Do ESG Risks Constitute a Financial Deterrent to Investment Attractiveness? An Empirical Multi-Country Analysis

Abstract

The growing incorporation of environmental, social, and governance (ESG) considerations into global financial systems has significantly influenced investment decision-making. Previous studies have mainly concentrated on ESG performance and their associated implications for businesses and have failed to examine the role of ESG risks in shaping barriers to cross-border investment. In this regard, this paper attempts to analyze the effects of ESG risks on foreign direct investment (FDI) inflows based on an unbalanced panel dataset for up to 250 countries spanning the years 2000 to 2024, coupled with cross-sectional data for 2020. This study uses a two-dimensional approach, whereby structural ESG risks are evaluated using panel FMOLS regression, while ESG risk exposures are assessed using cross-sectional models. This research also considers moderating factors such as economic development, industrial composition, and innovation capabilities. Based on the use of the national-level ESG risk, it is evident that ESG risks considerably reduce inward foreign direct investment.

1. Introduction

The incorporation of ESG issues in global finance systems has had a significant influence on how investments are viewed in light of risks. Issues of ESG, which include climate change, poor governance, and social conflicts, among others, have been noted to be key determinants in the movement of investments. With respect to foreign direct investment (FDI), risks related to ESG do not only focus on the firm level but also extend to macroeconomic aspects. The significance of ESG risks in determining FDI trends has gained prominence in recent years (UNCTAD 2024; International Monetary Fund 2009).

Much research focus in the existing literature has been directed towards ESG performance metrics and their influence on firm value, portfolio performance, and governance effectiveness (Caceres 2024). In comparison, relatively less focus has been placed on ESG risks, especially in the national-level investment landscape, which depends significantly on the country’s underlying structure and institutions. Another critical drawback of the past literature is that it has focused separately on the impacts of the dimensions of ESG (environmental, social, and governance), which has left gaps in knowledge about the overall impact on international investment. These gaps are relevant from the perspective of FDI, which considers the risk associated with the economy as a whole.

In this study, the research gap is clearly identified as the lack of a holistic, risk-based, macro-level empirical model that (a) focuses on ESG risks instead of ESG performance as drivers of FDI, (b) incorporates all three components of ESG (environmental, social, and governance) into a single analytical framework, and (c) identifies structural risks at the long-term level from exposure-based risks at the short-term level. Bridging the above-mentioned gap, the current study constructs an integrated analytical framework based on cross-country data through a two-axis methodology approach for ESG risks.

Through the use of ESG risks as macro-financial indicators for the investment attraction process, the study offers new insights into the issue of sustainability-related risks as applied in international investment. This is because the research empirically shows the influence of ESG risks on the pattern of FDI across nations and highlights the need to integrate risk-based evaluations of ESG issues into policy and decision-making processes.

The literature reveals a growing trend within academia of examining environmental, social, and governance (ESG) indicators as drivers of foreign direct investment (FDI) and the internationalization of companies. The empirical literature reveals that ESG indicators significantly and positively impact decisions about foreign investment. At the firm level, ESG indicators have been found to positively affect FDI since they enhance reputation, mitigate information problems, and increase trust among stakeholders (Yuan et al. 2025). In a similar vein, studies show that ESG indicators lead to bi-directional FDI activities, especially within large firms and those with a global market presence (Yuan et al. 2025).

At the macro level, indicators related to institutions and sustainability have gained increased recognition as major factors influencing FDI flows. Specifically, academic studies concentrating on emerging and Asian economies have concluded that the strength of environmental regulation, social progress metrics, and quality of governance are positively associated with the inflow of foreign investment (Tran and Trang 2025). Furthermore, it has been established that aspects of sustainable development, such as environmental protection and institutional quality, are critical in shaping patterns of FDI flows (Caetano et al. 2024).

Additionally, recent studies underscore the growing role of institutional and governance factors in mediating the effects of ESG criteria on FDI decisions. The quality of governance and specifically the effectiveness of the rule of law have been identified as important variables facilitating sustainable investment (Hamid and AlObaid 2025). Finally, the adoption of ESG standards by businesses operating abroad demonstrates that there is an ongoing trend towards responsible investments where environmental and social outcomes of operations are used to assess MNEs (Wiessner et al. 2024).

Despite such improvements, however, the literature review suggests that the ESG-FDI research domain suffers from the unequal treatment of ESG dimensions. Specifically, while environmental factors have been extensively considered in the literature, the social and governance factors have not been studied as often or in a combined manner (Rodríguez-Chávez et al. 2024). According to bibliometric research, the scope of the research in this area is continuously increasing; however, the literature in question is also fragmented, with very few studies relying on a comprehensive framework that allows for an analysis of the multi-faceted interaction between ESG factors and FDI (Zehouani and Ababou 2025).

The present paper fills a number of gaps in the current body of knowledge related to ESG factors and FDI. First, unlike many other studies, the present analysis considers ESG-related risks from the perspective of country-level risk. Second, the paper seeks to analyze the relationship among ESG risk factors and FDI from the standpoint of the theory of financial disincentives. Methodologically, the paper focuses on two types of variables, namely (i) structural risks, i.e., relatively slow-moving, long-term risks visible in the annual panel data of countries, and (ii) dimensions of exposure to ESG factors.

This cross-country study, spanning the period 2000 to 2024 with a maximum of 250 countries based on data availability, employs FMOLS panel estimation to detect the existence of long-term structural linkages, along with cross-sectional estimation for exposure effects in the base year. The findings show that an increase in ESG risk reduces inward FDI inflows, independently of conventional factors affecting FDI, including the market size, trade openness, and the institutional environment. There are also variations in the findings, where the negative relationship among ESG risks and FDI is weakened in economies with high levels of advancement and innovation capacity but strengthened in economies with industrial concentration in environmentally/socially sensitive sectors.

Apart from providing empirical evidence, this study also advances the conceptual discussion of the interaction between sustainable development and international business finance. In particular, by integrating ESG risk factors into a theoretical framework for FDI destination choice and illustrating contingent impacts in terms of innovation and sector structure, this paper provides useful insights into the design of public policies to reduce country risk perception and for investors who pursue sustainable development considerations when making location decisions.

The remaining sections of this paper are organized as follows. The literature review is given in Section 2. Section 3 details the study design, data used, variables, and estimation strategy. The results of the analysis are provided in Section 4, while Section 5 concludes the study with policy recommendations and suggestions for future research.

2. Literature Review on ESG Risks and Investment Attractiveness

From the existing academic literature, it can be seen that knowledge of the connection between ESG factors and investment outcomes depends largely on results that appear in numerous academic papers, which contradict each other regarding both the presence of any relationship and its nature—whether positive or negative—due to many reasons, including methodology and scope. On the one hand, some researchers state that better performance in terms of ESG practices contributes to higher business value and cash flows (Pedroni 2001); on the other hand, country-level ESG risks are considered to negatively affect investment due to the extra uncertainty created by them (Krueger et al. 2023; Bolton and Kacperczyk 2021). Differentiation between ESG performance and risk is necessary, as the first concept focuses on potential benefits and the second one on the drawbacks of investment. When this differentiation is considered in the framework of FDI theory, the risk of ESG can be defined as a macro-level locational disadvantage.

2.1. ESG Risks, Financial Risk, and Economic Performance

Environmental, social, and governance risks are being increasingly recognized, not only in the niche sustainability literature but also in mainstream discussions, as risks that have economic and financial implications. On a macroeconomic scale, environmental, social, and governance risks pertain to the structural vulnerabilities built into each country based on its environmental sensitivity, social resilience, and governance system. Risk-based analysis is different from the performance-based perspective of ESG analysis because the latter focuses on the risks involved, while the latter is focused on outcomes.

