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
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.
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
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
Variables
(1)
(2)
(3)
Energy consumption
Energy consumption
Energy consumption
−0.377***(0.017)
−0.418***(0.018)
−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
Fig.2
Fig.3
Fig.4
Fig.5
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
−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
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
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