WPS7728 Policy Research Working Paper 7728 Estimation of Climate Change Damage Functions for 140 Regions in the GTAP9 Database Roberto Roson Martina Sartori Development Economics Development Prospects Group June 2016 Policy Research Working Paper 7728 Abstract Climate change damage (or, more correctly, impact) func- damage functions are estimated for each of the 140 coun- tions relate variations in temperature (or other climate tries and regions in the Global Trade Analysis Project 9 data variables) to economic impacts in various dimensions, and set. To illustrate the salient characteristics of the estimates, are at the basis of quantitative modeling exercises for the the change in real gross domestic product is approximated assessment of climate change policies. This document pro- for the different effects, in all regions, corresponding to an vides a summary of results from a series of meta-analyses increase in average temperature of +3°C. After consider- aimed at estimating parameters for six specific damage func- ing the overall impact, the paper highlights which factor tions, referring to: sea level rise, agricultural productivity, is the most significant one in each country, and elaborates heat effects on labor productivity, human health, tourism on the distributional consequences of climate change. flows, and households’ energy demand. All parameters of the This paper is a product of the Development Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at roson@unive.it and martina.sartori@unive.it . The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Estimation of Climate Change Damage Functions for 140 Regions in the GTAP9 Database Roberto Roson Ca' Foscari University, Venice and IEFE, Bocconi University, Milan Martina Sartori University of Trento and IEFE, Bocconi University, Milan Keywords: Climate change, integrated assessment, computable general equilibrium, damage function, climate impacts. JEL Codes: C68, C82, D58, Q51, Q54. Acknowledgement: The research for this paper was supported by the Knowledge for Change multi- donor trust fund. The views and opinions expressed in this paper are solely those of the authors and do not represent the views of the World Bank Group. 1. Introduction Understanding how the ongoing climate change could ultimately affect our society and the well-being of current and future generations requires an evaluation of the complex interplay between human and natural systems. The human or anthropogenic influence on the earth climate is mainly associated with the emissions of greenhouse gases in the atmosphere, which is in turn related to the level of several economic activities. To forecast the future climate, physical scientists need to know the expected level of GHG emissions, which depend on scenarios of economic growth as well as on the possible implementation of climate mitigation policies. On the other hand, economic growth itself is influenced by the climate change, through its manifold impacts. As Tol (2015) puts it: “There are so many and so different effects: crops hit by worsening drought, crops growing faster because of carbon dioxide fertilization, heat stress increasing, cold stress decreasing, sea levels rising, cooling energy demand going up, heating energy demand going down, infectious disease spreading, and species going extinct. It is hard to make sense of this. Therefore, aggregate indicators are needed to assess whether climate change is, on balance, a good thing or a bad thing and whether the climate problem is small or large relative to the many other problems that we have.”. Damage functions have been introduced to this purpose, that is to “translate” physical impacts in terms of economic variables inside CGE, IAM and other numerical economic models. Therefore, damage functions are one or more relationships between climate variables (typically average temperature, but sometimes also humidity or “heating days”) and economic variables (potential income, productivity, resource endowments, etc.). It is generally acknowledged that damage functions constitute a weak link in the economics of climate change (Weitzman, 2010). Various methodologies have been employed for the estimation of their parameters, from subjective expert assessment (Nordhaus, 1994) to panel methods (Dell, Jones and Olken, 2014) to meta-analyses of non-economic literature (Tol, 2002). Also, the functions may be built by summing up different effects into a single aggregate, or they may retain some sectoral detail. The first approach is typical of earlier models like RICE (Nordhaus and Yang, 1996, Nordhaus and Boyer, 1999), MERGE (Manne, Mendelsohn and Richels, 1995) and CETA (Peck and Teisberg, 1992), where a relationship is posited between loss of potential income (GDP) and temperature. More recent contributions, based on multi- sectoral models like DART (Deke et al., 2001), GTEM (Pant, 2002), ICES (Eboli, Parrado and Roson, 2010) and ENVISAGE (Roson and van der Mensbrugghe, 2012) keep the sectoral detail and attribute the various impacts to different variables and parameters in a disaggregated macroeconomic model, which typically has a general equilibrium structure. The main advantage of holding distinct the different economic effects of climate change, despite the cost of higher computational complexity, is that it is possible to trace the various mechanisms through which the climate can affect the economic system. Furthermore, in a general equilibrium formulation, it is possible to account for second-order effects linked to variations in relative prices, which are often very relevant. This document illustrates the methodology and presents some results for the estimation of damage functions parameters, for all 140 countries and regions in the GTAP9 dataset, and for six climate impacts: sea level rise, variation in crop yields, heat effects on labor productivity, human health, tourism and household energy demand. Effects from 1°C up to 5°C average temperature increments are separately considered, as most impacts are non-linear. The GTAP social accounting matrix has become a de-facto standard for the calibration and implementation of computable general equilibrium models, or integrated assessment models with a CGE core, so our set of estimates can be seen as a “ready-to-use” information source for the realization of climate-related numerical experiments with a general equilibrium structure. Our parameters are obtained by processing information coming from many diverse studies, based on different approaches and methodologies, as we are undertaking an interdisciplinary assessment of 2 climate change impacts. This means that, although we are trying to build a standardized data set, the original information remains intrinsically heterogeneous. Consequently, our results have the same strengths and weaknesses as their primary references, which are difficult to judge, except for the fact that most of them are from published sources. For the same reason, we provide central values (or best estimates) of climate change impacts in the various categories, but we refrain from tackling any analysis of uncertainty, or from evaluating the overall robustness of our findings. Actually, some of the original studies do not supply information like standard errors of the parameters, whereas for those in which such information is available (in some way), converting it to a different spatial and temporal scale would be a rather complicated process. We understand that assessing uncertainty in climate change impacts is essential from both a scientific and a practical policy perspective, but we leave the issue for further future research. The full impact of climate change is a slowly unfolding event, and data continue to be gathered by experts in great efforts such as the Inter-governmental Panel on Climate Change (IPCC). New evidence will be available, and confidence on data and parameters will improve over time. Nonetheless, climate change impacts are and will remain differentiated among sectors and regions, which requires both a continuous interdisciplinary cooperation and the development of modeling platforms for the simultaneous appraisal of multiple impacts. The paper is structured as follows. Sections from 2 to 7 are devoted to presenting the methodology and some estimates for the six impact typologies, whereas detailed numerical results are available in the Appendix at the end of the paper. Section 8 provides a synthesis of the findings by showing first-order approximations of the change in national GDPs triggered by the various effects, when the average temperature is assumed to increase by three Celsius degrees. The results are then discussed in a final concluding section. 2. Climate change impact #1: Sea Level Rise A large number of studies reviewed by the Fifth IPCC Assessment Report (IPCC, 2014) have shown that the increase in global temperature brings about an increase in the level of the sea. Sea level rise (hereafter SLR) affects the land stock through the erosion, inundation or salt intrusion along the coastline. This phenomenon is in turn generated by (i) the thermal expansion of water bodies and (ii) glaciers’ melting. The share of land which may be lost (in terms of economic production factor) depends on several country-specific characteristics, like: (i) the composition of the shoreline (cliffs and rocky coasts are less subject to erosion than sandy coasts and wetlands); (ii) the total length of the country coast; (iii) the share of the coast which is suitable for productive purposes (i.e. in agriculture); (iv) the vertical land movement (VLM). The latter is a generic term for all processes affecting the elevation at a given location (tectonic movements, subsidence, ground water extraction), causing the land to move up or down. This is typically a slow process with values commonly between -10 mm/year (sinking) and +10 mm/year (rising). Local vertical land movement becomes relevant when looking at the local effects of sea level rise. The orders of magnitude are comparable, and VLM can thus either exacerbate or dampen the SLR. The literature offers several studies dealing with the SLR, but they are mainly local and country-level studies or macro-level studies, where countries are aggregated into large macro-regions. Perhaps the most employed model is DIVA (Vafeidis et al., 2008), which is an integrated model of coastal systems that assesses biophysical and socio-economic consequences of SLR. 2.1 Methodology The latest IPCC Assessment Report (IPCC AR5) reports the global mean SLR (in meters) associated with the global mean surface temperature change (in °C), at the time intervals [2046-2065] and [2080- 3 2100]. These estimates, plotted in Figure 1, suggest that there exist a positive relationship between SLR and the increase in global mean surface temperature, but also a time component, related to the substantial inertia of the physical processes involved. 4 Increase in Temperature (°C) 3.5 3 2.5 Avg increase in temp 2055 2 Avg increase in 1.5 temp 2090 1 0.5 0 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 SLR (in meters) Figure 1. Global mean surface temperature change (°C) and global mean sea level rise (m) To better understand the nature of the relationship between the global mean SLR, the increase in the mean global temperature and time, we ran a series of regressions, finding that the following equation provides a satisfactory fit for the relationship: SLR=[( α + βΔt )( T − 2000 )] (1) where Δt is the change in average global temperature with respect to the baseline [1985-2005], and T is the year period. A panel estimation of equation (1) gives a value for the α coefficient of 0.000954281, whereas the corresponding value for β is 0.003421296. To account for the vertical land movement (V), equation (1) can be modified as follows: aSLR =[( α + βΔt − V )( T − 2000 )] (2) where aSLR is the adjusted sea level rise. Data on VLM by country have been retrieved from the SONEL database (www.sonel.org). For example, the adjusted SLR associated with an increase in temperature of +1°C and VLM of +0.001 m/yr (rising) at the year 2050 is: 0.16878 =[( 0.000954281 + 0.003421296 x 1 − 0.001 )( 2050 − 2000 )] that is, about 0.17 meters. Using the DIVA v2.04 model, Arnell et al. (2014) provide estimates of the percentage loss in the coastal wetland for 16 macro-regions and 3 single countries. These estimates, reported in Table 1, are associated with a future global mean SLR of 0.16 m, predicted by the HadCM3 climate model under the A1b SRES scenario. Table 1. % change in coastal wetland at 0.16 m of SLR by macro-region (from Arnell et al. 2014) Region/country % change in coastal Region/country % change in coastal 4 wetland by 0.16 m wetland by 0.16 m of SLR of SLR West Africa -0.07 Australasia -0.12 Central Africa -0.13 North Africa -0.21 East Africa -0.12 West Asia -0.22 South Africa -0.17 West Europe -0.17 South Asia -0.1 Central Europe -0.2 South-East Asia -0.12 East Europe -0.19 East Asia -0.22 Canada -0.06 Central Asia 0 USA -0.24 Meso-America -0.18 South America -0.19 Brazil -0.09 - - Each of the 140 GTAP9 database regions has been associated to one macro-region of Table 1. The percentage loss in coastal wetland (Table 1) has been multiplied by the percentage of erodible coast and applied to the whole coast. For the European regions, the shares of erodible coast have been obtained from the Eurosion project (www.eurosion.org), while for the remaining countries we have adopted the 70% value suggested by Bird (1987, 2010). Considering which fraction of total coast is suitable for agricultural and other productive activities we have estimated the fraction of agricultural land which is lost when SLR equals 0.16 meters. Scaling up, we got the share of productive land which is lost for one meter of SLR, labelled LR. Data on coastline length are provided by the CIA database (www.cia.gov); data on the fraction of coast suitable for agricultural activities have been obtained from UNEP (2005). The percentage change in the land stock by year and country, LRT, is computed by multiplying the percentage of effective land change by meter of SLR, LR , and the predicted adjusted SLR, as follows: L RT = L R [( α + βΔt − V R )( T − 2000)] (3) Notice that the impact function (3) has four parameters. Two parameters ( α , β) are common across all regions, two other parameters (LR and VR) are country/region specific. Table A1 in the Appendix shows, for each GTAP9 region, the percentage loss of land by meter of SLR, corresponding to the parameter LR in (3), and the vertical land motion (VLM), corresponding to the parameter VR. Table A2 in the Appendix illustrates the percentage losses of productive land endowments for +1, +2, +3, +4 and +5 °C increases in average temperature, at the years 2050 and 2100, for all 140 countries and regions. As one can see, relevant physical effects of SLR are concentrated in a few countries, in particular: small island states of Oceania, Central America and Asia, Hong Kong SAR, China, Japan, Singapore, Jamaica, Puerto Rico, Trinidad and Tobago, Cyprus, Croatia, Bahrain, Kuwait, Qatar, United Arab Emirates and Mauritius. 3. Climate change impact #2: Variation in crop yields (agricultural productivity) Climate change is expected to bring about higher temperature, higher concentration of carbon dioxide in the atmosphere, and a different regional pattern of precipitation. These are all factors affecting crop yields and agricultural productivity. Not surprisingly, effects of climate change on agricultural production volumes are perhaps the most studied area of sectoral impacts. 5 Despite the many studies realized and the extensive empirical evidence produced, however, it is still difficult to identify some sort of “consensus” for the most likely impacts of climate change on agricultural productivity, especially for all world regions. This is because the issue is intrinsically complex and the eventual effect depends on several factors, which are difficult to evaluate ex-ante, for example: (i) the role of adaptation behavior by farmers, firms and organizations, including variety selection, crop rotation, sowing times, etc.; (ii) the amount of fertilization due to higher CO 2 concentration; (iii) the actual level of water available for irrigation, and irrigation techniques. Some studies in this area are based on controlled experiments. Others are based on crop models applied to different crops in different regions and on the basis of different climate scenarios. This heterogenous information is summarized in the latest IPCC Assessment Report (2014), while efforts are under way to standardize the process of agronomic experiments and modeling (AgMIP, 2014). Because of the heterogeneity of the underlying available information, we follow here two distinct approaches. The first approach, similar to the one adopted by Roson and Sartori (2010), relies on a meta-analysis provided in the Fifth IPCC Assessment Report (2014), providing central estimates for variations in the yields of maize, wheat and rice. We elaborate on these results to get estimates of productivity changes for these three crops, in all 140 regions and for the five levels of temperature increase, from +1°C to +5°C. The second approach is similar to that of Cline (2007), and brings about an estimate of productivity changes for the whole agricultural sector in the various regions. The decision about which estimates to use in a general equilibrium simulation depends on the level of industrial disaggregation of the model. We suggest to use the first set of parameters if maize, wheat and rice are considered as separate industries, and the second set for the rest, or for the whole agricultural sector if this is regarded as a single aggregate industry. 3.1 Methodology The IPCC AR5, similarly to the previous one, provides a graphical summary (Figure 7-4 in IPCC (2014)) for estimates of changes in productivity of maize, wheat and rice obtained by several studies. It distinguishes between tropical and temperate regions and identify a kind of non-linear interpolation function for the two cases, with and without simple agronomic adaptation. The figure is reproduced here below (Figure 2). 6 Figure 2. Percentage simulated yield change as a function of local temperature change (from IPCC(2014)) We first express the central values (without adaptation) of Figure 2 as percentage variations in the following table: Table 2. Central values of the percentage simulated yield change as a function of local temperature change Temperate Tropical +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C Maize -1% -3% -4% -7% -11% -4% -8% -10% -12% -14% Wheat -5% -6% -7% -8% -9% 4% -4% -20% -34% -44% Rice -4% -3% -2% -7% -16% 0% -2% -4% -6% -8% We then associate the type of region (temperate or tropical) to its latitude, assuming that the reference tropical region has a central latitude of 0° (the equator) and the reference temperate region has a central latitude of 40° (North or South). We compute the percentage variation VY in the yield of crop C in a region with latitude L as: VY ( C , L )=VY ( C , 0 )+(VY ( C , 40 )−VY ( C , 0 ))∗L / 40 (4) Therefore, we assume that the variation in the crop yield ranges linearly from its baseline value at the equator up (or down) to its value at 40° latitude and beyond. Considering the central latitude of all countries and regions in the GTAP9 dataset, we get the parameters shown in Table A3 of the Appendix. 