Dynamic Integrated Model Of Climate Research Articles

Regional carbon emission pathways under central-local coordination: Objective constraints and collaborative incentives

The commitment of carbon peak and carbon neutrality in China has attracted global attention. In high-emission countries, the coordination between the central and local governments will inevitably influence climate governance. This study establishes the Regional Collaborations Dynamic Integrated model of Climate and Economy, predicting carbon emission pathways and social cost of carbon for regions within China. The design of six scenarios incorporates objective constraints and collaborative incentives to identify the impact of central-local coordination on climate governance. Results indicate that objective constraints will mitigate 0.17–0.60 Gt carbon emissions in all regions, reaching carbon peak 5–10 years earlier. Collaborative incentives will contribute to achieving carbon neutrality for regions in the long term, especially the Yangtze River Economic Belt advancing 7 years. The combination of objective constraints and collaborative incentives will facilitate China reaching carbon peak in 2029 and achieving carbon neutrality in 2057. The social cost of carbon will vary across regions due to socioeconomic conditions, initiative choices, and mitigation efforts. Moreover, the analysis of structural uncertainty can help China to undertake timely climate actions and avoid excessive mitigation costs. Based on these findings, this study proposes policy suggestions to improve climate governance by dynamic objective management, innovative collaborative forms, enhancing synergistic effects.

Revisiting permafrost carbon feedback and economic impacts

Quantifying permafrost carbon feedback (PCF) is a critical step in conveying the significance of permafrost carbon emissions to decision-makers and stakeholders and achieving sustainable development goals. Simply assuming a rapid reduction in permafrost area may be an overaggressive approach. This study revisited PCF by incorporating relatively clear permafrost physics into the Dynamic Integrated model of Climate and the Economy. The results show that the total carbon released from permafrost regions in 2100 is 30.5 GtC, which is accompanied by an additional atmospheric warming of 0.038 °C, much lower than previous studies. This study provides a potential perspective to scrutinize the climate feedback and related economic impacts due to permafrost thawing. We may need to pay more attention to carbon processes during nongrowing seasons and sudden changes in permafrost.

The climate economic effect of technology spillover

There is increasing interest in the global CO2 emissions transfer caused by international trade. However, the reduction potential of technology spillover is rarely considered. More importantly, the effect of technology spillover on climate change and the social cost of carbon (SCC) should be further investigated to inform climate policy-makers. In this study, we investigate the climate economic impact of technology spillover through unidirectional coupling of the emissions embodied in bilateral trade (EEBT) method and the dynamic integrated model of climate and economy (DICE). The results indicate that technological progress in the electricity sector could contribute to climate change mitigation and SCC lowering. The trade-related CO2 emissions of key CO2 exporters could be substantially reduced, such as China, India, Russia, and resource- and labor-intensive countries. High-tech countries outsourced large amounts of CO2 emissions to labor- and resource-intensive countries. However, a large income gap remains between high-tech and labor/resource-intensive countries. These results indicate the great importance of technological progress and sustainable management throughout the global supply chain.

Measurement and economic valuation of carbon sequestration in Nova Scotian wetlands

Carbon sequestration and methane flux in wetlands in Nova Scotia are measured. The social benefits associated with carbon storage are estimated using the net sequestration rate and estimates of the social cost of carbon from the Dynamic Integrated model of Climate and the Economy (DICE model). The net benefits of restoring wetlands in agricultural cropland are estimated based on these values and costs of restoration from the literature. The aim is to put a value on wetlands in Nova Scotia using original data rather than benefit transfers from other regions, thereby informing policy aimed at wetlands management in the region. Based on the results of this study, wetlands in Nova Scotia sequester 6.45 tCO2eha−1 yr−1 on average, and release 1.46 tCO2e ha−1 yr−1 as methane. The total benefits of carbon sequestration in wetlands in Nova Scotia are roughly $124–$373 ha−1 yr−1, and range from $5105 to $39,795 ha−1 in total. The social benefit of wetlands in terms of carbon sequestration is as high as $9.66 billion in Nova Scotia. Results indicate that protection of existing wetlands can be warranted on economic grounds. On average, it is not optimal to create wetlands for carbon sequestration, although it may be economically viable to target wetlands that are particularly productive in terms of storing CO2. It may also be viable to restore wetlands if ecosystem services are considered along with carbon sequestration.

