Bibliographic Details
| Title: |
Data-driven decarbonization framework with machine learning |
| Authors: |
Ayush Jain, Manikandan Padmanaban, Jagabondhu Hazra, Ranjini Guruprasad, Shantanu Godbole, Heriansyah Syam |
| Source: |
Environmental Data Science, Vol 3 (2024) |
| Publisher Information: |
Cambridge University Press, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Environmental sciences
LCC:Electronic computers. Computer science |
| Subject Terms: |
AI, carbon accounting, decarbonization, hotspot identification, palm oil supply chain, Environmental sciences, GE1-350, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: |
Eight major supply chains contribute to more than 50% of the global greenhouse gas emissions (GHG). These supply chains range from raw materials to end-product manufacturing. Hence, it is critical to accurately estimate the carbon footprint of these supply chains, identify GHG hotspots, explain the factors that create the hotspots, and carry out what-if analysis to reduce the carbon footprint of supply chains. Towards this, we propose an enterprise decarbonization accelerator framework with a modular structure that automates carbon footprint estimation, identification of hotspots, explainability, and what-if analysis to recommend measures to reduce the carbon footprint of supply chains. To illustrate the working of the framework, we apply it to the cradle-to-gate extent of the palm oil supply chain of a leading palm oil producer. The framework identified that the farming stage is the hotspot in the considered supply chain. As the next level of analysis, the framework identified the hotspots in the farming stage and provided explainability on factors that created hotspots. We discuss the what-if scenarios and the recommendations generated by the framework to reduce the carbon footprint of the hotspots and the resulting impact on palm oil tree yield. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2634-4602 |
| Relation: |
https://www.cambridge.org/core/product/identifier/S263446022400027X/type/journal_article; https://doaj.org/toc/2634-4602 |
| DOI: |
10.1017/eds.2024.27 |
| Access URL: |
https://doaj.org/article/7591b0a4227e4b9ea08c18ab41cb779f |
| Accession Number: |
edsdoj.7591b0a4227e4b9ea08c18ab41cb779f |
| Database: |
Directory of Open Access Journals |
