An Empirical Analysis of the Pollution Haven Hypothesis for India

An Empirical Analysis of the Pollution Haven Hypothesis for India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Empirical Analysis of the Pollution Haven Hypothesis for India Sharmiladev J. C. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9343604/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose : The objective of this paper is to identify the presence of the Pollution Haven Hypothesis for India for the time period 1990 to 2023, considering cointegration equilibrium and causality relations, considering the foreign direct investment flow, agglomeration, market size, exports, natural resources, labour productivity, inflation, control of corruption and carbon dioxide emission. Methodology & Findings : The ARDL model was found to be a suitable fit for the study. Bounds test results confirmed the presence of a long-term cointegration relation, as did the Engle-Granger and Phillips-Ouliaris statistics. The stock of foreign direct investment is showing bi-directional causality with exports and corruption. The long-term and short-term results of the ARDL model, as well as the causality results, suggest that foreign direct investment is associated with increased carbon dioxide emissions. The Impulse Response function indicates that the foreign direct investment flow exhibits asymmetric responses to the independent variables. Originality : The study confirms the pollution haven hypothesis for India. Reliability and stability diagnostics are found to be significant. Implications : This research also suggests economic and environmental policy implications based on the study outcomes. JEL Classification Code : Q54, Q57, Q58, O11 foreign direct investment economic growth pollution carbon dioxide emission autoregressive distributed lag international trade Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Foreign direct investment (FDI) can be an effective tool to achieve the United Nation’s Sustainable Development Goals 8, 9 and 13 on economic growth, innovation development and climate action to combat climate change and its impacts (McIntyre et al., 2022 ; Stephenson et al., 2021 ; Van Tulder et al., 2021 ). This necessitates understanding the holistic impact of inward FDI into host economies to better recognize the determining and deterring factors, especially when FDI flows from developed to developing and emerging economies. FDI environment relationship includes i) environmental effects of FDI flows, (ii) competition for FDI and its outcomes on environmental standards, and (iii) cross-border environmental performance (Pazienza, 2015 ). Academic research remains unsettled regarding the outcomes of this phenomenon (Erdogan, 2014 ; Pazienza, 2015 ). Due to international integration and factor mobility, FDI can impact the environment through the scale effect - moving from a small to global scale, technique effect - adoption of cleaner technology and the composition effect - a shift in preferences to cleaner products and greater environmental protections with increased income (Kathuria, 2008 ; Pazienza, 2015 ). Considering strict environmental regulations and compliances in developed countries, multinational enterprises’ (MNEs), location decisions are tilted towards developing and poor nations, and the FDI outcomes of such movements are ambiguous and controversial and gave forth to the concept of Pollution Haven Hypothesis (PHH) (Dunning & Lundan, 2008 ; Taylor, 2005 ; Dörrenbächer & Gammelgaard, 2019 ; Copeland & Taylor, 2004 ; Fetscherin et al, 2010 ; Paul & Benito, 2018 ; Hoffmann et al., 2005 , Becker-Ritterspach et al, 2019 ; Yu et al, 2023 ). The internationalisation models followed by MNEs are mostly by acquiring intangible assets and optimisation of the resources of host country firms, which gives them the ability to establish as global competitors, enabling commercial operations in multiple countries through subsidiaries, associates and branches leading to many strategic activities, partnerships, business development and innovations (Bartlett & Ghoshal, 2002 ; Nohria & Ghoshal, 1997 ; Acharyya, 2009 ). The PHH argues that strict environmental regulations in developed countries raise firms' costs, encouraging pollution-intensive firms to relocate to developing economies with laxer standards. To attract FDI, developing countries often weaken environmental regulations, intensifying competition and potentially triggering a “race to the bottom” (Lee et al., 2014 ; Naughton, 2014 ; Grimes et al, 2003). Understanding PHH using recent data can clarify the implications of FDI for developing economies. Prior studies highlight conflicting views on FDI’s environmental impacts in host economies (Krugman et al., 2000; Eskeland & Harrison, 2003 ; Zhang & Zhou, 2016; Zhang et al, 2017 ). While FDI may transfer cleaner technologies and enhance environmental quality under the Pollution Halo Hypothesis (Dörrenbächer et al., 2024 ), its joint effects on economic growth and the environment remain contested, largely depending on appropriate host-country policies (Lucas, 1988; Romer, 1990 ; Fetscherin et al., 2010 ; Clunies-Ross et al., 2009 ). This study examines the presence of PHH in India, a fast-growing economy (6.5% in 2024) with substantial FDI inflows (USD 28.08 billion in 2023) and high per-capita CO₂ emissions (2.13 tons). Given India’s position among major emitters, as shown in Table I, understanding the FDI–growth–environment nexus is crucial, especially given the mixed evidence on PHH for India. Table I: CO2 Profiles Income Groups CO2 Emission Per Capita Countries CO2 Emission Per Capita High-Income Countries 9.93 United States 14.3 Upper Middle-Income Countries 6.16 United Kingdom 4.41 Lower Middle-Income Countries 1.58 China 8.37 Lower-Income Countries 0.27 India 2.13 Source: Our World in Data India has progressively liberalised its FDI regime, allowing most investments to proceed through the automatic route, while sustaining strong economic growth (6–7%), trade openness, and rising FDI inflows, supported by a young and skilled workforce (Bajpai & Dasgupta, 2004 ). However, despite its economic progress, India faces environmental challenges and has committed to significant emission reductions and sustainable development, reaffirmed through international forums (UNFCCC, 2022 ) and the 2025 BRICS summit (MOE, 2025 ). This study presents an evidence-based trade-off for the tripartite relationship among international trade, the environment, and economic growth, leading to CO2 emissions that pose environmental challenges and question the growth outcomes. India’s experience can serve as a valuable addition to other emerging economies seeking to achieve their economic and sustainable development goals through foreign investment. The study's outcomes can provide additional insights into the interactions among economic growth, international trade, and the environment, and contribute to policy discussions on balancing these outcomes by offering empirical evidence and recommendations for achieving sustainable development. If the existence of PHH is confirmed, it will re-establish the fact that enhancing international trade, led by FDI flows, can be one of the reasons for pollution and environmental deterioration in India. This will require accepting FDI with greater caution and revising policy areas to enhance the quality of FDI that India receives. 2. Review of Literature This section provides an overview of theoretical frameworks, encompassing discussions on the triangular linkages between trade, the environment, and the FDI nexus. It also succinctly encapsulates the past empirical studies that explore the interconnections among FDI, trade, environment and economic growth. 2.1 Theoretical Frameworks The Environmental Kuznets Curve (EKC) hypothesis, the Pollution Haven Hypothesis (PHH), and the Pollution Halo Hypothesis (PHH) collectively indicate the relationship between economic growth and the environment. The EKC hypothesis posits an inverse relationship between economic growth and environmental degradation within an economy, as environmental degradation is initially more severe because economic development is prioritised in the early stages (Grossman & Krueger, 1991 ). This tendency will continue until the country reaches a threshold level of income, suggesting that economic growth can have both positive and negative impacts on the environment (Dinda, 2004 ). The origins of PHH and PH emerge from international capital movements realised by multinational enterprises, and both hypotheses offer opposing views on the role of international capital, particularly regarding FDI and its environmental outcomes in host countries. The refuting and opposing views regarding these two hypotheses have enriched the literature on international capital movement through FDI and its environmental implications. As India's economic growth is also driven by foreign capital and the country is one of the highest recipients of FDI in recent times, it calls for an examination of the relationship in areas such as macroeconomics, international trade, the environment, and emissions. The relationships among FDI, economic growth, exports, labour productivity, inflation, control of corruption, natural resources endowment and carbon dioxide emission for India hold significance for the country’s progress towards achieving economic growth, innovation, sustainable and low-carbon development, aligning with the United Nations SDGs, especially Goals 8, 9 and 13. The study outcomes can offer valuable insights for policies and actions aimed at achieving the SDGs related to economic growth, innovation & development, as well as climate change mitigation, considering the broader implications for India’s development agenda (United Nations Assembly, 2015 ). 2.2 Empirical Reviews Empirical studies support PHH due to relocation of firms to countries with weaker environmental regulations (Zugravu & Ben Kheder, 2008 ; Pao & Tsai, 2011 ). Environmental advancements achieved in developed countries are the result of shifting and relocating dirty and pollution-intensive production to developing nations with lax and weaker environmental standards and compliance, which also leads to compromised environmental and social standards to gain a comparative advantage, resulting in a race to the bottom and further environmental damage (Bu et al., 2013 ). Murthy & Gambhir’s ( 2017 ) study suggests that trade openness and international trade are realities of globalisation, resulting in MNEs moving across borders in search of greener pastures, which creates collective outcomes that have enhanced gross domestic product and international trade. Parallelly, this phenomenon also created environmental consequences, such as high CO2 emissions, since the focus areas of MNE activities are on harnessing urban and energy resources. Therefore, the authors argue that an understanding of the relationship between CO2 emissions and FDI can be gained through the PHH. Murthy & Gambhir ( 2018 ), using data from 1991 to 2014 for India, validate PHH in both aggregate and per capita terms, indicating that this phenomenon cannot be ignored, despite its minimal numerical impact. Rana & Sharma ( 2019 ) study confirms the presence of PHH, indicating that India imports more pollution-intensive manufacturing goods, and FDI is causing economic growth, but through CO2 emissions. Analysing 21 developed and developing countries with high carbon emissions, Singhania & Saini ( 2021 ) found evidence of PHH in developing countries. A recent study by Ozturk et al. ( 2024 ) for South Asian economies, including India, confirms the presence of PHH. Holtbrügge & Raghavan ( 2025 ) confirm PHH in Indian manufacturing and transportation industries, metallurgy and chemical sectors. In contrast to studies supporting PHH in India, the literature finds no evidence of its existence. Input–output analyses and index-based approaches indicate that India has shifted away from pollution-haven dynamics. Currently, FDI is being driven by infrastructure, market access, and factor endowments than by weak environmental regulations. Recent time-series evidence also fails to confirm PHH for India (Mukhopadhyay & Chakraborty, 2006 ; Dietzenbacher & Mukhopadhyay, 2007 ; Kathuria, 2018 ; Gogoi & Hussain, 2024 ). 