Assessment Models Of Climate Change Research Articles

Climate change and economic prosperity: Evidence from a flexible damage function

The damage function used to assess the economic impact of secular changes in temperature is one of the most speculative components of integrated assessment models of climate change. Existing work informing this debate is based on pooled empirical models incorporating limited non-linearity and giving little regard to dynamics. We use aggregate and agricultural data for 151 countries over the past six decades to estimate dynamic heterogeneous models which (a) allow the weather-output nexus to differ freely across countries, (b) help distinguish short-run from long-run effects, and (c) account for unobserved time-varying heterogeneity. Overall, we find that, in low-income or high-temperature countries, a permanent 1 °C rise in temperature is associated with a fall in income per capita of about 1.3% in the short-run and 8.5% in the long run. These long-run effects are substantially larger than those commonly suggested in the literature.

An assessment of different transition pathways to a green global economy

Transitioning to a green economy is urgently needed to achieve the climate targets by the end of this century. Here we investigate alternative pathways for the transition of the global economy from one dominated by the fossil-fueled (brown) sector to one dominated by the low-emission (green) sector. We modify a well-known integrated assessment model of climate change and economy to consider three transition pathways: Linear, Delayed, and Fast. Our results indicate that the main burden of the green transition lies on capital formation, accumulation, and transfers facilitated by full R&D investment in the green sector’s productivity. We also find that transition pathways rely on different mechanisms to achieve their targets. The Delayed pathway relies on the combination of higher green capital investment and an increase in green capital productivity through R&D investment, while the Fast pathway requires substantial transfers of capital from the brown sector coupled with high abatement efforts.

Validity and Validation of Computer Simulations—A Methodological Inquiry with Application to Integrated Assessment Models

Our purpose is to advance a reasoned perspective on the scientific validity of computer simulation, using an example—integrated assessment modeling of climate change and its projected impacts—that is itself of great and urgent interest to policy in the real world. The spirited and continuing debate on the scientific status of integrated assessment models (IAMs) of global climate change has been conducted mostly among climate change modelers and users seeking guidance for climate policy. However, it raises a number and variety of issues that have been addressed, with various degrees of success, in other literature. The literature on methodology of simulation was mostly skeptical at the outset but has become more nuanced, casting light on some key issues relating to the validity and evidentiary standing of climate change IAMs (CC-IAMs). We argue that the goal of validation is credence, i.e., confidence or justified belief in model projections, and that validation is a matter of degree: (perfect) validity is best viewed as aspirational and, other things equal, it makes sense to seek more rather than less validation. We offer several conclusions. The literature on computer simulation has become less skeptical and more inclined to recognize that simulations are capable of providing evidence, albeit a different kind of evidence than, say, observation and experiments. CC-IAMs model an enormously complex system of systems and must respond to several challenges that include building more transparent models and addressing deep uncertainty credibly. Drawing on the contributions of philosophers of science and introspective practitioners, we offer guidance for enhancing the credibility of CC-IAMs and computer simulation more generally.

Time of emergence of economic impacts of climate change

We introduce the concept of time of emergence of economic impacts (ToEI), which identifies the initial moment when the climate change impact signal exceeds a previously defined threshold of past economic output shocks in a given geographic area. We compute the ToEI using probabilistic climate change projections and impact functions from three integrated assessment models of climate change: DICE, RICE and CLIMRISK. Our results demonstrate that, in terms of the business-as-usual carbon emissions scenario, the global economy could reach its ToEI by 2095. Regional results highlight areas that are likely to reach the ToEI sooner, namely Western Europe by 2075, India by 2083, and Africa by 2085. We also explore local-scale variations in the ToEI demonstrating that, for example, Paris already reached the ToEI around 2020, while Shanghai will reach it around 2080. We conclude that the ToEI methodology can be applied to impact models of varying scales when sufficient historical impact data are available. Moreover, unprecedented impacts of climate change in the 21st century may be experienced even in economically developed regions in the US and Europe. Finally, moderate to stringent climate change mitigation policies could delay the extreme economic impacts of climate change by three decades in Latin America, the Middle East, and Japan, by two decades in India, Western Europe, and the US, and by one decade in Africa. Our results can be used by policymakers interested in implementing timely climate policies to prevent potentially large economic shocks due to climate change.