Under the environmental angle, climate risk plays an important role in macroeconomic risk. Exposure to climatic risks may adversely impact the infrastructure, economic production, and investment of any country, especially when the country does not have sufficient resilience in the face of these risks. According to prior studies, climate risk negatively impacts output and growth rates, making countries more exposed to risks (Krueger et al. 2023; Bolton et al. 2023).

Apart from environmental and corporate governance risks, social risks are also important in the context of ESG. High inequality, social division, volatility in the labor market, and demographic issues can lead to social and political unrest. Such cases can lead to uncertainties in policies and economic activities and raise transaction costs for firms. According to empirical studies, political risks and instabilities are associated with poor levels of investment and economic growth, particularly in emerging economies (Al-Harbi 2025).

The issue of governance risk is relevant due to problems such as weak regulation, corruption, poor transparency, and inadequate rule of law. Institutions influence economic incentives, property rights, and contracts, which have to be respected in order to invest in an economy. Many studies confirm that low-quality governance increases uncertainty, decreases productivity, and discourages investment (Acemoglu et al. 2012; Daude and Stein 2007). Therefore, the governance element of ESG risk is an essential part of country risk.

It is worth noting that ESG risks are connected with each other and complement each other, so the environment can worsen social relations, and poor governance will hinder a nation’s efforts to solve both environmental and social problems. Such interconnections indicate the need to evaluate ESG risks using a holistic approach due to the impacts that they have on economic results and risks. In turn, several recent pieces of research point out that such interaction could increase macroeconomic risks and the persistence of shocks (Bolton et al. 2023; Krueger et al. 2023).

There is also empirical evidence indicating that ESG risks are being incorporated into market prices, indicating their financial effects. Organizations with higher scores for ESG have lower costs of debt and equity, as it reduces risks, transparency, and the asymmetry of information in the market (Ernst and Woithe 2024; Sun 2025). Thus, ESG risks are not just related to reputation but have a tangible financial impact.

With respect to asset pricing, ESG factors are increasingly perceived as systemic risk variables. Research reveals that ESG metrics, which comprise rating stability and momentum, enter the calculation of expected returns and determine the cost of capital for companies (Magnani et al. 2024). This suggests that ESG risks form part of investors’ expectations.

In addition, the degree of sensitivity to ESG risks differs among different financial markets. While debt markets are more responsive to ESG risks compared to equity markets, better ESG ratings lower the probability of defaults and lower funding costs (Al-Harbi 2025; Tian 2023). Meanwhile, the reaction of equity markets to ESG risks is more varied, depending on the preferences and return expectations of individual investors.

Financial institutions such as banks also consider ESG factors in credit decisions. Poor ESG ratings translate to wider loan margins and tighter borrowing conditions, which implies that ESG risks are priced in credit risk management (Carnevale and Drago 2024).

Indeed, the literature has revealed that ESG-related risks are systematically priced in financial markets, resulting in changes in the cost of capital, the valuation of firms, and economic outcomes. However, such changes depend on factors including the market architecture, industry concentration, and the institutional framework.

Empirically, both physical climate risks and transition risks shape investors’ decisions through changes in the expectation of returns, increases in uncertainty, and the incremental cost of capital (Bolton et al. 2023; Giglio et al. 2021). Indeed, financial markets have been increasingly directing resources from carbon-intensive industries to low-carbon assets (Krueger et al. 2023). Moreover, vulnerable countries receive relatively few capital flows compared to those with robust climate policies (Pankratz et al. 2023a).

The expansion of sustainable financial products, such as sustainability-linked loans, green bonds, and ESG funds, has also contributed to the greater financial significance of ESG factors. Sustainable financial products have a role to play in lowering financing costs, promoting efficient markets, and effective risk management due to the consideration of long-term sustainability risks in pricing models (Giglio et al. 2021). Countries that have developed advanced sustainable finance systems tend to show greater resilience and access to international financing.

Based on the above facts, it is possible to conclude that ESG risks are closely related to financial risks and economic performance because they affect macroeconomic stability and investor sentiments. Measures that use a risk approach evaluate risks from negative environmental, governance, and social impacts, thus making countries more exposed to country risk. In countries with higher ESG risks, investors have to pay higher costs for funding, face lower levels of confidence, and find themselves less inclined to invest (Krueger et al. 2023; Bolton and Kacperczyk 2021; International Monetary Fund 2009).

In conclusion, it can be said that the incorporation of ESG factors into the concept of country risk provides a comprehensive view of their effects on economic results. As far as economic growth, institutional performance, and the predictability of policies are concerned, ESG factors make a significant contribution to the broader macroeconomic context for foreign direct investment.

2.2. ESG Considerations and Foreign Direct Investment

FDI is associated with host-country risks as it requires heavy financial investments that are difficult to reverse. It differs from portfolio investment because the latter does not involve making any specific promises in terms of the geographical location where the financial resources will be placed, as it may be changed at will. Consequently, traditional host-country risks, such as political and regulatory risks, and institutional quality have been highly important in FDI for several years (Al-Harbi 2025; Daude and Stein 2007).

In this context, ESG risks can be considered an extension of the existing risk factors influencing the FDI process of decision-making. Environmental risks like the impacts of climatic risks, resource shortages, and environmental pollution may affect infrastructure, efficiency, and supply chain management, thus increasing the operational expenses of foreign firms. There is some research indicating that environmental risks can reduce the attractiveness of the FDI decision, particularly in economies that are highly exposed or dependent on climate-related industries (Bolton and Kacperczyk 2021; Krammer 2021).

Equally important is that social risks play an important role in determining FDI. Risks related to income disparity, rigidity in the labor market, demographics, and social instability lead to disruptions in the manufacturing process, policy uncertainty, and high transaction costs. Research indicates that both social and political risks tend to negatively affect FDI since investors prefer not to conduct business in an environment marked by uncertainties and social instability (Al-Harbi 2025; Katsampoxakis et al. 2026).

Linkages between ESG and foreign direct investment are significantly dependent on the question of governance. A lack of good laws, corruption, a lack of transparency, and ineffective law enforcement make it less likely for investors to have trust, which discourages investment. The majority of empirical research shows that good governance positively impacts the credibility of host nations and encourages FDI (Berg et al. 2022).

According to the recent literature, it is imperative to mention that the pricing of ESG risks plays a key role in the influence of investment decisions in the international context. With the increasing bearing of ESG risks in financial markets, such risks impact the cost of capital, expected returns on investment, and risk premiums, thereby impacting capital allocation among various countries. Companies and countries with better ESG performance receive larger amounts of investment from overseas investors because they are low-risk and financially stable (Ernst and Woithe 2024; Moussa and Elmarzouky 2024).

Asset pricing theory highlights that ESG risks can be classified as components of systematic risk, affecting global portfolios. Evidence suggests that ESG risk factors are included in asset pricing equations and impact the expected return on investment (Dobrick et al. 2025).

Moreover, ESG risk pricing influences financial performance through its impact on cash flow, valuation, and investment capabilities. ESG risks can be related to the cost of doing business, low efficiency, and high costs of financing, which may negatively influence foreign investment. On the contrary, low levels of ESG risk mean stability and sustainability, thus increasing the attractiveness of the country for the purpose of investment.

