Under the Natural Capital and Ecosystem Assessment (NCEA) Pilot, Natural England and the Botanical Society of Britain and Ireland (BSBI) have been working in partnership to use BSBI's vast database of plant records to inform the evidence base for tree-planting activities. Poorly targeted tree planting risks damaging wildlife and carbon-rich habitats, therefore using these data we aim to ensure that areas of high conservation value are preserved in the landscape. The summarised botanical value map provides an easily interpretable output which categorises monads (1 x 1 km grid squares) as being of Low, Moderate or High botanical value according to the presence of Rare, Scarce and Threatened (RST) plant species and/or the proportion of Priority Habitat Positive Indicator (PHPI) species that were recorded within the 1 x 1 km grid square between 1970 and 2022. The PHPI species are a combination of BSBI axiophytes, positive indicators for common standards monitoring and ancient woodland indicators. The dataset includes an overall botanical value, as well as values based on only the presence of RST plant species, and a value for each broad habitat type based on the PHPI species records. By viewing the different attributes, you can gain insights into how valuable a monad is for different habitat types and for plant species of conservation concern, as well as an indication of how well a particular monad has been surveyed. The categories of 'No indicators, poor survey coverage' and 'No indicators, good survey coverage' indicate where no indicator species have been recorded and survey coverage either is above or below a threshold of 3 'recorder days'. A 'recorder day' is defined as being when 40 or more species have been recorded on a single visit and 3 recorder days is assumed sufficient to achieve good survey coverage within a 1 x 1 km grid square. This map is not intended to be used to carry out detailed assessments of individual site suitability for tree planting, for which the RST plant species heatmap at 100 x 100 m resolution and the PHPI heatmaps at 1 x 1 km resolution have been developed by BSBI and Natural England. However, the summarised botanical value map can provide useful insights at a strategic landscape scale, to highlight monads of high value for vascular plants and inform spatial planning and prioritisation, and other land management decision-making. These should be used alongside other environmental datasets and local knowledge to ensure decisions are supported by the appropriate evidence. Please get in contact if you have any queries about the data or appropriate uses at botanicalheatmaps@naturalengland.org.uk.Datasets used:BSBI botanical heatmap data - BSBIOS Grids - OSONS Country boundaries - ONSCommon Standards Monitoring guidance - JNCC 2004BSBI's Axiophyte list - Walker 2018Ancient Woodland Indicators - Glaves et al. 2009Plantatt - Hill et al. 2004Further information can be found in the technical report at:Botanical Heatmaps and the Botanical Value Map: Technical Report (NERR110)Full metadata can be viewed on data.gov.uk.

The Department for Business, Enterprise & Regulatory Reform's wind speed database is available from this website. It contains estimates of the annual mean wind speed throughout the UK. The data is the result of an air flow model that estimates the effect of topography on wind speed. There is no allowance for the effect of local thermally driven winds such as sea breezes or mountain/valley breezes. The model was applied with 1km square resolution and takes no account of topography on a small scale or local surface roughness (such as tall crops, stone walls or trees), both of which may have a considerable effect on the wind speed. The data can only be used as a guide and should be followed by on-site measurements for a proper assessment. Each value stored in the database is the estimated average for a 1km square at either 10m, 25m or 45m above ground level (agl). The database uses the Ordnance Survey grid system for Great Britain and the grid system of the Ordnance Survey of Northern Ireland.

The Scotland Heat Map provides the locations of existing and planned heat networks. Both communal and district heat networks are included. Data about each network includes, where available, heat capacity size category, network name, status (either 'operational' or 'in development') and the main technology used (for example, 'boiler'). There is only one point location for each network, the data does not show all connected properties or pipe layouts. Networks can serve domestic properties, non-domestic properties or a mixture of the two. Heat networks have the potential to reduce carbon emissions from heating buildings. Alongside other heat map datasets, information on existing and planned networks is used to identify further opportunities to reduce carbon emissions. For example, by connecting more buildings to an existing network or by replacing the energy source with a nearby lower carbon alternative. Data on heat networks comes from two sources. These are: the UK Department for Energy Security and Net Zero's Heat Networks (Metering and Billing) Regulations (HNMBR) dataset and Zero Waste Scotland's Low Carbon Heat Database (LCHD). The most recent data available is up to end July 2022 for the HNMBR dataset (though the majority of the HNMBR data included in the heat map is up to end December 2018) and January 2022 for the LCHD. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

