Denmark

This dataset describes the emissions to the atmosphere released as a result of production and consumption processes. The productive activities are broken down by economic activity and the household consumption activities are broken down by purpose (transport, heating, and other activities). Air emissions include emissions of individual greenhouse gases (GHG) as well as air pollutants such as sulphur dioxide, nitrogen oxides, and particulate matter. The emissions are also aggregated (using equivalence factors) to report on environmental pressures: global warming potential, acidifying gases and ozone precursors.

This metadata refers to three datasets based on the data collection on air emissions accounts (AEA): 1.Air emissions accounts by NACE Rev. 2 activity [env_ac_ainah_r2] This data set reports the emissions of greenhouse gases and air pollutants broken down by 64 industries (classified by NACE Rev. 2) plus households. Concepts and principles are the same as in national accounts. Complete data starts from reference year 2008. 2. Air emissions intensities by NACE Rev. 2 activity [env_ac_aeint_r2] This data set presents intensity-ratios relating AEA emissions (see previous) to economic parameters (value added, production output) for 64 industries (classified by NACE Rev. 2). 3. Air emissions accounts totals bridging to emission inventory totals [env_ac_aibrid_r2] This data set includes so-called bridging items showing the differences between the national totals as derived from two internationally established approaches/methods for reporting emissions of greenhouse gases and air pollutants: a) Air emissions accounts (AEA), i.e. the dataset mentioned above under 1. The AEA national totals refer to the residents of the reporting country (so-called residence principle as established in national accounts). b) National emission inventories, i.e. greenhouse gas inventories (providing emission data under the United Nations Framework Convention on Climate Change (UNFCCC)) and air pollutant inventories (providing emission data under the United Nations Economic Commission for Europe (UNECE), Convention on Long-range Transboundary Air Pollution (CLRTAP) and the EU National Emission Ceilings Directive (NEC). The national totals refer widely to the territory of the reporting country. The European Environment Agency (EEA) collects national inventories for greenhouse gases and other air pollutants and compiles the EU aggregates. Eurostat republishes the most relevant data from these inventories in [env_air_emis] and [env_air_gge]. The two methodologies are based on slightly different concepts and principles and the totals at national and EU level correspondingly differ. The bridging items explicitly present these differences.

This metadata refers to three datasets based on the data collection on air emissions accounts (AEA): 1.Air emissions accounts by NACE Rev. 2 activity [env_ac_ainah_r2] This data set reports the emissions of greenhouse gases and air pollutants broken down by 64 industries (classified by NACE Rev. 2) plus households. Concepts and principles are the same as in national accounts. Complete data starts from reference year 2008. 2. Air emissions intensities by NACE Rev. 2 activity [env_ac_aeint_r2] This data set presents intensity-ratios relating AEA emissions (see previous) to economic parameters (value added, production output) for 64 industries (classified by NACE Rev. 2). 3. Air emissions accounts totals bridging to emission inventory totals [env_ac_aibrid_r2] This data set includes so-called bridging items showing the differences between the national totals as derived from two internationally established approaches/methods for reporting emissions of greenhouse gases and air pollutants: a) Air emissions accounts (AEA), i.e. the dataset mentioned above under 1. The AEA national totals refer to the residents of the reporting country (so-called residence principle as established in national accounts). b) National emission inventories, i.e. greenhouse gas inventories (providing emission data under the United Nations Framework Convention on Climate Change (UNFCCC)) and air pollutant inventories (providing emission data under the United Nations Economic Commission for Europe (UNECE), Convention on Long-range Transboundary Air Pollution (CLRTAP) and the EU National Emission Ceilings Directive (NEC). The national totals refer widely to the territory of the reporting country. The European Environment Agency (EEA) collects national inventories for greenhouse gases and other air pollutants and compiles the EU aggregates. Eurostat republishes the most relevant data from these inventories in [env_air_emis] and [env_air_gge]. The two methodologies are based on slightly different concepts and principles and the totals at national and EU level correspondingly differ. The bridging items explicitly present these differences.

This database includes annual, quarterly and monthly information on carbon dioxide (CO2) emissions related to commercial passenger, freight, and general aviation flights, on both a territory and a residence basis, for 186 countries. These CO2 emissions are estimated by the OECD, based on a consistent methodology across countries. The main source used for the estimation of these CO2 emissions is a database compiled by the International Civil Aviation Organisation (ICAO) with all commercial passenger and freight flights around the world.

This indicator tracks trends in anthropogenic atmospheric emissions of ammonia by agriculture.

The indicator measures the amount of ammonia (NH3) emissions as a result of the agricultural production. The EU inventory on air pollution compiled by the European Environment Agency (EEA) under the Convention on Long-range Transboundary Air Pollution (LRTAP Convention) is fully consistent with national air pollution inventories compiled by the EU Member States. Ammonia emissions per hectare are calculated using the total utilised agricultural area (UAA) of the relevant year as denominator.

