Planning for Wind Power: A Study of Public Engagement in Uddevalla, Sweden
Bibliographic record
Abstract
Despite seemingly widespread support, wind power initiatives often experience controversial development processes that may result in project delays or cancelations. Wind power planning – often derided for ignoring the concerns of local residents – is ideally positioned to engage citizens in determining if and where development may be appropriate. Following the process of a dialogue based landscape analysis in Uddevalla, Sweden, the research endeavours to better understand the ties between landscape and attitudes towards wind power, how concerned parties express these attitudes, and how these attitudes may change through public engagement. In contrast to many existing quantitative studies, the research uses one-on-one interviews with participants of the planning processes to provide a rich qualitative resource for the exploration of the topic. Five themes emerging from the interviews and their analysis, are explored in depth. These themes include: landscape form and function; the expression of public attitudes; changing attitudes; frustration with politicians and processes; and engagement and representation. Consideration is also given to landscape analysis as a method, wind power and planning in the Canadian context, and planning theory. The research indicates that attitudes towards wind power development are tied to planning processes, specifically: that the collective landscape shapes values and attitudes, is developed through discourse, and is composed of competing visions; that when formal avenues of discourse are closed, citizens will self-organize to exert political pressure with potentially damaging side-effects on relationships within the community; and that planning, when directed towards recognising and addressing concerns, can generate positive outcomes concerning attitudes towards wind power. Through engagement processes that embrace the multifaceted nature of landscape, value qualitative factors, are fair and transparent, better localize the benefits of development, and generate unique place-based policies, it is possible to acknowledge and address local concerns while still achieving global renewable energy goals. The research findings and recommendations are expected to be of interest to municipalities, citizens, and renewable energy developers to help form a shared and democratic sustainable energy future.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".