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Now showing 1 - 9 of 9
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    Improving the evidence base: A methodological review of the quantitative climate migration literature
    (Amsterdam [u.a.] : Elsevier, 2021) Hoffmann, Roman; Šedová, Barbora; Vinke, Kira
    The question whether and how climatic factors influence human migration has gained both academic and public interest in the past years. Based on two meta-analyses, this paper systematically reviews the quantitative empirical literature on climate-related migration from a methodological perspective. In total, information from 127 original micro- and macro-level studies is analyzed to assess how different concepts, research designs, and analytical methods shape our understanding of climate migration. We provide an overview of common methodological approaches and present evidence on their potential implications for the estimation of climatic impacts. We identify five key challenges, which relate to the i) measurement of migration and ii) climatic events, iii) the integration and aggregation of data, iv) the identification of causal relationships, and v) the exploration of contextual influences and mechanisms. Advances in research and modelling are discussed together with best practice cases to provide guidance to researchers studying the climate-migration nexus. We recommend for future empirical studies to employ approaches that are of relevance for and reflect local contexts, ensuring high levels of comparability and transparency.
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    The impact of climate conditions on economic production. Evidence from a global panel of regions
    (Amsterdam [u.a.] : Elsevier, 2020) Kalkuhl, Matthias; Wenz, Leonie
    We present a novel data set of subnational economic output, Gross Regional Product (GRP), for more than 1500 regions in 77 countries that allows us to empirically estimate historic climate impacts at different time scales. Employing annual panel models, long-difference regressions and cross-sectional regressions, we identify effects on productivity levels and productivity growth. We do not find evidence for permanent growth rate impacts but we find robust evidence that temperature affects productivity levels considerably. An increase in global mean surface temperature by about 3.5°C until the end of the century would reduce global output by 7–14% in 2100, with even higher damages in tropical and poor regions. Updating the DICE damage function with our estimates suggests that the social cost of carbon from temperature-induced productivity losses is on the order of 73–142$/tCO2 in 2020, rising to 92–181$/tCO2 in 2030. These numbers exclude non-market damages and damages from extreme weather events or sea-level rise. © 2020 The Authors
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    The social cost of carbon and inequality: When local redistribution shapes global carbon prices
    (Amsterdam [u.a.] : Elsevier, 2021) Kornek, Ulrike; Klenert, David; Edenhofer, Ottmar; Fleurbaey, Marc
    The social cost of carbon is a central metric for optimal carbon prices. Previous literature shows that inequality significantly influences the social cost of carbon, but mostly omits heterogeneity below the national level. We present an optimal taxation model of the social cost of carbon that accounts for inequality between and within countries. We find that climate and distributional policy can generally not be separated. If only one country does not compensate low-income households for disproportionate damages, the social cost of carbon tends to increase globally. Optimal carbon prices remain roughly unchanged if national redistribution leaves inequality between households unaffected by climate change and if the utility of households is approximately logarithmic in consumption.
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    A protocol to develop Shared Socio-economic Pathways for European agriculture
    (Amsterdam [u.a.] : Elsevier, 2019) Mitter, Hermine; Techen, Anja-K.; Sinabell, Franz; Helming, Katharina; Kok, Kasper; Priess, Jörg A.; Schmid, Erwin; Bodirsky, Benjamin L.; Holman, Ian; Lehtonen, Heikki; Leip, Adrian; Le Mouël, Chantal; Mehdi, Bano; Michetti, Melania; Mittenzwei, Klaus; Mora, Olivier; Øygarden, Lillian; Reidsma, Pytrik; Schaldach, Rüdiger; Schönhart, Martin
    Moving towards a more sustainable future requires concerted actions, particularly in the context of global climate change. Integrated assessments of agricultural systems (IAAS) are considered valuable tools to provide sound information for policy and decision-making. IAAS use storylines to define socio-economic and environmental framework assumptions. While a set of qualitative global storylines, known as the Shared Socio-economic Pathways (SSPs), is available to inform integrated assessments at large scales, their spatial resolution and scope is insufficient for regional studies in agriculture. We present a protocol to operationalize the development of Shared Socio-economic Pathways for European agriculture – Eur-Agri-SSPs – to support IAAS. The proposed design of the storyline development process is based on six quality criteria: plausibility, vertical and horizontal consistency, salience, legitimacy, richness and creativity. Trade-offs between these criteria may occur. The process is science-driven and iterative to enhance plausibility and horizontal consistency. A nested approach is suggested to link storylines across scales while maintaining vertical consistency. Plausibility, legitimacy, salience, richness and creativity shall be stimulated in a participatory and interdisciplinary storyline development process. The quality criteria and process design requirements are combined in the protocol to increase conceptual and methodological transparency. The protocol specifies nine working steps. For each step, suitable methods are proposed and the intended level and format of stakeholder engagement are discussed. A key methodological challenge is to link global SSPs with regional perspectives provided by the stakeholders, while maintaining vertical consistency and stakeholder buy-in. We conclude that the protocol facilitates systematic development and evaluation of storylines, which can be transferred to other regions, sectors and scales and supports inter-comparisons of IAAS. © 2019 Elsevier Ltd
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    Increasing compound warm spells and droughts in the Mediterranean Basin
    (Amsterdam [u.a.] : Elsevier, 2021) Vogel, Johannes; Paton, Eva; Aich, Valentin; Bronstert, Axel
    The co-occurrence of warm spells and droughts can lead to detrimental socio-economic and ecological impacts, largely surpassing the impacts of either warm spells or droughts alone. We quantify changes in the number of compound warm spells and droughts from 1979 to 2018 in the Mediterranean Basin using the ERA5 data set. We analyse two types of compound events: 1) warm season compound events, which are extreme in absolute terms in the warm season from May to October and 2) year-round deseasonalised compound events, which are extreme in relative terms respective to the time of the year. The number of compound events increases significantly and especially warm spells are increasing strongly – with an annual growth rates of 3.9 (3.5) % for warm season (deseasonalised) compound events and 4.6 (4.4) % for warm spells –, whereas for droughts the change is more ambiguous depending on the applied definition. Therefore, the rise in the number of compound events is primarily driven by temperature changes and not the lack of precipitation. The months July and August show the highest increases in warm season compound events, whereas the highest increases of deseasonalised compound events occur in spring and early summer. This increase in deseasonalised compound events can potentially have a significant impact on the functioning of Mediterranean ecosystems as this is the peak phase of ecosystem productivity and a vital phenophase.
