Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions

We present how uncertainty and learning are classically studied in economic models. Specifically, we study a standard expected utility model with two sequential decisions, and consider two particular cases of this model to illustrate how uncertainty and learning may affect climate policy. While uncertainty has generally a negative effect on welfare, learning has always a positive, and thus opposite, effect. The effects of both uncertainty and learning on decisions are less clear. Neither uncertainty nor learning can be used as a general argument to increase or reduce emissions today without studying the specific intertemporal costs and benefits. Considering limits in applying the expected utility framework to climate change problems, we then consider a more recent framework with ambiguity-aversion which accounts for situations of imprecise or multiple probability distributions. We discuss both the impact of ambiguity-aversion on decisions and difficulties in applying such a non-expected utility framework to a dynamic context.     

Multiple adaptation types with mitigation: A framework for policy analysis

Effective climate policy will consist of mitigation and adaptation implemented simultaneously in a policy portfolio to reduce the risks of climate change. Previous studies of the tradeoffs between mitigation and adaptation have implicitly framed the problem deterministically, choosing the optimal paths for all time. Because climate change is a long-term problem with significant uncertainties and opportunities to learn and revise, critical tradeoffs between mitigation and adaptation in the near-term have not been considered. We propose a new framework for considering the portfolio of mitigation and adaptation that explicitly treats the problem as a multi-stage decision under uncertainty. In this context, there are additional benefits to near-term investments if they reduce uncertainty and lead to improved future decisions. Two particular features are fundamental to understanding the relevant tradeoffs between mitigation and adaptation: (1) strategy dynamics over time in reducing climate damages, and (2) strategy dynamics under uncertainty and potential for learning. Our framework strengthens the argument for disaggregating adaption as has been proposed by others. We present three stylized classes of adaptation investment types as a conceptual framework: short-lived “flow” spending, committed “stock” investment, and lower capacity “option” stock with the capability of future upgrading. In the context of sequential decision under uncertainty, these subtypes of adaptation have important tradeoffs among them and with mitigation. We argue that given the large policy uncertainty that we face currently, explicitly considering adaptation “option” investments is a valuable component of a near-term policy response that can balance between the flexible flow and committed stock approaches, as it allows for the delay of costly stock investments while at the same time allowing for lower-cost risk management of future damages.     

The long-term policy context for solar radiation management

We examine the potential role of “solar radiation management” or “sunlight reduction methods” (SRM) in limiting future climate change, focusing on the interplay between SRM deployment and mitigation in the context of uncertainty in climate response. We use a straightforward scenario analysis to show that the policy and physical context determine the potential need, amount, and timing of SRM. SRM techniques, along with a substantial emission reduction policy, would be needed to meet stated policy goals, such as limiting climate change to 2 °C above pre-industrial levels, if the climate sensitivity is high. The SRM levels examined by current modeling studies are much higher than the levels required under an assumption of a consistent long-term policy. We introduce a degree-year metric, which quantifies the magnitude of SRM that would be needed to keep global temperatures under a given threshold.     

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.     

The impact of potential abrupt climate changes on near-term policy choices

We investigate an important scientific uncertainty facing climate-change policymakers, namely, the impact of potential abrupt climatic change. We examine sequential decision strategies for abating climate change where near-term policies are viewed as the first of a series of decisions which adapt over the years to improving scientific information. We compare two illustrative near-term (1992–2002) policies - moderate and aggressive emission reductions - followed by a subsequent long-term policy chosen to limit global-mean temperature change to a specified ‘climate target’. We calculate the global-mean surface temperature change using a simple climate/ocean model and simple models of greenhouse-gas concentrations. We alter model parameters to examine the impact of abrupt changes in the sinks of carbon dioxide, the sources of methane, the circulation of the oceans, and the climate sensitivity, ΔT _2x. Although the abrupt changes increase the long-term costs of responding to climate change, they do not significantly affect the comparatively small cost difference between near-term strategies. Except for an abrupt increase in ΔT _2x, the investigated abrupt climate changes do not significantly alter the values of the climate target for which each near-term strategy is preferred. In contrast, innovations that reduce the cost of limiting greenhouse-gas emissions offer the potential for substantial abatement cost savings, regardless of which level of near-term abatement is selected.     

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.     

