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Marginal abatement cost of CO2: A convex quantile non-radial directional distance function regression method considering noise and inefficiency

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  • Hu, Shuo
  • Wang, Ailun
  • Lin, Boqiang

Abstract

By purchasing emission reduction equipment or reducing production scale, it is possible to effectively decrease CO2. Therefore, understanding the marginal abatement cost (MAC) associated with these methods is crucial for making decisions. However, previous studies using the directional distance function (DDF) approach often mis-specified production functions and neglected data noise. They also assumed decision-making units (DMUs) to be on the production frontier and used proportional changes in inputs and outputs as abatement paths. This paper addresses these limitations by developing the convex quantile non-radial directional distance function (CQR-NDDF) method, which estimates the MAC of CO2 and determines optimal abatement paths for DMUs without assuming a specific production function, employing linear programming techniques. Applying this method to 30 provinces in mainland China from 2011 to 2019, the study finds that China's CO2 MAC increased from 182 to 247 yuan/ton. The lowest-cost abatement path varies by province and time. The club convergence and ordered probit model are employed to conclude that the second industry and urbanization increase the MAC of CO2, while factors such as foreign direct investment, openness level, and human capital decrease the MAC. Moreover, the CQR-NDDF method yields significantly lower MAC estimates than the NDDF method. In conclusion, this paper provides new insights into China's CO2 MAC, emphasizing the importance of considering inefficiency and data noise in MAC estimation. We anticipate that utilizing CO2 MAC as a benchmark for carbon trading market prices could lead to an increase in prices within China's carbon trading market.

Suggested Citation

  • Hu, Shuo & Wang, Ailun & Lin, Boqiang, 2024. "Marginal abatement cost of CO2: A convex quantile non-radial directional distance function regression method considering noise and inefficiency," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s036054422401096x
    DOI: 10.1016/j.energy.2024.131323
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