The “ALFS Summary tables” dataset is a subset of the Annual Labour Force Statistics database which presents annual labour force statistics and broad population series for 34 OECD member countries plus Brazil, Columbia and Russian Federation and 4 geographical areas (Major Seven, Euro area, European Union and OECD-Total). Data are presented in thousands of persons, in percentage or as indices with base year 2010=100. This dataset contains estimates from the OECD Secretariat for the latest years when countries did not provide data. These estimates are necessary to compile aggregated statistics for the geographical areas for a complete span of time. Since 2003, employment data by sector for the United States are compiled following the North American Industrial Classification System (NAICS); therefore they are not strictly comparable with other countries’ data. Euro area and European Union data were extracted from Eurostat (LFS Series, Detailed annual survey results in New Cronos). Euro area refer to Euro area with 17 countries (geo = ea17). European Union refers to European Union with 27 countries (geo = eu27).

This indicator is defined as the average emissions of carbon dioxide per kilometre by new passenger cars registered in a given year.

The indicator is defined as the average carbon dioxide (CO2) emissions per km by new passenger cars in a given year. The reported emissions are based on type-approval and can deviate from the actual CO2 emissions of new cars.

Consumption-based accounting (CBA) of emissions (also known as carbon footprints) accounts for emissions associated with imported and exported goods. CBA reports the total emissions associated with final demand in each country. Production-based accounting (PBA) -accounts for  physically occurring emissions in a country or territorial emissions. 

Data cited at https://www.transparency.org/en/cpi/2020/index/nzl by Transparency International is licensed under CC-BY-ND 4.0. Global Corruption Barometer is the largest world-wide public opinion survey on corruption. see more at https://www.transparency.org/en/cpi/2020/index/nzl Transparency International(TI) defines corruption as the abuse of entrusted power for private gain. This definition encompasses corrupt practices in both the public and private sectors. The Corruption Perceptions Index (CPI) ranks countries according to the perception of corruption in the public sector. The CPI is an aggregate indicator that combines different sources of information about corruption, making it possible to compare countries. The CPI ranks almost 200 countries by their perceived levels of corruption, as determined by expert assessments and opinion surveys.

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. Data cited at: https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/

This dataset provides selected information on national emissions of air pollutants: man-made emissions of sulphur oxides (SOx), nitrogen oxides (NOx), particulate matter (PM), carbon monoxide (CO) and volatile organic compounds (VOC). Categories presented are based on the NFR (nomenclature for reporting) 2014 classification. 

The dataset presents estimates of air emissions 'embodied' in products (goods and services) for final use - also referred to as 'footprints'. The estimates are the result of environmental input-output modelling for the aggregated European Union (EU) economy. For example, the carbon footprint is a measure of how much CO2 was emitted along the full production chain of a product that ends up in the EU as final consumption or investment, irrespective of the industry or country where the CO2 emission occurred. These emissions are sometimes referred to as emissions 'embodied' in EU consumption, although they are not literally included in the final products, and these products are not only consumed, but may also be investment goods. Air emission footprints offer a complementary perspective to greenhouse gas inventories and air emissions accounts. The latter two record emissions on the production side, at the origin of the emissions. In contrast, carbon footprints are estimated from the perspective of the final product and where it ends up, and are therefore also referred to as consumption-based accounts.   The model estimations are based on two main source datasets (see 18.1 for more detail): ESA supply, use and input–output tablesAir emissions accounts    The dataset reports on emissions of greenhouse gases and air pollutants from final use of products for: European Union2008-2016using the CPA 2008 classificationwithin the ESA 2010 framework   Estimates for the most recent year in the dataset (t) are estimated based on early estimates of air emissions accounts for year t.    Footprints can be derived from this dataset by combining the estimates for final consumption expenditure (P3) and gross capital formation (P5) from the dimension INDUSE (for ORIGIN equal to Total/WORLD).   The underlying modelling assumes that the production technology in the rest of the world economy is the same as in the EU. Hence, the estimated 'embodied' emissions in the rest of the world constitute rather emissions avoided in the EU production system. For carbon dioxide (CO2) the difference between 'avoided in EU' and 'actually emitted in rest of the world' is most likely smaller than for other pollutants such as sulphur dioxide (SO2) for which very efficient abatement technologies were introduced in the EU.

As of Jun 2021, estimates of greenhouse gas (GHG) emissions from agriculture and land use are disseminated under the FAOSTAT heading Climate Change/Emissions. This heading consolidates and replaces two previous distinct headings: Emissions-Agriculture and Emissions-Land Use, which no longer exist. Several other domains were consolidated to facilitate data access. https://fenixservices.fao.org/faostat/static/documents/GT/README_Changes_in_2021.pdf   The domain Emissions Totals consolidatesin one single dataset the information previously presented in two separate datasets: Agriculture total (previously: http://www.fao.org/faostat/en/#data/GT; containing data on non-CO2 emissions from agriculture); and Land Use total (previously: http://www.fao.org/faostat/en/#data/GL; containing CO2 and non-CO2 emissions from land use, land use change and forestry. This link no longer works).