Robust abatement pathways to tolerable climate futures require immediate global action

Disentangling the relative importance of climate change abatement policies from the human–Earth system (HES) uncertainties that determine their performance is challenging because the two are inexorably linked, and the nature of this linkage is dynamic, interactive and metric specific1. Here, we demonstrate an approach to quantify the individual and joint roles that diverse HES uncertainties and our choices in abatement policy play in determining future climate and economic conditions, as simulated by an improved version of the Dynamic Integrated model of Climate and the Economy2,3. Despite wide-ranging HES uncertainties, the growth rate of global abatement (a societal choice) is the primary driver of long-term warming. It is not a question of whether we can limit warming but whether we choose to do so. Our results elucidate important long-term HES dynamics that are often masked by common time-aggregated metrics. Aggressive near-term abatement will be very costly and do little to impact near-term warming. Conversely, the warming that will be experienced by future generations will mostly be driven by earlier abatement actions. We quantify probabilistic abatement pathways to tolerable climate/economic outcomes4,5, conditional on the climate sensitivity to the atmospheric CO2 concentration. Even under optimistic assumptions about the climate sensitivity, pathways to a tolerable climate/economic future are rapidly narrowing. Uncertainties are often cited as a reason for mitigation inaction. Here, millions of scenarios are evaluated to assess the relative importance of human–earth system uncertainties and policy variables. The growth rate of global abatement is found to be the primary driver of long-term warming.

Social cost of carbon under shared socioeconomic pathways

Abstract The Social Carbon Cost (SCC) measures present value of future economic damages caused by an additional ton of carbon emissions, and is widely used by governments to design climate policies. Although the use of SCC is very extensive, its predictions are very difficult. Because the SCC is defined by social welfare associated with economic growth and population, its estimation is necessarily dependent on future assumptions that are difficult to project. Many approaches consider the impact of population or economic growth on the SCC, but these socioeconomic factors must be grounded on solid assumptions concerning political, technological and environmental developments. Over the past seven years, the climate change research community has established five plausible socioeconomic narratives, called ‘Shared Socioeconomic Pathways’ (SSPs), numbered SSP1–SSP5. These scenarios provide descriptions of how the future might unfold in several key areas. To this end, we use the Dynamic Integrated model of Climate and the Economy (DICE) to update the SCC under the five socioeconomic pathways, while also considering alternative damage functions and the social welfare discount rate to address uncertainty. The result of the China Climate Change integrated assessment model (C3IAM) were used to re-estimate parameters in DICE, therefore characterize the SSPs. The results show that, in a world developing towards regional rivalry (SSP3), the SCC today will likely double compared with other scenarios. If emerged developing countries will follow the same path as previous industrializations (SSP5), the SCC will experience a rapid increase after 2060. Inequality (SSP4) will experience low mitigation pressure under a sustainable development scenario (SSP1), while the historical development pattern (SSP2) will have a moderate SCC with higher uncertainty. The results can provide carbon price benchmarks for policy makers who hold different attitudes towards the future and can help address the need to avoid regional rivalries and fossil-fueled development, which may counteract mitigation efforts.

