Reading through this discussion and speaking from professional experience I have to say that the real challenge isn’t just specific bans, it’s the administrative cost and the inertia of a permitting paradigm designed in the 1960s and 70s. We’re still managing complexity with relics of a regulatory architecture built for a different era — one with paper files, siloed agencies, and a bias toward “check-the-box” compliance rather than real world outcomes.

That’s why so many solutions feel like de facto bans: not because the environmental goal isn’t valid, but because the cost of compliance in time, paperwork, and legal risk creates a barrier that only well-resourced actors can navigate. The real economic deadweight loss isn’t always in the policy text — it’s in the thousands of hours and tens of thousands of dollars spent just to prove you did the minimum.

There’s enormous opportunity right now with data tools and AI agents for qualitative assessment. We don’t have to keep defaulting to rigid checklists that presume every context is the same. With modern sensors, realtime monitoring, and AI that can synthesize qualitative evidence with quantitative data, we can finally shift toward performance-based permits that look at actual impacts rather than adherence to outdated procedural triggers.

Imagine a system where:

Sensors and connected data streams show real emissions or ecological outcomes,

AI agents help translate diverse evidence into risk profiles,

Permits adapt based on performance instead of fixed thresholds divorced from context.

That’s not just a tech fantasy — that’s a pathway to reducing administrative drag while improving environmental protection. The status quo isn’t sustainable environmentally or economically. If we cling to 20th-century process dogma, we’ll keep seeing well-intentioned policies backfire into de facto bans, regulatory bottlenecks, and inequitable access to compliance.