Decarbonising cities: exploring regional energy justice implications
Bibliographic record
Abstract
To meet energy demand and achieve climate and energy decarbonisation targets, cities adopt a range of mechanisms to facilitate renewable electricity development from their surrounding regions. These mechanisms are likely to have implications for regional community co-benefits, social acceptance of renewable energy projects, and energy justice. This research used document analysis to identify the procurement mechanisms being used by cities to source renewable electricity from surrounding regions and the types of actors involved. The analysis focussed on 27 cities pursuing ambitious 100% renewable energy or carbon neutrality goals and whose plans indicate engagement with their surrounding regions. The results point to eight types of mechanisms used by cities to develop renewable energy in their surrounding region. Of the 56 occurrences identified, 55 involved public actors, 25 involved private actors, and 12 involved civic actors. The findings demonstrate that cities are overcoming their local energy constraints by seeking to develop renewable electricity in their surrounding regions utilising mechanisms that are dominated by the involvement of public and private actors, leaving civic actors underrepresented. Key policy highlightsCities with ambitious renewable energy goals require large amounts of renewable energy to decarbonise. To achieve their decarbonisation goals, cities are adopting a range of mechanisms to facilitate renewable electricity development in the regions that surround them.This study identifies eight types of mechanisms used by cities to drive renewable energy development within their surrounding region; power purchase agreements, project acquisition, city-led project development, incumbent-city collaborative project development, niche-city collaborative project development, centralised decision making, advocacy, and market stimulation. Of the 56 occurrences identified, most were dominated by public (n = 55/56) and private actors (n = 25/56), with little involvement of civic actors (n = 12/56) such as households, citizens and community organisations.Limited citizen involvement in renewable energy development can hinder equitable benefits and social acceptance for regional communities. Civic participation in regional energy development is essential for a just and successful energy transition.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".