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AbstractAlthough canopy height has long been a focus of interest in ecology, it has remained difficult to study at large spatial scales. Recently, satellite‐borne LiDAR equipment produced the first systematic high resolution maps of vegetation height worldwide. Here we show that this new resource reveals three marked modes in tropical canopy height ~40, ~12, and ~2 m corresponding to forest, savanna, and treeless landscapes. The distribution of these modes is consistent with the often hypothesized forest‐savanna bistability and suggests that both states can be stable in areas with a mean annual precipitation between ~1,500 and ~2,000 mm. Although the canopy height states correspond largely to the much discussed tree cover states, there are differences, too. For instance, there are places with savanna‐like sparse tree cover that have a forest‐like high canopy, suggesting that rather than true savanna, those are thinned relicts of forest. This illustrates how complementary sets of remotely sensed indicators may provide increasingly sophisticated ways to study ecological phenomena at a global scale.

AbstractAlthough canopy height has long been a focus of interest in ecology, it has remained difficult to study at large spatial scales. Recently, satellite‐borne LiDAR equipment produced the first systematic high resolution maps of vegetation height worldwide. Here we show that this new resource reveals three marked modes in tropical canopy height ~40, ~12, and ~2 m corresponding to forest, savanna, and treeless landscapes. The distribution of these modes is consistent with the often hypothesized forest‐savanna bistability and suggests that both states can be stable in areas with a mean annual precipitation between ~1,500 and ~2,000 mm. Although the canopy height states correspond largely to the much discussed tree cover states, there are differences, too. For instance, there are places with savanna‐like sparse tree cover that have a forest‐like high canopy, suggesting that rather than true savanna, those are thinned relicts of forest. This illustrates how complementary sets of remotely sensed indicators may provide increasingly sophisticated ways to study ecological phenomena at a global scale.

Abstract In the Swiss canton of Geneva, an energy efficiency portfolio has been operated since 2009. In contrast, such programs have been in place for more than 40 years in the US. There are numerous lessons to be learnt from their experience. We have therefore conducted a comparative analysis including Geneva and 11 leading states in the US, with the objective to identify explanatory factors for the success of energy efficiency programs. First, we compared total investment in energy efficiency programs between 2006 and 2015 and public policies related to energy efficiency in each state. Second, we analyzed the levelized cost of saved energy (LCSE) of the programs from the perspective of program administrators. Our cost-effectiveness analysis showed that the program investment scale has been strongly influenced by i) the existence of public mandates, which require program administrators to set ambitious targets and maximize their investment in cost-effective energy efficiency measures, ii) financial instruments that ensure program expenditure to be recovered, and iii) complementary funding from other programs. Moreover, learning and accumulated experience of the program administrators allow to reduce the LCSE. We conclude that the combination of financial instruments with public mandates can ensure a high level of effectiveness.

Abstract In the Swiss canton of Geneva, an energy efficiency portfolio has been operated since 2009. In contrast, such programs have been in place for more than 40 years in the US. There are numerous lessons to be learnt from their experience. We have therefore conducted a comparative analysis including Geneva and 11 leading states in the US, with the objective to identify explanatory factors for the success of energy efficiency programs. First, we compared total investment in energy efficiency programs between 2006 and 2015 and public policies related to energy efficiency in each state. Second, we analyzed the levelized cost of saved energy (LCSE) of the programs from the perspective of program administrators. Our cost-effectiveness analysis showed that the program investment scale has been strongly influenced by i) the existence of public mandates, which require program administrators to set ambitious targets and maximize their investment in cost-effective energy efficiency measures, ii) financial instruments that ensure program expenditure to be recovered, and iii) complementary funding from other programs. Moreover, learning and accumulated experience of the program administrators allow to reduce the LCSE. We conclude that the combination of financial instruments with public mandates can ensure a high level of effectiveness.

Abstract In its 2016 Heating and Cooling Strategy, the European Commission (EC) highlighted the strategic importance of heating demand for the energy demand reduction, and further noted that District Heat Networks (DHN) can play an important role in decarbonising this sector. This study applied a thermal atlas approach to map the potential for district heat networks in Switzerland. It extended existing methods with a novel approach to estimating linear thermal demand density in DHN at a national scale. DHN potential for current-generation high temperature networks as well as cutting-edge low temperature networks were compared for current building space heating and hot water demand as well as for two demand reduction scenarios. The method was tested by comparing its results to those of a local engineering study conducted for a Swiss municipality (Brig-Glis). The potential percentage of demand supplied by high temperature DHN was shown to decrease from 66% to 41% with energy saving while the potential for low temperature systems increased significantly from 2.1% to 42%. The percentage of heat demand covered by heat networks decreases less than the percentage of buildings covered, reflecting the strength of heat networks for supplying large fractions of thermal demand in geographically confined areas.

Abstract In its 2016 Heating and Cooling Strategy, the European Commission (EC) highlighted the strategic importance of heating demand for the energy demand reduction, and further noted that District Heat Networks (DHN) can play an important role in decarbonising this sector. This study applied a thermal atlas approach to map the potential for district heat networks in Switzerland. It extended existing methods with a novel approach to estimating linear thermal demand density in DHN at a national scale. DHN potential for current-generation high temperature networks as well as cutting-edge low temperature networks were compared for current building space heating and hot water demand as well as for two demand reduction scenarios. The method was tested by comparing its results to those of a local engineering study conducted for a Swiss municipality (Brig-Glis). The potential percentage of demand supplied by high temperature DHN was shown to decrease from 66% to 41% with energy saving while the potential for low temperature systems increased significantly from 2.1% to 42%. The percentage of heat demand covered by heat networks decreases less than the percentage of buildings covered, reflecting the strength of heat networks for supplying large fractions of thermal demand in geographically confined areas.

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.