Delivering urban energy infrastructure: the capacity of planning and governance networks in the cases of Barcelona, Burlington, Lerwick, London, and Toronto
Bibliographic record
Abstract
District heating and cooling (DHC) systems are a sound solution to environmental, energy security, climate change, and fuel poverty concerns in cities. As an environmental policy goal not fully controlled by government and subject to complex multi-actor negotiations, building DHC can be understood as requiring network governance patterns of behaviour. This thesis investigates the role of planning in delivering DHC, employing a conceptual framework of Actor-Centered Institutionalism to unravel the relationships between industry, government, and citizen organizations in governance networks. It contributes to a growing area of study on the intersection of urban development, energy infrastructure, and environmental policy goals. It responds to calls for further practical research on governance patterns of behaviour, applying a recognised network governance approach to the analysis of five international case studies. It also investigates the weight of institutional context and the purported connection between network interaction characteristics and policy outcomes. The research categorises a range of potential roles for planning organisations and planning interventions in governance networks for DHC. The comparative analysis suggests that planning organisations rarely negotiate for DHC but that planning interventions are regularly used to initiate governance networks for DHC and to shape negotiations by other actors. The findings outline a number of specific institutional factors, actor orientations and capabilities as well as qualities of interaction which affect the capability of governance networks to deliver DHC. The research contributes to the explanatory ambitions of network governance research and expanded understanding of the capacity of planning in building and managing urban energy infrastructure. The findings can potentially be extended to other urban utility infrastructure and environmental policy goals.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".