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Energy conservation potential analysis of Chinese manufacturing industry: the case of Jiangsu province

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Abstract

Improving energy conservation efficiency is one of the prerequisites for China’s manufacturing industry to transform and upgrade. Jiangsu province which presents the maximum economic volume in manufacturing and its economic status in eastern China is comparable to Shanghai. Research on the sustainable development capacity of Jiangsu’s manufacturing industry gives important guidance for upgrading the manufacturing industry all over China. The core of China’s manufacturing transition to a manufacturing power is to enhance its independent innovation capabilities to improve energy efficiency and its position in the global value chain. Therefore, it is important to study the impact of technological factor on energy conservation potential and the transformation and upgrading of manufacturing. In this paper, multivariate regression research method combined with risk analysis is developed to explore the influence of the research and development factor on energy conservation while introducing macroeconomic variables. Additionally, energy conservation of manufacturing in Jiangsu province in 2020 and 2025 based on historical data from 1985 to 2015 is predicted. Compared with the business-as-usual scenario, the advanced scenario could reduce by 44.07 Mtce and 87.60 Mtce in 2020 and 2025, respectively. Thus, the results indicate that there is much room for improvement in terms of the energy efficiency for Jiangsu province.

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References

  • Ahmed R, Stater M (2017) Is energy efficiency underprovided? An analysis of the provision of energy efficiency in multi-attribute products. Resour Energy Econ 49:132–149

    Article  Google Scholar 

  • Amer M, Tugrul UD, Antonie J (2013) A review of scenario planning. Futures 46:23–40

    Article  Google Scholar 

  • Andor AM, Fels MK (2018) Behavioral economics and energy conservation—a systematic review of non-price interventions and their causal effects. Ecol Econ 148:178–210

    Article  Google Scholar 

  • Chen H, Kang J, Liao H, Tang B, Wei Y (2017) Costs and potentials of energy conservation in China’s coal-fired power industry: a bottom-up approach considering price uncertainties. Energy Policy 104:23–32

  • Dong K, Sun S, Hochman G, Li H (2018) Energy intensity and energy conservation potential in China: a regional comparison perspective. Energy 155:782–795

  • Du Q, Li Z, Li Y, Bai LB, Li XH (2019) Rebound effect of energy efficiency in China’s construction industry: a general equilibrium analysis. Environ Sci Pollut Res 26(12):12217–12226

    Article  CAS  Google Scholar 

  • Engle RF, Granger CWJ (1987) Co-integration and erro correction: representation, estimation, and testing. Econometrica 55(2):251–276

    Article  Google Scholar 

  • Gamtessa S, Olani AB (2018) Energy price, energy efficiency, and capital productivity: empirical investigations and policy implications. Energy Econ 72:650–666

  • Ghosh NK, Blackhurst MF (2014) Energy savings and the rebound effect with multiple energy services and efficiency correlation. Ecol Econ 105:55–66

    Article  Google Scholar 

  • Handgraaf MJJ, Van lidth de Jeude MA, Appelt KC (2013) Public praise vs. private pay: effects of rewards on energy conservation in the workplace. Ecol Econ 86:86–92

    Article  Google Scholar 

  • Hasanbeigi A, Menke C, Therdyothin A (2010) The use of conservation supply curves in energy policy and economic analysis: the case study of Thai cement industry. Energy Policy 38(1):392–405

    Article  Google Scholar 

  • Hasanbeigi A, Morrow W, Sathaye J, Masanet E, Xu TF (2013) A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry. Energy 50:315–325

    Article  Google Scholar 

  • He AWW, Kwok JTK, Wan ATK (2010) An empirical model of daily highs and lows of West Texas Intermediate crude oil prices. Energy Econ 32(6):1499–1506

    Article  Google Scholar 

  • Hu WQ, Jin T, Liu Y (2019) Effects of environmental regulation on the upgrading of Chinese manufacturing industry. Environ Sci Pollut Res 26(26):27087–27099

    Article  Google Scholar 

  • Huang JB, Yang TC, Jia J (2019) Determining the factors driving energy demand in the Sichuan–Chongqing region: an examination based on DEA-Malmquist approach and spatial characteristics. Environ Sci Pollut Res 26(31):131654–131666

