What is affecting the improvement of green innovation efficiency in the old industrial base: Evidence from Northeast China

Abstract

Abstract Green innovation is an important driving force for high-quality development and is vital for reinvigorating the old industrial bases in Northeast China. As such, this study investigates the spatial-temporal evolution characteristics and factors influencing green innovation efficiency (GIE) in Northeast China from 2005 to 2020, using the super-efficient EBM-Malmquist model, kernel density estimation, and random forest model. The results show the following. 1) The "growth effect" of technological change is the main force driving GIE improvement; the "horizontal effect" of pure technical efficiency change has started to play an important role; and the club convergence characteristics of GIE in Northeast China have started to be optimized. 2) GIE in Northeast China shows significant spatial differentiation. The urban agglomeration of Mid-southern Liaoning and Harbin-Changchun has had high values for GIE, indicating that green innovation has a cyclic cumulative effect and the spatial pattern of green innovation needs to be further optimized. 3) The random forest model is more accurate and provides more trustworthy results compared with the traditional multiple linear regression model. The results of random forest model measurement illustrate that the number of digital economy enterprises, public finance expenditure, GDP per capita, and vegetation coverage play a positive role in promoting GIE. The proportion of the non-farm population and industrial agglomeration plays a negative role in GIE. In the same period, the contribution of the number of digital economy enterprises ≥ 0.41, public expenditure ≥ 0.47, GDP per capita ≥ 0.39, and vegetation coverage ≥ 0.36 to GIE reach maximum values and then remain unchanged.