Implementation Plan for Low-carbon Resilient City towards Sustainable Development Goals: Challenges and Perspectives
Bibliographic record
Abstract
Mitigation and adaptation are two climate approaches for reducing risks and preparing for hazards caused by the climate change. The development of low-carbon resilient cities must integrates mitigation and adaptation, which has great potential in decarbonization and natural disaster prevention However, limited studies have focused on systematizing the low-carbon resilience approach. To address this problem, this study aims to develop an implementation plan of low-carbon resilient cities. A theoretical framework is designed to analyze integrated strategies and key performance indicators for low-carbon resilient cities. Two related schemes, the “SIEGE” scheme and “5P” scheme, are provided to assist in the planning and governance. Challenges for implementing a low-carbon resilience approach are discussed from institutional, regulatory, financial, technical and communication perspectives. Guided by the theoretical frameworks and practical strategies, cities will be able to move towards social equality, energy-efficiency, hazards preparedness, governing-effectiveness and innovation.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".