How to Run a Carbon Hotspot Analysis on a Product in 5 Steps

A PCF number sitting in a spreadsheet does nothing.

The total CO₂e figure tells how much a product emits. It does not tell where those emissions come from, which inputs drive them, or where reducing effort will have the most impact. Without that breakdown, a carbon programme has a measurement but no direction.

That is what a carbon hotspot analysis provides. A carbon hotspot analysis identifies the biggest emissions contributors within a product's supply chain — enabling manufacturers to benchmark performance, prioritize reduction efforts, and set meaningful targets. It is the step that turns a PCF output into an actionable decarbonisation plan.

Making a 1% improvement in a hotspot could be more valuable than a 50% improvement in a non-hotspot. The analysis is not about doing more work — it is about directing the work that already exists toward the inputs that actually move the emissions number.

Here is the complete 5-step process.

Before the steps, a precise definition matters — because "hotspot" is used loosely in sustainability work, and the analytical approach depends on what is being identified.

In the PEF framework, a hotspot is defined using a clear threshold: when lifecycle stages, processes, or elementary flows are ranked from most to least impact, those that together make up 80% of the cumulative impact to any most relevant impact category are considered relevant hotspots.

This 80% cumulative threshold is the operational definition used across most PCF hotspot analysis work. It is not a fixed list of inputs — it is a ranked output from the PCF calculation itself. For one product, the top 80% of emissions might sit in three BOM materials. For another, they might sit in one manufacturing process and one transport route.

Hotspots can be identified at different levels of granularity: impact category, lifecycle stage, process, or elementary flow.

A hotspot analysis can be run at any of these levels — but for most manufacturers, the most actionable starting point is lifecycle stage and then BOM material or component.

A hotspot analysis cannot be run on a single total CO₂e number. The first requirement is that the PCF output is disaggregated — broken down by the lifecycle stages included in the system boundary.

The total carbon footprint should be broken down by lifecycle stage — raw materials, manufacturing, packaging, transport, use, end-of-life — using charts or tables to show the contribution of different processes or materials to the total carbon footprint.

For a cradle-to-grave PCF, use phase and end-of-life disposal are added.

If the PCF calculation was done correctly, each of these stages should have a subtotal expressed in kg CO₂e. If the output is a single aggregated number with no stage breakdown, the calculation needs to be disaggregated before the hotspot analysis can proceed. This is not additional work — it is a requirement of both ISO 14067 and the GHG Protocol Product Standard for any PCF intended for reporting or disclosure.

What to produce at the end of Step 1: A table or chart showing each lifecycle stage and its CO₂e contribution, ranked from highest to lowest, with each stage expressed as both an absolute figure (kg CO₂e) and a percentage of the total.

With the lifecycle stage breakdown complete, apply the 80% cumulative threshold to identify which stages are hotspots.

Sort the stages from highest to lowest CO₂e contribution. Starting from the top, accumulate the percentages until the running total reaches 80%. Every stage included in that 80% cumulative threshold is a hotspot. Every stage below the threshold is a non-hotspot.

In the PEF approach, the life cycle stages contributing cumulatively to at least 80% of the characterized impacts are selected as the most relevant — in a documented example, the most relevant life cycle stages identified were raw materials acquisition and pre-processing, product manufacturing, and end-of-life.

For most manufacturers of physical products, the raw materials stage will appear in the top 80% of almost every PCF. A PCF identifies the most emitting steps in the lifecycle — and for most manufactured products, raw material extraction and processing is where the largest share of emissions sits.

This does not mean manufacturing energy and transport are irrelevant. It means that if raw materials account for 65% of total PCF emissions and manufacturing accounts for 20%, the 85% combined total means both are hotspots — while packaging at 3% and inbound transport at 4% are not.

What to produce at the end of Step 2: A ranked list of hotspot stages with their cumulative emissions share, clearly separating the stages above and below the 80% threshold.

Stage-level hotspot identification tells which lifecycle phases matter most. Component-level hotspot analysis tells which specific inputs within those phases drive the emissions — and that is where the reduction levers are.

For each stage identified as a hotspot in Step 2, break the emissions down to the individual BOM material or process level:

Most product emissions concentrate in a handful of stages or inputs. A PCF reveals those hotspots with data that can be acted on — enabling material swap decisions, recycled content targets, energy consumption tuning on a critical process, re-thinking of packaging formats, or changes in shipment modes.

For a metal component manufacturer, the raw materials stage hotspot might break down to show that primary aluminium accounts for 58% of total PCF emissions while steel fasteners account for 4% and polymer seals account for 2%. The aluminium is the material-level hotspot. The fasteners and seals are not.

A tonne of aluminium can range between 3 and 20 tonnes of CO₂e in carbon intensity depending on how and where it is produced. This material-level variation is exactly why component-level hotspot analysis is necessary — the stage-level breakdown identifies where to look; the component-level breakdown identifies what to act on.

Apply the same 80% cumulative ranking logic at the component level within each hotspot stage. The components that together account for 80% of that stage's emissions are the material-level hotspots.

What to produce at the end of Step 3: For each hotspot stage, a ranked list of materials or processes and their individual CO₂e contributions, with the 80% cumulative threshold marked.

A hotspot identified using secondary data — industry-average emission factors from ecoinvent or similar databases — is a different level of finding from a hotspot identified using primary, supplier-specific data.

If the hotspot is resin production or heat-intensive forming, invest in better data. Using generic factors for critical processes introduces uncertainty precisely where accuracy matters most.

Before moving to reduction planning, assess the data quality behind each material-level hotspot:

Sensitivity analysis assesses the impact of changes in key assumptions on the results — it is best practice under ISO 14044 to conduct sensitivity analysis on the highest-impact inputs to understand how the hotspot finding would change if the underlying emission factor changed.

A simple sensitivity check involves substituting the secondary emission factor with the high and low end of the plausible range for that material and recalculating. If the material remains a hotspot under both the high and low case, the finding is robust. If the material falls out of the top 80% under the low case, primary data collection is warranted before committing to a reduction programme targeting it.

What to produce at the end of Step 4: A data quality assessment for each material-level hotspot — noting whether the underlying emission factor is primary or secondary, and the sensitivity range.

The output of a carbon hotspot analysis is only as useful as the reduction decisions it informs. Step 5 maps each identified hotspot to the specific interventions that can meaningfully reduce it.

Following a PCF hotspot identification, emissions reduction actions include substituting raw materials for more sustainable alternatives, replacing manufacturing equipment with more efficient models, switching energy suppliers to include more renewable electricity, investing in electric vehicle fleets for distribution, and implementing waste reduction and recycling processes.

Based on hotspot findings, engaging with suppliers can lead to collaborative efforts to reduce emissions and improve sustainability across the supply chain — supplier engagement is one of the highest-leverage actions following a hotspot analysis, particularly when the hotspot sits in upstream purchased materials.

For each hotspot, document the specific lever, the responsible team or function, the estimated emissions reduction potential, the timeline for implementation, and the data improvement needed to verify the reduction in the next PCF calculation cycle.

What to produce at the end of Step 5: A hotspot reduction map — a structured document linking each material-level hotspot to at least one reduction lever, with ownership, timeline, and estimated impact.

The hotspot analysis produces three outputs that serve different purposes: