This statistic shows the gross domestic product (GDP) per capita in Italy from 1987 to 2024, with projections up until 2030. GDP refers to the total market value of all goods and services that are produced within a country per year. It is an important indicator of the economic strength of a country. In 2024, the GDP per capita in Italy was around 40,224.01 U.S. dollars. Italy's struggling economy Italy’s GDP per capita has been unstable since 2008, often experiencing slight increases and decreases annually. The third largest economy of the euro area not only suffered from the global financial crisis, they were also one of the primary victims of the euro area crisis. One of the outcomes is the significant growth of Italy’s national debt, which saw continued upsurges every year over the past decade. With the collapse of investments and loss of industrial production, the Italian state was forced to resort to increase taxation and decrease spending. Additionally, Italy was forced to borrow more, which in turn increased national debt and furthermore their debt-to-GDP ratio. A debt-to-GDP ratio is significant to help determine if a country can pay off its debts without incurring more. Increased taxation and decrease spending helped with reducing expenditures as well as raising revenues, however Italy still maintained a trade balance deficit, which has only recently< started to recover. Several reasons for Italy’s downturn as a country are unnecessary spending and incompetent leadership.

The map identifies those countries that are most vulnerable to food insecurity. A country’s vulnerability is estimated according to: (1) population growth in 2000 to 2050 projected by the United Nations; (2) wealth expressed in GDP per capita in 2005; (3) land potential for rain-fed cereal production per capita of 2050 population; (4) total renewable water resources per capita of 2050 population; and (5) impact of climate change projected in 2050 on crop production potential. High income countries with 2005 GDP per capita exceeding US$ 7500 (in 1990 US$) are assumed not to be vulnerable to food insecurity. Source: Data compilation by authors from various sources (United Nations, World Bank, FAO, GAEZ 2009).

IntroductionThe combined populations of China and India were 2.78 billion in 2020, representing 36% of the world population (7.75 billion). Wheat is the second most important staple grain in both China and India. In 2019, the aggregate wheat consumption in China was 96.4 million ton and in India it was 82.5 million ton, together it was more than 35% of the world's wheat that year. In China, in 2050, the projected population will be 1294–1515 million, and in India, it is projected to be 14.89–1793 million, under the low and high-fertility rate assumptions. A question arises as to, what will be aggregate demand for wheat in China and India in 2030 and 2050?MethodsApplying the Vector Error Correction model estimation process in the time series econometric estimation setting, this study projected the per capita and annual aggregate wheat consumptions of China and India during 2019-2050. In the process, this study relies on agricultural data sourced from the Food and Agriculture Organization of the United States (FAO) database (FAOSTAT), as well as the World Bank's World Development Indicators (WDI) data catalog. The presence of unit root in the data series are tested by applying the augmented Dickey-Fuller test; Philips-Perron unit root test; Kwiatkowski-Phillips-Schmidt-Shin test, and Zivot-Andrews Unit Root test allowing for a single break in intercept and/or trend. The test statistics suggest that a natural log transformation and with the first difference of the variables provides stationarity of the data series for both China and India. The Zivot-Andrews Unit Root test, however, suggested that there is a structural break in urban population share and GDP per capita. To tackle the issue, we have included a year dummy and two multiplicative dummies in our model. Furthermore, the Johansen cointegration test suggests that at least one variable in both data series were cointegrated. These tests enable us to apply Vector Error Correction (VEC) model estimation procedure. In estimation the model, the appropriate number of lags of the variables is confirmed by applying the “varsoc” command in Stata 17 software interface. The estimated yearly per capita wheat consumption in 2030 and 2050 from the VEC model, are multiplied by the projected population in 2030 and 2050 to calculate the projected aggregate wheat demand in China and India in 2030 and 2050. After projecting the yearly per capita wheat consumption (KG), we multiply with the projected population to get the expected consumption demand.ResultsThis study found that the yearly per capita wheat consumption of China will increase from 65.8 kg in 2019 to 76 kg in 2030, and 95 kg in 2050. In India, the yearly per capita wheat consumption will increase to 74 kg in 2030 and 94 kg in 2050 from 60.4 kg in 2019. Considering the projected population growth rates under low-fertility assumptions, aggregate wheat consumption of China will increase by more than 13% in 2030 and by 28% in 2050. Under the high-fertility rate assumption, however the aggregate wheat consumption of China will increase by 18% in 2030 and nearly 50% in 2050. In the case of India, under both low and high-fertility rate assumptions, aggregate wheat demand in India will increase by 32-38% in 2030 and by 70-104% in 2050 compared to 2019 level of consumption.DiscussionsOur results underline the importance of wheat in both countries, which are the world's top wheat producers and consumers, and suggest the importance of research and development investments to maintain sufficient national wheat grain production levels to meet China and India's domestic demand. This is critical both to ensure the food security of this large segment of the world populace, which also includes 23% of the total population of the world who live on less than US $1.90/day, as well as to avoid potential grain market destabilization and price hikes that arise in the event of large import demands.

