Dynamic Integrated Climate-Economy Research Articles

From a dynamic integrated climate economy (DICE) to a resilience integrated model of climate and economy (RIMCE)

The emergence of cascaded risks we face at the Anthropocene raises new problems and demands for action. Climate change can be seen as the most critical world risk: it reflects the effect of human actions that promote several risks, it potentiates disasters and catastrophes caused by natural phenomena, and it can, ultimately, threaten human survival on the planet. Risks in the Anthropocene are a matter of governance and local actions. A reflexive analysis, in the context of the so-called risk societies, and of resilient local communities to cope with the impact of risks of climate change is carried out. A Resilience Integrated Model of Climate and Economy (RIMCE) is proposed. The model is based on the idea of a social economic view, which involves nation-states that focus on local communities, citizen responsibility, rights, and behavior, materialized through networks of social capital and solidarity. This local cooperation to cope with climate crises, is the motivation for a new tax, the resilience social tax. With this reflection, we hope to contribute to the understanding of the economic ties attached to anthropogenic risks, proposing criteria to change behaviors, and advocating public participation in defining local actions to restore the optimal operating conditions of the Earth System.

Climate uncertainty impacts on optimal mitigation pathways and social cost of carbon

Emissions pathways used in climate policy analysis are often derived from integrated assessment models. However, such emissions pathways do not typically include climate feedbacks on socioeconomic systems and by extension do not consider climate uncertainty in their construction. We use a well-known cost-benefit integrated assessment model, the Dynamic Integrated Climate-Economy (DICE) model, with its climate component replaced by the Finite-amplitude Impulse Response (FaIR) model (v2.1). The climate uncertainty in FaIR is sampled with an ensemble that is consistent with historically observed climate and Intergovernmental Panel on Climate Change (IPCC) assessed ranges of key climate variables such as equilibrium climate sensitivity (ECS). By varying discounting assumptions, three scenarios are produced: a pathway similar to the ‘optimal welfare’ scenario of DICE that has similar warming outcomes to current policies, and pathways that limit warming to ‘well-below’ 2 and 1.5 with a short-term overshoot, aiming to meet Paris Agreement long-term temperature goals. Climate uncertainty alone is responsible for a factor of five variation (5%–95% range) in the social cost of carbon (SCC) in the 1.5 overshoot scenario, with the spread in SCC increasing in relative terms with increasing stringency of climate target. CO2 emissions trajectories resulting from the optimal level of emissions abatement in all pathways are also sensitive to climate uncertainty, with 2050 emissions ranging from −12 to +14 GtCO2 yr−1 in the 1.5 scenario. ECS and the strength of present-day aerosol effective radiative forcing are strong determinants of SCC and mid-century CO2 emissions. This shows that narrowing climate uncertainty leads to more refined estimates for the social cost of carbon and provides more certainty about the optimal rate of emissions abatement. Including climate and climate uncertainty in integrated assessment model derived emissions scenarios would address a key missing feedback in scenario construction.

Social cost of carbon: A revisit from a systems analysis perspective

The social cost of carbon (SCC) is estimated by integrated assessment models (IAMs) and is widely used by government agencies to value climate policy impacts. Although there is an ongoing debate about obtained numerical estimates and related uncertainties, little attention has been paid so far to the SCC calculation method itself. This work attempts to fill the gap by providing the theoretical background and economic interpretation of the SCC calculation approach implemented in the DICE (Dynamic Integrated Climate-Economy) IAM. Our analysis indicates that the present calculation method is unable to reflect the linkages between two key IAM components—complex interconnected systems—climate and economy, both influenced by emission abatement policies. Within the modeling framework of DICE, the presently estimated SCC valuates emissions, which are beyond policy control, against consumption of products, which cannot be produced by the economy. This makes the SCC irrelevant for application in climate-economic policies and, therefore, calls for a replacement by a more appropriate indicator. An apparent SCC alternative, which can be considered for policy formulation, is the direct output of the DICE model, the socially optimal marginal abatement cost (SMAC), which corresponds to technological possibilities at the optimal level of carbon emissions abatement. In policymaking, because of the revealed SCC deficiency, great attention needs to be paid to the use of estimates obtained earlier.

