Implications of circular strategies on energy, water, and GHG emissions in informal housing in Lima
Bibliographic record
Abstract
Informal and self-built housing developments worldwide are expanding rapidly, often following incremental construction phases that drive substantial resource demands and emissions as communities consolidate. In Lima, Peru, informal settlements exemplify this trend, revealing significant environmental impacts as progressive construction intensifies urban growth. Informal housing construction in Lima progresses through four key stages: Inception, Development, Completion, and Consolidation, each characterized by material additions and upgrades that increase embodied environmental impacts over time and across all life cycle phases. A Life Cycle Assessment (LCA) was applied to evaluate total greenhouse gas (GHG) emissions, water use, and fossil energy consumption across these four building consolidation stages. Additionally, three end-of-life scenarios—landfilling, recycling, and selective deconstruction were analyzed to assess the potential benefits of adopting a circular model in construction material use. The results show that the inception stage emits 1.6 Mt(CO2-eq), the development stage 23.15 Mt(CO2-eq), the completion stage 100 Mt(CO2-eq), and the consolidation stage 58 Mt(CO2-eq), totaling 183 Mt(CO2-eq). Due to lower average occupation density, informal housing emits approximately 450 % more GHG emissions per person than its formally constructed counterpart. The linear construction model exacerbates these impacts by generating substantial waste and requiring continual extraction of new materials. Selective deconstruction could reduce carbon emissions by 81 %, water use by 82 %, and fossil energy use by 80 % compared to landfilling. Recycling offers smaller reductions of 69 %, 68 %, and 67 %, respectively. These findings highlight the environmental benefits of integrating circular economy strategies and the need for sustainable material management policies in rapidly urbanizing areas.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".