Research Article

Physical and economical consequences of climate alter in Europe

See all Hide authors and affiliations

  1. Edited past Hans-Joachim Schellnhuber, Potsdam Constitute for Climate Bear on Enquiry, Potsdam, Germany, and approved December thirty, 2022 (received for review Baronial eleven, 2022)

Loading

Abstruse

Quantitative estimates of the economic amercement of climate change usually are based on aggregate relationships linking boilerplate temperature change to loss in gross domestic product (Gross domestic product). Yet, there is a clear need for farther particular in the regional and sectoral dimensions of touch assessments to design and prioritize adaptation strategies. New developments in regional climate modeling and physical-impact modeling in Europe permit a better exploration of those dimensions. This commodity quantifies the potential consequences of climate modify in Europe in 4 market impact categories (agronomics, river floods, littoral areas, and tourism) and one nonmarket impact (man health). The methodology integrates a set of coherent, high-resolution climatic change projections and physical models into an economic modeling framework. Nosotros find that if the climate of the 2080s were to occur today, the annual loss in household welfare in the European Matrimony (EU) resulting from the four market impacts would range between 0.two–1%. If the welfare loss is assumed to be constant over time, climate change may halve the European union's annual welfare growth. Scenarios with warmer temperatures and a higher rise in body of water level consequence in more severe economic damage. However, the results show that there are large variations across European regions. Southern Europe, the British Isles, and Central Europe N appear most sensitive to climate change. Northern Europe, on the other manus, is the only region with net economic benefits, driven mainly past the positive effects on agriculture. Coastal systems, agriculture, and river flooding are the most important of the iv market impacts assessed.

  • climate adaptation policy
  • climate impact and accommodation assessment
  • integrated assessment model
  • computable general equilibrium

Adaptation is becoming a key issue of post-2012 international climate policy negotiations. The December 2009 Copenhagen Accord (1) establishes that past 2022 developed countries volition provide US$ 100 billion per year to address the needs of developing countries, including funding for adaptation. Indeed, fifty-fifty ambitious mitigation policies [east.g., the 2 °C target proposed by the European Matrimony (EU) and endorsed by the G8 (2, 3)] will demand to be complemented by adaptation strategies to lessen the affect of residual warming (iv). Europe is preparing for a coordinated adaptation climate strategy from 2022, equally set up out in the European Commission White Paper on Accommodation (5). One of its main conclusions is that much still is unknown almost the potential impacts of climate change on the European economy as a whole or with respect to different economic sectors and geographical regions of Europe (half-dozen–9).

The quantitative assessment of the economic impacts of climate change is vital for justifying strategies to curb global warming and minimize detrimental consequences. Evaluating the effects of climatic change in the very long term is an extremely complex issue because of incomplete scientific methodologies and data gaps. For this reason, the cess must account for the many sources of uncertainty, including time to come climate, demographic change, economic development, and technological change.

Most studies (e.g., 10–15) take focused on climate damage functions as reduced-form formulations linking climate variables to economic impacts [ordinarily boilerplate global temperature to gross domestic product (GDP)]. Nevertheless, such a top-down approach is unsatisfactory for the following reasons: Damage estimates often are derived from the literature, thus originating from dissimilar and possibly inconsistent climate scenarios. They also lack the necessary geographical resolution for assessing regional impacts and prioritizing adaptation policies. Moreover, only the average temperature and precipitation usually are included, ignoring other moments of the probabilistic distribution and other relevant climate variables.

We present here a high-resolution, regionally focused, and integrated assessment of the concrete and economic effects of climate change in Europe. The assay is innovative because it integrates (i) climate information with loftier space–time resolution; (two) detailed modeling tools specific for each affect category considered; and (iii) a multisectoral, multiregional economic model. The appraisal presented herein builds on examples of assessments fabricated elsewhere (e.g., for the United states of america, meet refs. 16–20; for a global assessment, see ref. 21).