Co-Benefits of CO2 Mitigation for NOX Emission Reduction: A Research Based on the DICE Model

Actions to reduce carbon emissions often entail co-benefits for environmental protection, like air pollutants reduction. Previous studies made contributions to estimate these co-benefits, but few considered the feedbacks from the socioeconomic system and the natural system. This paper extends the Dynamic Integrated model of Climate and the Economy (DICE) model, a classical Integrated Assessment model (IAM), into the Dynamic Integrated model of Climate, Air pollution and the Economy (DICAE) model. Through the hard link between a new air pollution module and the other modules in the original DICE, this paper quantifies the co-benefits of mitigating CO2 emissions for NOX emission reduction, and compares the predicted climate change, economic output and social utility under seven mixed policy scenarios. In addition, uncertainty analysis based on Monte Carlo simulation is carried out to verify the robustness of the DICAE model. The results indicate that the NOX emissions co-emitted with CO2 emissions would be over 0.6 Gt/year in a no-policy scenario. In policy scenarios, mitigating CO2 emissions can simultaneously reduce at least 15% of the NOX emissions, and the more severe the climate mitigation target is, the more obvious co-benefits for NOX emission reduction. Although these co-benefits can offset some mitigation costs, it will not be cost-effective when NOX emission reduction is achieved completely depending on ambitious carbon mitigation, so the end-of-pipe technology for NOX emission is also indispensable. For policymakers, they should recognize the co-benefits of climate policies, actively taking mitigation actions. Moreover, they are encouraged to combine CO2 mitigation with NOX emission reduction and coordinate their policy intensities to make wise use of the co-benefits.

MPC-DICE: An open-source Matlab implementation of receding horizon solutions to DICE

The most widely used integrated assessment model for studying the economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE). DICE is a nonlinear, time-varying discrete-time system whereby important quantities are calculated by solving an associated optimal control problem. Here, we describe an open-source Matlab implementation of the DICE model which uses Model Predictive Control (MPC) to approximately solve the DICE optimal control problem.

Climate mitigation roadmap: assessing low carbon scenarios for Malaysia

This study evaluates three proposals against the baseline scenario of existing practices to reduce climate damage in Malaysia using an ‘Empirical Regional Downscaling Dynamic Integrated model of Climate and the Economy’, which was deployed using a top-down disaggregation approach. This model took account of a temperature increase cap of 1.5°C, (which was defined at the Paris Declaration of 2015), reasonable changes in the macroeconomic variables, and the climate variables of carbon emission reduction, climate damage, carbon emission control, and carbon concentration that were estimated based on records of global warming climate factors and predicted climate targets over the period 2010–2110. The three proposals evaluated are Stern, Nordhaus, and Malaysia's Intended Nationally Determined Contribution (INDC) to COP21. The costs of climate damage under the prevailing scenario will reach MYR40,128 (US$ 11,631) billion. The commensurate climate damage costs of the Stern and Nordhaus proposals will be MYR5,483 (US$ 1,589) billion and MYR6,068 (US$ 1,759) billion respectively. It will only be MYR5,264 (US$ 1526) billion under Malaysia's INDC of the COP21 regime. Thus, the most effective proposal for reducing climate damage in Malaysia over the period 2010–2110 is Malaysia's INDC to the COP21 proposal.

О распределении бремени сокращения выбросов парниковых газов между поколениями

In this paper we propose an extension to the Dynamic Integrated model of Climate and Economy (DICE) by William Nordhaus, where we introduce overlapping generations (OLG) in the original model, and consider competitive equilibrium with the government, which follows Ramsey optimal policy on greenhouse gases (GHG) emissions reduction. In this framework it is possible to distinguish between subjective discount rate of individuals and social discount rate of the government. Thus the model provides two discount rates in the OLG model: the social discount rate under which cost-benefits analysis of climate projects are evaluated and the market discount rate for investments in physical capital. Using numerical simulations methods we show that the introduction of overlapping generations in the model could streamline more and early GHG emissions reduction, without causing a significant impact on the return on capital market. Long term gain from this policy is approximately 1 B C.

Inequality, climate impacts on the future poor, and carbon prices

Integrated assessment models of climate and the economy provide estimates of the social cost of carbon and inform climate policy. We create a variant of the Regional Integrated model of Climate and the Economy (RICE)-a regionally disaggregated version of the Dynamic Integrated model of Climate and the Economy (DICE)-in which we introduce a more fine-grained representation of economic inequalities within the model's regions. This allows us to model the common observation that climate change impacts are not evenly distributed within regions and that poorer people are more vulnerable than the rest of the population. Our results suggest that this is important to the social cost of carbon-as significant, potentially, for the optimal carbon price as the debate between Stern and Nordhaus on discounting.

