We construct green growth indicators for 203 economies. A detailed description of the data and the approach used to generate the global data are presented in the Data Descriptor published in Scientific Data under the same title.

For the first time, the full results from the Global Green Economy Index (GGEI) are available in the public domain. Historically, only the aggregate results have been publicly accessible. The full dataset has been paywalled and accessible to our subscribers only. But the way in which we release GGEI data to the public is changing. Read on for a quick explanation for how and why.

First, the how. The GGEI file publicly accessible today represents that dataset officially compiled in 2022. It contains the full results for each of the 18 indicators in the GGEI for 160 countries, across the four main dimensions of climate change & social equity, sector decarbonization, markets & ESG investment and the environment. Some (not all) of these data points have since been updated, as new datasets have been published. The GGEI is a dynamic model, updating in real-time as new data becomes available. Our subscribing clients will still receive this most timely version of the model, along with any customizations they may request.

Now, the why. First and foremost, there is huge demand among academic researchers globally for the full GGEI dataset. Academic inquiry around the green transition, sustainable development, ESG investing, and green energy systems has exploded over the past several years. We receive hundreds of inquiries annually from these students and researchers to access the full GGEI dataset. Making it publicly accessible as we are today makes it easier for these individuals and institutions to use these GGEI to promote learning and green progress within their institutions.

More broadly, the landscape for data has changed significantly. A decade ago when the GGEI was first published, datasets existed more in silos and users might subscribe to one specific dataset like the GGEI to answer a specific question. But today, data usage in the sustainability space has become much more of a system, whereby myriad data sources are synthesized into increasingly sophisticated models, often fueled by artificial intelligence. Making the GGEI more accessible will accelerate how this perspective on the global green economy can be integrated to these systems.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

All formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of the Philippines, the listing from the PSA’s List of Establishments (LE), a registrar of businesses operating in the Philippines, was used. The registration agency is the Securities and Exchange Commission (SEC).

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Philippines 2024 World Bank Enterprise Survey Green Economy Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the Philippines 2024 WBES Green Economy included additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics.

Response rate

Overall survey response rate was 76.4%.

Graph and download economic data for Nasdaq Clean Edge Global Green Income Index TR (NASDAQGGINCT) from 2023-02-13 to 2025-11-07 about NASDAQ, income, and indexes.

Amidst global sustainability challenges, green finance emerges as a crucial instrument for advancing sustainable development, garnering increasing attention for its pivotal role in fostering high-quality economic development (HQED), particularly within the dynamic economic landscape of China. This study delves into the nexus between green finance and HQED across 30 Chinese provinces from 2012 to 2021. Employing the entropy method, indices for green finance and HQED index system are calculated, and their interaction is analyzed through a panel data model, incorporating tests for moderating effects of FinTech and green technological innovation, as well as assessing the heterogeneity across diverse regions. The findings highlight green finance’s significant role in enhancing HQED, with notable regional disparities. Specifically, the eastern region shows the strongest impact, followed by the central region, while the western and northeastern regions exhibit weaker influences. The study also identifies FinTech and green technological innovation as pivotal moderators, amplifying green finance’s positive effect on HQED. These insights underscore green finance’s importance in driving sustainable economic growth and highlight the necessity for region-specific strategies to optimize its impact. Policy recommendations based on these findings include prioritizing the development of green finance, formulating region-specific strategies, and leveraging the catalytic roles of FinTech and green technological innovation to enhance the efficacy of green finance in achieving HQED.

During RIO+20 meeting, the sustainable green economy for protecting environmental health via income increasing and poor eradication were discussed. The successful countries for sustainable green economy depend on efficiency of integrated water management and provision of water supply and sanitary services. Water security index was another issue that had been proposed to monitor the national socio-economical development which comprised of household, urban water, economic water (including irrigation water), river health and resilience. The water security index was proposed and determined the water security status of Thailand compared with the world and ASEAN countries. The article reviewed water use per capita and grouped countries by GDP level to reflect water resources development status of both world scale and Thailand. From the analysis, the strength and weakness of Thailand water management status were discussed and the issues to be considered in the framework of secured, sustainable green economy concepts were recommended.

