What factors influence PM_2.5 emissions in China? An analysis of regional differences using a combined method of data envelopment analysis and logarithmic mean Divisia index

This study uses a combined data envelopment analysis and logarithmic mean Divisia index (DEA-LMDI) method to decompose affecting factors for PM_2.5 emissions into effects related to the potential emission intensity (PEI), environmental efficiency and technology, production efficiency and technology, regional economic structure, and national economic growth, and investigates differences in the effects on PM_2.5 emissions, considering the diversity among different areas and periods in China. This study provides a new insight in the decomposition method, which can decompose the emissions into new effects compared with the exiting studies. This study reveals that the regional environmental-based technology (EBT) effect is the key curbing factor for PM_2.5 emissions, followed by the regional PEI effect. The curbing effect of regional EBT on PM_2.5 emissions is strong in East China and weak in Northeast China. The environment-oriented scale efficiency (ESE), environment-oriented management efficiency (EME), production-oriented scale efficiency (PSE), production-oriented management efficiency (PME), and production-based technology (PBT) had relatively small effects on PM_2.5 emissions on the whole. The effects differ among different areas and periods in China. The emission reduction potential of these efficiency effects has not been realized. The national economic growth greatly promotes PM_2.5 emissions. The regional economic structure effect slightly increases PM_2.5 emissions because of the unbalanced development of regional economy. The relative policy suggestions are put forward based on the findings of this study.