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Institut
- Fakultät Nachhaltigkeit (92)
- Fakultät Wirtschaftswissenschaften (60)
- Institut für Ökologie (IE) (27)
- Nachhaltigkeitsmgmt./-ökologie (18)
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This dissertation concerns the question of how economics can contribute to the analysis of trade-offs between values (or normative objectives). The analysis is illustrated for the case of policies that pursue the goal of sustainability. Methodologically, this is done by reflecting economic concepts in light of philosophical theories and using generic models to analyze trade-offs between particular values. In sum, the work shows how economics can help in analyzing the factual relationships between values by clarifying the set of feasible acts and outcomes. The first paper of this cumulative dissertation concerns the question what a general definition of efficiency with respect to normative objective implies about relationships between two values. In order to conceptualize relationships between values carefully, the analysis distinguishes instrumental from intrinsic values and discusses the question whether there is one intrinsic value (value monism) or many intrinsic values (value pluralism). Next, a small economic model is used to show that there can be different relationships between values such as win-win relationships and trade-offs in value-efficient states if there are three or more values. Further, the distinction between Pareto-efficiency (based on individual preferences) and value-efficiency (which can also include non-preference values) is used to study relationships between values. The second paper uses the definition of sustainability as inter- and intragenerational justice to discuss the relationship between these two objectives. The general aim of this paper is to discuss what economic concepts can contribute to the discussion of tradeoffs between justices. For this, a syntax of the concept of justice is employed, different relationships between justices are defined and economic concepts such as scarcity, efficiency and opportunity costs are transferred to the justice context. One result from this analysis is that there must be a trade-off between these two justices in such respective efficient outcomes. The third paper concerns an intertemporal mechanism leading to the well-known equity-efficiency trade-off in an intergenerational setting. For this, two central characteristics of intergenerational policy making are taken into account: irreversibility and ignorance (or unawareness). A pertinent example is the irreversible use of fossil fuels before and after the discovery of the effect of CO2 emissions on climate change. The trade-off between Pareto-efficiency and intergenerational equity that results from these two characteristics is shown in a model with two non-overlapping generations which use a non-renewable resource. In the model there is initial unawareness about an intergenerational externality from resource use that is only discovered after the irreversible use of the resource. A central result of the paper is the trade-off between intergenerational equity and efficiency that emerges if initially unknown sustainability problems arise after irreversible policies have been enacted. The fourth paper concerns the question what the concept of merit goods can contribute to discussions of sustainability. For this, the history of the concept is discussed, then merit goods are defined and connected to the philosophical literature on different conceptions of well-being. In the next step different challenges and opportunities of merit good arguments are discussed for the sustainability context. For example, it becomes clear that merit good arguments concern conceptions of well-being and do not directly concern the aspect of intergenerational distribution in sustainability problems.
This doctoral dissertation aims to contribute to clarification of the potential of learning for water governance. The goal is to trace and understand the environmental impacts of learning through participation (research aim 1) and adaptive management (research aim 2), and the effect of learning on participation as a governance mode (research aim 3). For this goal, the researcher engages in a predominantly qualitative research design following the case study method. For every specific research aim cases are selected and analysed qualitatively according to conceptual categories and mechanisms which are defined beforehand. Quantitative studies are used to corroborate the results for research aim 1 and 2 in a mixed-method approach to enhance the validity of results. The empirical research context is European water governance, the implementation of the EU Water Framework and EU Floods Directive (WFD, FD) specifically. Eight cases of participatory decision-making across three European countries and five cases of adaptive management in Northern Germany for WFD implementation are examined to identify whether learning in these processes enhanced environmental outcomes. To detect whether governance learning by public officials occurred, the design of participatory processes for FD implementation in ten German federal states is assessed. The findings of research aim 1, understanding learning through participation and its effects on water governance, reveal that participatory planning led to learning through improved understandings at an individual and group level. Learning did, however, hardly shape effective outcomes. In the AM cases (research aim 2) managers and participants of implementing networks improved their knowledge as well as capacities, and spread the results. Nonetheless, environmental improvement was not necessarily linked to ecological learning. Regarding learning about participation as a governance mode (research aim 3) all interviewed public officials in German federal states reported some degree of governance learning, which emerged not systematically but primarily drawing on own experiences and intuition. These findings are condensed into three overarching lessons for learning in water governance: (1) Interactive communication seems to form the overall frame for participant and group learning. Framing of learning experiences turned out to play an important and potentially distorting role, for which professional facilitation and structured knowledge aggregation methods might be an im-portant counterbalance. (2) Learning did not automatically enhance environmental outcomes. It may thus not be an explanatory variable for policy outcomes, but a conditioning or intervening variable related to collective action, motivation for participation, and situating the issue at hand at wider societal levels. (3) The concepts of puzzling and powering might help understand learning as a source for effectiveness in the long-term when complemented with interest-based debates for creat-ing sufficient political agency of policy issues. Learning seen as puzzling processes might instruct acceptance and legitimization for new powering efforts. The perpetuation of learning in systematic ways and structures appears to characterize an alternative to this reflexive and strategic interplay, for which the water-related EU directives provide the basis.
