Movements shaping climate futures: A systematic mapping of protests against fossil fuel and low-carbon energy projects
Bibliographic record
Abstract
Abstract In this article we undertake a systematic mapping of 649 cases of resistance movements to both fossil fuel (FF) and low carbon energy (LCE) projects, providing the most comprehensive overview of such place-based energy-related mobilizations to date. We find that (1) Place-based resistance movements are succeeding in curbing both fossil-fuel and low-carbon energy projects. Over a quarter of projects encountering social resistance have been cancelled, suspended or delayed. (2) The evidence highlights that low carbon, renewable energy and mitigation projects are as conflictive as FF projects, and that both disproportionately impact vulnerable groups such as rural communities and Indigenous peoples. Amongst LCE projects, hydropower was found to have the highest number of conflicts with concerns over social and environmental damages. (3) Repression and violence against protesters and land defenders was rife in almost all activities, with 10% of all cases analysed involving assassination of activists. Violence was particularly common in relation to hydropower, biomass, pipelines and coal extraction. Wind, solar and other renewables were the least conflictive and entailed lower levels of repression than other projects. The results caution that decarbonization of the economy is by no means inherently environmentally innocuous or socially inclusive. We find that conflicts and collective action are driven by multiple concerns through which community mobilization seeks to reshape the energy regime and its impacts. These include claims for localization, democratic participation, shorter energy chains, anti-racism, climate-justice-focused governance, and Indigenous leadership. Climate and energy policymakers need to pay closer attention to the demands and preferences of these collective movements pointing to transformative pathways to decarbonization.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".