Scope 3 emissions categories.

Investors’ sophistication on climate risk is increasing and as part of this they require high-quality and comprehensive Scope 3 emissions data. Accordingly, we investigate Scope 3 emissions data divergence (across different providers), composition (which Scope 3 categories are reported) and whether machine-learning models can be used to predict Scope 3 emissions for non-reporting firms. We find considerable divergence in the aggregated Scope 3 emissions values from three of the largest data providers (Bloomberg, Refinitiv Eikon, and ISS). The divergence is largest for ISS, as it replaces reported Scope 3 emissions with estimates from its economic input-output and life cycle assessment modelling. With respect to the composition of Scope 3 emissions, firms generally report incomplete composition, yet they are reporting more categories over time. There is a persistent contrast between relevance and completeness in the composition of Scope 3 emissions across sectors, with low materiality categories such as travel emissions being reported more frequently than typically high materiality ones, such as the use of products and processing of sold products. Finally, machine learning algorithms can improve the prediction accuracy of the aggregated Scope 3 emissions by up to 6% and up to 25% when each category is estimated individually and aggregated into total Scope 3 emissions. However, absolute prediction performance is low even with the best models, with the accuracy of estimates primarily limited by low observations in specific Scope 3 categories. We conclude that investors should be cognizant of Scope 3 emissions data divergence, incomplete reporting of Scope 3 categories, and that predictions for non-reporting firms have high absolute errors even when using machine learning models. For both reported and estimated data, caveat emptor applies.投资者对气候风险的认知日益深化，在此背景下，他们亟需高质量且全面的范围三（Scope 3）排放数据。据此，本研究针对不同数据供应商提供的范围三排放数据的分歧、构成（即企业所披露的范围三类别范围），以及机器学习模型能否用于预测未披露排放企业的范围三排放量开展了研究。研究发现，全球三大主流数据供应商——彭博（Bloomberg）、路孚特Eikon（Refinitiv Eikon）与ISS——所提供的汇总范围三排放数值存在显著差异，其中ISS的差异幅度最大，因其会采用自身经济投入产出与生命周期评估模型生成的估算值，替代企业已披露的范围三排放数据。就范围三排放的构成而言，企业普遍存在披露结构不完整的问题，但随着时间推移，其披露的类别数量正逐步增加。不同行业的范围三排放构成在相关性与完整性之间始终存在持久的反差：诸如差旅排放这类低重要性的类别，其披露频率往往高于产品使用、已售产品加工这类通常具备高重要性的类别。最后，机器学习算法可提升汇总范围三排放的预测精度，最高可达6%；若针对每个类别单独估算后再汇总为总范围三排放量，预测精度的提升幅度最高可达25%。但即便采用最优模型，绝对预测性能仍处于较低水平，估算精度的主要限制因素在于特定范围三类别的观测数据匮乏。本研究最终得出结论：投资者应警惕范围三排放数据的分歧、范围三类别的不完整披露问题，且即便使用机器学习模型，针对未披露企业的排放预测仍存在较高的绝对误差。无论是已披露数据还是估算数据，均适用"购者自慎"原则。