    Google Scholar 

  • Johansen S (1995) Likelihood-based inference in cointegrated vector autoregressive models. General Inf 14:517–524

    Google Scholar 

  • Johansen S, Juselius K (1990) Maximum likelihood estimation and inference on cointegration-with applications to the demand for money. Oxf Bull Econ Stat 52:169–210

    Article  Google Scholar 

  • Kasman A, Duman YS (2015) CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis. Econ Model 44:97–103

    Article  Google Scholar 

  • Li K, Lin B (2014) The nonlinear impacts of industrial structure on China’s energy intensity. Energy 69:258–265

    Article  Google Scholar 

  • Li K, Lin B (2016) Impact of energy conservation policies on the green productivity in China’s manufacturing sector: evidence from a three-stage DEA model. Appl Energy 168:351–363

  • Lin B, Lin J (2017) Evaluating energy conservation in China’s heating industry. J Clean Prod 142:501–502

    Article  Google Scholar 

  • Lin B, Chen G (2018) Energy efficiency and conservation in China’s manufacturing industry. J Clean Prod 174:492–501

  • Lin B, Ouyang X (2014) Electricity demand and conservation potential in the Chinese nonmetallic mineral products industry. Energy Policy 68:243–253

  • Lin B, Tan R (2017) Estimating energy conservation potential in China’s energy intensive industries with rebound effect. J Clean Prod 156:899–910

  • Lin B, Xie X (2015) Energy conservation potential in China’s petroleum refining industry: evidence and policy implications. Energy Convers Manag 91:377–386

  • Lin B, Zhang L, Wu Y (2012) Evaluation of electricity saving potential in China’s chemical industry based on co-integration. Energy Policy 44:320–330

  • Lundgren T, Marklund P, Zhang S (2016) Industrial energy demand and energy efficiency—evidence from Sweden. Resour Energy Econ 43:130–152

    Article  Google Scholar 

  • MacKinnon JG, Haug AA, Michelis L (1999) Numerical distribution functions of likelihood ratio tests for cointegration. J Appl Econ 14(5):563–577

  • Mi Z, Wei Y, Wang B et al (2017) Socioeconomic impact assessment of China’s CO2 emissions peak prior to 2030. J Clean Prod 142:2227–2236

  • Paparoditis E, Politis D (2002) The tapered block bootstrap for general statistics from stationary sequences. J Econ 5:131–148

  • Shao Q, Schaffartzik A, Mayer A, Krausmann F (2017) The high ‘price’ of dematerialization: a dynamic panel data analysis of material use and economic recession. J Clean Prod 167:120–132

  • Wang A, Wang G (2015) S-curve model of relationship between energy consumption and economic development. Nat Resour Res 24(1):53–64

  • Worrell E, Martin N, Price L (2000) Potentials for energy efficiency improvement in the US cement industry. Energy 25(12):1189–1214

  • Yang M, Yang F (2016) Energy-efficiency policies and energy productivity improvements: evidence from China’s manufacturing industry. Emerg Mark Financ Trade 52(6):1395–1404

  • Yuan C, Liu S, Wu J (2009) Research on energy-saving effect of technological progress based on Cobb–Douglas production function. Energy Policy 37(8):2842–2846

  • Yuan C, Liu S, Wu J (2010) The relationship among energy prices and energy consumption in China. Energy Policy 38(1):197–207

  • Zaim O (2004) Measuring environmental performance of state manufacturing through changes in pollution intensities: a DEA framework. Ecol Econ 48:37–47

    Article  Google Scholar 

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  1. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, 230009, China

    Manli Cheng

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  1. Manli Cheng
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Correspondence to Manli Cheng.

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Cheng, M. Energy conservation potential analysis of Chinese manufacturing industry: the case of Jiangsu province. Environ Sci Pollut Res 27, 16694–16706 (2020). https://doi.org/10.1007/s11356-020-08084-w

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  • DOI: https://doi.org/10.1007/s11356-020-08084-w

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