Harnessing nearshoring for energy justice: a fuzzy TOPSIS-based framework for equitable energy transition in Mexico
Bibliographic record
Abstract
Abstract Nearshoring is reshaping Mexico’s industrial geography and electricity demand, creating opportunities for growth while raising energy-justice concerns over who benefits, who pays, and whose needs are recognized. This study synthesizes policy evidence and four cases (Monterrey, Ciudad Juárez, Oaxaca, Volkswagen’s clean-sourcing) to examine distributional, procedural, and recognition dimensions. To translate qualitative insights into priorities, this work implements a compact, desk-based linguistic Fuzzy TOPSIS with six policy packages evaluated against nine criteria. Each alternative–criterion pair is rated using evidence-linked linguistic labels mapped to triangular fuzzy numbers. It was reported equal weights and two justice-scenario weights sets (distributional-first, recognition-first) and assess robustness via leave-one-criterion-out tests. Community microgrids with virtual net metering ranks first, followed by targeted T&D with community-benefit agreements. The top-two remain unchanged under both justice scenarios, only dropping the environmental criterion flips their order. This work concludes that nearshoring can accelerate a just energy transition if community-scale solutions are sequenced with grid reinforcement and embedded participation/benefit-sharing. Without guardrails, nearshoring risks deepening inequities and conflict.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".