Some aspects of the guardrail approach already appear in the ‘backcasting’ method for energy policy analysis (Robinson, 1982). In a backcasting analysis, future goals and objectives (for energy policy) are first defined in an explicitly normative way. The analysis works then backwards from this future end-point to the present in order to determine the physical feasibility of that future and the policy mea- sures that would be required to reach it. In the systematic suggested by Morgan and Henrion (1990, Chapter 3), the guardrail approach is a satisficing method. Since the guardrail approach applies a hybrid decision criterion that includes rights-based and utility-based decision criteria, it may be characterized as a ‘bounded-risk bounded-cost’ strategy. Yet in contrast to the decision-analytical frameworks dis- cussed in Morgan and Henrion (1990), the guardrail approach aims at characterizing the complete set of acceptable policy strategy rather than just determining a single acceptable policy path. The guardrail approach may be considered as a dynamical generalization of the ‘critical loads’ concept, which proved very successful in the negotiation process of the Second Sulphur Protocol (Batterman, 1990; Alcamo et al., 1990; Hettelingh et al., 1995). In this tradition, Swart and Vellinga (1994) called for “a new ap- proach to climatechange research” that starts with defining “critical levels” of ecosystem response on a regional level, and to work backwards to determine “ultimate objective levels of GHG concentration changes”. However, the proposed approach was not implemented in any IAM. The guardrail approach enables the implementation of the ‘pessimization paradigm’ and more complex paradigms for sustain- able development (Schellnhuber and Wenzel, 1998; Schellnhuber, 1999). However, a detailed discussion of that topic is beyond the scope of this thesis. From an economic perspective, the guardrail approach borrows features from multi-criteria analysis, cost-benefit analysis, and scenario analysis (cf. Bruckner et al., 2003b), which are combined with elements of the ‘bounded rationality’ concept (Simon, 1972).
Most of the current analyses on agricultural trade liberalization pay no attention to the impact on water use and problems related to water availability. Some authors have looked at the potential impact on sustainable development in developing countries including water as an environmental service. George and Kirkpatrick  argue that further trade liberalisation would lead to an improved overall availability of water through increased efficiency in all developing countries. 1 Their study does not distinguish between different developing countries nor is a quantitative assessment provided. Other studies related to water issues investigate the implications of the GATS negotiations on service trade liberalisation on water management and the ability of governments to regulate water services (see e.g. [9,10]). All these analyses are qualitative assessments not based on economic models. Berrittella et al.  is an exception. They use a global CGE model including water resources (GTAP-W, version 1) to analyze the economic impact of hypothetical Doha-like liberalization of agricultural trade on water use. The Doha Development Agenda , launched in 2001, is meant to improve the situation for developing countries, but is subject to seemingly interminable delays.
The concept as such can be used also for other extreme weather events like droughts, which might affect utility and productivity of land as well. Heat events might rather affect labor pro- ductivity.
Introducing sector-specific loss functions, thereby taking into account regional sectoral vulnera- bility (beyond the dependence on a certain production factor) would help to make more detailed statements about economic impacts. Furthermore, climatechange induced shifts in the demand, i.e. , consumption preferences for goods of certain sectors like energy could be integrated into the model framework.
which the inherent variability is large and there are no meaningful paired samples. If one line lies always above the other, it may be said to dominate it, in the sense that any given value of the variable will be exceeded more frequently in that series than the other. It also provides a clear visualization of how frequently a given value is exceeded, which is particularly relevant when consider- ing heat stress. In addition to the results for the individual enterprises, there is a more detailed discus- sion of the likely effects ofclimatechange on grassland systems generally. The results are shown for scenario IS92a in the year 2050 only, because it was found that the variation in impacts between scenarios a, c and f was always negligible, as would be expected from the small differences in climatic data in Table 1. The gross margin calculations use current prices to represent the ®nancial situation if there were no changes in the relative prices of all inputs and outputs. In order to use a consistent set of commodity prices, the prices for beef are those prior to the depression in the market caused by measures to control BSE in the UK, and other recent falls in agricultural commodity prices.
