Scientific evolution of climate change justice: A bibliometric review
Bibliographic record
Abstract
The recognition of climate justice approaches for adaptation and mitigation by the United Nations Convention on Climate Change (UNFCCC) resulted in actions on ‘climate change justice’ in the early 2000s. Since then, the field has experienced rapid growth. This research identifies studies on climate change justice to understand how they highlight transformative actions. The study used a systematic review by a bibliometric analysis using 1464 documents indexed on the Web of Science (WOS), covering 26 years of research on climate change justice. The data were analyzed by SciMAT and ArcGIS for science mapping, detecting major focus areas, and understanding the development of the academic base of this field over time and the major themes in this evolution. The published documents were categorized into four distinct time frames: the post Kyoto Protocol (1997–2010), the climate change justice awakening (2011–2015), the post-Paris consensus (2016–2019), and the climate, biodiversity, and justice nexus (2020–2023) periods. The first period mainly focuses on a few themes, such as discourse, hazardous waste, and climate change justice principles. They become more diversified in the following periods to acknowledge the multidimensional characteristics of climate change justice. Moreover, political aspects are still dominant in these publications, while other important socio-economic subjects, e.g. transformative governance, collective actions, and participation, are poorly represented. Discourse, transitions, impacts, and policy are major thematic areas in configuring the advancement of climate change justice knowledge. This research can be a benchmark for researchers seeking to explore knowledge gaps related to climate change justice and its development.
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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 | 0.034 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.254 | 0.346 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".