Cost-Risk Analysis Reconsidered—Value of Information on the Climate Sensitivity in the Integrated Assessment Model PRICE

Infeasible solutions or negative expected values of future climate information are undesired problems if climate policies are adopted under Cost-Effectiveness Analysis (CEA) to reach uncertain temperature targets. Cost-Risk Analysis (CRA) was developed to resolve these issues. It allows for a trade-off between expected welfare losses of mitigation and avoided risk of transgressing a climate target with a certain probability of compliance (Safety). Some of the significant contributions of this paper are: (i) It updates the Probabilistic Integrated model of Climate and the Economy (PRICE) as a probabilistic version of the latest version of the Dynamic Integrated Climate-Economy model (DICE) 2016, and it extends the model to run welfare-maximizing decision analytic frameworks readily. (ii) It highlights that the standard method of applying CRA (Old CRA) leads to an extra welfare cost. (iii) It proposes revised instruction on how to use CRA. (iv) It simulates and compares welfare-maximizing decision analytic frameworks on the level of risk, damages, and carbon prices. (v) It measures the value of information using risk-based methods and compares them with the value of information calculated using the damage-based method. (vi) It measures the carbon prices for the CRA scenarios for the first time. The results show that the choice of the disutility function governs the magnitude of the value of information. Using a damage function or Old CRA, the value of information is significantly high for new information arriving between 2020 and 2060. If the New CRA is applied, however, such benefits are negligible.

Investigating Long-Term Commitments to Replace Electricity Generation with SMRs and Estimates of Climate Change Impact Costs Using a Modified VENSIM Dynamic Integrated Climate Economy (DICE) Model

During the last few years, nuclear energy has received great attention due to the increase in climate change awareness. According to the Paris agreement, global temperature is to be kept below 2 °C and preferably below 1.5 °C by 2050. This approach has been substantially confirmed in the recent COP 26 in Glasgow. This research investigates the effects of integrating SMR nuclear power plants (small modular reactors) into the Nordhaus Dynamic Integrated Climate Economy (DICE) model for reducing the CO2 emissions in the atmosphere by substituting all existing fossil-fueled power plants (FPPP). The software is based on the VENSIM dynamic systems modeling platform. Simulations were carried out from the year 2019 to 2100 using 10-year increments. Several scenarios were thus simulated replacing roughly 70,000 FPPPs operating at this time in the world. Simulations indicate a CO2 reduction of approximately 12.63% relative to the initial conditions used and using 87,830 SMR core units of 80 MWe electric each to meet such demand. The DICE model further predicts the cost of climate damage impacting the upper ocean and atmospheric temperatures, and the deep ocean temperature as USD 1.515 trillion (US Dollar; (US) trillion = 1,000,000,000,000 (1 × 1012)) by the end of this century. From a modified section of the model, a cost of USD 1.073 trillion is predicted as the toll on human health costs. This is thus equal to a USD 2.59 trillion loss in the economy.

From a Dynamic Integrated Climate Economy (DICE) to a Resilience Integrated Model of Climate and Economy (RIMCE)

From a Dynamic Integrated Climate Economy (DICE) to a Resilience Integrated Model of Climate and Economy (RIMCE)

Implications of temperature overshoot dynamics for climate and carbon dioxide removal policies in the DICE model

