AI for Social Good: An Investigation of the Causal Relationship Between Environmental Regulations and Their Effects on Air Pollution in London, UK
arXiv:2606.15257v1 Announce Type: new Abstract: 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 moni
The increasing availability of daily hyper-local environmental data and advancements in AI/Bayesian deep learning frameworks are enabling more precise analyses of complex policy effects.
This study demonstrates how advanced AI can provide clearer insights into the effectiveness of environmental regulations, offering a more robust basis for public health governance and policy-making.
The ability to quantify the causal impact of environmental policies more accurately shifts policy discussions from qualitative assessments to data-driven, evidence-based evaluations using advanced AI methods.
- · Environmental policy makers
- · Urban planners
- · AI researchers in causal inference
- · Public health organizations
- · Industries resistant to environmental regulation
- · Traditional statistical modeling approaches without causal inference
Improved understanding of air pollution regulation efficacy.
More targeted and effective environmental policies leading to better public health outcomes.
Potential for AI-driven predictive modeling for urban environmental management and early warning systems.
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Read at arXiv cs.LG