Transforming the International Food Supply – Sustainable Practices in Small Intermediary Businesses
(2021)
The global food system faces many complex challenges, and there is general agreement that a transformation is needed. While localizing food has been proposed as a means to this end, changing global food supply chains may also lead to sustainable food systems. Because most food systems today have an international dimension and are likely to remain connected, on one way or another, to other ones across the globe, it is necessary to find solutions to problems such as exploitation or environmental degradation. Addressing this challenge and the related gap in the literature, this study examines the emerging practices of small intermediary food businesses, which act between agricultural producers and consumers, and may have the potential to advance sustainability in international food supply. Including a systematic review of the literature on food systems change (Study#1), this dissertation adopts a transformational sustainability research methodology, which is solution-oriented, aims to integrate system, target and transformation knowledge, and is characterized by a transdisciplinary research practice. It conceptualizes challenges of international food supply and empirically investigates entrepreneurial solution approaches to address these challenges (Study#2). Two transdisciplinary research projects with small coffee businesses located in Germany, Mexico, and the U.S. were conducted to examine how these approaches could be implemented (Study#3, Study#4, Workshop reports 1+2). Overall, this study shows that challenges in international food supply chains can be conceptualized as negative effects of large geographical and relational distances. It also identifies five entrepreneurial solution approaches specified by twelve sustainability-oriented design principles to address these negative effects. Creating relational proximity between supply chain actors, that is, strong relationships based on knowledge and care, seems to be a key factor to advance sustainability in international food supply.The results also suggest that by building such strong relationships and changing the fundamental principles of international food trade, small intermediary businesses could be important agents in food system transformations.
Transformative learning is increasingly set to become an essential component in sustainability transformation. Despite, little has been done to systematically explore the contribution to sustainability transformation. This learning theory developed decades ago independently of sustainability discourses; however, it provides an analytical framework for understanding the learning processes, outcomes and conditions in individual and social learning towards sustainability transformation. Against this background, the following research question arises: To which extent can transformative learning lead to sustainability transformation? This doctoral work aims to explore transformative learning processes, outcomes, and conditions occurring and advancing towards sustainability transformation of the textile-fashion industry in Mexico. Taking an exploratory approach, the methods employed were literature reviews to untangle concepts and to construct theoretical pillars to support the empirical research design and data analysis. For data collection, snowball-sampling techniques were used to explore the practice field of the textile-fashion industry in Mexico. Qualitative interviews were employed to gather data about the learning experiences of actors. Qualitative and quantitative methods were required to perform the respective data analysis, the qualitative codification of interviewees' responses. Analysis of social media content was also utilised to understand the communication and business practices of projects involved in the transformation of the textile-fashion sector. As a result, this work comprises three articles, one a systematic literature review and two empirical research articles, investigating the transformative learning processes of entrepreneurs in the development of sustainability niches. As for the findings of this doctoral work, the use of transformative learning in sustainability transformation requires a careful study of the theory and its conceptual elements. Regarding the case study, transformative learning is inherent in forming and developing sustainability niches as entrepreneurs venture into them: It is individual prior learning, expectations and actions that initiate the path of sustainability transformation while disorienting dilemmas, critical reflection, and discourse accelerate them. Through these stages, it is when individual learning turns into social learning. On the other hand, based on the multi-level perspective, the interplay between the niche, regime and landscape levels generates a space for sustainability transformation and transformative learning.