Inaction and climate stabilization uncertainties lead to severe economic risks

Climate stabilization efforts must integrate the actions of many socio-economic sectors to be successful in meeting climate stabilization goals, such as limiting atmospheric carbon dioxide (CO2) concentration to be less than double the pre-industrial levels. Estimates of the costs and benefits of stabilization policies are often informed by Integrated Assessment Models (IAMs) of the climate and the economy. These IAMs are highly non-linear with many parameters that abstract globally integrated characteristics of environmental and socio-economic systems. Diagnostic analyses of IAMs can aid in identifying the interdependencies and parametric controls of modeled stabilization policies. Here we report a comprehensive variance-based sensitivity analysis of a doubled-CO2 stabilization policy scenario generated by the globally-aggregated Dynamic Integrated model of Climate and the Economy (DICE). We find that neglecting uncertainties considerably underestimates damage and mitigation costs associated with a doubled-CO2 stabilization goal. More than ninety percent of the states-of-the-world (SOWs) sampled in our analysis exceed the damages and abatement costs calculated for the reference case neglecting uncertainties (1.2 trillion 2005 USD, with worst case costs exceeding $60 trillion). We attribute the variance in these costs to uncertainties in the model parameters relating to climate sensitivity, global participation in abatement, and the cost of lower emission energy sources.

Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis

Integrated assessment models for climate change (IAMs) couple representations of economic and natural systems to identify and evaluate strategies for managing the effects of global climate change. In this study we subject three policy scenarios from the globally-aggregated Dynamic Integrated model of Climate and the Economy IAM to a comprehensive global sensitivity analysis using Sobol' variance decomposition. We focus on cost metrics representing diversions of economic resources from global world production. Our study illustrates how the sensitivity ranking of model parameters differs for alternative cost metrics, over time, and for different emission control strategies. This study contributes a comprehensive illustration of the negative consequences associated with using a priori expert elicitations to reduce the set of parameters analyzed in IAM uncertainty analysis. The results also provide a strong argument for conducting comprehensive model diagnostics for IAMs that explicitly account for the parameter interactions between the coupled natural and economic system components.

