Economics and Econometrics of Climate Change: evaluating the drivers, socio-economic and development impacts, and policies of climate change

Understanding the impact of climate change on natural and socio-economic outcomes plays an important role in informing a range of national and international policies, including energy, agriculture and health. Furthermore, studying this interplay between natural and human systems sheds light on progress and future challenges required to achieve many of the UN Sustainable Development Goals. However economic models of (and those designed to include) climate impacts that guide decision makers rely on multiple components, for example projections of future climate change, damage functions, and policy responses, each of which comes with its own modelling challenges and uncertainties.

We invite research using process-based (e.g. Integrated Assessment Models) and empirical models of climate change to investigate future human and natural impacts, together with policy evaluation to explore effective mitigation, technology and adaptation pathways. Furthermore, we invite research on changes to, and new developments of climate-economic and econometric modelling.

Co-organized by
Convener: Luke JacksonECSECS | Co-conveners: Sam Heft-Neal, Susana Campos-MartinsECSECS, Felix PretisECSECS, David Stainforth
vPICO presentations
| Wed, 28 Apr, 13:30–15:00 (CEST)

vPICO presentations: Wed, 28 Apr

Modelling climate
Jingying Lykke, Eric Hillebrand, and Mikkel Bennedsen

Energy Balance models (EBMs) condense the complicated processes underlying temperature change into a single equation that describes the disequilibrium between absorbed radiation and emitted radiation, where the relation between temperature change and radiative forcing is established. The two-component EBM divides the climate into a mixed shallow ocean/atmosphere layer and a deep ocean layer, thereby accommodating the heat exchange between these two layers. However, the predominant nature of non-stationarity in the observations of climate variables poses challenges for standard statistical inference.

This study maps the two-component EBM into a versatile linear state space system (named EBM-SS model) of temperatures in the mixed layer and in the deep ocean layer with radiative forcing. This EBM-SS model allows for the modeling of non-stationarity and time-varying behaviors, the incorporation of multiple alternative variables for one object of interest, and the handling of missing observations. It opens up the possibility to couple with other frameworks to identify the drivers underlying the temperature evolution while maintaining consistency with physical theory. We decompose the latent state of radiative forcing, which is exogenous in this system, into a smooth component and a rough component. The smooth component is modeled as a random walk process with drift to represent the deterministic and stochastic trends of radiative forcing, while the rough component captures the transitory episodes in forcing following major volcanic eruptions.

We conduct an empirical analysis on data series at the global level from the period 1955 -- 2019, where the maximum likelihood estimates of the physical parameters are obtained via outputs from the Kalman Filter. We employ proxy variable for the temperature in the deep ocean layer, which is an integral quantity of the ocean temperature and represents the heat storage in the ocean.

How to cite: Lykke, J., Hillebrand, E., and Bennedsen, M.: A State Space Representation of a Two-Component Energy Balance Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-559,, 2021.

Eric Hillebrand, Mikkel Bennedsen, and Siem Jan Koopman

We propose a dynamic statistical model of the Global Carbon Budget (GCB) as represented in the annual data set made available by the Global Carbon Project (Friedlingsstein et al., 2019, Earth System Science Data 11, 1783--1838), covering the sample period 1959--2018. The model connects four main objects of interest: atmospheric CO2 concentrations, anthropogenic CO2 emissions, the absorption of CO2 by the terrestrial biosphere (land sink) and by the ocean and marine biosphere (ocean sink).  The model captures the global carbon budget equation, which states that emissions not absorbed by either land or ocean sinks must remain in the atmosphere and constitute a flow to the stock of atmospheric concentrations. Emissions depend on global economic activity as measured by World gross domestic product (GDP), and sink activity depends on the level of atmospheric concentrations and the Southern Oscillation Index (SOI). We use the model to determine the time series dynamics of atmospheric concentrations, to assess parameter uncertainty, to compute key variables such as the airborne fraction and sink rate, to forecast the GCB components from forecasts of World-GDP and SOI, and to conduct scenario analysis based on different possible future paths of World-GDP.

How to cite: Hillebrand, E., Bennedsen, M., and Koopman, S. J.: A Statistical Model of the Global Carbon Budget, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-826,, 2021.

