The COACCH Scenario Explorer is conceived as a user friendly, non technical, intuitive tool to navigate and getting aquainted with the COACCH research.

The COACCH (CO-designing the Assessment of Climate CHange costs) research project is funded by the European Union’s Horizon 2020 Research and innovation Program and is conducted by a consortium of 14 European organizations.

The objective of COACCH is to produce improved and more spatially detailed assessment of the risks and costs of climate change in Europe that can be of direct usability by research, business, investment, and policy making.  To this end, COACCH developed an innovative science-practice integrated approach to knowledge co-design and co-delivery of outcomes with its stakeholder community.

The COACCH Scenario Explorer presents just a partial, condensed and selected, albeit representative, overview of project findings. For a full overview of COACCH results visit the project website.

The COACCH research integrates modelling and non-modelling investigation approaches for a 360° climate change impact assessment. The integration is not only vertical, from climate to the economy, but also horizontal across different impact areas (Figure 1). The full description of the COACCH integration strategy is presented in Watkiss et al (2018) and Bosello and Parrado (2018).

Model integration in COACCH

 

The Scenario Explorer showcases the results of the project multidisciplinary research effort leading to the macroeconomic assessment of climate change impacts on: agriculture forestry fisheries sea-level rise riverine-floods transport energy supply energy demand labour productivity.

Each impact has been quantified firstly in physical terms or direct economic losses by specific process based or econometric models. Then physical impacts and direct losses have been translated into macro economic costs, i.e. impacts on economic performances,  by a regionalized computable general equilibrium model. All the results are reported for the period 2020-2070 and for 138 European Union territorial units.

To fully characterize the uncertainty space, impacts are also specified for a worst, a medium and a best impact case. The range is obtained using as input to the macroeconomic model, for each impact, in each year, in each region, the highest and the lowest value produced by the sectoral impact assessment exercises. These, on their turn, depend mostly upon the different climate model used to perturb the sectoral impact model. Finally, all the results are presented under two different assumptions of capital mobility across the EU sub regions. One case represents a highly integrated EU where investments can rapidly move inter-regionally to pursuit higher returns. In this case economic shocks, especially those associated to impact on capital stock like sea-level rise and riverine floods, are also easier to propagate within the EU. The second case features a lower interregional mobility of capital implying that negative impacts tend to be more circumscribed within the region where they occur. Both assumptions are equally plausible, therefore both are reported to offer an overview as comprehensive as possible of future developments. 

COACCH impact assessments unfold across nine different combinations of climate change and social economic scenarios. The scenario space has been chosen to highlight the differential effect of economic and climate drivers on the impacts.

Four Shared Social Economic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) have been selected to span the uncertainty across economic and climatic futures. This is the “COACCH core set of scenarios” (see Figure 1 ). At the two extremes we have the sustainability - low climate change combination of SSP1-RCP2.6 and the fossil based high climate change combination of SSP5-RCP8.5.

Figure 1: COACCH core set of scenarios

SCENARIOS AND THEIR USE

Scenarios are defined as internally consistent descriptions of how the future may unfold. They do not provide predictions but offer “narratives” or “storylines” that support scientists in framing their investigations, when these are referred to future and inherently unknown time periods. The Representative Concentration Pathways – RCPs - (van Vuuren et al. 2011) and the Shared Social Economic Pathways – SSPs - (O’Neill et al 2014) are very popular and amply used to characterize climate change futures (Figure 2).

The four RCPs, or climate scenarios, span a range of possible future emission trajectories over the next century. Each corresponds to a level of total radiative forcing, a measure of warming potential (in W/m2) in the year 2100 which is reported in their name. Accordingly, RCP2.6 is a deep mitigation scenario that leads to a very low forcing level of 2.6 W/m2 by the end of the century. This level is only marginally higher compared to today forcing: 2.29 W/m2, (IPCC, 2013). In terms of emissions, it is a “peak-and-decline” scenario leading net zero emissions in 2060 and to very low greenhouse gas concentration levels. This scenario has a good chance of keeping the global mean temperature increase below 2°C respect to pre-industrial level.