Assessing climate policies that involve temporary overshoot of temperature targets requires an accurate representation of carbon cycle and climate dynamics. Here, we compare temperature overshoot climate policies obtained with the dynamic integrated climate–economy (DICE) integrated assessment model using two different climate-carbon cycle sub-models: first, the original DICE implementation, and second an implementation of the finite amplitude impulse response (FaIR) simple climate model. We analyze in a cost-effectiveness framework the minimum abatement and carbon dioxide removal costs for compliance against a (future) ceiling on temperatures. In our setup, the magnitude of the overshoot is not limited by temperature impacts, but simply by the temperature dynamics such that from a certain compliance date onwards a temperature ceiling cannot be exceeded anymore. We show that the rather sluggish temperature response and underestimation of carbon sinks in the most recent version of DICE implies that the additional future temperature change after a cessation of a given CO2 emission scenario is significantly overestimated compared to the zero emission commitments obtained with FaIR and complex earth system models. However, investigating climate policies which allow for a temporary temperature overshoot, this inertia translates into more than twice as high optimal carbon prices compared to FaIR and consequently in rather strict climate policies. For compliance with the 1.5 °C target from 2100 onward and non-CO2-warming of 0.2 °C, the mean optimal carbon prices in the year 2030 are 173USD/tCO2 and 56USD/tCO2 for DICE and FaIR, respectively. Still, the dynamics towards the target suggest that improved understanding of and accounting for (limited) reversibility of vulnerable Earth system components is required to derive appropriate overshoot climate policies.

Solely economic mitigation strategy suggests upward revision of nationally determined contributions

Solely economic mitigation strategy suggests upward revision of nationally determined contributions

DICE and the Carbon Budget for Ambitious Climate Targets

AbstractThe Dynamic Integrated Climate‐Economy (DICE) model is one of the most influential Integrated Assessment Models available. Its founder Professor William Nordhaus was recently awarded Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel due to his pioneering work on the economics of climate change. In a recent paper in American Economic Journal: Economic Policy, Nordhaus uses the model to conclude that a 2.5°C target is almost out of reach. In this paper, we update DICE 2016 R2 with state‐of‐the‐art models of the carbon cycle, heat uptake into the oceans, and the role of non‐CO2 forcers. We find that the allowable remaining carbon budget (over the period 2015–2100) to meet a 2.5°C target to be 2,360 GtCO2 whereas the estimate obtained using DICE 2016 R2 is about 460 GtCO2. Nordhaus's estimate of the remaining carbon budget for this target is hence five times lower than estimates made by our updated DICE. We also compare our results with estimates by the Intergovernmental Panel on Climate Change (IPCC), and find our results to be in line with the carbon budgets presented in IPCC SR 1.5. We explain the reasons behind the difference between our result and that of Nordhaus and propose that an updated climate module in DICE is warranted.

Adaptive mitigation strategies hedge against extreme climate futures

The United Nations Framework Convention on Climate Change agreed to “strengthen the global response to the threat of climate change, in the context of sustainable development and efforts to eradicate poverty” (UNFCCC 2015). Designing a global mitigation strategy to support this goal poses formidable challenges. For one, there are trade-offs between the economic costs and the environmental benefits of averting climate impacts. Furthermore, the coupled human-Earth systems are subject to deep and dynamic uncertainties. Previous economic analyses typically addressed either the former, introducing multiple objectives, or the latter, making mitigation actions responsive to new information. This paper aims at bridging these two separate strands of literature. We demonstrate how information feedback from observed global temperature changes can jointly improve the economic and environmental performance of mitigation strategies. We focus on strategies that maximize discounted expected utility while also minimizing warming above 2 °C, damage costs, and mitigation costs. Expanding on the Dynamic Integrated Climate-Economy (DICE) model and previous multi-objective efforts, we implement closed-loop control strategies, map the emerging trade-offs and quantify the value of the temperature information feedback under both well-characterized and deep climate uncertainties. Adaptive strategies strongly reduce high regrets, guarding against mitigation overspending for less sensitive climate futures, and excessive warming for more sensitive ones.