Luca Margaritella, Marina Friedrich, and Stephan Smeekes

We use the framework of Granger-causality testing in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global as well as hemispheric temperatures. By allowing for high dimensionality in the model we can enrich the information set with all relevant natural and anthropogenic forcing variables to obtain reliable causal relations. These variables have mostly been investigated in an aggregated form or in separate models in the previous literature. An additional advantage of our framework is that it allows to ignore the order of integration of the variables and to directly estimate the VAR in levels, therefore avoiding accumulating biases coming from unit-root and cointegration tests. This is of particular appeal for climate time series which are often argued to contain specific stochastic trends as well as yielding long memory. We are thus able to display the causal networks linking radiative forcings to global and hemispheric temperatures but also to causally connect radiative forcings among themselves, therefore allowing for a careful reconstruction of a timeline of causal effects among forcings. The robustness of our proposed procedure makes it an important tool for policy evaluation in tackling global climate change.

How to cite: Margaritella, L., Friedrich, M., and Smeekes, S.: High-Dimensional Granger Causality for Climatic Attribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8020,, 2021.

Marco Quatrosi

The following paper analyses monthly trends for CO2 emissions from energy consumption for 31 European countries, four primary fuels (i.e., Crude Oil, Natural Gas, Hard Coal, Lignite) and three secondary fuels (i.e., Gas/Diesel Oil, LPG, Naphta, Petroleum Coke) from 2008 to 2019. Carbon dioxide emission has been estimated following the Reference Approach in the 2006 IPCC Guidelines for National Greenhouse Gasses Inventories. Country-specific (e.g. Tier 2) coefficient were retrieved from the IPCC Emission Factor Database and the UN Common Reporting Framework. Data on fuel consumption (e.g., Gross Inland Deliveries) were taken from the Eurostat database. This paper will fill some knowledge gap analysing monthly trends of carbon dioxide emissions for major EU Countries. As the progressive phase-out of carbon is taking place pretty much in all Europe, Crude Oil exerted the largest amount of carbon dioxide emissions in the period considered. Analysis of selected countries unveiled several clusters within the EU in terms of major source of emissions. As final step, the paper has endeavoured the task of fitting a model for monthly CO2 forecasting. The whole series presents two structural breaks and can be explained by an autoregressive model of the first order. Indeed, further speculations on a more appropriate fit and more fuels in the estimation, is demanded to other works.

How to cite: Quatrosi, M.: Analysis of monthly CO2 emission trends for major EU Countries: a time series approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1132,, 2021.

Modelling impacts
Caterina Santi

We propose a measure of investors’ climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. We find that investors’ climate sentiment generates a mispricing in the Emission-minus-Clean (EMC) portfolio (Choi et al., 2020), the portfolio that invests in emission stocks and goes short on clean stocks. Specifically, when investors share a positive attitude towards climate change, they tend to overvalue the negative externalities produced by emission stocks. Moreover, we show that carbon prices are a successful incentive to reduce CO2 emissions. Finally, our model can predict the price of the EMC portfolio also for long-term horizons.

How to cite: Santi, C.: Investors’ Climate Sentiment and Financial Markets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2016,, 2021.

Filippo Pavanello and Teresa Randazzo

Do remittances improve how households adapt to global warming? We explore this question exploiting a nationally-representative household data from Mexico - a country that experiences a large flow of remittances. Mexican households respond to excess heat by purchasing air conditioning and remittances can be used to adopt and use cooling devices that contribute to maintaining thermal comfort at home. Our results show that recipient households have a higher probability to adopt air conditioning at home with important implication on electricity consumption. The effect is even larger for those households living in high-temperature areas showing an important role of remittances in the climate adaptation process.

How to cite: Pavanello, F. and Randazzo, T.: Climate change and air conditioning adoption: the role of remittances, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7840,, 2021.

