A Project Report On Sustainable Analytics of 10 companies in Energy Sector
INTRODUCTION
The modern corporate world has been seriously shifted towards
non-financial operations by investors and the globally growing awareness of
environmental hazards, social responsibility, and ethical governance. Such
corporations have come under greater pressure from their investors, employees,
suppliers, consumers, and the government to be vigilant, mitigate risks, and
report accurately about their performance on ESG aspects.
Where companies are now also
considering ESG actions not just as a compliance issue but as strategic
investment decisions. These ESG investment and resource allocation decisions
are closely reviewed in board meetings and relevant committees concerning their
financial rationale. The following report tries to analyse the causality of the
ESG performance and the general corporate performance of the energy sector
based on both financial and ESG performance
For Freeman, successful organizations
are one that develops and implements processes that align with the interest of
the stakeholders; thus, it enhances their sustainability. Organizations must be
oriented not only to shareholder profit but also to the interest of key
stakeholders such as customers, employees, and communities, the environment,
and suppliers. Business roundtable is a group composed of CEOs from the biggest
corporations in the US that support the stakeholder model. They advocate for
corporations to cater to an array of stakeholders of a company their own rather
than just the shareholders.
ESG measures have become
standard ways to measure the performance and position of a firm in areas of
importance to stakeholders, much like financial measures. More recently, public
companies have increased ESG information disclosure dramatically as part of a
general move towards stakeholder involvement, investor interest, reputation
enhancement, and industry crisis and competitive challenge. This practice of
sustainability-whether it be part of the economic mainstream or a
differentiator for competitive advantage-is dynamic and continues to take many
dimensions. In fact, companies worldwide are voluntarily adopting more ESG
practices, which may indicate that there is an economic advantage in doing so.
International organizations, sector institutions, and governments encourage the
trend, and the stock exchanges themselves promote it via the United Nations
Sustainable Stock Exchange Initiative-the SSE. According to the latter, 66 out
of 120 stock exchanges publish guidance on ESG.
ESG issues are increasingly
becoming a prime factor in the decisions of corporations and investors.
Academics and business researchers have debated for a long time the efficiency
of ESG performance in influencing corporate value and profitability. Several
studies investigated how corporate governance influences stock prices. With the
focus shifting toward issues such as climate change, circular economy, and
biodiversity, studies regarding environmental performance and its relation with
the stock market began piling up. Apart from the already prevailing
fast-changing demographics, social issues with respect to stock returns,
COVID-19 has underlined health, safety, and well-being and human capital
management issues such as employee.
The dataset consists of
financial and ESG performance variables for ten major energy companies:
Reliance Industries Limited, Tata Power Company Limited, NTPC Limited, Indian
Oil Corporation Limited, Oil and Natural Gas Corporation, Adani Green Energy
Limited, Hindustan Petroleum Corporation Limited, GAIL (India) Limited, Coal
India Limited, and JSW Energy Limited. Through analysis of such variables, the
project would provide detailed insight into how ESG performance is driving
overall corporate performance.
Most of the researchers
have examined the ESG and corporate financial performance. Recently, most of
studies report positive correlations, but also some articles advocate for
shareholder theory, where the main goal of the firm is maximize shareholder
profit. This project will add to this debate by adding new light into the
selected companies within the energy.
Project Objectives
1. Analysing Financial Performances Variables: Analyse the financial
performance variables such as ROE and ROA for the selected companies for the
last five years.
2. Analyse ESG Performance Indicators: Focus on main ESG metrics,
including Carbon Emission, Energy Consumption, and Employee Turnover Rate.
3. Establish the Causal Relationship: See if there is a causal
relationship between ESG performance and all-round corporate performance.
4. Stakeholder Insights: Produce relevant insights for investors,
corporate managers, policy makers, and other stakeholders on the benefits and
challenges that may arise in integrating ESG practices into core corporate
strategy.
This in-depth
analysis would focus more on how sustainable practices could influence
corporate performance and, therefore, help the stakeholders make better
decisions in this dynamic and changing energy sector.