This doctoral thesis contributes to the vibrant discourse on boundary-crossing collaboration in the German teacher education system. It offers theoretical advancements, programmatic guidelines, and empirical findings which advocate for a transdisciplinary perspective. In order to do so, the framing paper critically links persistent challenges and current reform processes in the teacher education system with theoretical foundations and conceptual positions of transdisciplinarity. Against this backdrop, four articles provide further insights on: a) how to expand the prevalent systematic of innovation and transfer approaches (top-down, bottom-up, cooperative) by a transdisciplinary perspective, b) outlining guiding principles for the realization of transdisciplinary collaboration in the context of a boundary-crossing research and development project, c) providing empirical findings on effect relationships between transdisciplinary dimensions of integration characteristics, and d) identifying empirical types of actors based on specific assessment patterns towards these characteristics.
To counteract species loss and preserve the remaining biodiversity, with its important ecosystem functioning and services essential to human well-being, there is an urgent need to develop promising and long-term conservation strategies. In order to achieve these goals, extensive research to gain a better understanding of the general mechanisms underlying community diversity is of greatest importance. Especially, the identification of intrinsic ecological and distributional species traits is receiving increased attention in ecology and conservation biology research. Depending on the expression of their traits, species perform particular ecosystem functions and respond in a specific manner to environmental conditions. Although insects make up the largest part of animal diversity, the majority of studies on extinctions have mainly focused on vertebrates. Among invertebrates either charismatic taxa or those targeted by conservation laws have been investigated until now (e.g. butterflies or saproxylic beetles). Being highly species-rich and trait-diverse, ground beetles (Coleoptera: Carabidae) should be even more suitable for conducting trait-based analyses. Thus, using ground beetles as a model taxon, four case studies focusing on the analyses of traits form the basis of this doctoral thesis. The work of this thesis was conducted with the aim of gaining general insights on the influence of species traits on ground beetle community compositions, such as habitat occupancy and species vulnerability to extinction, for instance. An important aspect when investigating species traits is the consideration of confounding factors, such as dependent relations between the different traits. Compiling a large dataset of 555 Central European species, the author identified that dependent relations between the six tested traits of ground beetles (distribution range size, habitat specialization, body size, hind-wing morphology, breeding season and trophic level) are highly common. Across all identified dependent trait relations, the relation between body size and hind wing morphology or range size and hind wing morphology showed the strongest significant dependencies. Since the consideration of trait relations is necessary to provide reliable interpretations, all analyses of this thesis tested several traits simultaneously and considered possible trait interactions. Studies on local communities found specific traits characterizing the local species pools of certain habitat types. Here, the species pools of seven different habitat types (coastal, forest, mountain, open, riparian, wetland and special habitat) were used to determine habitat-specific trait filters. The identified traits, characteristic for certain habitat types, were in most cases in accordance with the previous findings on local communities. Across Germany, the species of frequently disturbed habitat types, namely coastal, riparian and wetland habitats were characterized by small body size, high amount of macroptery, intermediate to high habitat specialization, spring breeding, and predatory feeding behavior. The species of stable habitat types (forest, mountain, and open habitats), however, were found to be generally larger in body size and more frequently breeding in autumn, further displaying greater variations in the other traits. The gained knowledge on the habitat-specific filtering of traits improve our understanding of the organization and assembly of communities, and can thereby help to detect alterations in the habitat-specific species pool due to natural or human-induced environmental changes. Furthermore, traits can provide evidence on species occurrences and vulnerability to extinction. Three case studies of this thesis aimed to gain new insights on this topic, through the investigations on the following research questions: (1) Which traits drive species extinction risks of Central European ground beetle species? (2) How traits influence the species occurrences of 28 forest species within a large area in Central Europe? (3) Whether certain traits are related to long-term population trends of the species pool from an ancient forest in northern Germany? The results indicated, that depending on the habitat type and tested species pool, different traits prove to be good predictors for the vulnerability of species. Nevertheless, across different geographical and taxonomical scales, especially species with small range sizes and high habitat specialization faced a greater risk of extinction. Therefore, the two traits distributional range size and habitat specialization emerge as reliable predictors of ground beetles vulnerability to extinction. Interestingly, body size did not display a consistent response; while increasing body size led to higher extinction risk in riparian, wetland and open habitats and large macropterous species showed higher extinction risks across the entire species pool, smaller species showed long-term population declines in an ancient forest.
