The Driving Factors of Green Technology Innovation Efficiency—A Study Based on the Dynamic QCA Method

Abstract

The problems of environmental pollution and resource shortages are becoming increasingly prominent with the advances in technology and the improvements in social productivity levels. How to coordinate the innovating subjects, and strengthen the interaction and cooperation among the subjects to improve the green technology innovation efficiency (GTI efficiency) is an important issue to be solved urgently. This paper constructs a multisubject collaborative analysis framework of “government–market–society” for the GTI efficiency based on the pluralistic co-governance theory. Using 30 provinces (or regions, cities) in China from 2016 to 2020 as research samples, we analyzed the driving factors of GTI efficiency using the dynamic QCA method. Furthermore, the configuration effects of the driving factors were further analyzed in the time and spatial dimensions. The results show that: (1) The single driving factor does not constitute the requisite condition for high GTI efficiency. (2) There are three types of paths to realize high GTI efficiency: “government–market driven”, “market–society driven”, and “government–market–society synergistic promotion”. Among them, the “government–market-driven” path is subdivided into “government—internal-market driven” and “government-supported–external-market driven”. (3) In the time dimension, the “market–society driven” path has prominently enhanced its driving effect on GTI efficiency, while the driving effect of the other three paths is little changed. In the spatial dimension, the coverage of the “government-supported–external-market driven” path appears to have obvious regional differences, and the explanation degree of the central region is higher, while the other three paths do not have obvious regional differences. Based on the perspective of configuration, these research results enrich the related research on the driving factors of GTI efficiency in the time and spatial dimension. It provides enlightenment for boosting the coordinated development of regional GTI efficiency and realizing sustainable economic growth.

The Impact of Yangtze River Delta Regional Integration on the Scientific and Technological Achievements Transformation in Higher Education: A Dynamic QCA Analysis Based on Provincial Panel Data

“…However, pathways to high-level scienti c and technological transformation in higher education are diverse, and traditional QCA is relatively static, lacking consideration of the temporal dimension of con gurations (Pagliarin & Gerrits, 2020). In contrast, dynamic QCA could analyze how changes in conditional variables over time affect outcomes (Fan et al, 2023). Dynamic QCA also allows for further measurement from between-group, within-group, and pooled dimensions, using consistency distance to capture changes in consistency over time and across cases.…”

Exploring the Multiple Paths of Digital Government Development: A Dynamic QCA Analysis Based on Organizational Transformation

“…Based on Fan, Z. T et al [93], the 95%, 50% and 5% quantiles were used as three qualitative anchors for full affiliation, midpoint and full nonaffiliation. To observe the changes in the variables in different years, an overall calibration scheme was adopted, drawing on existing studies [94]. To avoid cases with an affiliation of 0.5 losing their analytical validity, 0.01 was added to them.…”

“…The consistency distances currently reported for panel QCA analyses in R are Euclidean distances, and the criterion for determining whether a distance is too large is affected by the sample size, which can be adapted to datasets with arbitrary sample sizes by means of adjusted consistency distances [75]. For this purpose, the consistency distance was adjusted and reported according to the adjustment scheme given by Garcia-Castro R et al Referring to established research [94], the decision criterion for determining whether the consistencyadjusted distance is too large is set at 0.2. When the consistency-adjusted distance is greater than 0.2, it indicates the presence of a significant time effect or individual effect (clustering).…”

“…Given that the current panel QCA has not developed appropriate methods to further explore within-group effects, Garcia-Castro R also suggested that other statistical methods could be combined to aid in the study. Drawing on Fan et al [94] and incorporating data characteristics, the Kruskal-Wallis H test was used to identify specific sources of spatial variation. The differences between East, Central and West are a significant spatial feature of China.…”