A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Over the years, we have developed distinct competencies in numerous areas so that our clients can rely on the Business Information Reports they purchase from us. Operating on the globe, we are able to provide local/regional intelligence with the most up-to-date and accurate information.

We have local presence in each country we provide information to, through our own offices or via our global network of partners that extends to more than 227 countries worldwide.

Our International Credit Reports include, among other, data on
• Shareholders & Directors • Secretary • Registered Number & Registered Address • Date of registration • Capital • Charges • Company Activities • Shareholding and/or Director Relationships • Detrimental Data • Payment records • Financial statements • Credit Scoring Assessment

Our Due Diligence Report, include: • Relationship Checks • Global KYC Screening • Negative & Local language media checks • Site & Reputation Check • Legal cases relating to the primary subject and its related entities • General media information • Passport/ID authentication • Official Documents and Certificates

Our KYC Reports investigate the subject entity against the following Global lists, amongst other categories: • Sanction Lists • Enforcement Lists • Arms Trafficking • Drug Trafficking • Fraud • Money Laundering • Terrorism • Adverse Media • Political Exposed Persons • State Owned Entities

Our local knowledge and understanding of languages, laws, customs, culture economy and commercial parameters in every country, provide us the advantage of having reliable and relevant products and services no matter where your target company is located.

This database automatically includes metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Immigrants aged 15 or more by activity status, nationality, country of previous residence and sex, Slovenia, annually”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

Abstract

Estache and Goicoechea present an infrastructure database that was assembled from multiple sources. Its main purposes are: (i) to provide a snapshot of the sector as of the end of 2004; and (ii) to facilitate quantitative analytical research on infrastructure sectors. The related working paper includes definitions, source information and the data available for 37 performance indicators that proxy access, affordability and quality of service (most recent data as of June 2005). Additionally, the database includes a snapshot of 15 reform indicators across infrastructure sectors.

This is a first attempt, since the effort made in the World Development Report 1994, at generating a database on infrastructure sectors and it needs to be recognized as such. This database is not a state of the art output—this is being worked on by sector experts on a different time table. The effort has however generated a significant amount of new information. The database already provides enough information to launch a much more quantitative debate on the state of infrastructure. But much more is needed and by circulating this information at this stage, we hope to be able to generate feedback and fill the major knowledge gaps and inconsistencies we have identified.

Geographic coverage

The database covers the following countries: - Afghanistan - Albania - Algeria - American Samoa - Andorra - Angola - Antigua and Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas, The - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia and Herzegovina - Botswana - Brazil - Brunei - Bulgaria - Burkina Faso - Burundi - Cambodia - Cameroon - Canada - Cape Verde - Cayman Islands - Central African Republic - Chad - Channel Islands - Chile - China - Colombia - Comoros - Congo, Dem. Rep. - Congo, Rep. - Costa Rica - Cote d'Ivoire - Croatia - Cuba - Cyprus - Czech Republic - Denmark - Djibouti - Dominica - Dominican Republic - Ecuador - Egypt, Arab Rep. - El Salvador - Equatorial Guinea - Eritrea - Estonia - Ethiopia - Faeroe Islands - Fiji - Finland - France - French Polynesia - Gabon - Gambia, The - Georgia - Germany - Ghana - Greece - Greenland - Grenada - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong, China - Hungary - Iceland - India - Indonesia - Iran, Islamic Rep. - Iraq - Ireland - Isle of Man - Israel - Italy - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Korea, Dem. Rep. - Korea, Rep. - Kuwait - Kyrgyz Republic - Lao PDR - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao, China - Macedonia, FYR - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall Islands - Mauritania - Mauritius - Mayotte - Mexico - Micronesia, Fed. Sts. - Moldova - Monaco - Mongolia - Morocco - Mozambique - Myanmar - Namibia - Nepal - Netherlands - Netherlands Antilles - New Caledonia - New Zealand - Nicaragua - Niger - Nigeria - Northern Mariana Islands - Norway - Oman - Pakistan - Palau - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto Rico - Qatar - Romania - Russian Federation - Rwanda - Samoa - San Marino - Sao Tome and Principe - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Slovak Republic - Slovenia - Solomon Islands - Somalia - South Africa - Spain - Sri Lanka - St. Kitts and Nevis - St. Lucia - St. Vincent and the Grenadines - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syrian Arab Republic - Tajikistan - Tanzania - Thailand - Togo - Tonga - Trinidad and Tobago - Tunisia - Turkey - Turkmenistan - Uganda - Ukraine - United Arab Emirates - United Kingdom - United States - Uruguay - Uzbekistan - Vanuatu - Venezuela, RB - Vietnam - Virgin Islands (U.S.) - West Bank and Gaza - Yemen, Rep. - Yugoslavia, FR (Serbia/Montenegro) - Zambia - Zimbabwe

