Rice Model Research Articles

OPTIMAL CLIMATE POLICY WITH NEGATIVE EMISSIONS

We can limit the future temperature impact of climate change in two ways: (i) reducing our use of CO2 emitting fuels as an energy source (abatement), and (ii) using negative emission technologies (NETs) to remove existing CO2 from the atmosphere (removal). Using a modification of the DICE model, we analyze the optimal use of these two policy responses to climate change. After calibrating the marginal costs of abatement and CO2 removal to the latest scientific information, we find that carbon removal must play a very large role in an optimal policy. If this policy is followed, we find that the Paris-Agreement 1.5–2[Formula: see text]C warming by 2100 target is not just aspirational, but optimal. When an important role is played by NETs to control global warming, the decrease in carbon emissions can be more gradual, reducing transition risk and social dislocations. We examine the impact on the economy of large-scale carbon removal programs, the potential for moral hazard and the logistical problems associated with the storage of the removed carbon.

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.

Green bonds, transition to a low-carbon economy, and intertemporal welfare allocation: Evidence from an extended DICE model

<p>Short-term reductions in social welfare, expected to be caused by a currently imposed carbon tax, are among the obstacles to a rapid transition to a low-carbon economy. Using an extended DICE model, we studied the potential of green bonds to both accelerate this transition and smoothen welfare losses and gains in a socially optimal way. We showed that green bonds can indeed accelerate the transition to a low-carbon economy and that lower interest rates on bonds speed up this acceleration. Moreover, bonds can reduce short-term welfare losses; however, to eliminate welfare losses, additional compensation mechanisms are needed. For example, bonds at a 3% interest rate can decrease the peak atmospheric carbon concentrations by about 20% and shorten the initial time, during which society is worse off from 75 to 45 years. Retaining at least the same consumption level as in the no-mitigation scenario, without using bonds, is possible only through a decrease in abatement efforts. Green bonds of sufficiently low interest rates allow improving intertemporal welfare as well as achieving a more pronounced climate change mitigation with respect to both mitigation and no-mitigation scenarios without bonds.</p>

Big Data Simulation Analysis of the Impact of China’s Carbon Tax Levy on Carbon Emission Behavior under the Background of “Dual Carbon”

Abstract This study focuses on the impact of China’s carbon tax on carbon emission behavior under the background of “double carbon”, using big data simulation analysis. The study utilizes the DICE model, combined with general equilibrium theory, to conduct in-depth theoretical analysis and numerical simulation of the carbon tax policy. It is found that the optimal carbon tax rate should be flexibly adjusted in combination with social and economic development and technological progress, and the optimal tax rate gradually rises from RMB 2.83/tonne of carbon dioxide to RMB 17.62/tonne from 1997 to 2023. Simulation results show that increasing the carbon tax rate can effectively promote the low-carbon emission reduction behavior of enterprises and thus achieve the emission reduction target. Under the fixed carbon tax policy, a 50% tax rate can significantly improve environmental quality and economic output. In contrast, steady-state values of output and carbon emissions decrease with the increase of tax rates. At the same time, the combination of carbon trading and carbon tax can more effectively incentivize enterprises to reduce carbon emissions. The results of the study show that a flexible and reasonable carbon tax policy can significantly affect carbon emission behavior, which is of great significance for realizing China’s “double carbon” goal.

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.

Flatbands in frustrated lattice X3MnN3 (X = Ca, Sr, Ba): A first-principles study

Frustrated lattices with dispersionless band structures and fully localized states are an exciting platform for exploring many-body physics. In this work, we identify X3MnN3 (X= Ca, Sr, Ba) as a frustrated lattice and investigate its physical properties in the ferromagnetic (FM) and antiferromagnetic (AFM) states based on first-principles calculations. Our results show that all three materials in FM and A-type AFM configurations have flatbands with band touching in the kz∼0 and kz∼π planes with bandwidths less than 0.2 eV. Intriguingly, the flatband is tuned to the Fermi level when X3MnN3 is transformed into the FM state. Furthermore, we find the two-dimensional dice model hidden in X3MnN3 by treating the coupled Mn and N atoms as a basic site, revealing that the compact localized state is generated by destructive interferences between the hopping amplitudes, and the dice models stack the system staggered along the c-axis. Our work provides new candidate materials for exploring strong correlation physics and reveals the mechanism of their localized state.

EfficientNetB0 cum FPN Based Semantic Segmentation of Gastrointestinal Tract Organs in MRI Scans.

