Direct energy rebound effect for road transportation in China

doi:10.1007/s42524-023-0276-y

Front. Eng

2023, Vol. 10

Issue (4) : 597-611 https://doi.org/10.1007/s42524-023-0276-y

Urban Management: Developing Sustainable, Resilient, and Equitable Cities Co-edited by Wei-Qiang CHEN, Hua CAI, Benjamin GOLDSTEIN, Oliver HEIDRICH and Yu LIU

Direct energy rebound effect for road transportation in China

Donglan ZHA(

), Pansong JIANG, Xue ZHANG

College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

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Abstract

The enhancement of energy efficiency stands as the principal avenue for attaining energy conservation and emissions reduction objectives within the realm of road transportation. Nevertheless, it is imperative to acknowledge that these objectives may, in part or in entirety, be offset by the phenomenon known as the energy rebound effect (ERE). To quantify the long-term EREs and short-term EREs specific to China’s road transportation, this study employed panel cointegration and panel error correction models, accounting for asymmetric price effects. The findings reveal the following: The long-term EREs observed in road passenger transportation and road freight transportation range from 13% to 25% and 14% to 48%, respectively; in contrast, the short-term EREs in road passenger transportation and road freight transportation span from 36% to 41% and 3.9% to 32%, respectively. It is noteworthy that the EREs associated with road passenger transportation and road freight transportation represent a partial rebound effect, falling short of reaching the magnitude of a counterproductive backfire effect. This leads to the inference that the upsurge in energy consumption within the road transportation sector cannot be solely attributed to advancements in energy efficiency. Instead, various factors, including income levels, the scale of commodity trade, and industrial structure, exert more substantial facilitating influences. Furthermore, the escalation of fuel prices fails to dampen the demand for energy services, whether in the domain of road passenger transportation or road freight transportation. In light of these conclusions, recommendations are proffered for the formulation of energy efficiency policies pertinent to road transportation.

Keywords road transportation direct energy rebound effect asymmetric price effects panel data model

Corresponding Author(s): Donglan ZHA

Just Accepted Date: 31 October 2023 Online First Date: 21 November 2023 Issue Date: 07 December 2023

Cite this article:

Donglan ZHA,Pansong JIANG,Xue ZHANG. Direct energy rebound effect for road transportation in China[J]. Front. Eng, 2023, 10(4): 597-611.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0276-y
https://academic.hep.com.cn/fem/EN/Y2023/V10/I4/597

Fig.1 Direct energy rebound effect.

Fig.2 Decomposition of the fuel price index.

Tab.1 The statistical description of the variables

Tab.2 Results of the panel unit root test

Tab.3 Results of the panel cointegration test in the road passenger transportation equation

Variables	Whole country	Eastern	Central	Western
ln INC_it	0.649^***	?0.072	0.551^***	0.334^***
$ln ? P i t max$	3.123^***	15.378^***	1.124^*	3.186^***
$ln ? P i t cut$	?0.157^***	?0.126	0.144	?0.254^**
$ln ? P i t rec$	0.089	0.448^***	?0.034	0.214^*
ln URB_it	?1.125^***	?1.139^***	?0.488^***	?0.224^*
C	?17.256^***	?69.978^***	?5.405^*	?13.370^***

Tab.4 Estimation of the long cointegration equation in road passenger transportation

Variables	Whole country	Eastern	Central	Western
$Δ ln ? I N C i t$	0.776^***	1.004^***	1.024^***	0.596^***
$Δ ln ? P i t max$	0.957^***	?4.440^***	1.799^***	1.617^***
$Δ ln ? P i t cut$	?0.410^***	?0.408^***	?0.360^***	?0.401^***
$Δ ln ? P i t rec$	0.292^***	0.379^***	0.315^***	0.080
$Δ ln ? U R B i t$	0.069	?0.103	0.146	0.189
$e c m i t ? 1$	?0.016^***	?0.060^***	?0.004	?0.005^*
C	?0.086^***	?0.058^***	?0.125^***	?0.066^***

Tab.5 Estimation of the panel error correction equation in road passenger transportation

