Computable general equilibrium implementation in agriculture under the prism of sustainability and climate change: a systematic literature review

Abstract Given the challenges posed by climate change and resource overexploitation, sustainable solutions in agriculture requires integrated tools that harmonize economic viability with ecological stewardship. Previously, policies prioritized maximization of short-term productivity, leading to significant resource depletion and soil degradation. In this context, bioeconomic models, particularly Computable General Equilibrium (CGE) models, have emerged as essential tools for assessing the interconnection among the tripartite pillars of sustainability. This study aims to evaluate CGE implementation in agriculture by defining comprehensive criteria, analyzing the Water-Energy-Food-Ecosystem (WEFE) Nexus integration across the pillars of sustainability, and providing a roadmap for future research. Therefore, this study employs a Systematic Literature Review (SLR), assessing 131 full-text studies for eligibility from an initial pool of 9,673 documents. Of these full-text studies, 115 papers were selected for inclusion and classified into 8 thematic categories and 30 defined criteria, which enhance the accuracy of CGE implementation outcomes. Results indicate that while CGE implementation predominantly evaluates economic impacts (97%), it increasingly internalizes environmental (54%) and social (43%) pillars. A critical insight rooted in this study is the transformative role of the WEFE Nexus, and particularly, food security acts as a multidimensional bridge linking environmental ( p = 0.013) and social impacts ( p = 0.037), while water management serves as a foundational cross-cutting driver across all sectors. Integrating the WEFE Nexus into CGE models enhances climate mitigation efficacy by quantifying critical trade-offs between economic growth and resource preservation, as well as capturing the concurrent interdependencies between Nexus pillars. Finally, this SLR identifies existing research gaps, including the ineffectiveness of capturing granular, real-time farmer behavioral responses and localized technological adoption rates, and recommends further scientific pathways for CGE modeling with a view to agricultural sustainability. Further research should focus on the concurrent implementation of CGE modeling approaches with, for instance Agent-Based Modeling (ABM), Machine Learning, and Artificial Intelligence (AI) techniques to improve the spatial resolution of climate-impact simulations.