With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database. The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). For more information, refer to the Labour Force Statistics (LFS and STLFS) database description.

With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database. The employed comprise all persons of working age who, during a specified brief period, were in one of the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work). Data disaggregated by economic activity are provided according to the latest version of the International Standard Industrial Classification of All Economic Activities (ISIC) available for that year. Data may have been regrouped from national classifications, which may not be strictly compatible with ISIC. For more information, refer to the Labour Force Statistics (LFS and STLFS) database description.

Data cited at: Wendling, Z. A., Emerson, J. W., Esty, D. C., Levy, M. A., de Sherbinin, A., et al. (2018). 2018 Environmental Performance Index. New Haven, CT: Yale Center for Environmental Law & Policy. https://epi.yale.edu/   The Environmental Performance Index (EPI) is constructed through the calculation and aggregation of 20 indicators reflecting national-level environmental data. These indicators are combined into nine issue categories, each of which fit under one of two overarching objectives. The two objectives that provide the overarching structure of the EPI are Environmental Health and Ecosystem Vitality. Environmental Health measures the protection of human health from environmental harm. Ecosystem Vitality measures ecosystem protection and resource management. These two objectives are further divided into nine issue categories that span high-priority environmental policy issues, including air quality, forests, fisheries, and climate and energy, among others. The issue categories are extensive but not comprehensive. Underlying the nine issue categories are 20 indicators calculated from country-level data and statistics. After more than 15 years of work on environmental performance measurement and six iterations of the EPI, global data are still lacking on a number of key environmental issues. These include: freshwater quality, toxic chemical exposures, municipal solid waste management, nuclear safety, wetlands loss, agricultural soil quality and degradation, recycling rates, adaptation, vulnerability, and resiliency to climate change, desertification.

The yearly “EU energy in figures - statistical pocketbook” provides an annual overview of energy-related structural statistics in the EU and in individual EU countries. Data cited at: European Commission, Directorate-General for Energy, EU energy in figures – Statistical pocketbook 2023, Publications Office of the European Union, 2023, https://data.europa.eu/doi/10.2833/502436

Data cited at: tax justice network - https://fsi.taxjustice.net/en/introduction/fsi-results;  License term - https://fsi.taxjustice.net/en/introduction/copyright-license     Jurisdiction Note for Year 2020: Footnote 1: The territories marked in dark color are Overseas Territories (OTs) and Crown Dependencies (CDs) where the Queen is head of state; powers to appoint key government officials rest with the British Crown; laws must be approved in London; and the UK government holds various other powers (see here for more details: www.financialsecrecyindex.com/PDF/UnitedKingdom.pdf). Territories marked in light color are British Commonwealth territories which are not OTs or CDs but whose final court of appeal is the Judicial Committee of the Privy Council in London (see here for more details: http://www.taxjustice.net/cms/upload/pdf/Privy_Council_and_Secrecy_Scores.pdf). Footnote 6: For jurisdictions marked with 2, we provide special narrative reports exploring the history and politics of their offshore sectors. You can read and download these reports by clicking on the country name.

Data cited at: Fragile States Index - https://fragilestatesindex.org/ The FSI focuses on the indicators of risk and is based on thousands of articles and reports that are processed by our CAST Software from electronically available sources. Measures of fragility, like Demographic Pressures,Refugees and IDPs and etc., have been scaled on 0 to 10 where 10 is highest fragility and 0 no fragility.

Freedom Status obtained by Combined Average of the Political Rights and Civil Liberties Ratings (Freedom Rating) Range 1-7; 1-2.5 Free; 3-5: Partly Free and 5.5-7: Not Free The score for the A. Electoral Process subcategory The score for the B. Political Pluralism and Participation subcategory The score for the C. Functioning of Government subcategory The score for the Political Rights category The score for the D. Freedom of Expression and Belief subcategory The score for the E. Associational and Organizational Rights subcategory The score for the F. Rule of Law subcategory The score for the G. Personal Autonomy and Individual Rights subcategory The score for the Civil Liberties category Total aggregate Score for all categories  

The Global Carbon Project facilitates access to data to encourage its use and promote a good understanding of the carbon cycle. Respecting original data sources is key to help secure the support of data providers to enhance, maintain and update valuable data. 

