Estimating Corporate Carbon Footprints with Externally Available Data
Bibliographic Data
| ID | 19469232 |
|---|---|
| Authors | Bernhard Goldhammer, Christian Busse (0000-0001-9927-6944, ETH Zurich, corresponding author), Timo Busch (0000-0001-6405-5252) |
| Year | 2017 |
| Volume | 21 |
| Issue | 5 |
| Pages | 1165-1179 |
| Publication date | 2017-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Industrial Ecology (JOURNAL) |
| Journal identifiers | ISSN: 1088-1980 • E-ISSN: 1530-9290 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1111/jiec.12522 |
| OpenAlex | W2562983349 |
| Language | EN |
| Citations received | 9 |
| References cited | 51 |
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 However, many firms do not publish information about their emissions.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
Business · Centrality · Econometrics · Economics · Environmental economics · Explanatory power · Greenhouse gas · Industrial organization · Microeconomics · Publication · Statistics · Climate Change Policy and Economics · Environmental Impact and Sustainability · Mathematics · Sustainable Supply Chain Management
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| Unique citing works | 9 |
|---|---|
| Citations per year | 1,29 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 9 |