🌍 Carbon reporting — GHG inventory and reduction plan Live model
You run the carbon accounting of a generic manufacturer. Every month the model turns fuel, electricity and purchasing into tonnes of CO₂-equivalent with published emission factors, sorts them into scopes 1, 2 and 3 as the GHG Protocol does, tracks how complete and how uncertain the data are, and compares the rolling twelve months with a 4.2 %-per-year reduction pathway from the base year. You decide on metering, supplier engagement, renewable-electricity certificates, carbon credits and four abatement projects with real lead times and a budget. Educational simulation only — not accounting, assurance, legal or investment advice.
What you will learn
How activity data and emission factors become a scoped inventory — and why a first inventory is usually incomplete and gets restated.
Location-based vs. market-based scope 2, and why electrifying heat only cuts emissions when the electricity is clean.
Why spend-based scope 3 cannot show supplier progress, why lead times and the carbon budget punish late action, and why credits never count as reductions.
Simulator
Time 0 mo
1Scope 1 — fuels burned on site
2Scope 2 — purchased electricity (market-based)
3Scope 3 — purchased goods
○Project not funded
⚙Project being built
✓Running
Controls
Sub-meters, fuel logs and invoice capture for every site: metered share of activity data rises to 95 % within a few months. 10 k€ per month.
Engaged suppliers share product-level emission data (replacing the spend-based average) and, once they have set targets, cut their own intensity. Costs 4 k€ + 50 k€ × share per month.
Energy attribute certificates set the covered share to zero in market-based scope 2. Location-based scope 2 does not change. About 4 € per MWh.
Credits lower only the "net" figure. Gross emissions and progress against the target are unchanged. About 15 € per tonne.
About 100 MWh per month on average, more in summer. Cuts both scope 2 methods. 1,000 k€, ready in 6 months.
Takes over half of the gas-fired process heat (boiler efficiency 0.9 → heat pump COP 3): less scope 1, more electricity. 2,000 k€, ready in 9 months.
Replaces 80 % of diesel vans; electric vans need about a third of the energy. 800 k€, ready in 8 months.
Indicators
Gap to the reduction pathway (rolling 12 months)
0.0%
warning
Inventory uncertainty (95 %)
0.0%
normal
Budget used
0%
normal
Base-year restatements
0
normal
Gross emissions this month
0 t CO₂e
Scope 1 (fuels)
0 t CO₂e
Scope 2, market-based
0 t CO₂e
Scope 2, location-based
0 t CO₂e
Scope 3, purchased goods
0 t CO₂e
Scope 3 share
0 %
Net after credits (not used for targets)
0 t CO₂e
Rolling 12-month gross emissions
0.0 kt CO₂e
Pathway allowance for the same 12 months
0.0 kt CO₂e
Cumulative emissions vs. pathway (carbon budget)
0 %
Monthly emissions per base-year output
0 t CO₂e
Reported vs. true emissions this month
0.0 %
Metered activity data
90 %
Spend with supplier-specific data
0 %
Money spent
0 k€
Production vs. base year
100 %
Trend
Crisis scenarios
Level 1 · First inventory, patchy data
The company is preparing its first greenhouse-gas inventory; this year becomes the base year. Only about 30 % of fuel and electricity use is metered — the rest is estimated from partial records, and some sites and invoices are surely missing. Purchased goods are counted with spend-based averages. The base year is published after month 12, and the first limited-assurance engagement looks at it in month 15. Avoid a restatement and bring the uncertainty down, within a 1.2 M€ budget.
No base-year restatement
Inventory uncertainty ≤ ±15 % at month 24
Spending within budget
Level 2 · Growth years on a 1.5 °C pathway
The base year has just been published: about 22 kt CO₂e, half of it gas-fired process heat. All electricity is already covered by renewable certificates. A new order book will lift production by 12 % a year for the next two years, and the target is absolute: in two years the rolling twelve months must be 8.4 % below the base year, and cumulative emissions must stay within the pathway's carbon budget. You have 3 M€. A board member suggests buying credits instead.
Rolling 12 months at or below the pathway at month 24
Cumulative emissions within the pathway's carbon budget
Spending within budget
Level 3 · A footprint made by suppliers
An assembler that buys most of what it sells: about 79 % of its published base year (≈ 37 kt CO₂e) is purchased goods, counted with a spend-based average factor; the rest is mostly process gas, a diesel van fleet and electricity. Purchasing grows 6 % a year with sales. The target covers all scopes: in two years the rolling twelve months must sit on the 4.2 %-a-year pathway and cumulative emissions within its budget, with the uncertainty below ±20 %. Budget 4 M€.
Rolling 12 months at or below the pathway at month 24
Cumulative emissions within the pathway's carbon budget
Inventory uncertainty below ±20 %
Spending within budget
Basis — the model behind the numbers
Every relation the simulator uses, with its source. Constants marked as assumptions are illustrative calibrations.
Scope 1 — direct emissions from fuels the company burns: activity × emission factor (IPCC defaults for natural gas and diesel).
Scope 2 is reported twice: location-based with the grid-average factor, market-based with the factors of the electricity actually contracted — zero for certificate-covered power, the residual mix for the rest.