INTEGRATED ASSESSMENT AND CLIMATE CHANGE

The crowning achievement of the many published papers and books that William Nordhaus has published on climate change is the development of a simple Integrated Assessment Model of climate change. Embedding natural science insights into an economic framework reveals one can “solve” this difficult problem “for the greatest good, for the greatest number, and for the longest time”. Making certain that all the pieces are empirically based, and fit tightly together, and are internally consistent reveals this to be a masterpiece in the fine art of Integrated Assessment.

Grounding Social Foundations for Integrated Assessment Models of Climate Change

AbstractIntegrated assessment models (IAMs) are commonly used by decision makers in order to derive climate policies. IAMs are currently based on climate‐economics interactions, whereas the role of social system has been highlighted to be of prime importance on the implementation of climate policies. Beyond existing IAMs, we argue that it is therefore urgent to increase efforts in the integration of social processes within IAMs. For achieving such a challenge, we present some promising avenues of research based on the social branches of economics. We finally present the potential implications yielded by such social IAMs.

Societal Transformations in Models for Energy and Climate Policy: The Ambitious Next Step

Whether and how long-term energy and climate targets can be reached depend on a range of interlinked factors: technology, economy, environment, policy, and society at large. Integrated assessment models of climate change or energy-system models have limited representations of societal transformations, such as behavior of various actors, transformation dynamics in time, and heterogeneity across and within societies. After reviewing the state of the art, we propose a research agenda to guide experiments to integrate more insights from social sciences into models: (1) map and assess societal assumptions in existing models, (2) conduct empirical research on generalizable and quantifiable patterns to be integrated into models, and (3) build and extensively validate modified or new models. Our proposed agenda offers three benefits: interdisciplinary learning between modelers and social scientists, improved models with a more complete representation of multifaceted reality, and identification of new and more effective solutions to energy and climate challenges.

Even the Representative Agent Must Die: Using Demographics to Inform Long-Term Social Discount Rates

We develop a demographically-based approach for estimating the utility discount rate (UDR) portion of the Ramsey rule. We show how age-specific mortality rates and life expectancies imply a natural UDR for individuals at each age in a population, and these can be aggregated into a population-level social UDR. We then provide empirical estimates for nearly all countries and for the world as a whole. A striking part of the analysis is how the estimated UDRs fall within the range of those currently employed in the macroeconomics and climate change literatures. We use our results to derive heterogenous social discount rates across countries and explore the consequences for an integrated assessment model of climate change. We find that introducing regional heterogeneity of UDRs into the RICE model has little impact on the business-as-usual trajectory of global emissions. It does, however, change the trajectory of optimal emissions, the corresponding optimal carbon tax, and the distribution of emission reductions across countries.

Managing Catastrophic Climate Risks Under Model Uncertainty Aversion

We propose a robust risk management approach to deal with the problem of catastrophic climate change that incorporates both risk and model uncertainty. Using an analytical model of abatement, we show how aversion to model uncertainty influences the optimal level of mitigation. We disentangle the role of preferences from the structure of model uncertainty, which we define by means of a simple measure of disagreement across models. With data from an expert elicitation about climate change catastrophes, we quantify the relative importance of these two effects and calibrate a numerical integrated assessment model of climate change. The results indicate that the structure of model uncertainty, and specifically how model disagreement varies in abatement, is the key driver of optimal abatement and that model uncertainty warrants a higher level of climate change mitigation. This paper was accepted by Manel Baucells, decision analysis.

Solution algorithms for regional interactions in large-scale integrated assessment models of climate change

We present two solution algorithms for a large-scale integrated assessment model of climate change mitigation: the well known Negishi algorithm and a newly developed Nash algorithm. The algorithms are used to calculate the Pareto-optimum and competitive equilibrium, respectively, for the global model that includes trade in a number of goods as an interaction between regions. We demonstrate that in the absence of externalities both algorithms deliver the same solution. The Nash algorithm is computationally much more effective, and scales more favorably with the number of regions. In the presence of externalities between regions the two solutions differ, which we demonstrate by the inclusion of global spillovers from learning-by-doing in the energy sector. The non-cooperative treatment of the spillover externality in the Nash algorithm leads to a delay in the expansion of renewable energy installations compared to the cooperative solution derived using the Negishi algorithm.