Conceptualization and implementation of ecosystems-based adaptation

While ecosystems-based adaptation (EbA) has been received with great interest, the requirements for EbA implementation and its precise benefits under future climate change are unclear. Furthermore, EbA’s overlap with environmental, and development policy agendas leads to ambiguity regarding what actions fall under the rubric of EbA. We analyze the projects identified by the UNFCCC as examples of EbA to understand how EbA is conceptualized and promoted by the international community. Addressing climate change is the primary objective of 58% of the EbA projects; the other 42% of projects provide adaptation benefits yet are not primarily driven by climate change. A project’s adaptation targeting is tied to its information needs. Projects whose primary objective is to address climate change are more likely to use detailed climate projections than projects whose primary objective is to address natural hazards, development or ecosystems degradation. A majority of projects do not address uncertainty in future climate change or in adaptation benefits, nor do they track adaptation outcomes. This prevalent lack of monitoring highlights the possibility of a gap between expected and realized adaptation outcomes. It also represents a lost opportunity for improving knowledge of the thresholds of effectiveness of EbA and of factors influencing EbA efficacy.     

IPCC AR6报告解读：未来的全球气候——基于情景的预估和近期信息

依据IPCC第六次评估报告（AR6）第一工作组报告第四章的内容,对未来全球气候的预估结果进行解读。报告对21世纪全球表面气温、降水、大尺度环流和变率模态、冰冻圈和海洋圈的可能变化进行了系统评估,并对2100年以后的气候变化做了合理估计。评估指出全球平均表面气温将在未来20年内达到或超过1.5℃,平均降水也将增加,但随季节和区域而异,同时变率将增大。大尺度环流和变率模态受内部变率影响较大。到21世纪末,北冰洋可能出现无冰期;全球海洋会继续酸化,平均海平面将持续上升,百年内上升幅度依赖不同排放情景,都在2100年后继续升高。在最新的评估中采用多种约束方法,减小了预估不确定性的范围。AR6对于低排放情景以及“小概率高增暖情节”的关注为应对气候变化提供了更多、更完整的信息。综合报告的评估结果指出,未来需要进一步减小区域,特别是季风区气候预估的不确定性,并从科学研究和模式发展两方面加强我国气候预估能力的建设。     

Global-scale climate impact functions: the relationship between climate forcing and impact

Although there is a strong policy interest in the impacts of climate change corresponding to different degrees of climate change, there is so far little consistent empirical evidence of the relationship between climate forcing and impact. This is because the vast majority of impact assessments use emissions-based scenarios with associated socio-economic assumptions, and it is not feasible to infer impacts at other temperature changes by interpolation. This paper presents an assessment of the global-scale impacts of climate change in 2050 corresponding to defined increases in global mean temperature, using spatially-explicit impacts models representing impacts in the water resources, river flooding, coastal, agriculture, ecosystem and built environment sectors. Pattern-scaling is used to construct climate scenarios associated with specific changes in global mean surface temperature, and a relationship between temperature and sea level used to construct sea level rise scenarios. Climate scenarios are constructed from 21 climate models to give an indication of the uncertainty between forcing and response. The analysis shows that there is considerable uncertainty in the impacts associated with a given increase in global mean temperature, due largely to uncertainty in the projected regional change in precipitation. This has important policy implications. There is evidence for some sectors of a non-linear relationship between global mean temperature change and impact, due to the changing relative importance of temperature and precipitation change. In the socio-economic sectors considered here, the relationships are reasonably consistent between socio-economic scenarios if impacts are expressed in proportional terms, but there can be large differences in absolute terms. There are a number of caveats with the approach, including the use of pattern-scaling to construct scenarios, the use of one impacts model per sector, and the sensitivity of the shape of the relationships between forcing and response to the definition of the impact indicator.     

Learning and climate change

Abstract

Learning—i.e. the acquisition of new information that leads to changes in our assessment of uncertainty—plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.     

Invasive species and climate change: an agronomic perspective

In the current review we wish to draw attention to an additional aspect of invasive species and climate change, that of agricultural productivity and food security. We recognize that at present, such a review remains, in part, speculative, and more illustrative than definitive. However, recent events on the global stage, particularly in regard to the number of food riots that occurred during 2008, even at a time of record harvests, have prompted additional interest in those factors, including invasive species, which could, through climatic uncertainty, alter food production. To that end, as agricultural scientists, we wish to begin an initial evaluation of key questions related to food production and climate change including: how vulnerable is agriculture to invasive species?; are current pest management strategies sufficient to control invasive outbreaks in the future?; what are the knowledge gaps?; can we provide initial recommendations for scientists, land managers and policy makers in regard to available resources? Our overall goals are to begin a synthesis of potential impacts on productivity, to identify seminal research areas that can be addressed in future research, and to provide the scientific basis to allow agronomists and land managers to formulate mitigation and adaptation options regarding invasive species and climate change as a means to maintain food security.     

