Evaluation of green technology innovation efficiency in a regional context: A dynamic network slacks-based measuring approach

Qian Wang and Shuming Ren

Technological Forecasting and Social Change, 2022, vol. 182, issue C

Abstract: As green technology innovation efficiency is considered an effective indicator to evaluate energy conservation and emission mitigation, the question of how to estimate it has become a hot topic. Research primarily calculates the cross-section efficiency from a static perspective or the network efficiency alone; however, few studies have considered dynamic characteristics and the network structure of the innovation process simultaneously. To address this gap, this paper employs slacks-based dynamic network data envelopment analysis to calculate the overall, intertemporal, and divisional green technology innovation efficiency covering China's 30 provinces over 2012–2019. The results indicate that (1) a strong spatial dependence of efficiency scores exists, accounting for 60 % of the aggregate statistics. (2) China's overall scores are low, and inefficient regions need to improve efficiency by at least 31.41 % to become efficient. (3) Economically developed regions, provinces with advanced manufacturing, and nonresource-based areas have relatively high scores. (4) The period scores fluctuate greatly up and down around the overall scores, showing a significant spatial imbalance within consecutive multiple periods. (5) Regarding divisional scores, the internal structure of green technology innovation efficiency in China is inferior in R&D and superior in commercialization. Generally, policy-makers should improve local conditions for green technology innovation efficiency.

Keywords: Green technology innovation efficiency; Regional study; Dynamic DEA; Network DEA; Slacks-based measure (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:182:y:2022:i:c:s0040162522003602

DOI: 10.1016/j.techfore.2022.121836

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