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Performance evaluation of wind power industry chain based on three-stage DEA

With the development of the economy and society, the problems of resources and the environment are becoming more and more prominent. As an environmentally friendly and clean energy, wind energy is developing rapidly with the support of relevant policies and technologies. At the same time, the development of the wind power industry is also faced with some problems, as the “abandoning wind” phenomenon of wind farms is still relatively common; the continuous decline of wind turbine prices and the sharp rise in operation and maintenance costs have further reduced the profit space of wind power manufacturers. The fundamental reason for the series of difficulties in the development of the wind power industry is the imbalance between the various links in the wind power industry chain. There is an urgent need to evaluate the performance of the wind power industry chain to improve the weak links. In this paper, by using the three-stage data envelopment analysis (DEA) model, the input-oriented BCC model is used in the first stage, and the stochastic Frontier analysis method is used to analyze the input relaxation variables in the second stage to eliminate two exogenous factors. In the third stage, the adjusted input variables are recalculated in the first stage, and the chain performance value of the wind power industry excluding environmental factors and random errors is obtained in the performance evaluation. This paper takes China's wind power industry as an example and makes an overall analysis of the upstream, middle, and downstream of the wind power industry chain from three aspects: technology performance, scale performance, and pure technology performance. The results show that under the support of policy and economy, the technical performance of the upper and lower reaches of China's wind power industry chain is higher, while the performance of the wind power industry chain is underestimated by environmental factors. The three-stage DEA model can eliminate the influence of environmental and random errors, which makes the evaluation of the wind power industry chain more objective. In addition, according to the evaluation results, the optimization direction of each structure of the industrial chain is put forward.