Data cited at: The World Economic Forum https://www.weforum.org/ Topic: The Global Competitiveness Report Publication URL: https://www.weforum.org/reports/the-global-competitiveness-report-2020 License: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

The average global index score stagnated in 2022. Despite expectations of a rebound after the lifting of pandemic-related restrictions, the score was almost unchanged, at 5.29 (on a 0-10 scale), compared with 5.28 in 2021. The positive effect of the restoration of individual freedoms was cancelled out by negative developments globally. The scores of more than half of the countries measured by the index either stagnated or declined. Western Europe was a positive outlier, being the only region whose score returned to pre-pandemic levels. 

The GEM Adult Population Survey (APS) measures the level and nature of entrepreneurial activity around the world. It is administered to a representative national sample of at least 2000 respondents. The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. Through a vast, centrally coordinated, internationally executed data collection effort, GEM is able to provide high quality information, comprehensive reports and interesting stories, to enhance the understanding of the entrepreneurial phenomenon.

Data cited at: The World Economic Forum https://www.weforum.org/ Topic: Global Gender Gap Report 2023 Publication URL: https://www.weforum.org/reports/global-gender-gap-report-2023 License: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode   The Global Gender Gap Index annually benchmarks the current state and evolution of gender parity across four key dimensions (Economic Participation and Opportunity, Educational Attainment, Health and Survival, and Political Empowerment). It is the longest-standing index tracking the progress of numerous countries’ efforts towards closing these gaps over time since its inception in 2006. This year, the 17th edition of the Global Gender Gap Index benchmarks gender parity across 146 countries, providing a basis for robust cross country analysis.

Direct greenhouse gases: Carbon Dioxide (CO2), Methane (CH4), Nitrous Oxide (N2O), Hydrofluorocarbons (HFC-23, 32, 125, 134a, 143a, 152a, 227ea, 236fa, 245fa, 365mfc, 43-10-mee), Perfluorocarbons (PFCs: CF4, C2F6, C3F8, c-C4F8, C4F10, C5F12, C6F14, C7F16), Sulfur Hexafluoride (SF6), Nitrogen Trifluoride (NF3) and Sulfuryl Fluoride (SO2F2). Emissions are calculated by individual countries using country-specific information. The countries are organized in different world regions for illustration purposes. Emissions of some small countries are presented together with other countries depending on country definition and availability of activity statistics. Source: European Commission, Joint Research Centre (JRC)/PBL Netherlands Environmental Assessment Agency.

The Global Innovation Index 2023 captures the innovation ecosystem performance of 132 economies and tracks the most recent global innovation trends.  The GII 2023 tracks global innovation trends against a background of uncertainty caused by slow economic recovery from the COVID-19 pandemic, high-interest rates, and geopolitical conflict, but with the promise of the Digital Age and Deep Science innovation waves and technological progress.  

Text from source:Please note that this content is no longer being updated. The tax rate information on this page was last updated in January 2021 and the below historical tax rate data is available for reference only. For the latest information and insights on Global Tax rates, and what they may mean for your organization, please speak with your local KPMG Tax professional. Thank you.

This dataset presents trends in man-made emissions of major greenhouse gases and emissions by gas.   Data refer to total emissions of CO2 (emissions from energy use and industrial processes, e.g. cement production), CH4 (methane emissions from solid waste, livestock, mining of hard coal and lignite, rice paddies, agriculture and leaks from natural gas pipelines), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). Data exclude indirect CO2.   Intensities (per unit of GDP and per capita) as well as index are calculated on gross direct emissions excluding emissions or removals from land-use, land-use change and forestry (LULUCF).   The GDP used to calculate intensities is expressed in USD at 2010 prices and PPPs.

Data cited at: CAIT, retrieved from Climate Watch Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2019, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report.

This dataset includes data on greenhouse gas emissions inventory, as reported to the European Environment Agency (EEA). Note that Eurostat is not the producer of these data, only re-publishes them. The European Union (EU) as a party to the United Nations Framework Convention on Climate Change (UNFCCC) reports annually its greenhouse gas inventory for the year t-2 and within the area covered by its Member States. The inventory contains data on carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). The EU inventory is fully consistent with national greenhouse gas inventories compiled by the EU Member States. Values flagged with 'd' have the notation key 'IE' (included elsewhere) in the original dataset published by the EEA. Notation key combinations that include NE (not estimated), as well as values reported with the additional notation key used by EEA ' – ', are reported as missing values in the air_env_gge dataset. All notation keys combinations that include a C (confidential) are flagged as confidential. The notation keys NA and NO are not associated with a flag. EEA notation keys: NO – not occurring. NE – not estimated. NA – not applicable. IE – included elsewhere. C – confidential.

This indicator tracks trends in greenhouse gas (GHG) emissions by agriculture, estimated and reported under the United Nations Framework Convention on Climate Change (UNFCCC), the Kyoto Protocol and the Decision 525/2013/EC.

