Climate-economy Model Research Articles

Endogenous Preference for Nonmarket Goods in Carbon Abatement Decisions

Long-term decisions, such as decisions about carbon abatement, are usually based on the implausible assumption of constant social preference. This paper focuses on a specific case of market and nonmarket goods, and it investigates the optimal climate policy when social preference for them is also changed by climate policy in a seminal climate-economy model: the Dynamic Integrated Model of Climate and the Economy (DICE) model with a single objective to maximize market goods consumption. We amend DICE to study an optimization problem that trades off the values of market and nonmarket goods, considering endogenous preferences. The relative price of non-market goods grows over time because of increases in both relative scarcity and the appreciation that represents the preference of it. As nonmarket goods are increasingly valuable relative to market ones, climate damages on nonmarket goods are increasingly expensive. The social cost of carbon, which aggregated climate impact on both goods, is consequently higher. Because abatement decision affects the valuation of nonmarket goods in the utility function, unlike previous climate-economy models, we solve the model iteratively by taking the obtained abatement rates from the last run as inputs in the current run. The results in baseline calibration advocate a more stringent climate policy, where endogenous social preference to climate policy raises the social cost of carbon further by roughly 12%–18% this century. Moreover, neglecting changing social preference leads to an underestimate of nonmarket goods damages by 15%. Climate policy is self-reinforced if it favors the more expensive consumption good. The proposed method can be applied to other long-term problems with endogenous preferences. Funding: H. Liao appreciates the funding from the National Natural Science Foundation of China [Grants 71925008, 72293603, and 72488101].

From planetary scenarios to planetary sensing: Models, observations, and political legibility

This paper explores the political uses of images generated by Earth System science. It argues that images of possible climate futures, maps of potential worlds of heatwaves and wildfires, are made legible to policymakers by an alliance with a class of climate-economy models that associate scientific estimates of climate impacts with a prescribed international policy and technology mix. While environmental models have successfully mobilized policymakers in the past by providing images of “planetary scenarios” accompanying different emissions pathways, with climate change a political actor outside the administrative state is required to overcome the entrenchment of fossil capital. The paper suggests such actors are empowered not by the rhetoric of scenario modeling but by the emerging practice of “planetary sensing,” where activists and stakeholders directly mobilize the planetary images generated by Earth System science as they work to evacuate prisons, track pollutants, and repair pipelines.

Projecting progress in sustainable development goals vis-à-vis climate action in climate-economy models

Projecting progress in sustainable development goals vis-à-vis climate action in climate-economy models

Understanding climate damages: Consumption versus investment

Existing climate-economy models assume climate change has equal impacts on the productivity of firms that produce consumption and investment goods and services. We develop a model of structural change to show that the split between damage to consumption and investment productivity matters for the aggregate consequences of climate change. When investment is more vulnerable to climate, we find smaller short-run consumption losses than leading models suggest, but larger long-run consumption losses. We provide a quantitative illustration of these effects for one type of climate damage in the US economy: labor productivity losses from heat stress. We find that accounting for heterogeneous damages increases the welfare cost of the climate damage from heat stress by approximately 4 to 23%, depending on the discount factor.

Global sensitivity and domain‐selective testing for functional‐valued responses: An application to climate economy models

AbstractUnderstanding the dynamics and evolution of climate change and associated uncertainties is key for designing robust policy actions. Computer models are key tools in this scientific effort, which have now reached a high level of sophistication and complexity. Model auditing is needed in order to better understand their results, and to deal with the fact that such models are increasingly opaque with respect to their inner workings. Current techniques such as Global Sensitivity Analysis (GSA) are limited to dealing either with multivariate outputs, stochastic ones, or finite‐change inputs. This limits their applicability to time‐varying variables such as future pathways of greenhouse gases. To provide additional semantics in the analysis of a model ensemble, we provide an extension of GSA methodologies tackling the case of stochastic functional outputs with finite change inputs. To deal with finite change inputs and functional outputs, we propose an extension of currently available GSA methodologies while we deal with the stochastic part by introducing a novel, domain‐selective inferential technique for sensitivity indices. Our method is explored via a simulation study that shows its robustness and efficacy in detecting sensitivity patterns. We apply it to real‐world data, where its capabilities can provide to practitioners and policymakers additional information about the time dynamics of sensitivity patterns, as well as information about robustness.