V impact categories have been addressed in this study: agronomics, river basins, coastal systems, tourism, and human health. These five aspects are highly sensitive to changes in mean climate and climate extremes. For this report the Eu has been divided into 5 regions to simplify estimation (Fig. S1): Northern Europe, the British Isles, Central Europe North, Key Europe Due south, and Southern Europe.

Methodological Framework

The consistent methodological framework that integrates the climate information, physical-impact models, and economic models is shown in Fig. S2. In the first phase, daily and 50-km resolution climate information are obtained from climate models. In the second stage, these data are used as input to run the five physical-impact models. (See SI Text for detailed explanations on the models and Table S1 for their specific climate data input.)

In the third stage, the concrete-touch models and their associated straight economic furnishings are introduced into a multisectoral computable general equilibrium (CGE) model (22), General Equilibrium Model for Energy-Economic system-Surround Interactions (Jewel-E3 Europe), which models well-nigh Eu countries individually.

This study has other distinctive features. We take modeled the economic effects of futurity climate change (projected for the 2080s) on the current economy as of 2022. Several authors have followed this approach (due east.g., 23). This quasi-static analysis would be the equivalent of having the 2080s climate in today's economy. The alternative approach (followed, e.g., in ref. 24) would be to model the effect of the time to come climate on the future economy. Implementing a static arroyo has the advantage that hypotheses on the future evolution of the economic system over the side by side eight decades are not needed, thereby minimizing the number of assumptions. Moreover, the interpretation of the results becomes simpler. Within this quasi-static economical metrics, nosotros also present undiscounted impacts. Fourth dimension discounting is a fundamental and controversial event in evaluating the impacts of climate change (25, 26).

A baseline scenario has been run for 2022 assuming no climatic change. The culling scenario considered the influence of climatic change in the economy. The results presented compare the values of welfare and GDP of the climate scenario with those of the baseline scenario.

This study likewise has estimated "potential impacts" (27), which do not consider public adaptation policies. The assessment of potential impacts in various sectors facilitates the identification of priorities in public adaptation policies. In the models applied in this analysis, only private adaptation actions take been taken into account (due east.k., farm level adaptation in agriculture), but no new explicit public adaptation policies have been considered. Although the littoral systems DIVA model uses a more sophisticated cost–benefit framework to decide the optimal level of adaptation, in this study, this option has been disabled to measure the potential impact of sea-level rise (SLR).

Scenarios.

We accept considered four climate futures for the 2080s (Table 1) to reflect the uncertainty associated with the driving forces of global emissions and the response of climate to greenhouse gas (GHG) concentration. 2 global socioeconomic scenarios have been selected from the Intergovernmental Panel on Climate Alter (IPCC) Special Report on Emissions Scenarios (SRES) (28): the high-emission A2 scenario and the lower-emission B2 scenario (CO2 concentration of 709 ppm and 560 ppm past the finish of this century, respectively). For each SRES scenario, climate output from ii land-of-the-art regional climate models (RCMs), nested inside a global climate model (GCM), accept been selected from the Prediction of Regional Scenarios and Uncertainties for Defining European Climate change Risks and Effects (PRUDENCE) project (29). Daily RCM output at 50-km resolution has been used to drive the concrete-impact models. In the following, all climate change numbers refer to a comparison of the 30-y periods 1961–1990 and 2071–2100.

View this table:

  • View inline

Table 1.

Summary of socioeconomic and climate scenarios

Temperature and Precipitation.

