It refers to the foreign currency assets (including foreign currency cash, foreign currency deposits, and securities denominated in foreign currency) held by the central bank for non-residents, which can be freely used and utilized as needed to alleviate international balance of payments deficits.

Explore macroeconomic statistics and indicators, including GDP, Gross Fixed Capital Formation, National Income, and more. This dataset covers a wide range of countries such as Afghanistan, Albania, Algeria, Australia, Brazil, China, Germany, India, United States, and many more.

GDP, Gross Domestic Product, Capita, GFCF, Gross Fixed Capital Formation, Value, Added, Gross, Output, National, Income, Manufacturing, Agriculture, Population, National Accounts

Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Democratic Republic of the Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States of America, Uruguay, Uzbekistan, Vanuatu, Venezuela, Yemen, Zambia, Zimbabwe

Follow data.kapsarc.org for timely data to advance energy economics research.

The original database. There are all the raw data of this study to calculate the Gini coefficient. The others census data can be found in the web site of National Bureau of Statistics of the People's Republic of China. (XLS)

This is the data for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).Data SourcesWorld Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-casesMinistry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

The central bank provides interest rates for banks to carry out various forms of financing operations, such as rediscounting qualified bills, short-term financing, and secured loan refinancing.

Recommended citation

Gütschow, J.; Pflüger, M. (2023): The PRIMAP-hist national historical emissions time series v2.5 (1750-2022). zenodo. doi:10.5281/zenodo.10006301.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

• Use of the dataset and full description
• Abstract
• Support
• Sources
• Files included in the dataset
• Notes
• Data format description (columns)
• References
• Changelog

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2022, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC. For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "common reporting format" (CRF). For developing countries, non-AnnexI in terms of the UNFCCC, this is the data available through the UNFCCC DI interface (di.unfccc.int) with additional country submissions read from pdf and where available xls(x) or csv files. For a list of these submissions please see below. For South Korea the 2022 official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC.

Gaps in the country reported data are filled using third party data such as CDIAC, BP (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR v7.0 (all sectors). Lower priority data are harmonized to higher priority data in the gap-filling process.

For the third party priority time series gaps in the third party data are filled from country reported data sources.

Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions.

The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years. Thus the present version 2.5 includes data for 2022. For energy CO2 growth rates from the BP statistical review of world energy are used to extend the country reported (CR) or CDIAC (TP) data to 2022. For CO2 from cement production Andrew cement data are used. For other gases and sectors we have to rely on numerical methods until data becomes available.

Version 2.4.2 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.

The PRIMAP-hist v2.5 dataset is an updated version of

Gütschow, J.; Pflüger, M. (2023): The PRIMAP-hist national historical emissions time series v2.4.2 (1750-2021). zenodo. doi:10.5281/zenodo.7727475

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset.

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (mail@johannes-guetschow.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation.

We have also, for the first, time included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag "v2.5" in any issue you create regarding this dataset.

If you need support in using the dataset or have any other questions regarding the dataset, please contact mail@johannes-guetschow.de.

Sources

• Global CO2 emissions from cement production v220919 (Andrew 2022) data, paper: Andrew
  (2023), Andrew (2019b)
• BP Statistical Review of World Energy website: Energy Institute (2023)
• CDIAC data: Boden et al. (2017): Gilfillan et al. (2020), paper Gilfillan and Marland (2021)
• EDGAR version 7.0: data, website, Reports: JRC (2022), JRC (2021)
• EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)
• FAOSTAT database data: Food and Agriculture Organization of the United Nations (2023)
• RCP historical data data, paper: Meinshausen et al. (2011)
• UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal website, slightly updated version of data: UNFCCC (2023d), Pflüger and Gütschow (2023)
• UNFCCC Bnnial Update Reports, National Communications, and National Inventory
  Reports for developing countries website-BURs, website-NCs, data: UNFCCC (2023c), UNFCCC (2023a). Note: Not all BUR and NC submissions are included as reading the data is time consuming and not all submission contain sufficient data to be used in PRIMAP-hist. Not all submissions included in PRIMAP-hist are available in the github repository as we do not (yet) have code that we can publish for all submissions.
• UNFCCC Common Reporting Format (CRF) website, paper, data (23-09-26): UNFCCC (2023b) (processed as described in Jeffery et al. (2018a))
• Official country repositories (non-UNFCCC)
  • Taiwan / Republic of China: website, data: Republic of China - Environmental Protection

1. Current number of aboriginal households (before the year 100 of the Republic of China)2. Unit: households, individuals3. For detailed explanations of each field, refer to the New Taipei City Statistical Yearbook (website: http://www.bas.ntpc.gov.tw/home.jsp?idOTE) or contact the Department of Budget, Accounting and Statistics for inquiries.

