• Energy Research

Responses to future changes in climatic and socio-economic conditions can be expected to vary between sectors and regions, reflecting differential sensitivity to these highly uncertain factors. A sensitivity analysis was conducted using a suite of impact models (for health, agriculture, biodiversity, land use, floods and forestry) across Europe with respect to changes in key climate and socio-economic variables. Depending on the indicators, aggregated grid or indicative site results are reported for eight rectangular sub-regions that together span Europe from northern Finland to southern Spain and from western Ireland to the Baltic States and eastern Mediterranean, each plotted as scenario-neutral impact response surfaces (IRSs). These depict the modelled behaviour of an impact variable in response to changes in two key explanatory variables. To our knowledge, this is the first time the IRS approach has been applied to changes in socio-economic drivers and over such large regions. The British Isles region showed the smallest sensitivity to both temperature and precipitation, whereas Central Europe showed the strongest responses to temperature and Eastern Europe to precipitation. Across the regions, sensitivity to temperature was lowest for the two indicators of river discharge and highest for Norway spruce productivity. Sensitivity to precipitation was lowest for intensive agricultural land use, maize and potato yields and Scots pine productivity, and highest for Norway spruce productivity. Under future climate projections, North-eastern Europe showed increases in yields of all crops and productivity of all tree species, whereas Central and East Europe showed declines. River discharge indicators and forest productivity (except Holm oak) were projected to decline over southern European regions. Responses were more sensitive to socio-economic than to climate drivers for some impact indicators, as demonstrated for heat-related mortality, coastal flooding and land use. Regional Environmental Change, 19 (3) ISSN:1436-3798 ISSN:1436-378X

Responses to future changes in climatic and socio-economic conditions can be expected to vary between sectors and regions, reflecting differential sensitivity to these highly uncertain factors. A sensitivity analysis was conducted using a suite of impact models (for health, agriculture, biodiversity, land use, floods and forestry) across Europe with respect to changes in key climate and socio-economic variables. Depending on the indicators, aggregated grid or indicative site results are reported for eight rectangular sub-regions that together span Europe from northern Finland to southern Spain and from western Ireland to the Baltic States and eastern Mediterranean, each plotted as scenario-neutral impact response surfaces (IRSs). These depict the modelled behaviour of an impact variable in response to changes in two key explanatory variables. To our knowledge, this is the first time the IRS approach has been applied to changes in socio-economic drivers and over such large regions. The British Isles region showed the smallest sensitivity to both temperature and precipitation, whereas Central Europe showed the strongest responses to temperature and Eastern Europe to precipitation. Across the regions, sensitivity to temperature was lowest for the two indicators of river discharge and highest for Norway spruce productivity. Sensitivity to precipitation was lowest for intensive agricultural land use, maize and potato yields and Scots pine productivity, and highest for Norway spruce productivity. Under future climate projections, North-eastern Europe showed increases in yields of all crops and productivity of all tree species, whereas Central and East Europe showed declines. River discharge indicators and forest productivity (except Holm oak) were projected to decline over southern European regions. Responses were more sensitive to socio-economic than to climate drivers for some impact indicators, as demonstrated for heat-related mortality, coastal flooding and land use. Regional Environmental Change, 19 (3) ISSN:1436-3798 ISSN:1436-378X

AbstractClimate change increases workers' exposure to heat stress. To prevent heat‐related illnesses, according to occupational‐health recommendations, labor capacity must be reduced. However, this preventive measure is expected to be costly, and the costs are likely to rise as the scale and scope of climate change impacts increase over time. Shifting the start of the working day to earlier in the morning could be an effective adaptation measure for avoiding the impacts of labor capacity reduction. However, the plausibility and efficacy of such an intervention have never been quantitatively assessed. Here we investigate whether working time shifts can offset the economic impacts of labor capacity reduction due to climate change. Incorporating a temporally (1 hr) and spatially (0.5° × 0.5°) high‐resolution heat exposure index into an integrated assessment model, we calculated the working time shift necessary to offset labor capacity reduction and economic loss under hypothetical with‐ and without‐realistic‐adaptation scenarios. The results of a normative scenario analysis indicated that a global average shift of 5.7 (4.0–6.1) hours is required, assuming extreme climate conditions in the 2090s. Although a realistic (<3 hr) shift nearly halves the economic cost, a substantial cost corresponding to 1.6% (1.0–2.4%) of global total gross domestic product is expected to remain. In contrast, if stringent climate‐change mitigation is achieved, a realistic shift limits the remaining cost to 0.14% (0.12–0.47%) of global total gross domestic product. Although shifting working time is shown to be effective as an adaptation measure, climate‐change mitigation remains indispensable to minimize the impact.

