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 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.

Those countries have used the highest electricity in 2019, on the basis of that pie chart has been created so that it can be easier to find out which country was at the highest rank in the case of consuming electricity.

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

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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 ---

This dataset acompanies the paper "Post-communist countries and their participation in international forums on energy". This explorative analysis investigates in which international forums on energy Post-Communist countries participate and what motivates their participation. Theoretically, the article draws from theories of policy diffusion to explain why the Post-Communist countries have joined the 11 international forums on climate governance selected. It contends that the wish to follow the example of high-status countries or organizations, considerations concerning economic competitiveness, and the wish to obtain access to knowledge are potential factors explaining membership. Empirically, the article uses explorative methods to probe the plausibility of the three hypotheses. The database comprises information on the participation of 28 Post-Communist countries in 11 pertinent international forums, which are all characterized by a low degree of formalization and voluntary cooperation. Our findings show that neither the European Union nor Russia as a high-status organization or country had a robust impact on the Post-Communist countries’ decision to join the international forums on energy of interest. Instead, our indicative and preliminary findings suggest that access to knowledge was the most relevant driver of participation.

About the Project Increasing energy productivity holds some of the greatest possibilities for enhancing the welfare countries get out of their energy systems. It also recasts energy efficiency in terms of boosting competitiveness and wealth, more powerfully conveying its profound benefits to society. KAPSARC and UNESCWA have initiated this project to explore the energy productivity potential of the Arab region, starting with the six GCC countries and later extending to other countries. Aimed at policymakers, this project highlights the social gains from energy productivity investments, where countries are currently at, and pathways to achieving improved performance in this area. Key Points This paper describes our analysis of the cost-effectiveness of designing and retrofitting residential buildings in Bahrain and outlines our analytical approach. The study focuses on residential buildings since households consume more than 48 percent of electricity used in the country. As expected, residential buildings constitute the vast majority of Bahrain’s building stock, with about 76 percent of the total and projected annual growth in energy consumption of around 3 percent in the next few years. The optimization analysis outlined in this paper assesses the potential benefits from retrofitting both individual buildings and the entire national building stock, as well as the benefits of applying proven measures and technologies to improve the energy efficiency of the building sector. Our conclusions are: The development and enforcement of a more stringent energy efficiency code can potentially improve the energy efficiency of the new building stock with a reduction of more than 320 GWh in annual electricity consumption and 87 MW in peak demand. Retrofitting the existing building stock in Bahrain has the potential to cost-effectively reduce energy consumption in the building sector by 62 percent, with a 55 percent reduction in peak electricity demand compared with the business as usual scenario. The avoided costs of building new power plants would be sufficient to offset the implementation costs for a basic level of energy retrofitting of existing residential buildings. We estimate that as much as 31,700 job-years of employment can be created when retrofitting the existing building stock. More than 3,000 jobs would be needed annually in order to retrofit existing buildings over a 10-year period.

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 ‘Power consumption in India(2019-2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/twinkle0705/state-wise-power-consumption-in-india on 28 January 2022.

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

Context

India is the world's third-largest producer and third-largest consumer of electricity. The national electric grid in India has an installed capacity of 370.106 GW as of 31 March 2020. Renewable power plants, which also include large hydroelectric plants, constitute 35.86% of India's total installed capacity. During the 2018-19 fiscal year, the gross electricity generated by utilities in India was 1,372 TWh and the total electricity generation (utilities and non-utilities) in the country was 1,547 TWh. The gross electricity consumption in 2018-19 was 1,181 kWh per capita. In 2015-16, electric energy consumption in agriculture was recorded as being the highest (17.89%) worldwide. The per capita electricity consumption is low compared to most other countries despite India having a low electricity tariff.

In light of the recent COVID-19 situation, when everyone has been under lockdown for the months of April & May the impacts of the lockdown on economic activities have been faced by every sector in a positive or a negative way. With the electricity consumption being so crucial to the country, we came up with a plan to study the impact on energy consumption state and region wise.

The dataset is exhaustive in its demonstration of energy consumption state wise.

Content

Data is in the form of a time series for a period of 17 months beginning from 2nd Jan 2019 till 23rd May 2020. Rows are indexed with dates and columns represent states. Rows and columns put together, each datapoint reflects the power consumed in Mega Units (MU) by the given state (column) at the given date (row).

Acknowledgements

Power System Operation Corporation Limited (POSOCO) is a wholly-owned Government of India enterprise under the Ministry of Power. It was earlier a wholly-owned subsidiary of Power Grid Corporation of India Limited. It was formed in March 2009 to handle the power management functions of PGCIL.