Francesco Colelli, Enrica De Cian, Malcolm Mistry, and Irene Mammi

Relevance: Extreme temperature events, both heatwaves and cold spells, can put pressure on power systems’ reliability by pushing power demand to record highs. Within the literature assessing the impacts of climate change on the energy sector,  gathering new evidence on the drivers of peak load is a pressing issue for multiple reasons. First, peaks in power load must be accommodated by exceptional ramp-up requirements of power generating units, so that in the future adapting to climate change may involve the construction of plentiful under-utilized peak generation plants, putting pressures on the decarbonization goals and increasing stranded assets risks. Furthermore, peak load shocks induced by extreme temperatures can coincide with reduced transmission and distribution capacity, further challenging the operation of electricity grids [1].

Both the empirical and modeling literature assessing the impacts of climate change on the energy sector have generally focused on aggregated electricity demand rather than on its peaks. Few available empirical studies  investigate how extreme events can affect peak demand focus on industrialized countries and estimate reduced-form models, that hold adaptation, economic growth, technology, and current infrastructure constant [2,3]. Our paper aims to fill this gap by identifying if and how climatic and socio-economic drivers can affect the magnitude of the peak load response to extreme weather events.

Methods: We assess these interrelated dynamics by exploiting high-frequency power demand data collected from load balancing authorities. Specifically, we assemble a novel dataset spanning for the last two decades across more than 100 power markets, comprising both countries (European Member States, Asian and African countries) and large sub-national regions (power markets in Japan, Australia and Russia and Federal States or Provinces in the US, Canada, Brazil and India). The dataset includes: i) daily peak and total load; ii) daily population-weighted exposure to weather from 3 hourly near surface temperature data at 0.25 degrees gridded resolution; iii) quarterly and yearly regional statistics and indicators on demography, economy, education and innovation. We investigate how daily peak load responds to extreme temperatures by adopting a suite of time-series and panel econometric methods that fully exploit the high-frequency and sub-national disaggregation of our dataset.

Results: Utilizing the innovative methodological framework proposed, we: i) identify how peak load responds to temperature extremes in different regions; ii) test if and how such response can be modulated by regional climatic and socio-economic characteristics; iii) derive cost implications due to the amplification of peak demand deriving from future increases in the intensity and frequency of extreme events.


[1] Yalew, S. G., van Vliet, M. T., Gernaat, D. E., Ludwig, F., Miara, A., Park, C., ... & Van Vuuren, D. P. (2020). Nature Energy, 5(10), 794-802.

[2] Auffhammer, M., Baylis, P., & Hausman, C. H. (2017). PNAS, 114(8), 1886-1891.

[3] Wenz, L., Levermann, A., & Auffhammer, M. (2017). PNAS, 114(38), E7910-E7918.

How to cite: Colelli, F., De Cian, E., Mistry, M., and Mammi, I.: Implications of extreme temperatures and socio-economic development on power markets’ peak demand across the world, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7923,, 2021.

Maximilian Kotz, Leonie Wenz, Annika Stechemesser, Matthias Kalkuhl, and Anders Levermann

Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate–economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.

How to cite: Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M., and Levermann, A.: Day-to-day temperature variability reduces economic growth, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9973,, 2021.

Anthony Harding

Weather matters at the local level. Microeconomic climate econometric analyses find evidence that weather has localized effects on labor supply, agricultural yields, mortality rates, and other socio-economic measures. However, macroeconomic analyses at the national level find no evidence that weather affects macroeconomic aggregates, such as GDP or aggregate productivity in the US and other developed economies. These results present a seeming contradiction. In this paper, I develop a general equilibrium theoretical model of an economy with localized weather shocks to bridge the gap between microeconomic and macroeconomic studies. The theoretical model provides a simple, modular framework for aggregating weather shock impacts. I apply the findings to an empirical setting in the US, a prime example of the contradictory findings. I first estimate the microeconomic impacts of weather on labor productivity growth across county-industry pairs in the US from 2002 to 2017. I then apply these to construct annual estimates of the impact of weather shocks across the economy on US GDP according to the theoretical framework. I construct confidence intervals using the estimated microeconomic impact uncertainty. Across the sample years, I find no evidence that the annual impacts are distinct from $0. I then deconstruct the aggregate impacts, again following the theoretical framework, to examine what generates this no-effect result. I find consistent evidence of statistically significant but heterogeneous effects across a majority of counties and industries. For example, within a given year, over two-thirds of counties are consistently and significantly impacted by their local weather. This effect is positive for some counties and negative for others. I show that it is the aggregation of these heterogeneous impacts across the spatial distribution and industrial composition of the economy that masks the impact of weather. This finding highlights the importance of understanding micro-level economic impacts and changes in the composition of economic activity for projections of future macroeconomic climate change impacts.

