The radiative forcing (RF) of contrail cirrus is substantial, though short-lived, uncertain, and heterogeneous, whereas the RF from CO₂ emissions is long-term and more predictable. To balance these impacts, we calculate the social costs of CO₂ and contrail cirrus using a modified Dynamic Integrated Climate Economy (DICE) model, spanning three discount rates, two damage functions, and three climate pathways. The main case estimate of the global social cost ratio of contrail cirrus to aviation CO₂ emissions ranges from 0.075 to 0.57, depending on assumptions. Accounting for uncertainty in contrail cirrus RF and climate efficacy further widens this range. We also quantify flight-specific social costs of contrail cirrus by analyzing nearly 500,000 flights over the North Atlantic, revealing substantial variability due to meteorological conditions. While uncertainty is considerable, our findings suggest that carefully implemented operational contrail avoidance could offer climate benefits even when the social cost of additional CO₂ emissions is considered.

Governments worldwide are confronted with a need to curtail emissions of greenhouse gases to achieve the global climate targets outlined in the Paris Agreement. While carbon dioxide (CO2) remains the primary greenhouse gas targeted in climate policies, it is important to address emissions of non-CO2 forcers.Trade-offs between CO2 and other climate forcers are often determined based on Global Warming Potentials (GWP). An alternative approach is to use climate-economic approaches, estimating the social cost of different forcers. Here we focus particularly on aviation CO2 emissions and contrail cirrus. Specifically, we explore how the social cost of contrail cirrus can be estimated using a revised version of the integrated assessment model DICE. We analyze contrail forcing from a flight-specific model that considers their spatio-temporal variability. Further, DICE has been revised in particular with respect to the geophysical model (being based on the emulator FaIR 2.0.0), and also with changes in the parameterization of the discounting and damage functions. Additionally, we examine how the social cost of short-lived forcers (contrail cirrus) and long-lived emissions (CO2) is influenced by the discount rate and the future temperature pathway.Concerning spatio-temporal variability, we observe that both energy forcing and the social cost of contrail cirrus are strongly dependent on flight specific conditions, including as a strong diurnal variability. Furthermore, we find that the comparison between the social costs of contrail cirrus and the social cost of CO2 depends very strongly on the discount rate and the climate path the economy is following. This follows from the fact that the climate impacts of contrail cirrus are short lived, making their social cost less contingent than CO2 on how future climate impacts are valued through the discount rate, and correspondingly less affected by the long-term changes in global mean surface temperature. We also explore the additional insights gained from analyzing the ratio of the social cost of contrail cirrus to the social cost of CO2, beyond the information provided by analyzing the corresponding GWP values. Finally, the potential policy implications of the variability of the social cost of contrail cirrus are discussed.

Aviation contrail cirrus has important climate impacts. To construct efficient policies to reduce the uncertain, heterogeneous and short-lived climate impacts of contrail cirrus and balance these against the certain, homogeneous and long-lived climate impact of CO2 emissions the climate impact of CO2 and contrail cirrus need to be placed on a common scale. We analyze the social cost of CO2and of contrail cirrus as well their ratio using an updated version of the Dynamic Integrated Climate Economy (DICE) model with three different discount rates and three different future climate pathways. The social cost of contrail cirrus is less sensitive to the discount rate and less affected by the long-term temperature pathway than the social cost of CO2. However, the social cost of contrail cirrus is strongly dependent on specific meteorological conditions, which makes the social cost associated with individual flights vary by several orders of magnitude.

Rajamani et al. have presented estimates for a fair and equitable allocation of the remaining global greenhouse gas emissions that are compatible with meeting the temperature targets of the Paris Agreement. In this paper, we find that their approach yields a high emission allowance per capita to currently high-emitting countries such as Australia, South Africa, Saudi Arabia, Canada, and China. In fact, Rajamani et al. propose that these countries should get two to three times more allowances (emission space on a per capita basis) than for instance India and Ghana and they refer to this as a 'fair' allocation despite the fact that the latter countries have significantly lower per capita emissions, per capita income, and historical emissions. Furthermore, the allocation to several Western European countries, e.g. the UK and Sweden, is strongly negative. Hence, their approach tends to reward countries with high emissions and discriminate against countries with low emissions per capita despite the fact that Rajamani et al. argue that grandfathering cannot be seen as a fair principle for allocating emissions allowances. Our findings are not only of academic interest, but they carry important implications for the debates about climate litigation since several organizations have sued states based on essentially the same method as that used by Rajamani et al. Key policy insights center dot The allocation approach suggested by Rajamani et al. (2021) rewards high- emitting countries which undermines fairness principles like responsibility and equality. center dot Generous allocations to high-emitting countries reduce available emissions space for developing countries and low-emitting wealthy countries. center dot The suggested method by Rajamani et al. (2021) lacks transparency, making it difficult to justify why developing countries such as India and Ghana receive fewer allowances than the global average. center dot The allocation method suggested by Rajamani et al. (2021) is likely inappropriate for policy or litigation purposes since it is unclear in what respect the results obtained should be considered fair. center dot A complicated and non-transparent model is not a substitute for a democratic debate about different allocation approaches and the results they yield.

