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Results:

• 41. Kok_JGEC658_2009

  and that paving major roads will lead to deforestation. C3: Conservation units. A large percentage of the Amazon (close to 33%) is classified as some kind of protected area. This includes indigenous reserves, federal parks and national parks. Only about 5% is strictly protected, although it is argued that ‘paper parks’ can also be effective. C4: Forest accessibility. The amount of forest /media/loftslag/Kok_JGEC658_2009.pdf

• 42. Public-Choice-2012---McGuiness-and-Walker---Foundations-of-the-Ostrom-workshop---institutional-analysis,-polycentricity,-and-self-governance-of-the-commons

  The well-being of any human community is critically dependent on continued access to natural and man-made resources. Decades before sustainability became a popular slogan, the Ostroms began to investigate the conditions under which natural resources can be man- aged in a sustainable manner. The origins of the Workshop approach can be traced back at least as far as “State Administration of Natural /media/loftslag/Public-Choice-2012---McGuiness-and-Walker---Foundations-of-the-Ostrom-workshop---institutional-analysis,-polycentricity,-and-self-governance-of-the-commons.pdf

• 43. Public-Choice-2012---Teyssier---Inequity-and-risk-aversion-in-sequential-public-good-games

  individuals act like homo-œconomicus agents (see for example Andreoni 1988; Berg et al. 1995; Camerer 2003; Forsythe et al. 1994; Isaac et al. 1984). Recent developments in public-choice theory have taken a behavioral approach to broaden the analysis of collective action. The introduction of social preferences, such as altruism, inequity aversion or trust, may mean that optimal collective choices /media/loftslag/Public-Choice-2012---Teyssier---Inequity-and-risk-aversion-in-sequential-public-good-games.pdf

• 44. Hare-2011-ParticipatoryModelling

  are labelled according to the geographical area of application. Examples such as Zürich and Hase II have already been reviewed and categorized in Hare et al. (2003) and Bots and van Daalen (2008), respectively. The entire Iskar process, of which Iskar (Phase I) is a part, has been categorized by Barreteau et al. (2010). All are included in this review to provide alternative analytical perspectives /media/loftslag/Hare-2011-ParticipatoryModelling.pdf

• 45. Lorenzoni_Pidgeon_2006

  for people to conceptualise and to relate to their daily activities, arguably because it cannot be easily translated into the language of popular culture (Ungar, 2000; see also mental models of cli- mate change by Bostrom et al., 1994; Kempton, 1997; discussed later). Secondly, the various datasets available detailing public opinions and attitudes on climate PUBLIC VIEWS ON CLIMATE CHANGE: EUROPEAN /media/loftslag/Lorenzoni_Pidgeon_2006.pdf

• 46. 2013_001_Nawri_et_al

  and a pressure of 1013.25 hPa. In the case of Iceland, the climate deviates significantly from these standard atmospheric conditions. Additionally, terrain elevation varies considerably across the island. Therefore, seasonal and annual differences in air density from the standard value, as well as spatial variability, need to be taken into account. Approximate air density can be calculated by assuming /media/vedurstofan/utgafa/skyrslur/2013/2013_001_Nawri_et_al.pdf

• 47. VI_2014_005

  lines). Additionally, the aver- age profiles for offshore distances to the coast of up to 30 km are shown by the black lines. For temperature, the dashed lines indicate linear projection from the two lowest model levels to 2 m above ground. A comparison of monthly averages of simulated 2-m temperature with station measurements, both for SURFEX and projected values, is shown in Figure 8. On average /media/vedurstofan/utgafa/skyrslur/2014/VI_2014_005.pdf

• 48. 2010_005_

  Century control runs, as well as 21st Century forecast runs, submitted by various institutions to the Intergovernmental Panel on Climate Change (IPCC) for their Forth 11 Table 1. General circulation and regional climate models that were considered in this study. Model Version Model Name, Institute BCCR BCM 2.0 Bergen Climate Model, Bjerknes Centre for Climate Research, Bergen, Norway CCCMA CGCM 3.1 /media/ces/2010_005_.pdf

• 49. Water_resources_man_Veijalainen_etal

  range 90% 93.15–93.29 93.27–93.36 75.43–75.56 94.59–94.71 2040–2069 range 90% 93.09–93.31 93.28–93.40 75.41–75.56 94.72–94.83 2070–2099 range 90% 93.02–93.32 93.24–93.43 75.38–75.61 94.79–94.91 Lowest water level (in the 30 year period) (m) Reference period 92.86 92.72 75.20 94.27 2010–2039 range 90% 92.62–92.91 92.96–93.13 75.24–75.33 94.47–94.59 2040–2069 range 90% 92.55–92.82 92.88–93.12 75.19 /media/ces/Water_resources_man_Veijalainen_etal.pdf

• 50. 2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343

  e in relatio n to climat echang eadapt ation .X ,X X ,XX X is a ge n era lguid eo n th e relativ e imp ortanc e leve lo fth e sourc es , alth oug h it mus tb e em phasise d tha tth e imp ortanc e o fth e indi vidua lsou rce s o fun certaint y is co n tex tspe cifi c St ep si n cl im at e ch an ge ad ap tat io n an al ys es (ch ain in u n ce rta in ty ca sc ad e, Fi g. 2) So ur ce s o fu n ce rta /media/loftslag/2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343.pdf