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Threshold effect of foreign direct investment on environmental degradation

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Abstract

The aim of this paper is to investigate the threshold effect of foreign direct investment (FDI) on environmental degradation. In empirical analysis, FDI and environmental degradation are jointly determined under the given threshold variable and other exogenous variables. Using carbon dioxide (CO2) emissions per capita as a proxy for environmental degradation, the results show that increasing FDI worsens CO2 emissions after a threshold level of corruption has been reached. Our results demonstrate that increasing FDI will increase CO2 emissions when the degree of corruptibility is relatively high. The study suggests that further FDI and improved environmental quality are competing rather than compatible objectives in high-corruption countries and are compatible rather than competing objectives in low-corruption countries. Higher trade liberalization in low-corruption countries could contribute to negative environmental consequences because of the increased output or economic activity which results from increased trade. The robustness estimation confirms the evidence that pollution and economic development increase together up to a certain income level, after which the trend reverses.

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Notes

  1. Cole et al. (2006) found that FDI has two opposite effects on environmental pollution (i.e., a negative effect caused by a high degree of local government corruptibility and a positive effect due to a low degree of local government corruptibility).

  2. That is, FDI × CORRUPT. CORRUPT denotes corruptibility.

  3. Regarding the “pollution halo effect,” some studies argue that multinationals engaging in FDI tend to spread greener technology in the host country (Birdsall and Wheeler 1993; Zarsky 1999).

  4. CHECKS denotes the number of checks and balances within government. POLCON denotes the number of political constraints within the legislature.

  5. We assumed that ρ is restricted to a bounded set \( T\supset \left[\underline{\rho},\overline{\rho}\right] \). For each ρ ∈ Τ, \( {\widehat{\beta}}_1\left(\rho \right)={\left({\displaystyle \sum_{i=1}^n{x}_i{x}_i\hbox{'}I\left({q}_i\le \rho \right)}\right)}^{-1}{\displaystyle \sum_{i=1}^n{x}_i\left(FD{I}_i\right)\hbox{'}I\left({q}_i\le \rho \right)} \), and \( {\widehat{\beta}}_2\left(\rho \right)={\left({\displaystyle \sum_{i=1}^n{x}_i{x}_i\hbox{'}I\left({q}_i>\rho \right)}\right)}^{-1}{\displaystyle \sum_{i=1}^n{x}_i\left(FD{I}_i\right)\hbox{'}I\left({q}_i>\rho \right)} \). The residual from Eq. (4) is \( {\widehat{u}}_i\left(\rho \right)=FD{I}_i\hbox{-} {\widehat{\beta}}_1{\left(\rho \right)}^{\prime }{x}_iI\left({q}_i\le \rho \right)\hbox{-} {\widehat{\beta}}_2{\left(\rho \right)}^{\prime }{\mathrm{x}}_{\mathrm{i}}I\left({q}_i>\rho \right) \).

  6. We make an assumption that FDI is an endogenous explanatory variable. This is an important feature of the model, but is substantially different from Hansen’s (1999) model. Because Hansen’s (1999) static panel threshold model does not handle the endogenous explanatory variable, our model will require a distinct estimator.

  7. The threshold value γ is restricted to a bounded set \( \varGamma \supset \left[\underline{\gamma},\overline{\gamma}\right] \).

  8. Where the “tilde” over the Θ 1 and Θ 2 denotes estimated values.

  9. http://www.prsgroup.com/ICRG_Methodology.aspx.

  10. The whole sample did not include Portugal because data for the secondary school enrollment ratio for 1984–1985 and 1997, for the ratio of value added of industry to GDP for 1984–1994, and for GDP per unit of energy use for 1984–1989 are not available in the WDI database.

  11. In double-threshold effects, 14, 18, and 33 countries fall into the three different regimes, respectively. Thus, with triple-threshold effects, estimated results cannot be precisely obtained because at least one regime has few countries falling into it. The estimated results of the marginal impact of FDI on environmental pollution from a few countries are not meaningful.

  12. The three subsamples (i.e., high-, medium- , and low-corruption countries) are split by using estimated threshold values. Because the subsample countries are not derived arbitrarily, we cannot remove individual outliers in the individual subsamples. If we remove specific countries in the whole sample and re-estimate threshold values, the numbers of subsample countries will be changed.

  13. When we remove outliers that appear in Figs. 1 and 3 in whole sample, the numbers of moderate- and low-corruption countries decrease: moderate-corruption countries fall from 18 to 17, and low-corruption countries decrease from 33 to 20. However, the number of high-corruption countries increases from 14 to 24.

  14. Some studies found that FDI inflows lead to an increase in CO2 emissions (Cole and Elliott 2005; Feridun 2006). Other studies showed that FDI inflows reduce CO2 emissions (List and Co 2000; Tamazian et al. 2009).

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  1. Department of Business Administration, National Formosa University, Yunlin, Taiwan

    Shu-Chen Chang

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Correspondence to Shu-Chen Chang.

Appendix

Appendix

Table A

Table 8 Country sample
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Table B

Table 9 Effect of FDI on environmental degradation when removing countries with a high ratio of net FDI to GDP
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Table C

Table 10 Effect of FDI on environmental degradation without thresholds when removing countries with a high ratio of net FDI to GDP
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Chang, SC. Threshold effect of foreign direct investment on environmental degradation. Port Econ J 14, 75–102 (2015). https://doi.org/10.1007/s10258-015-0112-3

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