Green hydrogen’s role in global decarbonisation depends on its cost and carbon footprint. These vary widely in existing modelling studies, prohibiting a coherent global evaluation. Here, we present underlying data that assesses global levelised costs and embodied carbon for green hydrogen from wind, solar PV and hybrid systems, using spatially granular renewable profiles over 96,000 locations and country-specific costs of capital to optimise electrolyser sizing relative to the on-site renewable capacity. Required costs and carbon intensity of grid imports of electricity to not worsen the average carbon footprint or levelised cost of hydrogen are also assessed.Further details alongside an interactive web interface can be found here: https://greenhydrogenninja.streamlit.app/. The hydrogen model used to develop these results can be found here: https://github.com/LukeHatton21/HydrogenModel.

Green hydrogen’s role in global decarbonisation depends on its cost and carbon footprint. These vary widely in existing modelling studies, prohibiting a coherent global evaluation. Here, we present underlying data that assesses global levelised costs and embodied carbon for green hydrogen from wind, solar PV and hybrid systems, using spatially granular renewable profiles over 96,000 locations and country-specific costs of capital to optimise electrolyser sizing relative to the on-site renewable capacity. Required costs and carbon intensity of grid imports of electricity to not worsen the average carbon footprint or levelised cost of hydrogen are also assessed.Further details alongside an interactive web interface can be found here: https://greenhydrogenninja.streamlit.app/. The hydrogen model used to develop these results can be found here: https://github.com/LukeHatton21/HydrogenModel.

The cost of capital is an important input for power sector and energy system models, used widely across industry, academia and government to explore future decarbonisation scenarios. The levelised cost of electricity is sensitive to the assumed cost of capital (particular for renewables due to their high capital intensity), but empirical data on the cost of capital is typically outdated, geographically limited and difficult to access. It is also limited to markets which have seen recent deployment of corresponding electricity generation technologies, driving a data availability gap for emerging and developing economies.Here, we present a database which includes historical, current and future estimates of the cost of capital for 10 electricity generation technologies from 2015-2024: 1) solar photovoltaic (PV), 2) onshore wind, 3) offshore wind, 4) hydroelectric power, 5) biomass, 6) natural gas combined cycle turbines (CCGTs), 7) CCGTs equipped with carbon capture and storage, 8) geothermal, 9) tidal and 10) wave power. Note that nuclear is excluded due to the unique risks faced by project developers. In addition, we provide short term forecasts of the cost of capital out to 2030, which are an important input for accurate technoeconomic assessments. The data are global in scope but with national and technology specificity, covers the years 2015 through to 2030, and span 27,640 datapoints across 176 countries. The database addresses the limited empirical data on cost of capital available and enables enables modellers to select and justify model input data. The estimates presented here have been verified through comparison to historical data, with the estimation approach informed by stakeholder engagement and results from an expert elciitiation survey. Version two has been adjusted to include more in-depth treatment of technology risk premiums.An interactive webtool is also provided to visualise and explore the results, including underlying contributions to the cost of capital: https://wacc-forecaster.streamlit.app/

The cost of capital is an important input for power sector and energy system models, used widely across industry, academia and government to explore future decarbonisation scenarios. The levelised cost of electricity is sensitive to the assumed cost of capital (particular for renewables due to their high capital intensity), but empirical data on the cost of capital is typically outdated, geographically limited and difficult to access. It is also limited to markets which have seen recent deployment of corresponding electricity generation technologies, driving a data availability gap for emerging and developing economies.Here, we present a database which includes historical, current and future estimates of the cost of capital for 10 electricity generation technologies from 2015-2024: 1) solar photovoltaic (PV), 2) onshore wind, 3) offshore wind, 4) hydroelectric power, 5) biomass, 6) natural gas combined cycle turbines (CCGTs), 7) CCGTs equipped with carbon capture and storage, 8) geothermal, 9) tidal and 10) wave power. Note that nuclear is excluded due to the unique risks faced by project developers. In addition, we provide short term forecasts of the cost of capital out to 2030, which are an important input for accurate technoeconomic assessments. The data are global in scope but with national and technology specificity, covers the years 2015 through to 2030, and span 27,640 datapoints across 176 countries. The database addresses the limited empirical data on cost of capital available and enables enables modellers to select and justify model input data. The estimates presented here have been verified through comparison to historical data, with the estimation approach informed by stakeholder engagement and results from an expert elciitiation survey. Version two has been adjusted to include more in-depth treatment of technology risk premiums.An interactive webtool is also provided to visualise and explore the results, including underlying contributions to the cost of capital: https://wacc-forecaster.streamlit.app/

Data inputs and results of the study "A cross-country analysis  of the impacts  of emissions trading schemes and subsidies on emissions and renewable energy"

Data inputs and results of the study "A cross-country analysis  of the impacts  of emissions trading schemes and subsidies on emissions and renewable energy"