Sustainable landscape development is the main goal of decision makers worldwide. Achieving this goal in the long term leads to achieving social, economic and environmental sustainability. Remote sensing has been playing an essential role in monitoring remote areas. This study has employed part of the role of remote sensing in supporting the direction of decision makers towards sustainable landscape development. The study has focused on some of the main elements affecting sustainable environment: land uses, specifically agricultural land uses, water quality, forests, and water hazards such as floods. Three research programs were undertaken to investigate the role of Terrasar-x imagery, as a source of remote sensing data, in monitoring the environment and achieving the previous stated elements. The investigation was intended to investigate the effectiveness of TSX imagery in identifying the cropping pattern of selected study areas by employing a pixel-based supervised maximum likelihood classifier, as published in Paper I, assessment of the efficiency of using TSX imagery in determining land use and the flood risk maps by applying an object-based decision tree classifier as published in Paper II, and determination of the potential of inferential statistics tests such as the two samples Z-test and multivariate analysis, for example Factor Analysis, for identifying the kind of forest canopy, based on the backscattering coefficient of TSX imagery of forest plots, as presented in Paper III. Papers I and II covered two pilot areas in the Lower Saxonian Elbe Valley Biosphere Reserve “das Biosphärenreservat "Niedersächsische Elbtalaue" around Walmsburger Werder and Wehninger Werder. Paper III focused on the Fuhrberger Feld water protection area near Hanover in Germany. The inputs for this research were mainly SAR Imagery and the ground truth data collected from field surveys, in addition to databases, geo-databases and maps. The study presented in Paper I used two filters to decrease speckle noise namely De-Grandi as multi-temporal speckle filter, and Lee as an adaptive filter. A multi-temporal classification method was used to identify the different crops using a pixel-based maximum likelihood classifier. The classification accuracy was assessed based on the external user accuracy for each crop, the external producer accuracy for each crop, the Kappa index and the external total accuracy for the entire classification. Three cropping pattern maps were produced namely the cropping pattern map of Wehninger Werder in 2011 and the cropping pattern maps of Walmsburger Werder in 2010 and in 2011. The study showed that image filtering was essential for enhancing the accuracy of crop classification. The multi-temporal filter De-Grandi enhanced the producer accuracy by about 10% compared to the Lee filter. Furthermore, gathering and utilizing large ground truth data greatly enhanced the accuracy of the classification. The research verified that using sequence images covering the growing season usually improved the classification results. The results exposed the effect of the polarization and demonstrate that the majority of the classifications produced according to the crop calendar had higher total producer accuracy than using all acquisitions. The study demonstrated undertaken in Paper II applied the decision tree object-based classifier in determining the major land uses and the inundation extent areas in 2011 and 2013 using the Lee-filtered imagery. Based on the maps produced for the land uses and inundation areas, the hazard areas due to the floods in 2011 and 2013 were identified. The study illustrated that 95% of the inundated area was classified correctly, that 90% of vegetated lands were accurately determined, and around 80% of the forest and the residential areas were correctly recognized. The research undertaken in Paper III statistically analyzed the backscattering coefficient of the Lee-filtered TSX in some forest plots by the Factor Analysis and two sample Z-test. The study showed that Factor analysis tools succeeded in differentiating between the coniferous forest and the deciduous forest and mixed forest, but failed to discriminate between the deciduous and the mixed forest. On one hand, only one factor was extracted for each sample plot of the coniferous forest with approximately equal loadings during the whole acquisition period from March 2008 to January 2009. On the other hand, two factors were extracted for each deciduous or mixed forest sample plot, where one factor had high loadings during the leaf-on period from May to October, and the other one had high loadings during the leaf-off period from November to April. Furthermore, the research revealed that the two sample Z-test enabled not only differentiation between the deciduous and the mixed forest against the coniferous forest, but also discrimination between deciduous forest and the mixed forest. Statistically significant differences were observed between the mean backscatter values of the HH-polarized acquisitions for the deciduous forest and the mixed forest during the leaf-off period, but no statistically significant difference was found during the leaf-on period. Moreover, plot samples for the deciduous forest had slightly higher mean backscattering coefficients than those for the mixed forest during the leaf-off period.
