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Temperature Conversion Model

Temperature Conversion Model

To calculate a asset classes' contribution to the increase in temperature caused by its Carbon emissions and to identify the highest contributors.

Goal

To calculate a asset classes' contribution to the increase in temperature caused by its Carbon emissions and to identify the highest contributors.

Overview

The dramatic increase in carbon emissions poses a dire threat to our economies, assets and lives through global warming and higher weather volatility. $2.5 trillion of financial assets are at risk from climate change if  temperature rise 2.5 degree by the end of this century. Here at 15 Rock we have combine AI, Machine Learning and Financial Engineering to analyze the emissions of all publicly listed companies in the US, UK and Canada. Our Temperature Conversion Model transforms the raw carbon emissions of each company into easily understood units of expression, Degrees Celsius and/or Fahrenheit . Empowering asset owners and managers to understand the contributions of a company's impact on climate change. Using this model, Investors can see the impact their portfolio has on the world around them.

The model presents aggregated results at an industry or portfolio level. Our clients can evaluate the overall impact on temperature caused by their investments, and evaluate the highest contributors within that group and to enable them to align their investments with their core beliefs.

H. Damon Matthews, Nathan P. Gillett, Peter A. Stott & Kirsten Zickfeld wrote a research paper called "The proportionality of global warming to cumulative carbon emissions"  This analytical model is based on their Carbon-Climate Response (CCR) model. The CCR represents the net climate response to Carbon dioxide emissions.

Model values of CCR range from 1 to 2.1 Degree Celsius per trillion tonnes of carbon, with a mean value of 1.6 Degree Celsius per trillion tonnes of carbon.

{{Matthews, H., Gillett, N., Stott, P. et al. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009). https://doi.org/10.1038/nature08047}}

Design and Modeling

Assumptions

Uncertainty in land-use CO2 emissions and aerosol forcing, however, means that higher observationally constrained values cannot be excluded.

Inputs

  • Carbon Climate Response
  • 1 trillion tones of carbon increases temperature by 1.6 Degrees Celsius (Mean Value of Carbon Climate Response)
  • 5th percentile value of the CCR = 1 degree Celsius
  • 95th percentile value of the CCR = 2.1 degree
  • 15 Rock's state of the art Machine Learning models obtain the carbon emitted by a company and/or an industry in any year
  • http://api.15rock.com/company/aapl.us/industry-sum
  • Years of carbon is a variable in the model to allow for greater analytical flexibility
  • default is 1 year yet the model can aggregate data from any range of years selected by our clients

Formula

  • Convert Carbon from tonnes into teratonnes
  • divide carbon by 10^12
  • Multiply by 1.6 to get the mean increase in temperature
  • Multiply by 1 to get the lower range of the temperature increase
  • Multiply by 2.1 to get the higher range of temperature increase

Insights

Mean increase in temperature caused by carbon emissions of Exxon mobile from 2001 till 2010 was 0.006 Degree Celsius. In the next decade, they increased global temperature by 0.0072 Degree Celsius. Their overall contribution to temperature increases have been 0.0132 degrees since 2001.