The dataset has been scraped from the weekly energy reports of POSOCO.

Inspiration

Extensive research on power usage in the country is what inspired us to compile the dataset. We are making it public along with our research of the same. This is our first step towards independent data-based research. We are open to suggestions, compliments and criticism alike.

Do leave an upvote if you found the dataset helpful. It keeps me motivated to keep working hard :)

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

The domain of interest is Energy; however, the focus is to observe the trends between the different sources used for electricity generation among Canada and its provinces from 2005 to 2016, and to compare the trends for electricity generation to electricity consumption in Canada from 2005 to 2015. The main problem that will be investigated is how much of a particular source is used for electricity generation in Canada over these eleven years and what is the least and most used source of electricity generation over Canada. It will also be observed whether the proportion of electricity generated by each source in Canada during 2016, is consistent with the proportion of electricity generated by each source in every province. Additionally electricity consumption for the provinces will be studied to determine which province consumes the most and least amounts of electricity in Canada. The significance of this problem is to understand which sources are highly used to generate electric power in the provinces and in Canada. If a source is being used the most in Canada and in the provinces, it will lead us to find possible ways to generate electricity from the least used sources, so the country and its provinces do not depend on one source for electric power. It will also be observed if the electricity generation by each province has increased, decreased or remain constant from 2005-2016. From this data we can also infer which province generates the most and least amount of electric power and determine which abundant resources are available to each province for its electricity generation. Moreover, by comparing the trends for electricity consumption and electricity generation it will be observed if any province consumes more electricity than it generates. If so we can find ways to provide that province with more electrcity by importing it from other provinces.

Economic growth requires energy. Energy fuels industry and manufacturing, improves livelihoods, and connects markets. Consuming more energy is part of transforming into a modern economy. The historical relationship is quite clear: over time, economies which grow continue to do so while using more energy. Comparing per capita income and energy consumption figures across countries today shows a non-linear relationship. Moving away from low, almost non-existent, levels of energy use, there is a sharp uptick in incomes. Once a country is wealthy, the positive relationship between energy use and income seems to level off. Energy, therefore, seems crucial at the early stages of development.

About the project Quantifying the determinants of a country’s energy productivity trends answers the fundamental question of whether its economy is becoming more energy productive because of technological and efficiency gains, or whether it is due to structural economic shifts. Using three types of analysis, this paper investigates the drivers of energy productivity changes occurring in 39 countries during 1995-2009. The comparison between countries allows for examining whether demographic and economic characteristics contribute to energy productivity performance and the rates of improvement. The findings of the analysis can help inform policy-making efforts focused on improving energy productivity.Summary Quantifying the determinants of a country’s energy productivity trends answers the fundamental question of whether its economy is becoming more energy productive because of technological and efficiency gains, or whether it is due to structural economic shifts. This paper uses three types of analysis to investigate the drivers of energy productivity changes occurring in 39 countries during the 1995-2009 period. Several key findings about global energy productivity trends emerged: - Sectoral energy productivity improvements from efficiency gains and changes in product mix were the primary drivers behind country level energy productivity improvements. - Structural economic shifts away from industry and towards more service-oriented sectors played a lesser role in aggregate energy productivity improvements. - Nations with similar demographic and economic characteristics showed similar levels of energy productivity and rates of improvement. - Former communist countries and nations undergoing economic liberalization exhibited the highest rates of improvement—although they remain less energy productive than developed nations. - Long-standing hypotheses that higher levels of income per capita and higher energy prices are associated to greater energy productivity are reinforced by the analysis. - Higher levels of investment are also associated with aggregate energy productivity improvements, although the response from the investments may take a few years to materialize.