The segmentation of gastrointestinal (GI) organs is crucial in radiation therapy for treating GI cancer. It allows for developing a targeted radiation therapy plan while minimizing radiation exposure to healthy tissue, improving treatment success, and decreasing side effects. Medical diagnostics in GI tract organ segmentation is essential for accurate disease detection, precise differential diagnosis, optimal treatment planning, and efficient disease monitoring. This research presents a hybrid encoder-decoder-based model for segmenting healthy organs in the GI tract in biomedical images of cancer patients, which might help radiation oncologists treat cancer more quickly. Here, EfficientNet B0 is used as a bottom-up encoder architecture for downsampling to capture contextual information by extracting meaningful and discriminative features from input images. The performance of the EfficientNet B0 encoder is compared with that of three encoders: ResNet 50, MobileNet V2, and Timm Gernet. The Feature Pyramid Network (FPN) is a top-down decoder architecture used for upsampling to recover spatial information. The performance of the FPN decoder was compared with that of three decoders: PAN, Linknet, and MAnet. This paper proposes a segmentation model named as the Feature Pyramid Network (FPN), with EfficientNet B0 as the encoder. Furthermore, the proposed hybrid model is analyzed using Adam, Adadelta, SGD, and RMSprop optimizers. Four performance criteria are used to assess the models: the Jaccard and Dice coefficients, model loss, and processing time. The proposed model can achieve Dice coefficient and Jaccard index values of 0.8975 and 0.8832, respectively. The proposed method can assist radiation oncologists in precisely targeting areas hosting cancer cells in the gastrointestinal tract, allowing for more efficient and timely cancer treatment.

Endogenous economic structure, climate change, and the optimal abatement path

This paper endogenizes economic structure in Nordhaus's standard DICE model, and builds a dynamic intertemporal optimization model to determine long-term CO2 mitigation policy. Two basic macroeconomic sectors that vary in carbon intensity and suffer different damage from climate change are incorporated: one producing consumer goods, and another producing capital goods. The model finds a bidirectional interaction between climate change and economic structure. Climate change is able to reverse structure transition by decreasing investment rate. Meanwhile, economic structure evolution affects the choice of policies for managing climate change. Compared to the standard DICE model, our results show that we are likely to face a more severe climate situation; therefore, deeper mitigation efforts are called for, which requires investing an average of 3.2% of GDP per year for 2 °C warming goal. Mitigation efforts can become more effective for the economy as economic structure evolves and evokes future investment response, which will be determined not only by the normal intertemporal trade-off between present-day and future consumption, but also by another trade-off between high and low carbon-intensive goods.

Singular flat bands in the modified Haldane-Dice model

Flat bands can be divided into singular and non-singular ones according to the behavior of their Bloch wave function around band-crossing points in momentum space. We analyze the flat band in the Dice model, which can be tuned by a uniaxial strain in the zigzag direction and a Haldane-type next-nearest neighbor interaction, and derive the topological phase diagram of the modified Haldane-Dice model to obtain all band-gap closings with the central band. When the central band is flat, we determine its compact localized state and classify its behavior at all band-touching points by means of the Hilbert–Schmidt quantum distance. We find that the flat band remains singular for all band-touching points (topological phase transitions) with a maximal quantum distance and give expressions for the resulting non-contractible loop states on the real-space torus.

A study on loss function against data imbalance in deep learning correction of precipitation forecasts

Sample imbalance has always been an important issue to be solved in heavy precipitation forecasts. Introducing appropriate constraints into the loss function of a machine learning model can mitigate the negative impact of sample imbalance on model training. This study explores the role of Dice loss function in dealing with sample imbalance and verifies its application on the correction of heavy precipitation forecasts using the U-Net neural network. On this basis, the application of ordinal classification in forecasts correction is further verified. The results show that the concept of Dice loss is highly similar to that of threat score, which can suppress the negative impact of sample imbalance on heavy precipitation forecasts by ignoring most of the events that are insensitive to heavy precipitation forecasts. Model correction experiments further indicate that the correction models trained with weighted cross entropy, differentiable threat score loss and Dice loss (WCE, DTS and Dice models) improve the precipitation forecasts of ECMWF (European Centre for Medium-Range Weather Forecasts) model. For the forecasts of all precipitation levels, Dice model effectively suppresses false alarms while improving hits, whereas WCE model greatly promotes false alarms while significantly improving hits. In contrast, DTS model significantly suppresses large precipitation false alarms while improving its hits, but causes substantial small precipitation false alarms while significantly promoting its hits. As a result, for large precipitation forecasts, both Dice and DTS models perform significantly better than WCE model, while for small precipitation forecasts, Dice model performs better than DTS and WCE models. Furthermore, the correction model combining ordinal classification and Dice loss exhibits the best forecasting skill. This model further suppresses the false alarms of large precipitation while improving its hits.

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.

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.

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.

