01 · Measure · Inventory
Every stream, every factor, every month
The full greenhouse-gas inventory behind the headline number: where each tonne comes from, which factor produced it, how certain it is, and how it moved month by month. Every tonne here is computed by the engine.
Footprint
11,208 tCO₂e/yr
± 3,225 t (29%) from factor uncertainty, one standard deviation
Carbon intensity
315 t per $1M
$35.5M of operating spend behind the footprint
Estimated from spend
47% of tonnes
Dollar-based factors: the lowest tier of the GHG Protocol data-quality hierarchy
Coverage
168 records
14 streams · 12 months · 5 source files · Jan 2026 – Dec 2026
The inventory, stream by stream
Annual activity × emission factor = tCO₂e. Largest stream first.
✓ Scope subtotals reconcile with the engine
✓ Tonnes ÷ activity reproduce every factor
| Stream | Scope | Annual activity | Emission factor (kgCO₂e / unit) | ± | tCO₂e | Share | Spend | kg / $ |
|---|---|---|---|---|---|---|---|---|
| Procurement spend procurement.csv · 12 rows | Scope 3 | 19.0M USD | 0.28 EPA Supply Chain GHG Emission Factors (spend-based), sector average (2025) Spend-based | 60% | 5,315.2 | 47.4% | $19M | 0.28 |
| Electricity kWh electricity.csv · 12 rows | Scope 2 | 4.3M kWh | 0.712 Indonesia MEMR grid emission factor (Jamali) / IEA (2025) | 12% | 3,064.7 | 27.3% | $495K | 6.19 |
| Diesel litre fuel.csv · 12 rows | Scope 1 | 381K litre | 2.5123 UK Government GHG Conversion Factors 2026 — diesel (average biofuel blend) (2026) | 6% | 958.4 | 8.6% | $420K | 2.28 |
| Flight passenger km travel.csv · 12 rows | Scope 3 | 6.2M passenger km | 0.152 UK Government GHG Conversion Factors 2026 — domestic/short-haul/long-haul weighted by typical corporate mix, with RF (2026) | 30% | 941.4 | 8.4% | $1.9M | 0.51 |
| Cloud compute vCPU hour cloud_usage.csv · 12 rows | Scope 3 | 549.9M vCPU hours | 0.0004 Cloud Carbon Footprint methodology (min/max watts per vCPU x PUE 1.2, + embodied) (2025) | 40% | 220.0 | 2.0% | $11.5M | 0.019 |
| Petrol litre fuel.csv · 12 rows | Scope 1 | 95,845 litre | 2.1681 UK Government GHG Conversion Factors 2026 — petrol (average biofuel blend) (2026) | 6% | 207.8 | 1.9% | $115K | 1.81 |
| Natural gas kWh electricity.csv · 12 rows | Scope 1 | 733K kWh | 0.1829 UK Government GHG Conversion Factors 2026 — natural gas (2026) | 5% | 134.1 | 1.2% | $37K | 3.66 |
| Waste landfill kg procurement.csv · 12 rows | Scope 3 | 241K kg | 0.4588 UK Government GHG Conversion Factors 2026 — waste disposal (2026) | 30% | 110.6 | 1.0% | $12K | 9.18 |
| Car km travel.csv · 12 rows | Scope 3 | 478K km | 0.1704 UK Government GHG Conversion Factors 2026 — average car (2026) | 25% | 81.4 | 0.7% | $215K | 0.38 |
| Hotel night travel.csv · 12 rows | Scope 3 | 7,434 room night | 10.4 UK Government GHG Conversion Factors 2026 — hotel stay (2026) | 35% | 77.3 | 0.7% | $1.3M | 0.058 |
| Cloud network gb cloud_usage.csv · 12 rows | Scope 3 | 715K GB | 0.0590 implied: engine tonnes ÷ recorded activity | — | 42.2 | 0.4% | $64K | 0.66 |
| Train passenger km travel.csv · 12 rows | Scope 3 | 960K passenger km | 0.0298 implied: engine tonnes ÷ recorded activity | — | 28.6 | 0.3% | $77K | 0.37 |
| Water m³ procurement.csv · 12 rows | Scope 3 | 96,221 m³ | 0.177 UK Government GHG Conversion Factors 2026 — water supply + treatment (2026) | 25% | 17.0 | 0.2% | $115K | 0.15 |
| Cloud storage GB month cloud_usage.csv · 12 rows | Scope 3 | 11.6M GB month | 0.0008 Cloud Carbon Footprint methodology (SSD/HDD energy x PUE x replication factor) (2025) | 40% | 9.3 | 0.1% | $268K | 0.035 |
| Total · 14 streams | 11,207.9 | $35.5M | 0.32 | |||||
Factors come from the engine’s curated library (UK Government 2026 conversion factors, Indonesia MEMR grid factor, Cloud Carbon Footprint, US EPA supply-chain factors), each with its year and published uncertainty. Scopes follow the GHG Protocol Corporate Standard.
