A collaborative analysis based on multi-objective programming method for energy consumption reduction and governance investment

This paper delves into how to collaboratively reduce emissions of sulfur dioxide, nitrogen oxides, and carbon dioxide through rational energy consumption and governance investment strategies with limited funds. The main research contents include: employing the Granger causality test to analyze the causal relationship between air quality and pollutant emissions; using functional analysis to determine the quantitative relationship between energy consumption and the emissions of various pollutants; applying multi-objective programming method to establish an integrated model for collaborative emission reduction optimization that considers both energy consumption and governance investment, and analyzing the optimality conditions of the model; and conducting an empirical analysis of the model using Tianjin’s social development data from 2005 to 2021. The optimal carbon dioxide emissions calculated by the model are significantly lower than the actual emissions, with an average optimization efficiency of 38.43%. Through reasonable energy allocation and governance investment strategies, it is possible to effectively reduce pollutant emissions while ensuring production demands. The research results of this paper provide a theoretical basis and practical guidance for formulating rational energy use and governance investment strategies.