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.

The health of environment and the health of human beings are major concerns today for developing nations. Sustainable development goals are acting as a strategic concern for every nation’s policy framework, but also parallels the aggressive development that an economy strives to achieve. Therefore, it is necessary to find such ways that are practical in terms of adaptability. The current study focuses on two relevant SDGs (Sustainable Development Goals), i.e., SDG 13 and SDG 3. SDG 13 will act as an indicator for climate health, and SDG 3 will act as an indicator for human health. The study systematically derives the existing gaps and opportunities in integrating climate-resilient policies into healthcare systems to enhance synergies between SDG 13 and SDG 3. For doing this, relevant research documents were identified through a systematic literature review for targeting two SDGs through single policy frameworks. The core idea is that if targeting one SDG can help in the achievement of another, the game would be easier to win. On conducting a detailed review, it was found that the gaps exist in policy coordination, financing, and infrastructure, while opportunities lie in climate-smart healthcare planning and cross-sectoral governance. If these gaps are filled through future studies and government interventions, by targeting SDG 13, SDG 3 will be automatically achieved to the greatest. The study further suggests that to reduce health inequalities, it is necessary to mitigate the adverse effects of climate change by using renewable energy sources and reducing carbon footprints.