Evaluating the Effectiveness and Challenges of Climate Change Policies in Afghanistan
Bibliographic record
Abstract
ABSTRACT Climate change poses a significant global issue affecting a wide range of sectors and countries. Despite emitting relatively low levels of greenhouse gases, Afghanistan is one of the countries most susceptible to climate change crises owing to its political, geographical, and socioeconomic factors. To tackle this global challenge, international initiatives such as the UNFCCC, Kyoto Protocol, and the Paris Agreement have been established to implement mitigation and adaptation strategies. In this study, we employed a policy analysis framework to evaluate Afghanistan's climate change policies and strategies. We conducted an extensive review of relevant documents and used Walt and Gilson's policy triangle framework to assess Afghanistan's approach to climate change governance. Our findings indicate that Afghanistan has actively participated in international climate agreements and has established institutional mechanisms, such as the NEPA, to address climate change. Policies and strategies, including the National Environment Strategy, the National Environmental Action Plan, and the Climate Change Strategy and Action Plan, demonstrate Afghanistan's commitment to integrating climate change considerations into its national development agenda. However, regarding effectiveness, these policies were largely theoretical and remained on paper, failing to have a significant impact on combating climate change. Afghanistan still faces several challenges, such as political instability, limited resources, and restricted access to climate financing, which hinder the successful implementation of these policies. Afghanistan's susceptibility to climate change requires concerted efforts to mitigate its impact and enhance its resilience. While international agreements offer frameworks for action, it is crucial to address these challenges through global cooperation and support, enabling Afghanistan to combat climate change effectively and safeguard its environment and population.
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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 | 0.040 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".