This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2015 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2015 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2015 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2016 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2016 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2016 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2017 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2017 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2017 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2018 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2018 questionnaire.

This dataset contains public data for percentage energy mix in cities based on responses to the CDP Cities 2018 questionnaire.

This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. This dataset contains public data on cities' percentage energy mix as reported by cities in 2019.

This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. This dataset contains public data on cities' percentage energy mix as reported by cities in 2019.

This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. This dataset contains public data on cities' percentage energy mix as reported by cities in 2019.

This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. This dataset contains public data on cities' percentage energy mix as reported by cities in 2020. View cities questionnaire guidance https://www.cdp.net/en/guidance/guidance-for-cities. Please contact cities@cdp.net if you have any questions.

This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. This dataset contains public data on cities' percentage energy mix as reported by cities in 2020. View cities questionnaire guidance https://www.cdp.net/en/guidance/guidance-for-cities. Please contact cities@cdp.net if you have any questions.

This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. This dataset contains public data on cities' percentage energy mix as reported by cities in 2020. View cities questionnaire guidance https://www.cdp.net/en/guidance/guidance-for-cities. Please contact cities@cdp.net if you have any questions.

This dataset contains 2021 data on cities energy mix, as reported by cities through the CDP-ICLEI Unified Reporting System in response to question 8.1 ("Please indicate the source mix of electricity consumed in your city.") in the 2021 Cities questionnaire. View the Cities questionnaire at https://www.cdp.net/en/guidance/guidance-for-cities. Please contact cities@cdp.net if you have any questions about the data. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

This dataset contains 2021 data on cities energy mix, as reported by cities through the CDP-ICLEI Unified Reporting System in response to question 8.1 ("Please indicate the source mix of electricity consumed in your city.") in the 2021 Cities questionnaire. View the Cities questionnaire at https://www.cdp.net/en/guidance/guidance-for-cities. Please contact cities@cdp.net if you have any questions about the data. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

This dataset contains 2021 data on cities energy mix, as reported by cities through the CDP-ICLEI Unified Reporting System in response to question 8.1 ("Please indicate the source mix of electricity consumed in your city.") in the 2021 Cities questionnaire. View the Cities questionnaire at https://www.cdp.net/en/guidance/guidance-for-cities. Please contact cities@cdp.net if you have any questions about the data. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

This dataset contains 2022 data on cities' climate action plans, including adaptation, mitigation, and energy access and/or poverty. All data was self reported by cities through CDP-ICLEI Track in 2022. Browse the header menu to access supporting resources - including 2022 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

This dataset contains 2022 data on cities' climate action plans, including adaptation, mitigation, and energy access and/or poverty. All data was self reported by cities through CDP-ICLEI Track in 2022. Browse the header menu to access supporting resources - including 2022 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

The 2022 Cities Energy Mix dataset contains data on the energy mix of electricity, heating and cooling, self-reported by cities and local governments in 2022. Grid policy is often set at state or utility level and local governments may not have direct control over their energy mix. The dataset also identifies cities with a renewable energy target. Browse the header menu to access supporting resources - including 2022 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

The 2022 Cities Energy Mix dataset contains data on the energy mix of electricity, heating and cooling, self-reported by cities and local governments in 2022. Grid policy is often set at state or utility level and local governments may not have direct control over their energy mix. The dataset also identifies cities with a renewable energy target. Browse the header menu to access supporting resources - including 2022 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

This dataset details fuel mileage and gallons/kilowatt hours for each agency, mode, and type of service (TOS) as reported by agencies submitted data to the National Transit Database (NTD) for Report Year 2022. This file is based on the database file 2022 Energy Consumption available at https://transit.dot.gov/ntd/ntd-data Data Tables organize and summarize data from the 2022 NTD in a manner that is more useful for quick reference and summary analysis. Only Full Reporters report energy consumption. Other reporter types do not appear in this dataset. Demand Response Taxi (DR/TX) mode and type of service combination does not report energy consumption and does not appear in this dataset. Finally, Non-dedicated fleets report energy consumption but not miles traveled. Thus for some agencies the given data for miles traveled are incomplete. Non-dedicated fleets represent about 7% of the data reflected in this dataset. In versions of the data tables from 2014-2021, you can find data on fuel and energy in the file called "Fuel and Energy" available from the NTD program website.

This dataset details fuel mileage and gallons/kilowatt hours for each agency, mode, and type of service (TOS) as reported by agencies submitted data to the National Transit Database (NTD) for Report Year 2022. This file is based on the database file 2022 Energy Consumption available at https://transit.dot.gov/ntd/ntd-data Data Tables organize and summarize data from the 2022 NTD in a manner that is more useful for quick reference and summary analysis. Only Full Reporters report energy consumption. Other reporter types do not appear in this dataset. Demand Response Taxi (DR/TX) mode and type of service combination does not report energy consumption and does not appear in this dataset. Finally, Non-dedicated fleets report energy consumption but not miles traveled. Thus for some agencies the given data for miles traveled are incomplete. Non-dedicated fleets represent about 7% of the data reflected in this dataset. In versions of the data tables from 2014-2021, you can find data on fuel and energy in the file called "Fuel and Energy" available from the NTD program website.

