concentration of activity in these spaces and also reinforce social inequalities, increasing poverty. The reduction of well-being in rural areas may generate pressure on urban clusters, although there may be sectors and regions that benefit from the process. The biggest losses will probably take place in the interior areas ofthe country. Theimpactsofclimatechangeon urban infrastructure require further studies. The areas most vulnerable to climatechange in Brazil are the Amazon and the Northeast, which are exactly the poorest regions. In the Amazon, gradual warming may reach 7-8°C by 2100 in scenario A2-BR, meaning a radical change in the Amazon Forest – so called ‘savannisation’. One ofthe key questions to be answered by scientists is: What are possible tipping points after which the savannisation process ofthe Amazon would be irreversible? Without a doubt this is one ofthe most relevant and complex issues related to climatechange in Brazil and research is still in its initial stages. In the case ofthe Northeast, rainfall levels tend to decrease during the 21st Century, at a rate of 2-2.5 mm/day. This will lead to agricultural losses in all states ofthe region and its
Laborde (2011) analyses theimpactsofclimate-induced yield changes on agriculture in South Asia, and investigates the potential for trade policy options to mitigate the latter. A modified version ofthe MIRAGE CGE model was used, where yield estimates were first obtained via the IMPACT model for 12 climate scenarios representing alternative pathways for future agricultural productivity. The latter are constructed from the IPCC Special Report on Emission Scenarios (SRES) and general circulation models (GCM) for theclimate. The SRES emission scenarios correspond to assumption about the evolution in the emission of greenhouse gases (GHG) based onthe dynamics of projected economic growth, technological progress, and demographic pressures. The GCM models are numerical representations oftheclimate system based onthe physical, chemical, and biological properties of their components (IPCC 2007). The study makes use of 3xSRES scenarios (A1B, B1, and A2) in combination with 4xGCMS (CNR, CSIRO, ECH, and MIROC) to obtain the 12 climate scenarios used in the analysis. The latter are introduced as exogenous shocks in the modified MIRAGE CGE model, where baseline results are contrasted with the results from eight different trade policy landscapes for the region. The findings suggest that pinpointing optimal trade policy is difficult in the light of uncertainty in potential climate-induced impactson yields.
West African farmers are among those most likely to suffer from climatechange, partly due to the agro-climatic characteristics ofthe regional system and to their limited scope for coping with shocks. Climatechange adaptation has thus been touted as a necessary path for rural poverty reduction and development in the region. Yet, do farm households who implemented climatechange adaptation earn higher income compare to those who did not? We attempt to answer this question in the context of crop and livestock income in the Savana region of Togo. To that end, we build a bio-economic model based on farm household model theory. Using survey data collected from a representative sample of 450 savanna farm households ofthe agricultural year 2014/2015, we identify farm-household types through cluster analysis and apply them in the simulation model. From the simulation results, we conclude that at their current costs, soil and water conservation techniques and irrigation practives can on average provide higher income even under climatechange, since they are able to mitigate at least 63 % oftheimpactsofclimatechangeon crop and livestock income. By contrast, reducing the quantity of applied fertilizer, mentioned as an adaptation option by farmers, increases the farm households’ vulnerability to climatechange. The policy message we draw from this study is to encourage Soil and Water Conservation techniques and sustainable irrigation as sound strategies for higher income under climatechange in the region. These are “no regret options” with a positive impact on livelihoods while preserving the resource base.
The analysis presented in this paper has assumed, in common with almost all assessments ofclimatechangeimpacts, that the physical properties ofthe landscape (other than those changed fundamentally by urbanization) remain constant. Typically, available soil property data (such as bulk density, water retention and hydraulic conductivity), which have been collected over a number of past years by soil survey organizations, are used to parameterise the simulation of infiltration / runoff / soil water availability. Once calibrated, the model is then used predictively, with the soil properties unchanged. However, there is mounting evidence that the condition of temperate soils are changing at a range of temporal scales (Rounsevell et al. 1999), and this has concomitant implications for assessments of future groundwater recharge.
Climatechange is happening. We have to deal with it. The Federal Ministry of Education and Research in Ger- many (BMBF) is funding the research priority "KLIMZUG – Managing climatechange in theregions for the fu- ture". The objective of KLIMZUG is the development of in- novative strategies for adaptation to climatechange and related weather extremes in regions. Here, the anticipated changes in climate shall be integrated in processes of regional planning and development. The funding acti- vity particularly stresses the regional aspect since global problems such as climatechange must be tackled by measures at regional and local level. The future compe- titiveness ofregions, also in a changing climate, must be ensured. Also, KLIMZUG is meant to advance the deve- lopment and use of new technologies, procedures and strategies for adapting to climatechange in theregions. KLIMZUG contributes especially to the German High-Tech Strategy onClimate Protection as well as to the German National Adaptation Strategy. It also complements BMBF’s first funding activity on research and develop- ment of measures to deal with climatechange "Research for climate protection and protection from climateimpacts" (klimazwei).