S2_location = MWh × EF_grid; S2_market = MWh × (1 − certificate share) × EF_residual[2][9]Assumption: grid and residual-mix factors, the input–output factor, uncertainty ranges, first-inventory gaps, supplier response, costs, prices and project sizes are illustrative values for a generic company; the accounting rules, IPCC fuel factors, uncertainty propagation and the 4.2 % pathway rate are the published ones.
Scope 3 purchased goods — hybrid method: supplier-specific data where suppliers provide it, spend × an input–output average factor elsewhere.
S3 = Σ engaged spend × supplier intensity + Σ other spend × EEIO factor (0.40 kg/€)[3][4]Assumption: grid and residual-mix factors, the input–output factor, uncertainty ranges, first-inventory gaps, supplier response, costs, prices and project sizes are illustrative values for a generic company; the accounting rules, IPCC fuel factors, uncertainty propagation and the 4.2 % pathway rate are the published ones.
Estimated activity data in a first inventory misses sites and invoices; only the metered share is complete.
reported activity = true × (1 − (1 − q_metered) × gap), gap ~ N(μ, 0.04) per source[1]Assumption: grid and residual-mix factors, the input–output factor, uncertainty ranges, first-inventory gaps, supplier response, costs, prices and project sizes are illustrative values for a generic company; the accounting rules, IPCC fuel factors, uncertainty propagation and the 4.2 % pathway rate are the published ones.
Uncertainties combine as in the IPCC guidelines: in quadrature for a product (activity × factor), weighted by size for a sum of sources.
The base year is recalculated when a better method or an error found by assurance moves it by 5 % or more, so that every year is compared like for like.
recalculate base year if |B_today’s method / B_published − 1| ≥ 5 % (annual close, assurance finding)[1][7]
Absolute-contraction pathway: the allowance falls by 4.2 % of the base year every year. The gap compares the last twelve months with that allowance, the base year and the window both read with today's method (like for like).
allowed(t) = B × (1 − 0.042 · years since base); gap = rolling 12-month gross / allowed − 1 (B and the window both read with today’s method)[8][7]
Targets are measured on gross emissions. Carbon credits are disclosed separately and never counted as reductions.
gross = S1 + S2_market + S3 (targets); net = gross − credits retired (shown apart, never counted)[7][9]
Marginal abatement cost of a project: yearly capital cost minus yearly energy savings, per tonne avoided. A negative value means the project saves money — on paper.
MAC = (capex × CRF(8 %, 15 y) − annual energy savings) / annual t abated; CRF = r(1+r)ⁿ/((1+r)ⁿ − 1)[10][11]Assumption: grid and residual-mix factors, the input–output factor, uncertainty ranges, first-inventory gaps, supplier response, costs, prices and project sizes are illustrative values for a generic company; the accounting rules, IPCC fuel factors, uncertainty propagation and the 4.2 % pathway rate are the published ones.
What each abatement project changes in the activity data.
efficiency −10 % electricity · heat pump −50 % gas → + gas × 0.9 × 277.8 / COP 3 MWh · rooftop PV −100 MWh/mo (±40 % seasonal) · fleet −80 % diesel → + diesel × 277.8 / 3 MWh[1]Assumption: grid and residual-mix factors, the input–output factor, uncertainty ranges, first-inventory gaps, supplier response, costs, prices and project sizes are illustrative values for a generic company; the accounting rules, IPCC fuel factors, uncertainty propagation and the 4.2 % pathway rate are the published ones.
Other operating constants used by the model.
grid 0.35 t/MWh, residual mix 0.42 t/MWh · EEIO 0.40 kg/€ · U: metered 2 %, estimated 20 %, grid 10 %, spend-based 50 %, supplier data 15 % · metering → 95 % metered, τ = 3 mo, 10 k€/mo · supplier programme 4 + 50 × share k€/mo, data τ = 6 mo, targets in place after a further τ = 6 mo, then engaged suppliers cut ≈ N(10 %, 1 %)/yr · certificates 4 €/MWh · credits 15 €/t · capex: efficiency 250 k€ (2 mo), PV 1 000 k€ (6 mo), heat pump 2 000 k€ (9 mo), fleet 800 k€ (8 mo) · prices: electricity 150 €/MWh, gas 40 €/MWh, diesel 1.5 €/L · first-inventory gap of estimated data N(μ, 4 %) per source · assurance re-performs the base year with complete activity data · ±3 % monthly activity noise · gas ±20 % seasonalAssumption: grid and residual-mix factors, the input–output factor, uncertainty ranges, first-inventory gaps, supplier response, costs, prices and project sizes are illustrative values for a generic company; the accounting rules, IPCC fuel factors, uncertainty propagation and the 4.2 % pathway rate are the published ones.
Randomness: a seeded mulberry32 generator; distributions used — uniform, exponential (inverse CDF), normal (Box–Muller), Poisson (Knuth). The seed is shown and shareable.
Capital recovery factor A/P = r(1+r)ⁿ/((1+r)ⁿ − 1) — standard engineering-economics annuity factor (e.g. W. G. Sullivan et al., Engineering Economy) — Pearson, 2019