Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q1 2025 about per capita, real, GDP, and USA.

Analysis of ‘Gross domestic product (GDP) per inhabitant by region’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/846d9f94-854e-5016-a0f1-f7d0d33cfa2f on 18 January 2022.

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

Definition: Economic performance varies widely within North Rhine-Westphalia. Gross domestic product (GDP) per inhabitant is an indicator of regional economic power, which makes it possible to illustrate regional differences. GDP per inhabitant is shown in current prices, i.e. in the current year’s prices.

Note: A general audit was carried out in 2014. This mainly served to introduce the new European System of Accounts (ESA 2010) across Europe. ESA 2010 is based on the global new System of National Accounts (SNA 2008) and replaced the previous ESA 1995.

Data source:
Working Group on National Accounts of the Länder

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

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

This dataset provides statistics on real gross domestic product (GDP) and real GDP per capita for subnational regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

To allow comparability over time and between countries, data at current prices are transformed into constant prices and purchasing power parity measures. Regional GDP per capita is calculated by dividing regional GDP by the average annual population of the region.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

This dataset provides economic indicators for FUAs of more than 250 000 inhabitants, including GDP, GDP per capita, jobs and labour productivity.

   <h3>Data sources and methodology</h3>
   <p align="justify">
   When economic statistics are unavailable at a more granular level than the FUA (e.g. municipal level), indicators are estimated by adjusting regional (OECD TL2 and TL3 regions) values to FUA boundaries, based on the population distribution in each region. Regional values (GDP and jobs) in TL3 regions are used as data inputs and combined with gridded population data <a href=https://doi.org/10.2760/098587>(European Commission, GHSL Data Package 2023)</a>. FUA boundaries are intersected with TL3 borders to compute the share of the regional population that lives within FUAs in each region. This share is then applied to the variable of interest (e.g. GDP) and allocated to the FUA. In case several regions intersect the FUA, the adjusted values of intersecting regions are summed. For countries where TL3-level data is not available, data for TL2 regions is used. This approach assumes that the variable of interest has the same spatial distribution as population. Therefore, the modelled indicators should be interpreted with caution.<br /><br />
   When a more granular level is available, data is aggregated for each FUA. For example in the United States, GDP estimates are available at the county-level (<a href=https://www.bea.gov/data/employment/employment-county-metro-and-other-areas>US Bureau of Economic Analysis</a>), and then aggregated by FUA.
   </p>

   <h3>Defining FUAs and cities</h3>
   <p align="justify">The OECD, in cooperation with the EU, has developed a harmonised <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">definition of functional urban areas</a> (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns <a href=https://doi.org/10.1787/9789264174108-en>(OECD, 2012)</a>. FUAs consist of:
   <ol>
   <li><b>A city</b> – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city.</li>
   <li><b>A commuting zone</b> – including all local areas where at least 15% of employed residents work in the city.</li>
   </ol>
   The delineation process includes:
   <ul>
   <li>Assigning municipalities surrounded by a single FUA to that FUA.</li>
   <li>Excluding non-contiguous municipalities.</li>
   </ul>
   The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries.</p>
   <h3>Cite this dataset</h3>
   <p>OECD Regions, cities and local areas database (<a href="http://data-explorer.oecd.org/s/1e5">Economy - FUAs</a>), <a href=http://oe.cd/geostats>http://oe.cd/geostats</a></p>

   <h3>Further information</h3>
   <ul> 
   <li> <a href=https://localdataportal.oecd.org/>OECD Local Data Portal </a> </li>
   <li> <a href=https://www.oecd.org/en/publications/oecd-regions-and-cities-at-a-glance-2024_f42db3bf-en.html/>OECD Regions and Cities at a Glance </a> </li>
   </ul>
   <p align="justify">For questions and/or comments, please email <a href="mailto:CitiesStat@oecd.org">CitiesStat@oecd.org</a>

