Smith School of Enterprise and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
Lloyds Banking Group, Gresham Street, City of London, EC2V 7HN, UK.
Sci Data. 2023 Oct 13;10(1):696. doi: 10.1038/s41597-023-02599-w.
Cement 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.
水泥生产商及其投资者正在应对不断变化的风险和机遇，因为该行业的气候和可持续性影响变得更加突出。虽然许多公司现在都披露了温室气体排放，但来自碳密集型行业的排放似乎被转移到了效率较低的供应商身上。认识到这一点，我们强调有必要建立一个全球统一的资产层面数据集，承认生产投入的出处。我们的方法不仅整合了来自开发银行和政府等既定来源的数据，还创新性地将工厂的年龄和原材料的采购模式作为资产层面数据的两个基础变量进行整合。这些变量对于建模水泥生产利用率至关重要，而利用率反过来又会对公司的温室气体排放产生重大影响。我们的方法成功地结合了地理空间计算机视觉和大型语言模型技术，以确保对全球水泥生产动态有一个全面和整体的了解。