The Scotland Heat Map provides estimates of annual heat demand for almost 3 million properties in Scotland. Demand is given in kilowatt-hours per year (kWh/yr). Property level estimates can be combined to give values for various geographies. Both domestic and non-domestic properties are included. This dataset gives the total estimated heat demand of properties within each 2020 Settlement in Scotland in kilowatt-hours per year (kWh/yr). Heat demand is calculated by combining data from a number of sources, ensuring that the most appropriate data available is used for each property. The data can be used by local authorities and others to identify or inform opportunities for low carbon heat projects such as district heat networks. The Scotland Heat Map is produced by the Scottish Government. The most recent version is the Scotland Heat Map 2022, which was released to local authorities in November 2023. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

This dataset provides the first map and synthesis of the temperature of Britain's coalfields. It was created to support low-temperature heat recovery, cooling and storage schemes using mine water in abandoned workings. This baseline spatial mapping and synthesis of coalfield temperatures offers significant benefit to those planning, designing and regulating heat recovery and storage in Britain's abandoned coalfields. The data has been developed jointly by the Coal Authority and the British Geological Survey. It is delivered as a hexgrid representing mine water blocks, identifying equilibrium mine temperatures at 10 depth intervals (100m > 1000m) and pumped mine temperatures at 6 depth intervals (100m > 600m).

The British Geological Survey (BGS) in collaboration with the Environment Agency (EA) has developed a web-based tool that provides an indication of whether suitable conditions exist in a given area for Open-loop Ground Source Heat Pumps (GSHP). The tool is developed within a GIS and maps the potential for open-loop GSHP installations (heating/cooling output >100kW) in England and Wales at the 1:250,000 scale. Data layers from this tool are available to view in this service. The data in this service is available to access for free on the basis it is only used for your personal, teaching, and research purposes provided all are non-commercial in nature as described on http://www.bgs.ac.uk/about/copyright/non_commercial_use.html. Where commercial use is required, licences are available from the British Geological Survey (BGS). Your use of any information provided by the BGS is at your own risk. BGS gives no warranty, condition or representation as to the quality, accuracy or completeness of the information or its suitability for any use or purpose. All implied conditions relating to the quality or suitability of the information, and all liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average percentage change between the 'lower' values before and after this update is -1%.]What does the data show? A Heating Degree Day (HDD) is a day in which the average temperature is below 15.5°C. It is the number of degrees above this threshold that counts as a Heating Degree Day. For example if the average temperature for a specific day is 15°C, this would contribute 0.5 Heating Degree Days to the annual sum, alternatively an average temperature of 10.5°C would contribute 5 Heating Degree Days. Given the data shows the annual sum of Heating Degree Days, this value can be above 365 in some parts of the UK.Annual Heating Degree Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of HDD to previous values.What are the possible societal impacts?Heating Degree Days indicate the energy demand for heating due to cold days. A higher number of HDD means an increase in power consumption for heating, therefore this index is useful for predicting future changes in energy demand for heating.What is a global warming level?Annual Heating Degree Days are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Heating Degree Days, an average is taken across the 21 year period. Therefore, the Annual Heating Degree Days show the number of heating degree days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each warming level and two baselines. They are named ‘HDD’ (Heating Degree Days), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'HDD 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'HDD 2.5 median' is 'HDD_25_median'. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘HDD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, Annual Heating Degree Days were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

The location of existing and planned sources of energy, both electricity and heat, is provided as part of the Scotland Heat Map. Alongside data on heat demand, this is used to identify opportunities to reduce carbon emissions from heat in buildings, either by connecting supply and demand in a more efficient manner or by using lower carbon alternatives to existing supply. Data on each energy supply point includes, where available, capacity size category, main technology used (e.g., ‘wind’, ‘biomass’) and planning status (e.g., ‘operational’, ‘in development’). This dataset is new for the Scotland Heat Map 2022 (which was released to local authorities in November 2023). It replaces the data on existing and planned energy supply in earlier versions of the heat map. The Scotland Heat Map is produced by the Scottish Government. Data on existing and planned energy supply comes three sources. Two are UK Government sources: the Renewable Energy Planning Database (REPD) and the Major Power Producers (MPP) dataset. The third is the Energy Saving Trust’s (EST’s) Renewable Heat Database (RHD). Records from the MPP dataset have only been included where they have a fuel type of fossil fuel or nuclear, or where they have a renewable fuel type but their installed capacity is less than 1 MW. This is to avoid overlap with the REPD as much as possible. Records from the RHD have only been included where they output heat only, their installed capacity is 1 MW or higher and they can be shared. The 2020 quarter 4 extract of REPD has been used. MPP data was provided by the UK Government in late 2020. The RHD provides installation information as at end December 2021. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

