Open Access
| Issue |
E3S Web Conf.
Volume 580, 2024
2024 2nd International Conference on Clean Energy and Low Carbon Technologies (CELCT 2024)
|
|
|---|---|---|
| Article Number | 02010 | |
| Number of page(s) | 8 | |
| Section | Low Carbon and Energy Saving Technologies and Environmental Sustainability | |
| DOI | https://doi.org/10.1051/e3sconf/202458002010 | |
| Published online | 23 October 2024 | |
- Guo, R., Yuan, Y. J. (2019) Producer services agglomeration, manufacturing agglomeration and environmental pollution: based on provincial panel data analysis. Econ. Sci. (01), 82–94. [Google Scholar]
- Tang, C. A., Qiu, J. W., Zhang, L. J., Li H. Y. (2021) Spatial econometric analysis on the influence of elements flow and industrial collaborative agglomeration on regional economic growth: based on manufacturing and producer service. Econ. Geo., 41: 146–154. [Google Scholar]
- Li, X. F., Zhang, H. C. (2019) Research on the impact of industrial agglomeration on carbon emission: nonlinear analysis with urbanization level as the threshold. Ecolo. Econ., 35: 36–36+57. [Google Scholar]
- Yang, C. (2022) The effect mechanism of industrial agglomeration on the carbon emission level of the logistics industry under the trend of carbon neutrality. J. of Comm. Econ., (13): 103–106. [Google Scholar]
- Kou, D. X., Huang, J. (2021) The emission reduction effect of agglomeration of producer service on manufacturing agglomeration: based on 285 cities’ panel data from 2003 to 2019. Chn., Bus. & Mar., 35: 78–88. [Google Scholar]
- Chen, G., Yuan, K., Xu, W. (2020) Research on the formation mechanism and spatial spillover effects of industrial collaborative agglomeration. Z.J. Univ. P., Chn. [Google Scholar]
- Yan, F. (2021) Spatial correlation and influencing factors of synergistic agglomeration of logistics industry and manufacturing industry. Stat. & Deci., 37: 113–117. [Google Scholar]
- Liu, M. (2021) The influence of collaborative agglomeration of logistics industry and manufacturing industry on high-quality economic development——the empirical research on 283 cities at the prefecture level and above. Chn. Busi. & Mkt, (09): 22–32. [Google Scholar]
- Cheng, Y. Q., Wang, Z. Y., Ye, X. Y., Wei, Y. H. (2014) Spatiotemporal dynamics of carbon intensity from energy consumption in China. J. of Geo. Sci., 68: 631–650. [CrossRef] [Google Scholar]
- Wang, X., Zhang, L. J., Qin, Y. C., Zhang, J. F. (2020) Spatiotemporal changes of carbon emissions in highcarbon manufacturing industry in China and driving factors. Rsrc Sci., 42: 323–333. [Google Scholar]
- Hou, W. Y., Jin, J. X., Yan, T., Liu, Y. (2022). A satellite-based dataset of global atmospheric carbon dioxide concentration with a spatial resolution of 2°x 2.5°from 1992 to 2020. J.l of Glo. Chan. D. & Disc., (02): 191-199+363-371. [Google Scholar]
- Chu, Y. L. (2018) Innovation effect of coagglomeration between KIBS and manufacturing industry. Kno. Mgt. Forum, 3: 198–207. [Google Scholar]
- Xu, D. Y. (2008). A theoretical explanation and verification of industrial structure upgrading form determination and measurement. Pub. Fin. Res, (1): 46–49. [Google Scholar]
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