(Credit: Pixabay)As demand grows for reliable and comprehensive carbon data, Bloomberg has increased its carbon emissions dataset to cover 100,000 companies. This dataset consists of company reported carbon data and estimates based on either a machine-learning smart model or Bloomberg's newly developed industry-implied model accompanied with a PCAF reliability score.
"Greater precision in Scope 1, 2, and 3 carbon emissions data will enable asset managers and banks to measure the extent of climate change risk within their investment and lending portfolios, and across their operations as a whole," said Brad Foster, Head of Enterprise Data Content at Bloomberg, in a press release.
When company reported carbon emissions data is not available, Bloomberg applies estimation techniques guided by a waterfall principle to increase the scope of companies covered and thus provide a more complete picture of emissions. The first estimation technique uses Bloomberg's machine learning-based smart model incorporating 800+ data points to estimate Scope 1 and 2 emissions with historical data going back to 2010. Last year, Bloomberg also released Scope 3 estimates for Oil and Gas, Metals and Mining, and Services industries using a methodology combining a bottom-up model with a top-down machine learning model.
When there is not enough data available to apply these machine-learning smart models, the waterfall technique automatically reverts to Bloomberg's new industry-implied model. This method uses peer emissions and sales data to estimate a company's emissions. These estimates are accompanied by a reliability score, using the scale that has been proposed by the Partnership for Carbon Accounting Financials (PCAF) so investors can understand the quality of the underlying datapoint.
Bloomberg's carbon emissions data is available via the Bloomberg Terminal. A subset of this carbon data is also available to Bloomberg Data License clients and can be accessed via Bloomberg's ready-to-use data website, data.bloomberg.com.
