project mobilization costs (i.e. the initial costs of getting equipment and materials on-site). Hence, whilst information on overhead costs was not sought explicitly through this research, some initial indicative analysis is possible.

To a first approximation, the costs of compiling and submitting a tender for a project are unrelated to its size since the effort required is determined by the tendering process rather than site size per se (although site complexity may increase required tendering effort). Similarly, again to a first approximation, haulage costs for equipment and materials relate primarily to the charge for moving a transporter carrying such items rather than carrying individual items themselves per se, implying that mobilization costs are likely to increase in a lumpy manner depending on how many haulage events are required rather than linearly with site size (e.g. if two diggers can be hauled on one transporter, mobilization costs will be the same for a small site requiring one digger and a larger site requiring two; only if more than two diggers are required will the larger site see an increase in mobilization costs – with scale still diluting the additional costs).

Contractor interviewees suggested that tendering takes two to three (eight hour) days. If contractors value their managerial time at £30/hour this equates to £480 to £720. If they value their time at £50/hour it equates to £800 to £1200. Online haulage costs suggest generic (i.e. not peatland) individual digger transportation costs mostly lie in the £400 to £500 range, depending on digger size and the distance moved (UShip, 2024; WHC, 2024). Taken together, these imply project overhead costs of c.£900 to £1700. For a five-hectare site these equate to unit costs of c.£180/ha to c.£340/ha. For a 20-hectare site they equate to c.£45/ha to £85/ha. This highlights the potential magnitude of economies of scale effects. A better understanding could be established with further investigation, including how contractors value their managerial time, the effort devoted to tendering and actual mobilizations costs (including for multiple diggers and for items other than diggers).

Appendix A5.4 Additional analysis regarding temporal trends

Area

Table A5.9: Summary statistics outlining the average areas (ha) of restored sites per each funding year.

Types of restoration measures

Table A5.10: Number of sites restored using a measure category (A – dams & blocking, B – surface measures (bunding, mulching, replanting), C – forest & scrub removal) per funding year.

Land cover

Table A5.11: Area (ha) of each pre-restoration land cover category restored per each year.

Regions

Table A5.10: Number of sites restored in each restoration zone per funding year.

Appendix B6 Opportunities and challenges for contractors

Appendix B6.1 Detailed methodological approach

Eight interviews were conducted with contractors providing peatland restoration services in Scotland. Interviews were conducted using an interview script (Appendix Table B6.1) to guide the conversation, yet allowing some flexibility for the discussion to move into other topics that were important to the participants. A semi- structured approach was selected because this is considered most appropriate where the topic of research is novel or under researched, as is the case for research concerning the experience of peatland restoration contractors.

Participants were selected for interview by purposive sampling, from a publicly available list of contractors willing to offer peatland restoration services (7), and from a list of new entrants to peatland restoration that was provided by NatureScot (1). A sampling frame was used to guide recruitment to ensure perspectives were obtained from contractors of different sizes and across geographic areas (Table 6.1).

Interviews were scheduled for thirty minutes, though ranged from 15 minutes to one hour and were conducted as video conference calls using Microsoft Teams (N=7), and by phone (N=1). Most interviews were conducted by interviewer 1 and 2 together (N=6), with interviewer 1 leading the interview. Two more were conducted by interviewer 2 alone.

An initial draft interview script was presented to the project steering group and revised to incorporate their feedback. With the consent of participants, interviews were recorded and later transcribed for analysis. Pre-approval for the overall approach and research instruments was received from the SRUC Ethics committee (Ref. 149 / 89056833).

Interview notes and transcripts were reviewed to identify commonalities and points of difference in contractor perspectives of the tender process and wider factors affecting the industry.

Pre-populated brief of contractor

Add here information collated e.g. from online sources on the contractor, if any

This may include – type of services offered, information on location, range of operation, experience & examples of past work, references, availability of machinery and staff capacity.

• Contractor name:
• Contact(s):
• Website:
• Useful info:

Type contractor (can be filled and/or revised after interview)

• Experienced & active contractors focusing on restoration
• Experienced & active contractors with wide range of business (e.g. forestry, estate management & road construction/maintenance)
• Occasional contractors focusing on other business & who do not systematically look for restoration opportunities

Adjustments to questions needed if contractor falls into the following categories:

• Tendering but unsuccessful
• Not (yet) tendering

Introduction (to be tailored and aligned with contact emails and information provided therein)

We’re conducting research on behalf of the Scottish Government and its Centre of Expertise on Climate Change, looking at peatland restoration undertaken by contractors.

We’re interested in your views on peatland restoration – your experience as a contractor with the tendering process, how you approach costing bids for restoration work, and what influences restoration costs.

Your input will help with further development of funding schemes for restoration, for example by helping delivery partners and funders in having a better idea of the information that should be considered as relevant and make tendering easier for you.

Any information you provide will only be reported in anonymized form.

On this basis is this acceptable?

If not provided consent in email response, ask verbally for consent.

Table B6.1: Interview script.

Table B6.2: Final interview schedule for interviews with (potential) contractors of peatland restoration services.

© The University of Edinburgh, 2025
Prepared by SRUC on behalf of ClimateXChange, The University of Edinburgh. All rights reserved. DOI: http://dx.doi.org/10.7488/era/5570

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
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1. Restoration and rewetting are used interchangeably in this report. In doing so, we do not imply that it is likely that peatlands will be restored to their historic undisturbed state, but emphasise the aim of restoring the functioning of the area as a wetland. This is done through raising water tables, i.e. rewetting. ↑

2. Although the 2032 emission targets have now been acknowledged as unachievable, the peatland restoration target remains in place. ↑

3. Unless noted otherwise, we will refer to restoration costs as the capital requirements to implement restoration on site. This does not include certain transaction (program administration and monitoring) costs borne by funders, the opportunity costs of restoration related to income forgone (see Moxey et al. 2016), or any private financial benefits of restoration e.g. related to carbon scheme participation or transfer payments. Such costs can make up a considerable amount of total cost of investing in nature based solutions (Kang et al. 2023). ↑

4. Forestry and Land Scotland and the Cairngorms National Park Authority also hold data on restoration costs (as do Loch Lomond and Trossachs National Park), but these databases were beyond the scope of this project. ↑

5. For further insights, the search goes beyond peatland and peatland restoration only, including habitat (e.g. wetlands, grassland) restoration more generally but also other land-based sectors requiring similar contracted land management services (e.g. forestry, landscape gardening civil engineering). ↑

6. For example, Spencer (1989), Cohan (2018), Benjaminsson et al. (2019), Kronholm et al. (2021), Oo et al. (2022), Binshakir et al. (2023), Johansson et al. (2023), Olatunji et al. (2023). ↑

7. We note that the magnitudes of the factors cannot be compared (see Appendix Figure A5.3 for a version of the Figure where magnitudes can be compared). ↑

8. Potentially contributing to relatively lower restoration costs, earlier projects especially in the Flow Country may have been subject to sequencing of restoration measures at the same site over several years; with yearly progress entered as new projects into the SRUC cost database. The extent to which such sequencing might have taken place is, however, unclear. ↑

9. Note that the analysis only includes forest-to-bog restoration by NatureScot PA projects (and not forest-to-bog restoration through, for example, FLS). ↑

10. At the time of interviewing, interviewees may not have been aware that listing on PCS had recently been made compulsory rather than simply preferable. ↑

11. It should be noted, however, that interviews were mostly undertaken before an announcement was made regarding funding for an additional 7000ha. ↑

Why it is important

The Scottish Government is committed to support the transition to net zero, whilst restoring and regenerating biodiversity. Organic farming practices have the potential to deliver to both agendas. Therefore, in the 2021-2022 Programme for Government, the Scottish Government committed to doubling the land area devoted to organic farming in Scotland by 2026 and supporting the growth of organic food production.

Given these aims, Government asked ClimateXChange to commission a robust evidence review to help understand how organic farming practices contribute to climate change mitigation and adaptation.

Findings from the study demonstrate the environmental benefits of different organic farming practices and how they could help farmers adapt to a changing climate. This has informed Scottish Government’s decision-making about agricultural policy.

Furthermore, the report clarifies the definition of organic farming, addressing terminology challenges identified in previous work.

How ClimateXChange supported policymakers

Organic farming has become a widely used term, applied across a range of circumstances. ClimateXChange supported the project team in designing the research and subsequent report to clearly set out important distinctions. As a result, this report presents a clear summary of what constitutes organic farming practices, which is different from organic farming business certification.

The study included a rapid evidence assessment of academic and grey literature, followed by two stakeholder workshops to test the emerging findings and gather opinions from those directly involved in this sector. Published literature is – by definition – dated, given the time required for research, reporting, publication and dissemination. Stakeholder engagement ensured that the research captured up-to-date Scotland-specific practice and experience.

The project team had considerable expertise in organic farming research. They synthesised complex academic knowledge and mapped the state of knowledge and key evidence gaps that could be explored further. ClimateXChange brought expertise in knowledge exchange to communicate the results effectively with a wider policy audience who were not experts on the topic.

Impact

The findings shaped the development of the Scottish Government’s agriculture reform work, informing decisions on what organic farming practices to support. Furthermore, the report also informed the revised draft of Scotland’s Organic Action Plan. This has been presented to teams across Government as well as to ministers and will update the shared agenda for the sector, with actions to be taken forward by all parties.  

The Organic Action Plan is due to be published in June 2025 and will be used to support the growth of the organic sector in Scotland, through encouraging market growth and increasing the area of farmland under organic management.

The report contributed not only with knowledge but also to building relationships between the Scottish Government and the Scottish Organic Stakeholders Group.

“Thanks to the report, we learned about the benefits of organic farming on important aspects such as biodiversity, soil health and food waste reduction. This knowledge has been valuable in informing our work on the Organic Action Plan. It also helped set organic farming in the context of the wider agriculture support framework.”

– Callum Neil, Agri-Environment Senior Policy Adviser and Organic Action Plan Lead
Scottish Government

“The report was really helpful, particularly for our agricultural reform work, highlighting that organic farming is not a uniform set of practices. This provides a more holistic approach that farmers and crofters can take in selecting from a list of measures they can adopt to increase the sustainability of agriculture.”

– Stephanie Davies, Senior Agri-Environment Policy Adviser
Scottish Government

Related reports

Organic farming, net zero impacts and the impact of a changing climate

The potential for an agroecological approach in Scotland: Policy brief

Related links

Agricultural Reform Route Map

Biodiversity: delivery plan 2024 to 2030

The planning system in Scotland provides opportunities to adapt to current and future risks from climate change, and the potential to promote nature recovery and restoration.

Development planning, which outlines how places should change and where development should and should not happen, requires planning authorities to publish local development plans. These should account for and address current and future climate risks, and enable places to adapt accordingly. Accurate data, identifing geographic features such as rivers and utilities, is vital for the creation of effective plans with a sound evidence base to evaluate climate risks.

This project explored the geospatial resources that are available to planning authorities, with a view to improving access to geospatial data on climate risk. The research involved an evidence review and stakeholder engagement with planning authorities at various stages of evidence report development.