7 A second and different methodology is based on the Mendelsohn and Schlesinger (1999) reduced form Agricultural Response Functions in the formulation proposed by Cline (2007), where the variation ( DY) in output per hectare is expressed as a function of temperature T, precipitation P and CO2 concentration K: 2 DY =115.992 DT − 9.936 DT +0.4752 DP + 7.884 DK / K (5) We need to link changes in yield to variations in average temperature only. To this purpose, we rely on temperature and precipitation data from the USGS Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Change Viewer (GCCV), averaging results from many Global Circulation Models1. We collected information on baseline levels and variation in average annual temperature and annual precipitation, by country, comparing the period 1980-2004 (central year 1992) with the period 2050-2074 (central year 2062) under the RCP 8.5 scenario. We also assume that from 1992 to 2062 (70 years) the concentration of CO2 rises (from a baseline level of 365 ppm) at an annual rate of 2.11 ppm. We use the variation in temperature as an indicator, expressing how much the climate has changed. By dividing the country-specific variation in precipitation with the one of temperature we get a precipitation to temperature coefficient p. In the same way, we get a CO 2 concentration to temperature coefficient k, so that we can write: 2 DY =( 115.992 + 0.4752 p + 7.884 k / 365 ) DT − 9.936 DT (6) Finally, we need to transform DY to percentage changes DY/Y, which can be done by dividing DY by the output per hectare Y, in millions of dollars. Cline (2007) uses estimated values for the year 2003 which, unfortunately, vary widely (for example, from 29 in Australia to 8707 in the Republic of Korea), ultimately producing unrealistically volatile percentage changes for agricultural productivity. Here we follow a different strategy, which is based on the “calibration” of the output per hectare Y. The latter is chosen so that the percentage change for +3°C is “in line” with a simple mathematical average of estimated variations in the yield of the three crops maize, wheat and rice, for the same temperature change. “In line” means in the range +/-1%, but conditional on a minimum level for Y of 500 and a maximum level of 10,000. After calibrating the output per hectare, the percentage variation of agricultural output for 1, 2, 3, 4 and 5°C increases in temperature can be computed for each of the 140 GTAP9 countries and regions. The results are shown in Table A4 of the Appendix. The variation in temperature refers to the average annual temperature specific to each country or region, which may differ from the variation in the global average temperature. On the basis of actual global and regional temperature variations, we estimated for each region a correction factor, which can be used to get an approximated regional variation in temperature through multiplication from the global change. These correction factors are displayed in Table A5 of the Appendix. When only information on the change in global temperature is available, one could therefore estimate the corresponding change in regional temperature using the correction factors. A quick inspection of the table reveals that variations in regional temperature are typically wider at a higher latitude and whenever the region has limited or no access to the sea or ocean. 1 http://regclim.coas.oregonstate.edu/visualization/gccv/cmip5-global-climate-change-viewer/ 8 4. Climate change impact #3: Heat and labor productivity Labor productivity is affected by working conditions. Heat stress, determined by high temperature and humidity, implies more frequent pauses, interruptions, lower speed and higher probability of injury (Tawasupa et. al., 2013). Even if acclimatization, on one hand, and protective measures like air conditioning, on the other hand, can help curbing the negative effects of heat stress, the effectiveness and applicability of any adaptation mean is limited and dependent on the context. Previous work with the ENVISAGE model (Roson and van der Mensbrugghe, 2012), has shown that the impact of increased heat on average labor productivity can be substantial and, furthermore, very much differentiated between developing and developed countries. To our knowledge, Kjellström et al. (2009) is the only paper investigating the relationship between climate change, heat stress and labor productivity at a global scale. Other works have considered local impacts, or produced regional maps of occupational heat exposure (Hyatt et al., 2010). In this section we estimate heat damage functions, which are relationships between average temperature and labor productivity. The functions are estimated for three sectors: Agriculture (A), Manufacturing (M) and Services (S) and for 1, 2, 3, 4 and 5 °C increases in average temperature, bringing about a total of 140 x 3 x 5 = 2100 estimated parameter values. 4.1 Methodology Most quantitative standards to protect workers from heat injury use the “wet bulb globe temperature” (WBGT) to define the percentage of a typical working hour that a person can work assuming the remaining time is rest. The heat exposure index WBGT (unit=°C) is a combination of the natural wet bulb temperature (measured with a wetted thermometer exposed to the wind and heat radiation at the site), the black globe temperature (measured inside a 150 mm diameter black globe), and the air temperature (measured with a “normal” thermometer shaded from direct heat radiation). Lemke and Kjellström (2012) propose a methodology to estimate the WBGT from meteorological data. In this study, following Kjellström et al. (2009), we compute average monthly WBGT using average temperature and relative humidity, on the basis of the Australian Bureau of Meteorology equations: WBGT =0.567 T + 3.94 + 0.393 E (7) E =( RH / 100 )× 6.105 × exp ( 17.27 T /( 237.7 +T )) (8) where T is the average air temperature in °C; E is the average absolute humidity (water vapour pressure) in hPa; and RH is the average relative humidity in %. Monthly average temperature (and precipitation) by country has been obtained from the Weatherbase website (http://www.weatherbase.com/weather/countryall.php3). Unfortunately, data on average relative humidity is not generally available for all countries in our set, but only for specific locations (from http://www.weather-and-climate.com), for example New Delhi (Figure 3). 9 Figure 3. Average relative humidity in New Dehli In order to approximate the relative humidity from temperature and precipitation data, we ran a series of regressions, finding that the following equation provides a satisfactory estimation: 2 RH = 67.1082− 0.8438 T + 0.2305 P −0.0005 P (9) where P is precipitation in mm. Therefore, we have computed monthly WBGT for all countries, using temperature and precipitation, in order to assess labor productivity in the three sectors. Kjellström et al. (2009) produced a graph of “work ability” as the maximum percentage of an hour that a worker should be engaged working (Figure 4). The four curves represent four different work intensities. We assume that 200 W corresponds to office desk work and service industries; 300 W to average manufacturing industry work and 500 W to agricultural work. Figure 4. Work ability as a function of WBTG (°C) at four work intensities (Watts), acclimatized (left panel); rescaled (right panel) We found that curves in Figure 4 (left panel) would give rise to a too rapid and unrealistic decline in productivity at high temperature, especially because we are considering here aggregate averages. We have therefore replaced the relationships depicted in the left panel of Figure 4 with the ones shown in the right panel of Figure 4. These are characterized by: (a) a minimum threshold, below which no heat effects are felt (26°C for Agriculture, 28°C for Manufacturing, 30°C for Services), (b) a minimum level of 25% for productivity, reached at 36°C for Agriculture, 43°C for Manufacturing and 50°C for Services. We computed the percentage level of productivity for all months, sectors and countries. Monthly values have subsequently been aggregated in a yearly average, since economic flows in many CGE and other numerical models are expressed on an annual basis. We scaled up temperature levels from 1 to 5 Celsius degrees, assuming that the monthly distribution of temperature will be unaffected and relative humidity stays the same. Finally, we computed the relative percentage change in (annual) productivity with respect to the baseline, for all countries and sectors. 4.2 Results Overview Table A6 in the Appendix presents our estimates for the 140 countries and regions in the GTAP data base. Column headers refer to the sectors (S, M, A) and to the increment in temperature (1, 2, 3, 4 and 5 °C). 10 The boxplots in Figure 5 display the distribution of impacts on labor productivity for the three sectors, for the various changes in temperature. In the services, impacts are minimal for a +1°C increase, with a mean of -0.17% (maximum impact -1.67% in Thailand), but no impacts for 108 out of 140 regions. At five degrees, some effects are felt in about half of the regions (73), with a mean of -3.71% and maximum impact -18.16% in Singapore. For the manufacturing industries, the effects are more significant, but the distributions are still very much skewed, with 88 regions with no impacts for +1°C, 47 for +5%. The mean percentage variation in labor productivity ranges from -0.90% to -8.12%. The most significant effects are perceived in Singapore, from -5.96% to -31.46%. Agriculture is the sector most significantly affected by higher heat stress. Some effects are felt by about half of the countries (73) already at +1°C, but at +5°C only those countries located at sufficiently high latitudes (32) do not experience reductions in labor productivity. The mean percentage variation ranges from -2.52% to -17.48%. Figure 5. Distribution of impacts on labor productivity in the three sectors, for the various changes in temperature 11 5. Climate change impact #4: Human Health This section describes the methodology and presents some estimates of the effects of increases in temperature on labor productivity, due to changes in mortality and morbidity incidence of some diseases. The approach follows the one in Bosello, Roson and Tol (2006) by considering some vector-borne diseases (malaria, dengue, schistomiasis), heat and cold related diseases, and diarrhea. It does not consider other diseases and impacts mentioned in the IPCC AR5 (2014), like effects of extreme events, heat exposure effects on labor productivity (separately considered), hemorrhagic fever with renal syndrome, plague, chikungunya fever, japanese and tick-borne encephalitis, cholera and other (non- diarrhea) enteric infections, air quality and nutrition related diseases, allergic diseases, mental health. Because of lack of data, it is not possible to ascertain possible non-linear impacts of temperature, so the results are expressed as changes in average labor productivity for a +1°C increase in temperature (implicitly assuming that the relationship is approximately linear). Also, the focus is on impacts on labor productivity, whereas other impacts, like those on private and public expenditure for health services, or non-market impacts (e.g., value of life for retired persons) are not taken into account. We consider only the direct effect of temperature on the incidence of the various diseases, despite the fact that other variables (most notably economic development expressed through income levels) are very important (especially for vector-borne and diarrhea illnesses). To this end, the projected income levels at the year 2050 are taken as reference values for determining the degree of vulnerability in each region. This method implies that indirect effects on human health are not taken into account. For instance, climate change could bring about a reduction of income and a worsening of living conditions, making a society more vulnerable to the direct effects on health. 5.1 Methodology The starting point of the analysis presented in Bosello, Roson and Tol (2006), which is in turn based on Tol (2002), is a survey of the epidemiological, medical and interdisciplinary literature, with the aim of obtaining best estimates for the number of extra cases of mortality and morbidity (for a set of diseases) associated with a given increase in average temperature. These estimates often specify the distribution of cases in the age/sex structure of a population, as well as the length of the illness period (if applicable). This information can therefore be combined with data on the structure of the working population, to infer the number of lost working days or other variables. For example, Bosello, Roson and Tol (2006), present the following Table 3, expressing the “additional years of life diseased in 2050 by region and disease”. Table 3. Additional years of life diseased in 2050 by region and disease Malaria Schistom. Dengue Cardio Respiratory Diarrhea TOT USA 0 0 0 -167,357 22,257 83,070 -62,030 Europe Un. 0 0 0 -171,908 20,936 25,608 -125,364 E.E.F.S.U. 0 0 0 -259,884 46,884 57,717 -155,283 Japan 0 0 0 -65,353 33,161 912 -31,280 Rest Ann.I 0 0 0 -45,232 11,108 1,361 -32,763 Energy Exp. 7,219 -1,088 29 -66,363 1,706,267 112,633 1,758,697 China India 632 0 0 -1,119,902 770,340 156,271 -192,659 Rest World 232,737 -154,375 203 -194,383 3,683,042 834,294 44,01,518 12 In this study, we review the most recent literature on health impacts, and in particular some studies mentioned in IPCC (2014), to modify the figures contained in Table 3 above, with the aim of scaling up or down the variation in labor productivity calculated by Roson and Sartori (2010). For example, the change in labor productivity assumed for Japan, for +1°C, was +0.034%, which corresponds to the -31280 decrease in diseased years in Table 3. Our updated estimates for the number of diseased years in Japan point to an increase in the number of years (+57894), corresponding to a change in labor productivity of -0.063%. The procedure is slightly more complicated if several countries are included in the same macro-region, especially if those estimates of changes in productivity showed in Roson and Sartori (2010) have different sign. In this case, the original estimates are still multiplied by a correction factor, but the magnitude of the factor is determined by a mathematical optimization software, ensuring that the average variation in productivity for the whole group is consistent with the updated figures of diseased years. For malaria, our primary source is Béguin et al. (2011), who suggest that extra cases of malaria, net of the effect due to income growth, should only be found in Africa and China/India. Correspondingly, we set to zero the impact for Energy Exporting Countries, while increasing by 1/3 the number of cases (diseased years) in Africa and China/India. For schistomiasis, it is unclear why in the original estimates by Tol (2002) an increase in temperature should produce a decrease in the number of cases, if the effect of temperature is considered net of the impact of higher income levels. Actually, some studies highlight that climate change is expected to create the conditions for a potential spreading of the disease in some regions, for example in China (Zhou et al., 2008). Therefore, we decide to disregard any impact for schistomiasis, by putting zeros in the corresponding column. Dengue is the most rapidly spreading mosquito-borne viral disease, showing a 30-fold increase in global incidence over the past 50 years (WHO, 2013). However, according to Åström et al. (2012) the geographic distribution of dengue is strongly dependent on both climatic and socioeconomic variables. They present a model showing that, under a scenario of constant per capita GDP, global climate change results in a modest but important increase in the global population at risk of dengue. Under scenarios of high GDP growth, this adverse effect of climate change is counteracted by the beneficial effect of socioeconomic development. With higher income sets at projected 2050 levels, the vulnerability to dengue fever is rather low. We accommodate for this information by concentrating all extra cases of dengue in Africa, and by setting the figures of diseased years at 10% of their original levels in the benchmark Table 3. Among heat-related illnesses we consider, in line with Tol (2002), respiratory and a share of cardiovascular diseases. As the recent literature on heat risks for health (e.g., Honda et al. 2013) does not present very significant changes from earlier estimates, the contribution of heat-related diseases to the overall variation in labor productivity has been kept unchanged. The same reasoning applies to health impacts of changes in diarrhea cases (Kolstad and Johansson, 2011). On the contrary, our assumptions about cold-related diseases are dramatically different. In Bosello, Roson and Tol (2006), consistently with Table 3, a reduction of cold-related cases brings about a reduction of mortality/morbidity in most countries, and an increase in labor productivity. However, the recent epidemiological literature has questioned the finding of a positive effect of higher temperature levels on winter mortality and morbidity. For example, Ebi and Mills (2013) argue that although there is a physiological basis for increased cardiovascular and respiratory disease mortality during winter months, the limited evidence suggests cardiovascular disease mortality is only weakly associated with temperature. This is because several illnesses have a strong seasonal component, in which relative temperature, not absolute temperature, actually matters. Correspondingly, we disregard any effect of climate change on cold-related diseases. This has very important implications for our estimates, because now all health impacts become negative in all countries. 13 5.2 Results Overview The estimated percentage variation of labor productivity for 140 regions and for a +1°C increase in temperature is presented in Table A7 of the Appendix. The unweighed average is -0.27%, and the range is from -0.75% (India, Nepal and Sri Lanka) to 0% (Canada). The variations can be grouped in 32 classes. Figure 6 displays the number of countries in each class. The three most numerous classes are: -0.631% (African countries), -0.034% (Western Europe), -0.135% (Central America). 