Natural capital in integrated assessment models of climate change

In integrated assessment models (IAMs) economic activity leads to global warming, which causes future economic costs. However, typical IAMs do not explicitly represent the role of natural capital. In this paper, the DICE model by Nordhaus (2008) is expanded with a natural capital variable that is affected both by climate change and by depletive effects of economic activity. Due to a synergy between the two effects, the optimal policy of the expanded model features more and earlier abatement of CO2 emissions than DICE. Interestingly, the policy implications are different from what follows if one tries to capture the depletive effects on natural capital by simply reducing factor productivity growth in DICE. Acknowledging considerable uncertainty, simulations show that climate- and savings rate policies from the expanded model are more robust in the long term than policies that do not consider non-climatic depletion effects on natural capital.

Likelihood of environmental coalitions and the number of coalition members: evidences from an IAM model

To hold the grand coalition voluntarily in an economy with detrimental externalities, the allocations should be in the core. Identifying the scope or ‘size’ of the core allocations is of vital importance for understanding such a coalition. Furthermore, the relationship between the number of agents and the ‘size’ of the core reveals some crucial characteristics of coalition formation. In this paper, a cooperative game of stock externality provision is constructed to study its core properties of an economy with detrimental externality. Particularly, methods and algorithms for testing the shrinking core hypothesis are developed in the RICE model, an integrated assessment model of climate change. The calculation results show that the size of the core shrinks as the number of regions increases in RICE. The paper also evaluates the policy implications of the shrinking core phenomenon with respect to the environmental coalitions.

Expediting the transition from non-renewable to renewable energy via optimal control

Much recent climate research suggests that the transition from non-renewable to renewable energy should be expedited. To address this issue we use an optimal control model, based on an integrated assessment model of climate change that includes two types energy production. After setting up the model, we derive necessary optimality conditions in the form of a Pontryagin type Maximum Principle. We use a numerical discretization method for optimal control problems to explore various policy scenarios. The algorithm allows to compute both state and co-state variables by providing a consistent numerical approximation for the adjoint variables of the various scenarios. Our numerical method applies to control and state-constrained control problems as well as to delayed control problems. In the policy scenarios, we explore ways how the transition from non-renewable to renewable energy can be expedited.

A 4-Stated DICE: Quantitatively Addressing Uncertainty Effects in Climate Change

We introduce a version of the DICE-2007 model designed for uncertainty analysis. DICE is a wide-spread deterministic integrated assessment model of climate change. Climate change, long-term economic development, and their interactions are highly uncertain. The quantitative analysis of optimal mitigation policy under uncertainty requires a recursive dynamic programming implementation of integrated assessment models. Such implementations are subject to the curse of dimensionality. Every increase in the dimension of the state space is paid for by a combination of (exponentially) increasing processor time, lower quality of the value or policy function approximations, and reductions of the uncertainty domain. The paper promotes a state-reduced, recursive dynamic programming implementation of the DICE-2007 model. We achieve the reduction by simplifying the carbon cycle and the temperature delay equations. We compare our model’s performance and that of the DICE model to the scientific AOGCM models emulated by MAGICC 6.0 and find that our simplified model performs equally well as the original DICE model. Our implementation solves the infinite planning horizon problem in an arbitrary time step. The paper is the first to carefully analyze the quality of the value function approximation using two different types of basis functions and systematically varying the dimension of the basis. We present the closed form, continuous time approximation to the exogenous (discretely and inductively defined) processes in DICE, and we present a numerically more efficient re-normalized Bellman equation that, in addition, can disentangle risk attitude from the propensity to smooth consumption over time.

A 4-Stated Dice: Quantitatively Addressing Uncertainty Effects in Climate Change

We introduce a version of the DICE-2007 model designed for uncertainty analysis. DICE is a wide-spread deterministic integrated assessment model of climate change. Climate change, long-term economic development, and their interactions are highly uncertain. The quantitative analysis of optimal mitigation policy under uncertainty requires a recursive dynamic programming implementation of integrated assessment models. Such implementations are subject to the curse of dimensionality. Every increase in the dimension of the state space is paid for by a combination of (exponentially) increasing processor time, lower quality of the value or policy function approximations, and reductions of the uncertainty domain. The paper promotes a state reduced, recursive dynamic programming implementation of the DICE-2007 model. We achieve the reduction by simplifying the carbon cycle and the temperature delay equations. We compare our model's performance and that of the DICE model to the scientific AOGCM models emulated by MAGICC 6.0 and find that our simplified model performs equally well as the original DICE model. Our implementation solves the infinite planning horizon problem in an arbitrary time step. The paper is the first to carefully analyze the quality of the value function approximation using two different types of basis functions and systematically varying the dimension of the basis. We present the closed form, continuous time approximation to the exogenous (discretely and inductively defined) processes in DICE, and we present a numerically more efficient re-normalized Bellman equation that, in addition, can disentangle risk attitude from the propensity to smooth consumption over time.