The report identifies existing data, data gaps, barriers and resources needed for evidence-based planning and delivery.

Findings

• A wide range of data is required to assess climate vulnerabilities and impacts, some of which require substantial climate and data expertise to interpret.
• Most required data is free for planning authorities.
• Planning authorities tend to rely on datasets familiar to them – such as Flood Maps (SEPA), Dynamic Coast, Scottish Index of Multiple Deprivation (SIMD), and OS MasterMap to assess climate risks like flooding, coastal erosion, and social vulnerability.
• There are additional datasets and tools which would benefit from further adoption by planning authorities, especially the Local Authority Climate Service.
• Significant data gaps exist for urban heat islands, storm damage, health, water, infrastructure and landslides.
• Planning authority use of spatial data is limited, despite support for it in the Local Development Planning Guidance.
• Planning authorities find it challenging and time-consuming to gather data from multiple providers.
• There is value in carrying out Climate Risk and Vulnerability Assessments (CRVAs) to better direct the use of data but there is no consistent approach or simple tool available for planning authorities to use.
• Collaboration across planning authorities allows knowledge and resources sharing, which leads to more consistent and effective outcomes.

Briefing note

The briefing note provides guidance on more usable and interpretable data that can be used for assessing climate risks and vulnerabilities.

If you require the report in an alternative format, such as a Word document, please contact info@climatexchange.org.uk or 0131 651 4783.

Research completed: January 2025

DOI: http://dx.doi.org/10.7488/era/5650

Executive summary

Background

The planning system in Scotland is used to make decisions about future developments and use of land in towns, cities and countryside. Planning authorities are required to prepare evidence reports as part of the local development plan (LDP) process. These provide a summary of the baseline data used and explain the implications of the LDP.

Supported by National Planning Framework 4 (NPF4), planning LDPs should account for and address current and future climate risks, and enable places to adapt accordingly. Accurate, sub-national spatial data, which identifies geographic features such as rivers and utilities, is vital to create effective plans with a sound evidence base to evaluate climate risks. Fully evidencing climate risk requires an understanding of hazards, but also exposure and vulnerability, typically requiring interpretation of multiple datasets at once.

This report explores the geospatial resources that are available to support the evidence gathering stage with a view to improving access to geospatial data on climate risk. It identifies existing data, data gaps, barriers, and resources needed for evidence-based planning and delivery.

Key findings

Through engagement with a selection of Scottish Planning Authorities, we found:

Data for evidence reports

• A wide range of data is required to assess climate vulnerabilities and impacts, some of which require substantial climate and data expertise to interpret.
• Most required data is free for planning authorities.
• Planning authorities tend to rely on datasets familiar to them – such as Flood Maps (SEPA), Dynamic Coast, Scottish Index of Multiple Deprivation (SIMD), and OS MasterMap to assess climate risks like flooding, coastal erosion, and social vulnerability. These datasets are highly usable, with consistent coverage and quality across Scotland, but sometimes require geospatial expertise for analysis.
• There are additional datasets and tools which would benefit from further adoption by Planning Authorities, especially the Local Authority Climate Service. Additional datasets related to wildfire risk, air quality and land use may offer value, but would require some transformation, processing and interpretation to the climate context.
• Significant data gaps exist for urban heat islands, storm damage, health, water, infrastructure and landslides. Proxies (e.g. combining urban form, green space, and housing quality data) are suggested for urban heat island assessments. However, these must be approached in a considered way, which balances the potential effort to develop the interpretation against the likely risk.

Planning authorities’ approach

• There is a knowledge gap on how climate risk impacts planning. Some planning authorities have limited prior experience on climate risk, fewer technical data skills within their teams and no dedicated climate change professional. This leads to planning authorities mainly focusing on flood risk, where they have more familiarity.
• Planning authority use of spatial data is limited, despite support for it in the Local Development Planning Guidance. This underuse may result from limited awareness of the guidance and expectations of evidence reports, and a lack of capacity and skills to interpret geospatial data.
• Planners expressed a wish for a simplified approach to incorporate climate adaptation considerations into their plans.
• Planning authorities find it challenging and time-consuming to gather data from multiple providers.
• There is value in carrying out Climate Risk and Vulnerability Assessments (CRVAs) to better direct the use of data but there is no consistent approach or simple tool available for planning authorities to use.
• Collaboration across planning authorities allows knowledge and resources sharing, which leads to more consistent and effective outcomes.
• Given the wide range of potential data and analysis, planning authorities benefit from instances where work had been undertaken ahead of the LDP process to provide a view of which risks are most impactful, allowing a more focused approach to data.

Briefing note for planning authorities

• Many planning authorities lack clarity on which data should be used for assessing climate risks and vulnerabilities, and how to interpret it. We have created an accompanying briefing note (Section 9.5), which should help by providing guidance on more usable and interpretable data.

Glossary / Abbreviations table

Context and approach

Context

The reduction of emissions and adaptation to current and future risks of climate change is a challenge which is vital to be addressed via the planning system. The planning system provides opportunities to adapt to both current and future risks of climate change, as well as the potential to promote nature recovery and restoration in the area.

As part of the effort to modernise and update the planning system, the Scottish Government aims to align land-use planning with an outcomes-based approach to deliver sustainable development. This approach supports the National Performance Framework National Outcomes (Scottish Government, 2015) and supports the United Nations Sustainable Development Goals (United Nations, 2015).

Development planning, which outlines how places should change and where development should and should not happen, requires planning authorities to prepare and publish a local development plan (LDP)^[1], updating on a 5 yearly basis.

The National Planning Framework 4 (NPF4) (Scottish Government, 2023c),puts climate change adaptation and resilience front and centre. A clear understanding of the impact of hazards and risks related to climate is required for an effective plan, and this must be underpinned by the effective use of climate risk data.

Defining climate risk

In this report, we refer climate risk in line with existing climate literature. Risk is defined as a combination of hazard, exposure and vulnerability (IPCC 2014).

Hazards are physical events which may have adverse effects, such as sea level rise & increased heat.

Exposure indicates the presence of people, resources, infrastructure which could be impacted by the hazard, and the extent to which they can be reached by the hazard. Physical proximity is one key consideration in understanding degree of exposure.

Vulnerability indicates to which extent people, resources or infrastructure could be more or less impacted by a hazard

Crucially, data can indicate hazard, exposure, vulnerability, or potentially a combination of the factors if indicating risk. However, if a dataset was not designed with the above in mind, it would need to be reinterpreted to a climate risk context. As an example, Ordnance Survey provides extensive data on the location of buildings, infrastructure and natural features, but geospatial analysis would be required to derive metrics such location to flood zones to indicate risk of flooding.

There is a substantial range of potential hazards associated with climate change in Scotland (Grace et. Al 2025). For this report, our engagement with the planning authorities focussed on the applicability of data, therefore a simplified grouping of hazards and risks was used (Table 1). In instances where datasets were particularly applicable to vulnerability and exposure, this is discussed in detail in Section 4.

Table 1 – Hazards and risks, as summarised in this report

Local development plan evidence reports

Evidence reports are an early, statutory step in the development of a local plan. It provides a summary of the baseline data and other information which will form the basis of the plan.

This research focuses on the evidence gathering stage for climate risk – specifically, the tasks of early engagement and data collection, preparation of the evidence reports and a gate check by the Planning and Environmental Appeals Division (DPEA).

Evidence reports should be proportionate, with planning authorities having the discretion to tailor them to local characteristics and conditions. The Local Development Planning Guidance (Scottish Government, 2023b) provides guidance to support planning authorities in preparing evidence reports, including potential datasets relevant to NPF4 policies for climate change adaptation planning.

In addition to data access, there is a need to draw out implications of the data for the plan. It is not just about accessing the geospatial climate risk dataset but also ensuring its usability to accurately inform local development plans.

Rationale for this research

The lack of easily accessible spatial data on climate risk at a sub-national resolution has been identified as a key barrier to localised understanding of climate change adaptation by local authority planning officers.

Data gaps and accessibility issues create barriers to planning authorities producing proportionate, evidence-based plans. The aim of this research is to establish options for improved, simplified access by Scottish planning authorities to geospatial data that enables consistent, collaborative climate adaptation in local planning.

The intended audience for this research includes the Scottish Government and planning authorities. The work was commissioned on behalf of the Scottish Government, with particular interest for colleagues from the Climate Change Division and Planning, Architecture and Regeneration Directorate. A standalone briefing note and data catalogue (see Section 9.5) has also been produced specifically for planning authorities to showcase the available datasets.

Research methodology

The research involved an evidence review including a review of relevant literature, planning requirements and an in-depth data analysis of available risk data and its characteristics. Stakeholder engagement was conducted with planning authorities which were at various stages of evidence report development (see Figure 1) from early planning to successful completion. The engagement included interviews, as well as a wider workshop (See 9.3). Findings from these activities were the analysed to understand current needs, challenges and possible solutions to improve the process.

Figure 1 – Planning authorities engaged with in this study included Western Isles, Moray, West Dumbarton, Glasgow City, the Lothians, the Borders and Fife

Climate risk data

Effective data is central to the local development plan, and it is key that the right data is used and referenced for the evidence report. Additionally, the evidence report is expected to rely on spatial data, for which baseline evidence sources should be accessible.

There is a large range of data potentially available for use in evidence reports. This section provides a consolidated assessment of key datasets.

Our engagement with planning authorities identified:

• Five climate-related datasets that were familiar to planning authorities and were – or were intended to be – used to produce the evidence report.
• Eight datasets which would be valuable if used more extensively by planning authorities in the preparation of evidence reports.
• Five areas of concern to planning authorities in relation to the evidence report which did not have a dataset available, or a clear methodology, documented below as data gaps.

In this section, key aspects of the data are provided, such as name, the organisation providing the data. The data license under which the data is made available is provided, the full implications of the license to usage by planning authorities is detailed in Section 9.1.2.

The majority of datasets reviewed are updated and published at a rate sufficient for their purpose, though we have noted instances where there may not be a clear long term plan for the maintenance of the data.

Several other datasets were identified as having potential value. For the full list of all data sources reviewed please see the accompanying data catalogue (Section 9.5)., which catalogue includes details of the metadata and access links.

Key datasets already in use

Through the interviews and workshop, there were multiple datasets already in use by planning authorities in the production of the evidence reports, though not consistently in all cases. The most popular datasets are discussed in this section, along with a narrative of how the datasets were applied and if any issues were faced.

Table 4.1 – Key datasets already in use by planning authorities

Flood Maps

The consensus among most participants was that the Flood Maps from the Scottish Environment Protection Agency (SEPA) were a useful source for assessing increased flooding risks, which could be an outcome of both increased magnitude and frequency of rainfall, storms and sea level rise. The SEPA data is presented using a simple index (high, medium and low risk) down to ‘street’ level, which lends to easy interpretation by all stakeholders. The SEPA data also distinguishes between current flood risk, and future flood risk up to the 2080s, in the ‘Future Flood Maps’ layer. One participant also commented that the support provided by SEPA is also incredibly useful. Given the flood data also overlaps with other planning use cases out with the evidence report, there is a lot of familiarity with the data.

