| Title | Public attention, big data technology, and green innovation efficiency: empirical analysis based on spatial metrology |
|---|---|
| ID_Doc | 30410 |
| Authors | Chen, YR; Hu, J; Chen, H; Chu, ZZ; Hu, MJ |
| Title | Public attention, big data technology, and green innovation efficiency: empirical analysis based on spatial metrology |
| Year | 2023 |
| Published | |
| Abstract | This study employs the undesirable output super-efficiency SBM-DEA model to reassess the green innovation efficiency (GIE) of 30 Chinese provinces from 2011 to 2020. We pioneer the examination of public attention (PA) influence on GIE and spatial spillover effects, employing the spatial Durbin model. Additionally, a spatial mediation model, incorporating big data technology as a mediator, is adopted. Key findings are as follows: 1) Significant spatial correlations exist in PA and GIE. 2) Improved PA in one province can help enhance the GIE in neighboring provinces but cannot directly impact the local GIE. 3) The positive impact of PA on local GIE follows an indirect path. Specifically, PA elevates the level of big data technology in the local and neighboring provinces, and this positive technological spillover effect significantly enhances the GIE across the entire region. 4) Industrial structure and research and development intensity also influence GIE to some extent. |
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