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In 2015, representatives from more than 196 countries met in Le Bourget, France, a commune in the northeastern suburbs of Paris, to sign the Paris Agreement. The legally binding treaty limits global warming to well below 2 degrees Celsius compared to preindustrial levels, preferably 1.5 degrees. While the Agreement doesn’t spell out the means by which the undersigners need to achieve this, 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. The ability to see the carbon footprint of companies will be critical for countries to comply with the measures, for example. 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 that their model could be used by investors so that they can align their investments with international regulatory measures and achieve net-zero goals. Some regions, including the European Union, require that investors apply “the 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.
Training the model
Prior work attempted to estimate companies’ carbon emissions using a combination of conventional statistical approaches and machine learning. But according to the researchers, these approaches relied on assumptions that don’t always hold in reality, like that companies in the same industry emit roughly the same level of emissions.
To train the model, the researchers identified more than 1,000 different features and 24,052 rows of disclosed emissions from datasets containing company financials (like balance sheets and income statements), corporate locations, and ESG records. The ESG records had over 500 metrics alone, including for carbon emissions and resource and energy use, human rights and diversity and inclusion, and criteria based on management structure, executive compensation, and employee relations.
In an experiment designed to evaluate the model’s accuracy, the researchers say that it closely estimated the emissions of companies in industries including health care, technology, financial, materials, real estate, utilities, energy, communications, and more. In future work, the team plans to add more features from datasets like corporate policies, supply chain, and factory data.
“By training a machine learning model on disclosed … emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions,” the researchers wrote. “In this paper, we show that our model provides accurate estimates of corporate … emissions.”
While studies suggest that some forms of machine learning can contribute significantly to greenhouse gases, the technology has been proposed in recent years as a tool to combat climate change. For example, an IBM project delivers farm cultivation recommendations from digital farm “twins,” which simulate the future weather and soil conditions of real-world crops. Other researchers are using AI-generated images to help visualize climate change, and nonprofits like WattTime are working to reduce households’ carbon footprint by automating when electric vehicles, thermostats, and appliances are active based on where renewable energy is available.
Facebook chief AI scientist Yann LeCun and Google Brain cofounder Andrew Ng, among others, have argued that mitigating climate change and promoting energy efficiency are worthy challenges for AI researchers.
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