ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909243973566464 |
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| author | Guo, Yanming Guan, Charles Ma, Jin |
| author_facet | Guo, Yanming Guan, Charles Ma, Jin |
| contents | The Environmental Extended Multi-Regional Input-Output analysis is the predominant framework in Ecological Economics for assessing the environmental impact of economic activities. This paper introduces ExioML, the first Machine Learning benchmark dataset designed for sustainability analysis, aimed at lowering barriers and fostering collaboration between Machine Learning and Ecological Economics research. A crucial greenhouse gas emission regression task was conducted to evaluate sectoral sustainability and demonstrate the usability of the dataset. We compared the performance of traditional shallow models with deep learning models, utilizing a diverse Factor Accounting table and incorporating various categorical and numerical features. Our findings reveal that ExioML, with its high usability, enables deep and ensemble models to achieve low mean square errors, establishing a baseline for future Machine Learning research. Through ExioML, we aim to build a foundational dataset supporting various Machine Learning applications and promote climate actions and sustainable investment decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09046 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability Guo, Yanming Guan, Charles Ma, Jin Machine Learning Databases The Environmental Extended Multi-Regional Input-Output analysis is the predominant framework in Ecological Economics for assessing the environmental impact of economic activities. This paper introduces ExioML, the first Machine Learning benchmark dataset designed for sustainability analysis, aimed at lowering barriers and fostering collaboration between Machine Learning and Ecological Economics research. A crucial greenhouse gas emission regression task was conducted to evaluate sectoral sustainability and demonstrate the usability of the dataset. We compared the performance of traditional shallow models with deep learning models, utilizing a diverse Factor Accounting table and incorporating various categorical and numerical features. Our findings reveal that ExioML, with its high usability, enables deep and ensemble models to achieve low mean square errors, establishing a baseline for future Machine Learning research. Through ExioML, we aim to build a foundational dataset supporting various Machine Learning applications and promote climate actions and sustainable investment decisions. |
| title | ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2406.09046 |