Overview
The link between ESG performance and financial performance is one of the
most handsome areas in research over the last years. The following section
undertakes a literature review on the existing body of knowledge concerning the
impact of ESG factors on the financial performance of companies, with a
specific focus on the energy sector. This review will also discuss the
relevance of sustainability practice in the energy sector for the ten selected
companies, namely, Reliance Industries Limited, Tata Power Company Limited,
NTPC Limited, Indian Oil Corporation Limited, Oil and Natural Gas Corporation,
Adani Green Energy Limited, Hindustan Petroleum Corporation Limited, GAIL, Coal
India Limited, and JSW Energy Limited.
ESG and Corporate Performance
ESG Integration and Financial Performance A large number of empirical
studies have focused on the relationship between ESG integration and corporate
financial performance. Friede, Busch, and Bassen (2015) conducted a
meta-analysis of over 2,000 empirical studies whose results showed that about
90% of those studies found a non-negative relationship between ESG factors and
corporate financial performance, the majority suggesting a positive
relationship.
Sectoral Analyses
The energy sector has been one of the highest and most direct
environmental impactors to date. As proved by Eccles, Ioannou, and Serafeim
(2014), companies with strong sustainability policies tend to outperform their
peers along both stock market and accounting performance. Such a notion will
also apply to the energy companies since the prevailing regulatory pressures,
coupled with a multi-stakeholder scrutiny, significantly raises the stakes.
On the other hand, Clark, Feiner and Viehs (2015) research underlined
that environmental performance is one of the significant factors which could
determine the financial performance of a firm in the long run. According to the
work, the firms which could boast good environmental practice tend to depict
higher stock returns and low volatility. It is relevant in the case of energy
companies because of their huge carbon footprint and the shift of the world
towards renewable sources of energy.
Governance and Social Factors Recent literature has also pointed out that
social and governance factors have a strong influence on corporate performance.
For example, the World Economic Forum report of 2020 depicted that companies
taking care of their employees, therefore ensuring their well-being, and those
embracing diversity within the organization can be guaranteed to have
operational performance. Along the same line, Bauer, Derwall, and Hann (2009)
discovered that good governance practices have less risk and heightened
investor confidence.
Case Studies: Selected Energy
Companies
Reliance
Industries Limited: It is one of the largest conglomerates in India, with a
wide array of interests in energy, petrochemicals, textiles, and
telecommunications. It has indeed made great strides in ESG initiatives,
especially renewable energy and sustainability of its operations.
Tata
Power Company Limited: Tata Power is amongst the largest integrated private
sector companies in the Indian power industry. Its commitment to clean energy
can be viewed as unmatched: tremendous investment in renewable projects, solar
and wind energy, are some of its major fields with huge environmental
sustainability.
NTPC
Limited: NTPC is the largest energy conglomerate of India, mainly dealing in
generation of electricity. The company has been striving to reduce carbon
footprint by increasing the share of renewable energy in its portfolio and by
implementing various efficiency measures.
Indian
Oil Corporation Limited: Indian Oil stands among the foremost oil and gas
companies in India. The company has implemented various measures so as to
enhance its performance on critical environmental issues of energy efficiency
and greenhouse gas emissions.
Oil
and Natural Gas Corporation (ONGC): ONGC is the leading integrated oil and gas
exploration and production company in the country and is into renewable energy
business with considerable investment. ONGC undertakes many initiatives for
improvement in ESG performance, such as biodiversity conservation and community
development.
Adani
Green Energy Limited: The Company is among the largest renewable energy
businesses in the country. The company develops, constructs, and operates solar
and wind energy power plants. It aims to achieve sustainability by reducing
carbon footprints.
Hindustan
Petroleum Corporation Limited: HPCL is one of the major players within the
space of oil refining and marketing in India. The various initiatives
undertaken by the company on reduction of energy consumption, minimization of
wastes, and improvement of welfare of communities are aiding it to perform better
in terms of ESG parameters.
GAIL
(India) Limited: GAIL is the leading player in natural gas in India. In the
sphere of sustainability, some of the initiatives undertaken by the company
include reduction of methane emissions, improvement of energy efficiency, and
promoting social development projects.
Coal
India Limited: Coal India is the largest coal-producing company in the world.
Its environmental performance has improved through its adoption of cleaner coal
technology and the emergence of renewable energy projects.
JSW Energy Limited: JSW Energy is an integrated power company and one of the leading companies in India, comprising a diversified portfolio of Thermal, Hydro, and Solar Power projects. About Sustainability: The Company focuses on reducing the carbon footprint while improving its ESG performance at the same time.