DICE/RICE模型中碳循环模块的比较研究

碳循环模型的正确构建是影响综合集成评估模型IAM(Integrated Assessment Model)模拟结果的重要因素之一.DICE/RICE模型中的碳循环模型主要有两个,即Nordhaus单层碳库模型和Nordhaus三层碳库模型,但这两个模型的主要缺陷是不考虑陆地生态系统在碳循环中的贡献,因此,引入了包含陆地生态系统的Svirezhev碳循环模型,并将其与Nordhaus单层碳库模型、Nordhaus三层碳库模型展开比较研究.结果表明,在基于历史数据的模型检验中,Svirezhev碳循环模型对全球二氧化碳浓度模拟的准确度优于其他两个模型.对于未来全球气候变化的模拟,3个模型模拟得到了至2100年的温度预测值分别为2.98,3.54,2.91℃,二氧化碳浓度值分别为608.04,733.04,594.70μL/L.其中,Svirezhev碳循环模型的模拟值在3个模型中最低,表明了陆地生态系统和海洋对二氧化碳的吸收作用对抑制全球升温的贡献;而分析也发现Nordhaus三层碳库模型对陆地生态系统和海洋碳库的模拟与实际观测值偏离较大.最后,通过敏感性分析,研究发现DICE/RICE模型中使用的气候响应模块在短期温度模拟中对地表温度的初值较为敏感,在长期温度模拟中敏感度显著下降.总之,从碳循环机制的模拟性能而言,Svirezhev碳循环模型优于其他两个模型,而Nordhaus单层碳库模型虽然机制较为简单却保证了模拟的准确性,但Nordhaus三层碳库模型虽然丰富了碳库的表征,实际上各碳库的模拟准确性差,降低了模型的可靠性.;Modeling of the carbon cycle is one of the most important issues in research of the Integrated Assessment Model (IAM). The carbon-cycle module can not only implement the carbon balance among different carbon reservoirs, but can also provide an interface for climate adaptation and mitigation through management of carbon sinks and land use change. As one of the most popular IAMs in the world, the dynamic integrated model of climate and the economy/regional integrated model of climate and the economy (DICE/RICE) model has two versions of carbon-cycle models. These are the solo-reservoir (N1-N) and three-reservoir (N3-N) models. However, there is an obvious drawback of the two models. This is that terrestrial carbon storage is not considered. Therefore, this work examines the effectiveness of the carbon-cycle models within DICE/RICE, and compares the two models with another carbon-cycle model presented by Svirezhev (S-N model).By inputting global historical emission data into the three models (N1-N, N3-N and S-N), we obtain simulations of historical temperature and CO₂ concentration during 2001 to 2008. The results are calibrated with observed historical CO₂ concentrations and temperature changes, by developing a correlation test. The results show that correlations of CO₂ concentration based on the N1-N, N3-N and S-N models are 0.9967, 0.9971 and 0.9970, respectively, and corresponding correlations of temperature are 0.452, 0.447 and 0.451. It was found that there was a significant correlation between simulated and observed CO₂ concentration data, but simulated and observed temperature data were uncorrelated. This result is verified by an analysis of variance for the simulated and observed data. Although the correlations between the N1-N, N3-N and S-N models are very similar, the standard errors of CO₂ concentration data are 2.53, 2.76 and 0.89, respectively. This shows that the simulation based on the S-N model is much more accurate in relation to the observed data. The N1-N, N3-N and S-N models were used to project climate change by the year 2100, for which the temperatures are 2.98, 3.54℃ and 2.91℃, respectively, and the CO₂ concentrations are 608.04,733.04, 594.70 ppm, respectively. Projections with smallest values were produced by the terrestrial ecosystem and ocean carbon reservoir represented in the S-N model. This indicates that when carbon is absorbed by the terrestrial ecosystem and ocean in that model, atmospheric carbon is less than those in the other models. Although there are also three reservoirs in the DICE/RICE N3-N model, its results deviate substantially from actual observations. The climate response model used by DICE/RICE was also found to be sensitive to the initial value of land surface temperature, when applied in a short-term projection. However, the sensitivity becomes weaker when applied in a long-term projection. Therefore, the S-N model turns out to be superior to the other models in terms of a much more detailed model mechanism and more accurate modeling performance. In spite of the simplification of the N1-N model, its simulation results are still better than those of the N3-N, in which carbon in the biosphere and ocean is significantly different from observations.

What are robust strategies in the face of uncertain climate threshold responses?

We use an integrated assessment model of climate change to analyze how alternative decision-making criteria affect preferred investments into greenhouse gas mitigation, the distribution of outcomes, the robustness of the strategies, and the economic value of information. We define robustness as trading a small decrease in a strategy’s expected performance for a significant increase in a strategy’s performance in the worst cases. Specifically, we modify the Dynamic Integrated model of Climate and the Economy (DICE-07) to include a simple representation of a climate threshold response, parametric uncertainty, structural uncertainty, learning, and different decision-making criteria. Economic analyses of climate change strategies typically adopt the expected utility maximization (EUM) framework. We compare EUM with two decision criteria adopted from the finance literature, namely Limited Degree of Confidence (LDC) and Safety First (SF). Both criteria increase the relative weight of the performance under the worst-case scenarios compared to EUM. We show that the LDC and SF criteria provide a computationally feasible foundation for identifying greenhouse gas mitigation strategies that may prove more robust than those identified by the EUM criterion. More robust strategies show higher near-term investments in emissions abatement. Reducing uncertainty has a higher economic value of information for the LDC and SF decision criteria than for EUM.