Kind of data

Aggregate data [agg]

Mode of data collection

Face-to-face [f2f]

Response rate

Sector Performance Indicators

Energy The energy sector is relatively well covered by the database, at least in terms of providing a relatively recent snapshot for the main policy areas. The best covered area is access where data are available for 2000 for about 61% of the 207 countries included in the database. The technical quality indicator is available for 60% of the countries, and at least one of the perceived quality indicators is available for 40% of the countries. Price information is available for about 41% of the countries, distinguishing between residential and non residential.

Water & Sanitation Because the sector is part of the Millennium Development Goals (MDGs), it enjoys a lot of effort on data generation in terms of the access rates. The WHO is the main engine behind this effort in collaboration with the multilateral and bilateral aid agencies. The coverage is actually quite high -some national, urban and rural information is available for 75 to 85% of the countries- but there are significant concerns among the research community about the fact that access rates have been measured without much consideration to the quality of access level. The data on technical quality are only available for 27% of the countries. There are data on perceived quality for roughly 39% of the countries but it cannot be used to qualify the information provided by the raw access rates (i.e. access 3 hours a day is not equivalent to access 24 hours a day).

Information and Communication Technology The ICT sector is probably the best covered among the infrastructure sub-sectors to a large extent thanks to the fact that the International Telecommunications Union (ITU) has taken on the responsibility to collect the data. ITU covers a wide spectrum of activity under the communications heading and its coverage ranges from 85 to 99% for all national access indicators. The information on prices needed to make assessments of affordability is also quite extensive since it covers roughly 85 to 95% of the 207 countries. With respect to quality, the coverage of technical indicators is over 88% while the information on perceived quality is only available for roughly 40% of the countries.

Transport The transport sector is possibly the least well covered in terms of the service orientation of infrastructure indicators. Regarding access, network density is the closest approximation to access to the service and is covered at a rate close to 90% for roads but only at a rate of 50% for rail. The relevant data on prices only cover about 30% of the sample for railways. Some type of technical quality information is available for 86% of the countries. Quality perception is only available for about 40% of the countries.

Institutional Reform Indicators

Electricity The data on electricity policy reform were collected from the following sources: ABS Electricity Deregulation Report (2004), AEI-Brookings telecommunications and electricity regulation database (2003), Bacon (1999), Estache and Gassner (2004), Estache, Trujillo, and Tovar de la Fe (2004), Global Regulatory Network Program (2004), Henisz et al. (2003), International Porwer Finance Review (2003-04), International Power and Utilities Finance Review (2004-05), Kikukawa (2004), Wallsten et al. (2004), World Bank Caribbean Infrastructure Assessment (2004), World Bank Global Energy Sector Reform in Developing Countries (1999), World Bank staff, and country regulators. The coverage for the three types of institutional indicators is quite good for the electricity sector. For regulatory institutions and private participation in generation and distribution, the coverage is about 80% of the 207 counties. It is somewhat lower on the market structure with only 58%.

Water & Sanitation The data on water policy reform were collected from the following sources: ABS Water and Waste Utilities of the World (2004), Asian Developing Bank (2000), Bayliss (2002), Benoit (2004), Budds and McGranahan (2003), Hall, Bayliss, and Lobina (2002), Hall and Lobina (2002), Hall, Lobina, and De La Mote (2002), Halpern (2002), Lobina (2001), World Bank Caribbean Infrastructure Assessment (2004), World Bank Sector Note on Water Supply and Sanitation for Infrastructure in EAP (2004), and World Bank staff. The coverage for institutional reforms in W&S is not as exhaustive as for the other utilities. Information on the regulatory institutions responsible for large utilities is available for about 67% of the countries. Ownership data are available for about 70% of the countries. There is no information on the market structure good enough to be reported here at this stage. In most countries small scale operators are important private actors but there is no systematic record of their existence. Most of the information available on their role and importance is only anecdotal.

Information and Communication Technology The report Trends in Telecommunications Reform from ITU (revised by World Bank staff) is the main source of information for this sector. The information on institutional reforms in the sector is however not as exhaustive as it is for its sector performance indicators. While the coverage on the regulatory institutions is 100%, it varies between 76 and 90% of the countries for more of the other indicators. Quite surprisingly also, in contrast to what is available for other sectors, it proved difficult to obtain data on the timing of reforms and of the creation of the regulatory agencies.

