Food security remains a critical global challenge, increasingly threatened by the adverse effects of climate change on agricultural productivity and food supply chains. Ensuring the stability, availability, and accessibility of food resources necessitates innovative strategies to assess and mitigate climate-related risks. This study presents a comprehensive analysis of the impact of climate change on global food systems, focusing on the risk assessment and optimization of food supply chains from the perspective of importers. Deploying the Analytical Hierarchy Process (AHP), this study evaluates climate change risks associated with seven different suppliers for three key crops, considering a range of factors, including surface temperature, arable land, water stress, and adaptation policies. Utilizing these assessments, a multi-objective optimization model is developed and solved using MATLAB (R2018a)’s Genetic Algorithm, aiming to identify optimal suppliers to meet Qatar’s food demand, with consideration of the economic, environmental, and risk factors. The findings underscore the importance of a comprehensive approach in managing food supply chains and offer insights to enhance the resilience and sustainability of global food systems amid climate uncertainties. This study contributes to the literature by applying AHP and multi-objective optimization in climate risk management within food systems, providing valuable perspectives for policymakers and stakeholders in the agricultural sector. Furthermore, the multi-objective optimization model analyzed three crop networks, yielding total costs of USD 16 million, USD 6 million, and USD 10 million for tomatoes, onions, and cucumbers, respectively, with associated CO₂eq emissions and risk percentages. The findings reveal concentrated global vegetable markets, with major importers accounting for over 60% of imports, though the leading importers differ across crops, highlighting regional demand and production disparities, potentially impacting food security and supply chain resilience.

Jatropha curcas L. (JCL) is one of the most prominent energy crops due to its superior agronomical traits, where it can grow in non-arable lands and harsh climates with minimal water requirements. A significant number of studies were published on the utilisation of JCL for biofuel production, whereas there are no studies on its use in greenbelt (GB) or windbreak technologies reported thus far. Meanwhile, a few approaches on the delineation of greenbelts to fight desertification in the arid regions exist in literature. This study presents a novel approach to delineate a multipurpose energy-greenbelt using JCL crop for biofuel production, as well as to preserve the soil and enhance air quality, thereby helping to combat desertification and sand-dust storms (SDS). The methodology is demonstrated using a case study in the state of Qatar for the diversification of its renewable energy resources. Moreover, Qatar is also suffering from land degradation due to erosion factors and desert creep. A multi-dimensional approach is proposed for this purpose using satellite and meteorological data to initially select the optimal plantation sites that potentially contribute to the highest possible biofuel yield. The spatial analysis was carried out using the analytical hierarchy process (AHP) technique for multi-criteria decision making in the geographic information system (ArcGIS). In addition, the Landsat and MODIS satellite imagery were utilised in combination with historical records from the weather stations to evaluate the patterns of SDS, land degradation and urban expansion, to best define optimal GB pathway. COMSOL Multiphysics software was subsequently employed to evaluate the performance of Jatropha-GB and determine its optimal density. The different solutions for GB delineation spans 166.6-227.8 km length and (6 × 6 m) of field density. It is expected that the economic and environmental benefits from the derived GB configuration include: (a) protection of up to 87% of Qatar farms against further deterioration; (b) yield of up to 36 M gallon of green liquid fuels; (c) capture of 0.33 M tonnes of CO₂ per 1 km GB-depth annually; and (d) provide a better air quality for around 95% of the Qatar population.