While broadly consistent, the modeled decline in agricultural GDP during an extreme drought year is smaller than the observed decline in 1991/92 (i.e., 33 percent). One reason for this difference is the substantial change in the composition of agriculture that occurred between 1991 and 2006 (i.e., the base year for our analysis). Farmers in Zambia increased their production of drought-tolerant sorghum and millet and reduced maize production. This change in crop composition was due to the removal of unsustainable maize subsidies during the 1990s (see Thurlow and Wobst 2006). Moreover, non-traditional exports, including sugarcane and cotton, expanded dramatically, especially in the drought-affected zones 1, 2 and 4. These crops are more drought-resistant than traditional food crops and also benefit from irrigation. Given these changes, the agricultural sector as a whole has become more drought resistant over time, which is the main reason for the smaller GDP losses in our economic model.
replacement of sources of energy in the production process and consumption by the residential sector, agricultural yields and land use, etc. These, in turn, are dependent on climate variables, future water supply and other economic factors.
Development of the two socio-economic scenarios for Brazil relied on an integrated modelling system for the generation of temporal scenarios, with a SCGE model as its core model. Its overall objective is to specify and implement an integrated information system for macro- economic, sector-specific and regional projections, and the analysisof economic policies. Once the baseline trends were defined (two scenarios with no global climatechange), the next step was to establish deviations in relation to these trends caused by climatechange. Inputs from other models that provide the shocks to be fed into the main model were: (i) changes to the allocation among cropping, pasture and forestry, by state (UF); (ii) changes to agricultural yields, by UF; and (iii) changes to Brazil’s energy matrix.
Human influence on the climate is evident. Since the industrialisation in the 18th century, the atmospheric concentration of carbon dioxide has raised to levels which are unprece- dented in the last 800,000 years. Other anthropogenic emissions have also increased significantly since the begin of the industrial era. The Fifth Assessment Report (AR5) of the Intergovernmental Panel on ClimateChange (IPCC) reports that human impacts on the climate system are extremely likely the main reason for the global warming ob- served since the 1950s. In 2011, the carbon dioxide concentration had reached a value of 391 ppm which is by about 40% higher than the pre-industrial level. Further, ice cores records show that the rate of CO 2 increase over the past century is the highest in the last 22,000 years. Mainly emissions from fossil fuel burning and land use changes are responsible for this strong increase. (IPCC, 2013)
obtained from the Land Surface Hydrology Research Group at Princeton University (Sheffield et al. 2006). This dataset was adjusted to match the CRU monthly dataset.
Hydrologic and crop model
Climate Runoff Model (CLIRUN-II), a two-layer one-dimensional rainfall-runoff model, was used to simulate the hydrologic response of Congo and Zambezi River Basins for the different projected climate scenarios of precipitation and temperature discussed in 3.6. CLIRUN-II is one of the latest models in a family of hydrologic models developed specifically for the analysisof impact ofclimatechange on runoff (Strzepek et al. 2008) which has a built-in modified Hargreaves model (Droogers and Allen 2002) to compute Potential evapotranspiration. Reader is referred to Strzepek et al. (2011), Arndt et al. (2012), Fant et al. (2012) and Gebretsadik et al. (2012) for further reference on some previous application of CLIRUN-II on climate impact studies. The modeling procedure involves calibration of model parameters for historical observed runoff data and using the calibrated parameters to generate the corresponding runoffs for each ensemble of scenario.
Does climate policy slow economic growth of countries in Sub-Saharan Africa? The answer to this question largely determines the incentives in this world region for participation in ambitious climate policy regimes. Turning away from proven development pathways based on fossil fuels requires costly additional investments into the energy system. Nevertheless, large renewable energy potentials, in particular for solar energy, and international technology diffusion could ease the transformation towards a low carbon economy and thus facilitate the adoption of emission reduction commitments. Additionally, countries in Sub-Saharan Africa could benefit from interactions with other world regions in the form ofclimate finance, international technology policies, and exports of bioenergy. While Sub- Saharan Africa consists of very heterogeneous countries, we consider a focus on the region as a whole a useful starting point for understanding the implications of an ambitious global climate policy regime. In this paper we provide an aggregate and quantitative assessment of ambitious climatechange mitigation on economic development in Sub-Saharan Africa. This assessment includes costs, in particular for the low-carbon transformation of the energy system, and benefits like climate finance and bioenergy trade. We take the renewable energy potential of Sub-Saharan Africa, international fossil fuel markets, and technology diffusion from other world regions into account. We find costs and benefits ofclimatechange mitigation to be on the same order of magnitude, allowing Sub-Saharan Africa to participate in global mitigation efforts at roughly net zero costs. Additional benefits ofclimate policy would result from avoided climateimpacts, which are not even taken into account in this study.
