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# Forecasting forest-carbon offset outcomes
Multimodal learning, counterfactual baselines, and calibrated uncertainty
## Abstract
Forest-carbon offset forecasts depend on both future carbon storage and the unobserved no-project baseline. We propose a multimodal model combining satellite time series, climate covariates, and project histories to predict annual net carbon benefit. Predictions include calibrated uncertainty intervals and are evaluated on geographically held-out projects.
## 1. Introduction
An accurate carbon-stock forecast alone does not establish additional climate benefit. A project must also be compared with a credible no-project scenario. We separate predictive accuracy from claims about additionality, permanence, and credit eligibility.
Our research question is whether multimodal learning improves out-of-region forecasts while making baseline uncertainty and reversal risk explicit.
## 2. Data and prediction target
The illustrative dataset links annual satellite composites, weather histories, forest inventories, and project metadata. Features are restricted to information available at the forecast date; future monitoring records are reserved for evaluation.
The target is annual net carbon benefit in tonnes of CO2-equivalent per hectare per year: project carbon change minus a modeled no-project baseline, leakage, and reversal losses. The baseline is estimated rather than directly observed.
## 3. Model and evaluation
A temporal encoder summarizes satellite trajectories. A tabular encoder processes climate and project features. Their representations are fused into a quantile prediction head; a separate calibration split adjusts the nominal 90% prediction intervals.
Evaluation holds out entire projects, spatial regions, and later forecast years. Imputation, normalization, feature selection, and interval calibration use training or calibration data only.
Compare against persistence, linear regression, and gradient-boosted trees. Report mean absolute error, interval coverage, and interval width by region and project age. Ablate satellite and climate inputs separately.
## 4. Illustrative results
Figure 1 shows a small simulated held-out sample. Predictions broadly track the reference target, but several projects depart from the identity line. No aggregate accuracy or coverage claim is made from this demonstration.
## 5. Figures and model architecture
The calibration plot and architecture below describe the same prediction task. The reference outcome includes a modeled baseline; it is not a direct measurement of issued or verified credits.
Figure 1. Predicted versus reference annual net carbon benefit on simulated held-out projects. The dashed identity line denotes exact agreement; both axes use tCO2e/ha/year. Reference outcomes include baseline-model assumptions.
| Reference net benefit (tCO2e/ha/year) | Predicted net benefit (tCO2e/ha/year) |
| --- | --- |
| 1 | 1.6 |
| 2 | 1.8 |
| 3 | 3.8 |
| 4 | 3.2 |
| 5 | 5.6 |
| 6 | 5.3 |
| 7 | 7.8 |
| 8 | 6.9 |
| 9 | 9.4 |
| 10 | 8.8 |
| 11 | 10.3 |
```mermaid
flowchart TD
n0["Satellite time series"]
n1["Climate + project data"]
n2["Temporal encoder"]
n3["Tabular encoder"]
n4["Feature fusion"]
n5["Quantile prediction head"]
n6["Held-out interval calibration"]
n7["Net benefit + 90% interval"]
n0 --> n2
n1 --> n3
n2 --> n4
n3 --> n4
n4 --> n5
n5 --> n6
n6 --> n7
```
Figure 2. Multimodal forecasting architecture. Satellite and tabular encoders feed a shared quantile head. Independent calibration adjusts intervals before evaluation on unseen projects and regions.
## 6. Limitations and responsible use
Distribution shifts, sparse inventories, uncertain counterfactuals, and disturbance events can invalidate confident predictions. Evaluate coverage under regional and temporal shifts, and propagate baseline uncertainty rather than treating the target as exact.
This model is intended for research and monitoring support. It does not establish additionality, guarantee permanence, or certify tradable offsets. Independent verification and transparent assumptions remain necessary.
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