Dynamic and Spatial Character Analysis of Regional Marginal Abatement Costs of CO2 Emissions from Energy Consumption: A Provincial Aspect
Qiaozhi Zhao 1  
,   Qingyou Yan 1  
,   Lijun Zhang 2  
,   Hairui Zhao 1  
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School of Economics and Management, North China Electric Power University, Baoding, China
State Grid Zhejiang Economy Research Institute, Hangzhou, China
Qiaozhi Zhao   

North China Electric Power University, #639 North Yonghua Road, Baoding City, P.R.China, 071003 Baoding, China
Submission date: 2018-02-04
Final revision date: 2018-04-20
Acceptance date: 2018-04-24
Online publication date: 2019-01-09
Publication date: 2019-03-01
Pol. J. Environ. Stud. 2019;28(4):2501–2511
The Chinese government has made a commitment to achieve a 60-65% reduction of CO2 emissions by 2030 compared with that in 2005. Most provinces are assigned differentiated reduction tasks due to different natural resources endowment, energy consumption structure, and economic developments. Marginal abatement cost (MAC) supplies cost information on regional pollutant reduction processes and should be an important evaluation indicator of policies. In this study, we build a quadratic parametric directional distance function (DDF) to estimate provincial MAC of CO2 emissions in China during 2000-2015. Linear programming is used to solve the parameter estimation problem. Results are as follows:
1) LP method supplies efficient parameter estimation results and obtains 98.33% reliable MACs during the research period.
2) MAC keeps a growing trend for most provinces in 2000-2015. Especially when China enters the New Normal stage in 2012, this growing trend has been accelerated. These trends reveal that MAC gradually becomes a more important indicator to evaluate emission reduction measurements.
3) From a spatial distribution aspect, positive cluster feature has experienced such fluctuations as “apparent rise→significant decline→close to zero.” In this stage, their spatial cluster is close to random distribution state.
Spatial heterogeneity turns to being enlarged, especially among provinces at higher MAC range. These evolutionary trends will have important influence on their carbon reduction measure implementing process. Eastern regions should turn more focus on low-carbon technology innovation to push their lowcarbon transformation. For middle and western regions, they should promote their production efficiency and obtain more technology spillovers from eastern provinces in the future to stimulate their economic growth and low-carbon transformation.