The indicator calculation is based on the emissions covered under the Effort Sharing Decision (406/2009/EC). The Effort Sharing Decision sets national annual binding targets for emissions not covered under the EU emission trading scheme (ETS). The ESD emissions are calculated by deducting ETS verified emissions, CO2 emissions from domestic aviation and NF3 emissions from national total emissions. Total emissions are national totals reported under the UNFCCC (excluding LULUCF, international aviation and international maritime transport). For the period 2005-2012, additional emission estimates are deducted in order to reflect the current scope of the EU ETS, following the European Environment Agency methodology for calculating consistent time series.

The indicator measures total national emissions of the so called ‘Kyoto basket’ of greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and the so-called F-gases (hydrofluorocarbons, perfluorocarbons, nitrogen triflouride (NF3) and sulphur hexafluoride (SF6)). Using each gas’ individual global warming potential (GWP), they are being integrated into a single indicator expressed in units of CO2 equivalents. Emissions data are submitted annually by the EU Member States as part of the reporting under the United Nations Framework Convention on Climate Change (UNFCCC). The average population of the reference year (calculated as the arithmetic mean of the population on 1st January of two consecutive years) is used as denominator (per capita). The indicator does not include emissions and removals related to land use, land-use change and forestry (LULUCF); it does not include emissions reported as a memorandum item according to UNFCCC Guidelines but does include emissions from international aviation as well as indirect CO2 emissions.

This indicator shows trends in total man-made emissions (of both the ESD and ETS sectors) of the ‘Kyoto basket’ of greenhouse gases. It presents annual total emissions in relation to 1990 emissions The ‘Kyoto basket’ of greenhouse gases includes: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and the so-called F-gases (hydrofluorocarbons, perfluorocarbons, nitrogen triflouride (NF3) and sulphur hexafluoride (SF6)). These gases are aggregated into a single unit using gas-specific global warming potential (GWP) factors. The aggregated greenhouse gas emissions are expressed in units of CO2 equivalents. The indicator does not include emissions and removals related to land use, land-use change and forestry (LULUCF); nor does it include emissions from international maritime transport. It does however include emissions from international aviation. CO2 emissions from biomass with energy recovery are reported as a Memorandum item according to UNFCCC Guidelines and not included in national greenhouse gas totals. The EU as a whole is committed to achieving at least a 20% reduction of its greenhouse gas emissions by 2020 compared to 1990. This objective implies: - a 21 % reduction in emissions from sectors covered by the EU ETS (emission trading scheme) compared to 2005 by 2020; - a reduction of 10 % in emissions for sectors outside the EU ETS covered by the ESD (effort sharing decision). To achieve this 10% overall target each Member State has agreed country-specific greenhouse gas emission limits for 2020 compared to 2005 (Council Decision 2009/406/EC). Data Source: European Environment Agency

Data cited at: OSCE Office for Democratic Institutions and Human Rights:  http://hatecrime.osce.org/what-do-we-know 41 OSCE participating States have submitted hate crime information to ODIHR for 2021. Of these, 35 provided statistics, while 23 provided statistics disaggregated by bias motivation. The official figures are complemented by reports on hate incidents from 114 civil society groups, covering 44 participating States. These contributions amount to 6,391 hate incidents, including 2,363 disaggregated statistical incidents and 4,028 descriptive incidents. This information includes incidents provided by the Holy See, the UNHCR and OSCE missions.

Global Burden of Disease Study 2016 (GBD 2016) Healthcare Access and Quality Index Based on Amenable Mortality 1990–2016. Global Burden of Disease Study 2016 (GBD 2016) estimates were used in an analysis of personal healthcare access and quality for 195 countries and territories, as well as selected subnational locations, over time. This dataset includes the following global, regional, national, and selected subnational estimates for 1990-2016: age-standardized risk-standardized death rates from 24 non-cancer causes considered amenable to healthcare; age-standardized mortality-to-incidence ratios for 8 cancers considered amenable to healthcare; and the Healthcare Access and Quality (HAQ) Index and individual scores for each of the 32 causes on a scale of 0 to 100. Code used to produce the estimates is also included. Results were published in The Lancet in May 2018 in "Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016

Data Cited at: UNITED NATIONS DEVELOPMENT PROGRAMME, Human Development Data Center The Human Development Index (HDI) is a summary measure of achievements in three key dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living. The HDI is the geometric mean of normalized indices for each of the the three dimensions.

Data cited at, 1. https://kapsarc.opendatasoft.com/explore/dataset/population-human-mortality-database  

Data cited at: ICTD/UNU-WIDER, ‘Government Revenue Dataset’, 2018, https://www.wider.unu.edu/project/government-revenue-dataset' ICTD Government Revenue Dataset, 2018 A major obstacle to cross-country research on the role of revenue and taxation in development has been the weakness of available data. Government Revenue Dataset (GRD), developed through the International Centre for Tax and Development (ICTD), is aimed at overcoming this obstacle. It meticulously combines data from several major international databases, as well as drawing on data compiled from all available International Monetary Fund (IMF) Article IV reports. It achieves marked improvements in data coverage and accuracy, including a standardized approach to revenue from natural resources, and holds the promise of significant improvement in the credibility and robustness of research in this area. Dataset contains Central, General and merged government revenue data reported as % of GDP.

