Application of emergy as environmental performance indicator for assisting sustainable cities planning
Bibliographic record
Abstract
Constant increase in resource consumption and the associated waste generation rate in urban regions are leading to a less and less bearable ecological footprint. These problems are directly or indirectly associated to deficient planning, particularly, high resource utilization by the residential sector. One key point to better inform planning decisions on urban development contexts is the influence of urban form, that is the nature and intensity of occupation of the city’s territory, on resource consumption intensity. \n \nThe general objective of this work was to explore the appropriateness of emergy synthesis, an environmental accounting method, to assist urban planning decision-making efforts, applying the principles of the systems approach to the analysis of the urban environment, particularly from the perspective of the energy footprint through time (emergy) generated by the primary function of accommodating people (residential land use), that is, giving the highest priority to the understanding of the entire system behaviour from the exploration of the structural relationships of its main elements: the housing, food, transportation, spending, natural resources and generated wastes subsystems. To achieve this objective, three geographic scales were assessed, the urban agglomeration, the housing unit and the borough, through the analysis of material, energy and economic flows, to explore the performance of emergy-based indicators under different densities, per capita living spaces, and other variables, such as per household income and household size. \n \nResults allowed the identification of availability of space and per capita Gross Domestic Product as important variables affecting emergy use intensity at the city level. At the housing unit level, results showed that, while a higher per household income increased per capita emergy use in all the analyzed cases, increasing the availability of space per resident did not result in a decrease of empower density after 50 m2/person. Finally, at the borough level, the results confirmed income, household size and distance to downtown as the variables affecting more noticeably emergy use intensity. \n \nFrom the findings, the methodological procedure and the data management conducted for the three scales of analysis reviewed in the work, it is possible to develop a tool for the rapid calculation of emergy use in areas subject to urban planning or future development plans, which associated to a geographic information systems platform, allows the spatial distribution diagnosis of emergy use intensity by means of emergy indicators maps. It is feasible to scale up the emergy calculator up to become a dynamic simulation tool.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".