How to cite: Harding, A.: From Micro-level Weather Shocks to Macroeconomic Impacts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13189,, 2021.

Judit Carrillo, Albano González, Juan C. Pérez, Francisco J. Expósito, and Juan P. Díaz

Tourism is an essential sector of the economy of the Canary Islands. Tourism Climate Index (TCI) and Holiday Climate Index (HCI) are good indicators of environmental conditions for leisure activities. Regional climate model (RCM) has been addressed to analyze the impact of climate change on the indices of tourist areas. The initial and boundary conditions for future scenarios are prescribed through three CMIP5 models (GFDL, IPSL and MIROC)  surface and lateral boundary conditions within the Meteorological Research and Forecast (WRF), with a high resolution, 3x3 km. Two time periods (2030 – 2059, and 2070-2099) and two Representative Concentration Pathways (RCPs 4.5 and 8.5) are considered. Tourism indicators are projected to improve significantly during the winter and shoulder seasons, but will worsen in the summer months, including October, in the southeast, which is where hotels are currently located.

How to cite: Carrillo, J., González, A., Pérez, J. C., Expósito, F. J., and Díaz, J. P.: Impact of climate change on the future of tourism areas in the Canary Islands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11981,, 2021.

Future strategies
Biswadeep Bharali, Basanta Rajbanshi, Tashi Yangzom, Himal Dahal, Muden Rai, and Bhuwan Sewa

Net positive buildings can be the solution to slow down climate change. Old buildings and minimum code buildings only strive for structural protection, but they do not play a part in climate change mitigation solutions. In this study, we try to demonstrate net positive buildings' contribution in reducing global greenhouse gas emissions by taking the Guwahati region (India) as a study area. First, we developed a north-facing 3-B-H-K residential building plan with a two-car garage using the most commonly used construction materials in the region as a base case scenario. The weather data (like Temperature, Relative Humidity, and Airspeed) for 2020 is collected. With these inputs, the annual total energy consumption for the present climatic condition is simulated using the Ecotect tool. Then three different scenarios (modification of walls, modification of roofs, and floor modification) were created. The energy interpretation for the overall modified case was done and compared with the base case scenario. The result indicates that the total annual energy consumption for the overall modified case was reduced by 70% as compared to the base case model. The remaining 30% of the energy usage was supplied by renewable energy sources using photovoltaic cells to make net energy consumption zero.  These findings suggest that the old building can be renovated and modified to act as a mitigation solution to climate change.

How to cite: Bharali, B., Rajbanshi, B., Yangzom, T., Dahal, H., Rai, M., and Sewa, B.: Promoting Sustainable Housing with Fundamental Shift beyond Net-Zero and Green Building, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15833,, 2021.

Paolo Gazzotti, Andrea Castelletti, and Massimo Tavoni

International Environmental Agreements (IEAs) on greenhouse-gases (GHG) emissions reductions have demonstrated to be extremely hard to achieve. Even after the Paris Agreement, global cooperation still may be cursed by free-riding threats and the risk of withdrawals, discouraging countries from increasing their voluntary commitment. 

Several studies have already addressed the problem of agreement stability, self-enforcing strategies and coalition formation. Most of them are supported by models grounded on game theory, which account for participation rationales and address research questions about coalition-formation and optimal transfer incentives. However, diplomacy on climate change is a considerably complex problem, not exhaustively tractable by any game-theoretical framework, as it combines several deep international issues. Historical disappointments (i.e., the COP15 in Copenhagen, 2009) as well as encouraging achievements (i.e., the Paris Agreement in 2015) have also demonstrated the importance of negotiation and interaction rules in facilitating common ground for cooperation. 