Transport Information on transport institutions and reforms is not systematically generated by any agency. Even though more data are needed to have a more comprenhensive picture of the transport sector, it was possible to collect data on railways policy reform from Janes World Railways (2003-04) and complement it with

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

Live tracking of recent earthquakes near San Diego Country Estates, California from the past 30 days. Real-time updates of M1.5+ quakes with interactive map visualization.

The dataset on Common Database on Designated Areas (CDDA) was created in 2014 by Cogea for the European Marine Observation and Data Network. This dataset is entirely based on GIS Data from the European Environmental Agency (EEA), plus additional info and selected EEA tabular data added as feature attributes, as well as the Cogea's calulation of marine and coastal location of features. It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The CDDA is commonly known as 'Nationally designated areas' and it is the official source of protected areas information from the 38 European countries, members of the Eionet, to the World Database of Protected Areas (WDPA). The data are delivered by the Eionet partnership countries as spatial and tabular information. The inventory began in 1995 under the CORINE programme of the European Commission. It is now one of the agreed Eionet priority data flows maintained by EEA with support from the European Topic Centre on Biological Diversity. The dataset is used by the EEA and e.g. the UNEP-WCMC for their main European and global assessments, products and services. Geographical coverage: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kosovo (under UNSC Resolution 1244/99), Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden and Switzerland. EEA does not have permission to distribute some or all sites reported by Estonia, Ireland and Turkey. In the webmap the dataset has been filtered in order to show only marine and coastal areas. Where available each polygon has the following main attributes: CDDA ID, country code, country name, site name, legal foundation date, national ID, area type/code (Designated Boundary or Site), IUCN category/description (Ia: Strict Nature Reserve; Ib: Wilderness Area; II: National Park; III: Natural Monument or Feature; IV: Habitat/Species Management Area; V: Protected Landscape/ Seascape; VI: Protected area with sustainable use of natural resources; Not applicable; Not assigned; Not reported), area (ha), major ecosystem type (Marine, Marine and terrestrial, Terrestrial), marine area percentage, spatial resolution (Scale 100K-250K, Scale Larger 100K, Unknown), remarks, marine/coastal location (1). For further information please visit the EEA's website. Compared with the previous release, this one includes the updated dataset 'CDDA_2023_v01_public' published by the EEA in June 2023.

Abstract

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

       The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
       - Sample Size by Country, Area and Consumption Segment (Number of Households)
       - Population 2010 by Country, Area and Consumption Segment
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
       - Population 2010 by Country, Age Group, Sex and Consumption Segment
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
       - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
       - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

Geographic coverage notes

For all countries, estimates are provided at the national level and at the urban/rural levels. For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1): - Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins - India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal - South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape

Kind of data

Data derived from survey microdata

Following a request from the European Commission for a review of European dietary reference values (DRVs), the EFSA’s Panel on Dietetic Products, Nutrition and Allergies (NDA) has prepared a number of Scientific Opinions on DRVs for micronutrients. The DATA Unit supported this activity by estimating the nutrient intake of a number of micronutrients in nine selected European countries and different age groups. In addition, the DATA Unit also provided information on average content of food sources of the respective nutrients per country based on the composition database, as well as main food group contributors to nutrient intakes and assessed the comparability of the provided data with pertinent published intake data.

Intake estimates have been assessed using food consumption data from the EFSA Comprehensive Food Consumption Database (EFSA, 2011a) and the EFSA Nutrient composition database. Food composition data used to populate the Nutrient composition database were provided to EFSA through the EFSA procurement project ‘Updated food composition database for nutrient intake’ (Roe at al., 2013). Data were provided following the EFSA specification for standard sample description for food and feed and were classified according to the FoodEx2 classification system of EFSA (EFSA, 2011b).

The food composition data used in these assessments and here published cover the following vitamins and minerals: calcium (Ca); copper (Cu); cobalamin (vitamin B12); magnesium (Mg); niacin; phosphorus (P); potassium (K); riboflavin; thiamin; iron (Fe); selenium (Se); vitamin B6; vitamin K, zinc (Zn), and vitamin E1. The food composition dataset contains data from seven2 countries: Finland, France, Germany, Italy, Netherlands, Sweden, and United Kingdom. This dataset version has been checked for outliers but is prior to data completion for missing foods and nutrient values.

