Assessment of Global Sustainable Development, Environmental Sustainability, Economic Development and Social Development Index in Selected Economies
Bibliographic record
Abstract
This study assesses the association of sustainable development (SD) with environmental technologies, forest area and developmental indictors in selected 39 economies. It develops global sustainable development index (GSDI) as an integration of environmental sustainability index (ESI), economic development index (EDI) and social development index (SDI) during 2000-2016 using composite Z-score technique. Thereupon, it explores the influence of environmental technologies, deforestation, ESI, EDI and SDI on GSDI using country-wise panel data. The results infer that there exists a high inequality in SD due to diversity in socio-economic structure of selected countries. Most developed economies have a better position in SD due to their relatively better position in environmental, economic and social developmental related variables. India, South Africa and Tunisia have low values of ESI, EDI and SDI, thus, these countries are in worst position in SD. Empirical results exhibit that SD is positively associated with environmental, economic and social development, forest area and environmental technologies. It recommended that protection of forest area maintains the quantity and quality of natural resources and provide ecological security. Accessibility of electricity for all community, discovery of environmental technologies, use of green technologies in production activities may be effective to increase socio-economic, environmental and sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".