Assessing the economic implications of carbon emissions on climate change: Estimating the impact using methane-adjusted DICE model

This study aims to highlight the importance of accurately estimating methane emissions, one of the most dangerous and important greenhouse gases in the context of climate change. By incorporating methane emissions as a variable within the integrated assessment model DICE (Dynamic Integrated Climate-Economy model), we investigate how these emissions influence temperature changes and subsequently impact economic policies, including climate economic policies, carbon pricing, and non-price factors. We use the existing DICE-2020 and DICE-2023 models as references for our analysis. In addition to industrial emissions that can be detected through satellite observations, we address the challenge of estimating natural emissions from wetlands and permafrost, which leak gradually and are difficult to detect. By considering these emissions, we account for their exogenous nature and their divergence from the current situation. Our study reveals that incorporating methane emissions into the DICE model has significant implications for global temperature outcomes and subsequent policy changes. We find that by implementing existing methane reduction policies, which includes cutting the level of methane emissions in half and increase the carbon price in 4 times to 500 USD per ton, it is possible to achieve the more ambitious goal of limiting the global temperature increase to 1.5 °C by 2100 in an optimistic scenario instead of the common target of 2 °C. More pessimistic scenarios that do not imply big change in methane emissions, but the same numerical data for carbon price, still suggests the possibility of keeping the global temperature below 2 °C. The findings of this study emphasize the importance of estimating methane emissions in efforts to mitigate climate change. Recognizing the impact of methane on global temperature change, policymakers can make informed decisions regarding economic policies, carbon pricing, to effectively address the challenges posed by climate change. This research contributes to the field by incorporating methane emissions into the DICE model, providing a more complete understanding of its influence on global climate outcomes and economic implications. Additionally, by highlighting the potential benefits of methane reduction measures, this study provides information on how to achieve more ambitious climate targets.

Multi-Dimensional Impacts of Climate Change on China’s Food Security during 2002–2021

Climate change poses a significant threat to food security, necessitating a thorough examination across multiple dimensions. Establishing appropriate food security evaluation indicators that align with the evolving concept of food security is imperative. This study enhances food security evaluation by designing a multi-dimensional framework and analyzing the impact of climate variations across various regions from 2002 to 2021. By constructing a food security evaluation system that encompasses the production quantity and quality, sustainability, affordability, and resources, and utilizing the entropy method for accurate weighting, the impacts of climate variations on food security are accessed using a climate–economic model. The food security structure in China largely mirrors the regional division of grain, with the production quantity being the primary contributor. Overall, China’s food security has generally demonstrated improvements across various dimensions, with the exception of production quality. Regarding climate change, which encompasses variations in mean states and climate extremes, the panel regression analysis uncovers a negative linear relationship between food security and temperature. Conversely, the impact of precipitation on food security is non-linear, manifesting as inverse U-shaped patterns. In regions with balanced production and consumption, both accumulated temperatures and extreme high temperatures have a negative linear effect on food security. On the other hand, both accumulated and extreme precipitation exhibit inverse U-shaped non-linear impacts on food security in the main production and main consumption regions. These findings highlight the intricate interplay between climate change, regional disparities, and food security in China, emphasizing the need to consider multi-dimensional factors and regional variations in addressing food security challenges. These insights are invaluable for policymaking and planning aimed at enhancing food security in China.

Balancing the health benefits and climate mortality costs of haemodialysis

Extensive work is underway to quantify the carbon footprint of specific healthcare interventions and identify ways to minimise healthcare-related emissions; however, it remains unclear how to balance the relative benefits from delivering healthcare with the harm from the associated carbon footprint. To estimate emissions-related harms, we used the Mortality Cost of Carbon, a recently developed metric from environmental economics, which presents the impacts of carbon emissions in the form of excess deaths. We convert deaths into years of life lost and compare this with the healthy life years gained, under two temperature scenarios: 'Dynamic Integrated Climate Economy Model with an Endogenous Mortality Response' (DICE-EMR) (2.4°C) and 'DICE-Baseline' (4.1°C). As a case study, we use haemodialysis, a life-prolonging intervention with a large carbon footprint. We estimate that 19-53 and 10-25 healthy life years are gained from haemodialysis per year of life lost from the associated emissions in the DICE-EMR and DICE-Baseline scenarios, respectively, depending on the country and treatment regimen. This brings the distribution of harms, benefits and tradeoffs inherent to the decarbonisation of healthcare into sharper focus. More fully accounting for the harm imposed by carbon emissions could result in better value investments to lower the carbon footprint of interventions and support the implementation of the net-zero healthcare agenda.