Variable	LLC test	IPS test	HT test	Conclusion
$ln ? F T K M$	0.251	1.776	0.872	Nonstationary
$Δ ln ? F T K M$	?8.328^***	?13.231^***	?0.002^***	Stationary
$ln ? C O N S$	10.218	2.334	0.873	Nonstationary
$Δ ln ? C O N S$	?7.045^***	?10.752^***	0.084^***	Stationary
$ln ? P max$	?2.638^***	?4.574^***	0.826	Stationary
$Δ ln ? P max$	?7.390^***	?11.287^***	0.172^***	Stationary
$ln ? P cut$	5.865	3.901	0.930	Nonstationary
$Δ ln ? P cut$	?6.882^***	?13.124^***	?0.045^***	Stationary
$ln ? P rec$	0.427	4.676	0.919	Nonstationary
$Δ ln ? P rec$	?10.912^***	?12.550^***	?0.041^***	Stationary
$ln ? S T R$	?1.453^*	0.831	0.920	Nonstationary
$Δ ln ? S T R$	?8.391^***	?8.402^***	0.132^***	Stationary

Tab.6 Results of the panel unit root test

Tab.7 Results of the panel cointegration test in the road freight transportation equation

Variables	Whole country	Eastern	Central	Western
$ln ? C O N S i t$	0.548^***	0.514^***	0.763^***	0.603^***
$ln ? P i t max$	5.142^***	15.969^***	0.186	3.969^***
$ln ? P i t cut$	?0.483^***	?0.241^**	?0.140^**	?0.406^***
$ln ? P i t rec$	1.157^***	0.467^***	0.927^***	1.176^***
$ln ? S T R i t$	1.089^***	0.548^***	1.061^***	0.890^***
C	?22.104^***	?74.376^***	0.183	?17.090^***

Tab.8 Estimation of the long cointegration equation in road freight transportation

Variables	Whole country	Eastern	Central	Western
$Δ ln ? C O N S i t$	0.753^***	0.613^***	0.872^***	0.305^***
$Δ ln ? P i t max$	9.008^***	21.832^***	2.168^***	7.234^***
$Δ ln ? P i t cut$	?0.233^***	?0.039	?0.260	?0.318^**
$Δ ln ? P i t rec$	1.030^***	0.463^***	1.058^***	1.175^***
$Δ ln ? S T R i t$	0.830^***	0.498^***	0.720^***	0.752^***
$e c m i t ? 1$	?0.045^***	?0.013^*	?0.052^**	?0.093^***
C	?0.035^***	?0.001^*	?0.046	0.025

Tab.9 Estimation of the panel error correction equation in road freight transportation

Variables	$ln ? P T K M$	Variables	$Δ ln ? P T K M$	Variables	$ln ? F T K M$	Variables	$Δ ln ? F T K M$
$ln ? I N C i t$	0.645^***	$Δ ln ? I N C i t$	0.973^***	$ln ? C O N S i t$	0.668^***	$Δ ln ? C O N S i t$	0.814^***
$ln ? P i t max$	2.010^*	$Δ ln ? P i t max$	1.403	$ln ? P i t max$	8.848^***	$Δ ln ? P i t max$	9.823^***
$ln ? P i t cut$	?0.158^***	$Δ ln ? P i t cut$	?0.286^*	$ln ? P i t cut$	?0.586^***	$Δ ln ? P i t cut$	?0.459^**
$ln ? P i t rec$	?0.404^**	$Δ ln ? P i t rec$	0.308^*	$ln ? P i t rec$	0.839^***	$Δ ln ? P i t rec$	1.436^***
$ln ? U R B i t$	?1.213^***	$Δ ln ? U R B i t$	?0.733	$ln ? S T R i t$	1.686^***	$Δ ln ? S T R i t$	1.265^***
C	?13.975^***	$e c m i t ? 1$	?0.038	C	?40.509^***	$e c m i t ? 1$	?0.202^***
		C	0.110			C	?1.181^***

