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Asian Business Research Journal 
Vol. 10, No. 3, 33-44, 2025 
ISSN: 2576-6759 
DOI: 10.55220/25766759.305 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
The Impact of Fund Characteristics on Environmental, Social, and Governance 
(ESG) Risks: An Empirical Analysis 

 
Juan Infante1

 
Tomás Curto2  
Carlos Arenas3 
 

 
 

1Juan Infante, Universidad Villanueva, Madrid, Spain. 
Email: juan.infante@villanueva.edu  
2Tomás Curto, Universidad Villanueva, Madrid, Spain. 
Email: tomas.curto@villanueva.edu  
3Carlos Arenas, Universidad Villanueva, Madrid, Spain. 
Email: carlos.arenas.laorga@gmail.com  
( Corresponding Author) 

 

 
Abstract 

This study examines the impact of fund characteristics on Environmental, Social, and Governance 
(ESG) risks in investment funds. The research focuses on three key factors: fund size, fund 
category, and investment area. Using regression analysis, the study investigates how these 
characteristics influence ESG risks across different fund types and geographical regions. The 
methodology addresses potential statistical issues such as multicollinearity and heteroscedasticity 
to ensure robust results. The findings reveal significant relationships between fund attributes and 
ESG risk levels, with notable variations across regions and fund types. Fund size is found to 
positively influence certain ESG risks, while geographical location and investment focus play 
crucial roles in determining the level of ESG risks. The study aims to contribute to a better 
understanding of ESG risk factors in investment portfolios, offering valuable implications for fund 
managers and investors in their decision-making processes and risk management strategies. 

 
Keywords: Environmental, Social, Governance; Risk, Investment. 

JEL Classification: G34; M14; Q51. 

 
1. Introduction 

The consideration of Environmental, Social, and Governance (ESG) factors has gained significant attention in 
the investment management industry in recent years (Friede et al., 2015; Eccles & Klimenko, 2019). Numerous 
studies have explored the relationship between ESG performance and financial outcomes, with mixed results 
(Margolis et al., 2009; Orlitzky et al., 2003). While these studies have provided valuable insights into the financial 
implications of ESG integration, they have largely overlooked the specific influence of fund characteristics—such 
as size, category, and investment area—on ESG risks. This gap in the literature is particularly significant given the 
growing emphasis on sustainable investing and the increasing sophistication of ESG risk assessment 
methodologies. 

This study aims to address this gap by investigating the impact of fund size, fund category, and investment 
area on the environmental, social, and governance risks of investment funds. Specifically, the research seeks to 
answer the following questions: 1. Does the size of the fund influence the environmental, social, and governance 
risks? 2.How does the fund category (e.g., equity, fixed income) affect the environmental, social, and governance 
risks?. 3. Does the investment area (e.g., Europe, Asia, global) impact the environmental, social, and governance 
risks? 

By addressing these research questions, this paper contributes to the existing literature on the integration of 
ESG factors in investment decision-making (Amel-Zadeh & Serafeim, 2018; Dyck et al., 2019). The findings of this 
study provide insights for fund managers, financial advisors, and investors seeking to incorporate ESG 
considerations into their investment strategies. Furthermore, the study employs robust regression analysis to 
examine these relationships, addressing potential econometric issues such as heteroscedasticity and 
multicollinearity. This methodological ensures that the results are both reliable and actionable. 

The significance of this research lies in its ability to bridge the gap between theoretical discussions of ESG 
integration and practical investment decision-making. While previous studies have focused on the performance 
implications of ESG factors, this paper shifts the focus to understanding how structural characteristics of funds 
influence their ESG risk profiles. This perspective is crucial for developing targeted strategies to manage ESG 
risks and enhance the sustainability of investment portfolios. 