1 Vitamin E is defined as alpha-tocopherol (AT) only, however as most food composition databases in the EU contain values as alpha-tocopherol equivalents (TE), data on TE are also provided

2 For the nutrient intake estimates of Ireland and Latvia present in the opinions of the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA), food composition data from UK and Germany were respectively used

This study examines the validity of the assumption that international large-scale land acquisition (LSLA) is motivated by the desire to secure control over water resources, which is commonly referred to as ‘water grabbing’. This assumption was repeatedly expressed in recent years, ascribing the said motivation to the Gulf States in particular. However, it must be considered of hypothetical nature, as the few global studies conducted so far focused primarily on the effects of LSLA on host countries or on trade in virtual water. In this study, we analyse the effects of 475 intended or concluded land deals recorded in the Land Matrix database on the water balance in both host and investor countries. We also examine how these effects relate to water stress and how they contribute to global trade in virtual water. The analysis shows that implementation of the LSLAs in our sample would result in global water savings based on virtual water trade. At the level of individual LSLA host countries, however, water use intensity would increase, particularly in 15 sub-Saharan states. From an investor country perspective, the analysis reveals that countries often suspected of using LSLA to relieve pressure on their domestic water resources—such as China, India, and all Gulf States except Saudi Arabia—invest in agricultural activities abroad that are less water-intensive compared to their average domestic crop production. Conversely, large investor countries such as the United States, Saudi Arabia, Singapore, and Japan are disproportionately externalizing crop water consumption through their international land investments. Statistical analyses also show that host countries with abundant water resources are not per se favoured targets of LSLA. Indeed, further analysis reveals that land investments originating in water-stressed countries have only a weak tendency to target areas with a smaller water risk.

The first version of this data base originally was set up for testing and validation of the so-called Integrated Model of the Greenhouse Effect (the IMAGE model; see Alcamo, 1994), developed at RIVM. The main aim of the model is to use state-of-the-art models to assist policy makers in the development and evaluation of future scenarios to mitigate the negative effects of global change. The modelling framework consists of several subsystems that cover the different aspects of the earth system.

    Many calculations in IMAGE and other models are performed on a 0.5o by 0.5o
    longitude/latitude grid. This is because nearly all potential impacts of
    climate change (impacts on ecosystems, agriculture and coastal flooding) have a
    strong spatial variability. Moreover, land use related greenhouse gas emissions
    depend on local environmental conditions and human activity. There are also
    other reasons for using grid-scale information. First, policy makers are
    interested in regional/national policies to address climate change. Secondly,
    grid-scale information makes model caluclations more testable against
    observations as compared to more aggregated models.

    Nevertheless, it is infeasable to perform grid-based calculations for economic
    models, because of the difficulty in specifying economic/demographic factors on
    a country scale for the entire world over the long horizon of the model.
    Therefore, the world has been divided into 19 world regions, according to
    economic and geographic similarity. This classification also takes into account
    the regional aggregations used by the IPCC, OECD, FAO, UN and IEA. It should be
    noted, however, that IMAGE has the additional requirement that countries within
    a region be adjacent or nearby because of the model's approach to global land
    cover simulation.

    An important initiative for the update the previous version of HYDE (Klein
    Goldewijk, 2001) was the publication of a new population density data base, the
    Gridded World Population v.3 (Balk et al, 2005), which is now used as a
    starting point for historical gridded population calculations. Because
    population data are important in many calculations, it resulted in modified
    land cover estimates, as well as estimates for GDP, value added, private
    consumption. Furthermore, numerous new data have been incorporated in many
    tables.

    Besides the testing of IMAGE, HYDE has already been used for integrated
    environmental assessents such as the Global Environmental Outlook (GEO) of the
    United Nations Enviromental Programme (UNEP, 1997), technical background
    reports for GEO (RIVM/UNEP, 1997), the TARGETS project (Rotmans and De Vries,
    1997), the Dutch National Environmental Outlook (RIVM, 1997) and the Mappae
    Mundi project (Goudsblom and De Vries, 2002). Also, HYDE has contributed to
    other research e.g. in the field of historical atmospheric trace gas
    inventories (e.g. Kroeze et al, 1999; den Elzen et al, 1999; van Aardenne et
    al, 2001; Pitman et al, 2000; Pielke et al, 2003 ), biological diversity (e.g.
    Gaston et al, 2003), and climate reconstructions (e.g. Matthews et al, 2003;
    Brovkin et al, 2004).

    Furthermore, this effort very much fits within the Land-Use and Land-Cover
    Change LUCC project, (activity 3; database development), part of the the
    International Human Dimensions Project (IHDP), and the PAGES (Human
    Interactions in Past Environmental Changes) - focus 3: Human Impacts on
    Terrestrial Ecosystems (HITE) initiative. PAGES is the International
    Geosphere-Biosphere Programme (IGBP) core project charged with providing a
    quantitative understanding of the Earth's past climate and environment.

