Air pollutant and CO2 emissions mitigation in urban energy systems through a fuzzy possibilistic programming method under uncertainty

Abstract

Abstract To address the increasing tension between energy activities and environmental protection, an energy and environment optimization model based on fuzzy possibilistic programming method is proposed. The proposed model can (i) generate optimized solutions for energy and environmental systems; (ii) tackle the uncertainties expressed as fuzzy sets in the goal, left-hand (e.g., economic data), and right-hand (e.g., energy demands) sides of constraints; (iii) analyze the integrated and individual impacts of energy conversion efficiency, air pollutant removal rate, and electric power mix on energy, environmental, and economic systems; and (iv) explore cost-effective approaches for emissions reductions. The proposed model can be applied at city and regional scales, and its effectiveness was verified using Tianjin Municipality, Beijing Municipality, and Hebei Province. Several policy implications for local decision makers are suggested from the proposed model. Firstly, the optimization of the electric power mix and improvement of energy conversion efficiencies are cost-effective ways to reduce pollutant and CO2 emissions. Secondly, the improvement of pollutant removal rates can effectively reduce SO2, NOx, and particulate matter emissions, but with increased system costs. Thirdly, oil refining, coke processing, and coal-fired power are identified as major sectors for potential emissions reduction. The proposed model can effectively support local policymakers in the adjustment of current energy and environmental strategies sustainably and robustly.