Do Carbon Price Forecasts Improve Compliance Procurement? Evidence from European Union Allowances

2026-07-25

AI Summary

This study addresses how firms can optimize compliance procurement decisions under the EU Emissions Trading System by leveraging carbon price forecasts. Using daily data from 2019 to 2025, the authors develop direct multi-step forecasting models for horizons of one to five days and embed them within a dynamic procurement optimization framework that accounts for execution costs, market impact, capacity constraints, and tail risk. For the first time, they demonstrate—under a fixed information set and predefined trading rules—that short-term predictability in carbon prices translates into tangible cost savings, with gains arising from intra-horizon reallocation of purchases rather than directional timing. The proposed approach significantly outperforms 14 benchmarks across all horizons, achieving an out-of-sample R² of 15.5% for the five-day forecast; for a 100,000 EUA order, it reduces average procurement costs by 8.5–38.5 basis points compared to uniform execution.

Abstract

Firms covered by emissions trading systems need forecasts not only to value allowances, but also to decide when to buy them. This paper asks whether European Union Allowance (EUA) prices contain short-horizon predictability that survives a forecast-origin information design and improves simulated compliance procurement. Using daily data from 2019 to 2025, we produce direct forecasts for one to five trading days ahead. All predictors are observable at the forecast origin, and calibration and model-selection rules are fixed before the final holdout. The released forecast has the lowest point-estimate RMSE at every horizon among fourteen benchmarks, with the strongest loss-difference evidence at horizons three and four. Relative to a random walk, out-of-sample R^2 rises from 1.2% at one day to 15.5% at five days. We then use the forecast path in a constrained procurement problem with execution costs, market impact, capacity limits, and tail risk; sensitivity exercises add demand uncertainty. For a fixed 100,000-EUA order, optimized schedules lower average realized costs by 8.5 to 38.5 basis points relative to uniform execution across horizons h=2 to h=5. The gains come from reallocating purchases within a fixed window, not from reliable next-day directional timing.

Problem

• carbon price forecasting
• compliance procurement
• emissions trading system
• EUA
• short-horizon predictability

Innovation

• carbon price forecasting
• compliance procurement
• emissions trading system
• out-of-sample predictability
• optimal execution