Dynamic Coast

Data from Dynamic Coast is used by multiple planning authorities. This project undertook a wide range of analyses, from coastal change due to sea level rise to the social disadvantage of the population exposed to coastal erosion. The output is a series of datasets on coastal erosion, intended as a broad planning tool for ‘street’ to ‘regional scale’ mitigation. The data also includes social vulnerability as an indicator. For coastal planning authorities, the data was seen as valuable and usable, though may not be as accurate or applicable in estuarine areas. The outputs include a mixture of OGL and PSGA data, so while most of the data is fully open, not all layers can be supplied to all stakeholders.

Scottish Index of Multiple Deprivation

The Scottish Index of Multiple Deprivation (SIMD) dataset provides a range of indices which can be used to highlight areas of high deprivation that may face a higher impact from climate risks. The data is presented at Lower Layer Super Output Area (LSOA) level (e.g. ‘neighbourhood’ level) and summarises social issues in simple to interpret indices. The housing index specifically accounts for houses which are overcrowded, and those which do not have central heating – key factors to consider when assessing risks related to several climate hazards.

OS MasterMap

There is a large range of data available from the Ordnance Survey (OS), which can cover a range of topics from housing, infrastructure to green space and biodiversity. OS also provides products which can be used as backdrop maps to improve the accessibility of data when shared with stakeholders. The OS MasterMap range of datasets has been used in local government for various purposes since its launch in 2001, so there is likely to be organisational familiarity, especially in GIS teams. OS has been in the process of refreshing its key products with the introduction of the National Geographic Database (NGD). This is intended to add additional data to OS’s products to serve further analytical use cases and adds data such as the presence of green roofs and solar panels on buildings (coming in future release), habitat classifications and building ages. OS data is largely licensed under PSGA or OGL, and access is provided via the OS Datahub. OS also has products which identify areas of greenspace, namely MasterMap Greenspace and OS Open Greenspace. OS data is all high resolution, ‘street’ level data.

OS data is of high quality and coverage, providing street level data across Great Britain, which is updated frequently. Indicators for climate hazards, however would need to be derived through analysis. This would generally require geospatial skills and tools, but additionally, OS datasets tend to be large and complex. OS has made some efforts to address the complexity of accessing large data, including ‘Select+Build’ features, and API access. All planning authorities, as PSGA members, can access direct technical support from OS.

Light Detection and Ranging (LiDAR)

LiDAR data from the Scottish Remote Sensing portal is valuable for assessing risks related to flooding. Digital Surface Model (DSM)/Digital Terrain Model (DTM) data from LiDAR can be easily interpreted and integrated with other steps in the analysis. The Scottish Remote Sensing portal has OGL licensed LiDAR data in a relatively easily accessible form – however, the coverage of the data is mostly focussed on the Central Belt, limiting the ability for some planning authorities to use. Additional coverage for the data was announced as part of the Future Farming Investment Scheme^[5], which should improve the usability of this data in the future. The data is high resolution, supporting analysis at ‘street’ level.

Suggested datasets for future wider use

The following datasets were discussed in interviews and workshops, but we found that not all planning authorities sampled were using them. For some datasets, the low uptake by planning authorities was due to difficulty in the use, or accessing of the dataset, whereas for others low uptake was down to a lack of familiarity.

In this section we have provided a narrative for these datasets to indicate where they may be of value for planning authorities to use going forward.

Table 4.2 – Suggested datasets which could be used by Planning authorities for further value

Local Authority Climate Service

The newly launched Local Authority Climate Service (LACS) from the Met Office aims to provide planning authorities across the UK with crucial information on climate change to support decision-making. The LACS provides a simple interface for analysing changes related to key hazards and includes climate averages and climate indicators. A Climate Report can be generated through the Climate Explorer. Planning authorities can add data and then use it in other applications such as Excel and Power BI. It is built using geospatial technology from Esri UK and is part of the Climate Data Portal (Met Office, 2024b) which hosts the information within the Local Authority Climate Projections Explorer. The LACS also includes guidance on the process of assessing climate risk with ‘regional’ level data. The Met Office launched the new beta service on 9 October, so as a new service has not yet seen widespread adoption in planning authorities. The Met Office are inviting feedback to help drive improvements of the LACS – the conclusions of which could be used as the basis to feed into this improvement process. Additionally, it could increase the number of Scottish planning authorities involved, increasing their awareness and knowledge of the system, and also make sure the LACS delivers the service that Scottish planners need. The LACS is not currently configured to provide reports for National Park planning authorities, but does cover all Scottish local authorities.

Habitat Map of Scotland

The Habitat Map of Scotland (HabMoS) is a composite dataset including different layers of detailed habitat data. All have been given a common Habitat Coding from the European Nature Information System (EUNIS). Using this data, a mapping of the existing habitats in a planning authority can be created. High value, or at risk habitats can then be identified, and habitat loss due to hazards such as sea level rise can be accounted for . The data is OGL licensed, with ‘street’ level resolution. HabMoS brings together habitat and land use data from multiple sources into one map, but the data is not interpreted in the context of climate hazards, so further interpretation and combination with additional datasets would be required to draw conclusions.

European Local Climate Zones

The European Local Climate Zone (LCZ) data creates a simple typology for the built environment and landcover which is intended to support decision-making around climate risks. The data aims to characterise the urban landscape into broad categories (such as low-rise and high-rise housing) so that interactions between urban form and risks such as poor air quality, flooding and heatwaves can be modelled. Data is provided at ‘neighbourhood’ level resolution. One workshop participant reported that they had undertaken a ground truthing exercise in their local authority and confirmed that the data was broadly valid. As the data was recently, in 2022 and was aimed at the climate academic community, this dataset has not yet found widespread use in planning authorities. There are not currently any regular updates or revisions published for the LCZ data. Given the data is at ‘neighbourhood’ resolution, there is less need for it to be updated frequently, as only large changes to the urban landscape would be detected. As well as the detailed methodology being public, an LCZ Generator tool is provided by Ruhr University Bochum^[9] which provides potential opportunities for updated datasets to be created for Scotland in the future.

UK Climate Risk Indicators (UK-CRI)

UK-CRI data simplifies analysis of many risks into indices. For temperature related risks, the data includes an estimation of days (or events) per year of events including heat waves, amber heat-health alerts, tropical nights (nights with a minimum temp of 20 °C). This extends to heat related impacts on infrastructure, such as road melting and high temperatures on rail. The impact of hazards on agriculture such as growing season and heat stress are also reported. Rather than creating new climate data, the UK-CRI is an interface on existing datasets (primarily Met Office) which simplifies complex data into more easily interpreted indices. The Met Office publishes annual updates to its climate data, though the UK-CRI tool does not receive updates as frequently. At ‘regional’ scale, it is less critical that the data is frequently updated, though after 5-10 years if the tool does not receive data updates, it may become far less appropriate to use.

River Basin Management Plans

River basin management plans set out actions to address current issues affecting water quality, water resources and fish. The management plans can be used in context with other data sources to understand risks which impact river health. River basin management plans are not explicitly geospatial datasets but relate to river basins which can be represented geospatially. The issues faced using this data mainly lie in the river basin boundaries not aligning with local authority boundaries, so requires some analysis. In addition, the key use case of the dataset is not climate risk or hazard related, so will require reinterpreting to the climate context.

Neighbourhood Flood Vulnerability Index (NFVI) and Social Flood Risk Index (SFRI)

A national flood vulnerability dataset was created by the Joseph Rowntree Foundation and is publicly available via the ClimateJust Maps tool. This dataset provides and easily to use, ready-made index describing flood vulnerability by combing physical flood risk with several factors which represent socio-economic vulnerability to flooding. However, it is based on ‘street level’ data published in 2011, which at this scale becomes quickly outdated. England and Wales had their index updated in 2022. An updated Scottish equivalent would be a useful tool for planning authorities to explore the vulnerability to this specific and pressing hazard.

UK Climate Projections 2018 (UKCP18)

The Met Office is the authoritative source for key climate projection data for the UK. UKCP18 products are commonly used for temperature and precipitation projections, but it can also provide data on humidity, wind and sea level rise. The climate projections generally support analysis at a ‘neighbourhood’ to ‘regional’ level, dependent on the specific data UKCP18 product.

The Met Office provides a UKCP18 User Interface for querying and extracting the data, graphs and pre-paired maps (plus access to the full data catalogue for those experienced in handling large datasets), but this does require some expertise in the underlying data to navigate, limiting its usability to planning authorities that have GIS teams or capability. This was reflected in the workshops, as some participants expressed concern that the climate data accessed from the UKCP18 portal was sometimes difficult to use. In addition, there is further interpretation work required to convert a numerical value from the data into a clear indicator which can be used to influence a decision. This interpretation of the climate data and translation into implications for the LDP was also found challenging , with some planning authorities being unable to fully explore what the data means for their plan.

The UKCP18 data is a crucial underpinning to climate analysis and has been used by some planning authorities. More recently the data has been made more usable with a set of pre-prepared indicators by tools such as the Met Office Local Authority Climate Service (where GIS users can also visualise the mapped data and also add their own geospatial data), and UK Climate Risk Indicators.

GeoSure, GeoCoast and GeoClimate

Participants expressed an interest in data from the British Geological Survey, which has the potential to address risks such as coastal erosion and landslides. The BGS GeoSure, GeoCoast and GeoClimate datasets indicate risks arising from multiple hazards, with a range of open and licensed datasets. The use of BGS data was not widespread among participants, partly due to the licensing cost associated with the premium datasets.

There may be more value to be gained from these datasets, but it would likely require the supporting geotechnical knowledge and interpretation, unless a simpler way of indicating future risks is provided.

Perceived gaps and ways to address gaps

When discussing risks, participants expressed several areas where they felt there was insufficient data available to meet their needs. This was due more to the limited understanding of what data was required to support the analysis, rather than specific datasets lacking appropriate spatial and temporal resolution or having gaps in coverage.

It should also be considered that if these data gaps were closed, what value would they provide to the evidence reports in each planning authority, and to what extent would the planning process be able to take useful action on the data. Urban heat islands serve as a useful example – while it would be possible to carry out a detailed analysis in each planning authority, for rural, or northerly authorities, the risk could be understood to be minimal by using an understanding of the local context and long term heat risk from tools like the Local Authority Climate Data Service (see 4.2.1).

In this section, we list the key gaps and explore some datasets and approaches which could be used to address those gaps.

Urban heat islands

Participants generally expressed a lack of data to understand the risks associated with the urban heat island (UHI) effect. The overheating risk methodology can be derived from both UKCCRA3 (Built Environment chapter) and the previous Environmental Audit Committee evidence reviews (e.g. 2018). Determining the extent of the effect of UHIs in urban areas can be done using a temperature sensor network (at high spatial resolution), modelling (e.g. using dedicated products such as Envi-MET, adapting more commonly used modelling approaches, e.g. atmospheric dispersion modelling systems (ADMS) (Zhong et al., 2024), or analysis of high-resolution satellite data products. However, these approaches may not be suitable for all planning authorities due to resource or lack of specialist knowledge. Overheating risk is likely to be greater in areas where urban form is compact, where there is less green and open space, and where the housing quality is poor. As such, combining datasets on Local Climate Zones (to give urban form), green space, and Scottish Index of Multiple Deprivation may act as a proxy for estimating urban heat island magnitude (e.g. Ferranti et al., 2023). Housing quality can also be indicated in further detail by Home Analytics data from the Energy Savings Trust which provides specific attributes on building fabric. This is a simpler approach using GIS datasets that planning authorities may be able to use for their evidence reports.