Where the money goes is not where the carbon is
Share of annual tCO₂e against share of operating spend, by category
Electricity is 1.5% of spend but 28.5% of the carbon: 6.02 kg CO₂e per dollar, 263× a dollar of cloud. Cutting cost and cutting carbon are different exercises; ranking actions on one alone misses the other.
Share of carbon
Share of spend
- Procurement 0.28 kg/$ Procurement: 47.4% of carbon, 53.4% of spend
- Electricity 6.02 kg/$ Electricity: 28.5% of carbon, 1.5% of spend
- Fleet 2.18 kg/$ Fleet: 10.4% of carbon, 1.5% of spend
- Travel 0.32 kg/$ Travel: 10.1% of carbon, 9.8% of spend
- Cloud 0.02 kg/$ Cloud: 2.4% of carbon, 33.4% of spend
- Other 1.00 kg/$ Other: 1.1% of carbon, 0.4% of spend
Where the uncertainty lives
Each stream’s share of the footprint’s variance
Procurement spend is 47% of the tonnes but 98% of the uncertainty: its factor is ±60%. Every other stream together adds only ± 478 t. Better data for this one line is the biggest single improvement to the inventory.
-
Procurement spend ± 3,189 t · 97.8%
-
Electricity kWh ± 368 t · 1.3%
-
Flight passenger km ± 282 t · 0.8%
-
Cloud compute vCPU hour ± 88 t · 0.1%
-
Diesel litre ± 58 t · 0.0%
-
Waste landfill kg ± 33 t · 0.0%
Method: each factor’s published ±% is read as one standard deviation; streams are treated as independent, so their errors add in quadrature (σ = √Σ(tᵢ·uᵢ)²). 2 stream(s) without a reported factor uncertainty (0.6% of tonnes) are left out of the band rather than guessed.
Month by month, stream by stream
Each cell compares a month with that stream’s own 12-month median, the centre the engine’s robust anomaly test uses
Below median
Above median
Steps at ±5%, ±15%, ±30%, ±60%
⚠Flagged anomaly (robust z ≥ 3)
| Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Median t | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Procurement spend | Procurement spend, Jan 2026: 409.1 t, −8% vs median | Procurement spend, Feb 2026: 430.6 t, −3% vs median | Procurement spend, Mar 2026: 426.8 t, −4% vs median | Procurement spend, Apr 2026: 438.3 t, −1% vs median | Procurement spend, May 2026: 428.6 t, −3% vs median | Procurement spend, Jun 2026: 446.2 t, +1% vs median | Procurement spend, Jul 2026: 450.8 t, +2% vs median | Procurement spend, Aug 2026: 441.0 t, −1% vs median | Procurement spend, Sep 2026: 462.0 t, +4% vs median | Procurement spend, Oct 2026: 448.1 t, +1% vs median | Procurement spend, Nov 2026: 467.0 t, +5% vs median | Procurement spend, Dec 2026: 466.7 t, +5% vs median | 443.6 |
| Electricity kWh | Electricity kWh, Jan 2026: 243.5 t, −5% vs median | Electricity kWh, Feb 2026: 253.6 t, −1% vs median | Electricity kWh, Mar 2026: 252.3 t, −2% vs median | Electricity kWh, Apr 2026: 262.1 t, +2% vs median | Electricity kWh, May 2026: 270.2 t, +5% vs median | Electricity kWh, Jun 2026: 262.9 t, +2% vs median | Electricity kWh, Jul 2026: 259.7 t, +1% vs median | Electricity kWh, Aug 2026: 264.9 t, +3% vs median | Electricity kWh, Sep 2026: 259.7 t, +1% vs median | Electricity kWh, Oct 2026: 246.7 t, −4% vs median | Electricity kWh, Nov 2026: 244.2 t, −5% vs median | Electricity kWh, Dec 2026: 244.8 t, −5% vs median | 256.7 |