METHODOLOGY AND DATA COLLECTION:
General regression
analysis is a method that expresses the dependence of one dependent variable
upon one or more independent variables. The theoretical steps for this
methodology include:
1. Formulate the Model: You
have to identify what variables you want to analyze. The dependent is what you
want to predict or explain, and the independents are the factors you believe
will affect it.
2. Data Collection: Gather the
dependent and independent data variables that are relevant and adequate for any
trend or relationship consideration.
3. Fit the Model: Use statistical techniques to arrive at estimates of the actual relationship between the variables. This involves a decision on the best fit line or curve that describes the data.
4. Interpret the Results: Look at the fitted model for insight into how
the dependent variable changes with changes in the independent variables. One
could think of examining the coefficients to check on the strength and
direction of the relationships.
5. Model Validation: Its accuracy and reliability should be checked
using techniques such as cross-validation techniques that will ensure the model
performs well on new data.
6. Make Predictions: Employ the model to predict the dependent variable
for new values of the independent variables.
This helps in understanding the trend and thus making better decisions
based on the data.
First and foremost, one needs to understand the dependent and
independent variables in regression analysis.
Dependent Variable: This is the outcome to be forecast or explained; it
means it depends upon the independent variables. In other words, suppose a study
is done that investigates how the hours studied affect exam scores.
These are predictors or explanatory variables whose effect on the
dependent variable is measured. In other words, these are all the variables
that are not dependent on other factors within the framework of the model.
That means it essentially tries to determine the relationship which may
exist between a dependent variable with one or more independent variables so as
to know how changes in independent variables will affect the dependent variable.
The consequences of the regression analysis screen
varying ranges of courting among the ESG factors and ROA across the 10 Energy
sector comapnies. Each dating is discussed in detail beneath:
ROE vs. Carbon Emissions:
The f regression analysis
gives information on the relationship between ROE and carbon emission for a
sample of 50 observations. The Multiple R value of 0.336 expresses a moderate
positive relationship that exists between the two variables. The R Square value
of 0.113 indicates that about 11.3% of variation in ROE is explained by carbon
emissions. The Adjusted R Square of 0.094 accounts for the number of predictors
in the model.
From the ANOVA table, it can
be noticed that the regression model is significant statistically; the F value
is 6.099, and the p-value is 0.017, indicating that carbon emission has a
significant impact on ROE. The coefficients table presents the intercept as
9.806 and the slope for carbon emission as 0.135, while the p-value is 0.017.
It infers that the increase in ROE due to one-unit increase in carbon emission
is 0.135 units, keeping the rest of the factors constant.
This therefore generally
suggests that carbon emissions are positively related to ROE; it is
statistically significant, though the model explained a very small portion of
the variance in ROE. It therefore infers that while carbon emission impacts the
ROE, other factors play a vital role in determining the ROA.
ROE vs. Employee Turnover:
These results of regression analysis show the
relationship between ROE versus employee turnover for a sample of 50
observations. By considering the Multiple R value of 0.220, it infers that
there is a weak negative relation between both variables. The R Square value of
0.048 shows that only 4.8% of the variability in ROE is explained by employee
turnover. The adjusted R Square is 0.029; it considers the number of predictors
within the model, hence is a slight improvement from the R Square.
In this regard, the p-value is 0.125, and the F
= 2.443 that indicates, from ANOVA table, the regression model is not
significant because employee turnover is insignificantly associated with ROE at
5% significance level. Coefficients: Intercept - 20.042 and slope for employee
turnover is - 0.893, its p-value is 0.125 from the coefficients table. That
means that with every unit increase in employee turnover, ROE decreases by
0.893 units, though the result is not statistically significant.
Overall, analyses point toward a weak,
nonsignificant inverse relationship with ROE; in other words, even though there
might be some indication of a trend to lower ROEs when employee turnover is
higher, this effect neither is strong nor significant in this sample.
ROE vs. Energy Consumption:
The following regression
analysis would provide further insight into the relationship of ROE and energy
consumption from the sample data of 50 observations. A low value of Multiple R
points to a weak positive correlation between these two variables. R Square
becomes 0.101, and hence, about 10.1% of the variability in ROE probably could
be explained by energy consumption. This adjusted R Square value of 0.082 means
the explanatory power of the model is slightly lower when accounting for the
number of predictors.