Storm and wind damage

While there are multiple datasets for inundation and coastal erosion, we did not find much work done to understand wind damage to buildings, or from trees. Tree fall risk is a statutory responsibility so it may be that planning authorities have some of this data held within parks or urban forestry teams. There are datasets which use remote sensing techniques to identify trees. One such dataset is National Tree Map from BlueSky – however, as this is a proprietary, licensed dataset it is unclear if the cost of this dataset outweighs the value which can be gained.

Landslides

While participants did discuss landslide risk, there was no broad consensus on the approach, nor most appropriate data. The open data published by the BGS could serve as a potential baseline assessment of current risk which, if found to be sufficiently high, further research could be carried out incorporating premium data, or input from specialists.

Health infrastructure

Data on the locations of key health infrastructure are available from NHS Scotland and accessible via the Spatial Hub. However, the use of these datasets in the context of the evidence reports would require further interpretation in order to drive decision making in the climate context. Whilst it would be possible to interpret which areas could be exposed to hazards such flooding and coastal erosion, understanding the magnitude of the risk on health infrastructure from hazards such as heating would require additional data to determine vulnerability such as building age and fabric. In the workshops, these aspects were not raised by participants, suggesting that this has not been a focus for planning authorities thus far.

Water infrastructure

Relevant data on water infrastructure, from Scottish Water for example, for the climate context is either available piecemeal, or not published. To understand which data would be required, planning authorities would need more knowledge as to which risks are likely to require water infrastructure data to assess.

Further observations

Wildfire risk was one aspect investigated by some planning authorities. Seasonal risk forecasts, as well as real-time monitoring is published by the European Forest Fire Information System^[10] (EFFIS). This is a valuable resource for assessing the current risk landscape for fires, but additional context would be required for evaluating future risk (see UK-CRI in Section 4.3 above).

Datasets such as the Scottish Air Quality Database^[11] provide information on air quality monitoring, analysis and interpretation of data, and pollutant trends at national and local levels. Historical data can also be accessed via the Met Office. Since these are observational datasets, they can be used for assessing current risk, but additional context would be required for evaluating future risk.

For more rural or agricultural planning authorities, there was also value in land use and land cover data from NatureScot, which allows risk to peatlands and croplands to be assessed. For coastal areas this data could also be analysed alongside Dynamic Coast data.

Based on the interviews and workshop discussions, participants expressed several areas where they had difficulty using data for specific outcomes or were not sure what to use.

Most of the datasets which were found to be of value for the evidence report were not hosted by a single source such as the Improvement Service Spatial Hub. The overhead effort of data acquisition for the planning authorities could be improved by more of the data providers providing a copy of their data to the Spatial Hub. However, this approach would not be straightforward with all datasets, such as those which are licensed (e.g. BGS), or those were the provider includes an analytical interface for extracting key indicators (Met Office LACS or UK-CRI).

Analytical tools

We reviewed several different analytical tools, such as CLIMADA^[12] and OpenCLIM^[13] which are designed to support users in analysing climate datasets and produce new data outputs indicating risk. These tools are open source, adaptable and suitable for academic use cases. In our interviews and workshops, we did not receive any feedback from planning authorities on these tools, suggesting they do not use them. As they require a high degree of specific technical proficiency (e.g. running python code), they may not be particularly suited to the planning authority teams who are producing evidence reports.

Current Approach

Climate Risk and Vulnerability Assessments

Climate Risk and Vulnerability Assessments (CRVAs) or Climate Change Risk Assessments (CCRAs) are available for some planning authorities and some regions of Scotland. These include the Clyde area, and one in preparation for south east Scotland.

Nationally, there is the UK CCRA Independent Assessment (Climate Change Committee, 2021a) and the National Summary for Scotland (Sniffer, 2021). These documents are long, difficult to navigate, and have a comprehensive list of wide-ranging risks. For anyone with limited familiarity with climate science and/or individual sectors, it is hard to understand which risks are most relevant to their planning authority or which risks are most important to planning. Risks in these documents are categorised with urgency and magnitude scores, and there is no scoring of impact or likelihood (apart from flooding likelihood) at a national, regional or local scale. Planning authorities need this information for their evidence report requirements, but it is not provided in the national CCRA3.

National documentation on adaptation (i.e. Scottish Climate Change Adaptation Programme: progress report 2023 to 2024) does not directly relate to local planning process and/or is difficult for the untrained person to see the links. The wider list of literature reviewed is given in Section 9.4.1.

Local climate risk assessment barriers and challenges – findings from the academic literature

Research related to mapping climate risk has increased rapidly in recent years. Studies are usually area- and problem-specific, which means that there is no standardised approach. Some maps focus on the local level, such as a city scale, but some have also looked nationally. Some also consider both spatial scales. Similarly, maps that assess climate risk can vary in perspective, such as focusing on one climate hazard because it disproportionately affects the study area the map is produced for. While many do take a multi-hazard approach, some focus on specific challenges such as heat, flooding, and drought.

Methodological process can also vary greatly across such assessments which may affect results so, for decision-makers, it can be challenging to decide which method is most appropriate to use. One key feature of many CRVA maps is the weighting of variables, which affects the extent of which specific variables may influence overall scoring. The SIMD dataset from the Scottish Government, as an example, weights income and employment indicators more heavily than housing in its determination of the deprivation index. However, in a climate risk context, a different weighting may be more appropriate. From a local perspective, weighting variables may be beneficial as they can provide more accurate results for decision-makers. However, in some cases it is difficult to achieve and an unweighted approach is preferred. Reasons can include

• a lack of data or local studies
• the risk of politicisation that may underpin the decisions upon weighting which links to subjectivity and
• complexities around how different climate hazards may weight other variables differently.

Ultimately, adaptation to climate change should be a process that is iterative and embedded into organisational practices. Knowledge underpinning decisions may be imperfect, incomplete, or comprise other challenges such as those outlined above. It is important nevertheless that the process is started with the best knowledge and data available at the time. In repeating the process, more experience is gained, and the challenges can begin to be addressed (Greenham et al., 2024b).

Approach to the evidence report

Current position

In both the interviews and workshop, the planning authorities were at different stages of preparing their evidence reports. This ranged from those at very early stages of preparation through to authorities who have drafted their evidence report and received feedback from the Gate Check^[14]. It is important to note that the small number of authorities having received the Gate Check at the point of the interviews and workshop, meaning a small sample may have impacted some of the feedback, alongside the relatively small sample of planning authorities that could be engaged during this short project.

The participants’ attitudes towards producing the evidence report were slightly more positive than their understanding of climate risks in general, with a generally positive sentiment (Figure 2)

Figure 2 – Sentiment captured during the workshop from the participants

Different approaches

The approaches used by planning authorities varied significantly, with different methods to identify data including policy review, evidence audits and workshops.

The teams undertaking evidence reports ranged in number of staff from 1-2 to 4-5 people. The use of specialist data or climate specialist colleagues in other departments within the planning authorities varied.

There appears to be a disparity on the anticipated timescales and resources required to undertake the evidence report. This depends on the extent to which planning authorities have already undertaken a climate change risk or vulnerability assessment and could be more reflective of local authority capabilities to conduct and deliver the output.

Some authorities have access to pre-prepared local or regional climate risk assessments or are part of existing climate ready projects (see Section 5.6). Others have access to climate change profiles, which provided an overview of expected future climate change. We also found some authorities had not explored climate risk and therefore had little existing evidence or experience to work from.

However, it was noted that even those planning authorities which had undertaken previous assessments found it difficult to access primary data. They were mainly using the conclusions of the past risk assessments to inform their evidence reports.

The implications of interpreting the data in a climate context and what the evidence actual means for informing or changing the local development plans was not always clear.

Using data to produce the reports

Concerns were raised by participants over the dynamic nature of the data, new data being published, and old data being updated. Not only did this make it hard to identify the latest datasets, but it also gave rise to concerns about evidence becoming out of date soon after reports were developed^[15].

Concerns were also raised about the complex array of caveats and limitations that are inherent in much of the data. This included concerns about their own understanding and interpretation, and how these limitations should be portrayed in the reports in a non-technical manner.

Another issue raised was an inability to find locally specific data at a sub-local authority resolution; one local authority wanted to take a ‘neighbourhood’-level approach but felt that data did not exist to support this.

Data accessibility challenges

Challenges in accessing and fully utilising data exist at several points, and in ways which varied across planning authorities.

Firstly, a very broad set of potential datasets which could be used exists. The planning authorities had to locate many different data sources to compile the data they needed.

Next, the data needed to be downloaded and formatted from the different data sources and while most of the data required is licensable freely to planning authorities, we found a subset where the licensing implications and restrictions were unclear. In the case where the work was being carried out by organisations external to the planning authorities (e.g. Sniffer), additional barriers were faced as access to PSGA licensed data is not immediately granted, and additional contractor licenses need to be provided. PSGA contractor licenses are free, and they limit the scope of external use of the data to specifically the contracted work.

Once the data is obtained, its application to understanding climate risks and hazards is not always straightforward. Some information is in a readily usable format, while others require expert input before analysis is possible. In the case of datasets such as Dynamic Coast, or data presented via the Met Office Local Authority Climate Service, data is pre-transformed and interpreted in a climate context. As an example, the Met Office LACS provides simple indices such as “Average Number of Extreme Summer Days”. This contrasts with datasets such as UKCP18, where a user will need to download the dataset, isolate the area of interest, extract the climate values, and determine what metric to rate them against. This is a time-consuming process, requiring both geospatial and climate expertise.

Understanding how to link the data back to the guidance and the requirements of the evidence report is a key required outcome, and the extent to which accessing this insight from the data can be achieved varies widely across the datasets used.

Understanding climate risks

Concerns were expressed in the workshop by participants from smaller planning and development teams about resourcing, where there was little or no dedicated resource within the team, or even access to a dedicated individual with climate change knowledge. This makes the process more difficult and time consuming for these planning authorities.

Risks vary from area to area, but additionally will vary over time as the climate changes. We found that many workshop participants were focused on a current view of risk, rather than being informed on how risks might change based on future projections. Risks across different hazard areas discussed, and whether they seemed well represented or potentially underrepresented in the workshops are outlined in Table 1. Additionally, not all planning authorities had fully defined which hazards were most appropriate for their region.

Given many planning authorities already have a track record in modelling flood risk, and have a greater understanding of flood risks specifically, there is a heavy focus on flooding. There is less awareness of other climate risks, specifically future climate risks, and how they may relate to development planning. In some cases, there is confusion between climate mitigation (through reduction of greenhouse gas emissions) and adaption to reduce climate risk.

The value of pre-existing work

In some instances, planning authorities were (or will be) able to build upon pre-existing work. Of particular value is work focused on climate risk, and the production of data layers specifically to allow easy interpretation from a wide range of users.

