Synergies between climate change adaptation and mitigation in development
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand how cities at different stages of development each subject to its own challenges in adapting to climate change can manage greenhouse gas (GHG) emissions. Design/methodology/approach Case studies are undertaken for three cities: Amman, Jakarta and Dar es Salaam, including determination of GHG emissions and analysis of climate change data (where available) for each. Findings In Amman, the most climate‐sensitive municipal service is water; Jordan is exceptionally dry, and nearly 15 per cent of all electricity consumption is by the water authority. Jakarta has already experienced extreme flooding. The climate vulnerabilities associated with sea‐level rise are intensified by subsidence in parts of Jakarta. Alternating floods and droughts are climate impacts already experienced in Dar es Salaam. Droughts have impacted Tanzania's electricity infrastructure disrupting hydroelectricity production, requiring new natural gas infrastructure to maintain power, thereby increasing GHG emissions. Nonetheless, Dar es Salaam's GHG emissions at 0.56 t CO2e/cap are small compared to Amman and Jakarta at 3.66 and 4.92 t CO2e/cap., respectively. Originality/value Synergist development strategies, addressing climate change mitigation and adaptation are suggested. In Amman an increased share of photovoltaic electricity production might be used for service provision, especially for energy needs surrounding water supply. Advanced slum upgrading in Jakarta could see relocation of the at‐risk poor to safe areas with energy efficient homes connected to public transit and decentralized, community‐based electricity generation. The focus in Dar es Salaam community‐based waste‐to‐energy facilities would reduce climate change impacts and vulnerabilities while addressing energy poverty in poor communities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".