Here we present an attempt to reproduce and investigate IEAs on GHGs mitigation though an Agent-Based negotiating framework. It follows a bottom-up approach, based on the insights of complex systems theory,  by modelling the behaviour of each region-representative negotiator. Single agents generate and update their mitigation proposals accounting for personal multi-objective evaluations over potential upcoming scenarios informed by Integrated Assessment Models projections, reactions to other participants proposals, and private negotiation strategies. Few and simple interaction rules, shared as common-knowledge, regulate the negotiation process and guarantee termination and agreement, although not imposing any minimum participation level. Several negotiations follow one another on regular time intervals, allowing all participants to rediscuss and modify their commitment.

Preliminary results point out the importance of agents multi-objective evaluations, as the potential co-benefit estimated may foster personal participation and satisfaction from the agreement achieved. The high flexibility provided by this Agent-Based approach allows to easily vary and test several implementations and settings, searching for the best conditions to obtain cooperation as emerging behaviour in a complex yet realistic dynamic. 

How to cite: Gazzotti, P., Castelletti, A., and Tavoni, M.: An Agent-Based Approach for International Environmental Negotiations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13698,, 2021.

David Hendry and Jennifer Castle

To achieve greenhouse gas (GHG) emissions targets of net zero requires an integrated strategy to remove all fossil fuel and other GHG emitters, less natural absorption and carbon capture and storage (CCS), possibly combined with atmospheric CO2 extraction. Clean electricity generation is achievable with known technologies, but storage is essential for when renewables cannot generate power. Small modular nuclear reactors (SMRs) could help with background supply, but storage can be facilitated by decarbonizing the transport sector then using electric vehicles plugged into an intelligent vehicle-to-grid network also helping balance electricity flows. Batteries alone seem inadequate for this, so we propose supplying electric vehicles with supercapacitors using graphene-based nanotubes (GNTs) which can charge and discharge rapidly, offset by reducing costs in vehicle manufacture from eliminating catalytic convertors. GNTs could supply trains in place of diesel-electric, and are very light so help developments in electric aircraft. By ensuring continuity of renewables electricity supply, capacity can expand. This could sustain methane pyrolosis or electrolysis production of hydrogen gas when electricity demand is low, for fuel cells and to replace households’ methane use while liquid hydrogen offers a high heat source for industry. New buildings must be constructed as net zero.

Renewables electricity is fully price competitive, especially given free storage from GNT vehicles; graphene prices are falling and there may be `Moore’s laws’ for nanotube manufacture and SMRs. Hydrogen is a more expensive fuel than methane, but its production at `off-peak’ could be cost saving by sustaining 100% continuous renewables’ generation. All these developments interact and should maintain employment in new industries with real per-capita growth, while retrofitting vehicles and housing. Relevant skills already exist, from off-shoring, manufacturing and supply, through making electric engines. Taxing non-recyclable and high-carbon content products (as with plastic bags) would incentivise alternatives. The usual tools of carbon pricing, cap and trade, research support, prizes for great ideas etc., remain available.

Methane, nitrous oxide and CO2 emissions are by-products of modern food production. Ruminant emissions can be reduced by dietary changes, and nitrous oxide by reducing nitrogen fertiliser use, replacing some by basalt dust that also absorbs CO2. Animal dietary changes could be cost saving with lower feed input, as their methane production wastes energy; and mineral rich basalt dust is far cheaper than artificial fertilisers. Crop production efficiency can be greatly improved, benefitting the environment and reducing cropland, along with vertical and underground farms. Aquaculture (including seaweed production) could be greatly improved, noting that off-shore wind farms also act as marine reserves. Human dietary changes to eating less mammal meat are feasible. Pandemic responses confirm rapid adjustment is feasible.

The analysis is illustrated by the UK because it created the Industrial Revolution leading to the GHG problem; its Climate Change Act  of 2008 has markedly reduced its emissions at little aggregate cost; and we have modelled its performance in economic and climate terms.

How to cite: Hendry, D. and Castle, J.: A strategy for achieving net-zero emissions by 2050, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4688,, 2021.