Nonlinear El Niño impacts on the global economy under climate change

The El Niño-Southern Oscillation (ENSO) is a consequential climate phenomenon affecting global extreme weather events often with largescale socioeconomic impacts. To what extent the impact affects the macroeconomy, how long the impact lasts, and how the impact may change in a warming climate are important questions for the field. Using a smooth nonlinear climate-economy model fitted with historical data, here we find a damaging impact from an El Niño which increases for a further three years after initial shock, amounting to multi-trillion US dollars in economic loss; we attribute a loss of US$2.1 T and US$3.9 T globally to the 1997-98 and 2015-16 extreme El Niño events, far greater than that based on tangible losses. We find impacts from La Niña are asymmetric and weaker, and estimate a gain of only US$0.06 T from the 1998-99 extreme La Niña event. Under climate change, economic loss grows exponentially with increased ENSO variability. Under a high-emission scenario, increased ENSO variability causes an additional median loss of US$33 T to the global economy at a 3% discount rate aggregated over the remainder of the 21st century. Thus, exacerbated economic damage from changing ENSO in a warming climate should be considered in assessments of mitigation strategies.

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.

A climate–economy model in a stochastic differential equilibrium with fractional Brownian motions and Poisson jumps

A climate–economy model in a stochastic differential equilibrium with fractional Brownian motions and Poisson jumps

Who pays the bill? Climate change, taxes, and transfers in a multi-region growth model

This paper presents a quantitative study of a dynamic climate-economy model with multiple regions to evaluate how implementing an optimal climate tax affects production, emissions, and welfare in each region. We develop a numerical algorithm which is generally applicable to compute equilibria in the presence of arbitrarily many regions and under alternative climate policies. Our simulation model distinguishes six major world regions and incorporates a wide array of regional heterogeneities including a detailed description of the energy production process in each region. We also quantify the full range of Pareto-improving transfers under which each region has an incentive to join the global climate agreement. Our results show that optimal taxation reduces coal consumption in each region substantially by about 70% and leads to higher GDP within the next 100 years in most regions. The only exception is China which suffers losses in GDP for the next 130 years due to its strong dependence on coal and must be incentivized via transfer payments to implement the optimal tax. We also show that the increase in global temperature under optimal taxation is compatible with the two-degree target.

Understanding expectations from and capabilities of climate-economy models for measuring the impact of crises on sustainability

The multi-faceted emergencies of our time (recession, pandemics, and international conflict) can disrupt progress towards sustainable development goals (SDGs). While climate-economy models can show pathways out of these disruptions, numerous stakeholder-informed enhancements are required to navigate through the interconnected SDG landscape. We introduce a novel Fuzzy Cognitive Maps (FCMs) approach that integrates Monte Carlo simulations, aiming to capture and understand the differences among modellers' and experts' perceptions of the impacts of such crises on broader sustainability, considering models’ existing/planned capacity and uncertainty over perceived magnitude and propagation of impacts across SDGs. Our application shows that expert stakeholders appeared more concerned over the implications of these crises for sustainable development than modellers, but expectations were considerably constrained by model capabilities. SDGs 8 (growth), 7 (energy-sector sustainability), and 13 (climate action) were robustly found as most prone to examined shocks. Our proposed Monte Carlo Fuzzy Cognitive Mapping (MCFCM) approach also highlighted potentially important propagation effects as well as gaps in integrated assessment model capabilities to holistically study the SDG spectrum.

Expanding the possible: exploring the role for heterodox economics in integrated climate-economy modeling

This paper explores the degree to which heterodox economics can contribute to the development and use of climate-economy integrated assessment models. To do so, it introduces the field of integrated assessment modeling, with a focus on the core economic methodology used by various types of models. It then summarizes some of the literature critiquing these models and how they inform policy. The paper then provides an extended classification of ways in which heterodox economics could be applied to climate-economy models and presents a number of storylines, or pathways, which could be created using insights and methods from heterodox schools. The paper concludes with an assessment of the scope for heterodox economics to answer the criticisms of climate-economy models, finding that despite not resolving all issues, the heterodoxy has a substantial role to play.

