Carbon Emissions in the Yellow River Basin: Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors Based on a Logarithmic Mean Divisia Index (LMDI) Decomposition Method

Abstract

The “14th Five-Year Plan” period is a critical period and a window to obtain emission peak and carbon neutrality in China. The Yellow River Basin, a vital location for population activities and economic growth, is significant to China’s emission peak by 2030. Analyzing carbon emissions patterns and decomposing the influencing factors can provide theoretical support for reducing carbon emissions. Based on the energy consumption data from 2000–2019, the method recommended by Intergovernmental Panel on Climate Change (IPCC) is used to calculate the carbon emissions in the Yellow River Basin. The Logarithmic Mean Divisia Index (LMDI) decomposition method decomposes the influence degree of each influencing factor. The conclusions are as follows: First, The Yellow River Basin has not yet reached the peak of carbon emissions. Regional carbon emissions trends are different. Second, Shandong, Shanxi, Henan and Inner Mongolia consistently ranked in the top four in total carbon emissions, with low carbon emission efficiency. Third, Economic development has the most significant contribution to carbon emissions; other factors have various effects on nine provinces.

Study on Influencing Factors and Spatial Effects of Carbon Emissions Based on Logarithmic Mean Divisia Index Model: A Case Study of Hunan Province

“…Second, economic development is one of the most important factors of carbon emission increase in the research region. This finding is similar to the results of previous studies [27,46]. The results show that the relationship between economic development and carbon emissions is consistent with the Environmental Kuznets Curve.…”

“…Ang pointed out that the LMDI method has a good theoretical basis [10], strong adaptability [11], simple operation [12], no residual after decomposition [13], and the results of the method are easily comprehensible [14]. Since the early 1990s, the LMDI method has been widely used in the study of regional environmental influencing factors in various countries and regions, including Latin America (Sheinbaum et al [15], González and Martínez [16,17]), Ireland (Mahony [18], Tadhg et al [19]), United Kingdom (Hammonda and Normanb [20]) and Turkey (Ipek et al [21]), South Korea (Oh et al [22], Jung et al [23]) and China (Lee and Oh [24], Li [25], Wang et al [26], Liu et al [27]). ( 2) Principal component analysis, general regression analysis, and IPAT model analysis are commonly used in econometric models, and some of them are combined for analysis.…”

Carbon Emissions Drivers and Reduction Strategies in Jiangsu Province

“…In terms of permanent population, Nanjing has 4.34 million more people than Suqian. If we only analyze the driving factors based on regional GDP, population, energy consumption, and energy efficiency, it is difficult to accurately reflect the differences in driving factors of carbon emissions among different regions [36,37]. This article mainly considers the impact of energy and economy on carbon emissions, introducing two structural factors, energy structure and industrial structure, which refer to the impact of energy structure adjustment and changes in the secondary industry structure on carbon emissions, respectively.…”