The Baseline Scenario: What Would Have Happened Without the Project?
The counterfactual
The baseline scenario represents the continuation of pre-project farming practices, what would have kept happening on this land if no carbon project existed. Carbon credits are calculated as the difference between project performance and baseline. A stronger baseline = more credits (all else equal).
Why Does the Baseline Matter So Much?
The baseline is the single most important number in a carbon credit project. Without it, you have no way to prove that your project actually made a difference. Here is why:
The core formula behind every carbon credit:
Carbon Credit Equation
Carbon Credits Earned
The net emission reductions or removals that can be issued as VCUs, measured in tCO₂e
Baseline Emissions
The greenhouse gases the farm would have released (or carbon it would not have stored) under pre-project practices, in tCO₂e
Project Emissions
The greenhouse gases the farm actually releases after adopting new practices, in tCO₂e
Leakage
Emissions that shifted outside the project boundary as a result of the project activity, in tCO₂e
Uncertainty Deduction
A conservative deduction reflecting measurement and model imprecision, in tCO₂e
In plain language: you only get credits for the improvement over what would have happened anyway. The baseline is the "would have happened anyway" part. Let's break this down:
- Baseline emissions = the greenhouse gases the farm would have released (or the carbon it would not have stored) if the farmer had kept doing what they were doing before. For example: "This farm was ploughing 3 times a year, applying 150 kg N/ha, and burning stubble, producing an estimated 3.2 tCO₂e/ha/yr."
- Project emissions = the greenhouse gases the farm actually releases after adopting new practices. For example: "After switching to no-till and reducing fertilizer, the farm now emits 1.4 tCO₂e/ha/yr."
- The difference = 3.2 - 1.4 = 1.8 tCO₂e/ha/yr. That difference (minus leakage and uncertainty deductions) is what gets issued as carbon credits.
Analogy: Your Electricity Bill
Imagine your electricity bill was 5,000/month before you installed solar panels. After installation, it dropped to 2,000/month. Your "savings" are 3,000/month, but only because you can prove what you were paying before. If you had no old bills to show, you could not prove you saved anything. The baseline is your old electricity bill. Without it, there is no way to calculate the improvement, and no credits can be issued.
Why "a stronger baseline = more credits":
The bigger the gap between the old practices (baseline) and the new practices (project), the more credits you earn. Consider two farms that both switch to no-till:
| Farm | Baseline Emissions | Project Emissions | Difference (Credits) |
|---|---|---|---|
| Farm A, was ploughing heavily, burning stubble, heavy fertilizer | 4.5 tCO₂e/ha/yr | 1.5 tCO₂e/ha/yr | 3.0 tCO₂e/ha/yr |
| Farm B, was already doing reduced tillage and moderate fertilizer | 2.0 tCO₂e/ha/yr | 1.5 tCO₂e/ha/yr | 0.5 tCO₂e/ha/yr |
Both farms end up at the same project emissions, but Farm A earns 6x more credits because its starting point (baseline) was much worse. This is why the baseline must be documented carefully and honestly, it directly determines how many credits you can claim.
How the baseline feeds into credit registration:
- You document the baseline (this lesson) and submit it in the Project Description Document (PDD).
- An independent auditor (VVB) checks whether your baseline data is real, correctly sourced, and conservative.
- At each verification event (every 1-5 years), the auditor compares your actual project emissions against the baseline estimate.
- The difference (after leakage and uncertainty deductions) is submitted to Verra, which issues that many VCUs (Verified Carbon Units), each VCU = 1 tonne of CO₂e.
- Those VCUs are listed on the Verra registry, where corporate buyers purchase and retire them to offset their own emissions.
In short: no baseline - no difference - no credits - no revenue. Everything starts here.
How the Baseline Is Built
Before you can measure improvement, you need to establish a starting point. The baseline asks: "What would this land have done if no one had intervened?"
For each quantification unit, the baseline is constructed by documenting farming practices over the historical look-back period (minimum 3 years, must include at least one complete crop rotation).
Step-by-Step: Building a Schedule of Activities
What Exactly Is the "Schedule of Activities"?
This term comes up repeatedly in VM0042. Think of it as a detailed farming calendar, a month-by-month (or season-by-season) record of everything a farmer does on their land in a typical year. It is the foundation of the entire baseline: without it, you cannot model or measure what emissions would have been.
What the schedule must cover:
Crops & Planting
- What crop is planted and when (e.g., "wheat sown in November")
- Crop varieties used
- Harvest date and what happens to the residue (burned, retained, removed)
- Any fallow periods, and how long the land sits empty
Tillage & Soil Work
- Type of tillage (conventional plough, disc, chisel, or none)
- Number of passes per season
- Depth of tillage (e.g., 15 cm, 25 cm)
- Timing of each pass (e.g., "disc after harvest in April, plough before sowing in October")
Fertilizers & Amendments
- Type of fertilizer (urea, DAP, compost, manure)
- Application rate (kg/ha) and timing
- Whether lime is applied, and how much
- Any organic amendments (compost, biochar, crop residue incorporation)
Water & Livestock
- Irrigation method (flood, drip, rainfed) and schedule
- For rice: flooding duration and drainage events
- If livestock graze the land: stocking rate (heads/ha), grazing pattern, and season
- Manure management (left on field, collected, composted)
Analogy: A Cooking Recipe for the Farm
Imagine you want to know how many calories someone ate last year. You would not just ask "what do you eat?", you would need a week-by-week meal plan: what was eaten, how much, and when. The schedule of activities is exactly that, but for the farm. It tells the model: "In January, the field was fallow. In March, the farmer ploughed at 20 cm depth. In April, 120 kg/ha urea was applied. In May, wheat was sown..." and so on through the year. This level of detail is what allows the biogeochemical model (Approach 1) or the IPCC calculations (Approach 3) to estimate emissions accurately.
