Environmental Impact Assessment and Optimization of Urban Energy Systems
Bibliographic record
Abstract
Over the last century, the world has witnessed rapidly increasing urbanization trends. Consequently, the urban governments of this époque require the measure and monitoring of their cities’ expansion, as well as the impacts that this development has on the environment, the economy and the society. The energy sector in particular, plays a determining role in maintaining acceptable conditions in all these domains. The concept of sustainable development appears to combine a number of disciplines, which assess it in different manners. This research attempts to show how a combination of methods can provide further insight to a city’s energy system. More specifically, the concepts of life cycle assessment and mixed-integer optimization are brought together and applied to a hypothetical urban energy systems case study looking at three different environmental impacts: global warming potential, resource depletion and air quality. The model chooses the types of energy technologies that are most suitable when aiming to minimize each environmental impact, showing that a carefully selected energy systems design can perhaps achieve lower overall environmental impact within an urban area. Life cycle assessment, material flow analysis and ecological footprint methodologies are further performed on two case studies: a UK eco-town and the city of Toronto. Five energy technology scenarios are compared based on these environmental impact assessment methodologies and conclusions drawn as to which scenario achieves the lowest values. Attention is drawn to stakeholder involvement and how interpretation of environmental impact is “vulnerable” depending to which priorities are set.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".