3. Hypothesis Development and Variable Selection As PHH acts as an enabling factor for inward FDI, one needs to examine the factors responsible for attracting FDI into India to understand the presence/absence of PHH. Multiple theoretical and empirical models explain the occurrence of FDI, starting from Hymer ( 1968 , 1976 ), Kindleberger ( 1969 ), Buckley and Casson ( 1976 ), Vernon ( 1979 ), Dunning ( 1979 ). These theories create an overlap of many variables as determinants and attraction factors for FDI into India. The OLI (Ownership, Location and Internationalisation) paradigm of Dunning provides a more acceptable and comprehensive explanation for the reasons and directions for FDI. Also, the UNCTAD (1998) has classified the determinants of FDI into three categories: policy determinants, economic determinants, and business facilitation determinants. The scope of this paper is only restricted to the economic determinants, and these determinants also stem from Dunning’s motivations of market seeking, resource seeking, efficiency seeking and strategic asset seeking motives for attracting FDI (Dunning, 1979 ). As this paper is concerned only with India and the focus of this paper is restricted to understanding the motivations for inward FDI and its relation with economic growth, considering the Environmental Pollution Haven Hypothesis, the author narrows down the discussion of the literature concerning the same and conducts a discourse of the studies below. A list of selected variables with their proxies are mentioned in Table II Table II: Variables List Variables Used Proxies FDI net flows FDI net inflows Market growth Gross Domestic Product Per Capita Agglomeration Lagged FDI stocks, all stocks. Natural Resource Endowments Ores and metals exports (% of merchandise exports)+ Fuel exports (% of merchandise exports) Inflation Inflation, GDP deflator (annual %) Control of Corruption Governance Indicator Control of Corruption: Percentile Rank Labour Productivity GDP per hour worked Pollution Carbon dioxide emission Source: Authors Own Agglomeration As investors are always faced with uncertainties, due to volatility factors, they always look into the previous flow of investments across geographies. This concept of agglomeration stems from the philosophy of spillovers of FDI, such as knowledge, technology, and strategy. Lagged FDI stocks, all stocks are the proxies used (Krugman, 1997 ). Market growth and Openness to Trade These two variables form part of Duning’s market seeking motive, and these two variables are influential for FDI studies and are extensively researched across time ((Dunning, 2001 ; Lim, 1983 ; Kang & Jiang, 2012 ; Wadhwa & Reddy, 2011 ) Natural Resource Endowments This variable is part of Dunning’s resource-seeking motive. Natural resources act as an inviting factor for attracting FDI, ensuring cross-country intra-industry manufacturing for overseas affiliates. This variable is not considered in many studies, except for a few, such as Hassaballa ( 2014 ) and Miniesy & Tarek ( 2019 ). Ores & metals, and fuel exports, as a percentage of merchandise exports, are proxied in this case. Inflation The level of inflation reflects the macroeconomic stability of an economy, which is fundamental and crucial for any capital investments, and this is also true for FDI by MNEs, as inflation directly depends on profitability and stability (Singhania & Gupta, 2011 ). Control of Corruption Investment environment is directly related to institutional quality, and corruption is a deterrent factor for any capital investment, including FDI, as it creates uncertainties, reduces profit (Mathur & Singh, 2013 ; Hasan et al, 2017 ). Corruption prevents fair and efficient development of market conditions (Boatright, 2000 ). Control of Corruption is one of the four indicators, as mentioned by the World Bank governance indicators Labour Productivity This variable is part of Dunning’s strategic asset-seeking motive. Human resources are considered equivalent to capital resources, which are considered as an asset, and have a direct connection for enhancing the absorptive capacity of spillovers of FDI in host economies (Borensztein et al, 1998 ). GDP per hour worked is proxied for this variable. Pollution Carbon dioxide emissions (CO2) constitute about 75% of greenhouse gas emissions in the world (Akpan & Akpan, 2012 ; Asongu et al., 2018 ). Understanding the prevailing emission levels can help to compare and estimate the development outcomes that ensure sustainability. Carbon dioxide emission is used as a proxy for pollution. 4. Methodology 4.1 Data and Variables This research is an applied research following an empirical design. The objectives of this research is to identify the presence/absence of Pollution Haven Hypothesis, by considering variables like FDI flows, market growth, agglomeration, natural resources endowments, inflation, control of corruption, labour productivity and CO2 emission. The data source is the World Bank World Development Indicators, and the time period is from 1990 to 2023. Data descriptions with their proxies are shown in Table 2. 4.2 Model Based on the discussions in the previous section, the main model can be written as follows FIDF = f ( GDPPC, EXP, LP, INF, CORR, NAT, STOCK, CO2 ) ----------------- (1) The above model is expressed in linear equation form below in Eq. 2 FDIF t = f (GDPPC t , EXP t , LP t INF t CORR t NAT t STOCK t , CO 2t ) --------------- (2) All variables are converted into their natural logarithmic form to maintain consistency and overcome multicollinearity issues (Bekhet & Othman, 2017 ). The long run and short run forms of the equation are shown in Eq. (3) Where t denotes the year from 1990–2023, β represents short-run coefficient and \(\:\lambda\:\) represents the long run coefficients for the dependent variables. β0 is the constant and ε t is the error term. The Cointegration test is conducted using the F test using the combined significance of variables, which follows the path as mentioned in the hypothesis below. H0 : λ 1 = λ 2 = λ 3 = λ 4 = λ 5 = 0 H1 : λ 1 ≠ λ 2 ≠ λ 3 ≠ λ 4 ≠ λ 5 = 0 The null hypothesis of H 0 represents no cointegration against the alternative H 1 when cointegration exists. Comparing the F-statistics with the lower and upper bounds' critical values helps to check the hypothesis. We cannot reject the null hypothesis if the F-stat value is below the I(0). However, if the F-stat is above the I(0) value, we reject the null hypothesis of no cointegration. It is vital to establish the cointegration relation, after which the next step is to check for diagnostic tests to ensure model stability. 4.3 Econometric Specifications, Data Analysis and Interpretation The econometric analyses undertaken in this study are shown in Fig. 2 . Firstly, the data is checked for the presence of unit roots to understand the stationarity properties. Augmented Dicky Fuller unit root test is used to check the presence of unit roots. Results of the unit root test are given in Table III. Except for three variables – GDPPC, LP, Corruption, and natural resources, all other variables have unit roots. So those variables are converted to their first difference, and the same is considered for further analysis. This makes the order of integration of the variable combination of I(0) and I(1). Table III: Unit Root Test – Augmented Dicky Fuller Test (Trend & Intercept) Variables At Level At First Difference IFDI 0.56 (-2.02) 0.00 (-7.57) GDPPC 0.00 (-4.33) EX 0.92 (-1.04) 0.00 (-5.15) LP 0.05 (-3.51) INF 0.50 (-1.53) 0.00 (-4.10) CORR 0.01 (-4.08) NAT 0.00 (-4.42) PAT 0.21 (-2.77) 0.00 (-5.02) STOCK 0.89 (-1.19) 0.01 (-4.27) CO2 0.88 (-1.19) 0.01 (-4.23) Source: Authors own extracted from Eviews Since the variables are a mixture of I (0) at the level and I (1) at the first difference, the data is suitable for further analysis to understand their relationships (Narayan & Smyth, 2005 ). After establishing stationarity, further analysis is carried out with the Auto Regressive Distributed Lag (ARDL) model. This method is extensively used to understand the long-run dynamic relations, when the variables are of a combination of I(0) & I(1), and when the sample observations are small, it also successively addresses the endogeneity issues and helps to capture the dynamic relations of the variables in the long run (Pesaran et al, 2001 ; Pesaran & Shin, 1995 ). Thirdly, ARDL bounds test approach indicates the presence of long-term cointegrating relation among the variables. Fourthly, error correction estimates indicate the speed of adjustments in the disequilibrium for the model. Before running the model, the lag order is identified by calculating the lag selection criterion, as shown in Table IV. Table IV: Results of VAR lag order selection criteria VAR Lag Selection Criteria Endogenous Variables: lnifdi1 lngdppc1 lntop1 lnlp1 lninf1 lncorr1 lnnat1 lnpat1 lnstock lnco2 Exogenous Variable: C Lag LogL LR test statistic FPE- Final Prediction Error AIC- Akaike information criterion SC- Schwarz information criterion HQ- Hannan-Quinn information criterion 0 76.99 NA 7.44 -4.68 -4.26 -4.55 1 245.51 220.82* 2.32* -10.72* -6.48* -9.39* Notes : *indicates lag order selected by the criterion at 5% level Source: Author's own extracted from Eviews Lag selection as per the VAR (Vector Auto Regression) Lag order selection indicates that the optimum lag is at 1, as indicated by the 5 lag selection criterion recommendations of LR, FPE, AIC and HQ, shown in Table III. Table V: Bounds Test Results Model Sig Level Lower bound Upper bound F-Stat Critical Values for the Bounds Test 10% 5% 1.85 2.11 2.85 3.15 12.12 Note: - No long-run relationship β 1 = β 2 = β 3 = β 4 = β5 No short-run relationship : λ 1 = λ 2 = λ 3 = λ 4 = λ 5 Source: Authors own extracted from Eviews 4.1 ARDL Model Results The ARDL bounds test is conducted to understand the presence of a cointegrating relationship among the variables. From Table V, we can infer that there is cointegration among the variables, as the F-stat value of 12.12 exceeds the lower and upper bounds at 1%, 2.5%, 5%, and 10% levels of significance. So, we reject the null hypothesis of no cointegration and accept the alternative hypothesis, indicating the presence of cointegrating relation among the variables. Table VI: Cointegration Test Results Cointegration Test Value Probability scores Engle-Granger tau-statistic -11.04 0.00 Engle-Granger z-static -42.52 0.00 Phillips-Ouliaris tau-statistic -11.17 0.00 Phillips-Ouliaris z-statistic -42.65 0.00 Source: Author's own extracted from Eviews 4.2 Cointegration Results The cointegration, as established by the ARDL bounds test, is also confirmed by other cointegration tests, including the Engle-Granger and Phillips-Ouliaris tau & Z statistics, with a probability score that is significant at the 5% level of significance, as shown in Table VI. Table VII: Long-run and Short-run ARDL Estimations Independent variables Coefficients Standard Error t stat Probability Long-run estimates LNGDPPC1 -0.14 0.20 -0.68 0.50 LNEXP 0.69 0.73 0.94 0.36 LNLP 0.51 1.99 0.25 0.80 LNNAT 0.21 0.16 1.27 0.23 LNCORR 0.15 0.25 0.58 0.56 LNFDISTOCK1 2.55 0.72 3.51 0.00 LNINF -0.35 0.18 -1.87 0.08 LNCO21 1.20 1.11 1.08 0.30 Short-run estimates D(LNGDPGR) 0.03 0.07 0.53 0.60 D(LNEXP1) 0.73 0.34 2.15 0.05 D(LNLP) -14.6 3.85 -3.79 0.00 D(LNNAT) -0.25 0.17 -1.44 0.17 D(LNCORR) 0.19 0.09 2.02 0.06 D(LNFDISTOCK1) 2.01 0.34 5.85 0.00 D(LNINF) -0.03 0.14 -0.21 0.83 D(LNCO21) 0.35 0.70 0.50 0.62 ECT(-1) -1.43 0.09 -14.8 0.00 R-Square – 0.85 Adjusted R-Square – 0.62 Durbin Watson Stat – 2.91 F Stat – 3.72 Prob (F-Stat) – 0.01 Heteroskedasticity Breusch-Pagan-Godfrey : 0.47 Jarque-Bera (Prob) : 0.59 Breusch-Godfrey Serial Correlation LM test : 0.04 Source: Authors own extracted from Eviews After confirming cointegration, the study analyses long- and short-run dynamics to assess PHH. Table VII shows the long-run and short-run dynamics with the error correction term. Short-run coefficients show that FDI flow has a positive relation with GDPPC, merchandise exports, control of corruption, stock of FDI, and CO2 emissions. Labour productivity, control of corruption, and the stock of FDI are negatively associated with FDI flows and are significant. The probability score of merchandise export is significant, but the coefficient is positive. The short-run dynamics are different from those of the long run. In the long run, the stock of FDI and inflation have significant probability scores. The coefficient of inflation is negative, indicating an inverse relationship between FDI flow and inflation. GDPPC also shows negative relations, although not significant. Merchandise exports, labour productivity, natural resources, control of corruption, stock of FDI, and CO2 emissions show positive relations with FDI flow. The ARDL model is stable, as indicated by the F-statistic probability scores of 0.01. The model is able to explain the variations in FDI flow to the tune of 85%. The ECT is negative and significant, indicating that the ARDL model is stable and able to return to equilibrium at a rate of 14.3%. The ECT score is not very high, but its coefficient scores are negative, and the probability is significant at the 5% level, which is a necessary condition for ensuring stability. Diagnostic tests to understand the presence of heteroskedasticity, normality, and serial correlation, indicated through Heteroskedasticity Breusch-Pagan-Godfrey, Jarque-Bera, and Breusch-Godfrey Serial Correlation LM test, respectively, are validated to ensure model stability. Recursive estimates, as indicated by CUMSUM and CUSUM of squares, as shown in Fig. 3 , are also significant. The majority of the variables, including carbon dioxide, in the long and short run, are not significant and have a positive relationship with FDI flow, which leads to the conclusion that there exists the Pollution Haven Hypothesis exists in India 4.3 Recursive Estimates 4.4 Impulse Response Function In the Impulse Response Function (IRF) shown in Fig. 4 , the red line shows 95 per cent confidence intervals, computed as +/-2 standard error confidence