Let’s be honest about embodied carbon
Most people in the industry agree on one thing: embodied carbon matters.
What we don’t agree on, at least not yet, is how it should actually work in practice.
Today, embodied carbon and life cycle assessment (LCA) are often treated as specialist exercises. They’re careful, detailed, and technically sound but also slow, expensive, and disconnected from the decisions that shape a project.
By the time results land in a report, the design is usually set. Materials are locked in. Budgets are agreed. The opportunity to meaningfully reduce carbon has already passed.
That’s not a failure of intent.
It’s a failure of timing.
Carbon analysis shouldn’t be a post-mortem
If the goal is to reduce emissions, then embodied carbon analysis needs to happen while choices are still flexible, not after the fact.
Right now, we mostly use LCA to answer questions like:
How much carbon did this project end up with?
Does it meet a target or rating requirement?
Those are important questions but they’re not the most useful ones.
The more powerful questions are:
What should we change to reduce carbon?
Where are the biggest opportunities right now?
What trade-offs are we making between cost, carbon, and performance?
These are decision questions, not reporting questions. And they need fast answers.
The real problem isn’t methodology, it’s friction
The science behind embodied carbon is not the issue. The standards are not the issue. The data is getting better every year.
The real problem is friction.
Too much manual setup.
Too many spreadsheets.
Too much specialist translation between “project data” and “carbon data”.
Too much waiting.
This friction turns embodied carbon into something teams endure rather than use.
And when something is slow and painful, it gets pushed later in the process—or skipped entirely.
In the AI era, this no longer makes sense
We are living through a shift in how complex information is processed.
AI is already changing how we write, design, code, and analyse data. It is especially good at the exact tasks that make LCA slow today:
Interpreting messy, real-world inputs like cost plans and BOQs
Mapping materials to the right categories and datasets
Applying consistent calculation logic at scale
Surfacing patterns and insights from large datasets
This is not about cutting corners or dumbing things down.
It’s about removing the unnecessary effort that sits between questions and answers.
Embodied carbon needs to become decision-ready
In a world shaped by AI, embodied carbon analysis should feel less like an audit and more like live intelligence.
That means:
Results in minutes or hours, not weeks
Simple ways to test options and scenarios
Clear visibility of assumptions and confidence levels
Carbon insight that evolves as the project evolves
When carbon becomes fast and intuitive, it stops being something only sustainability teams worry about. It becomes part of everyday design and commercial conversations.
That’s when reduction actually happens.
This changes the role of experts for the better
There’s a common fear that AI will replace expertise. In reality, it does the opposite.
By automating repetitive and mechanical tasks, AI gives specialists more time to:
Interpret results
Challenge assumptions
Guide better decisions
Focus on what actually matters
Expertise doesn’t disappear. It scales.
Why we built Nulla
We built Nulla because we kept seeing the same pattern repeat itself.
Good teams.
Strong intent.
Late carbon insight.
Nulla is designed around a simple idea: embodied carbon analysis should be fast enough to influence decisions, not just document them.
Instead of asking teams to restructure how they work, Nulla works with the information they already have. It uses AI to turn raw project data into carbon LCA insight—quickly, transparently, and in a way that encourages exploration rather than compliance.
The aim isn’t to produce better reports. It’s to enable better choices.
This is a mindset shift, not just a technology shift
Re-imagining embodied carbon isn’t about chasing the latest tools. It’s about changing how we think about the role of LCA in projects.
If embodied carbon stays slow and specialist-only, it will always sit at the edges of decision-making. If it becomes fast, accessible, and trustworthy, it can sit at the centre.
The AI era gives us the chance to make that shift.
The future of LCA is not a report
It’s a conversation.
It’s a design input.
It’s a commercial signal.
It’s intelligence you can act on.
If we want embodied carbon reduction to scale across the industry, we need to make it easier to do the right thing than to do nothing at all.
The tools now exist. The urgency is clear.
The question is whether we’re ready to let embodied carbon evolve