Social entropic risk potential in Lima’s concrete production: A social life cycle assessment using social hotspot database
Bibliographic record
Abstract
■ S-LCA of 1 m³ concrete in Peru using Social Hotspots Database (SHDB) ■ Aligns LCA and S-LCA, targeting 11 high-entropy social risk subcategories ■ Peru accounts for 82.6% MRHEq; cement 38%, oil-related processes 4.8% ■ “Health & Safety” and “Labour Rights” subcategories show the highest social risk level ■ Calls for local data integration to improve social risk hotspot accuracy Concrete production has significant impacts on multiple environmental dimensions, yet the social risks embedded in its supply chains remain less examined. This study applies a Social Life Cycle Assessment (S-LCA), guided by the United Nations Environment Programme (UNEP) and the Society of Environmental Toxicology and Chemistry (SETAC) methodology, to evaluate the social risks levels associated with producing 1 m³ of concrete in Lima, Peru. Building on a previous environmental LCA for the same functional unit and system boundaries, processes for water, sand, gravel, and cement extraction, production, and end-of-life disposal were mapped to the Social Hotspots Database (SHDB) to enable a harmonized assessment. The UNEP subcategories were then aligned with SHDB social themes. From this alignment, 11 subcategories were prioritized for their relevance to entropic implications, defined here as irreversible changes in nature caused by material and energy transformations in urban systems. Results are presented across 5 general categories, 30 subcategories, and 11 detailed themes. Local social risks dominate, with Peru contributing 82.6% of MRHEq. Cement production is the most significant contributor to the total risk estimated by the SHDB, although oil-related processes in South Central Africa also play a role. The SHDB categories contributing most to the estimated social risk levels are “Health and Safety” and “Labour Rights and Decent Work,” while ‘Poverty and Inequality” and “State of Environmental Sustainability” lead at the subcategory level. The methodology and results provide a transferable framework for S-LCA of building materials in contexts reliant on local resources. Findings highlight the value of S-LCA and SHDB for identifying social hotspots, while underscoring the need to integrate regional and qualitative data to ensure context-specific, realistic assessments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".