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Frontiers of Engineering Management

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Front. Eng    2022, Vol. 9 Issue (3) : 439-451    https://doi.org/10.1007/s42524-022-0210-8
RESEARCH ARTICLE
Energy rebound effect in China’s manufacturing sector: Fresh evidence from firm-level data
Zicheng ZHOU1, Luojia WANG2(), Kerui DU2, Shuai SHAO3()
1. School of Public Affairs, Zhejiang University, Hangzhou 310058, China
2. School of Management, China Institute for Studies in Energy Policy, Xiamen University, Xiamen 361005, China
3. School of Business, East China University of Science and Technology, Shanghai 200237, China
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Abstract

The rebound effect refers to the phenomenon that individuals tend to consume more energy in the face of energy efficiency improvement, which reduces the expected energy-saving effect. Previous empirical studies on the rebound effect of regions and sectors do not provide microscopic evidence. To fill this gap, we use China’s firm-level data to estimate the rebound effect in China’s manufacturing subsectors, providing a detailed picture of China’s rebound effect across different sectors and different regions in 2001–2008. Results show that a partial rebound effect robustly appears in all industries, and the disparity between sectors is quite broad, ranging from 43.2% to 96.8%. As for the dynamic rebound effect of subsectors, most subsectors present an upward trend, whereas few subsectors show a clear downward trend. As a whole, the declined trend of the rebound effect is driven by the descent of minority sectors with high energy consumption and high energy-saving potential. In addition, we find that the disparity of the rebound effect across sectors is more significant than that across regions.
Keywords energy rebound effect      energy efficiency      manufacturing sector      firm-level data      China     
Corresponding Author(s): Luojia WANG,Shuai SHAO   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.
Just Accepted Date: 20 June 2022   Online First Date: 11 August 2022    Issue Date: 05 September 2022
 Cite this article:   
Zicheng ZHOU,Luojia WANG,Kerui DU, et al. Energy rebound effect in China’s manufacturing sector: Fresh evidence from firm-level data[J]. Front. Eng, 2022, 9(3): 439-451.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-022-0210-8
https://academic.hep.com.cn/fem/EN/Y2022/V9/I3/439
Fig.1  Energy consumption in China (Note: The data are from China’s National Bureau of Statistics).
Reference Subsector Time period Region Method Rebound effect estimates
Du et al. (2021a) Urban residents 2001–2014 30 Chinese provinces/cities Stochastic energy demand frontier model 65.4% on average
Zha et al. (2022) Urban households 2002–2017 30 Chinese provinces/cities Environmentally extended multiregional input–output model, price elasticity model, and re-spending model The direct carbon rebound effect is 50%, and the indirect rebound effect will be lower than 20%
Wang et al. (2019) Residents / / Consumer expenditure reallocation with cumulative energy consumption through integrating a re-spending framework and an input–output analysis under different scenarios /
Chen et al. (2022) Urban households 2002–2017 30 Chinese provinces/cities An elasticity approach with an individual fixed-effect variable coefficient panel data model 59.9% on average
Zhao and Li (2020) Residential electricity use 2010–2016 30 Chinese provinces/cities Panel data model and panel threshold model 84.94%
Lin and Zhu (2021) Household electricity consumption 2010–2018 25 Chinese provinces/cities Stochastic energy demand frontier model 48.0% on average
Du et al. (2021b) Residential buildings 1994–2016 China’s urban and rural areas Linear approximation of the almost ideal demand system model 79.4%–110.0% in urban areas and 115.3%–120.4% in rural areas
Tab.1  Related literature about the rebound effect in the residential sector
Variable Observation Mean Standard deviation Min Max
Energy efficiency 218731 0.210 0.340 0.000 2.615
Energy consumption 218731 19507.000 150618.400 2.914 5067999.000
Output price 218731 114.900 24.873 93.125 285.753
Labor price 218731 13757.090 10131.210 1104.252 128119.700
Capital price 218731 0.036 0.052 0.000 0.935
Energy price 218731 525.132 434.167 139.192 2634.507
Tab.2  Summary statistics
Variables (1) (2) (3)
Energy consumption Energy consumption Energy consumption
ln?τ 95 −0.377***(0.017)
ln?τ 92 −0.418***(0.018)
ln?τ 98 −0.335***(0.016)
Constant 3.905***(0.318) 3.990***(0.309) 3.783***(0.320)
Control variables Yes Yes Yes
Firm fixed effect Yes Yes Yes
Observations 199127 199127 199127
R-squared 0.947 0.949 0.944
RE 62.3% 58.2% 66.5%
Tab.3  Estimated results for baseline models
Fig.2  Dynamic trend of the rebound effect during 2001–2008.
