Abstract
How to utilize human, financial, and material resources reasonably in the technological innovation of new energy vehicles to obtain the maximum benefit with the least investment is an important issue that needs to be solved urgently. This paper employs the stochastic frontier model to analyze the innovation efficiency and its influencing factors of new energy vehicles in China. The investment of innovation is divided into four dimensions: human resource input, R&D input, technology acquirement input and environment support input. The results demonstrate that the mean of innovation efficiency of the new energy vehicle industry is low in general. The Yangtze River Delta and Pearl River Delta regions have the highest innovation efficiency comparable to that of other regions. There is a significant positive impact of asset turnover ratio, per capita compensation of management, and education level on innovation efficiency. The effects of asset-liability ratio and government support on innovation efficiency are not significant. But the government support has a significant positive moderation effect on innovation efficiency through financial factors. Based on the conclusions, this paper proposes insights to promote the development of the new energy vehicle industry.
Graphic abstract
Similar content being viewed by others
Abbreviations
- ALR:
-
Asset-liability ratio
- ATR:
-
Asset turnover ratio
- BEV:
-
Battery electrical vehicle
- DEA:
-
Data envelopment analysis
- DEV:
-
Dimethyl ether vehicle
- Edu:
-
Education level
- FCEV:
-
Fuel cell electric vehicle
- Gov:
-
Government support
- HEV:
-
Hydrogen engine vehicle
- HV:
-
Hybrid vehicle
- IE:
-
Innovation efficiency
- K:
-
Capital input
- NEV:
-
New energy vehicle
- Pay:
-
Per-capita compensation of management
- PCA:
-
Principal component analysis
- PHEVs:
-
Plug-in hybrid-electric vehicles
- RD:
-
R&D input
- SFA:
-
Stochastic frontier analysis
- TA:
-
Technology acquisition
- Y:
-
Industrial gross value
References
Aigner D, Lovell CK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37
Aigner DJ, Chu S-F (1968) On estimating the industry production function. Am Econ Rev 58:826–839
Arsad R, Isa Z, Shaari SNM (2018) Estimating efficiency performance of decision-making unit by using SFA and DEA Method: a cross-sectional data approach. Int J Eng Technol 7:25–31. https://doi.org/10.14419/ijet.v7i4.33.23478
Battese GE, Coelli T (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Econ 20:325–332
Broekel T, Rogge N, Brenner T (2013) The innovation efficiency of German regions—a shared-input DEA approach. Working Papers on Innovation and Space, No 0813:1–33
Buesa M, Heijs J, Pellitero MM, Baumert T (2006) Regional systems of innovation and the knowledge production function: the Spanish case. Technovation 26:463–472. https://doi.org/10.1016/j.technovation.2004.11.007
Charnes A, Cooper WW, Rhodes E (1978) Measuring efficiency of decision-making units. Eur J Oper Res 2:429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen K, Kenney M (2007) Universities/Research institutes and regional innovation systems: the cases of Beijing and shenzhen. World Devel 35:1056–1074. https://doi.org/10.1016/j.worlddev.2006.05.013
Cheng SX, Liu JP (2014) China's R&D production efficiency and impact factors. Transnational Corporations Rev 6:362–378
Coelli T, Perelman S (1999) A comparison of parametric and non-parametric distance functions: with application to European railways. Eur J Oper Res 117:326–339. https://doi.org/10.1016/S0377-2217(98)00271-9
Cullmann A, Schmidt-Ehmcke J, Zloczysti P (2011) R&D efficiency and barriers to entry: a two stage semi-parametric DEA approach. Oxford Econ Pap 64:176–196
Debreu G (1951) The coefficient of resource utilization. Econometrica 19:273–292. https://doi.org/10.2307/1906814
Deng LZ (2015) Analysis of technological innovation efficiency of China’s automobile enterprises and its affecting factors—evidence from A-share listed companies. Technoecon Manage Res, pp 26–31
