使用深度学习和统计过程控制监测碳排放:政府碳减排政策影响评估策略
Chinedu Pascal Ezenkwu1, San Cannon2, Ebuka Ibeke2
1School of Creative and Cultural Business, Robert Gordon University, Aberdeen, UK. p.ezenkwu@rgu.ac.uk.
Environmental monitoring and assessment
|February 2, 2024
概括
本研究介绍了一种使用长短期内存 (LSTM) 和统计过程控制 (SPC) 来监控碳减排政策的AI框架. 人工智能模型有效地跟踪英国二氧化碳排放的变化,将其与政府的气候战略相关联.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气候变化政策 气候变化政策
背景情况:
- 世界各地的政府正在实施碳减排政策,以应对气候变化.
- 有效监测这些政策对于实现减排目标至关重要.
- 目前用于监测碳减排战略的AI应用有限.
研究的目的:
- 提出一个人工智能框架,用于监测政府碳减排政策的影响.
- 利用人工智能 (AI) 来跟踪碳排放的变化.
- 分析英国减少碳排放战略的有效性.
主要方法:
- 开发了一个整合长短期记忆 (LSTM) 和统计过程控制 (SPC) 的AI框架.
- 使用LSTM创建英国年均二氧化碳排放 (人均) 1750-2021年间的替代模型.
- 应用SPC (Shewhart控制图表) 来分析与LSTM模型的偏差.
主要成果:
- 与ARIMA,指数平滑和ANN相比,LSTM显示出对二氧化碳排放的更高的预测准确性.
- 分析显示,从20世纪90年代中期到2021年,英国二氧化碳排放量存在显著变化.
- 这些变化与英国政府承诺降低碳排放有关.
结论:
- 拟议的AI框架有效监测与政府政策相关的碳排放变化.
- 这种方法可以确定政策驱动的偏离正常的二氧化碳排放趋势.
- 该研究提供了一种评估气候变化缓解战略影响的新方法.
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