The integration of ESG criteria into investment decisions shows a trend of structural change in global finance, which implies that ESG risks are increasingly perceived as financially material. The emergence of numerous investment products focusing on ESG has strengthened the importance of ESG scores in determining international investments (Katsampoxakis et al. 2026).

Finally, climate risks, both physical and transitional, affect FDI flows due to their influence on the regulatory environment, expected returns, and longevity of assets (Giglio et al. 2021; Krueger et al. 2023). High levels of climate vulnerability negatively correlate with levels of investment, whereas countries with sustainable development policies attract stable investments (Pankratz et al. 2023b). Modern multinational corporations also consider ESG risks when deciding about locations in which to operate (Bolton et al. 2023).

In addition to this trend, the development of sustainable financial assets, including sustainability-linked loans and green bonds, contributes further to allocating capital to sustainable and socially responsible investments. States with a developed sustainable finance framework and robust ESG regulatory structure would be able to attract more steady foreign investments because of the lower perceived risk and high institutional legitimacy (Pankratz et al. 2023a).

Nevertheless, this relationship may be conditional on certain structural aspects that define the economic system of the state. States with a diversified economic structure and high levels of human capital can cope with ESG risks better than others. Thus, the negative effect of ESG risks on the volume of FDI is less pronounced for such economies.

It is reasonable to conclude that considering ESG risks in the framework of foreign direct investment is an additional step in evaluating sustainable development factors’ influence on investment. The introduction of ESG criteria to traditional risk models enables one to take into account structural and institutional aspects of foreign investment.

2.3. Heterogeneity, Research Gaps, and Conceptual Framework

The link between ESG risk factors and foreign direct investment will differ from one nation to another because of differences in economic structure and adaptation to environmental changes. High ESG risk factors in any country can impact the process of decision-making among investors in terms of investing their money in particular projects. This will all be dependent on the nature of the economy under consideration.

Another crucial factor that affects the extent of the effect in question is the level of economic development. In economically developed countries, one can find effective institutions, sound infrastructure, and technology that would enable the effective handling of ESG risks. As such, the effect that ESG risks may have on foreign direct investment in such countries would be smaller than the effect in other countries at a less advanced level of economic development. This is because poor countries might be deprived of certain crucial elements, such as well-functioning financial markets.

A further aspect associated with heterogeneity concerns the sectoral composition. Countries where the economic structure includes sectors vulnerable to environmental and/or social impacts (e.g., countries that are resource-based, agricultural countries, etc.) will be more susceptible to potential negative consequences that can arise in connection with the adoption of the proposed ESG framework. In other words, the presence of high levels of risk related to the mentioned sectors will mean additional expenses and lower profit rates for foreign investors. On the contrary, countries that have a diversified economy and rely on services and innovation will not be affected by these risks (Al-Harbi 2025; Berg et al. 2022).

The ability to innovate represents another critical factor affecting the impact of ESG criteria on FDI. Technological development and research will allow ESG issues to be addressed and minimize the risks involved, thus enabling countries to maintain their investment attractiveness. Innovation economies have a strong ability to mitigate possible negative financial implications of applying ESG criteria.

All of these aspects are interlinked and together determine how ESG risks impact foreign investment. Aspects like economic development, institutional development, the diversification of sectors, and innovative capabilities usually develop simultaneously, determining the resilience of countries to sustainability risks. Therefore, considering ESG risks separately might lead to erroneous outcomes due to omitted interaction effects.

While there has been an increasing amount of literature written about ESG and investment issues, there is still a clear gap regarding research on how ESG risks, broken down into their components, interact with other factors at the macro level and impact foreign direct investment. Moreover, despite greater awareness of the ESG risk dimensions and their valuation, current research either treats ESG risks as a monolithic concept or analyzes each of the dimensions independently, without considering any possible interaction between the two.

This study attempts to bridge this research gap by presenting a holistic theoretical model for understanding the relationship between ESG risks and foreign direct investment in terms of heterogeneity at the country level.

2.4. Development of Hypotheses

Foreign direct investments are irreversible forms of investment that expose multinational corporations to risks in the destination countries for prolonged periods of time. Financially speaking, investors integrate ESG risks into their location choices only if they play a role in determining the expected return, continuity of operations, and asset value of foreign investments. Based on previous research, this paper defines ESG risks as multidimensional sources of financial risk and proposes hypotheses that are congruent with the two-axis approach.

The basis for the differentiation of Axis 1 and Axis 2 lies in distinguishing long-term, structural ESG risks from short-term risks of exposure. Axis 1 addresses the enduring macroeconomic risks that change over time and determine the attractiveness of foreign investment in the long term. Conversely, Axis 2 deals with the contemporaneous risks of exposure, which can affect the decisions of investors in the short run.

2.4.1. ESG Structural Risks and Investment Attractiveness (Axis 1)

Structural ESG risks refer to characteristics associated with a country’s environmental, social, and governance aspects. The risks change slowly and shape the investment climate in the long run. Environmental damage, social unrest, and poor governance mechanisms may lead to higher costs of production, lower productivity, and regulatory uncertainties, reducing the anticipated profitability of foreign investments (Bolton and Kacperczyk 2021; Pankratz et al. 2023b).

The assessment of ESG risks among investors has shifted towards a more forward-looking approach, especially for systemic and persistent risks (Ilhan et al. 2021). For countries, greater structural ESG risks may discourage foreign investment through concerns about economic stability and cash flow in the future. Because of the long-term nature of FDI, multinational corporations are especially sensitive to such risks.

The present analysis aligns with the existing international investment literature that indicates that risks at the country level and institutional weaknesses limit FDI by generating uncertainties and low returns on expectations (Al-Harbi 2025; Daude and Stein 2007).

H1: 

Higher levels of structural ESG risk negatively affect investment attractiveness, as measured by inward FDI inflows.

2.4.2. ESG-Specific Risk Dimensions and Investment Attractiveness (Axis 2)

ESG issues are also present as exposure-based risks, which impact the perspective of the investor. Some examples of environmental risks are climate change risks and environmental liability issues. Social risks can be related to labor issues or demographic risks. Lastly, governance risks cover regulation, corruption, and property right enforcement problems (World Bank 2024).

Exposure to these risks can discourage foreign investment because they will disrupt operations, create compliance expenses, and create reputation issues for investors (Daude and Stein 2007). Even though previous studies have studied these aspects independently, it is common practice for investors to evaluate them together in terms of their ESG risk exposure. Being exposed to these risks can prompt investors to postpone, limit, or move their investments elsewhere.

H2: 

Higher exposure to specific ESG-related risk dimensions negatively affects inward foreign direct investment.

2.4.3. The Moderating Roles of Economic Development, Sectoral Structure, and Innovation Capacity

It is expected that the effects of ESG risks on the desirability of investment will differ across nations based on their structural aspects. The level of economic development, industrial structure, and innovation capacity will have a bearing on how well a nation can manage sustainability risks. Nations with high-income economies, diverse industrial structures, and advanced innovation capacities will prove to be less vulnerable and hence reduce the likelihood of negative impacts of ESG risks on investment (Al-Harbi 2025).

Meanwhile, nations with low diversity in terms of industry, lower technological capabilities, and reliance on sectors where there is environmental or social risk will face greater negative impacts of ESG risks on investment.

H3: 

The negative impact of ESG risks on investment attractiveness is moderated by economic development, the sectoral structure, and the innovation capacity, such that the deterrent effect is weaker in more developed, diversified, and innovative economies.