The scenarios considered atomic number 82 to an average temperature increase in Europe between 2.5 °C and 5.4 °C (Table i). These figures depend on the GHG emission scenario called and the climate model used (temperature and precipitation maps appear in Figs. S3 and S4). Hereafter, the climate futures are called "scenarios" and are distinguished by the Eu temperature increment: 2.5 °C (B2 HadAM3-HIRHAM), three.9 °C (A2 HadAM3-HIRHAM), 4.1 °C (B2 ECHAM4-RCAO), and 5.4 °C (A2 ECHAM4-RCAO). Northern Europe is the expanse with the highest temperature increment in the 2.five °C and 3.nine °C scenarios, whereas in the iv.1 °C and v.4 °C scenarios, Primal Europe Due south and Southern Europe would feel the largest temperature increase. The more than oceanic British Isles have the everyman temperature increase throughout all scenarios. The global temperature increase of the scenarios is in the range of 2.3–iii.1 °C. 1 A2 simulation shows lower warming than ane of the B2 simulations; it should exist kept in listen that modeled projections of regional climate change have a larger spread than projections of global modify.

The regional atmospheric precipitation blueprint is similar in all scenarios. The Central Europe S and Southern Europe regions would experience almanac decreases compared with the 1961–1990 control menses, whereas virtually other EU regions would have positive precipitation changes in all scenarios but with large seasonal differences.

Ocean-Level Ascension.

Co-ordinate to the IPCC (30, 31), the incertitude range of the projected SLR is wide. Given recent evidence on accelerated SLR (32), we consider just the loftier-climate-sensitivity case. This case leads to a global SLR in the range of 49–59 cm by the end of the century (Tabular array 1). The high range of SLR of the IPCC Third Assessment Report (TAR), 88 cm, also has been studied for the coastal organization impact every bit a variant of the 5.iv °C scenario.

Results

Agriculture.

Because the production and quality of cultivated crops and their use of water are influenced directly by local climate variables and atmospheric COtwo, agronomics is particularly susceptible to climate change (33–36). Agronomics is the main user of land and water and withal plays a dominant economic role in many rural areas of Europe. Previous studies have shown that the stress imposed by climate change on agriculture will intensify the regional disparities between European countries (7, 8, xiv).

Nosotros investigated the response of the distribution of premium and productivity of crops in Europe to potential climatic change induced by increased GHG forcing. The assessment linked biophysical and statistical models in a rigorous and testable methodology, based on the electric current understanding of processes of crop growth and development, to quantify ingather responses to changing climate weather condition.

We found that the 2080s climate would have a rather dramatic spatial agricultural issue and a serious impact on aggregated regional production (Table two). In the 2080s the scenarios of lower warming could pb to pocket-size changes in EU yields, whereas the v.4 °C scenario could mean a reduction in crop yields by 10%. All 2080s scenarios show considerable regional disparities in impacts on agriculture. Southern Europe would experience yield losses that would become relatively high under the 5.4 °C scenario—about 25%. The Cardinal Europe regions would experience moderate changes in yield. In all scenarios Northern Europe would benefit from positive yield changes, and, to a lesser extent, the British Isles would benefit in the 4.i °C and 5.4 °C scenarios. These effects result from the dominance of the longer growing season. A group of countries (e.chiliad., Ireland, Belgium, Germany, France and the Netherlands) may be at risk if limitations on the use of fertilizers in agronomics are considered. Romania would experience higher potential gain, partly because of the considerable weight of agriculture in its economy.

View this table:

  • View inline

Table ii.

Physical almanac impacts in agriculture, river basins, coastal systems, and tourism of 2080s climate-modify scenarios in the electric current European economic system

River Floods.

River floods are the most common natural disaster in Europe (37), resulting in large economical losses through direct impairment to infrastructure, property, and agricultural land and through indirect losses inside flooded areas and beyond. The costs arising from floods have increased rapidly during the terminal decades, although the observed upward tendency in flood damage can be attributed largely to socioeconomic factors (38). Global warming generally is expected to increase the magnitude and frequency of farthermost atmospheric precipitation events (39, twoscore), which may pb to more intense and more frequent river floods.