30 25 20 15 10 5 0 -0.031 -0.053 -0.063 -0.113 -0.132 -0.139 -0.145 -0.184 -0.213 -0.235 -0.241 -0.305 -0.404 -0.578 -0.631 -0.747 0.000 -0.034 -0.059 -0.070 -0.127 -0.135 -0.145 -0.149 -0.199 -0.219 -0.237 -0.242 -0.366 -0.538 -0.592 -0.684 productivity % var. Figure 6. Number of countries in each class 6. Climate change impact #5: Tourism Climate is one of the main drivers of international tourism, and tourism revenue is a fundamental pillar of the economy in many countries. It is surprising that the tourism literature pays little attention to climate and climatic change and, when it does so, the analysis is typically based on local case studies. It is equally surprising that the climate change impact literature pays little attention to tourism. Previous work with the ENVISAGE model (Roson and van der Mensbrugghe, 2012) has shown that the impact of changing tourism attractiveness can be substantial, bringing about a sizable redistribution of income among various countries. Perhaps the only study conducting a quantitative assessment of climate impacts on international tourism flows, at a global scale, is Hamilton, Maddison and Tol (2005). We start from some functions and parameters computed in this study to elaborate data on arrivals, departures, temperature and expenditure. The ultimate goal is estimating a relationship between average temperature changes and net inflow of foreign currency and expenditure of foreign tourists in the hosting country. 6.1 Methodology Hamilton, Maddison and Tol (2005) have built an econometric model for the estimation of international tourism flows. They used econometric techniques to estimate parameters of two functions. In the first function, the logarithm of yearly arrivals of tourists in a country is expressed as a function of land area, average temperature, length of coastline and per capita income. In a second function, the logarithm of the ratio of departures over population is expressed as a function of temperature, income, land area and number of countries with shared land borders. 14 We take these two functional relationships to get equations linking arrivals ( A) and departures (D) in a region solely to its average temperature (T), in Celsius degrees: 2 A = K A × exp ( 0.22 T − 0.00791 T ) (10) 2 D = K D × exp (− 0.18 T + 0.00438 T ) (11) where KA a n d KD are country-specific constants, accounting for all other factors different from temperature. We calibrate these parameters on the basis of regional data on yearly arrivals, departures and average temperature. We can see that both relationships are non-linear. The maximum number of arrivals is obtained at the optimal average temperature of 13.9°C. The minimum number of departures is obtained at 18.6°C. For increases in temperature below the 13.9°C threshold, arrivals increase and departures decrease, therefore a country gets a beneficial net inflow of foreign currency. The opposite is found for increases in temperature above the 18.6°C threshold. For variations between 13.9°C and 18.6°C, effects are ambiguous, not only because arrivals and departures push to different directions, but also because the average expenditure level of an incoming tourist may be different from the expenditure level of an outgoing tourist2. We estimated changes in arrivals and departures for 1, 2, 3, 4 and 5 °C increases in average temperature from its baseline level, for all 140 countries and regions. Variations in arrivals multiplied by per capita expenditure minus variations in departures multiplied by per capita expenditure give a first estimate of changes in net foreign currency inflow. Of course, changes can be be both positive and negative. Furthermore, summing up all changes does not typically gives a zero result. However, as it will be made clearer in Sub-section 6.3, if foreign currency flows are interpreted as international income transfers, we would actually need to impose that all variations sum up to one. To this end, we scaled up or down all our estimates, by subtracting the average net inflow if positive, or adding it if it turns out to be negative. One possible interpretation of this ex-post rescaling is in terms of relative competitiveness, since flows are not only affected by local conditions, but also by conditions in competing destinations. 6.2 Results overview Our rescaled estimates of changes in net foreign currency inflows, relative to the 2011 GDP level, are displayed in Table A8 of the Appendix. These variations follows a rather non-linear path. Limited increases of temperature are beneficial but higher levels are detrimental in China, the Republic of Korea, Italy and Turkey. Vice versa, initial negative impacts turn positive at +5°C in Mongolia, Estonia, Lithuania, Slovak Republic, Slovenia, Bulgaria, Belarus, Romania and Kazakhstan. Benefits are concentrated in a few countries. For example, at +3°C only 26 countries get an increase in tourism revenue, whereas as many as 97 countries experience a relative loss. Benefitted countries include North European and North American countries, Japan and the Russian Federation, which are all rich nations: tourism impacts have adverse distributional consequences. Furthermore, the dispersion of income flows gets larger as temperature rises. The standard deviation of the distribution of net revenue inflows increases progressively from about 1.48 billions US$ at +1°C up to around 5.36 billions US$ at +5°C. 2 We estimated per capita expenditure data on the basis of IMF data on tourism revenue (IMF, 2014). 15 6.3 Inclusion of Tourism Impacts in a CGE Model Our estimates of net currency inflows are meant to be used as inputs in a CGE model, assessing economic impacts of climate change. The exogenous shock can be inserted as a variation in international income transfers and, possibly, as a shift in the pattern of final consumption. Most CGE models are based on a “territorial” definition of income. In other words, GDP rather than GNP is taken as the reference value for income and other macroeconomic variables. This implies that there is no distinction between nationals and foreigners when income is spent inside a country boundaries. However, the purchasing power of foreigners comes from income generated abroad. In order to consider this important aspect, Berrittella et al. (2006) and Bigano et al. (2008) simulate the occurrence of some international income transfers, whose magnitude corresponds to the estimated change in net currency inflows. Since foreign tourists are unlikely to have a structure of consumption similar to that of the representative household in a country, a further step is simulating an exogenous increase (or decrease) in the consumption of tourism (hotels, restaurants, recreation facilities) and domestic transport services, which can be implemented by inserting some shifting parameters in the final demand for these items. 7. Climate change impact #6: Household Energy Demand Household energy demand is directly affected by variations in temperature. This relationship is rather complex, as the impact on energy consumption depends on the season, the source of energy and the climatic condition of the country. For instance, an increase in winter temperatures would cause a decrease in energy used for heating purposes, whereas an increase in summer temperatures is likely to cause an increase of energy consumed for cooling purposes, depending on the latitude of the country (i.e., tropical, temperate, cold). In what follows, the impact of increasing average temperature on energy demand is computed, taking into account all these factors. 7.1 Methodology Our estimates are based on De Cian, Lanzi and Roson (2013), who computed parameters of a model for household energy demand, by energy source and season, using econometric techniques and a global panel database. Energy demand is expressed as dependent, among other factors, on the (natural logarithm of) seasonal average temperature, expressed in °F. Seasonal long run temperature elasticities by energy source and by climate region (Table 4) are those estimated by De Cian, Lanzi and Roson (2013.). Since we are interested in the variation of total energy demand, elasticities in Table 4 have been scaled down by considering the share of energy used for heating and cooling purposes (Table 5). The adjusted elasticities are shown in Table 6. Data on average seasonal temperature by country are obtained from the Weather Database (www.weatherbase.com), whereas each country has been classified as Cold, Mild or Hot, according to its latitude.3 Applying the model estimated by De Cian, Lanzi and Roson (2013), to the percentage variation in temperature corresponding to 1°C (and 2, 3, 4, 5°C) increase in seasonal average temperature has been multiplied by the elasticities reported in Table 6. 3 Hot countries: latitude<27°; mild countries: 27°63°. For aggregated regions the latitude has been computed as a weighted sum of the latitude of each single country. 16 7.2 Result overview Table A9 in the Appendix shows our estimates of the percentage variations in household energy demand corresponding to a +1, +2, +3, +4 and +5°C increase in the average seasonal temperature. Estimates are provided for the 140 GTAP9 regions, but they are available for more countries. A quick inspection of the table reveals that: (i) household demand for electricity rises, especially in the hot countries, as this source of energy is mainly used for air conditioning. The highest relative growth is expected in the African countries; (ii) household demand for energy from oil products dramatically decreases in all countries, especially in cold countries; (iii) the effect on household demand for energy from gas is positive (negative) in mild and cold (hot) countries. Table 4. Long run temperature elasticities from De Cian et al. (2013) Season Climate Electr. Gas Oil.P. Winter Cold -0.085 -0.422 -0.406 Mild -0.085 -0.422 -0.406 Hot -0.085 -0.422 -0.406 Spring Cold 0.522 0.686 -0.395 Mild -0.077 0.686 -0.395 Hot 0.263 0.686 -0.395 Summer Cold -0.321 -1.008 -0.912 Mild 0.2 -1.008 -0.912 Hot 0.174 -1.008 -0.912 Fall Cold - 0.685 0.0002 Mild - 0.685 0.0002 Hot - 0.685 0.0002 Table 5. Share of energy demanded for heating and cooling purposes, by energy source and climate region. Source: U.S. Residential Energy Demand Database (www.eia.gov) Electricity Gas Oil P. Climate Heating Cooling Heating Heating Cold 8% 5% 72% 88% Mild 9% 17% 56% 86% Hot 7% 28% 48% 86% 17 Table 6. Adjusted long run temperature elasticities Season Climate Electr. Gas Oil.P. Winter Cold -0.0111 -0.3053 -0.3558 Mild -0.0221 -0.2345 -0.3496 Hot -0.0300 -0.2008 -0.3496 Spring Cold 0.0682 0.4962 -0.3462 Mild -0.0200 0.3812 -0.3401 Hot 0.0929 0.3264 -0.3401 Summer Cold -0.0419 -0.7292 -0.7993 Mild 0.0519 -0.5602 -0.7853 Hot 0.0614 -0.4797 -0.7853 Fall Cold - 0.4955 0.0002 Mild - 0.3807 0.0002 Hot - 0.3260 0.0002 8. Aggregation of impacts and first-order effects on GDP The illustration of our estimates for the different impacts of the climate change has made clear that the impacts are different in sign, magnitude and relevance for the various countries and regions. Therefore, it would be interesting to see what is the net aggregate effect, for example in terms of real income or GDP, of the combined impacts. A full fledged analysis of this kind would require a global, disaggregated macroeconomic model, in which our estimates would be employed to shock exogenous parameters. For instance, an exogenous reduction in agricultural productivity would reduce the relative competitiveness for the domestic agricultural sector, increasing imports from abroad, inducing a real devaluation, expanding production and exports in manufacturing and services. Such kind of analysis is beyond the scope of this paper. Nonetheless, we can provide here a first-order approximation of the impact on the real GDP, because most of the impacts affect variables which are components of the Gross Domestic Product, with the exception of the variation in energy demand. Because of that, an approximated impact on the GDP can be readily obtained by multiplying the variation of one GDP component by its share, and in particular: • impacts of sea level rise on GDP can be gauged by multiplying the estimated changes in available land resources by the share of land rents income on total GDP; • agricultural productivity variations can be evaluated by multiplying the changes by the share of agricultural value added on total GDP; • the reduction in labor productivity due to heat stress has an effect on the GDP that can be estimated as the sum of variations in labor productivity in the three sectors (agriculture, manufacturing, services) multiplied by the shares of (sectoral) labor income on total GDP; • human health effects can be obtained by multiplying the estimated changes by the share of labor income on total GDP; • the net inflow of foreign currency due to tourism flows can be directly expressed as relative to a baseline GDP level. Even if the sum of the different impacts on GDP is only limited to first-order effects and does not consider general equilibrium feedbacks, we believe that such an approximation of the composite GDP footprint could reveal important insights about the order of magnitude, relevance, and distribution of 18 the various impacts. Tables 7-1 and 7-2 present our estimates, corresponding to an increase in average temperature of +3°C4 for the five categories above and their total algebraic sum. We highlight with a green background color the positive net variations in GDP, with a yellow background moderate reductions (from -1% to -5%) and with a red background the large reductions (below -5%). In addition, we identify, for each country, which among the three types of impact is the one which contributes the most to the overall effect on GDP.5 A quick inspection of Tables 7-1 and 7-2 reveals a number of thought-provoking facts. Only a few countries (Mongolia, Canada, and central-northern European countries, including Russia) are expected to get moderate gains from a +3°C increase in temperature, and these gains are typically due to an increase in tourists' arrivals (and diminished outgoing domestic tourists). Many countries (whose estimates are highlighted in red) are expected to suffer from dramatic reductions in GDP. The most negatively affected countries are Togo in Africa (-18.29%) and Cambodia in South-East Asia (-18.25%), where again Tourism is the most important factor. In addition to tourism income, variations in agricultural and labor productivity are also very relevant in many countries. Sea level rise, on the other hand, never appears as the primary factor, because of its limited incidence on total land and the relative small share of land income on GDP. Remarkably, Tourism is (possibly with Heat) the least studied effect of climate change, maybe because it causes a redistribution of income and wealth, but it has negligible consequences at the global level. 4 This refers to changes in the global average temperature. For agricultural productivity, we consider regional variations, which could be larger or smaller than the global one. Furthermore, sea level rise does not depend only on temperature levels, but on time. For this estimation, we set the year 2100 as the one corresponding to the +3°C temperature increment. 5 Therefore, it has the same sign of the total variation. 19 Table 7-1. Impact on GDP of +3°C by country Incidence on Dominant N Code SLR AGR HEAT HEALTH TOURISM GDP of +3°C impact 1 AUS 0.0000% -0.1686% -0.0162% -0.2370% -0.5029% -0.92% TOURISM 2 NZL -0.0005% -0.0975% 0.0000% -0.2073% 0.1806% -0.12% HEALTH 3 XOC -0.0095% -0.3135% -1.3971% -0.3030% 0.0000% -2.02% HEAT 4 CHN 0.0000% 0.1975% -0.5449% -0.8164% 0.0890% -1.07% HEALTH 5 HKG -0.0118% -0.0480% -1.6329% -0.7237% -5.2541% -7.67% TOURISM 6 JPN -0.0005% -0.0765% -0.2334% -0.0967% 0.0205% -0.39% HEAT 7 KOR -0.0006% -0.1113% -0.2600% -0.0843% 0.2123% -0.24% HEAT 8 MNG 0.0000% 0.5520% 0.0000% -0.4409% 0.9466% 1.06% TOURISM 9 TWN -0.0004% -0.1019% -2.4258% -0.9099% -2.0929% -5.53% HEAT 10 XEA -0.0010% -0.3961% -4.2472% -0.1915% 0.0000% -4.84% HEAT 11 BRN -0.0001% -0.0059% -2.0021% -0.1206% -2.6786% -4.81% TOURISM 12 KHM -0.0002% -2.1774% -5.2924% -0.1315% -10.6492% -18.25% TOURISM 13 IDN -0.0010% -1.1587% -4.7511% -0.1790% -0.7110% -6.80% HEAT 14 LAO 0.0000% -3.5049% -4.1597% -0.1425% -5.7644% -13.57% TOURISM 15 MYS -0.0005% -0.7494% -4.8378% -0.1816% -4.4406% -10.21% HEAT 16 PHL -0.0028% -0.9965% -4.6830% -0.1445% -1.5898% -7.42% HEAT 17 SGP -0.0020% -0.0200% -4.4945% -0.2987% -5.9202% -10.74% TOURISM 18 THA -0.0001% -0.7803% -3.7029% -0.1419% -4.5046% -9.13% TOURISM 19 VNM -0.0006% -1.3580% -3.3932% -0.1501% -2.1889% -7.09% HEAT 20 XSE -0.0010% -3.2015% -6.4740% -0.1549% 0.0000% -9.83% HEAT 21 BGD -0.0001% -1.2004% -3.2480% -0.2020% -0.3383% -4.99% HEAT 22 IND -0.0001% -1.3077% -3.3046% -1.0484% -0.5829% -6.24% HEAT 23 NPL 0.0000% -0.0773% -1.1111% -0.9108% -1.8753% -3.97% TOURISM 24 PAK 0.0000% -1.7497% -1.2167% -0.0985% -0.2498% -3.31% AGR 25 LKA -0.0008% -1.3164% -2.9340% -0.8583% -1.2886% -6.40% HEAT 26 XSA 0.0000% -1.9427% -2.8045% -0.1434% 0.0000% -4.89% HEAT 27 CAN -0.0001% 0.1723% 0.0000% 0.0000% 1.1003% 1.27% TOURISM 28 USA 0.0000% 0.0159% -0.0048% -0.2896% 0.1152% -0.16% HEALTH 29 MEX 0.0000% -0.3420% -0.1530% -0.2326% -0.4177% -1.15% TOURISM 30 XNA -0.0033% 0.0118% -0.0037% -0.3277% 0.0000% -0.32% HEALTH 31 ARG 0.0000% -0.2384% -0.1037% -0.3114% -0.2509% -0.90% HEALTH 32 BOL 0.0000% -1.3641% 0.0000% -0.1476% -1.3293% -2.84% AGR 33 BRA 0.0000% -0.5921% -0.8644% -0.3432% -0.3293% -2.13% HEAT 34 CHL -0.0002% 0.0103% 0.0000% -0.2737% 0.0007% -0.26% HEALTH 35 COL -0.0001% -0.7781% -0.9717% -0.1258% -0.6461% -2.52% HEAT 36 ECU -0.0004% -1.0763% 0.0000% -0.1526% -0.7002% -1.93% AGR 37 PRY 0.0000% -1.9012% -2.2562% -0.1768% -1.4291% -5.76% HEAT 38 PER -0.0002% -1.4078% 0.0000% -0.1868% -0.3127% -1.91% AGR 39 URY -0.0001% -0.4524% -0.0572% -0.2972% -1.3583% -2.17% TOURISM 40 VEN -0.0001% -0.6564% -0.9783% -0.1686% -0.3473% -2.15% HEAT 41 XSM -0.0013% -0.4069% -0.0462% -0.1470% 0.0000% -0.60% AGR 42 CRI -0.0011% -0.8385% -1.9108% -0.2989% -3.1429% -6.19% TOURISM 43 GTM -0.0002% -1.4468% -0.3188% -0.1860% -1.6208% -3.57% TOURISM 44 HND -0.0005% -1.3208% -4.0728% -0.1931% -3.5740% -9.16% HEAT 45 NIC -0.0006% -1.8717% -5.0354% -0.1958% -5.0277% -12.13% HEAT 46 SLV -0.0002% -0.6504% -2.7781% -0.1926% -1.4962% -5.12% HEAT 47 PAN -0.0019% -1.2835% -0.9629% -0.1481% -7.1071% -9.50% TOURISM 48 XCA -0.0044% -1.1027% -3.3145% -0.1863% 0.0000% -4.61% HEAT 49 DOM -0.0006% -0.6860% -1.8276% -0.1301% -4.2142% -6.86% TOURISM 50 JAM -0.0006% -0.3236% -2.3722% -0.1938% -8.4870% -11.38% TOURISM 51 PRI -0.0006% -0.1014% -1.6726% -0.1793% -0.7814% -2.74% HEAT 52 TTO -0.0009% -0.1245% -2.4513% -0.1207% -2.1839% -4.88% HEAT 53 XCB -0.0017% -0.5995% -3.3617% -0.2107% -3.6624% -7.84% TOURISM 54 AUT 0.0000% 0.0197% 0.0000% -0.0472% 1.9809% 1.95% TOURISM 55 BEL 0.0000% 0.0062% 0.0000% -0.0482% 1.2519% 1.21% TOURISM 56 CYP -0.0004% -0.4306% -0.1406% -0.0426% -3.9984% -4.61% TOURISM 57 CZE 0.0000% 0.0369% 0.0000% -0.0383% 1.4414% 1.44% TOURISM 58 DNK 0.0000% 0.0271% 0.0000% -0.0506% 1.8480% 1.82% TOURISM 59 EST 0.0000% 0.1165% 0.0000% -0.0379% 2.1074% 2.19% TOURISM 60 FIN 0.0000% 0.1317% 0.0000% -0.0471% 1.3954% 1.48% TOURISM 61 FRA 0.0000% 0.0002% 0.0000% -0.0501% 0.3515% 0.30% TOURISM 62 DEU 0.0000% 0.0115% 0.0000% -0.0530% 0.7933% 0.75% TOURISM 63 GRC -0.0001% -0.2039% -0.0545% -0.0329% -1.0597% -1.35% TOURISM 64 HUN 0.0000% 0.0191% 0.0000% -0.0376% 0.9476% 0.93% TOURISM 65 IRL 0.0000% 0.0116% 0.0000% -0.0404% 0.7150% 0.69% TOURISM 66 ITA 0.0000% -0.1355% 0.0000% -0.0417% -0.0005% -0.18% AGR 67 LVA 0.0000% 0.1817% 0.0000% -0.0396% 0.8261% 0.97% TOURISM 68 LTU 0.0000% 0.1642% 0.0000% -0.0379% 0.9750% 1.10% TOURISM 