UNU-WIDER, Katajanokanlaituri 6 B, 00160 Helsinki, Finland, wider.unu.edu
The views expressed in this publication are those of the author(s). Publication does not imply endorsement by the Institute or the United Nations University, nor by the programme/project sponsors, of any of the views expressed.
Abstract: The paper provides estimates of economic impactsofclimatechange, compares these with historical impactsof drought spells, and estimates the extent to which the current Moroccan agricultural development and investment strategy, the Plan Maroc Vert, helps in agricultural adaptation to climatechange and uncertainty. We develop a regionalized Morocco Computable General Equilibrium model to analyse the linkages ofclimate-induced productivity losses (gains) at the level of administrative and economic regions in Morocco. Yield projections are obtained from the joint-study by the Moroccan Ministry of Agriculture and Fisheries and the World Bank, in collaboration with the National Institute for Agricultural Research, the Food and Agriculture Organization of the United Nations, and the Direction of National Meteorology. We model the climatechangeimpacts as productivity (or yield) shocks in the agricultural sector, and which are region- and crop-specific. The yield projections are for 2050, and introduced with respect to a 2003 baseline. With no adaptation, GDP impacts range from -3.1 per cent (worst-case scenario) to +0.4 per cent (best case scenario). The decline in GDP under the worst-case scenario results from a general contraction in economic aggregates. Accounting for the adaptation measures in the Plan Maroc Vert, the GDP impacts from climatechange are reduced and range from -0.3 per cent to +3 per cent. Nonetheless, the adaptation potential of the Plan Maroc Vert is based upon the assumption of achieving the identified productivity-enhancement targets, and which remains questionable.
4.2. An indicator measuring water security
In order to conduct both the qualitative and the quantitative analysis, I developed an indicator measuring water security, namely the Water Security Index (WSI), since a widely accepted indicator was lacking in the literature. The vast majority of the existing indicators does not include all the dimensions of the concept as indicated in the definition by the United Nations. Moreover, from different definitions and approaches, different scales ofanalysis descend and this contributes to make further distinctions among the existing indices and indicators. Indeed, in the development of the WSI a major concern was given by the scale ofanalysis, since the water realm is by nature heterogeneous, as variations in physical water availability and water quality often occur at basin level. Certainly, country-level indices may hide differences existing among regions, urban and rural populations and genders. However, considering the challenges it poses and how integrated these are, water security is by definition a national issue; consequently, a certain level of integration among policy makers and the engaged institutions is also requested to address it. Moreover, the choice of the scale ofanalysis is also constrained by data availability, which is far broader at country level rather than at basin level; in addition, for policy purposes, an index measured at national level allows comparisons among countries, forming a meaningful management tool. The indicator proposed is an outcome indicator that includes the four dimensions of water security individuated by the United Nations, each corresponding to a variable of the indicator:
The utility effect of a specific absolute loss of income will in general not be the same to a poor person as to a rich. Integrated assessment models used to assess the marginal social costs of carbon emissions (SCC) often handle this aspect using equity weights of some form. This implies weighing together the monetized welfare (income) losses from climatechange across regions of disparate incomes, under the assumption of a supranational social planner (Fankhauser et al. 1997, Pearce 2003, Johansson-Stenman 2005, Anthoff et al. 2009). But in the absence of this supranational planner, how should national policy makers account forclimatechangeimpacts in other countries? One could argue that this should depend on local, national preferences over global impacts. Importantly, as pointed out by Anthoff and Tol (2010), it is unusual in national policy assessments in general to take into account the impacts a national policy may have on citizens of other countries. Anthoff and Tol (2010) develop a handful of alternative models for how a national government may account - or not - for welfare losses in other countries resulting from national policies. They show that varying assumptions about the concern for others have significant impact on what costs a national government would be willing to carry.
