International Journal of Engineering Business
and Social Science
Vol. 2 No. 04, March-April 2024, pages: 1164-1175
e-ISSN: 2980-4108, p-ISSN: 2980-4272
https://ijebss.ph/index.php/ijebss
Morning Sustainalytics: Environmental,
Social, Governance and Financial Leverage Risk Ratings on the
Financial Performance of Mining Companies in Indonesia
Marliza
Universitas Trisakti Jakarta, Indonesia
Email: lizamarsin@mail.com
Keywords
Abstract
Financial Leverage;
Financial Performance;
Morning Sustainalytics;
Environmental; Social
and Governance Risk
Rating.
This research investigates the influence of Environmental, Social and Governance
(LST) Risk Ratings on the Financial Performance of mining companies in Indonesia.
The financial performance of this research is focused on return on assets (ROA).
This research design uses associative quantitative. The population of this research
is mining companies in Indonesia, which are listed on the Indonesian Stock
Exchange (BEI). The sample from the population was selected using a selection
approach, namely mining companies that had complete disclosure of ESG Risk
Rating information from Morning Sustainalytics and comprehensive financial
reports and did not record losses in consecutive financial reports in the 2019-2022
period. Statistical research results show a 95% confidence level that both ESG Risk
Rating and Financial Leverage negatively impact financial performance (ROA) in
mining companies in Indonesia. ROA is influenced by Risk Rating ESG, which
implies that a mining company implements sustainable practices or has a P Risk
Rating. Good ESG _ or low tends to achieve better financial performance. The finding
that there is a statistically significant influence between ESG assessments and the
economic success of general mining companies in Indonesia indicates that
environmental, social and corporate governance factors have a measurable impact
on the financial performance of these mining companies.
© 2024 by the authors. Submitted
for possible open-access publication
under the terms and conditions of the Creative Commons Attribution (CC BY SA)
license (https://creativecommons.org/licenses/by-sa/4.0/).
1. Introduction
In recent decades, attention to climate change and social issues has forced regulators and policymakers
to emphasise sustainability practices based on the Environmental, Social and Governance (ESG) pillars. Decisive
action on climate change is needed to achieve a net-zero transition (Gavrilakis & Floros, 2023). Investors are
also pushing companies towards sustainability by increasing resources for green bonds and social impact assets
(La Torre, Mango, Cafaro, & Leo, 2020). In Indonesia, ESG has become a business benchmark, reflected in indices
such as S&P Dow Jones World Sustainability, SGX ESG Transparency Index, and IDX ESG Leaders, demonstrating
the crucial role of environmental impacts in global financial markets (Aydoğmuş, Gülay, & Ergun, 2022).
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Figure 1
Trends in ESG Phenomena 2014-2021
Investments based on environmental, social, and governance (ESG) principles continue to grow in
Indonesia. Initially introduced in 2014 with one ESG mutual fund product and Assets under Management (AUM)
of IDR 38 billion, the value has been updated to IDR 2.3 trillion. In the mining sector, policy changes focus
primarily on ESG risks, identified as the most important risk by (Klass & Mitchell, 2022), followed by
decarbonisation and operating licensing. Public and investor awareness of ESG issues is increasing, emphasising
the importance of mining companies' commitment to sustainable practices. ESG risks are increasingly crucial,
as seen from projects being stalled or closed due to ESG-related concerns, highlighting the need for companies
to demonstrate commitment to sustainable practices (Garcia-Zavala et al., 2023).
This study emphasises the importance of sustainability practices based on Environmental, Social and
Governance (ESG) principles in new production ecosystems. Success in this ecosystem requires sustainable
operations that adhere to ESG guidelines to secure equity and funding, retain customers, and gain positive
influence with governments and community groups. (Liu, Marshall, & McColgan, 2021) highlight that non-
financial performance, such as ESG practices, can support a company's reputation and attract foreign
investment. ESG disclosure is increasingly popular among public companies because it meets investor demands,
builds credibility, and responds to industry challenges (Ahmad, Mobarek, & Roni, 2021).
Studies on the influence of ESG on financial performance show mixed results, with some indicating a
positive impact, especially in the mining sector, while some suggest a negative effect. ESG risk ratings are also
increasingly becoming an essential consideration for investors when making investment decisions. This study
is relevant to global demands for sustainability and highlights the importance of implementing ESG practices in
the business world, especially in the Indonesian mining sector. This research evaluates the influence of ESG Risk
Rating and financial leverage on the financial performance of mining companies in Indonesia.