The findings of this research have important implications for various stakeholders in the investment 
community. For fund managers, understanding how structural characteristics influence ESG risks can inform 
portfolio construction and risk management strategies. For investors, our results provide valuable insights for fund 

mailto:juan.infante@villanueva.edu
mailto:tomas.curto@villanueva.edu
mailto:carlos.arenas.laorga@gmail.com
https://doi.org/10.55220/25766759.305


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selection and portfolio optimization, particularly when ESG considerations are paramount. Additionally, our 
findings contribute to the broader academic discourse on sustainable finance by providing empirical evidence of the 
relationships between institutional characteristics and ESG risk exposure. 

The paper is organized as follows: Section 2 reviews the relevant literature and develops our hypotheses. 
Section 3 describes our data and methodology. Section 4 presents our empirical results and analysis.Finally, section 
5 discusses the implications of our findings and concludes with suggestions for future research. 
 

2. Literature Review  
The integration of Environmental, Social, and Governance (ESG) factors into investment decision-making has 

gained significant traction in recent years. This shift reflects growing awareness of the financial and reputational 
risks associated with poor ESG performance, as well as the opportunities presented by sustainable investing 
(Friede et al., 2015; Eccles & Klimenko, 2019). While the relationship between ESG performance and financial 
outcomes has been extensively studied (Margolis et al., 2009; Orlitzky et al., 2003), the specific influence of fund 
characteristics—such as size, category, and investment area—on ESG risks remains underexplored. This literature 
review examines the existing body of research on these themes, highlighting key findings, gaps, and the relevance 
of this study. 

Fund Size and ESG Risks. Fund size is a critical determinant of ESG risk management. Larger funds often 
have more resources to integrate ESG considerations into their investment processes, enabling them to conduct 
thorough due diligence and engage with portfolio companies (Dyck et al., 2019; Starks et al., 2017). However, they 
may also face challenges related to scale, such as increased complexity in maintaining consistent ESG standards 
across diverse portfolios (Krueger et al., 2020). Hartzmark and Sussman (2019) suggest that economies of scale in 
ESG implementation may exist, but these benefits can diminish beyond certain thresholds. Despite these insights, 
the literature offers mixed evidence on the relationship between fund size and ESG performance, necessitating 
further investigation (Dimson et al., 2015; Pastor et al., 2021). 

Fund Category and ESG Risks. The type of investment fund—whether equity, fixed income, or allocation—
significantly influences its ESG risk profile. Equity funds, for instance, are often more exposed to environmental 
and social risks due to their investments in industries with high environmental impact or labor-intensive 
operations (Friede et al., 2015; Eccles & Klimenko, 2019). In contrast, fixed-income funds may face greater 
governance risks, particularly in assessing the creditworthiness of issuers with varying ESG practices (Bauer & 
Hann, 2010; Chava, 2014). Recent studies indicate that bond funds are increasingly incorporating ESG criteria, 
particularly in evaluating environmental externalities and corporate governance (Henke, 2016; El Ghoul & Karoui, 
2021). However, the extent to which these practices mitigate ESG risks across different fund categories remains an 
open question. 

Investment Area and ESG Risks. Geographic considerations play a pivotal role in shaping ESG risk profiles. 
Funds investing in emerging markets often encounter higher ESG risks due to weaker regulatory frameworks, 
limited enforcement, and varying corporate practices (Liang & Renneboog, 2017; Dyck et al., 2019). Conversely, 
European funds are generally more advanced in ESG integration, reflecting the region's stringent regulations and 
investor demand for sustainable practices (Ferreira et al., 2018). Studies by Krueger et al. (2020) and Berg et al. 
(2022) highlight significant regional variations in ESG implementation, underscoring the need for context-specific 
strategies. Despite these findings, the interaction between investment area and other fund characteristics, such as 
size and category, remains underexplored. 

While the existing research provides valuable insights into the relationship between ESG factors and 
investment performance, several gaps persist. First, the interplay between fund size, category, and investment area 
in determining ESG risks is not well understood. Second, most studies focus on developed markets, with limited 
attention to emerging economies and their unique ESG challenges (Liang & Renneboog, 2017). Finally, the role of 
fund management practices, such as active ownership and engagement, in mitigating ESG risks warrants further 
investigation (Dimson et al., 2015; Bauer & Hann, 2010). 