Delayed action and uncertain stabilisation targets. How much will the delay cost?

Despite the growing concern about actual on-going climate change, there is little consensus on the scale and timing of actions needed to stabilise the concentrations of greenhouse gases. Many countries are unwilling to implement mitigation strategies, at least in the short term, and no agreement on an ambitious global stabilisation target has yet been reached. It is thus likely that international climate policies will be characterized by a high degree of uncertainty over the stringency of the climate objective, and that some countries might delay their participation to global action. What additional economic costs will this delay in the adoption of mitigation measures imply? What would the optimal short-term strategy be given the uncertainty surrounding the climate policy to come? Is there a hedging strategy that decision makers can adopt to cope with delayed action and uncertain targets? This paper addresses these questions by quantifying the economic implications of delaying mitigation action, and by computing the optimal abatement strategy in the presence of uncertainty about a global stabilisation target (which will be agreed upon in future climate negotiations). Results point to short-term inaction as the key determinant for the economic costs of ambitious climate policies. They also indicate that there is an effective hedging strategy that could minimise the cost of climate policy uncertainty over the global stabilisation target: a short-term moderate climate policy would be a good strategy to reduce the costs of delayed action and to cope with uncertainty about the outcome of future climate negotiations. By contrast, failing to curb emissions in the short term imposes rapidly increasing additional costs of compliance.     

Integrated Global System Model for Climate Policy Assessment: Feedbacks and Sensitivity Studies

Alternative policies to address global climate change are being debated in many nations and within the United Nations Framework Convention on Climate Change. To help provide objective and comprehensive analyses in support of this process, we have developed a model of the global climate system consisting of coupled sub-models of economic growth and associated emissions, natural fluxes, atmospheric chemistry, climate, and natural terrestrial ecosystems. The framework of this Integrated Global System Model is described and the results of sample runs and a sensitivity analysis are presented. This multi-component model addresses most of the major anthropogenic and natural processes involved in climate change and also is computationally efficient. As such, it can be used effectively to study parametric and structural uncertainty and to analyze the costs and impacts of many policy alternatives. Initial runs of the model have helped to define and quantify a number of feedbacks among the sub-models, and to elucidate the geographical variations in several variables that are relevant to climate science and policy. The effect of changes in climate and atmospheric carbon dioxide levels on the uptake of carbon and emissions of methane and nitrous oxide by land ecosystems is one potentially important feedback which has been identified. The sensitivity analysis has enabled preliminary assessment of the effects of uncertainty in the economic, atmospheric chemistry, and climate sub-models as they influence critical model results such as predictions of temperature, sea level, rainfall, and ecosystem productivity. We conclude that uncertainty regarding economic growth, technological change, deep oceanic circulation, aerosol radiative forcing, and cloud processes are important influences on these outputs.     

To what extent can a long-term temperature target guide near-term climate change commitments?

The question of appropriate timing and stringency of future greenhouse gas (GHG) emission reductions remains an issue in the discussion of mitigation responses to the climate change problem. It has been argued that our near-term action should be guided by a long-term vision for the climate, possibly a long-term temperature target. In this paper, we review proposals for long-term climate targets to avoid ‘dangerous’ climate change. Using probability estimates of climate sensitivity from the literature, we then generate probabilistic emissions scenarios that satisfy temperature targets of 2.0, 2.5, and 3.0°C above pre-industrial levels with no overshoot. Our interest is in the implications of these targets on abatement requirements over the next 50 years. If we allow global industrial GHG emissions to peak in 2025 at 14 GtCeq, and wish to achieve a 2.0°C target with at least 50% certainty, we find that the low sensitivity estimate in the literature suggests our industrial emissions must fall to 9 GtCeq by 2050: equal to the level in 2000. However, the average literature sensitivity estimate suggests the level must be less than 2 GtCeq; and in the high sensitivity case, the target is simply unreachable unless we allow for overshoot. Our results suggest that in light of the uncertainty in our knowledge of the climate sensitivity, a long-term temperature target (such as the 2.0°C target proposed by the European Commission) can provide limited guidance to near-term mitigation requirements.     

