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How does Foreign Direct Investment Affect Pollution? Toward a Better Understanding of the Direct and Conditional Effects


This paper seeks to investigate the impact of foreign direct investments (FDIs) on industrial pollution (\(\hbox {CO}_{2}, \hbox {SO}_{2}, \hbox {NO}_\mathrm{x}\) and BOD emissions) on a large sample of highly heterogeneous countries. By using panel data on manufacturing FDIs from France, Germany, Sweden, and the United Kingdom between 1995 and 2008, and by developing an empirical model with “first” and “second order” interaction terms, we investigate the existence and the conditionality of the most controversial FDI-induced effects on industrial emissions, i.e., Pollution Haven, Factor Endowments and Pollution Halo hypotheses. The paper has three main findings: (1) the central hypotheses linking pollution to FDI are found to act simultaneously, with opposing effects; (2) FDIs are associated with pollution reduction, i.e., predominating pollution halo induced effect, in countries with low to average capital-to-labour ratio but not too lax environmental regulation; (3) FDIs are found to increase pollution, i.e., prevailing pollution haven and/or factor endowments induced effects, in countries with average capital endowments and lax environmental regulations, as well as in all the capital abundant countries, though with a smaller magnitude in countries having strict environmental regulations and/or a high-skilled labour force. Some specific and interesting findings are discussed regarding different FDI-origin countries and FDI-host country groups.

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Fig. 1


  1. See for example Mani and Wheeler (1998), Antweiler et al. (2001), Cole and Elliott (2003b, 2005).

  2. See Copeland and Taylor (2004), Brunnermeier and Levinson (2004), Levinson (2008) and Millimet and Roy (2012) for definitions and extensive reviews of the literature related to the pollution haven hypothesis.

  3. Antweiler et al. (2001) are investigating the different effects on pollution of openness to trade, whereas Cole and Elliott (2005) examine the US dirty sector FDI’s determinants (of which factor endowments) and not explicitly their impact on pollution.

  4. We note that Ben Kheder and Zugravu (2012) found empirical support for this relationship for French FDIs, which appear to be attracted by a tightening of environmental regulation in some countries (in particular less developed and ex-soviet economies).

  5. Kim and Adilov (2012) suggest that the effects of FDIs on pollution vary by the country’s income level. However, their regressions being performed on separate samples of countries (developed and developing), the statistical significance of the difference in the estimated coefficients is not discussed. Moreover, the country’s income alone does not allow discussing the potential pollution halo or pollution haven FDI-induced effects. Theory driven variables conditioning FDI’s effect on pollution, like environmental regulation, relative factor endowments, human capital, etc. should be closer investigated. Considering them as country-specific characteristics captured through country-fixed effects should bias results and lead to misleading or partial conclusions. In this study, we’ll show that different (even opposite) FDI effects on pollution may be found for countries in the same revenue country group, conditional to their specific characteristics in terms of factor endowments, environmental regulation and human capital.

  6. To avoid ambiguity and misunderstanding, we make distinction between the terms (i) ‘pollution haven hypothesis’, (ii) ‘pollution haven effect’ and (iii) ‘pollution haven induced effect’ (or FDI’s impact on pollution induced by the pollution haven hypothesis/effect). As regards the two first terms, the common view in the related literature is that the pollution haven hypothesis is a stronger version of the pollution haven effect. According to Copeland and Taylor (2004), the pollution haven effect states that differences in environmental regulation influence, at the margin and along with other factors, the firms’ location decisions, while the pollution haven hypothesis predicts that under free trade, environmental regulation acts as the prevailing determinant factor in the location decision of polluting firms. Since in this study we do not investigate MNCs’ behaviour and hence the factors influencing their location decisions (e.g., ‘pollution haven hypothesis/effect’) but only their direct impact on pollution once established, we’ll employ the expression of ‘pollution haven induced effect’ to differentiate the environmental impact of foreign firms looking for environmental regulation comparative advantages.

  7. Also called “effect-modifier” or moderation model.

  8. See also Mani and Wheeler (1998), Cole and Elliott (2003b, 2005), Feridun et al. (2006), Managi et al. (2009), for evidence linking capital intensity and pollution intensity.

