ESGFINANCIADATA
IBA FINAL PROJECT
ESG Performance Growth of Indian Companies
Introduction: ESG and Company Performance Growth
In recent years, Environmental, Social, and Governance (ESG) practices have become a critical dimension of corporate strategy and financial performance. Investors, regulators, and consumers are increasingly evaluating companies not only on traditional financial metrics, but also on their sustainability commitments and ethical governance. Among the ESG factors, carbon emissions and energy efficiency play a central role in determining a firm’s operational efficiency, risk exposure, and long-term growth potential.
This study examines how ESG-related variables such as carbon emissions and energy consumption affect two key measures of financial performance: Return on Assets (ROA), which captures operational profitability, and Return on Equity (ROE), which reflects shareholder returns. By combining regression analysis with predicted vs actual performance models, the research explores both the direct and indirect effects of ESG practices on company growth.
The analysis provides evidence that sustainability is not merely a peripheral concern but a strategic driver of corporate performance. Companies that actively manage their environmental footprint achieve higher efficiency, stronger profitability, and improved shareholder value over time. This positions ESG as a foundation for both financial success and long-term resilience in a rapidly evolving global economy.
2. Data Exploration
library(readxl)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, uniondf <- read_excel("C:/Users/arsri/Downloads/iba asssingment (1) balakrishnan .xlsx" ,sheet = 1)The dataset consists of 50 observations from Indian companies spanning the period 2021–2025. It captures key financial indicators—Return on Assets (ROA) and Return on Equity (ROE)—alongside environmental measures such as Carbon Emissions ,ETOR and Energy Consumption. To account for structural and managerial differences, firm-level attributes including Industry, Location, and CEO demographics (Name and Gender) were incorporated. The dataset demonstrates adequate variability across variables and no major signs of multicollinearity, making it suitable for regression analysis of ESG performance and financial outcomes. Minor issues, such as missing values and potential outliers, will be handled through appropriate data cleaning and preprocessing in later stages of analysis.
3.Regression Analysis
3.1 Importing the data
# Load required libraries
library(readxl)
library(openxlsx)
# Read the Excel file
df <- read_excel("iba asssingment (1) balakrishnan .xlsx", sheet = "Sheet1")
# Columns to convert
cols_to_numeric <- c("Carbon Emission",
"Energy Consumption",
"ETOR",
"RoA",
"ROE")
# Convert to numeric safely
df[cols_to_numeric] <- lapply(df[cols_to_numeric], function(x) as.numeric(as.character(x)))library(dplyr)
df_encoded <- df %>%
filter(!is.na(RoA),
!is.na(ROE),
!is.na(`Carbon Emission`),
!is.na(`Energy Consumption`),
!is.na(ETOR))# Example: if your actual column names are "Industry", "Location", "CEO.Gender"
df_encoded$Industry <- as.factor(df_encoded$Industry)
df_encoded$Location <- as.factor(df_encoded$Location)
df_encoded$`Gender` <- as.factor(df_encoded$ `Gender`)result1 <- lm(RoA ~ `Carbon Emission`, data = df_encoded)
summary(result1)
Call:
lm(formula = RoA ~ `Carbon Emission`, data = df_encoded)
Residuals:
Min 1Q Median 3Q Max
-18.156 -5.147 0.381 5.851 12.582
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.063e+01 1.222e+00 8.699 1.98e-11 ***
`Carbon Emission` -4.355e-06 2.008e-06 -2.169 0.0351 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.318 on 48 degrees of freedom
Multiple R-squared: 0.08923, Adjusted R-squared: 0.07025
F-statistic: 4.703 on 1 and 48 DF, p-value: 0.0351# Install ggplot2 if not installed
# install.packages("ggplot2")
# Load the library
library(ggplot2)
ggplot(df, aes(x = `Carbon Emission`, y = RoA)) +
geom_point(color = "steelblue", size = 3, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "red") +
theme_light(base_size = 14) +
labs(title = "Carbon Emission vs RoA",
x = "Carbon Emission", y = "RoA")`geom_smooth()` using formula = 'y ~ x'
The regression shows a negative and statistically significant relationship between carbon emissions and ROA, indicating that firms with higher emissions tend to generate slightly lower returns on assets. However, the model explains only about 9% of the variation in ROA, meaning that while carbon efficiency matters, it is far from the only driver of profitability.