The gross domestic product (GDP) per capita in Serbia amounted to 13,540 U.S. dollars in 2024. Between 1999 and 2024, the GDP per capita rose by 11,830 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP per capita will steadily rise by 10,090 U.S. dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product per capita at current prices. Thereby, the gross domestic product was first converted from national currency to U.S. dollars at current exchange rates and then divided by the total population. The gross domestic product is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

From the IPCC website: The B2 world is one of increased concern for environmental and social sustainability. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R&D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity. Data are available for the following periods: 1961-1990, 2010-2039; 2040-2069; and 2090-2099 Mean monthly and change fields.

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). Compared to the first version of the dataset (https://zenodo.org/record/79880387), this updated dataset is based on model runs where the Inflation Reduction Act (IRA) are implemented in the model scenarios. In addition to the queried and post-processed key output variables related to residential energy sector in .csv tables, we also upload the full model output databases in this repository, so that users can query their desired model outputs.

GCAM-USA operates within the Global Change Analysis Model (GCAM), 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 September 24, 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 2015-2050 (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 full model output databases and key output variables related to the residential energy sector under the four scenarios, including:

• income shares by consumer groups at each state over 2015-2050 (Casper et al., 2023)
• residential energy consumption per capita by service, fuel, state, and income group, 2015-2050
• residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, state, and income group, 2015-2050
• estimated energy burden (Eq.1), by state and income group, 2015-2050
• estimated satiation gap (Eq.2), by service, state, and income group, 2015-2050
• residential heating service inequality (Eq.3), by state, 2015-2050

Table 1

Eq. 1

\(Energy\ burden_{i,k} = \dfrac{\sum_j (service\ output_{i,j,k} * service\ cost_{j,k})}{GDP_{i,k}}\)

for income group i and state k, that sums over all residential energy services j.

Eq. 2

\(Satiation\ Gap_{i,j,k} = \dfrac{satiation\ level_{j,k} - service\ output_{i,j,k}} {satiation\ level_{j,k}}\)

for service j, income group i, and state k. Note that the satiation level and service output are per unit of floorspace.

Eq. 3

\(Residential\ heating\ service\ inequality_j = \dfrac{S_j^{d10}}{(S_j^{d1} +S_j^{d2} + S_j^{d3} + S_j^{d4})}\)

for service j 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. Among the key output variables in this repository, we provide the residential heating service inequality output table as an example.

Reference

Casper, K. C., Narayan, K. B., O'Neill, B. C., Waldhoff, S. T., Zhang, Y., & Wejnert-Depue, C. (2023). Non-parametric projections of the net-income distribution for all U.S. states for the shared socioeconomic pathways. Environmental Research Letters. http://iopscience.iop.org/article/10.1088/1748-9326/acf9b8.

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].

Acknowledgement

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.

Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population; not only is it better able to describe the change pattern of population and biomass, but it is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of changing industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.

Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita),the corresponding industrial structure scenarios in each period were set, and each industry’s output value was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP, and, therefore, it was adjusted according to the need of the future industrial structure scenarios of the research area.

The gross domestic product (GDP) per capita in Bulgaria stood at 17,434.51 U.S. dollars in 2024. Between 1980 and 2024, the GDP per capita rose by 13,148.3 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP per capita will steadily rise by 12,073.02 U.S. dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product per capita at current prices. Thereby, the gross domestic product was first converted from national currency to U.S. dollars at current exchange rates and then divided by the total population. The gross domestic product is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

The waste sector is the third-largest source of anthropogenic methane emissions in China, accounting for approximately 12% of total emissions. Within this sector, MSW treatment contributes nearly 58%.Since 1978 (China’s reform and opening up), the rapid economic growth and urbanization have led to a substantial increase in the generation of municipal solid waste (MSW).Waste sector represents a significant source of anthropogenic methane emissions in China, however, its spatial distribution, future trajectories, and mitigation potential remain insufficiently characterized. This study presented a provincial-level methane emission inventory for landfills in China from 2003 to 2021.The data utilized in this study were obtained from multiple authoritative sources to ensure reliability and scientific rigor. The volume of MSW landfilled at the provincial level was primarily collected from official statistical publications, including the China Statistical Yearbook, China Environmental Statistical Annual Report and China Environmental Yearbook. Forecast data for population and GDP per capita in 34 provinces up to 2050 were derived from the SSPs database.We have uploaded the complete dataset to Figshare, which can be accessed via the DOI link. This ensures that all interested researchers can access the full data used in our study for replication and further analysis. We believe that these measures have improved the transparency and repeatability of our work.

APAC Bath Fitting Market size was valued at USD 12.8 Billion in 2023 and is projected to reach USD 21.3 Billion by 2031 growing at a CAGR of 6.8% from 2024 to 2031.

Key Market Drivers:

Rapid Urbanization and Housing Development: According to the Asian Development Bank (ADB), Asia's urban population is projected to increase from 1.84 billion in 2017 to 3.3 billion by 2050. This tremendous urbanization is driving unprecedented demand for housing infrastructure, especially in rising nations such as India, Indonesia and Vietnam. The urbanization trend has resulted in the construction of over 200 smart city projects around APAC, each requiring contemporary bathroom infrastructure.

Rising Disposable Income and Living Standards: According to the World Bank, the average GDP per capita in East Asia and the Pacific region increasing from $10,091 in 2010 to $14,280 by 2019.

The statistic shows the gross domestic product (GDP) per capita in Canada from 1987 to 2024, with projections up until 2030. In 2024, the gross domestic product per capita in Canada was around 54,473.19 U.S. dollars. Canada's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country. In 2014, Canada had one of the largest GDP per capita values in the world, a value that has grown continuously since 2010 after experiencing a slight downturn due to the financial crisis of 2008. Canada is seen as one of the premier countries in the world, particularly due to its strong economy and healthy international relations, most notably with the United States. Canada and the United States have political, social and economical similarities that further strengthen their relationship. The United States was and continues to be Canada’s primary and most important trade partner and vice versa. Canada’s economy is partly supported by its exports, most notably crude oil, which was the country’s largest export category. Canada was also one of the world’s leading oil exporters in 2013, exporting more than the United States. Additionally, Canada was also a major exporter of goods such as motor vehicles and mechanical appliances, which subsequently ranked the country as one of the world’s top export countries in 2013.