Estimates of changes in the frequency and severity of river floods are based on simulations using the LISFLOOD hydrological model (41). This model has been adult for operational flood forecasting at the European scale and is a combination of a filigree-based water-balance model and a 1-dimensional hydrodynamic aqueduct flow-routing model. Because it is spatially distributed, the model can take account of the spatial variation in state use, soil backdrop, and climate variables. The LISFLOOD model transfers the climate-forcing data (temperature, precipitation, radiation, wind-speed, and humidity) into estimates of river runoff. Past using extreme value analysis, changes in flood magnitude at different return periods are derived (42). From the calculated flood inundation depths, expected annual economical damage and the population exposed are estimated using state-specific flood depth–damage functions, information on land use, and information on population density. The projections assume no growth in exposed values and population or adjustments of electric current inundation protection standards and hence consider just the effects of climatic change on overflowing risk.

River flooding would affect 250,000–400,000 additional people per yr in Europe by the 2080s (Table 2). The increase in direct impairment from river floods in the 2080s ranges from €7.7 billion to €15 billion, more than doubling the annual average amercement during the catamenia 1961–1990. In full general, the higher the hateful temperature increase, the higher are the projected increment in people exposed to floods and the expected amercement. The impacts at the regional level vary substantially, deviating strongly from the Eu average. Flood damages and people afflicted are projected to increment across much of Western Europe, the British Isles, and the Key Europe regions. Decreases in overflowing damage are projected consistently for northeastern parts of Europe considering of a reduction in jump snowmelt floods.

Coastal Systems.

Coastal regions are areas where wealth and population are full-bodied and are undergoing rapid increases in population and urbanization (43, 44). SLR is a direct threat to productive infrastructures and to the residential and natural heritage zones.

The bottom-up coast assessment is based on the Dynamic and Interactive Vulnerability Assessment (DIVA) tool, an integrated impact–adaptation model allowing interaction between a series of biophysical and socioeconomic modules to assess the impacts of SLR (45). A major weakness of before studies is that they examined merely a subset of the physical consequences of SLR; DIVA allows all the major direct impacts of SLR to exist evaluated quantitatively in physical terms. These effects include (i) straight impacts on erosion, (2) increased alluvion chance and inundation, (iii) coastal wetland loss and alter, and (iv) surface salinization. Accommodation is an explicit part of the model, and the benefits of a range of homogenous adaptation options can be explored together with their costs, including options from no protection to full protection, together with intermediate options which narrate more realistic accommodation responses in the context of Europe.

The number of people annually affected by sea floods in the reference year (1995) is estimated to exist 36,000. Without adaptation, the number of people affected annually past flooding in the 2080s increases significantly in all scenarios and ranges from 775,000–5.5 1000000 people (Table 2). The British Isles, Central Europe North, and Southern Europe are the areas potentially most affected by littoral floods. Even so, when adaptation (dikes and embankment nourishment) is taken into business relationship, the number of people potentially exposed to floods is reduced significantly.

The economic costs to people who might migrate because of land loss (through submergence and erosion) also increases substantially under a high rate of SLR, bold no adaptation, and the costs increase over fourth dimension. When adaptation measures are implemented, this displacement of people becomes a small-scale affect. This result indicates the important benefit of adaptation to coastal populations afflicted by SLR.

Tourism.

Tourism is a major economic sector in Europe, with the current annual flow of tourists from Northern to Southern Europe bookkeeping for one in every six tourist arrivals in the globe (46). Climate change has the potential to modify tourism patterns in Europe radically past inducing changes in destinations and seasonal need structure (47).

The tourism report faux the major outdoor international tourism flows within Europe. The study represents an improvement on before work because information technology integrates the climate component of tourist activeness (climate suitability was expressed with the tourism climate alphabetize, see ref. 48) with the economical assay of tourist demand flows, considering also seasonality effects in a tourist regional demand model.

For the 2080s, the distribution of climatic conditions in Europe is projected to change significantly. For the spring season, all climate model results show a articulate extension toward the n of the zone under practiced conditions. Excellent conditions in bound, which are found mainly in Spain in the baseline menstruum, could spread across nearly of the Mediterranean littoral areas by the 2080s. Changes in autumn are comparable to the ones in spring. In summer, the zone of skillful weather condition also expands toward the north only at the expense of the south, where climatic atmospheric condition would deteriorate. These results match the findings of earlier studies into the impact of climate change on climate suitability for tourism (e.g., 49).

