Evaluation and Influencing Factors of Regional Green Innovation Efficiency Based on the Lasso Method

Long Yu, Yang Liao, Renyong Hou and Weihua Peng ()
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Long Yu: School of Management, Wuhan University of Technology, Wuhan 430070, China
Yang Liao: School of Management, Wuhan University of Technology, Wuhan 430070, China
Renyong Hou: School of Management, Wuhan University of Technology, Wuhan 430070, China
Weihua Peng: School of Management, Wuhan University of Technology, Wuhan 430070, China

Sustainability, 2024, vol. 16, issue 20, 1-16

Abstract: Regional green innovation efficiency is affected by multiple factors. Based on the undesirable output of 11 provinces in the Yangtze River Economic Belt from 2012–2021, this paper uses the Super-SBM model containing undesirable outputs to measure and analyze the regional green innovation efficiency and tests the variables affecting regional green innovation efficiency through lasso regression. Stepwise regression and least squares estimation are used to prove the rationality of the lasso regression model. The results show that the regional green innovation efficiency of the Yangtze River Economic Belt during 2012–2021 among different regions has differences that manifest as the efficiency of the lower, middle, and upper reaches decreases successively, and the differences decrease gradually over time. All the variables affecting the regional green innovation efficiency pass the lasso regression variable screening test, and most of the influencing factors positively promote the regional green innovation efficiency and are more explanatory than the stepwise regression and the least squares proof model.

Keywords: green innovation efficiency; lasso regression; variable selection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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