RCP4.5 is a medium-low emission scenario. Also in this scenario, annual emissions (of CO2) will need to sharply reduce in the second half of the century, which will require significant climate policy (mitigation), but not as hard as in RCP2.6. By the end of the century the median temperature increases consistent with RCP4.5 is roughly 2.5°C respect to pre-industrial level.

RCP6.0 is a medium-high emission scenario. It assumes some mitigation effort, but weaker than that of RCP4.5. By the end of the century the median temperature increases consistent with RCP6.0 is 3°C respect to pre-industrial level.

RCP8.5 is one emission-rising non-stabilization scenario, representative of a non-climate policy future. GHGs carry on increasing over the century leading to very high concentrations by 2100. By the end of the century the median temperature increase consistent with RCP8.5 is beyond 4°C respect to pre-industrial level.

Figure 2: Temperature projections for SRES scenarios and the RCPs.

(a) Time-evolving temperature distributions (66% range) for the four RCP scenarios computed with the ECS distribution from Rogelj et al. (2012) and a model setup representing closely the carbon-cycle and climate system uncertainty estimates of the AR4 (grey areas). Median paths are drawn in yellow. Red shaded areas indicate time periods referred to in panel b. (b) Ranges of estimated average temperature increase between 2090 and 2099 for SRES scenarios and the RCPs respectively. Note that results are given both relative to 1980–1999 (left scale) and relative to pre-industrial (right scale). Yellow ranges indicate results obtained by Rogelj et al. (2012). Colour-coding of AR4 ranges is chosen to be consistent with AR4 (Meehl et al., 2007b). RCP2.6 is labelled as RCP3-PD here.
Source: Collins et al. (2013)

The SSPs, that can be used in combination with or independently from RCPs, describe instead social economic futures. The five SSPs are characterized through storylines following different paths for macro drivers like GDP and population, by the type of development, more or less sustainability oriented, and by the challenges they pose to adaptation and mitigation action (see Figure 3).

In SSP1 (sustainability) the world shifts gradually, but pervasively, toward a more sustainable path, with more inclusive development that respects environmental boundaries. Management of the global commons slowly improves. Educational and health investments accelerate the demographic transition, and economic growth shifts its emphasis on human well-being. Inequality is reduced both across and within countries. Consumption is oriented toward low material growth and lower resource and energy intensity.

In SSP2 (middle of the road) social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceeds unevenly, with some countries making relatively good progress while others fall short of expectations. Slow progress is made in achieving sustainable development goals. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Global population growth is moderate and levels off in the second half of the century. Income inequality persists or improves only slowly and challenges to reducing vulnerability to societal and environmental changes remain.

In SSP3 (regional rivalry) resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time. Population growth is low in industrialized and high in developing countries. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions.

In SSP4 (inequality) highly unequal investments in human capital, combined with increasing disparities in economic opportunity and political power, lead to increasing inequalities and stratification both across and within countries. Over time, a gap widens between an internationally connected society that contributes to knowledge- and capital-intensive sectors of the global economy, and a fragmented collection of lower-income, poorly educated societies that work in a labor intensive, low-tech economy. Social cohesion degrades and conflict and unrest become increasingly common. Technology development is high in the high-tech economy and sectors. The globally connected energy sector diversifies, with investments in both carbon-intensive fuels like coal and unconventional oil, but also low-carbon energy sources. Environmental policies focus on local issues around middle and high-income areas.

In SSP5 (fossil fueled development) the world places increasing faith in competitive markets, innovation and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, the push for economic and social development is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy intensive lifestyles around the world. All these factors lead to rapid growth of the global economy, while global population peaks and declines in the 21st century. Local environmental problems like air pollution are successfully managed. There is faith in the ability to effectively manage social and ecological systems, including by geo-engineering if necessary.

