Academy of Management Annual Meeting Proceedings includes abstracts of all papers and symposia presented at the annual conference, plus 6-page abridged versions of the “Best Papers” accepted for inclusion in the program (approximately 10%). Papers published in the Proceedings are abridged because presenting papers at their full length could preclude subsequent journal publication. Please contact the author(s) directly for the full papers.


Estimating Corporate Carbon Footprints with Externally Available Data

    Corporate Carbon Footprints (CCFs) are a core tool in greenhouse gas emissions reporting. Extant 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 However, many firms do not publish information about their emissions.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 CCFs from an external perspective. The study employs a regression analysis approach, using actual firm-internally computed CCFs to assess their degree of predictability from the outside. Data was collected from 94 companies in three different sectors. As predictors, we use five measures that are computed based on publicly available corporate data: firm size, level of vertical integration, capital intensity, centrality of production, and carbon intensity of the national energy mix. The confirmatory analysis shows that significant explanatory power for the CCF can be observed for size, capital intensity, and centrality of production. We explore the obtained results further, with consideration for the impact of the model, delineation of the data, and operationalization of the measures. The results suggest that the best estimation results are achieved when data from different sectors is integrated into a comprehensive all-sector model, while accounting for sector-specific emission intensities by means of dummy variables. The proposed procedure is capable of estimating CCFs very accurately, yet also efficiently. 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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