Estimating Corporate Carbon Footprints with Externally Available Data

Goldhammer, Bernhard; Busse, Christian; Busch, Timo

doi:10.1111/jiec.12522

Cited by 26 publications

(8 citation statements)

References 59 publications

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“…As an alternative dependent variable, we use carbon_intensity, which is the natural logarithm of total carbon emissions scaled by total revenues. Due to comparability concerns regarding scope 3 carbon emissions across companies (Goldhammer et al, 2017), we also limit the analysis to scope 1 and scope 2 carbon emissions. The corresponding variable is denoted as carbon12 [4].…”

Section: Panel Data Analysismentioning

confidence: 99%

Market perceptions on the role of female leadership in adapting to climate change

Kordsachia

Bassen²,

Fieberg

et al. 2023

JRF

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PurposeThis empirical study aims to examine the association between gender-diverse boards and corporate carbon emissions and estimates the effect of board gender diversity on stock price reactions to climate activism. This study contributes to the inconclusive literature on the link between gender-diverse boards and firms' financial performance by examining a single and plausibly isolated channel of association (i.e. attention to climate change).Design/methodology/approachThe authors use parametric and non-parametric panel data techniques to examine the association between gender-diverse boards to corporate carbon emission. The system generalized methods of moments (SYS-GMM) estimator is used to address endogeneity concerns. The authors use the event study methodology to examine difference in stock price reactions to climate activism.FindingsThe results show that high board gender diversity is associated with lower corporate carbon emissions and higher stock returns to climate activism.Originality/valueThis is the first study to isolate public attention to climate change as a relevant channel through which gender-diverse boards have an impact of firms' financial performance. This study is timely and important due to the immediate threat of global warming and the recent introduction of mandatory board gender quotas in many countries around the world.

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Section: Panel Data Analysismentioning

confidence: 99%

Market perceptions on the role of female leadership in adapting to climate change

Kordsachia

Bassen²,

Fieberg

et al. 2023

JRF

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“…The third external constraint resembles a problem that is well known within the context of GHG emissions. Energy consumption may be deliberately removed from direct corporate influence by means of outsourcing (Goldhammer et al, 2016). Logistics organizations often outsource processes such as fleet management or transport operations to (other) logistics service providers (Tian et al, 2010).…”

Section: Pbcsmentioning

confidence: 99%

Managerial perceptions of energy in logistics

Busse

Regelmann

Chithambaram³

et al. 2017

IJPDLM

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Purpose Because of the major contribution of logistics to the greenhouse gas effect, logistics research has begun to address the topic of energy, but it has not yet targeted the role of energy within logistical decision-making processes. To facilitate such endeavors, the purpose of this paper is to explore the perceptions of energy which managers in logistics organizations portray. Design/methodology/approach This investigation is based on interviews with 17 managers. The findings are embedded in the theory of planned behavior (TPB) and stakeholder theory (SHT). Findings The study depicts initial insights on which energy-related perceptions exist, how they can be categorized into attitudes, subjective norms, and perceived behavioral controls (PBCs), and how different stakeholders seem to affect them. Research limitations/implications This research suggests a tentative comprehensive conceptual framework that describes the role of energy in logistical decision-making processes. The findings on attitudes and subjective norms appear to be unspecific to the logistics domain, whereas some of the PBCs are presumably unique to the logistics context. Future logistics research should hence focus its efforts on the PBCs. Generalizability and completeness of the managerial perceptions must be validated by future research. Practical implications The findings help logistics organizations in scrutinizing managerial perspectives on energy and in developing awareness-raising measures. Originality/value The behavioral perspective applied in this study can complement extant, more technically oriented views. The conceptual framework that integrates the TPB and SHT may also be useful for organizational research beyond the logistics domain.

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“…Further, Scope 3 represents the most significant emissions reduction opportunities going forward, and a full assessment of Scope 3 emissions is critical for understanding the end-to-end impacts of carbon taxes and climate policies on individual firms [10]. However, the analyses of firm-level emissions by external stakeholders are usually limited to Scope 1 and Scope 2 emissions [11][12][13]. This is due to the following three issues associated with Scope 3:…”

Section: Introductionmentioning

confidence: 99%

Scope 3 emissions: Data quality and machine learning prediction accuracy

Nguyen,

Diaz-Rainey,

Kitto

et al. 2023

PLOS Clim

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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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Estimating Corporate Carbon Footprints with Externally Available Data

Cited by 26 publications

References 59 publications

Market perceptions on the role of female leadership in adapting to climate change

Market perceptions on the role of female leadership in adapting to climate change

Managerial perceptions of energy in logistics

Scope 3 emissions: Data quality and machine learning prediction accuracy

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