AI on BBNJ High Seas Biodiversity Agreement for Legal teams at Renewables & Clean Energy firms in international jurisdictions

Executive Summary

Legal teams at Renewables & Clean Energy firms operating in international jurisdictions increasingly encounter the BBNJ High Seas Biodiversity Agreement — the first legally binding global instrument governing activities in areas beyond national jurisdiction — when scoping offshore project development, marine cable routing, or operations that interact with the high seas. The Agreement's environmental impact assessment (EIA) regime is one of its most operationally significant provisions, establishing screening thresholds that determine whether a planned activity must undergo formal assessment before proceeding.

In testing AI tools against this regulation, we found that AI assistants misstate the core EIA trigger, replacing the Agreement's precautionary "may have" threshold with a stricter "likely to have" standard and citing a wrong article reference. Even when the initial response sounds authoritative, the AI — when challenged — acknowledged uncertainty about its answer. For a Legal team advising on project viability or drafting internal compliance positions, that single substitution silently narrows the firm's compliance obligations and exposes operations to challenge.

How AI gets this regulation wrong

The table below catalogues how AI tools go wrong when answering questions about the BBNJ Agreement's environmental impact assessment obligations. The dominant failure mode here is an AI that states an incorrect rule with confidence — and only retreats to uncertainty when pressed, rather than flagging its limitations upfront. In this regulation, that means quietly raising the bar for when an EIA is required and pointing to the wrong article as authority.

What that means for your team

The table below maps how AI errors on the BBNJ Agreement translate into concrete risk categories for Legal teams at Renewables & Clean Energy firms. For this regulation, the risk concentrates in a single but highly consequential category: a wrong deliverable — advice, a policy brief, or a project opinion that rests on a misread compliance threshold. In a regime where the EIA trigger is expressly precautionary, that misread systematically understates the firm's obligations.

When this affects your department

A Legal team at a Renewables & Clean Energy firm touches the BBNJ Agreement wherever the firm's activities extend to — or interact with — areas beyond national jurisdiction. Offshore wind development in international waters, subsea power cable routes, floating energy platforms, and marine renewable energy feasibility studies all potentially trigger the Agreement's EIA regime. Legal teams are commonly asked to provide a rapid view on whether a proposed project activity will require environmental assessment, to map the firm's compliance obligations for internal risk registers, or to brief the business development team ahead of a project decision gate.

AI tools are a natural first port of call for this kind of regulatory mapping work, particularly when the Agreement is newly in force and in-house expertise is still being built.

The problem is that the EIA screening threshold — the question of exactly when an activity is caught — is the single most consequential legal question the Agreement poses for project developers. The Agreement uses a precautionary "may have more than a minor or transitory effect" standard: if it is even possible that the effect exceeds that threshold, assessment is required. An AI that substitutes "likely to have" raises the bar substantially, potentially placing activities outside the EIA obligation that the Agreement is designed to capture.

A Legal team that relies on this AI output when advising on project scope, drafting an internal compliance note, or preparing a board presentation on regulatory risk carries that error directly into the firm's decision-making.

The downstream consequences for a Renewables & Clean Energy firm can be significant. Operating without a required EIA in a high-seas context exposes the firm to challenge from flag states, coastal states with adjacent interests, or intergovernmental bodies charged with enforcement. It also creates reputational risk in a sector where ESG credentials and environmental compliance are closely scrutinised by investors, lenders, and offtake counterparties. Where the firm has already committed capital on the basis of a flawed legal position, course-correction costs escalate sharply.

The findings at a glance

The table below summarises the findings identified for this regulation and audience, covering the question tested, what AI tools said, and how it diverges from the Agreement's text.

Aggregate impact

The finding for this regulation and audience centres on a single, structurally important error: AI tools misstate the EIA screening threshold in a way that consistently favours a narrower compliance obligation. The Agreement's text — "may have more than a minor or transitory effect on the marine environment, or the effects of which are unknown or poorly understood" — is deliberately precautionary. It captures uncertainty as a trigger in its own right. The AI substitution of "likely to have" removes that precautionary limb and replaces it with a probability standard that the Agreement's drafters explicitly did not adopt.

