Integrating Land-Use and Renewable Energy Planning Decisions: A Technical Mapping Guide for Local Government
Bibliographic record
Abstract
Land-based, utility-scale renewable energy (RE) systems using wind or solar resources to generate electricity is becoming a decisive solution to meet long-term carbon emission reduction goals. Local governments are responding in kind, by adopting their own goals and/or establishing policies to facilitate successful implementations of RE in their jurisdiction. One factor to successful RE development is to locate the most suitable lands, while continuing to sustain land-based economies and ecosystem services. Local governments often have limited resources; and this is especially true for small, land-constrained local governments. In this paper, we illustrate how a standardized RE technical mapping framework can be used by local governments to advance the implementation of RE in land-constrained areas, through a case study in the Town of Canmore, Alberta. Canmore has a limited municipal area surrounded by the Canadian Rockies, along with complex land-use bylaw and environmentally sensitive habitats. This mapping framework accounts for these conditions as it considers theoretical resources, technically recoverable lands, legally accessible lands, and the spatial capital cost of connecting new RE facilities. Different land-use planning scenarios are considered including changing setback buffers and expanding restrictions on development to all environmentally sensitive districts. The total RE potentials are then estimated based on the least-conflict lands. Technically speaking, even under restrictive land suitability scenarios, Canmore holds enough land to achieve ambitious RE targets, but opportunities and challenges to implementation remain. To eventually succeed in its long-term emission reduction goal, the most decisive step for Canmore is to balance the growth of energy demands, land-use changes, and practicable RE development. Mapping systems that can study the influence of land-use planning decisions on RE potential are critical to achieving this balance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".