Population, uncertainty, and learning in climate change decision analysis

The prospect of learning about various uncertainties relevant to analyses of the climate change issue is important because it can affect estimates of the costs of both damages and mitigation, and it can influence the optimal timing of emissions reductions. Baseline scenarios representing future emissions in the absence of mitigation are one of the major sources of uncertainty. Here we investigate how fast we might realistically expect to learn about the outlook for long-term population growth, as one determinant of future baseline emissions. That is, we estimate how long it might take to substantially revise current estimates of the likelihood of various population size outcomes over the twenty-first century. We draw on recent work showing that, because population growth is path dependent, we can learn about the long term outlook by waiting to observe how population changes in the short term. We then explore the implications of uncertainty and of this learning potential for mitigation costs and for optimal emissions. Using a simple model, we show that uncertainty in population growth translates into an uncertainty in the optimal tax rate of about $200/tC by 2050 for a range of stabilization levels. When learning is taken into account, it allows for mitigation strategies to change in response to new information, leading to a slight reduction in the expected value of mitigation costs, and a substantial reduction in the likelihood of high cost outcomes. We also find that while learning can lead to large revisions over the next few decades in anticipated population growth, this potential does not imply large changes in near-term optimal emissions reductions. Results suggest that further work on the potential for learning about other determinants of emissions could have larger effects on expected mitigation costs.     

Optimal carbon emissions trajectories when damages depend on the rate or level of global warming

In this paper we extend our earlier work with the Carbon Emissions Trajectory Assessment model (CETA) to consider a number of issues relating to the nature of optimal carbon emissions trajectories. We first explore model results when warming costs are associated with the rate of temperature rise, rather than with its level, as in our earlier work. We find that optimal trajectories are more strongly affected by the degree of non-linearity in the warming cost function than by whether the cost function is driven by the warming level or the warming rate. Next we briefly explore the implications of simple uncertainty and risk aversion for optimal emissions trajectories. We find that uncertainty and risk aversion cause optimal emissions trajectories to be somewhat lower, but that the effect is not noticeable in the near term and not dramatic in the long term; the long term effect on the shadow price of carbon is more marked, however. Finally, we experiment with scaling up the warming cost functions until optimal policies are approximately the same as a policy of stabilizing emissions at the 1990 level. Based on the results of this experiment, we conclude that damages would have to be very high to justify anything like a stabilization policy; and even in this case, a policy allowing intertemporal variation in emissions would be better.This paper does not represent the position of EPRI or of its members.     

Trading biomass or GHG emission credits?

Global biomass potentials are considerable but unequally distributed over the world. Countries with Kyoto targets could import biomass to substitute for fossil fuels or invest in bio-energy projects in the country of biomass origin and buy the credits (Clean Development Mechanism (CDM) and Joint Implementation (JI)). This study analyzes which of those options is optimal for transportation fuels and looks for the key variables that influence the result. In two case studies (Mozambique and Brazil), the two trading systems are compared for the amount of credits generated, land-use and associated costs. We found costs of 17–30 euro per ton of carbon for the Brazilian case and economic benefits of 11 to 60 euros per ton of carbon avoided in the Mozambique case. The impact of carbon changes related to direct land-use changes was found to be very significant (both positive and negative) and can currently only be included in emission credit trading, which can largely influence the results. In order to avoid indirect land-use changes (leakage) and consequent GHG emissions, it is crucial that bioenergy crop production is done in balance with improvements of management of agriculture and livestock management. Whatever trading option is economically most attractive depends mainly on the emission baseline in the exporting (emission credit trading) or importing (physical trading) country since both bio- and fossil fuel prices are world market prices in large scale trading systems where transportation costs are low. Physical trading could be preferential since besides the GHG reduction one could also benefit from the energy. It could also generate considerable income sources for exporting countries. This study could contribute to the development of a methodology to deal with bio fuels for transport, in Emission Trading (ET), CDM and the certification of traded bio fuels.     

Hedging the climate sensitivity risks of a temperature target

This paper addresses the problem of meeting a predetermined temperature target cost-effectively under uncertainty and gradual learning on climate sensitivity. The firstorder optimality conditions to a stochastic cost-minimization problem with a temperature constraint are first provided, portraying how marginal costs evolve with an optimal hedging strategy. Then, numerical stochastic scenarios with cost curves fitted to recent climate changemitigation scenarios are presented, illustrating both the range of possible future pathways and the effect of uncertainty to the solution. Last, the effect of several different sets of assumptions on the optimal hedging strategy are analyzed. The results highlight that the hedging of climate sensitivity risk calls for deeper early reductions, although the possibility of different assumptions prevents providing accurate policy guidance.     