The hypotheses have been developed from the links among ESG risk and FDI on the basis of theory through risk-adjusted returns and location-based advantages. Hypothesis 1 represents long-term structural constraints, whereas Hypothesis 2 indicates risks associated with short-term effects on the sentiments of investors. The examination of these hypotheses offers an empirical way to understand the relationship among FDI and ESG risk.

3. Research Design and Methodology

3.1. Sample Description and Data Sources

The analysis makes use of an unbalanced panel dataset that involves up to 250 countries between 2000 and 2024, depending on data availability.

The selected countries were determined using the availability and consistency of the ESG risk factors and macroeconomic variables. The inclusion of a country in the sample required having sufficient time-series observations to allow for reliable panel estimations. ESG risk factor data are obtained from reliable international databases, which offer standardized risk measurements for each country. Countries included are from North America, Europe, the Asia-Pacific, Latin America, and Africa. Data on foreign direct investment and other control variables are sourced from internationally recognized organizations such as the World Bank and International Monetary Fund.

This study uses aggregate ESG risk measures and individual ESG risk dimension measures. The aggregates are employed to assess the structure of ESG risks, while individual ESG risk dimensions are computed using cross-sectional analysis for the year 2020. The choice of 2020 was informed by data availability and its appropriateness in measuring the global risk environment.

The dependent variable, net inflows of foreign direct investment (FDI), is drawn from the UNCTADstat dataset and expressed in USD at current prices. Foreign direct investments are defined as a type of long-term investment across borders with high sunk costs, making them sensitive to the economic environment of the host country.

The macroeconomic and moderation variables are from the World Bank dataset—World Development Indicators—while the quality of institutions variable is based on the worldwide governance indicators—specifically the rule of law indicator.

Structural breaks that could have happened over the course of the studied period, such as global financial crisis episodes and disruptions caused by the COVID-19 pandemic, are considered as variables that could affect the association between ESG and FDI. In response to this, the study highlights the importance of considering long-run associations in the panel setting (Axis 1) and supports this with a cross-sectional analysis (Axis 2).

3.2. Research Hypotheses and Empirical Strategy

The empirical analysis aims to test the possibility that ESG risks serve as financial disincentives for investment attractiveness, as reflected in foreign direct investment flows. In light of the theoretical perspective presented in Section 2.4, the research tests Hypotheses H1–H3 through a rigorous econometric approach based on the two-axis approach.

Hypothesis H1, centered around structural ESG risks, is examined using panel cointegration methods. As the cross-country dataset is heterogeneous, and there could exist some cointegrating relationships between variables, the FMOLS estimator will be employed for estimating the long-run relationship among ESG risks and FDI flows.

As regards Hypothesis H2,whichconsiders the exposure-related risks associated with ESG, cross-sectional regression is usedwith2020 as the reference year. In this way, the immediate effect of the exposure to ESG risk components is captured through differences in the levels of country-level exposure at a certain point in time. This research design facilitates short-run impact assessment and complements the long-run panel data model.

With Hypothesis H3, the addition of interaction terms among the ESG risk indicators and moderators such as economic development, sector composition, and innovation is achieved. Through these interaction effects, the impacts of country-specific factors on the connection between ESG risks and FDI can be examined.

Standard control variables that have been used extensively in foreign direct investment (FDI) studies are also incorporated into the empirical models in order to identify the impacts of ESG risks. Robustness tests are run to ensure consistency in the empirical output regardless of the different variable definitions and model formulations.

Overall, the approach taken in empirical testing is quite versatile as both panel and cross-sectional analysis techniques are utilized in order to consider both long-term structural impacts and short-term exposure dynamics.

3.3. Variables and Measurements

The research uses a number of dependent, independent, and control variables for an empirical analysis of the association between ESG risks and foreign direct investment. The inward foreign direct investment (FDI) net inflow in current US dollars is considered the dependent variable. It denotes the magnitude of foreign investment received by a country and can be used as a measure of investment attractiveness. Since there are some deviations from a normal distribution in FDI statistics, the variable is logged.

3.3.1. Endogenous Variable

The endogenous variable is the net inward foreign direct investment (FDI) inflow, which is defined in accordance with the International Monetary Fund as an investment that involves lasting interest and effective control by a foreign investor. FDI inflows are commonly used as a proxy for investment attractiveness and international capital allocation.

Because of the highly skewed distribution of the FDI data and the presence of extreme observations, the variable is transformed using the natural logarithm after the appropriate treatment of zero and negative values, as is common practice in the empirical FDI literature.

3.3.2. Explanatory Variables

(i)

Structural ESG-related risk indicators (Axis 1)

The structural ESG-related risks are quantified through aggregate country-level ESG risk indicators that capture the persistent country-level vulnerabilities associated with environmental, social, and governance issues. The indicators change every year from 2000 to 2024 and capture the long-term properties of the country-level economic, social, and institutional frameworks. The indicators are higher in countries that are more vulnerable to ESG-related risks. These indicators are intended to capture the structural, slow-moving sources of country risk, rather than short-term variations.

(ii)

ESG-specific risk dimensions (Axis 2)

The ESG-specific risk dimensions capture exposure-driven risks associated with environmental, social, and governance issues. The indicators capture the sources of uncertainty that are specific to ESG issues, such as regulatory uncertainty, social unrest, and environmental risks. Because of data limitations, the indicators are measured for the reference year 2020 and analyzed in a cross-sectional setup. This is because the ESG-specific risks are exposure-driven and therefore time-specific and are not well suited to panel estimation.

3.3.3. Moderating Variables

To address the heterogeneity in the ESG-FDI relationship, three groups of moderating variables are considered:

• Economic development, represented by the GDP per capita, capturing market sophistication and stability.
• Sectoral composition, represented by the share of agriculture, industry, and services in the GDP, reflecting economic diversification.
• Innovation capabilities, represented by R&D expenditure as a percentage (%) of the GDP, capturing a country’s ability to adapt to and reduce ESG risks.

These moderating variables are then interacted with the ESG-related risk indicators to examine heterogeneous effects across countries.

3.3.4. Control Variables

In line with the knowledge capital theory of MNEs, the analysis controls for traditional determinants of foreign direct investment location through control variables. These variables include the market size (GDP), trade openness, tariff barriers, and institutional quality, which is quantified by the rule of law index. The inclusion of these variables enables the model to control for both horizontal and vertical incentives driving FDI. All control variables are included to isolate the effect of ESG risks on foreign direct investment.

The principal independent variable for Axis 1 is the aggregate score of ESG risks. It represents the degree of exposure of a country to various ESG risks as a whole and can be considered structural in nature.

Regarding Axis 2, disaggregation of ESG risks will be considered. This will include environmental risk, social risk, and governance risk. The risk measures here are exposure-related risk measures, measured cross-sectionally in terms of the reference year 2020. They will enable us to examine the varying effects of ESG risks on FDI.

In order to address possible heterogeneity, the following moderating variables that affect the effects of ESG risks on FDI will be taken into consideration. The first is the level of economic development of the host nation, which can be quantified using the GDP per capita. Another moderating variable is the sectoral structure, which can be measured using the proportion of economically sensitive or environmentally sensitive sectors.

Control variables are included to estimate the influence of ESG risks on FDI. The control variables are the market size, which is indicated through the GDP; trade openness, which is captured using the trade-to-GDP ratio; and institutional quality, which is captured through indicators of governance, like the rule of law. These control variables are well-known factors impacting foreign direct investment and are used to minimize any potential issues related to omitted variable bias.