Greenland GDP per Capita: PPP data was reported at 68,086.460 Intl $ in 2021. This records an increase from the previous number of 64,857.629 Intl $ for 2020. Greenland GDP per Capita: PPP data is updated yearly, averaging 40,283.810 Intl $ from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 68,086.460 Intl $ in 2021 and a record low of 20,092.096 Intl $ in 1993. Greenland GDP per Capita: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greenland – Table GL.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides per capita values for gross domestic product (GDP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries. Total population is a mid-year population based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Weighted average;

Faroe Islands GDP per Capita: PPP data was reported at 78,103.483 Intl $ in 2023. This records an increase from the previous number of 74,282.307 Intl $ for 2022. Faroe Islands GDP per Capita: PPP data is updated yearly, averaging 54,000.238 Intl $ from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 78,103.483 Intl $ in 2023 and a record low of 39,330.596 Intl $ in 2009. Faroe Islands GDP per Capita: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Faroe Islands – Table FO.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides per capita values for gross domestic product (GDP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries. Total population is a mid-year population based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Weighted average;

Andorra GDP per Capita: PPP data was reported at 71,730.669 Intl $ in 2023. This records an increase from the previous number of 68,470.076 Intl $ for 2022. Andorra GDP per Capita: PPP data is updated yearly, averaging 44,215.495 Intl $ from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 71,730.669 Intl $ in 2023 and a record low of 22,958.326 Intl $ in 1993. Andorra GDP per Capita: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Andorra – Table AD.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides per capita values for gross domestic product (GDP) expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. GDP is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. conversion factor is a spatial price deflator and currency converter that controls for price level differences between countries. Total population is a mid-year population based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Weighted average;

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

This dataset provides statistics on gross domestic product and gross domestic product per capita, for large regions (TL2) and small regions (TL3).

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, data in current prices are transformed into constant prices and PPP measures (link). Regional GDP per capita is calculated by dividing the regional GDP by the average annual population of the region.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting, see the list of OECD metropolitan regions (xlsx) and the EU methodology (link).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

This Alberta Official Statistic presents annual per cent change for Alberta and Canada real Gross Domestic Product (GDP) for Primary Agriculture Industries, 2008-2014. Gross Domestic Product (GDP) is a measure of the economic production which takes place within a geographical area. The term "gross" in GDP means that capital consumption costs, that is the costs associated with the depreciation of capital assets (buildings, machinery and equipment), are included. The production estimates are prepared for 215 separate industries using the North American Industrial Classification System (NAICS). Real GDP is gross domestic product adjusted for price changes. By taking out the impact of fluctuation in prices, real GDP allows people to more accurately measure the changes in total output and service for a jurisdiction. GDP measures are part of the Canadian System of National Accounts (SNA). The SNA provides a conceptually integrated framework of statistics for studying the state and behavior of the Canadian economy. The accounts are centered on the measurement of activities associated with the production of goods and services, the sales of goods and services in final markets, the supporting financial transactions, and the resulting wealth positions.

This dataset provides economic indicators aggregated at national level and broken down by territorial typology according to the population's access to cities.

Data source and definition

The indicators include GDP, GDP per capita, gross value added, employment at place of work and labour productivity by type of territory. Data is collected from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

The indicators are aggregated data at the national level, using the typology of small (TL3) regions to calculate totals or averages for all metropolitan large regions, metropolitan midsize regions, near a midsize/large FUA regions, near a small FUA regions and remote regions.

Territorial typology on the population's access to cities

Territorial typologies helps to assess differences in socio-economic trends in regions, both within and across countries and to highlight the specific issues faced by each type of region.