Trade-offs between mitigation costs and temperature change

This paper uses the MERGE integrated assessment model to identify the least-cost mitigation strategy for achieving a range of climate policies. Mitigation is measured in terms of GDP foregone. This is not a benefit-cost analysis. No attempt is made to calculate the reduction in damages brought about by a particular policy. Assumptions are varied regarding the availability of energy-producing and energy-using technologies. We find pathways with substantial reductions in temperature change, with the cost of reductions varying significantly, depending on policy and technology assumptions. The set of scenarios elucidates the potential energy system transformation demands that could be placed on society. We find that policy that allows for “overshoot” of a radiative forcing target during the century results in lower costs, but also a higher temperature at the end of the century. We explore the implications of the costs and availability of key mitigation technologies, including carbon capture and storage (CCS), bioenergy, and their combination, known as BECS, as well as nuclear and energy efficiency. The role of “negative emissions” via BECS in particular is examined. Finally, we demonstrate the implications of nationally adopted emissions timetables based on articulated goals as a counterpoint to a global stabilization approach.     

The REMIND-R model: the role of renewables in the low-carbon transformation—first-best vs. second-best worlds

Can near-term public support of renewable energy technologies contain the increase of mitigation costs due to delays of implementing emission caps at the global level? To answer this question we design a set of first and second best scenarios to analyze the impact of early deployment of renewable energy technologies on welfare and emission timing to achieve atmospheric carbon stabilization by 2100. We use the global multiregional energy?Ceconomy?Cclimate hybrid model REMIND-R as a tool for this analysis. An important design feature of the policy scenarios is the timing of climate policy. Immediate climate policy contains the mitigation costs at less than 1% even if the CO₂ concentration target is 410?ppm by 2100. Delayed climate policy increases the costs significantly because the absence of a strong carbon price signal continues the carbon intensive growth path. The additional costs can be decreased by early technology policies supporting renewable energy technologies because emissions grow less, alternative energy technologies are increased in capacity and their costs are reduced through learning by doing. The effects of early technology policy are different in scenarios with immediate carbon pricing. In the case of delayed climate policy, the emission path can be brought closer to the first-best solution, whereas in the case of immediate climate policy additional technology policy would lead to deviations from the optimal emission path. Hence, technology policy in the delayed climate policy case reduces costs, but in the case of immediate climate policy they increase. However, the near-term emission reductions are smaller in the case of delayed climate policies. At the regional level the effects on mitigation costs are heterogeneously distributed. For the USA and Europe early technology policy has a positive welfare effect for immediate and delayed climate policies. In contrast, India looses in both cases. China loses in the case of immediate climate policy, but profits in the delayed case. Early support of renewable energy technologies devalues the stock of emission allowances, and this effect is considerable for delayed climate policies. In combination with the initial allocation rule of contraction and convergence a relatively well-endowed country like India loses and potential importers like the EU gain from early renewable deployment.     

Costs of Reducing Carbon Emissions: An Integrated Modeling Framework Approach

This paper presents an approach to estimating world-regional carbon mitigation cost functions for the years 2020, 2050, and 2100. The approach explicitly includes uncertainty surrounding such carbon reduction costs. It is based on the analysis of global energy-economy-environment scenarios described for the 21st century. We use one baseline scenario and variants thereof to estimate cumulative costs of carbon mitigation as a function of cumulative carbon emission reductions. For our baseline for estimating carbon mitigation cost curves, we use the so-called IIASA F scenario. The F scenario is a high-growth, high-emissions scenario designed specifically to be used as a reference against which to evaluate alternatives. Carbon emissions and energy systems costs in the F scenario are then compared with (reduced) emissions and (higher) costs (including macroeconomic adjustment costs) of alternative scenarios taken from the IIASA scenario database. As a kind of sensitivity analysis of our approach, we also present the results of a scenario involving assumptions on particularly rapid technological progress.     