69 LUX 0.0000% 0.0057% 0.0000% -0.0497% 2.8828% 2.84% TOURISM 70 MLT -0.0001% -0.1480% -0.0711% -0.0361% -6.2965% -6.55% TOURISM 20 Table 7-2. Impact on GDP of +3°C by country Incidence on Dominant N Code SLR AGR HEAT HEALTH TOURISM GDP of +3°C impact 71 NLD 0.0000% 0.0103% 0.0000% -0.0506% 0.7591% 0.72% TOURISM 72 POL 0.0000% 0.0511% 0.0000% -0.0405% 0.9494% 0.96% TOURISM 73 PRT 0.0000% -0.1230% 0.0000% -0.0486% -0.7612% -0.93% TOURISM 74 SVK 0.0000% 0.0359% 0.0000% -0.0392% 1.2305% 1.23% TOURISM 75 SVN 0.0000% 0.0273% 0.0000% -0.0523% 1.3031% 1.28% TOURISM 76 ESP 0.0000% -0.1623% 0.0000% -0.0521% -0.5523% -0.77% TOURISM 77 SWE 0.0000% 0.0566% 0.0000% -0.0516% 1.7159% 1.72% TOURISM 78 GBR 0.0000% 0.0099% 0.0000% -0.0551% 0.6373% 0.59% TOURISM 79 CHE 0.0000% 0.0151% 0.0000% -0.0665% 1.4678% 1.42% TOURISM 80 NOR -0.0001% 0.0756% 0.0000% -0.0487% 1.4445% 1.47% TOURISM 81 XEF 0.0000% 0.0364% 0.0000% -0.0742% 0.0000% -0.04% HEALTH 82 ALB -0.0002% -0.5880% -0.0018% -0.0837% -1.8545% -2.53% TOURISM 83 BGR 0.0000% -0.2314% 0.0000% -0.0836% 1.0793% 0.76% TOURISM 84 BLR 0.0000% 0.1365% 0.0000% -0.1016% 0.1481% 0.18% TOURISM 85 HRV -0.0059% -0.1818% 0.0000% -0.0475% -0.4174% -0.65% TOURISM 86 ROU 0.0000% 0.0507% 0.0000% -0.0406% 0.2620% 0.27% TOURISM 87 RUS -0.0001% 0.2438% 0.0000% -0.0620% 1.2058% 1.39% TOURISM 88 UKR 0.0000% 0.0614% 0.0000% -0.0829% 0.9421% 0.92% TOURISM 89 XEE 0.0000% 0.0685% 0.0000% -0.0887% 0.0000% -0.02% HEALTH 90 XER 0.0000% 0.0479% 0.0000% -0.0396% 0.0000% 0.01% AGR 91 KAZ 0.0000% 0.0489% 0.0000% -0.0843% 0.3404% 0.31% TOURISM 92 KGZ 0.0000% 0.7822% 0.0000% -0.0638% -1.7649% -1.05% TOURISM 93 XSU 0.0000% 0.1312% 0.0000% -0.0568% 0.0000% 0.07% AGR 94 ARM 0.0000% 0.2216% 0.0000% -0.0714% 0.0175% 0.17% AGR 95 AZE 0.0000% -0.5908% -0.0988% -0.0414% -0.1307% -0.86% AGR 96 GEO -0.0003% 0.1385% -0.0522% -0.0843% -1.9215% -1.92% TOURISM 97 BHR -0.0005% -0.0683% -1.1748% -0.4204% -3.2314% -4.90% TOURISM 98 IRN 0.0000% -0.4277% -0.1860% -0.1181% -0.0843% -0.82% AGR 99 ISR 0.0000% -0.1655% -0.0400% -1.2584% -0.7563% -2.22% HEALTH 100 JOR 0.0000% -0.3556% -0.1463% -0.5373% -4.0531% -5.09% TOURISM 101 KWT 0.0000% -0.0182% -0.7005% -0.2407% -1.5365% -2.50% TOURISM 102 OMN 0.0000% -0.0558% -0.7102% -0.3094% -1.5583% -2.63% TOURISM 103 QAT -0.0001% -0.0346% -1.2702% -0.3952% -0.8283% -2.53% HEAT 104 SAU 0.0000% -0.0700% -1.4904% -0.5016% -1.2991% -3.36% HEAT 105 TUR -0.0001% -0.4687% 0.0000% -0.3499% -0.0075% -0.83% AGR 106 ARE -0.0002% -0.1686% -1.3851% -0.4344% -2.8718% -4.86% TOURISM 107 XWS 0.0000% -0.7620% -0.2868% -0.1673% 0.0000% -1.22% AGR 108 EGY -0.0005% -1.1341% -0.6905% -0.4656% -1.5531% -3.84% TOURISM 109 MAR -0.0001% -1.1070% -0.0555% -0.7353% -1.8221% -3.72% TOURISM 110 TUN -0.0001% -0.7579% -0.2464% -0.5286% -1.5935% -3.13% TOURISM 111 XNF 0.0000% -0.3463% -0.1242% -0.4551% 0.0000% -0.93% HEALTH 112 BEN 0.0000% -2.2061% -5.8667% -0.7895% -4.7655% -13.63% HEAT 113 BFA 0.0000% -2.3843% -5.8824% -0.6710% -2.9330% -11.87% HEAT 114 CMR 0.0000% -2.4157% -2.6122% -1.0351% -2.0672% -8.13% HEAT 115 CIV 0.0000% -2.6715% -7.3540% -1.1743% -1.8351% -13.03% HEAT 116 GHA 0.0000% -2.5318% -7.6143% -1.2015% -1.8443% -13.19% HEAT 117 GIN 0.0000% -2.2525% -2.4491% -0.5128% -4.9416% -10.16% TOURISM 118 NGA 0.0000% -4.0968% -8.2096% -0.9791% -0.6444% -13.93% HEAT 119 SEN 0.0000% -1.5615% -3.6766% -0.6634% -3.6789% -9.58% TOURISM 120 TGO 0.0000% -2.9926% -6.7908% -0.8777% -7.6318% -18.29% TOURISM 121 XWF 0.0000% -1.5088% -3.9966% -0.5685% 0.0000% -6.07% HEAT 122 XCF 0.0000% -0.4709% -0.8100% -0.6294% -0.4675% -2.38% HEAT 123 XAC 0.0000% -0.9863% -0.0461% -0.7702% 0.0000% -1.80% AGR 124 ETH 0.0000% -3.4512% 0.0000% -0.8943% -1.4763% -5.82% AGR 125 KEN 0.0000% -2.8648% -0.1698% -0.9299% -1.6563% -5.62% AGR 126 MDG -0.0002% -2.9062% -2.5131% -0.9861% -3.5947% -10.00% TOURISM 127 MWI 0.0000% -2.6408% -1.5485% -0.9422% -4.6332% -9.76% TOURISM 128 MUS -0.0009% -0.7158% -2.1495% -0.8996% -8.3783% -12.14% TOURISM 129 MOZ -0.0001% -1.1773% -2.7128% -0.9728% -3.0178% -7.88% TOURISM 130 RWA 0.0000% -3.7427% 0.0000% -1.0299% -4.8945% -9.67% TOURISM 131 TZA -0.0001% -2.5945% -1.4315% -1.0207% -3.7480% -8.79% TOURISM 132 UGA 0.0000% -2.3230% -0.3320% -0.8564% -4.0730% -7.58% TOURISM 133 ZMB 0.0000% -1.1479% -0.4776% -1.1182% -1.5571% -4.30% TOURISM 134 ZWE 0.0000% -0.9073% -0.1290% -0.7963% -2.9613% -4.79% TOURISM 135 XEC 0.0000% -1.2136% -2.4070% -0.9955% 0.0000% -4.62% HEAT 136 BWA 0.0000% -0.5257% -0.3916% -0.7531% -1.7062% -3.38% TOURISM 137 NAM 0.0000% -0.9395% -0.0747% -0.7110% -3.3564% -5.08% TOURISM 138 ZAF 0.0000% -0.2159% -0.0003% -0.8577% -0.5198% -1.59% HEALTH 139 XSC 0.0000% -1.0459% -0.0015% -0.7656% 0.0000% -1.81% AGR 140 XTW -0.0013% -0.1252% 0.0000% -0.7543% 0.0000% -0.88% HEALTH 21 It is also evident that effects are similar among similar countries, that is when they belong to the same region or are characterized by comparable socio-economic conditions. Figure 7 presents a scatter plot of total percentage variations of GDP against per capita income levels. The correlation between these two variables is positive and as large as 0.445, confirming a robust finding from previous studies (e.g. Eboli, Parrado and Roson, 2010, Roson and van der Mensbrugghe, 2012) that climate change impacts act like a highly regressive tax, often making poor countries poorer, and rich countries richer. 5% 0% Impact on GDP of +3°C -5% -10% -15% -20% 0 20,000 40,000 60,000 80,000 100,000 120,000 GDP per capita Figure 7. Percentage variation of GDP against per capita income level It is known that economic development is itself correlated with geographical location and temperature: in contemporary data, national income falls 8.5% per degree Celsius in the world cross-section (Dell, Jones and Olken, 2009). We do not discuss here any causality or interpretation for this correlation. Rather, we show in Figures 8 and 9 another two scatter plots, this time contrasting GDP variations with average temperature and latitude. The corresponding correlation factors are, respectively, -0.785 and 0.732. 5% Impact on GDP of +3°C 0% -5% -10% -15% -20% -5 0 5 10 15 20 25 30 Average Temperature Figure 8. Percentage variation of GDP against average temperature 22 5% 0% Impact on GDP of +3°C -5% -10% -15% -20% 0 0.2 0.4 0.6 0.8 1 1.2 Latitude Figure 9. Percentage variation of GDP against latitude 9. Conclusion In this paper, a new set of climate change damage functions has been presented, improving earlier estimates in several ways. First, functions and parameters are provided with a large regional disaggregation (140 countries) and in a format which, by referring to the latest GTAP social accounting matrix, makes them easily employable in many general equilibrium models. 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(2002), “Estimates of the Damage Costs of Climate Change - Part 1: Benchmark Estimates”, Environmental and Resource Economics, 21: 47–73. Tol, R.S.J. (2015), “Who Benefits and Who Loses from Climate Change?”, Handbook of Climate Change Mitigation and Adaptation, Springer Science Business Media, New York. Forthcoming. Weitzman, M. (2010), “What is the “Damages Function” for Global Warming — and What Difference Might It Make?”, Climate Change Economics, 1(1): 57–69. WHO (2013), Impact of Dengue, World Health Organization (WHO), Geneva, Switzerland, www.who.int/csr/disease/dengue/impact/en/. Zhou, X., Yang, G., Yang, K., Wang, X., Hong, Q., Sun, L., Malone, J.B., Kristensen, T.K., Bergquist, N.R., and Utzinger, J. (2008). “Potential impact of climate change on schistosomiasis transmission in China”, American Journal of Tropical Medicine and Hygiene , 78(2): 188-194. 25 Appendix Table A0. Country codes. Number Code Description Number Code Description 1 AUS Australia 71 NLD Netherlands 2 NZL New Zealand 72 POL Poland 3 XOC Rest of Oceania 73 PRT Portugal 4 CHN China 74 SVK Slovak Republic 5 HKG Hong Kong SAR, China 75 SVN Slovenia 6 JPN Japan 76 ESP Spain 7 KOR South Korea 77 SWE Sweden 8 MNG Mongolia 78 GBR United Kingdom 9 TWN Taiwan, China 79 CHE Switzerland 10 XEA Rest of East Asia 80 NOR Norway 11 BRN Brunei Darassalam 81 XEF Rest of EFTA 12 KHM Cambodia 82 ALB Albania 13 IDN Indonesia 83 BGR Bulgaria 14 LAO Lao People's Democratic Republic 84 BLR Belarus 15 MYS Malaysia 85 HRV Croatia 16 PHL Philippines 86 ROU Romania 17 SGP Singapore 87 RUS Russian Federation 18 THA Thailand 88 UKR Ukraine 19 VNM Vietnam 89 XEE Rest of Eastern Europe 20 XSE Rest of Southest Asia 90 XER Rest of Europe 21 BGD Bangladesh 91 KAZ Kazakhstan 22 IND India 92 KGZ Kyrgyzstan 23 NPL Nepal 93 XSU Rest of Former Soviet Union 24 PAK Pakistan 94 ARM Armenia 25 LKA Sri Lanka 95 AZE Azerbaijan 26 XSA Rest of South Asia 96 GEO Georgia 27 CAN Canada 97 BHR Bahrain 28 USA United States 98 IRN Iran, Islamic Republic of 29 MEX Mexico 99 ISR Israel 30 XNA Rest of North America 100 JOR Jordan 31 ARG Argentina 101 KWT Kuwait 32 BOL Bolivia 102 OMN Oman 33 BRA Brazil 103 QAT Qatar 34 CHL Chile 104 SAU Saudi Arabia 35 COL Colombia 106 TUR Turkey 36 ECU Ecuador 105 ARE United Arab Emirates 37 PRY Paraguay 107 XWS Rest of Western Asia 38 PER Peru 108 EGY Egypt, Arab Rep. 39 URY Uruguay 109 MAR Marocco 40 VEN Venezuela, RB 110 TUN Tunisia 41 XSM Rest of South America 111 XNF Rest of North Africa 42 CRI Costa Rica 112 BEN Benin 43 GTM Guatemala 113 BFA Burkina Faso 44 HND Honduras 114 CMR Cameroon 45 NIC Nicaragua 115 CIV Cote d'Ivoire 46 SLV El Salvador 116 GHA Ghana 47 PAN Panama 117 GIN Guinea 48 XCA Rest of Central America 118 NGA Nigeria 49 DOM Dominican Republic 119 SEN Senegal 50 JAM Jamaica 120 TGO Togo 51 PRI Puerto Rico 121 XWF Rest of Western Africa 52 TTO Trinidad and Tobago 122 XCF Rest of Central Africa 53 XCB Caribbean 123 XAC Rest of South Central Africa 54 AUT Austria 124 ETH Ethiopia 55 BEL Belgium 125 KEN Kenya 56 CYP Cyprus 126 MDG Madagascar 57 CZE Czech Republic 127 MWI Malawi 58 DNK Denmark 128 MUS Mauritius 59 EST Estonia 129 MOZ Mozambique 60 FIN Finland 130 RWA Rwanda 61 FRA France 131 TZA Tanzania 62 DEU Germany 132 UGA Uganda 63 GRC Greece 133 ZMB Zambia 64 HUN Hungary 134 ZWE Zimbabwe 65 IRL Ireland 135 XEC Rest of Eastern Africa 66 ITA Italy 136 BWA Botswana 67 LVA Latvia 137 NAM Namibia 68 LTU Lithuania 138 ZAF South Africa 69 LUX Luxembourg 139 XSC Rest of South African Customs Union 70 MLT Malta 140 XTW Rest of the World 26 Table A1. Sea level rise: percentage change of land stock by meter of SLR and VLM. Countries with asterisk do not have coastline. % of land change % of land change N. Code by meter of SLR VLM (m/yr) N. Code by meter of SLR VLM (m/yr) 1 AUS -0.0026% -0.0009 71 NLD -0.0021% -0.0005 2 NZL -0.0567% -0.0014 72 POL -0.0010% 3 XOC -0.5611% -0.0010 73 PRT -0.0069% -0.0005 4 CHN -0.0013% 0.0017 74 SVK* 0.0000% 5 HKG -4.6796% 0.0017 75 SVN -0.0046% 6 JPN -0.2873% 0.0006 76 ESP -0.0011% -0.0006 7 KOR -0.0614% 0.0007 77 SWE -0.0013% 0.0056 8 MNG* 0.0000% 78 GBR -0.0094% 0.0004 9 TWN -0.0715% 0.0010 79 CHE* 0.0000% 10 XEA -0.0422% 80 NOR -0.0296% 0.0018 11 BRN -0.3443% 81 XEF -0.0016% 0.0064 12 KHM -0.0019% 82 ALB -0.0056% 13 IDN -0.0256% 0.0033 83 BGR -0.0010% 14 LAO* 0.0000% 84 BLR* 0.0000% 15 MYS -0.0145% 85 HRV -0.2170% 16 PHL -0.0750% 0.0027 86 ROU -0.0004% 17 SGP -6.8252% 87 RUS -0.0067% -0.0001 18 THA -0.0039% 88 UKR -0.0020% 0.0002 19 VNM -0.0082% 89 XEE* 0.0000% 20 XSE -0.0051% 90 XER -0.0025% 0.0001 21 BGD -0.0013% 91 KAZ* 0.0000% 22 IND -0.0008% 0.0003 92 KGZ* 0.0000% 23 NPL* 0.0000% 93 XSU* 0.0000% 24 PAK -0.0008% 94 ARM* 0.0000% 25 LKA -0.0105% 95 AZE* 0.0000% 26 XSA -0.0003% 0.0000 96 GEO -0.0047% 27 CAN -0.0381% 0.0029 97 BHR -0.8829% 28 USA -0.0023% -0.0010 98 IRN -0.0023% 0.0016 29 MEX -0.0033% -0.0009 99 ISR -0.0237% 0.0021 30 XNA -2.2812% 0.0011 100 JOR -0.0012% 31 ARG -0.0021% 0.0014 101 KWT -0.1477% 32 BOL* 0.0000% 102 OMN -0.0529% 0.0004 33 BRA -0.0008% 0.0003 103 QAT -0.3812% 34 CHL -0.0252% -0.0006 104 SAU -0.0007% 0.0002 35 COL -0.0047% -0.0022 106 TUR -0.0082% 0.0002 36 ECU -0.0186% -0.0005 105 ARE -0.1082% 37 PRY* 0.0000% 107 XWS -0.0028% 38 PER -0.0070% 108 EGY -0.0351% 0.0001 39 URY -0.0028% 109 MAR -0.0031% -0.0002 40 VEN -0.0081% 110 TUN -0.0059% 41 XSM -0.0888% 0.0013 111 XNF -0.0025% 42 CRI -0.0420% 112 BEN -0.0006% -0.0020 43 GTM -0.0056% 0.0006 113 BFA* 0.0000% 44 HND -0.0151% 0.0022 114 CMR -0.0014% 45 NIC -0.0104% 115 CIV -0.0004% 46 SLV -0.0116% 116 GHA -0.0006% 0.0013 47 PAN -0.0648% 117 GIN -0.0004% 48 XCA -0.1487% 118 NGA -0.0002% 49 DOM -0.0308% 119 SEN -0.0010% 50 JAM -0.1333% 120 TGO -0.0003% 51 PRI -0.1568% -0.0012 121 XWF -0.0004% 52 TTO -0.3925% 122 XCF -0.0007% 0.0013 53 XCB -0.0832% -0.0009 123 XAC -0.0007% 54 AUT* 0.0000% 124 ETH* 0.0000% 55 BEL -0.0013% 0.0006 125 KEN -0.0006% -0.0017 56 CYP -0.1232% 0.0000 126 MDG -0.0048% 57 CZE* 0.0000% 127 MWI* 0.0000% 58 DNK -0.0210% 0.0008 128 MUS* -0.0798% 59 EST -0.0001% 0.0044 129 MOZ -0.0021% 60 FIN 0.0000% 0.0065 130 RWA* 0.0000% 61 FRA -0.0020% -0.0002 131 TZA -0.0016% 0.0020 62 DEU -0.0021% 0.0007 132 UGA* 0.0000% 63 GRC -0.0260% 133 ZMB* 0.0000% 64 HUN* 0.0000% 134 ZWE* 0.0000% 65 IRL -0.0037% 135 XEC -0.0012% -0.0037 66 ITA -0.0082% 0.0003 136 BWA* 0.0000% 67 LVA -0.0054% 0.0017 137 NAM -0.0017% 68 LTU -0.0004% 138 ZAF -0.0012% -0.0001 69 LUX* 0.0000% 139 XSC 0.0000% 70 MLT -0.0423% 140 XTW -0.4119% 0.0018 27 Table A2-1. Sea level rise: percentage losses of land for +1, +2, +3, +4 and +5 °C, years 2050 and 2100. Values lower than -0.1% in red. 2050 2100 N. Code +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C 1 AUS -0.0007% -0.0011% -0.0016% -0.0020% -0.0025% -0.0014% -0.0023% -0.0032% -0.0041% -0.0050% 2 NZL -0.0163% -0.0260% -0.0357% -0.0454% -0.0551% -0.0326% -0.0520% -0.0714% -0.0908% -0.1102% 3 XOC -0.1505% -0.2465% -0.3424% -0.4384% -0.5344% -0.3009% -0.4929% -0.6849% -0.8769% -1.0688% 4 CHN -0.0002% -0.0004% -0.0006% -0.0008% -0.0010% -0.0003% -0.0008% -0.0012% -0.0016% -0.0021% 5 HKG -0.6151% -1.4156% -2.2161% -3.0166% -3.8171% -1.2302% -2.8312% -4.4322% -6.0333% -7.6343% 6 JPN -0.0549% -0.1041% -0.1532% -0.2023% -0.2515% -0.1098% -0.2081% -0.3064% -0.4047% -0.5030% 7 KOR -0.0112% -0.0217% -0.0322% -0.0427% -0.0532% -0.0224% -0.0433% -0.0643% -0.0853% -0.1063% 8 MNG* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 9 TWN -0.0121% -0.0243% -0.0366% -0.0488% -0.0610% -0.0242% -0.0487% -0.0731% -0.0976% -0.1221% 10 XEA -0.0092% -0.0164% -0.0237% -0.0309% -0.0381% -0.0185% -0.0329% -0.0473% -0.0617% -0.0762% 11 BRN -0.0753% -0.1342% -0.1931% -0.2520% -0.3109% -0.1506% -0.2684% -0.3862% -0.5040% -0.6217% 12 KHM -0.0004% -0.0008% -0.0011% -0.0014% -0.0018% -0.0009% -0.0015% -0.0022% -0.0028% -0.0035% 13 IDN -0.0014% -0.0058% -0.0102% -0.0146% -0.0189% -0.0028% -0.0116% -0.0203% -0.0291% -0.0379% 14 LAO* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 15 MYS -0.0032% -0.0056% -0.0081% -0.0106% -0.0131% -0.0063% -0.0113% -0.0162% -0.0212% -0.0262% 16 PHL -0.0064% -0.0192% -0.0320% -0.0449% -0.0577% -0.0128% -0.0384% -0.0641% -0.0897% -0.1154% 17 SGP -1.4932% -2.6608% -3.8283% -4.9959% -6.1634% -2.9864% -5.3215% -7.6566% -9.9917% -12.3268% 18 THA -0.0009% -0.0015% -0.0022% -0.0029% -0.0035% -0.0017% -0.0031% -0.0044% -0.0057% -0.0071% 19 VNM -0.0018% -0.0032% -0.0046% -0.0060% -0.0074% -0.0036% -0.0064% -0.0092% -0.0120% -0.0148% 20 XSE -0.0011% -0.0020% -0.0028% -0.0037% -0.0046% -0.0022% -0.0040% -0.0057% -0.0074% -0.0092% 21 BGD -0.0003% -0.0005% -0.0007% -0.0009% -0.0011% -0.0006% -0.0010% -0.0014% -0.0019% -0.0023% 22 IND -0.0002% -0.0003% -0.0004% -0.0006% -0.0007% -0.0003% -0.0006% -0.0009% -0.0011% -0.0014% 23 NPL* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 24 PAK -0.0002% -0.0003% -0.0005% -0.0006% -0.0007% -0.0004% -0.0006% -0.0009% -0.0012% -0.0015% 25 LKA -0.0023% -0.0041% -0.0059% -0.0077% -0.0095% -0.0046% -0.0082% -0.0118% -0.0154% -0.0189% 26 XSA -0.0001% -0.0001% -0.0002% -0.0002% -0.0003% -0.0001% -0.0003% -0.0004% -0.0005% -0.0006% 27 CAN -0.0027% -0.0092% -0.0158% -0.0223% -0.0288% -0.0054% -0.0185% -0.0315% -0.0445% -0.0576% 28 USA -0.0006% -0.0010% -0.0014% -0.0018% -0.0022% -0.0013% -0.0021% -0.0029% -0.0037% -0.0045% 29 MEX -0.0009% -0.0014% -0.0020% -0.0026% -0.0031% -0.0018% -0.0029% -0.0040% -0.0052% -0.0063% 30 XNA -0.3788% -0.7690% -1.1592% -1.5494% -1.9397% -0.7575% -1.5380% -2.3184% -3.0989% -3.8793% 31 ARG -0.0003% -0.0007% -0.0011% -0.0014% -0.0018% -0.0006% -0.0014% -0.0021% -0.0028% -0.0036% 32 BOL* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 33 BRA -0.0002% -0.0003% -0.0004% -0.0006% -0.0007% -0.0003% -0.0006% -0.0009% -0.0012% -0.0014% 34 CHL -0.0063% -0.0106% -0.0149% -0.0192% -0.0235% -0.0125% -0.0212% -0.0298% -0.0384% -0.0470% 35 COL -0.0015% -0.0023% -0.0031% -0.0039% -0.0047% -0.0031% -0.0046% -0.0062% -0.0078% -0.0094% 36 ECU -0.0046% -0.0077% -0.0109% -0.0141% -0.0173% -0.0091% -0.0155% -0.0218% -0.0282% -0.0345% 37 PRY* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 38 PER -0.0015% -0.0027% -0.0039% -0.0051% -0.0063% -0.0030% -0.0054% -0.0078% -0.0102% -0.0126% 39 URY -0.0006% -0.0011% -0.0015% -0.0020% -0.0025% -0.0012% -0.0022% -0.0031% -0.0040% -0.0050% 40 VEN -0.0018% -0.0032% -0.0045% -0.0059% -0.0073% -0.0035% -0.0063% -0.0091% -0.0119% -0.0146% 41 XSM -0.0136% -0.0288% -0.0440% -0.0592% -0.0744% -0.0272% -0.0576% -0.0880% -0.1184% -0.1488% 42 CRI -0.0092% -0.0164% -0.0235% -0.0307% -0.0379% -0.0184% -0.0327% -0.0471% -0.0614% -0.0758% 43 GTM -0.0011% -0.0020% -0.0030% -0.0039% -0.0049% -0.0021% -0.0040% -0.0059% -0.0078% -0.0097% 44 HND -0.0017% -0.0042% -0.0068% -0.0094% -0.0120% -0.0033% -0.0085% -0.0137% -0.0188% -0.0240% 45 NIC -0.0023% -0.0040% -0.0058% -0.0076% -0.0093% -0.0045% -0.0081% -0.0116% -0.0152% -0.0187% 46 SLV -0.0025% -0.0045% -0.0065% -0.0085% -0.0105% -0.0051% -0.0090% -0.0130% -0.0169% -0.0209% 47 PAN -0.0142% -0.0253% -0.0363% -0.0474% -0.0585% -0.0283% -0.0505% -0.0727% -0.0948% -0.1170% 48 XCA -0.0325% -0.0580% -0.0834% -0.1088% -0.1343% -0.0651% -0.1159% -0.1668% -0.2176% -0.2685% 49 DOM -0.0067% -0.0120% -0.0173% -0.0226% -0.0278% -0.0135% -0.0240% -0.0346% -0.0451% -0.0557% 50 JAM -0.0292% -0.0519% -0.0747% -0.0975% -0.1203% -0.0583% -0.1039% -0.1495% -0.1951% -0.2407% 51 PRI -0.0435% -0.0703% -0.0972% -0.1240% -0.1508% -0.0870% -0.1406% -0.1943% -0.2480% -0.3016% 52 TTO -0.0859% -0.1530% -0.2201% -0.2873% -0.3544% -0.1717% -0.3060% -0.4403% -0.5745% -0.7088% 53 XCB -0.0221% -0.0363% -0.0506% -0.0648% -0.0790% -0.0442% -0.0726% -0.1011% -0.1296% -0.1580% 54 AUT* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 55 BEL -0.0002% -0.0005% -0.0007% -0.0009% -0.0011% -0.0005% -0.0009% -0.0014% -0.0018% -0.0022% 56 CYP -0.0270% -0.0481% -0.0692% -0.0903% -0.1113% -0.0540% -0.0962% -0.1384% -0.1805% -0.2227% 57 CZE* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 58 DNK -0.0037% -0.0073% -0.0109% -0.0145% -0.0181% -0.0075% -0.0147% -0.0219% -0.0291% -0.0363% 59 EST 0.0000% 0.0000% -0.0001% -0.0001% -0.0001% 0.0000% -0.0001% -0.0001% -0.0002% -0.0002% 60 FIN 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 61 FRA -0.0004% -0.0008% -0.0011% -0.0015% -0.0018% -0.0009% -0.0016% -0.0022% -0.0029% -0.0036% 62 DEU -0.0004% -0.0008% -0.0011% -0.0015% -0.0018% -0.0008% -0.0015% -0.0022% -0.0030% -0.0037% 63 GRC -0.0057% -0.0101% -0.0146% -0.0190% -0.0235% -0.0114% -0.0203% -0.0292% -0.0380% -0.0469% 