This raster dataset represents the temperature distribution at 7 km depth in the UK. Method described in Busby, J. and Terrington, R., 2017. Assessment of the resource base for engineered geothermal systems in Great Britain. Geothermal Energy, 5, pp.1-18 and were used to calculated the heat-in-place and recoverable heat in EGS systems.

For an urban heat island map during an average summer see this dataset. A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer have historically been associated with health problems and an increase in mortality. The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. This urban heat island map was produced using LondUM, a specific set-up of the Met Office Unified Model version 6.1 for London. It uses the Met Office Reading Surface Exchange Scheme (MORUSES), as well as urban morphology data derived from Virtual London. The model was run from May until September 2006 and December 2006. This map shows average surface temperatures over the summer period of 2006 at a 1km by 1km resolution. To find out more about LondUM, see the University of Reading’s website. The hourly outputs from LondUM have been aggregated and mapped by Jonathon Taylor, UCL Institute for Environmental Design and Engineering. Variables include: WSAVGMAX= the average of the maximum daily temperatures across the summer period (May 26th-August 31st) WSAVG=the average temperature across the summer period WSAVGMIN = the average minimum daily temperature across the summer period HWAVGMAX= the average of the maximum daily temperatures across the 2006 heatwave (July 16th-19th) HWAVG=the average temperature across the across the 2006 heatwave HWAVGMIN = the average minimum daily temperature across 2006 heatwave period The maps are also available as one combined PDF. The gif below maps the temperatures across London during the four-day period of 16-19th July, which was considered a heatwave. If you make use of the LondUM data, please use the following citation to acknowledge the data and reference the publication below for model description: LondUM (2011). Model data generated by Sylvia I. Bohnenstengel (), Department of Meteorology, University of Reading and data retrieved from http://www.met.reading.ac.uk/~sws07sib/home/LondUM.html. () Now at Metoffice@Reading, Email: sylvia.bohnenstengel@metoffice.gov.uk Bohnenstengel SI, Evans S, Clark P and Belcher SeE (2011) Simulations of the London Urban Heat island. Quarterly journal of the Royal Meteorological Society, 137(659). pp. 1625-1640. ISSN 1477-870X doi 10.1002/qj.855. LondUM data (2013).