Wants and needs in mitigation policy

Disagreements about morally appropriate mitigation policies arise in part from implicit disagreements about the nature and moral significance of needs. One key question is what, if anything, distinguishes “needs” from “mere wants.” One approach, prominent in economics and implemented in existing integrated assessment models of climate change, rejects a hard distinction between needs and wants. An alternative approach, prominent in the philosophical literature on needs, identifies needs with the requirements for autonomous agency, which is the capacity to set and pursue one’s own goals. A second key question is in what sense, if any, the satisfaction of needs should take precedence over the satisfaction of wants. Those who reject the distinction between wants and needs can say only that some desires should be weighted more heavily than others. Those who endorse the distinction can say that, given certain ethical assumptions, it is wrong to frustrate one person’s needs in order to satisfy others’ mere wants. Thus, rejecting the distinction between wants and needs tends to justify less aggressive mitigation policies, in which satisfying the so-called “wants” of present generations compensates for frustrating the so-called “needs” of future generations. Endorsing the distinction between wants and needs, along with certain ethical assumptions, tends to justify more aggressive mitigation policies. Both positions are intellectually defensible; understanding them helps illuminate disagreements over mitigation policy.

The value of information for integrated assessment models of climate change

We estimate the value of information (VOI) for three key parameters of climate integrated assessment models (IAMs): marginal damages at low temperature anomalies, marginal damages at high temperature anomalies, and equilibrium climate sensitivity. Most empirical studies of climate damages have examined temperature anomalies up to 3°C, while some recent theoretical studies emphasize the risks of “climate catastrophes,” which depend on climate sensitivity and on marginal damages at higher temperature anomalies. We use a new IAM to estimate the VOI for each parameter over a range of assumed levels of study precision based on prior probability distributions calibrated using results from previous studies. We measure the VOI as the maximum fixed fraction of consumption that a social planner would be willing to pay to conduct a new study before setting a carbon tax. Our central results suggest that the VOI is greatest for marginal damages at high temperature anomalies.

Optimal climate change mitigation under long-term growth uncertainty: Stochastic integrated assessment and analytic findings

Economic growth over the coming centuries is one of the major determinants of today׳s optimal greenhouse gas mitigation policy. At the same time, long-run economic growth is highly uncertain. This paper is the first to evaluate optimal mitigation policy under long-term growth uncertainty in a stochastic integrated assessment model of climate change. The sign and magnitude of the impact depend on preference characteristics and on how damages scale with production. We explain the different mechanisms driving optimal mitigation under certain growth, under uncertain technological progress in the discounted expected utility model, and under uncertain technological progress in a more comprehensive asset pricing model based on Epstein–Zin–Weil preferences. In the latter framework, the dominating uncertainty impact has the opposite sign of a deterministic growth impact; the sign switch results from an endogenous pessimism weighting. All of our numeric scenarios use a DICE based assessment model and find a higher optimal carbon tax than the deterministic DICE base case calibration.

Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches

Uncertainty plays a key role in the economics of climate change, and research on this topic has led to a substantial body of literature. However, the discussion on the policy implications of uncertainty is still far from being settled, partly because the uncertainty of climate change comes from a variety of sources and takes diverse forms. To reflect the multifaceted nature of climate change uncertainty better, an increasing number of analytical approaches have been used in the studies of integrated assessment models of climate change. The employed approaches could be seen as complements rather than as substitutes, each of which possesses distinctive strength for addressing a particular type of problems. We review these approaches—specifically, the non-recursive stochastic programming, the real option analysis, and the stochastic dynamic programming—their corresponding literatures and their respective policy implications. We also identify the current research gaps associated with the need for further developments of new analytical approaches.

Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches

Uncertainty plays a key role in the economics of climate change, and the discussions surrounding its implications for climate policy are far from settled. We give an overview of the literature on uncertainty in integrated assessment models of climate change and identify some future research needs. In the paper, we pay particular attention to three different and complementary approaches that model uncertainty in association with integrated assessment models: the discrete uncertainty modeling, the most common way to incorporate uncertainty in complex climate-economy models: the real options analysis, a simplified way to identify and value flexibility: the continuous-time stochastic dynamic programming, which is computationally most challenging but necessary if persistent stochasticity is considered.