Southern Europe, which currently accounts for more than half of the total Eu capacity of tourist accommodation, could be the merely region with a reject in bed nights, estimated to range betwixt 1% and 4% depending on the climate scenario (Tabular array 2). The remainder of Europe is projected to have large increases in bed nights, in the range of 15–25% for the two warmest scenarios. The changes in bed nights caused by irresolute climate weather tin be estimated econometrically, leading to changes in expenditure associated with bed nights. In all climate scenarios in that location would exist additional expenditures, with a relatively small Eu-wide positive bear upon of €4–xviii billion, depending on the scenario and climate model used.

A cardinal supposition is that the tourism arrangement has full flexibility in responding to climate change. Climatic change can bear on overall demand, and the seasonal distribution of tourists is determined exclusively by climate factors. However, if institutional factors (e.m., school holidays) limit that seasonal flexibility, results could be quite different. In that case, for case, Southern Europe might not exist able to recoup for the summertime losses with gains in the shoulder seasons.

Human Health.

Climate change has a range of complex interlinkages with health (l), including direct impacts, such as temperature-related illness and decease and the wellness impacts of extreme atmospheric condition events. Other impacts follow more than indirect pathways, such as those that give rise to water- and nutrient-borne diseases, vector-borne diseases, or food and h2o shortages.

At that place is a direct relationship betwixt bloodshed and temperature that differs by climatic zone and geographical area (51). High ambient temperature is associated with mortality from heat stroke and also illnesses (e.thousand., cardiovascular diseases). Nevertheless, rise temperatures also reduce wintertime excess deaths. The projections of the impacts of climate change on heat-related and cold-related bloodshed were based on empirical relationships between mortality and current temperature (51–53). The study used daily projected temperature information at a fifty-km filigree resolution across Europe, combined with country-specific information from socioeconomic scenarios for population and age structure and with groundwork health incidence data for both electric current and future periods. Impacts were estimated using temperature-response functions, which provide relationships between daily mortality and daily temperature. These functions usually are represented as separate functions for oestrus and cold effects, reflecting the fact that mortality increases at low or high temperatures above certain threshold levels, i.eastward., around a broad central range over which at that place is little response.

In the 2080s, without accommodation measures and physiological acclimatization, the upshot of estrus- and cold-related mortality changes depends on the set of exposure-response functions used. The range of estimates for the annual increment in mortality (caused by rut and without acclimatization) is between 60,000 and 165,000. Physiological and behavioral responses to the warmer climate would accept a very pregnant effect in reducing this mortality, potentially reducing the estimates by a factor of five. The range of estimates for the subtract in common cold-related mortality is between 60,000 and 250,000, although there too may be a pass up in the sensitivity of mortality to cold. It is notable that, in aggregate, the decreases in cold-related bloodshed may outweigh the increases in estrus-related mortality. This event can be understood because, based on the impact functions used in the report, the current baseline climate of Europe is associated with more than deaths in the winter than in the summertime. The cold- and oestrus-related impacts are estimated using unproblematic epidemiologically derived functions for daily mortality; nonetheless, there are important differences in the bear on pathways, linkages with morbidity, exposure patterns, and other determinants between the heat- and cold-related deaths.

Touch on the Overall Economy.

The consequences of climate modify on the four market place affect categories (agriculture, river floods, coastal systems, and tourism) tin can be valued in monetary terms because they directly affect sectoral markets and—via the cross-sector linkages—the overall economy. They also influence the consumption behavior of households and therefore household welfare.

If the climate of the 2080s occurred today, the annual damage of climate change to the EU economy in terms of GDP loss is estimated to be between €xx billion for the 2.5 °C scenario and €65 billion for the v.four °C scenario with high SLR (Fig. i).

Fig. 1.

