Abstract
AbstractCement producers and their investors are navigating evolving risks and opportunities as the sector’s climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company’s greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics.
Funder
Children's Investment Fund Foundation
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Cited by
13 articles.
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