Thriving through innovation: Boosting green tech performance in China's new energy sector

Xin Cheng, Jingyue Gao, Yanting Liu, Ziyi Yu, Yuhan He and Wenhao Yu

Renewable Energy, 2024, vol. 237, issue PA

Abstract: In response to the challenges of sustainable development, green technological innovation (GTI) is considered as a key driver of high-quality growth. However, current research often lacks a comprehensive assessment of GTI's multidimensional performance and rarely explores improvement pathways from a structural perspective. This study evaluates the GTI performance of China's new energy sector using data from 62 listed companies between 2015 and 2021, employing a Back Propagation (BP) neural network model optimized by a genetic algorithm. Additionally, it explores performance enhancement paths through fuzzy set Qualitative Comparative Analysis (fsQCA). The findings indicate that: Industrial structure and human resource levels significantly boost GTI performance in the new energy sector; Optimizing and upgrading industrial structures plays a crucial role in GTI improvement; High Research and development (R&D) investment and human capital strengthen market competitiveness, leading to higher GTI levels; The development of GTI in the new energy sector exhibited a general upward trend, with notable differences across sub-industries and regions from 2015 to 2021. Based on these results, this study recommends differentiated industry support, targeted regional policies, and enhanced talent cultivation to improve GTI performance in China's emerging energy industry.

Keywords: New energy industry; Green technological innovation; Genetic-algorithm-optimized back propagation neural network model; Fuzzy-set qualitative comparative analysis; Performance evaluation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:237:y:2024:i:pa:s0960148124016215

DOI: 10.1016/j.renene.2024.121553

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