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@ARTICLE{Nguyen2026CO2TaxationML,
    author = {Nguyen, Huy-Duc and Wannier, David and Genoud, Dominique},
  keywords = {CO₂ taxation, Delphi method, Environmental taxation, Internal combustion vehicles, machine learning, Real-world consumption, sustainable mobility},
     title = {Optimization of CO₂ Emission Taxation in Switzerland Using Machine Learning},
   journal = {ISEC 2026 – 4th International Sustainable Energy Conference},
      year = {2026},
     pages = {1–15},
      note = {DOI placeholder: will be filled in by TIB Open Publishing. Published date placeholder: will be filled in by TIB Open Publishing. Corresponding authors: Huy-Duc Nguyen, David Wannier, Dominique Genoud.},
  abstract = {This research aims to improve the taxation of CO₂ emissions from internal combustion
engine passenger cars using a Machine Learning (ML) approach. The current tax system
in Switzerland relies heavily on WLTP type-approval values, which often differ from real-world
fuel consumption and emissions, limiting its effectiveness.
Using European OBFCM data and the citiwatts.eu dataset [1], the study develops predictive
models to estimate actual fuel consumption and CO₂ emissions based on technical vehicle
characteristics such as mass, engine power, and displacement. Ensemble algorithms, including
LightGBM and Random Forest, demonstrate strong predictive accuracy, showing that ML
can reliably approximate real driving emissions.
To validate the model, a qualitative assessment was conducted through a Delphi study with
mobility experts, surveys of Swiss corporate fleet managers, and interviews with politicians
from multiple parties. This ensured that the findings are grounded in practical, political, and
economic realities.
The results indicate the need to revise local and national vehicle taxation to better reflect real
emissions and to more rigorously assess plug-in hybrid vehicles, whose environmental impact
is often underestimated. The research proposes a differentiated, science-based tax framework
aligned with actual vehicle performance, improving both environmental effectiveness and fiscal
fairness.
It also recommends enhancing consumer awareness and replacing the A–G energy labeling
scale with more precise, continuous indicators.}
}