All variables have been taken from internationally recognized sources, ensuring consistency and compatibility for comparisons between countries. Where required, the variables have been adjusted or standardized to ensure consistency.

In general, the choice of variables conforms to the theoretical framework and will facilitate the analysis of ESG risk impacts on foreign investment opportunities.

3.4. Empirical Model

The empirical model has been formulated to determine the effects of ESG risks on inward FDI while considering country-level heterogeneity.

The empirical model includes control variables based on international business theory, which takes into consideration structural factors affecting FDI flows. Factors such as the GDP per capita serve as indicators of market size and development, trade openness indicates openness to the international environment, and innovation serves as a measure of technological absorptive capacity. Such control variables have theoretical relevance to location-specific advantages in the complex paradigm that affects the expected profitability and risks perceived by investors. Equations for both the Axis 1 and Axis 2 models have been formulated in order to ensure identification through the control of structural variables and also take into consideration country-level heterogeneity.

The issue of endogeneity, especially from the possibility of reverse causality between ESG risks and FDI flows, is mitigated by the chosen modeling approach. The employment of FMOLS within the panel context helps to address the issues of endogeneity and serial correlation in long-run models, while the incorporation of suitable control variables ensures that the problem of omitted variable bias does not become an issue. Furthermore, suitable lags have been considered wherever necessary to avoid any problems of simultaneity. The study does not use instruments; however, the modeling process follows past empirical studies, which emphasize long-run relationships only.

3.4.1. Panel Data Models: Structural ESG-Related Risks

To test Hypothesis H1, inward FDI inflows are modeled as a function of structural ESG-related risks and control variables:

FDI_it = α_i + β₁ESG_it + β₂X_it + ε_it

(1)

To investigate heterogeneous effects (Hypothesis H3), the model is extended to incorporate the interface between ESG-related risks and the moderating variables:

FDI_it = α_i + β₁ESG_it + β₂M_it + β₃(ESG_it × M_it) + β₄X_it + ε_it

(2)

where FDI_it denotes inward foreign direct investment inflows in country i at time t, ESG_it represents structural ESG risk indicators, M_it denotes the vector of moderating variables, and X_it is a vector of control variables. Nation-specific fixed impacts α_i capture unobserved heterogeneity.

Equations (1) and (2) jointly provide empirical tests of Hypotheses H1 and H3.

3.4.2. Endogeneity and Identification Strategy

One key methodological problem in analyzing the link between ESG-related risks and FDI flows involves the issue of endogeneity. The sources of endogeneity include reverse causality, omitted variable biases, and simultaneity. On the one hand, ESG risks could drive FDI flows. However, on the other hand, incremental FDI would bring about better ESG policies because of technology transfer, spillover effects, and improved regulatory compliance (Demena and van Bergeijk 2019; Krammer 2021).

Other variables, including institutions, macroeconomic stability, or country-specific factors, could have a simultaneous effect on both ESG risks and FDI flows, leading to a biased coefficient estimate (Hayakawa et al. 2013). Another problem associated with endogeneity is measurement errors in ESG risk indicators (Berg et al. 2022).

In regard to the aforementioned limitations, the following methodological tools are used in this research. Firstly, the inclusion of control variables such as the GDP per capita, trade openness, and the rule of law assists in minimizing the problem of omitted variables since the abovementioned variables are important factors that affect the dependent variable. Secondly, using the robust weighted least squares (RWLS) technique with robust estimators improves the validity of the coefficient estimates given the non-ideal nature of errors. Thirdly, the interaction variables used in the expanded model assist in dealing with the problem of simultaneity.

Nevertheless, it must be noted that these methods might not completely resolve the problem of endogeneity. Further studies can use identification techniques, such as IV regression, system GMM, or natural experiments to prove causality (Arellano and Bover 1995; Blundell and Bond 1998).

3.4.3. Cross-Sectional Models: ESG-Specific Risk Dimensions

To test Hypothesis H2, the following cross-sectional specification is estimated for the reference year:

FDI_i = γ₀ + k∑_γkESGRisk_ik + δZ_i + u_i

(3)

To assess heterogeneity in exposure-based effects, the model is extended by including interface terms among ESG-specific risk dimensions and the moderating variables:

FDI_i = γ₀ + k∑_γkESGRisk_ik + _m∑ϕ_m(ESGRisk_ik × M_mi) + δZ_i + u_i

(4)

Equations (3) and (4) represent a test of Hypothesis H2, but they also help to test for heterogeneous treatment effects. The regression coefficients can only be regarded as conditional relationships since it would be inappropriate to regard them as causative given the observational nature of the data used and the lack of an identification strategy.

3.5. Estimation Method

For the panel data regressions in Equations (1) and (2), FMOLS estimators will be employed as a means of dealing with serial correlation and potential endogeneity in heterogeneous panel data. The use of FMOLS is more suitable for estimating the existence of long-run relationships with respect to slowly moving macroeconomic and institutional factors.

For the cross-sectional regressions in Equations (3) and (4), robust weighted least squares (RWLS) estimators are employed due to heteroskedasticity and the possibility of having outlier values within FDI data. The use of estimation techniques such as robust regression helps in mitigating the effects of outliers.

While various identification approaches may be employed, the selected estimation techniques are appropriate for the objective of analyzing the long-run and exposure-related relationships among ESG-related risks and foreign direct investment.

RWLS estimation is used to deal with heteroscedasticity caused by differences between countries in their exposure to ESG risks and the size of their economies. By giving observations unequal weight in such a way that those observations with larger variability in the error terms are given less importance, RWLS increases the efficiency and ensures that the effect of such observations on the analysis is minimal. This is especially appropriate in the scenario of a cross-country analysis, where there are large discrepancies with regard to the quality of data and the size of the economy in question. Furthermore, RWLS is appropriate for the cross-sectional model of Axis 2 due to its variance-instability problem.

Econometric methods are selected based on their association with the dataset and research objectives. The selection of FMOLS is appropriate because of the non-stationarity of panel data and the estimation of long-term relationships, addressing issues of endogeneity and serial correlation. Concerning Axis 2, RWLS is suitable due to heteroskedasticity, as well as differences between countries in terms of ESG risk vulnerability. Possible issues of endogeneity related to the reverse influence of ESG risks on FDI can be partially overcome through the use of lag structures and robustness tests; however, this study is primarily concerned with showing associations rather than causation.

4. Empirical Results and Discussion

First, the findings consistently indicate the presence of a strong negative correlation among ESG risks and foreign direct investment in different nations. According to the analysis on the basis of the panel FMOLS estimation (Axis 1), there is a strong, statistically significant inverse correlation among the level of total ESG risk and foreign direct investment. The more countries suffer from structural ESG risks, the less likely it is that they will be able to draw investors, which is explained by the significance of the issue for the overall evaluation of country risk.

At the same time, the cross-sectional data analysis (Axis 2) provides additional support to the results of Axis 1 by revealing negative correlations between ESG risks in certain areas (environmental risks, social risks, and governance risks) and FDI inflows. Among the three mentioned types of ESG risk, governance risks demonstrate the strongest impact on FDI. The influence of environmental and social risks is also significant but differs among countries.

The presence of moderator variables points to the significant heterogeneity in the relationship between ESG and FDI. The interaction terms show that the negative influence of ESG risks is smaller in those countries that are more economically developed, where their economies have better diversification, and where the innovation level is higher. In turn, these aspects increase resilience and adaptability and thus reduce the negative effects of ESG risks on their attractiveness for investment.

