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

Min Shang,Ji Luo

doi:10.3390/ijerph18042101

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.

Highlights

IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Due to the complexity and openness of the economic and social system, the driving factors of the carbon footprint were divided into four primary indices from a system perspective, namely, city size, economic development, social system, and technological progress
In the analysis of driving factors of carbon footprint, the auto-encoder in deep learning technology was combined with the parallel algorithm in cloud computing

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. From the beginning of the Industrial Revolution to 2009, the CO2 content in the atmosphere has increased by nearly 38% and the methane content has increased by 148%, most of which have increased in the past 50 years [1]. The island of Arctic Circle in 2019 has a daily melting amount of up to 2 billion tons. In Europe, heat waves hit many countries, with record-breaking high temperatures in Germany, the UK, Belgium, the Netherlands and Greece [2,3,4]. In July 2019, the National Hightemperature Early Warning Map issued by the China Meteorological Administration classified high temperatures as “swelling”, “twisting”, “melting”, and “evaporating” [5]

Methods

Results

Discussion

Conclusion