Carbon emissions and economic growth in the Yellow River Basin: Decoupling and driving factors

Abstract

In the context of global countries’ pursuit of sustainable development and “dual carbon” goals of China, this paper combines the Tapio decoupling model, Kaya’s equation and LMDI decomposition method to analyze the relationship between carbon emissions and economic growth and the driving factors of carbon emissions in the Yellow River Basin (YRB) of China from 2001 to 2019. It is found that the decoupling state of CO2 and economic growth in the Yellow River Basin shows a trend of expansion negative decoupling - expansion connection - weak decoupling - strong decoupling, but different regions shows some heterogeneity. The economic intensity effect and population size effect generally play a positive role in driving carbon emissions, while carbon emission intensity, industrial structure and energy structure effect play a negative role in driving carbon emissions in most years. From the regional perspective, the energy consumption structure in the middle reaches is the most important factor to promote carbon emissions. In terms of time period, the industrial structure effect is the driving factor of carbon emissions in the decoupling state overall as expansionary linkage, while it is the inhibitor of carbon emissions in the decoupling state overall as weak decoupling. This study also puts forward countermeasures such as optimize the energy structure and upgrade the industrial structure to achieve green and high-quality economic development in the YRB.

Spatiotemporal Evolution Characteristics and Driving Factors of Water-Energy-Food-Carbon System Vulnerability: A Case Study of the Yellow River Basin, China

“…Energy-related data were acquired from the "China Energy Statistical Yearbook" and the "Statistical Bulletin of National Economic and Social Development (2010-2019)" for each city. Urban carbon emissions and carbon sequestration data were based on relevant literature [49][50][51] from 1997 to 2017. Other than that, the time series model was optimally selected using the SPSS expert modeler, and was finally predicted by the ARIMA model and the Brownian model, with the goodness of fit exceeding 97.5%.…”

Analysis of the Impact of Industrial Structure Upgrading and Energy Structure Optimization on Carbon Emission Reduction

“…Liu et al, Zhao et al, and Gu et al used the STIRPAT model to verify the significant effect of industrial structure transformation and upgrades on reducing carbon emissions [10][11][12]. Developing the Tapio decoupling model, Kaya's equation and LMDI decomposition method, Han et al [13] found that the industrial structure and energy structure effect play negative roles in driving carbon emissions. The degree of coupling coordination among industrial structure, carbon emissions, and the regional innovation of Shandong Province were calculated, and their spatial characteristics and the aggregation effects of both coupled and coordinated development were explored by Wang et al [14].…”

How Does Network Infrastructure Construction Affect Livestock Carbon Emissions?

“…Currently, climate change caused by greenhouse gases is harmful to human survival and sustainable economic development, and controlling greenhouse gas emissions, such as carbon dioxide, has become a global consensus [1]. China has consistently held the title of the world's foremost emitter since 2007.…”

“…After endogenous treatment, rigorous robustness checks, and heterogeneity analysis, the study delves into mediation mechanisms, which encompass the livestock industry scale effect, technology innovation effect, and factor allocation effect. The potential contributions of this study are as follows: (1) it extends the research on the environmental effects of NIC to the field of livestock industry. (2) This study concludes that NIC can suppress LCEs, providing policy references for the coordinated realization of NIC goals and dual-carbon targets.…”