Inputs optimization to reduce the undesirable outputs by environmental hazards: a DEA model with data of PM2.5 in China

Abstract

Currently, the hazy weather in China is increasingly serious. It is urgent for China to reduce haze emissions in environmental governance. A feasible way is to control haze emissions by optimizing the input sources. This paper proposed an innovative method in which the haze emission is controlled by readjusting input indicators. The output efficiency of input indicators in 29 provinces in China is calculated through 7 input indicators (namely, SO2 emissions, NOX emissions, soot emissions, coal consumption, car ownership, capital, and labor force) as well as GDP (desirable output) and PM2.5 emissions (undesirable output). The results showed that the input indicators are excessive in redundancy on condition that PM2.5 emissions and GDP are equal. The input indicators are high in redundancy rate except labor force. The redundancy rates of soot emissions, SO2 emissions and coal consumption are relatively high and, respectively, are 78, 67.18, and 61.14%. Moreover, all the provinces are redundant in inputs except Beijing, Tianjin, and Shanghai which are optimal in input–output efficiency. The redundancy of middle and western provinces, such as Ningxia, Guizhou, and Shanxi, is relatively large. The ideas and methods proposed in this paper can provide a reference for the future researches that aim to reduce the input indicators of undesirable output, and the empirical results can provide empirical support for the PM2.5 abatement in China.