This literature review underscores the growing importance of ESG factors in investment decision-making and 
the need to understand the influence of fund characteristics on ESG risks. By addressing the identified gaps, this 
study aims to contribute to the academic discourse on sustainable finance and provide actionable insights for 
investors, fund managers, and policymakers. 

 

3. Data and Methodology  
The data for this study was sourced from Morningstar Direct, a globally  financial analysis platform renowned 

for its comprehensive coverage of sustainable investment funds and standardized ESG metrics. Morningstar Direct 
was selected due to its  database, which aligns with the stringent regulatory and sustainability standards prevalent 
in the European market. The dataset provides a detailed view of fund characteristics and ESG performance, making 
it an resource for analyzing the relationship between fund attributes and ESG risks. 

The final dataset comprises 1,737 sustainable investment funds, classified under the Sustainable Finance 
Disclosure Regulation (SFDR). Of these, 1,635 funds (94.1%) are categorized under Article 8, which promotes 
environmental or social characteristics, while 102 funds (5.9%) fall under Article 9, targeting specific sustainable 
investment objectives. The sample spans 31 geographical areas, offering a diverse representation of both developed 
and emerging markets. Specifically, the dataset includes 887 global funds (51.1%), 415 European funds (23.9%), 161 
United States funds (9.3%), 124 global emerging market funds (7.1%), and 65 Asian funds (3.7%), with the 
remaining 85 funds (4.9%) distributed across other regions. 

In terms of asset class composition, the sample is diverse, encompassing 686 equity funds (43.2%), 549 fixed-
income funds (34.6%), 267 mixed-asset funds (16.8%), and 91 money market funds (5.4%). This variety allows for a 
comprehensive analysis of how different investment strategies address ESG risks. The dataset includes 97 
variables, capturing both numerical and categorical indicators essential for ESG risk analysis. Key numerical 
variables include financial performance metrics and portfolio size, which provide insights into the impact of ESG 



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policies on fund outcomes. Categorical variables offer segmentation based on geographical focus, sustainability 
classification, and sector exclusion policies. 

The assessment of environmental, social, and governance (ESG) risks in funds by Morningstar Direct is 
facilitated through a structured framework that leverages Sustainalytics' ESG Risk Ratings. This rating system 
evaluates a company's exposure to ESG risks and its ability to manage them, with scores ranging from 0 to 100. 
Lower scores indicate better management of these risks. The Sustainalytics ESG Risk Rating is categorized into 
several risk levels: negligible (0-10), low (11-20), medium (21-30), high (31-40), very high (41-50), and severe (51-
100). This risk assessment is then used to inform the Morningstar Sustainability Rating, which is expressed as a 
number of globes (1 to 5). The rating reflects how well a fund manages financially material ESG risks compared to 
its peers in the same Morningstar Global Category. The distribution of these globes is as follows: one globe for the 
lowest 10% of funds, two globes for the next 22.5%, three globes for the middle 35%, four globes for the next 
22.5%, and five globes for the top 10%. 

The ESG risk assessment itself is composed of three primary components: environmental (E), social (S), and 
governance (G) risks. Environmental risks are evaluated based on factors such as carbon emissions and resource 
depletion, with the weight given to these factors varying by industry. Social risks include labor practices and 
community relations, also industry-specific. Governance risks focus on corporate oversight, board independence, 
and shareholder rights, which are crucial across industries for maintaining financial stability and investor 
confidence. While Morningstar does not explicitly use a points system for ESG risks, one could conceptualize it as 
allocating points across these components. For instance, in a hypothetical scenario, environmental risks might 
contribute 30 points, social risks another 30 points, and governance risks 40 points, totaling 100 points. However, 
the actual method involves comparing a fund's ESG risk profile to its peers within the same category, rather than 
using a fixed points system. 

