The impact of cross-region industrial structure optimization on economy, carbon emissions and energy consumption: A case of the Yangtze River Delta
Graphical abstract
A reasonable industrial division and collaboration system has been formed through the guidance of cross-region industrial structure optimization.
Introduction
China has achieved great success in economy after reform and opening up. The rapid economy growth rate often comes at the expense of the environmental development that has led to the depletion of resources and ecological environment deterioration, high energy consumption, high pollution phenomenon in a few places (Z.X. Zhou et al., 2020). China's economic losses caused by unilateral pursuit of environmental pollution account for about 8%–15% of gross domestic product (GDP). Sustainable regional economic development is under serious threat (X. Liu et al., 2015b). Realizing high economic growth, continuous utilization of resources, and effective environmental protection have become the primary problem in regional economic and social development (Cheng et al., 2014).
Over a decade now, researchers conduct related researches on economic production and various measures for energy saving and emissions reduction. Some studies focus on certain specific industries. They investigate the impact of production methods (Hosseinzadeh-Bandbafha et al., 2018; Elsoragaby et al., 2019; Mostashari-Rad et al., 2019) and technological progress (Bahman et al., 2018; Jafari-Sejahrood et al., 2019) on energy and carbon emissions. For example, Lv et al. (2019) estimated iron life cycle energy consumption and greenhouse gas emissions, providing solutions for energy conservation and emissions reduction in China's steel industry. Other studies focus on the impact of regional or national industries on energy and carbon emissions. These researches (González and Martínez, 2012; Wang et al., 2016; Wurlod and Noailly, 2018) often investigate the carbon emissions or energy efficiency of the industry. Especially, some researches (Xiong et al., 2019; D. Li et al., 2020c; Zhu and Shan, 2020) analyze the impact of industry structure optimization on energy consumption and carbon emissions, and conclude that the industry structure adjustment not only is an important way to achieve high-speed regional economic growth, but also directly determines the consumptions structure and utilization efficiency of energy. Energy consumption is closely related to carbon emissions. So the industry structure adjustment is an important way to solve environmental problems and reduce carbon emissions (Wu et al., 2018). Through industry structure adjustment each region can increase investment in technology-intensive and knowledge-intensive industries, improve technological progress and support emerging industries, reduce investment in high-pollution and energy-intensive industries, and control energy consumption and pollution emissions from the source.
However, there is little literature focus on the cross-regional industrial structure adjustment. China's current industrial reconstructuring mainly relies on industrial policies implementation and intervention. And the similar industrial policies of local governments may lead to the isomorphism of industries among regions and the intensification of low-level repeated construction. Local governments in backward areas encourage the development of some high-tech industries that do not have a comparative advantage in the short term, to improve their returns in the future regional division. The result of this kind of rational behavior of local government is just a round of repeated construction. If all the regions optimize their industrial structure without mutual cooperation, industrial homogeneity may occur. The rational layout of industries will be affected, and the improvement of industrial energy efficiency will also be reduced (Han et al., 2018).
Therefore, under the circumstances of energy-saving and emissions reduction, how to optimize the cross-regional industrial structure to achieve high-speed economic growth has become a hot issue of concern to academia. Existing researches have played an important role in explaining and guiding regional industrial structure optimization, but there are also some obvious limitations. These researches (Tian et al., 2014; Chang, 2015; Y. Yu et al., 2018; Chen et al., 2019) either considered the industry structure optimization within a region or country, or do not effectively guided the effective division of labor in each area. Very few kinds of literature went further to discuss the cooperation between regions, and adjust industrial structure through industrial transfer and division to achieve economic development, energy conservation, and emissions reduction. Thus we still don't know how to optimize industrial structure and form a reasonable division of labor system. The solution of the serious environmental pollution problem caused by the regional development of the championship model needs to be found.
