范围 3 排放:数据质量和机器学习预测准确性

Quyen Nguyen, I. Diaz‐Rainey, Adam Kitto, Ben I. McNeil, Nicholas A. Pittman, Renzhu Zhang
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引用次数: 0

摘要

投资者对气候风险的认识正在不断提高,作为其中的一部分,他们需要高质量和全面的范畴 3 排放数据。因此,我们调查了范围 3 排放数据的差异(不同提供商之间的差异)、构成(报告了哪些范围 3 类别)以及机器学习模型是否可用于预测未报告企业的范围 3 排放量。我们发现,三个最大的数据提供商(彭博、Refinitiv Eikon 和 ISS)的范围 3 排放汇总值存在相当大的差异。ISS 的分歧最大,因为它用经济投入产出和生命周期评估模型的估计值取代了报告的范畴 3 排放量。关于范畴 3 排放的构成,企业通常报告不完整的构成,但随着时间的推移,它们报告了更多的类别。在各部门的范畴 3 排放构成中,相关性和完整性之间始终存在着反差,低重要性类别(如差旅排放)的报告频率高于典型的高重要性类别(如产品的使用和已售产品的加工)。最后,机器学习算法可将范围 3 排放总量的预测准确率提高 6%,而将每个类别单独估算并汇总为范围 3 排放总量时,预测准确率可提高 25%。然而,即使使用最好的模型,绝对预测性能也很低,估计的准确性主要受限于特定范围 3 类别的低观测值。我们的结论是,投资者应认识到范围 3 排放数据的偏差、范围 3 类别报告的不完整,以及即使使用机器学习模型,对未报告企业的预测也存在较高的绝对误差。对于报告数据和估算数据,都应谨慎对待。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scope 3 emissions: Data quality and machine learning prediction accuracy
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.
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