{"title":"Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China","authors":"Yonghui Han, Shuting Tan, Chaowei Zhu, Yang Liu","doi":"10.1108/ijccsm-06-2022-0074","DOIUrl":null,"url":null,"abstract":"\nPurpose\nCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.\n\n\nDesign/methodology/approach\nThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.\n\n\nFindings\nResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.\n\n\nOriginality/value\nThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.\n","PeriodicalId":46689,"journal":{"name":"International Journal of Climate Change Strategies and Management","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"46","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Climate Change Strategies and Management","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1108/ijccsm-06-2022-0074","RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 46
Abstract
Purpose
Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.
Design/methodology/approach
This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.
Findings
Results suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.
Originality/value
This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.
期刊介绍:
Effective from volume 10 (2018), International Journal of Climate Change Strategies and Management is an open access journal. In the history of science there have been only a few issues which have mobilized the attention of scientists and policy-makers alike as the issue of climate change currently does. International Journal of Climate Change Strategies and Management is an international forum that addresses the need for disseminating scholarly research, projects and other initiatives aimed to facilitate a better understanding of the subject matter of climate change. The journal publishes papers dealing with policy-making on climate change, and methodological approaches to cope with the problems deriving from climate change. It disseminates experiences from projects and case studies where due consideration to environmental, economic, social and political aspects is given and especially the links and leverages that can be attained by this holistic approach. It regards climate change under the perspective of its wider implications: for economic growth, water and food security, and for people''s survival – especially those living in the poorest communities in developing countries.