Modeling the drivers of climate impact in food systems: an interpretive structural modeling approach based on life cycle assessment

The agri-food systems have been found to be major contributors to the greenhouse gases emission in the world because of the overlapping interactions in the production, processing, and supply chain processes. Despite being an extremely effective instrument of attracting environmental hotspots, the life cycle assessment (LCA) is often incapable of capturing interdependencies between other important drivers. This study addresses this gap by integrating LCA-based insights with interpretive structural modeling (ISM) and MICMAC analysis to examine the structural relationships among climate impact drivers in food systems. The sample size has been established as 18 drivers based on the comprehensive review of literature and validated by the experts of the domain. A hierarchical model was developed with the help of ISM, and MICMAC analysis was also developed in which the variables were clustered in terms of their power of driving and dependence. The results show that the system is very interdependent and has a clear top-driven structure that has carbon and water footprint capability, resource use efficiency, and technological performance as the major driving forces. The policy support and complexity of the supply chains are the dependent variables, and the land use change and energy consumption are the variables that mediate the system dynamics. The findings also indicate that to effectively reduce climate and provide practical recommendations to the policymakers and industry players, systems-based approach is necessary to focus on high-impaction points of interventions.