Leadership and Climate Change Mitigation: A Systematic Literature Review
Bibliographic record
Abstract
This systematic literature review (SLR) explores leadership and climate change mitigation in cities. In doing so, it investigates explicit meanings of leadership, enablers of leadership, and leadership similarities and differences across regions. The review utilized three databases on 8 March 2024—Scopus, ProQuest, and Web of Science—curating an initial 496 results, resulting in 30 studies in the final analysis, using a two-reviewer screening process to limit bias and ensure consistency of approach. Inclusion criteria included English-language peer-reviewed articles over a ten-year period. The timeframe used was limited to January 2014 to December 2023 (10 years) to focus on the lead up to and post-implementation of the Paris Agreement. Further, empirical and conceptual studies were included to provide readers of this review with a thorough understanding of leadership work completed since 2014. Exclusion criteria included any studies that focus on adaptation measures and forms of leadership where the focus is on the private business, state, or national level, including leadership and climate change mitigation outside the influence of the local government. The study highlights five distinct meanings of leadership using the Braun and Clarke method of thematic analysis. It found leadership themes related to people (e.g., mayors), policy (e.g., ambitious climate plans), ideas (e.g., new concepts), collective action (e.g., motivating others), and mobilizing power (e.g., through regulations). The enablers of leadership included polycentricity, social capital influences, co-creational and mayor leadership, climate governance, and multi-actor coordination. This review segments the studies based on the findings from the literature, which focus on three continents (North America, Europe, and Asia) with a distinct difference in the meaning and enablers of leadership based on region. The 30 articles shared similarities in content, such as strong mayoral influence, but also had some distinct differences, such as how leadership is enacted based on leveraging market mechanisms, policy, and horizontal and vertical coordination. Finally, research gaps were identified, such as the scant focus on leadership and climate change mitigation in the Global South, to enable future research. Limitations of this study include the utilization of three databases, a focus on only English-language peer-reviewed articles, and a strong climate change mitigation focus.
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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 | 0.026 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".