This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2022, figures for 2023 are revised provisional figures and figures for 2024 are provisional.

Changes as of July 2025: Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the outdated data from June 2025. The most recent figures are 5 percent higher for 2024 solar electricity production. These figures are in these two tables (in Dutch): - StatLine - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - StatLine - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update is scheduled in November 2025. From that moment all figures will be fully consistent again. We apologize for the inconvenience.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe have further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels determine the amount of electricity the average person requires in each country.

Analysis of ‘Global Energy Consumption & Renewable Generation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jamesvandenberg/renewable-power-generation on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Content

4 of these datasets outline the quantity of terawatt hours (TWh) produced through various sources of energy, comparing both renewable and non-renewable sources, while highlighting the renewable use of the top 20 countries. The Renewables Power Generation dataset includes a 1997-2017 timeline that outlines the progress of the main renewable energy sectors : Hydro, Wind, Biofuel, Solar PV, and Geothermal. Additionally, the Top 20 Countries Power Generation dataset includes the national data for each of the renewable categories as outlined above. The last 2 datasets include the global TWh generated from renewable and non-renewable sources.

In the latest version, I added two datasets which contain the global consumption figures on national and continental/international group levels, which help provide context about the quantity of energy required, how that is changing over time, and how we are doing in terms of transitioning from non-renewable to renewable energy use.

Source

Renewable Energy: Reddy, Vamsi., Kalananda, Aala., Komanapalli, Narayana. "Nature Inspired Optimization Algorithms for Renewable Energy Generation, Distribution and Management - A Comprehensive Review. 2021.

Consumption: https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html (data converted from mTOE to TWh)

Inspiration

As temperatures rise and storms grow more fierce, improving the efficiency and increasing the use of renewable energy sources is critical. In turn, understanding which nations are leading the way and which require more immediate transformations will help target efforts and hopefully, reach global goals.

Which types of renewables are improving the fastest? Which countries using which types of renewables? At the increasing rate of returns on renewables, how long will it take to meet global demands and eliminate non-renewables, or atleast, break 50%?

--- Original source retains full ownership of the source dataset ---

DOI Abstract copyright UK Data Service and data collection copyright owner. The International Energy Agency (IEA) World Energy Prices database includes annual energy prices data for gasoline, automotive diesel, electricity and other products.Energy prices are a significant part of our domestic expenditures, play an important role for industrial competitiveness and influence energy consumption patterns. End-use prices-paid by final consumers- are affected by movements in commodity markets as well as policy decisions. As countries move away from regulated pricing, monitoring energy end-use prices around the world has become increasingly important for analysts and policy makers. World Energy Prices aims to serve this purpose by being the most reliable database that uses official sources with transparent and documented methodologies for each country. Main Topics: Topics covered include:ElectricityTransportTransport FuelsConsumer Price IndicesOther products Aggregation 1969 2022 AGRICULTURE Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan BOILERS Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi CALORIES CHEMICALS COAL CONSTRUCTION ENGINE... CONSUMPTION Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Rep... Chad Channel Islands Chile China Colombia Comoros Congo Costa Rica Croatia Cuba Curacao Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic ELECTRIC POWER ENERGY EXPORTS AND IMPORTS Ecuador Egypt El Salvador Energy and natural ... Equatorial Guinea Estonia Ethiopia Europe European Union Coun... FISHING INDUSTRY FOOD FORESTRY FOSSIL FUELS Faroe Islands Finland France GAS FUELS GEOTHERMAL ENERGY Gabon Gambia Georgia Germany October 1990 Ghana Gibraltar Greece Grenada Guatemala Guinea Guinea Bissau HEATING SYSTEMS HYDROPOWER Honduras Hong Kong Hungary INDUSTRIAL PLANTS INDUSTRIAL PRODUCTION INDUSTRIES IRON Iceland India Indonesia Iran Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kosovo Kuwait Kyrgyzstan LEATHER Latvia Lebanon Lesotho Liberia Lithuania Luxembourg MACHINES MARKETING METALS MINERALS MINING Macao Macedonia Madagascar Malawi Malaysia Mali Malta Mauritania Mauritius Mexico Moldova Montenegro Morocco Mozambique Multi nation NUCLEAR ENERGY Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway PAPER PEAT PETROLEUM PRODUCTS PRODUCTION PUBLIC SERVICES PUMPS RAILWAY TRAVEL RENEWABLE ENERGY RESIDENTIAL BUILDINGS ROAD TRAFFIC ROADS Romania Russia Rwanda SHARES SOLAR ENERGY Saint Lucia Saint Martin Saint Vincent Saotome Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Korea Spain Sri Lanka Sudan Surinam Swaziland Switzerland TEXTILE INDUSTRY TEXTILE PRODUCTS TOBACCO TRANSPORT Tajikistan Tanzania Thailand Togo Trinidad and Tobago Turkey Turkmenistan Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela Vietnam Virgin Islands USA WASTES WAXES WIND ENERGY WOOD Zambia Zimbabwe

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center.