    Please note that this data base is far from complete. Work is continuous in
    progress to update and extent the data series where possible.

    [Summary provided by MNP]

FAO Agricultural Capital Stock Database. Activity coverage: Agriculture, forestry, fishery (ISIC Rev.3: A+B). Main indicators: Agricultural Gross Fixed Capital Formation (GFCFAFF), Agricultural Net and Gross Capital Stock (NCSAFF & GCSAFF), Agricultural Consumption of Fixed Capital (CFCAFF), Agricultural Investment ratio (AIR), and the Agriculture Orientation Index in physical investment flows (INV_AOI). As part of the FAO Agricultural Capital Stock database, ESS-FAO publishes country-by-country data on annual physical investment flows in agriculture, forestry and fishery as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF).The FAO Capital Stock Database is an analytical database. For most countries, published series start in 1990. Whenever available, the database integrates national accounts data harvested from UNSD National Accounts Official Country Data (UNSD OCD) or OECD STAN and OECD Annual National Accounts (OECD ANA). To make data comparable across countries and over time, national data series have been rescaled to pair the ISIC Rev. 3 levels (linking of series is done by applying ratios computed on overlapping years of data) and re-referenced using the UNSD National Accounts Estimates of Main Aggregates database. If the full set of national accounts data on the above-mentioned set of agriculture capital related variables is not available for a specific country from these sources, estimation procedures are employed to construct complete time series. For a description of the procedures implemented to obtain complete time series on GFCFAFF, net and gross CSAFF, and CFCAFF, see the “Data Compilation†section underneath. Country data on Gross Fixed Capital Formation (GFCF) in agriculture, forestry and fishery, either as complete time series or just data for a few individual years, are available for just over 100 countries, originating mainly from the UNSD NA OCD, and OECD STAN and OECD ANA. Country data on agricultural Net Capital Stock (NCSAFF), Gross Capital Stock (GCSAFF) and Consumption of Fixed Capital (CFCAFF) are available only for a limited number of countries - to a large extent from OECD countries and included in the OECD STAN database. For some 20 other countries data are also availed from the UNSD National Accounts Official Country Data. Data on Gross Capital Stock (GCS) is available only for a few OECD countries. Based on the dataset on agriculture GFCF, FAO calculates NCSAFF, GCSAFF and CFCAFF series for all countries for which country data are not available from the above mentioned sources. To that end, a variation of the perpetual inventory method is used (for further details, see “Data Compilation†section below). Series are also presented in Constant prices. The total economy GFCF deflators from UNSD National Accounts Estimates have been used for non-OECD countries. As for OECD countries, GFCFAFF specific deflator series in ISIC Rev.3 A+B are used when available. For other cases, the GFCF-total economy deflator for GFCF has been used. The same deflators as for GFCFAFF have been used for GCSAFF, NCSAFF and CFCAFF.

Establishment of a bio-optical database of Australian Waters (SRS-OC-BODBAW) By harvesting all bio-geochemical and bio-optical data from all the IMOS data streams (ANMN, NRS, ANFOG, SRS Ocean Colour - Lucinda Jetty) and from legacy databases (e.g SRFME/WAMSI, Great Barrier Reef Long Term Monitoring Program, Aquafin CRC, Port Hacking transect, Research cruises in the Southern Ocean) it is possible to establish an Australian Database of bio-geochemical and bio-optical data. This SRS dataset covers the southern oceans worldwide and Indonesian waters. The Australian Antarctic Division (AAD) and the Australian Institute of Marine Science (AIMS) are providing some of the data to the bio-optical database. This activity aims to collate measurements undertaken prior to the commencement of IMOS by several members of the Australian biological oceanography community. This database is used to assess accuracy of Satellite ocean colour products for current and forthcoming satellite missions for the Australian Waters. The bio-optical database underpins the assessment of ocean colour products in the Australian region (e.g. chlorophyll a concentrations, phytoplankton species composition and primary production). Such a data set is crucial to quantify the uncertainty in the ocean colour products in our region. Further, it is essential to assessing new ocean colour data products generated for the Australian region. The contribution of such a database to international space agencies ensures that the accuracy of global algorithm developed for future sensors will increase for Australian waters as bias towards Northern Hemisphere observation will be reduced. The database contains bio-optical data (i.e. HPLC, Chlorophyll by spectrophotometric methods, full spectral absorptions, TSS ) and in situ optical data (Vertical attenuation, water leaving radiance, reflectances, Atmospheric Optical Depth, spectral and single channel absorption and backscattering).

