通过实时监测和计量经济学来减少建筑碳排放的综合框架
Fan Bai1, Qiyuan Cai1, Xichuan Zheng1
1Guangzhou Design Institute Group Co., Ltd, Guangzhou, 510630, China.
Scientific reports
|October 1, 2025
概括
本研究介绍了一种基于数据的框架,使用网络物理系统和人工智能实时跟踪建筑排放. 它将运营效率与金融激励联系在一起,帮助减少碳排放的努力.
科学领域:
- 环境科学 环境科学
- 土木工程 土木工程是指土木工程.
- 数据科学数据科学数据科学
背景情况:
- 建筑行业对全球二氧化碳排放做出了重大贡献 (39%).
- 缺乏实时可验证的建筑数据阻碍了脱碳.
- 现有的方法难以提供细粒度,可操作的排放洞察力.
研究的目的:
- 开发和验证一个集成的,数据驱动的框架,用于实时监测建筑排放.
- 弥合运营数据和可验证的碳足迹减少之间的差距.
- 将建筑效率与可持续性金融激励联系起来.
主要方法:
- 实施带有无线传感器的网络物理系统 (CPS),用于从机器收集高分辨率数据.
- 开发和培训长期短期记忆 (LSTM) 模型,用于设备层面的排放预测.
- 应用固定效应面板计量模型来评估绿色金融指数对碳强度的影响.
主要成果:
- 在预测设备级二氧化碳排放方面,LSTM模型取得了很高的准确性 (RMSE:0.0196t CO2,MAE:0.015t CO2).
- 绿色金融指数增加1个单位与建筑碳强度显著下降相关 (β = -0.082,p < 0.01).
- 该框架成功地将细分的现场数据转化为可操作的见解,将运营绩效与财务结果联系起来.
结论:
- 开发的框架允许实时跟踪和验证建筑排放.
- 它建立了一个基于绩效的碳管理模式,将运营效率与财务回报相结合.
- 这种方法支持政策制定者和实践者通过知情决策和绿色融资整合来实现碳中立目标.
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