The Vale of Evesham constitutes one ofthe most important regions for irrigated horticultural production in England, providing a focus for intensive production of vegetables, salad crops and soft fruit. These high value crops are increasingly dependant on supplemental irrigation to meet the exacting, high quality standards for produce being demanded by the major multiples and processors. As in other parts of England where irrigated production is concentrated, the underlying demand for irrigation in this region is growing steadily. In Worcestershire, these rising abstractions for irrigation, particularly in dry years, are impacting on local surface and groundwater resources, such as the Badsey Brook and Offenham gravels. Indeed, the Warwickshire Avon CAMS Report (EA, 2006) highlights that over the last decade restrictions at the Offenham control point have been applied every year for between 54-246 days to protect the environment from over-abstraction. Climatechange will worsen the situation, with higher summer temperatures, less summer rainfall and more evaporation being predicted (Hulme et al., 2002). Climatechange could therefore result in a reduction in summer water available for abstraction, and an increase in irrigation water demand.
5. This section ofthe study focusses on identifying a set of representative future climate projections for the UIB. Although a large number of GCM’s predictor sets are nowadays available in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation along with other factors such as time, human resources or computational constraints, makes it imperative to sort out the most appropriate, single or small-ensemble set of GCMs for the assessment ofclimatechangeimpacts in a region. Here a various approaches are adopted and applied for a step-wise shortlist and selection of appropriate climate models for the UIB for two representative concentration pathways (RCPs), RCP 4.5 and RCP 8.5, based on, a) range of projected mean changes, b) range of projected extreme changes, and c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions, like the UIB, in addition to projections that provide the closest representation ofthe mean future climatechange, all possible future extreme scenarios (wet-warm, wet-cold, dry-warm, dry-cold) are also considered. Based on this two-fold procedure a limited number ofclimate models is pre-selected, out of which the final selection is done by assigning ranks to the weighted score for each ofthe mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB, for subsequent use as climate drivers in the SWAT- model.
Still, higher temperatures were also found to have a pronounced effect on runoff by changing its seasonal distribution. In the snow-fed Mesohora and Sykia basins, earlier snowmelt processes could increase surface runoff during winter. This would result in a marked reduction of spring and summer runoff, ultimately leading to a prolongation ofthe dry period (Mimikou et al., 1991). These findings were corroborated in a subsequent study by Mimikou et al. (1999), where two equilibrium (UKHI, CCC) and one transient scenario (UKTR) were applied to investigate theimpactsofclimatechangeonthe Aliakmon river and three of its subbasins in northern Greece. According to these experiments, the projected increase in runoff during November, December and January would lead to a reduction in spring runoff, thus causing a severe prolongation ofthe dry season. Furthermore, all scenarios indicated a decrease in mean annual and mean winter runoff (Nov-Apr), as well as a severe reduction in summer runoff (May- Oct) over the course of this century. According to the UKHI scenario, decreases in annual and seasonal runoff in the three subbasins could be around 20-40% by the 2050s. Overall, runoff is projected to become more extreme with maximum annual values expected to increase and minimum annual runoff projected to decrease (Mimikou et al., 1999). Additional modelling studies (HadCM2, UKHI) for the Ali Efenti basin in central Greece projected again a significant reduction of mean monthly runoff for almost all months ofthe year 2050. The highest decreases could occur again during the summer months, especially in June when reductions of 26-46% are projected (Mimikou et al., 2000).
There is a strong demand for regionalised risk assessments and adaptation strategies by weather- sensitive economic sectors like the insurance industry, international airports, and water management. The same holds for national weather services like the DWD in Germany in its efforts for optimisation of forecasts and warnings of such convective extreme events. The adaptation of existing building codes with respect to wind loads and precipitation maxima to climatic trends in extreme weather events is also economically relevant. From these target groups, Munich international airport and the Munich Reinsurance Group were chosen as exemplary users.
10. One significant example is the fact that the last IPCC report, the most comprehensive literature review onthe subject, has few references to family farming in either its reports on vulnerability and adaptation (Working Group II) or mitigation (Working Group III).
