Measuring sustainable technology R&D innovation in China: A unified approach using DEA-SBM and projection analysis

Measuring Green Technology R&D Efficiency (GTR&DE) and identifying improvement potentials are of paramount importance for governmental policymaking on sustainable technology R&D investment. However, comprehensive evaluation of GTR&DE and integration with projection analysis are largely lacking. To support government policymaking, we propose a new DEA-SBM-PA approach integrating Data Envelopment Analysis – Slack Based Measurement (DEA-SBM) and projection analysis (PA) to identify how well sustainable R&D innovations are performing and how much improvements are needed. Based on the theory of decoupling, this study constructs a systemic framework of GTR&DE including economic, energy, and environmental performance indicators. Subsequently, the GTR&DE scores at provincial, regional, and national levels are measured by DEA-SBM model based on panel data from China’s 30 provinces during 2011–2017, and the potential GTR&DE improvements for the provinces deemed inefficient are assessed by projection analysis. Our findings reveal that implementation of green technology R&D innovation has positive impact in achieving comprehensive benefits covering economy, energy, and environment. Furthermore, the major contribution of this research is to develop a unifying framework to provide insights for policymakers, including assessment of GTR&DE at provincial, regional, and national levels, analysis of both spatial and temporal differences of GTR&DE scores at different levels, identification of efficient and inefficient provinces, and finally projecting GTR&DE improvement potentials for the provinces deemed inefficient. The findings have significant policy implications, particularly because they demonstrate the impact of an important government policy adjustment in 2015 by analyzing both before and after effects. Finally, it is discussed how government initiatives for sustainable technology R&D innovation may be supported with additional analysis in future.