Skip to main content

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

Environmental and Resource Economics volume 66, pages 293–338 (2017)Cite this article

Abstract

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.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1

Notes

  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. 3.1–3.3), 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.

Abbreviations

CEE:

Central and Eastern Europe

CEECs:

Central and Eastern European Countries

CIS:

Commonwealth of Independent States

ER:

Environmental regulation (index)

FDI:

Foreign direct investment

MEA:

Multilateral environmental agreement

MNCs:

Multinational corporations

NGO:

Non-governmental organization

PCA:

Principal component analysis

VIF:

Variance inflation factors

References

  • Albornoz F, Cole MA, Elliott RJR, Ercolani MG (2009) In search of environmental spillovers. World Econ 32(1):136–163

  • Antweiler W, Copeland BR, Taylor MS (2001) Is free trade good for the environment? Am Econ Rev 91(4):877–908

    Article  Google Scholar 

  • Arimura T, Hibiki A, Johnstone N (2007) Empirical study of environmental R&D: what encourages facilities to be environmentally innovative? In: Johnstone N (ed) Corporate behaviour and environmental policy. Edward Elgar, OECD/Cheltenham, Paris. http://www.e-elgar.co.uk/Bookentry_contents.lasso?id=12551)

  • Asghari M (2013) Does FDI promote MENA region’s environment quality? Pollution halo or pollution haven hypothesis. Int J Sci Res Environ Sci 1(6):92–100

    Google Scholar 

  • Ben Kheder S, Zugravu N (2012) Environmental regulation and french firm location abroad: an economic geography model in an international comparative study. Ecol Econ 77:48–61

    Article  Google Scholar 

  • Birdsall N, Wheeler D (1993) Trade policy and industrial pollution in Latin America: where are the pollution havens? J Environ Dev 2(1):137–147

    Article  Google Scholar 

  • Blanco LR, Gonzalez F, Ruiz I (2013) The impact of FDI on \({\text{ CO }_2}\) emissions in latin America. Oxf Dev Stud 14(1):104–121

    Article  Google Scholar 

  • Brunnermeier S, Levinson A (2004) Examining the evidence on environmental regulations and industry location. J Environ Dev 13:6–41

    Article  Google Scholar 

  • Cao X, Prakash A (2012) Trade competition and environmental regulations: domestic political constraints and issue visibility. J Polit 74(1):66–82

  • Cole MA, Elliott RJR (2001) Factor endowments or environmental regulations? Determining the trade-environment composition effect. University of Birmingham Department of Economics Discussion Paper no. 01-06

  • Cole MA, Elliott RJR (2003a) Do environmental regulations influence trade patterns? Testing old and new trade theories. World Econ 26(8):1163–1186

    Article  Google Scholar 

  • Cole MA, Elliott RJR (2003b) Determining the trade-environment composition effect: the role of capital, labor and environmental regulations. J Environ Econ Manag 46(3):363–383

    Article  Google Scholar 

  • Cole MA, Elliott RJR (2005) FDI and the capital intensity of ‘dirty’ sectors: a missing piece of the pollution haven puzzle. Rev Dev Econ 9:530–548

    Article  Google Scholar 

  • Cole MA, Elliott RJR, Fredriksson PG (2006) Endogenous pollution havens: does FDI influence environmental regulations? Scand J Econ 108(1):157–178

    Article  Google Scholar 

  • Cole MA, Elliott RJR, Strobl E (2008) The environmental performance of firms: the role of foreign ownership, training, and experience. Ecol Econ 65(3):538–546

    Article  Google Scholar 

  • Copeland BR, Taylor MS (2004) Trade, growth and the environment. J Econ Lit 42(1):7–71

    Article  Google Scholar 

  • Coughlin CC, Segev E (2000) Foreign direct investment in China: a spatial econometric study. World Econ 23(1):1–23

    Article  Google Scholar 

  • Dam L, Scholtens B (2012) The curse of the haven: the impact of multinational enterprise on environmental regulation. Ecol Econ 78:148–156

    Article  Google Scholar 

  • Damania R, Fredriksson PG, List J (2003) Trade liberalization, corruption, and environmental policy formation: theory and evidence. J Environ Econ Manag 46(3):490–512

    Article  Google Scholar 

  • Dardati E, Saygili M (2012) Multinationals and environmental regulation: are foreign firms harmful? Environ Dev Econ 17:163–186

    Article  Google Scholar 

  • Dasgupta S, Mody A, Roy S, Wheeler D (2001) Environmental regulation and development: a cross-country empirical analysis. Oxf Dev Stud 29(2):173–187

