Optimizing Energy Allocation for Sustainability: A Mathematical Modeling Approach

Against the backdrop of global climate change and energy crises, the energy sector, which accounts for over 70% of global greenhouse gas emissions, has become a key focus for climate change mitigation. While renewable energy investments are growing, fossil fuels still dominate the global energy mix, especially in developing countries, highlighting the urgency of optimizing energy allocation for sustainable development. The paper adopts a mathematical modeling approach, specifically linear programming, and utilizes data from open energy databases such as those of the IEA and Our World in Data. It constructs a national-level energy allocation model involving five energy sources (coal, natural gas, nuclear, wind, and solar) to explore the optimal energy mix that balances total cost and CO emissions under constraints like capacity limits, renewable portfolio requirements, and emission caps. Additionally, scenario analyses with different carbon prices and emission caps are conducted to derive policy insights. The paper finds that an energy mix excluding coal, consisting of nuclear, wind, solar, and natural gas, can meet emission targets at a reasonable cost. Scenario analyses reveal that carbon pricing and emission caps significantly influence the energy configuration and the trade-offs between cost and emissions, emphasizing the effectiveness of mathematical optimization in balancing economic and environmental goals in energy planning.