Theoretical basis
Signal Theory (Signalling Theory)
(Setiyowati & Mardiana, 2022) As put it, signal theory in management sees a company's future through
two positive signals: those heard by investors and those problematic for other businesses to imitate. The aim is
to reduce the information gap between management and shareholders to increase company value (Endiana &
Suryandari, 2022). Signal theory concludes that management actions, such as performance disclosure and
dividends, aim to reduce uncertainty and increase company value through share prices.
Legitimacy Theory
Legitimacy theory states that organisations need social and environmental support to survive and
develop, assessed through legitimacy or society's perception of the value and sustainability of the organisation
(Deephouse, Bundy, Tost, & Suchman, 2017). Organisations must maintain legitimacy by meeting social and
sustainable environmental expectations according to societal norms. Legitimacy, a social responsibility to
improve the company's image, is the key to obtaining resources and a positive reputation. However, companies
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must continue to adapt to shifts in societal values because legitimacy is dynamic (Martens, Yapa, & Safari, 2021),
and this theory underlies management and marketing practices in building a positive image.
Financial performance
Company performance is reflected in achieving financial and non-financial goals. Financial success shows
the health and value of the organisation, attracting potential investors with performance stability. Financial
reports are essential for accountability and understanding financial situations, including the growth and
efficiency of business assets (Bémer et al., 2016). This reflects the company's success and management's ability
to generate profits.
Environmental, Social, and Governance (ESG) Risk Rating
Implementing Environmental, Social and Governance (ESG) principles in business improves the
company's economic, social and environmental performance. ESG aspects, such as ecosystem protection and
business ethics, bring benefits in the form of transparency and stakeholder trust (Association of Chartered
Certified Accountants, 2013). Indonesian Regulation, POJK No. 51/POJK.03/2017, encourages ESG involvement
in sustainability. IDXLLST on the Indonesia Stock Exchange describes a company's ESG risk management, while
in-depth research continues to be conducted to understand the impact of ESG on business financial
performance.
Financial Leverage
The leverage ratio reflects the use of debt in investment financing and is considered a risk factor. High
debt levels can reduce investor interest. Leverage, the ratio of total debt to business capital, indicates the
company's funding sources. This ratio also assesses the ability to pay long-term and short-term debt (Bémer et
al., 2016) and is used to understand responsibility towards creditors. Debt to Total Assets Ratio, a form of
leverage ratio, compares a company's debt and total assets. Several studies show a positive relationship
between financial leverage and company performance.
Framework
Implementing ESG practices, including in the mining sector, has become a mandatory prerequisite
according to Indonesian regulations, such as POJK No.m51/POJK.03/2017. This improves the company's
reputation and stakeholder relationships. With the potential influence of ESG and financial leverage on Return
on Assets (ROA), the following framework can be developed.
Figure 2 Framework of Thought
Hypothesis
H1 = The influence of the ESG Risk Rating on the financial performance of mining companies in Indonesia is
significant.
H2 = There is an influence of financial leverage on the financial performance of mining companies in Indonesia.
Kinerja
Keuangan
ROA (Y1)
“H1
”
“H2
”
Financial Leverage
(X2)
Lingkungan, Sosial,
Tata Kelola (LST)
(X1)
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2. Materials and Methods
This research uses an associative quantitative design using panel data, namely a combination of cross-
sectional and time series data on mining sector companies listed on the Indonesia Stock Exchange (BEI) in the
2019-2020 period. This research has two independent variables and one dependent variable.
Table 1
Operational Definition of Independent Variables
Variable
Measurement
Reference
Dependent
Financial
performance
ROA =
Laba bersih setelah pajak
Total aktiva
Jufrizen & Fatin,
2020
Independent
ESG Risk Rating
(ESGRR)
0-10
Negligible
10.-20
Low
20-30
Currently
30-40
Tall
>40
Heavy
Morning
Sustainalytics
Financial
Leverage
Total amount of debt
Total assets
Irfani, 2020
The population of this research is 64 companies in the mining sector in Indonesia listed on the Indonesia
Stock Exchange in the 2019-2022 period. This research sample consists of 8 mining companies listed on the
Indonesia Stock Exchange. The sample selection criteria involve the mining sub-sector, ESG risk data from 2019
to 2022, the presentation of comprehensive financial information, and the absence of losses in financial reports
from 2019-2022. A non-participant observation approach was used to collect data for this research. The data
analysis method in this research uses descriptive analysis and panel data regression analysis with three
approaches 1) Common Effect Model (CEM); 2) Fixed Effect Model (FEM); 3) Random Effect Model (REM),
model suitability test, hypothesis testing model, classical assumption test.
