Structural transformation of manufacturing, natural resource dependence, and carbon emissions reduction: Evidence of a threshold effect from China

Li, Zhi; Shao, Shuai; Shi, Xunpeng; Yong, Sun; Zhang, Xiaoling

doi:10.1016/j.jclepro.2018.09.241

Cited by 222 publications

(82 citation statements)

References 21 publications

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“…Taken into consideration that the advance of economic complexity is accompanied by higher energy demand and energy intensity in manufacture and industrial sectors, the paper's results are validated by the studies of and Wang et al, 2019 [39] and Li et al, 2019 [40], demonstrating that the manufacturing and industrial structure rationalisation and upgrading can help to curb CO 2 emissions. As Cheong et al (2018) [124] underlined, energy saving policy interventions may reduce energy intensity and improve energy efficiency in economy.…”

Section: Discussionmentioning

confidence: 57%

“…In the framework of a low-carbon economy, exploring the efficiency of carbon emissions (ratio of inputs to desirable and undesirable outputs) becomes more important. Recent studies explore the role of natural resources abundance as driving factor, among others (e.g., industrial structure) [39,40] of efficiency of carbon emissions. A negative correlation between resources abundance, carbon abatement potential (the excess in undesirable outputs) and carbon emissions efficiency) is identified in the Chinese provinces, and industrial structure plays an important role in emission reduction and improvement of carbon emissions efficiency [39].…”

Section: Introductionmentioning

confidence: 99%

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The Link between Economic Complexity and Carbon Emissions in the European Union Countries: A Model Based on the Environmental Kuznets Curve (EKC) Approach

Neagu

2019

Sustainability

170

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The aim of the paper is to apply the Environmental Kuznets Curve (EKC) model in order to explore the link between economic complexity index (ECI) and carbon emissions, in 25 selected European Union (EU) countries from 1995–2017. The study examines a cointegrating polynomial regression (CPR) for a panel data framework as well as for simple time series of individual countries. In the model is also included the variable ‘energy intensity’ as main determinant of carbon emissions. Depending on economic complexity, the CO2 emissions pattern was found to exhibit an inverted U-shaped curve: in the initial phase, pollution increases when countries augment the complexity of the products they export using and after a turning point the rise of economic complexity suppress the pollutant emissions. The panel cointegration test indicates a long-run relationship between economic complexity, energy intensity and carbon emissions. It was also found that a rise of 10% of energy intensity would lead to a 3.9% increase in CO2 emissions. The quadratic model incorporating ECI is validated for the whole panel as well as for six countries (Belgium, France, Italy, Finland, Sweden and the United Kingdom). The graphical representation of the EKC in these countries is discussed. Policy implications are also included.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

The Link between Economic Complexity and Carbon Emissions in the European Union Countries: A Model Based on the Environmental Kuznets Curve (EKC) Approach

Neagu

2019

Sustainability

170

View full text Add to dashboard Cite

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“…However, there are few studies on energy-rich cities.The most widely used methods to analyze drivers of CO 2 emissions are econometric methods and decomposition analysis methods. In terms of econometric methods, STIRPAT models are widely used to analyze the effects of population, affluence and technology on CO 2 emissions [23,24]. and Zhou et al (2013) constructed a panel regression method to estimate impacts of these three factors and institutional factors on CO 2 emissions [25,26].…”

mentioning

confidence: 99%

Economic Structure Transformation and Low-Carbon Development in Energy-Rich Cities: The Case of the Contiguous Area of Shanxi and Shaanxi Provinces, and Inner Mongolia Autonomous Region of China

