| 英文摘要: | Perhaps the most ‘dangerous’ aspect of future climate change is the possibility that human activities will push parts of the climate system past tipping points, leading to irreversible impacts1. The likelihood of such large-scale singular events2 is expected to increase with global warming1, 2, 3, but is fundamentally uncertain4. A key question is how should the uncertainty surrounding tipping events1, 5 affect climate policy? We address this using a stochastic integrated assessment model6, based on the widely used deterministic DICE model7. The temperature-dependent likelihood of tipping is calibrated using expert opinions3, which we find to be internally consistent. The irreversible impacts of tipping events are assumed to accumulate steadily over time (rather than instantaneously8, 9, 10, 11), consistent with scientific understanding1, 5. Even with conservative assumptions about the rate and impacts of a stochastic tipping event, today’s optimal carbon tax is increased by ~50%. For a plausibly rapid, high-impact tipping event, today’s optimal carbon tax is increased by >200%. The additional carbon tax to delay climate tipping grows at only about half the rate of the baseline carbon tax. This implies that the effective discount rate for the costs of stochastic climate tipping is much lower than the discount rate7, 12, 13 for deterministic climate damages. Our results support recent suggestions that the costs of carbon emission used to inform policy12, 13 are being underestimated14, 15, 16, and that uncertain future climate damages should be discounted at a low rate17, 18, 19, 20. Integrated assessment models (IAMs) are key tools to assist climate policymaking7, 12, 13, which attempt to capture two-way interactions between climate and society. There is much debate over what discount rate to assume for evaluating future damages due to global temperature rise17, which in turn partly determines how much we should be willing to pay now to avoid or delay those damages. The Stern Review21 followed a prescriptive (and controversial22, 23, 24) approach; based on ethical arguments it assumed a near-zero rate for discounting the utility of future generations, implying a low discount rate for monetized damages of climate change and a high willingness to pay now. In contrast, studies using a descriptive approach7, 12, 13 generally evaluate the costs of climate change using much higher market rates of return as discount rates. Most studies are deterministic, but uncertainty will also affect the rate at which future levels of climate damage are discounted17, 18, 19, 20. Climate tipping points and their impacts are a key source of uncertainty, for several reasons1, 3, 4. First, our knowledge of thresholds, in terms of, for example, regional warming, is imperfect, and the mapping from global temperature rise to regional thresholds is also uncertain. Second, even if we knew a tipping point precisely, stochastic internal variability in the climate system could trigger tipping at a range of times and corresponding global temperatures4. Several IAM approaches to model climate tipping points are fundamentally deterministic8, 9, 14, 25, 26, whereas only a few studies include stochastic climate damages10, 11, 27 (see Supplementary Discussion). In common with deterministic IAMs, they generally assume10, 11 that the impacts of passing a tipping point are felt instantaneously, whereas in reality impacts will accumulate over time at a rate determined by the dynamics of the system that has been tipped1. One recent study27 assumes that tipping instantaneously increases climate sensitivity or weakens carbon sinks, which then causes damages to accumulate at an increased rate; but this is scientifically questionable (see Supplementary Discussion) and leads to increased discounting of future damages27. Here, we examine how a more realistic treatment of stochastic climate tipping points affects the optimal policy choice, including the discount rate to evaluate future damages. Our stochastic integrated assessment model6, DSICE (Fig. 1a), builds on the deterministic Dynamic Integrated Climate and Economy (DICE) model7 (2007 version) as used in the 2010 US federal assessment of the social cost of carbon12. The federal assessment13 and the DICE model28 have since been updated, in ways that tend to increase the estimated social cost of carbon (see Supplementary Methods). Hence the reader should focus on our relative changes in carbon tax due to stochastic climate tipping more than the absolute values.
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