Development of a Factorial Hypothetical Extraction Model for Analyzing Socioeconomic Environmental Effects of Carbon Emission Intensity Reduction

Abstract

China has pledged to peak its carbon emissions before 2030 and achieve the net-zero ambition in the 2060s. Reducing the national carbon emission intensity can help achieve these ambitions effectively. To explore the tradeoff between emission reduction and system health, a factorial hypothetical extraction method has been proposed. It was applied to identify key carbon emission sectors, and further help formulate countermeasures on reducing the national emission intensity. A seven-factor factorial analysis was developed to evaluate the effects of factors (i.e., 7 countermeasures) and the combinations (i.e., 128 scenarios) on responsive variables (i.e., system health). Main effects and interactions for response variables were also detected between these factors. Results show that the most effective combination, i.e., simultaneously enlarging the production scales of agriculture and other services, and lessening those of metallurgy sectors, can help reduce emission intensity by −19.2%. Lessening the production scales of electricity-generation/metallurgy, and enlarging those of wholesale and retailing sectors, can help reduce national emission intensity, while these factors negatively impacted system sustainability and robustness. The mitigation effects of these countermeasures will be weakened if these countermeasures are implemented simultaneously. Enlarging the production scales of leasing and commercial services and other services sectors can help achieve a win-win outcome. China has pledged to reduce the national carbon emission intensity (NEI) by 60%–65% in 2030 compared to the level in 2015. However, emission reduction policies may come at the expense of system health. In this research, key carbon emission sectors are extracted and seven countermeasures targeting on these sectors are proposed. Then, there are 128 combinations of these countermeasures. Under all the combinations, the changing rates of NEI are evaluated under all the combinations. In addition, the contribution effects of all the combinations on system health (i.e., robustness and sustainability) are also assessed. Accordingly, enlarging the production scales of leasing and commercial services, and other service sectors can positively impact system health, as well as NEI. Lessening the production scales of electricity-generation/metallurgy, and enlarging those of wholesale and retailing sectors, can help reduce NEI, while these factors negatively impacted system sustainability and robustness.