Data cited at: World Economic Forum The Inclusive Development Index (IDI) is an annual assessment of 103 countries’ economic performance that measures how countries perform on eleven dimensions of economic progress in addition to GDP. It has 3 pillars; growth and development; inclusion and; intergenerational equity – sustainable stewardship of natural and financial resources. The IDI is a project of the World Economic Forum’s System Initiative on the Future of Economic Progress, which aims to inform and enable sustained and inclusive economic progress through deepened public-private cooperation through thought leadership and analysis, strategic dialogue and concrete cooperation, including by accelerating social impact through corporate action.

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

Data cited at: The World Bank https://datacatalog.worldbank.org/ Topic: Millennium Development Goals Publication: https://datacatalog.worldbank.org/dataset/millennium-development-goals License: http://creativecommons.org/licenses/by/4.0/   Relevant indicators drawn from the World Development Indicators, reorganized according to the goals and targets of the Millennium Development Goals (MDGs). The MDGs focus the efforts of the world community on achieving significant, measurable improvements in people's lives by the year 2015: they establish targets and yardsticks for measuring development results. Gender Parity Index (GPI)= Value of indicator for Girls/ Value of indicator for Boys. For e.g GPI=School enrolment for Girls/School enrolment for Boys. A value of less than one indicates differences in favor of boys, whereas a value near one (1) indicates that parity has been more or less achieved. The greater the deviation from 1 greater the disparity is.

This indicator is the ratio between the number of persons aged 65 and over (age when they are generally economically inactive) and the number of persons aged between 15 and 64. The value is expressed per 100 persons of working age (15-64).

Data cited at: Open data research Network  Lisence: http://creativecommons.org/licenses/by/4.0/

  Political Terror Scale Levels 1 - Coun­tries un­der a se­cure rule of law, people are not im­prisoned for their views, and tor­ture is rare or ex­cep­tion­al. Polit­ic­al murders are ex­tremely rare. 2 - There is a lim­ited amount of im­pris­on­ment for non­vi­ol­ent polit­ic­al activ­ity. However, few per­sons are af­fected, tor­ture and beat­ings are ex­cep­tion­al. Polit­ic­al murder is rare. 3 - There is ex­tens­ive polit­ic­al im­pris­on­ment, or a re­cent his­tory of such im­pris­on­ment. Ex­e­cu­tion or oth­er polit­ic­al murders and bru­tal­ity may be com­mon. Un­lim­ited de­ten­tion, with or without a tri­al, for polit­ic­al views is ac­cep­ted. 4 - Civil and polit­ic­al rights vi­ol­a­tions have ex­pan­ded to large num­bers of the pop­u­la­tion. Murders, dis­ap­pear­ances, and tor­ture are a com­mon part of life. In spite of its gen­er­al­ity, on this level ter­ror af­fects those who in­terest them­selves in polit­ics or ideas. 5 - Ter­ror has ex­pan­ded to the whole pop­u­la­tion. The lead­ers of these so­ci­et­ies place no lim­its on the means or thor­ough­ness with which they pur­sue per­son­al or ideo­lo­gic­al goals.   Note- NA_Status_A, NA_Status_H, and NA_Status_S corresponds to  PTS_A, PTS_H, and PTS_S respectively0= The value ‘0’ is assigned where the respective human rights report was available and has been coded66= The value ‘66’ is reserved for missing PTS scores due to missing reports 77=The value ‘77’ is assigned where reports no-longer exist, or do not exist yet88=The value ‘88’ is assigned for units that exist  but no report was published and thus no PTS score is assigned99= The value ‘99’ is assigned where human rights report was published but no PTS score was assigned  

Source is not publishing the dataset anymore: "The dataset identified with code t2020_rk300 does not exist"   6.1. Reference area

This is part of a supporting dataset for Lester R. Brown, Full Planet, Empty Plates: The New Geopolitics of Food Scarcity (New York: W.W. Norton & Company, 2012).

The datasets are composed by baseline population projections and the 'No migration' sensitivity test. Data is available as follows: •           Projected population on 1 January by age and sex, and by single year time interval; •           Assumptions on future age-specific fertility rates, age-specific mortality rates and international net migration levels (including statistical adjustment); •           Corresponding approximated values of the life expectancy by age and sex. Moreover, for the baseline projections, the following demographic balances and indicators are available: •           Total numbers of the projected live births and deaths; •           Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median age of the population. The time horizon covered is: 2018 to 2100.

