Challenges of Integrating Renewable Energy in Land Use
Bibliographic record
Abstract
This thesis examines the challenges of integrating land-based, utility-scale renewable energy (RE) systems, specifically wind and solar, into diverse urban and rural environments. It highlights these systems' pivotal role in achieving long-term carbon emission reduction goals. The study focuses on local government responses, including adopting specific goals and policies to facilitate successful RE implementation within their jurisdictions. Through detailed case studies of Canmore, Alberta, Canada, and the Indira Paryavaran Bhawan in New Delhi, India, this research compares strategies for incorporating solar photovoltaics (PV) in Canmore and building-integrated photovoltaics (BIPV) in the Indira Paryavaran Bhawan. The thesis comprehensively explains the challenges, obstacles, and strategies associated with renewable energy integration by integrating these case studies with a broad literature review.\nThe findings highlight the critical importance of balancing technological advancements with environmental preservation, fostering robust community engagement, and implementing innovative design solutions tailored to specific contexts. In Canmore, the primary challenges involve maintaining the town’s natural beauty and addressing seasonal weather variations while integrating PV systems. The city emphasizes careful site selection, aesthetic integration, and strong community involvement. Conversely, in the high-density urban environment of New Delhi, the challenges include maximizing energy efficiency within limited space and mitigating the urban heat island effect. Using BIPV in Indira Paryavaran Bhawan demonstrates the potential of innovative architectural solutions to achieve net zero energy status and optimize space usage.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".