A rapid and sustained reduction of methane emissions has been proposed recently as a key strategy to meet the climate targets of the Paris Agreement. The social cost of methane (SCM), which expresses the climate damage cost associated with an additional metric ton of methane emitted, is a metric that can be used to design policies to reduce the emissions of this gas. Here, we extend the DICE-2016R2 model so that it includes an improved carbon cycle and energy balance model as well as methane emissions, methane abatement cost, and an atmospheric methane cycle explicitly to be able to provide consistent estimations of the SCM. We estimate the SCM to lie in the range 880–8100 USD/tCH 4 in 2020, with a base case estimate of 4000 USD/tCH 4 . We find our base case estimate to be larger than the average SCM presented in other studies mainly due to the revised damage function we use. We also estimate the social cost of carbon (SCC) and find that SCM estimates are less sensitive to variations in the social discount rate than the SCC due to the relatively short lifetime of methane. Changes in the parameterization of the damage function have similar relative impacts on both SCM and SCC. Furthermore, we evaluate the ratio of SCM to SCC as an alternative metric to GWP-100 of CH 4 to facilitate tradeoffs between these two gases. We find this ratio to lie in the range 7–33 in 2020, with a base case estimate of 21, based on an extensive sensitivity analysis with respect to the discount rate, damage cost, and underlying emission scenarios. We also show that the global warming potential (GWP) and the SCM to SCC ratio are almost the same if the inverse of the effective discounting (in the social cost calculations) is equal to the time horizon used to evaluate the GWP. For comparison, the most widely used GWP, i.e., with a time horizon of 100 years, equals 27, hence in the upper range of the ratio we find using the SCM to SCC ratio.

Decarbonizing the electricity system in order to contribute to climate change mitigation is a key policy goal. Yet, uncertain political and economic conditions (e.g., electricity prices) create uncertainty for energy companies. The dynamics of carbon price developments and aversion to uncertainty may have decisive impacts on companies' investment decisions and thus environmental and distributional outcomes. In this paper, we incorporate a dynamic portfolio approach in a simulation model of investments in the electricity sector to explore and disentangle the impacts of both uncertainty and risk aversion on companies' investment decisions. We find that policy uncertainty and risk aversion tend to delay the transition to a low-carbon energy system, with higher levels of either factor causing even further delays. However, the mechanism for the delay depends on how risk aversion is modeled, e.g. whether companies are averse to losses, or variances or if they use a higher discount rate in uncertain situations. Employing the loss-averse approach, the company prefers technology with a low likelihood of negative returns for the portfolio; meanwhile, the mean-variance approach indicates an aversion to both positive and negative deviations in returns. With a high discount rate, investors favor less capital-intensive technologies. To account for the impact of risk aversion in policy framework we, therefore, need more empirical work on understanding these behavioral traits of energy companies.

To achieve the climate goals of the Paris Agreement, greenhouse gas emissions from the electricity sector must be substantially reduced. We develop an agent-based model of the electricity system with heterogeneous agents who invest in power generating capacity under uncertainty. The heterogeneity is characterised by the hurdle rates the agents employ (to manage risk) and by their expectations of the future carbon prices. We analyse the impact of the heterogeneity on the transition to a low carbon electricity system. Results show that under an increasing CO2 tax scenario, the agents start investing heavily in wind, followed by nuclear and to some extent in natural gas fired power plants both with and without carbon capture and storage as well as biogas fired power plants. However, the degree to which different technologies are used depend strongly on the carbon tax expectations and the hurdle rate employed by the agents. Comparing to the case with homogeneous agents, the introduction of heterogeneity among the agents leads to a faster CO2 reduction. We also estimate the so called “cannibalisation effect” for wind and find that the absolute value of wind does not drop in response to higher deployment levels, but the relative value does decline.