64 HUN* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 65 IRL -0.0008% -0.0014% -0.0021% -0.0027% -0.0033% -0.0016% -0.0029% -0.0041% -0.0054% -0.0066% 66 ITA -0.0017% -0.0031% -0.0045% -0.0059% -0.0073% -0.0034% -0.0062% -0.0090% -0.0118% -0.0146% 67 LVA -0.0007% -0.0017% -0.0026% -0.0035% -0.0044% -0.0015% -0.0033% -0.0052% -0.0070% -0.0089% 68 LTU -0.0001% -0.0002% -0.0002% -0.0003% -0.0004% -0.0002% -0.0003% -0.0005% -0.0007% -0.0008% 69 LUX* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 70 MLT -0.0093% -0.0165% -0.0237% -0.0309% -0.0382% -0.0185% -0.0330% -0.0474% -0.0619% -0.0764% 28 Table A2-2. Sea level rise: percentage losses of land for +1, +2, +3, +4 and +5 °C, years 2050 and 2100. Values lower than -0.1% in red. 2050 2100 N. Code +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C 71 NLD -0.0005% -0.0009% -0.0012% -0.0016% -0.0019% -0.0010% -0.0017% -0.0024% -0.0031% -0.0038% 72 POL -0.0002% -0.0004% -0.0006% -0.0007% -0.0009% -0.0004% -0.0008% -0.0011% -0.0014% -0.0018% 73 PRT -0.0017% -0.0029% -0.0041% -0.0053% -0.0064% -0.0034% -0.0058% -0.0081% -0.0105% -0.0129% 74 SVK* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 75 SVN -0.0010% -0.0018% -0.0026% -0.0034% -0.0041% -0.0020% -0.0036% -0.0051% -0.0067% -0.0083% 76 ESP -0.0003% -0.0005% -0.0006% -0.0008% -0.0010% -0.0005% -0.0009% -0.0013% -0.0017% -0.0020% 77 SWE 0.0001% -0.0001% -0.0004% -0.0006% -0.0008% 0.0001% -0.0003% -0.0007% -0.0011% -0.0016% 78 GBR -0.0019% -0.0035% -0.0051% -0.0067% -0.0083% -0.0037% -0.0070% -0.0102% -0.0134% -0.0166% 79 CHE* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 80 NOR -0.0038% -0.0088% -0.0139% -0.0190% -0.0240% -0.0075% -0.0176% -0.0278% -0.0379% -0.0481% 81 XEF 0.0002% -0.0001% -0.0004% -0.0007% -0.0009% 0.0003% -0.0002% -0.0008% -0.0013% -0.0019% 82 ALB -0.0012% -0.0022% -0.0032% -0.0041% -0.0051% -0.0025% -0.0044% -0.0063% -0.0083% -0.0102% 83 BGR -0.0002% -0.0004% -0.0006% -0.0007% -0.0009% -0.0004% -0.0008% -0.0011% -0.0015% -0.0018% 84 BLR* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 85 HRV -0.0475% -0.0846% -0.1217% -0.1588% -0.1960% -0.0950% -0.1692% -0.2435% -0.3177% -0.3919% 86 ROU -0.0001% -0.0001% -0.0002% -0.0003% -0.0003% -0.0002% -0.0003% -0.0004% -0.0006% -0.0007% 87 RUS -0.0015% -0.0027% -0.0038% -0.0050% -0.0061% -0.0030% -0.0053% -0.0076% -0.0099% -0.0123% 88 UKR -0.0004% -0.0007% -0.0011% -0.0014% -0.0017% -0.0008% -0.0015% -0.0022% -0.0028% -0.0035% 89 XEE* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 90 XER -0.0005% -0.0010% -0.0014% -0.0018% -0.0022% -0.0011% -0.0019% -0.0028% -0.0036% -0.0045% 91 KAZ* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 92 KGZ* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 93 XSU* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 94 ARM* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 95 AZE* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 96 GEO -0.0010% -0.0018% -0.0027% -0.0035% -0.0043% -0.0021% -0.0037% -0.0053% -0.0069% -0.0086% 97 BHR -0.1932% -0.3442% -0.4952% -0.6462% -0.7973% -0.3863% -0.6884% -0.9904% -1.2925% -1.5945% 98 IRN -0.0003% -0.0007% -0.0011% -0.0015% -0.0019% -0.0006% -0.0014% -0.0022% -0.0030% -0.0037% 99 ISR -0.0027% -0.0067% -0.0108% -0.0148% -0.0189% -0.0053% -0.0134% -0.0215% -0.0296% -0.0377% 100 JOR -0.0003% -0.0005% -0.0007% -0.0009% -0.0011% -0.0005% -0.0009% -0.0013% -0.0017% -0.0022% 101 KWT -0.0323% -0.0576% -0.0828% -0.1081% -0.1334% -0.0646% -0.1151% -0.1657% -0.2162% -0.2667% 102 OMN -0.0105% -0.0196% -0.0286% -0.0377% -0.0467% -0.0210% -0.0391% -0.0572% -0.0753% -0.0934% 103 QAT -0.0834% -0.1486% -0.2138% -0.2790% -0.3442% -0.1668% -0.2972% -0.4276% -0.5581% -0.6885% 104 SAU -0.0001% -0.0003% -0.0004% -0.0005% -0.0006% -0.0003% -0.0005% -0.0008% -0.0010% -0.0012% 106 TUR -0.0017% -0.0031% -0.0045% -0.0059% -0.0073% -0.0034% -0.0062% -0.0090% -0.0119% -0.0147% 105 ARE -0.0237% -0.0422% -0.0607% -0.0792% -0.0977% -0.0473% -0.0843% -0.1213% -0.1583% -0.1953% 107 XWS -0.0006% -0.0011% -0.0016% -0.0021% -0.0025% -0.0012% -0.0022% -0.0032% -0.0041% -0.0051% 108 EGY -0.0076% -0.0136% -0.0196% -0.0256% -0.0316% -0.0152% -0.0272% -0.0392% -0.0512% -0.0632% 109 MAR -0.0007% -0.0012% -0.0018% -0.0023% -0.0028% -0.0014% -0.0025% -0.0035% -0.0046% -0.0057% 110 TUN -0.0013% -0.0023% -0.0033% -0.0043% -0.0053% -0.0026% -0.0046% -0.0066% -0.0086% -0.0106% 111 XNF -0.0005% -0.0010% -0.0014% -0.0018% -0.0022% -0.0011% -0.0019% -0.0028% -0.0036% -0.0045% 112 BEN -0.0002% -0.0003% -0.0004% -0.0005% -0.0006% -0.0004% -0.0006% -0.0008% -0.0010% -0.0012% 113 BFA* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 114 CMR -0.0003% -0.0005% -0.0008% -0.0010% -0.0012% -0.0006% -0.0011% -0.0015% -0.0020% -0.0025% 115 CIV -0.0001% -0.0002% -0.0002% -0.0003% -0.0004% -0.0002% -0.0003% -0.0005% -0.0006% -0.0008% 116 GHA -0.0001% -0.0002% -0.0003% -0.0004% -0.0005% -0.0002% -0.0004% -0.0006% -0.0008% -0.0010% 117 GIN -0.0001% -0.0001% -0.0002% -0.0003% -0.0003% -0.0002% -0.0003% -0.0004% -0.0006% -0.0007% 118 NGA 0.0000% -0.0001% -0.0001% -0.0001% -0.0002% -0.0001% -0.0001% -0.0002% -0.0003% -0.0003% 119 SEN -0.0002% -0.0004% -0.0005% -0.0007% -0.0009% -0.0004% -0.0008% -0.0011% -0.0014% -0.0017% 120 TGO -0.0001% -0.0001% -0.0001% -0.0002% -0.0002% -0.0001% -0.0002% -0.0003% -0.0004% -0.0005% 121 XWF -0.0001% -0.0001% -0.0002% -0.0003% -0.0003% -0.0002% -0.0003% -0.0004% -0.0005% -0.0007% 122 XCF -0.0001% -0.0002% -0.0003% -0.0005% -0.0006% -0.0002% -0.0005% -0.0007% -0.0009% -0.0012% 123 XAC -0.0002% -0.0003% -0.0004% -0.0005% -0.0006% -0.0003% -0.0006% -0.0008% -0.0010% -0.0013% 124 ETH* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 125 KEN -0.0002% -0.0003% -0.0004% -0.0005% -0.0006% -0.0003% -0.0005% -0.0007% -0.0009% -0.0011% 126 MDG -0.0011% -0.0019% -0.0027% -0.0035% -0.0044% -0.0021% -0.0038% -0.0054% -0.0071% -0.0087% 127 MWI* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 128 MUS* -0.0175% -0.0311% -0.0448% -0.0584% -0.0721% -0.0349% -0.0622% -0.0895% -0.1169% -0.1442% 129 MOZ -0.0005% -0.0008% -0.0012% -0.0015% -0.0019% -0.0009% -0.0016% -0.0023% -0.0030% -0.0037% 130 RWA* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 131 TZA -0.0002% -0.0005% -0.0007% -0.0010% -0.0013% -0.0004% -0.0009% -0.0015% -0.0020% -0.0025% 132 UGA* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 133 ZMB* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 134 ZWE* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 135 XEC -0.0005% -0.0007% -0.0009% -0.0011% -0.0013% -0.0009% -0.0013% -0.0017% -0.0021% -0.0025% 136 BWA* 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 137 NAM -0.0004% -0.0006% -0.0009% -0.0012% -0.0015% -0.0007% -0.0013% -0.0019% -0.0024% -0.0030% 138 ZAF -0.0003% -0.0005% -0.0007% -0.0009% -0.0011% -0.0005% -0.0009% -0.0013% -0.0017% -0.0021% 139 XSC 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 140 XTW -0.0539% -0.1244% -0.1948% -0.2653% -0.3358% -0.1079% -0.2488% -0.3897% -0.5306% -0.6715% 29 Table A3-1. Agriculture: percentage variation in multi-factor productivity. Values lower than -10% in red. MAIZE WHEAT RICE N. Code +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C 1 AUS -1.94% -4.56% -5.88% -8.56% -11.94% -2.19% -5.38% -11.06% -16.13% -19.94% -2.75% -2.69% -2.63% -6.69% -13.50% 2 NZL -0.78% -2.63% -3.55% -6.63% -10.78% -5.68% -6.15% -6.03% -6.05% -6.38% -4.30% -3.08% -1.85% -7.08% -16.60% 3 XOC -3.03% -6.38% -8.05% -10.38% -13.03% 1.08% -4.65% -15.78% -25.55% -32.63% -1.30% -2.33% -3.35% -6.33% -10.60% 4 CHN -1.34% -3.56% -4.68% -7.56% -11.34% -3.99% -5.78% -8.46% -10.93% -12.94% -3.55% -2.89% -2.23% -6.89% -15.10% 5 HKG -2.35% -5.25% -6.70% -9.25% -12.35% -0.95% -5.10% -12.85% -19.70% -24.75% -2.20% -2.55% -2.90% -6.55% -12.40% 6 JPN -1.49% -3.81% -4.98% -7.81% -11.49% -3.54% -5.68% -9.11% -12.23% -14.69% -3.35% -2.84% -2.33% -6.84% -14.70% 7 KOR -1.34% -3.56% -4.68% -7.56% -11.34% -3.99% -5.78% -8.46% -10.93% -12.94% -3.55% -2.89% -2.23% -6.89% -15.10% 8 MNG -0.48% -2.13% -2.95% -6.13% -10.48% -6.58% -6.35% -4.73% -3.45% -2.88% -4.70% -3.18% -1.65% -7.18% -17.40% 9 TWN -2.20% -5.00% -6.40% -9.00% -12.20% -1.40% -5.20% -12.20% -18.40% -23.00% -2.40% -2.60% -2.80% -6.60% -12.80% 10 XEA -3.06% -6.44% -8.13% -10.44% -13.06% 1.19% -4.63% -15.94% -25.88% -33.06% -1.25% -2.31% -3.38% -6.31% -10.50% 11 BRN -3.66% -7.44% -9.33% -11.44% -13.66% 2.99% -4.23% -18.54% -31.08% -40.06% -0.45% -2.11% -3.78% -6.11% -8.90% 12 KHM -3.06% -6.44% -8.13% -10.44% -13.06% 1.19% -4.63% -15.94% -25.88% -33.06% -1.25% -2.31% -3.38% -6.31% -10.50% 13 IDN -3.81% -7.69% -9.63% -11.69% -13.81% 3.44% -4.13% -19.19% -32.38% -41.81% -0.25% -2.06% -3.88% -6.06% -8.50% 14 LAO -2.61% -5.69% -7.23% -9.69% -12.61% -0.16% -4.93% -13.99% -21.98% -27.81% -1.85% -2.46% -3.08% -6.46% -11.70% 15 MYS -3.66% -7.44% -9.33% -11.44% -13.66% 2.99% -4.23% -18.54% -31.08% -40.06% -0.45% -2.11% -3.78% -6.11% -8.90% 16 PHL -3.03% -6.38% -8.05% -10.38% -13.03% 1.08% -4.65% -15.78% -25.55% -32.63% -1.30% -2.33% -3.35% -6.33% -10.60% 17 SGP -3.93% -7.88% -9.85% -11.88% -13.93% 3.78% -4.05% -19.68% -33.35% -43.13% -0.10% -2.03% -3.95% -6.03% -8.20% 18 THA -3.06% -6.44% -8.13% -10.44% -13.06% 1.19% -4.63% -15.94% -25.88% -33.06% -1.25% -2.31% -3.38% -6.31% -10.50% 19 VNM -2.84% -6.06% -7.68% -10.06% -12.84% 0.51% -4.78% -14.96% -23.93% -30.44% -1.55% -2.39% -3.23% -6.39% -11.10% 20 XSE -3.06% -6.44% -8.13% -10.44% -13.06% 1.19% -4.63% -15.94% -25.88% -33.06% -1.25% -2.31% -3.38% -6.31% -10.50% 21 BGD -2.20% -5.00% -6.40% -9.00% -12.20% -1.40% -5.20% -12.20% -18.40% -23.00% -2.40% -2.60% -2.80% -6.60% -12.80% 22 IND -2.31% -5.19% -6.63% -9.19% -12.31% -1.06% -5.13% -12.69% -19.38% -24.31% -2.25% -2.56% -2.88% -6.56% -12.50% 23 NPL -1.90% -4.50% -5.80% -8.50% -11.90% -2.30% -5.40% -10.90% -15.80% -19.50% -2.80% -2.70% -2.60% -6.70% -13.60% 24 PAK -1.75% -4.25% -5.50% -8.25% -11.75% -2.75% -5.50% -10.25% -14.50% -17.75% -3.00% -2.75% -2.50% -6.75% -14.00% 25 LKA -3.40% -7.00% -8.80% -11.00% -13.40% 2.20% -4.40% -17.40% -28.80% -37.00% -0.80% -2.20% -3.60% -6.20% -9.60% 26 XSA -2.31% -5.19% -6.63% -9.19% -12.31% -1.06% -5.13% -12.69% -19.38% -24.31% -2.25% -2.56% -2.88% -6.56% -12.50% 27 CAN 0.69% -0.19% -0.63% -4.19% -9.31% -10.06% -7.13% 0.31% 6.63% 10.69% -6.25% -3.56% -0.88% -7.56% -20.50% 28 USA -1.23% -3.38% -4.45% -7.38% -11.23% -4.33% -5.85% -7.98% -9.95% -11.63% -3.70% -2.93% -2.15% -6.93% -15.40% 29 MEX -2.20% -5.00% -6.40% -9.00% -12.20% -1.40% -5.20% -12.20% -18.40% -23.00% -2.40% -2.60% -2.80% -6.60% -12.80% 30 XNA -1.23% -3.38% -4.45% -7.38% -11.23% -4.33% -5.85% -7.98% -9.95% -11.63% -3.70% -2.93% -2.15% -6.93% -15.40% 31 ARG -1.11% -3.19% -4.23% -7.19% -11.11% -4.66% -5.93% -7.49% -8.98% -10.31% -3.85% -2.96% -2.08% -6.96% -15.70% 32 BOL -2.76% -5.94% -7.53% -9.94% -12.76% 0.29% -4.83% -14.64% -23.28% -29.56% -1.65% -2.41% -3.18% -6.41% -11.30% 33 BRA -2.54% -5.56% -7.08% -9.56% -12.54% -0.39% -4.98% -13.66% -21.33% -26.94% -1.95% -2.49% -3.03% -6.49% -11.90% 34 CHL -1.26% -3.44% -4.53% -7.44% -11.26% -4.21% -5.83% -8.14% -10.28% -12.06% -3.65% -2.91% -2.18% -6.91% -15.30% 35 COL -3.70% -7.50% -9.40% -11.50% -13.70% 3.10% -4.20% -18.70% -31.40% -40.50% -0.40% -2.10% -3.80% -6.10% -8.80% 36 ECU -3.89% -7.81% -9.78% -11.81% -13.89% 3.66% -4.08% -19.51% -33.03% -42.69% -0.15% -2.04% -3.93% -6.04% -8.30% 37 PRY -2.28% -5.13% -6.55% -9.13% -12.28% -1.18% -5.15% -12.53% -19.05% -23.88% -2.30% -2.58% -2.85% -6.58% -12.60% 38 PER -3.33% -6.88% -8.65% -10.88% -13.33% 1.98% -4.45% -17.08% -28.15% -36.13% -0.90% -2.23% -3.55% -6.23% -9.80% 39 URY -1.56% -3.94% -5.13% -7.94% -11.56% -3.31% -5.63% -9.44% -12.88% -15.56% -3.25% -2.81% -2.38% -6.81% -14.50% 40 VEN -3.51% -7.19% -9.03% -11.19% -13.51% 2.54% -4.33% -17.89% -29.78% -38.31% -0.65% -2.16% -3.68% -6.16% -9.30% 41 XSM -1.56% -3.94% -5.13% -7.94% -11.56% -3.31% -5.63% -9.44% -12.88% -15.56% -3.25% -2.81% -2.38% -6.81% -14.50% 42 CRI -3.29% -6.81% -8.58% -10.81% -13.29% 1.86% -4.48% -16.91% -27.83% -35.69% -0.95% -2.24% -3.53% -6.24% -9.90% 43 GTM -2.80% -6.00% -7.60% -10.00% -12.80% 0.40% -4.80% -14.80% -23.60% -30.00% -1.60% -2.40% -3.20% -6.40% -11.20% 44 HND -2.91% -6.19% -7.83% -10.19% -12.91% 0.74% -4.73% -15.29% -24.58% -31.31% -1.45% -2.36% -3.28% -6.36% -10.90% 45 NIC -3.03% -6.38% -8.05% -10.38% -13.03% 1.08% -4.65% -15.78% -25.55% -32.63% -1.30% -2.33% -3.35% -6.33% -10.60% 46 SLV -3.36% -6.94% -8.73% -10.94% -13.36% 2.09% -4.43% -17.24% -28.48% -36.56% -0.85% -2.21% -3.58% -6.21% -9.70% 47 PAN -2.99% -6.31% -7.98% -10.31% -12.99% 0.96% -4.68% -15.61% -25.23% -32.19% -1.35% -2.34% -3.33% -6.34% -10.70% 48 XCA -3.03% -6.38% -8.05% -10.38% -13.03% 1.08% -4.65% -15.78% -25.55% -32.63% -1.30% -2.33% -3.35% -6.33% -10.60% 49 DOM -2.58% -5.63% -7.15% -9.63% -12.58% -0.28% -4.95% -13.83% -21.65% -27.38% -1.90% -2.48% -3.05% -6.48% -11.80% 50 JAM -2.65% -5.75% -7.30% -9.75% -12.65% -0.05% -4.90% -14.15% -22.30% -28.25% -1.80% -2.45% -3.10% -6.45% -11.60% 51 PRI -2.65% -5.75% -7.30% -9.75% -12.65% -0.05% -4.90% -14.15% -22.30% -28.25% -1.80% -2.45% -3.10% -6.45% -11.60% 52 TTO -3.18% -6.63% -8.35% -10.63% -13.18% 1.53% -4.55% -16.43% -26.85% -34.38% -1.10% -2.28% -3.45% -6.28% -10.20% 53 XCB -2.65% -5.75% -7.30% -9.75% -12.65% -0.05% -4.90% -14.15% -22.30% -28.25% -1.80% -2.45% -3.10% -6.45% -11.60% 54 AUT -0.44% -2.06% -2.88% -6.06% -10.44% -6.69% -6.38% -4.56% -3.13% -2.44% -4.75% -3.19% -1.63% -7.19% -17.50% 55 BEL -0.21% -1.69% -2.43% -5.69% -10.21% -7.36% -6.53% -3.59% -1.18% 0.19% -5.05% -3.26% -1.48% -7.26% -18.10% 56 CYP -1.38% -3.63% -4.75% -7.63% -11.38% -3.88% -5.75% -8.63% -11.25% -13.38% -3.50% -2.88% -2.25% -6.88% -15.00% 57 CZE -0.25% -1.75% -2.50% -5.75% -10.25% -7.25% -6.50% -3.75% -1.50% -0.25% -5.00% -3.25% -1.50% -7.25% -18.00% 58 DNK 0.24% -0.94% -1.53% -4.94% -9.76% -8.71% -6.83% -1.64% 2.73% 5.44% -5.65% -3.41% -1.18% -7.41% -19.30% 59 EST 0.43% -0.63% -1.15% -4.63% -9.58% -9.28% -6.95% -0.82% 4.35% 7.63% -5.90% -3.48% -1.05% -7.48% -19.80% 60 FIN 0.88% 0.13% -0.25% -3.88% -9.13% -10.63% -7.25% 1.13% 8.25% 12.88% -6.50% -3.63% -0.75% -7.63% -21.00% 61 FRA -0.51% -2.19% -3.03% -6.19% -10.51% -6.46% -6.33% -4.89% -3.78% -3.31% -4.65% -3.16% -1.68% -7.16% -17.30% 62 DEU -0.18% -1.63% -2.35% -5.63% -10.18% -7.48% -6.55% -3.43% -0.85% 0.63% -5.10% -3.28% -1.45% -7.28% -18.20% 63 GRC -1.11% -3.19% -4.23% -7.19% -11.11% -4.66% -5.93% -7.49% -8.98% -10.31% -3.85% -2.96% -2.08% -6.96% -15.70% 64 HUN -0.44% -2.06% -2.88% -6.06% -10.44% -6.69% -6.38% -4.56% -3.13% -2.44% -4.75% -3.19% -1.63% -7.19% -17.50% 65 IRL -0.02% -1.38% -2.05% -5.38% -10.03% -7.93% -6.65% -2.78% 0.45% 2.38% -5.30% -3.33% -1.35% -7.33% -18.60% 66 ITA -0.89% -2.81% -3.78% -6.81% -10.89% -5.34% -6.08% -6.51% -7.03% -7.69% -4.15% -3.04% -1.93% -7.04% -16.30% 67 LVA 0.24% -0.94% -1.53% -4.94% -9.76% -8.71% -6.83% -1.64% 2.73% 5.44% -5.65% -3.41% -1.18% -7.41% -19.30% 68 LTU 0.13% -1.13% -1.75% -5.13% -9.88% -8.38% -6.75% -2.13% 1.75% 4.13% -5.50% -3.38% -1.25% -7.38% -19.00% 69 LUX -0.29% -1.81% -2.58% -5.81% -10.29% -7.14% -6.48% -3.91% -1.83% -0.69% -4.95% -3.24% -1.53% -7.24% -17.90% 70 MLT -1.34% -3.56% -4.68% -7.56% -11.34% -3.99% -5.78% -8.46% -10.93% -12.94% -3.55% -2.89% -2.23% -6.89% -15.10% 30 Table A3-2. Agriculture: percentage variation in multi-factor productivity. Values lower than -10% in red. MAIZE WHEAT RICE N. Code +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C 71 NLD -0.06% -1.44% -2.13% -5.44% -10.06% -7.81% -6.63% -2.94% 0.13% 1.94% -5.25% -3.31% -1.38% -7.31% -18.50% 72 POL -0.10% -1.50% -2.20% -5.50% -10.10% -7.70% -6.60% -3.10% -0.20% 1.50% -5.20% -3.30% -1.40% -7.30% -18.40% 73 PRT -1.04% -3.06% -4.08% -7.06% -11.04% -4.89% -5.98% -7.16% -8.33% -9.44% -3.95% -2.99% -2.03% -6.99% -15.90% 74 SVK -0.33% -1.88% -2.65% -5.88% -10.33% -7.03% -6.45% -4.08% -2.15% -1.13% -4.90% -3.23% -1.55% -7.23% -17.80% 75 SVN -0.55% -2.25% -3.10% -6.25% -10.55% -6.35% -6.30% -5.05% -4.10% -3.75% -4.60% -3.15% -1.70% -7.15% -17.20% 76 ESP -1.00% -3.00% -4.00% -7.00% -11.00% -5.00% -6.00% -7.00% -8.00% -9.00% -4.00% -3.00% -2.00% -7.00% -16.00% 77 SWE 0.65% -0.25% -0.70% -4.25% -9.35% -9.95% -7.10% 0.15% 6.30% 10.25% -6.20% -3.55% -0.90% -7.55% -20.40% 78 GBR 0.16% -1.06% -1.68% -5.06% -9.84% -8.49% -6.78% -1.96% 2.08% 4.56% -5.55% -3.39% -1.23% -7.39% -19.10% 79 CHE -0.48% -2.13% -2.95% -6.13% -10.48% -6.58% -6.35% -4.73% -3.45% -2.88% -4.70% -3.18% -1.65% -7.18% -17.40% 80 NOR 0.84% 0.06% -0.32% -3.94% -9.16% -10.51% -7.23% 0.96% 7.93% 12.44% -6.45% -3.61% -0.78% -7.61% -20.90% 81 XEF -0.48% -2.13% -2.95% -6.13% -10.48% -6.58% -6.35% -4.73% -3.45% -2.88% -4.70% -3.18% -1.65% -7.18% -17.40% 82 ALB -0.89% -2.81% -3.78% -6.81% -10.89% -5.34% -6.08% -6.51% -7.03% -7.69% -4.15% -3.04% -1.93% -7.04% -16.30% 83 BGR -0.81% -2.69% -3.63% -6.69% -10.81% -5.56% -6.13% -6.19% -6.38% -6.81% -4.25% -3.06% -1.88% -7.06% -16.50% 84 BLR 0.01% -1.31% -1.98% -5.31% -9.99% -8.04% -6.68% -2.61% 0.77% 2.81% -5.35% -3.34% -1.33% -7.34% -18.70% 85 HRV -0.66% -2.44% -3.33% -6.44% -10.66% -6.01% -6.23% -5.54% -5.08% -5.06% -4.45% -3.11% -1.78% -7.11% -16.90% 86 ROU -0.55% -2.25% -3.10% -6.25% -10.55% -6.35% -6.30% -5.05% -4.10% -3.75% -4.60% -3.15% -1.70% -7.15% -17.20% 87 RUS 0.61% -0.31% -0.78% -4.31% -9.39% -9.84% -7.08% -0.01% 5.98% 9.81% -6.15% -3.54% -0.93% -7.54% -20.30% 88 UKR -0.40% -2.00% -2.80% -6.00% -10.40% -6.80% -6.40% -4.40% -2.80% -2.00% -4.80% -3.20% -1.60% -7.20% -17.60% 89 XEE -0.40% -2.00% -2.80% -6.00% -10.40% -6.80% -6.40% -4.40% -2.80% -2.00% -4.80% -3.20% -1.60% -7.20% -17.60% 90 XER -0.18% -1.63% -2.35% -5.63% -10.18% -7.48% -6.55% -3.43% -0.85% 0.63% -5.10% -3.28% -1.45% -7.28% -18.20% 91 KAZ -0.40% -2.00% -2.80% -6.00% -10.40% -6.80% -6.40% -4.40% -2.80% -2.00% -4.80% -3.20% -1.60% -7.20% -17.60% 92 KGZ -0.96% -2.94% -3.93% -6.94% -10.96% -5.11% -6.03% -6.84% -7.68% -8.56% -4.05% -3.01% -1.98% -7.01% -16.10% 93 XSU -0.40% -2.00% -2.80% -6.00% -10.40% -6.80% -6.40% -4.40% -2.80% -2.00% -4.80% -3.20% -1.60% -7.20% -17.60% 94 ARM -0.96% -2.94% -3.93% -6.94% -10.96% -5.11% -6.03% -6.84% -7.68% -8.56% -4.05% -3.01% -1.98% -7.01% -16.10% 95 AZE -1.00% -3.00% -4.00% -7.00% -11.00% -5.00% -6.00% -7.00% -8.00% -9.00% -4.00% -3.00% -2.00% -7.00% -16.00% 96 GEO -0.81% -2.69% -3.63% -6.69% -10.81% -5.56% -6.13% -6.19% -6.38% -6.81% -4.25% -3.06% -1.88% -7.06% -16.50% 97 BHR -2.05% -4.75% -6.10% -8.75% -12.05% -1.85% -5.30% -11.55% -17.10% -21.25% -2.60% -2.65% -2.70% -6.65% -13.20% 98 IRN -1.56% -3.94% -5.13% -7.94% -11.56% -3.31% -5.63% -9.44% -12.88% -15.56% -3.25% -2.81% -2.38% -6.81% -14.50% 99 ISR -1.68% -4.13% -5.35% -8.13% -11.68% -2.98% -5.55% -9.93% -13.85% -16.88% -3.10% -2.78% -2.45% -6.78% -14.20% 100 JOR -1.68% -4.13% -5.35% -8.13% -11.68% -2.98% -5.55% -9.93% -13.85% -16.88% -3.10% -2.78% -2.45% -6.78% -14.20% 101 KWT -1.83% -4.38% -5.65% -8.38% -11.83% -2.53% -5.45% -10.58% -15.15% -18.63% -2.90% -2.73% -2.55% -6.73% -13.80% 102 OMN -2.39% -5.31% -6.78% -9.31% -12.39% -0.84% -5.08% -13.01% -20.03% -25.19% -2.15% -2.54% -2.93% -6.54% -12.30% 103 QAT -2.13% -4.88% -6.25% -8.88% -12.13% -1.63% -5.25% -11.88% -17.75% -22.13% -2.50% -2.63% -2.75% -6.63% -13.00% 104 SAU -2.24% -5.06% -6.48% -9.06% -12.24% -1.29% -5.18% -12.36% -18.73% -23.44% -2.35% -2.59% -2.83% -6.59% -12.70% 