As UK planning regulations undergo the most sweeping reforms in a generation, automation is invaluable in expediting the process. Tech expert Alistair Walker explains how to responsibly use artificial intelligence for EIA reporting.
The application of AI is moving rapidly. In Environmental Impact Assessments (EIAs), it now assists in classifying habitats, scanning documents, predicting environmental effects and summarising consultation responses. That brings opportunities for better analysis and more efficient reporting, but also questions about accuracy, accountability and trust.
As lead author of a new Institute of Sustainability and Environmental Professionals (ISEP) Advice Note, Using AI in EIA, my purpose has been to give practitioners and decision makers a clear view of both sides. AI can strengthen the EIA process, but only if we use it carefully, explain it openly and retain human oversight throughout.
The use of AI in EIAs
AI describes computer systems that carry out tasks that normally need human intelligence, such as learning from data, recognising patterns or producing text. In EIA, the two most relevant groups of tools are analytical systems built on machine learning and so-called generative AI tools which can draft text or summarise information based on a user prompt.
Neither group replaces the EIA process. Screening, scoping, assessment and mitigation all still rest on human judgement. AI tools support these stages by handling large datasets, organising information or highlighting issues for further review, but they do not determine the conclusions.
The added value of AI
AI is already being used across the EIA lifecycle. In baseline studies, it can analyse satellite images, classify habitats or flag environmental changes that merit a closer look on the ground. In more technical topics, machine learning tools can help explore model outputs or check consistency across datasets.
Another area where AI can help is document control. EIAs typically often involve hundreds of pages and multiple contributors. Tools that support version control, track changes in commitments or identify possible gaps can save time and reduce error. Generative AI can also help structure non-technical summaries, provided all statements are checked carefully against the evidence.
There is also potential in engagement. Systems that summarise consultation responses or identify Frequently Asked Questions can help project teams understand community concerns early in the process. But while digital tools can assist, but the practitioner must decide what matters and why.
Barriers and risks
For all its usefulness, AI brings challenges. Perhaps the most significant is data quality. AI tools cannot compensate for poor or incomplete inputs. If data is biased or out of date, this will apply to the outputs too. Similarly if data models are hard to interpret, it will be difficult to explain how a conclusion was reached.
This can lead to legal and ethical concerns. EIA work often involves sensitive environmental, commercial or personal data. Practitioners need to know where data is stored, how it is used and whether information is being shared with third party systems outside the organisation.
There are cultural challenges too. Entry level analytical tasks help early career professionals learn how environmental systems behave but if AI removes those steps without clear planning and training, we risk weakening the skills base that EIA depends on.
Bias is another issue. AI models trained on historic data can reflect patterns that do not align with current policy or community priorities. Recognising this and checking for it is essential.
Principles for responsible use
The ISEP Advice Note proposes six principles. The first is responsibility: practitioners remain accountable for all outputs, regardless of the tools used. Understanding the strengths and weaknesses of each system is part of that responsibility.
The second is alignment with regulatory frameworks and professional standards: AI outputs must stand up to challenge and match accepted methodologies.
Transparency is vital. If AI has been used, this should be stated clearly in the EIA report, which should also state which tool was used, when and for what purpose.
The next step is verification. AI tools are fallible. And so all outputs must be checked against underlying evidence, with a clear audit trail of any changes made.
Finally, AI should be treated as a utility, not a substitute. It should help practitioners understand issues more clearly, not short-cut the learning process.
Training and procedures
As AI tools are taken up in the industry, companies are developing AI policies for their use to ensure that employees understand their responsibilities when using them and assisting their understanding. It is recommended that all companies look to ensure they have one in place to avoid liability.
AI training programmes are starting to be rolled out in the sector, notably incorporating modules on data ethics and data management. Language model courses also help users learn about the effective use of high-quality prompts to ensure the generative output of the AI tool is as closely aligned to what is required.
Looking ahead
AI is already shaping the way we prepare and review EIAs and that trend will continue. So the question is not whether the sector should use AI, but how. My view is that we need structured trials on live projects, shared learning between practitioners and regulators and clear internal policies on data handling and tool selection.
Most importantly, we must hold on to the purpose of EIA: to provide reliable, evidence-based information that helps decision makers and communities understand the environmental consequences of development. AI can support that aim, but only if we apply it with care.
With the right safeguards, AI will strengthen the profession and help us produce clearer, more consistent and more transparent assessments. However, if we lose sight of human judgement, we will undermine the very process we are trying to improve.
Alistair Walker is Technical Director at Lanpro, a multi-disciplinary, environment-led planning consultancy.
Image: Danist Soh / Unsplash
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