bands, and the blue lines show the IRF of the variable. The X-axis represents periods (quarters), and the Y-axis represents percentage variations. IRF measures a one-standard-deviation change in FDI flow with the dependent variables. This change can be a shock, an impulse, or an innovation to FDI flow to the dependent variables. The Cholesky table, as shown in Table VIII, indicates the short and long-run outcomes of shocks in the dependent variable. Table VIII: Cholesky IRF Table Source: Authors own extracted from Eviews In the IRF, the positive and negative changes in FDI flows indicate that GDPPC exhibits a positive flow in the 2nd ,9th and 10th periods and follows a negative flow in all other periods. EXP is positive in the 3rd ,9th, and 10th periods only, indicating that there is a close relation between GDPPC and exports with FDI flow. LP, NAT, and INF are negative across the time period. CORR is positive from the 4th period onwards. STOCK is negative only in the 3rd period. CO2 levels decline and become negative from the 1st to 5th periods, and then remain negative from the 6th to 10th periods. This phenomenon indicates that changes in the FDI flow in response to the variables are not uniform, and the outcomes of FDI flows on all the dependent variables are asymmetrical. Table 8 Granger Causality Results Null Hypothesis F stat Probability Decision Paths GDPGR does not Granger cause FDI flow 4.9 0.03* GDP to FDI flow Unidirectional LNLP does not Granger cause FDI flow 4.4 0.04* Labour Productivity to FDI flow LNCORR does not Granger cause FDI flow 4.4 0.04* Carbon emission to Inward FDI CO2 does not Granger cause FDI flow 9.01 0.00* CO2 to FDI flow GDPGR does not Granger cause exports 5.35 0.02* GDP growth rate to Merchandise exports GDPGR does not Granger cause natural resources 3.42 0.07** GDP growth rate to Corruption GDPGR does not Granger cause FDI stock 5.7 0.02* GDP growth rate to FDI stock Labour productivity does not Granger cause Corruption 12.6 0.00* Labour Productivity to Corruption Labour productivity does not Granger cause Stock of FDI 0.00* Labour Productivity to Stock of FDI Inflation does not Granger cause labour productivity 10.2 0.00* Inflation to Labour Productivity CO2 does not Granger cause labour productivity 4.54 0.04* CO2 to Labour Productivity Natural resources does not Granger cause corruption 3.8 0.05* Natural Resources to Corruption Inflation does not Granger cause Corruption 6.1 0.01* Inflation to Corruption CO2 does not Granger cause Corruption 5.5 0.02* CO2 to Corruption CO2 does not Granger cause Stock of FDI 6.28 0.01* CO2 to Stock of FDI Inflation does not Granger cause CO2 6.29 0.01* Inflation to CO2 Stock of FDI Granger causes Exports Exports does not Granger cause Stock of FDI 3.36 6.31 0.07** 0.08** Stock of FDI to Exports & Exports to Stock of FDI Bidirectional Stock of FDI does not Granger cause Corruption Corruption does not Granger cause Stock of FDI 3.64 6.12 0.06** 0.01* Stock of FDI to Corruption & Corruption to Stock of FDI *5% level of significance ** 10% level of significance Source: Authors own extracted from Eviews 4.5 Causality Results The causality results, as checked using the Granger causality test, indicate two-way causality relations between the stock of FDI and merchandise exports, as well as with the control of corruption. The unidirectional causality relations of other variables are shown in Table 8 . 5 Discussion of Results The objective of this study is to identify the presence/absence of the Pollution Haven Hypothesis (PHH) for India, considering factors such as FDI flows, market growth and openness, agglomeration, natural resource endowments, inflation, corruption, labour productivity, and CO2 emissions. Inward FDI, gross domestic product per capita, stock of FDI, ores, metals, fuel exports, GDP deflator, control of corruption rank, and GDP per hour worked are the proxy variables selected for the study, and data were collected from the World Bank’s World Development Indicators from 1990 to 2023. FDI flow is the dependent variable, and all other variables are independent variables. After checking the data for unit roots and ensuring data stability, the ARDL model was estimated, including a bounds test and error correction. The model is able to explain 85 per cent of the total changes in FDI flows and also found to be significant, considering the probability scores. Cointegration relation is established using bounds test scores and confirmed through the Engle-Granger and Phillips-Ouliaris statistics. In the short term, FDI flows exhibit a positive relationship with GDP growth rates, merchandise exports, control of corruption, the stock of FDI, and CO2 emissions. Labour productivity, control of corruption, and the stock of FDI have negative relationships with FDI flow, and they are also significant. The probability score of merchandise export is significant, but the coefficient is positive. In the long run, the stock of FDI and inflation have significant probability scores; an inverse relationship is observed between FDI flow, inflation and GDPPC. Merchandise exports, labour productivity, natural resources, control of corruption, stock of FDI, and CO2 emissions show positive relations with FDI flow. The ECT satisfies the conditions of stability, showing a negative and significant coefficient score of 14.3%, indicating a 14.3% rate of returning to equilibrium. Stability and diagnostic tests, as well as recursive estimates, indicate significant results. The results of the impulse response function indicate that FDI flow exhibits an asymmetrical relationship with all the dependent variables. Considering the causality relations, a. there is a one-way significant causality effect moving from GDPGR, LP, CORR, and CO2 to FDI flow, b. FDI stock and export are having a two-way relationship, c. FDI stock and corruption have two-way causality relations. This study confirms the presence of PHH for India. The majority of the variables, including carbon dioxide, in the long and short run, are not significant and have a positive relationship with FDI flow, which leads to the conclusion that there exists the Pollution Haven Hypothesis in India, considering the above selected variables and following the path as suggested by Dunning ( 1979 ) for receiving FDI. This finding aligns with those of Miniesy & Tarek ( 2019 ), Murthy & Gambhir ( 2017 ), Rana & Sharma ( 2019 ), and Dagar et al. ( 2022 ). The asymmetrical FDI relations are also in sync with Nguyen et al. (2020), Muhammad et al. ( 2021 ), and Mujtaba & Jena ( 2021 ), who confirmed the presence of PHH for India. This study confirms the argument that developing countries have high air pollution due to high carbon dioxide emissions, accompanied by lax environmental laws [Hoffmann et al, 2005 ; Hassaballa, 2004; Doytch & Uctum, 2011 ; Kim & Adilov, 2012 ; Asghari, 2013 ; Badri & Parvizkhanlu, 2014 ]. India is one of the top 20 countries with the highest air pollution levels, as per the urban air quality database released by the World Health Organisation (WHO, 2016). As per this study, every 1% increase in FDI flow leads to a 1.20% increase in CO2 emission in the long run and 0.35% increase in the short run, confirms the fact that India’s economic growth has got a significant and positive impact upon carbon emission, as growing economies consume high energy mainly supplied from fossil fuels which results in the generation of CO2 emissions. Studies by Rajpurohit and Sharma ( 2021 ) and Song ( 2021 ) also support this idea. Although this is not encouraging, but convincing, as it is claimed that MNEs coming into India from developed countries start using clean energy and hybrid technologies, as a result of which the emission levels can be reduced (Zhu et al, 2022 ; Waqih et al, 2019 ), but this study's results do not reflect the above argument. A 1% increase in FDI flow leads to a 0.69% increase in merchandise exports in the long run, and a 0.73% increase in the short run reflects the fact that export growth aligns with FDI flows. This relation is also confirmed by the bi-directional causal relation between the stock of FDI and merchandise exports. For a long time, literature has suggested that FDI acts as an engine for enhancing exports and promoting economic growth, a claim confirmed in this study (Sethi & Sucharita, 2009 ). Labour productivity is significant in the short run; a 1% increase in FDI flow leads to a 14.6% decrease in labour productivity. This is one of the unique findings of this study, as there is very little literature that studies labour productivity and FDI flow. Literature claims that FDI flows have the ability to increase the skill base of host countries through learning by doing, on-the-job training, and other mechanisms, as they originate from developed countries with a state-of-the-art skill base, creating positive knowledge and technology spillovers (Perri & Peruffo, 2016 ). This research study supports the results of Ombuki et al ( 2025 ), indicating that productivity and knowledge spillovers are absent in this case. The causality from LP moves to corruption and FDI stock leads to the non-definitive conclusion that necessitates more intensive firm-level studies The natural resources endowment and control of corruption moderate the quality of institutions, thereby influencing FDI flows; therefore, the role played by these two variables cannot be studied separately. Natural resources exhibit a negative relationship with FDI flow in the short run, and in the long run, it is positive, but not statistically significant in either time period. A 1% increase in FDI flow reduces natural resources by 25% in the short run, indicating that resource-seeking FDI is targeting the Indian economy; however, in the long run, its focus shifts. Institutional quality and the prevailing legal climate play a significant role in influencing resource-seeking FDI (Chiyaba & Singleton, 2023 ). Our research results for the Control of corruption show a positive relationship with FDI flow in the short run and a negative relationship in the long run, aligning with current realities. Control of corruption, one of the key ingredients for good governance, influences economic health and thereby enhances the flow of FDI (Asongu & Odhiambo, 2020 ). The results of this study align with a recent study by Khan ( 2025 ), which shows that FDI exhibits a negative relationship with the control of corruption. Inflation exhibits a negative relationship with FDI flow in both the short and long run; the causality effect runs one way from labour productivity, CO2, and corruption, indicating that these factors have strong negative relationships, a finding established in most studies (Khan & Mitra, 2014 ; Siddiqui & Aumeboonsuke, 2014 ). As corruption is an important barrier to entry of MNEs, a recent study indicates that MNEs learn to adjust to cope with corruption by reducing corruption cost, and this experience helps them to sail across such environments and bring out country-specific entry policies (Thede & Karpaty, 2023 ) The study results for CO2 and FDI flows are positive in both the short and long run, with causality running from CO2 to the stock of FDI. This is a clear indication that the flow of FDI and the increase in CO2 are happening simultaneously. The results of this study support the clear existence of PHH and align with those of Joshua et al. ( 2024 ) and Khan et al. ( 2021 ). These study results contradict the findings of Waqih et al. ( 2019 ) and Shekhawat et al. ( 2022 ), who did not confirm the presence of PHH for SAARC countries, which also includes India. The opposing views of PHH indicate favourable environmental benefits to host countries, due to FDI flows, as MNCs adopt modern production techniques in their entire production and supply chain processes, which can result in environmentally friendly outcomes that reduce degradations in the long run (Tamazian & Chousa, 2009). 6 Conclusion This study is a significant contribution to the literature on FDI, as it reveals the inherent relationship among the variables that significantly impact the flow of FDI in India. Examining the determining factors of FDI with recent data sets can provide new insights and understanding of the current dynamics, which can help in understanding the ground realities across time and serve as an effective guide when framing policies. Additionally, this study helps assess the quality of FDI received by India during the studied period, which can also aid in understanding the motivations of investors and inform the development of India's future FDI policies. The motivations and objectives of MNEs when investing in India can also be understood. 