Suchergebnisse

In 2010, the U.S. government adopted its first consistent estimates of the social cost of carbon (SCC) for government-wide use in regulatory cost-benefit analysis. Here, the authors examine a number of limitations of the estimates identified in the U.S. government report and elsewhere and review recent advances that could pave the way for improvements. The authors consider in turn socio-economic scenarios, treatment of physical climate response, damage estimates, ways of incorporating risk aversion, and consistency between SCC estimates and broader climate policy.

In 2010, the U.S. government adopted its first consistent estimates of the social cost of carbon (SCC) for government-wide use in regulatory cost-benefit analysis. Here, we examine a number of the limitations of the estimates identified in the U.S. government report and elsewhere and review recent advances that could pave the way for improvements. We consider in turn socioeconomic scenarios, treatment of physical climate response, damage estimates, ways of incorporating risk aversion, and consistency between SCC estimates and broader climate policy.

Sea‐level rise sits at the frontier of usable climate climate change research, because it involves natural and human systems with long lags, irreversible losses, and deep uncertainty. For example, many of the measures to adapt to sea‐level rise involve infrastructure and land‐use decisions, which can have multigenerational lifetimes and will further influence responses in both natural and human systems. Thus, sea‐level science has increasingly grappled with the implications of (1) deep uncertainty in future climate system projections, particularly of human emissions and ice sheet dynamics; (2) the overlay of slow trends and high‐frequency variability (e.g., tides and storms) that give rise to many of the most relevant impacts; (3) the effects of changing sea level on the physical exposure and vulnerability of ecological and socioeconomic systems; and (4) the challenges of engaging stakeholder communities with the scientific process in a way that genuinely increases the utility of the science for adaptation decision making. Much fundamental climate system research remains to be done, but many of the most critical issues sit at the intersection of natural sciences, social sciences, engineering, decision science, and political economy. Addressing these issues demands a better understanding of the coupled interactions of mean and extreme sea levels, coastal geomorphology, economics, and migration; decision‐first approaches that identify and focus research upon those scientific uncertainties most relevant to concrete adaptation choices; and a political economy that allows usable science to become used science.

Sea level rise will cause spatial shifts in economic activity over the next 200 years. Using a spatially disaggregated, dynamic model of the world economy, this paper estimates the consequences of probabilistic projections of local sea level changes. Under an intermediate scenario of greenhouse gas emissions, permanent flooding is projected to reduce global real GDP by 0.19 percent in present value terms. By the year 2200, a projected 1.46 percent of the population will be displaced. Losses in coastal localities are much larger. When ignoring the dynamic response of investment and migration, the loss in real GDP in 2200 increases from 0.11 percent to 4.5 percent. (JEL E23, F01, Q54, Q56) ; Desmet and Rossi- Hansberg acknowledge the support and hospitality of PERC while doing part of this research. Kopp, Kulp, and Strauss were supported in part by US National Science Foundation grant ICER- 1663807 and by National Aeronautics and Space Administration grant 80NSSC17K0698. Nagy acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Program for Centers of Excellence in R&D (SEV- 2015-0563) and the Juan de la Cierva Grant (FJCI- 2017- 34728); and from the Government of Catalonia, through CERCA and SGR Program (2017-SGR-1393). Oppenheimer acknowledges support from US National Science Foundation Award Number 1520683.