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    Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
    (Amsterdam [u.a.] : Elsevier, 2017) Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco; Asseng, Senthold; Baranowski, Piotr; Basso, Bruno; Bodin, Per; Buis, Samuel; Cammarano, Davide; Deligios, Paola; Destain, Marie-France; Dumont, Benjamin; Ewert, Frank; Ferrise, Roberto; François, Louis; Gaiser, Thomas; Hlavinka, Petr; Jacquemin, Ingrid; Kersebaum, Kurt Christian; Kollas, Chris; Krzyszczak, Jaromir; Lorite, Ignacio J.; Minet, Julien; Minguez, M. Ines; Montesino, Manuel; Moriondo, Marco; Müller, Christoph; Nendel, Claas; Öztürk, Isik; Perego, Alessia; Rodríguez, Alfredo; Ruane, Alex C.; Ruget, Françoise; Sanna, Mattia; Semenov, Mikhail A.; Slawinski, Cezary; Stratonovitch, Pierre; Supit, Iwan; Waha, Katharina; Wang, Enli; Wu, Lianhai; Zhao, Zhigan; Rötter, Reimund P.
    Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
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    Understanding adaptive capacity of smallholder African indigenous vegetable farmers to climate change in Kenya
    (Amsterdam [u.a.] : Elsevier, 2020) Chepkoech, Winifred; Mungai, Nancy W.; Stöber, Silke; Lotze-Campen, Hermann
    Understanding the adaptive capacity (AC) of farmers is crucial to planning effective adaptation. Action to promote farmers’ AC is required because climate change (CC) is resulting in unpredictable alterations in weather patterns. Based on the sustainable livelihoods framework (SLF), this study explored how access to natural, physical, financial, social and human capitals enhances the AC. Quantitative data from 269 African indigenous vegetable (AIV) farmers in three selected agro-climatic zones in Kenya were analysed. Four indicators in each capital were selected based on previous studies and judgments collected from an expert online ranking survey (n = 35). The Kruskal-Wallis H test and an independent sample t-test were used to test the independence of AC scores and access to the different resources. The findings showed that the majority of farmers (53%) had a moderate AC, while fewer (32%) and (15%) had low or high AC levels respectively. Disparities in adaptive capacity scores were recorded between respondents in terms of their age, marital status and location. Farmers had high access to social capital but low access to financial, natural and human capitals. Female farmers showed lower capacities in the areas of financial, human and natural resources, while their male counterparts had low access to some human and social capitals. Resilient interventions that target individuals with low adaptive capacities are required. © 2020 The Authors
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    Bio-IGCC with CCS as a long-term mitigation option in a coupled energy-system and land-use model
    (Amsterdam [u.a.] : Elsevier, 2011) Klein, D.; Bauer, N.; Bodirsky, B.; Dietrich, J.P.; Popp, A.
    This study analyses the impact of techno-economic performance of the BIGCC process and the effect of different biomass feedstocks on the technology's long term deployment in climate change mitigation scenarios. As the BIGCC technology demands high amounts of biomass raw material it also affects the land-use sector and is dependent on conditions and constraints on the land-use side. To represent the interaction of biomass demand and supply side the global energy-economy-climate model ReMIND is linked to the global land-use model MAgPIE. The link integrates biomass demand and price as well as emission prices and land-use emissions. Results indicate that BIGCC with CCS could serve as an important mitigation option and that it could even be the main bioenergy conversion technology sharing 33% of overall mitigation in 2100. The contribution of BIGCC technology to long-term climate change mitigation is much higher if grass is used as fuel instead of wood, provided that the grass-based process is highly efficient. The capture rate has to significantly exceed 60 % otherwise the technology is not applied. The overall primary energy consumption of biomass reacts much more sensitive to price changes of the biomass than to technoeconomic performance of the BIGCC process. As biomass is mainly used with CCS technologies high amounts of carbon are captured ranging from 130 GtC to 240 GtC (cumulated from 2005-2100) in different scenarios.
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    Tackling long-term climate change together: The case of flexible CCS and fluctuating renewable energy
    (Amsterdam [u.a.] : Elsevier, 2011) Ludig, S.; Haller, M.; Bauer, N.
    The present study aims at shedding light into the interaction of fluctuating renewables and the operational flexibility of post-combustion capture plants in the framework of a long-term model. We developed a model of the electricity sector taking into account both long-term investment time scales to represent plant fleet development under economic and climate constraints as well as short time scales to consider fluctuations of demand and renewable energy sources. The LIMES model allows us to determine the respective roles of renewables and CCS in climate change mitigation efforts within the electricity sector. Furthermore, we assess the influence of natural gas prices on fuel choice and investigate the shares of competing CCS approaches in the technology mix. We find that the optimal technology mix includes large shares of renewables and simultaneously different competing CCS technologies, depending on emission constraints and fuel prices.