At the same time, in less developed countries, where there is less diversification and less innovation, the impact of ESG risks on foreign investment is more prominent since their vulnerabilities make the risks of facing problems related to sustainability even more visible and thus discourage investors.

The conclusions are supported by the different empirical approaches used, which increases the credibility of the conclusions. The coefficients of control variables like the size of markets and openness of trade have reasonable sign values, which confirms the consistency of the models.

In summary, the outputs are consistent with the theoretical framework and the literature in recognizing that ESG risks are more than just a question of morality and reputation and have important financial implications that impact foreign direct investment globally. Through its exploration of the structural and exposure approaches, this research provides insights into the role of ESG risks in shaping foreign direct investment flows around the world.

4.1. Descriptive Statistics and Correlation Analysis

Table 1 illustrates the descriptive statistics of the core variables employed in the empirical analysis, namely inward foreign direct investment, ESG-related risk indicators, moderating variables, and control variables. The statistics indicate the existence of considerable time-series and cross-sectional heterogeneity in both inward FDI and ESG-related risks, which is in line with the global coverage of the dataset.

Inward FDI inflows are characterized by a highly skewed distribution, with considerable differences between large host economies and countries that have experienced net outflows over extended periods of time. Structural ESG-related risk indicators are characterized by moderate time-series variability but considerable cross-sectional variability, which is in line with the presence of entrenched environmental, social, and governance risks embedded in national systems. ESG-related risk dimensions also demonstrate considerable cross-sectional variability in the reference year.

Table 2 provides the correlation matrix for the core variables. Although there are some correlations between ESG-related risk indicators and institutional quality indicators, the correlation coefficients are below conventional cutoff levels, indicating that multicollinearity is unlikely to be a serious issue in the subsequent regression analysis.

Table 1. Descriptive statistics.

Variable	Obs.	Mean	Std. Dev.	Min	Max
ln(FDI inflows)	4850	6.42	2.11	−2.30	12.90
Structural ESG risk	4850	52.7	12.4	21.3	78.9
Environmental risk	210	55.1	14.8	24.5	82.6
Social risk	210	48.9	13.1	22.8	79.4
Governance risk	210	50.6	15.7	19.6	84.1
GDP per capita (USD, log)	4850	9.31	1.12	6.71	11.62
R&D expenditure (% GDP)	3920	1.21	0.93	0.02	4.35
Agriculture VA (% GDP)	4850	13.8	9.6	0.4	52.1
Industry VA (% GDP)	4850	29.4	9.1	11.3	58.7
Trade openness (%)	4850	76.2	38.5	19.4	211.6
Rule of law	4850	−0.02	0.94	−2.35	1.89

Notes: FDI in USD, log-transformed. ESG-specific risks observed in 2020.

Table 1. Descriptive statistics.

Variable	Obs.	Mean	Std. Dev.	Min	Max
ln(FDI inflows)	4850	6.42	2.11	−2.30	12.90
Structural ESG risk	4850	52.7	12.4	21.3	78.9
Environmental risk	210	55.1	14.8	24.5	82.6
Social risk	210	48.9	13.1	22.8	79.4
Governance risk	210	50.6	15.7	19.6	84.1
GDP per capita (USD, log)	4850	9.31	1.12	6.71	11.62
R&D expenditure (% GDP)	3920	1.21	0.93	0.02	4.35
Agriculture VA (% GDP)	4850	13.8	9.6	0.4	52.1
Industry VA (% GDP)	4850	29.4	9.1	11.3	58.7
Trade openness (%)	4850	76.2	38.5	19.4	211.6
Rule of law	4850	−0.02	0.94	−2.35	1.89

Notes: FDI in USD, log-transformed. ESG-specific risks observed in 2020.

Notes for Table 1: Corporate Biodiversity Risk Integration (CBRI) Index Composition

• The CBRI index is a composite score ranging from 0 (zero) to 100, representing the extent of biodiversity risk integration at the firm level.
• The index comprises 15 indicators grouped under three pillars, Governance and Strategy, Metrics and Targets, and Performance and Disclosure, aligned with impact materiality and financial materiality dimensions.

Measurement Units and Variables:

• Binary (Yes/No): Dummy variable coded as 1 = Yes, 0 = No (e.g., board-level oversight, TNFD alignment, scenario analysis, science-based targets, third-party verification).
• Score (0–3): Ordinal scale indicating increasing levels of implementation or sophistication (e.g., biodiversity policy, ERM integration, site-level assessments, ecosystem monitoring, dependency quantification).
• Scaled by revenue: Continuous variable measuring financial commitment relative to firm revenue (e.g., restoration investment).
• Log(amount): Logarithm of monetary value to normalize distribution (e.g., environmental fines).
• % of total revenue: Proportion indicating exposure to biodiversity-sensitive sectors (e.g., revenue from high-impact sectors).
• Score (A–F to 0–5): Converted categorical rating into numerical scale (e.g., CDP Forests score).
• Inverted score (0–100): Reverse-coded index where higher values indicate lower controversy risk (e.g., ESG Controversy Score).
• Data for all indicators are manually collected and coded from sustainability reports, annual reports, and CDP disclosures.
• The index (Cronbach’s Alpha = 0.84) demonstrates high internal consistency.

Table 2. Correlation matrix.

Variable	ln(FDI)	Structural ESG Risk	Environmental Risk	Social Risk	Governance Risk
ln(FDI)	1.00
Structural ESG risk	−0.34	1.00
Environmental risk	−0.29	0.61	1.00
Social risk	−0.26	0.57	0.49	1.00
Governance risk	−0.31	0.68	0.44	0.46	1.00

Notes: No correlation exceeds |0.70|.

Table 2. Correlation matrix.

Variable	ln(FDI)	Structural ESG Risk	Environmental Risk	Social Risk	Governance Risk
ln(FDI)	1.00
Structural ESG risk	−0.34	1.00
Environmental risk	−0.29	0.61	1.00
Social risk	−0.26	0.57	0.49	1.00
Governance risk	−0.31	0.68	0.44	0.46	1.00

Notes: No correlation exceeds |0.70|.

Notes for Table 2: Variable Definitions and Data Sources

• CBRI (Corporate Biodiversity Risk Integration): Considered as a score ranging from 0 (zero) to 100, representing the composite index of biodiversity risk integration constructed by the authors.
• Treat × Post: Measured as an interaction dummy variable, where Treat indicates firms issuing nature-linked bonds and Post indicates the period after issuance.
• RegPressure (Regulatory Pressure): Quantified as a composite index ranging from 0 to 10, combining the OECD Environmental Policy Stringency (EPS) Index and a binary indicator for mandatory TNFD-aligned disclosure regulations.
• Size: Measured as the logarithm of total assets, representing the firm size.
• ROA (Return on Assets): Quantified as net income divided by total assets, representing firm profitability.
• Leverage: Measured as total debt divided by total equity, representing the financial structure.
• R&D Intensity: Measured as R&D expenditure divided by revenue, representing innovation capacity.
• Capex (Capital Expenditure): Quantified as capital expenditure divided by total assets, representing investment intensity.
• Data sources include constructed data (for CBRI), Bloomberg and Refinitiv (for Treat × Post), OECD and national legislation (for RegPressure), and Refinitiv Eikon (for all control variables).

4.2. Structural ESG Risks and Foreign Direct Investment (Axis 1)

Table 3 and Table 4 show the FMOLS results for Equations (1) and (2), respectively, while Table 5 and Table 6 display the RWLS results for Equations (3) and (4), respectively.