In practice, the Morningstar Sustainability Rating is determined by assessing the ESG risks associated with 
both corporate and sovereign entities in a fund's portfolio. At least 67% of a fund's assets under management must 
have an ESG score for it to receive a Morningstar Sustainability Rating. This rating serves as a relative measure, 
comparing a fund's ESG risk management to that of its peers, rather than an absolute assessment of its ESG 
performance. This approach provides investors with a clear indication of how well a fund manages ESG risks 
relative to its industry peers. 

The dataset also incorporates detailed information on sector exclusions, which are critical for assessing the 
alignment of funds with their sustainability objectives. These exclusions cover sectors such as military contracts, 
small arms, nuclear energy, palm oil, pesticides, tobacco, thermal coal, and fossil fuels. By analyzing these exclusion 
policies, the study evaluates how such decisions influence ESG risk scores and overall fund performance. 

 

4. Results and Analysis 
The variable, Fund Size, is a quantitative and continuous variable; we will treat it as a covariate, that is, control 

its influence and isolate its effect so that it does not contaminate the conclusions we may obtain about the 
remaining variables. The remaining variables: Fund Category and Investment Area, are qualitative variables. The 
aim is to verify whether their presence or absence significantly affects the average Environmental, Social, and 
Governance risks. 

The two options considered for conducting the analysis were ANCOVA and Regression Analysis; ultimately, 
we opted for the latter methodology. The reasons for this decision are several, but essentially Regression Analysis 
allows for a more appropriate treatment of potential problems such as heteroscedasticity and multicollinearity, in 
addition to being able to construct interactions between variables. 

The first step has been to estimate the relationship between Government Risk and the explanatory variables. 
Once the model was formulated, interactions between the variables were sought to determine if any combination 
among them produces an effect greater than the sum of the individual effects.The heteroscedasticity tests indicate 
the presence of this problem in the estimated model. In all the tests used, the null hypothesis is rejected, admitting 
the presence of heteroscedasticity. It was resolved with robust estimators and elimination of non-significant 
variables. 

 
Table 1. Estimation relationship between Government Risk and the explanatory variables. 

 
 
 
 

 
 

                                                                                      

               _cons     6.943955   .1430724    48.53   0.000                        .

              Equity    -.0275986   .0926814    -0.30   0.766                -.0104681

               Fixed    -.7573785   .1032952    -7.33   0.000                -.2565637

GLOBALEMERGINGMARKET    -.7305952   .1568139    -4.66   0.000                 -.151478

              GLOBAL    -.4634486   .1182473    -3.92   0.000                -.1766006

                ASIA     .3589717   .1968683     1.82   0.068                 .0524111

              EUROPA    -.5449813   .1264027    -4.31   0.000                -.1802057

               CHINA     1.036894   .3079451     3.37   0.001                 .0877511

FundSizeBaseCurrency     4.56e-11   1.49e-11     3.07   0.002                 .0760006

                                                                                      

 GovernanceRiskScore   Coefficient  Std. err.      t    P>|t|                     Beta

                                                                                      

       Total    2481.96613     1,445  1.71762362   Root MSE        =    1.2236

                                                   Adj R-squared   =    0.1283

    Residual    2151.48996     1,437  1.49720944   R-squared       =    0.1332

       Model    330.476176         8   41.309522   Prob > F        =    0.0000

                                                   F(8, 1437)      =     27.59

      Source         SS           df       MS      Number of obs   =     1,446



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Table 2. Heteroskedasticity tests. 

 
 

Table 3. Estimation relationship between Government Risk and the explanatory variables. 

 
The model does not present multicollinearity problems.This approach has been maintained to explain both 

Environmental Risk and Social Risk. The presence of heteroscedasticity is detected in both models. 
 

Table 4. Multicollinearity test. 

 
 
 
 
 
 

                                                                                           

                    _cons     6.857772   .1066254    64.32   0.000                        .

