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Spatial impact of foreign direct investment on ecological footprint in Africa

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Abstract

This study examines the spatial impact of FDI on ecological footprint of 31 African countries. In achieving this, the study uses the Driscoll-Kraay (1998) random effect model, fixed-effect instrumental variable regression, and the spatial Durbin model. There are three main important findings from this empirical study. First, FDI has a nonlinear impact on ecological footprint in Africa. At the initial stage, FDI reduces ecological footprint up to a threshold of $404.75–$669.96 million, before the impact increases ecological degradation. This result is robust to the instrumental regression model. Second, the results further reveal a significant spatial spillover of FDI on ecological footprint in Africa. Third, the empirical results provide evidence of both direct and spillover effects of environmental degradation determinant in Africa. This denotes that environmental quality of a particular country influences the environmental quality of other neighbouring countries. While it is important to attract significant amount of foreign investment to Africa, this study recommends that African governments need to improve their environmental regulations and laws to achieve transfer of energy-saving technology from foreign investors.

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Fig. 1

Source: UNCTAD (2021)Query

Fig. 2

Source: Global Footprint Network (GFN)

Fig. 3

Source: Authors’ calculation using Global Footprint Network Database

Fig. 4

Source: Authors’ computation using data from Global Footprint Network Database

Fig. 5

Source: Authors’ computation using data from Global Footprint Network and UNCTAD Database

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. http://www.footprintnetwork.org/our-work/ecological-footprint/

  2. http://www.footprintnetwork.org/resources/data/

  3. https://data.footprintnetwork.org/#/

  4. Quadrant A in Fig. 3 are countries with negative spatial clustering of low ecological footprint, while B indicates countries with positive spatial clustering of high ecological footprint, C indicates positive spatial cluster of countries with low ecological footprint, and D is negative spatial cluster of high ecological footprint.

  5. The selected countries account for 91% of total stock of FDI inflows in Africa in 2017. This makes our sample more representative.

  6. The fixed-effect instrumental regression is used to address endogeneity in the model. Lagged FDI was used as instruments in the model.

  7. This study empirically confirms the presence of spatial autocorrelation in Eq. (2) with the Moran \(Ii\) test. This is done through the residual of the pooled regression (not reported in this study but available on request).

  8. See Elhorst (2014) for more on the mathematical derivation.

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Authors and Affiliations

  1. College of Business and Economics, University of Johannesburg, Auckland Park Kingsway Campus, PO Box 524, Auckland Park, Johannesburg, South Africa

    Sodiq Arogundade, Biyase Mduduzi & Adewale Samuel Hassan

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  1. Sodiq Arogundade
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Sodiq Arogundade conceptualized the major idea of this research paper. He prepared the methodology, data collection, analysis, interpretation, and conclusion section. Adewale Hassan prepared the introduction, and Biyase Mduduzi prepared the literature review. The three authors have read and approved the final version of this manuscript.

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Correspondence to Sodiq Arogundade.

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Appendices

Appendix 1

(Table 7)

Table 7 Moran’s \({I}_{i}\) residual of ecological footprint regression by country*
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Appendix 2

(Table 8)

Table 8 United Nation regional classification
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Appendix 3

(Table 9)

Table 9 SAR vs. SEM vs. SDM model
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Arogundade, S., Mduduzi, B. & Hassan, A.S. Spatial impact of foreign direct investment on ecological footprint in Africa. Environ Sci Pollut Res 29, 51589–51608 (2022). https://doi.org/10.1007/s11356-022-18831-w

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