Data may differ from nationally reported figures and the Global SDG Indicators Database due to differences in sources and/or reference years. The share of youth not in education, employment or training (also known as the NEET rate) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. It provides a measure of youth who are outside the educational system, not in training and not in employment, and thus serves as a broader measure of potential youth labour market entrants than youth unemployment, since it also includes young persons outside the labour force not in education or training. This indicator is also a better measure of the current universe of potential youth labour market entrants compared to the youth inactivity rate, as the latter includes those youth who are not in the labour force and are in education, and thus cannot be considered currently available for work. For more information, refer to the Labour Market-related SDG Indicators (ILOSDG) database description.

The main objective of the research is to address the theoretical and empirical problems of how political institutions of high quality can be created and maintained. The second objective is to study the effects of Quality of Government on a number of policy areas, such as health, environment, social policy, and poverty. Data citation: Teorell, Jan, Aksel Sundström, Sören Holmberg, Bo Rothstein, Natalia Alvarado Pachon, Cem Mert Dalli, Rafael Lopez Valverde & Paula Nilsson. 2024. The Quality of Government Standard Dataset, version Jan24. University of Gothenburg: The Quality of Government Institute, https://www.gu.se/en/quality-government doi:10.18157/qogstdjan24

The Regional well-being dataset presents eleven dimensions central for well-being at local level and for 395 OECD regions, covering material conditions (income, jobs and housing), quality of life (education, health, environment, safety and access to services) and subjective well-being (social network support and life satisfaction). The set of indicators selected to measure these dimensions is a combination of people's individual attributes and their local conditions, and in most cases, are available over two different years (2000 and 2014). Regions can be easily visualised and compared to other regions through the interactive website [www.oecdregionalwellbeing.org]. The dataset, the website and the publications "Regions at a Glance" and "How’s life in your region?" are outputs designed from the framework for regional and local well-being. The Regional income distribution dataset presents comparable data on sub-national differences in income inequality and poverty for OECD countries. The data by region provide information on income distribution within regions (Gini coefficients and income quintiles), and relative income poverty (with poverty thresholds set in respect of the national population) for 2013. These new data complement international assessments of differences across regions in living conditions by documenting how household income is distributed within regions and how many people are poor relatively to the typical citizen of their country. For analytical purposes, the OECD classifies regions as the first administrative tier of sub-national government, so called Territorial Level 2 or TL2 in the OECD classification. This classification is used by National Statistical Offices to collect information and it represents in many countries the framework for implementing regional policies. Well-being indicators are shown for the 395 TL2 OECD regions, equivalent of the NUTS2 for European countries, with the exception for Estonian where well-being data are presented at a smaller (TL3) level and for the Regional Income dataset, where Greece, Hungary and Poland data are presented at a more aggregated (NUTS1) level.

Note: Nominal prices on April, 01 2021 Prices are not necessarily comparable between carbon pricing initiatives because of differences in the number of sectors covered and allocation methods applied, specific exemptions, and different compensation methods. Due to the dynamic approach to continuously improve data quality and fluctuating exchange rates, data of different years may not always be comparable and could be amended following new information from official government sources. In addition, data for a limited number of initiatives may be incomplete as they are in the process of being validated and will be updated following confirmation from official government sources. 

Data cited at:  The World Justice Project (WJP) The World Justice Project (WJP) Rule of Law Index® is a quantitative assessment tool designed by the World Justice Project to offer a detailed and comprehensive picture of the extent to which countries adhere to the rule of law in practice. Factors of the WJP Rule of Law Index include: 1. Constraints on Government Powers 2. Absence of Corruption 3. Open Government 4. Fundamental Rights 5. Order and Security 6. Regulatory Enforcement 7. Civil Justice 8. Criminal Justice (Data is collected for a 9th factor, Informal Justice, but it is not included in aggregated scores and rankings. This is due to the complexities of these systems and the difficulties in measuring their fairness and effectiveness in a matter that is both systematic and comparable across countries.) Every year WJP collects data from representative samples of the general public and legal professionals to compute the index scores. The data, once collected, are carefully processed to arrive at country-level scores. The respondent level data is first edited to exclude partially-completed surveys, suspicious data, and outliers. Individual answers are then mapped on to the 44 sub-factors of the index. Answers are coded so that all values ​​fall between 0 (least rule of law) and 1 (most rule of law), and aggregated at country level using the simple, or unweighted, average of all respondents. Note: 2012-2013 values ​​given for year 2013 and 2017-2018 given for year 2018.

The SCImago Institutions Rankings (SIR) is a classification of academic and research-related institutions ranked by a composite indicator that combines three different sets of indicators based on research performance, innovation outputs and societal impact measured by their web visibility. It provides a friendly interface that allows the visualization of any customized ranking from the combination of these three sets of indicators. Additionally, it is possible to compare the trends for individual indicators of up to six institutions. For each large sector it is also possible to obtain distribution charts of the different indicators.  