The variables that resulted as statistically significant are shown in Table 1. TEMPERATURE presented a negative coefficient for shrimp output, but a positive one for sardine output. Such results suggest that CC has a meaningful influence on the fish catch; furthermore, such effects will be differentiated according to the fishery and provinces (see the next section). Indeed, our estimates involve the monetary value of fish landings due to CC impacts (either positive or negative). Similar results have already been observed on either a global (e.g. Cheung et al. 2010; Hanna 2010) or a regional scale (e.g. Lam et al. 2012. It is worth mentioning that LABOR and CAPITAL (i.e. the number of boats) positively affect fish production for both shrimp and sardine fisheries, just as fisheries economic theory predicts (Hannesson 1993). It is important to note that LABOR was an aggregated variable and boats from the artisanal fleet were not included in our models. This is a drawback to our analysis, but, as noted by McClanahan et al. (2013), obtaining accurate data on tropical fisheries is rather difficult. In spite of this, our results are congruent with similar reports in the literature (as noted above). FINANCING resulted as positively significant for shrimp output, presumably due to the subsidies granted on fuel to the shrimp fleet.
• Investigating farmer costs associated with adapting to climatechange. Better information is needed to help both growers and the regulatory agencies assess the relative costs and benefits of various adaptation options;
• Working with local agribusinesses, to test the UKICIP “climate adaptation: risk, uncertainty and decision-making framework” tool. This could help farmers, the regulatory authority and stakeholders assess the local risks posed by climatechange, and work out how best to respond. The tool has been used in other vulnerable sectors to judge the significance of the climatechange risk, compared to other risks, so that the appropriate adaptation measures can be implemented. It would involve undertaking case study farm assessments, comparing the different adaptation options and their financial and environmental impacts. It would also provide information to help avoid mal-adaptations that might be unbeneficial at the catchment level (in terms of water resources management) or at individual farm level (such as investments in additional water storage);
elasticity of the value ofclimatechangeimpacts.
In this paper, we systematically explore the relative importance of these various factors. To our knowledge, no single study has looked at all three aspects and their various interactions. In a previous paper (Anthoff et al. 2009b), we only considered the pure rate of time preference and the income elasticity of marginal utility. We here use a slightly updated version of the model, and add (baseline) scenario uncertainty to the Monte Carlo analysis. We also extend the time horizon of the analysis, which affects some of the results in an intriguing way. Finally, we add an investigation of effective discount rates in this paper. The paper proceeds as follows. Section 2 sets out the conceptual and theoretical preliminaries. Most of this material is standard, but we include it here for reasons of clarity and to suggest why it is important to look at all three aspects together; there are, in particular, no a priori reasons by which we can even predict the sign of changes driven by anything but time preference. Section 3 presents
number of examples where historical data from multiple locations in countries have been systematically and rigorously checked, cleaned, analyzed and presented in easy to interpret graphical products. From these examples there is emerging best practice which includes: ownership of and responsibility for data remaining with national meteorological services (NMS); capacity development within NMS in approaches and programmes that support data rescue and management, quality control, and analysis and presentation of historical information (e.g. using CLIMSOFT and RINSTAT); NMS staﬀ providing historical climate information products rather than data (thereby avoiding sensitivities about access to data); NMS staﬀ being actively involved in successful climate services initiatives and therefore realizing the practical value of histori- cal information along with other products. There remains how- ever an urgent need for NMS to devote adequate resources to these activities and for more NMS to recognize the value of his- torical information. The research presented here reveals that approaches like PICSA are urgently needed to ensure that insights from meteorological analysisof historical data can be eﬀectively explored together with local experiences and percep- tions of the climateimpacts being experienced, in order to enhance abilities to select development options that support current agricultural livelihood decisions and open up pathways towards eﬀective adaptation As the reach and power of narra- tives about the impacts about climatechange continues to increase, the use of analyzed historical climate information in participatory development activities opens up opportunities for eﬀective cross-scale engagement between global organiz- ations driving funding agendas and those on the ground who are dealing ﬁrst hand with the negative livelihood impactsof a changing climate.
