Summary of Exioml: Eco-economic Dataset For Machine Learning in Global Sectoral Sustainability, by Yanming Guo et al.
ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability
by Yanming Guo, Charles Guan, Jin Ma
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces ExioML, the first benchmark dataset designed specifically for sustainability analysis in Ecological Economics, leveraging machine learning (ML) to assess environmental impact. The study evaluates sectoral sustainability by conducting a crucial greenhouse gas emission regression task, comparing traditional shallow models with deep learning models using a diverse Factor Accounting table and incorporating various categorical and numerical features. The results show that ExioML enables deep and ensemble models to achieve low mean square errors, establishing a baseline for future ML research. This development aims to build a foundational dataset supporting various ML applications and promote climate actions and sustainable investment decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new tool called ExioML that helps figure out the environmental impact of economic activities. It’s like a big puzzle piece that connects machine learning and ecological economics together, making it easier for researchers in both fields to work together. The study shows how well different models perform on this task, comparing simple ones with more complex deep learning models. This research is important because it can help us make better decisions about the environment and invest in sustainable projects. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Regression