Relative Prices and Climate Policy: How the Scarcity of Nonmarket Goods Drives Policy Evaluation

Climate change not only impacts production and market consumption but also the relative scarcity of nonmarket goods, such as environmental amenities. We study fundamental drivers of the resulting relative price changes, their potential magnitude, and their implications for climate policy in Nordhaus’s Dynamic Integrated Climate-Economy (DICE) model, thereby addressing one of its key criticisms. We propose plausible ranges for these relative prices changes based on best available evidence. Our central calibration reveals that accounting for relative prices is equivalent to decreasing pure time preference by 0.6 percentage points and leads to a more than 50 percent higher social cost of carbon. (JEL D61, H43, Q51, Q54, Q58)

Climate–Society Feedback Effects: Be Wary of Unidentified Connections

Feedbacks within the climate–economy system are complex. The research analyzing the relationship between human activities and the climate is considerable, with particular focus on intra-system feedback effects: environmental tipping points, and climate-triggered social tipping points, like migration, to a lesser extent (Kopp et al., 2016; van Ginkel et al., 2018). Due to their cross-disciplinary nature (Guerrero et al., 2018), two-way interactions between the environment and society, whereby movement in either system can trigger inter-system feedbacks (Lade et al., 2013; Yletyinen et al., 2019) as humans respond to a changing environment thereby further changing the environment, have only recently received attention by a growing inter-disciplinary research community. With the aim of improving climate policy and its tools, such as the social cost of carbon, we describe these social–ecological system (SES) feedbacks and place them in the existing taxonomy for tipping points applied by mainstream climate–economy models. Drawing lessons from SES research and related interdisciplinary literature, we discuss the value of and method by which to modify social-cost integrated assessment models (SC-IAMs), like Nordhaus' Dynamic Integrated Climate Economy. As it is critical that climate policy include these risks to the stability of the climate–economy system, we conclude with a research agenda for the identification, quantification, and integration of climate–society feedbacks into SC-IAMs.

WHAT THE FUTURE MIGHT HOLD: DISTRIBUTIONS OF REGIONAL SECTORAL DAMAGES FOR THE UNITED STATES — ESTIMATES AND MAPS IN AN EXHIBITION

The text and associated Supplemental Materials contribute internally consistent and therefore entirely comparable regional, temporal, and sectoral risk profiles to a growing literature on regional economic vulnerability to climate change. A large collection of maps populated with graphs of Monte-Carlo simulation results support a communication device in this regard — a convenient visual that we hope will make comparative results tractable and credible and resource allocation decisions more transparent. Since responding to climate change is a risk-management problem, it is important to note that these results address both sides of the risk calculation. They characterize likelihood distributions along four alternative emissions futures (thereby reflecting the mitigation side context); and they characterize consequences along these transient trajectories (which can thereby inform planning for the iterative adaptation side). Looking across the abundance of sectors that are potentially vulnerable to some of the manifestations of climate change, the maps therefore hold the potential of providing comparative information about the magnitude, timing, and regional location of relative risks. This is exactly the information that planners who work to protect property and public welfare by allocating scarce resources across competing venues need to have at their disposal — information about relative vulnerabilities across time and space and contingent on future emissions and future mitigation. It is also the type of information that integrated assessment researchers need to calibrate and update their modeling efforts — scholars who are exemplified by Professor Nordhaus who created and exercised the Dynamic Integrated Climate-Economy and Regional Integrated Climate-Economy models.

Climate economics support for the UN climate targets

Under the UN Paris Agreement, countries committed to limiting global warming to well below 2 °C and to actively pursue a 1.5 °C limit. Yet, according to the 2018 Economics Nobel laureate William Nordhaus, these targets are economically suboptimal or unattainable and the world community should aim for 3.5 °C in 2100 instead. Here, we show that the UN climate targets may be optimal even in the Dynamic Integrated Climate–Economy (DICE) integrated assessment model, when appropriately updated. Changes to DICE include more accurate calibration of the carbon cycle and energy balance model, and updated climate damage estimates. To determine economically ‘optimal’ climate policy paths, we use the range of expert views on the ethics of intergenerational welfare. When updates from climate science and economics are considered jointly, we find that around three-quarters (or one-third) of expert views on intergenerational welfare translate into economically optimal climate policy paths that are consistent with the 2 °C (or 1.5 °C) target. The economic optimality of limiting global warming to below 2 °C has been questioned. This analysis shows that the 2 °C target is economically optimal in a version of the DICE model that includes updated climate science, climate damage estimates and evidence on social discount rates.