A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office generally uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer, and have historically been associated with health problems and an increase in mortality. The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. For an example of an urban heat island map during an average summer, see this dataset. For an example of an urban heat island map during a warm summer, see this dataset. As well as outdoor temperature, an individual’s heat exposure may also depend on the type of building they are inside, if indoors. Indoor temperature exposure may depend on a number of characteristics, such as the building geometry, construction materials, window sizes, and the ability to add extra ventilation. It is also known that people have different vulnerabilities to heat, with some more prone to negative health issues when exposed to high temperatures. This Triple Jeopardy dataset combines: Urban Heat Island information for London, based on the 55 days between May 26th -July 19th 2006, where the last four days were considered a heatwave An estimate of the indoor temperatures for individual dwellings in London across this time period Population age, as a proxy for heat vulnerability, and distribution From this, local levels of heat-related mortality were estimated using a mortality model derived from epidemiological data. The dataset comprises four layers: Ind_Temp_A – indoor Temperature Anomaly is the difference in degrees Celsius between the estimated indoor temperatures for dwellings and the average indoor temperature estimate for the whole of London, averaged by ward. Positive numbers show dwellings with a greater tendency to overheat in comparison with the London average HeatMortpM – total estimated mortality due to heat (outdoor and indoor) per million population over the entire 55 day period, inclusive of age effects HeatMorUHI – estimated mortality per million population due to increased outdoor temperature exposure caused by the UHI over the 55 day period (excluding the effect of overheating housing), inclusive of age effects HeatMorInd - estimated mortality per million population due to increased temperature exposure caused by heat-vulnerable dwellings (excluding the effect of the UHI) over the 55 day period, inclusive of age effects More information is on this website and in the Triple Jeopardy leaflet. The maps are also available as one combined PDF. More information is on this website and in the Triple Jeopardy leaflet.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.2.]What does the data show? The Annual Count of Hot Summer Days is the number of days per year where the maximum daily temperature is above 30°C. It measures how many times the threshold is exceeded (not by how much) in a year. Note, the term ‘hot summer days’ is used to refer to the threshold and temperatures above 30°C outside the summer months also contribute to the annual count. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Hot Summer Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of hot summer days to previous values.What are the possible societal impacts?The Annual Count of Hot Summer Days indicates increased health risks, transport disruption and damage to infrastructure from high temperatures. It is based on exceeding a maximum daily temperature of 30°C. Impacts include:Increased heat related illnesses, hospital admissions or death.Transport disruption due to overheating of railway infrastructure. Overhead power lines also become less efficient. Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Extreme Summer Days (days above 35°C) and the Annual Count of Tropical Nights (where the minimum temperature does not fall below 20°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Hot Summer Days is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Hot Summer Days, an average is taken across the 21 year period. Therefore, the Annual Count of Hot Summer Days show the number of hot summer days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘HSD’ (where HSD means Hot Summer Days), the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Hot Summer Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Hot Summer Days 2.5 median’ is ‘HotSummerDays_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘HSD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Hot Summer Days was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