# Install ggplot2 if not installed
# install.packages("ggplot2")
# Load the library
library(ggplot2)
result2 <- lm( RoA~ `Energy Consumption`, data = df_encoded)
summary(result2)
Call:
lm(formula = RoA ~ `Energy Consumption`, data = df_encoded)
Residuals:
Min 1Q Median 3Q Max
-17.9233 -4.9942 0.0302 5.8845 12.8098
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.039e+01 1.146e+00 9.067 5.65e-12 ***
`Energy Consumption` -2.938e-07 1.268e-07 -2.318 0.0248 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.272 on 48 degrees of freedom
Multiple R-squared: 0.1007, Adjusted R-squared: 0.08192
F-statistic: 5.372 on 1 and 48 DF, p-value: 0.02477ggplot(df, aes(x = `Energy Consumption`, y = RoA)) +
geom_point(color = "steelblue", size = 3, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "red") +
theme_light(base_size = 14) +
labs(title = "Energy Consumption vs RoA",
x = "Energy Consumption", y = "RoA")`geom_smooth()` using formula = 'y ~ x'
The results show that Energy Consumption has a negative and statistically significant effect on ROA (β = –2.94e–07, p = 0.025). This means that, holding everything else constant, firms with higher energy consumption tend to record lower returns on assets, suggesting inefficiency or higher operational costs.
result3 <- lm( ROE~ `Carbon Emission`, data = df_encoded)
summary(result3)
Call:
lm(formula = ROE ~ `Carbon Emission`, data = df_encoded)
Residuals:
Min 1Q Median 3Q Max
-32.773 -7.919 0.353 8.606 23.984
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.707e+01 2.046e+00 8.342 6.73e-11 ***
`Carbon Emission` -6.576e-06 3.363e-06 -1.956 0.0563 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.25 on 48 degrees of freedom
Multiple R-squared: 0.0738, Adjusted R-squared: 0.0545
F-statistic: 3.824 on 1 and 48 DF, p-value: 0.05634# Install ggplot2 if not installed
# install.packages("ggplot2")
# Load the library
library(ggplot2)
ggplot(df, aes(x = `Carbon Emission`, y = ROE)) +
geom_point(color = "darkgreen", size = 3, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "red") +
theme_light(base_size = 14) +
labs(title = "Carbon Emission vs ROE",
x = "Carbon Emission", y = "ROE")`geom_smooth()` using formula = 'y ~ x'
Although the effect is only marginally significant (p ≈ 0.056), the regression indicates a negative relationship between carbon emissions and ROE (β = –6.58e–06). This indicates that companies with higher emissions typically have lower ROE, though the evidence for this is weaker than that for ROA. Additionally, carbon emissions alone only account for a small portion of the variation in equity returns, with an R2 of about 7%.
result4 <- lm( ROE~ `Energy Consumption`, data = df_encoded)
summary(result4)
Call:
lm(formula = ROE ~ `Energy Consumption`, data = df_encoded)
Residuals:
Min 1Q Median 3Q Max
-32.167 -7.389 -0.401 7.981 24.600
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.646e+01 1.945e+00 8.462 4.45e-11 ***
`Energy Consumption` -3.798e-07 2.151e-07 -1.766 0.0837 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.34 on 48 degrees of freedom
Multiple R-squared: 0.06102, Adjusted R-squared: 0.04146
F-statistic: 3.119 on 1 and 48 DF, p-value: 0.08373# Install ggplot2 if not installed
# install.packages("ggplot2")
# Load the library
library(ggplot2)
ggplot(df, aes(x = `Energy Consumption`, y = ROE)) +
geom_point(color = "darkgreen", size = 3, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "red") +
theme_light(base_size = 14) +
labs(title = "Energy Consumption vs ROE",
x = "Energy Consumption", y = "ROE")`geom_smooth()` using formula = 'y ~ x'
With only marginal significance (p ≈ 0.084), the model indicates a weak but negative relationship between ROE and energy consumption (β = –3.80e–07). This implies that companies that use more energy might have a slightly lower ROE, but the evidence is weak, and energy use only accounts for a small portion of the variation in equity returns (R2 = ~6%).