Can you trust a model whose output keeps changing? Interpreting changes in the social cost of carbon produced by the DICE model

The social cost of carbon (SCC) measures the present value of the economic damages caused by emitting one marginal ton of carbon dioxide into the atmosphere. It plays a crucial role in climate policy analysis, where it is used to suggest optimal carbon prices or quantify the benefits of actions that reduce emissions. One prominent framework used to estimate the SCC is the Dynamic Integrated Climate-Economy (DICE) model. As updated versions of DICE have been introduced, its SCC estimates have changed, sometimes by amounts that would appear significant. For example, the SCC in 2020 produced by DICE rose 54% from its 2013R version to its 2016R2 version. We address two important questions. First, what changes to DICE explain this increase in its SCC? Second, how surprising is the magnitude of this increase, relative to the uncertainty present in DICE’s input parameters? We find that changes in scientific parameters and updated initial conditions due to near-term forecasting errors accounted for the largest shares of the SCC increase. The later SCC estimate falls within the 80% probability interval produced using the earlier model with uncertainty. Therefore, the 54% increase should not be considered surprising or dispositive regarding the quality of DICE itself.

Multi-objective optimal control of a simple stochastic climate-economy model

Integrated assessment modelling of climate change aims to provide quantitative solutions to inform international climate policy by employing models where socio-economic and climatic systems are integrated. Among these models, DICE (Dynamic Integrated Climate-Economy), is used to perform cost-benefit analysis that returns as output the optimal emission reduction pathway. The model makes some important assumptions: future socioeconomic and climate system evolution is deterministic and economic damages of climate change are a quadratic function of the atmospheric temperature. In this study, propose a multi-objective stochastic optimal control problem formulation of the DICE model in order to account for stochastic disturbances and to align with physical targets posed by international agreements on climate change mitigation. The solutions are control policies that can handle stochastic disturbances outperforming the static inter-temporal optimization approach traditionally adopted. Moreover, such control policies are able to deal with multiple objectives making explicit the trade-offs between economic and environmental objectives.

Economic Modeling of Climate Risks

Climate change is a subject that has been largely addressed from both macroeconomic and energetic standpoints. Integration of climate variables and natural capital into the traditional economic framework can appear conflicting with the notion of infinitely growing economies exploiting finite resources, which questions the sustainability of neoclassic economic growth models. Moreover, the temporal dimension is of paramount importance and the integration of inter-temporal utility is not a trivial issue. The construction of complex general equilibrium models is a way to model the response of economic systems to shocks. Their use is somewhat limited because of their lack of transparency, computational scaling issues and non-equal attitudes toward uncertainty. If they are properly calibrated to model scenarios of interest, these models can however constitute an additional module for assisting short- and medium-term decisions. The dynamic integrated climate economy (DICE) seminal model of William Nordhaus allows to set an optimal global control trajectory with respect to a set of constraints and assumptions. Similar and more sophisticated macroeconomic models can provide the optimal allocation with respect to long-term constraints. The complexity of the academic literature might have clouded a rather simple question. Will we efficiently reduce the negatives implied by our economic activity or face the consequences? Consequently, the two aspects an investor wishes to assess is to what extent his portfolio contributes to the reduction of social and environmental negatives, and how it contributes to the improvement of global resiliency. The first dimension can be approached with integrated assessment models (IAMs) similar to the DICE, with clear and fair expression of trajectories required from each sector and region. The remaining pitfalls are to set commonly accepted abatement cost curves and to obtain full disclosure of the research and development investment dedicated to climate change and to set issuer-specific deviation from the optimal path. Similarly, commonly accepted accounting techniques are required to meet this goal. Regarding the second dimension and the question of adaptation, there is a lack of behavioral modeling and indicators of the resiliency dimension where huge uncertainty remains and will not be dealt without the consideration of the social dimension.