    Article  Google Scholar 

  • Dean JM, Lovely ME, Wang H (2009) Are foreign investors attracted to weak environmental regulations? Evaluating the evidence from China. J Dev Econ 90(1):1–13

    Article  Google Scholar 

  • Dinda S, Coondoo D, Pal M (2000) Air quality and economic growth: an empirical study. Ecol Econ 34:409–423

    Article  Google Scholar 

  • Dinda S (2006) Globalization and environment: can pollution haven hypothesis alone explain the impact of globalization on environment? In: MPRA paper, no 50590

  • Elliott RJR, Zhou Y (2013) Environmental regulation induced foreign direct investment. Environ Resour Econ 55(1):141–158

    Article  Google Scholar 

  • Eskeland GS, Harrison AE (2003) Moving to greener pastures multinationals and the pollution haven hypothesis. J Dev Econ 70(1):1–23

    Article  Google Scholar 

  • Feridun M, Sunday Ayadi F, Balouga J (2006) Impact of trade liberalization on the environment in developing countries: the case of Nigeria. J Dev Soc 22(1):39–56

    Google Scholar 

  • Fredriksson PG, Millimet D (2004) Electoral rules and environmental policy. Econ Lett 84(2):237–244

    Article  Google Scholar 

  • Fung KC, Lizaka H, Parker S (2002) Determinants of U.S. and Japanese direct investment in China. J Comp Econ 30:567–578

    Article  Google Scholar 

  • Gallagher K, Zarsky L (2007) The enclave economy: foreign investment and sustainable development in Mexico’s Silicon Valley. MIT Press, Cambridge

    Google Scholar 

  • Grossman GM (1995) Pollution and growth: what do we know? In: Goldin Winter (ed) The economics of sustainable development. Cambridge University Press, Cambridge

    Google Scholar 

  • Grossman GM, Krueger AB (1993) Environmental impacts of a North American free trade agreement. In: Garber PM (ed) The Mexico–US free trade agreement. MIT Press, Cambridge

    Chapter  Google Scholar 

  • Grossman GM, Krueger AB (1995) Economic growth and the environment. Q J Econ 110(2):353–377

    Article  Google Scholar 

  • Harris M, Konya L, Matyas L (2003) Modelling the impact of environmental regulations on bilateral trade flows: OECD, 1990–1996. World Econ 25(3):387–405

    Article  Google Scholar 

  • He J (2006) Pollution haven hypothesis and environmental impacts of foreign direct investment: the case of industrial emissions of sulphur dioxide (\({\text{ SO }_2}\)) in Chinese provinces \({\text{ SO }_2}\). Ecol Econ 60:228–245

    Article  Google Scholar 

  • He J (2009) China’s industrial \({\text{ SO }_2}\) emissions and its economic determinants: EKC’s reduced vs. structural model and the role of international trade. Environ. Dev Econ 14(2):227–262

    Article  Google Scholar 

  • He J (2010) What is the role of openness for China’s aggregate industrial \({\text{ SO }_2}\) emission?: A structural analysis based on the Divisia decomposition method. Ecol Econ 69(4):868–886

    Article  Google Scholar 

  • He J, Fu J (2011) Is «pollution haven» hypothesis valid for China’s manufacture sectors? An empirical analysis based on carbon embodied in trade, Cahiers de recherche, Departement d’Economique de la Faculte d’administration à l’Universite de Sherbrooke. http://EconPapers.repec.org/RePEc:shr:wpaper:11-12

  • Hejazi W (2009) Does China receive more regional FDI than gravity would suggest? Eur Manag J 27:327–335

    Article  Google Scholar 

  • Hilton F, Levinson A (1998) Factoring the environmental Kuznets curve: evidence from automotive lead emissions. J Environ Econ Manag 35(2):126–141

    Article  Google Scholar 

  • Jaffe AB, Peterson SR, Portney PR, Stavins RN (1995) Environmental regulation and the competitiveness of U.S. manufacturing: what does the evidence tell us? J Econ Lit 33(1):132–163

    Google Scholar 

  • Johnstone N (2007) Environmental policy and corporate behaviour. Edward Elgar Publishing, Northampton

    Book  Google Scholar 

  • Johnstone N, Hai I, Popp D (2010) Renewable energy policies and technological innovation: evidence based on patent counts. Environ Resour Econ 45:133–155

    Article  Google Scholar 

  • Kellenberg D (2009) An empirical investigation of the pollution haven effect with strategic environment and trade policy. J Int Econ 78(2):242–255

    Article  Google Scholar 

  • Keller W, Levinson A (2001) Pollution abatement costs and foreign direct investment inflow to U.S. states. Rev Econ Stat 84:691–703