106 TUR -1.08% -3.13% -4.15% -7.13% -11.08% -4.78% -5.95% -7.33% -8.65% -9.88% -3.90% -2.98% -2.05% -6.98% -15.80% 105 ARE -2.16% -4.94% -6.33% -8.94% -12.16% -1.51% -5.23% -12.04% -18.08% -22.56% -2.45% -2.61% -2.78% -6.61% -12.90% 107 XWS -1.68% -4.13% -5.35% -8.13% -11.68% -2.98% -5.55% -9.93% -13.85% -16.88% -3.10% -2.78% -2.45% -6.78% -14.20% 108 EGY -1.98% -4.63% -5.95% -8.63% -11.98% -2.08% -5.35% -11.23% -16.45% -20.38% -2.70% -2.68% -2.65% -6.68% -13.40% 109 MAR -1.64% -4.06% -5.28% -8.06% -11.64% -3.09% -5.58% -9.76% -13.53% -16.44% -3.15% -2.79% -2.43% -6.79% -14.30% 110 TUN -1.49% -3.81% -4.98% -7.81% -11.49% -3.54% -5.68% -9.11% -12.23% -14.69% -3.35% -2.84% -2.33% -6.84% -14.70% 111 XNF -1.49% -3.81% -4.98% -7.81% -11.49% -3.54% -5.68% -9.11% -12.23% -14.69% -3.35% -2.84% -2.33% -6.84% -14.70% 112 BEN -3.29% -6.81% -8.58% -10.81% -13.29% 1.86% -4.48% -16.91% -27.83% -35.69% -0.95% -2.24% -3.53% -6.24% -9.90% 113 BFA -3.10% -6.50% -8.20% -10.50% -13.10% 1.30% -4.60% -16.10% -26.20% -33.50% -1.20% -2.30% -3.40% -6.30% -10.40% 114 CMR -3.44% -7.06% -8.88% -11.06% -13.44% 2.31% -4.38% -17.56% -29.13% -37.44% -0.75% -2.19% -3.63% -6.19% -9.50% 115 CIV -3.44% -7.06% -8.88% -11.06% -13.44% 2.31% -4.38% -17.56% -29.13% -37.44% -0.75% -2.19% -3.63% -6.19% -9.50% 116 GHA -3.40% -7.00% -8.80% -11.00% -13.40% 2.20% -4.40% -17.40% -28.80% -37.00% -0.80% -2.20% -3.60% -6.20% -9.60% 117 GIN -3.25% -6.75% -8.50% -10.75% -13.25% 1.75% -4.50% -16.75% -27.50% -35.25% -1.00% -2.25% -3.50% -6.25% -10.00% 118 NGA -3.33% -6.88% -8.65% -10.88% -13.33% 1.98% -4.45% -17.08% -28.15% -36.13% -0.90% -2.23% -3.55% -6.23% -9.80% 119 SEN -2.91% -6.19% -7.83% -10.19% -12.91% 0.74% -4.73% -15.29% -24.58% -31.31% -1.45% -2.36% -3.28% -6.36% -10.90% 120 TGO -3.36% -6.94% -8.73% -10.94% -13.36% 2.09% -4.43% -17.24% -28.48% -36.56% -0.85% -2.21% -3.58% -6.21% -9.70% 121 XWF -3.36% -6.94% -8.73% -10.94% -13.36% 2.09% -4.43% -17.24% -28.48% -36.56% -0.85% -2.21% -3.58% -6.21% -9.70% 122 XCF -3.51% -7.19% -9.03% -11.19% -13.51% 2.54% -4.33% -17.89% -29.78% -38.31% -0.65% -2.16% -3.68% -6.16% -9.30% 123 XAC -4.00% -8.00% -10.00% -12.00% -14.00% 4.00% -4.00% -20.00% -34.00% -44.00% 0.00% -2.00% -4.00% -6.00% -8.00% 124 ETH -3.33% -6.88% -8.65% -10.88% -13.33% 1.98% -4.45% -17.08% -28.15% -36.13% -0.90% -2.23% -3.55% -6.23% -9.80% 125 KEN -4.00% -8.00% -10.00% -12.00% -14.00% 4.00% -4.00% -20.00% -34.00% -44.00% 0.00% -2.00% -4.00% -6.00% -8.00% 126 MDG -2.58% -5.63% -7.15% -9.63% -12.58% -0.28% -4.95% -13.83% -21.65% -27.38% -1.90% -2.48% -3.05% -6.48% -11.80% 127 MWI -3.03% -6.38% -8.05% -10.38% -13.03% 1.08% -4.65% -15.78% -25.55% -32.63% -1.30% -2.33% -3.35% -6.33% -10.60% 128 MUS -2.43% -5.38% -6.85% -9.38% -12.43% -0.73% -5.05% -13.18% -20.35% -25.63% -2.10% -2.53% -2.95% -6.53% -12.20% 129 MOZ -2.61% -5.69% -7.23% -9.69% -12.61% -0.16% -4.93% -13.99% -21.98% -27.81% -1.85% -2.46% -3.08% -6.46% -11.70% 130 RWA -3.85% -7.75% -9.70% -11.75% -13.85% 3.55% -4.10% -19.35% -32.70% -42.25% -0.20% -2.05% -3.90% -6.05% -8.40% 131 TZA -3.51% -7.19% -9.03% -11.19% -13.51% 2.54% -4.33% -17.89% -29.78% -38.31% -0.65% -2.16% -3.68% -6.16% -9.30% 132 UGA -3.89% -7.81% -9.78% -11.81% -13.89% 3.66% -4.08% -19.51% -33.03% -42.69% -0.15% -2.04% -3.93% -6.04% -8.30% 133 ZMB -3.03% -6.38% -8.05% -10.38% -13.03% 1.08% -4.65% -15.78% -25.55% -32.63% -1.30% -2.33% -3.35% -6.33% -10.60% 134 ZWE -2.61% -5.69% -7.23% -9.69% -12.61% -0.16% -4.93% -13.99% -21.98% -27.81% -1.85% -2.46% -3.08% -6.46% -11.70% 135 XEC -2.61% -5.69% -7.23% -9.69% -12.61% -0.16% -4.93% -13.99% -21.98% -27.81% -1.85% -2.46% -3.08% -6.46% -11.70% 136 BWA -2.31% -5.19% -6.63% -9.19% -12.31% -1.06% -5.13% -12.69% -19.38% -24.31% -2.25% -2.56% -2.88% -6.56% -12.50% 137 NAM -2.28% -5.13% -6.55% -9.13% -12.28% -1.18% -5.15% -12.53% -19.05% -23.88% -2.30% -2.58% -2.85% -6.58% -12.60% 138 ZAF -1.86% -4.44% -5.73% -8.44% -11.86% -2.41% -5.43% -10.74% -15.48% -19.06% -2.85% -2.71% -2.58% -6.71% -13.70% 139 XSC -1.86% -4.44% -5.73% -8.44% -11.86% -2.41% -5.43% -10.74% -15.48% -19.06% -2.85% -2.71% -2.58% -6.71% -13.70% 140 XTW -1.00% -3.00% -4.00% -7.00% -11.00% -5.00% -6.00% -7.00% -8.00% -9.00% -4.00% -3.00% -2.00% -7.00% -16.00% 31 Table A4-1. Agriculture: percentage variation in multi-factor productivity. Negative values in red. N. Code Base Y ref.imp. adj Base T Delta T Base P Delta P P/T Ratio C/T Ratio +1°C +2°C +3°C +4°C +5°C 1 AUS 5000 -6.5 -0.19% 21.95 2.7 1.59 -0.02 -0.0074 54.7037 -1.91% -4.01% -6.31% -8.81% -11.51% 2 NZL 500 -3.8 -2.03% 12.08 2 3.58 0.1 0.0500 73.8500 1.39% 0.80% -1.78% -6.35% -12.90% 3 XOC 4000 -9.1 0.86% 25.24 1.84 5.14 0.23 0.1250 80.2717 -3.06% -6.37% -9.92% -13.73% -17.78% 4 CHN 10000 -5.1 -6.86% 5.3 3.4 2.37 0.21 0.0618 43.4412 0.68% 1.26% 1.74% 2.11% 2.40% 5 HKG 5000 -7.5 -0.83% 22.84 2.14 4.5 0.13 0.0607 69.0187 -2.02% -4.24% -6.65% -9.27% -12.08% 6 JPN 500 -5.5 -0.34% 12.13 2.94 4.43 0.23 0.0782 50.2381 0.28% -1.43% -5.13% -10.81% -18.48% 7 KOR 500 -5.1 -0.24% 12.07 2.99 3.16 0.26 0.0870 49.3980 0.36% -1.26% -4.88% -10.48% -18.06% 8 MNG 10000 -3.1 -6.52% -0.4 3.66 1.01 0.13 0.0355 40.3552 1.24% 2.38% 3.41% 4.35% 5.19% 9 TWN 5000 -7.1 -0.48% 22.84 2.14 4.5 0.13 0.0607 69.0187 -2.02% -4.24% -6.65% -9.27% -12.08% 10 XEA 5000 -9.1 -0.95% 25.3 2.34 5.07 0.22 0.0940 63.1197 -2.53% -5.27% -8.20% -11.33% -14.66% 11 BRN 4000 -10.5 -0.30% 25.41 2.17 7.9 0.43 0.1982 68.0645 -3.17% -6.58% -10.24% -14.16% -18.32% 12 KHM 5000 -9.1 -0.95% 25.3 2.34 5.07 0.22 0.0940 63.1197 -2.53% -5.27% -8.20% -11.33% -14.66% 13 IDN 4000 -10.9 -0.55% 25.53 2.2 0.13 0.39 0.1773 67.1364 -3.20% -6.65% -10.35% -14.30% -18.49% 14 LAO 4000 -8.1 -0.31% 21.89 2.53 3.75 0.2 0.0791 58.3794 -2.35% -4.94% -7.78% -10.87% -14.21% 15 MYS 4000 -10.5 -0.29% 25.4 2.21 7.86 0.48 0.2172 66.8326 -3.17% -6.59% -10.26% -14.17% -18.34% 16 PHL 5000 -9.1 -0.42% 26.23 2.06 6.76 0.5 0.2427 71.6990 -2.68% -5.56% -8.64% -11.91% -15.39% 17 SGP 4000 -11.2 0.21% 26.96 2.11 7.5 0.18 0.0853 70.0000 -3.54% -7.33% -11.37% -15.66% -20.20% 18 THA 4000 -9.1 0.65% 24.63 2.46 4.27 0.18 0.0732 60.0407 -3.02% -6.28% -9.80% -13.56% -17.57% 19 VNM 4000 -8.6 -0.04% 23.05 2.36 4.42 0.25 0.1059 62.5847 -2.61% -5.47% -8.58% -11.93% -15.54% 20 XSE 5000 -9.1 -0.95% 25.3 2.34 5.07 0.22 0.0940 63.1197 -2.53% -5.27% -8.20% -11.33% -14.66% 21 BGD 5000 -7.1 0.49% 24.24 2.54 3.9 0.4 0.1575 58.1496 -2.34% -4.89% -7.63% -10.57% -13.71% 22 IND 5000 -7.4 -0.30% 23.24 2.77 2.65 0.27 0.0975 53.3213 -2.17% -4.53% -7.10% -9.86% -12.82% 23 NPL 500 -6.4 -6.19% 11.24 3.15 4.99 0.43 0.1365 46.8889 1.91% 1.83% -0.24% -4.29% -10.34% 24 PAK 4000 -6.1 -0.35% 18.82 3.38 0.87 0.05 0.0148 43.6982 -1.66% -3.57% -5.73% -8.14% -10.80% 25 LKA 5000 -9.9 -0.93% 26.87 2.04 4.87 0.53 0.2598 72.4020 -2.80% -5.81% -9.01% -12.41% -16.01% 26 XSA 5000 -7.4 -0.30% 23.24 2.77 2.65 0.27 0.0975 53.3213 -2.17% -4.53% -7.10% -9.86% -12.82% 27 CAN 10000 -0.4 -4.87% -4.13 4.54 1.83 0.23 0.0507 32.5330 1.59% 3.08% 4.48% 5.77% 6.96% 28 USA 10000 -4.9 -5.62% 8.52 3.63 2.37 0.13 0.0358 40.6887 0.35% 0.60% 0.76% 0.81% 0.77% 29 MEX 3000 -7.1 0.88% 19.35 2.89 2.54 -0.2 -0.0692 51.1073 -2.34% -5.01% -8.02% -11.35% -15.02% 30 XNA 10000 -4.9 -5.62% 8.52 3.63 2.37 0.13 0.0358 40.6887 0.35% 0.60% 0.76% 0.81% 0.77% 31 ARG 2000 -4.6 -0.21% 14.57 2.21 2.1 0.02 0.0090 66.8326 -0.97% -2.43% -4.39% -6.84% -9.79% 32 BOL 3000 -8.4 0.13% 19.82 3.17 4.11 -0.06 -0.0189 46.5931 -2.53% -5.39% -8.58% -12.10% -15.95% 33 BRA 5000 -7.9 0.21% 24.91 2.91 4.01 -0.08 -0.0275 50.7560 -2.51% -5.22% -8.13% -11.23% -14.54% 34 CHL 10000 -4.9 -5.24% 10.61 2.27 2.87 -0.19 -0.0837 65.0661 0.20% 0.29% 0.29% 0.19% -0.01% 35 COL 3500 -10.6 0.11% 23.95 2.76 6.37 0.14 0.0507 53.5145 -3.30% -6.88% -10.74% -14.89% -19.32% 36 ECU 3000 -11.1 -0.75% 21.86 2.49 7.73 0.43 0.1727 59.3173 -3.11% -6.55% -10.32% -14.42% -18.86% 37 PRY 5000 -7.3 0.23% 23.9 2.99 2.89 0 0.0000 49.3980 -2.32% -4.83% -7.54% -10.45% -13.56% 38 PER 2500 -9.8 0.51% 19.89 2.91 5.6 0.22 0.0756 50.7560 -3.02% -6.45% -10.26% -14.48% -19.10% 39 URY 3000 -5.6 0.75% 18.15 2.09 2.51 0.21 0.1005 70.6699 -1.80% -3.93% -6.39% -9.19% -12.31% 40 VEN 4000 -10.2 0.17% 25.2 2.85 3.35 -0.25 -0.0877 51.8246 -3.21% -6.66% -10.36% -14.31% -18.51% 41 XSM 3000 -5.6 0.75% 18.15 2.09 2.51 0.21 0.1005 70.6699 -1.80% -3.93% -6.39% -9.19% -12.31% 42 CRI 4500 -9.7 -0.15% 25.96 2.26 3.44 -0.16 -0.0708 65.3540 -2.95% -6.12% -9.52% -13.13% -16.97% 43 GTM 4500 -8.5 -0.05% 24.15 2.75 2.79 -0.24 -0.0873 53.7091 -2.61% -5.44% -8.49% -11.76% -15.25% 44 HND 4500 -8.8 -0.16% 24.52 2.45 2.49 -0.25 -0.1020 60.2857 -2.66% -5.54% -8.64% -11.96% -15.50% 45 NIC 4500 -9.1 0.20% 25.5 2.38 2.34 -0.22 -0.0924 62.0588 -2.87% -5.95% -9.26% -12.79% -16.54% 46 SLV 4500 -9.8 -0.40% 25.89 2.2 5.35 0.14 0.0636 67.1364 -2.93% -6.07% -9.44% -13.03% -16.84% 47 PAN 4500 -9.0 0.39% 25.54 2.6 2.06 -0.22 -0.0846 56.8077 -2.90% -6.02% -9.36% -12.93% -16.71% 48 XCA 4500 -9.1 0.20% 25.5 2.38 2.34 -0.22 -0.0924 62.0588 -2.87% -5.95% -9.26% -12.79% -16.54% 49 DOM 5000 -8.0 0.13% 25.28 2.19 2.22 -0.25 -0.1142 67.4429 -2.51% -5.22% -8.13% -11.24% -14.55% 50 JAM 5000 -8.2 0.52% 26.34 2.04 2.27 -0.23 -0.1127 72.4020 -2.70% -5.60% -8.70% -12.00% -15.50% 51 PRI 5000 -8.2 -0.10% 25.38 1.95 1.82 -0.26 -0.1333 75.7436 -2.50% -5.19% -8.09% -11.18% -14.47% 52 TTO 4500 -9.4 -0.11% 25.8 2.01 2.26 -0.3 -0.1493 73.4826 -2.88% -5.98% -9.30% -12.84% -16.60% 53 XCB 5000 -8.2 0.52% 26.34 2.04 2.27 -0.23 -0.1127 72.4020 -2.70% -5.60% -8.70% -12.00% -15.50% 54 AUT 10000 -3.0 -4.01% 7.89 3.15 2.62 0 0.0000 46.8889 0.43% 0.76% 0.98% 1.11% 1.14% 55 BEL 10000 -2.5 -2.85% 10.18 2.66 2.62 0.03 0.0113 55.5263 0.22% 0.34% 0.36% 0.28% 0.10% 56 CYP 4000 -5.2 0.60% 19.18 2.63 1.38 -0.16 -0.0608 56.1597 -1.69% -3.62% -5.80% -8.24% -10.91% 57 CZE 10000 -2.6 -3.48% 8.2 3.11 2.34 0.03 0.0096 47.4920 0.40% 0.70% 0.90% 1.00% 1.00% 58 DNK 10000 -1.4 -2.42% 8.11 2.66 2.38 0.14 0.0526 55.5263 0.42% 0.75% 0.98% 1.10% 1.13% 59 EST 10000 -1.0 -2.92% 4.66 3.64 1.96 0.17 0.0467 40.5769 0.74% 1.37% 1.91% 2.34% 2.68% 60 FIN 10000 0.0 -3.22% 0.01 4.14 1.9 0.23 0.0556 35.6763 1.19% 2.27% 3.26% 4.15% 4.94% 61 FRA 10000 -3.2 -3.21% 11.26 2.85 2.75 -0.12 -0.0421 51.8246 0.10% 0.11% 0.01% -0.18% -0.48% 62 DEU 10000 -2.4 -3.17% 8.76 2.85 2.54 0.05 0.0175 51.8246 0.35% 0.60% 0.76% 0.81% 0.77% 63 GRC 3000 -4.6 -0.26% 15.59 3.05 1.63 -0.2 -0.0656 48.4262 -1.11% -2.56% -4.34% -6.45% -8.89% 64 HUN 10000 -3.0 -3.27% 10.28 3.36 2.03 -0.03 -0.0089 43.9583 0.18% 0.27% 0.25% 0.14% -0.07% 65 IRL 10000 -2.1 -2.63% 10.02 1.84 3.29 0.09 0.0489 80.2717 0.29% 0.48% 0.57% 0.56% 0.45% 66 ITA 1500 -4.1 0.23% 13.4 3.02 2.05 -0.13 -0.0430 48.9073 -0.77% -2.21% -4.30% -7.06% -10.48% 67 LVA 10000 -1.4 -3.06% 5.67 3.48 2.04 0.16 0.0460 42.4425 0.64% 1.18% 1.62% 1.96% 2.20% 68 LTU 10000 -1.7 -3.06% 6.6 3.36 2.05 0.13 0.0387 43.9583 0.55% 1.00% 1.35% 1.60% 1.75% 69 LUX 10000 -2.7 -3.13% 9.76 2.82 2.93 0.01 0.0035 52.3759 0.25% 0.41% 0.46% 0.42% 0.27% 70 MLT 4000 -5.1 0.37% 18.85 2.44 0.86 -0.1 -0.0410 60.5328 -1.58% -3.41% -5.49% -7.81% -10.39% 32 Table A4-2. Agriculture: percentage variation in multi-factor productivity. Negative values in red. N. Code Base Y ref.imp. adj Base T Delta T Base P Delta P P/T Ratio C/T Ratio +1°C +2°C +3°C +4°C +5°C 71 NLD 10000 -2.1 -2.57% 10.03 2.52 2.49 0.08 0.0317 58.6111 0.24% 0.38% 0.42% 0.37% 0.21% 72 POL 10000 -2.2 -3.19% 8.38 2.26 2.02 0.07 0.0310 65.3540 0.42% 0.74% 0.96% 1.08% 1.10% 73 PRT 2500 -4.4 0.55% 15.53 2.69 2.13 -0.32 -0.1190 54.9071 -1.26% -2.92% -4.97% -7.42% -10.27% 74 SVK 10000 -2.8 -3.44% 8.88 3.28 2.23 -0.01 -0.0030 45.0305 0.33% 0.55% 0.68% 0.70% 0.63% 75 SVN 10000 -3.3 -3.74% 9.61 3.27 2.43 -0.07 -0.0214 45.1682 0.25% 0.41% 0.46% 0.42% 0.27% 76 ESP 2000 -4.3 -0.25% 14 2.95 1.8 -0.25 -0.0847 50.0678 -0.87% -2.23% -4.09% -6.44% -9.29% 77 SWE 10000 -0.5 -3.27% 1.75 3.49 2.2 0.22 0.0630 42.3209 1.03% 1.96% 2.79% 3.52% 4.15% 78 GBR 10000 -1.6 -2.35% 9.22 2.16 2.81 0.09 0.0417 68.3796 0.34% 0.58% 0.73% 0.77% 0.72% 79 CHE 10000 -3.1 -4.30% 7.22 3.07 3.41 -0.05 -0.0163 48.1107 0.50% 0.89% 1.19% 1.39% 1.49% 80 NOR 10000 0.0 -3.10% 0.89 3.3 2.89 0.31 0.0939 44.7576 1.12% 2.14% 3.06% 3.88% 4.60% 81 XEF 10000 -3.1 -4.30% 7.22 3.07 3.41 -0.05 -0.0163 48.1107 0.50% 0.89% 1.19% 1.39% 1.49% 82 ALB 1500 -4.1 0.16% 13.3 3.21 2.28 -0.22 -0.0685 46.0125 -0.75% -2.16% -4.23% -6.97% -10.36% 83 BGR 1000 -3.9 -0.67% 12.23 3.29 1.74 -0.11 -0.0334 44.8936 -0.08% -1.16% -3.22% -6.29% -10.34% 84 BLR 10000 -2.0 -3.24% 6.82 3.53 2.01 0.1 0.0283 41.8414 0.52% 0.95% 1.27% 1.50% 1.62% 85 HRV 500 -3.5 -1.89% 11.43 3.28 2.29 -0.09 -0.0274 45.0305 1.43% 0.88% -1.66% -6.19% -12.70% 86 ROU 10000 -3.3 -3.54% 10.25 3.4 1.96 -0.06 -0.0176 43.4412 0.19% 0.27% 0.26% 0.15% -0.07% 87 RUS 10000 -0.6 -5.20% -4.64 4.6 1.53 0.23 0.0500 32.1087 1.64% 3.18% 4.62% 5.97% 7.21% 88 UKR 10000 -2.9 -3.34% 9.74 3.44 1.7 0.03 0.0087 42.9360 0.24% 0.37% 0.41% 0.34% 0.18% 89 XEE 10000 -2.9 -3.34% 9.74 3.44 1.7 0.03 0.0087 42.9360 0.24% 0.37% 0.41% 0.34% 0.18% 90 XER 10000 -2.4 -3.17% 8.76 2.85 2.54 0.05 0.0175 51.8246 0.35% 0.60% 0.76% 0.81% 0.77% 91 KAZ 10000 -2.9 -3.86% 7.88 3.9 0.92 0.08 0.0205 37.8718 0.41% 0.72% 0.93% 1.04% 1.05% 92 KGZ 10000 -4.2 -6.68% 2.88 3.63 1.54 0.06 0.0165 40.6887 0.91% 1.72% 2.44% 3.05% 3.57% 93 XSU 10000 -2.9 -3.86% 7.88 3.9 0.92 0.08 0.0205 37.8718 0.41% 0.72% 0.93% 1.04% 1.05% 94 ARM 10000 -4.2 -5.14% 8.1 3.44 1.79 -0.07 -0.0203 42.9360 0.40% 0.70% 0.90% 1.00% 1.00% 95 AZE 1500 -4.3 0.09% 13.44 3.09 1.25 0 0.0000 47.7994 -0.81% -2.29% -4.43% -7.23% -10.69% 96 GEO 10000 -3.9 -4.55% 8.96 3.3 2.32 -0.04 -0.0121 44.7576 0.32% 0.53% 0.65% 0.67% 0.59% 97 BHR 6000 -6.8 0.44% 25.72 3.21 0.2 0.01 0.0031 46.0125 -2.24% -4.65% -7.23% -9.97% -12.87% 98 IRN 3000 -5.6 -0.03% 16.74 3.51 0.63 -0.03 -0.0085 42.0798 -1.54% -3.41% -5.62% -8.15% -11.02% 99 ISR 4000 -5.9 0.15% 19.36 3.05 0.4 -0.07 -0.0230 48.4262 -1.77% -3.79% -6.06% -8.58% -11.34% 100 JOR 4000 -5.9 -0.36% 18.56 3.39 0.28 -0.04 -0.0118 43.5693 -1.60% -3.45% -5.54% -7.89% -10.48% 101 KWT 6000 -6.3 0.52% 24.73 3.55 0.24 0.01 0.0028 41.6056 -2.10% -4.36% -6.78% -9.37% -12.13% 102 OMN 6000 -7.6 0.09% 26.71 2.86 0.25 0.05 0.0175 51.6434 -2.39% -4.94% -7.66% -10.54% -13.59% 103 QAT 6000 -7.0 0.64% 26.45 3.29 0.18 0.01 0.0030 44.8936 -2.37% -4.90% -7.60% -10.47% -13.50% 104 SAU 6000 -7.2 -0.85% 23.91 3.56 0.23 0.03 0.0084 41.4888 -1.96% -4.08% -6.38% -8.83% -11.45% 106 TUR 500 -4.5 0.58% 11.99 3.33 1.79 -0.17 -0.0511 44.3544 0.29% -1.41% -5.09% -10.77% -18.42% 105 ARE 6000 -7.0 0.97% 27.28 3.28 0.19 0.01 0.0030 45.0305 -2.51% -5.18% -8.01% -11.01% -14.18% 107 XWS 4000 -5.9 -0.36% 18.56 3.39 0.28 -0.04 -0.0118 43.5693 -1.60% -3.45% -5.54% -7.89% -10.48% 108 EGY 5000 -6.6 0.00% 22.27 3.18 0.09 -0.01 -0.0031 46.4465 -2.00% -4.21% -6.61% -9.21% -12.01% 109 MAR 4000 -5.8 -0.50% 18.32 3.2 0.53 -0.12 -0.0375 46.1563 -1.53% -3.30% -5.32% -7.60% -10.12% 110 TUN 4000 -5.5 0.41% 19.14 2.98 0.46 -0.07 -0.0235 49.5638 -1.71% -3.67% -5.88% -8.34% -11.04% 111 XNF 4000 -5.5 0.41% 19.14 2.98 0.46 -0.07 -0.0235 49.5638 -1.71% -3.67% -5.88% -8.34% -11.04% 112 BEN 5000 -9.7 -0.73% 26.36 2.72 3.11 0.04 0.0147 54.3015 -2.78% -5.76% -8.95% -12.32% -15.90% 113 BFA 5000 -9.2 0.34% 27.29 3.02 2.09 0.08 0.0265 48.9073 -2.99% -6.18% -9.57% -13.16% -16.94% 114 CMR 3500 -10.0 0.29% 23.5 2.65 5.08 0.21 0.0792 55.7358 -3.15% -6.59% -10.31% -14.32% -18.61% 115 CIV 4000 -10.0 0.37% 25.33 2.65 4.04 0.04 0.0151 55.7358 -3.22% -6.68% -10.39% -14.35% -18.56% 116 GHA 4500 -9.9 -0.16% 26.17 2.59 3.5 0.03 0.0116 57.0270 -3.04% -6.30% -9.77% -13.47% -17.39% 117 GIN 4000 -9.6 0.92% 25.41 2.81 3.54 -0.03 -0.0107 52.5623 -3.25% -6.75% -10.50% -14.50% -18.75% 118 NGA 4000 -9.8 0.88% 25.63 2.73 3.2 0.09 0.0330 54.1026 -3.30% -6.85% -10.64% -14.68% -18.98% 119 SEN 5500 -8.8 0.17% 27.88 2.75 0.99 -0.02 -0.0073 53.7091 -2.81% -5.79% -8.96% -12.31% -15.84% 120 TGO 4500 -9.8 -0.03% 26.21 2.65 3.46 0.01 0.0038 55.7358 -3.05% -6.33% -9.82% -13.53% -17.47% 121 XWF 4500 -9.8 -0.03% 26.21 2.65 3.46 0.01 0.0038 55.7358 -3.05% -6.33% -9.82% -13.53% -17.47% 122 XCF 4000 -10.2 -0.48% 24.37 2.79 4.02 0.26 0.0932 52.9391 -2.99% -6.23% -9.72% -13.45% -17.44% 123 XAC 3500 -11.3 -0.68% 24 2.44 1.78 0.23 0.0943 60.5328 -3.27% -6.82% -10.65% -14.77% -19.17% 124 ETH 3500 -9.8 -0.50% 22.25 2.68 2.11 0.29 0.1082 55.1119 -2.80% -5.89% -9.26% -12.91% -16.85% 125 KEN 3500 -11.3 -0.68% 24 2.44 1.78 0.23 0.0943 60.5328 -3.27% -6.82% -10.65% -14.77% -19.17% 126 MDG 4000 -8.0 0.33% 22.74 2.33 3.56 -0.12 -0.0515 63.3906 -2.53% -5.31% -8.34% -11.62% -15.14% 127 MWI 3000 -9.1 0.67% 21.08 2.86 3.33 -0.17 -0.0594 51.6434 -2.91% -6.15% -9.72% -13.63% -17.86% 128 MUS 5000 -7.7 -0.32% 24.3 1.77 2.88 -0.11 -0.0621 83.4463 -2.25% -4.70% -7.34% -10.19% -13.23% 129 MOZ 4500 -8.1 -0.25% 23.21 2.68 3 -0.16 -0.0597 55.1119 -2.39% -5.01% -7.84% -10.90% -14.18% 130 RWA 2200 -11.0 0.26% 19.66 2.72 4.83 0.46 0.1691 54.3015 -3.30% -7.04% -11.24% -15.89% -21.00% 131 TZA 3000 -10.2 -0.35% 21.31 2.63 3.26 0.08 0.0304 56.1597 -2.95% -6.24% -9.85% -13.80% -18.07% 132 UGA 3000 -11.1 -0.30% 22.29 2.54 3.5 0.4 0.1575 58.1496 -3.26% -6.85% -10.78% -15.03% -19.61% 133 ZMB 3000 -9.1 0.63% 20.97 3.07 3.45 -0.12 -0.0391 48.1107 -2.90% -6.13% -9.69% -13.58% -17.81% 134 ZWE 3500 -8.1 0.28% 21.06 3.04 2.46 -0.15 -0.0493 48.5855 -2.51% -5.30% -8.37% -11.73% -15.38% 135 XEC 4000 -8.1 0.73% 23.21 2.68 3 -0.16 -0.0597 55.1119 -2.69% -5.63% -8.82% -12.26% -15.95% 136 BWA 4000 -7.4 0.02% 21.07 3.38 2.01 -0.15 -0.0444 43.6982 -2.22% -4.69% -7.41% -10.38% -13.60% 137 NAM 4000 -7.3 0.27% 21.35 3.17 1.49 -0.12 -0.0379 46.5931 -2.28% -4.80% -7.57% -10.60% -13.87% 138 ZAF 2500 -6.3 0.41% 16.93 2.94 2.32 -0.08 -0.0272 50.2381 -1.85% -4.11% -6.75% -9.80% -13.25% 139 XSC 2500 -6.3 0.41% 16.93 2.94 2.32 -0.08 -0.0272 50.2381 -1.85% -4.11% -6.75% -9.80% -13.25% 140 XTW 2000 -4.3 -0.25% 14 2.95 1.8 -0.25 -0.0847 50.0678 -0.87% -2.23% -4.09% -6.44% -9.29% 33 Table A5. Adjustment factors for regional temperature changes. N. Code T. adj. N. Code T. adj. 1 AUS 0.95 71 NLD 0.85 2 NZL 0.70 72 POL 0.88 3 XOC 0.64 73 PRT 0.79 4 CHN 1.19 74 SVK 0.94 5 HKG 0.75 75 SVN 1.15 6 JPN 1.03 76 ESP 1.15 7 KOR 1.05 77 SWE 1.03 8 MNG 1.28 78 GBR 1.22 9 TWN 0.75 79 CHE 0.76 10 XEA 0.82 80 NOR 1.08 11 BRN 0.76 81 XEF 1.16 12 KHM 0.82 82 ALB 1.08 13 IDN 0.77 83 BGR 1.12 14 LAO 0.89 84 BLR 1.15 15 MYS 0.77 85 HRV 1.24 16 PHL 0.72 86 ROU 1.15 17 SGP 0.74 87 RUS 1.19 18 THA 0.86 88 UKR 1.61 19 VNM 0.83 89 XEE 1.20 20 XSE 0.82 90 XER 1.20 21 BGD 0.89 91 KAZ 1.00 22 IND 0.97 92 KGZ 1.37 23 NPL 1.10 93 XSU 1.27 24 PAK 1.18 94 ARM 1.37 25 LKA 0.71 95 AZE 1.20 26 XSA 0.97 96 GEO 1.08 27 CAN 1.59 97 BHR 1.16 28 USA 1.27 98 IRN 1.12 29 MEX 1.01 99 ISR 1.23 30 XNA 1.27 100 JOR 1.07 31 ARG 0.77 101 KWT 1.19 32 BOL 1.11 102 OMN 1.24 33 BRA 1.02 103 QAT 1.00 34 CHL 0.80 104 SAU 1.15 35 COL 0.97 106 TUR 1.25 36 ECU 0.87 105 ARE 1.17 37 PRY 1.05 107 XWS 1.15 38 PER 1.02 108 EGY 1.19 39 URY 0.73 109 MAR 1.11 40 VEN 1.00 110 TUN 1.12 41 XSM 0.73 111 XNF 1.04 42 CRI 0.79 112 BEN 1.04 43 GTM 0.96 113 BFA 0.95 44 HND 0.86 114 CMR 1.06 45 NIC 0.83 115 CIV 0.93 46 SLV 0.77 116 GHA 0.93 47 PAN 0.91 117 GIN 0.91 48 XCA 0.83 118 NGA 0.98 49 DOM 0.77 119 SEN 0.96 50 JAM 0.71 120 TGO 0.96 51 PRI 0.68 121 XWF 0.93 52 TTO 0.70 122 XCF 0.93 53 XCB 0.71 123 