用因果AI分析伦敦环保政策对空气质量的影响，发现政策使PM2.5平均下降1.88微克/立方米。
AI for Social Good: An Investigation of the Causal Relationship Between Environmental Regulations and Their Effects on Air Pollution in London, UK
- 构建贝叶斯深度学习框架，融合气象、社会经济与政策数据，识别因果效应。
- 估算出2010-2020年伦敦政策使PM2.5平均下降1.88 μg/m³，相对降幅12.35%。
- 适合关注环境治理、公共健康与可解释AI的政策制定者与研究者。
空气污染治理是城市公共健康治理的核心，但政策效果评估困难，因政策非随机实施，且污染变化受气象、社会经济、时间趋势及多重干预共同影响。本研究构建不确定性感知的贝叶斯深度学习框架，评估2010至2020年伦敦空气污染政策对PM₂.₅浓度的总体影响。模型整合内伦敦监测站的每日PM₂.₅观测值、气象协变量、年度社会经济指标、月度与星期指标，以及32项政策措施的每日实施状态。贝叶斯LSTM捕捉环境与社会经济协变量的时间依赖性，贝叶斯嵌入层表示时间与政策状态输入，政策状态预测分支支持基于倾向得分的非随机实施调整。通过对比实际观测与假设无政策情景下的反事实预测，结合重复贝叶斯训练与自助抽样，量化不确定性。结果表明，伦敦政策平均使PM₂.₅浓度降低1.88 μg/m³（95%置信区间1.64–2.12 μg/m³），相对减少12.35%。政策效应在2013年前较弱，2013–2017年逐步显现，2018–2019年达到最强。研究显示持续累积的监管干预推动了伦敦空气质量的可测量改善。该工作展示了不确定性感知因果AI如何支撑环境问责、公共卫生保护与基于证据的环境决策。
原文摘要 · Abstract (English)
Air pollution regulation is central to urban public health governance, but estimating its effects is difficult because policies are implemented non-randomly and pollution trajectories are shaped by meteorology, socioeconomic change, temporal trends, and overlapping interventions. This study develops an uncertainty-aware Bayesian deep learning framework to estimate the aggregate effect of air pollution regulations on PM$_{2.5}$ concentrations in London from 2010 to 2020. The framework integrates daily PM$_{2.5}$ observations from Inner London monitoring stations, meteorological covariates, annual socioeconomic indicators, month-of-year and day-of-week indicators, and daily regulation status data for 32 policy measures. A Bayesian LSTM captures temporal dependencies in environmental and socioeconomic covariates, Bayesian embedding layers represent temporal and regulation status inputs, and a regulation status prediction branch supports propensity score-based adjustment for non-random policy implementation. Regulatory effects are estimated by comparing observed PM$_{2.5}$ concentrations with counterfactual predictions under a hypothetical no-regulation scenario, with uncertainty summarized across repeated Bayesian training runs and bootstrap resampling. Results show that London's regulations were associated with an average PM$_{2.5}$ reduction of 1.88 $μ$g/m$^3$, a relative reduction of 12.35%, with a 95% confidence interval of 1.64-2.12 $μ$g/m$^3$. Estimated effects were limited before 2013, became clearer from 2013 to 2017, and were strongest in 2018 and 2019. The findings suggest that sustained and cumulative regulatory interventions contributed to measurable improvements in London's air quality. This study demonstrates how uncertainty-aware causal AI can support environmental accountability, public health protection, and evidence-based governance for environmental decision-making.
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