Volume 62, Issue 341, May - August 2026

Prescriptive AI for Climate Action: A Causal Physics-Informed Optimization Framework for High- Impact Methane Abatement

Bharat Khushalani^♦

Picosoft Research, 5529 163rd CT NE Redmond, WA, 98052 USA

^♦Corresponding Author
Bharat Khushalani, Picosoft Research, 5529 163rd CT NE Redmond, WA, 98052 USA

ABSTRACT

The pressing need of global warming requires shifting from area-wide forecasting to site-specific, action-oriented reduction approaches, especially for high-impact climate-warming gases like methane CH₄. Present frameworks are inadequate for critical resource distribution. Examples include ranking the sealing of large numbers of abandoned wells, an action priced at about $100,000 per well. A major gap remains in measuring the cause-and-effect what-if impact of a measure compared with a strategy of "inaction". Conventional empirical models are unreliable for this problem as they fail to guarantee the physics validity needed for reliable policy choices. We present the Causal physics-inspired policy optimization (C-PIPO) Framework. This new method systematically combines a Physics-inspired neural network (PINN) at its core. This network imposes the scientific limits of the Transport-diffusion equation. It helps produce physically coherent counterfactuals. These cause-and-effects are passed into a multi-criteria optimization module. The module recommends the best policy ordering by weighing climate gain against budget cost and environmental justice. C-PIPO provides cost-effective, clear, and auditable decision rule guidance. It changes AI’s function from forecasting warning to action-guiding decision-making for faster climate response.

Keywords: Methane concerns; Physics-informed neural networks; Optimization; Environment

Discovery, 2026, 62, e18d3283

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Published: 12 July 2026