Conference Agenda

Facing uncertain market and political conditions, industrial firms continue to postpone investments in innovative low-carbon technologies. To address this problem, Carbon Contracts for Difference (CCfDs) have rapidly emerged as a central policy instrument to support industrial decarbonisation. Yet, while the investment incentives of such instruments have been extensively discussed, their welfare and fiscal implications have been heavily understudied. Building on a newly-introduced measure of the cost of public risk, the Cost of Earmarked Funds, this paper investigates on how alternative CCfD implementations allocate risk between firms and the public sector. By framing the industrial firm’s abatement decision as a continuous-time investment problem with multiple sources of risk, I analytically show how these different policy designs reshape the distribution of payoffs for the producers. From a policy perspective, I highlight the trade-offs of each CCfD design, and compare their fiscal and welfare implications. I show that, when considering multiple risk sources, some design architectures can increase public budget exposure while barely having any effect on incentives. Finally, I perform Monte-Carlo simulations to quantitatively analyse current CCfD implementations - and compare the fiscal and welfare impacts of the French and German schemes to those of the standard CCfD design. The results show that, across all scenarios considered, the standard CCfD is never welfare-dominant, highlighting the structural limitations of this widely discussed instrument in the context of industrial decarbonisation. I then conclude on how national design choices reflect different policy priorities, and the outcomes that can be expected from each of these schemes.

Climate stress testing aims to assess firm-level transition risks and how these propagate to financial institutions and the financial system. We examine how climate stress-testing scenario design affects the quantification and comparability of firm-level transition risks under a common transition shock. We develop a stylized model of firm-level investment in low-carbon production capacity under climate policy uncertainty, applied to the European steel sector. Two identical firms differ only in their expectations about future climate policy scenarios, capturing belief-driven early- and late-mover behavior in low-carbon production investment. Firm cash flows depend on investment timing, capital and operating costs, policy support, carbon pricing and demand for primary steel. We show that stress-testing scenarios based primarily on carbon pricing generate broad and overlapping valuation outcomes for the early and late mover firm, reflecting uncertainty in cost pass-through and carbon leakage protection. By contrast, an accelerated policy-mix scenario combining price incentives with binding product bans and demand reductions produces a sharper transition shock, leading to a clear differentiation between early and late movers. Overall, the results show that the use of a sufficiently stringent transition scenario is critical for ensuring the comparability and informativeness of firm-level climate stress tests for financial risk management.

U.S. federal and state biofuel policies interact in complex ways, but existing models insufficiently capture these relationships, limiting analyses and the effective design of new policies. We develop a detailed partial equilibrium model of road and air transportation fuel markets to compare policy options to incentivize the deployment of sustainable aviation fuel (SAF). We find several federal policy options to result in similar emissions reductions and costs but different incidence across jurisdictions, fuel users, and taxpayers. Our lowest cost policy scenario entails replacing current biofuels policies with a modest carbon tax on fossil transportation fuels and a SAF tax credit.

Using a discrete choice experiment, this study examines household preferences for staying versus relocating under future coastal flood risk in metropolitan France. We estimate mixed logit models to analyse how experimentally varied environmental and economic attributes, together with selected self-reported behavioural variables, shape residential relocation preferences. Flood frequency, beach amenities, insurance premiums, local economic conditions, perceived ability to relocate, homeownership, and prior network exposure significantly influence relocation choices. Counterfactual simulations show that insurance premiums provide a strong price signal: changes in insurance premiums generate substantial predicted changes in staying probabilities, even relative to large increases in flood probability. At the same time, high-income households exhibit slightly higher average staying probabilities across all policy scenarios. These findings suggest that the uniform pricing structure of the French CatNat system may weaken location-specific financial incentives and contribute to socioeconomic differences in coastal exposure while spreading risk-related costs more broadly. As a possible reform direction, we propose finer-scale risk-based pricing complemented by means-tested vouchers to preserve affordability.

Integrated assessment models used to inform climate policy typically do not explicitly represent adaptation, based on the assumption that implicit optimal adaptation does not meaningfully interact with mitigation decisions. I revisit this assumption by extending the regional RICE model to include both flow and stock adaptation alongside mitigation. I show that the non-interaction result depends on a specific property of the damage function: adaptation must reduce damages without affecting marginal damages. When this condition holds, explicit adaptation leaves efficient mitigation pathways unchanged regardless of whether adaptation is modelled as a flow or a capital stock. However, when I introduce constraints on total climate investment—reflecting the limited capacity of resource-constrained regions to simultaneously invest in mitigation and adaptation—the distribution of mitigation burdens shifts. Under binding investment constraints and a fixed global carbon budget, mitigation burdens are reallocated from poor to rich regions. This counterintuitive result arises because shifting mitigation to poor regions would crowd out adaptation investment where climate damages are highest. These findings provide an efficiency-based argument for reallocating mitigation burdens towards richer regions, complementing equity-based arguments that have dominated this debate.

Standard IAMs typically use CRRA welfare, where a single curvature parameter jointly governs intergenerational weighting and interregional equity, so comparative statics in ``inequality aversion'' necessarily change discounting. I implement Fehr--Schmidt (FS) inequality aversion in the RICE model to separate directional interregional equity (envy and guilt) from intergenerational weighting, with an optional discounting adjustment that matches CRRA one-period welfare weights. This separation changes predictions. In cooperative solutions, stronger interregional inequality aversion shifts mitigation burdens toward richer regions (under FS, even with Negishi weights). For total abatement, FS can generate a non-monotonic temperature response once rich regions reach full abatement, whereas under CRRA the response to higher curvature is dominated by steeper intergenerational weighting and therefore lower aggregate mitigation. In non-cooperative Nash equilibria, the direction of equity concerns matters: Guilt raises abatement whereas envy lowers it. In a climate coalition game, FS preferences admit a fully stable coalition with substantial participation in one specification, while under CRRA no coalition larger than two regions is internally stable.

Many studies have evaluated climate adaptation as a major strategy to reduce the potential negative economic impacts of climate change induced disasters, focusing primarily on property damage. However, averting stock losses is not the only measure of benefits of adaptation; it is also important to consider reducing the loss of the flow of goods and services stemming from property damage, typically measured in terms of loss of net revenues or GDP. These losses begin when the disaster strikes and continue until damaged property is restored. This paper presents a formal model of optimal economic recovery from disasters for the individual firm as a lower bound estimate of flow losses, blending aspects of climate change economics and the economics of disasters. The paper combines important objectives, background considerations and functional relationships in an optimal control theory approach to analyze how investment in repair and reconstruction (R&R) reduces disaster flow losses in the assessment of the benefits of adaptation measures such as the construction of seawalls, installation of storm shutters, or enhanced vegetation management practices. R&R can be facilitated and enhanced by tactics that accelerate the pace and lower the cost of that investment. We refer to these tactics as dynamic economic resilience, which promotes the efficient allocation of resources over time. We distinguish two classes of tactics: those that hasten the implementation of the investment and those that and lower its cost. Tactics to accomplish the former include speeding up the removal of debris, insurance payments, and applying for public assistance. Tactics to accomplish the latter include shortening the gestation period for reconstruction activity and improving the efficiency of the repair/reconstruction effort. The results yield insights into optimizing behavior, trade-offs, and recovery time-paths with and without dynamic economic resilience.