7 Policy Implications Drawing from empirical findings, a set of policy recommendations emerges, grounded among the complex dynamics of foreign investment, GDP growth rate, exports, labour productivity, natural resources, inflation, control of corruption, agglomeration and CO2 emissions within the context of India. The first policy consideration necessitates promoting sustainable FDI. By incentivising investments in green technology and emission reduction, we can mitigate the effects of CO2 emissions. Tax holidays and tax incentives can also show encouraging outcomes. Secondly, FDI policies and goals must be made more environmentally friendly, which can act as a deterrent for those MNEs that invest in India with the objective of relocating polluting industries from their home country. Thirdly, the manufacturing practices followed in MNE investing industries must be as clean and green as possible. Fourthly, the government must monitor, control, and regulate the sustainability practices followed by MNE affiliates, branches, and their subsidiaries in India, and this must become part of the approval mechanism. Fifthly, improving institutional quality through good governance can reduce the effect of corruption, natural resources exploitation and can expedite the flow of FDI Sixthly, modifying the entry process to focus on local content development can facilitate spillovers in multiple areas of development, thereby enhancing the skill base and productivity. Configuring the above policy implications into development strategies can make India more attractive for foreign investments and also positively favour sustainable development. Need-based and time-specific modifications, with regular audits and monitoring, can create a more streamlined effect on the FDI entering into India. 8. Limitations and Directions for Future Research Structural breaks in the data were not considered, which can be a limitation of the study. Future studies can follow the regime-shifting approach, identifying the structural breaks across different time periods to understand the determining factors. Understanding the sustainability profile of MNE affiliates and subsidiaries operating in India can help one identify and differentiate their economic and environmental motives. A comparative analysis of the sustainability profiles of MNEs in their home and host countries can also help to assess the extent to which they are vulnerable to environmental challenges when they move from their home base. Demonstrating the differential sustainability practices followed by multinationals in their host country affiliates versus those in their home country can shed light on the differences in approaches, and this can help discourage polluting MNEs from entering India. The study has implications for fine-tuning the impacts of international trade to ensure environmental sustainability. Declarations Declaration of Competing Interest: The author declares no known competing interests Data Availability Statement: The data used for this study are available from the author upon request and can also be downloaded from the source mentioned. Consent for Publication: Author gives her consent to this publication Consent to Participate: Not applicable Clinical Studies: Not applicable Conflict of Interest/Competing Interest: The author declares no conflict of interest Ethical Declaration and Compliance: Not Applicable Informed Consent/Consent to Participate: Not Applicable Funding: No funding or grants received to assist with the preparation of this manuscript. References Acharyya J. FDI, growth and the environment: Evidence from India on CO2 emission during the last two decades. J Economic Development. 2009;34(1):43. Asghari M. Does FDI promote MENA region’s environment quality? Pollution halo or pollution haven hypothesis. Int J Sci Res Environ Sci. 2013;1(6):92–100. Asongu SA, Odhiambo NM. Governance, CO2 emissions and inclusive human development in sub-Saharan Africa. Energy Explor Exploit. 2020;38(1):18–36. Asongu SA, Le Roux S, Biekpe N. Enhancing ICT for environmental sustainability in sub-Saharan Africa. 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C.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYDACCRBhYMHAcACIPqBIMePVIgHWcnAG8VoYIFqYeYhxl/zs5mefbhRIyPMd7zE8bPPHLlp+do/Zgw8MdvIM7LwHsGkxuHPMeHaOgYThzDNnDA7ntiXnbrhzxtxwBkOyYQMzXwJWLRIJxsxALYwbbqQlHM5tOJC7QSLHTJqHgTkB6E4DrA6bkf4ZpMV+w/1nCYct/hzInT8DqOUPQz1OLQw3csC2JG64wXzgMAPbgdyGG0AtDAyHcWoxuJFTDNKSPPNM8oGDvWC/HCuT7DE4btiG22GbmXP+2Nj2HT/Y/OHHH7vc+bObt0n8qKiW5+c/g91hmAASuQwMbESqh2kZBaNgFIyCUYAAAMz5Xq4eH6qIAAAAAElFTkSuQmCC","orcid":"","institution":"Symbiosis International University","correspondingAuthor":true,"prefix":"","firstName":"Sharmiladev","middleName":"J.","lastName":"C.","suffix":""}],"badges":[],"createdAt":"2026-04-07 10:39:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9343604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9343604/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109760785,"identity":"891c88cd-c114-463f-b172-7d36b444f8b3","added_by":"auto","created_at":"2026-05-22 07:29:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98783,"visible":true,"origin":"","legend":"\u003cp\u003eFDI net inflows \u0026amp; GDP Growth\u003c/p\u003e\n\u003cp\u003eSource: World Bank\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9343604/v1/ae72dbec622a105658046e93.png"},{"id":109760449,"identity":"e487cc50-7309-4073-bd1f-fa4ac9a53dfa","added_by":"auto","created_at":"2026-05-22 07:28:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69647,"visible":true,"origin":"","legend":"\u003cp\u003eEconometric Specifications\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9343604/v1/32922cbdc1ebff7b7d2cd546.png"},{"id":109480597,"identity":"5c308a1d-3bd7-4f21-840c-b59a69d21321","added_by":"auto","created_at":"2026-05-18 15:00:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37983,"visible":true,"origin":"","legend":"\u003cp\u003eRecursive Estimates\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9343604/v1/df1552667e65ae7e9cf4a326.png"},{"id":109760085,"identity":"f859ae38-11b3-482d-93c5-31f7daf146ba","added_by":"auto","created_at":"2026-05-22 07:28:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89496,"visible":true,"origin":"","legend":"\u003cp\u003eImpulse Response Function\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9343604/v1/eec609b34a6cc6008a16f7be.png"},{"id":109765128,"identity":"6cd8468f-931e-4a7b-be29-e18751255770","added_by":"auto","created_at":"2026-05-22 07:39:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":839161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9343604/v1/7c6f3bb8-0219-4f85-9f65-9d279f9adac3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Empirical Analysis of the Pollution Haven Hypothesis for India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForeign direct investment (FDI) can be an effective tool to achieve the United Nation\u0026rsquo;s Sustainable Development Goals 8, 9 and 13 on economic growth, innovation development and climate action to combat climate change and its impacts (McIntyre et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stephenson et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van Tulder et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This necessitates understanding the holistic impact of inward FDI into host economies to better recognize the determining and deterring factors, especially when FDI flows from developed to developing and emerging economies. FDI environment relationship includes i) environmental effects of FDI flows, (ii) competition for FDI and its outcomes on environmental standards, and (iii) cross-border environmental performance (Pazienza, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Academic research remains unsettled regarding the outcomes of this phenomenon (Erdogan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pazienza, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Due to international integration and factor mobility, FDI can impact the environment through the scale effect - moving from a small to global scale, technique effect - adoption of cleaner technology and the composition effect - a shift in preferences to cleaner products and greater environmental protections with increased income (Kathuria, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pazienza, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering strict environmental regulations and compliances in developed countries, multinational enterprises\u0026rsquo; (MNEs), location decisions are tilted towards developing and poor nations, and the FDI outcomes of such movements are ambiguous and controversial and gave forth to the concept of Pollution Haven Hypothesis (PHH) (Dunning \u0026amp; Lundan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Taylor, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; D\u0026ouml;rrenb\u0026auml;cher \u0026amp; Gammelgaard, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Copeland \u0026amp; Taylor, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Fetscherin et al, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Paul \u0026amp; Benito, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hoffmann et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Becker-Ritterspach et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu et al, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The internationalisation models followed by MNEs are mostly by acquiring intangible assets and optimisation of the resources of host country firms, which gives them the ability to establish as global competitors, enabling commercial operations in multiple countries through subsidiaries, associates and branches leading to many strategic activities, partnerships, business development and innovations (Bartlett \u0026amp; Ghoshal, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Nohria \u0026amp; Ghoshal, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Acharyya, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PHH argues that strict environmental regulations in developed countries raise firms' costs, encouraging pollution-intensive firms to relocate to developing economies with laxer standards. To attract FDI, developing countries often weaken environmental regulations, intensifying competition and potentially triggering a \u0026ldquo;race to the bottom\u0026rdquo; (Lee et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Naughton, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Grimes et al, 2003). Understanding PHH using recent data can clarify the implications of FDI for developing economies. Prior studies highlight conflicting views on FDI\u0026rsquo;s environmental impacts in host economies (Krugman et al., 2000; Eskeland \u0026amp; Harrison, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zhang \u0026amp; Zhou, 2016; Zhang et al, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While FDI may transfer cleaner technologies and enhance environmental quality under the Pollution Halo Hypothesis (D\u0026ouml;rrenb\u0026auml;cher et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), its joint effects on economic growth and the environment remain contested, largely depending on appropriate host-country policies (Lucas, 1988; Romer, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Fetscherin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Clunies-Ross et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study examines the presence of PHH in India, a fast-growing economy (6.5% in 2024) with substantial FDI inflows (USD 28.08\u0026nbsp;billion in 2023) and high per-capita CO₂ emissions (2.13 tons). Given India\u0026rsquo;s position among major emitters, as shown in Table I, understanding the FDI\u0026ndash;growth\u0026ndash;environment nexus is crucial, especially given the mixed evidence on PHH for India.\u003c/p\u003e \u003cp\u003eTable I: CO2 Profiles\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO2 Emission\u003c/p\u003e \u003cp\u003ePer Capita\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO2 Emission\u003c/p\u003e \u003cp\u003ePer Capita\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Income\u003c/p\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003eMiddle-Income\u003c/p\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Middle-Income\u003c/p\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower-Income Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Our World in Data\u003c/p\u003e \u003cp\u003eIndia has progressively liberalised its FDI regime, allowing most investments to proceed through the automatic route, while sustaining strong economic growth (6\u0026ndash;7%), trade openness, and rising FDI inflows, supported by a young and skilled workforce (Bajpai \u0026amp; Dasgupta, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, despite its economic progress, India faces environmental challenges and has committed to significant emission reductions and sustainable development, reaffirmed through international forums (UNFCCC, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the 2025 BRICS summit (MOE, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study presents an evidence-based trade-off for the tripartite relationship among international trade, the environment, and economic growth, leading to CO2 emissions that pose environmental challenges and question the growth outcomes. India\u0026rsquo;s experience can serve as a valuable addition to other emerging economies seeking to achieve their economic and sustainable development goals through foreign investment. The study's outcomes can provide additional insights into the interactions among economic growth, international trade, and the environment, and contribute to policy discussions on balancing these outcomes by offering empirical evidence and recommendations for achieving sustainable development. If the existence of PHH is confirmed, it will re-establish the fact that enhancing international trade, led by FDI flows, can be one of the reasons for pollution and environmental deterioration in India. This will require accepting FDI with greater caution and revising policy areas to enhance the quality of FDI that India receives.