Abstract
Indeed, the Belt and Road Initiative (BRI) plays an increasingly important role in global economic and climate change mitigation. However, scientists have insufficient attention to the issues related to the elements that contribute to justifying these impacts and bolstering its response in BRI nations. Accordingly, the existent study executed an in-depth examination of the spatial direct and spillover effects of foreign direct investment inflows (FDI) and biomass energy consumption (BEC) on greenhouse gas emissions (GHG) for 57 BRI countries (1992–2012). We applied the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM) with five different weights matrices to verify the existence of the pollution haven hypothesis (PHH), the pollution halo hypothesis (P-HH), and the N-shaped environmental Kuznets curve (EKC). We linked the study results with the implementation level of the sustainable Development Goals (SDGs). The findings of local Moran’s I (LMI) and Lagrange Multiplier (LM) tests confirm the existence of spatial autocorrelation (SAR). The empirical results revealed that FDI has a positive direct and spillover influence on GHG emissions, which supports the presence of PHH. Also, the nexus between economic growth and GHG emission is an N-shaped curve. The results revered that BEC has a negative sign for direct and spillover effects. In contrast to BEC, Fossil Fuel Energy Consumption (FFEC) and population positively sign for direct and indirect impact. Some policy proposals and future research directions are discussed for BRI countries.
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Source: Prepared by Authors depend on the Database of WDI (2020) and UN-IRP (2020)
Source: Prepared via the researcher based on UN-IRP Database 2020
Source: Prepared by authors
Source: Prepared by authors
Source: Prepared by Authors
Source: Prepared via the researcher relying on SDGs Database 2020
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The datasets generated and/or analyzed during the current study are not publicly available. Our team spent the time to collect and organize the data. We need to use it to do more studies about BRI countries. But the datasets are available from the corresponding author on reasonable request.
Notes
IPCC: Intergovernmental Panel on Climate Change.
GML: Global Monitoring Laboratory database.
WDI: world development indicators database.
United Nations — Environment Programme — International Resource Panel — Material Flows Database.
bp Energy economics database.
For an inclusive review of the EKC hypothesis see Shahbaz, M., & Sinha, A. 2019. Environmental Kuznets curve for CO2 emissions: a literature survey. Journal of Economic Studies, 46(1), 106–168. https://doi.org/10.1108/JES-09-2017-0249
Also, see the SU-Table 1 in the Supplementary material.
To know prominent advantages of the STIRPAT model, see York, R., Rosa, E. A., & Dietz, T. 2003. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3), 351–365. https://doi.org/10.1016/s0921-8009(03)00188-5
For more details of these limitations, see Fan, Y., Liu, L.-C., Wu, G., & Wei, Y.-M. 2006. Analyzing impact factors of CO2 emissions using the STIRPAT model. Environmental Impact Assessment Review, 26(4), 377–395. https://doi.org/10.1016/j.eiar.2005.11.007, and York, R., Rosa, E. A., & Dietz, T. 2003. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3), 351–365. https://doi.org/10.1016/s0921-8009(03)00188-5
The BRI countries list of this study available at SU-Table 2 in the Supplementary material.
For a more comprehensive theoretical and mathematical clarification of these effects including how they are estimated, see: P34 to P39 in LeSage, J., & Pace, R. (2009). Introduction to Spatial Regressions. In: Chapman and Hall/CRC.
This table available in the Supplementary material.
In this test, due to the limited publication space available in journals, and difficulty to display full results for all years in this paper, the average GHG was calculated during the period from 1992–2018 for each country separately, and then the general average of these averages was calculated for 57 countries according to each year separately, and then these last values were used in calculating a GMI and LMI tests statistic.
The GMI’s full results for 27 years available in SU-Table 4 in the Supplementary material.
The LMI’s results for selected three years (1992, 2005, and 2018) available in SU-FIG.1 in the Supplementary material.
Due to the limit spaces, the results of SLM, SEM are available in SU-Table 7 and SU-Table 5 in the Supplementary material.
The scale effect suggests that the FDI may increase emissions through its influence on economic activity as a result of scaling up the size of the economy, ceteris paribus. The technique effect captures the influence of the relocation and diffusion of modern technology and the introduction of new environmental regulations on environmental quality, which can be induced by the FDI inflow. The composition effect indicates that FDI can increase or decrease the emissions depending on whether a high FDI would shift the economic structure toward more or less polluting sectors. For more details, see.
Pazienza, P. 2015. The relationship between CO2 and Foreign Direct Investment in the agriculture and fishing sector of OECD countries: Evidence and policy considerations. Intellectual Economics, 9(1), 55–66. https://doi.org/10.1016/j.intele.2015.08.001, and Pazienza, P. 2019. The impact of FDI in the OECD manufacturing sector on CO2 emission: Evidence and policy issues. Environmental Impact Assessment Review, 77, 60–68. https://doi.org/10.1016/j.eiar.2019.04.002
Due to the limit spaces, the tables are available in the Supplementary material.
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AL-Barakani Abdo: conceptualization, methodology, formal analysis, investigation, writing — original draft preparation, writing — review and editing preparation, visualization, supervision. Bin Li: visualization, conceptualization, review and editing preparation. Qahtan Anwar Saeed Ahmed: data curation, resources, investigation. Alnoah Abdulsalama: resources, investigation. Aloqaba Abdullah: project administration, resources. Obadi Waleed: resources, investigation.
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Highlights
• This study assessed FDI and BEC’s spatial influence on GHGs emissions in BRI states.
• The spatial autocorrelation tests and spatial panel data model tests were employed.
• FDI increases GHG emissions, and PHH is valid in BRI countries.
• N-shaped nexus between GDP and GHG emissions is existing.
• BEC declines GHG emissions and improves environmental quality.
• The BRI countries still face challenges in achieving the SDGs.
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Abdo, AB., Li, B., Qahtan, A.S.A. et al. The influence of FDI on GHG emissions in BRI countries using spatial econometric analysis strategy: the significance of biomass energy consumption. Environ Sci Pollut Res 29, 54571–54595 (2022). https://doi.org/10.1007/s11356-022-19384-8
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DOI: https://doi.org/10.1007/s11356-022-19384-8