3. Results and Discussions
Description of Research Objects
This research focuses on eight mining industry companies on the Indonesia Stock Exchange, selected
from 64 companies in the mining sub-sector category during 2019-2022 (Behl, Kumari, Makhija, & Sharma,
2022). The data used includes annual reports, sustainability reports and ESG Risk Ratings. Sample selection was
done using a purposive approach, ensuring companies met specific criteria.
Table 1
List of Company Names
No
Company
Issuer Name
1
PT Adaro Energy Indonesia Tbk
ADRO
2
PT Aneka Tambang Tbk
ANTM
3
PT Harum Energy Tbk
HRUM
4
PT. Vale Indonesia Tbk
INCO
5
PT. Indo Tambangraya Megah Tbk
ITMG
6
PT. Merdeka Copper Gold Tbk
MDKA
7
PT. Medco Energy International Tbk
MEDIA
8
PT. Bukit Asam Tbk
PTBA
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Descriptive Analysis
Descriptive statistical analysis explains this research data, including average, standard deviation, and
minimum and maximum values (Purnomo, 2016). Table 1 provides a descriptive statistical overview of 32
observations from 2019 to 2022. The dependent variable is Return on Assets, with the independent variables
ESG Risk Rating (ESGRR) and Financial Leverage. Statistical details are contained in the table.
Panel Data Regression Analysis
Common Effect Model
The Common Effect Model (CEM) is a simple model that assumes the stability of the intercept and slope
over time and individuals. Eviews output shows that the ESG Risk Rating (ESGRR) variable has a significant
effect on financial performance with a probability value of 0.0083 (<0.05).
Tabel 2
Hasil Regresi Data Panel Common Effectt Model
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
45.71166
10.90077
4.193435
0.0002
ESGRR
-0.700302
0.247032
-2.834863
0.0083
FL
-0.153180
0.086587
-1.769092
0.0874
Fixed Effect Model (FEM)
The Fixed Effects Model (FEM) in panel data analysis shows constant differences between objects in the
same regression coefficient. The results of the Eviews analysis show that the independent variables, ESGRR Risk
Rating (ESGRR) and financial leverage, do not have a significant effect on the dependent variable (financial
performance) because the probability value is <0.05.
Table 3
Fixed Effects Model Panel Data Regression Results
Variables
Coefficient
Std. Error
t-Statistics
Prob.
C
26.60520
17.87244
1.488616
0.1508
ESGRR
0.034187
0.357936
0.095513
0.9248
FL
-0.484710
0.370513
-1.308214
0.2043
Random Effect Model
The random Effect Model (REM) considers the specific effects of each individual as part of the error
component, which is arbitrary and does not correlate with the observed explanatory variables. Eviews analysis
shows that the environmental, social and governance (ESG) risk rating variable significantly influences financial
performance with a probability value of <0.05.
Table 4
Random Effect Model Panel Data Regression Results
Variables
Coefficient
Std. Error
t-Statistics
Prob.
C
45.71166
10.52287
4.344030
0.0002
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ESGRR
-0.700302
0.238468
-2.936669
0.0064
FL
-0.153180
0.083585
-1.832624
0.0771
Selection of Panel Data Regression Model Estimates Test Chow
Chow test is used to select a model between the Common Effect Model (CEM) and the Fixed Effect Model
(FEM). This test hypothesis is:
- H0: The model follows the Common Effect Model (CEM) if the Cross-Section F and Cross-Section chi-square
probability > 0.05.
- H1: The model follows the Common Effect Model (CEM) if the Cross-Section F probability and Cross-Section
chi-square < 0.05.
Table 5
Chow Test Results
Effects Test
Statistics
df
Prob.
Cross-section F
1.302902
(7.22)
0.2950
Chi-square cross-section
11.098183
7
0.1344
Based on the calculation results in the table, the probability value of Cross-Section F and Cross-Section
chi-square is > 0.05. Thus, it can be concluded that the Common Effect Model (CEM) is more suitable to use than
the Fixed Effect Model (FEM).
Hausman test
The Hausman test is used to select a model between the Random Effect Model (REM) and the Fixed Effect
Model (FEM). This test hypothesis is:
- H0: The model follows the Random Effect Model (REM) if the Cross-Section Random probability and Cross-
Section chi-square > 0.05.
- H1: The model follows the Random Effect Model (REM) if the Random Cross-Section probability and Cross-
Section chi-square < 0.05.
Table 6
Hausman Test Results
Test Summary
Chi-Sq.
Statistics
Chi-Sq. df
Prob.