Tab.10 Robustness test results for changing the time window width

Variables	$ln ? P T K M$	Variables	$Δ ln ? P T K M$	Variables	$ln ? F T K M$	Variables	$Δ ln ? F T K M$
$ln ? I N C i t$	0.897^***	$Δ ln ? I N C i t$	0.903^***	$ln ? C O N S i t$	0.678^***	$Δ ln ? C O N S i t$	0.744^***
$ln ? P i t max$	1.073	$Δ ln ? P i t max$	1.364	$ln ? P i t max$	4.854^***	$Δ ln ? P i t max$	9.392^***
$ln ? P i t cut$	?0.183^***	$Δ ln ? P i t cut$	?0.316^**	$ln ? P i t cut$	?0.643^***	$Δ ln ? P i t cut$	?0.415^**
$ln ? P i t rec$	?0.739^***	$Δ ln ? P i t rec$	0.201	$ln ? P i t rec$	0.664^**	$Δ ln ? P i t rec$	1.470^***
$ln ? U R B i t$	?0.958^***	$Δ ln ? U R B i t$	?0.029	$ln ? S T R i t$	1.307^***	$Δ ln ? S T R i t$	1.222^***
C	?8.783^**	$e c m i t ? 1$	?0.053^**	C	?21.598^***	$η P E (S) = ? ln ? S / ? ln ? P E = 0$	?0.149^***
		C	0.186			C	?0.874^***