et al. 2020

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Energy-rich cities tend to rely on resource-based industries for economic growth, which leads to a great challenge for its low-carbon and sustainable economic development. The contiguous area of Shanxi and Shaanxi Provinces, and the Inner Mongolia Autonomous Region (SSIM) is one of the most important national energy bases in China. Its development pattern, dominated by the coal industry, has led to increasingly prominent structural problems along with difficult low-carbon transition. Taking energy-rich cities in the contiguous area of SSIM as examples, this study analyzes the main drivers of CO2 emissions and explores the role of economic structure transformation in carbon emission reduction during 2002–2012 based on structural decomposition analysis (SDA). The results show that CO2 emissions increase significantly with the coal industry expansion in energy-rich cities. Economic growth and structure are the main drivers of CO2 emission increments. An energy structure dominated by coal and improper product allocation structure can also cause CO2 emission increases. Energy consumption intensity is the main factor curbing CO2 emission growth in energy-rich cities. The decline of agriculture and services contributes to carbon emission reduction, while the expansion of mining and primary energy processing industries has far greater effects on CO2 emission growth. Finally, we propose that energy-rich cities must make more efforts to transform energy-driven economic growth patterns, cultivate new pillar industries by developing high-end manufacturing, improve energy efficiency through more investment in key technologies and the market-oriented reform of energy pricing and develop natural gas and renewable energy to accelerate low-carbon transition.

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“…From the perspective of structure and efficiency share, Pan et al [49] explored the changes in the carbon intensity of China's manufacturing industry and found that the decline in carbon intensity was caused by efficiency. Li et al [50] used STIRPAT model to examine the impact of rationalization and upgrading of manufacturing structure on carbon emissions in China from the perspective of natural resource dependence from 2003 to 2014. The results shown that rationalization and upgrading of manufacturing structure will help to curb carbon dioxide emissions, which is limited by a region's dependence on natural resources.…”

Section: Factors That Affect Manufacturing Carbon Emissionsmentioning

confidence: 99%

Analysis of CO2 Emissions in China’s Manufacturing Industry Based on Extended Logarithmic Mean Division Index Decomposition

et al. 2019

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China is the world’s largest emitter of CO2. As the largest sector of China’s fossil energy consumption and carbon emissions, manufacturing plays an important role in achieving emission reduction targets in China. Using the extended logarithmic mean division index (LMDI) decomposition model, this paper decomposed the factors that affect the CO2 emissions of China’s manufacturing industry into eight effects. The results show the following: (1) China’s manufacturing CO2 emissions increased from 1.91 billion tons in 1995 to 6.25 billion tons in 2015, with an average annual growth rate of 6%. Ferrous metal smelting and rolling were the largest sources of carbon dioxide emissions, followed by chemical raw materials and products and then non-metallic minerals. (2) During the research period, the industrial activity effects were the most important factor leading to increased CO2 emissions in manufacturing and energy intensity was the most important factor in promoting the reduction of CO2 emissions from manufacturing. The investment intensity was the second most influential factor leading to the increase in China’s manufacturing CO2 emissions after the industrial scale and this even exceeded the industrial activity effect in some time periods (2000–2005). R&D efficiency and R&D intensity were shown to have significant roles in reducing CO2 emissions in China’s manufacturing industry. The input of R&D innovation factors is an effective way to achieve emission reductions in China’s manufacturing industry. (3) There were differences in the driving factors of CO2 emissions in the manufacturing industry in different periods that were closely related to the international and domestic economic development environment and the relevant policies of the Chinese government regarding energy conservation and emission reduction. (4) Sub-sector research found that the factors that affect the reduction of CO2 emissions in various industries appear to be differentiated. This paper has important policy significance to allow the Chinese government to implement effective energy-saving and emission reduction measures and to reduce CO2 emissions from the manufacturing industry.

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Structural transformation of manufacturing, natural resource dependence, and carbon emissions reduction: Evidence of a threshold effect from China

Cited by 222 publications

References 21 publications

The Link between Economic Complexity and Carbon Emissions in the European Union Countries: A Model Based on the Environmental Kuznets Curve (EKC) Approach

The Link between Economic Complexity and Carbon Emissions in the European Union Countries: A Model Based on the Environmental Kuznets Curve (EKC) Approach

Economic Structure Transformation and Low-Carbon Development in Energy-Rich Cities: The Case of the Contiguous Area of Shanxi and Shaanxi Provinces, and Inner Mongolia Autonomous Region of China

Analysis of CO2 Emissions in China’s Manufacturing Industry Based on Extended Logarithmic Mean Division Index Decomposition

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