Integrating Climate Change Factors within China’s Environmental Impact Assessment Legislation: New Challenges and Developments
Bibliographic record
Abstract
Climate change and its undeniable impacts must be considered while applying the existing development tools. As a preventative instrument to identify, assess and mitigate the adverse environmental effects of proposed and current undertakings, the incorporation of the impacts of climate change into Environmental Impact Assessment (EIA) has been recommended. This article finds that EIA can be more beneficial with a ‘climate change - plan/project - environment’ interaction, where the climate change impacts on a proposed plan/project and the environment are also assessed. In that case, the integration of climate change issues within EIA can improve the resilience of the proposed plan/project. Although difficulties of integrating climate change within EIA are apparent (such as scientific uncertainty, the difficulty of separating climate variability and the interaction between climate change and economic activities), various approaches have been developed to overcome these challenges. Canada’s experience will be used as an example to illustrate how EIA in China can integrate climate change factors. However, given the ineffectiveness of China’s current EIA legislation, significant improvements are imperative to provide climate-friendly and climate-proofing solutions. This article also reveals that integration of climate change does not change the essential steps of EIA, but will inevitably influence some minor steps by accounting for climate change factors.
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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 | 0.024 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".