XAC 0.98 54 AUT 1.10 124 ETH 0.85 55 BEL 0.93 125 KEN 0.94 56 CYP 0.92 126 MDG 0.85 57 CZE 1.09 127 MWI 0.82 58 DNK 0.93 128 MUS 1.00 59 EST 1.27 129 MOZ 0.62 60 FIN 1.45 130 RWA 0.94 61 FRA 1.00 131 TZA 0.95 62 DEU 1.00 132 UGA 0.92 63 GRC 1.07 133 ZMB 0.89 64 HUN 1.18 134 ZWE 1.08 65 IRL 0.64 135 XEC 1.06 66 ITA 1.06 136 BWA 0.94 67 LVA 1.22 137 NAM 1.18 68 LTU 1.18 138 ZAF 1.11 69 LUX 0.99 139 XSC 1.03 70 MLT 0.85 140 XTW 1.03 34 Table A6-1. Heat impacts on labor productivity, by sector (percentage change). Values below -10% in red. AGRICULTURE MANUFACTURING SERVICES N. Code +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C 1 AUS 0.00% 0.00% -1.09% -2.86% -5.55% 0.00% 0.00% 0.00% -0.02% -0.87% 0.00% 0.00% 0.00% 0.00% 0.00% 2 NZL 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 3 XOC -5.53% -12.90% -21.93% -31.19% -40.70% -1.32% -4.24% -8.08% -13.20% -19.20% 0.00% -0.12% -1.53% -4.02% -7.30% 4 CHN -1.37% -3.01% -5.13% -7.31% -9.90% 0.00% -0.51% -1.46% -2.69% -4.17% 0.00% 0.00% -0.06% -0.58% -1.33% 5 HKG -3.76% -7.69% -12.36% -17.44% -23.14% -1.95% -3.99% -6.52% -9.13% -12.25% -0.39% -1.44% -2.96% -4.65% -6.63% 6 JPN -1.31% -2.78% -4.92% -7.50% -10.46% -0.07% -0.58% -1.54% -2.66% -4.15% 0.00% 0.00% -0.14% -0.64% -1.40% 7 KOR -1.39% -2.81% -4.28% -6.61% -9.55% -0.01% -0.68% -1.65% -2.64% -3.68% 0.00% 0.00% -0.11% -0.73% -1.50% 8 MNG 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 9 TWN -3.76% -7.69% -12.36% -17.44% -23.14% -1.95% -3.99% -6.52% -9.13% -12.25% -0.39% -1.44% -2.96% -4.65% -6.63% 10 XEA -8.05% -16.95% -27.22% -37.76% -47.95% -3.79% -8.33% -13.23% -18.66% -24.93% -1.22% -3.74% -6.71% -10.16% -13.89% 11 BRN -10.71% -21.70% -32.98% -44.57% -56.47% -5.75% -11.82% -18.06% -24.46% -31.04% -0.69% -3.70% -8.19% -12.82% -17.58% 12 KHM -8.05% -16.95% -27.22% -37.76% -47.95% -3.79% -8.33% -13.23% -18.66% -24.93% -1.22% -3.74% -6.71% -10.16% -13.89% 13 IDN -9.78% -19.81% -30.11% -40.67% -51.53% -4.03% -9.60% -15.56% -21.67% -27.95% 0.00% -1.82% -5.18% -9.64% -14.28% 14 LAO -5.34% -11.18% -17.57% -24.75% -32.71% -2.48% -5.53% -9.00% -12.79% -16.97% -0.11% -1.15% -3.22% -5.69% -8.40% 15 MYS -10.26% -20.79% -31.60% -42.70% -54.10% -5.30% -11.23% -17.32% -23.57% -29.99% -0.12% -2.23% -6.63% -11.23% -15.96% 16 PHL -10.03% -20.31% -30.86% -41.70% -52.83% -4.29% -9.81% -15.86% -22.07% -28.45% -0.50% -2.75% -6.18% -10.58% -15.24% 17 SGP -10.87% -22.03% -33.49% -45.25% -57.34% -5.96% -12.09% -18.37% -24.82% -31.46% -0.60% -4.21% -8.73% -13.38% -18.16% 18 THA -7.84% -16.92% -27.20% -37.95% -47.97% -3.69% -8.30% -13.07% -18.63% -24.90% -1.67% -4.25% -7.14% -10.61% -14.23% 19 VNM -5.40% -11.73% -18.42% -26.15% -34.56% -2.45% -5.23% -8.86% -12.96% -17.32% -0.45% -1.98% -3.88% -6.13% -9.09% 20 XSE -8.05% -16.95% -27.22% -37.76% -47.95% -3.79% -8.33% -13.23% -18.66% -24.93% -1.22% -3.74% -6.71% -10.16% -13.89% 21 BGD -5.07% -11.12% -18.06% -25.28% -31.73% -2.45% -5.33% -8.59% -12.38% -16.84% -1.01% -2.59% -4.47% -6.68% -9.23% 22 IND -5.21% -10.84% -16.71% -23.06% -29.08% -2.47% -5.44% -8.83% -12.44% -16.21% -0.74% -2.36% -4.29% -6.58% -9.25% 23 NPL -1.10% -3.53% -6.96% -10.48% -14.26% 0.00% -0.36% -1.29% -3.05% -5.45% 0.00% 0.00% -0.01% -0.38% -1.30% 24 PAK -3.60% -7.28% -11.05% -15.60% -20.24% -1.40% -3.43% -5.79% -8.21% -10.69% -0.78% -1.83% -2.91% -4.51% -6.33% 25 LKA -8.14% -17.23% -26.55% -36.11% -45.92% -1.70% -5.86% -11.23% -17.12% -23.16% 0.00% -0.51% -2.23% -5.76% -10.12% 26 XSA -5.21% -10.84% -16.71% -23.06% -29.08% -2.47% -5.44% -8.83% -12.44% -16.21% -0.74% -2.36% -4.29% -6.58% -9.25% 27 CAN 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 28 USA 0.00% 0.00% -0.59% -1.91% -3.66% 0.00% 0.00% 0.00% 0.00% -0.51% 0.00% 0.00% 0.00% 0.00% 0.00% 29 MEX -0.69% -3.19% -6.42% -10.08% -14.61% 0.00% 0.00% -0.73% -2.56% -4.86% 0.00% 0.00% 0.00% 0.00% -0.89% 30 XNA 0.00% 0.00% -0.59% -1.91% -3.66% 0.00% 0.00% 0.00% 0.00% -0.51% 0.00% 0.00% 0.00% 0.00% 0.00% 31 ARG -0.04% -0.81% -2.81% -5.11% -8.44% 0.00% 0.00% -0.09% -0.74% -2.15% 0.00% 0.00% 0.00% 0.00% -0.15% 32 BOL 0.00% 0.00% 0.00% 0.00% -0.23% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 33 BRA -3.99% -9.28% -15.65% -23.79% -32.32% -0.05% -1.59% -4.37% -8.06% -12.47% 0.00% 0.00% -0.19% -1.65% -3.93% 34 CHL 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 35 COL -5.20% -12.82% -21.22% -29.84% -38.69% 0.00% -0.58% -4.54% -10.03% -15.91% 0.00% 0.00% 0.00% -1.14% -4.51% 36 ECU 0.00% 0.00% 0.00% -1.66% -4.79% 0.00% 0.00% 0.00% 0.00% -0.04% 0.00% 0.00% 0.00% 0.00% 0.00% 37 PRY -3.66% -8.01% -13.30% -18.73% -25.33% -1.37% -3.49% -5.93% -9.02% -12.56% 0.00% -0.50% -1.67% -3.45% -5.48% 38 PER 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 39 URY 0.00% -0.12% -1.24% -3.18% -5.90% 0.00% 0.00% 0.00% -0.14% -0.98% 0.00% 0.00% 0.00% 0.00% 0.00% 40 VEN -4.99% -10.79% -18.25% -26.67% -35.30% 0.00% -1.45% -4.98% -9.01% -14.17% 0.00% 0.00% -0.02% -1.65% -4.56% 41 XSM 0.00% -0.12% -1.24% -3.18% -5.90% 0.00% 0.00% 0.00% -0.14% -0.98% 0.00% 0.00% 0.00% 0.00% 0.00% 42 CRI -6.29% -14.71% -23.38% -32.27% -41.40% -0.26% -2.69% -7.17% -12.92% -18.81% 0.00% 0.00% -0.47% -2.80% -6.61% 43 GTM 0.00% -0.57% -3.91% -8.59% -14.28% 0.00% 0.00% 0.00% -0.72% -3.30% 0.00% 0.00% 0.00% 0.00% 0.00% 44 HND -7.27% -16.38% -25.79% -35.44% -45.34% -2.61% -6.16% -10.96% -16.86% -22.92% 0.00% -0.99% -3.16% -6.15% -10.09% 45 NIC -9.03% -18.29% -27.79% -37.53% -47.52% -2.34% -7.09% -12.81% -18.69% -24.72% -0.19% -0.75% -3.01% -6.91% -11.41% 46 SLV -8.22% -17.46% -26.93% -36.65% -46.62% -2.87% -7.19% -12.40% -18.26% -24.28% 0.00% -0.77% -3.37% -6.77% -10.96% 47 PAN -2.31% -6.93% -12.00% -18.17% -26.01% 0.00% -0.30% -2.32% -5.59% -9.13% 0.00% 0.00% 0.00% -0.51% -2.41% 48 XCA -9.03% -18.29% -27.79% -37.53% -47.52% -2.34% -7.09% -12.81% -18.69% -24.72% -0.19% -0.75% -3.01% -6.91% -11.41% 49 DOM -5.87% -13.13% -22.08% -31.32% -40.81% -2.09% -5.27% -9.19% -14.00% -19.89% 0.00% -0.22% -2.19% -4.85% -8.02% 50 JAM -6.33% -14.64% -23.86% -33.31% -43.01% -2.62% -5.97% -10.13% -15.56% -21.50% 0.00% -0.79% -3.07% -5.73% -9.13% 51 PRI -5.80% -12.81% -21.72% -31.07% -40.66% -2.05% -5.31% -9.21% -13.89% -19.77% 0.00% -0.30% -2.26% -4.95% -8.14% 52 TTO -8.94% -18.61% -28.52% -38.70% -49.14% -3.60% -7.87% -13.47% -19.49% -25.66% -0.14% -2.22% -5.10% -8.48% -12.90% 53 XCB -6.33% -14.64% -23.86% -33.31% -43.01% -2.62% -5.97% -10.13% -15.56% -21.50% 0.00% -0.79% -3.07% -5.73% -9.13% 54 AUT 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 55 BEL 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 56 CYP -0.26% -1.46% -3.03% -5.38% -8.02% 0.00% 0.00% -0.12% -0.96% -2.04% 0.00% 0.00% 0.00% 0.00% -0.11% 57 CZE 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 58 DNK 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 59 EST 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 60 FIN 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 61 FRA 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 62 DEU 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 63 GRC 0.00% -0.85% -2.08% -4.09% -6.64% 0.00% 0.00% 0.00% -0.56% -1.41% 0.00% 0.00% 0.00% 0.00% 0.00% 64 HUN 0.00% 0.00% 0.00% 0.00% -0.12% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 65 IRL 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 66 ITA 0.00% 0.00% 0.00% -0.72% -2.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 67 LVA 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 68 LTU 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 69 LUX 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 70 MLT -0.29% -1.31% -2.99% -5.06% -8.17% 0.00% 0.00% -0.18% -0.87% -2.05% 0.00% 0.00% 0.00% 0.00% -0.15% 35 Table A6-2. Heat impacts on labor productivity, by sector (percentage change). Values below -10% in red. AGRICULTURE MANUFACTURING SERVICES N. Code +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C +1°C +2°C +3°C +4°C +5°C 71 NLD 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 72 POL 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 73 PRT 0.00% 0.00% 0.00% 0.00% -0.16% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 74 SVK 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 75 SVN 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 76 ESP 0.00% 0.00% 0.00% -0.25% -1.49% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 77 SWE 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 78 GBR 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 79 CHE 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 80 NOR 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 81 XEF 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 82 ALB 0.00% 0.00% -0.03% -1.19% -2.52% 0.00% 0.00% 0.00% 0.00% -0.03% 0.00% 0.00% 0.00% 0.00% 0.00% 83 BGR 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 84 BLR 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 85 HRV 0.00% 0.00% 0.00% -0.87% -2.19% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 86 ROU 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 87 RUS 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 88 UKR 0.00% 0.00% 0.00% 0.00% -0.23% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 89 XEE 0.00% 0.00% 0.00% 0.00% -0.23% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 90 XER 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 91 KAZ 0.00% 0.00% 0.00% -0.48% -1.12% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 92 KGZ 0.00% 0.00% 0.00% 0.00% -0.31% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 93 XSU 0.00% 0.00% 0.00% -0.48% -1.12% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 94 ARM 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 95 AZE 0.00% -0.37% -1.52% -2.90% -4.85% 0.00% 0.00% 0.00% -0.26% -1.07% 0.00% 0.00% 0.00% 0.00% 0.00% 96 GEO 0.00% 0.00% -0.45% -1.76% -3.56% 0.00% 0.00% 0.00% 0.00% -0.40% 0.00% 0.00% 0.00% 0.00% 0.00% 97 BHR -4.22% -8.54% -13.46% -19.24% -25.08% -1.95% -4.42% -7.11% -9.86% -12.98% -0.98% -2.28% -3.75% -5.63% -7.67% 98 IRN -1.06% -2.49% -4.35% -6.67% -9.25% 0.00% -0.22% -0.93% -1.90% -3.20% 0.00% 0.00% 0.00% -0.18% -0.74% 99 ISR 0.00% -0.96% -2.77% -5.23% -8.18% 0.00% 0.00% 0.00% -0.61% -1.83% 0.00% 0.00% 0.00% 0.00% 0.00% 100 JOR -0.58% -1.78% -4.07% -6.54% -9.27% 0.00% 0.00% -0.34% -1.18% -2.72% 0.00% 0.00% 0.00% 0.00% -0.27% 101 KWT -4.18% -8.78% -13.54% -17.56% -20.69% -2.28% -4.61% -7.19% -10.07% -13.13% -1.32% -2.76% -4.47% -6.22% -8.16% 102 OMN -4.70% -9.60% -14.61% -20.92% -27.53% -1.98% -4.26% -7.26% -10.42% -13.65% -0.54% -1.71% -3.23% -4.99% -7.29% 103 QAT -4.44% -9.53% -14.91% -21.05% -25.52% -2.25% -4.97% -7.75% -10.91% -14.26% -1.18% -2.78% -4.47% -6.52% -8.61% 104 SAU -3.66% -8.20% -13.27% -18.45% -23.72% -2.16% -4.37% -6.69% -9.58% -12.83% -0.85% -2.05% -3.70% -5.38% -7.15% 106 TUR 0.00% 0.00% 0.00% -0.45% -1.70% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 105 ARE -4.39% -9.49% -14.79% -20.91% -26.07% -2.23% -4.78% -7.54% -10.74% -14.08% -1.17% -2.63% -4.31% -6.23% -8.33% 107 XWS -0.58% -1.78% -4.07% -6.54% -9.27% 0.00% 0.00% -0.34% -1.18% -2.72% 0.00% 0.00% 0.00% 0.00% -0.27% 108 EGY -1.75% -4.18% -6.99% -10.57% -14.42% 0.00% -0.40% -1.54% -3.22% -5.14% 0.00% 0.00% 0.00% -0.31% -1.21% 109 MAR 0.00% 0.00% -0.53% -1.75% -3.54% 0.00% 0.00% 0.00% 0.00% -0.33% 0.00% 0.00% 0.00% 0.00% 0.00% 110 TUN -0.53% -1.87% -3.73% -6.17% -8.75% 0.00% 0.00% -0.32% -1.26% -2.56% 0.00% 0.00% 0.00% 0.00% -0.27% 111 XNF -0.53% -1.87% -3.73% -6.17% -8.75% 0.00% 0.00% -0.32% -1.26% -2.56% 0.00% 0.00% 0.00% 0.00% -0.27% 112 BEN -8.35% -17.67% -27.22% -37.01% -47.05% -2.73% -7.05% -12.24% -18.09% -24.09% -0.64% -1.94% -4.32% -7.75% -11.77% 113 BFA -8.25% -16.69% -26.25% -36.16% -45.00% -3.91% -7.91% -12.88% -17.98% -23.71% -1.29% -3.84% -6.77% -9.78% -13.52% 114 CMR -3.09% -9.09% -16.21% -24.63% -33.37% -0.15% -1.40% -3.75% -8.16% -13.06% 0.00% 0.00% -0.25% -1.49% -3.66% 115 CIV -7.63% -16.71% -26.08% -35.68% -45.54% -2.30% -6.35% -11.24% -17.04% -23.03% -0.06% -1.17% -3.20% -6.59% -10.44% 116 GHA -8.48% -17.71% -27.17% -36.87% -46.81% -2.58% -6.65% -12.05% -17.90% -23.90% -0.27% -1.55% -3.78% -7.06% -11.33% 117 GIN -3.94% -9.35% -15.21% -22.98% -31.00% -1.37% -3.34% -6.04% -9.63% -13.53% -0.05% -0.44% -1.71% -3.29% -5.51% 118 NGA -7.33% -15.37% -24.59% -34.45% -44.40% -3.45% -7.38% -11.94% -16.96% -22.68% -0.78% -2.23% -5.08% -8.12% -11.67% 119 SEN -6.25% -13.25% -21.13% -30.22% -38.92% -2.60% -5.71% -9.60% -14.01% -18.98% -1.10% -2.80% -4.78% -7.15% -10.13% 120 TGO -7.90% -17.03% -26.39% -35.98% -45.82% -2.31% -6.32% -11.32% -17.15% -23.12% -0.30% -1.41% -3.49% -6.79% -10.70% 121 XWF -7.90% -17.03% -26.39% -35.98% -45.82% -2.31% -6.32% -11.32% -17.15% -23.12% -0.30% -1.41% -3.49% -6.79% -10.70% 122 XCF -3.33% -9.48% -16.52% -24.76% -33.40% 0.00% -0.28% -3.12% -7.43% -12.44% 0.00% 0.00% 0.00% -0.47% -3.26% 123 XAC 0.00% 0.00% -0.94% -4.03% -9.32% 0.00% 0.00% 0.00% -0.06% -0.89% 0.00% 0.00% 0.00% 0.00% 0.00% 124 ETH 0.00% 0.00% 0.00% -0.34% -3.30% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 125 KEN 0.00% 0.00% -0.94% -4.03% -9.32% 0.00% 0.00% 0.00% -0.06% -0.89% 0.00% 0.00% 0.00% 0.00% 0.00% 126 MDG -2.58% -6.12% -10.34% -15.22% -20.99% 0.00% -0.33% -2.23% -4.86% -7.82% 0.00% 0.00% 0.00% -0.64% -2.17% 127 MWI -0.66% -3.97% -7.67% -12.47% -17.46% 0.00% 0.00% -0.81% -3.19% -5.81% 0.00% 0.00% 0.00% -0.04% -1.11% 128 MUS -3.74% -8.64% -14.26% -21.77% -30.86% -1.43% -3.84% -6.40% -9.70% -13.54% 0.00% -0.39% -1.73% -3.63% -5.73% 129 MOZ -3.51% -7.73% -12.82% -18.21% -24.87% -1.23% -3.14% -5.54% -8.41% -11.84% 0.00% -0.11% -1.33% -2.88% -4.81% 130 RWA 0.00% 0.00% 0.00% 0.00% -0.62% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 131 TZA -0.28% -3.97% -8.28% -13.83% -19.95% 0.00% 0.00% -0.50% -3.24% -6.34% 0.00% 0.00% 0.00% 0.00% -0.85% 132 UGA 0.00% 0.00% -1.90% -7.49% -15.51% 0.00% 0.00% 0.00% -0.10% -1.76% 0.00% 0.00% 0.00% 0.00% 0.00% 133 ZMB -0.07% -2.37% -6.38% -10.57% -15.69% 0.00% 0.00% -0.13% -2.09% -4.91% 0.00% 0.00% 0.00% 0.00% -0.35% 134 ZWE 0.00% -0.69% -3.33% -6.83% -10.99% 0.00% 0.00% 0.00% -0.75% -2.65% 0.00% 0.00% 0.00% 0.00% 0.00% 135 XEC -3.51% -7.73% -12.82% -18.21% -24.87% -1.23% -3.14% -5.54% -8.41% -11.84% 0.00% -0.11% -1.33% -2.88% -4.81% 136 BWA -1.68% -4.43% -8.02% -12.09% -16.26% 0.00% -0.30% -1.57% -3.52% -6.03% 0.00% 0.00% 0.00% -0.35% -1.48% 137 NAM 0.00% -0.43% -2.16% -5.26% -8.65% 0.00% 0.00% 0.00% -0.38% -1.63% 0.00% 0.00% 0.00% 0.00% 0.00% 138 ZAF 0.00% 0.00% -0.03% -1.12% -3.29% 0.00% 0.00% 0.00% 0.00% -0.08% 0.00% 0.00% 0.00% 0.00% 0.00% 139 XSC 0.00% 0.00% -0.03% -1.12% -3.29% 0.00% 0.00% 0.00% 0.00% -0.08% 0.00% 0.00% 0.00% 0.00% 0.00% 140 XTW 0.00% 0.00% 0.00% -0.25% -1.49% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 36 Table A7. Human health: percentage variation of labor productivity for +1°C. % var. in labor % var. in labor N. Code productivity N. Code productivity 1 AUS -0.1489 71 NLD -0.0337 2 NZL -0.1489 72 POL -0.0315 3 XOC -0.2423 73 PRT -0.0337 4 CHN -0.5378 74 SVK -0.0337 5 HKG -0.5378 75 SVN -0.0337 6 JPN -0.0631 76 ESP -0.0337 7 KOR -0.0631 77 SWE -0.0337 8 MNG -0.5378 78 GBR -0.0337 9 TWN -0.5378 79 CHE -0.0337 10 XEA -0.1315 80 NOR -0.0337 11 BRN -0.1840 81 XEF -0.0527 12 KHM -0.1315 82 ALB -0.0697 13 IDN -0.1453 83 BGR -0.0697 14 LAO -0.1315 84 BLR -0.0697 15 MYS -0.1453 85 HRV -0.0315 16 PHL -0.1315 86 ROU -0.0315 17 SGP -0.2375 87 RUS -0.0589 18 THA -0.1386 88 UKR -0.0589 19 VNM -0.1315 89 XEE -0.0697 20 XSE -0.1315 90 XER -0.0337 21 BGD -0.1386 91 KAZ -0.0589 22 IND -0.7468 92 KGZ -0.0589 23 NPL -0.7468 93 XSU -0.0589 24 PAK -0.1386 94 ARM -0.0589 25 LKA -0.7468 95 AZE -0.0589 26 XSA -0.1386 96 GEO -0.0589 27 CAN 0.0000 97 BHR -0.5921 28 USA -0.1447 98 IRN -0.2128 29 MEX -0.2412 99 ISR -0.6843 30 XNA -0.2349 100 JOR -0.5921 31 ARG -0.2191 101 KWT -0.5921 32 BOL -0.1131 102 OMN -0.5921 33 BRA -0.2191 103 QAT -0.5921 34 CHL -0.2191 104 SAU -0.5921 35 COL -0.1131 106 TUR -0.3050 36 ECU -0.1131 105 ARE -0.5921 37 PRY -0.1131 107 XWS -0.1990 38 PER -0.1131 108 EGY -0.3657 39 URY -0.2191 109 MAR -0.4044 40 VEN -0.1269 110 TUN -0.4044 41 XSM -0.1131 111 XNF -0.4044 42 CRI -0.2412 112 BEN -0.6308 43 GTM -0.1352 113 BFA -0.6308 44 HND -0.1352 114 CMR -0.6308 45 NIC -0.1352 115 CIV -0.6308 46 SLV -0.1352 116 GHA -0.6308 47 PAN -0.1352 117 GIN -0.6308 48 XCA -0.1352 118 NGA -0.6308 49 DOM -0.1352 119 SEN -0.6308 50 JAM -0.1352 120 TGO -0.6308 51 PRI -0.1352 121 XWF -0.6308 52 TTO -0.1352 122 XCF -0.6308 53 XCB -0.1352 123 XAC -0.6308 54 AUT -0.0337 124 ETH -0.6308 55 BEL -0.0337 125 KEN -0.6308 56 CYP -0.0337 126 MDG -0.6308 57 CZE -0.0315 127 MWI -0.6308 58 DNK -0.0337 128 MUS -0.5783 59 EST -0.0315 129 MOZ -0.6308 60 FIN -0.0337 130 RWA -0.6308 61 FRA -0.0337 131 TZA -0.6308 62 DEU -0.0337 132 UGA -0.6308 63 GRC -0.0337 133 ZMB -0.6308 64 HUN -0.0315 134 ZWE -0.6308 65 IRL -0.0337 135 XEC -0.6308 66 ITA -0.0337 136 BWA -0.6308 67 LVA -0.0315 137 NAM -0.6308 68 LTU -0.0315 138 ZAF -0.6308 69 LUX -0.0337 139 XSC -0.6308 70 MLT -0.0337 140 XTW -0.5783 37 Table A8. Tourism: changes in net foreign currency inflows (relative to 2011 GDP). Negative values in red. N. Code +1°C +2°C +3°C +4°C +5°C N. Code +1°C +2°C +3°C +4°C +5°C 1 AUS -0.14% -0.31% -0.50% -0.71% -0.94% 71 NLD 0.29% 0.55% 0.76% 0.93% 1.07% 2 NZL 0.06% 0.12% 0.18% 0.25% 0.33% 72 POL 0.33% 0.65% 0.95% 1.23% 1.48% 3 XOC 0.00% 0.00% 0.00% 0.00% 0.00% 73 PRT -0.25% -0.50% -0.76% -1.01% -1.24% 4 CHN 0.05% 0.08% 0.09% 0.08% 0.06% 74 SVK 0.30% 0.71% 1.23% 1.84% 2.55% 5 HKG -1.77% -3.53% -5.25% -6.93% -8.57% 75 SVN 0.21% 0.65% 1.30% 2.14% 3.16% 6 JPN 0.01% 0.02% 0.02% 0.02% 0.01% 76 ESP -0.13% -0.32% -0.55% -0.82% -1.12% 7 KOR 0.09% 0.16% 0.21% 0.24% 0.26% 77 SWE 0.60% 1.17% 1.72% 2.23% 2.71% 8 MNG -1.51% -1.18% 0.95% 4.83% 10.47% 78 GBR 0.26% 0.47% 0.64% 0.77% 0.86% 9 TWN -0.69% -1.39% -2.09% -2.80% -3.51% 79 CHE 0.52% 1.01% 1.47% 1.89% 2.26% 10 XEA 0.00% 0.00% 0.00% 0.00% 0.00% 80 NOR 0.52% 1.00% 1.44% 1.87% 2.27% 11 BRN -1.88% -2.75% -2.68% -1.68% 0.21% 81 XEF 0.00% 0.00% 0.00% 0.00% 0.00% 12 KHM -5.21% -8.74% -10.65% -11.02% -9.92% 82 ALB -1.57% -2.18% -1.85% -0.59% 1.63% 13 IDN -0.26% -0.50% -0.71% -0.90% -1.08% 83 BGR 0.20% 0.57% 1.08% 1.72% 2.49% 14 LAO -3.93% -5.82% -5.76% -3.81% 0.01% 84 BLR -0.20% -0.15% 0.15% 0.68% 1.44% 15 MYS -1.63% -3.10% -4.44% -5.66% -6.81% 85 HRV -0.15% -0.28% -0.42% -0.54% -0.62% 16 PHL -0.59% -1.11% -1.59% -2.03% -2.46% 86 ROU 0.02% 0.11% 0.26% 0.47% 0.74% 17 SGP -2.04% -4.01% -5.92% -7.84% -9.84% 87 RUS 0.43% 0.82% 1.21% 1.58% 1.95% 18 THA -1.72% -3.22% -4.50% -5.61% -6.55% 88 UKR 0.29% 0.61% 0.94% 1.30% 1.67% 19 VNM -0.82% -1.55% -2.19% -2.77% -3.29% 89 XEE 0.00% 0.00% 0.00% 0.00% 0.00% 20 XSE 0.00% 0.00% 0.00% 0.00% 0.00% 90 XER 0.00% 0.00% 0.00% 0.00% 0.00% 21 BGD -0.25% -0.37% -0.34% -0.18% 0.11% 91 KAZ 0.05% 0.16% 0.34% 0.58% 0.88% 22 IND -0.21% -0.40% -0.58% -0.76% -0.93% 92 KGZ -3.09% -3.63% -1.76% 2.45% 9.00% 23 NPL -1.46% -2.08% -1.88% -0.88% 0.92% 93 XSU 0.00% 0.00% 0.00% 0.00% 0.00% 24 PAK -0.15% -0.24% -0.25% -0.20% -0.08% 94 ARM -1.48% -1.45% 0.02% 2.88% 7.12% 25 LKA -0.72% -1.14% -1.29% -1.17% -0.78% 95 AZE -0.23% -0.27% -0.13% 0.20% 0.71% 26 XSA 0.00% 0.00% 0.00% 0.00% 0.00% 96 GEO -1.65% -2.28% -1.92% -0.59% 1.72% 27 CAN 0.40% 0.76% 1.10% 1.42% 1.71% 97 BHR -1.67% -2.74% -3.23% -3.18% -2.61% 28 USA 0.06% 0.10% 0.12% 0.11% 0.09% 98 IRN -0.05% -0.08% -0.08% -0.06% -0.02% 