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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

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Front. Eng    2022, Vol. 9 Issue (3) : 473-485    https://doi.org/10.1007/s42524-022-0213-5
RESEARCH ARTICLE
Evaluating R&D efficiency of China’s listed lithium battery enterprises
Shizhen BAI1, Xinrui BI1, Chunjia HAN2, Qijun ZHOU3, Wen-Long SHANG4(), Mu YANG2, Lin WANG5, Petros IEROMONACHOU3, Hao HE1
1. School of Management, Harbin University of Commerce, Harbin 150028, China
2. Department of Management, Birkbeck, University of London, London WC1E 7HX, UK
3. Department of Systems Management and Strategy, University of Greenwich, London SE10 9LS, UK
4. Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100008, China; Centre for Transport Studies, Imperial College London, London SW7 2AZ, UK
5. School of Business Administration, Chongqing Technology and Business University, Chongqing 400067, China
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Abstract

Promoting the growth of the lithium battery sector has been a critical aspect of China’s energy policy in terms of achieving carbon neutrality. However, despite significant support on research and development (R&D) investments that have resulted in increasing size, the sector seems to be falling behind in technological areas. To guide future policies and understand proper ways of promoting R&D efficiency, we looked into the lithium battery industry of China. Specifically, data envelopment analysis (DEA) was used as the primary approach based on evidence from 22 listed lithium battery enterprises. The performance of the five leading players was compared with that of the industry as a whole. Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019. However, during this period, a significant increase in R&D expenditure was witnessed. This finding was supported, as the results showed that the average technical efficiency of the 22 enterprises was 0.442, whereas the average pure technical efficiency was at 0.503, thus suggesting that they were suffering from decreasing returns to scale (DRS). In contrast, the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry’s average. Moreover, they were experiencing increasing scale efficiency (IRS). We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale; rather, it should also encompass assisting businesses in developing efficient managerial processes for R&D.

Keywords Data Envelopment Analysis      R&D investment efficiency      China’s listed lithium battery enterprises      technical efficiency      pure technical efficiency      scale efficiency     
Corresponding Author(s): Wen-Long SHANG   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 23 June 2022   Online First Date: 11 August 2022    Issue Date: 05 September 2022
 Cite this article:   
Shizhen BAI,Xinrui BI,Chunjia HAN, et al. Evaluating R&D efficiency of China’s listed lithium battery enterprises[J]. Front. Eng, 2022, 9(3): 473-485.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-022-0213-5
https://academic.hep.com.cn/fem/EN/Y2022/V9/I3/473
Index category Standard level Index name Data source
Input index R&D manpower The number of technical personnel Corporate annual report
The proportion of technical personnel Corporate annual report
R&D expenses R&D expenditure Corporate annual report
The proportion of R&D expenditure Corporate annual report
Output index Technical improvement The number of patent applications State Intellectual Property Office
Economic benefit Operating income Corporate annual report
Net profit Corporate annual report
Tab.1  R&D efficiency evaluation index system for listed lithium battery enterprises
Industrial chain link Name Main points
Cell and battery pack BYD Establish the world’s leading technical and cost advantages in the field of power batteries
Lithium raw materials Tianqi Lithium It is one of the few enterprises in the world that simultaneously distribute two kinds of raw material resources: High-quality lithium mine and salt lake brine mine
Ganfeng Lithium It is the world’s leading lithium ecological enterprise, with the production capacity of more than 40 kinds of lithium compounds and metal lithium products in five categories
Anode materials Hunan Zhongke Electric Graphite powder processing technology is internationally advanced; heat treatment process and graphite composite technology are leading in China
Cathode materials Beijing Easpring Material Technology Leading enterprise in lithium battery cathode material industry
Tab.2  Introduction of five leading enterprises
Year Technical efficiency Pure technical efficiency Scale efficiency RTS
2010 1.000 1.000 1.000
2011 0.981 0.986 0.995 irs
2012 1.000 1.000 1.000
2013 1.000 1.000 1.000
2014 0.986 1.000 0.986 irs
2015 1.000 1.000 1.000
2016 0.997 1.000 0.997 drs
2017 1.000 1.000 1.000
2018 0.960 0.981 0.978 irs
2019 1.000 1.000 1.000
Average 0.992 0.997 0.996
Tab.3  Efficiency scores and RTS of the whole lithium battery industry in 2010–2019
Fig.1  The growth ratio of R&D investment inputs and outputs (Note: The calculation of growth ratio is based on the data of 2010, all the indicators from the other years compared with the data from 2010. For example, the net profit growth ratio 2019 = (net profit 2019 ? net profit 2010)/net profit 2010).