This subsection discusses the output of the panel data analysis conducted to test Hypothesis H1, investigating the long-run relationship among structural ESG risks and inward foreign direct investment.

Table 3 shows the output of the FMOLS analysis for Equation (1). The coefficient of the structural ESG risk indicator is negative and statistically significant for all model specifications, suggesting that the higher the ESG risks, the lower the inward FDI inflows. This result lends support to the hypothesis that structural ESG risks are a financial deterrent to foreign investment.

The output shows that the coefficients are robust to the addition of control variables based on knowledge capital theory, implying that the relationship is not influenced by conventional FDI location factors. The results highlight the significance of long-term ESG risks in influencing international investment choices.

Table 3. FMOLS results: Structural ESG risks and FDI.

Variable	(1)	(2)	(3)
Structural ESG risk	−0.018 ***	−0.016 ***	−0.014 **
GDP (market size)		0.621 ***	0.587 ***
Trade openness		0.003 **	0.002 *
Tariff rate			−0.009 *
Rule of law			0.184 ***
Constant	2.11 ***	−1.92 ***	−2.36 ***
Country FE	Yes	Yes	Yes
Observations	4850	4850	4850
Countries	250	250	250

Notes: ***, **, * denote 1%, 5%, 10%.

Table 3. FMOLS results: Structural ESG risks and FDI.

Variable	(1)	(2)	(3)
Structural ESG risk	−0.018 ***	−0.016 ***	−0.014 **
GDP (market size)		0.621 ***	0.587 ***
Trade openness		0.003 **	0.002 *
Tariff rate			−0.009 *
Rule of law			0.184 ***
Constant	2.11 ***	−1.92 ***	−2.36 ***
Country FE	Yes	Yes	Yes
Observations	4850	4850	4850
Countries	250	250	250

Notes: ***, **, * denote 1%, 5%, 10%.

Notes for Table 3: Descriptive Statistics and Correlation Matrix

• CBRI: Measured as a score (0–100); mean = 42.30, standard deviation = 18.70.
• Treat × Post: Measured as an interaction dummy variable (0/1); mean = 0.12, standard deviation = 0.32.
• RegPressure: Measured as a score (0–10); mean = 5.85, standard deviation = 2.10.
• Size: Measured as log(total assets); mean = 22.45, standard deviation = 1.89.
• ROA: Measured as net income/total assets; mean = 0.05, standard deviation = 0.08.
• Leverage: Measured as total debt/total equity; mean = 0.65, standard deviation = 0.35.
• R&D intensity: Measured as R&D/revenue; mean = 0.04, standard deviation = 0.07.
• Capex: Measured as capital expenditure/total assets; mean = 0.08, standard deviation = 0.06.
• Correlation coefficients represent pairwise linear relationships between variables.
• Statistical significance levels are signified as p < 0.001; p < 0.01; p < 0.05
• Total observations: 7232 firm-year observations.

Table 4. FMOLS results with interaction terms.

Variable	Economic Development	Innovation Capacity	Sectoral Structure
Structural ESG risk	−0.020 ***	−0.017 ***	−0.019 ***
GDP per capita	0.512 ***
ESG × GDP pc	0.006 ***
R&D expenditure		0.284 ***
ESG × R&D		0.009 **
Sectoral structure			−0.031 **
ESG × sector			−0.005 **
Controls	Yes	Yes	Yes
Country FE	Yes	Yes	Yes
Observations	4850	3920	4850
Countries	250	210	250

Sectoral structure refers to the share of environmentally or socially sensitive sectors in the GDP.

Table 4. FMOLS results with interaction terms.

Variable	Economic Development	Innovation Capacity	Sectoral Structure
Structural ESG risk	−0.020 ***	−0.017 ***	−0.019 ***
GDP per capita	0.512 ***
ESG × GDP pc	0.006 ***
R&D expenditure		0.284 ***
ESG × R&D		0.009 **
Sectoral structure			−0.031 **
ESG × sector			−0.005 **
Controls	Yes	Yes	Yes
Country FE	Yes	Yes	Yes
Observations	4850	3920	4850
Countries	250	210	250

Sectoral structure refers to the share of environmentally or socially sensitive sectors in the GDP.

Notes for Table 4: Two-Way Fixed-Effects Regression Results

• Dependent variable (CBRI): Considered as a score ranging from 0 (zero) to 100, representing Corporate Biodiversity Risk Integration.
• Treat × Post: Measured as an interaction dummy variable (0/1) capturing the Difference-in-Differences effect. Coefficients reported with robust standard errors in parentheses.
• RegPressure: Measured as a score ranging from 0 to 10, representing regulatory pressure.
• Treat × Post × RegPressure: Interaction term capturing the moderating effect of regulatory pressure on the treatment effect.
• Size: Quantified as log(total assets).
• ROA: Quantified as net income/total assets.
• Leverage: Quantified as total debt/total equity.
• R&D intensity: Measured as R&D/revenue.
• Capex: Measured as capital expenditure/total assets.
• Coefficients: Represent estimated effects on CBRI; standard errors are clustered at the company level and mentioned in parentheses.
• Model specifications:

  ∘

  Model 1: Includes control variables with firm and year fixed effects.

  ∘

  Model 2: Adds Treat × Post (main effect).

  ∘

  Model 3: Adds RegPressure and interaction term.

• R-squared (within): Indicates explanatory power of the model (0.18, 0.21, 0.22).
• F-statistic: Tests overall model significance.
• Observations: 7232 firm-year observations across all models.
• Significance levels: Indicated as *** p < 0.001, ** p < 0.01.

Table 5. RWLS results: ESG-specific risk dimensions and FDI.

Variable	Environmental Risk	Social Risk	Governance Risk
Environmental risk	−0.021 ***
Social risk		−0.017 **
Governance risk			−0.026 ***
GDP per capita	0.644 ***	0.612 ***	0.658 ***
Trade openness	0.004 **	0.003 *	0.004 **
Rule of law	0.211 ***	0.196 ***	0.238 ***
Constant	−3.14 ***	−2.87 ***	−3.36 ***
Observations	210	210	210
R2 (robust)	0.41	0.38	0.44

Table 5. RWLS results: ESG-specific risk dimensions and FDI.

Variable	Environmental Risk	Social Risk	Governance Risk
Environmental risk	−0.021 ***
Social risk		−0.017 **
Governance risk			−0.026 ***
GDP per capita	0.644 ***	0.612 ***	0.658 ***
Trade openness	0.004 **	0.003 *	0.004 **
Rule of law	0.211 ***	0.196 ***	0.238 ***
Constant	−3.14 ***	−2.87 ***	−3.36 ***
Observations	210	210	210
R2 (robust)	0.41	0.38	0.44

Notes for Table 5: RWLS Results—ESG-Specific Risk Dimensions and FDI

• Environmental risk, social risk, governance risk: Measured as risk indices; coefficients represent their impacts on FDI, with values reported for each respective model.
• GDP per capita: Measured as a continuous economic indicator representing the income level of a country.
• Trade openness: Measured as a ratio-based indicator reflecting the degree of integration with global trade.
• Rule of law: Measured as an index score representing institutional quality and legal effectiveness.
• Constant: Represents the intercept term in each regression model.
• Observations: Total number of data points used in estimation (N = 210).
• R² (robust): Indicates the proportion of variance explained by each model (0.41, 0.38, 0.44).
• Significance levels: Indicated as *** p < 0.01, ** p < 0.05, * p < 0.10.
• Coefficients are estimated using robust weighted least squares (RWLS) with robustness adjustments.

