Abstract
The development of railways brings many positive externalities, such as the expansion of built environment, the growth of feeder roads, the rise of passenger mobility, and the creation of economic opportunities for locals. In the meantime, the railway transport system exerts some negative externalities on environmental sustainability, which intensifies climate change. This paper assesses the negative externalities of railway transport through the changing dynamics of the normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC). The spatial regression model was calibrated to understand the degree of these externalities. In addition, a prediction model was constructed based on machine learning techniques like cellular automata and Markov chain. The study reveals that the development of railway stations in Tripura, India has significant negative externalities on the environment.
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