Annual country-level estimates for 66 countries for the three indicators are presented by industry for 45 industries, for the years 1995-2018.CO₂ emissions from fuel consumption are in millions of metric tons of CO₂.CO₂ emissions intensities are in metric tons of CO₂ emissions per $1 million USD of output.CO₂ emissions multipliers are in metric tons of CO₂ emissions per $1 million USD of output.Sources: OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio; OECD (2021), Trade in embodied CO₂ (TeCO2) Database, https://www.oecd.org/sti/ind/carbondioxideemissionsembodiedininternationaltrade.htm; Organisation for Economic Co-operation and Development (OECD). 2021. Input-Output Tables (IOTs) (https://oe.cd/i-o).Category: Greenhouse Gas (GHG) EmissionsData series: CO2 emissionsCO2 emissions intensitiesCO2 emissions multipliersMetadata:Input-Output tables and Carbon Emissions for 66 Countries and 45 industries have been taken from the OECD’s compilation of indicators on “Carbon dioxide emissions embodied in international trade” (2021 ed.) which combines the Input-Output Database and Trade in embodied CO₂ (TeCO2) Database. In this release of TeCO2 sourced from OECD, emissions from fuels used for international aviation and maritime transport (i.e. aviation and marine bunkers) are also considered.The data series “CO₂ emissions, emission intensities; emission multipliers” was earlier referred to as “Carbon emissions from fuel combustion per unit of output” in the previous vintage of the Climate Change Indicator Dashboard.Methodology:CO₂ emission intensities are calculated by dividing the CO₂ emissions from fuel consumption by output from the OECD Inter-Country Input-Output (ICIO) Tables and multiplying the result by 1 million for scaling purposes. CO₂ emission multipliers are calculated by multiplying the Leontief inverse (also known as output multipliers matrix) from the OECD Inter-Country Input-Output (ICIO) Tables by the CO₂ emission intensities.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.Note on CO2 Emissions, Intensities, and Multipliers, June 2022Update of the CO₂ emissions by industry - April 2022

BackgroundIt is widely recognised that noncommunicable diseases are on the rise worldwide, partly due to insufficient levels of physical activity (PA). It is a particularly concerning health issue among children and adolescents in Arabic countries where cultural and environmental factors may limit their opportunity for engaging in physical activities.AimThis review sought to assess the effectiveness of school-based PA interventions for increasing PA among schoolchildren aged six to 18 years in Middle Eastern and Arabic-speaking countries.MethodsA systematic literature search was developed to identify studies reporting the evaluation of school-based PA interventions in Arabic-speaking countries. Four different databases were searched from January 2000 to January 2023: PubMed/MEDLINE, Web of Science, Scopus and CINAHL. Article titles and abstracts were screened for relevance. Full article scrutiny of retrieved shortlisted articles was undertaken. After citation searches and reference checking of included papers, full data extraction, quality assessment and narrative synthesis was undertaken for all articles that met the inclusion criteria. This review adhered to PRISMA guidelines for conducting systematic reviews.ResultsSeventeen articles met the inclusion criteria. Eleven articles reported statistically significant improvements in the levels of PA among their participants. Based largely on self-reported outcomes, increases in PA between 58% and 72% were reported. The studies with a follow-up period greater than three months reported sustained PA levels. There are a limited range of types of programmes evaluated and evaluations were only identified from 30% of the countries in the region. Relatively few studies focused solely on PA interventions and most of the interventions were multi-component (lifestyle, diet, education).ConclusionsThis review adds to the existing body of research about the efficacy of school-based interventions to increase physical activity levels. To date, few evaluations assess PA specific interventions and most of the interventions were multi-component including education components on lifestyle and diet. Long-term school-based interventions combined with rigorous theoretical and methodological frameworks are necessary to develop, implement and evaluate PA interventions for children and adolescents in Arabic-speaking countries. Also, future work in this area must also consider the complex systems and agents by which physical activity is influenced.

Analysis of ‘Food composition database for nutrient intake: selected vitamins and minerals in selected European countries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/food-composition-database on 07 January 2022.

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

Following a request from the European Commission for a review of European dietary reference values (DRVs), the EFSA’s Panel on Dietetic Products, Nutrition and Allergies (NDA) has prepared a number of Scientific Opinions on DRVs for micronutrients. The DATA Unit supported this activity by estimating the nutrient intake of a number of micronutrients in nine selected European countries and different age groups. In addition, the DATA Unit also provided information on average content of food sources of the respective nutrients per country based on the composition database, as well as main food group contributors to nutrient intakes and assessed the comparability of the provided data with pertinent published intake data.

