Working towards a Green Economy – Meaning, Measures, Policies & Implementation
Bibliographic record
Abstract
The transition towards a Green Economy is a critical human response to the imminent threat of climate change, driven primarily by anthropogenic global warming. This paper explores the multifaceted aspects of Green Economy, encompassing sustainable development and economic growth that mitigates environmental degradation. The concept is grounded in the UNEP definition of a Green Economy, emphasizing improved human well-being and social equity while reducing environmental risks. Key areas include renewable energy, sustainable transport, green building, water and waste management, and land management. Measurement of progress towards a Green Economy is examined through various indices like the Global Green Economy Index (GGEI) and methodologies proposed by OECD. The challenges faced by developing countries in monitoring and achieving Green Growth are discussed, highlighting the need for enhanced statistical capacities and integrated policy frameworks. Policies for transitioning to Green Economic Growth are analyzed, with a focus on developing countries and strategic sectors. The paper also delves into specific policy instruments such as environmental labeling, green subsidies, payments for ecosystem services, environmental taxes, and promotion of green energy investments. Additionally, it discusses strategic trade policies and innovation indicators, using China as a case study to illustrate the potential benefits and challenges. The conclusion underscores the necessity of harmonizing economic growth with sustainability, advocating for a model where Green Economic Growth serves as both a driver of economic development and a solution to environmental challenges. This holistic approach is essential to prevent economic regress and ensure a sustainable future for all.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".