Global primary energy consumption has increased dramatically in recent years and is projected to continue to increase until 2045. Only hydropower and renewable energy consumption are expected to increase between 2045 and 2050 and reach 30 percent of the global energy consumption. Energy consumption by country The distribution of energy consumption globally is disproportionately high among some countries. China, the United States, and India were by far the largest consumers of primary energy globally. On a per capita basis, it was Qatar, Singapore, the United Arab Emirates, and Iceland to have the highest per capita energy consumption. Renewable energy consumption Over the last two decades, renewable energy consumption has increased to reach over 90 exajoules in 2023. Among all countries globally, China had the largest installed renewable energy capacity as of that year, followed by the United States.

Content

4 of these datasets outline the quantity of terawatt hours (TWh) produced through various sources of energy, comparing both renewable and non-renewable sources, while highlighting the renewable use of the top 20 countries. The Renewables Power Generation dataset includes a 1997-2017 timeline that outlines the progress of the main renewable energy sectors : Hydro, Wind, Biofuel, Solar PV, and Geothermal. Additionally, the Top 20 Countries Power Generation dataset includes the national data for each of the renewable categories as outlined above. The last 2 datasets include the global TWh generated from renewable and non-renewable sources.

In the latest version, I added two datasets which contain the global consumption figures on national and continental/international group levels, which help provide context about the quantity of energy required, how that is changing over time, and how we are doing in terms of transitioning from non-renewable to renewable energy use.

Source

Renewable Energy: Reddy, Vamsi., Kalananda, Aala., Komanapalli, Narayana. "Nature Inspired Optimization Algorithms for Renewable Energy Generation, Distribution and Management - A Comprehensive Review. 2021.

Consumption: https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html (data converted from mTOE to TWh)

Inspiration

As temperatures rise and storms grow more fierce, improving the efficiency and increasing the use of renewable energy sources is critical. In turn, understanding which nations are leading the way and which require more immediate transformations will help target efforts and hopefully, reach global goals.

Which types of renewables are improving the fastest? Which countries using which types of renewables? At the increasing rate of returns on renewables, how long will it take to meet global demands and eliminate non-renewables, or atleast, break 50%?

The dataset reports annual estimates for primary energy per capita and GDP per capita for 185 countries for 1950 through 2014. The data allows investigating long-term joint evolution of economic activity and energy demand, which is important for both understanding the past energy needs of economic development, and forming useful baselines for scenario development, especially for integrated assessment modeling around climate change mitigation. Other commonly used datasets only go back to 1971 (International Energy Agency) for worldwide coverage and so extending the data back to 1950 allows analyzing a longer time period than before. The dataset also includes more individual country time series than IEA data thanks to data from the UN. 185 Countries as well as Czechoslovakia, East and West Pakistan, Soviet Union, Yugoslavia prior to their dissolution. Covers upward of 99% of global population after 1970. Data were downloaded from online repositories and then cleaned, harmonized and merged.

This dataset provides a set of 18 load profiles with an hourly temporal resolution that represent main industrial and tertiary sectors in France for the year 2018.The ELMAS dataset is derived from a total of 55,730 consumption time series initially split into 424 business sectors and three levels of subscribed capacity. The customer’s field of activity follows the Statistical Classification of Economic Activities in the European Community (NACE), which is a four-digit industry standard classification used in the European Union composed of 21 sections, 88 divisions, 272 groups, and 615 classes. For anonymity concerns, the initial times series are averaged according to their NACE coding and level of subscribed capacity.Discrepancies between the temporal patterns of customers that belong to the same NACE section highlight the need to resort to another clustering approach. Thus, a K-means algorithm is used to gather the business groups sharing similar temporal patterns into 18 clusters. The resulting clustering shows that numerous NACE sections are scattered over various clusters, which increases the global heterogeneity of the clustering while spoiling the interpretation. The proportion of these dispersed NACE classes in terms of annual energy consumption remains low, which suggests that a manual reorganisation has little impact on the global consistency of the clusters. This manual reclassification is conducted in such a way that scattered NACE classes are gathered in the cluster that possesses the highest share of the considered NACE section. The energy consumption time series dataset represents a limited panel composed of 55,730 customers, which may bias the output load profiles in comparison with the whole French panel of industrial and tertiary customers. To fill this gap, Enedis provides the annual energy consumption of a wider range of customers for the year 2019. This annual energy consumption dataset is used to generate weights implemented in the clustering approach and to derive weighted average time series for the clusters.