About the Project Increasing energy productivity holds some of the greatest possibilities for enhancing the welfare countries get out of their energy systems. It also recasts energy efficiency in terms of boosting competitiveness and wealth, more powerfully conveying its profound benefits to society. KAPSARC and UNESCWA have initiated this project to explore the energy productivity potential of the Arab region, starting with the six GCC countries and later extending to other countries. Aimed at policymakers, this project highlights the social gains from energy productivity investments, where countries are currently at, and pathways to achieving improved performance in this area. Key Points More than 75 percent of the total electricity consumed in Oman is attributed to buildings, with almost 50 percent used by households. The absence of mandatory energy efficiency regulations for buildings, coupled with population growth, has led to a significant increase in annual energy consumption and peak power demand in the country – both averaging growth rates of 10 percent over the last five years. We used an energy productivity analysis approach to analyze the benefits of large-scale energy efficiency programs in new and existing buildings. Our study finds: Investment in energy efficiency measures to retrofit existing buildings could lead to significant economic and environmental benefits. The potential for energy savings will vary depending on implementation costs and scale of retrofits. The benefits that can be realized for residential buildings are significantly higher than those obtained for commercial or governmental buildings. If a minimal Level-1 energy retrofit program is applied to existing residential buildings, savings of 957 GWh/year in electricity consumption and 214 MW in peak power demand can be achieved. Moreover, if a Level-3 deep retrofit of energy efficiency measures is implemented for the residential sector, savings soar to 6,000 GWh/year in electricity consumption and 1,300 MW in peak power demand. Also, 4 million metric tonnes per year of carbon emissions will be eliminated. A Level-3 retrofit of the entire building stock in Oman can result in savings of 10,000 GWh/year in electricity consumption and 2,300 MW in peak power demand. Additionally, there would be a 7 million metric tonnes per year of reduction in carbon emissions The economic impact of the buildings' energy efficiency retrofit program is the potential to create new employment in Oman. The direct effects for retrofitting buildings include jobs needed to implement energy efficiency measures while the indirect effects are associated with work needed to produce and supply energy efficiency equipment and materials.

This table shows the supply, transformation and the consumption of energy in a balance sheet. Energy is released - among other things - during the combustion of for example natural gas, petroleum, hard coal and biofuels. Energy can also be obtained from electricity or heat, or extracted from natural resources, e.g. wind or solar energy. In energy statistics all these sources of energy are known as energy commodities.

The supply side of the balance sheet includes indigenous production of energy, net imports and exports and net stock changes. This is mentioned primary energy supply, because this is the amount of energy available for transformation or consumption in the country.

For energy transformation, the table gives figures on the transformation input (amount of energy used to make other energy commodities), the transformation output (amount of energy made from other energy commodities) and net energy transformation. The latter is the amount of energy lost during the transformation of energy commodities.

Then the energy balance sheet shows the final consumption of energy. First, it refers to the own use and distribution losses. After deduction of these amounts remains the final consumption of energy customers. This comprises the final energy consumption and non-energy use. The final energy consumption is the energy consumers utilize for energy purposes. It is specified for successively industry, transport and other customers, broken down into various sub-sectors. The last form of energy is the non-energy use. This is the use of an energy commodity for a product that is not energy.

Data available: From 1946.

Status of the figures: All figures up to and including 2022 are definite. Figures for 2023 and 2024 are revised provisional.

Changes as of June 2025: Figures for 2024 have been updated.

Changes as of March 19th 2025: For all reporting years the underlying code for 'Total crudes, fossil fraction' is adjusted. Figures have not been changed.

Changes as of March 17th 2025: Provisional figures of 2024 have been added.

Changes as of November 15th 2024: The structure of the table has been adjusted. This concerns the classification into energy commodities, section 'other energy commodities'. The new classification ensures that it is now exactly in line with the classification used by Eurostat when publishing the Energy Balance Sheet. This table has also been revised for 2015 to 2021 as a result of new methods that have also been applied for 2022 and 2023. This concerns the following components: final energy consumption of LPG, distribution of final energy consumption of motor gasoline and transfer of energy consumption of the nuclear industry from industry to the energy sector. The natural gas consumption of the wood and wood products industry has also been improved so that it is more comparable over time. This concerns changes of a maximum of a few PJ.

Changes as of June 7th 2024: Revised provisional figures of 2023 have been added.

Changes as of April 26th 2024:

• Provisional figures of 2023 have been added.

The energy balance has been revised for 2015 and later on a limited number of points. The most important is the following: 1. For solid biomass and municipal waste, the most recent data have been included. Furthermore data were affected by integration with figures for a new, yet to be published StatLine table on the supply of solid biomass. As a result, there are some changes in imports, exports and indigenous production of biomass of a maximum of a few PJ. 2. In the case of natural gas, an improvement has been made in the processing of data for stored LNG, which causes a shift between stock changes, imports and exports of a maximum of a few PJ. 3. Data for final energy consumption of blended biofuels per subsector in transport were incorrectly excluded. These have now been made visible.

Changes as of March 25th 2024: The energy balance has been revised and restructured. It concerns mainly a different way of dealing with biofuels that are mixed with fossil fuels.

Previously, biofuels mixed with fossil fuels were counted as petroleum crude and products. In the new energy balance, blended biofuels count for renewable energy and petroleum crude and products and the underlying products (such as gasoline, diesel and kerosene) only count the fossil part of mixtures of fossil and biogenic fuels. To make this clear, the names of the energy commodities have been adjusted. The consequence of this adjustment is that part of the energy has been moved from petroleum to renewable. The energy balance remains the same for total energy commodities. The aim of this adjustment is to make the increasing role of blended biofuels in the Energy Balance visible and to better align with the Energy Balances published by Eurostat and the International Energy Agency. Within renewable energy and biomass, pure and blended biofuels are now visible as separate energy commodities.

