Evaluating environmental impacts of concrete in Lima: Bridging the gap between quantitative LCA results and local contexts
Bibliographic record
Abstract
Abstract Concrete production significantly contributes to environmental impacts in the built environment, especially in rapidly developing urban areas like Lima, Peru, where concrete is a predominant building material, accounting for over 80% of the weight in typical housing structures and significantly influencing environmental impacts. While traditional studies have concentrated on the environmental repercussions of cement production (10-20% of the concrete mix), a major contributor to carbon emissions, this study also focuses on the often-overlooked other impacts of concrete production. These impacts are often felt on a more local scale. They are associated with the other non-cement constituents of concrete, namely water, aggregates, and sand, representing about 80% of the mix mass. While the carbon impacts of non-cementitious materials may appear relatively low compared to cement, their significance becomes evident when viewed within the local context and their potentially correlated qualitative effects throughout the entire life cycle. In this sense, this paper does not only look at climate change impacts of cement production but also eight other critical environmental indicators, including particulate matter, land use, water use, and resource consumption, which are quantitively evaluated using a Life Cycle Assessment (LCA). Hence, this paper employs a quantitative approach to analyze the life cycle impacts of concrete use in Lima. The results offer valuable insights into the environmental impacts within the concrete supply chain in Lima, corroborating existing literature and affirming the robustness of the LCA methodology. Moreover, the results underline the pivotal role played by non-cementitious components like water, sand, and gravel in shaping the overall environmental footprint of concrete. However, the study primarily underscores the necessity of integrating alternative evaluation methods to comprehensively understand impacts, particularly for identifying overall local impacts. Consequently, a key contribution of this paper is highlighting the gaps in focusing solely on LCA to understand the sustainability of concrete production. It emphasizes how the results of an LCA may assist in identifying such gaps and pose questions for considering a more holistic understanding of the social and local impacts of concrete production. This approach aims to uncover entropic impacts across the production spectrum, paving the way for future research in unexplored areas of concrete production’s environmental and social implications.
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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 | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".