Global consumption of renewable energy has increased significantly over the last two decades. Consumption levels nearly reached ***** exajoules in 2024. This upward trend reflects the increasing adoption of clean energy technologies worldwide. However, despite its rapid growth, renewable energy consumption still remains far below that of fossil fuels. Fossil fuels still dominate energy landscape While renewable energy use has expanded, fossil fuels continue to dominate the global energy mix. Coal consumption reached *** exajoules in 2023, marking its highest level to date. Oil consumption also hit a record high in 2024, exceeding *** billion metric tons for the first time. Natural gas consumption has remained relatively stable in recent years, hovering around **** trillion cubic meters annually. These figures underscore the ongoing challenges in transitioning to a low-carbon energy system. Renewable energy investments The clean energy sector has experienced consistent growth over the past decade, with investments more than doubling from *** billion U.S. dollars in 2014 to *** billion U.S. dollars in 2023. China has emerged as the frontrunner in renewable energy investment, contributing *** billion U.S. dollars in 2023. This substantial funding has helped propel the renewable energy industry forward.

The Global Energy Balance Archive (GEBA) is a database for the central storage of the worldwide measured energy fluxes at the Earth's surface, maintained at ETH Zurich (Switzerland). This paper documents the status of the GEBA version 2017 dataset, presents the new web interface and user access, and reviews the scientific impact that GEBA data had in various applications. GEBA has continuously been expanded and updated and contains in its 2017 version around 500.000 monthly mean entries of various surface energy balance components measured at 2500 locations. The database contains observations from 15 surface energy flux components, with the most widely measured quantity available in GEBA being the shortwave radiation incident at the Earth's surface (global radiation). Many of the historic records extend over several decades. GEBA contains monthly data from a variety of sources, namely from the World Radiation Data Centre (WRDC) in St. Petersburg, from national weather services, from different research networks (BSRN, ARM, SURFRAD), from peer-reviewed publications, project and data reports, and from personal communications. Quality checks are applied to test for gross errors in the dataset. GEBA has played a key role in various research applications, such as in the quantification of the global energy balance, in the discussion of the anomalous atmospheric shortwave absorption, and in the detection of multi-decadal variations in global radiation, known as "global dimming" and "brightening". GEBA is further extensively used for the evaluation of climate models and satellite-derived surface flux products. On a more applied level, GEBA provides the basis for engineering applications in the context of solar power generation, water management, agricultural production and tourism. GEBA is publicly accessible through the internet via http://www.geba.ethz.ch. Submitted here are 2 different files in text format, one file containing the original energy flux data, and one file containing meta data in terms of station names, coordinates, station history with known changes in instrumentation, data evaluation procedures and data publication standards. The exact file formats are described in detail in http://www.geba.ethz.ch/data-retrieval/data-formats.html. All data in the dataset are stored in the Unit W/m**2.GEBA is accessible worldwide through the internet. The official URL address of GEBA version 2017 is http://www.geba.ethz.ch. To grant access to the data, a registration is required, which can be achieved by filling in the respective form provided on the GEBA website (http://www.geba.ethz.ch/register.html). Thereon, a specification of the intended usage of the data is also required. It is the policy of GEBA that the data are available at no cost for bona fide research. Direct commercial use, i.e., selling, of the data is not allowed.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center.

Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).

GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).

GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: https://jgcri.github.io/gcam-doc/toc.html

The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP per capita) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.

Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:

• income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)
• residential energy consumption per capita by service and fuel, by state and income group, 2015-2045
• residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, and technology, by state and income group, 2015-2045
• estimated energy burden (Eq.1), by state and income group, 2015-2045
• residential heating service inequality (Eq.2), by state, 2015-2045

Table 1

Eq. 1

\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)

for income group i and service j

Eq. 2

\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)

where S is the residential heating service output per capita of the highest income group (d10) divided by the sum of that of the lowest four income groups (d1, d2, d3, and d4), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.