The OECD territorial typology on access to cities uses the concept of functional urban areas (FUA) – composed of urban centres and their commuting areas – and classifies small (TL3) regions (Fadic et al., 2019) according to the following criteria:

• Metropolitan regions, if more than half of the population live in a FUA. Metropolitan regions are further classified into: metropolitan large, if more than half of the population live in a (large) FUA of at least 1.5 million inhabitants; and metropolitan midsize, if more than half of the population live in a (midsize) FUA of at 250 000 to 1.5 million inhabitants.
• Non-metropolitan regions, if less than half of the population live in a midsize/large FUA. These regions are further classified according to their level of access to FUAs of different sizes: near a midsize/large FUA if more than half of the population live within a 60-minute drive from a midsize/large FUA (of more than 250 000 inhabitants) or if the TL3 region contains more than 80% of the area of a midsize/large FUA; near a small FUA if the region does not have access to a midsize/large FUA and at least half of its population have access to a small FUA (i.e. between 50 000 and 250 000 inhabitants) within a 60-minute drive, or contains 80% of the area of a small FUA; and remote, otherwise.

List of OECD regions and typologies are presented in the OECD Territorial correspondence table (xlsx). Maps of OECD regions are presented in the OECD Territorial grid (pdf).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers:

1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332

2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344

3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567

The file with the database is available in excel.

DATA SOURCES

The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas.

With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index.

To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted:

• Eurostat [3]
• Directorate-General for Mobility and Transport (DG MOVE). European Union [4]
• The World Bank [5]
• World Health Organization (WHO) [6]
• European Transport Safety Council (ETSC) [7]
• European Road Safety Observatory (ERSO) [8]
• European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]
• EU BestPoint-Project [10]
• Ministerstvo dopravy, República Checa [11]
• Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]
• Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]
• National Statistics Office, Malta [14]
• Ministério da Economia e Transição Digital, Portugal [15]
• Ministerio de Fomento, España [16]
• Trafikverket, Suecia [17]
• Ministère de l’environnement de l’énergie et de la mer, Francia [18]
• Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]
• Statistisk sentralbyrå, Noruega [26-29]
• Instituto Nacional de Estatística, Portugal [30]
• Infraestruturas de Portugal S.A., Portugal [31–35]
• Road Safety Authority (RSA), Ireland [36]

DATA BASE DESCRIPTION

The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.

Table. Database metadata

Code	Variable and unit
fatal_pc_km	Fatalities per billion passenger-km
fatal_mIn	Fatalities per million inhabitants
accid_adj_pc_km	Accidents per billion passenger-km
p_km	Billions of passenger-km
croad_inv_km	Investment in roads construction per kilometer, €/km (2015 constant prices)
croad_maint_km	Expenditure on roads maintenance per kilometer €/km (2015 constant prices)
prop_motorwa	Proportion of motorways over the total road network (%)
populat	Population, in millions of inhabitants
unemploy	Unemployment rate (%)
petro_car	Consumption of gasolina and petrol derivatives (tons), per tourism
alcohol	Alcohol consumption, in liters per capita (age > 15)
mot_index	Motorization index, in cars per 1,000 inhabitants
den_populat	Population density, inhabitants/km2
cgdp	Gross Domestic Product (GDP), in € (2015 constant prices)
cgdp_cap	GDP per capita, in € (2015 constant prices)
precipit	Average depth of rain water during a year (mm)
prop_elder	Proportion of people over 65 years (%)
dps	Demerit Point System, dummy variable (0: no; 1: yes)
freight	Freight transport, in billions of ton-km

ACKNOWLEDGEMENTS

This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges.

Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.

REFERENCES

1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance.

2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020).

3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021).

4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021).

5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021).

6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021).

7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011;

8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021).

9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237.

10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic;

11. Bundesministerium

United States US: Military Expenditure: % of GDP data was reported at 3.149 % in 2017. This records a decrease from the previous number of 3.222 % for 2016. United States US: Military Expenditure: % of GDP data is updated yearly, averaging 4.864 % from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.063 % in 1967 and a record low of 2.908 % in 1999. United States US: Military Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Defense and Official Development Assistance. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; Weighted average; Data for some countries are based on partial or uncertain data or rough estimates.