In order to fill such research gaps, this paper investigates the effect of cross-regional industry structure optimization on economic development, energy and carbon emissions. Because the same industry in different regions will produce different carbon emissions and energy consumption during the production process. These regions can reasonably distribute industries in various regions through the form of industrial transfer to meet their economic development, energy consumption and carbon emissions requirements. This process can be achieved by constructing a cross-regional multi-objective planning model. We take the Yangtze River Delta region as an example. Regional integration in the Yangtze River Delta has become a national strategy. Inter-regional industrial transfer and coordinated development are conducive to the structural optimization and upgrading of the Yangtze River Delta region, and promote the regional economy to a higher level. Each province's industry structure is adjusted to realize the goals of energy conservation and emissions decrease while meeting the requirements of economic development in 2018. The model has five goals: the growth rate of GDP, energy consumption, energy and carbon intensities, as well as the proportion of service industry. Because some provinces are unable to complete all goals, this model adopts goal programming method. And different settings of priority in the goal programming model will get different results, the super-efficiency data envelopment analysis (DEA) model which can better sort schemes is used to choose the best solution. The result reflects the coordination of economic development and environment. Thus, we expand the previous analysis to the following aspects: (1) Carrying out not just optimization within a region, but an optimization problem across regions. And discussing the adjustment of industrial structure through industrial transfer and division to obtain the industry distribution of each province. (2) In terms of method, the goal programming model and the super-efficiency DEA model combine to compare the pros and cons of different results based on economy and environment coordination, and solve the regional industrial structure optimization problem.
The rest of this paper is organized as follows. Section 2 is the overview of relationship between industrial structure, emissions and consumption. Section 3 shows the goal programming model's construction. In Section 4 the Yangtze River Delta as a case is analyzed. Section 5, a general conclusion and discussion is given.
Section snippets
Literature review
Since economists Grossman and Krueger (1995) had put forward the famous environmental Kuznets curve, a series of relevant related researches on environment and economic growth were developed (Carson et al., 1997; Narayan and Smyth, 2008; Sari et al., 2008; Shahbaz et al., 2015). Galeotti et al. (2006) also proved this inverted U-shaped curve relationship for Organization for Economic Cooperation and Development (OECD) countries. Acaravci and Ozturk (2010) checked the validation of the
The model
For the purpose of examining the performance of cross-region industrial structure optimization on carbon emissions and energy consumption, two optimization models including the single-region and the cross-region industry structure optimization models are set up for comparison.
Industrial structure optimization is a multi-objective planning model which contains several constraints which are local governments' phased goals according to the targets of the 13th Five-Year Plan of each province. The
Study area
The Yangtze River Delta (YRD) is situated in the East Asia Geographic Center and the East Pacific route of the Western Pacific (as can be seen in Fig. 1). It is a key intersection of the Yangtze River Economic Belt and the “Belt and Road”. The YRD not only has excellent natural endowments with a mild climate and rich products, but also has strong comprehensive strength, such as convenient transportation conditions, a complete industrial system, rich scientific and technological achievements,
Sensitivity analysis settings
The results of the optimized models will vary with the changes of the parameters in the model. The main influencing factors include the following three categories:
The first category is the growth and decline limits of the six industries in each region in the model. They are based on the maximum fluctuation of historical data during the 12th Five Year Plan period. And the degree of industrial transfer is a key variable that expands the limits of industrial growth and decline. The second category
Conclusion
As the world's largest emitter of greenhouse gases, the development of a low-carbon economy has been the key task for China's sustainable development. The industry structure directly determines the energy consumption structure and utilization efficiency, and energy consumption is closely related to CO2 emissions. Therefore, accelerating the upgrading of industry structure has become not only a key driving force for long-term sustainable economic development, but also an important means to solve
Funding project
Anhui Philosophy and Social Science Planning Project (AHSKY2020D21).
CRediT authorship contribution statement
Bing Zhu: Methodology, Data curation, Writing – original draft. Tinglong Zhang: Conceptualization, Writing – review & editing.
Declaration of competing interest
The authors declare that there is no conflict of interests regarding the publication of this article.
References (84)
- et al.
On the relationship between energy consumption, CO2 emissions and economic growth in Europe
Energy
(2010) - et al.
Carbon emissions, energy consumption and economic growth: an aggregate and disaggregate analysis of the Indian economy
Energy Policy
(2016) Decomposition analysis for policymaking in energy: which is the preferred method?
Energy Policy
(2004)- et al.
Substitution between energy, capital and labour within industrial companies: a micro panel data analysis
Resour. Energy Econ.
(2007) - et al.
