The carbon dioxide marginal abatement cost calculation of Chinese provinces based on stochastic frontier analysis

• Published in: Natural hazards (Dordrecht) Vol. 85; no. 1; pp. 505 - 521
• Main Authors: Yang, Kejia, Lei, Yalin
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2017; Springer Nature B.V
• Subjects: Analysis; Carbon; Carbon dioxide; Carbon dioxide emissions; Civil Engineering; Correlation; Cost engineering; Earth and Environmental Science; Earth Sciences; Emissions control; Energy consumption; Environmental Management; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Government; Hydrogeology; Mathematical analysis; Natural Hazards; Natural resources; Original Paper; Random errors; Reduction; Stochastic models; China; Directional distance function; Policy suggestions; Marginal abatement cost; Stochastic frontier analysis; Carbon dioxide
• DOI: 10.1007/s11069-016-2582-8
• ISSN: 0921-030X

The Chinese government made a commitment to achieve a 40–45 % reduction in carbon emissions per unit of gross domestic product (GDP) by 2020 compared with 2005. Most provinces followed the national commitment due to unified task of 40–45 % reduction in carbon emissions. However, different industrial structures, energy consumption structures and natural resources endowment of each province vary the emission abatement costs. Each province should take the carbon dioxide abatement cost into consideration for the carbon dioxide reduction target. Data envelopment analysis (DEA) and linear programming (LP) methods were used to measure the marginal abatement cost in previous studies. In this paper, we built a quadratic parametric directional distance function (DDF) to measure the carbon dioxide marginal abatement cost of Chinese provinces. To overcome the flaw of ignoring random errors in previous research, this paper compared results of stochastic frontier analysis (SFA) method and DEA method. Because DEA method only considers the inefficiency and SFA method can distinguish the random error from inefficiency, the result of the average carbon dioxide marginal abatement cost of each province calculated by SFA was 55 % lower than DEA method. As the random error may be introduced by chosen function form, Spearman test and paired sample T test were used to test the correlation of two methods’ MAC results. The results show that the ranking order MAC results sequence of SFA method and DEA method is highly correlated. But the MAC value of SFA and DEA methods has significant difference. As half of the error comes from the random error, the MAC results calculated by SFA method are more precise than DEA method. So SFA method is more appropriate than DEA in this paper. This result reinforces the feasibility of the Chinese government carbon dioxide emission reduction target. However, this study proved that the carbon dioxide emissions and marginal abatement cost varied from province to province. Furthermore, there was no distinct correlation between carbon dioxide emissions and the marginal abatement cost. On the contrary, the marginal abatement cost was related to the industrial structures, energy consumption structures and natural resources endowment of each province. Therefore, two policy suggestions are proposed as CO 2 emission reduction principle: First, central government should establish CO 2 emission reduction targets based on MAC and local economic affordability. Second, resource endowments and embodied carbon transfer should be considered.