An anonymous reader shares a report: In 2015, representatives from more than 196 countries met in Le Bourget, France to sign the Paris Agreement. The legally binding treaty limits global warming to a rise of well below 2 degrees Celsius compared to preindustrial levels, preferably capping warming at 1.5 degrees. While the Paris Agreement doesn’t spell out how the undersigned are expected to achieve this goal, some countries have pledged to cut their net climate emissions to zero by 2050. For these and other steps to be successful, reliable data is key. While the ability to evaluate companies’ carbon footprints will be critical for countries seeking to comply with the measures, only a fraction of companies currently disclose their greenhouse gas emissions. But researchers at Bloomberg Quant Research and Amazon Web Services claim to have successfully trained a machine learning model to estimate the emissions of businesses that don’t disclose their emissions.
The researchers say investors could use this model to align their investments with international regulatory measures and achieve net-zero goals. Some regions, including the European Union, require investors to apply a “precautionary principle” that penalizes non-disclosing companies by overestimating their emissions. “Merely 2.27% of companies filing financial statements are disclosing their [greenhouse gas] emissions according to our environmental, social, and governance (ESG) datasets,” the coauthors wrote in a paper. “In order to make a meaningful change, we need to measure who is contributing [greenhouse gases] into the atmosphere and monitor their claims to decarbonize.”