  • Download figure
  • Open in new tab
  • Download powerpoint

Fig. i.

Annual impact of 2080s climate-alter scenarios in terms of Gdp loss (in million €). The scenarios are identified by the average EU temperature increase, although temperature is not the just determinant of economic impacts. Impacts are adamant past the combination of SRES socioeconomic scenarios and information, the associated emissions scenarios, and the utilize of alternative climate models, leading to different spatial patterns of the climate variables. These factors explain why, for example, the economic costs are higher in the European union overall and in well-nigh regions under the iii.9°C scenario than under the 4.1°C scenario.

Even so, the amercement expressed in GDP loss underestimate the bodily losses. For instance, the repair of damage to buildings acquired past river floods increases production (GDP), because it represents a kind of obliged consumption, but does not amend consumer welfare. (Table S2 details the changes in GDP and welfare for all scenarios and market bear on categories.) The aggregated touch on the four categories would lead to an Eu annual welfare loss between 0.2% for the 2.five °C scenario and i% for the v.4 °C scenario with a high SLR (88 cm) (Fig. 2). The long-term historic Eu annual growth of welfare is around ii%. Bold that the almanac loss is constant over fourth dimension, climate modify would reduce the annual welfare growth by betwixt 0.two% and 1%.

Fig. 2.

  • Download figure
  • Open in new tab
  • Download powerpoint

Fig. two.

Annual impact of 2080s climate-change scenarios expressed equally percent modify in welfare. The scenarios are identified by the average EU temperature increase, although temperature is non the only determinant of economic impacts. Impacts are determined by the combination of SRES socioeconomic scenarios and data, the associated emissions scenarios, and the use of alternative climate models, leading to different spatial patterns of the climate variables. These factors explain why, for example, the economical costs are higher in the EU overall and in most regions under the iii.9°C scenario than nether the 4.1°C scenario.

EU-aggregated economical impact figures hide a high variation across regions, climate scenarios, and impact categories. In all 2080s scenarios, most regions would undergo welfare losses, with the exception of Northern Europe, where gains in a range of 0.5–0.8% per year are driven largely by the improvement in agronomical yields. Southern Europe would be severely affected by climate change, with annual welfare losses of around one.iv% for the v.4 °C scenario.

The sectoral and geographical decomposition of welfare changes under the 2.5 °C scenario shows that aggregated European costs of climate modify are much higher for agriculture, river flooding, and coastal systems than for tourism (Fig. 3). The British Isles, Primal Europe N, and Southern Europe appear to be the most sensitive areas. Moreover, moving from a European climate scenario of 2.5 °C to ane of iii.9 °C aggravates the iii noted impacts in almost all European regions. In the Northern Europe area these impacts are offset by the increasingly positive effects on agriculture.

The 5.iv °C scenario would lead to an almanac EU welfare loss of 0.vii%, with more pronounced impacts in near sectors in all European union regions. The agronomical sector is the almost important impact category in the EU average, as was found in the United states of america (17). The significant amercement in Southern Europe and Primal Europe South are not compensated by the gains in Northern Europe. Impacts from river flooding likewise are more of import in this case than in the other scenarios, with particular aggravation in the British Isles and in Central Europe. In the 5.4 °C scenario with the loftier SLR (88 cm) variant, which would atomic number 82 to a ane% annual welfare loss in the European union, coastal systems would become the most important impact category, particularly in the British Isles.

Word

This written report has aimed to judge the regional distributional implications of climatic change in Europe, beyond amass impact estimates. Nosotros illustrated the feasibility of integrating the relevant scientific disciplines in an "end-to-end" way, ultimately providing estimates of physical and socioeconomic impacts on the sectoral and geographical scales relevant to the electric current argue on adaptation in the EU. This multidisciplinary cess represents an improvement on monodisciplinary assessments (54).