    Article  Google Scholar 

  • Kim MH, Adilov N (2012) The lesser of two evils: an empirical investigation of foreign direct investment-pollution tradeoff. Appl Econ 44:2597–2606

    Article  Google Scholar 

  • Klimont Z, Smith SJ, Cofala J (2013) The last decade of global anthropogenic sulfur dioxide: 2000–2011 emissions. Environ Res Lett 8(1):014003. doi:10.1088/1748-9326/8/1/014003

  • Lan J, Kakinaka M, Huang X (2012) Foreign direct investment, human capital and environmental pollution in China. Environ Resour Econ 51(2):255–275

    Article  Google Scholar 

  • Levinson A (2008) Pollution haven hypothesis. In: Steven ND, Lawrence EB (eds) The New Palgrave Dictionary of Economics, 2nd edn. Palgrave Macmillan, London. 14 August 2015. doi:10.1057/9780230226203.1302

  • Managi S, Hibiki A, Tsurumi T (2009) Does trade openness improve environmental quality? J Environ Econ Manag 58(3):346–363

    Article  Google Scholar 

  • Managi S (2011) Technology, natural resources and economic growth: improving the environment for a greener future. new horizons in environmental economics series. Edward Elgar Publishing, Cheltenham

  • Manderson E, Kneller R (2012) Environmental regulations, outward FDI and heterogeneous firms: are countries used as pollution havens? Environ Resour Econ 51(3):317–352

    Article  Google Scholar 

  • Mani M, Wheeler D (1998) In search of pollution havens? Dirty industry in the world economy, 1960–1995. J Environ Dev 7(3):215–247

    Article  Google Scholar 

  • Martinez V, Bengoa M, Sanchez-Robles B (2012) Foreign direct investment and trade: complements or substitutes? Empirical evidence for the European Union. Technol Invest 3:105–112

    Article  Google Scholar 

  • Millimet DL, Roy J (2012) Three new tests of the pollution haven hypothesis when environmental regulation is endogenous. In: IZA discussion paper #5911

  • Mulatu A, Gerlagh R, Rigby D, Wossink A (2010) Environmental regulation and industry location in Europe. Environ Resour Econ 45(4):459–479

    Article  Google Scholar 

  • Nesta L, Vona F, Nicolli F (2012) Environmental policies, product market regulation and innovation in renewable energy. In: FEEM working paper no. 90.2012

  • Prakash A, Potoski M (2006) New dependencies: FDI and the cross-country diffusion of ISO 14001 management systems. In: Tuck School of Business, Dartmouth College

  • Rezza AA (2013) FDI and pollution havens: evidence from the Norwegian manufacturing sector. Ecol Econ 90:140–149

    Article  Google Scholar 

  • Smarzynska JB, Wei SJ (2004) Pollution havens and foreign direct investment: dirty secret or popular myth? Contrib Econ Anal Policy 3(2):1–32

    Google Scholar 

  • Tang J (2015) Testing the pollution haven effect: does the type of FDI matter? Environ Resour Econ 60(4):549–578

    Article  Google Scholar 

  • UNCTAD (2013) World investment report: global value-chains: investment and trade for development. United Nations Publication. ISBN: 978-92-1-112868-0

  • Wagner UJ, Timmins CD (2009) Agglomeration effects in Foreign direct investment and the pollution haven hypothesis. Environ Resour Econ 43(2):231–256

    Article  Google Scholar 

  • Waldkirch A, Gopinath M (2008) Pollution control and foreign direct investment in Mexico: an industry-level analysis. Environ Resour Econ 41(3):289–313

    Article  Google Scholar 

  • Zarsky L (1999) Havens, halos and spaghetti: untangling the relationship between FDI and the environment. Foreign direct investment and the environment. OECD, Paris, pp 47–73

    Google Scholar 

  • Zhang K, Markusen J (1999) Vertical multinationals and hostcountry characteristics. J Dev Econ 59:233–253

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UFR des Sciences Sociales / CEMOTEV, University of Versailles Saint-Quentin-en-Yvelines, 47, Bd Vauban, 78280, Guyancourt, France

    Natalia Zugravu-Soilita

Authors
  1. Natalia Zugravu-Soilita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natalia Zugravu-Soilita.

Additional information

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.

Appendices

Appendix 1: Data Definition and Sources

Table 4 Data definition and sources
Full size table

Appendix 2: Data Summary

Table 5 General descriptive statistics (variables in level)
Full size table

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)
Full size table

Appendix 4: Robustness Tests and Extended Empirical Analysis

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1007/s10640-015-9950-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10640-015-9950-9

Keywords

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

JEL Classification

  • F18
  • F23
  • Q53
  • Q56