The gross domestic product (GDP) per capita in Kenya was estimated at 2,305.31 U.S. dollars in 2024. From 1980 to 2024, the GDP per capita rose by 1,449.85 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. Between 2024 and 2030, the GDP per capita will rise by 484.8 U.S. dollars, showing an overall upward trend with periodic ups and downs.This indicator describes the gross domestic product per capita at current prices. Thereby, the gross domestic product was first converted from national currency to U.S. dollars at current exchange rates and then divided by the total population. The gross domestic product is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

The gross domestic product (GDP) per capita in Croatia was estimated at 23,989.15 U.S. dollars in 2024. Between 1992 and 2024, the GDP per capita rose by 23,869.11 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP per capita will steadily rise by 8,905.4 U.S. dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product per capita at current prices. Thereby, the gross domestic product was first converted from national currency to U.S. dollars at current exchange rates and then divided by the total population. The gross domestic product is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

The United States gross domestic product (GDP) was forecast to reach over 30.1 trillion U.S. dollars in 2025. Furthermore, by 2035, it is expected to surpass 43.9 trillion U.S. dollars. GDP refers to the market value of all final goods and services produced within a country in a given period.

The gross domestic product (GDP) per capita in Thailand stood at 7,493.2 U.S. dollars in 2024. Between 1980 and 2024, the GDP per capita rose by 6,787.72 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP per capita will steadily rise by 1,797.77 U.S. dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product per capita at current prices. Thereby, the gross domestic product was first converted from national currency to U.S. dollars at current exchange rates and then divided by the total population. The gross domestic product is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

Germany’s GDP per capita stood at almost 54,989.76 U.S. dollars in 2024. Germany ranked among the top 20 countries worldwide with the highest GDP per capita in 2021 – Luxembourg, Ireland and Switzerland were ranked the top three nations. Rising annual income in Germany The average annual wage in Germany has increased by around 5,000 euros since 2000, reaching in excess of 39,000 euros in 2016. Germany had the tenth-highest average annual wage among selected European Union countries in 2017, ranking between France and the United Kingdom. Growing employment More than two thirds of the working population in Germany are employed in the service sector, which generated the greatest share of the country’s GDP in 2018. Unemployment in Germany soared to its highest level in decades in 2005, but the rate has since dropped to below 3.5 percent. The youth unemployment rate in Germany has more than halved since 2005 and currently stands around 6.5 percent.

This statistic shows the gross domestic product (GDP) per capita in Spain from 1987 to 2024, with projections up until 2030. GDP refers to the total market value of all goods and services that are produced within a country per year. It is an important indicator of the economic strength of a country. In 2024, the GDP per capita in Spain was around 35,091.65 U.S. dollars. Spain's struggling economy The Spanish economy is essential for the global market, as it remains one of the largest economies in the world as well as within Europe. The aftermath of the global financial crisis and the Eurozone crisis resulted in an economic collapse, which has yet to be completely resolved by the Spanish government. While unemployment has always been a general weakness for Spain, the occurrence of recent economic disasters has fueled the struggles in the country’s job market, resulting in a decade high unemployment rate. During the prime of both crises, not only millions of workers were laid off, but government spending also reached a new high, considerably exceeding national revenues earned. This not only resulted in further layoffs in the following years, but also burdened the country with almost double the amount of debt. Prior to the crisis, the public already assumed that the Spanish economy would decline, however the public opinion of the situation became conclusive post 2009. The lack of consumer confidence is only further damaging the Spanish economy, as investors have already pulled much capital from the troubled nation and are hesitant to reinvest their money.

The gross domestic product (GDP) per capita in Cambodia amounted to 2,682.95 U.S. dollars in 2024. Between 1986 and 2024, the GDP per capita rose by 2,656.45 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The GDP per capita will steadily rise by 1,131.15 U.S. dollars over the period from 2024 to 2030, reflecting a clear upward trend.This indicator describes the gross domestic product per capita at current prices. Thereby, the gross domestic product was first converted from national currency to U.S. dollars at current exchange rates and then divided by the total population. The gross domestic product is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

From the IPCC website: The B2 world is one of increased concern for environmental and social sustainability. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R&D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity. Data are available for the following periods: 1961-1990, 2010-2039; 2040-2069; and 2090-2099 Mean monthly and change fields.