11. The crops detailed in this report were selected from the main list considered by the funds supporting family farming (e.g. Seguro da Agricultura Familiar (SEAF); 50 crops ZARC/MAPA): pineapple, acaí berry, cotton, plums, peanuts, rice, oats, bananas, cocoa, coffee (arabica and robusta), cashew nuts, sugarcane, canola, barley, citrus (orange, lemon, lime, tangerine and grapefruit), coconut, palm, eucalyptus, beans (first, second and third harvest), cowpea bean, sesame, sunflower, guava, apple, papaya, castor (mamona), manioc or cassava, mango, passion fruit, watermelon, millet, corn, corn/Brachiaria, nectarine, cactus (palma forrageira), pear, peach, pepper, pine, peach palm (pupunha), rubber, sisal, soybeans, sorghum, wheat and grape (American and European).
Many studies focus on natural science aspects of water availability, but analyses onthe economic responses are important as well. Economies and in particular agricultural sectors of some developing countries might be hit particularly hard by a changing climate and a change in water availability. The agricultural sector is by far the largest consumer of water and farmers operate, directly or indirectly, at the world market for agricultural products. One ofthe few analyses oftheimpactsofclimate-change-induced changes in water resources on agriculture in the context of international trade is Calzadilla et al. . In addition to information on predicted changes in river flows under the IPCC SRES A1B and A2 scenarios from Falloon and Betts , they analyse the effects of temperature, precipitation and CO2 fertilization on crop yields. The SRES A1B scenario has relatively little warming while the SRES A2 scenario shows higher levels of greenhouse gas concentration in the atmosphere. The results show that global food production, welfare and GDP fall due to climatechange while food prices increase. Larger changes are observed under the SRES A2 scenario for the medium term (2020) and under the SRES A1B scenario for the long term (2050). The results are more pronounced if irrigation areas respond to water availability as well.
Source: Auffhammer (2018).
When you leave your house in the morning, you have to decide what to wear. If you look outside your window and it is cold and rainy, you will wear warmer clothes and bring an umbrella, if you have one. The next day it may be sunny and warm, and you will leave your home in a short-sleeved shirt. What you encounter on a day to day basis is weather. In most places, weather is highly variable across and even within seasons. Summer months are usually warmer and drier, while winter months are usually colder and wetter. Climate can coarsely be characterized as the full statistical distribution of weather. If that distribution is stationary—meaning the moments ofthe distribution are constant over time—one would still expect day to day variation in weather, but on average expect a similar number of hotter and cooler days during the summer, etc. Let’s use a sector which is highly sensitive to weather: electricity consumption. Assume a location with a cool pleasant climate, like San Francisco. Fashionable San Francisco old and young alike live in houses and apartments that usually do not have air conditioning equipment. What this means is that onthe occasional very hot day, San Franciscans will complain loudly and head to the park to eat ice cream, hoping for cooler days. Since these hot days are a rare occurrence in a world without climatechange, the cost of installing and operating air conditioning may be greater for most residents compared to the benefits they would derive from the few days they would use it. However, if San Francisco inherits theclimateof Beijing, which is much warmer and more humid, especially in the summer, most San Franciscans would probably find it optimal to install air conditioning. Hence electricity use would go up. We call this adaptation. The top left panel of Figure 4 displays the weather from a pre-climatechange world in light gray. The temperature series is mapped via the damage
Regarding theimpactsofclimatechangeon biodiversity, there are hardly any specific monitoring activities. Consequently, a corresponding indicator system has to be based predominantly on data that were originally collected for other purposes–in our case for biodiversity and climatechange issues separately. Several examples for ongoing monitoring activities or the development of such monitoring programs concerning different components of biodiversity do already exist, e.g., for bees, locusts, butterflies, dragonflies, birds, fish, and plankton at the species level, as well as high nature value farmland and inland and coastal waters at the habitat level. Monitoring ofthe natural environment in Germany is carried out by governmental agencies, administrations, or non-governmental organizations (often in combination with citizen science). These efforts, however, cover, only selected groups of species and habitats, so far, and the resulting data sets are often incomplete and heterogeneous, e.g., in data quality and sampling intensity. Climate data and phenological phases (annually recurring growth and development phenomena) of plants are gathered within the monitoring programs ofthe German Weather Service (Deutscher Wetterdienst—DWD) and provided, e.g., through its Climate Data Center (CDC) and the German Climate Service (Deutscher Klimadienst—DKD).
disturbances are independently distributed. Thus, estimations and inference based on models that do not account for common unobserved factors or shocks and non- stationarity of some data series can yield biased and misleading results, particularly in standard fixed effect panel estimators (see Phillips and Sul, 2003; Bai, 2009; Kapetanios et al., 2011). In the case of cross-sectional dependence, such common unobserved factors could for example be reflected in oil price shocks, a global financial crisis or local events that affect several countries via spillover effects. We test for cross-sectional independence ofthe different time series and reject it. To address these issues, we adopt the common correlated effects (CCE) estimator of Pesaran (2006), a sufficiently general and flexible econometric approach, which is applicable under both cross-sectional dependence and cross-country heterogeneity. Eberhardt and Teal (2012) use similar techniques in order to address the issue of parameter heterogeneity across countries, but they do not consider climate variables as agricultural production inputs. 5
Some aspects ofthe 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 ofthe ‘critical loads’ concept, which proved very successful in the negotiation process ofthe 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 ofthe ‘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 ofthe ‘bounded rationality’ concept (Simon, 1972).
