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.