Figure 3. The SSPs’ Space: Challenges for mitigation and adaptation.

Source: O'Neill et al. (2014)

This impact category quantifies crop yield changes induced by climate change (Boere et al. 2019) and the associated macroeconomic costs. These last are assessed by the ICES computable general equilibrium model that uses as input information yield changes.

Yield changes derive from the IIASA biophysical model EPIC (Balkovič et al., 2013). They have been aggregated to match the economic model regional and crop resolution using the GLOBIOM model (Havlík et al, 2011). The data have been generated from an ensemble of 10 Global Circulation Models (GCMs) (Table 1, Bosello et al. (2020).

Yield changes are implemented into the economic model as changes in the productivity of land that is a production factor used by agricultural firms in each of the model regions.

This impact category quantifies changes in net physical wood production induced by climate change (Boere et al. 2019) and the associated macroeconomic costs. These last are assessed by the ICES computable general equilibrium model that uses as input information forest yield changes.

Changes in net physical wood production per hectare are derived from the biophysical G4M model (Kindermann et al. 2008). The GLOBIOM model has then been used to match G4M model output to the regional resolution of the economic model.

Changes in net physical wood production are implemented in the economic model as changes in the productivity of wood natural resource that is a production factor used by the timber industry in each of the model regions.

For these simulations, only one GCM, the HadGEM2-ES model, has been used. Accordingly, it was not possible to characterize a high, low, and medium range for these impacts.

This impact category quantifies changes in marine fish catches (Boere et al. 2019) and the associated macroeconomic costs. These last are assessed by the ICES computable general equilibrium model that uses as input information changes in catches.

Changes in catches are assessed applying two bio-physical models: the Dynamic Bioclimate Envelope Model (DBEM) (Cheung et al., 2016) and the Dynamic Size‐based Food web model (DSFM) (Blanchard et al., 2012).

Results are available for the RCP2.6 and RCP 8.5 climate change scenarios. Potential impacts on catches in RCP 4.5 and 6.0 have been reconstructed for every country by linear interpolation between RCP 2.6 and RCP 8.5 assuming these two RCPs as the two extremes of the interpolation.   

Changes in catches are implemented in the in the economic model as changes in the productivity of the fish natural resource that is a production factor used by fish industry in each of the model regions. For this impact category, data are available at the national and not at the NUTS level. Accordingly, the same shock on the fish stock productivity has been imposed to all coastal regions belonging to the same country. The effect on freshwater fish is not examined.

This impact category quantifies a set of direct physical and economic consequences from sea level rise (Lincke et al. 2019), and the associated macroeconomic costs.

Direct impacts and adaptation costs are computed for a “no additional adaptation scenario”, assuming constant protection at 1995 levels. The COACCH project analyzed also a “with incremental adaptation scenario”, where the demand for safety increases with increasing affluence and higher dikes are built with rising sea-levels. These results are not shown here, but full results are available at the COACCH data repository.

Direct impacts assessed are:

• Annual land loss due to submergence (km²/year): Land is considered to be unusable, and thus lost, if it is situated below the 1-in-1 year flood water level and not protected by a dike.
• Expected annual damages to assets by sea floods (million US$/year).
• Expected annual number of people flooded per year (thousands/year).
• Annual protection costs including construction of new dikes, raising of existing dikes, and maintenance of existing dikes (million US$/year).

These data are obtained applying the DIVA model (Hinkel and Klein, 2009; Hinkel et al, 2013; Hinkel et al, 2014).

Macroeconomic costs are determined applying the ICES computable general equilibrium model where SLR impacts affect regional economic performances through land loss, labour productivity loss, and capital loss. In particular:

• Land loss is implemented in the economic model decreasing the quantity of productive land available to agriculture.
• Labour productivity is reduced assuming that people flooded are not able to work for 2 working weeks per year.
• Capital stock is decreased according to the expected annual damages to assets by sea floods.

