Mapping the Sustainability of Waste-to-Energy Processes for Food Loss and Waste in Mexico—Part 2: Environmental and Economic Analysis
Bibliographic record
Abstract
Mexico generated 8.9 million tons of food loss and waste (FLW) in the year 2022, which was primarily managed through linear economy schemes where final disposal sites are predominant, leading to significant contributions to climate change and economic losses. This study, as a continuation of a prior work (Part 1), aims to evaluate the economic and environmental feasibility of implementing waste-to-energy anaerobic digestion (WtE-AD) plants for FLW management across Mexico, identifying the key factors that influence the competitiveness of these systems compared to conventional waste management practices. Using the outcomes of an energy feasibility analysis (Part 1), alongside life cycle assessment (LCA), geographic information systems, and cost engineering, this research analyzes the eco-efficiency and viability of WtE-AD for FLW valorization throughout the Mexican territory. The results indicate that, with a conventional waste management fee of USD 35.4 t−1, only the largest WtE-AD plant in the State of Mexico (treatment capacity of 2536.8 t FLW d−1) achieved a positive net present value (NPV) of USD 70.1 million, while all the other plants remained economically unviable. Conversely, under an optimistic scenario with a fee of USD 139 t−1, plants processing over 50 t FLW d−1 achieved profitability, with the largest plant reaching an NPV of USD 1.15 billion. The environmental assessment showed that larger plants effectively act as carbon sinks, with the largest plant reducing emissions by up to −89.8 kg CO2 eq t−1 of FLW. These findings emphasize the need for appropriate waste management fees and governmental incentives to ensure the economic viability of WtE-AD plants. This study provides valuable insights for policymakers, highlighting the potential of WtE-AD technologies to contribute to Mexico’s energy transition and the shift toward a circular economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".