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.

This dataset provides values for ELECTRICITY PRICE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Using the new version of a database developed by the New Energy Foundation the country update report of direct uses of geothermal energy in Japan as of December 1999 is presented. Firstly, analyses are carried out for various aspects of direct uses of geothermal energy. Main results are as follows: 1) Installed thermal power is found to be 266.12 MWt, with the maximum in Hokkaido Pref. (91.71 MWt) for locality and in space heating including domestic hot water supply (136.70 MWt) for utilization type. 2) Thermal energy used is found to be 5399.59 TJ/yr, with the maximum in Hokkaido Pref. (1950.19 TJ/yr) for locality and in space heating including domestic hot water supply (2953.35 TJ/yr) for utilization type. Secondly, by comparing with other countries the economically severe condition for the use of geothermal energy as compared to fossil fuels in Japan is revealed statistically.

Recommended citation

Gütschow, J.; Busch, D.; Pflüger, M. (2024): The PRIMAP-hist national historical emissions time series v2.6.1 (1750-2023). zenodo. doi:10.5281/zenodo.15016289.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

Use of the dataset and full description

Abstract

Support

Sources

Files included in the dataset

Notes

Data format description (columns)

References

Changelog

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2023, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC.

For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "National Inventory Submissions". Until 2023 data was submitted in the "Common Reporting Format" (CRF). Since 2024 the new "Common Reporting Tables" (CRT) are used. For developing countries, non-AnnexI in terms of the UNFCCC, we use the "Biannial Transparency Reports" (BTR) which mostly come with data also using the "Common Reporting Tables". We also use older data available through the UNFCCC DI portal (di.unfccc.int) and additional country submissions from "Biannial Update Reports" (BUR), "National Communications" (NC), and "National Inventory Reports" (NIR) read from pdf and where available xls(x) or csv files. For a list of these submissions please see below. For South Korea the 2023 official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. We have mostly replaced the official data that has not been submitted to the UNFCCC used in v2.6 as countries have now submitted their data in CRT format, but had to make some exceptions as the CRT data was not usable for all countries.

Gaps in the country reported data are filled using third party data such as CDIAC, EI (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR 2024 (all sectors for CO2, CH4, N2O, HFCs, PFCs, SF6, NF3, except energy CO2). Lower priority data are harmonized to higher priority data in the gap-filling process.

For the third party priority time series gaps in the third party data are filled from country reported data sources.

Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions.

The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.6.1 includes data for 2023. For energy CO2 growth rates from the EI Statistical Review of World Energy are used to extend the country reported (CR) or CDIAC (TP) data to 2023. For CO2 from cement production Andrew cement data are used. For other gases and sectors we use EDGAR 2024 data. In a few cases we have to rely on numerical methods to estimate emissions for 2023.

Version 2.6.1 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.

The PRIMAP-hist v2.6.1 dataset is an updated version of

Gütschow, J.; Pflüger, M.; Busch, D. (2024): The PRIMAP-hist national historical emissions time series v2.6 (1750-2023). zenodo. doi:10.5281/zenodo.13752654.

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset. Detailed per country information is available from the detailed changelog which is available on the primap.org website and on zenodo.

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (johannes.guetschow@climate-resource.com) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation.

We have also included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag "v2.6.1" in any issue you create regarding this dataset.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@climate-resource.com.

Climate Resource makes this data available CC BY 4.0 licence. Free support is limited to simple questions and non-commercial users. We also provide additional data, and data support services to clients wanting more frequent updates, additional metadata or to integrate these datasets into their workflows. Get in touch at contact@climate-resource.com if you are interested.

Sources

Global CO2 emissions from cement production v250226 data, paper: Andrew(2025), Andrew (2019)

EI Statistical Review of World Energy website: Energy Institute (2024)

CDIAC data: Hefner and Marland (2023), data: Hefner (2024), paper: Gilfillan and Marland (2021)

CEDS: data: Hoesly et al. (2020), paper: Hoesly et al. (2018)

EDGAR 2024: data/website: European Commission, European Commision, JRC (2024), report: European Commission. Joint Research Centre & IEA. (2024)

EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)

FAOSTAT database data: Food and Agriculture Organization of the United Nations (2024)

RCP historical data data, paper: Meinshausen et al. (2011)

UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal website, data: UNFCCC (2024e), Pflüger and Gütschow (2024), github

UNFCCC Bnnial Update Reports, National Communications, and National Inventory Reports for developing countries website-BURs, website-NCs, data: UNFCCC (2024d), UNFCCC (2024b).

