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Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future

Qingwen Zeng, Hanlin Xu, Nanjun Xu, Zhenghao Zhao, Joakim Westerholm, Flora Salim, Junbin Gao, Huaming Chen

TL;DR

This work addresses how carbon emissions and carbon credits intersect with finance and FinTech, proposing computing-driven solutions to improve transparency and management. Using a systematic literature review, it analyzes four core areas: the impact of emission disclosure, drivers of carbon credit prices, methods for predicting prices, and methods for forecasting enterprise emissions, culminating in directions that integrate price and emission predictions. The review highlights that voluntary disclosure generally enhances market value and financing conditions in many contexts, while price-prediction methods increasingly rely on data-decomposition hybrids, multi-factor inputs, and interval predictions, including embodied-carbon considerations. By establishing a quantitative research foundation and outlining practical FinTech implications, the paper guides future efforts to optimize carbon management strategies, inform policy, and strengthen market trust in the global carbon credit market.

Abstract

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.

Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future

TL;DR

This work addresses how carbon emissions and carbon credits intersect with finance and FinTech, proposing computing-driven solutions to improve transparency and management. Using a systematic literature review, it analyzes four core areas: the impact of emission disclosure, drivers of carbon credit prices, methods for predicting prices, and methods for forecasting enterprise emissions, culminating in directions that integrate price and emission predictions. The review highlights that voluntary disclosure generally enhances market value and financing conditions in many contexts, while price-prediction methods increasingly rely on data-decomposition hybrids, multi-factor inputs, and interval predictions, including embodied-carbon considerations. By establishing a quantitative research foundation and outlining practical FinTech implications, the paper guides future efforts to optimize carbon management strategies, inform policy, and strengthen market trust in the global carbon credit market.

Abstract

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.
Paper Structure (14 sections, 3 figures)

This paper contains 14 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Research methodology
  3. Research questions
  4. Methodology
  5. For Finance: The Impact of Disclosing Carbon Emissions on organisations
  6. For Engineering: Factors Influencing Carbon Credit Prices
  7. Algorithms for Predicting Carbon Credit Prices
  8. Prediction of Enterprise Carbon Emissions
  9. Empirical Models
  10. Machine Learning Methods
  11. Predictive Models for Embodied Carbon Emission
  12. Prospects of Carbon Credits in the Financial and FinTech Fields
  13. Literature Review Comparison
  14. Conclusion

Figures (3)

  • Figure 1: Factors Influencing Carbon Emission Disclosure
  • Figure 2: Impact of carbon emissions disclosure
  • Figure 3: Evolutionary Trends in Corporate Carbon Emission Prediction Algorithms