TREND: World Bank. Climate Change Data [Archive]: GHG Emissions and Energy Use | Country: Algeria, Argentina, Australia, Austria, Azerbaijan, Bangladesh, Barbados, Belarus, Belgium, Brazil, Canada, Chile, China, Congo (Brazzaville), Cote D'Ivoire, Denmark, Djibouti, Eritrea, France, Gambia, Germany, Guatemala, Hong Kong, India, Indonesia, Israel, Japan, Kazakhstan, Malaysia, Mexico, New Zealand, Norway, Pakistan, Philippines, Russia, Saudi Arabia, Singapore, South Korea, Switzerland, United Kingdom, United States | Socioeconomic Indicator: CO2 emissions per units of GDP, 1990 - 2008. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 051-005-004
Bibliographic record
Abstract
datasets.shared.infosheet.CitationMgr@146c Dataset: Presents data on the emissions of greenhouse gases (GHG) and energy use, including measures of energy efficiency and carbon intensity of economy. The data are sourced by the World Bank from national and international organizations, including the Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory (Tennessee, United States), and the International Energy Agency. The World Bank Climate Change Database present statistics that relate to climate change, development, and the relationship between the two. The over 70 indicators included in the dataset are grouped into four areas: Size of the Economy, which presents data on population size and Gross Domestic Product (GDP); Exposure to Impacts, which provides data that suggest the degree to which a country or region may be exposed to changes in the prevailing climate and natural environment, with potential impacts on human health, food security, water supply, and physical infrastructure; Resilience, which includes data on the capacity of human systems to cope with the impacts of climate change, such as economic capacity, institutional capacity, and extent of public infrastructure; GHG Emissions and Energy Use, which includes data on the emissions of greenhouse gases (GHG) and energy use, including measures of energy efficiency and carbon intensity of economy. Data are shown for economies with populations greater than 30,000 or for smaller economies if they are members of the World Bank. Where available, data are provided for all countries and by nation covering the period 1990-2011. http://data.worldbank.org.ezproxy.snhu.edu/topic/climate-change Category: Natural Resources and Environment, Energy Resources and Industries Subject: Climate Change, Air Pollutants, Energy Consumption, Air Pollution, Energy Demand Source: World Bank Founded in 1944 as a result of the Bretton Woods Conference, the World Bank is an independent specialized agency of the United Nations that provides financial and technical assistance to developing countries in order to foster economic growth and reduce poverty. Today, the World Bank Group comprises five institutions owned by 187 member countries: the International Bank for Reconstruction and Development (IBRD), which focuses on middle-income and creditworthy poor countries; the International Development Association (IDA), which focuses on least developed countries; the International Finance Corporation (IFC); the Multilateral Guarantee Agency (MIGA); and the International Centre for the Settlement of Investment Disputes (ICSID). http://www.worldbank.org.ezproxy.snhu.edu/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.061 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".