Integrating Life Cycle Assessment and Response Surface Methodology for Optimizing Carbon Reduction in Coal-to-Synthetic Natural Gas Process

Coal-to-Synthetic Natural Gas (SNG) plays a crucial role in China’s decarbonization strategy but faces significant sustainability challenges due to its carbon-intensive nature. This study integrates Life Cycle Assessment (LCA) with Box–Behnken Design and Response Surface Methodology (BBD-RSM) to quantify and optimize key parameters for emission reduction. The LCA results indicate that 90.48% of total emissions originate from the SNG production stage, while coal mining accounts for 9.38%, leading to a carbon intensity of 660.92 g CO2eq/kWh, second only to conventional coal power. Through BBD-RSM optimization, the optimal parameter combination was identified as a raw coal selection rate of 62.5%, an effective calorific value of 16.75 MJ/kg, and a conversion efficiency of 83%, corresponding to an energy-based rate of return (ERR) of 49.79%. The optimized scenario demonstrates a substantial reduction in total life-cycle emissions compared with the baseline, thereby improving the environmental viability of coal-to-SNG technology. Furthermore, this study employs the energy-based rate of return (ERR) as a normalization and comparative evaluation metric to quantitatively assess emission reduction potential. The ERR, combined with BBD-RSM, enables a more systematic exploration of emission-driving factors and enhances the application of statistical optimization methods in the coal-to-SNG sector. The findings provide practical strategies for promoting the low-carbon transformation of the coal-to-SNG industry and contribute to the broader advancement of sustainable energy development.