Fig.2  The changing trend of average efficiency scores of 22 listed lithium battery enterprises from 2010 to 2019.
Fig.3  The changing trend of the RTS of 22 listed lithium battery enterprises from 2010 to 2019.
Number Name TE PTE SE RTS
2010 2019 2010 2019 2010 2019 2010 2019
1 Guangdong Fenghua Advanced Technology 0.457 0.170 0.491 0.212 0.931 0.801 irs drs
2 Hengdian Group DMEGC Magnetics 0.286 0.330 0.286 0.523 0.999 0.631 drs
3 Guoxuan High-tech 0.397 0.115 0.532 0.118 0.747 0.974 irs irs
4 Suzhou Good-Ark Electronics 0.326 0.331 0.436 0.396 0.749 0.837 irs irs
5 Sinoma Science & Technology 0.228 0.443 0.248 0.950 0.918 0.466 irs drs
6 Do-Fluoride Chemicals 0.483 0.266 0.573 0.274 0.843 0.971 irs irs
7 Ganfeng Lithium 0.632 0.829 0.782 0.895 0.809 0.926 irs drs
8 Tianqi Lithium 0.589 0.277 0.830 0.503 0.710 0.551 irs irs
9 BYD 0.644 1.000 0.685 1.000 0.941 1.000 irs
10 Eve Energy 0.520 0.250 0.665 0.509 0.782 0.492 irs drs
11 Hunan Zhongke Electric 0.781 0.315 0.793 0.375 0.985 0.840 irs irs
12 Beijing Easpring Material Technology 0.675 0.433 0.782 0.487 0.863 0.888 irs irs
13 Sunwoda Electronic 0.237 0.326 0.277 0.379 0.856 0.860 irs drs
14 Wanxiang Qianchao 0.210 0.336 0.268 0.337 0.783 0.994 drs irs
15 Shenzhen CLOU Electronics 0.435 0.123 0.441 0.126 0.988 0.975 irs irs
16 Shenzhen Topband 0.511 0.146 0.515 0.190 0.991 0.771 irs drs
17 Zhejiang Unifull Industrial Fibre 1.000 0.304 1.000 0.420 1.000 0.723 irs
18 Zhejiang Narada Power Source 0.596 0.398 0.624 0.400 0.955 0.996 irs drs
19 China CSSC Holdings 1.000 0.461 1.000 0.585 1.000 0.788 drs
20 Jiangsu Zhongtian Technology 0.408 1.000 0.412 1.000 0.990 1.000 drs
21 Neusoft Corporation 0.042 0.047 0.063 0.059 0.665 0.786 drs drs
22 Shenzhen Capchem Technology 1.000 0.304 1.000 0.316 1.000 0.960 drs
Mean 0.521 0.373 0.577 0.457 0.887 0.829
Tab.4  Efficiency scores of R&D investments in 22 China’s listed lithium battery enterprises in 2010 and 2019, respectively
Fig.4  The comparison of PTE and TE scores of 22 enterprises in 2010 and 2019, respectively.
Year BYD Tianqi Lithium Hunan Zhongke Electric Ganfeng Lithium Beijing Easpring Material Technology
2010 0.644 0.589 0.781 0.632 0.675
2011 0.578 0.567 0.671 0.472 0.534
2012 0.624 0.505 0.692 0.418 0.624
2013 0.618 0.736 0.674 0.696 0.618
2014 0.736 0.783 0.734 0.706 0.679
2015 1.000 0.900 0.996 0.392 0.598
2016 1.000 1.000 1.000 0.470 0.470
2017 0.993 0.865 0.491 1.000 0.452
2018 0.922 0.507 0.363 0.275 0.391
2019 1.000 0.277 0.315 0.829 0.433
Average 0.812 0.673 0.672 0.589 0.547
Rank 1 2 3 4 5
Tab.5  Technical efficiency scores of five leading enterprises from 2010 to 2019
Year BYD Tianqi Lithium Hunan Zhongke Electric Ganfeng Lithium Beijing Easpring Material Technology
2010 0.685 0.830 0.793 0.782 0.782
2011 0.642 0.793 0.690 0.602 0.611
2012 0.629 0.719 0.721 0.529 0.663
2013 0.628 0.946 0.715 0.702 0.675
2014 0.736 1.000 0.748 0.706 0.717
2015 1.000 0.979 1.000 0.399 0.652
2016 1.000 1.000 1.000 0.626 0.505
2017 1.000 1.000 0.521 1.000 0.493
2018 0.924 0.612 0.409 0.285 0.506
2019 1.000 0.503 0.375 0.895 0.487
Average 0.824 0.838 0.697 0.653 0.609
Rank 2 1 3 4 5
Tab.6  Pure technical efficiency scores of five leading enterprises from 2010 to 2019
Year BYD Tianqi Lithium Hunan Zhongke Electric Ganfeng Lithium Beijing Easpring Material Technology
2010 0.941 irs 0.710 irs 0.985 irs 0.809 irs 0.863 irs
2011 0.899 irs 0.714 irs 0.972 irs 0.784 irs 0.874 irs
2012 0.992 irs 0.703 irs 0.960 irs 0.790 irs 0.941 irs
2013 0.984 drs 0.777 irs 0.942 irs 0.991 irs 0.916 irs
2014 1.000 0.783 irs 0.981 irs 1.000 0.948 irs
2015 1.000 0.919 irs 0.996 irs 0.984 drs 0.917 irs
2016 1.000 1.000 1.000 0.751 drs 0.931 irs
2017 0.993 drs 0.865 drs 0.943 irs 1.000 0.918 irs
