Marginal Carbon Dioxide Emission Reduction Cost and Influencing Factors in Chinese Industry Based on Bayes Bootstrap

Di Peng and Haibin Liu ()
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Di Peng: School of Management, China University of Mining and Technology, Beijing 100083, China
Haibin Liu: School of Management, China University of Mining and Technology, Beijing 100083, China

Sustainability, 2023, vol. 15, issue 11, 1-19

Abstract: Accurate measurement of the shadow price of carbon dioxide (CO 2 ) is fundamental to the scientific assessment of the carbon emission reduction cost and the formulation and execution of China’s carbon emission mitigation policies. Underpinned by the directional distance function, this research uses a parametric linear programming method and a Bayes bootstrap estimation method to estimate the marginal CO 2 emission reduction cost of the industrial sector in China and to quantify the related influencing factors. The results revealed that the marginal reduction cost of industrial CO 2 is CNY 4565/ton. The marginal reduction cost of CO 2 varies by industry, with the textile industry being the highest and the petroleum, coking and nuclear fuel processing industries the lowest. Meanwhile, an increasing number of industries are shifting to cleaner production. Furthermore, the marginal reduction cost of industrial CO 2 has an “inverted U-shaped” relation with carbon intensity. Carbon emission reduction can be accomplished effectively if the carbon intensity is kept below the threshold value of 0.41 tons/CNY 10,000.

Keywords: shadow price; marginal emission reduction cost; influencing factors; directional distance function; Bayes bootstrap (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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