{"title":"Innovation ability of China's new energy industry from the perspective of new quality productivity","authors":"Qingli Tan, 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&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.