2018 0.998 irs 0.828 irs 0.889 irs 0.963 drs 0.773 irs
2019 1.000 0.551 irs 0.840 irs 0.926 drs 0.888 irs
Average 0.981 0.785 0.951 0.900 0.897
Rank 1 5 2 3 4
Tab.7  Scale efficiency scores of five leading enterprises from 2010 to 2019
1 Z J Acs, L Anselin, A Varga, ( 2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31( 7): 1069– 1085
https://doi.org/10.1016/S0048-7333(01)00184-6
2 N K Avkiran, T Rowlands, ( 2008). How to better identify the true managerial performance: State of the art using DEA. Omega, 36( 2): 317– 324
https://doi.org/10.1016/j.omega.2006.01.002
3 S Bandyopadhyay, A Gupta, R Srivastava, B Nandan, ( 2022). Bio-inspired design of electrospun poly(acrylonitrile) and novel ionene based nanofibrous mats as highly flexible solid state polymer electrolyte for lithium batteries. Chemical Engineering Journal, 440: 135926
https://doi.org/10.1016/j.cej.2022.135926
4 R D Banker, A Charnes, W W Cooper, ( 1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30( 9): 1078– 1092
https://doi.org/10.1287/mnsc.30.9.1078
5 S Berg ( 2010). Water Utility Benchmarking. London: IWA Publishing
6 Y Cao ( 2020). Research on R&D Efficiency of Listed Companies in New Energy Vehicle Industry — Based on Three-stage DEA and Malmquist Index Models. Dissertation for the Master’s Degree. Nanjing: Nanjing University of Posts and Telecommunications
7 A Charnes, W W Cooper, E Rhodes, ( 1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2( 6): 429– 444
https://doi.org/10.1016/0377-2217(78)90138-8
8 X Chen, Z Liu, Q Zhu, ( 2020). Performance evaluation of China’s high-tech innovation process: Analysis based on the innovation value chain. Technovation, 94−95: 102094
https://doi.org/10.1016/j.technovation.2019.102094
9 Y Chiu, C Huang, Y Chen, ( 2012). The R&D value-chain efficiency measurement for high-tech industries in China. Asia Pacific Journal of Management, 29( 4): 989– 1006
https://doi.org/10.1007/s10490-010-9219-3
10 D Chun, Y Chung, S Bang, ( 2015). Impact of firm size and industry type on R&D efficiency throughout innovation and commercialisation stages: Evidence from South Korean manufacturing firms. Technology Analysis and Strategic Management, 27( 8): 895– 909
https://doi.org/10.1080/09537325.2015.1024645
11 X Duan, W Zhu, Z Ruan, M Xie, J Chen, X Ren, ( 2022). Recycling of lithium batteries: A review. Energies, 15( 5): 1611
https://doi.org/10.3390/en15051611
12 H Fang, J Wu, C Zeng, ( 2009). Comparative study on efficiency performance of listed coal mining companies in China and the US. Energy Policy, 37( 12): 5140– 5148
https://doi.org/10.1016/j.enpol.2009.07.027
13 S Fang, X Xue, G Yin, H Fang, J Li, Y Zhang, ( 2020). Evaluation and improvement of technological innovation efficiency of new energy vehicle enterprises in China based on DEA-Tobit model. Sustainability, 12( 18): 7509
https://doi.org/10.3390/su12187509
14 Z Griliches, ( 1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28( 4): 1661– 1707
15 J Guan, K Chen, ( 2010). Measuring the innovation production process: A cross-region empirical study of China’s high-tech innovations. Technovation, 30( 5–6): 348– 358
https://doi.org/10.1016/j.technovation.2010.02.001
16 C Han, S R Thomas, M Yang, P Ieromonachou, H Zhang, ( 2017). Evaluating R&D investment efficiency in China’s high-tech industry. Journal of High Technology Management Research, 28( 1): 93– 109
https://doi.org/10.1016/j.hitech.2017.04.007
17 H Hollanders F Celikel Esser ( 2007). Measuring innovation efficiency: INNO-Metrics Thematic Paper. Brussels: European Commission-DG Enterprise
18 G Iglesias, P Castellanos, A Seijas, ( 2010). Measurement of productive efficiency with frontier methods: A case study for wind farms. Energy Economics, 32( 5): 1199– 1208