Urban areas are prone to climate change impacts. Simultaneously the world's population increasingly resides in cities. In this light, there is a growing need to equip urban decision makers with evidence-based climate information tailored to their specific context to adequately adapt to and prepare for future climate change. To construct climate information high-resolution regional climate models and their projections are pivotal. There is a need to move beyond commonly investigated variables, such as temperature and precipitation, to cover a wider breath of possible climate impacts. In this light, the research presented in this thesis is centered around enhancing the understanding about regional-to-local climate change in Berlin and its surroundings, with a focus on humidity. More specifically, following a regional climate modelling and data analysis approach, this research aims to understand the potential of regional climate models, and the possible added value of convection-permitting simulations, to support the development of high-quality climate information for urban regions, to support knowledge-based decision-making. The first part of the thesis investigates what can already be understood with available regional climate model simulations about future climate change in Berlin and its surroundings, particularly with respect to humidity and related variables. Ten EURO-CORDEX model combinations are analyzed, for the RCP8.5 emission scenario during the time period 1970-2100, for the Berlin region. The results are the first to show an urban-rural humidity contrast under a changing climate, simulated by the EURO-CORDEX ensemble, of around 6% relative humidity, and a robust enlarging urban drying effect, of approximately 2-4% relative humidity, in Berlin compared to its surroundings throughout the 21st century. The second part explores how crossing spatial scales from 12.5km to 3km model grid size affects unprecedented humidity extremes and related variables under future climate conditions for Berlin and its surroundings. Based on the unique HAPPI regional climate model dataset, two unprecedented humidity extremes are identified happening under 1.5°C and 2°C global mean warming, respectively SH>0.02 kg/kg and RH<30%. Employing a double-nesting approach, specifically designed for this study, the two humidity extremes are downscaled to the 12.5km grid resolution with the regional climate model REMO, and thereafter to the 3km with the convection-permitting model version of REMO (REMO NH). The findings indicate that the convection-permitting scale mitigates the SH>0.02kg/kg moist extreme and intensifies the RH<30% dry extreme. The multi-variate process analysis shows that the more profound urban drying effect on the convection-permitting resolution is mainly due to better resolving the physical processes related to the land surface scheme and land-atmosphere interactions on the 3km compared to the 12.5km grid resolution. The results demonstrate the added value of the convection-permitting resolution to simulate future humidity extremes in the urban-rural context. The third part of the research investigates the added value of convection-permitting models to simulate humidity related meteorological conditions driving specific climate change impacts, for the Berlin region. Three novel humidity related impact cases are defined for this research: influenza spread and survival; ragweed pollen dispersion; and in-door mold growth. Simulations by the regional climate model REMO are analyzed for the near future (2041-2050) under emission scenario RCP8.5, on the 12.5km and 3km grid resolution. The findings show that the change signal reverses on the convection-permitting resolution for the impact cases pollen, and mold (positive and negative). For influenza, the convection-permitting resolution intensifies the decrease of influenza days under climate change. Longer periods of consecutive influenza and mold days are projected under near-term climate change. The results show the potential of convection-permitting simulations to generate improved information about climate change impacts in urban regions to support decision makers. Generally, all results show an urban drying effect in Berlin compared to its surroundings for relative and specific humidity under climate change, respectively for the urban-rural contrast throughout the 21st century, for the downscaled future extreme conditions, and for the three humidity related impact cases. Added value for the convection-permitting resolution is found to simulate humidity extremes and the meteorological conditions driving the three impacts cases.
