The Tapio Decoupling Principle and Key Strategies for Changing Factors of Chinese Urban Carbon Footprint Based on Cloud Computing

Abstract

The expansion of Xi’an City has caused the consumption of energy and land resources, leading to serious environmental pollution problems. For this purpose, this study was carried out to measure the carbon carrying capacity, net carbon footprint and net carbon footprint pressure index of Xi’an City, and to characterize the carbon sequestration capacity of Xi’an ecosystem, thereby laying a foundation for developing comprehensive and reasonable low-carbon development measures. This study expects to provide a reference for China to develop a low-carbon economy through Tapio decoupling principle. The decoupling relationship between CO2 and driving factors was explored through Tapio decoupling model. The time-series data was used to calculate the carbon footprint. The auto-encoder in deep learning technology was combined with the parallel algorithm in cloud computing. A general multilayer perceptron neural network realized by a parallel BP learning algorithm was proposed based on Map-Reduce on a cloud computing cluster. A partial least squares (PLS) regression model was constructed to analyze driving factors. The results show that in terms of city size, the variable importance in projection (VIP) output of the urbanization rate has a strong inhibitory effect on carbon footprint growth, and the VIP value of permanent population ranks the last; in terms of economic development, the impact of fixed asset investment and added value of the secondary industry on carbon footprint ranks third and fourth. As a result, the marginal effect of carbon footprint is greater than that of economic growth after economic growth reaches a certain stage, revealing that the driving forces and mechanisms can promote the growth of urban space.