Carbon pricing is the cornerstone of European climate policy, yet its evaluation remains trapped in an average-effects paradigm. The most comprehensive meta-analysis to date, covering 80 ex-post evaluations across 21 carbon pricing schemes, establishes that carbon pricing reduces emissions by 5% to 21% in the first years of operation, but also documents that heterogeneity in outcomes is driven by policy design and context rather than by price levels or instrument type (Döbbeling-Hildebrandt e...

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a rigorous causal ML framework for estimating heterogeneous treatment effects in carbon pricing, with practical policy learning implications.

🏢実務担当者:Offers a method to identify which installations face competitiveness pressure and how to target compensation, useful for corporate strategy and engagement with regulators.

🏛政策担当者:Demonstrates how to design targeted compensation schemes under budget constraints, directly applicable to ETS2 and national climate funds.

  Carbon pricing is the cornerstone of European climate policy, yet its evaluation remains trapped in an average-effects paradigm. The most comprehensive meta-analysis to date, covering 80 ex-post evaluations across 21 carbon pricing schemes, establishes that carbon pricing reduces emissions by 5% to 21% in the first years of operation, but also documents that heterogeneity in outcomes is driven by policy design and context rather than by price levels or instrument type (Döbbeling-Hildebrandt et al., 2024). What the literature cannot answer is the question that matters most to regulators and finance ministries: which installations respond strongly to the carbon price, which suffer acute competitiveness pressure, and how should compensation be targeted rather than distributed by uniform rules? This manuscript develops an Estimation-to-Compensation framework that integrates double/debiased machine learning, spatially aware causal forests, and budget-constrained policy learning to estimate installation-level heterogeneous treatment effects of the EU Emissions Trading System (EU ETS) and to derive targeted compensation policies. The framework is designed for the empirical setting of the roughly 1,900 German installations regulated under the EU ETS, using the public European Union Transaction Log, German Emissions Trading Authority data, and firm-level financial registers, with validation against the Chinese pilot emissions trading experience, the only setting where causal forests have been applied to firm-level carbon market effects to date. A simulation study calibrated to published parameter values illustrates the framework: targeting compensation on estimated conditional average treatment effects and predicted competitiveness vulnerability outperforms uniform allocation rules on both abatement and welfare criteria under a fixed public budget. The framework speaks directly to the deployment of the EUR 100 billion German Climate and Transformation Fund, the EUR 86.7 billion EU Social Climate Fund, and the design of the forthcoming ETS2 for buildings and road transport. Keywords : causal machine learning; heterogeneous treatment effects; EU Emissions Trading System; carbon pricing; policy learning; targeted compensation; climate policy

• Zenodo https://zenodo.org/records/21880722first seen 2026-08-11 04:13:26