| Diesel litre | Diesel litre, Jan 2026: 74.2 t, −3% vs median | Diesel litre, Feb 2026: 76.4 t, −0% vs median | Diesel litre, Mar 2026: 77.4 t, +1% vs median | Diesel litre, Apr 2026: 76.5 t, −0% vs median | Diesel litre, May 2026: 76.0 t, −1% vs median | Diesel litre, Jun 2026: 76.8 t, +0% vs median | Diesel litre, Jul 2026: 74.0 t, −3% vs median | Diesel litre, Aug 2026: 76.7 t, +0% vs median | Diesel litre, Sep 2026: 120.9 t, +58% vs median · flagged anomaly, z 42.4 | Diesel litre, Oct 2026: 76.8 t, +0% vs median | Diesel litre, Nov 2026: 77.4 t, +1% vs median | Diesel litre, Dec 2026: 75.2 t, −2% vs median | 76.6 |
| Flight passenger km | Flight passenger km, Jan 2026: 76.6 t, −0% vs median | Flight passenger km, Feb 2026: 75.2 t, −2% vs median | Flight passenger km, Mar 2026: 88.0 t, +15% vs median · flagged anomaly, z 4.2 | Flight passenger km, Apr 2026: 77.5 t, +1% vs median | Flight passenger km, May 2026: 74.9 t, −2% vs median | Flight passenger km, Jun 2026: 82.8 t, +8% vs median | Flight passenger km, Jul 2026: 76.8 t, +0% vs median | Flight passenger km, Aug 2026: 74.8 t, −2% vs median | Flight passenger km, Sep 2026: 80.4 t, +5% vs median | Flight passenger km, Oct 2026: 74.5 t, −3% vs median | Flight passenger km, Nov 2026: 83.9 t, +9% vs median | Flight passenger km, Dec 2026: 75.9 t, −1% vs median | 76.7 |
| Cloud compute vCPU hour | Cloud compute vCPU hour, Jan 2026: 15.9 t, −2% vs median | Cloud compute vCPU hour, Feb 2026: 15.8 t, −3% vs median | Cloud compute vCPU hour, Mar 2026: 16.2 t, −0% vs median | Cloud compute vCPU hour, Apr 2026: 15.5 t, −5% vs median | Cloud compute vCPU hour, May 2026: 43.0 t, +165% vs median · flagged anomaly, z 56.8 | Cloud compute vCPU hour, Jun 2026: 16.3 t, +1% vs median | Cloud compute vCPU hour, Jul 2026: 15.6 t, −4% vs median | Cloud compute vCPU hour, Aug 2026: 16.5 t, +2% vs median | Cloud compute vCPU hour, Sep 2026: 15.9 t, −2% vs median | Cloud compute vCPU hour, Oct 2026: 16.5 t, +2% vs median | Cloud compute vCPU hour, Nov 2026: 16.2 t, +0% vs median | Cloud compute vCPU hour, Dec 2026: 16.6 t, +2% vs median | 16.2 |
| Petrol litre | Petrol litre, Jan 2026: 17.8 t, +3% vs median | Petrol litre, Feb 2026: 17.9 t, +4% vs median | Petrol litre, Mar 2026: 17.1 t, −1% vs median | Petrol litre, Apr 2026: 16.9 t, −2% vs median | Petrol litre, May 2026: 17.3 t, +1% vs median | Petrol litre, Jun 2026: 17.9 t, +4% vs median | Petrol litre, Jul 2026: 16.8 t, −3% vs median | Petrol litre, Aug 2026: 17.5 t, +1% vs median | Petrol litre, Sep 2026: 17.5 t, +2% vs median | Petrol litre, Oct 2026: 17.2 t, −1% vs median | Petrol litre, Nov 2026: 16.9 t, −2% vs median | Petrol litre, Dec 2026: 17.0 t, −1% vs median | 17.2 |