1. OverviewThis data and code repository provides supporting data and reproducible computational code for the empirical validation section of the model assumptions in the paper "Analysis of Key Development Paths for Smart Elderly Care Service Platforms Based on Evolutionary Games."This repository contains:Empirical Data: User review data from the Marta Smart Elderly Care Platform, used to validate the impact of platform additional services on user satisfaction, and data from China's Smart Health and Elderly Care Products and Services Promotion Catalogue and Baidu Index time series data for macro-level analysis.All code for data processing, sentiment analysis, and macro-indicator analysis.2. Data Description2.1 Marta Platform User Review Data (MartaReviewsEN.csv)Content: Contains 502 user reviews of the European Marta Smart Elderly Care Platform (original German reviews translated to English via Google Translate).Purpose: Serves as the raw input data for sentiment analysis in the paper, used to validate the impact of platform additional services on user satisfaction (supporting the empirical part of Figure 13 in the paper).Field Description: title(EN) (Review Title - English), title(DE) (Review Title - German), text(EN) (Review Body - English), text(DE) (Review Body - German), Rating (User Rating)2.2 Baidu Index Data (BaiduIndexData-2018-2024.csv)Content: Contains daily average time series data of the Baidu Index for the keyword "智慧养老" (Smart Elderly Care) from 2018 to 2024.Source: Official Baidu Index website.Purpose: Serves as data input for macro-level analysis, used to validate the government incentive effect (supporting the empirical part of Figure 14 in the paper).Field Description: Year, Baidu Index2.3 Smart Health and Elderly Care Products and Services Promotion Catalogue Data (Smart elderly care products and services promotion catalogue.csv)Content: Contains data from the Smart Health and Elderly Care Products and Services Promotion Catalogue published by the Chinese government from 2018 to 2024. This file includes extracted information for quantitative comparative analysis.Source: Catalogues published on the official websites of the Ministry of Industry and Information Technology (MIIT), Ministry of Civil Affairs (MCA), and National Health Commission (NHC) of the People's Republic of China.Original Data Language: The original promotion catalogue data is in Chinese, translated to English via Google Translate. The data in this file consists of processed and extracted key indicators.Purpose: Serves as data input for macro-level analysis, used to validate the government incentive effect (supporting the empirical part of Figure 14 in the paper).Field Description: Serial number, years, category, Subcategories, Product Name3. Code Description and Usage3.1 Marta Comment Analysis Code (Marta Comment Analysis.ipynb)Functionality: Contains code for processing the MartaReviewsEN.csv data, performing text mining and sentiment analysis, and generating the results shown in Figure 13 of the paper.Running Environment:Python 3.13Jupyter Notebook environmentAll necessary libraries (e.g., NLTK, Pandas, Matplotlib) are listed in the requirements.txt file.How to Run:Ensure you have Python and Jupyter Notebook installed.Install the required libraries: pip install -r requirements.txtNavigate to the `` folder in your terminal and run jupyter notebook.Open Marta Comment Analysis.ipynb in the Jupyter interface.Run all cells in the Notebook sequentially.3.2 Macroeconomic Indicator Validation Code (Macroeconomic indicator validation.ipynb)Functionality: Contains code for processing BaiduIndexData-2018-2024.csv and Smart elderly care products and services promotion catalogue.csv data, and generating the results shown in Figure 14 of the paper.Running Environment: (Same as above)How to Run: (Same as above, open and run this Notebook)

This dataset provides basic information on parks in Tainan City.

This dataset mainly provides daily water suspension information from Taiwan Water Corporation

The location of fire departments across the country and the position of disaster response centers use a coordinate system based on latitude and longitude.

Income source budget table, expenditure agency budget table, income item description summary table, expenditure plan summary and branch plan overview table, various expense summary table, expenditure use item analysis table, capital expenditure analysis table, personnel expense analysis table, budgeted staff details table, public vehicle details table, existing office space details table, transfer income and expenditure reconciliation table, donation fund analysis table, expenditure by functional and economic comprehensive classification table.