The Global Renewable Energy and Indicators Dataset is a comprehensive resource designed for in-depth analysis and research in the field of renewable energy. This dataset includes detailed information on renewable energy production, socio-economic factors, and environmental indicators from around the world. Key features include:

1.Renewable Energy Data: Covers various types of renewable energy sources such as solar, wind, hydro, and geothermal energy, detailing their production (in GWh), installed capacity (in MW), and investments (in USD) across different countries and years.

2.Socio-Economic Indicators: Includes data on population, GDP, energy consumption, energy exports and imports, CO2 emissions, renewable energy jobs, government policies, R&D expenditure, and renewable energy targets.

3.Environmental Factors: Provides information on average annual temperature, annual rainfall, solar irradiance, wind speed, hydro potential, geothermal potential, and biomass availability.

4.Additional Features: Contains relevant features such as energy storage capacity, grid integration capability, electricity prices, energy subsidies, international aid for renewables, public awareness scores, energy efficiency programs, urbanization rate, industrialization rate, energy market liberalization, renewable energy patents, educational level, technology transfer agreements, renewable energy education programs, local manufacturing capacity, import tariffs, export incentives, natural disasters, political stability, corruption perception index, regulatory quality, rule of law, control of corruption, economic freedom index, ease of doing business, innovation index, number of research institutions, renewable energy conferences, renewable energy publications, energy sector workforce, proportion of energy from renewables, public-private partnerships, and regional renewable energy cooperation.

This dataset is ideal for analysts, researchers, and policymakers aiming to study trends, impacts, and strategies related to renewable energy development globally.

The World Bank's ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories. In order to shift financial flows so that they are better aligned with global goals, the World Bank Group (WBG) is working to provide financial markets with improved data and analytics that shed light on countries’ sustainability performance. Along with new information and tools, the World Bank will also develop research on the correlation between countries’ sustainability performance and the risk and return profiles of relevant investments.

The present research examines the relationship between China’s mineral extraction-related carbon dioxide (CO2) emissions and factors such as the legislative law, openness, green economic growth, FDI, technology innovation, and green patent from 1989 to 2020. Depending on statistics from the China Statistical Yearbook as well as other global databases, it finds the legislative law and openness contribute to sustainable mineral extracting in China. The efficacies of legislative laws can demonstrate by the substantial decreases in CO2 that occur in response to a one percent rise in these variables. Reducing carbon dioxide emissions is another positive association with the green growth index. The absence of green patents, innovation (patent applications), and foreign direct investment (FDI) unexpectedly reveals consequences on the environment. Improving the long-term sustainability of China’s mineral extracting sector should be a priority for policymakers. To achieve this, we need to reinforce legal frameworks, encourage green economic growth (GEG), integrate foreign direct investment (FDI) with sustainable methods, incentivised green innovations and promote the import of green technology.

Graph and download economic data for Nasdaq Clean Edge International Green Energy Total Return Index (NASDAQCELSIT) from 2021-12-23 to 2025-12-01 about return, NASDAQ, energy, indexes, and USA.