This dataset contains 2023 data on cities' climate action plans, including adaptation, mitigation, and energy access and/or poverty. All data was self reported by cities through CDP-ICLEI Track in 2023. Browse the header menu to access supporting resources - including 2023 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through CDP-ICLEI Track. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

This dataset contains 2023 data on cities' climate action plans, including adaptation, mitigation, and energy access and/or poverty. All data was self reported by cities through CDP-ICLEI Track in 2023. Browse the header menu to access supporting resources - including 2023 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through CDP-ICLEI Track. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

The 2023 Cities Energy Mix dataset contains data on the energy mix of electricity, heating and cooling, self-reported by cities and local governments in 2023. Grid policy is often set at state or utility level and local governments may not have direct control over their energy mix. The dataset also identifies cities with a renewable energy target. The data is drawn from questions 3.1a, 3.1b, 6.1 and 0.1 in the Cities 2023 questionnaire. Browse the header menu to access supporting resources - including 2023 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

The 2023 Cities Energy Mix dataset contains data on the energy mix of electricity, heating and cooling, self-reported by cities and local governments in 2023. Grid policy is often set at state or utility level and local governments may not have direct control over their energy mix. The dataset also identifies cities with a renewable energy target. The data is drawn from questions 3.1a, 3.1b, 6.1 and 0.1 in the Cities 2023 questionnaire. Browse the header menu to access supporting resources - including 2023 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

The 2023 Cities Energy Mix dataset contains data on the energy mix of electricity, heating and cooling, self-reported by cities and local governments in 2023. Grid policy is often set at state or utility level and local governments may not have direct control over their energy mix. The dataset also identifies cities with a renewable energy target. The data is drawn from questions 3.1a, 3.1b, 6.1 and 0.1 in the Cities 2023 questionnaire. Browse the header menu to access supporting resources - including 2023 cities questionnaire guidance, our Open Data Portal user guide, and Introducing Cities, States and Regions Data webinar recording. This data is collected through the CDP-ICLEI Unified Reporting System. When using this data, please cite both organisations using the following wording: ‘This data was collected in partnership by CDP and ICLEI - Local Governments for Sustainability’. For further guidance on how to reference this data for use in external publications, please refer to the Open Data Portal Terms of Use available on the homepage.

About the Workshop:The objective of the workshop was to discuss a framework to add to current approaches for evaluating the effectiveness of policy instruments in delivering rapid transitions that meet their intended objectives.Evaluating the effectiveness of transition policy and incentives requires an understanding of the key dynamics that determine the pace and path of transitions. This workshop, and subsequent sessions will seek to provide policymakers with a framework for assessing the implications of policy instruments on achieving the intended objectives of their energy transition plans.Key Points:Energy technology and fuel transitions are impacted by a large number of actors and forces. This results in resistance to policy intervention and other potential unintended consequences.Existing literature on energy transitions includes several empirical analyses of historical transitions, theories of innovation and technological transition, and some recent modeling efforts.The growing demand for the new technology tightens its supply/demand balance, thereby placing upward pressure on its LCOE.Energy transitions can be conceptualized by representing the cost competitiveness between the incumbent and the new technologies. This conceptualization underscores the significance of technology supply chain evolution on the pace and path of transitions.Prioritizing transition policy objectives to address tradeoffsThe perfect storm of enablers defines or redefines transitions

What does the data show? A Cooling Degree Day (CDD) is a day in which the average temperature is above 22°C. It is the number of degrees above this threshold that counts as a Coolin Degree Day. For example if the average temperature for a specific day is 22.5°C, this would contribute 0.5 Cooling Degree Days to the annual sum, alternatively an average temperature of 27°C would contribute 5 Cooling Degree Days. Given the data shows the annual sum of Cooling Degree Days, this value can be above 365 in some parts of the UK.Annual Cooling 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 CDD to previous values.What are the possible societal impacts?Cooling Degree Days indicate the energy demand for cooling due to hot days. A higher number of CDD means an increase in power consumption for cooling and air conditioning, therefore this index is useful for predicting future changes in energy demand for cooling.In practice, this varies greatly throughout the UK, depending on personal thermal comfort levels and building designs, so these results should be considered as rough estimates of overall demand changes on a large scale.What is a global warming level?Annual Cooling 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 Cooling Degree Days, an average is taken across the 21 year period. Therefore, the Annual Cooling Degree Days show the number of cooling 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 global warming level and two baselines. They are named ‘CDD’ (Cooling Degree Days), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'CDD 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. 'CDD 2.5 median' is 'CDD_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 ‘CDD 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 Cooling 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.

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.

