College of Geography and Territorial Engineering, Yuxi Normal University, Yuxi 653100, China.
The School of Public Policy & Management, Tsinghua University, Beijing 100000, China.
Int J Environ Res Public Health. 2021 Feb 21;18(4):2101. doi: 10.3390/ijerph18042101.
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 CO 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.
西安市的扩张导致了能源和土地资源的消耗，引发了严重的环境污染问题。为此，本研究旨在衡量西安市的碳承载能力、净碳足迹和净碳足迹压力指数，并描述西安市生态系统的碳固存能力，从而为制定全面合理的低碳发展措施奠定基础。本研究期望通过 Tapio 脱钩原理为中国发展低碳经济提供参考。通过 Tapio 脱钩模型探讨了 CO 和驱动因素之间的脱钩关系。利用时间序列数据计算碳足迹。结合深度学习技术中的自动编码器和云计算中的并行算法，提出了一种基于云计算集群上的 Map-Reduce 的通用多层感知机神经网络，基于偏最小二乘（PLS）回归模型构建了驱动因素分析模型。结果表明，就城市规模而言，城市化率的变量重要性投影（VIP）输出对碳足迹增长具有很强的抑制作用，常住人口的 VIP 值排名最后；就经济发展而言，固定资产投资和第二产业增加值对碳足迹的影响分别排在第三和第四位。因此，在经济增长达到一定阶段后，碳足迹的边际效应大于经济增长，这表明驱动因素和机制可以促进城市空间的增长。
