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As haze intensifies in China, controlling haze emission has become the country's top priority for environmental protection. Because haze moves across different regions, it is necessary to develop a data envelopment analysis (DEA) model underpinned by both competition and cooperation to evaluate the haze emission efficiency in different provinces. This study innovatively adopts the spatial econometrics to construct the co‐opetition matrices of Chinese provinces, then builds the co‐opetition DEA model to evaluate the haze emission efficiency of them, and finally uses the haze data of 2015 as an example to assess the applicability of the model. The results of the study include the following: First, compared with the traditional CCR (A. Charnes & W. W. Cooper & E. Rhodes) model, this study constructs the co‐opetition DEA cross‐efficiency model that integrates haze's feature of cross‐border moving; thus, it is more in line with the reality of haze emission and movement. Second, compared with the efficiency value gained from the CCR model, the haze emission efficiency values for Tianjin and Guangdong, two decision‐making units, register greater variance when using the DEA model. The reason might lie in that they have a different spatial transportation relationship with their surrounding provinces. Third, the haze emission efficiency of provinces, according to the evaluation based on the co‐opetition DEA method, varies greatly: Those with high efficiency are mostly inland provinces with slow economic growth and adverse climatic conditions, whereas many of the provinces with low efficiency are located in the relatively prosperous East China. The specific co‐opetition DEA model constructed in this study enriches the research on the DEA model, which can be applied to the emission efficiency evaluation of similar pollutants around the world and can contribute empirical support to the haze reducing efforts of the government with its empirical results. [ABSTRACT FROM AUTHOR]

It is an urgent task for China to control haze emission in environmental treatment. Emission right trading is a feasible way to achieve the reduction of pollutant emission on the premise that the total pollutant emission amount is under control, and the allocation of initial emission rights based on the target total amount is the key to emission right trading. In this paper, via the input-oriented ZSG-DEA model, the inter provincial allocative efficiency of PM 2.5 emission rights is investigated under the condition that the target total amount is fixed. The results showed that (1) after initial emission rights were allocated in accordance with the ZSG-DEA model, PM 2.5 emission amounts of all provinces would be in a new common DEA frontier so as to realize the overall Pareto optimality with a set total amount; (2) two factors, namely land areas and atmospheric environmental capacities of all provinces, were considered in the actual allocation to avoid the homogenization of all evaluated units found in the previous evaluation literature on allocative efficiency, thereby making the evaluation results more in line with the actual situations in all provinces. Such an investigation approach can provide guidance on the allocation of initial emission rights in emission right trading; and the research results can offer empirical support for haze-reducing work load conducted by central and local governments of China. [ABSTRACT FROM AUTHOR]

Accurate evaluation of emission governance efficiency can build fundament to develop haze control strategy towards sustainable development. By features of the haze, we view the haze formation stage as the first sub-process and the haze control stage as the second sub-process. This paper proposes an additive aggregation network data envelopment analysis (DEA) model with undesirable intermediate measures and undesirable outputs, which have not been thoroughly studied in previous literature. We found the newly developed network DEA model was nonlinear and cannot be converted into a linear program, and then developed an improved second-order cone programming approach to solve this problem. After analyzing the data of haze control in China, we drew the following conclusions: Firstly, different weights of preference for two sub-process can lead to the variation in the overall efficiency. Under different weights of preference, although the efficiency of the haze formation has a very small change in some provinces, the efficiency of the haze control has a large change. Secondly, decision makers can achieve the adjust goal of reducing haze by adjusting their preferences on the information of the haze formation and haze control stages, which are helpful for policy making in haze control strategy and sustainable development. [ABSTRACT FROM AUTHOR]