  9. We should note that in the related literature, if a statistically significant effect on pollution is found for the K/L variable, its sign varies from one study to another (in particular depending on the country sample used, the pollutant and period considered and the estimation technique applied): e.g., positive effect in Antweiler et al. (2001), Cole and Elliott (2003b); negative effect in Feridun et al. (2006), Dinda et al. (2000), He (2009, 2010); non-linear effect in Dinda (2006), Managi et al. (2009). While explaining energy use, \(\hbox {CO}_{2}\) and \(\hbox {SO}_{2}\) emissions, Managi (2011) discusses the positive increasing slope for capital-to-labour ratio in the middle zone of K/L, and a declining (even negative) slope for the extreme values of the relative capital endowments. According to the author, high and low capital-to-labour ratios may capture a technique effect (e.g., the use of wood as fuel in the least capital intensive sectors). While explaining a similar finding, Dinda et al. (2000) show that the more industrialized an economy is, the lower and flatter its pollution–per capita income level curve (or EKC) would be. For all the coefficients of the composition effect, measured by K/L ratio, He (2010) shows also negative values even after being instrumented by trade intensity variables, or using totally different econometrical strategies (He 2009). For the factor endowment hypothesis, He and Fu (2011) also use K/L ratio as a representative proxy, suggesting that K/L can likewise be a measurement for a sector’s technology advancement: i.e., “for a given sector, the dynamic increase in capital intensity actually measures the progress in production technology [which, according to the authors, is true for both capital- and labour- intensive sectors] instead of a measurement of factor endowment intensity”.

  10. Indeed, our log-log model shall capture changes in pollution corresponding to a change in factor(s) explaining pollution intensity, like production techniques and/or management practices.

  11. See Jaffe et al. (1995) for a comprehensive literature review on environmental quality linked to ‘autonomous’ and ‘induced’ technological changes.

  12. The robustness of our results based on a composite index of environmental stringency is checked in the last sections by using the variable Energy efficiency (Eeff) as a proxy much closer to the theoretical variable, i.e., emission intensity.

  13. We can thus expect a negative sign for \(\hbox {FDI} \times \hbox {K/L}\) interaction term, in contrast to a positive sign predicted by FDI-induced composition effects’ theories.

  14. The expected negative sign for \(\hbox {FDI} \times \hbox {ER}\) interaction term should state for a pollution haven effect in countries lowering environmental legal requirements and, reciprocally, a pollution halo effect in countries that are improving their environmental regulation, due to available (less costly) cleaner technologies and/or production practices.

  15. We might expect the K/L, ER, and Educ variables to introduce some collinearity issues in our regressions, since they are all, in some sense, measures of development. A first look at their partial correlations, as well as at pair correlations of their coefficients in our regressions, does not indicate for potential multicollinearity problems (partial correlations \(<\)0.52). The variance inflation factor (VIF) of each of these variables is \(<\)2.5 (which corresponds to an \(\hbox {R}^{2}\) less that 0.60 with the other variables); we should mention that a commonly given rule is that VIFs of 10 or higher may be reason for concern. Hence, the multicollinearity would not be a real problem in our regressions (moreover, as we’ll see in the next sections, modifications like: considering different pollutants, country samples, including/excluding some variables or interaction terms, do not produce big shifts in the coefficients in the models regressed using the same estimation technique).

  16. One could generally assume an effect of FDI on pollution conditional to GDP (more particularly to GDP/cap), as suggested by the Environmental Kuznets Curve. But, once pollution havens, factor endowments and pollution halo hypotheses are properly taken into account, no other theory suggests existence of such a specific FDI effect. With “zero” effects conditional to capital abundance, environmental regulation (a particular element of the general institutional quality), and human capital in the host country, the FDI variable alone is meant to capture a direct and linear scale effect on pollution.