| Natural gas kWh | Natural gas kWh, Jan 2026: 10.7 t, −3% vs median | Natural gas kWh, Feb 2026: 10.9 t, −2% vs median | Natural gas kWh, Mar 2026: 10.7 t, −3% vs median | Natural gas kWh, Apr 2026: 11.1 t, −0% vs median | Natural gas kWh, May 2026: 11.2 t, +1% vs median | Natural gas kWh, Jun 2026: 10.7 t, −4% vs median | Natural gas kWh, Jul 2026: 12.0 t, +8% vs median | Natural gas kWh, Aug 2026: 12.2 t, +10% vs median | Natural gas kWh, Sep 2026: 11.3 t, +1% vs median | Natural gas kWh, Oct 2026: 10.8 t, −3% vs median | Natural gas kWh, Nov 2026: 11.1 t, +0% vs median | Natural gas kWh, Dec 2026: 11.3 t, +2% vs median | 11.1 |
| Waste landfill kg | Waste landfill kg, Jan 2026: 9.0 t, −3% vs median | Waste landfill kg, Feb 2026: 9.3 t, +1% vs median | Waste landfill kg, Mar 2026: 9.3 t, +1% vs median | Waste landfill kg, Apr 2026: 9.1 t, −2% vs median | Waste landfill kg, May 2026: 8.9 t, −4% vs median | Waste landfill kg, Jun 2026: 9.2 t, −1% vs median | Waste landfill kg, Jul 2026: 9.3 t, +0% vs median | Waste landfill kg, Aug 2026: 9.5 t, +3% vs median | Waste landfill kg, Sep 2026: 9.3 t, +1% vs median | Waste landfill kg, Oct 2026: 9.4 t, +1% vs median | Waste landfill kg, Nov 2026: 9.2 t, −1% vs median | Waste landfill kg, Dec 2026: 9.2 t, −0% vs median | 9.2 |
| Car km | Car km, Jan 2026: 6.7 t, −0% vs median | Car km, Feb 2026: 7.0 t, +4% vs median | Car km, Mar 2026: 6.9 t, +2% vs median | Car km, Apr 2026: 6.8 t, +0% vs median | Car km, May 2026: 6.6 t, −2% vs median | Car km, Jun 2026: 7.0 t, +4% vs median | Car km, Jul 2026: 6.8 t, +0% vs median | Car km, Aug 2026: 6.6 t, −1% vs median | Car km, Sep 2026: 6.6 t, −1% vs median | Car km, Oct 2026: 6.7 t, −0% vs median | Car km, Nov 2026: 7.0 t, +4% vs median | Car km, Dec 2026: 6.6 t, −1% vs median | 6.7 |
| Hotel night | Hotel night, Jan 2026: 6.2 t, −2% vs median | Hotel night, Feb 2026: 6.4 t, +0% vs median | Hotel night, Mar 2026: 7.4 t, +17% vs median · flagged anomaly, z 4.1 | Hotel night, Apr 2026: 6.1 t, −4% vs median | Hotel night, May 2026: 6.4 t, +1% vs median | Hotel night, Jun 2026: 6.8 t, +7% vs median | Hotel night, Jul 2026: 6.4 t, +0% vs median | Hotel night, Aug 2026: 6.1 t, −4% vs median | Hotel night, Sep 2026: 6.9 t, +8% vs median | Hotel night, Oct 2026: 6.3 t, −1% vs median | Hotel night, Nov 2026: 6.1 t, −4% vs median | Hotel night, Dec 2026: 6.3 t, −0% vs median | 6.4 |
| Cloud network gb | Cloud network gb, Jan 2026: 3.5 t, −1% vs median | Cloud network gb, Feb 2026: 3.5 t, +0% vs median | Cloud network gb, Mar 2026: 3.6 t, +4% vs median | Cloud network gb, Apr 2026: 3.4 t, −2% vs median | Cloud network gb, May 2026: 3.4 t, −2% vs median | Cloud network gb, Jun 2026: 3.6 t, +2% vs median | Cloud network gb, Jul 2026: 3.5 t, +1% vs median | Cloud network gb, Aug 2026: 3.5 t, −0% vs median | Cloud network gb, Sep 2026: 3.6 t, +4% vs median | Cloud network gb, Oct 2026: 3.6 t, +3% vs median | Cloud network gb, Nov 2026: 3.4 t, −2% vs median | Cloud network gb, Dec 2026: 3.5 t, −1% vs median | 3.5 |