The data and findings from examples like Climate Ready Clyde and Climate Ready South East Scotland can be re-used and built upon for consistency, as well as reducing the effort required for an evidence report specifically. However, planning authorities who have not benefited from these will be at a relative disadvantage.

Climate Ready Clyde

Climate Ready Clyde (CRC)^[16] is a leading cross-sector initiative funded by 12 member organisations and supported by Scottish Government to create and deliver a shared vision, strategy and action plan for an adapting Glasgow City Region. CRC have produced Glasgow City Region’s Adaptation Strategy and Action Plan (Sniffer, 2024a) which includes a webmap (created by Clydeplan) that shows the location of postcodes most vulnerable to the impacts of climate change (Clydeplan, 2022). This includes heat risk (derived from the 4EI Heat Hazard Index^[17]) and a layer highlighting postcodes within the top two heat risk bands. The work from CRC on the Climate Risk and Opportunity Assessment and data layers in the vulnerability map directly informed Glasgow City Council’s Evidence report.

Figure 3 – Glasgow City Region Climate Vulnerability Map

Climate Ready South East Scotland

A new project to support collaborative climate action in the Edinburgh and south east Scotland City Region. Climate Ready South East Scotland^[18] is led by Sniffer, working in partnership with the region’s six local authorities: City of Edinburgh, East Lothian, Fife, Midlothian, Scottish Borders and West Lothian.

Climate Ready South East Scotland plans to:

• Identify and prioritise the risks and opportunities from climate change to Edinburgh and south east Scotland’s society, economy and environment between now and 2080.
• Lay the foundation for a transformational approach to climate adaptation and resilience for the city region.
• Support a just transition to a net zero and climate resilient economy, in a way that delivers fairness and tackles inequality and injustice.

A detailed assessment of the climate risks and opportunities faced by the Edinburgh and south east Scotland City Region will be carried out, and is intended to be published by March 2025. This assessment will both draw on the best available scientific evidence, and work with communities across the region to gather and share their experiences of climate change. It will inform decision-making across the region, laying the foundation for collaborative climate adaptation action (Sniffer, 2024b).

Overall observations planning authorities’ approaches

We collated six overall observations, looking across the literature review, interviews and workshop.

A focus on flooding and a lack of awareness of available non-flood data

Knowledge and understanding of flood risk and applicable datasets is much more established with planning authorities. While this experience is valuable, it does lead to a focus on flooding to the detriment of the consideration of other risks.

In general, the participants revealed a lack of awareness around climate change projection data, including where to source it and how to use it. As an example, whilst some participants with backgrounds and expertise in climate had knowledge of the data, planners in general were far less familiar with UKCP18 data, when asked about data they used. Most questions on data were directed back to flood information. In one interview, when questioned more on UKCP18 there appeared to be no knowledge of where this data can be located and how to access it. Some statements suggested a lack of understanding of what climate projections are and different scenarios used, however this was not fully probed in the interviews. This data is key in understanding the risks that planning authorities will face in the future and the degree of potential impact.

Spatial data not always used

The use of spatial data appears limited even though the use of it in evidence reports is supported by the Local Development Planning Guidance (Scottish Government, 2023b). Whilst some spatial datasets are well known by planning authorities (e.g. SEPA flood maps) the use of further datasets is not extensive due to poor understanding of the geospatial data required and/or ability and access to staff with the right skills to use and interpret geospatial data.

Simple indices support interpretation

Interviewees generally favoured datasets which provide simple indices tailored to the climate risk context, such as SEPA flood maps and Dynamic Coast. These datasets allow interpretation by users without specific climate or data expertise. This contrasts with the UKCP18 data, as an example, which provides users with hazard data like temperature and rainfall values over time. Considerable interpretation would need to be done to translate this data into a measure of risk which can be interpreted. Users without climate or geospatial data experience can be supported in understanding the implications if the data is pre-prepared, and presented with relatively simple indicators.

Section 9.2 provides examples of tools and datasets which have been developed outwith Scotland, used to support users without climate expertise in understanding risks.

Simplification of the approach is needed

There is a need to simplify the approach that planners adopt, to enable them to incorporate climate adaptation into their plans. The evidence report is an important part of this process to help development of a baseline and support understanding of the climate risks faced by planning authorities now and in the future. To do this, a Climate Risk and Vulnerability Assessment (CRVA), which considers key hazard, vulnerability and exposure data, is a valuable prerequisite to identify those risks which can be mitigated by planning. Spatial data is key to undertaking an accurate and specific assessment, although there is currently no simple tool to support it. An example of this would be the methods and approach taken by the University of Birmingham for work done for one local authority (Birmingham) and one regional authority (West Midlands) in England (see Section 9.2). Aside from a tool, authorities would benefit from greater understanding of the key datasets which provide the best outcomes. This mirrors findings from the evidence report gate checks completed to date.

Prior work and climate data skills are advantageous

There was a general observation (which was also identified by several stakeholders) that some planning authorities faced greater challenges in the evidence report process. This is because they have little prior work on climate risk, less technical data skills within the team and no dedicated climate change professional within the Council.

Planning support and guidance is not specific on the application of data

The literature review illustrates that there is little information on climate risks and resilience that is written to support either local authority planners or the evidence report process. There is a wide range of data available, which could be used in many different approaches to be applied to understanding climate risk and appropriate adaptations. Furthermore, there is a gap in knowledge on how climate risk impacts planning and how planners can enhance climate resilience through planning requirements and the local development plan. Such information contextualised for Scottish planners would be invaluable to support the evidence report process and would allow adaptation to become business as usual within planning processes.

Conclusions

The following conclusions drawn from this research project to improve the evidence base for climate resilient planning policy are:

Accessibility and usability of data

• There are numerous and varied datasets required to consider the range of vulnerabilities to, and impacts from, climate risks. There are several key datasets which are underutilised by planning authorities currently.
• Many planning authorities do not have a clear and specific understanding of what data is needed to assess climate risk and vulnerabilities. The accompanying Briefing Note for Scottish Planning authorities to this research report (see Section 9.5) should provide support in providing signposted usable data.
• Significant data gaps exist for urban heat islands, storm damage, health, water infrastructure and landslides. Proxies can be used, e.g. combining urban form, green space, and housing quality to assess urban heat island risk. However, consideration should be taken to ensure a consistent methodology, and a proportional amount of effort given potential risk.
• The most useable and accessible data sources, such SEPA, Dynamic Coast, and Met Office LACS provide pre-determined, simple indices which provide a clear indication of climate risk. These allow planners to make clear decisions, without having to apply climate or data expertise to determine risk themselves.
• Not all datasets provide simple indices, however, there are several datasets which could be used by planning authorities in the evidence reports which are not widely used currently. A consistent methodology for planners on how the indicators can be derived and used to incorporate adaptation and resilience into local planning would be advantageous.
• Longer term, a single entry-point to these datasets would make this process easier for the planners and ensure that all Planning authorities have similar data to undertake the assessments
• The data required is also mostly free for planning authorities, however, there are some licensing differences to be aware of when publishing to the public. See 9.1.2 – Data Licensing for further detail.
• Accessibility could also be improved with further guidance on certain requirements mean and how they can be achieved. For example, the requirements ask planning authorities to assess the likelihood of risks occurring. This is a complex task, requiring knowledge of the climate hazards, how they will change, uncertainties, the ranges of climate outcomes depending on scenarios. Guidance on how to define likelihood and how to use data to evaluate likelihood is important, and can be paired with a specific indication of which dataset can be used for that purpose.

Understanding and capacity

• Planning authorities find it time-consuming and difficult to get the required data from all the different providers.
• Undertaking climate change risk assessments prior to the evidence report would provide a better understanding of which risks and hazards are most impactful.
• However, planning authorities may need help doing this (especially the smaller planning authorities and/or those without climate and GIS and data experts), including:
• Direct funding to outsource.
• Support to identify partners and apply for funding.
• Sharing or secondment of staff with climate resilience/climate science and GIS and data skills.
• Collaboration and co-funding with neighbouring/regional planning authorities, like the ‘Climate Ready’ regional projects.
• As an example, Sniffer is an independent charity that supports and coordinates the climate ready programmes in Scotland, including work such as webinars which discuss the use of different datasets. Sniffer currently hold a Scottish Government funded contract (Adaptation Scotland) to provide some capacity building support to planning authorities. This could be expanded to provide wider support for the climate change risk assessment process and support planners in translating the findings into the evidence report and the local development plans.
• It should also be noted that the assessment of likelihood of risks occurring, beyond flood risk, is not undertaken in the UKCCRA3 or Scotland’s national summary.
• It is useful to understand and address vulnerability at the evidence report stage, although stakeholders did not seem familiar with this as a concept or how it might be assessed.

Potential for action

Based on the findings from this research, the following actions could be explored in the short term to enhance and improve the coverage and usefulness of evidence reports:

• Engage with the Met Office for a review of planner-specific user experience when accessing the latest UK Climate Projections (UKCP18)
• The impression from most stakeholders was that UKCP18 projections are daunting and avoided by planners. The Met Office Local Authority Climate Service (LACS) (Met Office, 2024a) is still in beta, and feedback should be provided on how it could further meet the needs of Scottish planners.
• Explore whether non-flood related climate data could be sourced directly via an existing data service for the creation of bespoke Climate Risk and Vulnerability Assessment indices and geospatial data portal relevant to planning in Scotland.
• Explore the inclusion of features to support the National Park planning authorities in the LACS.
• Engage with the British Geological Survey (BGS) to explore expansion of access for the assessment of landslide risk – and potential inclusion of licensed data.
• Provide planners with further detail on which aspects of various datasets are valuable for their local plans, including the climate risks which planning should address (e.g. overheating, surface water flooding).
• Encourage cross-authority engagement and collaboration. Given the inconsistent availability of knowledge, skills and capacity, peer learning and support can potentially provide a valuable approach to improving quality and consistency.
• Share and promote the list of data (from this research) as a standalone resource, cross referenced with the relevant climate risks. See accompanying data catalogue (Section 9.5).
• This research also clarifies which data licences may be required to support evidence report production, and further guidance on the impacts of the different license types.
• Investigate funding options for regional Climate Risk and Vulnerability Assessments. As a first step, a CRVA provides a clear steer on what the key risks are, therefore allowing a more targeted approach to the Evidence reports.
• Scope out the requirements for a Climate Risk and Vulnerability Assessment data platform for centralising the hosting of key datasets. In addition, this should include the development of new datasets and indicators which allow interpretation to non-climate, or non-geospatial users more easily. This could be delivered as a new tool or an extension to an existing one.

There is potential in the longer term to make the key national datasets available, with pre-interpreted indices available in one location. The data could have simple (e.g. high, medium, low) indices with user friendly guidance on what the data is, what it means and caveats (this could be 4-5 key hazards, with a climate vulnerability index). It could link directly to the datasets that have been identified in this report and could also potentially use some of the data from the analytics tools. This would require further research and dialogue with potential providers.