Intake estimates have been assessed using food consumption data from the EFSA Comprehensive Food Consumption Database (EFSA, 2011a) and the EFSA Nutrient composition database. Food composition data used to populate the Nutrient composition database were provided to EFSA through the EFSA procurement project ‘Updated food composition database for nutrient intake’ (Roe at al., 2013). Data were provided following the EFSA specification for standard sample description for food and feed and were classified according to the FoodEx2 classification system of EFSA (EFSA, 2011b).

The food composition data used in these assessments and here published cover the following vitamins and minerals: calcium (Ca); copper (Cu); cobalamin (vitamin B12); magnesium (Mg); niacin; phosphorus (P); potassium (K); riboflavin; thiamin; iron (Fe); selenium (Se); vitamin B6; vitamin K, zinc (Zn), and vitamin E(1). The food composition dataset contains data from seven(2) countries: Finland, France, Germany, Italy, Netherlands, Sweden, and United Kingdom. This dataset version has been checked for outliers but is prior to data completion for missing foods and nutrient values.

(1) Vitamin E is defined as alpha-tocopherol (AT) only, however as most food composition databases in the EU contain values as alpha-tocopherol equivalents (TE), data on TE are also provided

(2) For the nutrient intake estimates of Ireland and Latvia present in the opinions of the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA), food composition data from UK and Germany were respectively used

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

Public species occurrence database such as GBIF provides specimen geographical records for global bee distribution, which is an invaluable resource for researchers studying bee diversity and pollination ecology. However, most records are biased toward specimens collected in North America and Europe. On the contrary, bees from Southeast Asia (SEA) are poorly understood and are not well represented in public databases. The Chulalongkorn University Natural History Museum (CUNHM) in Thailand holds a collection of more than 12,000 bee specimens from 4 families across more than 500 localities in the country's 77 provinces.

The initial purpose of this project is to mobilize at least 8,000 Thai bee specimen records deposited at CUNHM and publish in GBIF. Activities include photographing specimens, assigning QR codes, transcribing labels, formatting transcription of the data to enable publication in GBIF.org, mapping species distributions, and holding a workshop to showcase and demonstrate the use of the database.

For long-term sustainability of the project, we aim to establish an accurate and reliable digital bee database for the global audience and researchers whose interest are in pollination biology, conservation, bee taxonomy, and biodiversity informatics in Southeast Asia, a lesser-known area of bee diversity. Research fields in climate change, invasive species, and ecology of pollinators will benefit from this work, since information from tropical Asia is often limited and sometimes inaccessible.

Beside producing and publishing the database to GBIF, this effort provides a template for hosting other biodiversity information hosted and stored in Thailand by the National Science and Technology Development Agency (NSTDA), a partner that is providing matching funds. The processes and methods of digitization of bee records will be disseminated and shared with the country's other research collections, universities, and institutions through workshop and university lectures. Through these outreach activities, we hope to familiarize and educate audiences on how to utilize the data efficiently—both through the database and GBIF—and to persuade them the importance of pollinators to the public.

This vast repository houses crucial information on international trade transactions, capturing the intricate details of both export and import activities of India. The Export Database contains meticulous records of outbound shipments, offering valuable insights into the products, exporters, and destinations involved in each transaction. On the other hand, the Import Database provides a comprehensive view of inbound shipments, shedding light on the importers, origins, and details of the products acquired. Together, these two databases present a holistic perspective on global trade dynamics, encompassing critical metadata such as dates, product descriptions, quantities, values, and transportation specifics. Whether you are an analyst, researcher, or business professional, this comprehensive database will undoubtedly prove to be an invaluable resource for gaining a deep understanding of international trade patterns and market dynamics. Explore the wealth of information within and unlock new opportunities in the world of trade and commerce. The Export Database contains information related to export transactions. Each entry in the database represents a specific export event. The metadata fields in this database hold crucial details about the exported products and the transaction itself. The "DATE" field indicates the date of the export. "EXPORTER NAME" refers to the name of the entity or company responsible for exporting the goods. "DESTINATION COUNTRY" indicates the country to which the products are being shipped. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the exported item. The "BRAND" field specifies the brand associated with the product. "QUANTITY" indicates the total quantity of the product being exported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main exported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the exported products. "SUBITEM FOB VALUE" and "TOTAL FOB VALUE" denote the Free on Board (FOB) value of the subitem and the total FOB value of the export, respectively. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "ITEM NUMBER" is a unique identifier for each product item. "TRANSPORT TYPE" specifies the mode of transportation used for the export. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "CUSTOMS" indicates the customs information related to the export. "VARIETY" and "ATTRIBUTES" hold additional details about the product. The "OPERATION TYPE" field indicates the type of export operation, such as direct export or re-export. "MONTH" and "YEAR" represent the month and year when the export occurred. The Import Database contains information related to import transactions. Each entry in the database represents a specific import event. The metadata fields in this database hold crucial details about the imported products and the transaction itself. The "DATE" field indicates the date of the import. "IMPORTER NAME" refers to the name of the entity or company responsible for importing the goods. "SALES COUNTRY" indicates the country from which the products are being purchased. "ORIGIN COUNTRY" denotes the country where the imported products originate. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the imported item. "QUANTITY" indicates the total quantity of the product being imported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main imported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the imported products. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "TOTAL FREIGHT VALUE" and "TOTAL INSURANCE VALUE" represent the respective values for freight and insurance. "ITEM FOB VALUE," "SUBITEM FOB VALUE," and "ITEM CIF VALUE" denote the Free on Board (FOB) value of the item, subitem, and the cost, insurance, and freight value of the item, respectively. "ORIGIN PORT" specifies the port from which the products were shipped. "TRANSPORT TYPE" specifies the mode of transportation used for the import. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "ITEM NUMBER" is a unique identifier for each product item. "CUSTOMS" indicates the customs information related to the import. "OPERATION TYPE" field indicates the type of import operation, such as direct import...