Perceptions and responses to climate policy risks among California farmers

This paper considers how farmers perceive and respond to climate change policy risks, and suggests that understanding these risk responses is as important as understanding responses to biophysical climate change impacts. Based on a survey of 162 farmers in California, we test three hypotheses regarding climate policy risk: (1) that perceived climate change risks will have a direct impact on farmer's responses to climate policy risks, (2) that previous climate change experiences will influence farmer's climate change perceptions and climate policy risk responses, and (3) that past experiences with environmental policies will more strongly affect a farmer's climate change beliefs, risks, and climate policy risk responses. Using a structural equation model we find support for all three hypotheses and furthermore show that farmers’ negative past policy experiences do not make them less likely to respond to climate policy risks through participation in a government incentive program. We discuss how future research and climate policies can be structured to garner greater agricultural participation. This work highlights that understanding climate policy risk responses and other social, economic and policy perspectives is a vital component of understanding climate change beliefs, risks and behaviors and should be more thoroughly considered in future work.     

Process, Forcing, and Signal Analysis of Global Mean TemperatureVariations by Means of a Three-Box Energy Balance Model

An analytic solution of an energy balance model (EBM) is presented which can beused as a recursive filter for time series analysis. It is shown that the EBM can reproduce the solution of a coupled atmosphere-ocean general circulation model (AOGCM) experiment. Contrary to the AOGCM, the EBM easily allows for variations in climate sensitivity to satisfy the full range of uncertainty concerned with this parameter. The recursive filter is applied to two natural and two anthropogenic forcing mechanisms which are expressed in terms of heating rate anomaly time series: volcanism, solar activity, greenhouse gases (GHG), and anthropogenic tropospheric aerosols. Thus, we obtain modelled global mean temperature variations as a response to the different forcings and with respect to the uncertainty in the forcing approximations and climate sensitivity. In addition, it is shown that the observed (ENSO-corrected) global mean temperature time series within the period from 1866 to 1997 can be explained by the external forcings which have been considered and an additional white noise forcing. In this way we are able to separate different signals and compare them. As a result, global anthropogenic climate change due to GHG forcing can be detected at a high level of significance without considering spatial patterns of climate change but including natural forcing, which is usually not done. Furthermore, it is shown that solar forcing alone does not lead to significantclimate change, whereas solar and volcanic forcing together lead to a significant natural climate change signal. Anthropogenic climate change due to GHG forcing may partly be masked by anthropogenic aerosol cooling.     

Climate sensitivity estimated from ensemble simulations of glacial climate

The concentration of greenhouse gases (GHGs) in the atmosphere continues to rise, hence estimating the climate system’s sensitivity to changes in GHG concentration is of vital importance. Uncertainty in climate sensitivity is a main source of uncertainty in projections of future climate change. Here we present a new approach for constraining this key uncertainty by combining ensemble simulations of the last glacial maximum (LGM) with paleo-data. For this purpose we used a climate model of intermediate complexity to perform a large set of equilibrium runs for (1) pre-industrial boundary conditions, (2) doubled CO₂ concentrations, and (3) a complete set of glacial forcings (including dust and vegetation changes). Using proxy-data from the LGM at low and high latitudes we constrain the set of realistic model versions and thus climate sensitivity. We show that irrespective of uncertainties in model parameters and feedback strengths, in our model a close link exists between the simulated warming due to a doubling of CO₂, and the cooling obtained for the LGM. Our results agree with recent studies that annual mean data-constraints from present day climate prove to not rule out climate sensitivities above the widely assumed sensitivity range of 1.5–4.5°C (Houghton et al. 2001). Based on our inferred close relationship between past and future temperature evolution, our study suggests that paleo-climatic data can help to reduce uncertainty in future climate projections. Our inferred uncertainty range for climate sensitivity, constrained by paleo-data, is 1.2–4.3°C and thus almost identical to the IPCC estimate. When additionally accounting for potential structural uncertainties inferred from other models the upper limit increases by about 1°C.     

The economics of climate change impacts and policy benefits at city scale: a conceptual framework

Cities are particularly vulnerable to climate change and climate extremes in part because they concentrate many activities, people and wealth in limited areas. As a result they represent an important scale for assessment and understanding of climate change impacts. This paper provides a conceptual and methodological framework for urban economic impact assessment of climate change. The focus of the paper is on model-based analysis of future scenarios, including a framing of uncertainty for these projections, as one valuable input into the decision-making process. The paper highlights the main assessment difficulties, methods and tools, and selected examples across these areas. A number of challenges are unique to climate change impact assessment and others are unique to the problem of working at local scales. The paper also identifies the need for additional research, including the need for more integrated and systemic approaches to address climate change as a part of the urban development challenge as well as the need to assess the economic impacts of climate change and response policy at local scale.     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.