Fig.3  Energy rebound effect for each subsector in the manufacturing sector (Notes: The energy rebound effect for each subsector is an average over the period of 2001–2008; the red vertical line indicates the average energy rebound effect).
Fig.4  Energy rebound effects for China’s 29 manufacturing subsectors during 2001–2008 (Notes: The solid line connects the point estimates for the rebound effect; the two dashed lines indicate the upper and lower bound of the 95% confidence interval).
Fig.5  Energy rebound effect in three regions for 29 manufacturing subsectors.
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Energy-intensive Non-energy-intensive Pollution-intensive Non-pollution-intensive High industry concentration Low industry concentration Heavy industry Light industry High-degree of marketization Low-degree of marketization
ln?τ 95 −0.436***(0.0182) −0.330***(0.0186) −0.417***(0.0163) −0.271***(0.0185) −0.283***(0.0175) −0.496***(0.0205) −0.382***(0.0181) −0.366***(0.0195) −0.350***(0.0266) −0.400***(0.0208)
Constant 5.395***(0.219) 2.820***(0.508) 4.841***(0.224) 1.521**(0.690) 4.122***(0.344) 4.492***(0.236) 4.160***(0.326) 3.107***(0.570) 3.767***(0.475) 3.990***(0.477)
Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 97568 100914 161837 37290 89410 100596 124734 74393 97270 99747
R-squared 0.948 0.923 0.949 0.912 0.944 0.957 0.951 0.931 0.944 0.948
RE 56.4% 67.0% 58.3% 72.9% 71.7% 50.4% 61.8% 63.4% 65.0% 60.0%
Empirical p-value 0.000*** 0.000*** 0.000*** 0.018** 0.000***
Tab.4  Results of heterogeneity analysis
Complementary Identification Code Subsector Abbreviation
13 Processing of food from agricultural products Agricultural Food Processing
14 Manufacture of foods Food
15 Manufacture of beverages Beverage
16 Manufacture of tobacco Tobacco
17 Manufacture of textile Textile
18 Manufacture of textile wearing apparel, footwear and caps Apparel
19 Manufacture of leather, fur, feather and related products Leather
20 Processing of timber, manufacture of wood, bamboo, rattan, palm and straw products Wood Process
21 Manufacture of furniture Furniture
22 Manufacture of paper and paper products Paper
23 Printing, reproduction of recording media Printing
24 Manufacture of articles for culture, education and sport activities Cultural Articles
25 Processing of petroleum, coking, processing of nuclear fuel Petroleum Process
26 Manufacture of raw chemical materials and chemical products Chemical
27 Manufacture of medicines Medicine
28 Manufacture of chemical fibers Fibers
29 Manufacture of rubber products Rubber
30 Manufacture of plastics products Plastic
31 Manufacture of nonmetallic mineral products Nonmetal Manufacture
32 Smelting and pressing of ferrous metals Ferrous Press
33 Smelting and pressing of non-ferrous metals Non-Ferrous Press
34 Manufacture of metal products Metal Products
35 Manufacture of general purpose machinery General Machinery
36 Manufacture of special purpose machinery Special Machinery
37 Manufacture of transport equipment Transport Equipment
39 Manufacture of electrical machinery and equipment Electrical Equipment
40 Manufacture of communication equipment, computers and other electronic equipment Computers Electronic Equipment
41 Manufacture of measuring instruments and machinery for cultural activity and office work Measuring Instruments
42 Artwork and other manufacturing Artwork
  Table B1 List of manufacturing subsectors and abbreviations
Region Provinces or Municipalities
Eastern region Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan, Hebei, Shandong, Liaoning, Guangxi
Central region Heilongjiang, Jilin, Anhui, Henan, Hubei, Hunan, Jiangxi, Inner Mongolia, Shanxi
Western region Chongqing, Gansu, Guizhou, Ningxia, Qinghai, Sichuan, Xinjiang, Shaanxi, Yunnan
  Table C1 Classification of the three regions in China
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