PLAN OR REACT? ANALYSIS OF ADAPTATION COSTS AND BENEFITS USING INTEGRATED ASSESSMENT MODELS

This report examines adaptation and mitigation within an integrated framework. Global and regional costs of adaptation are assessed dynamically and the resulting benefits are quantified. This is accomplished by developing a framework to incorporate adaptation as a policy variable within three Integrated Assessment Models (IAMs); the global Dynamic Integrated model of Climate and the Economy (DICE), the Regional Integrated model of Climate and the Economy (RICE), and the World Induced Technical Change Hybrid (WITCH) model. The framework developed here takes into account investments in reactive adaptation and in adaptation "stocks", as well as investments in building adaptive capacity. This report presents the first inter-model comparison of results on adaptation costs using the emerging category of adaptation-IAMs. Results show that least-cost policy response to climate change will need to involve subsantial amounts of mitigation efforts, investments in adaptation stock, reactive adaptation measures and adaptive capacity to limit the remaining damages.

RECAST THE DICE AND ITS POLICY RECOMMENDATIONS

The DICE (for Dynamic Integrated model of Climate and the Economy) introduced two important policy aspects to the economic discussion of global warming. First, it argues for a “climate-policy ramp” that deems back-loading of mitigation optimal. Second, it demonstrates that an intergenerational tradeoff is at the heart of the mitigation problem. In this paper we argue that both of these findings rest on contestable assumptions. To demonstrate this a recast DICE is presented. Its outcomes show that DICE's predictions are not robust with higher migitations earlier on and slower temperature increases along the optimal path. The adoption of a baseline scenario in which pollution is a negative externality makes mitigating climate change a Pareto improvement. The alleged sacrifice of present generations vanishes. This strengthens the case for immediate policy action.

The optimal paths of climate change mitigation and adaptation under certainty and uncertainty

Tradeoffs between climate change mitigation and adaptation policies are explored under both certainty and uncertainty with learning using a numerical two-period decision model. We first replicate a version of the Adaptation in DICE climate model (AD-DICE) (de Bruin et al., 2009), which modifies the Dynamic Integrated model of Climate and the Economy (DICE) (Nordhaus and Boyer, 2000) to incorporate climate change adaptation explicitly into the traditional optimisation framework. Our model is then extended to include uncertainty over a probability distribution of expected Climate Sensitivity (CS) values and the implications for optimal mitigation and adaptation levels are then explored. In the certainty model runs, the results of previous studies that incorporated adaptation into the portfolio of climate change responses are largely confirmed. Modelling an uncertain CS with the same expected value as under certainty leads to several insights: before learning occurs, optimal levels of both mitigation and adaptation are lower under uncertainty than under certainty; in this same early period, optimal mitigation and adaptation levels are most sensitive to the respective cost of each strategy, with the mitigation level more dependent on adaptation costs than vice versa; variance in CS – a parameter with long-term effects – affects mitigation levels more than adaptation levels.

Warming the World: Economic Models of Global Warming

Humanity is risking the health of the natural environment through a myriad of interventions, including the atmospheric emission of trace gases such as carbon dioxide, the use of ozone-depleting chemicals, the engineering of massive land-use changes, and the destruction of the habitats of many species. It is imperative that we learn to protect our common geophysical and biological resources. Although scientists have studied greenhouse warming for decades, it is only recently that society has begun to consider the economic, political, and institutional aspects of environmental intervention. To do so raises formidable challenges of data modeling, uncertainty, international coordination, and institutional design.Attempts to deal with complex scientific and economic issues have increasingly involved the use of models to help analysts and decision makers understand likely future outcomes as well as the implications of alternative policies. This book presents in detail a pair of models of the economics of climate change. The models, called RICE-99 (for the Regional Dynamic Integrated model of Climate and the Economy) and DICE-99 (for the Dynamic Integrated Model of Climate and the Economy) build on the authors' earlier work, particularly their RICE and DICE models of the early 1990s. They can help policy makers design better economic and environmental policies.