AbstractThe future of the global ocean economy is currently envisioned as an advancement towards a ‘Blue Economy’—socially equitable, environmentally sustainable, and economically viable ocean industries. However, there are current tensions between development discourses from perspectives of natural capital versus social equity and environmental justice. Here we show there are stark differences in Blue Economy outlooks when social conditions and governance capacity beyond resource availability are considered, and highlight limits to establishing multiple overlapping industries. The key differences in regional capacities to achieve a Blue Economy are not due to available natural resources, but include factors such as national stability, corruption, and infrastructure, that can be improved through targeted investments and cross-scale cooperation. Knowledge gaps can be addressed by integrating historical natural and social science information on the drivers and outcomes of resource use and management, thus identifying equitable pathways to establishing or transforming ocean sectors. Policy-makers must engage researchers and stakeholders to promote evidence-based, collaborative planning that ensures that sectors are chosen carefully, local benefits are prioritized, and the Blue Economy delivers on its social, environmental, and economic goals. , MethodsThis dataset presents all results necessary to reproduce the figures and analysis in the corresponding peer-reviewed article. All input data are also included, but any use must give credit to their original authors and sources; we strongly urge users to personally contact corresponding authors. These are specifically noted in the Supplementary Information 3 file of our peer-reviewed article, and include: Hutchison J, Manica A, Swetnam R, Balmford A, Spalding M (2014) Predicting global patterns in mangrove forest biomass. Conservation Letters 7(3): 233–240. http://data.unep-wcmc.org/datasets/39 McOwen C, Weatherdon LV, Bochove J, Sullivan E, Blyth S, Zockler C, Stanwell- Smith D, Kingston N, Martin CS, Spalding M, Fletcher S (2017). A global map of saltmarshes. Biodiversity Data Journal 5: e11764. http://data.unep-wcmc.org/datasets/43 UNEP-WCMC, Short FT (2016). Global distribution of seagrasses (version 4.0). Fourth update to the data layer used in Green and Short (2003). Cambridge (UK): UNEP World Conservation Monitoring Centre. http://data.unep-wcmc.org/datasets/7 Zheng, C.-W., and Pan, J. 2014. Assessment of the global ocean wind energy resource. Renewable and Sustainable Energy Reviews 33: 382–391. doi:10.1016/j.rser.2014.01.065. Bonjean F. and G.S.E. Lagerloef, 2002 , Diagnostic model and analysis of the surface currents in the tropical Pacific ocean, J. Phys. Oceanogr., 32, 2,938-2,954 https://podaac.jpl.nasa.gov/dataset/OSCAR_L4_OC_third-deg General Bathymetric Chart of the Oceans (GEBCO). https://www.bodc.ac.uk/data/documents/nodb/301801/ Wessel, P., and W. H. F. Smith. 1996. A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. World Tourism Organization (UNWTO). 2018. Yearbook of tourism statistics. Data 2012-2016. UNWTO, Madrid. DOI: https://doi.org/10.18111/9789284419531 Gagné, T. O., Reygondeau, G., Jenkins, C. N., Sexton, J. O., Bograd, S. J., Hazen, E. L., & Van Houtan, K. S. 2020. Towards a global understanding of the drivers of marine and terrestrial biodiversity. PloS one, 15(2), e0228065. Reygondeau, G. 2019. Current and future biogeography of exploited marine exploited groups under climate change. In: Predicting Future Oceans (pp. 87-101). Elsevier. Cheung, William W. L., Vicky W. Y. Lam, Jorge L. Sarmiento, Kelly Kearney, Reg Watson, Dirk Zeller, and Daniel Pauly. 2010. “Large-Scale Redistribution of Maximum Fisheries Catch Potential in the Global Ocean under Climate Change.” Global Change Biology 16 (1): 24–35. doi:10.1111/j.1365-2486.2009.01995.x Oyinlola, M.A., Reygondeau, G., Wabnitz, C.C., Troell, M., and Cheung, W.W. 2018. Global estimation of areas with suitable environmental conditions for mariculture species. PLoS One 13(1): e0191086. The Fund For Peace (FFP). 2018. Fragile States Index. https://fragilestatesindex.org/2018/04/24/fragile-states-index-2018-annual-report/ United Nations Development Programme (UNDP). 2017. Gender Inequality Index. http://hdr.undp.org/en/content/gender-inequality-index-gii Daniel Kaufmann, Aart Kraay and Massimo Mastruzzi. 2010. The Worldwide Governance Indicators: A Summary of Methodology, Data and Analytical Issues. World Bank Policy Research Working Paper No. 5430. www.govindicators.org World Bank. 2018. DataBank. https://databank.worldbank.org/data/home Halpern et al. 2012. An index to assess the health and benefits of the global ocean. Nature 488(7413): 615–620. ... Visit https://dataone.org/datasets/sha256%3Af9e82883e4230fb763985d32319af3c4806e4b6a387dd4c60d278e1b9e020a28 for complete metadata about this dataset.