  17. In particular, Ben Kheder and Zugravu (2012) show that French FDIs are attracted by lax environmental regulations in the developed, emerging, and most Central and Eastern European (CEE) countries; in contrast, the pollution haven hypothesis was rejected for most Commonwealth of Independent States (CIS) and other developing countries included in their sample, where too lax environmental regulations seem rather to deter French firms. Although our study also addresses issues linked to the pollution haven hypothesis, among others, it is different from Ben Kheder and Zugravu (2012) by estimating the FDIs’ direct and conditional effects on pollution, while Ben Kheder and Zugravu (2012) investigate only French firms’ location choice with regard to the stringency of the environmental regulation. The studies are thus complementary in their empirical findings.

  18. These tests’ statistics are reported in Tables 1 and 9.

  19. It is worth to be mentioned that an indirect effect of FDI on pollution could pass through the stringency of environmental regulations, as suggested by Cole et al. (2006). This is not of high concern here since we investigate only the direct effect of FDI on pollution and any conditionality between FDI’s and ER’s effects should be captured by their interaction terms, discussed in Sect. 3.2.

  20. We keep the same country classification: ‘TrCEEC’ for CEE countries in transition, ‘TrCIS’ for transition economies of CIS, ‘Developed’, ‘Emerging’ and ‘Developing’ for developed, emerging and developing countries, respectively (see Table 4 in “Appendix 1” for definitions and data sources).

  21. We note that the negative sign of the interaction term [2] may also be interpreted as a validation of the pollution haven effect: i.e., expanding manufacturing FDIs, associated with lax environmental regulations (decreasing ER variable), appear to be harmful for the environment.

  22. This finding attests for the FDI-induced composition effect, linking positively pollution intensity to capital intensity (as suggested by Antweiler et al. 2001) in countries with relatively well-designed environmental policy and increasing FDI stock.

  23. As manufacturing FDIs require more skilled workforce to successfully implement capital-intensive plants, we use secondary and tertiary education rates (% in total labour) as proxies for the domestic labour’s qualification (see Fung et al. 2002 for the same proxy used). For reasons of data coverage across countries and time period, we use secondary education in models evaluating impact on pollution of French firms’ location between 1996 and 2002 (Sects. 3.13.3), and tertiary education in models considering more recent FDI flows from a selection of developed countries (Sect. 3.4). Estimating in the last section the impact of more recent French FDI monetary outflows conditional to tertiary education of local labour allows at the same time testing for the robustness of our reference empirical model, which is estimating the impact on pollution of French firms’ establishments in the late 1990s and the early 2000s, conditional to secondary education.

  24. Several debates exist on the calculation methods and the estimated trends for \(\hbox {SO}_{2}\) and \(\hbox {NO}_\mathrm{x}\) series (e.g., Klimont et al. 2013). Regarding BOD series, it should be noted that data by country/sector are generally poor or missing.

  25. For instance, Coughlin and Segev (2000) suggest that labour quality (productivity), proxied by education variable, is a positive determinant of FDI, while the rate of literacy is negative (see also Zhang and Markusen 1999; Hejazi 2009; Martinez et al. 2012).

  26. Note that in our case, the variables K/L, ER and Educ cannot take the value ‘0’. For more clarity in the interpretation, one could center these variables to their mean. Nevertheless, we can work with non- normalized variables, provided that caution is taken in their interpretation.

  27. See Table 6 (“Appendix 3”) for marginal effects computed at the average levels of our moderators, which are not displayed in the Fig. 1.

  28. In other words, the pollution haven effect seems to be partly offset by the pollution halo effect.

  29. VIF values greater than 10, or a tolerance (1/VIF) below 0.05, should warrant further examination. In our regressions, though, VIF suggest collinearity only for interaction terms, which is not unexpected and should probably not cause a problem for our analysis.

  30. See Table 4 in “Appendix 1” for data definitions and sources.

  31. Following the Hausman test statistics in Table 9, “Appendix 4”, the between-effects estimators appear to be more consistent than the fixed-effects estimators, eventually because of highly unbalanced panel data used in this section.

  32. The same models’ estimations with fixed-effects (‘1a’ models, Table 9 in “Appendix 4”) give results very comparable with those found in the previous sections, i.e., K/L with negative sign.