Doloreux D, Dionne S (2008) Is regional innovation system development possible in peripheral regions? Some evidence from the case of La Pocatière. Canada Entrepreneurship Reg Devel 20:259–283
Dong YZ, Hamilton R, Tippett M (2014) Cost efficiency of the Chinese banking sector: a comparison of stochastic frontier analysis and data envelopment analysis. Econ Modelling 36:298–308. https://doi.org/10.1016/j.econmod.2013.09.042
Farrell MJ (1957) The measurement of productive efficiency. J Roy Stat Soc Ser General 120:253–290. https://doi.org/10.2307/2343100
Feng ZX, Wang Q, Hou XH (2011) Government investment, degree of marketization and technological innovation efficiency of China’s industrial enterprises. J Quantitative Tech Econ, pp 3–17
Fu XL, Yang QG (2009) Exploring the cross-country gap in patenting: a stochastic Frontier approach. Res Pol 38:1203–1213. https://doi.org/10.1016/j.respol.2009.05.005
Guan JC, Chen KH (2010) Measuring the innovation production process: A cross-region empirical study of China's high-tech innovations. Technovation 30:348–358. https://doi.org/10.1016/j.technovation.2010.02.001
Guan JC, Chen KH (2012) Modeling the relative efficiency of national innovation systems Res Pol 41:102–115. https://doi.org/10.1016/j.respol.2011.07.001
Hao H, Ou X, Du J, Wang H, Ouyang M (2014) China’s electric vehicle subsidy scheme: Rationale and impacts. Energy Policy 73:722–732. https://doi.org/10.1016/j.enpol.2014.05.022
Hong J, Feng B, Wu YR, Lb W (2016) Do government grants promote innovation efficiency in China's high-tech industries? Technovation 57:4–13
Hsu FM, Hsueh CC (2009) Measuring relative efficiency of government-sponsored R&D projects: a three-stage approach. Eval Program Plann 32:178–186. https://doi.org/10.1016/j.evalprogplan.2008.10.005
Kimble C, Wang H (2013) China's new energy vehicles: value and innovation. J Bus Strategy 34:13–20. https://doi.org/10.1108/02756661311310413
Kumbhakar SC, Denny M, Fuss M (2000) Estimation and decomposition of productivity change when production is not efficient: a paneldata approach. Econometric Rev 19:312–320
Li C, Negnevitsky M, Wang X, Yue WL, Zou X (2019) Multi-criteria analysis of policies for implementing clean energy vehicles in China. Energy Policy 129:826–840. https://doi.org/10.1016/j.enpol.2019.03.002
Li XB (2009) China's regional innovation capacity in transition: An empirical approach Res Pol 38:338–357. https://doi.org/10.1016/j.respol.2008.12.002
Liu J-h, Meng Z (2017) Innovation model analysis of new energy vehicles: taking Toyota. Tesla BYD Example Procedia Eng 174:965–972. https://doi.org/10.1016/j.proeng.2017.01.248
Lu GQ, Wang Z, Zhang CY (2014) Research on the performance of subsidizing innovation for Chinese strategic emerging industry. Econ Res J 7:44–55
Niu MM, Feng B, Tang PC (2013) Research on technological innovation of chemical industry in China. Statist Decision, pp 113–115
Pope RD, Chavas J-P (1994) Cost functions under production uncertainty. Am J Agr Econ 76:196–204. https://doi.org/10.2307/1243621
Qiao S, Xu XL, Liu CK, Chen HH (2016) A panel study on the relationship between biofuels production and sustainable development. Int J Green Energy 13:94–101. https://doi.org/10.1080/15435075.2014.910784
Sharma S, Thomas VJ (2008) Inter-country R&D efficiency analysis: an application of data envelopment analysis. Scientometrics 76:483–501. https://doi.org/10.1007/s11192-007-1896-4
Shui HY, Jin XN, Ni J (2015) Manufacturing productivity and energy efficiency: a stochastic efficiency frontier analysis. Int J Energy Res 39:1649–1663. https://doi.org/10.1002/er.3368
Suo L, Hu Y-S, Li H, Armand M, Chen L (2013) A new class of Solvent-in-Salt electrolyte for high-energy rechargeable metallic lithium batteries. Nat Commun 4:1481. https://doi.org/10.1038/ncomms2513
Wang EC (2007) R&D efficiency and economic performance: a cross-country analysis using the stochastic frontier approach. J Pol Modeling 29:345–360