\u003c/p\u003e"},{"header":"2. Review of Literature","content":"\u003cp\u003eThis section provides an overview of theoretical frameworks, encompassing discussions on the triangular linkages between trade, the environment, and the FDI nexus. It also succinctly encapsulates the past empirical studies that explore the interconnections among FDI, trade, environment and economic growth.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Frameworks\u003c/h2\u003e \u003cp\u003eThe Environmental Kuznets Curve (EKC) hypothesis, the Pollution Haven Hypothesis (PHH), and the Pollution Halo Hypothesis (PHH) collectively indicate the relationship between economic growth and the environment. The EKC hypothesis posits an inverse relationship between economic growth and environmental degradation within an economy, as environmental degradation is initially more severe because economic development is prioritised in the early stages (Grossman \u0026amp; Krueger, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). This tendency will continue until the country reaches a threshold level of income, suggesting that economic growth can have both positive and negative impacts on the environment (Dinda, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The origins of PHH and PH emerge from international capital movements realised by multinational enterprises, and both hypotheses offer opposing views on the role of international capital, particularly regarding FDI and its environmental outcomes in host countries.\u003c/p\u003e \u003cp\u003eThe refuting and opposing views regarding these two hypotheses have enriched the literature on international capital movement through FDI and its environmental implications. As India's economic growth is also driven by foreign capital and the country is one of the highest recipients of FDI in recent times, it calls for an examination of the relationship in areas such as macroeconomics, international trade, the environment, and emissions. The relationships among FDI, economic growth, exports, labour productivity, inflation, control of corruption, natural resources endowment and carbon dioxide emission for India hold significance for the country\u0026rsquo;s progress towards achieving economic growth, innovation, sustainable and low-carbon development, aligning with the United Nations SDGs, especially Goals 8, 9 and 13. The study outcomes can offer valuable insights for policies and actions aimed at achieving the SDGs related to economic growth, innovation \u0026amp; development, as well as climate change mitigation, considering the broader implications for India\u0026rsquo;s development agenda (United Nations Assembly, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Reviews\u003c/h2\u003e \u003cp\u003eEmpirical studies support PHH due to relocation of firms to countries with weaker environmental regulations (Zugravu \u0026amp; Ben Kheder, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pao \u0026amp; Tsai, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Environmental advancements achieved in developed countries are the result of shifting and relocating dirty and pollution-intensive production to developing nations with lax and weaker environmental standards and compliance, which also leads to compromised environmental and social standards to gain a comparative advantage, resulting in a race to the bottom and further environmental damage (Bu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMurthy \u0026amp; Gambhir\u0026rsquo;s (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) study suggests that trade openness and international trade are realities of globalisation, resulting in MNEs moving across borders in search of greener pastures, which creates collective outcomes that have enhanced gross domestic product and international trade. Parallelly, this phenomenon also created environmental consequences, such as high CO2 emissions, since the focus areas of MNE activities are on harnessing urban and energy resources. Therefore, the authors argue that an understanding of the relationship between CO2 emissions and FDI can be gained through the PHH. Murthy \u0026amp; Gambhir (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), using data from 1991 to 2014 for India, validate PHH in both aggregate and per capita terms, indicating that this phenomenon cannot be ignored, despite its minimal numerical impact.\u003c/p\u003e \u003cp\u003eRana \u0026amp; Sharma (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) study confirms the presence of PHH, indicating that India imports more pollution-intensive manufacturing goods, and FDI is causing economic growth, but through CO2 emissions. Analysing 21 developed and developing countries with high carbon emissions, Singhania \u0026amp; Saini (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found evidence of PHH in developing countries. A recent study by Ozturk et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for South Asian economies, including India, confirms the presence of PHH. Holtbr\u0026uuml;gge \u0026amp; Raghavan (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) confirm PHH in Indian manufacturing and transportation industries, metallurgy and chemical sectors.\u003c/p\u003e \u003cp\u003eIn contrast to studies supporting PHH in India, the literature finds no evidence of its existence. Input\u0026ndash;output analyses and index-based approaches indicate that India has shifted away from pollution-haven dynamics. Currently, FDI is being driven by infrastructure, market access, and factor endowments than by weak environmental regulations. Recent time-series evidence also fails to confirm PHH for India (Mukhopadhyay \u0026amp; Chakraborty, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dietzenbacher \u0026amp; Mukhopadhyay, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kathuria, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gogoi \u0026amp; Hussain, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Hypothesis Development and Variable Selection","content":"\u003cp\u003eAs PHH acts as an enabling factor for inward FDI, one needs to examine the factors responsible for attracting FDI into India to understand the presence/absence of PHH. Multiple theoretical and empirical models explain the occurrence of FDI, starting from Hymer (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1968\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), Kindleberger (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1969\u003c/span\u003e), Buckley and Casson (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), Vernon (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), Dunning (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). These theories create an overlap of many variables as determinants and attraction factors for FDI into India. The OLI (Ownership, Location and Internationalisation) paradigm of Dunning provides a more acceptable and comprehensive explanation for the reasons and directions for FDI. Also, the UNCTAD (1998) has classified the determinants of FDI into three categories: policy determinants, economic determinants, and business facilitation determinants. The scope of this paper is only restricted to the economic determinants, and these determinants also stem from Dunning\u0026rsquo;s motivations of market seeking, resource seeking, efficiency seeking and strategic asset seeking motives for attracting FDI (Dunning, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). As this paper is concerned only with India and the focus of this paper is restricted to understanding the motivations for inward FDI and its relation with economic growth, considering the Environmental Pollution Haven Hypothesis, the author narrows down the discussion of the literature concerning the same and conducts a discourse of the studies below. A list of selected variables with their proxies are mentioned in Table II\u003c/p\u003e \u003cp\u003eTable II: Variables List\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProxies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI net flows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDI net inflows\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross Domestic Product Per Capita\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLagged FDI stocks, all stocks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Resource Endowments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOres and metals exports (% of merchandise exports)+ Fuel exports (% of merchandise exports)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflation, GDP deflator (annual %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl of Corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernance Indicator Control of Corruption: Percentile Rank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabour Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP per hour worked\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbon dioxide emission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Authors Own\u003c/p\u003e \u003cp\u003e \u003cem\u003eAgglomeration\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAs investors are always faced with uncertainties, due to volatility factors, they always look into the previous flow of investments across geographies. This concept of agglomeration stems from the philosophy of spillovers of FDI, such as knowledge, technology, and strategy. Lagged FDI stocks, all stocks are the proxies used (Krugman, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eMarket growth and Openness to Trade\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThese two variables form part of Duning\u0026rsquo;s market seeking motive, and these two variables are influential for FDI studies and are extensively researched across time ((Dunning, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lim, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Kang \u0026amp; Jiang, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wadhwa \u0026amp; Reddy, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eNatural Resource Endowments\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis variable is part of Dunning\u0026rsquo;s resource-seeking motive. Natural resources act as an inviting factor for attracting FDI, ensuring cross-country intra-industry manufacturing for overseas affiliates. This variable is not considered in many studies, except for a few, such as Hassaballa (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Miniesy \u0026amp; Tarek (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ores \u0026amp; metals, and fuel exports, as a percentage of merchandise exports, are proxied in this case.\u003c/p\u003e \u003cp\u003e \u003cem\u003eInflation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe level of inflation reflects the macroeconomic stability of an economy, which is fundamental and crucial for any capital investments, and this is also true for FDI by MNEs, as inflation directly depends on profitability and stability (Singhania \u0026amp; Gupta, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eControl of Corruption\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInvestment environment is directly related to institutional quality, and corruption is a deterrent factor for any capital investment, including FDI, as it creates uncertainties, reduces profit (Mathur \u0026amp; Singh, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hasan et al, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Corruption prevents fair and efficient development of market conditions (Boatright, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Control of Corruption is one of the four indicators, as mentioned by the World Bank governance indicators\u003c/p\u003e \u003cp\u003e \u003cem\u003eLabour Productivity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis variable is part of Dunning\u0026rsquo;s strategic asset-seeking motive. Human resources are considered equivalent to capital resources, which are considered as an asset, and have a direct connection for enhancing the absorptive capacity of spillovers of FDI in host economies (Borensztein et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). GDP per hour worked is proxied for this variable.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePollution\u003c/em\u003e \u003c/p\u003e \u003cp\u003eCarbon dioxide emissions (CO2) constitute about 75% of greenhouse gas emissions in the world (Akpan \u0026amp; Akpan, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Asongu et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Understanding the prevailing emission levels can help to compare and estimate the development outcomes that ensure sustainability. Carbon dioxide emission is used as a proxy for pollution.