Synergies and trade-offs between climate change adaptation options and gender equality: a review of the global literature
Bibliographic record
Abstract
Abstract Climate change impacts are being felt across sectors in all regions of the world, and adaptation projects are being implemented to reduce climate risks and existing vulnerabilities. Climate adaptation actions also have significant synergies and tradeoffs with the Sustainable Development Goals (SDGs), including SDG 5 on gender equality. Questions are increasingly being raised about the gendered and climate justice implications of different adaptation options. This paper investigates if reported climate change adaptation actions are contributing to advancing the goal of gender equality (SDG 5) or not. It focuses on linkages between individual targets of SDG 5 and climate change adaptation actions for nine major sectors where transformative climate actions are envisaged. The assessment is based on evidence of adaptation actions documented in 319 relevant research publications published during 2014–2020. Positive links to nine targets under SDG 5 are found in adaptation actions that are consciously designed to advance gender equality. However, in four sectors—ocean and coastal ecosystems; mountain ecosystems; poverty, livelihood, sustainable development; and industrial system transitions, we find more negative links than positive links. For adaptation actions to have positive impacts on gender equality, gender-focused targets must be intentionally brought in at the prioritisation, designing, planning, and implementation stages. An SDG 5+ approach, which takes into consideration intersectionality and gender aspects beyond women alone, can help adaptation actions move towards meeting gender equality and other climate justice goals. This reflexive approach is especially critical now, as we approach the mid-point in the timeline for achieving the SDGs.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".