Renewable and low-carbon energies as mitigation options of climate change for China

This article discusses how renewable and low-carbon energies can serve as mitigation options of climate change in China’s power sector. Our study is based on scenarios developed in PowerPlan, a bottom-up model simulating a countries’ power sector and its emissions. We first adjusted the model to China’s present-day economy and power sector. We then developed different scenarios based on story lines for possible future developments in China. We simulated China’s carbon-based electricity production system of today and possible future transitions towards a low-carbon system relying on renewable and low-carbon energies. In our analysis, we compare the business-as-usual scenarios with more sustainable energy scenarios. We found that by increasing the share of renewable and nuclear energies to different levels, between 17% and 57% of all CO₂ emissions from the power sector could be avoided by 2030 compared to the business-as-usual scenario. We also found that electricity generation costs increase when more sustainable power plants are installed. As a conclusion, China has two options: choosing for high climate change mitigation and high costs or choosing for moderate climate change mitigation and moderate costs. In case high climate change mitigation will be chosen, development assistance is likely to be needed to cover the costs.     

Sensitivity Study of Optimal CO2 Emission Paths Using a Simplified Structural Integrated Assessment Model (SIAM)

A structurally highly simplified, globally integrated coupled climate-economic costs model SIAM (Structural Integrated Assessment Model) is used to compute optimal paths of global CO₂ emissions that minimize the net sum of climate damage and mitigation costs. The model is used to study the sensitivity of the computed optimal emission paths with respect to various critical input assumptions. The climate module is represented by a linearized impulse-response model calibrated against a coupled ocean-atmosphere general circulation climate model and a three-dimensional global carbon-cycle model. The cost terms are represented by strongly simplified expressions designed for maximal transparency with respect to sensitive input assumptions. These include the discount rates for mitigation and damage costs, the inertia of the socio-economic system, and the dependence of climate damages on the change in temperature and the rate of change of temperature. Different assumptions regarding these parameters are believed to be the cause of the marked divergences of existing cost-benefit analyses based on more sophisticated economic models. The long memory of the climate system implies that very long time horizons of several hundred years need to be considered to optimize CO₂ emissions on time scales relevant for a policy of sustainable development. Cost-benefit analyses over shorter time scales of a century or two can lead to dangerous underestimates of the long term climatic impact of increasing greenhouse-gas emissions. To avert a major long term global warming, CO₂ emissions need to be reduced ultimately to very low levels. However, the draw-down can be realized as a gradual transition process over many decades and even centuries. This should nevertheless not be interpreted as providing a time cushion for inaction: the transition becomes more costly the longer the necessary mitigation policies are delayed. However, the long time horizon provides adequate flexibility for later adjustments. Short term energy conservation alone is insufficient and can be viewed only as a useful measure in support of the necessary long term transition to carbon-free energy technologies. For standard climate damage cost expressions, optimal emission paths limiting long term global warming to acceptable sustainable development levels are recovered only if climate damage costs are not significantly discounted. Discounting of climate damages at normal economic rates yields emission paths that are only weakly reduced relative to business as usual scenarios, resulting in high global warming levels that are incompatible with the generally accepted requirements of sustainable development. The solutions are nevertheless logically consistent with the underlying discounting assumption, namely that the occurrence of global warming damages in the distant future as a result of present human activities is of negligible concern today. It follows that a commitment to long term sustainable development, if it in fact exists, should be expressed by an intertemporal relation for the value of the earth's future climate which does not degrade significantly over the time horizon relevant for climate change. Since the future climate is a common assett whose value cannot be determined on the market, the appropriate discount rate for future climate damages should be determined by an assessment of the public willingness to pay today for the mitigation of future climate change. To translate our general conclusions into quantitative cost estimates required by decision makers, the present exploratory study needs to be extended using more detailed disaggregated climate damage and mitigation cost estimates and more realistic socio-economic models, including multi-actor interactions, inherent variability, the role of uncertainty and adaptive control strategies.     

Carbon dioxide sequestration: how much and when?

