ALBERT

Description: In this report, the European Scientific Advisory Board on Climate Change recommends a series of actions to put the EU on track towards climate neutrality. Based on an assessment of more than 80 indicators, the Advisory Board found that more efforts are needed across all sectors to achieve the EU climate objectives from 2030 to 2050, and particularly in buildings, transport, agriculture and forestry. The Advisory Board acknowledges the potential of the Fit for 55 policy package to speed up EU’s decarbonisation, but warns that additional measures are imperative if the EU is to achieve its climate neutrality objective by 2050 at the latest. With this in mind, the Advisory Board outlines 13 key recommendations for a more effective implementation and design of the EU climate policy framework. This will require action in the coming years, both to effectively implement recently agreed legislation and to start preparations for the post-2030 climate policy framework.

Description: The European Climate Law requires the EU to adopt a 2040 climate target, taking into account the advice from the European Scientific Advisory Board on Climate Change. The Advisory Board recommends keeping the EU’s cumulative greenhouse gas emissions below 11-14 Gt CO2e between 2030 and 2050, and reducing EU greenhouse gas emissions by 90-95 % by 2040, relative to 1990. A fair contribution to climate change mitigation requires ambitious reductions in domestic emissions, complemented by measures outside the EU. The EU 2030 target of at least a 55% reduction in greenhouse gas emissions compared to 1990 enables it to reach the recommended 2040 target range and climate neutrality by 2050. The recommended 2040 emission reductions can be achieved through several pathways. Despite their differences, they show a high degree of convergence on a range of issues.

Description: Climate change and socioeconomic developments will have a decisive impact on people exposed to hunger. This study analyses climate change impacts on agriculture and potential implications for the occurrence of hunger under different socioeconomic scenarios for 2030, focusing on the world regions most affected by poverty today: the Middle East and North Africa, South Asia, and Sub-Saharan Africa. We use a spatially explicit, agroeconomic land-use model to assess agricultural vulnerability to climate change. The aims of our study are to provide spatially explicit projections of climate change impacts on Costs of Food, and to combine them with spatially explicit hunger projections for the year 2030, both under a poverty, as well as a prosperity scenario. Our model results indicate that while average yields decrease with climate change in all focus regions, the impact on the Costs of Food is very diverse. Costs of Food increase most in the Middle East and North Africa, where available agricultural land is already fully utilized and options to import food are limited. The increase is least in Sub-Saharan Africa, since production there can be shifted to areas which are only marginally affected by climate change and imports from other regions increase. South Asia and Sub-Saharan Africa can partly adapt to climate change, in our model, by modifying trade and expanding agricultural land. In the Middle East and North Africa, almost the entire population is affected by increasing Costs of Food, but the share of people vulnerable to hunger is relatively low, due to relatively strong economic development in these projections. In Sub-Saharan Africa, the Vulnerability to Hunger will persist, but increases in Costs of Food are moderate. While in South Asia a high share of the population suffers from increases in Costs of Food and is exposed to hunger, only a negligible number of people will be exposed at extreme levels. Independent of the region, the impacts of climate change are less severe in a richer and more globalized world. Adverse climate impacts on the Costs of Food could be moderated by promoting technological progress in agriculture. Improving market access would be advantageous for farmers, providing the opportunity to profitably increase production in the Middle East and North Africa as well as in South Asia, but may lead to increasing Costs of Food for consumers. In the long-term perspective until 2080, the consequences of climate change will become even more severe: while in 2030 56% of the global population may face increasing Costs of Food in a poor and fragmented world, in 2080 the proportion will rise to 73%.

Description: A methodology to assess future development in patterns of vulnerability is presented which can support the assessment of global policies with regard to their impacts on specific vulnerabilities on the regional or local scale. Patterns of vulnerability, formalized by vulnerability profiles (e.g. for the livelihoods of dryland smallholder farmers) were investigated under different consistent indicator scenarios reflecting different global policies. After unfolding several principal possibilities to do such an analysis of temporal change in vulnerability patterns we could conclude that the concept of “Clusters of Change” (CoCs) is the most straight forward and promising approach. The main arguments are that each interpretation has necessarily to consider both, the starting situation and it’s change over time (”poor and heavily improving”, ”rich and stagnating” etc.). This implies that we are looking for patterns which represent typical combinations of present states AND expected future changes. An application of the CoC-concept to the drylands vulnerability patterns considering the indicator set for the present situation and the same indicator set for 2050 under a baseline scenario was performed as a test. Comparison of the present vulnerability cluster partition with the spatial distribution of the CoCs revealed that most of these clusters are separated into an improving and a deteriorating part which shows where winners and losers of the baseline scenario are – an interesting result which illustrates the appropriateness of the CoC – method. To explore the potential of CoCs for the dryland vulnerability we applied the method to two different sets of scenarios until 2050: a baseline vs. Climate policy scenario (OECD, 2012) and a ”policy first” scenario vs. ”security first” scenario (UNEP, 2007). The first one serves as an example for a policy assessment while the second compares the vulnerability consequences of two scenarios based on different story-lines of further global development. The main conclusion to be drawn from these calculations is that the CoCs are rather insensitive with regard to the small differences between the scenarios. Regarding the first set of scenarios the relatively short time horizon of relevant influences of climate policies on climate change impacts and several indicators which are not influenced at all generate only a very small difference. The only significant change in the resulting vulnerability profiles was in the values of change in water scarcity: it was lower for all profiles in the climate policy case. The second set of scenarios is not directly related to policy decisions but to different global story-lines which deviate stronger. This resulted in an increasing cluster number from 4 (policy first) to 5 (security first) clusters, about 20% of the pixels changing cluster membership, 3 clusters showing the same spatial extent for both scenarios but the 4th cluster (“policy first”) “losing” India which generates a separate cluster in the “security first” scenario. This allows for the interpretation that a further development according to the “security first”-storyline compared to the “policy first”-storyline would make a difference particularly for India. Closer inspection of the respective profile shows a qualitatively different situation indicating increased vulnerability compared to the “policy first” scenario where India shares one cluster with e.g., Northern Africa.

Description: In coupled human-environment systems where well established and proven general theories are often lacking cluster analysis provides the possibility to discover regularities – a first step in empirically based theory building. The aim of this report is to share the experiences and knowledge on cluster analysis we gained in several applications in this realm helping to avoid typical problems and pitfalls. In our description of issues and methods we will highlight well-known main-stream methods as well as promising new developments, referring to pertinent literature for further information, thus offering also some potential new insights for the more experienced. The following aspects are discussed in detail: data-selection and pre-treatment, selection of a distance measure in the data space, selection of clustering method, performing clustering (parameterizing the algorithm(s), determining the number of clusters etc.) and the interpretation and evaluation of results. We link our description – as far as tools for performing the analysis are concerned - to the R software environment and its associated cluster analysis packages. We have used this public domain software, together with own tailor-made extensions, documented in the appendix.