GIS for the Potential Application of Renewable Energy in Buildings towards Net Zero: A Perspective
Bibliographic record
Abstract
Environmental, economic, and social activities involve inherent spatial dimensions. The geospatial information system (GIS), a platform containing principles, methods, and tools to link, create, visualize, analyze, and model artificial activities and environment, provides the possibility to develop sustainability in the building sector. With globally political collaborations across governments, the demands to manage and visualize sustainable data (e.g., building energy and environment with geospatial reference) and implement more rigorous building modelling are increasing. A systematic mapping at multiple scales will help urban engineers, architectural engineers, policymakers, and energy planners identify emission hotspots, locate spatial resources, restructure district energy mix, and achieve net zero energy targets. To achieve net zero energy goals, it is crucial to minimize energy consumption, improve energy efficiency, and most importantly, apply renewable energy in buildings. However, these processes imply many aspects and challenges, regarding e.g., data availability, scalability, integrability, and a lack of clear and applicable frameworks. In this conceptional perspective paper, we aim to explore the potential of applying and installing renewable energy in net zero energy buildings using the GIS. More specifically, the described virtual framework will effectively support policy- and decision-makers in optimizing the energy structure, reducing building emissions, and applying renewable energy technologies. We also present challenges, limitations, and future directions for real practice.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".