References

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Clydeplan (2022) Glasgow City Region Climate Vulnerability Map. Available online: https://climatereadyclyde.org.uk/climate-vulnerability-map/ [last accessed October 2024]

Environmental Audit Committee (2018) Heatwaves: Adapting to Climate Change. UK Parliament Commons Select Committee. Environmental Audit. 2018. Available online: https://publications.parliament.uk/pa/cm201719/cmselect/cmenvaud/826/82604.htm [last accessed October 2024].

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Grace, E., Marcinko, C., Paterson, C., & Stobbs, W. (2025). Using future climate scenarios to support today’s decision making. CXC/Government Actuary’s Department.

Greenham, SV., Jones, SA., Ferranti, EJS., Zhong, J., Acton, WJF., MacKenzie, AR., Grayson, N., (2023) Mapping climate risk and vulnerability with publicly available data. A guidance document produced by the WM-Air project, University of Birmingham. Available online: https://doi.org/10.25500/epapers.bham.00004259 [last accessed October 2024]

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Appendices

Definition of Terms

Usability

Usability is summarised as follows:

• ‘High’ – a dataset which provides simple indices in a climate hazard and risk context and access and can be interpreted easily. Users will not need specific GIS or climate expertise to understand planning outcomes from these datasets.
• ‘Medium’ – data is relatively accessible but requires expertise to interpret or transform. To understand the data in a climate hazard and risk context, as well as planning outcomes, specific expertise in either climate, or GIS will be required.
• ‘Low’ – a dataset which requires specialist knowledge, expertise or skills. Extensive expertise, as well as time and effort will need to be applied to this data in order to arrive at indicators that can be used to make planning decisions.

Data Licensing

The information is provided as guidance on the description and general consequences of the common license types encountered for data. However, care should always be taken to ensure that if any data is used, the license and its limits should be validated before use or distribution.

Table 9.1 – Relevant data licences and their impact on planning authorities

Data Scale and Resolution

Table 9.2 – Description of the shorthand terms used to describe spatial resolution as used in this report. Inset maps © OpenStreetMap contributors

Scale	Spatial Resolution	Example
‘street’	<50cm
‘neighbourhood’	50cm – 1km
‘town’	1km – 15km
‘regional’	15km+
‘national’	100km+

Example Tools to support Interpretation

Climate Risk Vulnerability Assessment methods – the University of Birmingham

A Climate Risk and Vulnerability Assessment (CRVA) map is a method co-developed by the WM-Air project team at the University of Birmingham with local and regional stakeholders across Birmingham and the West Midlands. A CRVA map shows how geospatial climate risk data may be used by local planning authorities. It is pulled together using different environmental, physical, and socio-economic datasets to understand how climate risk varies across an area. The mapping approach prioritises using publicly accessible data and can be replicated by other planning authorities to improve climate resilience (Greenham et al., 2023).

Figure 4 – Birmingham City Council Climate Risk and Vulnerability Assessment map

Birmingham City Council recently published their CRVA (Greenham et al., 2023) on the city’s website (Birmingham City Council, 2024). The CRVA map scores areas of Birmingham based on compiling the presence and extent of 11 different factors that may influence the impact of climate change, where the higher the score, the more at-risk and vulnerable an area and its citizens are likely to be to climate change. The approach is considered a Minimum Viable Product (MVP), i.e. it works, and refinements can be made through use.

West Midlands Climate Risk and Vulnerability Assessment (WM-CRVA) Map

Figure 5 – West Midlands Combined Authority Climate Risk and Vulnerability Assessment Map

The University of Birmingham also collaborated with the West Midlands Combined Authority (WMCA); co-developing a CRVA map for the wider West Midlands (Greenham et al., 2024a). It takes forward the Birmingham MVP approach by including greater consideration of vulnerability. The overall CRVA map scores are based on 24 different factors, each of which is considered one of either a hazard, vulnerability, or exposure factor influencing climate risk.

Methodology

Our approach included two key phases the Discovery phase and the Analysis phase. The discovery phase involved a continuation of planning and refining the scope, and identifying the key tasks needed to ensure we had the full background and context to successfully undertake the research. The Analysis phase involved the main research tasks, including stakeholder engagement and a deep dive analysis of currently available climate risk and hazard data. Both are described in more detail below.

Discovery Phase

• A desktop literature review was conducted (August 2024). The literature included covered information on climate change methods, past climate risk or vulnerability studies (where spatial data was used), and information relevant to climate risk in Scotland as well as the latest policy documents around climate adaptation for Scotland. Here we identified which risks were commonly highlighted within Scotland and what data has been used by others to represent those risks. As well as summarising the key policies relevant to the topic climate change risk and adaptation for the Evidence reports. The literature review findings were summarised in an excel spreadsheet. The full references to the literature are included in Appendix Literature review (Section 9.4.1).
• Three of the currently available evidence reports were also reviewed, this allowed us to understand the current work by planning authorities and what approaches they took. We also began to identify gaps between the produced Evidence reports and the requirements.
• A long list of potential workshop invitees was developed, this was to be refined within the analysis phase.
• Identified a proposed list of relevant data with key search parameters for deep dive assessment. Research partners the University of Birmingham shared the data lists for both their CRVA mapping projects for Birmingham City Council and the West Midlands Combined Authority for review in the context of identifying the same UK wide datasets of their Scottish equivalents.
• Set out an initial stakeholder engagement plan for the approach to both the interviews and a workshop, which was reviewed by and agreed with CXC.

Analysis Phase

• Undertook the dataset deep dive and identified key practical characteristics including cost, availability, and accessibility
• In the stakeholder engagement plan the approach for both the interviews and workshops were also set out. For the interviews we identified a list of stakeholders, this included three planning authorities that had or were in the process of undertaking the Evidence reports. We asked to speak to relevant individuals who had written the reports or who would be or were significantly involved in gathering climate evidence. Here, knowledge gained from the literature review was used to develop appropriate questions to help us better understand the local authority’s approach to gathering evidence, their understanding of the requirements and any difficulties they had faced or anticipated facing, and confidence with the topic (a full list of the interview questions can be found in Appendix Interview responses, Section 9.4.2).
• When selecting stakeholders for both interviews and workshops we aimed to get a mix of stages within the development report process. We also ensured we had a good geographic spread of participants representing the wide range of planning authorities in Scotland. This included, coastal, city based, and Island based planning authorities.
• During this analysis phase we held three interviews with representatives of Fife Council (21^st August 2024), Comhairle Nan Eilean Siar (28^th August 2024) and Glasgow City Council (3^rd September 2024). Interviews included multiple members of the Arup team representing planning, Climate and data expertise as well as a note taker. All interviews were recorded with the permission of the participants. Interview findings were summarised in Appendix Interview responses (Section 9.4.2).
• After the initial interviews a virtual ‘Prioritisation Workshop’ (17^th September 2024) which included representatives from the planning authorities we interviewed, other planning authorities (across a geographic spread and at differing stages in their LDP) and other relevant wider stakeholders (such as representative from Sniffer). The workshop was developed using the findings of the interviews, so that the activities probed at areas of interest and or areas not fully covered by the interviews. The aim of this workshop was to further discuss how planning authorities can improve their access to geospatial data for climate adaptation.

Underlying assumptions

CXC facilitated introductions to key stakeholders for engagement. Engagements were virtual, via Microsoft Teams.

The sample of planning authorities involved was not aiming to be extensive or include all Scottish LPAs, given the scope, size and duration of this research project, but aimed to have good representation across geography, size and stage of progress with the Evidence report.

Literature review and stakeholder engagement

Literature review

A desktop literature review was conducted during the discovery phase, and a full list of references is provided here.

Table 9.3: Full list of references for literature review

Interview responses

This section provides the interview questions and a summary of the answer given by all three planning authorities interviewed.

Q1: What is your role in preparing the Evidence report?

• Most stakeholders interviewed were planning officers based in planning services or equivalent.
• Teams working on this topic of the Evidence report ranged from 1-2 to 4-5 members of staff as the core team. Though generally one or two key individuals took responsibility or a key co-ordination role.
• Use of expertise outside of teams also varied including some drawing on data individuals or climate and sustainability officers.

Q2: What is the status of your Evidence report? And what are the next steps?

• Ranged from early stages of prep to just complete and complete and addressing feedback.

Q3: How clear to you were the requirements / guidance for assessing climate risk through the Evidence report?

• Responses to this question were mixed, some stated the guidance “left a lot open to interpretation”, feeling it was hard to understand what they actually needed to do.
• One Local Authority said the guidance was clear in answer to this question but after further probing it appeared they were not sure on several of the elements within the requirements.
• One response stated that requirements were easier to interpret due to knowledge of other resources.

Q4: How did you assess climate risk/climate change within your Evidence report?

• In one interview the approach had not been developed and it was too early to discuss.
• The other two planning authorities relied heavily on information that had been previously produced for the area, such as past climate profiles or regional risk assessments. Using this information and interpreting it rather than new information or raw data designed specifically for the Evidence report.
• One Local Authority took a place-based approach but struggled with assessing climate risk on this level.

Q5: How have you assessed vulnerability to climate change and inequalities?

• This question was generally not well answered indicating that there was a lack of understanding on the requirements around assessing vulnerability.
• One Local Authority alluded to have a copy of a vulnerability index, but not sure where it was sourced from (potentially from SEPA).

Q6: Do you know what datasets were required to undertake climate risk assessment? 

• One Council in the early stages indicated that they did not yet know what datasets would be required to complete assessments but were aware of the recommended data sets.
• Another realised on using report summarising previous regional risk assessments and interpreted screenshots from these reports. They had tried to get the data but had difficulty locating/accessing it.
• Generally, when talking about data the focus was flood data. No participant mentioned raw climate projection data. Heat hazard data was mentioned by a single Local Authority but in the context of difficulties getting the data.

Q7: Do you know how to/have the ability to access them (within the team)?

• Answers for this question were mixed some had dedicated contacts they could reach out too for data others stated they struggled knowing which data was relevant.
• When probed there generally seemed to be a lack of knowledge on what data was out there, without even thinking about how to access, use and interpret the data.
• General staff resources were flagged as an issue and time needed to use datasets.
• One Local Authority had external GIS support for these kind of tasks from a cross local authority collaboration to share resource, with a 2-year secondment, though this was a resource with a limited timescale.

Q8: Is there anything else you would like to share about the process?

• One set of interviews mentioned that they were beginning to realise the size and scope of the task of gathering and analysing climate risk data as they begin process of evidence gathering.
• One Local Authority mentioned concern about understanding how all of this actually linked into to planning and how in general the evidence would be used to influence planning.
• One also mentioned they had a lot of support from their climate team and could sense check and problem solve with their guidance. This was an invaluable resource.

Workshop

Arup and the University of Birmingham undertook a stakeholder workshop on 17 September 2024 for ClimateXChange Scotland and the Scottish Government on Improving access to geospatial climate risk data. The purpose of this workshop was to discuss together how planning authorities can improve their access to geospatial data for climate adaptation in the context of development planning.