Effects through structural transformation
We start by focusing on how climatechange might affect fertility by altering the composition of production within an economy. In response to negative economic impacts from climatechange, labor is likely to reallocate towards agriculture, were large climateimpacts are expected relative to other sectors. This can occur for two reasons. First, the low elasticity of substitution in the demand for agricultural and non-agricultural goods implies that the scarcity of agricultural goods will increase prices and wages in this sector, creating incentives for labor reallocation [22, 23]. Second, if climate damages decrease income, consumers would have an incentive to spend a greater fraction of their income on agriculture goods when compared to a world without climatechange [23, 24]. This would again increase the relative wage of agricultural workers and motivate labor reallocation.
Traditionally, humanitarian aid responds to emergencies. To make humanitarian assistance more effective in the light of rapidly increasing risks of serious (climate) emergencies (e.g. annually recurring South Asia floods; South African cyclones Idai and Kenneth in 2019; East African droughts in 2011, 2017 and 2019), some experts involved in the panel discussion argued that moving humanitarian action from response to prevention and anticipation, from crisis management to risk management, can significantly reduce chaos and human suffering. For example, sophisticated early warning systems such as heat-wave warning present a considerable opportunity to avert food crises. It was furthermore submitted that,
The main goal ofthe trading scheme is to reduce Australia‟s carbon pollution, while creating long-term economic success in a low carbon economy. The scheme should correct the major market failure related with climatechange (Gar- naut, 2008). It is an opportunity to limit greenhouse gas pollution, while giving par- ticipants incentives to reduce their emissions (Australian Government, 2008b). The scheme will put a price on carbon by setting a cap onthe amount of carbon pollu- tion that industries can emit; emission rights become scarce which entails a price. Included businesses and industries will need a pollution permit for each ton of car- bon they emit, which will give strong incentives to reduce pollution. Companies with high abatement costs will buy permits, either at auctions or at a secondary trading market. Companies with low abatement costs will undertake abatement efforts in- stead of buying permits. The CPRS will mainly focus onthe biggest polluters in Australia. Around 1,000 companies will be included, other uncovered businesses will not have obligations given by the scheme. These biggest polluters are respon- sible for more than 25,000 tons of carbonized pollution each year. The income ofthe pollution permits will be used to help businesses to adjust to the scheme and to support low-carbon and energy efficient technologies. Australia committed to a five to 15 or even 25 percent medium-term reduction target below 2000 level by 2020, depending onthe efforts of other high emitting countries, and to a 60 percent re- duction long-term target by 2050. These goals should be reached in a cost effective way (Australian Government, 2008a; Australian Government, 2008c). The emission cap, within the Carbon Pollution Reduction Scheme, will be consistent with the
consumption level. It is also possible to determine which households in the sample are below the poverty line, in the sense that they face difficulties to accommodate that minimum expenditure on food within their budget.
One advantage of this methodology is the possibility to construct confidence intervals, using, for example, bootstrap methods (Davison and Hinkley, 1997; Hall, 1992) or first order approximations (Lehmann, 1999). Thus, one can also obtain confidence intervals for the cut-off per capita income level and for the number of poor households. However, given the computational effort involved in estimating monotonic expansions of B-splines, the use of bootstrap for the definition of confidence intervals was avoided. Besides, the complexity imposed by the restriction in the Weighted LS makes it difficult to obtain analytical results for the estimators asymptotic distribution, based on first-order approximations. Therefore, the option was made to use confidence intervals estimated the traditional LS. This alternative is computationally attractive, given its simplicity, and allows for the incorporation of observations with different weights (Draper and Smith, 1998).
For the neonatal mortality rate, the education ofthe mother seems to be an important reducer of mortality. The base dummy considers mother’s education higher than eight years. The worst education level for mortality seems to be the group of mothers who had not completed elementary school, a result that is consistent in all specifications. In terms of local infrastructure, sewage system had statistically significant coefficients, suggesting that these are the most important ways of reducing mortality. When it comes to medical care access, the number of nurses per inhabitant seems to be the most important medical input for the population studied. Nurses have an important role in increasing the knowledge of poor families, in terms of hygiene and primary care. The coefficients were statistically significant for robust estimations, but the significance vanished when the bootstrapped standard errors were estimated.