Parameter uncertainties in evaluating climate policies with dynamic integrated climate-economy model

Parameter uncertainties in evaluating climate policies with dynamic integrated climate-economy model

Optimal climate and fiscal policy in an OLG economy

AbstractThis paper develops a climate–economy model to study the joint design of optimal climate and fiscal policies in economies with overlapping generations (OLGs). I demonstrate how capital taxation, if optimal, drives a wedge between the market costs of carbon (the net present value of marginal damages using the market interest rate) and the Pigouvian tax (the net present value of marginal damages using the consumption discount rate of successive OLGs). In contrast to deterministic infinitely lived representative agent models, at the optimum, the capital income tax is positive, the carbon price equals the market costs of carbon but it falls short of the Pigouvian tax when (i) preferences are not separable over consumption and leisure; and (ii) labor income taxes cannot be age‐dependent. I also show that restrictions on climate change policy provide a novel rationale for positive capital income taxes.

Fertility and climate change*

AbstractA quarter of the total increase in emissions is attributable to the growth of emissions per capita, whereas three‐quarters are due to population growth. This evidence notwithstanding, demography in climate–economy models typically follows exogenous trends. We develop a climate–economy integrated model with endogenous fertility through a quality–quantity trade‐off. The decentralization of the social optimum requires two complementary instruments: a carbon pricing policy and family planning interventions. Global population increases and reaches a peak, depending on the scenario, between 11.6 billion in the social optimum and 14.6 billion if only carbon prices are implemented. Fertility costs (i.e., the net present value of the climate‐related costs per child) are in 2020 estimated to be about 22,000 euros in the “social optimum” scenario, and about 88,000 euros in the “second‐best with fertility taxes” scenario. Carbon pricing tends to have a rebound effect as it increases population growth leading to higher future emissions. Our results highlight the effects of fertility choices and global population on climate change, quantifying the cost of neglecting the interaction.

Understanding Climate Damages: Consumption versus Investment

Existing climate-economy models use aggregate damage functions to model the effects of climate change. This approach assumes climate change has equal impacts on the productivity of firms that produce consumption and investment goods or services. We show the split between damage to consumption and investment productivity matters for the dynamic consequences of climate change. Drawing on the structural transformation literature, we develop a framework that incorporates heterogeneous climate damages. When investment is more vulnerable to climate, we find short-run consumption losses will be smaller than leading models with aggregate damage functions suggest, but long-run consumption losses will be larger. We quantify these effects for the climate damage from heat stress and find that accounting for heterogeneous damages increases the welfare cost of climate change by approximately 4 to 24 percent, depending on the discount factor.

Self‐Adaptive Multi‐Objective Climate Policies Align Mitigation and Adaptation Strategies

AbstractIntensifying climate change impacts can divert the economic resources away from emission reduction toward adaptation to reduce rising damages, jeopardizing temperature stabilization within safe levels. Indeed, the traditional static welfare‐maximizing climate policy design leads to a conflict between mitigation and adaptation, invalidating the recently established consistency of cost‐benefit analysis with the Paris Agreement's targets. Here, we show that this tension can be resolved by integrating multi‐objective optimization and feedback control in the Dynamic Integrated Climate Economy model to design self‐adaptive climate policies trading off welfare maximization with the Paris Agreement compliance. These policies allow adjusting against uncertainty as information on the socio‐climatic system accumulates, thus representing the policy‐making process more realistically. We show that, the costs being the same as in traditional methods, warming above 2°C and the probability of overshooting can be drastically reduced, emphasizing the need for integrating adaptation and mitigation strategies and the value of embracing a self‐adaptive, multi‐objective perspective.

ABM-IAM: optimal climate policy under bounded rationality and multiple inequalities

Despite considerable concern about potentially inequitable effects of climate policy, models fall short in assessing their implications for policy design. To address this issue, we develop an agent-based climate-economy model, ABM-IAM, as a disaggregated, behavioural approach to integrated climate assessment. It describes networks of heterogeneous consumers, banks, power plants and firms, and is calibrated on patterns of growth and carbon dioxide emissions generated by the DICE model of Nordhaus. Whereas the latter assumes full employment and abstains from a financial sector and inequality considerations, our approach relaxes these restrictions to obtain a more reliable assessment of climate policy impacts. We show that inequalities in labour and capital income serve as essential but overlooked links between climate-change damages and optimal climate policy. Our result show that lower inequalities of labour income increase the social cost of carbon (SCC), while the impact of capital income inequalities on the SCC depends on the share of population receiving capital rents.