Introduction

Overly high carbon emissions are becoming a major cause of environmental deterioration and a barrier to sustainable growth. The achievement of business sustainability is largely dependent on corporate carbon performance. Promoting carbon performance requires a clear understanding of the variables that affect business carbon emissions. The primary source of climate change is still carbon emissions. Given the catastrophic effects of climate change and the enormous pace of greenhouse gas emissions, regulating carbon emissions reactively is no longer viable. (Oyewo, 2023). Carbon emissions that come from sources within the control or ownership of an organization are referred to as scope 1 emissions. Examples of these emissions are those brought on by the burning of fuel in boilers, furnaces, and cars. Indirect emissions under Scope 2 are those brought on by the purchase of energy, steam, heat, or cooling. Scope 2 emissions are accounted for in a company's inventory even though they are really produced at the plant where they are produced due to the organisation's energy consumption.
 

Problem Statement

This project has two parts:
  1. Climate-risk Modelling - analyse financial data and carbon emissions of global companies to understand the relationships between them, identify financial factors influencing carbon emissions, and explore opportunities for sustainable practices.
  1. Portfolio Decarbonisation - demonstrate the transition to a low-carbon economy using sustainable investing principles that leverage portfolio optimisation methods to reduce overall carbon footprint.
 
 

1. Climate-risk Modelling

 
The objective of the study is to explore the relationship between carbon emission and financial factors, and uncover opportunities for sustainable practices. The analysis employs regression models to test the relationships between independent financial variables and carbon emissions.
On the basis of literature review, the study formulates several hypotheses and develops regression models to test these hypotheses. The independent variables considered include efficiency ratio, liquidity ratio, valuation ratio, coverage ratio, profitability ratio, leverage ratio, and cost of capital. These variables are used to explain the dependent variable, carbon emissions.
 

1. 2. EDA:

After checking the data from the defined universe of 500 companies for any missing values, the final data comprised of 128 companies for a period of four years. To reduce the impact of extreme values and address non-normality and heteroscedasticity, natural logarithm transformation was applied to the carbon emissions data.
Exploratory Data Analysis was conducted to uncover relationships, patterns, and similarities between variables. Analysis included checking the distribution of carbon emission across sectors for the 128 companies, visualising the correlations across sectors and the sector performance on the carbon emission metric overtime. Further the carbon emission data was analysed to establish the Scope 1 and Scope 2 emission trends over time and across sectors.
 
notion image
notion image
Scope 1 emissions depicted a declining trend from 2018 to 2020, plateauing further until 2021. However, Scope 2 emissions remained mostly flat with no prominent decline in the emission levels.
Scope 1 emissions depicted a declining trend from 2018 to 2020, plateauing further until 2021. However, Scope 2 emissions remained mostly flat with no prominent decline in the emission levels.
 

1. 3. Modelling:

The panel OLS regression technique was employed to estimate five different models (Model 1, Model 2, Model 3, Model 4, and Model 5 based on combinations of Scope 1 and Scope 2 measures) using a dataset consisting of 512 observations repeated without constant. The R-squared values of these models ranged from 0.1083 to 0.1787, suggesting that the independent variables collectively explain between 10.83% and 17.87% of the variation observed in the dependent variable. Furthermore, the F-statistics for all models yielded significant results, with p-values of 0, indicating that the regression models as a whole are statistically significant. These findings emphasise the relationships between the independent variables (financial factors) and the dependent variable (carbon emission) in each respective model.
 

1. 4. Conclusion and Limitations of the study:

In conclusion, upon analysing both the modelling methods, it is evident that certain variables consistently exhibit statistical significance at a 1% level.
Asset Turnover, Current Ratio, and Weighted Average Cost of Capital (WACC) consistently demonstrated significance, indicating their strong impact on Scope1 emissions and their predictive power. On the other hand, variables such as Interest Coverage Ratio, Total Debt to Total Equity, and certain others did not exhibit significant relationships with Scope1 emissions.
These findings provide valuable insights into the variables that have the greatest influence on Scope1 emissions, helping to inform decision-making and further analysis in the context of the given models.
Limitations of the study include accuracy and reliability of the data, timeframe of data used may restrict the generalisability of findings to other time periods
 

2. Portfolio Decarbonisation

 
Sustainable investing reduces greenhouse gas emissions by decarbonising portfolios for a low-carbon economy. By doing so, we can mitigate climate risks, identify economic leaders and promote responsible environmental investing.
 

2. 1. Data Sources:

Carbon emissions data and financial data for 100 randomly selected companies from the Russell 3000 index.
 

2. 2. Methodology:

Three different reduction targets (50%, 25%, and 10%) were examined to identify an optimal balance between environmental responsibility and financial performance. The report provides a comprehensive portfolio performance evaluation by comparing key metrics such as carbon performance, Sharpe ratio, expected returns, volatility. Additionally, Environmental, Social, and Governance (ESG) scores across portfolios were analysed to illustrate the potential for sustainable investing.
Company's carbon emissions were computed using a "Carbon Intensity" measure. This new feature involved adding up the "Scope 1" and "Scope 2 Location" emissions and then dividing them by the company's "Revenue" value (in $mn).
A market-cap weighted portfolio was constructed to serve as a benchmark, while a mean-variance tangency portfolio was formulated with appropriate weights and optimal decarbonisation constraints. A sector balancing constraint was used to define and ensure the portfolio was well diversified.
 

2. 3. Findings:

ESG performance analysis across portfolio styles
ESG performance analysis across portfolio styles
 
Risk return measures across portfolio styles
Risk return measures across portfolio styles
 
Sector composition analysis
Sector composition analysis
 
 
 
 
 

References:

  1. King, A.A and Lenox, M.J. (2001). Does it really pay to be Green? An empirical study of Firm Environmental and Financial Performance. Journal of Industrial Ecology, 5(1), pp. 105-116.
  1. How to create a roadmap to decarbonising your asset portfolio - Wood
 
The code for this implementation is in the open source domain and can be accessed easily on GitHub. You can click here to access the codebase.
You can always contact me if you have any questions about the code or the implementation. I would be glad to help as best as I can.