Sustainable energy landscapes : designing, planning, and development
Bibliographic record
Abstract
In the near future the appearance and spatial organization of urban and rural landscapes will be strongly influenced by the generation of renewable energy. One of the critical tasks will be the re-integration of these sustainable energy landscapes into the existing environment—which people value and want to preserve—in a socially fair, environmentally sound, and economically feasible manner. Accordingly, Sustainable Energy Landscapes: Designing, Planning, and Development focuses on the municipal and regional scale, where energy-conscious interventions are effective, and stakeholders can participate actively in the transition process. This book presents state-of-the-art knowledge in the exciting new field of sustainable energy landscapes. It bridges the gap between theory and fundamental research on the one hand, and practice and education on the other. The chapters—written by experts in their fields—present a selection of interdisciplinary, cutting-edge projects from across the world, illustrating the inspiring challenge of developing sustainable energy landscapes. They include unique case studies from Germany, Taiwan, the United Kingdom, Canada, Denmark, Austria, Italy, and the United States. The editors and team of contributing authors aim to inspire readers, providing a comprehensive overview of sustainable energy landscapes, including principles, concepts, theories, and examples. The book describes various methods, such as energy potential mapping and heat mapping, multicriteria decision analysis, energy landscape visualization, and employing exergy and carbon models. It addresses how to quantify the impact of energy transition both on landscape quality and energy economy, issues of growing importance. The text infuses readers with enthusiasm to promote further research and action toward the important goal of building energy landscapes for a sustainable future.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".