Green innovation has become an important combination of high-quality economic growth and ecological sustainability. In this paper, the super-efficiency network SBM model was used to measure the two-stage green innovation efficiency of the industrial technology research and development (R&D) stage and achievement transformation stage in China (30 provinces and cities) from 2009 to 2019. The results show the following points. Firstly, in terms of temporal series, the efficiency of technology R&D and achievement transformation has experienced three stages of “upward-declining-revitalized period”. Secondly, in terms of spatial trend, the industrial green innovation efficiency gradually increases from northwest to southeast. The high-efficiency ...
Based on employing the global super efficiency epsilon-based measure (GSE-EBM) model to evaluation t...
Earlier studies on the innovation process in the high-tech manufacturing industry failed to take env...
This paper uses panel data from 30 provinces and cities in China between 2008 and 2017. It calculate...
Industrial green technology innovation has become an important content in achieving high-quality eco...
Green innovation is an important capability for an enterprise’s sustainable development. Evaluating ...
Green technology innovation is an important means to break out of the constraints of resources and t...
The industrial revolution has brought a leap in productivity; however, some severe ecological enviro...
Innovation is the first driving force for development, and green innovation efficiency (GIE) plays a...
Green innovation exchanges low emissions, low pollution and low output for economic development. At ...
Green technology innovation, containing economic, social and ecological triple value effects, plays ...
This paper selects panel data of 29 provinces in mainland China from 2010-2017 and combines a DEA-SB...
This study offers a RAGA-PP-SFA model to measure green technology’s innovation efficiency in the hig...
Improvements in green technology innovation efficiency is the core factor to promote to shape new ad...
Based on China’s provincial panel data from 2009 to 2019, this paper empirically tests and analyzes ...
ABSTRACT: To achieve sustainable development, it is vitally important to identify the factors drivin...
Based on employing the global super efficiency epsilon-based measure (GSE-EBM) model to evaluation t...
Earlier studies on the innovation process in the high-tech manufacturing industry failed to take env...
This paper uses panel data from 30 provinces and cities in China between 2008 and 2017. It calculate...
Industrial green technology innovation has become an important content in achieving high-quality eco...
Green innovation is an important capability for an enterprise’s sustainable development. Evaluating ...
Green technology innovation is an important means to break out of the constraints of resources and t...
The industrial revolution has brought a leap in productivity; however, some severe ecological enviro...
Innovation is the first driving force for development, and green innovation efficiency (GIE) plays a...
Green innovation exchanges low emissions, low pollution and low output for economic development. At ...
Green technology innovation, containing economic, social and ecological triple value effects, plays ...
This paper selects panel data of 29 provinces in mainland China from 2010-2017 and combines a DEA-SB...
This study offers a RAGA-PP-SFA model to measure green technology’s innovation efficiency in the hig...
Improvements in green technology innovation efficiency is the core factor to promote to shape new ad...
Based on China’s provincial panel data from 2009 to 2019, this paper empirically tests and analyzes ...
ABSTRACT: To achieve sustainable development, it is vitally important to identify the factors drivin...
Based on employing the global super efficiency epsilon-based measure (GSE-EBM) model to evaluation t...
Earlier studies on the innovation process in the high-tech manufacturing industry failed to take env...
This paper uses panel data from 30 provinces and cities in China between 2008 and 2017. It calculate...