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

• Published in: Energy policy Vol. 77; pp. 46 - 55
• Main Authors: Yu, Shiwei, Zhang, Junjie, Zheng, Shuhong, Sun, Han
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.02.2015; Elsevier Science Ltd
• Subjects: Abatement; Abatement potential estimation; Algorithms; Carbon; Carbon dioxide; Carbon dioxide emissions; Carbon emission; China; China (People's Republic); Computer simulation; Economic theory; Emissions control; Energy policy; Environmental economics; Estimating techniques; Estimation; GDP; Government; greenhouse gas emissions; Gross Domestic Product; Industry; Intelligence; issues and policy; Learning; Learning curves; least squares; Least squares method; Mitigation; Mongolia; Multivariate environmental learning curve; Optimization; Optimization algorithms; Particle swarm optimization–genetic algorithm; Policies; Pollution control; Provinces; Reduction; Simulation; Studies; Variables; China; China, People's Rep., Inner Mongolia; China, People's Rep., Beijing; China, People's Rep., Jiangsu; China, People's Rep., Tianjin; Carbon emission; Abatement potential estimation; Multivariate environmental learning curve; Particle swarm optimization–genetic algorithm
• ISSN: 0301-4215; 1873-6777
• DOI: 10.1016/j.enpol.2014.11.035

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 the proportion of the tertiary industry in GDP, to construct carbon intensity learning curves (CILCs), i.e., CO2 emissions per unit of GDP, of 30 provinces in China. Instead of the traditional ordinary least squares (OLS) method, a PSO–GA intelligent optimization algorithm is used to optimize the coefficients of a learning curve. The carbon intensity abatement potentials of the 30 Chinese provinces are estimated via PSO–GA under the business-as-usual scenario. The estimation reveals the following results. (1) For most provinces, the abatement potentials from improving a unit of the proportion of the tertiary industry in GDP are higher than the potentials from raising a unit of per capita GDP. (2) The average potential of the 30 provinces in 2020 will be 37.6% based on the emission's level of 2005. The potentials of Jiangsu, Tianjin, Shandong, Beijing, and Heilongjiang are over 60%. Ningxia is the only province without intensity abatement potential. (3) The total carbon intensity in China weighted by the GDP shares of the 30 provinces will decline by 39.4% in 2020 compared with that in 2005. This intensity cannot achieve the 40%–45% carbon intensity reduction target set by the Chinese government. Additional mitigation policies should be developed to uncover the potentials of Ningxia and Inner Mongolia. In addition, the simulation accuracy of the CILCs optimized by PSO–GA is higher than that of the CILCs optimized by the traditional OLS method. •A PSO–GA-optimized multi-factor environmental learning curve method is proposed.•The carbon intensity abatement potentials of the 30 Chinese provinces are estimated by the curve.•The average potential will be 37.6% in 2020 based on the emission's level of 2005.•China's total potentials weighted by GDP shares of the 30 provinces will be 39.4% in 2020.•The intensity cannot achieve the 40%–45% carbon intensity reduction target in BAU scenario.