Switzerland ImPI: Weights: AP: Beverage Crops: Green Coffee data was reported at 0.336 % in 2025. This stayed constant from the previous number of 0.336 % for 2024. Switzerland ImPI: Weights: AP: Beverage Crops: Green Coffee data is updated yearly, averaging 0.336 % from Dec 2022 (Median) to 2025, with 4 observations. The data reached an all-time high of 0.336 % in 2025 and a record low of 0.336 % in 2025. Switzerland ImPI: Weights: AP: Beverage Crops: Green Coffee data remains active status in CEIC and is reported by Swiss Federal Statistical Office. The data is categorized under Global Database’s Switzerland – Table CH.I025: Import Price Index: Weights.

Graph and download economic data for Nasdaq Clean Edge International Green Energy Index (NASDAQCELSI) from 2021-12-23 to 2025-11-11 about NASDAQ, energy, indexes, and USA.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Indonesia Wholesale Price Index: Agriculture: Seasonal Agriculture: Green Beans data was reported at 121.200 2018=100 in Dec 2023. This records a decrease from the previous number of 128.570 2018=100 for Nov 2023. Indonesia Wholesale Price Index: Agriculture: Seasonal Agriculture: Green Beans data is updated monthly, averaging 97.470 2018=100 from Jan 2020 (Median) to Dec 2023, with 48 observations. The data reached an all-time high of 128.570 2018=100 in Nov 2023 and a record low of 80.920 2018=100 in May 2021. Indonesia Wholesale Price Index: Agriculture: Seasonal Agriculture: Green Beans data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IB008: Wholesale Price Index: by Sector: Agricultural (Discontinued).

Imports of environmental goods comprise all environmental goods entering the national territory. A relatively high share of environmental goods imports indicates that an economy purchases a significant share of environmental goods from other economies. Exports of environmental goods comprise all environmental goods leaving the national territory. A relatively high share of environmental goods exports indicates that an economy produces and sells a significant share of environmental goods to other economies. An economy’s environmental goods trade balance is the difference between its exports and imports of environmental goods.Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, environmental goods), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in environmental goods, while a value of less than one indicates a relative disadvantage.Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. Accessed on 2023-06-28; International Monetary Fund (IMF) Direction of Trade Statistics (DOTS). https://data.imf.org/dot. Accessed on 2023-06-28. World Economic Outlook (WEO) Database. https://www.imf.org/en/Publications/WEO/weo-database/2022/April. Accessed on 2023-06-28; IMF staff calculations.Category: Cross-Border IndicatorsData series: Comparative advantage in environmental goodsEnvironmental goods exportsEnvironmental goods exports as percent of GDPEnvironmental goods exports as share of total exportsEnvironmental goods importsEnvironmental goods imports as percent of GDPEnvironmental goods imports as share of total importsEnvironmental goods trade balanceEnvironmental goods trade balance as percent of GDPTotal trade in environmental goodsTotal trade in environmental goods as percent of GDPMetadata:Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.Methodology:Environmental goods imports and exports are estimated by aggregating HS 6-digit commodities identified as environmental goods based on OECD and Eurostat, The Environmental Goods & Services Industry: Manual for Data Collection and Analysis, 1999, and IMF research. Total goods imports and exports are estimated by aggregating all commodities. Environmental goods trade balance is calculated as environmental goods exports less environmental goods imports. A positive trade balance means an economy has a surplus in environmental goods, while a negative trade balance means an economy has a deficit in environmental goods.Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are environmental goods to the proportion of global exports that are environmental goods. Total trade in environmental goods is calculated as the sum of environmental goods exports and environmental goods imports. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.Methodology Attachment Environmental Goods Harmonized System Codes

Russia Consumer Price Index (CPI): Weights: Food: Tea: Green data was reported at 0.116 % in 2024. This records a decrease from the previous number of 0.122 % for 2023. Russia Consumer Price Index (CPI): Weights: Food: Tea: Green data is updated yearly, averaging 0.111 % from Dec 2020 (Median) to 2024, with 5 observations. The data reached an all-time high of 0.122 % in 2023 and a record low of 0.079 % in 2021. Russia Consumer Price Index (CPI): Weights: Food: Tea: Green data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA030: Consumer Price Index: Weights.

