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.

Exploring the Dependence and Influencing Factors of Carbon Emissions from the Perspective of Population Development

“…In the long run, SD state may be difficult to maintain, which means that there is no real decoupling between carbon emissions and the PDI. The same decoupling trend is also reflected in the research of Shang and Luo [ 27 ]. Therefore, the key to decoupling is to effectively control the change rate of carbon emissions, while evaluating the effectiveness of the decoupling state requires examining the changes of carbon emissions and the PDI over a period, for example, taking 5 years as an evaluation cycle.…”

“…Economic output has become the main consideration in the study of carbon decoupling in China [ 27 ], and this consideration is also reflected in the research outside China [ 28 ]. However, the dependence of carbon emissions on other factors, such as population, has received little attention, and only a few studies have assessed the decoupling of carbon emissions from population-related factors.…”

Decoupling Relationship between Urbanization and Carbon Sequestration in the Pearl River Delta from 2000 to 2020

“…The degradation of carbon sequestration service capacity during urbanization is the main cause of global warming. Improving the carbon sequestration service capacity at the same time as urban expansion and economic development has become an urgent problem [50,51]. In recent years, the development and utilization of natural resources have often only focused on their short-term economic value and ignored the impact on climate change and the carbon cycle, resulting in a serious loss of ecological space and function, which is not conducive to ecological construction and the control of global warming.…”

The decoupling estimation effect as an indicator of the efficient use of natural capital in the agrifood in the southern geographical area of the Russian Federation

“…According to this approach, the decoupling situation can be divided into several basic types. In each of the basic cases, the values of the decoupling index are the main criterion for assigning a particular case to a specific situation [13].…”