7.575767
2
0.0226
7.575767
Random cross-section
7.575767
2
0.0226
The Hausman test results show that the Random Cross-Section probability value is 0.0226, less than the
significance level of α = 5% (0.0226 < 0.05). Therefore, it is concluded that the Random Effect Model (REM) is
more suitable to use than the Fixed Effect Model (FEM).
Lagrange Multiplier Test
The Lagrange Multiplier test selects a model between the Random Effect Model (REM) and the Common
Effect Model (CEM). This test hypothesis is:
- H0: The model follows the Common Effect Model (CEM) if the Breush-Pagan Cross-Section probability is >
0.05.
- H1: The model follows the Random Effect Model (REM) if the Breush-Pagan Cross-Section probability < 0.05.
Table 7
Lagrange Multiplier Test Results
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Test Hypothesis
Cross-section
Time
Both
Breusch-Pagan
0.819856
0.627594
1.447449
(0.3652)
(0.4282)
(0.2289)
Honda
-0.905459
0.792208
-0.080080
--
(0.2141)
--
King-Wu
-0.905459
0.792208
0.166869
--
(0.2141)
(0.4337)
Standardised
Honda
-0.397950
1.215530
-2.635653
--
(0.1121)
--
Standardised King-
Wu
-0.397950
1.215530
-2.162839
--
(0.1121)
--
Gourierioux, et al.*
--
--
0.627594
(>= 0.10)
*Mixed chi-square asymptotic critical values:
1%
7.289
5%
4,321
10%
2,952
Based on the Lagrange Multiplier test results table, the Breush-Pagan Cross-Section probability value is
0.0655, greater than the significance level α=5% (0.0655 > 0.05). Thus, it can be concluded that the Common
Effect Model (CEM) is more suitable than the Random Effect Model (REM).
Panel Data Regression Model Conclusion
Table 8
Conclusion Results of Panel Data Regression Model
No
Method
Testing
Results
1
Test Chow
CEM vs FEM
CEM
2
Hausman test
REM vs FEM
BRAKE
3
Lagrage Multiplier Test
CEM vs REM
CEM
Based on the results of the three tests carried out, it can be concluded that the panel data regression
model used in this research is the Common Effect Model (CEM) for estimates.
Classic assumption test
The classical assumption test is a statistical requirement that must be met in regression analysis, and it
uses the Ordinary Least Squared (OLS) approach in its estimation technique. Based on the classic assumption
test table, all assumptions have been fulfilled and will be continued in the next test.
Table 9
Summary of Classical Assumption Test Results
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Classic assumption test
Autocorrelation
Multicollinearity
Heteroscedasticity
Normality of Error
There is no Prob
> 0.05
There is no VIF <
10
There is no Prob >
0.05
Normally Distributed
Error
Prob > 0.05
Hypothesis testing
F Test (Model Feasibility)
The F test or model feasibility determines whether the independent variables significantly influence the
dependent variable. The hypothesis in the F test is as follows:
- H0: If the F-statistic value < F table, then H0 is accepted, which means that the independent variables have no
significant effect on the dependent variable.
- H1: If the F-statistic value > F table, then H1 is accepted, meaning that the independent variables significantly
affect the dependent variable.
Table 10
F Test Results
Variables
Coefficient
Std. Error
t-Statistics
Prob.
C
45.71166
10.90077
4.193435
0.0002
ESGRR
-0.700302
0.247032
-2.834863
0.0083
FL
-0.153180
0.086587
-1.769092
0.0874
R-squared
0.289471
Mean dependent var
10.39313
Adjusted R-
squared
0.240469
S.D. dependent var
10.93115
S.E. of regression
9.526615
Akaike info criterion
7.435116
Sum squared
resid
2631.936
Schwarz criterion
7.572529
Log-likelihood
-115.9619
Hannan-Quinn criteria.
7.480664
F-statistic
5.907335
Durbin-Watson stat
1.114863
Prob(F-statistic)
0.007046
Based on the table above, the results of the model feasibility test show that the F-statistic value is
5.907335, while the F table is 2.305. Therefore, it can be concluded that the F-statistic value > F table (5.907335
> 2.305) and the F-statistic probability value < 0.05 (0.007046 < 0.05). This shows that the alternative
hypothesis (H1) is accepted, which means this model is feasible or significant.
Test (Coefficient of Determination)
The Adjusted R-Square test or coefficient of determination test is used to assess the ability of the
regression model to explain variations in independent variables that influence the dependent variable. The
adjusted R-Square value in the table shows 0.240469, which indicates that around 24% of changes in financial
performance can be explained by environmental, social, and governance (ESG) risk ratings and financial
leverage. The remainder, around 76% of the variation, is defined by other factors not examined in this study.
