Estimating the embodied carbon in products is a key step towards understanding their impact, and undertaking mitigation actions. Precise carbon attribution ischallenging at scale, requiring both domain expertise and granular supply chaindata. As a first-order approximation, standard reports use Economic Input-Outputbased Life Cycle Assessment (EIO-LCA) which estimates carbon emissions perdollar at an industry sector level using transactions between different parts of theeconomy. For EIO-LCA, an expert needs to map each product to one of upwardsof 1000 potential industry sectors. We present CaML, an algorithm to automateEIO-LCA using semantic text similarity matching by leveraging the text descriptions of the product and the industry sector. CaML outperforms the previous manually intensive method, yielding a MAPE of 22% with no domain labels.