Ausgrid is providing the following data on electricity consumption within Maitland local government areas (LGA).Average electricity consumption by geographic area can vary significantly due to a number of factors. These include differences in climate, the use of natural gas, the type of housing (house or apartment), type of business (commercial or industrial), and varying household socio-demographics or business types.This data does not include high voltage customers and supply to services such as public lighting and bus shelters.Data also include associated Greenhouse Gas emission Scope 2 and Scope 3, in tonnes of CO2 equivalent.

The Digest of United Kingdom Energy Statistics (DUKES) is the annual energy statistics publication produced by DESNZ. It provides a detailed and comprehensive picture on the production and consumption of individual fuels and of energy as a whole. The digest, sometimes known as DUKES, is an essential source of energy information. It contains: - extensive tables, charts and commentary - separate sections on coal, petroleum, gas, electricity, renewables and combined heat and power - a comprehensive picture of energy production and use over the last 5 years, with key series taken back to 1970

About the Workshop: In February 2015, KAPSARC convened the fourth and fifth workshops in its Energy Productivity workshop series. The workshops were held over two days, with the first day dedicated to energy productivity in the six countries of the Gulf Cooperation Council (GCC) and the second to wider international energy productivity issues. The workshops also launched a collaborative project between KAPSARC and UNESCWA to explore the energy productivity potential of the Arab region. The workshop was held under the Chatham House Rule of capturing the discussion on a non-attribution basis.

Wind turbine locations in planning applications in The Highland Council area. This data is updated typically twice a year. Wind turbine locations from planning applications in The Highland Council area. Includes the following statuses: Constructed / Constructed-Removed / Under Construction / Approved / In Planning / Refused / Expired / Withdrawn / Scoping and Screening.

Here you can find out the approximate location of our national gas transmission network. You can also check if our transmission networks may be affected by your works by using the Linesearch website. These datasets are for indicative purposes only. They can only be used for emergency and land use planning and cannot be used for commercial purposes. They are owned by National Gas Transmission plc and you are required to acknowledge us in your product or application using "© National Gas Transmission". This data is supplied on a best-effort basis only, using available information as documented at the time by the transmission network operators. While every effort is made to make sure the information is accurate and up-to-date, we do not accept any liability for any direct, indirect, or consequential loss or damage of any nature--however caused--which may be sustained as a result of reliance upon such information. The datasets contain gas transmission data only. For gas distribution data, please contact the distribution company for your area. You can find your local distribution company by going to the Energy Networks website. If you are planning to undertake any ground works, please visit www.lsbud.co.uk and raise an enquiry with at least 14 days in advance. If you are having difficulties using this service, please contact our Asset Protection Team on 0800 970 7000 or at box.assetprotection@nationalgas.com All datasets are accompanied with Gemini 2.2 metadata.

The Electricity Ten Year Statement outlines the ESO’s view of the National Electricity Transmission System over the next ten to twenty years.

Open Power System Data is a free-of-charge data platform dedicated to electricity system researchers. We collect, check, process, document, and publish data that are publicly available but currently inconvenient to use. The project is a service provider to the modeling community: a supplier of a public good. Learn more about its background or just go ahead and explore the data platform.

The EDP Open Data hub shares operational data from EDP assets

This dataset contains number of solar installations and capacity (in kW) since 2001 and per locality. The data is organised by size of solar installation. It is provided by the Australian Photovoltaic Institute. 

We provide data from two models of different manufactures (A and B).The modules are located in Faro and each model has three different orientations: vertical, optimal and horizontal. The granularity of the data is one minute and the timezone is UTC (Coordinated Universal Time). All files have daylight saving time correction.

The EDP Open Data hub shares operational data from EDP assets

About the Workshop The workshop was attended by energy security experts from academia, government, multilateral and non-governmental organizations. It was conducted on the basis that the discussions could be reported on a non-attribution basis.About KAPSARC’s Research on Energy Vulnerability KAPSARC’s research on energy vulnerability looks at energy shocks and disruptions from the perspective of both exporting and importing economies. Our objective is to understand what are the macroeconomic fundamentals that increase the resilience of an economy to energy shocks and, in particular, the role of the energy mix in reducing vulnerability. This will lead to an analysis of policies that enhance the resilience of economies to energy shocks.Key PointsThe economic vulnerability of an economy to energy disruptions is traditionally viewed through the lens of price shocks. This 'energy vulnerability' is driven by the energy mix, the infrastructure and the supply chain resilience that an economy enjoys.Globally, the energy mix appears to be determined by the relative costs of fuels. Regionally, the mix may be distorted by policies addressing security, environmental, economic and other social aspects. Such policies may create economic vulnerabilities to the same extent as price shocks. Policies designed to stabilize energy prices rather than to insulate an economy from price volatility may, ironically, lead to greater swings in prices in the long-term. As technology and regulation change the relative competitiveness of energy sources, stabilization mechanisms may create unsustainable imbalances—creating even greater hardship when they unwind

The EDP Open Data hub shares operational data from EDP assets

The EDP Open Data hub shares operational data from EDP assets

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