This type of error is particularly hazardous for Legal teams because it is not obviously wrong. "Likely to have" and "may have" are close enough in ordinary usage that an attorney relying on an AI summary without cross-checking the treaty text could accept the paraphrase as accurate. The additional wrong article citation compounds the problem: a lawyer who attempts spot-verification by checking the cited article will not find the provision they expect, but may conclude they are looking at a drafting variation rather than an AI error.

The combination of a plausible-sounding paraphrase and a wrong cross-reference is a more dangerous error profile than an outright fabrication.

For Renewables & Clean Energy firms, the systemic risk is that project scoping, permitting strategy, and internal compliance thresholds built on this AI output will carry an incorrectly narrow EIA trigger through the entire project lifecycle. Because the BBNJ Agreement is a relatively recent instrument and authoritative secondary commentary is still limited, Legal teams face elevated reliance on AI tools precisely where AI performance is least reliable.

What your team should do

The default position for Legal teams should be to treat AI-generated summaries of the BBNJ Agreement's EIA provisions as a starting point for identifying the right questions, not as a reliable statement of the operative legal standard. The EIA screening threshold is a threshold question — the answer determines whether the entire assessment process is triggered — and that makes it exactly the kind of provision where a paraphrase error has the largest downstream impact.

Before any AI-assisted analysis of BBNJ EIA obligations reaches an internal opinion, board paper, or project-gate recommendation, the specific treaty text should be verified directly against the Agreement as deposited with the United Nations Treaty Collection.

Practical safeguards for Legal workflows on this regulation include: maintaining a verified reference card for the key operative provisions (EIA threshold, article cross-references, definitions of "activities" and "marine environment") that team members can use to quickly check AI summaries; building a verification step into any BBNJ regulatory mapping exercise before the output is shared with the business; and treating any AI response that cites a specific article number as requiring article-level verification, since article citation errors are a consistent pattern in AI responses on this treaty.

Where the firm is actively scoping projects that may interact with areas beyond national jurisdiction, external legal advice from specialists in international ocean law should be obtained rather than relying on in-house AI-assisted analysis for threshold determinations.

AI tools can add genuine value in the BBNJ Legal workflow when used appropriately: summarising the Agreement's overall architecture, identifying which provisions are likely relevant to a given project type, generating a checklist of questions to put to external counsel, or helping draft the non-operative sections of internal briefing notes. The risk concentrates in the operative legal standards themselves — the specific thresholds, article references, and procedural requirements that determine what the firm must actually do.

How RLB Can Help

RegLeg's published Hallucination Research gives the Legal team at a Renewables & Clean Energy firm a ready-made pre-flight check before placing weight on AI-generated output in regulatory matters. The research identifies, by regulator and regulatory instrument, the specific question types where AI tools have demonstrably produced wrong answers — misquoted thresholds, fabricated cross-references, outdated consenting conditions — so counsel can apply targeted scepticism rather than blanket caution or uncritical reliance.

Beyond the public research, RLB works with Legal teams on bespoke regulator deep-dives that map hallucination exposure to the firm's actual AI-supported workflows. In the renewables and clean energy sector those workflows commonly span permitting and grid-connection advice, emissions-reporting obligations, cross-border offtake structuring, and evolving subsidy or feed-in regime compliance — each carrying a different profile of regulatory complexity and AI failure risk. A deep-dive surfaces which tasks carry the highest exposure and where human review should be concentrated, so the team can allocate oversight resources proportionately.

RLB also offers a confidential review of the firm's existing AI-use policy against RegLeg's failure-mode catalogue, with a prioritised remediation plan that fits within the Legal function's existing governance structure. Alongside that, RLB can produce training material and CPD-aligned content — tailored to the firm's jurisdiction mix and practice areas — that the team can deploy internally, helping lawyers develop a durable, evidence-based instinct for where AI tools are reliable and where they are not.