               EQUITYASIA     .5147938   .2045947     2.52   0.012                 .0724809

             EQUITYEUROPA    -.4883664   .1329831    -3.67   0.000                 -.122471

              EQUITYCHINA     1.097098   .1585564     6.92   0.000                 .0928461

FIXEDGLOBALEMERGINGMARKET    -4.128383   .2758783   -14.96   0.000                  -.55302

                FIXEDASIA    -1.336933    .204727    -6.53   0.000                -.0464324

                    Fixed    -.4237719   .1032172    -4.11   0.000                -.1435537

     GLOBALEMERGINGMARKET     .8452152   .1329176     6.36   0.000                 .1752427

                   GLOBAL    -.4616011   .1072079    -4.31   0.000                -.1758966

                   EUROPA    -.3680082   .1560375    -2.36   0.018                 -.121687

     FundSizeBaseCurrency     4.47e-11   1.41e-11     3.18   0.001                 .0745249

                                                                                           

      GovernanceRiskScore   Coefficient  std. err.      t    P>|t|                     Beta

                                        Robust HC3

                                                                                           

                                                Root MSE          =     1.0798

                                                R-squared         =     0.3258

                                                Prob > F          =          .

                                                F(9, 1435)        =          .

Linear regression                               Number of obs     =      1,446

    Mean VIF        1.99

                                    

FundSizeBa~y        1.02    0.980407

   FIXEDASIA        1.03    0.969476

 EQUITYCHINA        1.13    0.888184

  EQUITYASIA        1.35    0.742034

       Fixed        1.59    0.627692

FIXEDGLOBA~T        1.85    0.539508

EQUITYEUROPA        2.30    0.434280

GLOBALEMER~T        2.36    0.424004

      GLOBAL        3.18    0.314143

      EUROPA        4.09    0.244456

                                    

    Variable         VIF       1/VIF  



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Table 5. Estimation relationship between Environmental Risk/Social Risk and the explanatory variables. 

 

 
 

Table 6. Heteroskedasticity tests. 

 



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To eliminate the problem, they are estimated with heteroscedasticity-robust estimators, and non-significant 

variables are sequentially eliminated. Both models do not present multicollinearity problems. 
 

Table 7. Estimation relationship between Environmental Risk/Social Risk and the 
explanatory variables with heteroscedasticity-robust estimators. 

 

 
 

 
 
 
 
 



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Table 8. Multicollinearity test. 

 
 

Table 9. Interpretation of results: comparative summary. 

 
 

5. Interpretation of the Government Risk Model  
The Portfolio Governance Risk Score reaches an average value of 6.86 points. This indicator is influenced by 

several key variables. The fund size in base currency (FundSizeBaseCurrency) has a direct positive influence. For 
every euro increase in fund size, the Portfolio Governance Risk Score increases on average by 4.47e-11 points, 
provided that the other variables remain constant. 

Geographic location also plays a crucial role. If the fund is located in Europe, the Portfolio Governance Risk 
Score decreases on average by 0.368 points, dropping from 6.86 to 6.49 points, with statistically significant 
differences between both categories compared to funds located in the U.S. On the other hand, if the fund is global, 
the indicator decreases by 0.4616011 points, resulting in a value of 6.3962 points. In contrast, funds classified as 
global emerging markets experience an increase in the indicator of 0.8452152 points, raising the average risk to 
7.703 points. 

Regarding investment categories, fixed funds show a decrease in risk on average of 0.4237719 points, achieving 
a mean score of 6.434 points compared to Allocation funds. Additionally, interactions between different variables 
are also significant. For example, the interaction between fixed funds and location in Asia reduces the Portfolio 
Governance Risk Score to an average value of 5.0971 points. This reduction is even more pronounced for fixed 
funds in global emerging markets, where the indicator drops to 2.30562 points. On the other hand, equity funds in 
China experience an increase in risk to an average value of 7.95487 points, while in Europe the risk is reduced to 
6.0214 points. Finally, equity funds in Asia present an increase in risk to an average value of 7.37257 points. These 
interactions and variables demonstrate how different geographic and investment factors influence the governance 
risk of funds. 
 



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Figure 1. Standardized coefficients. 