| Train passenger km | Train passenger km, Jan 2026: 2.4 t, −1% vs median | Train passenger km, Feb 2026: 2.4 t, −1% vs median | Train passenger km, Mar 2026: 2.4 t, +2% vs median | Train passenger km, Apr 2026: 2.4 t, +1% vs median | Train passenger km, May 2026: 2.4 t, +1% vs median | Train passenger km, Jun 2026: 2.4 t, +0% vs median | Train passenger km, Jul 2026: 2.4 t, +1% vs median | Train passenger km, Aug 2026: 2.3 t, −2% vs median | Train passenger km, Sep 2026: 2.4 t, −1% vs median | Train passenger km, Oct 2026: 2.5 t, +3% vs median | Train passenger km, Nov 2026: 2.3 t, −3% vs median | Train passenger km, Dec 2026: 2.4 t, −0% vs median | 2.4 |
| Water m³ | Water m³, Jan 2026: 1.5 t, +3% vs median | Water m³, Feb 2026: 1.5 t, +2% vs median | Water m³, Mar 2026: 1.4 t, +0% vs median | Water m³, Apr 2026: 1.4 t, +2% vs median | Water m³, May 2026: 1.4 t, −1% vs median | Water m³, Jun 2026: 1.4 t, −0% vs median | Water m³, Jul 2026: 1.4 t, −2% vs median | Water m³, Aug 2026: 1.4 t, −3% vs median | Water m³, Sep 2026: 1.4 t, +2% vs median | Water m³, Oct 2026: 1.4 t, −3% vs median | Water m³, Nov 2026: 1.4 t, −1% vs median | Water m³, Dec 2026: 1.5 t, +2% vs median | 1.4 |
| Cloud storage GB month | Cloud storage GB month, Jan 2026: 0.7 t, −4% vs median | Cloud storage GB month, Feb 2026: 0.7 t, −5% vs median | Cloud storage GB month, Mar 2026: 0.8 t, −2% vs median | Cloud storage GB month, Apr 2026: 0.8 t, −2% vs median | Cloud storage GB month, May 2026: 0.8 t, −2% vs median | Cloud storage GB month, Jun 2026: 0.8 t, −2% vs median | Cloud storage GB month, Jul 2026: 0.8 t, +4% vs median | Cloud storage GB month, Aug 2026: 0.8 t, +2% vs median | Cloud storage GB month, Sep 2026: 0.8 t, +3% vs median | Cloud storage GB month, Oct 2026: 0.8 t, +5% vs median | Cloud storage GB month, Nov 2026: 0.8 t, +7% vs median | Cloud storage GB month, Dec 2026: 0.8 t, +6% vs median | 0.8 |
Hover a cell for its value and deviation. Flagged cells link the inventory to Diagnose, where each one is attributed to its drivers.
Inside the largest line: procurement
$19M of purchases, 5,315 tCO₂e, split by supplier segment
| Segment | Spend | tCO₂e | |
|---|---|---|---|
| IT Infrastructure · 30% | $5.7M | 1,595 | |
| Cloud & SaaS · 25% | $4.7M | 1,329 | |
| Facilities & Utilities · 20% | $3.8M | 1,063 | |
| Logistics & Transport · 15% | $2.8M | 797 | |
| Professional Services · 10% | $1.9M | 532 |
Demo attribution: procurement is recorded as a single spend line, so these segments split it by fixed, documented shares. They are not measured counterparty emissions.
The grid behind Scope 2
4.3M kWh of purchased electricity, 3,065 tCO₂e
- Indonesia grid
- 705 g/kWh
- rank 19 of 20 in JAPAC · 69% coal
- Same kWh in Singapore
- 2,140 t
- -29.5% intensity vs home, lifecycle basis
A company cannot move its grid, but it can choose what it buys from it: a renewable power purchase agreement changes the factor behind every kWh, not just the number of kWh.
Compare all 20 JAPAC grids →