This impact category quantifies a set of direct physical and economic consequences from riverine floods (Lincke et al. 2019), and the associated macroeconomic costs. Direct impacts assessed are:

• Expected Annual Damages (EAD) in million USD PPP 2005 determined for three macro-areas: industry, commerce, and residential sector,
• Population exposed to floods.

These impacts have been computed applying the GLOFRIS model (Ward et al., 2020; Ward et al., 2017; Winsemius et al., 2016) and based upon climate forcing from an ensemble of 5 different global circulation models (NorESM1-M, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC-ESM-CHEM). The cross-model variability enabled the computation of a maximum, minimum and medium level of damage for each scenario combination.

Macroeconomic costs are determined applying the ICES computable general equilibrium model where riverine floods impacts affect regional performances through labour productivity loss, and capital productivity loss in the affected areas (for the matching between of GLOFRIS and the 24 sectors of the computable general equilibrium  model see Table 2 of Bosello et al. (2020)).
In particular:

• Loss of labour productivity is computed assuming that people affected by a flood event are unable to work for 2 weeks each year.

Loss of capital productivity is considered proportional to the fraction of capital lost to floods by each macro sector. This fraction is given by the ratio between EAD and the value of sectoral capital stock by region.

This impact category quantifies direct economic losses from climate change impacts on road transportation (Lincke et al. 2019), and the associated macroeconomic costs.

Direct economic losses have been assessed applying the OSDaMage model (van Ginkel et al, 2020) that computes direct infrastructural EAD to road assets in the former EU-28, with the exclusion of Malta and Cyprus. For this impact category data is available for RCP 4.5 and RCP8.5. Potential impacts in RCP 2.6 and 6.0 have been reconstructed for every region by linear interpolation. Impacts have been determined using climate forcing from an ensemble of 11 combinations of GCM-RCM which enabled to define the high, low, and medium level of potential damage.

Then, macroeconomic costs have been computed by the ICES computable general equilibrium model using direct losses (EAD) to determine changes in productivity of labour and capital production factors of the road transportation sector.

This impact category quantifies climate change impacts on wind and hydropower supply  (Schleypen et al., 2019) and the associated macroeconomic costs.

The relation between wind energy supply and climate change has been estimated running a panel regression with location (NUTS‐2) and multiple time (year, month, and hour) fixed‐effects. The econometric analysis links wind power supply data available at the hourly level for the 1986‐2015 period at the country‐level along with various levels of sub‐national aggregation (NUTS‐1 and NUTS‐2) from the EMHIRES database (Gonzalez Aparicio et al., 2016) and climate variables. Econometric estimates based on historical data are then used for projecting wind energy supply under the warming scenarios of all RCPs considered in COACCH using ensemble-mean temperature projections from four different regional climate models (KNMI-RACMO22E, IPSL‐CM5A‐MR, MPI‐ESM‐LR, and CNRM‐CM5).

For this impact category only the “central” or “medium” impact on the wind power generation measured by changes in Gwh is computed.

Changes in wind energy supply are assumed to be proportional to productivity changes in labour and capital production factors used by the regional wind-power sectors.

The relation between climate change and hydropower generation has been estimated econometrically merging data on electricity generation by energy source from the Global Energy & CO2 Data (Enerdata 2018) with high‐resolution climatic data from the Global Land Data Assimilation System (GLDAS v2.1) dataset (Rodell et al. 2004) for 1971 – 2016.

Impacts of future climate change at the sub-national level have been computed combining the econometric estimates with warming scenarios under RCP4.5 and RCP8.5 simulated using five different climate regional models (CCSM4, GFDL‐CM3, INM‐CM4, IPSL‐CM5A‐MR, and MIROC5).

Impacts under RCP6.0 have been obtained interpolating data from RCP4.5 and 8.5 using temperatures as a scaling factor. Impacts under RCP2.6 are set equal to zero given that the interpolation process originated extremely small numbers.

Changes in hydropower supply are assumed to be proportional to productivity changes in labour and capital production factors used by the regional hydropower sectors.

Macroeconomic costs are estimated by the ICES computable general equilibrium model that evaluates these productivity changes.

This impact category assesses climate change impacts on energy demand (Schleypen et., 2019) and the associated macroeconomic costs.

Demand elasticity to temperature of electricity, petroleum products, and natural gas for agriculture, industry, services, and the residential sector derive from De Cian and Sue Wing (2017). These data are used to estimate future trends in regional energy demand for each vector using high‐resolution ensemble-mean temperature projections from four Regional Climate Models (RCMs): KNMI RACMO22E, IPSL‐CM5A‐MR, MPI‐ESM‐LR, and CNRM‐CM5.

Macroeconomic consequences are assessed applying the ICES computable general equilibrium model. The desired change in energy demand in the agriculture, industry and services sectors are implemented as changes in the model sector-specific energy efficiency parameters. The residential sector has been approximated by the representative regional household. Residential energy demand shifts are obtained imposing exogenous shocks to household energy expenditure while keeping fixed the household budget constraint. This implies a re-adjustment of household consumption across all consumption items.

This impact category assesses climate change effects on labour productivity (Schleypen et al., 2019) and the associated macroeconomic costs.

The relation between climate and labour productivity are estimated applying a fixed‐effects panel regression method linking sectoral value added per working population to temperature, controlled for by including both the linear and its squared‐term. Historical climatic data comes from the Global Land Assimilation System (GLDAS v2.1). The estimated relation is then combined with future climate projections to obtain the future impacts. Climate projections derive from four high‐resolution Regional Climate Models (RCM): KNMI RACMO22E, IPSL‐CM5A‐MR, MPI‐ESM‐LR, and CNRM‐CM5.

Macroeconomic consequences are computed by the ICES computable general equilibrium model that uses as input information labour productivity changes in the agricultural and industrial sectors.

This simulation computes the macroeconomic estimates of all the sectoral impacts implemented jointly. It highlights the compounded effect and interactions of all the impacts on regional  GDP.

ICES

(Inter-temporal Computable Equilibrium System) is a top-down recursive-dynamic multi-sector and multi-country computable general equilibrium (CGE) model for the word economy, based upon the GTAP database. Its structure describes domestic and international linkages between economic activities, energy use, including renewables, and GHG emissions (including non-CO2 from agriculture and livestock). ICES is applied to provide a macroeconomic assessment of climate change impacts at NUTS2 level for Europe.

GLOBIOM

is an economic land use model for the EU agriculture and forestry sector. It provides a bio-economic assessment of climate impacts on these sectors regarding agricultural production, LULUCF, ecosystems, water, land-use, bioenergy, trade, and GHG-emissions at national and regional levels with high spatial resolution. In COACCH, it models climate effects on the agriculture (crops and livestock), forestry and fishery sector as well as feedback effects to other sectors. GLOBIOM is linked to EPIC and G4M.

EPIC

is a cropping system model that provides biophysical parameters and constraints of crop production to GLOBIOM for the estimation of water use, carbon, phosphorus and nitrogen cycling, as well as erosion and impacts of management practices on these cycles. Crop yields, biomass, and environmental variables are generated at the grid cell level by soil/site conditions, climate, and crop management information (tillage and crop residue management as well as fertilization and irrigation practices). Since EPIC is driven by daily weather data, it allows flexible projections of climate change impacts including weather extremity and drought events. In COACCH it assesses crop yields and associated environmental externalities under different climate scenarios through model linkages with GLOBIOM.

G4M

is a dynamic forest growth model that estimates how growth rates in forest net primary production (NPP) are affected by changes in climate drivers like temperature, precipitation, radiation, or CO2 concentrations. G4M provides the biophysical basis of the forestry sector in GLOBIOM. It models forest biomass and carbon stocks and estimates forest area change, carbon sequestration and emissions in forests, as well as impacts of carbon incentives (e.g., avoided deforestation) on the supply of biomass for bioenergy and timber.