The cost of capital is an important input for power sector and energy system models, used widely across industry, academia and government to explore future decarbonisation scenarios. The levelised cost of electricity is sensitive to the assumed cost of capital (particular for renewables due to their high capital intensity), but empirical data on the cost of capital is typically outdated, geographically limited and difficult to access. It is also limited to markets which have seen recent deployment of corresponding electricity generation technologies, driving a data availability gap for emerging and developing economies.Here, we present a database which includes historical, current and future estimates of the cost of capital for 10 electricity generation technologies from 2015-2024: 1) solar photovoltaic (PV), 2) onshore wind, 3) offshore wind, 4) hydroelectric power, 5) biomass, 6) natural gas combined cycle turbines (CCGTs), 7) CCGTs equipped with carbon capture and storage, 8) geothermal, 9) tidal and 10) wave power. Note that nuclear is excluded due to the unique risks faced by project developers. In addition, we provide short term forecasts of the cost of capital out to 2030, which are an important input for accurate technoeconomic assessments. The data are global in scope but with national and technology specificity, covers the years 2015 through to 2030, and span 27,640 datapoints across 176 countries. The database addresses the limited empirical data on cost of capital available and enables enables modellers to select and justify model input data. The estimates presented here have been verified through comparison to historical data, with the estimation approach informed by stakeholder engagement and results from an expert elciitiation survey.An interactive webtool is also provided to visualise and explore the results, including underlying contributions to the cost of capital: https://wacc-forecaster.streamlit.app/

The cost of capital is an important input for power sector and energy system models, used widely across industry, academia and government to explore future decarbonisation scenarios. The levelised cost of electricity is sensitive to the assumed cost of capital (particular for renewables due to their high capital intensity), but empirical data on the cost of capital is typically outdated, geographically limited and difficult to access. It is also limited to markets which have seen recent deployment of corresponding electricity generation technologies, driving a data availability gap for emerging and developing economies.Here, we present a database which includes historical, current and future estimates of the cost of capital for 10 electricity generation technologies from 2015-2024: 1) solar photovoltaic (PV), 2) onshore wind, 3) offshore wind, 4) hydroelectric power, 5) biomass, 6) natural gas combined cycle turbines (CCGTs), 7) CCUS-enabled CCGTs, 8) geothermal, 9) tidal and 10) wave power. Note that nuclear is excluded due to the unique risks faced by project developers. In addition, we provide short term forecasts of the cost of capital out to 2030, which are an important input for accurate technoeconomic assessments. The data are global in scope but with national and technology specificity, covers the years 2015 through to 2030, and span 25,980 datapoints across 177 countries. The database addresses the limited empirical data on cost of capital available and enables enables modellers to select and justify model input data. The estimates presented here have been verified through comparison to historical data, with the estimation approach informed by stakeholder engagement and results from an expert elciitiation survey.An interactive webtool is also provided to visualise and explore the results, including underlying contributions to the cost of capital: https://wacc-forecaster.streamlit.app/

The cost of capital is an important input for power sector and energy system models, used widely across industry, academia and government to explore future decarbonisation scenarios. The levelised cost of electricity is sensitive to the assumed cost of capital (particular for renewables due to their high capital intensity), but empirical data on the cost of capital is typically outdated, geographically limited and difficult to access. It is also limited to markets which have seen recent deployment of corresponding electricity generation technologies, driving a data availability gap for emerging and developing economies.Here, we present a database which includes historical, current and future estimates of the cost of capital for 10 electricity generation technologies from 2015-2024: 1) solar photovoltaic (PV), 2) onshore wind, 3) offshore wind, 4) hydroelectric power, 5) biomass, 6) natural gas combined cycle turbines (CCGTs), 7) CCUS-enabled CCGTs, 8) geothermal, 9) tidal and 10) wave power. Note that nuclear is excluded due to the unique risks faced by project developers. In addition, we provide short term forecasts of the cost of capital out to 2030, which are an important input for accurate technoeconomic assessments. The data are global in scope but with national and technology specificity, covers the years 2015 through to 2030, and span 25,980 datapoints across 177 countries. The database addresses the limited empirical data on cost of capital available and enables enables modellers to select and justify model input data. The estimates presented here have been verified through comparison to historical data, with the estimation approach informed by stakeholder engagement and results from an expert elciitiation survey.An interactive webtool is also provided to visualise and explore the results, including underlying contributions to the cost of capital: https://wacc-forecaster.streamlit.app/

The cost of capital is an important input for power sector and energy system models, used widely across industry, academia and government to explore future decarbonisation scenarios. The levelised cost of electricity is sensitive to the assumed cost of capital (particular for renewables due to their high capital intensity), but empirical data on the cost of capital is typically outdated, geographically limited and difficult to access. It is also limited to markets which have seen recent deployment of corresponding electricity generation technologies, driving a data availability gap for emerging and developing economies.Here, we present a database which includes historical, current and future estimates of the cost of capital for 10 electricity generation technologies from 2015-2024: 1) solar photovoltaic (PV), 2) onshore wind, 3) offshore wind, 4) hydroelectric power, 5) biomass, 6) natural gas combined cycle turbines (CCGTs), 7) CCUS-enabled CCGTs, 8) geothermal, 9) tidal and 10) wave power. Note that nuclear is excluded due to the unique risks faced by project developers. In addition, we provide short term forecasts of the cost of capital out to 2030, which are an important input for accurate technoeconomic assessments. The data are global in scope but with national and technology specificity, covers the years 2015 through to 2030, and span 26,550 datapoints across 177 countries. The database addresses the limited empirical data on cost of capital available and enables enables modellers to select and justify model input data. The estimates presented here have been verified through comparison to historical data, with the estimation approach informed by stakeholder engagement and results from an expert elciitiation survey.An interactive webtool is also provided to visualise and explore the results, including underlying contributions to the cost of capital: https://wacc-forecaster.streamlit.app/