Innovation ability of China's new energy industry from the perspective of new quality productivity

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL
Qingli Tan, Yihua Gan
{"title":"Innovation ability of China's new energy industry from the perspective of new quality productivity","authors":"Qingli Tan,&nbsp;Yihua Gan","doi":"10.1016/j.cesys.2025.100306","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>To quantitatively analyze the innovation capability of China's new energy industry from the perspective of new-quality productive forces, explore issues related to innovation efficiency, and provide references for enhancing industrial innovation capability and developing new-quality productive forces.</div></div><div><h3>Methods</h3><div>Based on the data of 23 new energy A-share listed enterprises in China from 2020 to 2023, this study constructs an innovation efficiency evaluation index system under the context of new productive forces. It applies the CCR-DEA and BCC-DEA models to analyze the static efficiency in 2023, and combines the Malmquist-DEA index to examine the dynamic efficiency from 2020 to 2023. Additionally, projection analysis is employed to identify issues of input redundancy and output deficiency, while the stochastic frontier approach is utilized to supplement the DEA analysis.</div></div><div><h3>Results</h3><div>The overall innovation efficiency of China's new energy enterprises was approximately effective but varied significantly. More than 60 % of enterprises needed to optimize resource allocation efficiency. About half of the enterprises faced decreasing returns to scale, with prominent issues of output shortages such as patent quantity and R&amp;D expenditure. From 2020 to 2023, total factor productivity showed a trend of first increasing and then decreasing, with technological regression being the main cause of declining innovation efficiency.</div></div><div><h3>Conclusion</h3><div>It is necessary to construct a resource utilization system, policy guidance mechanism, collaborative ecosystem, and talent supply system adapted to new-quality productive forces, so as to optimize the allocation of innovation resources, break through technological bottlenecks, and promote the high-quality development of the new energy industry.</div></div>","PeriodicalId":34616,"journal":{"name":"Cleaner Environmental Systems","volume":"18 ","pages":"Article 100306"},"PeriodicalIF":4.9000,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cleaner Environmental Systems","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666789425000522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Objective

To quantitatively analyze the innovation capability of China's new energy industry from the perspective of new-quality productive forces, explore issues related to innovation efficiency, and provide references for enhancing industrial innovation capability and developing new-quality productive forces.

Methods

Based on the data of 23 new energy A-share listed enterprises in China from 2020 to 2023, this study constructs an innovation efficiency evaluation index system under the context of new productive forces. It applies the CCR-DEA and BCC-DEA models to analyze the static efficiency in 2023, and combines the Malmquist-DEA index to examine the dynamic efficiency from 2020 to 2023. Additionally, projection analysis is employed to identify issues of input redundancy and output deficiency, while the stochastic frontier approach is utilized to supplement the DEA analysis.

Results

The overall innovation efficiency of China's new energy enterprises was approximately effective but varied significantly. More than 60 % of enterprises needed to optimize resource allocation efficiency. About half of the enterprises faced decreasing returns to scale, with prominent issues of output shortages such as patent quantity and R&D expenditure. From 2020 to 2023, total factor productivity showed a trend of first increasing and then decreasing, with technological regression being the main cause of declining innovation efficiency.

Conclusion

It is necessary to construct a resource utilization system, policy guidance mechanism, collaborative ecosystem, and talent supply system adapted to new-quality productive forces, so as to optimize the allocation of innovation resources, break through technological bottlenecks, and promote the high-quality development of the new energy industry.
新质量生产力视角下的中国新能源产业创新能力
目的从新型优质生产力视角定量分析中国新能源产业创新能力,探讨创新效率相关问题,为提升产业创新能力、发展新型优质生产力提供参考。方法基于2020 - 2023年中国23家新能源a股上市企业数据,构建新生产力背景下的创新效率评价指标体系。应用CCR-DEA和BCC-DEA模型对2023年的静态效率进行分析,结合Malmquist-DEA指数对2020 - 2023年的动态效率进行分析。此外,采用投影分析来识别输入冗余和输出不足的问题,并利用随机前沿方法来补充DEA分析。结果中国新能源企业整体创新效率近似有效,但差异显著。60%以上的企业需要优化资源配置效率。约半数企业面临规模收益递减,专利数量和研发支出等产出短缺问题突出。2020 - 2023年,全要素生产率呈现先上升后下降的趋势,技术回归是创新效率下降的主要原因。结论构建与新型优质生产力相适应的资源利用体系、政策引导机制、协同生态系统和人才供给体系,优化创新资源配置,突破技术瓶颈,促进新能源产业高质量发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Cleaner Environmental Systems
Cleaner Environmental Systems Environmental Science-Environmental Science (miscellaneous)
CiteScore
7.80
自引率
0.00%
发文量
32
审稿时长
52 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信