Tab.11 Robustness test results for excluding special samples

1	P Barla, B Lamonde, L F Miranda-Moreno, N Boucher, (2009). Traveled distance, stock and fuel efficiency of private vehicles in Canada: Price elasticities and rebound effect. Transportation, 36( 4): 389–402 https://doi.org/10.1007/s11116-009-9211-2
2	P H G Berkhout, J C Muskens, J W Velthuijsen, (2000). Defining the rebound effect. Energy Policy, 28( 6/7): 425–432 https://doi.org/10.1016/S0301-4215(00)00022-7
3	C Böhringer, N Rivers, (2021). The energy efficiency rebound effect in general equilibrium. Journal of Environmental Economics and Management, 109: 102508 https://doi.org/10.1016/j.jeem.2021.102508
4	J Chai, Y Yang, S Wang, K K Lai, (2016). Fuel efficiency and emission in China’s road transport sector: Induced effect and rebound effect. Technological Forecasting and Social Change, 112: 188–197 https://doi.org/10.1016/j.techfore.2016.07.005
5	M Chaudry, L Jayasuriya, S Blainey, M Lovric, J W Hall, T Russell, N Jenkins, J Wu, (2022). The implications of ambitious decarbonisation of heat and road transport for Britain’s net zero carbon energy systems. Applied Energy, 305: 117905 https://doi.org/10.1016/j.apenergy.2021.117905
6	Q Chen, D L Zha, M Salman, (2022a). The influence of carbon tax on CO2 rebound effect and welfare in Chinese households. Energy Policy, 168: 113103 https://doi.org/10.1016/j.enpol.2022.113103
7	Q Chen, D L Zha, L J Wang, G L Yang, (2022b). The direct CO2 rebound effect in households: Evidence from China’s provinces. Renewable & Sustainable Energy Reviews, 155: 111888 https://doi.org/10.1016/j.rser.2021.111888
8	Z N Chen, H B Du, J L Li, F Southworth, S F Ma, (2019). Achieving low-carbon urban passenger transport in China: Insights from the heterogeneous rebound effect. Energy Economics, 81: 1029–1041 https://doi.org/10.1016/j.eneco.2019.06.009
9	B de Borger, I Mulalic, (2012). The determinants of fuel use in the trucking industry: Volume, fleet characteristics and the rebound effect. Transport Policy, 24: 284–295 https://doi.org/10.1016/j.tranpol.2012.08.011
10	H S Dillon, J D Saphores, M G Boarnet, (2015). The impact of urban form and gasoline prices on vehicle usage: Evidence from the 2009 National Household Travel Survey. Research in Transportation Economics, 52( SI): 23–33 https://doi.org/10.1016/j.retrec.2015.10.006
11	A Dimitropoulos, W Oueslati, C Sintek, (2018). The rebound effect in road transport: A meta-analysis of empirical studies. Energy Economics, 75: 163–179 https://doi.org/10.1016/j.eneco.2018.07.021
12	R Feng, X K Fang, (2022). China’s pathways to synchronize the emission reductions of air pollutants and greenhouse gases: Pros and cons. Resources, Conservation and Recycling, 184: 106392 https://doi.org/10.1016/j.resconrec.2022.106392
13	M Frondel, J Peters, C Vance, (2008). Identifying the rebound: Evidence from a German household panel. Energy Journal, 29( 4): 145–163 https://doi.org/10.5547/ISSN0195-6574-EJ-Vol29-No4-7
14	M Frondel, C M Schmidt, (2005). Evaluating environmental programs: The perspective of modern evaluation research. Ecological Economics, 55( 4): 515–526 https://doi.org/10.1016/j.ecolecon.2004.12.013
15	D Gately, (1993). The imperfect price-reversibility of world oil demand. Energy Journal, 14( 4): 163–182 https://doi.org/10.5547/ISSN0195-6574-EJ-Vol14-No4-11
16	K Gillingham, (2014). Identifying the elasticity of driving: Evidence from a gasoline price shock in California. Regional Science and Urban Economics, 47( SI): 13–24 https://doi.org/10.1016/j.regsciurbeco.2013.08.004
17	K T Gillingham, D S Rapson, G Wagner, (2016). The rebound effect and energy efficiency policy. Review of Environmental Economics and Policy, 10( 1): 68–88 https://doi.org/10.1093/reep/rev017
18	L A Greening, D L Greene, C Difiglio, (2000). Energy efficiency and consumption: The rebound effect, a survey. Energy Policy, 28( 6/7): 389–401 https://doi.org/10.1016/S0301-4215(00)00021-5
19	J X Jia, G R Ma, C Qin, L Y Wang, (2020). Place-based policies, state-led industrialisation, and regional development: Evidence from China’s Great Western Development Programme. European Economic Review, 123: 103398 https://doi.org/10.1016/j.euroecorev.2020.103398
20	Z J Jiang, B Q Lin, (2013). China’s energy demand and its characteristics in the industrialization and urbanization process: A reply. Energy Policy, 60: 583–585 https://doi.org/10.1016/j.enpol.2013.04.059
21	J D Khazzoom, (1980). Economic implications of mandated efficiency in standards for household appliances. Energy Journal, 1( 4): 21–40
22	J L Li, A J Li, X Xie, (2018). Rebound effect of transportation considering additional capital costs and input-output relationships: The role of subsistence consumption and unmet demand. Energy Economics, 74: 441–455 https://doi.org/10.1016/j.eneco.2018.06.019
23	K Li, Y X Yan, X L Zhang, (2021). Carbon-abatement policies, investment preferences, and directed technological change: Evidence from China. Technological Forecasting and Social Change, 172: 121015 https://doi.org/10.1016/j.techfore.2021.121015
24	S J Li, Y Y Liu, A O Purevjav, L Yang, (2019). Does subway expansion improve air quality?. Journal of Environmental Economics and Management, 96: 213–235 https://doi.org/10.1016/j.jeem.2019.05.005
25	J Y Liu, Y J Zhang, (2021). Has carbon emissions trading system promoted non-fossil energy development in China?. Applied Energy, 302: 117613 https://doi.org/10.1016/j.apenergy.2021.117613