MAgPIE

links crop, livestock, pasture and bioenergy production, consumption, prices and trade with spatially explicit resource availability for water and land, option for flexible regions (in particular Europe). To assess climate impacts, it is coupled to the global gridded vegetation and hydrology model LPJml. In COACCH it simulates the climate impacts on forestry and fishery.

LPJmL

is a Global gridded vegetation, crop and hydrology model with process-based representation of photosynthesis and phenology. Land use, precipitation, water, soil type, CO2 concentrations and radiation are model inputs.

DIVA

is an engineering sea-level rise model which assesses both physical and direct economic implication of climate change impacts on coastal areas in their multidimensionality: land lost and agricultural activity, built environment and infrastructure, forced migration, ecosystems, beach tourism. The model can consider mean sea-level rise, extreme events (e.g. storm surges), low probability/high impact events, tipping situations. In COACCH it i) incorporates accommodate and retreat options, ii) downscales socio-economic coastal development and urbanisation scenarios, iii) accounts for optimal adaptation responses as well as adaptation under robust decision making.

LISFLOOD, GLOFRIS, OSdaMage

build together a risk assessment framework linking risk evaluation tools and physical models to provide an assessment of climate change impacts of river floods across several dimensions (built environment and infrastructures, agriculture, ecosystems) at the basin level. In COACCH they co-deliver improved cost estimates and refinement of flood risk management strategies.

SFM/FLAM

provides explicit estimation of burned areas, validated for the EU. The model will be used to assess impacts and risks related to forest fires under climate change and socio-economic scenarios. These will be expressed in terms of estimated burned areas. In COACCH, SFM/FLAM: i) provides updated impact estimates using latest climate projections for the European domain (EURO-CORDEX or ISI-MIP), ii) extends the set of management options to reflect measures limiting fire spread (e.g. fire breaks).

CRF-ES: Climate Response Function-Energy Supply (Hydropower)

A panel regression model is used to estimate the (reduced-form) relationship between runoff and temperature variability on hydropower generation at the country-level.

The relationship takes the form:

         (1)

• : log of annual hydropower generation
• : total annual runoff and mean annual temperature
• : vector of control variables controlling for installed hydroelectric capacity, final electricity consumption, share of hydropower capacity, and electricity production mix (gas, oil, coal, and nuclear).
• : time-invariant country fixed-effects
• : linear and quadratic time trends

Potential impacts of future climate change are computed by combining the estimated parameters from equation (1) with RCP6.0. Current and future climate is defined as the mean of the climatic variables between 1971-2000 (historical), 2031-2070, and 2071-2099, respectively.

CRF-ES: Climate Response Function-Energy Supply (Wind power)

A panel regression model is used to estimate the (reduced-form) relationship between wind intensity and wind power generation with a sub national (NUTS-2) resolution in the EU. The estimated relation is:

         (2)

• : log of hourly wind factor capacity in NUTS-2 region i
• : 10-m instant wind gust (and its second-degree polynomial) and air density at time t
• : time-invariant NUTS-2 fixed-effects
• : year, month, and hourly fixed-effects

The potential impacts of future climate change on wind energy production in Europe is estimated  by combining the parameters from Equation (2) with two Representative Concentration Pathway (RCP4.5 and RCP8.5).

CRF-ED: Climate Response Function-Energy Demand

De Cian and Sue Wing (2017) estimate the relationship between per capita demand for electricity, petroleum products, and natural gas, in the agriculture, industry, residential, and commercial sectors for tropical and temperate countries as a function of per capita gross domestic product (GDP) and exposure to hot (>27.5 °C) and cold (<12.5 °C) days. These estimates are combined with future projections of temperature in RCP4.5 and RCP8.5 to project future energy demand trends under climate change.