Artificial intelligence, most prominently in the form of machine learning, is shaping up to be one of the most transformational technologies of the 21st century. Auditors are among the professions forecasted to be the most affected by artificial intelligence, as the profession encompasses many highly structured and repetitive tasks. Automating such tasks would naturally increase the efficiency of financial statement audits. By allowing auditors to focus on higher value-added tasks, and the capability to analyze large volumes of data at a fracture of the time a human would need, artificial intelligence would also benefit the effectiveness of auditing. Despite these benefits, to this day, the actual adoption of artificial intelligence in the audit domain remains rather limited. The audit profession is highly regulated and has to consider requirements regarding, e.g. the application of professional standards, codes of conduct, and data protection obligations. Hence, the question arises of how audit firms can be supported in their efforts to adopt artificial intelligence and how machine learning systems can be designed to comply with the specific demands of the audit domain. The goal of this dissertation is to better understand the adoption of artificial intelligence in the audit domain and to actively support the adoption of artificial intelligence in auditing based on this understanding. To this end, we employ a mixture of research methods. On the one hand, the research presented here adopts a qualitative approach, examining the adoption of artificial intelligence and other advanced analytical technologies of the audit domain through taxonomy development and grounded theory. The findings of these studies inspire the second stream of work within this dissertation, which adopts a quantitative and design-oriented approach: It focuses on using machine learning to extract information from invoices for tests of details. Tests of details are essential substantive audit procedures used in nearly every audit. This dissertation proposes a new machine learning model architecture for information extraction from invoices, compares different machine learning models, and proposes design principles for machine learning pipelines for an audit application addressing the test of details through action design research.
Increased international compliance with human rights and democracy standards is a core issue for both human rights and democratizing actors as well as for victims of human rights abuse. International human rights organizations (IHROs) are expected to make positive contributions to this end, even though they possess low levels of authority. This authority has been renegotiated multiple times in various reform processes. An oversimplified expectation would have us assume that democracies would want to strengthen IHROs, and that autocracies would seek to weaken them. As the United Nations Human Rights Council (UNHRC) was reformed in 2006, 2007, 2010, and 2011, some autocracies strived to abolish parts of the UNHRC. Other autocracies aimed "merely" to weaken them. Democracies displayed an even larger variance. The question that drives this research work is how we can explain the broad variety of state preferences for strengthening or weakening IHROs. Previous research has mostly concentrated on democracies, leaving autocracies understudied. It also treated countries as black boxes. To account for such shortcomings, first, the author systematically tests the relationship between the UNHRC and its authoritarian and democratic members by means of inferential statistics. Second, he analyzes a bottom-up process inherent to New Liberalism. It scrutinizes the role of domestic societal actors, domestic institutions, as well as pressures on the international stage. The results reveal that societal actors, along with the interplay of wealth and regime type in the international realm, figure as the most important predictors of delegation preferences voiced by autocracies and democracies during the reform of the monitoring bureaucracy Special Procedures of the UNHRC. Societal actors play a more important role in democracies than in autocracies. Institutionalized domestic oversight mechanisms help societal actors to conduct effective lobbying at the domestic level. Oversight mechanisms are more important than the rule of law and electoral institutions. Regarding international coalition building, authoritarian regimes turn out to be better organized than democracies. The author concludes that supporters of strong IHROs shall 1. empower domestic societal actors; 2. disrupt cohesive delegation preferences of authoritarian regimes; and 3. invest in independent domestic oversight mechanisms.oversight mechanisms.