All GHiGs datasets cover the whole of Scotland and have been derived by Greenspace Scotland over the project period of September 2020 to April 2021. Principal third party data suppliers include: - Ordnance Survey (greenspace and water body data) - Scottish Government (Scotland's Heat Map) - Energy Saving Trust (Home Analytics) Please reference the Data Guide and Methodology report (attached to the metadata record as an associated resource) and send any further queries on the quality/ accuracy of the data to parkpower@greenspacescotland.org.uk. GHiGs Settlements: A public summary of indicators for GHiGs analysis of low carbon heat based on data aggregated to Scotland's 516 settlements. Settlement boundaries are from 2012 derived from National Records of Scotland to be consistent with those used by Scotland's Heat Map v.2. Settlements are defined as places with populations greater than 500. Approximately 90% of Scotland's population lives in settlements. It is not clear why Scotland's Heat Map is using the NRS 2012 settlement boundaries rather than the more recent NRS 2016 settlement boundaries. Attributes were derived from Scotland's Heat Map with additional attributes from GHiGs analysis and EST Home Analytics GHiGs Settlements by LA: A more comprehensive spreadsheet of tables used for National Findings Report and all indicators for GHiGs analysis of low carbon heat based on data aggregated to Scotland's 516 settlements and, separately, the 32 Local Authorities. Settlement data aggregated to Local Authority geographies and presented based on OS BoundaryLine Local Authority boundaries. The data excludes areas outside settlements and therefore does NOT represent figures for complete local authorities. This is particularly evident for Local Authorities with more significant populations and businesses located outside of settlements. It includes most indicators used in the GHiGs National Findings report based on analysis of low carbon heat related data aggregated to Scotland's 516 settlements and then aggregated to 32 Local Authorities. GHiGs greenspaces: Boundaries derived from OS Mastermap Greenspace. Attributes derived from Scotland's Heat Map v.2 with additional attributes from GHiGs analysis (see our Methodology Report) and EST Home Analytics GHiGs strategic greenspaces: Subset of GHiGs Greenspaces based on selection criteria to identify the 3% (3,446) of national greenspace sites with high potential for supply of ground source heat (based on areal size / capacity) and have been classified as 'high' based on local linear heat density. These sites are likely to be the strongest candidates for larger scale ground source heat solutions, potentially storing and feeding low grade heat into low carbon district heat networks. The 'GSHP_Strategic_Importance' indicator category of 'VERY HIGH' was used to select this subset GHiGs static water bodies: Relatively static water bodies greater than 1000m2 in area in proximity to urban settlements including canals, lochs, lakes, flooded quarries/pits etc. derived largely from OS Mastermap Greenspace. This data does not include rivers. GHiGs DHN highest viability (Linear Heat Density 16000 kWh/m/yr): Linear Heat Density model created by Ramboll to highlight areas where District Heat Networks (DHNs) have highest viability based on heat demand from all buildings. Areas identifies have high levels of heat demand density and are therefore highly suitable for DHNs - source of heat demand data: Scotland's Heat Map v2. GHiGs DHN high viability (Linear Heat Density 8000 kWh/m/yr): Linear Heat Density model created by Ramboll to highlight areas where District Heat Networks (DHNs) have high viability based on heat demand from all buildings - source of heat demand data: Scotland's Heat Map v2. GHiGs DHN viable (Linear Heat Density 4000 kWh/m/yr): Linear Heat Density model created by Ramboll to highlight areas where District Heat Networks (DHNs) are viable based on heat demand from all buildings. Threshold of 4000 is widely used across the industry for Linear Heat Density modelling to identify areas with DHN viability. Polygons of area less than 250m2 were deleted which reduced the number of polygon features by 80% to cut file size. Source of heat demand data: Scotland's Heat Map v2. GHiGs DHN highest viability public buildings only (Linear Heat Density 16000 kWh/m/yr): Linear Heat Density model created by Ramboll based on a best estimate of public buildings to highlight areas where District Heat Networks have highest viable based on heat demand from only public buildings. Source of heat demand data: Scotland's Heat Map v2. GHiGs DHN high viability public buildings only (Linear Heat Density 8000 kWh/m/yr): Linear Heat Density model created by Ramboll based on a best estimate of public buildings to highlight areas where District Heat Networks have high viability based on heat demand from only public buildings. Source of heat demand data: Scotland's Heat Map v2. GHiGs DHN viable public buildings only (Linear Heat Density 4000 kWh/m/yr): Linear Heat Density model created by Ramboll based on a best estimate of public buildings to highlight areas where District Heat Networks are viable based on heat demand from only public buildings. Threshold of 4000 is widely used across the industry for Linear Heat Density modelling to identify areas with DHN viability - source of heat demand data: Scotland's Heat Map v2. GHiGs public buildings: Subset of Scotland's Heat Map at building level where buildings are assessed as likely to be publicly owned based on a selection of 125 OS AddressBase codes (see GHiGs Methodology report for details). This is the best available approximation of publicly owned buildings but will exclude those publicly owned buildings which are leased to third parties for more commercial-type services. This same identification method was the basis for creating the 3 Linear Heat Density map layers for public buildings only. GHiGs public buildings with heat demand greater than 50 MWh/year: Subset of 'GHiGs public buildings' dataset based on a filter for all those public buildings with an annual heat demand of 50 MWh or more. Multi-occupancy buildings like flatted properties are treated as separate buildings and therefore they are unlikely to appear in this dataset. GHiGs public buildings (>200 MWh) near greenspaces (>200 MWh): Subset of 'GHiGs public buildings' dataset where: (1) buildings are assessed as likely to be publicly owned based on a selection of 125 OS AddressBase codes and have a heat demand of at least 200 MWh; AND (2) they are located within 50m of a greenspace that, based on 20% space utilisation, could meet at least 200 MWh in terms of heat production from its available area. In effect this is a subset of public building locations that offers the strongest opportunities for larger scale GSHP projects based on use of nearby greenspace. Multi-occupancy buildings like flatted properties are treated as separate buildings and therefore examples such as high rise flats next to larger areas of greenspace are unlikely to appear in this dataset. GHiGs waste disposal sites: Potential sources of waste heat from waste disposal sites to feed into district heat networks - source: SEPA registered waste sites All GHiGs datasets cover Scotland and have been derived over the project period of September 2020 to April 2021. Principal third party data suppliers include: * Ordnance Survey (greenspace and water body data) * Scottish Government (Scotland's Heat Map) * Energy Saving Trust (Home Analytics)

