Implementing Community Sustainability Plans through Partnership: Examining the Relationship between Partnership Structural Features and Climate Change Mitigation Outcomes
Bibliographic record
Abstract
Addressing society’s most complex challenges, such as climate change, requires bringing together stakeholders from the business, government, and nonprofit sectors. At the municipal level, multi-stakeholder partnerships are often formed to implement community sustainability plans. However, these partnerships can create new challenges, as it is cumbersome to coordinate action among a group that is made up of such diverse stakeholders. Past research suggests that it is important for these partnerships to have the appropriate structures in place to mitigate some of the coordination challenges to which they are prone. Yet, very few studies have examined the influence that different structural features have on plan outcomes. This article seeks to address this important research gap by using quantitative methods to examine five different features that can compose partnership structures—oversight, monitoring and evaluation, partner engagement, communication, and community wide-actions and their impact on climate change mitigation outcomes. Based on data collected through a global survey and publicly available greenhouse gases emission data from 72 different partnerships that implement community sustainability plans (CSPs), this study finds that structural features related to oversight and community-wide actions are positively associated with climate change mitigation outcomes. These results indicate that certain features of partnership structures may be more important for achieving desirable climate change mitigation outcomes, and thus contribute to research on collaborative governance structures and climate action.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".