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Due to the global phenomenon of climate change the region of Mara Siana is projected to increasingly face extreme weather events that particularly comprise prolonged droughts and
heavier rainfalls. To be able to adequately adapt to these changing circumstances and maintain their livelihoods communities need to build respective capacities. As the main objective, this research aims at determining landowners’ climate change adaptative capacity (CCAC) across different villages in Mara Siana. Accordingly, a semi-quantitative approach was carried out including qualitative interviews and the subsequent quantitative calculation of CCAC based on a multidimensional indicator set and a respective coding
system. In addition to predominantly positive results of socio-cultural characteristics and the quality of natural resources, this work reveals clear weaknesses and potential for improvement in the areas of income security and financial stability, the expansion and resilience of infrastructure, and the relationship between communities and local authorities. Moreover, differences in capacity results are not only identified between the investigated villages as well as between individual households but also systemic disadvantage in capacity building affecting female landowners and community members can be indicated from the obtained interview data. Therefore, this research gives concrete recommendations for the implementation and verification of suitable adaptive measures that are particularly tailored for the improvement of low-performance indicators while following a gendertransformative approach and thus hold the potential to increase CCAC in the long-term.
Acknowledging the ways in which design (as practices, forms of knowledge, and sets of objects) is accountable for ongoing social and environmental injustices, this anthology contains contributions that envision alternative ways of exploring and designing more livable futures. Attending to these futures requires a reckoning with a multiplicity of actors and contexts, from institutional norms and regulations, to pedagogies, curricula, programs, digital tools, infrastructures, and architectural environments. Last but not least, attention is drawn to the mechanisms and protocols by which these futures are imagined and shaped. This includes critically examining the ways in which design is talked about, taught, and learned in order to empower future designers to engage with the political issues, cultural conditions, and social and environmental implications of their work.
Elaeis guineensis Jacq. or oil palm is a native species of West Africa. Its oils, extracted from the fruit mesocarp and the kernel are widely used in the food industry, industrial applications, and bioenergy production. Due to its versatility, profitability and growing demand, the global oil palm agroindustry raises concerns regarding deforestation, effects in biodiversity, contamination and related to social issues such as labor conditions, poverty, and social conflicts. In Mexico, the establishment and subsequent growth of the oil palm industry was promoted by past government policies and financial support. In Chiapas the current main producer of the country, the expansion can be also attributed to oil palm resilience to floods, hurricanes, and the economic profitability.
The objective of this study is to evaluate the sustainability status of the oil palm production system within Acapetahua and Villa Comaltitlán Municipalities by analyzing the indicators of sustainability. To achieve this, the Evaluation Framework for Natural Resource Management Systems (MESMIS), was adapted to measure the attributes status of productivity, stability, reliability, resilience, self-management, equity, and adaptability, of the different dimensions of sustainability (environmental, social, political, and economic).
It was identified that MESMIS is an appropriate framework to study oil palm system in Acapetahua and Villa Comaltitlán municipalities. The methodology allowed the identification of critical points, and relevant indicators that include land use and vegetation cover changes, oil palm cashflow, good agricultural practices, farmers´ training, level of participation and farmers´ well-being. As a result, it was identified that vegetation and land use changes were principally from pastures land and previous oil palm plantations, and a positive profitability in the last two years. Soil and water conservation practices are implemented, and farmers have received different trainings principally from social mills, but other good agricultural practices and awareness of social problems should be improved, while the social participation evaluation showed a weak status of the political dimension.
The 16th Annual Meeting of the Sponsoring Group Reinsurance [Förderkreis Rückversicherung] was held 16 June 2023 in Niederkassel, near Cologne. Some 90 representatives of the (re)insurance companies involved in the Sponsoring Group
took part in the meeting, along with guests. Offered for the ninth time as part of the Annual Meeting, the Researchers’ Corner gave the six academic researchers at the Cologne Research Centre for Reinsurance an opportunity to deliver a presentation on the research project in which each is involved in 2023. Over the course of three sessions, the most important results of the scientific studies by the Cologne Research Centre for Reinsurance were presented and discussed.
The heterogeneity of the topics presented reflects the dovetailing of Cologne Research Centre with reinsurance practice.
Catastrophe insurance without premium payment – The concept of contigent liability in Switzerland
(2023)
No later than with the heavy rainfalls of 2021, discussions in Germany have resumed around the introduction of compulsory insurance for natural hazards. Natural hazards exhibit a high potential for loss, and insurance is a building block with which to bolster resilience. In practice, there are already a host of functioning solution concepts to provide cover for natural hazards, including insurance pools and state guarantees. All of the concepts, however, are predicated on payment of an ongoing insurance premium.
In its Renewables 2022 Report, the International Energy Agency (IEA) projects that the share of renewable energies in the global energy mix will increase from 22.8% in 2015 to 38.1% in 2027. This trend goes hand-in-hand with increasing construction of plants for the generation of renewable energies, leading to increased demand for (re)insurance. Comparable to the development of traditional energy sources, the hedging of current risks is a key element in the further development of renewable energies. According to projections by the IEA, by 2027 most of the energy from renewable sources will be generated using photovoltaics or solar as well as onshore and offshore wind.
The prolonged US-China trade tension, initiated in 2017, has led to significant consequences, impacting global supply chains and causing economic tension between the two largest economies. Particularly affecting the automotive sector, the trade war has influenced motor insurance premiums in China, contributing to a declining trend in non-life insurance growth rates from 2017 to 2021. However, a positive outlook is projected for 2023-2026, indicating potential recovery opportunities. The trade war's short-term impacts on the Chinese motor insurance market include increased costs, low premium growth, and economic challenges. In the long term, transformative changes, including market diversification, innovative products, data-driven pricing, and technology-enabled risk prevention, are expected to shape a dynamic and competitive motor insurance landscape in China, offering growth potential despite initial challenges.
In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.
Austria is committed to the net-zero climate goal along with the European Union. This requires all sectors to be decarbonized. Hereby, hydrogen plays a vital role as stated in the national hydrogen strategy. A report commissioned by the Austrian government predicts a minimum hydrogen demand of 16 TWh per year in Austria in 2040. Besides hydrogen imports, domestic production can ensure supply. Hence, this study analyses the levelized cost of hydrogen for an off-grid production plant including a proton exchange membrane electrolyzer, wind power and solar photovoltaics in Austria. In the first step, the capacity factors of the renewable electricity sources are determined by conducting a geographic information system analysis. Secondly, the levelized cost of electricity for wind power and solarphotovoltaics plants in Austria is calculated. Thirdly, the most cost-efficient portfolio of wind power and solar photovoltaics plants is determined using electricity generation profiles with a 10-min granularity. The modelled system variants differ among location, capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. Finally, selected variables are tested for their sensitivities. With the applied model, the hydrogen production cost for decentralized production plants can be calculated for any specific location. The levelized cost of hydrogen estimates range from 3.08 EUR/kg to 13.12 EUR/kg of hydrogen, whereas it was found that the costs are most sensitive to the capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. The novelty of the paper stems from the model applied that calculates the levelized cost of renewable hydrogen in an off-grid hydrogen production system. The model finds a cost-efficient portfolio of directly coupled wind power and solar photovoltaics systems for 80 different variants in an Austria-specific context.