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In the background of global “carbon neutrality,” China Railway Express (CRE) is contributing to the long-term development of low-carbon environmental protection while promoting economic growth. Based on panel data of 284 cities at the prefecture level and above in China from 2003 to 2019, we empirically investigate the impact and mechanism of CRE opening on urban carbon emissions using the multi-period DID model and mediating effect model. Under the assumption of meeting parallel trends, we find that the opening of CRE considerably lowers urban carbon emissions by .78 percentage points as compared to the control group. This finding holds after placebo testing, PSM-DID estimation, replacing key variables, excluding other policies and disturbances from the opening of the high-speed railway (HSR), and overcoming endogeneity problems. According to the findings of the mechanism tests, the influence of transportation substitution, improved industrial structure, economic agglomeration, and trade openness are crucial mechanisms for reducing carbon emissions by opening CRE. Further heterogeneity tests show that the carbon reduction effect of CRE opening is more pronounced in east-central China, larger and non-resource-based cities. Therefore, this study suggests that the Chinese government should pay attention to the positive effect of CRE on environmental protection. The government should accelerate the construction of comprehensive transportation infrastructure while rationalizing the layout of economic activities and population clustering.

The paper first gives an effective integration on high resolution RS image and multi-spectral RS data on the basis of RS image geometric correction, image mosaic and subset; then introduces many kinds of vegetation indices such as NDVI, PVI, RVI, SAVI, GVI, DVI and so on, and analyses the characteristic of vegetation indices, builds normalized difference vegetation index (NDVI) of study area; later plant information in the study area is extracted with high accuracy by decision tree method, according to different spectral features of different plants, so as to make thematic pictures of plant distribution. Finally it probes the method of planting area change monitoring and plant growth monitoring by the application of multi-temporal RS data in order to provide a reliable basis for decision making and for agriculture management..

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