The ANOVA table presents
the model as significant due to the obtained F = 5.394, p = 0.024, which
implies energy consumption significantly influences ROE. From the coefficients
table, the intercept is 11.547, and the slope for energy consumption is
approximately at the p-value of 0.024. That is, with one unit rise in energy
consumption, ROE increases by a very negligible units,
Overall, data analysis
suggests that there is a statistically significant but weak positive
relationship in energy consumption and ROE. While it is affected by the consumption
of energy, the size of its effect is very small, so other factors are also at
play in determining ROE.
ROA vs. Carbon Emissions:
From the regression
analysis results, one can have an idea about the relationship existing between
ROA and carbon emission for the sample of 50 observations. The value of
Multiple R is 0.383, indicating that there exists a moderate positive
relationship between the two variables. The R Square value of 0.147 indicates
that about 14.7% variation in ROA is explained by carbon emission. Adjusted R
Square is 0.129, and it denotes goodness of fit for the model in explaining
variation, with a slight adjustment for the number of predictors.
From the ANOVA table, it
can be seen that the regression model is statistically significant since the
F-statistic value stands at 8.254, with a p-value of 0.006; hence, there is a
significant impact of carbon emission variable on ROA. The coefficients table
shows that the intercept value stands at 4.257, and the carbon volatile organic
compound emissions have a slope of 0.109, with a p-value standing at 0.006.
This simply means that when carbon emissions increase by one unit, ROA rises by
0.109 units, ceteris paribus.
From the overall results,
a positive and statistically significant relationship between carbon emissions
and ROA is indicated. That is to say, with higher levels of carbon emissions
come higher ROA levels within this sample. The model explains only a moderate
portion of variance in ROA. The possible reason may be that some underlying
factors drive both carbon emission and financial performance, which need to be
researched further.
ROA vs. Employee Turnover:
Following is the
regression analysis that provides the relation between ROA and employee
turnover for a sample of 50 observations. The Multiple R value of 0.292
reflects a very low negative relationship between ROA and employee turnover.
The R Square value of 0.085 indicates that the variability in ROA induced by
the employee turnover is 8.5%. The adjusted R Square value of 0.066 considering
the number of predictors involved in the model indicates that there is a
smaller amount of explanation in variation.
As it is seen, the
overall regression model is significant according to the ANOVA table (F =
4.459; p-value = 0.040); thus, the employee turnover has a significant effect
on ROA. According to coefficients table, intercept is 13.122, the slope for
employee turnover is -0.835, and p-value as 0.040. This means when the employee
turnover increases by a single unit, the ROA diminishes by 0.835 units, ceteris
paribus.
Therefore, the analysis
shows that employee turnover and ROA have a negative and significant
relationship. The model explains just a small portion of the variance in ROA,
but with a higher employee turnover comes lower ROA. Thus, from this sample,
employee turnover negatively affects financial performance.
ROA vs. Energy
Consumption:
The results of the regression
analysis provide an indication of the relationship between ROA and energy
consumption for a sample size of 50 observations. The Multiple R value of 0.357
infers that there is, at most, a moderate positive correlation between the two
variables. In this respect, the R Square value of 0.127 implies that
approximately 12.7% of the variability in ROA can be accounted for through
energy consumption. The Adjusted R Square of 0.109 reflects an adjustment for
the number of predictors.
This ANOVA table indicates
that the regression model is significant, with F = 7.010 and p = 0.011;
therefore, energy consumption significantly influences ROA. Coefficient Table
From the coefficients table, it comes out that the intercept is 5.706, while
the slope for energy consumption is about 7.686×10 −8 7.686×10 −8 with a
p-value of 0.011. It therefore means that as energy consumption increases by
one unit, ROA increases by 7.686×10 −8 7.686×10 −8 units, while controlling for
other factors.
On the whole, the analysis
shows that energy consumption is positively related to ROA with a statistically
significant coefficient, though its magnitude is very small. Energy consumption
contributes to ROA; however, this relationship is so small in magnitude,
suggesting thereby that other factors all over contribute significantly toward
ROA determination.