It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 °C target of the Paris Agreement. Here we provide complementary perspectives to reiterate that substantial, broad, and sustained engagements beyond recovery packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from Integrated Assessment Models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.

在本文中,我们探讨了在向低碳发电转型过程中,可变可再生能源(VRE)和碳中和基本负荷技术之间的竞争,我们使用基于智能体的模型(ABM,也可翻译成智能体模型)研究了一个逐渐提高碳税的程式化系统,其中智能体是投资新发电容量的公司.智能体对不同投资选择的盈利能力进行预测.该模型中包含五种发电技术:煤炭、天然气、风能、太阳能光伏和更昂贵的碳中和基本负荷技术.我们将我们的模型与相应的优化模型进行了比较,得出了两个主要结论:(1)随着碳税的征收,VRE的初期装机容量呈现增长趋势.然而一旦碳税达到一定水平,VRE的装机容量开始下降,这是由于程式化的碳中和基本负荷技术开始扩张.(2)在有限的预见性下,我们发现,与优化模型在风能的过度投资相比,该模型在风电方面投资不足(前25).文中解释了这些动态现象的原因,并进行了广泛的敏感性分析.

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

Transitioning to a low-carbon electricity system requires investments on a very large scale. These investments require access to capital, but that access can be challenging to obtain. Most energy system models do not (explicitly) model investment financing and thereby fail to take this challenge into account. In this study, we develop an agent-based model, where we explicitly include power sector investment financing. We find that different levels of financing constraints and capital availabilities noticeably impact companies' investment choices and economic performances and that this, in turn, impacts the development of the electricity capacity mix and the pace at which CO2 emissions are reduced. Limited access to capital can delay investments in low-carbon technologies. However, if the financing constraint is too relaxed, the risk of going bankrupt can increase. In general, companies that anticipate carbon prices too high above or too far below the actual development, along with those that use a low hurdle rate, are the ones that are more likely to go bankrupt. Emissions are cut more rapidly when the carbon tax grows faster, but there is overall a greater tendency for agents to go bankrupt when the tax grows faster. Our energy transition model may be particularly useful in the context of the least financially developed markets.

In this paper we explore the competition between variable renewable energy sources (VRE) and a carbon-neutral baseload technology in the transition to a low-carbon power system. We study a stylized system subject to a gradually increasing carbon tax using an agent-based model where agents are power companies investing in new capacity. The agents make predictions of the profitability of different investment options. Five electricity generating technologies are available in the model: coal, gas, wind, solar PV and a more expensive carbon-neutral baseload technology. We compare the output from our model with a corresponding optimization model. We present two main findings: (1) installed capacity of VRE initially increases with a carbon tax. However, once the carbon tax has reached a certain level the installed capacity of VRE starts to decline due to competition with the stylized carbon-neutral baseload technology. (2) With limited foresight we find that the model underinvests (first 25 years) in wind and then overinvests in wind compared to the optimal solution. The reasons for these dynamic phenomena are explained and an extensive sensitivity analysis is carried out.

Global net-negative carbon emissions are prevalent in almost all emission pathways that meet the Paris temperature targets. In this paper, we generate and compare cost-effective emission pathways that satisfy two different types of climate targets. First, the common approach of a radiative forcing target that has to be met by the year 2100 (RF2100), and, second, a temperature ceiling target that has to be met over the entire period, avoiding any overshoot. Across two integrated assessment models (IAMs), we found that the amount of net-negative emissions—when global net emissions fall below zero—depends to a large extent on how the target is represented, i.e. implemented in the model. With a temperature ceiling (no temperature overshoot), net-negative emissions are limited and primarily a consequence of trade-offs with non-CO 2 emissions, whereas net-negative emissions are significant for the RF2100 target (temperature overshoot). The difference becomes more pronounced with more stringent climate targets. This has important implications: more stringent near-term emission reductions are needed when a temperature ceiling is implemented compared to when an RF2100 target is implemented. Further, in one IAM, for our base case assumptions, the cost-effective negative carbon emissions (i.e. gross anthropogenic removals) do not depend to any significant extent on how the constraint is implemented, only, largely, on the ultimate stringency of the constraint. Hence, for a given climate target stringency in 2100, the RF2100 target and the temperature ceiling may result in essentially the same amount of negative carbon emissions. Finally, it is important that IAM demonstrate results for diverse ways of implementing a climate target, since the implementation has implications for the level of near-term emissions and the perceived need for net-negative emissions (beyond 2050).

A Correction to this paper has been published: https://doi.org/10.1038/s41558-021-01021-w.