The impact of foreign direct investment on China's industrial carbon emissions based on the threshold model
- PMID: 37074607
- DOI: 10.1007/s11356-023-26803-x
The impact of foreign direct investment on China's industrial carbon emissions based on the threshold model
Abstract
In recent years, the number of countries concerned about environmental protection continues to increase. With a continuous expansion of economic scale, many emerging markets are also sustainably enhancing their management for industrial carbon emissions in foreign direct investment (FDI). Therefore, the impact of FDI on the host country's industrial carbon emissions has been a hot topic of researches. This study selects panel data of 30 medium and large cities in China from 2006 to 2019. Combined with dynamic panel GMM estimation and panel threshold model, this study empirically analyzes the impact factors of FDI on the host country's industrial carbon emissions. This study is based on the perspective of dual environmental management systems. This study draws the following conclusions: When taking the dual environmental management system factors as threshold variables into the empirical research process, only the FDI in Beijing, Tianjin, and Shanghai shows a certain inhibitory effect on Chinese industrial carbon emissions. The FDI in other cities increases the scale of industrial carbon emissions. At the same time, in the formal environmental management system, FDI has no significant impact on China's industrial carbon emissions. It indicates that the formal environmental management system of each city is not effective in policy formulation or implementation. In addition, the corresponding role of environmental management systems, such as innovation compensation and mandatory emission reduction, is not played. With the exception of Beijing and Shanghai, informal environmental management systems in other cities help curb the scale of industrial carbon emissions brought by FDI.
Keywords: Dual environmental management system; Foreign direct investment; Industrial carbon emissions; Threshold model.
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
References
-
- Acheampong AO, Boateng EB (2019) Modelling carbon emission intensity: application of artificial neural network. J Clean Prod 225:833–856 - DOI
-
- Acma B, Rc A (2020) The impact of foreign direct investment on emission reduction targets: evidence from high- and middle-income countries. Struct Chang Econ Dyn 55:107–118 - DOI
-
- Arain H, Sharif A, Akbar B (2020) Dynamic connection between inward foreign direct investment, renewable energy, economic growth and carbon emission in China: evidence from partial and multiple wavelet coherence. Environ Sci Pollut Res 29:51–68
-
- Ahmad I, Shahid I, Ali A (2021) Electronic, mechanical, optical and photocatalytic properties of two-dimensional monolayers. RSC advances 11:17230–17239
-
- Bhatti WA, Chwialkowska A, Glowik M (2020) The role of power and network positioning in technology firms' international expansion. Academy of Management Annual Conference 3:1–12
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Grants and funding
- (No. 22GJB127/the Philosophy and Social Science Research Planning Project of Heilongjiang Province
- 2022-KYYWF-1208/Heilongjiang Provincial Universities Basic Scientific Research Operation Fund Project of Heilongjiang University
- No. 20YJC790082/Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China
- No. GXZY2107/the Major Project of Party's Political Construction Research Center of Ministry of Industry and of Information Technology of the People's Republic of China
- No.22CGL030/the National Social Science Fund of China
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