Cluster Analysis
This figure includes a scatter plots matrix comparing all variables with all.
ROE: Return on Equity
(measures profitability relative to equity)
ROA: Return on Assets
(measures profitability relative to total assets)
Carbon emission: in GHG in
Million tCO2e [GHG in million tCO2e]
Energy Consumption: in GJ
[total energy consumed]
Employee turnover ratio: in %
[Attrition rate-proportion of employees leaving the organization].
Scatter Plot Interpretation:
Each plot conveys a message by accepting two variables' relationships. To
mention a few, they are
ROE vs. ROA-first row, second
column-tells how equity returns are associated with asset returns.
ROE vs. Carbon emission-first
row, third column-tells how the equity return is going with respect to GHG
emission.
Energy Consumption vs.
Employee turnover ratio-fifth row, last column-high consumption of energy may
relate to high employee attrition.
General
Observations
In a few scatter plots, there
may be clustering occur around single area, in which are possibly showing a
non-linear or a very weak relationship.A few of the variables like ROE and ROA,
are nearly fallen in clusters, which showing a strong relationship about such
two variables.The rest of the GHG emissions spread more against the other
financial metrics. Hence they are revealing no evident or a weak relationship.
Overall, this report compares
various financial and sustainability metrics. It seems to suggest that while
there is some correlation, such as ROE to ROA, environmental metrics—like
carbon emissions or energy consumption—are not strongly related to financial
returns. Elucidation of such observations would need deeper statistical
analysis.
The above figure illustrates
the correlation data on the association of three variables: carbon emission,
energy consumption, and employee turnover rate with financial performance
measured by "Return on Total Assets" and "Return on Net
Worth."
1)
Carbon Emissions:
Correlation with Return on
Total Assets 0.383
Correlation with Return on Net
Worth 0.336
Interpretation: In this case,
both correlations are positive; the rise in carbon emissions indicates a
moderate positive relationship with financial performance. There is a
possibility that companies that have higher carbon emissions have better
returns on assets and worth in their books, but this may imply that
industrialized or resource-intensive operations, which tend to produce more
carbon, also yield better returns.
2)
Energy Consumption:
Correlation with Return on
Total Assets: 0.357
Correlation with Return on Net
Worth: 0.318
Interpretation: Similar to
carbon emissions, energy consumption also has a positive correlation with
financial performance, however weaker in strength. This would imply that higher
usage of energy is more correlated with better financial returns. Once again,
this could be typical of energy-using industries where higher usage is
accompanied with higher production capacity and profitability.
3)
Employee Turnover Rate:
Correlation with Return on
Total Assets: -0.292
Correlation with Return on Net
Worth: -0.220
Interpretation. The
correlations are negative, signifying a relationship between employee turnover
rates and worse financial performance. Such a situation reveals that more
frequently exiting workers can hurt the returns of assets and net worth for the
organization. Operations are going to get disrupted, productivity reduced, and
hiring and training costs increased with high turnover.
The given association analysis
of ESG performance with Corporate Financial Performance for selected energy
companies in India has brought forth some of the important aspects of the said
relationship. Among others, there is a moderate statistically significant
positive association of carbon emission with ROE and ROA.
1.
The higher the carbon emission, the higher the ROE and
ROA - though the model explains only a small portion of the variance.
2.
Employee turnover is weakly negative and significantly
related to both ROE and ROA. Increasing employee turnover decreases ROE and
ROA, but the strength of this relation is not high.
3.
Energy consumption is weakly positively and
significantly associated with ROE. Increasing energy consumption is linked to higher
ROE, but the effect size for this relation is very small. The relation is
moderate and positive in the case of ROA1.
That would imply that, though
significant, ESG factors are indeed related to financial performance in the
energy sector; there are other drivers. The results cannot be conclusive, as
some ESG metrics are positively related, while others are negative. Further
research is needed to comprehend the complex interrelations that exist between
sustainability practices and corporate performance for this industry.
The insights brought forth by
this analysis can be used by investors, managers, and policy-makers in making
more informed choices about how to integrate ESG considerations into corporate
strategy in the fast-moving energy world.
REFERENCES :
9.
https://www.adanigreenenergy.com/
10.
https://www.gailonline.com/
12.
https://www.hindustanpetroleum.com/
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