29 MEX -0.15% -0.29% -0.42% -0.54% -0.65% 99 ISR -0.29% -0.54% -0.76% -0.92% -1.04% 30 XNA 0.00% 0.00% 0.00% 0.00% 0.00% 100 JOR -1.81% -3.16% -4.05% -4.46% -4.36% 31 ARG -0.09% -0.18% -0.25% -0.31% -0.35% 101 KWT -0.53% -1.04% -1.54% -2.05% -2.60% 32 BOL -1.10% -1.53% -1.33% -0.50% 0.95% 102 OMN -0.75% -1.27% -1.56% -1.64% -1.53% 33 BRA -0.11% -0.22% -0.33% -0.45% -0.58% 103 QAT -0.37% -0.64% -0.83% -0.94% -0.98% 34 CHL -0.05% -0.04% 0.00% 0.09% 0.22% 104 SAU -0.44% -0.87% -1.30% -1.74% -2.21% 35 COL -0.26% -0.48% -0.65% -0.77% -0.86% 106 TUR 0.03% 0.03% -0.01% -0.08% -0.18% 36 ECU -0.44% -0.67% -0.70% -0.54% -0.18% 105 ARE -0.97% -1.92% -2.87% -3.87% -4.94% 37 PRY -1.12% -1.59% -1.43% -0.65% 0.74% 107 XWS 0.00% 0.00% 0.00% 0.00% 0.00% 38 PER -0.18% -0.28% -0.31% -0.27% -0.16% 108 EGY -0.59% -1.11% -1.55% -1.91% -2.18% 39 URY -0.74% -1.19% -1.36% -1.24% -0.83% 109 MAR -0.69% -1.30% -1.82% -2.23% -2.50% 40 VEN -0.16% -0.27% -0.35% -0.38% -0.38% 110 TUN -0.85% -1.38% -1.59% -1.50% -1.07% 41 XSM 0.00% 0.00% 0.00% 0.00% 0.00% 111 XNF 0.00% 0.00% 0.00% 0.00% 0.00% 42 CRI -1.51% -2.56% -3.14% -3.28% -2.98% 112 BEN -3.88% -5.43% -4.77% -1.95% 3.01% 43 GTM -0.87% -1.41% -1.62% -1.51% -1.08% 113 BFA -2.55% -3.51% -2.93% -0.88% 2.65% 44 HND -2.15% -3.32% -3.57% -2.93% -1.40% 114 CMR -1.32% -2.00% -2.07% -1.55% -0.45% 45 NIC -3.36% -5.01% -5.03% -3.47% -0.35% 115 CIV -1.28% -1.88% -1.84% -1.17% 0.11% 46 SLV -1.01% -1.50% -1.50% -1.02% -0.08% 116 GHA -1.05% -1.65% -1.84% -1.64% -1.04% 47 PAN -3.02% -5.38% -7.11% -8.24% -8.80% 117 GIN -4.86% -6.45% -4.94% -0.40% 7.16% 48 XCA 0.00% 0.00% 0.00% 0.00% 0.00% 118 NGA -0.23% -0.44% -0.64% -0.85% -1.08% 49 DOM -1.83% -3.23% -4.21% -4.78% -4.95% 119 SEN -2.43% -3.63% -3.68% -2.60% -0.43% 50 JAM -4.23% -7.04% -8.49% -8.60% -7.42% 120 TGO -6.91% -9.38% -7.63% -1.76% 8.20% 51 PRI -0.44% -0.69% -0.78% -0.70% -0.46% 121 XWF 0.00% 0.00% 0.00% 0.00% 0.00% 52 TTO -1.45% -2.17% -2.18% -1.52% -0.18% 122 XCF -0.41% -0.56% -0.47% -0.13% 0.44% 53 XCB -1.49% -2.70% -3.66% -4.37% -4.85% 123 XAC 0.00% 0.00% 0.00% 0.00% 0.00% 54 AUT 0.71% 1.37% 1.98% 2.52% 2.98% 124 ETH -1.00% -1.48% -1.48% -0.98% 0.01% 55 BEL 0.48% 0.90% 1.25% 1.55% 1.79% 125 KEN -1.02% -1.57% -1.66% -1.28% -0.45% 56 CYP -1.88% -3.22% -4.00% -4.22% -3.86% 126 MDG -2.84% -4.01% -3.59% -1.62% 1.89% 57 CZE 0.47% 0.96% 1.44% 1.91% 2.37% 127 MWI -4.43% -5.93% -4.63% -0.60% 6.15% 58 DNK 0.68% 1.29% 1.85% 2.34% 2.78% 128 MUS -4.35% -7.13% -8.38% -8.13% -6.40% 59 EST 0.07% 0.79% 2.11% 4.00% 6.44% 129 MOZ -2.30% -3.29% -3.02% -1.52% 1.18% 60 FIN 0.43% 0.90% 1.40% 1.92% 2.48% 130 RWA -4.15% -5.74% -4.89% -1.65% 3.99% 61 FRA 0.16% 0.28% 0.35% 0.38% 0.37% 131 TZA -1.92% -3.16% -3.75% -3.69% -2.99% 62 DEU 0.31% 0.57% 0.79% 0.97% 1.11% 132 UGA -2.40% -3.75% -4.07% -3.39% -1.70% 63 GRC -0.35% -0.70% -1.06% -1.40% -1.71% 133 ZMB -1.37% -1.87% -1.56% -0.44% 1.48% 64 HUN 0.30% 0.61% 0.95% 1.30% 1.68% 134 ZWE -2.45% -3.42% -2.96% -1.12% 2.12% 65 IRL 0.23% 0.47% 0.72% 0.96% 1.22% 135 XEC 0.00% 0.00% 0.00% 0.00% 0.00% 66 ITA 0.04% 0.03% -0.00% -0.07% -0.16% 136 BWA -1.63% -2.18% -1.71% -0.23% 2.25% 67 LVA -0.23% 0.05% 0.83% 2.07% 3.77% 137 NAM -2.41% -3.51% -3.36% -1.97% 0.65% 68 LTU 0.00% 0.34% 0.98% 1.90% 3.11% 138 ZAF -0.18% -0.35% -0.52% -0.67% -0.81% 69 LUX 0.88% 1.85% 2.88% 3.96% 5.09% 139 XSC 0.00% 0.00% 0.00% 0.00% 0.00% 70 MLT -3.74% -5.83% -6.30% -5.15% -2.37% 140 XTW 0.00% 0.00% 0.00% 0.00% 0.00% 38 Table A9-1. Household energy demand (percentage variations). +1°C +2°C +3°C +4°C +5°C N. Code Electr. Gas Oil.P. Electr. Gas Oil.P. Electr. Gas Oil.P. Electr. Gas Oil.P. Electr. Gas Oil.P. 1 AUS 0.00% -0.03% -4.04% 0.00% -0.05% -7.98% 0.00% -0.08% -11.80% 0.00% -0.10% -15.52% 0.00% -0.13% -19.15% 2 NZL 0.00% -0.02% -4.74% 0.00% -0.04% -9.32% 0.00% -0.05% -13.76% 0.00% -0.07% -18.06% 0.00% -0.09% -22.23% 3 XOC 0.28% -0.05% -3.35% 0.55% -0.11% -6.62% 0.82% -0.16% -9.82% 1.08% -0.21% -12.94% 1.34% -0.26% -16.00% 4 CHN -0.06% -0.15% -4.76% -0.11% -0.28% -9.34% -0.15% -0.40% -13.77% -0.19% -0.50% -18.05% -0.23% -0.59% -22.18% 5 HKG 0.28% -0.01% -3.57% 0.55% -0.02% -7.06% 0.81% -0.03% -10.46% 1.07% -0.04% -13.78% 1.33% -0.05% -17.03% 6 JPN -0.04% -0.01% -4.53% -0.07% -0.02% -8.92% -0.10% -0.03% -13.16% -0.12% -0.03% -17.28% -0.15% -0.03% -21.27% 7 KOR -0.06% -0.12% -4.91% -0.12% -0.22% -9.63% -0.16% -0.30% -14.19% -0.21% -0.37% -18.59% -0.25% -0.43% -22.83% 8 MNG 0.10% -0.75% -8.16% 0.21% -1.30% -15.74% 0.31% -1.69% -22.80% 0.40% -1.96% -29.41% 0.50% -2.13% -35.62% 9 TWN 0.29% -0.03% -3.63% 0.56% -0.06% -7.18% 0.84% -0.09% -10.64% 1.10% -0.12% -14.01% 1.37% -0.15% -17.31% 10 XEA -0.12% -0.46% -6.17% -0.23% -0.84% -12.02% -0.32% -1.16% -17.58% -0.41% -1.42% -22.87% -0.48% -1.64% -27.93% 11 BRN 0.27% -0.06% -3.27% 0.54% -0.13% -6.46% 0.80% -0.19% -9.59% 1.06% -0.24% -12.64% 1.31% -0.30% -15.64% 12 KHM 0.26% -0.08% -3.24% 0.52% -0.16% -6.42% 0.78% -0.24% -9.52% 1.03% -0.32% -12.56% 1.27% -0.39% -15.54% 13 IDN 0.28% -0.08% -3.32% 0.55% -0.16% -6.57% 0.82% -0.24% -9.74% 1.08% -0.32% -12.85% 1.33% -0.39% -15.89% 14 LAO 0.27% -0.09% -3.41% 0.53% -0.17% -6.73% 0.79% -0.25% -9.98% 1.04% -0.33% -13.16% 1.29% -0.41% -16.27% 15 MYS 0.28% -0.07% -3.30% 0.54% -0.13% -6.52% 0.81% -0.20% -9.68% 1.07% -0.26% -12.76% 1.32% -0.32% -15.78% 16 PHL 0.27% -0.07% -3.28% 0.54% -0.14% -6.49% 0.80% -0.21% -9.62% 1.05% -0.27% -12.69% 1.30% -0.34% -15.70% 17 SGP 0.27% -0.06% -3.25% 0.54% -0.13% -6.44% 0.80% -0.19% -9.55% 1.05% -0.25% -12.60% 1.30% -0.31% -15.58% 18 THA 0.26% -0.09% -3.25% 0.52% -0.17% -6.43% 0.77% -0.25% -9.53% 1.02% -0.33% -12.58% 1.26% -0.40% -15.55% 19 VNM 0.27% -0.06% -3.40% 0.54% -0.11% -6.72% 0.80% -0.17% -9.97% 1.05% -0.22% -13.15% 1.30% -0.27% -16.25% 20 XSE 0.28% -0.11% -3.42% 0.55% -0.22% -6.76% 0.82% -0.32% -10.02% 1.08% -0.42% -13.22% 1.34% -0.52% -16.34% 21 BGD 0.26% -0.13% -3.37% 0.51% -0.25% -6.66% 0.76% -0.37% -9.88% 1.00% -0.48% -13.02% 1.24% -0.59% -16.10% 22 IND 0.26% -0.10% -3.42% 0.51% -0.20% -6.76% 0.76% -0.29% -10.02% 1.00% -0.39% -13.21% 1.24% -0.47% -16.33% 23 NPL 0.28% -0.10% -3.93% 0.56% -0.20% -7.76% 0.83% -0.29% -11.48% 1.10% -0.38% -15.11% 1.36% -0.47% -18.64% 24 PAK 0.26% -0.12% -3.69% 0.51% -0.23% -7.29% 0.76% -0.33% -10.79% 1.01% -0.44% -14.21% 1.25% -0.53% -17.55% 25 LKA 0.28% -0.07% -3.35% 0.55% -0.14% -6.62% 0.81% -0.21% -9.83% 1.07% -0.27% -12.96% 1.32% -0.34% -16.02% 26 XSA 0.00% -0.04% -3.86% 0.00% -0.08% -7.61% -0.01% -0.12% -11.27% -0.01% -0.16% -14.83% -0.01% -0.20% -18.31% 27 CAN 0.08% -1.10% -8.03% 0.15% -1.96% -15.47% 0.23% -2.64% -22.42% 0.31% -3.17% -28.93% 0.39% -3.58% -35.04% 28 USA -0.05% -0.04% -4.87% -0.09% -0.06% -9.57% -0.14% -0.08% -14.10% -0.17% -0.10% -18.48% -0.21% -0.10% -22.72% 29 MEX 0.30% -0.06% -3.73% 0.58% -0.13% -7.38% 0.86% -0.19% -10.93% 1.14% -0.25% -14.39% 1.41% -0.31% -17.77% 30 XNA 0.12% 0.17% -5.83% 0.22% 0.33% -11.42% 0.33% 0.47% -16.80% 0.43% 0.61% -21.97% 0.52% 0.74% -26.95% 31 ARG 0.00% -0.01% -4.02% -0.01% -0.03% -7.94% -0.01% -0.05% -11.74% -0.01% -0.06% -15.45% -0.02% -0.08% -19.06% 32 BOL 0.02% -0.11% -4.11% 0.03% -0.21% -8.11% 0.05% -0.31% -12.00% 0.07% -0.41% -15.78% 0.09% -0.51% -19.46% 33 BRA 0.02% -0.08% -3.43% 0.04% -0.16% -6.77% 0.05% -0.23% -10.04% 0.07% -0.31% -13.24% 0.09% -0.38% -16.36% 34 CHL 0.00% 0.00% -4.74% 0.01% -0.01% -9.33% 0.01% -0.02% -13.77% 0.02% -0.03% -18.07% 0.02% -0.04% -22.25% 35 COL 0.30% -0.08% -3.50% 0.58% -0.15% -6.92% 0.87% -0.23% -10.27% 1.14% -0.30% -13.53% 1.41% -0.37% -16.72% 36 ECU 0.32% -0.08% -3.87% 0.64% -0.15% -7.65% 0.95% -0.22% -11.32% 1.25% -0.29% -14.90% 1.54% -0.36% -18.40% 37 PRY 0.01% -0.03% -3.56% 0.01% -0.06% -7.03% 0.02% -0.08% -10.41% 0.02% -0.11% -13.72% 0.03% -0.14% -16.95% 38 PER 0.36% -0.07% -4.30% 0.70% -0.14% -8.47% 1.04% -0.20% -12.52% 1.36% -0.26% -16.46% 1.68% -0.33% -20.29% 39 URY 0.00% 0.01% -4.11% -0.01% 0.02% -8.10% -0.01% 0.02% -11.98% -0.01% 0.03% -15.75% -0.01% 0.03% -19.43% 40 VEN 0.29% -0.10% -3.47% 0.58% -0.19% -6.85% 0.85% -0.28% -10.16% 1.13% -0.37% -13.40% 1.39% -0.46% -16.56% 41 XSM 0.31% -0.08% -3.77% 0.62% -0.16% -7.44% 0.91% -0.23% -11.02% 1.20% -0.30% -14.51% 1.49% -0.37% -17.92% 42 CRI 0.28% -0.08% -3.42% 0.56% -0.15% -6.76% 0.83% -0.22% -10.02% 1.10% -0.29% -13.22% 1.36% -0.36% -16.34% 43 GTM 0.30% -0.09% -3.73% 0.60% -0.17% -7.38% 0.89% -0.26% -10.93% 1.17% -0.34% -14.39% 1.45% -0.42% -17.77% 44 HND 0.28% -0.07% -3.35% 0.54% -0.14% -6.62% 0.81% -0.21% -9.82% 1.07% -0.28% -12.95% 1.32% -0.34% -16.02% 45 NIC 0.27% -0.08% -3.30% 0.54% -0.15% -6.54% 0.80% -0.22% -9.70% 1.06% -0.29% -12.79% 1.31% -0.36% -15.81% 46 SLV 0.29% -0.08% -3.52% 0.57% -0.16% -6.96% 0.85% -0.23% -10.32% 1.12% -0.31% -13.60% 1.39% -0.38% -16.80% 47 PAN 0.28% -0.07% -3.32% 0.55% -0.14% -6.57% 0.81% -0.21% -9.75% 1.07% -0.28% -12.85% 1.33% -0.34% -15.89% 48 XCA 0.27% -0.07% -3.36% 0.54% -0.14% -6.64% 0.80% -0.21% -9.84% 1.06% -0.27% -12.98% 1.31% -0.34% -16.05% 49 DOM 0.28% -0.06% -3.38% 0.55% -0.11% -6.68% 0.82% -0.17% -9.90% 1.08% -0.22% -13.06% 1.34% -0.27% -16.15% 50 JAM 0.28% -0.05% -3.34% 0.55% -0.10% -6.60% 0.82% -0.15% -9.79% 1.08% -0.20% -12.91% 1.33% -0.24% -15.96% 51 PRI 0.28% -0.05% -3.41% 0.56% -0.11% -6.74% 0.83% -0.16% -10.00% 1.10% -0.21% -13.18% 1.36% -0.26% -16.29% 52 TTO 0.27% -0.07% -3.29% 0.54% -0.14% -6.51% 0.80% -0.21% -9.65% 1.06% -0.27% -12.73% 1.31% -0.34% -15.75% 53 XCB 0.28% -0.06% -3.30% 0.54% -0.11% -6.52% 0.81% -0.16% -9.68% 1.06% -0.21% -12.77% 1.32% -0.27% -15.79% 54 AUT -0.06% -0.05% -5.78% -0.12% -0.09% -11.32% -0.16% -0.12% -16.63% -0.21% -0.14% -21.72% -0.25% -0.15% -26.62% 55 BEL -0.03% 0.02% -5.30% -0.06% 0.04% -10.39% -0.08% 0.07% -15.31% -0.10% 0.09% -20.05% -0.12% 0.11% -24.63% 56 CYP -0.01% 0.01% -3.91% -0.02% 0.02% -7.71% -0.03% 0.03% -11.41% -0.04% 0.04% -15.02% -0.05% 0.05% -18.53% 57 CZE 0.08% -0.03% -5.84% 0.17% -0.04% -11.44% 0.24% -0.04% -16.80% 0.32% -0.04% -21.96% 0.39% -0.03% -26.92% 58 DNK 0.10% 0.12% -5.69% 0.19% 0.24% -11.16% 0.28% 0.36% -16.41% 0.37% 0.46% -21.47% 0.45% 0.56% -26.34% 59 EST 0.10% 0.00% -6.35% 0.20% 0.02% -12.41% 0.30% 0.06% -18.19% 0.39% 0.12% -23.71% 0.48% 0.18% -28.99% 60 FIN 0.12% -0.24% -7.56% 0.23% -0.39% -14.66% 0.34% -0.47% -21.35% 0.45% -0.50% -27.67% 0.55% -0.49% -33.66% 61 FRA -0.03% 0.03% -4.87% -0.05% 0.06% -9.57% -0.07% 0.09% -14.11% -0.08% 0.12% -18.51% -0.10% 0.14% -22.77% 62 DEU -0.05% 0.01% -5.50% -0.09% 0.02% -10.79% -0.12% 0.04% -15.87% -0.15% 0.06% -20.76% -0.18% 0.08% -25.48% 63 GRC -0.02% 0.05% -4.09% -0.04% 0.10% -8.07% -0.05% 0.15% -11.93% -0.07% 0.20% -15.69% -0.08% 0.24% -19.34% 64 HUN -0.05% -0.09% -5.17% -0.10% -0.16% -10.14% -0.15% -0.22% -14.93% -0.18% -0.27% -19.55% -0.22% -0.32% -24.00% 65 IRL 0.08% 0.07% -5.22% 0.15% 0.12% -10.26% 0.23% 0.17% -15.12% 0.29% 0.22% -19.82% 0.36% 0.26% -24.37% 66 ITA -0.02% 0.04% -4.52% -0.05% 0.08% -8.90% -0.07% 0.12% -13.14% -0.09% 0.16% -17.25% -0.10% 0.20% -21.24% 67 LVA 0.10% 0.03% -6.14% 0.20% 0.08% -12.00% 0.29% 0.14% -17.60% 0.38% 0.21% -22.98% 0.47% 0.28% -28.13% 68 LTU 0.09% -0.12% -6.13% 0.17% -0.21% -11.98% 0.26% -0.28% -17.57% 0.33% -0.32% -22.93% 0.41% -0.35% -28.06% 69 LUX -0.04% 0.03% -5.42% -0.08% 0.06% -10.63% -0.11% 0.09% -15.64% -0.14% 0.12% -20.47% -0.16% 0.15% -25.13% 70 MLT -0.01% 0.05% -3.92% -0.02% 0.09% -7.73% -0.02% 0.14% -11.44% -0.03% 0.17% -15.06% -0.04% 0.21% -18.58% 39 Table A9-2. Household energy demand (percentage variations). +1°C +2°C +3°C +4°C +5°C N. Code Electr. Gas Oil.P. Electr. Gas Oil.P. Electr. Gas Oil.P. Electr. Gas Oil.P. Electr. Gas Oil.P. 71 NLD -0.03% 0.02% -5.26% -0.05% 0.05% -10.32% -0.08% 0.07% -15.20% -0.10% 0.09% -19.91% -0.11% 0.11% -24.46% 72 POL 0.08% -0.07% -5.87% 0.17% -0.12% -11.48% 0.24% -0.16% -16.87% 0.32% -0.18% -22.04% 0.39% -0.19% -27.01% 73 PRT 0.00% 0.02% -4.24% 0.00% 0.03% -8.36% 0.00% 0.04% -12.36% 0.00% 0.05% -16.25% 0.00% 0.06% -20.03% 74 SVK -0.07% -0.12% -6.02% -0.13% -0.21% -11.77% -0.18% -0.29% -17.28% -0.23% -0.35% -22.55% -0.28% -0.39% -27.60% 75 SVN -0.05% -0.04% -5.54% -0.10% -0.07% -10.85% -0.14% -0.09% -15.96% -0.18% -0.11% -20.88% -0.21% -0.12% -25.61% 76 ESP -0.01% 0.04% -4.25% -0.02% 0.08% -8.38% -0.03% 0.11% -12.39% -0.03% 0.15% -16.29% -0.04% 0.17% -20.07% 77 SWE 0.11% 0.07% -6.59% 0.22% 0.15% -12.87% 0.32% 0.24% -18.85% 0.42% 0.35% -24.55% 0.52% 0.46% -30.01% 78 GBR 0.08% 0.07% -5.30% 0.16% 0.14% -10.42% 0.23% 0.19% -15.35% 0.31% 0.25% -20.12% 0.38% 0.29% -24.72% 79 CHE -0.06% -0.02% -6.02% -0.11% -0.04% -11.77% -0.16% -0.04% -17.28% -0.20% -0.04% -22.57% -0.23% -0.03% -27.64% 80 NOR 0.11% 0.05% -6.55% 0.21% 0.12% -12.80% 0.31% 0.19% -18.76% 0.40% 0.26% -24.46% 0.49% 0.34% -29.92% 81 XEF 0.09% 0.07% -5.89% 0.18% 0.14% -11.54% 0.27% 0.21% -16.97% 0.35% 0.27% -22.19% 0.43% 0.33% -27.21% 82 ALB -0.03% 0.06% -4.44% -0.05% 0.12% -8.75% -0.07% 0.17% -12.92% -0.09% 0.22% -16.97% -0.11% 0.27% -20.90% 83 BGR -0.05% -0.05% -5.25% -0.10% -0.10% -10.31% -0.14% -0.13% -15.17% -0.18% -0.16% -19.86% -0.21% -0.18% -24.38% 84 BLR 0.08% -0.28% -6.36% 0.16% -0.49% -12.41% 0.24% -0.66% -18.16% 0.31% -0.78% -23.65% 0.38% -0.87% -28.89% 85 HRV -0.03% 0.01% -4.66% -0.06% 0.02% -9.17% -0.09% 0.03% -13.54% -0.11% 0.05% -17.77% -0.14% 0.06% -21.86% 86 ROU -0.06% -0.12% -5.51% -0.12% -0.23% -10.80% -0.17% -0.31% -15.87% -0.21% -0.38% -20.75% -0.25% -0.44% -25.45% 87 RUS 0.10% -0.82% -8.24% 0.20% -1.44% -15.89% 0.29% -1.90% -23.02% 0.39% -2.23% -29.70% 0.48% -2.45% -35.97% 88 UKR -0.08% -0.11% -5.51% -0.14% -0.19% -10.79% -0.21% -0.26% -15.86% -0.26% -0.31% -20.72% -0.31% -0.34% -25.40% 89 XEE -0.07% -0.16% -5.33% -0.13% -0.29% -10.45% -0.18% -0.41% -15.37% -0.23% -0.50% -20.10% -0.28% -0.58% -24.66% 90 XER -0.03% 0.01% -4.95% -0.05% 0.02% -9.74% -0.07% 0.03% -14.36% -0.09% 0.04% -18.83% -0.11% 0.05% -23.15% 91 KAZ -0.20% -0.98% -7.30% -0.37% -1.76% -14.08% -0.52% -2.39% -20.39% -0.65% -2.88% -26.31% -0.77% -3.27% -31.88% 92 KGZ -0.11% -0.45% -5.93% -0.20% -0.83% -11.56% -0.29% -1.16% -16.93% -0.36% -1.43% -22.05% -0.43% -1.66% -26.95% 93 XSU -0.06% -0.11% -4.63% -0.11% -0.20% -9.10% -0.16% -0.27% -13.42% -0.20% -0.34% -17.59% -0.24% -0.39% -21.63% 94 ARM -0.10% -0.30% -6.07% -0.19% -0.55% -11.86% -0.27% -0.76% -17.37% -0.34% -0.93% -22.64% -0.41% -1.08% -27.67% 95 AZE -0.05% -0.05% -4.71% -0.10% -0.09% -9.26% -0.14% -0.12% -13.65% -0.18% -0.15% -17.90% -0.21% -0.17% -22.01% 96 GEO -0.03% 0.02% -4.53% -0.06% 0.04% -8.92% -0.08% 0.06% -13.17% -0.10% 0.08% -17.29% -0.12% 0.10% -21.28% 97 BHR 0.01% -0.17% -3.47% 0.01% -0.34% -6.85% 0.02% -0.49% -10.16% 0.02% -0.65% -13.39% 0.03% -0.79% -16.55% 98 IRN -0.04% -0.05% -4.25% -0.08% -0.09% -8.36% -0.11% -0.13% -12.35% -0.14% -0.16% -16.23% -0.17% -0.19% -19.99% 99 ISR -0.01% -0.04% -3.89% -0.02% -0.09% -7.67% -0.03% -0.13% -11.36% -0.03% -0.17% -14.94% -0.04% -0.20% -18.44% 100 JOR -0.02% -0.05% -3.95% -0.03% -0.10% -7.79% -0.05% -0.15% -11.52% -0.06% -0.19% -15.16% -0.08% -0.24% -18.70% 101 KWT 0.00% -0.21% -3.59% 0.00% -0.41% -7.10% -0.01% -0.60% -10.52% -0.01% -0.78% -13.85% -0.01% -0.96% -17.11% 102 OMN 0.26% -0.13% -3.36% 0.51% -0.26% -6.64% 0.76% -0.38% -9.85% 1.00% -0.50% -12.99% 1.24% -0.61% -16.06% 103 QAT 0.01% -0.19% -3.43% 0.02% -0.36% -6.77% 0.02% -0.54% -10.04% 0.03% -0.70% -13.24% 0.04% -0.86% -16.37% 104 SAU 0.00% -0.16% -3.49% 0.01% -0.31% -6.89% 0.01% -0.46% -10.22% 0.02% -0.60% -13.47% 0.02% -0.74% -16.64% 106 TUR -0.04% -0.01% -4.70% -0.08% -0.01% -9.24% -0.11% -0.02% -13.64% -0.14% -0.02% -17.89% -0.16% -0.01% -22.01% 105 ARE 0.26% -0.14% -3.40% 0.52% -0.27% -6.72% 0.77% -0.40% -9.97% 1.02% -0.53% -13.15% 1.26% -0.65% -16.25% 107 XWS 0.29% -0.01% -3.73% 0.56% -0.03% -7.37% 0.84% -0.04% -10.92% 1.10% -0.05% -14.38% 1.36% -0.06% -17.75% 108 EGY -0.01% -0.05% -3.64% -0.01% -0.10% -7.19% -0.01% -0.15% -10.65% -0.02% -0.19% -14.03% -0.02% -0.24% -17.32% 109 MAR -0.01% 0.03% -4.04% -0.02% 0.05% -7.98% -0.02% 0.07% -11.80% -0.03% 0.09% -15.52% -0.03% 0.11% -19.15% 110 TUN -0.01% 0.03% -3.95% -0.03% 0.07% -7.79% -0.04% 0.09% -11.53% -0.05% 0.12% -15.17% -0.06% 0.15% -18.72% 111 XNF -0.01% 0.01% -3.90% -0.03% 0.02% -7.69% -0.04% 0.02% -11.38% -0.05% 0.03% -14.97% -0.06% 0.04% -18.48% 112 BEN 0.27% -0.11% -3.28% 0.54% -0.22% -6.48% 0.80% -0.32% -9.62% 1.05% -0.42% -12.69% 1.30% -0.52% -15.69% 113 BFA 0.25% -0.14% -3.18% 0.50% -0.27% -6.30% 0.74% -0.40% -9.35% 0.98% -0.53% -12.33% 1.21% -0.65% -15.25% 114 CMR 0.29% -0.10% -3.51% 0.58% -0.21% -6.94% 0.85% -0.30% -10.29% 1.13% -0.40% -13.56% 1.39% -0.49% -16.75% 115 CIV 0.28% -0.10% -3.34% 0.55% -0.20% -6.60% 0.81% -0.30% -9.80% 1.07% -0.40% -12.92% 1.33% -0.49% -15.97% 116 GHA 0.28% -0.10% -3.30% 0.55% -0.20% -6.53% 0.81% -0.29% -9.69% 1.07% -0.39% -12.78% 1.32% -0.47% -15.81% 117 GIN 0.27% -0.11% -3.37% 0.54% -0.21% -6.66% 0.81% -0.32% -9.89% 1.06% -0.42% -13.04% 1.32% -0.51% -16.12% 118 NGA 0.27% -0.12% -3.29% 0.53% -0.23% -6.50% 0.78% -0.34% -9.65% 1.03% -0.44% -12.73% 1.28% -0.55% -15.74% 119 SEN 0.26% -0.11% -3.26% 0.52% -0.21% -6.44% 0.77% -0.32% -9.56% 1.02% -0.41% -12.60% 1.26% -0.51% -15.59% 120 TGO 0.28% -0.11% -3.32% 0.54% -0.22% -6.56% 0.81% -0.32% -9.73% 1.07% -0.42% -12.83% 1.32% -0.52% -15.87% 121 XWF 0.27% -0.10% -3.36% 0.54% -0.19% -6.65% 0.80% -0.28% -9.87% 1.06% -0.37% -13.01% 1.31% -0.46% -16.09% 122 XCF 0.28% -0.11% -3.41% 0.56% -0.21% -6.74% 0.83% -0.32% -10.00% 1.09% -0.42% -13.18% 1.35% -0.51% -16.29% 123 XAC 0.30% -0.09% -3.62% 0.59% -0.18% -7.15% 0.87% -0.27% -10.60% 1.15% -0.35% -13.97% 1.42% -0.43% -17.25% 124 ETH 0.34% -0.12% -3.98% 0.67% -0.23% -7.86% 0.99% -0.34% -11.63% 1.30% -0.44% -15.31% 1.61% -0.54% -18.89% 125 KEN 0.31% -0.09% -3.77% 0.62% -0.17% -7.45% 0.92% -0.25% -11.03% 1.21% -0.33% -14.53% 1.49% -0.41% -17.94% 126 MDG 0.30% -0.06% -3.64% 0.59% -0.11% -7.20% 0.87% -0.17% -10.66% 1.15% -0.22% -14.05% 1.42% -0.28% -17.35% 127 MWI 0.29% -0.10% -3.68% 0.57% -0.20% -7.27% 0.85% -0.30% -10.77% 1.12% -0.40% -14.19% 1.38% -0.49% -17.52% 128 MUS 0.29% -0.04% -3.49% 0.57% -0.08% -6.89% 0.85% -0.12% -10.21% 1.12% -0.16% -13.46% 1.38% -0.20% -16.64% 129 MOZ 0.28% -0.07% -3.48% 0.55% -0.13% -6.89% 0.82% -0.20% -10.21% 1.08% -0.26% -13.46% 1.34% -0.32% -16.63% 130 RWA 0.33% -0.08% -3.95% 0.65% -0.15% -7.79% 0.97% -0.22% -11.53% 1.27% -0.29% -15.18% 1.57% -0.36% -18.73% 131 TZA 0.30% -0.08% -3.62% 0.59% -0.16% -7.14% 0.87% -0.24% -10.59% 1.14% -0.31% -13.95% 1.41% -0.38% -17.24% 132 UGA 0.31% -0.07% -3.70% 0.61% -0.14% -7.32% 0.91% -0.21% -10.84% 1.20% -0.27% -14.28% 1.48% -0.33% -17.64% 133 ZMB 0.29% -0.12% -3.72% 0.57% -0.24% -7.34% 0.85% -0.36% -10.88% 1.12% -0.47% -14.33% 1.38% -0.58% -17.70% 134 ZWE 0.29% -0.10% -3.82% 0.58% -0.20% -7.54% 0.86% -0.30% -11.17% 1.14% -0.39% -14.71% 1.40% -0.48% -18.16% 135 XEC 0.29% -0.09% -3.43% 0.57% -0.19% -6.77% 0.85% -0.27% -10.05% 1.12% -0.36% -13.24% 1.38% -0.44% -16.37% 136 BWA 0.27% -0.08% -3.68% 0.54% -0.16% -7.26% 0.81% -0.23% -10.75% 1.06% -0.30% -14.17% 1.32% -0.37% -17.49% 137 NAM 0.29% -0.07% -3.77% 0.57% -0.14% -7.44% 0.85% -0.20% -11.02% 1.12% -0.27% -14.51% 1.39% -0.33% -17.91% 138 ZAF 0.00% -0.06% -4.08% 0.01% -0.11% -8.05% 0.01% -0.16% -11.91% 0.01% -0.21% -15.67% 0.02% -0.26% -19.32% 139 XSC 0.00% -0.05% -4.13% 0.01% -0.11% -8.14% 0.01% -0.16% -12.04% 0.02% -0.21% -15.84% 0.02% -0.25% -19.54% 140 XTW 0.12% -0.26% -7.58% 0.23% -0.45% -14.72% 0.33% -0.56% -21.44% 0.44% -0.63% -27.81% 0.53% -0.66% -33.83% 40