The average for 2021 based on 196 countries was 656013 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

Population of Russia

Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.

The average for 2021 based on 192 countries was 14.4 percent. The highest value was in Bangladesh: 60.5 percent and the lowest value was in Djibouti: 0.1 percent. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

The average for 2024 based on 180 countries was 54 points. The highest value was in Finland: 100 points and the lowest value was in Venezuela: 0 points. The indicator is available from 1995 to 2024. Below is a chart for all countries where data are available.

Russia is the largest country in Europe, and also the largest in the world, its total size amounting to 17 million square kilometers (km2). It should be noted, however, that over three quarters of Russia is located in Asia, and the Ural mountains are often viewed as the meeting point of the two continents in Russia; nonetheless, European Russia is still significantly larger than any other European country. Ukraine, the second largest country on the continent, is only 603,000 km2, making it about 28 times smaller than its eastern neighbor, or seven times smaller than the European part of Russia. France is the third largest country in Europe, but the largest in the European Union. The Vatican City, often referred to as the Holy Sea, is both the smallest country in Europe and in the world, at just one km2. Population Russia is also the most populous country in Europe. It has around 144 million inhabitants across the country; in this case, around three quarters of the population live in the European part, which still gives it the largest population in Europe. Despite having the largest population, Russia is a very sparsely populated country due to its size and the harsh winters. Germany is the second most populous country in Europe, with 83 million inhabitants, while the Vatican has the smallest population. Worldwide, India and China are the most populous countries, with approximately 1.4 billion inhabitants each. Cities Moscow in Russia is ranked as the most populous city in Europe with around 13 million inhabitants, although figures vary, due to differences in the methodologies used by countries and sources. Some statistics include Istanbul in Turkey* as the largest city in Europe with its 15 million inhabitants, bit it has been excluded here as most of the country and parts of the city is located in Asia. Worldwide, Tokyo is the most populous city, with Jakarta the second largest and Delhi the third.

In 2023, Australia was ranked first with an organic agricultural land area amounting to about ** million hectares. The land area used for organic agriculture in Australia is thus larger than the acreage of all the other leading countries combined. Australia is followed by India and Argentina, which each had areas of organic agriculture of approximately **** and **** million hectares, respectively. Organic farming in the United States Among the different States in the United States, the State of California has the largest number of organic farms and ranches, with approximately *****. California is followed by the States of Wisconsin and New York, which have about *** million hectares each. In terms of land ownership of organic acreage, over *** million acres of certified organic land operated in the United States was rented from others. Organic food worldwide Worldwide sales of organic foods have experienced nearly constant growth during the last two decades. The United States has by far the largest share of global retail sales of organic food, with ** percent. The sales value of organic food in the United States has more than quadrupled during the last 15 years. To classify as organic, specific farming standards have to be applied during food production. The global per capita consumption of organic food is highest in Denmark and Switzerland.

The average for 2021 based on 11 countries was 671160 sq. km. The highest value was in India: 2973190 sq. km and the lowest value was in Singapore: 718 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

The average for 2022 based on 189 countries was 38.55 percent. The highest value was in Turkmenistan: 84.55 percent and the lowest value was in Suriname: 0.45 percent. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

As of 2025, the leading country for the best intellectual property environment was the United States, with an overall score of ***** points. The International Property Index consists of five key sets of indicators to map the national intellectual property environment of 55 surveyed countries. The United Kingdom and France followed behind.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

The statistic shows the largest countries in South America, based on land area. Brazil is the largest country by far, with a total area of over 8.5 million square kilometers, followed by Argentina, with almost 2.8 million square kilometers.

Source: Food and Agriculture Organization of the United Nations (FAO), 2022. FAO, 2022 FAOSTAT Land, Inputs and Sustainability, Land Use https://www.fao.org/faostat/en/#data/RL, Rome, FAO; IMF staff calculations.Category: MitigationData series: Forest areaLand areaCarbon stocks in forestsShare of forest areaIndex of forest extentIndex of carbon stocks in forestsMetadata:The FAOSTAT Land Use domain contains data on twenty-one land use categories. The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). In terms of the 2030 SDG Agenda statistical processes, FAO land use classes - Land Area and Forest Land provide inputs into the computations of SDG indicator 15.1.1.Methodology:Data on land area, forest area and carbon stocks in forests for the years 1992-2020 have been sourced from FAOSTAT. The methodology adopted by FAO for the compilation of land cover datasets can be seen at-https://www.fao.org/faostat/en/#data/RL. For some of the countries that were formed during 1992-2020, the shares as in the year of formation have been used to impute the values for the previous years using the values of the originating country.The following three indicators/indices have been compiled to enable a macro-view of changes in the forests post the ratification of the UN Framework Convention for Climate Change (UNFCCC).1. Share of forest area: The indicator can be considered as identical to global SDG indicator 15.1.1 "Forest area as a proportion of total land area".2. Index of forest extent: The index shows the magnitude of the forest area of a given year with reference to the base year 1992, that is depicted as 100. 3. Index of carbon stocks in forests: The index shows the magnitude of the carbon stocks in living biomass in forests of a given year with reference to the base year 1992, that is depicted as 100. The indices and the indicators have also been compiled and presented by region and sub-region according to the M49 and the World Economic Outlook Classifications. The “World” estimates do not include emissions of selected small countries.Disclaimer:Users are encouraged to examine the documentation, metadata, and sources associated with the data. User feedback on the fit-for-use of this product and whether the various dimensions of the product are appropriate is welcome.

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

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

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

This paper revises and updates the Campi-Nuvolari index of intellectual property protection for plant varieties. The new index provides yearly scores for the period 1961–2018 for 104 countries, which have legislation on plant variety protection in force. The new evidence highlights the ongoing shift towards more similar and stronger systems of intellectual property rights (IPRs) worldwide, regardless of individual characteristics of countries. The signing of the TRIPS and trade agreements with TRIPS-Plus provisions are major drivers of this process. In addition, certain characteristics of countries such as the regulatory environment, the level of human capital, the importance of agricultural production, and openness to trade, are also significant determinants of the evolution of IPRs systems. We conclude by discussing other possible applications of the data.

State of Palestine (West Bank and Gaza) PS: Net Barter Terms of Trade Index data was reported at 84.941 2000=100 in 2017. This records a decrease from the previous number of 88.269 2000=100 for 2016. State of Palestine (West Bank and Gaza) PS: Net Barter Terms of Trade Index data is updated yearly, averaging 81.379 2000=100 from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 101.454 2000=100 in 2002 and a record low of 70.820 2000=100 in 2008. State of Palestine (West Bank and Gaza) PS: Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s State of Palestine (West Bank and Gaza) – Table PS.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.; ;

As of May 2024, Taiwan was ranked as having the highest degree of internet freedom among all countries and territories in the Asia-Pacific region, scoring 79 index points. In contrast, China and Myanmar were ranked the lowest, each scoring just 9 index points in terms of internet freedom for 2024.