CLIMATE POLICY WITHOUT INTERTEMPORAL DICTATORSHIP: CHICHILNISKY CRITERION VERSUS CLASSICAL UTILITARIANISM IN DICE

Unlike discounting and the damage function, the social welfare function has not received so much attention in the debate on climate economics. An important challenge has been to combine efficiency and equity considerations in a single social welfare framework. The Chichilnisky criterion is one way to resolve this. We consider its implementation in the climate-economy model Dynamic Integrated Climate-Economy (DICE), and compare results for different damage functions, namely the standard one in DICE and the one proposed by Weitzman implying potential large climate damages at high temperature increases. We calculate optimal climate policy for different parameter settings and compare the results with those under the green golden rule (only final utility matters) and classical utilitarianism (no discounting). Optimal emission abatement trajectories turn out to be very different between standard discounted utilitarianism, classical utilitarianism and Chichilnisky specifications. The results are very sensitive to the damage function, the climate sensitivity parameter and especially the “Chichilnisky weight” given to utility of generations in the far future. We discuss conditions and reasons for preferring either classical utilitarianism or the Chichilnisky criterion, and conclude that a critical factor is the time horizon used in climate policy analysis. Adopting sustainable preferences as formalized by the Chichilnisky criterion in climate policy analysis has the advantage that the very long-term implications of greenhouse gases in the atmosphere on the environment and human welfare are not downplayed.

Impact of population growth and population ethics on climate change mitigation policy

Future population growth is uncertain and matters for climate policy: higher growth entails more emissions and means more people will be vulnerable to climate-related impacts. We show that how future population is valued importantly determines mitigation decisions. Using the Dynamic Integrated Climate-Economy model, we explore two approaches to valuing population: a discounted version of total utilitarianism (TU), which considers total wellbeing and is standard in social cost of carbon dioxide (SCC) models, and of average utilitarianism (AU), which ignores population size and sums only each time period's discounted average wellbeing. Under both approaches, as population increases the SCC increases, but optimal peak temperature decreases. The effect is larger under TU, because it responds to the fact that a larger population means climate change hurts more people: for example, in 2025, assuming the United Nations (UN)-high rather than UN-low population scenario entails an increase in the SCC of 85% under TU vs. 5% under AU. The difference in the SCC between the two population scenarios under TU is comparable to commonly debated decisions regarding time discounting. Additionally, we estimate the avoided mitigation costs implied by plausible reductions in population growth, finding that large near-term savings ($billions annually) occur under TU; savings under AU emerge in the more distant future. These savings are larger than spending shortfalls for human development policies that may lower fertility. Finally, we show that whether lowering population growth entails overall improvements in wellbeing-rather than merely cost savings-again depends on the ethical approach to valuing population.

Evaluation of the ocean ecosystem: Climate change modelling with backstop technologies

This paper discusses the economic impacts of climate change, including those on ecosystems, and whether a new backstop technology should be used under conditions of strict temperature targets. Using the dynamic integrated climate-economy (DICE) model, we developed a new model to calculate the optimal path by considering new backstop technologies, such as CO2 capture and storage (CCS). We identify the effects of parameter changes based on the resulting differences in CO2 leakage and sites, and we analyse the feasibility of CCS. In addition, we focus on ocean acidification and consider the impact on economic activity. As a result, when CCS is assumed to carry a risk of CO2 leakage and acidification is considered to result in a decrease in utility, we find that CCS can only delay the effects of climate change, but its use is necessary to achieve strict targets, such as a 1.5°C limit. This observation suggests that if the target temperature is too tight, we might end up employing a technology that sacrifices the ecosystem too greatly.