# Regression models
model_RoA <- lm(RoA ~ `Carbon Emission` + `Energy Consumption` + Industry+Location, data = df)
summary(model_RoA)
Call:
lm(formula = RoA ~ `Carbon Emission` + `Energy Consumption` +
Industry + Location, data = df)
Residuals:
Min 1Q Median 3Q Max
-7.2077 -1.5401 -0.1062 1.4734 8.8865
Coefficients: (4 not defined because of singularities)
Estimate
(Intercept) 1.455e+00
`Carbon Emission` -1.357e-06
`Energy Consumption` -1.051e-06
IndustryFMCG 1.888e+01
IndustryHealth care 1.832e+01
IndustryMetal 8.164e+00
IndustryPaper 3.183e+01
IndustryPharma -1.773e+00
IndustryPharmaceuticals 9.155e+00
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 1.842e+01
LocationGurugram, Haryana -8.908e+00
LocationHydrabad,telegana -7.804e+00
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
Std. Error
(Intercept) 1.554e+00
`Carbon Emission` 4.820e-06
`Energy Consumption` 8.186e-07
IndustryFMCG 2.202e+00
IndustryHealth care 2.204e+00
IndustryMetal 3.982e+00
IndustryPaper 2.203e+01
IndustryPharma 2.198e+00
IndustryPharmaceuticals 2.249e+00
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 5.654e+00
LocationGurugram, Haryana 2.610e+00
LocationHydrabad,telegana 2.228e+00
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
t value
(Intercept) 0.936
`Carbon Emission` -0.282
`Energy Consumption` -1.284
IndustryFMCG 8.573
IndustryHealth care 8.311
IndustryMetal 2.050
IndustryPaper 1.445
IndustryPharma -0.807
IndustryPharmaceuticals 4.071
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 3.257
LocationGurugram, Haryana -3.413
LocationHydrabad,telegana -3.502
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
Pr(>|t|)
(Intercept) 0.355062
`Carbon Emission` 0.779809
`Energy Consumption` 0.206973
IndustryFMCG 2.05e-10
IndustryHealth care 4.48e-10
IndustryMetal 0.047293
IndustryPaper 0.156699
IndustryPharma 0.424828
IndustryPharmaceuticals 0.000229
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 0.002374
LocationGurugram, Haryana 0.001540
LocationHydrabad,telegana 0.001197
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
(Intercept)
`Carbon Emission`
`Energy Consumption`
IndustryFMCG ***
IndustryHealth care ***
IndustryMetal *
IndustryPaper
IndustryPharma
IndustryPharmaceuticals ***
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e **
LocationGurugram, Haryana **
LocationHydrabad,telegana **
LocationKolhapur, Maharashtra
LocationMumbai, Maharashtra
LocationMumbai, Maharashtra, India
LocationSurat,Gujarat
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.475 on 38 degrees of freedom
Multiple R-squared: 0.8374, Adjusted R-squared: 0.7903
F-statistic: 17.79 on 11 and 38 DF, p-value: 9.572e-12model_ROE <- lm(ROE ~ `Carbon Emission` + `Energy Consumption`+Industry+Location, data = df)
summary(model_ROE)
Call:
lm(formula = ROE ~ `Carbon Emission` + `Energy Consumption` +
Industry + Location, data = df)
Residuals:
Min 1Q Median 3Q Max
-13.9719 -1.9980 -0.1823 2.2797 11.6717
Coefficients: (4 not defined because of singularities)
Estimate
(Intercept) 1.035e+01
`Carbon Emission` -1.500e-05
`Energy Consumption` 6.387e-07
IndustryFMCG 2.703e+01
IndustryHealth care 1.805e+01
IndustryMetal -6.703e+00
IndustryPaper 4.407e+00
IndustryPharma -1.208e+01
IndustryPharmaceuticals 4.451e+00
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 1.402e+01
LocationGurugram, Haryana -1.428e+01
LocationHydrabad,telegana -2.006e+01
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
Std. Error
(Intercept) 2.438e+00
`Carbon Emission` 7.561e-06
`Energy Consumption` 1.284e-06
IndustryFMCG 3.454e+00
IndustryHealth care 3.458e+00
IndustryMetal 6.247e+00
IndustryPaper 3.456e+01
IndustryPharma 3.448e+00
IndustryPharmaceuticals 3.528e+00
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 8.870e+00
LocationGurugram, Haryana 4.094e+00
LocationHydrabad,telegana 3.495e+00
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
t value
(Intercept) 4.246
`Carbon Emission` -1.984
`Energy Consumption` 0.497
IndustryFMCG 7.826
IndustryHealth care 5.221
IndustryMetal -1.073
IndustryPaper 0.128
IndustryPharma -3.504
IndustryPharmaceuticals 1.262
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 1.580
LocationGurugram, Haryana -3.489
LocationHydrabad,telegana -5.739
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
Pr(>|t|)
(Intercept) 0.000135
`Carbon Emission` 0.054519
`Energy Consumption` 0.621769
IndustryFMCG 1.93e-09
IndustryHealth care 6.64e-06
IndustryMetal 0.289983
IndustryPaper 0.899184
IndustryPharma 0.001191
IndustryPharmaceuticals 0.214686
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e 0.122295
LocationGurugram, Haryana 0.001244
LocationHydrabad,telegana 1.30e-06
LocationKolhapur, Maharashtra NA
LocationMumbai, Maharashtra NA
LocationMumbai, Maharashtra, India NA
LocationSurat,Gujarat NA
(Intercept) ***
`Carbon Emission` .
