Exploring the focus of future CO₂ emission reduction in China's industrial sectors

Abstract

Marginal abatement cost (MAC) of CO 2 plays a fundamental role in formulating industry environmental policies. In this paper, we measured the MACs of energy-related CO 2 among 40 industrial sectors during 2005-2017 based on parametric directional distance function, and there were significant differences between the results. Then, industrial sectors were classified into four categories, and we found 'low carbon intensity' type (type I, II) has higher MACs of CO 2 than 'high carbon intensity' type (type III, IV); under the same level of carbon intensity, industrial sectors with lower CO 2 emissions have higher MACs (type I>II, type III>IV). Moreover, capital intensity, technology progress, and ownership structure have positive effects on MACs, while energy intensity and energy consumption structure are negatively correlated with MACs. The research suggests: (1) The 'double control' of carbon intensity and CO 2 emissions is necessary, however, reducing carbon intensity is still the primary target, and CO 2 emission should also be controlled appropriately at the present stage. (2) Industrial sectors in type IV should be chosen as key industrial sectors to be supervised. (3) China should consider industry characteristics and implement "common but differentiated" emission reduction policies.

Regional differential decomposition and the formation mechanism of the dynamic carbon emissions efficiency of industrial energy in China

“…A large and growing body of literature has investigated the concept (Teng et al, 2019;Zhang et al, 2020;Wang and Wei, 2014;Zhang et al, 2021), measurement (Yao et al, 2015;Liu et al, 2016a;Wang et al, 2017;Cai et al, 2019), impact factors (Zhou et al, 2016;Zhang et al, 2017;Yu and Zhang, 2021;Zhang et al, 2018;, and effects (Zhang and Cheng, 2009;Zhang, 2011;Long et al, 2015;Lu et al, 2018;Zhu et al, 2019;Wu and Gu, 2021) of carbon emission efficiency (CEE). The indicators for measuring CEE have shifted from a single factor measurement method (Ang, 1999;Mielnik and Goldemberg, 1999;Pretis and Roser, 2017;Ferreira et al, 2018) to a total factor measurement method (Kortelainen, 2008;Marklund and Samakovlis, 2007).…”