Utilising multiple emission, cost & function databases with ease in Unibloom to power a data-driven climate transition, cost-effectively

Sustainability & procurement teams everywhere share the same frustration: big targets, but no clear actionable costed path with priorities and scenarios of options.

“Net zero by 2050” sounds inspiring on paper, yet when it comes to reducing emissions today, many procurement and sustainability managers are left wondering what the first move should be and how to tackle it with suppliers and price it with customers. 

Before you can cut emissions, you first need to know where they come from.

If it’s a single ingredient, say, milk used in your ice cream mix or soybean meal in your chicken feed, you might think, “I’ll just ask my supplier.” But in reality, most suppliers don’t have this data readily available. You could hire a carbon accounting consultant to help for a large fee, and they might turn around results in a few weeks.

But what happens when your company uses tens of thousands of ingredients which are chosen by reliability, quality, function and cost, plus energy and transport inputs that cost a lot to change under Scope 1 and 2 emissions? 

Suddenly, this task becomes overwhelming. Primarily to show the business case from both carbon and cost impact.

Collecting and verifying this data can take months, even years.

And without accurate emission data & a high level cost input, it’s impossible to know your real carbon footprint and the cost of inaction, let alone decide where and how to reduce it at optimal cost. That delay doesn’t just cost time; it can also mean financial losses, whether through carbon credits, regulatory fines, or missed opportunities to collaborate with customers or shift to better suppliers.

Today, there are many Life Cycle Assessment (LCA) and emission databases available to fill data gaps. Some are commercial and require costly licences, while others are freely accessible through governments or NGOs. A few are broad and cover multiple sectors, whereas others focus on specific regions or industries. 

No single database captures all industries, product categories, or the full picture of global emissions, especially when regional differences come into play. 

For example, if you produce both chicken and vegetables, you’d need emission data that spans animal feed and crop production something few databases cover comprehensively. 

Even when you find most of your ingredients in one place, regional gaps remain. A feed ingredient like soy sourced from Argentina and barley from Denmark requires access to country-specific datasets to ensure accuracy. 

Unfortunately, many databases only provide global averages, limiting the precision of your analysis. That’s why sustainability teams often need to work across multiple sources. Databases such as the WRAP Emission Factor Database highlight clear contrasts, showing how meat and dairy drive far higher emissions than fruits or beverages. Meanwhile, the Australian LCI dataset reveals how even the same product, say, almonds, can have vastly different footprints depending on where and how it’s processed. In commercial real estate, EU carbon data tells a similar story: two identical buildings, one in Poland and one in France, can have dramatically different footprints.

It’s not just about juggling databases—it’s about turning complex emission data into real business cases and actionable insights. Sustainability teams often struggle to build credible scenarios that blend carbon reduction potential with cost implications. For instance, how would changing a raw material source affect both your total emissions and your bottom line?

Or how can you show that investing in greener transport options reduces emissions and operational costs over time? It might be difficult to know which database actually has the data you need, and even harder to decide which one to trust.

How would you know which database contains the right data for your product or region? And how can you ensure that methodologies remain consistent across all calculations so your comparisons and scenario modelling are reliable?

Calculating these trade-offs consistently across multiple datasets and methodologies is hard—and this is exactly where many organisations get stuck.

This is where Unibloom comes in.

Our team has been hard at work integrating multiple Life Cycle Inventory and emission databases directly into the Unibloom platform, all harmonised under a consistent and transparent methodology.

What does that mean in practice?

• Consistent data structure and units across all datasets, ensuring direct comparability.
• Unified emission boundaries and system definitions, so Scope 1, 2, and 3 impacts are calculated consistently.
• Transparent data lineage, showing exactly which source and method each data point comes from.
• Built-in quality and uncertainty checks, helping you make decisions based on trustworthy information.

This methodological alignment enables sustainability and procurement teams to work confidently with data that’s comparable, traceable, and scientifically rigorous—without having to manually clean or standardise datasets themselves. This means you can now access a vast library of high-quality public and commercial data within a single, streamlined workflow. No more switching between platforms or worrying about inconsistent calculations.

With Unibloom, you don’t just model your current operations — you model different shifts and scenarios: changing ingredients or materials; exploring alternative transport routes or suppliers, and comparing Scope 3 impacts across your value chain. Each scenario can be evaluated not just for emission reduction potential, but also for cost implications—helping you find the optimal balance between sustainability and profitability.

And for industries like food and agriculture, Unibloom even lets you explore outcomes down to the FLAG (Forest, Land, and Agriculture) level. This means you can measure how land-use changes, deforestation impacts, and agricultural emissions contribute to your total footprint—bringing deeper insight into your climate transition planning.

Our AI Scenario Optimiser takes this even further, automatically identifying low-carbon alternatives and recommending the most suitable datasets for your unique context, helping you move from insight to action effortlessly.

To make your sustainability modelling even more robust, Unibloom now includes:

• Ecoinvent – a globally recognised database with thousands of process-level emission factors across industries.
• Agrifootprint -  a comprehensive agricultural database that provides detailed Life Cycle Assessment (LCA) data for food, feed, and agricultural products, covering crop cultivation, animal production, and the processing stage.
• Australian LCI Database – covering materials, transport, and energy data specific to the Australasian context.
• WRAP Emission Factor Database – offering detailed data for food, beverage, and waste management sectors.
• Multiple other databases for other applications and regions

So, what’s next after reading this?

The takeaway is simple:
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If you want to move beyond reporting and start building a true climate transition plan, you need data & automated calculations that connects emissions, cost, and opportunity.

Unibloom empowers sustainability and procurement teams to:

• Identify data gaps and fill them with high-quality, harmonised datasets.
• Model various emission-reduction initiatives alongside financial impacts.
• Confidently prioritise initiatives that deliver both climate and business value.

Try Unibloom today by get started on an inception agile workshop for 55 minutes with your team or book a data analysis gap session to assess your current datasets.

Together, we can help you plan a data-driven climate transition and give your procurement teams the insight they need to act—quickly, confidently, and cost-effectively to hit Science Based Targets.

calendly.com/anna-sandgren or anna.sandgren@unibloom.world

https://unibloom.world