The impact of regional industrial structure differences on carbon emission differences in China: An evolutionary perspective

Zheng, Huiling; Gao, Xiangyun; Sun, Qingru; Han, Xiaodan; Wang, Ze

doi:10.1016/j.jclepro.2020.120506

Cited by 65 publications

(30 citation statements)

References 40 publications

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“…The results show that the industrial structure adjustment has a significant effect on carbon emission reduction, but there is an obvious hysteresis effect [17]. Zheng et al (2020) believe that the impact of regional industrial structure differences on regional carbon emissions differences has temporal heterogeneity [18]. Wu et al (2021) believe that the energy-dependent industries' structure is positively correlated with carbon emissions; however, industrial production and industrial structure upgrades have a negative regulatory effect on the impact of the energy-dependent industrial structures and indirectly reduce carbon emissions [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Industrial Structure Upgrading, Green Total Factor Productivity and Carbon Emissions

Yang

Wei

et al. 2022

Sustainability

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Carbon emission reduction is becoming a global issue. Methods of reducing carbon emissions in developing countries have become a hot topic of discussion. Based on the obvious structural transformation in developing countries, this paper discusses the logical mechanisms among industrial structure upgrading, green total factor productivity improvements, and carbon emission reduction. In addition, this paper empirically tests these relationships with provincial data from 2000 to 2017 in China. The conclusions are as follows: (1) industrial structure upgrades have a significant impact on carbon emissions. The industrial structure rationalization remains a noteworthy inhibition on carbon emissions. The industrial structure’s advancement has obvious features of development at the current stage, and its effect on carbon emissions shows an inverted “V” trend, which is initially accelerating but then restraining. (2) Upgrades to industrial structures will decrease carbon emissions by raising green total factor productivity. (3) The rise of green total factor productivity in a certain region will have a relatively obvious inhibitory effect on carbon emissions, but it will exhibit a negative spatial spillover effect on the adjacent areas.

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Section: Literature Reviewmentioning

confidence: 99%

Industrial Structure Upgrading, Green Total Factor Productivity and Carbon Emissions

Yang

Wei

et al. 2022

Sustainability

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“…Some scholars have studied the influencing factors on CO 2 emissions in different industrial sectors, such as transportation, agriculture, industry, and tourism, and results vary from study to study [11,12]. Zheng et al explored interregional differences in industry, construction, and transportation; warehousing industries had a significantly positive impact on interregional differences in CO 2 emissions from 2007 to 2016 [13]. Li et al suggested that governments should increase the proportion of high-tech industries through technological progress, vigorously develop resource-saving and environment-friendly tertiary industries, and develop a low-carbon economy by promoting clean production technology [14].…”

Section: Industrial Structurementioning

confidence: 99%

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Wang

Zheng²,

Sun

et al. 2021

Mathematics

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Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3 and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.

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“…Recently, many studies have demonstrated that CP varies by region, with spatial spillover effects [17,34,35]. Furthermore, most local governments in China generally prefer upgrading their industrial structures to reduce carbon emissions, but have failed to reach their goals [36]. It is, therefore, crucial to investigate the relationship between industrial development and CP as a method of achieving carbon neutrality since the provincial governments in China are the direct executive bodies in charge of emission reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We use the gravity model to estimate the spatial weight index because it incorporates spatial network relationships and more realistically identifies the spatial-economic relationship between regions. (2) Departing from previous studies focused on the relationship between CP and industrial structure upgrades or between carbon productivity and urbanization [23,36], our article aims to investigate correlations between industrial development and CP and between urbanization and CP, revealing the spatial effects of industrial development and urbanization on CP.…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

Zheng

et al. 2021

Sustainability

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This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

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The impact of regional industrial structure differences on carbon emission differences in China: An evolutionary perspective

Cited by 65 publications

References 40 publications

Industrial Structure Upgrading, Green Total Factor Productivity and Carbon Emissions

Industrial Structure Upgrading, Green Total Factor Productivity and Carbon Emissions

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

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