Carbon dioxide (CO₂) sequestration has been proposed as a key component in technological portfolios for managing anthropogenic climate change, since it may provide a faster and cheaper route to significant reductions in atmospheric CO₂ concentrations than abating CO₂ production. However, CO₂ sequestration is not a perfect substitute for CO₂ abatement because CO₂ may leak back into the atmosphere (thus imposing future climate change impacts) and because CO₂ sequestration requires energy (thus producing more CO₂ and depleting fossil fuel resources earlier). Here we use analytical and numerical models to assess the economic efficiency of CO₂ sequestration and analyze the optimal timing and extent of CO₂ sequestration. The economic efficiency factor of CO₂ sequestration can be expressed as the ratio of the marginal net benefits of sequestering CO₂ and avoiding CO₂ emissions. We derive an analytical solution for this efficiency factor for a simplified case in which we account for CO₂ leakage, discounting, the additional fossil fuel requirement of CO₂ sequestration, and the growth rate of carbon taxes. In this analytical model, the economic efficiency of CO₂ sequestration decreases as the CO₂ tax growth rate, leakage rates and energy requirements for CO₂ sequestration increase. Increasing discount rates increases the economic efficiency factor. In this simple model, short-term sequestration methods, such as afforestation, can even have negative economic efficiencies. We use a more realistic integrated-assessment model to additionally account for potentially important effects such as learning-by-doing and socio-economic inertia on optimal strategies. We measure the economic efficiency of CO₂ sequestration by the ratio of the marginal costs of CO₂ sequestration and CO₂ abatement along optimal trajectories. We show that the positive impacts of investments in CO₂ sequestration through the reduction of future marginal CO₂ sequestration costs and the alleviation of future inertia constraints can initially exceed the marginal sequestration costs. As a result, the economic efficiencies of CO₂ sequestration can exceed 100% and an optimal strategy will subsidize CO₂ sequestration that is initially more expensive than CO₂ abatement. The potential economic value of a feasible and acceptable CO₂ sequestration technology is equivalent – in the adopted utilitarian model – to a one-time investment of several percent of present gross world product. It is optimal in the chosen economic framework to sequester substantial CO₂ quantities into reservoirs with small or zero leakage, given published estimates of marginal costs and climate change impacts. The optimal CO₂ trajectories in the case of sequestration from air can approach the pre-industrial level, constituting geoengineering. Our analysis is silent on important questions (e.g., the effects of model and parametric uncertainty, the potential learning about these uncertainties, or ethical dimension of such geoengineering strategies), which need to be addressed before our findings can be translated into policy-relevant recommendations.     

The importance of health co-benefits in macroeconomic assessments of UK Greenhouse Gas emission reduction strategies

We employ a single-country dynamically-recursive Computable General Equilibrium model to make health-focussed macroeconomic assessments of three contingent UK Greenhouse Gas (GHG) mitigation strategies, designed to achieve 2030 emission targets as suggested by the UK Committee on Climate Change. In contrast to previous assessment studies, our main focus is on health co-benefits additional to those from reduced local air pollution. We employ a conservative cost-effectiveness methodology with a zero net cost threshold. Our urban transport strategy (with cleaner vehicles and increased active travel) brings important health co-benefits and is likely to be strongly cost-effective; our food and agriculture strategy (based on abatement technologies and reduction in livestock production) brings worthwhile health co-benefits, but is unlikely to eliminate net costs unless new technological measures are included; our household energy efficiency strategy is likely to breakeven only over the long term after the investment programme has ceased (beyond our 20 year time horizon). We conclude that UK policy makers will, most likely, have to adopt elements which involve initial net societal costs in order to achieve future emission targets and longer-term benefits from GHG reduction. Cost-effectiveness of GHG strategies is likely to require technological mitigation interventions and/or demand-constraining interventions with important health co-benefits and other efficiency-enhancing policies that promote internalization of externalities. Health co-benefits can play a crucial role in bringing down net costs, but our results also suggest the need for adopting holistic assessment methodologies which give proper consideration to welfare-improving health co-benefits with potentially negative economic repercussions (such as increased longevity).     

AD-DICE: an implementation of adaptation in the DICE model

Integrated Assessment Models (IAMS) have helped us over the past decade to understand the interactions between the environment and the economy in the context of climate change. Although it has also long been recognized that adaptation is a powerful and necessary tool to combat the adverse effects of climate change, most IAMS have not explicitly included the option of adaptation in combating climate change. This paper adds to the IAM and climate change literature by explicitly including adaptation in an IAM, thereby making the trade-offs between adaptation and mitigation visible. Specifically, a theoretical framework is created and used to implement adaptation as a decision variable into the DICE model. We use our new AD-DICE model to derive the adaptation cost functions implicit in the DICE model. In our set-up, adaptation and mitigation decisions are separable and AD-DICE can mimic DICE when adaptation is optimal. We find that our specification of the adaptation costs is robust with respect to the mitigation policy scenarios and parameter values. Our numerical results show that adaptation is a powerful option to combat climate change, as it reduces most of the potential costs of climate change in earlier periods, while mitigation does so in later periods.     

How harmful are restrictions on adapting to climate change?

