Two-stage data envelopment analysis with undesirable intermediate measures: an application to air quality improvement in China

Abstract

Air pollution continues to pose a major threat to human health in China and its neighbor countries including Japan. Although the general clean air trend in China is to replace older coal-fired generation with clean energy, $$60\%$$ coal consumption in the overall energy use still makes it the most coal-dependent country. Improvement of the coal-fired generation efficiency is fully expected to play a vital role for the foreseeable future. Focusing on the main air pollutants, this paper presents a comprehensive study from the target region selection in China to the benefit allocation analysis for a potential collaboration between China and Japan in Integrated coal Gasification Combined Cycle (IGCC). Both radial and non-radial two-stage data envelopment analysis models with undesirable intermediate measures are applied first to evaluate the regional air quality improvement efficiencies in China (2005–2014). Due to the extremely high initial and dynamic investment for IGCC, target regions with the highest priorities to be installed with this technology are selected by k-means clustering method based on the results of two-stage data transformation model under variable returns to scale. Finally, a benefit analysis by multi-criteria allocation game with the principal performance indices from two representative IGCC power plants in China and Japan provides further insights for the mutual motivations of this kind of collaboration and uncovers some international policy implications.