Stuart Jenkins, Eli Mitchell-Larson, Matthew Ives, Stuart Haszeldine, and Myles Allen

Integrated Assessment Model (IAM) design philosophy currently focuses on demand-side global carbon pricing as the principal policy tool to drive mitigation. However, ambitious mitigation scenarios produced with these IAMs rely heavily on the availability of carbon capture and storage (CCS) technologies in the mid-century, at the scale of billions of tonnes. If integrated assessment continues to employ demand-side policies exclusively we risk a gap forming between the requirements of economically-optimal mitigation trajectories in these IAMs and the reality of developed CCS capacity.

If CCS capacity fails to keep up with the ambition of mitigation policy, carbon prices could rise well above the cost of direct air capture as markets aim to drive residual emissions down. To avoid this, scenarios could include both supply and demand-side policies in tandem, where supply-side policies are targeted to increase CCS capacity to appropriate levels.

One such supply-side policy option is a Carbon Takeback Obligation (CTBO), where suppliers of fossil carbon are required to recapture and store an increasing fraction of the carbon in their products. This ‘stored fraction’ would be increased from near zero at present, up to 100% at the time of net-zero. By applying such a policy suppliers of fossil carbon products are forced to take responsibility for decarbonising their own products and provide the drive to develop the CCS capacity necessary to achieve net-zero emissions in the mid-century. In theory, if a CTBO was enforced globally the costs associated with the production of one tonne of CO2 would be capped around the price for the capture, transport and storage of diffuse, mobile, or otherwise hard-to-abate CO2 emission sources (i.e. the cost of direct air capture).

Here, we discuss the implementation of a global CTBO. Using an Integrated Assessment Model emulator, tuned to existing IAM carbon price/abatement rate relationships, we explore the total policy cost of applying a CTBO globally to achieve net-zero by 2050. Using the emulator we harmonise the combined CTBO and demand-side carbon price policies, and show how a SSP2-26 level of ambition can be achieved using these policies with a similar total policy cost. Further, we explore what additional near-term carbon prices can be included to achieve SSP2-19 level policy ambition. These results suggest there are significant benefits to defining climate policy around measures targeting suppliers of fossil carbon, including for long-term planning, implementation and governance of the policy, and overall cost. For further insight, and to provide a greater variety of policy options feeding into IPCC’s WG3, we argue IAMs should look to include CTBO-like policies in future scenario design.

How to cite: Jenkins, S., Mitchell-Larson, E., Ives, M., Haszeldine, S., and Allen, M.: Using an emulator to apply a Carbon Takeback Obligation alongside demand-side carbon pricing in Integrated Assessment Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10961,, 2021.

Angelo Carlino, Massimo Tavoni, and Andrea Castelletti

DICE (Dynamic Integrated Climate Economy) and other cost-benefit integrated assessment models are used to study the economically optimal climate policy or to evaluate economic performance of alternative policies, such as 2°C compliant emission trajectories.

Recently, DICE has been updated to provide economically optimal climate policies keeping global warming in line with the Paris Agreement. Yet, explicit uncertainty and adaptation modelling are still overlooked. Introducing these components requires a transition from the traditional perfect-foresight static decision-making framework to a dynamic one, able to change strategy in order to react to the realization of uncertainties.

In this work, starting from the updates proposed by Hansel et al. (2020), we present an updated DICE model that: i) explicitly represents adaptation in the form of temporary and long-term adaptation investment; ii) explicitly describes stochastic, parametric and structural uncertainty over the physical and socio-economic components of the model including adaptation efficiency and climate damages specification; iii) leverages self-adaptive control policies to implement a more realistic decision-making scheme that allows to adjust climate policy after that new information arises.

Results show that the self-adaptive policies allow for a reduction in the discrepancy between economically optimal climate policy and the 2°C temperature target set with the Paris Agreement, which resurfaces when introducing adaptation, also in presence of uncertainty. When using self-adaptive policies, average adaptation costs remain low and, thanks to the ability to modulate adaptation choices depending on the scenario eventually unfolding, also climate damages are maintained at a low level. As a result, more economic resources are made available for mitigation in the short-term resulting in a reduced temperature increase in 2100 for a same level of welfare.

How to cite: Carlino, A., Tavoni, M., and Castelletti, A.: Improving the decision-making in DICE: self-adaptive climate policies to handle explicit uncertainty and adaptation modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11500,, 2021.