Linear features (shown as polylines) represent six classes of geological structural features e.g. faults, folds or landforms e.g. buried channels, glacial drainage channels at the ground or bedrock surface (beneath superficial deposits). Linear features are associated most closely with the bedrock theme either as an intrinsic part of it for example marine bands or affecting it in the case of faults. However landform elements are associated with both bedrock and superficial deposits. The linear features are organised into seven main categories: Alteration area indicating zones of change to the pre-existing rocks due to the application of heat and pressure that can occur round structural features such as faults and dykes. Fault where a body of bedrock has been fractured and displaced by a large scale process affecting the earth's crust. Fold where strata are bent or deformed resulting from changes or movement of the earth's surface creating heat and pressure to reshape and transform the original horizontal strata. Folds appear on all scales, in all rock types and from a variety of causes. Fossil horizons where prolific fossil assemblages occur and can be used to help establish the order in which deposits were laid down (stratigraphy). These horizons allow correlation where sediments of the same age look completely different due to variations in depositional environment. Mineral vein where concentrations of crystallised mineral occur within a rock, they are closely associated with faulting, but may occur independently. Landforms define the landscape by its surface form; these include glacial features such as drumlins, eskers and ice margins. Rock identifies key (marker) beds, recognised as showing distinct physical characteristics or fossil content. Examples include coal seams, gypsum beds and marine bands. The data are available in vector format (containing the geometry of each feature linked to a database record describing their attributes) as ESRI shapefiles and are available under BGS data licence.

This online appendix was generated as a part of my MPhil thesis work carried out at the department of chemical engineering and biotechnology, University of Cambridge in collaboration with our industrial partner MedImmune, using data from their databases. This appendix should be read in conjunction with the dissertation titled 'Analysis of Historical Data for Mammalian Cell Culture Processes Producing Monoclonal Antibodies'.

The Properties Vulnerable to Heat Impact report, produced by Arup, maps London's heat risk across homes, neighbourhoods, and essential properties in the wake of climate change.

The study focused on essential settings, emphasising areas where occupants are especially vulnerable to heat-related hazards. This included schools, hospitals, care homes residential properties and neighbourhoods.

Properties Vulnerable to Heat Impact Report | London City Hall

This vector dataset represents the calculated, estimated temperature distribution at 100 m depth. Method described in Busby, J., Lewis, M., Reeves, H. and Lawley, R., 2009, August. Initial geological considerations before installing ground source heat pump systems. Geological Society of London.

Heatwaves are becoming more frequent and intense, yet many countries remain inadequately prepared to manage their impacts. Existing heat risk plans and responses often fail to account for the complex interdependencies among the various causes and impact pathways of heat waves.

Effective planning requires a system-level understanding of these interdependencies to identify strategic entry points for action. This research employs a participatory system mapping approach to explore the interconnections among causes, impacts, and response actions during the UK heatwave events of summer 2022. Cognitive maps were developed shortly after the events, incorporating input from 38 stakeholders across sectors involved in the heatwave response. These maps informed a forensic disaster analysis designed to provide a holistic understanding of the heatwave’s causes, impacts, and adaptation measures.

By analysing the interdependencies among these factors, we identified cascading effects and amplifiers that significantly intensified heat risk in the UK. Notably, we find that the primary heatwave impacts were often indirect, emerging or worsening due to cascading effects such as wildfires, drought, transportation disruptions, and the overburdening of first responders. In many cases, adaptation measures were reactive, addressing isolated, short-term impacts, while proactive, system-level approaches tackling interconnected impacts and root causes—such as vulnerable buildings, at-risk populations, and behavioural barriers—were largely absent. Additionally, we found notable variations in heat risk perceptions among groups. While individual sectors displayed a limited understanding of the broader heat risk system, a system-level perspective emerged through the aggregation of cognitive maps. The implications for adaptation research and policy are discussed.

The Place-Based Climate Action Network (P-CAN) seeks to strengthen the links between national and international climate policy and local delivery through place-based climate action. The Network is innovative in its focus on local decision making. Clear policy signals by the government are essential, but the key to continued climate action increasingly lies at the local level, with the participation of local actors, businesses and citizens. Important decisions about low-carbon business opportunities, renewable energy investment, urban transport, energy management, buildings efficiency and the management of climate risks are decentralised and taken across the UK.