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data and Variables\u003c/h2\u003e \u003cp\u003eThis research is an applied research following an empirical design. The objectives of this research is to identify the presence/absence of Pollution Haven Hypothesis, by considering variables like FDI flows, market growth, agglomeration, natural resources endowments, inflation, control of corruption, labour productivity and CO2 emission. The data source is the World Bank World Development Indicators, and the time period is from 1990 to 2023. Data descriptions with their proxies are shown in Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Model\u003c/h2\u003e \u003cp\u003eBased on the discussions in the previous section, the main model can be written as follows\u003c/p\u003e \u003cp\u003eFIDF\u0026thinsp;=\u0026thinsp;\u003cem\u003ef\u003c/em\u003e ( GDPPC, EXP, LP, INF, CORR, NAT, STOCK, CO2 ) ----------------- (1)\u003c/p\u003e \u003cp\u003eThe above model is expressed in linear equation form below in Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003eFDIF\u003csub\u003et\u003c/sub\u003e= \u003cem\u003ef\u003c/em\u003e (GDPPC\u003csub\u003et\u003c/sub\u003e, EXP\u003csub\u003et\u003c/sub\u003e, LP\u003csub\u003et\u003c/sub\u003e INF\u003csub\u003et\u003c/sub\u003e CORR\u003csub\u003et\u003c/sub\u003e NAT\u003csub\u003et\u003c/sub\u003e STOCK\u003csub\u003et\u003c/sub\u003e, CO\u003csub\u003e2t\u003c/sub\u003e) --------------- (2)\u003c/p\u003e \u003cp\u003eAll variables are converted into their natural logarithmic form to maintain consistency and overcome multicollinearity issues (Bekhet \u0026amp; Othman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The long run and short run forms of the equation are shown in Eq.\u0026nbsp;(3)\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"732\" height=\"109\"\u003e\u003c/p\u003e \u003cp\u003eWhere t denotes the year from 1990\u0026ndash;2023, β represents short-run coefficient and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e represents the long run coefficients for the dependent variables. β0 is the constant and ε\u003csub\u003et\u003c/sub\u003e is the error term. The Cointegration test is conducted using the F test using the combined significance of variables, which follows the path as mentioned in the hypothesis below.\u003c/p\u003e \u003cp\u003eH0 : \u003cem\u003eλ\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e4\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e5\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003cp\u003eH1 : \u003cem\u003eλ\u003c/em\u003e1\u0026thinsp;\u0026ne;\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e2\u0026thinsp;\u0026ne;\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e3\u0026thinsp;\u0026ne;\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e4\u0026thinsp;\u0026ne;\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e5\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003cp\u003eThe null hypothesis of H\u003csub\u003e0\u003c/sub\u003e represents no cointegration against the alternative H\u003csub\u003e1\u003c/sub\u003e when cointegration exists. Comparing the F-statistics with the lower and upper bounds' critical values helps to check the hypothesis. We cannot reject the null hypothesis if the F-stat value is below the I(0). However, if the F-stat is above the I(0) value, we reject the null hypothesis of no cointegration. It is vital to establish the cointegration relation, after which the next step is to check for diagnostic tests to ensure model stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Econometric Specifications, Data Analysis and Interpretation\u003c/h2\u003e \u003cp\u003eThe econometric analyses undertaken in this study are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFirstly, the data is checked for the presence of unit roots to understand the stationarity properties. Augmented Dicky Fuller unit root test is used to check the presence of unit roots. Results of the unit root test are given in Table III. Except for three variables \u0026ndash; GDPPC, LP, Corruption, and natural resources, all other variables have unit roots. So those variables are converted to their first difference, and the same is considered for further analysis. This makes the order of integration of the variable combination of I(0) and I(1).\u003c/p\u003e \u003cp\u003eTable III: Unit Root Test \u0026ndash; Augmented Dicky Fuller Test (Trend \u0026amp; Intercept)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt First Difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003cp\u003e(-2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(-7.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(-4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(-1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(-5.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(-3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(-1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(-4.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCORR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(-4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003cp\u003e(-2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e(-5.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003cp\u003e(-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-4.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(-4.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Authors own extracted from Eviews\u003c/p\u003e \u003cp\u003eSince the variables are a mixture of I (0) at the level and I (1) at the first difference, the data is suitable for further analysis to understand their relationships (Narayan \u0026amp; Smyth, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). After establishing stationarity, further analysis is carried out with the Auto Regressive Distributed Lag (ARDL) model. This method is extensively used to understand the long-run dynamic relations, when the variables are of a combination of I(0) \u0026amp; I(1), and when the sample observations are small, it also successively addresses the endogeneity issues and helps to capture the dynamic relations of the variables in the long run (Pesaran et al, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Pesaran \u0026amp; Shin, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Thirdly, ARDL bounds test approach indicates the presence of long-term cointegrating relation among the variables. Fourthly, error correction estimates indicate the speed of adjustments in the disequilibrium for the model. Before running the model, the lag order is identified by calculating the lag selection criterion, as shown in Table IV.\u003c/p\u003e \u003cp\u003eTable IV: Results of VAR lag order selection criteria\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eVAR Lag Selection Criteria\u003c/p\u003e \u003cp\u003eEndogenous Variables: lnifdi1 lngdppc1 lntop1 lnlp1 lninf1 lncorr1 lnnat1 lnpat1 lnstock lnco2\u003c/p\u003e \u003cp\u003eExogenous Variable: C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLR test statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFPE- Final Prediction Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAIC- Akaike information criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSC- Schwarz information criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHQ- Hannan-Quinn information criterion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220.82*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.72*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.39*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes\u003c/em\u003e: *indicates lag order selected by the criterion at 5% level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Author's own extracted from Eviews\u003c/p\u003e \u003cp\u003eLag selection as per the VAR (Vector Auto Regression) Lag order selection indicates that the optimum lag is at 1, as indicated by the 5 lag selection criterion recommendations of LR, FPE, AIC and HQ, shown in Table III.\u003c/p\u003e \u003cp\u003eTable V: Bounds Test Results\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-Stat\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical Values for the Bounds Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: -\u003c/p\u003e \u003cp\u003eNo long-run relationship β\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e4\u003c/sub\u003e \u0026thinsp;=\u0026thinsp;β5\u003c/p\u003e \u003cp\u003eNo short-run relationship : \u003cem\u003eλ\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e4\u0026thinsp;=\u0026thinsp;\u003cem\u003eλ\u003c/em\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Authors own extracted from Eviews\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 ARDL Model Results\u003c/h2\u003e \u003cp\u003eThe ARDL bounds test is conducted to understand the presence of a cointegrating relationship among the variables. From Table V, we can infer that there is cointegration among the variables, as the F-stat value of 12.12 exceeds the lower and upper bounds at 1%, 2.5%, 5%, and 10% levels of significance. So, we reject the null hypothesis of no cointegration and accept the alternative hypothesis, indicating the presence of cointegrating relation among the variables.\u003c/p\u003e \u003cp\u003eTable VI: Cointegration Test Results\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCointegration Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003cp\u003escores\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngle-Granger tau-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngle-Granger z-static\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-42.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhillips-Ouliaris tau-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhillips-Ouliaris z-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-42.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Author's own extracted from Eviews\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Cointegration Results\u003c/h2\u003e \u003cp\u003eThe cointegration, as established by the ARDL bounds test, is also confirmed by other cointegration tests, including the Engle-Granger and Phillips-Ouliaris tau \u0026amp; Z statistics, with a probability score that is significant at the 5% level of significance, as shown in Table VI.\u003c/p\u003e \u003cp\u003eTable VII: Long-run and Short-run ARDL Estimations\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabh\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLong-run estimates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNGDPPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNNAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNCORR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNFDISTOCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNCO21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eShort-run estimates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNGDPGR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNEXP1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNLP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNNAT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNCORR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNFDISTOCK1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNINF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(LNCO21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECT(-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eR-Square \u0026ndash; 0.85\u003c/p\u003e \u003cp\u003eAdjusted R-Square \u0026ndash; 0.62\u003c/p\u003e \u003cp\u003eDurbin Watson Stat \u0026ndash; 2.91\u003c/p\u003e \u003cp\u003eF Stat \u0026ndash; 3.72\u003c/p\u003e \u003cp\u003eProb (F-Stat) \u0026ndash; 0.01\u003c/p\u003e \u003cp\u003eHeteroskedasticity Breusch-Pagan-Godfrey : 0.47\u003c/p\u003e \u003cp\u003eJarque-Bera (Prob) : 0.59\u003c/p\u003e \u003cp\u003eBreusch-Godfrey Serial Correlation LM test : 0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Authors own extracted from Eviews\u003c/p\u003e \u003cp\u003eAfter confirming cointegration, the study analyses long- and short-run dynamics to assess PHH. Table VII shows the long-run and short-run dynamics with the error correction term. Short-run coefficients show that FDI flow has a positive relation with GDPPC, merchandise exports, control of corruption, stock of FDI, and CO2 emissions. Labour productivity, control of corruption, and the stock of FDI are negatively associated with FDI flows and are significant. The probability score of merchandise export is significant, but the coefficient is positive. The short-run dynamics are different from those of the long run. In the long run, the stock of FDI and inflation have significant probability scores. The coefficient of inflation is negative, indicating an inverse relationship between FDI flow and inflation. GDPPC also shows negative relations, although not significant. Merchandise exports, labour productivity, natural resources, control of corruption, stock of FDI, and CO2 emissions show positive relations with FDI flow.