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Updated: Sep 12, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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通过使用随机区块模型分析社区一级的支出行为,这些行为有助于高碳排放,使用随机区块模型
Ognyan Simeonov1,2, Valerio Restocchi3, Benjamin D Goddard4
1School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton St, Newington, Edinburgh, UK. O.O.Simeonov@sms.ed.ac.uk.
Scientific reports
|August 8, 2025
概括
金融交易数据揭示了消费者支出模式,以确定高碳活动. 随机区块建模有助于确定有针对性的碳减排战略的排放热点.
科学领域:
- 数据科学数据科学数据科学
- 环境科学 环境科学
- 网络分析 网络分析
背景情况:
- 金融交易数据集越来越多地用于估计消费者支出产生的碳排放.
- 了解消费者支出行为对于政策制定者和金融机构制定有效的碳减排战略至关重要.
研究的目的:
- 开发一种方法来识别大型金融交易数据集中的消费者支出模式.
- 通过将支出模式与高排放商业类别联系起来,实现有针对性的碳减排战略.
主要方法:
- 利用随机区块建模在双边网络上进行社区检测.
- 为双边网络开发了修改后的权重系统,以保持各类社区平均支出保持不变.
主要成果:
- 成功形成了具有相似消费模式的消费者社区.
- 确定了为不同消费者群体贡献高碳排放的特定商家类别.
- 展示了一个分析支出,能源使用和运输习惯相互作用的框架.
结论:
- 交易数据集和随机区块建模为旨在脱碳的金融机构提供了宝贵的见解.
- 该研究为研究人员提供了一个可控的框架,以分析消费者行为对碳减排努力的影响.
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