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.

翻译：碳排放是导致气候变化的重要因素，而碳信用已成为减轻环境损害、帮助组织管理碳足迹的关键工具。尽管碳信用在各行业的重要性日益增长，但充分运用碳信用仍面临挑战。本研究探讨了增强碳排放管理的工程实践与金融科技解决方案。我们首先回顾了碳排放未披露的负面影响，揭示了其对金融稳定性和市场价值的不利影响。鼓励组织积极管理排放并披露相关数据以降低风险。接着，我们分析了影响碳价格的因素，并综述了优化碳信用购买策略的先进预测算法，这些算法有助于降低成本并提高效率。此外，我们研究了企业碳排放预测模型，这些模型能够提供准确的绩效评估，并有助于规划未来的碳信用需求。通过整合碳价格与排放预测，我们提出了包括企业碳管理成本预测在内的研究方向。本研究为未来关于碳管理实践对金融和市场影响的定量研究奠定了基础，也是首个聚焦于碳信用计算解决方案与工程实践的系统性综述。