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Tu C, Zang C, Wu A, Long H, Yu C, Liu Y. Assessing the impact of industrial intelligence on urban carbon emission performance: Evidence from China. Heliyon 2024; 10:e30144. [PMID: 38779025 PMCID: PMC11108847 DOI: 10.1016/j.heliyon.2024.e30144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/03/2024] [Accepted: 04/20/2024] [Indexed: 05/25/2024] Open
Abstract
With the growing emphasis on sustainable development, there has been increasing attention given to measures aimed at promoting environmental improvements and reducing carbon emissions, including the adoption of intelligent industry. Recent studies have analyzed the influence of industrial intelligence on urban carbon emission performance while ignore the spatial spillover effects and lack in-depth discussion of the mechanisms, which reduces the reliability of the assessment of industrial intelligence's impact on carbon emission performance. To address this issue, the paper examines direct effect, spatial spillover effects, and mechanisms, utilizing a balanced panel data from 2008 to 2019 for 238 Chinese cities. The findings reveal that a 1 % improvement in industrial intelligence results in a 2.747 % enhancement of local carbon emission performance. Moreover, through the spatial spillover analysis, we determined that industrial intelligence has a notable negative impact on the carbon emission performance of surrounding areas. The mechanism analysis demonstrated that industrial intelligence affects the carbon emission performance of local and neighboring areas by influencing the agglomeration of productive services. Furthermore, our study illustrates that the industrial intelligence's enhancement effect on carbon emission performance shows more significantly in eastern, resource-dependent, and ordinary prefecture-level cities. Finally, endogeneity and robustness tests conducted yielded consistent conclusions. Our study provides a new perspective on industrial intelligence's carbon reduction effect and contributes to the development of policies related to industrial upgrading and green development.
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Affiliation(s)
- Chenglin Tu
- School of Management, Guangzhou University, University Town Outer Ring West Road 230, 510006, Guangzhou, China
- Academy of Guangzhou Development, Guangzhou University, University Town Outer Ring West Road 230, 510006, Guangzhou, China
| | - Chuanxiang Zang
- School of Management, Guangzhou University, University Town Outer Ring West Road 230, 510006, Guangzhou, China
| | - Anqi Wu
- NTU Entrepreneurship Academy, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
| | - Hongyu Long
- NTU Entrepreneurship Academy, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
- Innovation, Policy and Entrepreneurship Thrust, The Hong Kong University of Science and Technology, Guangzhou, 511455, China
| | - Chenyang Yu
- Academy of Guangzhou Development, Guangzhou University, University Town Outer Ring West Road 230, 510006, Guangzhou, China
| | - Yuqing Liu
- School of Humanities, Guangzhou University, Guangzhou, 510006, China
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He Q, Ye T, Wang W, Luo M, Song Y, Zhang M. Spatiotemporally continuous estimates of daily 1-km PM 2.5 concentrations and their long-term exposure in China from 2000 to 2020. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 342:118145. [PMID: 37210817 DOI: 10.1016/j.jenvman.2023.118145] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/17/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023]
Abstract
Monitoring long-term variations in fine particulate matter (PM2.5) is essential for environmental management and epidemiological studies. While satellite-based statistical/machine-learning methods can be used for estimating high-resolution ground-level PM2.5 concentration data, their applications have been hindered by limited accuracy in daily estimates during years without PM2.5 measurements and massive missing values due to satellite retrieval data. To address these issues, we developed a new spatiotemporal high-resolution PM2.5 hindcast modeling framework to generate the full-coverage, daily, 1-km PM2.5 data for China for the period 2000-2020 with improved accuracy. Our modeling framework incorporated information on changes in observation variables between periods with and without monitoring data and filled gaps in PM2.5 estimates induced by satellite data using imputed high-resolution aerosol data. Compared to previous hindcast studies, our method achieved superior overall cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.90 and 12.94 μg/m3 and significantly improved the model performance in years without PM2.5 measurements, raising the leave-one-year-out CV R2 [RMSE] to 0.83 [12.10 μg/m3] at a monthly scale (0.65 [23.29 μg/m3] at a daily scale). Our long-term PM2.5 estimates show a sharp decline in PM2.5 exposure in recent years, but the national exposure level in 2020 still exceeded the first annual interim target of the 2021 World Health Organization air quality guidelines. The proposed hindcast framework represents a new strategy to improve air quality hindcast modeling and can be applied to other regions with limited air quality monitoring periods. These high-quality estimates can support both long- and short-term scientific research and environmental management of PM2.5 in China.
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Affiliation(s)
- Qingqing He
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China.
| | - Tong Ye
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China
| | - Weihang Wang
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China
| | - Ming Luo
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yimeng Song
- School of the Environment, Yale University, New Haven, CT, 06511, USA
| | - Ming Zhang
- School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China
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Gan T, Li Y, Jiang Y. The impact of air pollution on venture capital: evidence from China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:90615-90631. [PMID: 35869345 DOI: 10.1007/s11356-022-21972-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Utilizing a dataset of VC investment from 2005 to 2018, we investigate the relationship between air pollution and venture capital (VC) investment. We find that startups suffering severe air pollution receive less investment from VCs and experience a lower probability of financing from VCs. Our findings are robust to considering endogeneity concerns and various robustness checks. Moreover, air pollution is detrimental to startup innovation ability and entrepreneurial activities through which air pollution negatively affects VC investment. In response, VCs prefer to co-invest with other VCs to mitigate the adverse effect of air pollution. Our paper sheds new light on the impact of air pollution on the private financial markets.
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Affiliation(s)
- Tian Gan
- School of Economics and Management, Tongji University, Shanghai, 200092, China
- Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China
| | - Yumin Li
- SILC Business School, Shanghai University, Shanghai, 201800, China
| | - Yan Jiang
- College of Business, Shanghai University of Finance and Economics, Shanghai, 200433, China.
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