Quantitative Analysis of Sulfur Dioxide Emissions in the Yangtze River Economic Belt from 1997 to 2017, China

Hui Guo, Feng Zhou, Yawen Zhang and Zhen’an Yang ()
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Hui Guo: Shihezi University, Shihezi 832000, China
Feng Zhou: Key Laboratory of Southwest China Wildlife Resources Conservation, Ministry of Education, China West Normal University, Nanchong 637009, China
Yawen Zhang: Key Laboratory of Southwest China Wildlife Resources Conservation, Ministry of Education, China West Normal University, Nanchong 637009, China
Zhen’an Yang: College of Science, Shihezi University, Shihezi 832000, China

IJERPH, 2022, vol. 19, issue 17, 1-15

Abstract: Economic development is responsible for excessive sulfur dioxide (SO 2 ) emissions, environmental pressure increases, and human and environmental risks. This study used spatial autocorrelation, the Environmental Kuznets Curve (EKC), and the Logarithmic Mean Divisia Index model to study the spatiotemporal variation characteristics and influencing factors of SO 2 emissions in the Yangtze River Economic Belt (YREB) from 1997 to 2017. Our results show that the total SO 2 emissions in the YREB rose from 513.14 × 10 4 t to 974.00 × 10 4 t before dropping to 321.97 × 10 4 t. The SO 2 emissions from 11 provinces first increased and then decreased, each with different turning points. For example, the emission trends changed in Yunnan in 2011 and in Anhui in 2015, while the other nine provinces saw their emission trends change during 2005–2006. Furthermore, the SO 2 emissions in the YREB showed a significant agglomeration phenomenon, with a Moran index of approximately 0.233–0.987. Moreover, the EKC of SO 2 emissions and per capita GDP in the YREB was N-shaped. The EKCs of eight of the 11 provinces were N-shaped (Shanghai, Zhejiang, Anhui, Jiangxi, Sichuan, Guizhou, Hunan, and Chongqing) and those of the other three were inverted U-shaped (Jiangsu, Yunnan, and Hubei). Thus, economic development can both promote and inhibit the emission of SO 2 . Finally, during the study period, the technical effect (approximately −1387.97 × 10 4 –130.24 × 10 4 t) contributed the most, followed by the economic (approximately 27.81 × 10 4 –1255.59 × 10 4 t), structural (approximately −56.45 × 10 4 –343.90 × 10 4 t), and population effects (approximately 4.25 × 10 4 –39.70 × 10 4 t). Technology was the dominant factor in SO 2 emissions reduction, while economic growth played a major role in promoting SO 2 emissions. Therefore, to promote SO 2 emission reduction, technological innovations and advances should be the primary point of focus.

Keywords: spatial autocorrelation; environmental Kuznets Curve; logarithmic mean divisia index; driving factor; technological innovation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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