26	M Malmaeus, L Hasselstrom, A Mellin, A Nyblom, J Akerman, (2023). Addressing rebound effects in transport policy: Insights from exploring five case studies. Transport Policy, 131: 45–55 https://doi.org/10.1016/j.tranpol.2022.12.004
27	F J F Matos, F J F Silva, (2011). The rebound effect on road freight transport: Empirical evidence from Portugal. Energy Policy, 39( 5): 2833–2841 https://doi.org/10.1016/j.enpol.2011.02.056
28	K Mizobuchi, (2008). An empirical study on the rebound effect considering capital costs. Energy Economics, 30( 5): 2486–2516 https://doi.org/10.1016/j.eneco.2008.01.001
29	J Odeck, K Johansen, (2016). Elasticities of fuel and traffic demand and the direct rebound effects: An econometric estimation in the case of Norway. Transportation Research Part A: Policy and Practice, 83: 1–13 https://doi.org/10.1016/j.tra.2015.10.003
30	T D Peng, Z Y Yuan, X M Ou, G H Wang, (2017). Analysis of future vehicle fuel demand and direct CO2 emissions in China. Energy Procedia, 142: 2767–2772 https://doi.org/10.1016/j.egypro.2017.12.223
31	M Rottoli, A Dirnaichner, R Pietzcker, F Schreyer, G Luderer, (2021). Alternative electrification pathways for light-duty vehicles in the European transport sector. Transportation Research Part D: Transport and Environment, 99: 103005 https://doi.org/10.1016/j.trd.2021.103005
32	K Safarzyńska, den Bergh J C J M van, (2018). A higher rebound effect under bounded rationality: Interactions between car mobility and electricity generation. Energy Economics, 74: 179–196 https://doi.org/10.1016/j.eneco.2018.06.006
33	K A Small, K van Dender, (2007). Fuel efficiency and motor vehicle travel: The declining rebound effect. Energy Journal, 28( 1): 25–51 https://doi.org/10.5547/ISSN0195-6574-EJ-Vol28-No1-2
34	P Song, X Q Mao, Z Y Li, Z X Tan, (2023). Study on the optimal policy options for improving energy efficiency and co-controlling carbon emission and local air pollutants in China. Renewable & Sustainable Energy Reviews, 175: 113167 https://doi.org/10.1016/j.rser.2023.113167
35	S Sorrell, J Dimitropoulos, (2008). The rebound effect: Microeconomic definitions, limitations and extensions. Ecological Economics, 65( 3): 636–649 https://doi.org/10.1016/j.ecolecon.2007.08.013
36	S Sorrell, J Dimitropoulos, M Sommerville, (2009). Empirical estimates of the direct rebound effect: A review. Energy Policy, 37( 4): 1356–1371 https://doi.org/10.1016/j.enpol.2008.11.026
37	S Sorrell, L Stapleton, (2018). Rebound effects in UK road freight transport. Transportation Research Part D: Transport and Environment, 63: 156–174 https://doi.org/10.1016/j.trd.2018.05.006
38	L Stapleton, S Sorrell, T Schwanen, (2016). Estimating direct rebound effects for personal automotive travel in Great Britain. Energy Economics, 54: 313–325 https://doi.org/10.1016/j.eneco.2015.12.012
39	M Tamannaei, H Zarei, M Rasti-Barzoki, (2021). A game theoretic approach to sustainable freight transportation: Competition between road and intermodal road–rail systems with government intervention. Transportation Research Part B: Methodological, 153: 272–295 https://doi.org/10.1016/j.trb.2021.09.002
40	A Q Wang, Z B Yuan, X H Liu, M L Wang, J Yang, Q E Sha, J Y Zheng, (2022). Measurement-based intermediate volatility organic compound emission inventory from on-road vehicle exhaust in China. Environmental Pollution, 310: 119887 https://doi.org/10.1016/j.envpol.2022.119887
41	H Wang, J Y Han, M Su, S L Wan, Z C Zhang, (2021). The relationship between freight transport and economic development: A case study of China. Research in Transportation Economics, 85: 100885 https://doi.org/10.1016/j.retrec.2020.100885
42	H Wang, P Zhou, D Q Zhou, (2012). An empirical study of direct rebound effect for passenger transport in urban China. Energy Economics, 34( 2): 452–460 https://doi.org/10.1016/j.eneco.2011.09.010
43	Z H Wang, M L Lu, (2014). An empirical study of direct rebound effect for road freight transport in China. Applied Energy, 133: 274–281 https://doi.org/10.1016/j.apenergy.2014.07.090
44	J J Winebrake, E H Green, (2017). Environmental policy, decision making, and rebound effects in the US trucking sector. Research in Transportation Business & Management, 23: 54–63 https://doi.org/10.1016/j.rtbm.2017.02.008
45	D Xia, L Zhang, (2022). Coupling coordination degree between coal production reduction and CO2 emission reduction in coal industry. Energy, 258: 124902 https://doi.org/10.1016/j.energy.2022.124902
46	D L Zha, Q Chen, L J Wang, (2022). Exploring carbon rebound effects in Chinese households’ consumption: A simulation analysis based on a multi-regional input-output framework. Applied Energy, 313: 118847 https://doi.org/10.1016/j.apenergy.2022.118847
47	S S Zhang, B Q Lin, (2018). Investigating the rebound effect in road transport system: Empirical evidence from China. Energy Policy, 112: 129–140 https://doi.org/10.1016/j.enpol.2017.10.010
48	Y J Zhang, Z Liu, C X Qin, T D Tan, (2017). The direct and indirect CO2 rebound effect for private cars in China. Energy Policy, 100: 149–161 https://doi.org/10.1016/j.enpol.2016.10.010
49	Y J Zhang, H R Peng, Z Liu, W Tan, (2015). Direct energy rebound effect for road passenger transport in China: A dynamic panel quantile regression approach. Energy Policy, 87( SI): 303–313 https://doi.org/10.1016/j.enpol.2015.09.022
50	Y J Zheng, H C Xu, R N Jia, (2022). Endogenous energy efficiency and rebound effect in the transportation sector: Evidence from China. Journal of Cleaner Production, 335: 130310 https://doi.org/10.1016/j.jclepro.2021.130310

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