\u003c/p\u003e \u003cp\u003eThe ARDL model is stable, as indicated by the F-statistic probability scores of 0.01. The model is able to explain the variations in FDI flow to the tune of 85%. The ECT is negative and significant, indicating that the ARDL model is stable and able to return to equilibrium at a rate of 14.3%. The ECT score is not very high, but its coefficient scores are negative, and the probability is significant at the 5% level, which is a necessary condition for ensuring stability. Diagnostic tests to understand the presence of heteroskedasticity, normality, and serial correlation, indicated through Heteroskedasticity Breusch-Pagan-Godfrey, Jarque-Bera, and Breusch-Godfrey Serial Correlation LM test, respectively, are validated to ensure model stability. Recursive estimates, as indicated by CUMSUM and CUSUM of squares, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, are also significant. The majority of the variables, including carbon dioxide, in the long and short run, are not significant and have a positive relationship with FDI flow, which leads to the conclusion that there exists the Pollution Haven Hypothesis exists in India\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Recursive Estimates\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Impulse Response Function\u003c/h2\u003e \u003cp\u003eIn the Impulse Response Function (IRF) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the red line shows 95 per cent confidence intervals, computed as +/-2 standard error confidence bands, and the blue lines show the IRF of the variable. The X-axis represents periods (quarters), and the Y-axis represents percentage variations. IRF measures a one-standard-deviation change in FDI flow with the dependent variables. This change can be a shock, an impulse, or an innovation to FDI flow to the dependent variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Cholesky table, as shown in Table VIII, indicates the short and long-run outcomes of shocks in the dependent variable.\u003c/p\u003e \u003cp\u003eTable VIII: Cholesky IRF Table\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"746\" height=\"459\"\u003e\u003c/p\u003e\u003cp\u003eSource: Authors own extracted from Eviews\u003c/p\u003e \u003cp\u003eIn the IRF, the positive and negative changes in FDI flows indicate that GDPPC exhibits a positive flow in the 2nd ,9th and 10th periods and follows a negative flow in all other periods. EXP is positive in the 3rd ,9th, and 10th periods only, indicating that there is a close relation between GDPPC and exports with FDI flow. LP, NAT, and INF are negative across the time period. CORR is positive from the 4th period onwards. STOCK is negative only in the 3rd period. CO2 levels decline and become negative from the 1st to 5th periods, and then remain negative from the 6th to 10th periods. This phenomenon indicates that changes in the FDI flow in response to the variables are not uniform, and the outcomes of FDI flows on all the dependent variables are asymmetrical.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGranger Causality Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePaths\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPGR does not Granger cause FDI flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP to FDI flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003eUnidirectional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNLP does not Granger cause FDI flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabour Productivity to FDI flow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNCORR does not Granger cause FDI flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarbon emission to Inward FDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2 does not Granger cause FDI flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO2 to FDI flow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPGR does not Granger cause exports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP growth rate to Merchandise exports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPGR does not Granger cause natural resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP growth rate to Corruption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPGR does not Granger cause FDI stock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP growth rate to FDI stock\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabour productivity does not Granger cause Corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabour Productivity to Corruption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabour productivity does not Granger cause Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabour Productivity to Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation does not Granger cause labour productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInflation to Labour Productivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2 does not Granger cause labour productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO2 to Labour Productivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural resources does not Granger cause corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural Resources to Corruption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation does not Granger cause Corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInflation to Corruption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2 does not Granger cause Corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO2 to Corruption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2 does not Granger cause Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO2 to Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation does not Granger cause CO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInflation to CO2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStock of FDI Granger causes Exports\u003c/p\u003e \u003cp\u003eExports does not Granger cause Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003cp\u003e6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07**\u003c/p\u003e \u003cp\u003e0.08**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStock of FDI to Exports\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003eExports to Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBidirectional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStock of FDI does not Granger cause Corruption\u003c/p\u003e \u003cp\u003eCorruption does not Granger cause Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06**\u003c/p\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStock of FDI to Corruption\u003c/p\u003e \u003cp\u003e\u0026amp;\u003c/p\u003e \u003cp\u003eCorruption to Stock of FDI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*5% level of significance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e** 10% level of significance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Authors own extracted from Eviews\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Causality Results\u003c/h2\u003e \u003cp\u003eThe causality results, as checked using the Granger causality test, indicate two-way causality relations between the stock of FDI and merchandise exports, as well as with the control of corruption. The unidirectional causality relations of other variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion of Results","content":"\u003cp\u003eThe objective of this study is to identify the presence/absence of the Pollution Haven Hypothesis (PHH) for India, considering factors such as FDI flows, market growth and openness, agglomeration, natural resource endowments, inflation, corruption, labour productivity, and CO2 emissions. Inward FDI, gross domestic product per capita, stock of FDI, ores, metals, fuel exports, GDP deflator, control of corruption rank, and GDP per hour worked are the proxy variables selected for the study, and data were collected from the World Bank\u0026rsquo;s World Development Indicators from 1990 to 2023. FDI flow is the dependent variable, and all other variables are independent variables. After checking the data for unit roots and ensuring data stability, the ARDL model was estimated, including a bounds test and error correction. The model is able to explain 85 per cent of the total changes in FDI flows and also found to be significant, considering the probability scores. Cointegration relation is established using bounds test scores and confirmed through the Engle-Granger and Phillips-Ouliaris statistics.\u003c/p\u003e \u003cp\u003eIn the short term, FDI flows exhibit a positive relationship with GDP growth rates, merchandise exports, control of corruption, the stock of FDI, and CO2 emissions. Labour productivity, control of corruption, and the stock of FDI have negative relationships with FDI flow, and they are also significant. The probability score of merchandise export is significant, but the coefficient is positive. In the long run, the stock of FDI and inflation have significant probability scores; an inverse relationship is observed between FDI flow, inflation and GDPPC. Merchandise exports, labour productivity, natural resources, control of corruption, stock of FDI, and CO2 emissions show positive relations with FDI flow.\u003c/p\u003e \u003cp\u003eThe ECT satisfies the conditions of stability, showing a negative and significant coefficient score of 14.3%, indicating a 14.3% rate of returning to equilibrium. Stability and diagnostic tests, as well as recursive estimates, indicate significant results. The results of the impulse response function indicate that FDI flow exhibits an asymmetrical relationship with all the dependent variables. Considering the causality relations, a. there is a one-way significant causality effect moving from GDPGR, LP, CORR, and CO2 to FDI flow, b. FDI stock and export are having a two-way relationship, c. FDI stock and corruption have two-way causality relations. This study confirms the presence of PHH for India.\u003c/p\u003e \u003cp\u003eThe majority of the variables, including carbon dioxide, in the long and short run, are not significant and have a positive relationship with FDI flow, which leads to the conclusion that there exists the Pollution Haven Hypothesis in India, considering the above selected variables and following the path as suggested by Dunning (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) for receiving FDI. This finding aligns with those of Miniesy \u0026amp; Tarek (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Murthy \u0026amp; Gambhir (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Rana \u0026amp; Sharma (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Dagar et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The asymmetrical FDI relations are also in sync with Nguyen et al. (2020), Muhammad et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Mujtaba \u0026amp; Jena (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who confirmed the presence of PHH for India. This study confirms the argument that developing countries have high air pollution due to high carbon dioxide emissions, accompanied by lax environmental laws [Hoffmann et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hassaballa, 2004; Doytch \u0026amp; Uctum, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kim \u0026amp; Adilov, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Asghari, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Badri \u0026amp; Parvizkhanlu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e]. India is one of the top 20 countries with the highest air pollution levels, as per the urban air quality database released by the World Health Organisation (WHO, 2016).\u003c/p\u003e \u003cp\u003eAs per this study, every 1% increase in FDI flow leads to a 1.20% increase in CO2 emission in the long run and 0.35% increase in the short run, confirms the fact that India\u0026rsquo;s economic growth has got a significant and positive impact upon carbon emission, as growing economies consume high energy mainly supplied from fossil fuels which results in the generation of CO2 emissions. Studies by Rajpurohit and Sharma (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Song (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also support this idea. Although this is not encouraging, but convincing, as it is claimed that MNEs coming into India from developed countries start using clean energy and hybrid technologies, as a result of which the emission levels can be reduced (Zhu et al, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Waqih et al, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but this study's results do not reflect the above argument.