Data cited at: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Socio-Demographic Index (SDI) 1950–2019. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), 2020.

The SGI is a platform built on a cross-national survey of governance that identifies reform needs in 41 EU and OECD countries. The SGI brings together a broad network of experts and practitioners aiming to understand what works best in sustainable governance. Advocating the exchange of best practices, we offer full access to our data set and enable the comparisons that generate innovation in governance.

The Global Slavery Index, the flagship report of the Walk Free Foundation. The Global Slavery Index estimates the number of people in modern slavery in 167 countries. It is a tool for citizens, non government organisations, businesses and public officials to understand the size of the problem, existing responses and contributing factors, so they can build sound policies that will end modern slavery. The Global Slavery Index answers the following questions: What is the estimated prevalence of modern slavery country by country, and what is the absolute number by population? How are governments tackling modern slavery? What factors explain or predict the prevalence of modern slavery? Government Response Rating by Country A - 70 to 79.9 BBB - 60 to 69.9 BB - 50 to 59.9 B - 40 to 49.9 CCC - 30 to 39.9 DC - 20 to 29.9 C - 10 to 19.9 D - <0 to 9.9

Data cited at: UN E-Government Knowledgebase - https://publicadministration.un.org/egovkb/en-us/ 1. The EGDI is based on a comprehensive Survey of the online presence of all 193 United Nations Member States, which assesses national websites and how e-government policies and strategies are applied in general and in specific sectors for delivery of essential services. The assessment rates the e-government performance of countries relative to one another as opposed to being an absolute measurement. The results are tabulated and combined with a set of indicators embodying a country’s capacity to participate in the information society, without which e-government development efforts are of limited immediate use. Although the basic model has remained consistent, the precise meaning of these values varies from one edition of the Survey to the next as understanding of the potential of e-government changes and the underlying technology evolves. This is an important distinction because it also implies that it is a comparative framework that seeks to encompass various approaches that may evolve over time instead of advocating a linear path with an absolute goal. 2. E-Government Development Index-EGDI Very High-EGDI (Greater than 0.75) High-EGDI (Between 0.50 and 0.75) Middle-EGDI (Between 0.25 and 0.50) Low-EGDI (Less than 0.25)

With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database. The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. For more information, refer to the Labour Force Statistics (LFS and STLFS) database description.

Data cited at: UNICEF Data: Monitoring the situation of children and women Millions of children are not protected against Violence, Child labor, Trafficking, Sexual exploitation, Female genital mutilation/cutting, Child marriage and worldwide they experience the worst kinds of rights violations. Millions more children, not yet victims, are inadequately protected against them. UNICEF uses the term ‘Child Protection’ to refer to prevention and response to violence, exploitation and abuse of children in all contexts. This includes reaching children who are especially vulnerable to these threats, such as those living without family care, on the streets or in situations of conflict or natural disasters. Note: Year 2010-2016 is taken as 2016

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

The primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates

The World Energy Council’s Energy Trilemma Index tool, produced in partnership with Oliver Wyman, ranks countries on their ability to provide sustainable energy through 3 dimensions: Energy security, Energy equity (accessibility and affordability), Environmental sustainability. The ranking measures overall performance in achieving a sustainable mix of policies and the balance score highlights how well a country manages the trade-offs of the Trilemma with "A" being the best.

The Worldwide Governance Indicators (WGI) project reports aggregate and individual governance indicators for over 200 countries and territories over the period 1996–2020, for six dimensions of governance:Voice and AccountabilityPolitical Stability and Absence of ViolenceGovernment EffectivenessRegulatory QualityRule of LawControl of Corruption The Worldwide Governance Indicators (WGI) are a research dataset summarizing the views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents in industrial and developing countries. These data are gathered from a number of survey institutes, think tanks, non-governmental organizations, international organizations, and private sector firms. The WGI do not reflect the official views of the World Bank, its Executive Directors, or the countries they represent. The WGI are not used by the World Bank Group to allocate resources. Measure description: Estimate:-Estimate of governance (ranges from approximately -2.5 (weak) to 2.5 (strong) governance performance) Standard error (StdErr):-Standard error reflects variability around the point estimate of governance. Number of sources (NumSrc):-Number of data sources on which estimate is based Rank:-Percentile rank among all countries (ranges from 0 (lowest) to 100 (highest) rank) Lower:-Lower bound of 90% confidence interval for governance, in percentile rank terms Upper:-Upper bound of 90% confidence interval for governance, in percentile rank terms

With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training) during a specified reference period (e.g., one week). For statistical purposes, youth are defined as persons between the ages of 15 and 24 years. For more information, refer to the Labour Force Statistics (LFS and STLFS) database description.