The Climate Change Challenge: A Review of the Barriers and Solutions to Deliver a Paris Solution

Global greenhouse gas (GHG) emissions have continued to grow persistently since 1750. The United Nations Framework Convention on Climate Change (UNFCCC) entered into force in 1994 to stabilize GHG emissions. Since then, the increasingly harmful impacts of global climate change and repeated scientific warnings about future risks have not been enough to change the emissions trend and enforce policy actions. This paper synthesizes the climate change challenges and the insofar insufficient mitigation responses via an integrated literature review. The fossil industry, mainstream economic thinking, national rather than international interests, and political strive for short-term interests present key barriers to climate mitigation. A continuation of such trends is reflected in the Dice model, leading to a 3.5 °C temperature increase by 2100. Despite receiving the Nobel Prize for integrating climate change into long-run macroeconomic analysis via the Dice model, increases in global mean temperatures overshooting the 1.5 °C to 2 °C Paris targets imply an intensified disruption in the human–climate system. Past and present policy delays and climate disruption pave the way for solar radiation management (SRM) geoengineering solutions with largely unknown and potentially dangerous side effects. This paper argues against SRM geoengineering and evaluates critical mitigation solutions leading to a decrease in global temperatures without overshooting the Paris targets. The essential drivers and barriers are discussed through a unified approach to tipping points in the human–climate system. The scientific literature presents many economically and technologically viable solutions and the policy and measures required to implement them. The present paper identifies the main barriers to integrating them in a globally cooperative way, presenting an efficient, long-term, and ethical policy approach to climate change.

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.

RICE50+: DICE model at country and regional level

Benefit-cost Integrated Assessment Models (IAMs) have been largely used for optimal policies and mitigation pathways countering climate change. However, the available models are relatively limited in the representation of regional heterogeneity. This is despite strong evidence of significant variation of local mitigation costs and benefits, institutional capacity, environmental and economic priorities. Here, I introduce RICE50+, a benefit-cost optimizing IAM with more than 50 independently deciding regions or countries. Its core foundation is the DICE model, improved with several original contributions. These include new calibrations on actual mitigation cost data, full integration of recent empirically based impact functions, alternative socioeconomic reference projections as well as normative preferences, including welfare specifications explicitly featuring inequality aversion. Due to its high level of regional detail, the model can support researchers in better investigating the role of heterogeneity in international cooperation, cross-country inequalities, and climate change impacts under a variety of mitigation pathways and scenarios.

Zigzag dice lattice ribbons: Distinct edge morphologies and structure-spectrum correspondences

Ribbons of two-dimensional lattices have properties depending sensitively on the morphology of the two edges. For regular ribbons with two parallel straight edges, the atomic chains terminating the two edges may have more than one choice for a general edge orientation. We enumerate the possible choices for zigzag dice lattice ribbons, which are regular ribbons of the dice lattice with edges parallel to a zigzag direction, and explore the relation between the edge morphologies and their electronic spectra. A formula is introduced to count the number of distinct edge termination morphologies for the regular ribbons, which gives 18 distinct edge termination morphologies for the zigzag dice lattice ribbons. For the pure dice model, because the equivalence of the two rim sublattices, the numerical spectra of the zigzag ribbons show qualitative degeneracies among the different edge termination morphologies. For the symmetrically biased dice model, we see a one-to-one correspondence between the 18 edge termination morphologies and their electronic spectra, when both the zero-energy flat bands and the dispersive or nonzero-energy in-gap states are considered. We analytically study several interesting features in the electronic spectra, including the number and wave functions of the zero-energy flat bands, and the analytical spectrum of unique in-gap states. The in-gap states of the zigzag dice lattice ribbons both exhibit interesting similarities and show salient differences when compared to the spectra of the zigzag ribbons of the honeycomb lattice.

Robust Enough? Exploring Temperature-Constrained Energy Transition Pathways under Climate Uncertainty

In this paper, we study how uncertainties weighing on the climate system impact the optimal technological pathways the world energy system should take to comply with stringent mitigation objectives. We use the TIAM-World model that relies on the TIMES modelling approach. Its climate module is inspired by the DICE model. Using robust optimization techniques, we assess the impact of the climate system parameter uncertainty on energy transition pathways under various climate constraints. Unlike other studies we consider all the climate system parameters which is of primary importance since: (i) parameters and outcomes of climate models are all inherently uncertain (parametric uncertainty); and (ii) the simplified models at stake summarize phenomena that are by nature complex and non-linear in a few, sometimes linear, equations so that structural uncertainty is also a major issue. The use of robust optimization allows us to identify economic energy transition pathways under climate constraints for which the outcome scenarios remain relevant for any realization of the climate parameters. In this sense, transition pathways are made robust. We find that the abatement strategies are quite different between the two temperature targets. The most stringent one is reached by investing massively in carbon removal technologies such as bioenergy with carbon capture and storage (BECCS) which have yields much lower than traditional fossil fuelled technologies.

Valley-contrasting interband transitions and excitons in symmetrically biased dice model

We study the exciton states in the symmetrically biased dice model, the electronic structures of which have an isolated flat band between two dispersive bands. At 1/3 or 2/3 filling, the model describes a two-dimensional semiconductor with the band edge at two degenerate valleys. Because of qualitative changes in the eigenvectors resulting from the bias term, the interband transition between the flat band and a dispersive band is valley contrasting under circularly polarized light. In terms of an effective-mass model and a realistic electron-hole interaction, we numerically calculate the spectrum and wave functions of the intravalley excitons, which are treated as Wannier-Mott excitons. We also discuss the fine structures of the exciton spectrum induced by the intravalley and intervalley exchange interactions. The symmetrically biased dice model thereby proves to be a new platform for valley-contrasting optoelectronics.