AbstractClimate change increases workers' exposure to heat stress. To prevent heat‐related illnesses, according to occupational‐health recommendations, labor capacity must be reduced. However, this preventive measure is expected to be costly, and the costs are likely to rise as the scale and scope of climate change impacts increase over time. Shifting the start of the working day to earlier in the morning could be an effective adaptation measure for avoiding the impacts of labor capacity reduction. However, the plausibility and efficacy of such an intervention have never been quantitatively assessed. Here we investigate whether working time shifts can offset the economic impacts of labor capacity reduction due to climate change. Incorporating a temporally (1 hr) and spatially (0.5° × 0.5°) high‐resolution heat exposure index into an integrated assessment model, we calculated the working time shift necessary to offset labor capacity reduction and economic loss under hypothetical with‐ and without‐realistic‐adaptation scenarios. The results of a normative scenario analysis indicated that a global average shift of 5.7 (4.0–6.1) hours is required, assuming extreme climate conditions in the 2090s. Although a realistic (<3 hr) shift nearly halves the economic cost, a substantial cost corresponding to 1.6% (1.0–2.4%) of global total gross domestic product is expected to remain. In contrast, if stringent climate‐change mitigation is achieved, a realistic shift limits the remaining cost to 0.14% (0.12–0.47%) of global total gross domestic product. Although shifting working time is shown to be effective as an adaptation measure, climate‐change mitigation remains indispensable to minimize the impact.

The impact of climate change on power demand in Japan is a matter of concern for the Japanese authorities and power companies as it may have consequences on the power grid. We trained random forest models against daily power data in ten Japanese regions and for different types of power generation to project changes in future power production and its carbon intensity. To do so, we used twelve predictors: six climate variables, five variables accounting for human exposure to climate, and one variable for the level of human activities. We then used the models trained from the present-day period to estimate the future power demand, carbon intensity, and pertaining CO2 emissions over the period 2020-2100 under three SSPs scenarios (Shared Socioeconomic Pathways: SSP126, SSP370, and SSP585). The impact of climate change on CO2 emissions via power generation shows seasonal and regional disparities. In cold regions, a decrease in power demand during winter under future warming leads to an overall decrease in power demand over the year. In contrast, the decrease in winter power demand in hot regions can be overcompensated by an increase in summer power demand because of more frequent hot days, leading to an overall annual increase. From our regional models, the power demand should increase the most in most Japanese regions in May, June, September, and October and not in the middle of summer, as has been found in older studies. Such an increase could result in regular power outages during those months if not considered, as the power grid could be particularly tense. Overall, we observed that power demand in regions with extreme climates is more sensitive to global warming than in temperate regions. The impact of climate change on power demand induces a net annual decrease in CO emissions in all regions except for Okinawa, in which power demand strongly increases during the summer, resulting in a net annual increase in CO emissions. However, climate change’s impact on carbon intensity may reverse the trend in some regions (Shikoku, Tohoku). We also assessed the relative impacts of socioeconomic factors such as population, GDP, and environmental policies on CO emissions. When combined with these factors, we found that the climate change effect is more important than when considered individually and significantly impacts total CO emissions under SSP585.

The impact of climate change on power demand in Japan is a matter of concern for the Japanese authorities and power companies as it may have consequences on the power grid. We trained random forest models against daily power data in ten Japanese regions and for different types of power generation to project changes in future power production and its carbon intensity. To do so, we used twelve predictors: six climate variables, five variables accounting for human exposure to climate, and one variable for the level of human activities. We then used the models trained from the present-day period to estimate the future power demand, carbon intensity, and pertaining CO2 emissions over the period 2020-2100 under three SSPs scenarios (Shared Socioeconomic Pathways: SSP126, SSP370, and SSP585). The impact of climate change on CO2 emissions via power generation shows seasonal and regional disparities. In cold regions, a decrease in power demand during winter under future warming leads to an overall decrease in power demand over the year. In contrast, the decrease in winter power demand in hot regions can be overcompensated by an increase in summer power demand because of more frequent hot days, leading to an overall annual increase. From our regional models, the power demand should increase the most in most Japanese regions in May, June, September, and October and not in the middle of summer, as has been found in older studies. Such an increase could result in regular power outages during those months if not considered, as the power grid could be particularly tense. Overall, we observed that power demand in regions with extreme climates is more sensitive to global warming than in temperate regions. The impact of climate change on power demand induces a net annual decrease in CO emissions in all regions except for Okinawa, in which power demand strongly increases during the summer, resulting in a net annual increase in CO emissions. However, climate change’s impact on carbon intensity may reverse the trend in some regions (Shikoku, Tohoku). We also assessed the relative impacts of socioeconomic factors such as population, GDP, and environmental policies on CO emissions. When combined with these factors, we found that the climate change effect is more important than when considered individually and significantly impacts total CO emissions under SSP585.