Thematic area - Climate change Name of Indicator - Greenhouse gas emission DPSIR - Pressure Indicator type - B – performance indicator Definition of the indicator The indicator shows the quantities of greenhouse gas emissions into atmosphere on national level. The emissions are presented by greenhouse gas type. The indicator provides information on emissions in the following sectors: energy, industrial processes and solvents, agriculture, waste and net removals from land use, land use change and forestry (LULUCF). Annual aggregated GHG per capita, per km2 and per unit of GDP. Units - Mt/year CO2 equivalent Policy relevance of the indicator: The Republic of Moldova is a non-Annex I Party to the United Nations Framework Convention on Climate Change (ratified in 1995). In 2003 Moldova ratified the Kyoto Protocol. Government of the of the Republic of Moldova adopted Environment Strategy for the period 2014-2023 (Government Decision #301 from 24.04.2014) and Strategy on adaptation to climate change till 2020 and it’s Action Plan (Government Decision #1009 from 10.12.2014). Targets: According to Copenhagen Agreement, Republic of Moldova aims to reduce, to not less than 25% compared to the base year (1990), the total national level of greenhouse gas emissions by 2020, by implementing economic mechanisms focused on global climate change mitigation, in accordance with the principles and provisions of the United Nations Framework Convention on Climate Change. The Environmental Protection Strategy for the years 2014-2023 and the Action Plan for its implementation states that a 20 % GHG emissions reduction compared to the base line scenario has to be reached in the Republic of Moldova by 2020. Republic of Moldova’s iNDC states to reduce unconditional, by 2030, total emissions of national greenhouse gas emissions net, with no less than 67% compared to 1990, in support of the global effort on the trend of increasing global average temperature by 2100 in limit of up to 2 ° C. The objective of reducing emissions could increase up to 78% conditionally - according to an overall agreement that would address important issues such as financial resources with low costs, technology transfer and technical cooperation. Key question - What is the average trend of GHG emissions for the whole period? Specific question - What are the emission changes by sectors, by GHG, per capita, per km2, per unit of GDP? Assessment The base year for Republic of Moldova is 1990. The inventory data presents that for base year the total emissions of GHG in CO2 equivalent are 43,42 without net removals from LULUCF sector and 37,53 aggregated emissions including emissions/removals from LULUCF. For 1991-2013 (the last Inventory data) the net GHG emissions without/with removals decrease respectively from 43,42/37,53 Mt/year CO2 equivalent to 12,84/12,74 Mt/year CO2 equivalent compared with base year. This constitutes a reducing of GHG emissions with 30% and respectively 33% comparing with base year. Figure 1 presents the trend of the aggregated emissions (without and with LULUCF sector). Table 1 presents the aggregated emissions (without and with LULUCF sector), the main GHG emissions and the share of the total emissions compare with the base year. The analysis of the inventory presents that for the base year the big share of GHG type has CO2 emission (81%), followed by CH4 emissions (11%) and N2O emissions (7%). The trend is the same for the next years. So, in 2013 the share of CO2 emissions continue to be the highest (65%), CH4 emissions are the second with 21% and the third one are N2O emissions with 13% share from total emissions. The difference between 1990 and 2013 is the share from total emissions between these GHG. During 1990-2010 the share of CO2 emissions decreases, while the share of CH4 and NO2 increase. Nevertheless, during 1990-2013 the emissions of GHG decrease: CO2 emissions with 23,6%, CH4 with 55,3% and N2O with 52,1% (see Figure 2). Halocarbons emissions (HFCs, PFCs) and sulphur hexafluoride (SF6) emissions have been registered in the Republic of Moldova starting with 1995. This year is considered as a reference year for F-gases (HFCs, PFCs and SF6). Evolution of these emissions denotes a steady trend towards increase in the last years, though their share in the total national emissions structure is insignificant. The observed sectors in inventory are energy sector, industrial process, solvent and other product use, agriculture, land use, land use change, forestry and waste. The total GHG emissions by sectors are presented in Table 2 and the trend is presented in Figure 3. In general, Energy Sector has the greatest contribution to national GHG emissions, with an average share of 70% in 1990 and 65% in 2013 (see Figure 4 and Figure 5). Agriculture Sector was the second sector contributor with an average share of 10%, followed by Industrial Processes with average share of 4% for 1990. The trend of the share of different sectors for 2013 has changed and Industrial Processes has been replaced by Waste Sector with a share of 12% from the total emissions. Figure 6 shows that starting with 1992 till 2004 there was a reduction of total GHG emissions from the Waste Sector. This trend is explained by the economic decline that occurred in the Republic of Moldova during the period under review, by a significant drop in the wellbeing of population, and respectively, capacity to generate solid and other types of wastes. At the same time, starting with 2005, there has been a clear growing trend of direct GHG emissions from the Waste Sector. The main indicator for the assessment of the GHG emissions in the international aspects are GHG per capita. The emission of GHG per capita decrease from 9,95 tons CO2 equivalent in 1990 to 3,16 tons CO2 equivalent in 2013. The lower level was during 2007 – 2.18 tons CO2 equivalent per capita (see Figure 7). For comparison the average European level of this indicator is 9.4 tons CO2 equivalent per capita in 2013. The emission of the GHG are directly linked with economic growth of the country, because with increasing of economic activity the consumption of energy and resources increase to. For the period 1990 to 2013 aggregated GHG emissions per unit of GDP decrease from 4.39 tons CO2 equivalent to 1.91 tons CO2 equivalent. Between 1990 to 2007 emissions of GDP in the most European countries decrease for more than 30%. The trend in the aggregated GHG emissions per km2 is the same as the trends of GHG emission per capita and per GDP (see Figure 7). Key messages: For the period 1990 to 2013: • the total emission throughout the inventory have decrease with 30%. • the emissions of the GHG per capita decrease with 32%. • the energy sector has the greatest contribution to national GHG emissions. Trend - positive. Data coverage - 1990-2013 Data source - Republic of Moldova’s Third National Communication to United Nation Framework Convention on Climate Change (UNFCCC), Ministry of Environment. Methodology To calculate GHG emissions as well as GHG inventories, the methodology provided by UNFCCC/IPCC is used. Methodology is based on the calculation of GHGs as a product from the rate of activity for individual sectors and emission factors. The national inventory is structured to match the reporting requirement of the UNFCCC and is divided into six main sectors: (1) Energy, (2) Industrial Processes, (3) Solvents and Other Products Use, (4) Agriculture, (5) Land Use, Land-Use Change and Forestry and (6) Waste. Emissions of direct (CO2, CH4, N2O, HFCs, PFCs and SF6) and indirect (NOx, CO, NMVOC, SO2) greenhouse gases were estimated based on methodologies contained in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Reporting obligations - UNFCCC