\u003c/p\u003e \u003cp\u003eA 1% increase in FDI flow leads to a 0.69% increase in merchandise exports in the long run, and a 0.73% increase in the short run reflects the fact that export growth aligns with FDI flows. This relation is also confirmed by the bi-directional causal relation between the stock of FDI and merchandise exports. For a long time, literature has suggested that FDI acts as an engine for enhancing exports and promoting economic growth, a claim confirmed in this study (Sethi \u0026amp; Sucharita, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Labour productivity is significant in the short run; a 1% increase in FDI flow leads to a 14.6% decrease in labour productivity. This is one of the unique findings of this study, as there is very little literature that studies labour productivity and FDI flow. Literature claims that FDI flows have the ability to increase the skill base of host countries through learning by doing, on-the-job training, and other mechanisms, as they originate from developed countries with a state-of-the-art skill base, creating positive knowledge and technology spillovers (Perri \u0026amp; Peruffo, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This research study supports the results of Ombuki et al (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), indicating that productivity and knowledge spillovers are absent in this case. The causality from LP moves to corruption and FDI stock leads to the non-definitive conclusion that necessitates more intensive firm-level studies\u003c/p\u003e \u003cp\u003eThe natural resources endowment and control of corruption moderate the quality of institutions, thereby influencing FDI flows; therefore, the role played by these two variables cannot be studied separately. Natural resources exhibit a negative relationship with FDI flow in the short run, and in the long run, it is positive, but not statistically significant in either time period. A 1% increase in FDI flow reduces natural resources by 25% in the short run, indicating that resource-seeking FDI is targeting the Indian economy; however, in the long run, its focus shifts. Institutional quality and the prevailing legal climate play a significant role in influencing resource-seeking FDI (Chiyaba \u0026amp; Singleton, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our research results for the Control of corruption show a positive relationship with FDI flow in the short run and a negative relationship in the long run, aligning with current realities. Control of corruption, one of the key ingredients for good governance, influences economic health and thereby enhances the flow of FDI (Asongu \u0026amp; Odhiambo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results of this study align with a recent study by Khan (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which shows that FDI exhibits a negative relationship with the control of corruption. Inflation exhibits a negative relationship with FDI flow in both the short and long run; the causality effect runs one way from labour productivity, CO2, and corruption, indicating that these factors have strong negative relationships, a finding established in most studies (Khan \u0026amp; Mitra, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Siddiqui \u0026amp; Aumeboonsuke, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As corruption is an important barrier to entry of MNEs, a recent study indicates that MNEs learn to adjust to cope with corruption by reducing corruption cost, and this experience helps them to sail across such environments and bring out country-specific entry policies (Thede \u0026amp; Karpaty, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe study results for CO2 and FDI flows are positive in both the short and long run, with causality running from CO2 to the stock of FDI. This is a clear indication that the flow of FDI and the increase in CO2 are happening simultaneously. The results of this study support the clear existence of PHH and align with those of Joshua et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Khan et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These study results contradict the findings of Waqih et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Shekhawat et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who did not confirm the presence of PHH for SAARC countries, which also includes India. The opposing views of PHH indicate favourable environmental benefits to host countries, due to FDI flows, as MNCs adopt modern production techniques in their entire production and supply chain processes, which can result in environmentally friendly outcomes that reduce degradations in the long run (Tamazian \u0026amp; Chousa, 2009).\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study is a significant contribution to the literature on FDI, as it reveals the inherent relationship among the variables that significantly impact the flow of FDI in India. Examining the determining factors of FDI with recent data sets can provide new insights and understanding of the current dynamics, which can help in understanding the ground realities across time and serve as an effective guide when framing policies. Additionally, this study helps assess the quality of FDI received by India during the studied period, which can also aid in understanding the motivations of investors and inform the development of India's future FDI policies. The motivations and objectives of MNEs when investing in India can also be understood.\u003c/p\u003e"},{"header":"7 Policy Implications","content":"\u003cp\u003eDrawing from empirical findings, a set of policy recommendations emerges, grounded among the complex dynamics of foreign investment, GDP growth rate, exports, labour productivity, natural resources, inflation, control of corruption, agglomeration and CO2 emissions within the context of India.\u003c/p\u003e \u003cp\u003eThe first policy consideration necessitates promoting sustainable FDI. By incentivising investments in green technology and emission reduction, we can mitigate the effects of CO2 emissions. Tax holidays and tax incentives can also show encouraging outcomes.\u003c/p\u003e \u003cp\u003eSecondly, FDI policies and goals must be made more environmentally friendly, which can act as a deterrent for those MNEs that invest in India with the objective of relocating polluting industries from their home country.\u003c/p\u003e \u003cp\u003eThirdly, the manufacturing practices followed in MNE investing industries must be as clean and green as possible.\u003c/p\u003e \u003cp\u003eFourthly, the government must monitor, control, and regulate the sustainability practices followed by MNE affiliates, branches, and their subsidiaries in India, and this must become part of the approval mechanism.\u003c/p\u003e \u003cp\u003eFifthly, improving institutional quality through good governance can reduce the effect of corruption, natural resources exploitation and can expedite the flow of FDI\u003c/p\u003e \u003cp\u003eSixthly, modifying the entry process to focus on local content development can facilitate spillovers in multiple areas of development, thereby enhancing the skill base and productivity.\u003c/p\u003e \u003cp\u003eConfiguring the above policy implications into development strategies can make India more attractive for foreign investments and also positively favour sustainable development. Need-based and time-specific modifications, with regular audits and monitoring, can create a more streamlined effect on the FDI entering into India.\u003c/p\u003e"},{"header":"8. Limitations and Directions for Future Research","content":"\u003cp\u003eStructural breaks in the data were not considered, which can be a limitation of the study. Future studies can follow the regime-shifting approach, identifying the structural breaks across different time periods to understand the determining factors. Understanding the sustainability profile of MNE affiliates and subsidiaries operating in India can help one identify and differentiate their economic and environmental motives.\u003c/p\u003e \u003cp\u003eA comparative analysis of the sustainability profiles of MNEs in their home and host countries can also help to assess the extent to which they are vulnerable to environmental challenges when they move from their home base. Demonstrating the differential sustainability practices followed by multinationals in their host country affiliates versus those in their home country can shed light on the differences in approaches, and this can help discourage polluting MNEs from entering India. The study has implications for fine-tuning the impacts of international trade to ensure environmental sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eDeclaration of Competing Interest: The author declares no known competing interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Availability Statement: The data used for this study are available from the author upon request and can also be downloaded from the source mentioned.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for Publication: Author gives her consent to this publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to Participate: Not applicable\u003c/p\u003e\n\u003cp\u003eClinical Studies: Not applicable\u003c/p\u003e\n\u003cp\u003eConflict of Interest/Competing Interest: The author declares no conflict of interest\u003c/p\u003e\n\u003cp\u003eEthical Declaration and Compliance: Not Applicable\u003c/p\u003e\n\u003cp\u003eInformed Consent/Consent to Participate: Not Applicable\u003c/p\u003e\n\u003cp\u003eFunding: No funding or grants received to assist with the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcharyya J. 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The pollution haven hypothesis. a geographic economy model in a comparative study; 2008.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"foreign direct investment, economic growth, pollution, carbon dioxide emission, autoregressive distributed lag, international trade","lastPublishedDoi":"10.21203/rs.3.rs-9343604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9343604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: The objective of this paper is to identify the presence of the Pollution Haven Hypothesis for India for the time period 1990 to 2023, considering cointegration equilibrium and causality relations, considering the foreign direct investment flow, agglomeration, market size, exports, natural resources, labour productivity, inflation, control of corruption and carbon dioxide emission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology \u0026amp; Findings\u003c/strong\u003e: The ARDL model was found to be a suitable fit for the study. Bounds test results confirmed the presence of a long-term cointegration relation, as did the Engle-Granger and Phillips-Ouliaris statistics. The stock of foreign direct investment is showing bi-directional causality with exports and corruption. The long-term and short-term results of the ARDL model, as well as the causality results, suggest that foreign direct investment is associated with increased carbon dioxide emissions. The Impulse Response function indicates that the foreign direct investment flow exhibits asymmetric responses to the independent variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOriginality\u003c/strong\u003e: The study confirms the pollution haven hypothesis for India. Reliability and stability diagnostics are found to be significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e: This research also suggests economic and environmental policy implications based on the study outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification Code\u003c/strong\u003e: Q54, Q57, Q58, O11\u003c/p\u003e","manuscriptTitle":"An Empirical Analysis of the Pollution Haven Hypothesis for India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 14:59:50","doi":"10.21203/rs.3.rs-9343604/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-13T07:10:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T04:05:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275418110346970179677695096260983885537","date":"2026-05-12T04:03:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205994093027556683851782098628159868887","date":"2026-05-10T18:06:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233973073438755103590412094319721976527","date":"2026-05-08T05:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-08T05:19:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-22T06:23:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T11:07:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T11:07:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Environment","date":"2026-04-07T10:34:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"372ccca0-c5ab-45ab-9958-76e97af3ac66","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-13T07:10:17+00:00","index":45,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T04:05:55+00:00","index":43,"fulltext":""},{"type":"reviewerAgreed","content":"275418110346970179677695096260983885537","date":"2026-05-12T04:03:23+00:00","index":42,"fulltext":""},{"type":"reviewerAgreed","content":"205994093027556683851782098628159868887","date":"2026-05-10T18:06:23+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"233973073438755103590412094319721976527","date":"2026-05-08T05:32:02+00:00","index":37,"fulltext":""},{"type":"reviewersInvited","content":"15","date":"2026-05-08T05:19:25+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T14:59:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 14:59:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9343604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9343604","identity":"rs-9343604","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}