  33. That is, on a macro-economic level, stringent environmental regulations are supposed to reduce pollution through energy/pollution efficiency measures. At the same time, improved efficiency is usually believed to lead to lower prices and thus more demand, the later contributing further to increased levels of pollution. To avoid capturing rebound effects, one should investigate manufacturing pollution intensity (not emission levels) as the dependent variable. Again, we are limited to data availability across countries in the world. Moreover, we should mention that the investigation of rebound effects requires development of a more sophisticated empirical model allowing for indirect effects, which is not the objective of this study.

  34. We use formula \(z=\left( {\beta _{1} -\beta _{2} } \right) /\sqrt{\left( {se_{\beta 1} } \right) ^{2}+\left( {se_{\beta 2} } \right) ^{2}}\), with \(\beta _{1}\) and \(\beta _{2}\)—the coefficients of the same variable/interaction term from different models; more specifically, we compare coefficients from Sweden, Germany and the UK (\(\beta _{2}\)) with those found for France (\(\beta _{1}\)).

  35. This kind of transformation is not suitable if the concern is about dependent variables (which is not the case here) and would be controversial while dealing with predictors for which we don’t know whether 0 means “true zero”/“so small that it could not be detected” or “data is missing”. While in the former case adding a small (but not too small) number to the 0 values of the independent variables could be justified and thus considered as an acceptable modification, then in the later such transformation should be criticised, requiring more specific treatments to be applied (e.g., data categorization). In our study, we are more or less confident with the data treatment before log-transformation, because 0 values do not indicate for missing values but for a true zero (or too small to be detected value): i.e., \(\hbox {FDI}_{{\mathrm{FR}}}\) [nb of firms] comes from an exhaustive dataset of French firms established abroad, “zero” meaning no registered firm in a particular country; Eurostat reports separately missing observations and zero/negative values for our \(\hbox {FDI}_{{\mathrm{FR-UK}}}\) [M€] variables.



Central and Eastern Europe


Central and Eastern European Countries


Commonwealth of Independent States


Environmental regulation (index)


Foreign direct investment


Multilateral environmental agreement


Multinational corporations


Non-governmental organization


Principal component analysis


Variance inflation factors


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Correspondence to Natalia Zugravu-Soilita.

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I would like to thank the two anonymous referees from the Journal for very helpful comments and suggestions that have significantly improve this paper. I am also grateful to the participants in the CEMOTEV (UVSQ), UPEC, PSE seminars, the 7th Annual International Conference on Business and Society in a Global Economy for their valuable comments on the previous versions of the paper.


Appendix 1: Data Definition and Sources

Table 4 Data definition and sources

Appendix 2: Data Summary

Table 5 General descriptive statistics (variables in level)

Appendix 3: FDI Marginal Effects Depending on the Moderators’ Value

Table 6 FDI marginal effect (Delta method), \(\hbox {d}ln(CO_{2})/\hbox {d}ln(FDI)\) for different values of ln(K / L), ln(ER), ln(Educ)

Appendix 4: Robustness Tests and Extended Empirical Analysis

Table 7 Robustness tests with alternative estimation technique and control variables
Table 8 Robustness tests with alternative proxies for ER variable
Table 9 Model selection for the impact on industrial \(\hbox {CO}_{2}\) emissions of FDIs from different developed countries
Table 10 Impact on industrial \(\hbox {CO}_{2}\) emissions of FDIs from different developed countries
Table 11 Impact on industrial \(\hbox {SO}_{2}\) emissions of FDIs from different developed countries
Table 12 Impact on industrial \(\hbox {NO}_{x}\) emissions of FDIs from different developed countries
Table 13 Impact on industrial BOD emissions of FDIs from different developed countries

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Zugravu-Soilita, N. How does Foreign Direct Investment Affect Pollution? Toward a Better Understanding of the Direct and Conditional Effects. Environ Resource Econ 66, 293–338 (2017).

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  • Environmental regulation
  • FDI
  • Industrial air and water pollution
  • Pollution halo
  • Pollution haven

JEL Classification

  • F18
  • F23
  • Q53
  • Q56