Reference

Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7227128

IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

Solar Footprints in CaliforniaThis GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy https://www.blm.gov/style/medialib/blm/wo/MINERALS_REALTY_AND_RESOURCE_PROTECTION_/energy/solar_and_wind.Par.70101.File.dat/Public%20Webinar%20Dec%203%202014%20-%20Solar%20and%20Wind%20Regulations.pdfBLM CA Renewable Energy Projects | BLM GBP Hub (arcgis.com)Metadata: (5) Quarterly Fuel and Energy Report (QFER) California Power Plants - Overview (arcgis.com)

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center.

Analysis of ‘Traded Energy Share Domestic’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mathurinache/traded-energy-share-domestic on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Think about how much energy you use. Some common things come to mind: electricity to keep the lights on; heating to warm your home; the car or bus you might take to get to work.

But there’s also the energy needed to produce the goods we buy in the first place. Sometimes these goods are produced in our own country – and so that energy is reported in our country’s energy use data. But when we buy goods from overseas, this energy is included in their accounts. It’s missing from ours.1

When we compare energy use across the world we rarely adjust for the energy embedded in imports. But what happens when we do? What difference does it make to our energy footprint?

Content

Datas come from https://ourworldindata.org/energy-offshoring" alt="https://ourworldindata.org/energy-offshoring">

Acknowledgements

https://d346xxcyottdqx.cloudfront.net/wp-content/uploads/2018/07/energy-markets.jpg" alt="https://d346xxcyottdqx.cloudfront.net/wp-content/uploads/2018/07/energy-markets.jpg">

Inspiration

• Which countries are exporters and importers of embodied energy?
• How do production-based and consumption-based energy trends compare?
• Where do people consume the most energy, after trade?

--- Original source retains full ownership of the source dataset ---

A Global Database on Whole Life Carbon, Energy and Material Intensity of Buildings (CarbEnMats-Buildings)

• Data descriptor article (Preprint): https://doi.org/10.5281/zenodo.8378939
• Data records / database (this repo): https://doi.org/10.5281/zenodo.8363895
• Latest version: Available via https://github.com/mroeck/carbenmats-buildings (main)

Abstract

Globally, interest in understanding the life cycle related greenhouse gas (GHG) emissions of buildings is increasing. Robust data is required for benchmarking and analysis of parameters driving resource use and whole life carbon (WLC) emissions. However, open datasets combining information on energy and material use as well as whole life carbon emissions remain largely unavailable – until now.

We present a global database on whole life carbon, energy use, and material intensity of buildings. It contains data on more than 1,200 building case studies and includes over 300 attributes addressing context and site, building design, assessment methods, energy and material use, as well as WLC emissions across different life cycle stages. The data was collected through various meta-studies, using a dedicated data collection template (DCT) and processing scripts (Python Jupyter Notebooks), all of which are shared alongside this data descriptor.

This dataset is valuable for industrial ecology and sustainable construction research and will help inform decision-making in the building industry as well as the climate policy context.

Background & Summary

The need for reducing greenhouse gas (GHG) emissions across Europe require defining and implementing a performance system for both operational and embodied carbon at the building level that provides relevant guidance for policymakers and the building industry. So-called whole life carbon (WLC) of buildings is gaining increasing attention among decision-makers concerned with climate and industrial policy, as well as building procurement, design, and operation. However, most open buildings datasets published thus far have been focusing on building’s operational energy consumption and related parameters 1,2,2–4. Recent years furthermore brought large-scale datasets on building geometry (footprint, height) 5,6 as well as the publication of some datasets on building construction systems and material intensity 7,8. Heeren and Fishman’s database seed on material intensity (MI) of buildings 7, an essential reference to this work, was a first step towards an open data repository on material-related environmental impacts of buildings. In their 2019 descriptor, the authors present data on the material coefficients of more than 300 building cases intended for use in studies applying material flow analysis (MFA), input-output (IO) or life cycle assessment (LCA) methods. Guven et al. 8 elaborated on this effort by publishing a construction classification system database for understanding resource use in building construction. However, thus far, there is a lack of publicly available data that combines material composition, energy use and also considers life cycle-related environmental impacts, such as life cycle-related GHG emissions, also referred to as building’s whole life carbon.