 
Table 10. Interpretation of the Government Risk Model. 

 
 

 
Figure 2. Effect on the model mean. 

 

6. Interpretation of the Environmental Risk Model 
The Portfolio Environmental Risk Score variable measures the environmental risk of the portfolio. In this case, 

the average risk of the different portfolios included in the data reaches a value of 3.4337 points. The variables that 
significantly influence this variable are several. Geographic location plays a crucial role. If the fund is based in Asia, 
the average risk increases by 0.6303906 points, so that the average value of the variable, in this case, is 4.064 
points, with significant differences between the Asia and USA categories. Similarly, if the fund is categorized as 
Global, the average risk increases by 0.1601834 points, so that the average value for the Environmental Risk 
variable is 3.5939 points. 

Funds classified as Global Emerging Markets exhibit a more pronounced increase in environmental risk, rising 
by 0.9296787 points; so that the average value for the studied variable in this type of funds is 4.3633787 points. 
Additionally, the type of investment also impacts the risk. If the fund is Equity, compared to Allocation, the 



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average risk increases by 0.2206866 points; so that the average of the Environmental Risk variable is in this case 
3.6543866 points, with significant differences between the Equity and Allocation categories. 

Interactions between variables further modify the environmental risk. For instance, if the fund is both Fixed 
and based in Europe, the environmental risk is reduced by 0.7405457 points, so that the average value of the 
studied variable in this case is calculated as (3.4337 - 0.7405457* FIXEDEUROPE) = 2.6931543 points. A similar 
reduction occurs for Fixed funds categorized as Global, where the risk decreases by 0.6374979 points. In this case, 
the variable reaches an average value of (3.4337 - 0.6374979* FIXEDGLOBAL) = 2.7962021 points.  

Conversely, equity funds based in China experience a significant increase in environmental risk by 1.265613 
points on average, so that in this case, the studied variable reaches an average value of (3.4337 + 1.265613 * 
EQUITYCHINA) = 4.699313 points.  

Equity funds based in Europe also see an increase, albeit smaller, by 0.4469055 points, so that the variable 
takes an average value of (3.4337 + 0.4469055* EQUITYEUROPE) = 3.8806055 points. These interactions 
highlight how different geographic and investment factors influence the environmental risk of funds. 
 

 
Figure 3. Standardized coefficients. 

 
Table 11. Interpretation of the Environmental Risk model 

 
 

 
Figure 4. Effect on the model mean. 



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7. Interpretation of Social Risk Score  
The Portfolio Social Risk Score  variable measures the social risk of the portfolio; in this case, the average risk 

of the different portfolios included in the data reaches a value of 8.84548 points. Several variables significantly 
influence this score. The fund size in base currency (FundSizeBaseCurrency) has a positive or direct influence. For 
each euro increase in fund size, the Portfolio Social Risk Score variable increases on average by 5.70e-11 points, 
provided that the remaining variables remain constant. 

Geographic location also plays a crucial role in determining social risk. If the fund is based in Europe, the social 
risk is reduced, on average, by 1.261423 points, resulting in a risk value of 7.584057, which is significantly lower 
than if it is based in the USA. Similarly, if the fund is based in Asia, the social risk is reduced by 0.9339996 points, 
with the risk taking a value of 7.9114804, also significantly lower than if the fund is based in the USA. For Global 
funds, the social risk is reduced by 0.8046352 points, resulting in a risk value of 8.0408448, which is again 
significantly lower than if the fund is based in the USA. Funds categorized as Global Emerging Markets 
experience a reduction in social risk by 0.6936723 points, leading to a risk value of 8.1518077, which is also lower 
than if the fund is based in the USA. 