The dominant assumption in economic models of climate policy remains that adaptation will be implemented in an optimal manner. There are, however, several reasons why optimal levels of adaptation may not be attainable. This paper investigates the effects of suboptimal levels of adaptation caused by different types of adaptation restrictions, on the composition and level of climate change costs and on welfare. We find that especially restrictions to the effectiveness of adaptation at more extreme levels of climate change can be very harmful. Furthermore we show that the potential of mitigation to offset suboptimal adaptation varies from being essential in case adaptation becomes ineffective at higher temperature increases, to being largely ineffective in case of short-term inaction. However, in all cases the short-term recommendation is to increase mitigation levels slightly above what is normally recommended, and to keep mitigation policies flexible enough to be able to respond when adaptation restrictions become more prominent. It is clear that by reducing adaptation restrictions, in combination with adjusting the optimal level of mitigation may keep the costs of adaptation restrictions limited, and thus generally it is very harmful to ignore existing restrictions on adaptation when devising (efficient) climate policies.     

Disconcerting learning on climate sensitivity and the uncertain future of uncertainty

How will our estimates of climate uncertainty evolve in the coming years, as new learning is acquired and climate research makes further progress? As a tentative contribution to this question, we argue here that the future path of climate uncertainty may itself be quite uncertain, and that our uncertainty is actually prone to increase even though we learn more about the climate system. We term disconcerting learning this somewhat counter-intuitive process in which improved knowledge generates higher uncertainty. After recalling some definitions, this concept is connected with the related concept of negative learning that was introduced earlier by Oppenheimer et al. (Clim Change 89:155–172, 2008). We illustrate disconcerting learning on several real-life examples and characterize mathematically certain general conditions for its occurrence. We show next that these conditions are met in the current state of our knowledge on climate sensitivity, and illustrate this situation based on an energy balance model of climate. We finally discuss the implications of these results on the development of adaptation and mitigation policy.     

A comparison of a GCM response to historical anthropogenic land cover change and model sensitivity to uncertainty in present-day land cover representations

This study assesses the sensitivity of the fully coupled NCAR-DOE PCM to three different representations of present-day land cover, based on IPCC SRES land cover information. We conclude that there is significant model sensitivity to current land cover characterization, with an observed average global temperature range of 0.21 K between the simulations. Much larger contrasts (up to 5 K) are found on the regional scale; however, these changes are largely offsetting on the global scale. These results show that significant biases can be introduced when outside data sources are used to conduct anthropogenic land cover change experiments in GCMs that have been calibrated to their own representation of present-day land cover. We conclude that hybrid systems that combine the natural vegetation from the native GCM datasets combined with human land cover information from other sources are best for simulating such impacts. We also performed a prehuman simulation, which had a 0.39 K ~higher average global temperature and, perhaps of greater importance, temperature changes regionally of about 2 K. In this study, the larger regional changes coincide with large-scale agricultural areas. The initial cooling from energy balance changes appear to create feedbacks that intensify mid-latitude circulation features and weaken the summer monsoon circulation over Asia, leading to further cooling. From these results, we conclude that land cover change plays a significant role in anthropogenically forced climate change. Because these changes coincide with regions of the highest human population this climate impact could have a disproportionate impact on human systems. Therefore, it is important that land cover change be included in past and future climate change simulations.     

Evidence of Organized Large Eddies by Ground-Based Doppler Lidar, Sonic Anemometer and Sodar

In an experiment investigating the planetary boundary layer (PBL) wind and temperature fields, and PBL inversion height recorded by various instruments, the results reveal the presence of organized large eddies (OLE) or rolls. The measurements by lidars, anemometers, soundings and sodar gave an overview of the characteristics of the rolls and sources of energy production that maintain them. The experimental results obtained on two consecutive days are compared to model outputs. The agreement is excellent, showing that thermal stratification and wind shear are important factors in the structure and dynamics of OLE. A heterodyne Doppler lidar (HDL) is shown to be a useful tool in the study of OLE.     

