Counting costs and carbon: A decision model for greener healthcare policy.

BACKGROUND

Healthcare systems contribute approximately 5-8% of national greenhouse gas emissions in high-income countries, yet current evaluation frameworks seldom include environmental externalities. While many nations have committed to decarbonizing healthcare, operational tools that integrate environmental impacts into decision-making remain scarce.

OBJECTIVE

To test the feasibility and implications of a "health-economic-climate" model that incorporates monetized carbon emissions into conventional cost-utility analysis (CUA), using depression care in France as a case study.

METHODS

A semi-Markov model compared three first-line treatments for depression-antidepressants (AD), psychotherapy (PT), and combination therapy (COMB)-from the French healthcare system perspective over five years. Direct medical costs and carbon emissions were estimated using life-cycle data and monetized via a sector-adjusted social cost of carbon (SCC) of €90/tCO₂e. Incremental cost-effectiveness ratios (ICERs) and sensitivity analyses were conducted.

RESULTS

Annual carbon footprints were 104 kgCO₂e (AD), 177 kgCO₂e (PT), and 225 kgCO₂e (COMB). Over five years, PT was the least costly and equally effective compared with AD, while COMB was more effective but more expensive (ICER ≈ €35,000/QALY). Incorporating carbon costs modestly altered results but improved visibility of environmental impacts.

CONCLUSIONS

Integrating monetized carbon emissions within CUA is feasible, methodologically transparent, and compatible with existing HTA structures. This minimal adaptation supports gradual adoption of climate-aligned decision tools without institutional redesign. Future applications should expand to a societal perspective and explore multicriteria frameworks that make trade-offs between health, cost, and environmental goals explicit.