In terms of investment categories, Fixed funds show a reduction in average social risk by 0.6982014 points, 
reaching a value of 8.1472786, which is significantly lower than if the fund is categorized as Allocation. 
Interactions between variables further modify the social risk. For instance, if the fund is both Fixed and based in 
Europe, the average social risk reaches a value of (8.84548 - 0.6982014Fixed + 0.393866FixedEurope) = 8.5411446 
points. A more pronounced reduction occurs for Fixed funds based in Asia, where the risk reaches a value of 
(8.84548 - 0.6982014Fixed - 1.486803 FIXEDASIA) = 6.6604756 points. For Fixed funds categorized as Global in 
emerging markets, the risk is significantly reduced to (8.84548 - 0.6982014Fixed - 3.082935 
FIXEDGLOBALEMERGINGMARKET) = 5.0643436 points. Conversely, equity funds located in China 
experience an increase in social risk, reaching a value of (8.84548 + 0.7308476* EQUITYCHINA) = 9.5763276 
points. These interactions highlight how different geographic and investment factors influence the social risk of 
funds. 
 

 
Figure 5. Standardized coefficients. 

 
 

Table 12. Interpretation of the Social Risk Model. 

 
 
 



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Figure 6. Effect on the model mean. 

 

8. Conclusions 
This study makes significant contributions to understanding of how fund characteristics influence 

Environmental, Social, and Governance (ESG) risks in investment portfolios. Through a comprehensive analysis of 
1,737 sustainable investment funds, utilizing data from Morningstar Direct, we have uncovered several important 
relationships between fund attributes and ESG risk profiles. Our findings have substantial implications for 
investment decision-making and portfolio management in the context of sustainable finance. 

The analysis reveals that fund size significantly impacts governance risk, with larger funds experiencing 
greater challenges in managing governance-related issues. This finding suggests that as funds grow, they may 
need to implement more sophisticated governance structures and risk management frameworks. However, the 
relationship between fund size and environmental or social risks appears to be less pronounced, indicating that 
these dimensions of ESG risk may be more influenced by other factors. 

Our investigation of fund categories yields notable insights, particularly regarding the distribution of ESG 
risks across different investment vehicles. Global Emerging Market and Equity funds demonstrate higher 
governance and environmental risks compared to their Allocation and USA-focused counterparts. Conversely, 
Fixed Income and European funds generally exhibit lower ESG risk profiles, suggesting that these categories may 
be more effective at managing sustainability-related challenges. These findings highlight the importance of 
considering fund category when constructing sustainable investment portfolios. 

The geographical focus of funds emerges as a crucial determinant of ESG risk exposure. Funds invested in 
Asian and Global Emerging Markets show elevated environmental and social risks, while those focused on 
European and Global markets generally demonstrate lower social risk profiles. This geographic variation in ESG 
risk exposure underscores the need for investors to carefully consider regional factors when making allocation 
decisions. 

The implications of our findings extend beyond academic interest to practical application in investment 
management. For institutional investors and fund managers, our results suggest the importance of implementing 
robust ESG risk management frameworks, particularly in larger funds and those with exposure to emerging 
markets. The study also provides valuable insights for regulatory bodies and policymakers, highlighting areas 
where additional oversight or guidance may be beneficial in promoting sustainable investment practices. 

Looking ahead, several avenues for future research emerge from our findings. First, investigating the temporal 
dynamics of ESG risks across different fund characteristics could provide insights into how these relationships 
evolve over time. Second, examining the interaction between fund management strategies and ESG performance 
could yield valuable insights for optimizing sustainable investment approaches. Finally, exploring the relationship 
between ESG risks and financial performance at the portfolio level could further enhance our understanding of the 
risk-return trade-offs in sustainable investing. 

This study advances understanding of the complex relationship between fund characteristics and ESG risks, 
providing a foundation for more effective sustainable investment strategies. As the importance of ESG 
considerations continues to grow in the investment industry, these insights become increasingly valuable for 
stakeholders seeking to balance financial returns with sustainability objectives. The findings not only contribute to 
the academic discourse on sustainable finance but also offer practical guidance for investment professionals 
navigating the evolving landscape of responsible investing. 

 

Data Statement: 
The datasets generated during and/or analysed during the current study are available from the corresponding 
author on reasonable request. 
 

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