Work

Multidimensional Analysis of Technological Innovation Efficiency in New Energy Vehicles: Industrial Chain Heterogeneity and Key Drivers

Year: 2025

Type: article

Abstract: As the world accelerates efforts to combat climate change and transition toward a green, low-carbon economy, the new energy vehicle (NEV) industry has become a key driver of carbon reduction. Its ability to innovate efficiently is critical to long-term sustainable development. This study builds on the innovation value chain theory and introduces an evaluation framework that accounts for undesirable outputs such as energy consumption and pollutant emissions. Using a super-efficiency network SBM–Malmquist model and Tobit regression, we analyze the technological innovation efficiency of 272 A-share listed NEV enterprises in China from 2016 to 2023. Expanding beyond traditional overall assessments, we examine efficiency at different stages of the industry chain and find that: (a) overall technological innovation efficiency has declined, mainly due to weak pure technical efficiency, underscoring the need for better R&D management and resource allocation; (b) efficiency varies across the industry chain, with midstream firms performing better than those upstream and downstream, reflecting differences in technological accumulation and market conditions; (c) R&D tax deductions and market competition significantly boost innovation efficiency by creating pressure-driven incentives, while mismatched labor skills, the “welfare dependence” effect of tax incentives and financing constraints hinder progress. By introducing a two-stage innovation efficiency evaluation framework, this study not only pinpoints where efficiency losses occur along the industry chain but also provides empirical insights to guide targeted policy decisions, offering valuable implications for the sustainable growth of the global NEV industry.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj16040233

Authors: Yawei Xue, Yuchen Lu, Zhongshuai Wang

Institutions: Qingdao University of Technology, Qingdao University of Science and Technology

Language: English

Topic: Efficiency Analysis Using DEA

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $1,689