Table 6. RWLS results with interaction terms.

Variable	Economic Development	Innovation Capacity	Sectoral Structure
ESG-specific risk	−0.024 ***	−0.022 ***	−0.026 ***
ESG × GDP pc	0.007 ***
ESG × R&D		0.010 **
ESG × sector			−0.006 **
Controls	Yes	Yes	Yes
Observations	210	210	210
R2 (robust)	0.45	0.47	0.43

Sectoral structure refers to the share of environmentally or socially sensitive sectors in the GDP.

Table 6. RWLS results with interaction terms.

Variable	Economic Development	Innovation Capacity	Sectoral Structure
ESG-specific risk	−0.024 ***	−0.022 ***	−0.026 ***
ESG × GDP pc	0.007 ***
ESG × R&D		0.010 **
ESG × sector			−0.006 **
Controls	Yes	Yes	Yes
Observations	210	210	210
R2 (robust)	0.45	0.47	0.43

Sectoral structure refers to the share of environmentally or socially sensitive sectors in the GDP.

Notes for Table 6: RWLS Results with Interaction Terms

• ESG-specific Risk: Measured as a composite risk index; coefficients represent its effect across models.
• ESG × GDP per capita (GDP pc): Interaction term confining the moderating effect of economic development, where GDP per capita is a continuous economic indicator.
• ESG × R&D: Interaction term confining the moderating effect of innovation capacity, where R&D is measured as a ratio of research and development activity.
• ESG × sector: Interaction term capturing the moderating effect of sectoral structure, defined as the share of environmentally or socially sensitive sectors in the GDP.
• Controls: Indicates inclusion of control variables (Yes) in all models.
• Observations: Total number of data points used (N = 210).
• R² (robust): Represents the proportion of variance explained by each model (0.45, 0.47, 0.43).
• Significance levels: Indicated as *** p < 0.01, ** p < 0.05.
• Estimation method: Robust weighted least squares (RWLS) with robustness adjustments.

4.3. Moderating Effects and Cross-Country Heterogeneity

The outputs of the FMOLS estimates are presented in Table 4, including interaction terms between structural ESG-related risks and moderating variables, as specified in Hypothesis H3.

The interaction terms indicate that there is significant heterogeneity in the relationship among ESG-related risks and inward FDI. Specifically, the negative relationship among ESG-related risks and FDI is found to be less pronounced in countries that have advanced levels of economic development and innovation capabilities, as measured by the GDP per capita and R&D expenditure. By contrast, countries with economic structures that are more dominated by industries that are sensitive to the environment and society display a stronger deterrent effect.

These results indicate that structural factors have a pivotal role in determining the ability of countries to resist ESG-related risks and also affect the perception of foreign investors regarding sustainability-related risks.

4.4. ESG-Specific Risk Dimensions and Foreign Direct Investment (Axis 2)

This subsection reports the cross-sectional outcomes regarding the influence of ESG-specific risk factors on inward foreign direct investment, as proposed in Hypothesis H2.

Table 5 shows the outcomes of the RWLS estimations of Equation (3). The coefficients of ESG-specific risk factors are negative and significant for various risk factors, suggesting that higher sensitivity to environmental, social, or governance risks makes a country less attractive for investment. These findings emphasize the importance of sensitivity-driven ESG risks in influencing investors’ views of country risk.

The robustness of this conclusion is further established by the stability of coefficient estimates in the presence of macroeconomic and institutional factors.

4.5. Heterogeneity in Exposure-Based Effects

Table 6 provides the results of continuing the cross-sectional analysis by adding interaction terms between the ESG-specific risk dimensions and the moderating variables, as shown in Equation (4).

The output shows that the effects of ESG-specific risks are systematically different across countries. Specifically, higher levels of economic development and innovation capabilities offset the negative effect of ESG risk exposure on FDI inflows, while sectoral specialization exerts a magnifying effect on ESG risk exposure in more vulnerable economies. These results further confirm Hypothesis H3 and underscore the significance of country-specific factors in influencing investment reactions to ESG risks.

4.6. Summary of Empirical Findings

Overall, the empirical findings offer robust evidence that ESG-related risks are a material financial discouraging factor for foreign direct investment. Structural ESG risks have a negative long-run impact on FDI inflows, while ESG-specific risks, based on exposure, further lower investment attractiveness in the cross-section. Notably, the strength of these impacts differs across countries depending on their levels of development, industry composition, and innovation capabilities.

The empirical outcomes of the current research are compatible with, yet expand upon, the existing literature concerning the ESG risk–investment attractiveness relationship. As in the previous literature, the current research suggests that higher levels of ESG risks, especially those associated with inefficient governance and social problems, negatively affect FDI flows as they cause additional uncertainty and higher transaction costs to multinational companies investing abroad. As before, the outcomes of the research confirm that high-quality institutions and sustainable policies make investment more attractive. Nevertheless, unlike the previous empirical literature that has shown an ambiguous and often insignificant correlation between environmental risks and foreign investment, the current study finds that the adverse effects of environmental risks on FDI are more apparent and significant. The reason behind this difference might lie in the adopted two-axis approach, which allows for measuring not only long-term structural risk but also time-dependent ESG risks. Thus, the unique contribution of the current research to the literature is in showing that the effect of ESG risk depends on its temporal and qualitative characteristics.

The results offer strong empirical support for the importance of ESG-related risks as a macro-financial variable in international investment choices and offer empirical evidence for the hypotheses outlined in Section 2.

5. Conclusions and Policy Implications

This study examines whether ESG risks constitute a financial deterrent to investment attractiveness by analyzing their impacts on foreign direct investment across countries. Using a comprehensive multi-country dataset and a two-axis empirical framework, the findings consistently exhibit that higher ESG risks are linked with lower inward FDI inflows. Both long-term structural risks and short-term exposure-based risks significantly influence investment decisions, confirming the financial materiality of sustainability-related vulnerabilities.

The results highlight that ESG risks function as an integral component of country risk, affecting investor perceptions, expected returns, and the stability of the investment environment. Governance-related risks emerge as particularly influential, underscoring the critical role of institutional quality, regulatory effectiveness, and the rule of law in attracting foreign investment. Environmental and social risks also contribute to investment deterrence, especially in countries with limited resilience and higher exposure to sustainability challenges.

The study further discloses that the impact of ESG risks on investment attractiveness is not uniform across countries. Economic development, sectoral diversification, and innovation capacity play a moderating role, reducing the negative effects of ESG risks in more advanced and adaptable economies. In contrast, countries with structural vulnerabilities experience stronger deterrent effects, highlighting the significance of context-specific policy responses.

The findings suggest that improving ESG-related conditions can improve a country’s attractiveness to foreign investors from a policy perspective. Strengthening governance frameworks, promoting environmental sustainability, and addressing social challenges can reduce perceived risks and create a more stable investment climate. Policymakers should also focus on fostering innovation and economic diversification to build resilience against ESG-related shocks.

For investors, the results underline the significance of incorporating ESG risk assessments into processes of decision-making. Evaluating sustainability-related risks alongside traditional financial indicators can improve risk management and support long-term investment strategies.

Overall, the contribution of this study towards the growing literature on sustainable finance is its provision of an integrated, risk-based perspective on ESG factors and their role in shaping international investment flows. Future research could extend this analysis by incorporating company-level data, exploring causal relationships, and examining the dynamic interactions between ESG risk mitigation and investment behavior.