The online workshop brought together planning authorities (across a geographic spread and at differing stages in their LDP) to better understand their needs as a local planning authority and/or climate policy team:

• to prepare Evidence reports, 
• as users of this geospatial climate risk data and;
• understand any current challenges or gaps that need addressing

The workshop provided information on key hazards and risks the planning authorities had been or anticipated focussing on and what data would or had been used. The workshop built on the interviews by delving deeper into the data and methods of analysis. Providing further insight on data gaps, ease of use and challenges faced by the planning authorities.

Additional Resources

Data catalogue

The data catalogue spreadsheet is available online:

Improving access to geospatial climate risk data – Data catalogue

Briefing note for planning authorities

The briefing note for planning authorities is available online:

Geospatial climate data for evidence reports briefing note

How to cite this publication:

Corrigan, J., McNeill, C., Kent, N., Keane, M., Brown, C., Hanson, D., Halliday, C., Ferranti, E., Greenham, S., Horrocks, L., Doherty, J. (2025) ‘Improving access to geospatial climate risk data’, ClimateXChange. http://dx.doi.org/10.7488/era/5650

© The University of Edinburgh, 2025
Prepared by Arup and the University of Birmingham 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. 
   

   The LDP is required under The Town and Country Planning (Scotland) Act 1997 (Scottish Government, 1997), as amended by the Planning (Scotland) Act 2019 (Scottish Government, 2019). Relevant secondary legislation and published guidance includes The Town and Country Planning (Development Planning) (Scotland) Regulations 2023 (Scottish Government, 2023a) as well as Local Development Planning Guidance (Scottish Government, 2023b). ↑

   
   
2. 
   

   A detailed description of licensing terms and their implication, such as OGL, PSGA, BSD and CC-BY are provided in 9.1.2 – Data Licensing ↑

   
   
3. 
   

   The relevance of the dataset to the hazard groups as discussed with participants. Definitions are shown in Table 1 ↑

   
   
4. 
   

   Captures ease of use by local authority officials. See 9.1.1 – Usability for more detail ↑

   
   
5. 
   

   New deal for agriculture – gov.scot ↑

   
   
6. 
   

   A detailed description of licensing terms and their implication, such as OGL, PSGA, BSD and CC-BY are provided in 9.1.2 – Data Licensing ↑

   
   
7. 
   

   The relevance of the dataset to the hazard groups as discussed with participants. Definitions are shown in Table 1 ↑

   
   
8. 
   

   Captures ease of use by local authority officials. See 9.1.1 – Usability for more detail ↑

   
   
9. 
   

   LCZ Generator ↑

   
   
10. 
    

    https://forest-fire.emergency.copernicus.eu/apps/effis_current_situation/index.html ↑

    
    
11. 
    

    https://www.scottishairquality.scot/ ↑

    
    
12. 
    

    https://wcr.ethz.ch/research/climada.html ↑

    
    
13. 
    

    https://tyndall.ac.uk/projects/openclim/ ↑

    
    
14. 
    

    Administered by the Planning and Environmental Appeals Division, the Gate Check is a process through which the sufficiency of the evidence report is assessed, to confirm there is a sound evidence base on which to prepare a Local Development Plan ↑

    
    
15. 
    

    From an LDP guidance perspective the evidence available at the time of writing the report is proportionate and sufficient. ↑

    
    
16. 
    

    https://climatereadyclyde.org.uk/ ↑

    
    
17. 
    

    https://www.4earthintelligence.com/capabilities/heat/ ↑

    
    
18. 
    

    https://climatereadyses.org.uk/ ↑

    
    
19. 
    

    https://www.ordnancesurvey.co.uk/customers/public-sector/public-sector-licensing/publish-share-data ↑

    
    

The agriculture policies outlined in the Update to the Climate Change Plan (CCP) provide a route map for agricultural transformation to reduce greenhouse gas emissions. They take a co-development approach and work with stakeholders and farmer-led groups to secure increased uptake of low-emission farming measures through new schemes and approaches.

This project examined the potential reductions in livestock methane emissions through breeding, and the policy levers that could motivate these changes.

Findings

• Breeding could reduce methane emissions from the digestive process in livestock by up to 9.5% by 2045.
• Actions and behaviour changes will be required of Government policymakers, pre- and post-farm gate actors and farmers, with key barriers being lack of knowledge and perceived cost.
• Scotland has a well-developed research base around breeding livestock for reduced emissions, placing it in good stead to develop further work in this area.
• Relevant technologies that could be mainstream in Scotland by 2030 include a national breeding programme, sexed semen and the breeding potential of an animal for a specific trait.
• There are very few instances of methane detection methods being used on farm in the UK. The use of proxies such as mid-infrared spectra in milk could be encouraged to determine methane emissions.
• There are many reproductive technologies already in use, particularly in the dairy industry. The report estimates these to be mainstream across the cattle sector by 2045, with lower uptake in the sheep sector due to artificial insemination being a complex procedure.

For further details, please read the report.

If you require the report in an alternative format, such as a Word document, please contact info@climatexchange.org.uk or 0131 651 4783.

Research completed: May 2024

DOI: http://dx.doi.org/10.7488/era/5569

Executive summary

The agriculture policies outlined in the Update to the Climate Change Plan (CCP) provide a route map for agricultural transformation, to reduce greenhouse gas emissions. They take a co-development approach and work with stakeholders and farmer-led groups to secure increased uptake of low-emission farming measures through new schemes and approaches.

This project examined the potential reductions in livestock methane emissions through breeding, and the policy levers that could motivate these changes.

We began by exploring the technologies that detect and measure methane, manage data and are used in the breeding process. This included considering the availability of these technologies in Scotland in 2030 and 2045, with practical considerations for a Scottish context, and identifying the breeding traits that can lead to lower methane emissions.

We then identified the relevant policy levers and behaviour changes and considered what Government, the post-farm market, pre-farm gate actors and farmers can do differently to encourage methane reductions through breeding.

Key findings

• By 2045, breeding could reduce methane emissions from the digestive process in livestock, known as enteric methane, by up to 9.5% (382.2 kt CO₂ equivalent). This is under the “Policy changes” scenario, where legislation will require farmers to introduce methane reducing breeding techniques to their herds (with uptake rates of 100% in dairy, 80% in beef, and 60% in sheep).
  • This includes a 6.8% reduction in emissions from beef, 6% from dairy and 17.5% from sheep.
  • This reduction is achieved by selecting traits for methane efficiency (methane production, intensity and yield), feed efficiency, offspring carcass weight, milk yield and milk fat and protein when choosing breeding stock.
  • Our research highlighted selective breeding for feed efficiency as a promising option. This is because, despite its lower methane reduction potential, it builds on a practice that is already well understood by farmers.
• To achieve emission reductions, actions and behaviour changes will be required of Government policymakers, pre- and post-farm gate actors and farmers. We found key barriers were lack of knowledge and perceived cost.
• Scotland has a well-developed research base around breeding livestock for reduced emissions, placing it in good stead to develop further work in this area. Funding could be targeted towards building on this research, with more data points to support innovation and enhance the robustness of results. Further research could include the potential for a specific methane reduction target to increase clarity and focus action. Funding would be useful if targeted to better communication of the research findings to inform farm advisers, pre- and post-farm actors and supporting farmer peer-to-peer learning. Collaboration between stakeholder groups will achieve greater progress.
• Relevant technologies include methods to detect and measure enteric methane in animals, data management, reproductive technologies and genomics. Those that could be mainstream in Scotland by 2030 include a national breeding programme, sexed semen and the breeding potential of an animal for a specific trait, known as estimated breeding values. The interaction between technologies is key to success. For instance, the wide use of data management tools will depend on the wide use of genomics to collect data.
• We found very few instances of methane detection methods being used on farm in the UK. We therefore believe it is unlikely these will be used beyond research and innovators by 2045. As such, we recommend encouraging the use of proxies such as mid-infrared (MIR) spectra in milk to determine methane emissions.
• Many reproductive technologies are already in use, particularly in the dairy industry, so we estimate these to be mainstream across the cattle sector by 2045. We estimate lower uptake in the sheep sector due to artificial insemination being a complex procedure. However, as sheep start breeding at an early age and often have multiple births per animal, there is greater potential for emission reductions if low-emitting traits are introduced into the herd such as through a ram.

On this basis, we think there is a strong foundation for breeding for reduced methane emissions to contribute to Scottish Government’s methane and climate commitments and to support Scottish livestock farmers’ future resilience. 

Glossary / Abbreviations

Introduction

Methane is a powerful greenhouse gas (GHG), 28 times more potent than CO₂, produced as a by-product of the ruminant digestive process called enteric fermentation. During enteric fermentation, microbes digest feed in a specialised stomach, known as the rumen, subsequently releasing enteric methane. In 2021, enteric fermentation from ruminant livestock, such as cattle and sheep, was responsible for 48% of GHG emissions from agriculture in Scotland.

The UK signed the Paris Agreement, committing to limit global warming to 1.5°C and is a signatory of the Global Methane Pledge, aiming to reduce global methane emissions by at least 30% from 2020 levels by 2030. The Climate Change (Emissions Reduction Targets) (Scotland) Act 2019 outlines a net zero target for Scotland by 2045, with a 75% reduction in emissions by 2030. The strategy to meet these targets is laid out in Scotland’s Climate Change Plan (CCP) 2018-2032 and Climate Change Plan Update (CCPU).

One potential way to reduce emissions from the livestock sector is to select breeding traits in livestock that lead to lower methane emissions.

Traditional breeding programmes select cows or ewes producing offspring with desirable characteristics to either produce meat, milk or fibre, or to continue in the breeding herd. This method relies on waiting for the offspring to mature before the desired traits can be identified. The use of genetic technologies allows desired traits to be chosen at the point of breeding, giving a more assured outcome at an earlier stage.

Genetics are already used to facilitate precision breeding to improve livestock performance. As genetic changes are permanent and cumulative, it is an attractive option for targeting and reducing GHG emissions from ruminants (González-Recio et al., 2020; Manzanilla-Pech et al., 2021; Rowe et al., 2021).

Scottish research is at the forefront in breeding livestock for reduced methane. A recent project by the Roslin Institute highlighted the strong relationship between the rumen microbiome and methane emissions; SRUC has several relevant research studies (published and ongoing), with research facilities such as GreenCow measuring GHG emissions, and Moredun has researched the impact of livestock health and welfare on methane emissions. In 2023, Defra awarded £2.9 million to the sheep sector to launch ‘Breed for Ch4nge’ which aims to measure methane from 13,500 sheep to improve the efficiency of the UK flock; some of the research is taking place on Scottish farms.

The issue is of interest internationally. New Zealand research has shown that breeding for reduced emissions in sheep does not impact productivity and health; Canadian traders are marketing dairy semen with methane efficiency traits, and beef farmers in Ireland are being paid to take part in genomic programmes.

Please note, reducing methane through dietary amendments (such as feed additives) is out of scope for this project.

Project aims

This research project has two key aims:

1. To understand the methane emission reductions that could be achieved in Scotland through breeding. We do this by identifying technologies that detect and measure methane, manage data and are involved in the breeding process. We look at the likely availability of these technologies in Scotland in 2030 and 2045, with practical considerations. Finally, we identify the breeding traits that can lead to lower methane emissions and quantify these.
2. To identify what is needed to support this through policy levers and behaviour change. Using the findings of our literature review and stakeholder consultation, we suggest behaviour changes and discuss their impacts.

