A scalable tool for farm-level carbon accounting
Evidence from UK agriculture
Bibliographic Data
| ID | 15545698 |
|---|---|
| Authors | Jerome C Wells (0000-0002-3981-0364, University of Leeds, corresponding author), Anna Trendl (0000-0003-1994-4207, Lloyds Banking Group (United Kingdom), corresponding author), Alex Owens (0000-0002-3872-9900, University of Leeds, corresponding author), John Barrett (0000-0002-4285-6849, University of Leeds, corresponding author), Joseph Gridley (Soil Association, corresponding author), Norbert Jobst (Lloyds Banking Group (United Kingdom), corresponding author), David Leake (0000-0003-2361-9394, Lloyds Banking Group (United Kingdom), corresponding author) |
| Year | 2025 |
| Volume | 20 |
| Issue | 12 |
| Pages | 124046-124046 |
| Publication date | 2025-11-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ae20ab |
| OpenAlex | W4416329814 |
| Language | EN |
| References cited | 37 |
Accurately quantifying greenhouse gas (GHG) emissions at the farm level is crucial for agricultural decarbonisation, yet doing so remains challenging—particularly for smaller farming operations that lack resources for comprehensive data collection. To address this gap, we present a new framework to predict farm-level direct emission intensities (tCO 2 e/£) from a minimal set of input variables. Our approach leverages a unique and comprehensive dataset that integrates survey based GHG audits from 482 farms across the United Kingdom (UK) with financial transaction data, obtained through a collaboration with a major high-street bank and a leading UK charity promoting sustainable agriculture. By combining granular farm-level emissions metrics with financial records, we demonstrate that a limited set of input variables focused on the highest impact areas—particularly dairy and beef cattle intensities—yield robust predictions that can explain up to 91% of the variation in farm-level emissions. Although this model does not replace the depth and specificity of established carbon calculators, it provides a pragmatic, scalable alternative for emissions reporting. It offers farms a clear entry-point to carbon accounting and provides financial institutions with a data-efficient method for more accurate assessments of the environmental impact of their agricultural portfolios
Agriculture · Audit · Carbon accounting · Carbon price · Database transaction · Greenhouse gas · Scalability · Set (abstract data type · Agriculture Sustainability and Environmental Impact · Soil Carbon and Nitrogen Dynamics · Sustainable Agricultural Systems Analysis
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| Citation velocity | historical |
|---|---|
| Highly cited | No |