How nonlinear control can enhance the automobile efficiency and reduce harmful emissions: China case study
J. Clean. Prod.
(2019) Changing industrial structure to reduce carbon dioxide emissions: a Chinese application
J. Clean. Prod.
(2015)- et al.
What determines the diversity of CO2 emission patterns in the Beijing-Tianjin-Hebei region of China? An analysis focusing on industrial structure
J. Clean. Prod.
(2019) - et al.
Robust planning of energy management systems with environmental and constraint-conservative considerations under multiple uncertainties
E Energ Convers. Manag.
(2013) - et al.
The relationship between energy consumption structure, economic structure and energy intensity in China
Energy Policy
(2009) - et al.
An inexact multi-objective programming model for an economy-energy-environment system under uncertainty: a case study of Urumqi, China
Energy
(2017)
Reassessing the environmental Kuznets curve for CO2 emissions: a robustness exercise
Ecol. Econ.
Decomposition analysis of CO2 emissions in the Mexican industrial sector
Energy Sustain. Dev.
Urban agglomeration economies and industrial energy efficiency
Energy
Application of data envelopment analysis approach for optimization of energy use and reduction of greenhouse gas emission in peanut production of Iran
J. Clean. Prod.
Cost optimization of industrial steel building structures
Adv. Eng. Softw.
Comparison of future energy scenarios for Denmark: IDA 2050, CEESA (coherent energy and environmental system analysis), and climate commission 2050
Energy
The nonlinear impacts of industrial structure on China's energy intensity
Energy
The improvement gap in energy intensity: analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model
Energy
Examining industrial structure changes and corresponding carbon emission reduction effect by combining input-output analysis and social network analysis: a comparison study of China and Japan
J. Clean. Prod.
China's energy intensity change in 1997–2015: non-vertical adjusted structural decomposition analysis based on input-output tables
Struct. Change Econ. D
Changes in carbon intensity in China's industrial sector: decomposition and attribution analysis
Energy Policy
How might China achieve its 2020 emissions target? A scenario analysis of energy consumption and CO2 emissions using the system dynamics model
J. Clean. Prod.
What drives CO2 emissions from China's civil aviation? An exploration using a new generalized PDA method
Transport Res. A-Pol.
Life cycle energy consumption and greenhouse gas emissions of iron pelletizing process in China, a case study
J. Clean Prod.
Industrial structure, energy-saving regulations and energy intensity: evidence from Chinese cities
J. Clean. Prod.
Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing
J. Clean. Prod.
Distributed energy resources integration in single-phase microgrids: an application of IDA-PBC and PI-PBC approaches
Int. J. Elec. Power
Energy consumption and real GDP in G7 countries: new evidence from panel cointegration with structural breaks
Energ Econ.
A multi-objective multi-sectoral economy-energy-environment model: application to Portugal
Energy
Assessment of urban air quality in Istanbul using fuzzy synthetic evaluation
Atmos. Environ.
A scaling method for priorities in hierarchical structures
J. Math. Psychol.
A goal programming model for environmental policy analysis: application to Spain
Energy Policy
The relationship between disaggregate energy consumption and industrial production in the United States: an ARDL approach
Energ Econ.
How does industrial structure change impact carbon dioxide emissions? A comparative analysis focusing on nine provincial regions in China
Environ. Sci. Pol.
Realizing low-carbon development in a developing and industrializing region: impacts of industrial structure change on CO2 emissions in southwest China
J. Environ. Manag.
Prolonged SDA and reduced digestive efficiency under elevated CO2 may explain reduced growth in Atlantic cod (Gadus morhua)
Aquat. Toxicol.
Environmental assessment and investment strategies of provincial industrial sector in China — analysis based on DEA model
Environ. Impact Assess. Rev.
Assessing drivers of economy-wide energy use and emissions: IDA versus SDA
Energy Policy
Energy efficiency and influencing factors analysis on Beijing industrial sectors
J. Clean. Prod.
Resource abundance, industrial structure, and regional carbon emissions efficiency in China
Resour. Policy
Decoding the carbonization mode of the south coastal economic zone in China from the perspective of a dynamic industrial structure
J. Clean. Prod.
Energy and environmental efficiency measurement of China's industrial sectors: a DEA model with non-homogeneous inputs and outputs
Energ Econ.
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