Abstract

As China is undergoing economic transformation and facing increasing energy and environmental problems, it is essential to pay special attention to sustainable innovation governance. This research took industrial waste and total energy consumption into consideration and uses a super efficiency slack-based measure (SBM) model to empirically evaluate the regional innovation efficiency of Chinese provinces. The results showed that the efficiency of China’s regional sustainable innovation has not changed significantly over recent years. In addition, the results also showed large and varying degrees of innovation efficiency across different provinces. Eastern China, in comparison to central and western China, showed higher innovation efficiency. In addition, we found a slightly increasing trend in terms of innovation efficiency disparities between the three areas. On the basis of these findings, the reasons for the innovation efficiency gap between different regions were analyzed. The impacts of influential factors on sustainable innovation efficiency were further explored. We found that technology market maturity affected sustainable innovation efficiency positively, while government funding had a negative impact on sustainable innovation efficiency. Industrial structure and environmental regulations had no significant effect on sustainable innovation efficiency. Finally, some implications for improving governance performance in terms of sustainable innovation were provided.

Highlights

  • Technological innovation, in contemporary times, has had a significant impact on economic and productivity growth, and has become the core driver of regional sustainable development and economic growth [1,2]

  • Previous research has found that government funding (GOV) [58,59,60,61], technology market maturity (TEC) [3,62,63,64], industrial structure (IND) [65,66,67], and environmental regulations (ER) [68,69,70] could affect sustainable innovation efficiency (IE)

  • The data envelopment analysis (DEA) provides an ordinal ranking of relative efficiency within a set of comparable decision-making units (DMUs), and identifies the best practices leading to the identification of an efficient frontier

Read more

Summary

Introduction

Technological innovation, in contemporary times, has had a significant impact on economic and productivity growth, and has become the core driver of regional sustainable development and economic growth [1,2]. Technological innovation may impact negatively on energy conservation and environmental emissions reductions because of the rebound effects caused by efficiency improvements [7]. Technological innovation processes often produce industrial waste and carbon dioxide during the transformation period [8,9]. Such undesirable outputs in the innovation process are difficult to dispose of These massive emissions of industrial waste and carbon dioxide create new challenges for traditional technological innovation. Economic development, and environmental pollution, improving sustainable innovation efficiency is of theoretical and practical significance. No research has analyzed the factors influencing sustainable innovation efficiency from a quantitative perspective. We took industrial waste and total energy consumption into consideration and extended sustainable innovation governance to include undesirable outputs.

Sustainable Innovation Governance
Research on Innovation Efficiency
Super Efficiency DEA-SBM Model
Indicators Selection and Data Sources
Regression Model
Efficiency Evaluation Results
Analysis of Innovation Efficiency Changes
Regional Comparative AnJialilnysis
Hypotheses
Contribution to Theory
Contribution to Practice
Limitations
Future Research
Conclusions and Implications

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.