linearly dependent on the soil water content whereas WU2 also depend on the root length densities. In WU1 modelled root distribution and consequently root length densities in different soil layer have negligible effects on water uptake. Although it is conceivable to develop also an empirical relationship between water uptake and root length density I would suggest further precise studies with WU2 where modelled root resistances and soil water potentials can be validated by measured data along the soil-plant-atmosphere continuum. I found the highest compliance between observed and simulated drought years by the WU2 approach with high total root resistance. The accordance is between 33 and 100 % depending on simulated forest stand. The model proved to be a suitable tool for assessing drought related risks of mixed oak- pine forest stands. However, this study did not identify an approach which performs best regarding all three validation data sets. Daily simulated variables as soil water content and stand transpiration fit poorly to measured data. Also the correlation between measured and simulated index time series of diameter increment is not higher than 0.56. This lack of accordance at the overall view of carbon and water cycle within the forest stands does not allow a more detailed quantification of future drought risks for pine and oak trees as the more general approach of chapter 3. In addition, until the role of increasing atmospheric CO 2 -concentration with respect to
The papers discussed above mostly focus on single economies for individual sectors. In order to construct a damage function for all sectors across the world, one would need to write a few thousand empirical papers to get good coverage. I would encourage young scholars to start writing these papers. Another approach is to estimate a single damage function using GDP data, measuring the value of all goods and services produced for a country in a given year. Burke, Hsiang, and Miguel (2015) have written a paper providing a correlation between the growth rate of GDP and a nonlinear measure of changes in temperature to estimate how damaging climatechange will be to the major world economies. While there is discussion about the empirical model adopted in the paper, they show a highly nonlinear relationship between GDP growth rate and temperature which resembles an environmental Kuznets curve—an inverse U. Growth rates peak at about 13° Celsius and are increasing at lower temperatures and decreasing at higher temperatures. The issue with approaches like this is that GDP does not measure everything that has value. It excludes the value of non-market resources, which is likely to be significant. Furthermore, this paper again uses short run variation. For technical reasons discussed in McIntosh and Schlenker (2006), the approach here includes some adaptation response but not complete adaptation. In the next section, I will discuss some of these results and provide some context for the Asian economies.
Long-term changes ofclimate have already been detected and there is wide agreement that the climate will continue to warm over the 21st century (IPCC, 2001). Climate and agriculture are locally and globally interrelated. Climatechange is likely to have both detrimental and beneficial impacts on the agricultural sector (Adams et al., 1990; Lewandrowski and Schimmelpfennig, 1999). One concern of agriculturalists involves the effects ofclimatechange on pest populations. Several studies investigate the interaction of pests and climate (Patterson et al., 1999; Porter, et al., 1991). The results from these studies indicate that pest activity is likely to increase under climatechange, leading to greater risk of crop losses (Gutierrez et al. 2008, Patterson et al. 1999). There is a small but growing research field that focuses on how climatechange will affect pesticide applications. Chen and McCarl (2003) empirically study the relationship between pesticide and climate associated with treatment costs of pesticides use. Their results suggest that climatechange will increase pesticide treatment costs for major crops. In chapter 2 we use a similar approach but consider all major food crops and a more detailed classification of pesticides. We develop a panel data regression model and investigate how weather variability and climatechange affect the application of pesticides in US agriculture. Furthermore we link the regression results to downscaled climatechange scenarios from the Canadian and Hadley climate models. Our results indicate that for current crop area allocations, pesticide application rates mainly increase.
Climate-sensitive health problems kill millions every year and undermine the physical and psychological well-being of millions more. To identify the climateimpacts on dengue risk in Brazil, a comparative case study is used based on the synthetic controls approach. The South and Northeast regions of Brazil are compared to the rest of the country in order to identify those impacts. The results suggest that dengue is more prevalent in warmer regions, but the humidity conditions and amount of rainfall seem fundamental for increase of the disease’s prevalence in temperate climate regions or drier tropical regions of the country. On the other hand, the increase in rainfall in the rainiest tropical areas could diminish the disease’s prevalence, as standing water accumulations might be washed away. Therefore, due to expected climate changes in the future, the dengue fever distribution in the country might change, with the disease migrating from the north to the south. Public policy’s role in minimizing these effects in the country should be focused on anticipating the proper climate conditions for dengue incidence by using integrated actions among local authorities.