Game-theoretic DEA optimization for sustainable agricultural carbon trading: Evidence from Türkiye’s maize production
Bibliographic record
Abstract
• New carbon policy is introduced to agricultural contexts with limited MRV capacity. • DEA is used to classify farms by carbon-equivalent performance and efficiency levels. • A cooperative Nash game is developed to model group-level adoption of the policy. • A hybrid metaheuristic–gradient based optimization computes stable Nash equilibria. • A Türkiye maize case is presented to demonstrate policy impacts and feasibility. Effective agricultural carbon policy requires reliable monitoring and frameworks that consider efficiency, welfare, and adoption. Although monitoring systems are typically stronger in developed countries, they are costly to maintain, whereas in developing countries they are often limited or insufficient. We hypothesize that a performance-based carbon policy integrated with welfare and farm decision behavior improves suitability for heterogeneous agricultural settings under limited emissions data. This paper proposes a Forward-Looking Cooperative Cap-and-Trade Carbon Pricing (FCTCP), policy that assesses farms’ carbon performance using carbon-equivalent resource, operational, and agrochemical inputs instead of direct carbon emission data, while integrating farm-level decision-making, cooperative carbon trading, and a welfare indicator aligned with emission performance. In this framework, farms are classified using Data Envelopment Analysis into efficient, near-efficient, and inefficient categories, and their policy participation is modeled through a cooperative Nash game. Nonlinear strategic interactions are solved using a hybrid optimization scheme consisting of four metaheuristic algorithms, including Particle Swarm Optimization, Artificial Bee Colony, Grey Wolf Optimizer, and Genetic Algorithm, and a nonlinear quasi-Newton optimization method, L-BFGS-B, to identify robust Nash equilibria. A case study of 104 maize farms in Türkiye shows green welfare improvements of 9.65% for near-efficient farms and 15.23% for inefficient farms while achieving the low-carbon benchmark. Key determinants of adoption include efficiency-based caps, asymmetric trading rules, and carbon exchange without discounting. The results demonstrate that FCTCP is a practical, welfare-linked, and forward-looking policy mechanism capable of guiding early-stage carbon transitions in agricultural systems lacking mature carbon monitoring infrastructure.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".