CRF-L: Climate Response Function-Labour Productivity

A panel regression model is used to estimate the (reduced-form) relationship between hours worked and temperature. The analysis uses a large georeferenced dataset at the second administrative level with nearly 10,000 observations and global coverage. The estimated relation is:

         (3)

•  is the change in the log of the total number of hours worked in sector s in region i in year t
•  represents the non-linear impact of regional annual mean temperature on labour supply
•  are time-invariant regional fixed-effects
•  are year fixed-effects

 

To estimate the impact of future climate change, parameters estimated in (3) are combined with statistically downscaled (0.5° x 0.5°) simulations of daily global climate data from a multi-model ensemble of Global Climate Models (GCMs) from the CMIP5 ensemble.

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Meehl, G.A., Stocker, T.F., Collins, W.D., Friedlingstein, P., Gaye, A.T., Gregory, J.M., Kitoh, A., Knutti, R., Murphy, J.M., Noda, A., Raper, S.C.B., Watterson, I.G., Weaver, A.J. and Zhao, Z.-C. (2007) Global Climate Projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. and Miller, H.L., Eds., Climate Change 2007: The Physical Science Basis, Contribution of: Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 747-846

O'Neill, B.C., Kriegler, E., Riahi, K., Ebi, K.L., Hallegatte, S., Carter, T.R., Mathur, R., van Vuuren, D.P., (2014). A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Clim Change 122, 387-400.

 Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., (2017). "The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview". Global Environmental Change. 42: 153–168. doi:10.1016/j.gloenvcha.2016.05.009. ISSN 0959-3780.

Rodell, M.,  Houser, P. R.,  Jambor, U.,  Gottschalck, J.,  Mitchell, K.,  Meng, C.-J.,  Arsenault. K.,  Cosgrove, B., Radakovich, J.,  Bosilovich, M.,  Entin, J. K.,  Walker, J. P.,  Toll, D. L. (2004)  The  Global  Land  Data Assimilation System. Bull AmerMeteorolSoc85:381-394, doi:10.1175/BAMS-85-3-381.

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Schleypen, J.R., Dasgupta, S., Borsky, S., Jury, M., Ščasný, M., Bezhanishvili, L. (2019). D2.4 Impacts on Industry, Energy, Services, and Trade. Deliverable of the H2020 COACCH project.

van Ginkel, K. C. H., Dottori, F., Alfieri, L., Feyen, L., and Koks, E. E. Direct flood risk assessment of the European road network: an object-based approach, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-104, in review, 2020. https://nhess.copernicus.org/preprints/nhess-2020-104/

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Ward, P. J., Jongman, B., Aerts, J.C.J.H., Bates, P.D., Botzen, W.J.W., DIaz Loaiza, A., Hallegatte, S., Kind, J.M., Kwadijk, J., Scussolini, P., Winsemius, H.C., (2017). A global framework for future costs and benefits of river-flood protection in urban areas. Nat. Clim. Chang. https://doi.org/10.1038/nclimate3350

Watkiss, P., Hunt, A., Hof, A., van Vuuren, D., (2018). D1.6 Protocol for impact assessment studies. Deliverable of the H2020 COACCH project.

Winsemius, H. C., Aerts, J. C., van Beek, L. P., Bierkens, M. F., Bouwman, A., Jongman, B., ... & Ward, P. J. (2016). Global drivers of future river flood risk. Nature Climate Change, 6(4), 381.

COACCH results span over multiple dimensions: space (countries-regions), time (years), scenarios (combinations of SSPs and RCPs), climate change impacts. These are further reported with uncertainty ranges and under different assumptions on investment mobility. To enable the amplest possibility of comparisons across dimensions, provide comprehensive overviews, and allow users the possibility to focus on specific aspects of interests, two different visualization modes of project results are offered.

• Maps are used to emphasize and summarize the spatial distribution of impacts.
• Graphs are used to enable more specific explorations along the other dimensions.

Learn how to explore in our mini-tutorials and... enjoy the tour!

Exploring with maps

Exploring with graphs