Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917208482906112 |
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| author | Zeng, Qingwen Xu, Hanlin Xu, Nanjun Zhao, Zhenghao Westerholm, Joakim Salim, Flora Gao, Junbin Chen, Huaming |
| author_facet | Zeng, Qingwen Xu, Hanlin Xu, Nanjun Zhao, Zhenghao Westerholm, Joakim Salim, Flora Gao, Junbin Chen, Huaming |
| contents | Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage their carbon footprint. Despite their growing importance across sectors, fully leveraging carbon credits remains challenging. This study explores engineering practices and fintech solutions to enhance carbon emission management. We first review the negative impacts of carbon emission non-disclosure, revealing its adverse effects on financial stability and market value. Organizations are encouraged to actively manage emissions and disclose relevant data to mitigate risks. Next, we analyze factors influencing carbon prices and review advanced prediction algorithms that optimize carbon credit purchasing strategies, reducing costs and improving efficiency. Additionally, we examine corporate carbon emission prediction models, which offer accurate performance assessments and aid in planning future carbon credit needs. By integrating carbon price and emission predictions, we propose research directions, including corporate carbon management cost forecasting. This study provides a foundation for future quantitative research on the financial and market impacts of carbon management practices and is the first systematic review focusing on computing solutions and engineering practices for carbon credits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14750 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future Zeng, Qingwen Xu, Hanlin Xu, Nanjun Zhao, Zhenghao Westerholm, Joakim Salim, Flora Gao, Junbin Chen, Huaming Computers and Society Machine Learning Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage their carbon footprint. Despite their growing importance across sectors, fully leveraging carbon credits remains challenging. This study explores engineering practices and fintech solutions to enhance carbon emission management. We first review the negative impacts of carbon emission non-disclosure, revealing its adverse effects on financial stability and market value. Organizations are encouraged to actively manage emissions and disclose relevant data to mitigate risks. Next, we analyze factors influencing carbon prices and review advanced prediction algorithms that optimize carbon credit purchasing strategies, reducing costs and improving efficiency. Additionally, we examine corporate carbon emission prediction models, which offer accurate performance assessments and aid in planning future carbon credit needs. By integrating carbon price and emission predictions, we propose research directions, including corporate carbon management cost forecasting. This study provides a foundation for future quantitative research on the financial and market impacts of carbon management practices and is the first systematic review focusing on computing solutions and engineering practices for carbon credits. |
| title | Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2501.14750 |