Import Price Index: FF: AF: GC: Green Coffee data was reported at 292.800 Dec2007=100 in Mar 2025. This records an increase from the previous number of 259.700 Dec2007=100 for Feb 2025. Import Price Index: FF: AF: GC: Green Coffee data is updated monthly, averaging 132.600 Dec2007=100 from Dec 2007 (Median) to Mar 2025, with 208 observations. The data reached an all-time high of 292.800 Dec2007=100 in Mar 2025 and a record low of 84.800 Dec2007=100 in Mar 2009. Import Price Index: FF: AF: GC: Green Coffee data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.JA234: Import Price Index: by End Use: 2000=100.

This dataset collection provides a comprehensive overview of AI development trends, sustainability metrics, and global performance across various domains between 2010 and 2024. The data aims to support the advancement of the Three Zeros (Zero Emissions, Zero Waste, Zero Poverty) framework and foster AI-driven solutions for sustainable urban development. Below is a description of each dataset:

AI_Development_Trends_2010_2024.csv This file tracks the global advancements in artificial intelligence from 2010 to 2024, highlighting key trends, innovations, and technologies shaping the AI landscape.

AI_Index_Countries_2010_2024.xlsx This dataset presents the AI adoption index of various countries, reflecting their AI research, development, and implementation efforts over the past decade.

AI_data.txt A text-based dataset that includes raw AI-related data, such as research papers, patent filings, and global AI initiatives, enabling text-based analysis and natural language processing tasks.

GGGI_Country_Data.xlsx This file provides data on the Green Growth Index (GGGI) for different countries, focusing on their progress toward sustainable development, including economic growth, environmental preservation, and social equity.

Keyword_CoOccurrence_Matrix.xlsx This matrix captures the frequency of co-occurrence of key terms in AI and sustainability research articles, facilitating keyword analysis and identification of emerging topics.

Keyword_CoOccurrence_VOSviewer.xlsx A dataset that structures the keyword co-occurrence data into a format compatible with VOSviewer, enabling network visualization of research themes and trends.

Three_Zeros_Network.xlsx A dataset focused on the implementation of the Three Zeros framework, tracking countries' efforts in achieving zero emissions, zero waste, and zero poverty through AI and technological innovations.

bibliometric_data.csv.txt This dataset contains bibliometric information for AI-related publications, providing insights into publication patterns, citations, and collaborations in the AI research community.

co_occurrence_data.cs A dataset detailing the co-occurrence of specific keywords and topics within

Egypt Agricultural Production Index: Plants: VS: Vegetables: Green Beans data was reported at 100.800 2007-2008=100 in 2015. This records a decrease from the previous number of 142.700 2007-2008=100 for 2014. Egypt Agricultural Production Index: Plants: VS: Vegetables: Green Beans data is updated yearly, averaging 106.750 2007-2008=100 from Dec 2008 (Median) to 2015, with 8 observations. The data reached an all-time high of 142.700 2007-2008=100 in 2014 and a record low of 100.000 2007-2008=100 in 2008. Egypt Agricultural Production Index: Plants: VS: Vegetables: Green Beans data remains active status in CEIC and is reported by Ministry of Agriculture and Land Reclamation. The data is categorized under Global Database’s Egypt – Table EG.B009: Agricultural Production Index: 2007-2008=100. Rebased from 2007-2008=100 to 2012-2013=100 Replacement series ID: 402824827