Notes:

Not all BUR and NC submissions are included as reading the data is time consuming and not all submission contain sufficient data to be used in PRIMAP-hist.

Not all submissions included in PRIMAP-hist are available in the github repository as we do not (yet) have code that we can publish for all submissions.

No submissions have been added for PRIMAP-hist v2.6.1

UNFCCC First Biannial Transparency Reports website, [data] UNFCCC (2025)

Notes:

For a list of added submissions see section "Data source updates (v2.6.1)" in the changelog in the pdf data description.

UNFCCC Common Reporting Format (CRF) website, paper, data (24-01-08): UNFCCC (2024c) (processed as described in Jeffery et al. (2018))

Official country repositories (non-UNFCCC)

Belarus: Greenhouse gas statistics (1990-2022) website: National Statistical Committee of theRepublic of Belarus (2024)

EU, Iceland, Norway, Switzerland: National emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanism, April 2024 website: European Environment Agency(2024)

South Korea: 2023 Inventory website, data: Republic of Korea (2023)

Taiwan / Republic of China: 2023 Inventory website, data: Republic of China - EnvironmentalProtection Administration (2023)

For the pre-1990 LULUCF time-series we use the following additional data sources:

Houghton land use CO2 website: Houghton (2008)

HYDE land cover data website: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)

SAGE Global Potential Vegetation Dataset website: Ramankutty and Foley (1999)

FAO Country Boundaries website: Food and Agriculture Organization of the United Nations(2015)

Files included in the dataset

For each dataset we have three files:

Most of the existing carbon emission studies based on the IPAT framework considered the size effect rather than structure effect of population. However, it is proved with the micro-data household evidence that the demographic structure explains the unexpected trends better. To complete the framework, this study integrated the structure effects with the STIRPAT model base on the household life-cycle consumption theory as different age groups differ in carbon consumption behaviors. For further analysis with the frequent extreme weather events caused by global warming and their catastrophic effect on human activities, this study also harmonized Köppen criteria with the theories model by Syukuro Manabe and Klaus Hasselmann and considers climate factors precipitation (PRE), annual degree-day (DD), and temperature anomaly (TA) with the extended model to investigate whether population aging trend provides room for or creates barriers to carbon reduction. NASA night-time light (NTL) data DMSP/OLS and VIIRS/DNB is adopted as the proxy for population density to weight the relevant climate data from over 30,000 weather stations worldwide. The combined dataset is from 150 countries, and the period is during 1970–2013. The Panel Seemingly Unrelated Regression (SUR) method is used to solve the problems of cross-sectional correlation, non-stationarity, and endogeneity since sample countries are closely linked in the global meteorological system which make each cross-sectional disturbance term likely to be contemporaneously correlated, and endogeneity of carbon emission under the same global agreement constraint. The empirical results show that the age structure had significant and different impacts on carbon emissions. The general influence of age growth is an inverted U shape as the younger group consumes less than the older group, and offspring leave the family when the householder turns 50. The EKC theory is also checked with the threshold model of per capita income on carbon emissions to determine how many countries reached carbon peak. This study proved that the aggregated carbon consumption pattern is aligned with the microlevel evidence on household energy consumption. Another distinguished finding is that population aging may generally lead to an increase in heat and electricity carbon emissions, contrary to what some household energy consumption models would predict. We explain the uplifted tail as the “effect caused by the narrowed adaptation temperature range” when people are getting older and vulnerable. It should be noted that as the aging trend becomes severe worldwide and extreme weather events happen with higher frequency, the potential energy spending and thus carbon emission on air conditioning will undoubtfully overgrow. One important method is to improve the building energy efficiency by retrofitting old buildings’ insulations. Implementing new green building standards in carbon reduction must not be ignored. Evidence shows that if the insulation of pre-1990s houses is reconstructed with modern materials, carbon emissions caused by residential cooling and heating can be reduced by about 20% every year. Overall, promoting an efficient building style provides reduction capacity for the industrial sector, and it is a way to achieve sustainable growth.

The Gross available energy is one of the most important aggregate of the energy balance. For the total of all energy products this is the total energy delivered/consumed in a country. However, for individual products its interpretation is different. For primary products (those directly harvested from nature) it shows the available supply. For derived products (manufactured products, secondary products) it covers only their international trade and stock changes. Production of derived products is recorded in the transformation output. Consequently, Gross available energy for derived products can be negative - which means its original primary form of supply was accounted for in the form of the respective primary product.