Wang CH, Lu YH, Huang CW, Lee JY (2013a) R&D, productivity, and market value: an empirical study from high-technology firms. Omega 41:143–155
Wang ZH, Wang C, Hao Y (2013b) Influencing factors of private purchasing intentions of new energy vehicles in China. J Renew Sustain Energy 5:063133. https://doi.org/10.1063/1.4850516
Wang QW, Hang Y, Sun LC, Zhao ZY (2016) Two-stage innovation efficiency of new energy enterprises in China: a non-radial DEA approach Technol Forecast. Soc Change 112:254–261. https://doi.org/10.1016/j.techfore.2016.04.019
Wang ZH, Zhao CY, Yin JH, Zhang B (2017) Purchasing intentions of Chinese citizens on new energy vehicles: How should one respond to current preferential policy? J Clean Prod 161:1000–1010. https://doi.org/10.1016/j.jclepro.2017.05.154
Wei L, Li G, Zhu X, Sun X, Li J (2019) Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures. Energy Econ 80:452–460. https://doi.org/10.1016/j.eneco.2019.01.020
Xu J, Shang Y, Yu W, Liu F (2019) Intellectual capital. Technological innovation and firm performance: evidence from China’s manufacturing sector. Sustainability 11:1–15. https://doi.org/10.3390/su11195328
Xu XL, Chen HH (2018) Examining the efficiency of biomass energy: evidence from the Chinese recycling industry. Energy Policy 119:77–86. https://doi.org/10.1016/j.enpol.2018.04.020
Xu XL, Chen HH, Feng Y, Tang J (2018) The production efficiency of renewable energy generation and its influencing factors: Evidence from 20 countries. J Renew Sustain Energy 10:025901–025911 https://doi.org/10.1063/1.5006844
Xu XL, Chen HH, Li Y, Chen QX (2019) The role of equity balance and executive stock ownership in the innovation efficiency of renewable energy enterprises. J Renew Sustain Energy 11:055901–055911. https://doi.org/10.1063/1.5116849
Xu XL, Liu CK (2019) How to keep renewable energy enterprises to reach economic sustainable performance: from the views of intellectual capital and life cycle. Energy Sustainability Soc 9:1–10. https://doi.org/10.1186/s13705-019-0187-2
Xu XL, Qiao S, Chen HH (2020) Exploring the efficiency of new energy generation: evidence from OECD and non-OECD countries. Energy Environ 31:389–404. https://doi.org/10.1177/0958305x19871675
Ye YY (2013) Research on technical efficiency and influencing factors of listed companies in China's automobile industry. Shandong Soc Sci, pp 165–169
Yuan XL, Liu X, Zuo J (2015) The development of new energy vehicles for a sustainable future: a review. Renew Sustain Energy Rev 42:298–305. https://doi.org/10.1016/j.rser.2014.10.016
Zhang GW, Tang Q, Zeng XY (2015) Study on technical efficiency of energy conservation and environmental protection listed company based on superefficiency DEA-Tobit model. Enterprise Econ, pp 114–118
Zhang J, Wang R (2019) Research on the marketing strategy of new energy vehicles in SL company. Am J Indus Business Manage 9:306–314. https://doi.org/10.4236/ajibm.2019.92020
Zhang X, Bai X (2017) Incentive policies from 2006 to 2016 and new energy vehicle adoption in 2010–2020 in China. Renew Sustain Energy Rev 70:24–43 https://doi.org/10.1016/j.rser.2016.11.211
Acknowledgements
We sincerely thank the editor and reviewers for their very valuable and professional comments.
Funding
This work was funded by Youth Project of Humanities and Social Sciences of Ministry of Education in China [grant number 18YJC630213]; China Postdoctoral Science Foundation [grant number 2020M670473]; National Social Science Foundation of China [grant number 19CGL030]; Natural Science Foundation of Hunan Province [grant number 2019JJ50382]; and Key Project of Hunan Education Department [grant number 19A292].
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Xu, X.L., Chen, H.H. Exploring the innovation efficiency of new energy vehicle enterprises in China. Clean Techn Environ Policy 22, 1671–1685 (2020). https://doi.org/10.1007/s10098-020-01908-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10098-020-01908-w