Identifying the evidence

To better understand where and how methane emissions could be reduced for project aim 1, we performed a Rapid Evidence Assessment (REA) and a series of stakeholder interviews^[1] (now on referred to as our review) to understand:

• The technologies involved in reduced emission breeding;
• The important traits to select for reduced emissions;
• Emission reduction values;
• The benefits and challenges of breeding for reduced emissions;

The review also sought evidence on what is needed to support further uptake of these technologies in Scotland, for project aim 2.

The technologies involved in breeding for reduced emissions

We grouped the technologies used to identify livestock with low methane emissions into four categories: detection methods, data management, reproductive technologies and animal genomics.

We found little information regarding the timeline of availability for the technologies on farms in Scotland. In the stakeholder interviews, many were not aware of specific technologies being used unless they were directly involved in research. Our research did find international evidence, for example, portable accumulation chambers (PAC) in New Zealand support The Cool Sheep™ Programme. Due to this limited data on timing, we categorised the availability of the technology in Scotland in 2030 and 2045 under the following headings:

Experimental (E): used in research only, with no use on Scottish farms.

Innovative (I): used in trials on Scottish farms by a few innovators.

Mainstream (M): considered mainstream and being used on Scottish farms.

Future possibility (FP): unlikely the technology will be used by 2030 or 2045, however not ruling out its availability in the future.

Not applicable (NA): not relevant to the sector.

The rate of technology uptake will differ between and within sectors. For instance, dairy cattle are milked multiple times a day, providing an opportunity to closely assess individuals interacting with the technology. For the same assessment in the beef and sheep sectors, the grazing nature of the system may require cultural and habitual change for widespread uptake (Jones and Haresign, 2020). Farmers also have different interests, business structures, cash flow etc which impacts their decisions on changing farm practices.

Cost was excluded from our review due to the complexities in estimation. The cost of a technology is likely to depend on the individual farm situation, for example, the number of livestock or proximity to infrastructure or manufacturers. Technologies requiring installation may vary depending on whether adjustments are required to an existing building.

We understand the technologies presented below have the potential to be used in Scotland. The full list of technologies discovered in our research can be found in Appendix A, section 9.1.

Detection methods

Detection methods are used to detect and measure enteric methane to identify which animals emit less. Examples include a respiration chamber which measures the difference in methane emissions with and without the animal, while spot sampling uses head chamber systems or hand-held lasers to take short-term measurements from the animal’s breath (Tedeschi et al., 2022). Further examples can be seen in Table 1.

We found very few instances of detection methods being used in UK research, but we estimate that some will be available in 2030 and more by 2045 (see Table 1). However, as detection methods are primarily a research tool, it is unlikely they will be used beyond innovators by 2045. Practical constraints such as large technological components and measuring a few animals at a time make it challenging to introduce respiration chambers (which are considered the ‘gold standard’ of measurements) on a large scale (Manzanilla-Pech et al., 2021; Rowe et al., 2020). As such, we recommend encouraging use of proxies such as mid-infrared (MIR) spectra in milk to determine methane emissions.

Portable Accumulation Chambers (PACs) have been launched recently by scientists at Scotland’s Rural College (SRUC) for use across the UK. The two units (of 12 trailers) are currently only being used for research purposes. Each trailer holds 12 chambers and is capable of measuring between 60 – 80 sheep per day providing breeding values for methane emissions for representative samples of sheep within a breeding programme (Duthie et al., 2024)

New Zealand currently incorporates the use of PACs in breeding programmes through The Cool Sheep™ Programme, where breeders use PACs to measure and select for low-emitting rams available for breeding. Research trials are underway in countries such as Australia, Norway and Uruguay and now the UK. This technology provides a promising option for Scotland as it is transportable between farms and has a short measurement period which limits stress in livestock. However, current research trials on UK sheep systems need to be completed before PACs can be used widely (Duthie et al., 2024).

Data management

Data management technologies are essential to store, share and analyse data, while also tracking individuals and breeding lines with desired traits to improve target outcomes (including emissions reductions).

The dairy sector is advanced in this area compared to beef and sheep sectors, with established tools for monitoring and measuring production characteristics. Stakeholders discussed the possibility to enhance or repackage these tools and platforms, such as ScotEID, to incorporate methane traits. Using a tool that is familiar for farmers might reduce resistance for adoption.

1. Case study: New Zealand

N-Prove is a free website tool for New Zealand farmers to find the best rams for breeding. Using a series of button­­­­­s and slider scales, farmers can customise what traits they are looking for in a ram. NProve then generates a list of breeders with rams that best fit. Farmers can select terminal or maternal traits, as well as breeders based on location, breed and exclude certain flocks from results. Methane production is an option to select from the maternal traits. The tool is free to use and registration is not required. The tools anonymity means farmers can gather their options for the best breeder for their farm. NProve sources data from a central database and genetic evaluation service (SIL database) that holds information for more than 600 flocks, making it one of the largest genetic evaluations of sheep in the world. This tool could be used in a similar fashion for other species in other geographies as long as an appropriate database was available or was developed to source information.

Data technologies rely on wider infrastructure, such as website portals or cross-country collaboration, making it challenging to estimate the availability for 2030 and 2045. However, there is high potential. Stakeholders discussed that a risk for these technologies is the lack of interest and uptake from farmers, so it is important to inform and engage the industry regarding their benefits.

These technologies offer benefits for farmers by improving the understanding of the genetic qualities of their livestock and having a head-start on understanding the genetics and traits being brought into the herd. See Table 2 for the relevant data management technologies.

Table 1. Examples of the detection methods involved in breeding livestock for reduced methane emissions. Please see Appendix A, section 9.1 for the full list of the technologies found in our review.

Table 2. Examples of data management tools involved in the process of breeding livestock for reduced methane emissions. Please see Appendix A, section 9.1 for the full list of the technologies found in our review.

Reproductive technologies

Reproductive technologies are directly used for breeding. With many already in use on Scottish dairy farms, we estimate that it is likely most will be mainstream in the cattle sectors by 2045. We estimate lower progress in the sheep sector reflecting the current low uptake. Stakeholders discussed the reasons for low uptake in the sheep sector are due to the extensive nature of sheep farming in Scotland and less infrastructure for sheep in this area, such as semen collection and storage, the availability of which determines uptake. In addition to this, artificial insemination (AI) in sheep requires a vet to perform a surgical procedure (in cattle it can be done by a qualified farmer), adding a practical and financial hurdle.

Table 3. Examples of reproductive technologies involved in the process of breeding livestock for reduced methane emissions. Please see Appendix A, section 9.1 for the full list of the technologies found in our review.

Animal Genomics

Genomics is the study of the genome, a complete set of an organism’s DNA^[2]. Genomics provides the opportunity to better understand how well an animal will perform based on its DNA profile. DNA and management both determine performance qualities, such as milk yield. Precision breeding (which is not genetic modification) amends sections of DNA by adding or moving genetic material. This has been used in the cropping sector to improve yields and/or disease resistance. In the livestock sector, research focusses on increased resilience to bovine tuberculosis and mastitis. In 2023, England introduced The Precision Breeding Act, outlining classifications for using precision breeding on crops and livestock, including how the products from them should be regulated, “Neither the Scottish nor Welsh Parliaments have granted legislative consent to the Bill.”.

Table 4. Examples of animal genomic technologies involved in the process of breeding livestock for reduced methane emissions. Please see Appendix A, section 9.1 for the full list of the technologies found in our review.

Traits

Traits are specific characteristics of an individual (physical or behavioural) that are influenced by genes and environmental factors. Understanding the traits that lead to lower methane emissions is key to a successful breeding programme for methane emissions reduction.

It should be noted that the breeding focus, and therefore traits selected, depends on the farmer’s goals. For example, breeding for breeding stock would focus on selecting offspring traits, such as calving or lambing ease, while farms producing fat or store stock would focus on product traits, such as increased liveweight gain (Stakeholder comment, 2023). Currently, most traits are associated with productivity, such as increasing milk yield in the dairy sector. Progress in the beef and sheep sectors has been much slower, with fewer examples found of genetics used in breeding programmes.

The traits in Table 5 are used for breeding in global research to reduce methane emissions directly and indirectly. We have categorised these traits into the following groups:

Production – offspring: Traits associated with reproduction.

Production – product: Traits associated with products from the animal.

Functional: Traits that underpin the function of the animal and are not specific to production or emissions improvements.

Climate: Traits directly linked to reducing methane emissions.

Stakeholders and the literature emphasised that selection for methane reduction traits should ensure production traits, such as health, are not compromised (Stakeholder comment, 2023; Llonch et al., 2017). To look into this further, we examined performance and methane efficiency data from SEMEX. For their Holstein bulls with above average methane efficiency scores, we could not identify any clear relationship between this trait and the other traits. However, this is only for one breed of cattle from one company.

1. Case study: New Zealand
2. Research in New Zealand genotyped low emitting sheep which identified traits that lead to reduced methane emission. The research found no negative impacts on physiology, productivity and health when selecting for reduced emissions.

Our research highlighted the importance of selecting for feed efficiency. Despite this trait having a lower methane reduction potential than others, it will benefit farmers through more efficient use of feed through better feed conversion (Stakeholder comment, 2023).

Table 6 presents the traits that were selected for further analysis, including quantification and the technologies used to detect or select them. Only a few of the traits found in our review were taken forward because some of the traits did not have robust emission reduction values, so were therefore excluded from our calculations.

Table 5. Traits included in breeding indexes around the world, split by sector and type.

Table 6. The quantifiable traits in each sector, with the technologies which can be used to detect or select them.

Quantifying the potential emission savings

We calculated the potential methane emission reductions under different traits for dairy, beef and sheep. Further information can be found in Appendix E.

The traits identified in our review (see Section 4.2) were further evaluated to assess their applicability to emission reduction calculations, based on requirements for defined quantification of methane emission values (either absolute or relative) and values to have a comparative emission baseline. A summary of the applicable traits used in the quantification calculations are presented in Table 7 below, with further information presented in Appendix E, Section 10.6.4.

Table 7. Traits used in the calculations of emissions savings

The current uptake of genetic traits focused on methane emissions is estimated based on our review and discussions with Scottish Government. This was based on an understanding on the currently uptake of AI and breeding technologies used within the sector from expert knowledge and limited research able to be found online. This rate provides a baseline for the quantification of additional uptake in 2030 and 2045 under four scenarios (further described in Appendix E, Section 10.6.4). The scenarios include: no additional intervention, voluntary uptake, supplier demand and policy changes. Scenario uptake percentages are presented with the current baselines in Table 8 below. These values were developed based on technical expertise and discussion with both stakeholders and Scottish Government, as well as published research. The impact of other traits (such as functional, health related traits) could not be estimated in this work as relevant values for methane reduction potential could not be identified in the literature. Further information in the calculation methodology, including additional detail on the selected scenarios, traits selected and limitations to the data is presented in Appendix E, Section 10.6.

Table 8. Scenario implementation values for dairy, beef and sheep