P-CAN is about engagement, impact, and the co-creation and sharing of knowledge. The Network has the following components:

1. Place-based climate change commissions: We will develop three city-level climate commissions, in Belfast, Edinburgh and Leeds. The concept is currently being piloted in Leeds as an innovative structure for sustained two-way, multi-level engagement between national and local policy and practice. We will work on the replication of these commissions in other local context to further broaden our reach.

2. Thematic platforms: There will be two theme-based platforms, on business engagement and green finance. These virtual networks will focus on two stakeholder groups that are particularly important for place-based climate action. They will be co-created with representatives from the business and sustainable finance community.

3. The P-CAN Flexible Fund: We will open the Network to the wider community of climate change researchers and research users by commissioning 20-30 small grants. The grants will be awarded competitively, with a focus on engagement activities, user-oriented analysis, innovative approaches and support for early-career researchers.

4. Communication and user-oriented research synthesis: An active outreach strategy will connect the place-based activities and inform wider climate action by co-producing, synthesising and communicating decision-relevant analysis. This programme of user-oriented outreach will leverage the work of P-CAN's host institutions and other ESRC investments.

P-CAN is led by an experienced team of senior academics from a diversity of backgrounds. They all have strong track records of engaging with decision processes at the local, national and/or international level. Most of them have combined academic achievements with careers in business, finance, or international development. The Network PI is a former member of the Committee on Climate Change.

The core team is supported by a full-time Network Manager, a Communications Officer and a group of Network Analysts who will provide analytical, administrative and logistics support for the five platforms (three local commissions and two thematic platforms). P-CAN will, to the maximum extent possible, leverage the existing administrative, research and engagement capabilities of its host institutions, including the ESRC Centre for Climate Change Economics and Policy, the Edinburgh Centre for Carbon Innovation, and...

Linear features (shown as polylines) represent six classes of geological structural features e.g. faults, folds or landforms e.g. buried channels, glacial drainage channels at the ground or bedrock surface (beneath superficial deposits). Limited coverage within Great Britain, data exists for 167 10x10km tiles. Most primary geological mapping was carried out at 1:10 000 scale but in some areas of Wales and Scotland mapping at 1:25 000 was adopted as the norm including areas with complex geology or in some areas of classic geology. Linear features are associated most closely with the bedrock theme either as an intrinsic part of it for example marine bands or affecting it in the case of faults. However landform elements are associated with both bedrock and superficial deposits. The linear features are organised into seven main categories: Alteration area, indicating a zone of change to the pre-existing rocks due to the application of heat and pressure that can occur round structural features such as faults and dykes. Fault, where a body of bedrock has been fractured and displaced by a large scale process affecting the earth's crust. Fold, where strata are bent or deformed resulting from changes or movement of the earth's surface creating heat and pressure to reshape and transform the original horizontal strata. Folds appear on all scales, in all rock types and from a variety of causes. Fossil horizons, where prolific fossil assemblages occur and can be used to help establish the order in which deposits were laid down (stratigraphy). These horizons allow correlation where sediments of the same age look completely different due to variations in depositional environment. Landforms, define the landscape by its surface form; these include glacial features such as drumlins, eskers and ice margins. Mineral vein, where concentrations of crystallised mineral occur within a rock, they are closely associated with faulting but may occur independently. Rock, identifies key (marker) beds, recognised as showing distinct physical characteristics or fossil content. Examples include coal seams, gypsum beds and marine bands. The data are available in vector format (containing the geometry of each feature linked to a database record describing their attributes) as ESRI shapefiles and are available under BGS data licence. Another batch of tiles was added to the data in 2012 to bring the total to 167 for this version 2 release.

The Scotland Heat Map includes information on the percentage of households in each 2011 Data Zone that are renting their home from a council or a housing association (socially renting). Alongside other heat map datasets, this data is used to identify areas suitable for measures to reduce carbon emissions from heating homes and other buildings. For example, through the creation of heat networks. The 2011 Census provides the total number of households and the number of socially rented households in each 2011 Data Zone. Scotland's census is carried out by National Records of Scotland. Boundaries for Data Zones are created by the Scottish Government. Census data and Data Zone boundaries are updated approximately every 10 years. The Scotland Heat Map is a tool to help plan for the reduction of carbon emissions from heat in buildings. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/ The Scotland's Census website provides details on how the census is carried out and information on accessing publicly available census data, including geographical areas: https://www.scotlandscensus.gov.uk/