Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method

摘要: Abstract This study aims to estimate carbon intensity abatement potential in China at the regional level by proposing a particle swarm optimization–genetic algorithm (PSO–GA) multivariate environmental learning curve estimation method. The model uses two independent variables, namely, per capita gross domestic product (GDP) and proportion of tertiary industry GDP, construct curves (CILCs), i.e., CO 2 emissions unit 30 provinces China. Instead traditional ordinary least squares (OLS) method, PSO–GA intelligent optimization is used optimize coefficients curve. potentials Chinese are estimated via under business-as-usual scenario. reveals following results. (1) For most provinces, from improving GDP higher than raising GDP. (2) average 2020 will be 37.6% based on emission's 2005. Jiangsu, Tianjin, Shandong, Beijing, Heilongjiang over 60%. Ningxia only province without potential. (3) total weighted shares decline 39.4% compared with that cannot achieve 40%–45% reduction target set government. Additional mitigation policies should developed uncover Inner Mongolia. In addition, simulation accuracy CILCs optimized OLS