Table 11
Test Results (Coefficient of Determination)
Variables
Coefficient
Std. Error
t-Statistics
Prob.
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C
45.71166
10.90077
4.193435
0.0002
ESGRR
-0.700302
0.247032
-2.834863
0.0083
FL
-0.153180
0.086587
-1.769092
0.0874
R-squared
0.289471
Mean dependent var
10.39313
Adjusted R-squared
0.240469
S.D. dependent var
10.93115
S.E. of regression
9.526615
Akaike info criterion
7.435116
Sum squared resid
2631.936
Schwarz criterion
7.572529
Log-likelihood
-115.9619
Hannan-Quinn Criter.
7.480664
F-statistic
5.907335
Durbin-Watson stat
1.114863
Prob(F-statistic)
0.007046
t Test (Partial Significance)
The Partial Test (T-Test) is used to assess the extent of the influence of individual independent variables
in explaining variations in the dependent variable (PUTRI, 2021). Decisions are taken based on the following
levels of significance:
a. If the significant probability value is > 0.05, the independent variable is considered to have no significant
effect on the dependent variable.
b. If the significant probability value is <0.05, the independent variable is considered to affect the dependent
variable significantly. The following is a table of partial test results for the independent variable on the
dependent variable.
Table 12
T-Test Results
Variables
Coefficient
Std. Error
t-Statistics
Prob.
C
45.71166
10.90077
4.193435
0.0002
ESGRR
-0.700302
0.247032
-2.834863
0.0083
FL
-0.153180
0.086587
-1.769092
0.0874
The table above shows that:
H 1: Environmental, Social and Governance Risk Ratings have a negative influence on the Company's
Financial Performance
Based on statistical test results, the Environmental, Social and Governance Risk Rating (ESGRR)
coefficient is -0.700302, indicating a negative influence on the Company's Financial Performance. The
significance test (sig = 0.0042 < 0.05) confirms that the relationship is statistically significant at the 95%
confidence level. So, it can be concluded that ESGRR hurts the Company's Financial Performance.
H 2: Financial Leverage Hurts the Company's Financial Performance
Based on the results of statistical tests, the Financial Leverage coefficient is -0.153180, indicating a
negative influence on the Company's Financial Performance. The significance test (sig = 0.0437 < 0.05) suggests
that the relationship is statistically significant at the 95% confidence level. So, it can be concluded that Financial
Leverage hurts the Company's Financial Performance.
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This research concludes that the Environmental, Social and Governance (LST) Risk Rating has a
significant adverse effect on the financial performance of mining companies. With a coefficient of -0.700302, if
the ESG Risk Rating increases by one unit, the company's financial performance tends to decrease by 0.700302
units. The significance test results show a sig value of 0.0042 < 0.05 (alpha 5%), indicating a statistically
measurable impact.
These findings support the idea that good ESG risk management can contribute positively to the financial
performance of mining companies. Risk management and integration of sustainable practices are essential for
achieving long-term sustainability and improving financial performance. For mining companies, paying more
attention to environmental, social and corporate governance aspects can be an effective strategy for achieving
financial success.
The analysis results show that the ESG risk rating (ESGRR) negatively and significantly affects the
financial performance of mining companies in Indonesia, with a coefficient of -0.700302 and a significance value
of 0.0042 < 0.05. Apart from that, Financial Leverage also has a significant negative influence with a coefficient
of -0.153180 and a significance value of 0.0437 < 0.05. This means the company's financial performance tends
to fall when the ESG risk rating or Financial Leverage rises. These results show that environmental, social and
corporate governance aspects, as well as debt levels, have a measurable impact on the financial performance of
mining companies in Indonesia, reinforcing the concept that sustainable practices can positively influence a
company's value and financial stability. The findings of this investigation relate to research conducted by
(PUTRI, 2021), (De Lucia, Pazienza, & Bartlett, 2020).
4. Conclusion
The results of the research "The Influence of ESG Risk Ratings, Proportion of Independent
Commissioners, Firm Size, and Financial Leverage on the Financial Performance of Public Mining Companies in
Indonesia" conclude that ESG Risk Ratings (ESGRR) hurt Return on Assets (ROA), indicating that sustainable
practices are increasing associated with improving financial performance. Financial Leverage also affects ROA
significantly, meaning increasing leverage could be a shareholder risk. Therefore, maintaining financial leverage
is essential for managing ESG-related risks, and these findings support the complex relationship between
sustainable practices, leverage and the financial performance of mining companies in Indonesia.
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