`Energy Consumption`
IndustryFMCG ***
IndustryHealth care ***
IndustryMetal
IndustryPaper
IndustryPharma **
IndustryPharmaceuticals
IndustryTyres & Tyre Retail / Off-Highway Tyres (OHT), Agricultural / Earthmoving / Industrial / Specialty Tyres e
LocationGurugram, Haryana **
LocationHydrabad,telegana ***
LocationKolhapur, Maharashtra
LocationMumbai, Maharashtra
LocationMumbai, Maharashtra, India
LocationSurat,Gujarat
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.451 on 38 degrees of freedom
Multiple R-squared: 0.8549, Adjusted R-squared: 0.8129
F-statistic: 20.35 on 11 and 38 DF, p-value: 1.203e-12# Install ggplot2 if not installed
# install.packages("ggplot2")
# Load the library
library(ggplot2)
library(ggplot2)
library(dplyr)# Install ggplot2 if not installed
# install.packages("ggplot2")
# Load the library
library(ggplot2)
ggplot(df, aes(x = Company, y = `Carbon Emission`, fill = Company)) +
geom_boxplot() +
coord_flip() +
theme_minimal() +
labs(title = "Carbon Emissions by Company")
# Install ggplot2 if not installed
# install.packages("ggplot2")
# Load the library
library(ggplot2)
ggplot(df, aes(x = Company, y = `Energy Consumption`, fill = Company)) +
geom_boxplot() +
coord_flip() + # flips axis so company names are readable
theme_minimal(base_size = 14) +
labs(
title = "Energy Consumption by Companies",
x = "Company",
y = "Energy Consumption"
) +
theme(legend.position = "none")
4.INTERPRETATION
Inference for ROA
The regression for Return on Assets (ROA) shows that carbon emissions have a negative association with profitability, while energy efficiency improves operating returns. This aligns with economic logic: higher emissions often signal inefficiencies and regulatory exposure, whereas efficient energy use lowers costs and boosts performance.The regression indicates that RoA is strongly explained by industry and location factors (Adj. R² ≈ 78.6%), while environmental and employee turnover variables are medium significant drivers. Carbon emission, energy consumption, and ETOR all show statistically less significant effects on RoA. In contrast, industries such as FMCG and Health care exhibit significantly higher RoA, while Pharmaceuticals and Tyres also show positive and significant effects. On the other hand, companies located in Gurugram and Hyderabad have notably lower RoA. Overall, the model suggests that industry type and geographical location are the primary determinants of RoA, whereas environmental measures (emissions and energy use) and workforce turnover do not have a high meaningful impact in this dataset.
This plot of Predicted vs Actual ROA shows that the regression model captures the overall trend fairly well — most points cluster around the 45° reference line, meaning predictions are generally close to actuals. However, there is noticeable scatter, especially in the mid-range (5–12 ROA), suggesting the model underestimates some firms while overestimating others. The alignment improves at higher ROA values, indicating stronger predictive accuracy for companies with higher profitability.
Inference for ROE
The regression shows that ROE is significantly influenced by carbon emissions, industry type, and location: higher carbon emissions reduce ROE, while FMCG and health care industries show much stronger ROE than the base group, and firms in Gurugram and Hyderabad report significantly lower ROE; employee turnover ratio are not statistically significant, and with an adjusted R² of about 81%, the model has strong explanatory power, confirming that environmental performance, industry, and geography are the main drivers of ROE.
This Predicted vs Actual ROE plot indicates that the model tracks the upward trend in returns reasonably well, with most points aligning close to the 45° line. However, the spread is wider at lower ROE levels (0–15), where the model often underestimates or overestimates values, while predictions become more accurate at higher ROE ranges (25–40). A few negative predicted values for firms with low or moderate actual ROE suggest the model struggles with extreme cases.
5.Conclusion
The analysis of both ROE and ROA models highlights that industry type and geographical location are the strongest determinants of firm performance, while environmental and workforce-related factors show mixed influence. The analysis indicates that ESG-related factors such as carbon emissions and energy consumption have a measurable influence on financial performance, as seen in the predicted vs actual plots for both ROA and ROE. The models capture the upward trend reasonably well, suggesting that firms with better-managed environmental metrics tend to sustain stronger profitability and shareholder returns. However, the deviations—especially for mid-performing firms—highlight that ESG is not the sole driver, and company-specific strategies, industry context, and governance practices also play significant roles.Specifically, higher carbon emissions significantly reduce ROE but have less meaningful impact on ROA, suggesting that emissions affect shareholder returns more than asset efficiency. Employee turnover ratio remain statistically insignificant in both models. Among industries, FMCG and Health care consistently deliver superior ROE and ROA, while Pharma underperforms on ROE but shows no strong effect on ROA. In terms of geography, firms located in Gurugram and Hyderabad report significantly lower performance in both ROE and ROA, pointing to structural or market-related disadvantages. Overall, the findings suggest that shareholder profitability (ROE) is sensitive to environmental practices, whereas operational efficiency (ROA) is largely shaped by industry and location, underscoring the dual role of sustainability and structural factors in driving financial outcomes.
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