Energy Reports (Jun 2024)
Environmental impact assessment for a meta-model-based food-energy-water-nexus system
Abstract
A growing global population uses essential resources such as food, energy, and water to drive economic growth. However, population growth, urbanization, and human activities strain these limited resources, resulting in serious harm, particularly from global warming and climate change. Sustainable resource allocation in the Nexus system is required for improved resource security. This issue is essential since it impacts resource management, sustainability, and the environment. This research proposes developing an integrated assessment model (IAM) for the FEW-N system. The system undergoes a comprehensive evaluation that considers sustainability, economics, social aspects, and the environment. This evaluation combines an environmental impact assessment with a meta-model-based approach to the FEW-N system. A multi-objective optimization model is utilized to optimize the system with three key targets: maximum economic benefit, positive environmental impact, and least negative environmental impact. The findings show significant improvements in resource security within a community-level FEW-N system, with the Nexus system sectors contributing to both food and energy security. Greenhouse farming, rainfed farming, and irrigated farming supply 55.6%, 11.9%, and 32.5% of food security, respectively. Similarly, bioenergy, solar/wind hybrid renewable energy, and hydropower contribute 52.7%, 40.1%, and 7.2% of total energy security, respectively. The FEW-N system optimizes resource allocation for sustainability, reliability, and efficiency while emphasizing the specific contributions of diverse sectors to food and energy security. These findings provide critical insights for making educated resource allocation policy decisions in South Africa, emphasizing balancing societal, economic, and environmental factors. The study has increased the evidence base for the FEW-N environmental impact assessment framework to address resource allocation issues.