Regarding the lessons learned, one key decision concerns the careful pick of climate scenarios, taking into account the data needed by impacts modelers and the desirability of working with country-of-fine art climate models while being aware of the variability of climate model data for the same underlying socioeconomic scenario. Making such a decision requires scientific coordination actively involving all impacts-modeling teams as well as climate experts.

Despite the breadth and depth of this study, the results still may be viewed as indicative or only illustrative because both the issue and the proposed methodology are circuitous and subject to many caveats and uncertainties. Uncertainties are present in all models of the integrated assessment, both in their input and structural specification. The socioeconomic scenarios driving GHG emissions, the sensitivity of the climate models to GHG concentrations, the specific physical-impact models, and the assumptions regarding economic valuation are all subject to dubiousness, and all are key in influencing the terminal results.

Adaptation has been modeled to diverse degrees in the affect models, because the toll–do good analysis of accommodation strategies is not achievable currently on a European scale. Earlier assessments for the coastal systems indicate that adaptation policies could be particularly toll efficient for this sector (55).

The coverage of bear upon categories has some limitations, considering it does not consider potentially important impacts (e.g., on forestry and transport and energy systems, migration phenomena, and biodiversity losses). The effects caused by climate extremes such equally heat waves, storms, and droughts have not been considered explicitly, nor accept major economical amercement acquired by low-probability high-impact events (such as collapse or slowdown of the thermohaline circulation). Furthermore, possible intersectoral furnishings, which often lead to greater vulnerabilities, are non considered. Thus, this study possibly underestimates the climate impacts on the EU economy.

The side by side steps in the research calendar consist of the extension of the impact coverage to include nonmarket effects, weather extremes and catastrophic impacts, the modeling of cross-sectoral effects, the cost–do good analysis of adaptation, the apply of dynamic state-use scenarios, and a probabilistic assessment of impacts. Equity issues also could exist considered more than explicitly and going beyond the standard efficiency analysis by identifying winners and losers in the infinite and time resolution of the adaptation cess.

Despite the quasi-static modeling framework of this application, impacts tin be interpreted genuinely in annual terms, because the physical-impact models evangelize results on a year-past-yr ground. Assessing the impacts on long-term economic growth would require a truly dynamic multisectoral approach, simulating the economy and climate change to the end of this century and specifically dedicated to addressing the problems missing in the present analysis, such as capital letter spillovers and path-dependent effects. Such a dynamic setup likewise would allow a improve analysis of the times scales of change of adaptation policy.

In determination, there seems to be a need to improve the conceptual framework underlying the multidisciplinary assessment of climate impacts and adaptation, specially by better integrating the different disciplines in a consequent way, e.1000., overcoming the limitations of the standard cost–benefit assay to include fat-tailed uncertainty (56).

Acknowledgments

We thank C. Bamps and K. Bódis for work on climate maps, and we acknowledge the comments made by T. Carter, West. Cramer, S. Fankhauser, D. Tirpak, J. McCallaway, Due north. Kouvaritakis, and R. Mendelsohn. This work benefited greatly from past projects of the Directorate Full general for Inquiry. We acknowledge the PRUDENCE project and the Rossby Center (Norrköping, Sweden) of the Swedish Meteorological and Hydrological Institute for providing climate information. This work was funded by the European Commission Joint Research Eye project, Projection of Economical Impacts of Climate Change in Sectors of the European Union Based on Bottom-upward Analysis (PESETA).

Footnotes

  • 1To whom correspondence should be addressed. E-mail: juan-carlos.ciscar{at}ec.europa.eu .

  • Author contributions: J.-C.C. and A.S. designed research; J.-C.C., A.I., 50.F., 50.South., D.V.R., B.A., R.Due north., P.W., O.B.C., R.D., L.1000., C.One thousand.Grand., A.H., A.M., J.R., and A.South. performed research; and J.-C.C., A.I., L.F., C.M.G., and A.S. wrote the newspaper.

  • The authors declare no disharmonize of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting data online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1011612108/-/DCSupplemental.

Freely available online through the PNAS open admission option.