Sources: OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio; International Monetary Fund (IMF), Statistics Department Questionnaire; IMF staff calculations.Category: Climate FinanceData series:Carbon Footprint of Bank Loans (Based on emission intensities)Carbon Footprint of Bank Loans (Based on emission intensities - normalized)Carbon Footprint of Bank Loans (Based on emission multipliers)Carbon Footprint of Bank Loans (Based on emission multipliers - normalized)Metadata:For relevant literature see Guan, Rong, Haitao Zheng, Jie Hu, Qi Fang, and Ruoen Ren. 2017. “The Higher Carbon Intensity of Loans, the Higher Non-Performing Loan Ratio: The Case of China.” Sustainability 9 (4) (April 22): 667. https://dx.doi.org/10.3390/su9040667.Methodology:The IMF has developed the Carbon Footprint of Bank Loans (CFBL) indicator for selected countries. CFBL indicator requires (i) deposit takers’ domestic loans by industry data, and (ii) the estimation of carbon emission factors (CEFs) by industry.The IMF has conducted a data collection exercise to obtain deposit takers’ domestic loans by industry data. The CEFs are calculated based on (i) direct metric tons of carbon emissions from fuel consumption per million $US of output by country and industry (CO2 emission intensities), and (ii) direct and indirect carbon emissions from fuel consumption per million $US of output by country (CO2 emission multipliers). The output multipliers and carbon emission intensities for 66 countries and 45 industries are sourced from the OECD Input-Output Database. Direct and indirect carbon emission factors are calculated by multiplying the Leontief inverse (also known as input-output multipliers) from the OECD World Input-Output Table by the carbon emissions from fuel consumption intensities.CFBL indicator is obtained by multiplying domestic loans to a specific industry by their corresponding carbon emission factors, summing over all industries and dividing the final result by total domestic loans. For a limited number of countries, updated CFBL information until 2018 will be posted in due course. CFBL is an experimental indicator. The index requires a nuanced reading. For instance, a sharp increase in the share of a brown industry in the deposit takers’ loans portfolio may create a negative impact on this indicator in the short term, but longer term results could diverge significantly if these loans were allocated for transition to low carbon environment or for continuing unsustainable brown activities. The emission coefficients applied to loans related to the emissions of the industry and not the emissions resulting from the consumption of the goods the industry produces. Also, the estimation methodology has a number of limitations. First, class level ISIC data could be more appropriate for the CFBL estimation, as it offers more detailed information to evaluate carbon footprint by industry. However, carbon emission factors are not available at this granularity. Also, the ISIC structure is not fully aligned with the needs of climate finance.Second, the granularity of the deposit takers’ domestic loans by industry data availability is not fully consistent across jurisdictions. It is not possible to obtain the loans by industry data at the same level of granularity from all participating countries. Third, the country coverage is limited as carbon intensity factors are available for only 66 countries. Fourth, input-output multipliers have limiting assumptions. Input-output multipliers are static (i.e., assume that there is a fixed input structure and fixed ratios for production for each industry) and do not take into account supply-side constraints or budget constraints. Please see additional information in this link.

OLS regression results with country and year fixed effects.