Key point: The schedule is built from the look-back period (the 3-5 years before the project started). If the farmer grew rice-wheat in rotation, the schedule captures both seasons. This schedule then repeats on loop for the entire baseline period, the assumption being that without the project, the farmer would have kept doing the same thing year after year.
Sample Schedule: Rice-Wheat Farm in Uttar Pradesh
| Month | Activity | Details |
|---|---|---|
| Jun | Rice transplanting | Paddy flooded, variety: Pusa Basmati 1121 |
| Jun-Oct | Continuous flooding | Field stays waterlogged for ~120 days |
| Jul | Urea top-dress | 60 kg N/ha applied |
| Oct | Rice harvest | Stubble burned on field |
| Nov | Tillage for wheat | 2 passes, disc + cultivator at 15 cm |
| Nov | Wheat sowing + DAP | Variety: HD-2967; 60 kg N/ha as DAP at sowing |
| Jan | Urea top-dress | 60 kg N/ha applied to wheat |
| Apr | Wheat harvest | Straw removed for animal feed |
| May | Fallow + tillage | 1 plough pass at 20 cm before rice season |
This schedule tells the model everything it needs: total N applied (180 kg/ha/yr across both crops), flooding duration (120 days, needed for CH₄ calculation), tillage intensity (3 passes/yr, needed for SOC and fossil fuel calculations), and residue fate (rice stubble burned, wheat straw removed). Without any one of these details, the baseline estimate would be incomplete.
The Data Hierarchy (Box 1 of VM0042)
VM0042 specifies a ranked hierarchy for data used to establish baseline practices. Higher-tier data = less uncertainty = more credits. Auditors will scrutinise which tier was used and may require you to justify why higher-tier data was unavailable:
| Tier | Data Source | Example | Preference |
|---|---|---|---|
| 1 (Best) | Historical management records + documented evidence | Farm management logs, purchase receipts, GPS field records, satellite imagery | Most preferred |
| 2 | Historical management plans with written documentation | Agronomist recommendations, management plan documents | Preferred |
| 3 | Signed attestation from farmer/landowner | Farmer statement supported by evidence from similar nearby fields | Acceptable |
| 4 (Weakest) | Regional/national average statistics | USDA National Agricultural Statistics, government census data (≤20 years old) | Least preferred |
Practical guidance: Working down the tiers
You start at Tier 1 and move down only when higher-tier data is unavailable. The methodology requires a justification for each step down:
- Tier 1 - Tier 2: Acceptable if the farm has no digital records, but there are written agronomist recommendations or management plan documents covering the look-back period. Common in well-documented commercial operations.
- Tier 2 - Tier 3: The farmer provides a signed statutory declaration of their historical practices, supported by at least two corroborating lines of evidence (e.g., neighbouring farm records, dealer records, satellite imagery confirming crop patterns).
- Tier 3 - Tier 4: Only where individual farm data is completely unattainable. Regional averages introduce the most uncertainty, typically requiring larger uncertainty deductions.
A mixed-tier approach is common in practice: Tier 1 for tillage (satellite imagery confirms), Tier 3 for fertilizer rate (farmer attestation), Tier 4 for irrigation (no records available).
Baseline reassessment: The baseline schedule of activities must be reassessed every 10 years (or every 5 years where regional production practices change rapidly or new data becomes available). At reassessment, if the regional common practice has changed significantly, the baseline must be updated, which can reduce or eliminate the project's additionality for new practice changes that became common.
Worked Example: Australia Wheat Baseline
Project: 3,000 ha of wheat farmland in Western Australia switching to no-till + cover crops.
Historical look-back: 5 years (t=-5 to t=-1)
| Data Collected | Tier | Practice Documented |
|---|---|---|
| 5 years of farm management records | Tier 1 | Conventional tillage (3 passes/yr), 120 kg N/ha urea, no cover crops, stubble burned |
| Sentinel-2 satellite imagery | Tier 1 | Bare soil confirmed in fallow periods; crop types verified |
| ABS regional survey data | Tier 3 | Confirms these practices are standard for the region |
Resulting baseline schedule of activities (repeating):
"Annual wheat crop; conventional tillage 3x per year at 15 cm depth; 120 kg N/ha urea in autumn; all stubble burned at harvest."
Project practice: No-till, stubble retention, 80 kg N/ha urea + 20 kg N/ha compost, winter field-pea cover crop.
Conservativeness Principle
When multiple values are available for a baseline parameter, VM0042 requires using the value that results in the lowest expected emissions (or highest SOC) in the baseline scenario. This is conservative, it means you earn fewer credits when uncertain, rather than over-claiming.
Example of conservativeness in practice:
An agronomist recommendation says "apply 100-150 kg N/ha." For the baseline, you use 100 kg N/ha (the lower end), this produces lower N₂O baseline emissions, which means the project's N₂O reduction gets fewer credits. Conservativeness always favours the environment, not the developer.
Key Takeaways
- 1The baseline scenario represents the continuation of pre-project farming practices - credits are the difference between baseline and project performance
- 2The Schedule of Activities is a detailed farming calendar documenting every practice (crops, tillage, fertilizer, water, grazing) during the look-back period
- 3VM0042 uses a 4-tier data hierarchy: historical records (Tier 1, best) down to regional averages (Tier 4, weakest) - justify each step down
- 4The baseline must be reassessed every 10 years (or 5 years if regional practices change rapidly)
- 5The conservativeness principle requires using the value that results in the fewest credits when multiple values are available for a baseline parameter