▲圖片標題(來源： Chase Dekker Wild-Life Images/Getty)
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.”
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 true in reality, like the idea that companies in the same industry emit roughly the same level of emissions.
To train their model, the researchers identified more than 1,000 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, covering areas like 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 the model 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 across areas like corporate policy, 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 some forms of machine learning contribute significantly to greenhouse gas emissions, the technology has also been proposed as a tool to combat climate change. For example, an IBM project delivers farm cultivation recommendations from digital farm “twins” that 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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