Eco-efficiency measurement of coal-fired power plants in China using super efficiency data envelopment analysis. (January 2018) - Wu, Yunna
Highlights: A novel framework is proposed to measure efficiency and identify influencing variables. Environmental effects from resource, policy and economy aspects are verified. Tobit regression is constructed under the consideration of coal power industry chain. Effective suggestions are proposed from the government's perspective. Large installed capacity tend to reduce ignition consumption and stably operate. Abstract: The measurement of coal-fired power plant eco-efficiency and identification of influencing variables are of great importance for the government to develop relative policy decisions. In this article, an improved two-stage analysis model is employed to analyze eco-efficiency of 58 Chinese coal-fired power plants: firstly, principal component analysis is selected for pre-treatment of variables in order to reduce dimensionality and distinguish prioritized factors; secondly, the super efficiency data envelopment analysis is chosen to assess eco-efficiency with overall ranking; thirdly, Kruskal-Wallis rank sum tests are adopted to figure out macro-environmental factors; finally, considering interactions in the coal power industry chain, plus time effect, we apply the Tobit regression to determine direct external factors. According to the result, over 60% of the coal-fired power plants work in an acceptable productive condition, while some still face improper investment problems. Plants with large installed capacity tend to stably operate. Then, macro-environmental factors like policy preference and economic situation can greatly affect local plants' performance, while resource distribution has few impacts because of the perfect transport condition. In addition, the increasing thermal coal price help improve eco-efficiency while the high power unit age and feed-in tariff do harm to production performance. Based on the analysis, this paper also proposes some effective suggestions for the government.