https://doi.org/10.1016/j.eneco.2010.03.004
19 R Jing, J Wang, N Shah, M Guo, ( 2021). Emerging supply chain of utilising electrical vehicle retired batteries in distributed energy systems. Advances in Applied Energy, 1: 100002
https://doi.org/10.1016/j.adapen.2020.100002
20 M I Kafouros, C Wang, ( 2008). The role of time in assessing the economic effects of R&D. Industry and Innovation, 15( 3): 233– 251
https://doi.org/10.1080/13662710802041638
21 G Kozmetsky, P Yue, ( 1998). Comparative performance of global semiconductor companies. Omega, 26( 2): 153– 175
https://doi.org/10.1016/S0305-0483(97)00011-X
22 N U R Lashari, M Zhao, J Wang, X He, I Ahmed, M M Liang, S Tangsee, X Song, ( 2022). Improved cycling performance of polypyrrole coated potassium trivanadate as an anode for aqueous rechargeable lithium batteries. Journal of Industrial and Engineering Chemistry, 108: 366– 373
https://doi.org/10.1016/j.jiec.2022.01.015
23 H Lee, Y Choi, H Seo, ( 2020). Comparative analysis of the R&D investment performance of South Korean local governments. Technological Forecasting and Social Change, 157: 120073
https://doi.org/10.1016/j.techfore.2020.120073
24 B Lin, B Xu, ( 2018). How to promote the growth of new energy industry at different stages?. Energy Policy, 118: 390– 403
https://doi.org/10.1016/j.enpol.2018.04.003
25 S Lin, J Sun, D Marinova, D Zhao, ( 2018). Evaluation of the green technology innovation efficiency of China’s manufacturing industries: DEA window analysis with ideal window width. Technology Analysis and Strategic Management, 30( 10): 1166– 1181
https://doi.org/10.1080/09537325.2018.1457784
26 J Liu, K Lu, S Cheng, ( 2018). International R&D spillovers and innovation efficiency. Sustainability, 10( 11): 3974
https://doi.org/10.3390/su10113974
27 Z Liu, Q Qian, B Hu, W Shang, L Li, Y Zhao, Z Zhao, C Han, ( 2022). Government regulation to promote coordinated emission reduction among enterprises in the green supply chain based on evolutionary game analysis. Resources, Conservation and Recycling, 182: 106290
https://doi.org/10.1016/j.resconrec.2022.106290
28 L Lv, Y Wang, W Huang, Y Wang, G Zhu, H Zheng, ( 2022). Effect of lithium salt type on silicon anode for lithium-ion batteries. Electrochimica Acta, 413: 140159
https://doi.org/10.1016/j.electacta.2022.140159
29 R Ma, H Cai, Q Ji, P Zhai, ( 2021). The impact of feed-in tariff degression on R&D investment in renewable energy: The case of the solar PV industry. Energy Policy, 151: 112209
https://doi.org/10.1016/j.enpol.2021.112209
30 M Mohsin, I Hanif, F Taghizadeh-Hesary, Q Abbas, W Iqbal, ( 2021). Nexus between energy efficiency and electricity reforms: A DEA-based way forward for clean power development. Energy Policy, 149: 112052
https://doi.org/10.1016/j.enpol.2020.112052
31 S Niewerth, P Vogt, M Thewes, ( 2022). Tender evaluation through efficiency analysis for public construction contracts. Frontiers of Engineering Management, 9( 1): 148– 158
https://doi.org/10.1007/s42524-020-0119-z
32 A Pakes, Z Griliches, ( 1980). Patents and R&D at the firm level: A first report. Economics Letters, 5( 4): 377– 381
https://doi.org/10.1016/0165-1765(80)90136-6
33 Industrial Research Institute Qianzhan ( 2021). Deep analysis! A detailed understanding of the current market situation, competition pattern and development prospect of China’s lithium battery industry in 2021 (in Chinese)
34 S Reinhard, C A Knox Lovell, G J Thijssen, ( 2000). Environmental efficiency with multiple environmentally detrimental variables: Estimated with SFA and DEA. European Journal of Operational Research, 121( 2): 287– 303
https://doi.org/10.1016/S0377-2217(99)00218-0
35 S Rousseau, R Rousseau, ( 1997). Data envelopment analysis as a tool for constructing scientometric indicators. Scientometrics, 40( 1): 45– 56
