What question did this study set out to answer?

The research aims to operationalize a new decision-making framework, Cost-Benefit-Risk Analysis (CBRA), to better assess economic impacts under climate uncertainty.

March 5, 2026Open Access

Modified cost-risk analysis as a bridge between target-based and trade-off-based decision-making frameworks

Key Points

The research aims to operationalize a new decision-making framework, Cost-Benefit-Risk Analysis (CBRA), to better assess economic impacts under climate uncertainty.
Developed CBRA by integrating a partial damage function with Cost-Risk Analysis (CRA) and a risk function.
Utilized a regionally and sectorally disaggregated Computable General Equilibrium (CGE) model for economic assessments.
Implemented the CBRA in the MIND integrated assessment model with a modified FaIR climate model to evaluate climate sensitivity.
Explicit damages from agriculture, labor productivity, and human health account for 58% of the precautionary risk associated with a 2°C target at a 65% safety level.
When updated with FaIR, CRA and CEA yield nearly identical outcomes, with minimal differences in peak emissions and temperatures.
The framework suggests a shift towards more flexible targets as damage estimates improve, narrowing the gap between cost-benefit models.

Abstract

Abstract Decision-analytic frameworks under climate uncertainty include Cost-Benefit Analysis (CBA), which maximizes welfare by trading mitigation costs against quantified damages; Cost-Effectiveness Analysis (CEA), used here in its probabilistic form, which minimizes the cost of meeting a predefined temperature target via a chance constraint that accounts for uncertainty when damages cannot be reliably estimated; and Cost-Risk Analysis (CRA), which reinterprets adherence to the temperature target within an unconstrained utility-maximization framework by penalizing the probability of target exceedance via a risk function. This study operationalizes Cost-Benefit-Risk Analysis (CBRA), a novel framework that extends CRA by retaining its risk function while adding an explicit, partial damage function, thereby internalizing quantified impacts and leaving residual, unquantified impacts to be represented by the reduced risk term. In our application, the partial global damage function is derived from a forward-looking, regionally and sectorally disaggregated Computable General Equilibrium (CGE) model. This allows us to assess how much of the precautionary risk embedded in climate targets is captured by explicit economic losses. We implement CBRA in the integrated assessment model MIND, coupling a modified version of the FaIR climate model that accounts for climate sensitivity uncertainty. Our findings reveal that explicit damages from agriculture, labor productivity, and human health explain 58% of the risk captured by a 2 ^ C target under a 65% safety level. We demonstrate that when MIND is updated with FaIR, CRA and CEA deliver near-equivalent outcomes (differences of 1. 3% in peak emissions, 0. 40% in peak temperature, and 0. 36% in cumulative emissions), confirming the theoretical equivalence suggested in previous studies. These results suggest that as damage estimates improve, a greater share of precautionary risk is accounted for within cost-benefit models, reducing the need for rigid precautionary targets and narrowing the gap between CBA and CEA. However, uncertainty in climate sensitivity remains a dominant factor, highlighting the need for a more precise understanding of the climate system response to guide policy.

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