Analysis of the Potential Impacts on China’s Industrial Structure in Energy Consumption

Tian, Yushen; Xiong, Siqin; Ma, Xiaoming

doi:10.3390/su9122284

Cited by 24 publications

(11 citation statements)

References 34 publications

(35 reference statements)

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“…In China, industrial sectors account for about 60% of the country's total energy use and are mainly responsible for the majority of its CO 2 emissions [16]. However, the intensity of energy consumption and carbon emissions is expected to decrease with nationwide technological improvements, industrial structure adjustments, and the gradual shift to a service economy [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Most previous researchers have focused on TEC/TCEM or IEC/ICEM to determine the spatial-temporal pattern and driving factors. Although some have explored the energy consumption and related carbon emissions from the single aspect of industrial or residential sectors, few researchers have conducted comparative analyses of both aspects [13,17,30,31]. This research gap may lead to inappropriately optimistic judgments about China's future energy consumption and carbon emissions considering its industrial transformation and significant decline in energy intensity in the industrial sector [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Sun

2018

Sustainability

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Improvements of manufacturability and living standard driven by industrialization and urbanization typically cause a spike in total energy consumption (TEC) and related carbon emissions (TCEM). However, there have been few comparative studies to include industrial and residential energy consumption (IEC and REC, respectively) and related carbon emissions (ICEM and RCEM, respectively). Since China is a major emerging industrial country wherein urbanization is still ongoing, the present study was conducted in an attempt to analyze the temporal evolution of China’s continued energy consumption and related carbon emissions regarding both industrialization and urbanization. The influencing factors of TCEM, RCEM and ICEM are determined via the log-mean divisia index (LMDI) model. The results showed that both TEC and TCEM gradually increased (apart from a slight decrease in 2014); REC and RCEM increased steadily with no sharp peak; while IEC and ICEM declined sharply. TCEM was positively affected by economic output, consumption level, and population size; the influence of consumption level became more and more significant. Per capita GDP and per capita expenditure were the most significant driving factors for RCEM, while industrial added value (IAV) was the main driving factor for ICEM. The temporal evolution and influencing factors of energy consumption and carbon emissions had stage-related characteristics in accordance with Shanghai’s three stages of development. The Shanghai case study provided a comprehensive understanding of energy consumption and related carbon emissions from the dual perspective of industrialization and urbanization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Sun

2018

Sustainability

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“…However, it remains unclear how energy efficiency can be increased. Most relevant studies attempted to answer this question at the level of industrial structure [22][23][24]. The "structural bonus hypothesis" suggests that due to the significant differences between the industrial sector and the service sector with regard to their energy efficiency, the transfer of energy from a low-productivity sector to a high-productivity sector would increase the overall energy efficiency of an economy that comprises various sectors.…”

Section: Introductionmentioning

confidence: 99%

Retraction Notice: Influence of Agglomeration of Manufacturing and the Producer Service Sector on Energy Efficiency

Shen¹,

Deng²,

Wang³

2019

Pol. J. Environ. Stud.

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Due to the gradual agglomeration of economic activities and the continuous reinforcement of spatial linkages in specific geographic locations, the geospatial factor should become an important starting point to understand the relationship between industrial restructuring and energy conservation and emission reduction. This paper first introduces a non-separable hybrid DEA model that considers undesirable output to measure the energy efficiencies of 285 prefecture or higher-level cities in China during 2003-2016; then, a dynamic spatial panel model is used to investigate the influence of different types of industrial agglomerations and agglomeration modes on energy efficiency. According to the obtained study results, for the investigation period, the overall energy efficiency of China with regard to pollutants remained at a low level and presented a "U-shaped" decreasing-increasing trend. To be specific, China's energy efficiency distribution presented a trend of "high in the east and low in the west." The energy efficiency of East China changed relatively gently, while the energy efficiencies of central China and western China changed dramatically. China's energy efficiency also presented a significant spatial agglomeration effect, i.e., cities with close energy efficiencies are usually adjacent to each other. At the national level, agglomeration of the manufacturing sector significantly inhibited the increase of energy efficiency; the agglomeration of the producer service sector and the co-agglomeration of the manufacturing sector and the producer service sector both facilitated an increase of energy efficiency. The influence of industrial agglomeration on energy efficiency differed across different city scale grades. Based on these conclusions, the paper proposes the following policy implications: 1) make full use of the energy savings and emission reduction effect of agglomeration; 2) accelerate the optimization of industrial layout; 3) develop high-end service industry and productive service industry; and 4) create an agglomeration environment that encourages benign industrial competition.

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“…Yu et al [14] explored China's industrial restructuring for realizing its energy-saving and economy-growth goals through formulating a multi-objective GP model based on the dynamic environment input-output analysis model. Tian et al [15] analyzed the potential impacts on China's industrial structure by formulated a multiobjective optimization model to achieve the target of the maximization of GDP and minimization of energy consumption. Papadakis and Markaki [3] used particle swarm optimization model to optimize the domestic technical coefficients structure of each sector for minimizing the GHG emissions intensity in Greek.…”

Section: Introductionmentioning

confidence: 99%

Regional Input–Output Multiple Choice Goal Programming Model and Method for Industry Structure Optimization on Energy Conservation and GHG Emission Reduction in China

Lin¹,

Li²,

Jiang³

et al. 2019

IJCIS

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To assess the potential of China's industrial restructuring on energy conservation and greenhouse gas (GHG) emission reduction in 2020, this study proposes an input-output multi-choice goal programming model and method. In this model, the goals include the maximization of gross domestic product (GDP), minimization of energy consumption and GHG emission. They are subjected to the input-output balance, economy development, energy supply, and industry diversity. And four scenarios with different decision preferences are taken into accounted in the solutions of the industrial structure optimization model. The results demonstrate that industrial restructuring has potential in energy saving and emission reducing. First, after optimization, energy consumption intensity and GHG emission intensity can drop by 13.88246% and 5.33767% over 2012, and GDP can grow up at annual growth rate 6.6% from 2013-2020. Second, promoting the development of the low energy-intensive and low GHG emission intensive sectors is an effective method for energy conservation and emission reduction. Three, compared to energy intensity reduction, GHG emission intensity reduction is less effective for four scenarios. Four, there are several difficulties to achieve the amounts and intensity control targets of energy conservation and GHG emission reduction simultaneously. It is suggested that China had better strive to promote progress of technologies of energy conservation and GHG emission reduction while adjusting the industrial structure.

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Analysis of the Potential Impacts on China’s Industrial Structure in Energy Consumption

Cited by 24 publications

References 34 publications

Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

Retraction Notice: Influence of Agglomeration of Manufacturing and the Producer Service Sector on Energy Efficiency

Regional Input–Output Multiple Choice Goal Programming Model and Method for Industry Structure Optimization on Energy Conservation and GHG Emission Reduction in China

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