全球海洋资料同化系统ZFL_GODAS的研制和初步评估试验

本研究发展了一个全球海洋资料同化系统ZFL_GODAS。该系统是一个短期气候数值预测业务系统的子系统,为短期气候预测海气耦合模式提供全球海洋初始场。系统能够同化的观测资料包括卫星高度计资料、卫星海表温度(SST)资料,以及Argo、XBT、TAO等各种不同来源的现场温盐廓线资料。系统使用的海洋模式为中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室开发的气候系统海洋模式LICOM1.0,同化方案为集合最优插值(EnOI)方案。系统使用一个由海洋模式自由积分得到的静态样本来估计背景场误差协方差。这样的基于集合样本的背景场误差协方差具有多变量协变、各向异性的特征,且能反映海洋物理过程固有的空间尺度特征。针对EnOI同化程序的特点,开发了一套特色鲜明、负载均衡、高效的并行化同化程序。本文通过与不同类型观测资料的比较,对同化系统的性能进行了评估。通过比较海表温度和海面高度的年际变率,海表温度异常随时间的变化,SST、海面高度异常(SLA)以及次表层温盐预报产品的均方根误差,5年平均温度偏差廓线、平均盐度廓线、平均纬向流速廓线等发现:系统工作正常、同化效果较好;经过同化以后,各变量都更加接近观测,误差更小,与观测场的相关性更好,可以为短期气候预测系统提供较好的海洋初始场,也可以为物理海洋学的研究提供有效的再分析资料。     

The safe carbon budget

Cumulative emissions drive peak global warming and determine the carbon budget needed to keep temperature below 2 or 1.5 °C. This safe carbon budget is low if uncertainty about the transient climate response is high and risk tolerance (willingness to accept risk of overshooting the temperature target) is low. Together with energy costs, this budget determines the optimal carbon price and how quickly fossil fuel is abated and replaced by renewable energy. This price is the sum of the present discounted value of all future losses in aggregate production due to emitting one ton of carbon today plus the cost of peak warming that rises over time to reflect the increasing scarcity of carbon as temperature approaches its upper limit. If policy makers ignore production losses, the carbon price rises more rapidly. If they ignore the peak temperature constraint, the carbon price rises less rapidly. The alternative of adjusting damages upwards to factor in the peak warming constraint leads initially to a higher carbon price which rises less rapidly.     

Assessment of climate change adaptation costs for the U.S. road network

The U.S. road network is one of the nation's most important capital assets and is vital to the functioning of the U.S. economy. Maintaining this asset involves approximately $134 billion of government funds annually from Federal, State, and local agencies. Climate change may represent a risk or an opportunity to this network, as changes in climate stress will affect the resources necessary for both road maintenance and construction projects. This paper develops an approach for estimating climate-related changes in road maintenance and construction costs such that the current level of service provided by roads is maintained over time. We estimate these costs under a baseline scenario in which annual mean global temperature increases by 1.5 °C in 2050 relative to the historical average and a mitigation scenario under which this increase in mean temperature is limited to 1.0 °C. Depending on the nature of the changes in climate that occur in a given area, our analysis suggests that climate change may lead to a reduction in road maintenance and/or construction costs or an increase in costs. Overall, however, our analysis shows that climate change, if unchecked, will increase the annual costs of keeping paved and unpaved roads in service by $785 million in present value terms by 2050. When not discounted, this figure increases to $2.8 billion. Policies to reduce greenhouse gas emissions are estimated to reduce these costs by approximately $280 million in present value terms and by $885 million when not discounted. These costs vary substantially by region and time period, information that should be important for transportation planners at the national, state, and local levels.     

Limitations of integrated assessment models of climate change

The integrated assessment models (IAMs) that economists use to analyze the expected costs and benefits of climate policies frequently suggest that the “optimal” policy is to go slowly and to do relatively little in the near term to reduce greenhouse gas emissions. We trace this finding to the contestable assumptions and limitations of IAMs. For example, they typically discount future impacts from climate change at relatively high rates. This practice may be appropriate for short-term financial decisions but its extension to intergenerational environmental issues rests on several empirically and philosophically controversial hypotheses. IAMs also assign monetary values to the benefits of climate mitigation on the basis of incomplete information and sometimes speculative judgments concerning the monetary worth of human lives and ecosystems, while downplaying scientific uncertainty about the extent of expected damages. In addition, IAMs may exaggerate mitigation costs by failing to reflect the socially determined, path-dependent nature of technical change and ignoring the potential savings from reduced energy utilization and other opportunities for innovation. A better approach to climate policy, drawing on recent research on the economics of uncertainty, would reframe the problem as buying insurance against catastrophic, low-probability events. Policy decisions should be based on a judgment concerning the maximum tolerable increase in temperature and/or carbon dioxide levels given the state of scientific understanding. The appropriate role for economists would then be to determine the least-cost global strategy to achieve that target. While this remains a demanding and complex problem, it is far more tractable and epistemically defensible than the cost-benefit comparisons attempted by most IAMs.