https://doi.org/10.1007/BF02459261
36 W L Shang, J Chen, H Bi, Y Sui, Y Chen, H Yu, ( 2021). Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis. Applied Energy, 285: 116429
https://doi.org/10.1016/j.apenergy.2020.116429 pmid: 33519037
37 Z Shi, X Zhang, W Guo, Q Xu, Y Min, ( 2022). Interfacial electric field effect of Double-Network composite electrolyte for Ultra-Stable lithium batteries. Chemical Engineering Journal, 440: 135779
https://doi.org/10.1016/j.cej.2022.135779
38 X X Sun ( 2021). Sound of the material industry chain of the national “Two Sessions” in 2021. Advanced Materials Industry, 321( 2): 2– 19 (in Chinese)
39 L Tong, R Ding, ( 2008). Efficiency assessment of coal mine safety input by data envelopment analysis. Journal of China University of Mining and Technology, 18( 1): 88– 92
https://doi.org/10.1016/S1006-1266(08)60019-X
40 E C Wang, W Huang, ( 2007). Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36( 2): 260– 273
https://doi.org/10.1016/j.respol.2006.11.004
41 J Wang, D Han, Y Wang, ( 2020). Empirical research on innovation efficiency in China based on SFA model. IOP Conference Series: Earth and Environmental Science, 474( 7): 072055
https://doi.org/10.1088/1755-1315/474/7/072055
42 J Wu, J Sun, L Liang, ( 2021). Methods and applications of DEA cross-efficiency: Review and future perspectives. Frontiers of Engineering Management, 8( 2): 199– 211
https://doi.org/10.1007/s42524-020-0133-1
43 F Yang, M Wang, ( 2020). A review of systematic evaluation and improvement in the big data environment. Frontiers of Engineering Management, 7( 1): 27– 46
https://doi.org/10.1007/s42524-020-0092-6
44 Z Yang, W Shang, H Zhang, H Garg, C Han, ( 2022). Assessing the green distribution transformer manufacturing process using a cloud-based q-rung orthopair fuzzy multi-criteria framework. Applied Energy, 311: 118687
https://doi.org/10.1016/j.apenergy.2022.118687
45 Q Yeh, ( 1996). The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation. Journal of the Operational Research Society, 47( 8): 980– 988
https://doi.org/10.1057/jors.1996.125
46 J Yoo, S Lee, S Park, ( 2019). The effect of firm life cycle on the relationship between R&D expenditures and future performance, earnings uncertainty, and sustainable growth. Sustainability, 11( 8): 2371
https://doi.org/10.3390/su11082371
47 L Zhang, L Li, H Rui, D Shi, X Peng, L Ji, X Song, ( 2020). Lithium recovery from effluent of spent lithium battery recycling process using solvent extraction. Journal of Hazardous Materials, 398: 122840
https://doi.org/10.1016/j.jhazmat.2020.122840 pmid: 32516726
48 M Zhang, ( 2020). The analysis of the influencing factors of high-tech industry collaborative innovation efficiency in China based on two-stage DEA-Tobit model. International Journal of Frontiers in Engineering Technology, 2( 1): 84– 94
https://doi.org/10.25236/IJFET.2020.020108
49 W Zhong, W Yuan, S X Li, Z Huang, ( 2011). The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data. Omega, 39( 4): 447– 455
https://doi.org/10.1016/j.omega.2010.09.004
50 J Zhou, Y Zhang, Y Zhang, W Shang, Z Yang, W Feng, ( 2022). Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning. Applied Energy, 314: 118877
https://doi.org/10.1016/j.apenergy.2022.118877
51 P Zhou, B W Ang, K L Poh, ( 2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189( 1): 1– 18
https://doi.org/10.1016/j.ejor.2007.04.042
52 L Zhu, M Chen, ( 2020). Development of a two-stage pyrolysis process for the end-of-life nickel cobalt manganese lithium battery recycling from electric vehicles. Sustainability, 12( 21): 9164
https://doi.org/10.3390/su12219164
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