This paper focuses on the analysis of the impact that FDI has on pollution. In this study, countries are divided into their income levels as the transference of polluting industries happens mainly between countries with different development and income levels. The ARDL model provides a useful disaggregation of the impacts, making it possible to better understand the impacts extended in time. This paper contributes to the enlargement of the literature about FDI-environment nexus with empirical evidence of a linkage between variables that could change impacts across time. Besides that, this paper innovates by considering factors as regulation, innovation, and efficiency levels in the countries under scrutiny. Furthermore, the discussion about overall emissions and those from the industry sector provides robust support for PHH in middle-income countries, while high-income countries benefit from FDI phenomenon. The technology absorptive capacity of the middle-income countries plays a critical role in analysing the impacts of FDI, although the positive effect of FDI in overall emissions happens slowly. The countries in this study are facing a trade-off between FDI and meeting pollution reduction targets. In this paper, policymakers gain useful guidance to help them understand that it is possible to increase the income of a country through the inflow of FDI while at the same time preserving the environment.
Having this in mind, is it crucial to establish a stable legal structure, since regulation has an important role in this theme.
Regulation can shift attitudes, encouraging investment in R&D, and increasing RES use. Given the unexpected result that regulation shows in high-income countries, these countries must combine different policy tools to reach the goal of decreased emissions. For instance, not only regulation in the form of fees and taxes but also subsidies are recommended. The incorporation of subsidies for efficient firms with a high innovation level and, highly qualified workers, for example, could encourage investment in R&D. These subsidies must reward the investment that companies are obliged to carry out. The creation of direct subsidies for researchers and the foundation of research centers should be strongly recommended. Increasing the human capital level of countries will increase their environmental awareness. An increase in the use of RES is also required. However, given that renewable energies present higher costs than fossil fuels, policymakers should introduce policies to increase the competitiveness of RES, decreasing investment costs.
Increasing the stringency of the environmental laws of the countries under analysis is required. If their human capital level escalates, that will be reflected in more innovation and efficiency. Furthermore, middle-income countries must improve their evaluation criteria for FDI quality, and to strengthen their attractiveness for the entry of new multinational enterprises, enterprises that could transfer their advanced and eco-friendly technologies and efficient management skills. Recipient countries must absorb these
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technologies to change their industrial structure. Co-operation between countries has high applicability to guarantee the transference of knowledge and efficiency. It is also important to remember that corruption is a severe concern that is difficult to control. Policymakers from high-income countries must increase the stringency of outflow FDI, controlling all external headquarters of domestic firms, checking that these companies are investing in innovation and transferring their knowledge, and not avoiding environmental compliance costs and just relocating their emissions. With this co-operation and the combination of different policy tools mutually, high-income countries would be discouraged from transferring their polluting industries, and middle-income countries would not accept their entrance.
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Table A3. Correlation matrices and VIF statistics (high-income - industry sector)
LCO2I LFDI LY LE LTO LPAT LREG DLCO2I DLFDI DLY DLE DLTO DLPAT DLREG
CD-test 22.34*** 29.06*** 10.21*** 17.02*** 25.91*** 4.10*** 5.49*** 9.552*** 16.816*** 14.552*** 0.487 22.29*** 0.755 3.789***
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Table A4. Correlation matrices and VIF statistics (middle-income - industry sector)
LCO2I LFDI LY LE LTO LPAT LREG DLCO2I DLFDI DLY DLE DLTO DLPAT DLREG
LCO2I 1.000 DLCO2I 1.000
LFDI 0.523 1.000 DLFDI 0.217 1.000
LY 0.665 0.793 1.000 DLY 0.278 0.306 1.000
LE -0.653 -0.214 -0.076 1.000 DLE -0.092 -0.102 0.004 1.000
LTO -0.153 0.197 0.022 0.016 1.000 DLTO 0.071 -0.221 0.058 0.335 1.000
LPAT 0.753 0.616 0.769 -0.383 0.230 1.000 DLPAT 0.105 -0.002 0.076 -0.009 0.056 1.000
LREG 0.783 0.558 0.725 -0.387 0.183 0.787 1.000 DLREG 0.015 0.080 -0.138 0.030 0.127 -0.105 1.000
VIF 3.71 8.55 1.99 1.51 4.50 3.47 1.19 1.16 1.14 1.21 1.02 1.06
VIFMEAN 3.96 1.13
Cross-sectional dependence
CD-test 5.057*** 17.545*** 15.859*** 4.56*** 2.413** -2.407** 2.538** 1.886* 5.209*** 6.718*** -0.261 8.943*** -1.127 2.994***
Table A5. Diagnostic tests (industry sector)
High-income Middle-income
Robust Hausman test 134.02*** 34.59***
Wooldridge test 18.626*** 31.100***
Breusch Pagan LM 106.085** 41.991**
Modified Wald test 321.73*** 224.62***
Notes: *** , ** denote significance at 1%, 5% level, respectively. The null hypothesis for the Robust Hausman test is “difference in coefficients is not systematic”.