Data published by Our World in Data based on EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir)

Variable time span 1900 – 2010

This dataset has been calculated and compiled by Our World in Data based on raw disaster data published by EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir). EM-DAT publishes comprehensive, global data on each individual disaster event – estimating the number of deaths; people affected; and economic damages, from UN reports; government records; expert opinion; and additional sources. Our World in Data has calculated annual aggregates, and decadal averages, for each country based on this raw event-by-event dataset. Decadal figures are measured as the annual average over the subsequent ten-year period. This means figures for ‘1900’ represent the average from 1900 to 1909; ‘1910’ is the average from 1910 to 1919 etc. We have calculated per capita rates using population figures from Gapminder (gapminder.org) and the UN World Population Prospects (https://population.un.org/wpp/). Economic damages data is provided by EM-DAT in concurrent US$. We have calculated this as a share of gross domestic product (GDP) using the World Bank’s GDP figures (also in current US$) (https://data.worldbank.org/indicator). Definitions of specific metrics are as follows: – ‘All disasters’ includes all geophysical, meteorological, and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. – People affected are those requiring immediate assistance during an emergency situation. – The total number of people affected is the sum of injured, affected, and homeless.Link www.emdat.be

Private Debt to GDP in the United States decreased to 142 percent in 2024 from 147.50 percent in 2023. United States Private Debt to GDP - values, historical data, forecasts and news - updated on July of 2025.