The Global Database on Whole Life Carbon, Energy Use, and Material Intensity of Buildings (CarbEnMats-Buildings) published alongside this descriptor provides information on more than 1,200 buildings worldwide. The dataset includes attributes on geographical context and site, main building design characteristics, LCA-based assessment methods, as well as information on energy and material use, and related life cycle greenhouse gas (GHG) emissions, commonly referred to as whole life carbon (WLC), with a focus on embodied carbon (EC) emissions. The dataset compiles data obtained through a systematic review of the scientific literature as well as systematic data collection from both literature sources and industry partners. By applying a uniform data collection template (DCT) and related automated procedures for systematic data collection and compilation, we facilitate the processing, analysis and visualization along predefined categories and attributes, and support the consistency of data types and units. The descriptor includes specifications related to the DCT spreadsheet form used for obtaining these data as well as explanations of the data processing and feature engineering steps undertaken to clean and harmonise the data records. The validation focuses on describing the composition of the dataset and values observed for attributes related to whole life carbon, energy and material intensity.

The data published with this descriptor offers the largest open compilation of data on whole life carbon emissions, energy use and material intensity of buildings published to date. This open dataset is expected to be valuable for research applications in the context of MFA, I/O and LCA modelling. It also offers a unique data source for benchmarking whole life carbon, energy use and material intensity of buildings to inform policy and decision-making in the context of the decarbonization of building construction and operation as well as commercial real estate in Europe and beyond.

Files

All files related to this descriptor are available on a public GitHub repository and related release via Zenodo (https://doi.org/10.5281/zenodo.8363895). The repository contains the following files:

• README.md is a text file with instructions on how to use the files and documents.
• CarbEnMats_attributes.XLSX is a table with the complete attribute description.
• CarbEnMats_materials.XLSX is the table of material options and mappings.
• CarbEnMats_dataset.XLSX is the building dataset in MS Excel format.
• CarbEnMats_dataset.txt is the building dataset in tab-delimited TXT format.

Further information

Please consult the related data descriptor article (linked at the top) for further information, e.g.:

• Methods (Data collection; data processing)
• Data records (Files; Sources; Attributes)
• Technical validation (Data overview; Data consistency)
• Usage Notes (Attribute priority; Scope summary, Missing information)

Code availability (LICENSE)

The dataset, the data collection template as well as the code used for processing, harmonization and visualization are published under a GNU General Public License v3.0. The GNU General Public License is a free, copyleft license for software and other kinds of works. We encourage you to review, reuse, and refine the data and scripts and eventually share-alike.

Contributing

The CarbEnMats-Buildings database is the results of a highly collaborative effort and needs your active contributions to further improve and grow the open building data landscape. Reach out to the lead author (email, linkedin) if you are interested to contribute your data or time.

Cite as

When referring to this work, please cite both the descriptor and the dataset:

• Descriptor: RÖCK, Martin, SORENSEN, Andreas, BALOUKTSI, Maria, RUSCHI MENDES SAADE, Marcella, RASMUSSEN, Freja Nygaard, BIRGISDOTTIR, Harpa, FRISCHKNECHT, Rolf, LÜTZKENDORF, Thomas, HOXHA, Endrit, HABERT, Guillaume, SATOLA, Daniel, TRUGER, Barbara, TOZAN, Buket, KUITTINEN, Matti, ALAUX, Nicolas, ALLACKER, Karen, & PASSER, Alexader. (2023). A Global Database on Whole Life Carbon, Energy and Material Intensity of Buildings (CarbEnMats-Buildings) [Preprint]. Zenodo. https://doi.org/10.5281/zenodo.8378939
• Dataset: Martin Röck. (2023). mroeck/carbenmats-buildings: Pre-release (0.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8363895

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center.

The leading countries for installed renewable energy in 2024 were China, the United States, and Brazil. China was the leader in renewable energy installations, with a capacity of around 1,827 gigawatts. The U.S., in second place, had a capacity of around 428 gigawatts. Renewable energy is an important step in addressing climate change and mitigating the consequences of this phenomenon. Renewable energy capacity and productionRenewable power capacity is defined as the maximum generating capacity of installations that use renewable sources to generate electricity. The share of renewable energy in the world’s power production has increased in recent years, surpassing 30 percent in 2023. Renewable energy consumption varies from country to country. The leading countries for renewable energy consumption are China, the United States, and Canada.Renewable energy sourcesThere are various sources of renewable energy used globally, including bioenergy, solar energy, hydropower, and wind energy, to name a few. Globally, China and Brazil are the top two countries in terms of generating the most energy through hydropower. Regarding solar power, China, the United States, and Japan boast the highest installed capacities worldwide.