Upstream Carbon Dioxide Assessment at the Product Level

Goldhammer, Bernhard; Abrashkina, Polina; Busse, Christian

doi:10.1007/978-3-642-32838-1_18

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…For firms that have many products with different variants, PA is very labor intensive and costly (Goldhammer et al. ). This downside has motivated researchers to invent procedures that facilitate a more “industrialized” CF calculation (see Meinrenken et al.…”

Section: Conceptual Backgroundmentioning

confidence: 99%

“…However, given that not every aspect of a firm or part of the supply chain can be investigated in such great detail, PA requires the setting of system boundaries, and therefore it often lacks completeness Suh and Huppes 2009;Suh et al 2004). For firms that have many products with different variants, PA is very labor intensive and costly (Goldhammer et al 2013). This downside has motivated researchers to invent procedures that facilitate a more "industrialized" CF calculation (see Meinrenken et al 2012).…”

Section: Carbon Emission Assessment Between Accuracy and Efficiencymentioning

confidence: 99%

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Estimating Corporate Carbon Footprints with Externally Available Data

Goldhammer¹,

Busse²,

Busch³

2016

J of Industrial Ecology

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Summary Corporate carbon footprints (CCFs) are a core tool in greenhouse gas emissions reporting. Established approaches for CCF calculation are based on an internal perspective that requires detailed corporate information. However, many firms do not publish information about their emissions. We seek to close this data gap by estimating scope 1 and 2 CCFs from an external perspective. The study uses a regression analysis approach, using actual firm‐internally computed CCFs to assess their degree of predictability from the outside. Data were collected from 93 European companies belonging to the chemicals, construction and engineering, and industrial machinery sectors. As predictors, we use five measures that are computed with publicly available corporate data: firm size; level of vertical integration; capital intensity; centrality of production; and carbon intensity of the national energy mix. The analysis shows that significant explanatory power for the CCF can be observed for size, capital intensity, and centrality of production. The best estimation results are achieved when data from different sectors are integrated into a comprehensive all‐sector model, while accounting for sector‐specific emission intensities by means of dummy variables. With an adjusted R² value of 0.817, the proposed procedure estimates CCFs in an accurate, yet also efficient, manner. Moreover, the study enhances trust in the current CCF calculation practices by showing that their results are plausible from a third‐party perspective.

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Section: Conceptual Backgroundmentioning

confidence: 99%