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This dissertation investigates the impact of the economic and fiscal crisis starting in 2008 on EU climate policy-making. While the overall number of adopted greenhouse gas emission reduction policies declined in the crisis aftermath, EU lawmakers decided to introduce new or tighten existing regulations in some important policy domains. Existing knowledge about the crisis impact on EU legislative decision-making cannot explain these inconsistencies. In response, this study develops an actor-centred conceptual framework based on rational choice institutionalism that provides a micro-level link to explain how economic crises translate into altered policy-making patterns. The core theoretical argument draws on redistributive conflicts, arguing that tensions between ‘beneficiaries’ and ‘losers’ of a regulatory initiative intensify during economic crises and spill over to the policy domain. To test this hypothesis and using social network analysis, this study analyses policy processes in three case studies: The introduction of carbon dioxide emission limits for passenger cars, the expansion of the EU Emissions Trading System to aviation, and the introduction of a regulatory framework for biofuels. The key finding is that an economic shock causes EU policy domains to polarise politically, resulting in intensified conflict and more difficult decision-making. The results also show that this process of political polarisation roots in the industry that is the subject of the regulation, and that intergovernmental bargaining among member states becomes more important, but also more difficult in times of crisis.
The Himalayas are a region that is most dependent, but also frequently prone to hazards from changing meltwater resources. This mountain belt hosts the highest mountain peaks on earth, has the largest reserve of ice outside the polar regions, and is home to a rapidly growing population in recent decades. One source of hazard has attracted scientific research in particular in the past two decades: glacial lake outburst floods (GLOFs) occurred rarely, but mostly with fatal and catastrophic consequences for downstream communities and infrastructure. Such GLOFs can suddenly release several million cubic meters of water from naturally impounded meltwater lakes. Glacial lakes have grown in number and size by ongoing glacial mass losses in the Himalayas. Theory holds that enhanced meltwater production may increase GLOF frequency, but has never been tested so far. The key challenge to test this notion are the high altitudes of >4000 m, at which lakes occur, making field work impractical. Moreover, flood waves can attenuate rapidly in mountain channels downstream, so that many GLOFs have likely gone unnoticed in past decades. Our knowledge on GLOFs is hence likely biased towards larger, destructive cases, which challenges a detailed quantification of their frequency and their response to atmospheric warming. Robustly quantifying the magnitude and frequency of GLOFs is essential for risk assessment and management along mountain rivers, not least to implement their return periods in building design codes.
Motivated by this limited knowledge of GLOF frequency and hazard, I developed an algorithm that efficiently detects GLOFs from satellite images. In essence, this algorithm classifies land cover in 30 years (~1988–2017) of continuously recorded Landsat images over the Himalayas, and calculates likelihoods for rapidly shrinking water bodies in the stack of land cover images. I visually assessed such detected tell-tale sites for sediment fans in the river channel downstream, a second key diagnostic of GLOFs. Rigorous tests and validation with known cases from roughly 10% of the Himalayas suggested that this algorithm is robust against frequent image noise, and hence capable to identify previously unknown GLOFs. Extending the search radius to the entire Himalayan mountain range revealed some 22 newly detected GLOFs. I thus more than doubled the existing GLOF count from 16 previously known cases since 1988, and found a dominant cluster of GLOFs in the Central and Eastern Himalayas (Bhutan and Eastern Nepal), compared to the rarer affected ranges in the North. Yet, the total of 38 GLOFs showed no change in the annual frequency, so that the activity of GLOFs per unit glacial lake area has decreased in the past 30 years. I discussed possible drivers for this finding, but left a further attribution to distinct GLOF-triggering mechanisms open to future research.
This updated GLOF frequency was the key input for assessing GLOF hazard for the entire Himalayan mountain belt and several subregions. I used standard definitions in flood hydrology, describing hazard as the annual exceedance probability of a given flood peak discharge [m3 s-1] or larger at the breach location. I coupled the empirical frequency of GLOFs per region to simulations of physically plausible peak discharges from all existing ~5,000 lakes in the Himalayas. Using an extreme-value model, I could hence calculate flood return periods. I found that the contemporary 100-year GLOF discharge (the flood level that is reached or exceeded on average once in 100 years) is 20,600+2,200/–2,300 m3 s-1 for the entire Himalayas. Given the spatial and temporal distribution of historic GLOFs, contemporary GLOF hazard is highest in the Eastern Himalayas, and lower for regions with rarer GLOF abundance. I also calculated GLOF hazard for some 9,500 overdeepenings, which could expose and fill with water, if all Himalayan glaciers have melted eventually. Assuming that the current GLOF rate remains unchanged, the 100-year GLOF discharge could double (41,700+5,500/–4,700 m3 s-1), while the regional GLOF hazard may increase largest in the Karakoram.
To conclude, these three stages–from GLOF detection, to analysing their frequency and estimating regional GLOF hazard–provide a framework for modern GLOF hazard assessment. Given the rapidly growing population, infrastructure, and hydropower projects in the Himalayas, this thesis assists in quantifying the purely climate-driven contribution to hazard and risk from GLOFs.
A contemporary challenge in Ecology and Evolutionary Biology is to anticipate the fate of populations of organisms in the context of a changing world. Climate change and landscape changes due to anthropic activities have been of major concern in the contemporary history. Organisms facing these threats are expected to respond by local adaptation (i.e., genetic changes or phenotypic plasticity) or by shifting their distributional range (migration). However, there are limits to their responses. For example, isolated populations will have more difficulties in developing adaptive innovations by means of genetic changes than interconnected metapopulations. Similarly, the topography of the environment can limit dispersal opportunities for crawling organisms as compared to those that rely on wind. Thus, populations of species with different life history strategy may differ in their ability to cope with changing environmental conditions. However, depending on the taxon, empirical studies investigating organisms’ responses to environmental change may become too complex, long and expensive; plus, complications arising from dealing with endangered species. In consequence, eco-evolutionary modeling offers an opportunity to overcome these limitations and complement empirical studies, understand the action and limitations of underlying mechanisms, and project into possible future scenarios. In this work I take a modeling approach and investigate the effect and relative importance of evolutionary mechanisms (including phenotypic plasticity) on the ability for local adaptation of populations with different life strategy experiencing climate change scenarios. For this, I performed a review on the state of the art of eco-evolutionary Individual-Based Models (IBMs) and identify gaps for future research. Then, I used the results from the review to develop an eco-evolutionary individual-based modeling tool to study the role of genetic and plastic mechanisms in promoting local adaption of populations of organisms with different life strategies experiencing scenarios of climate change and environmental stochasticity. The environment was simulated through a climate variable (e.g., temperature) defining a phenotypic optimum moving at a given rate of change. The rate of change was changed to simulate different scenarios of climate change (no change, slow, medium, rapid climate change). Several scenarios of stochastic noise color resembling different climatic conditions were explored. Results show that populations of sexual species will rely mainly on standing genetic variation and phenotypic plasticity for local adaptation. Population of species with relatively slow growth rate (e.g., large mammals) – especially those of small size – are the most vulnerable, particularly if their plasticity is limited (i.e., specialist species). In addition, whenever organisms from these populations are capable of adaptive plasticity, they can buffer fitness losses in reddish climatic conditions. Likewise, whenever they can adjust their plastic response (e.g., bed-hedging strategy) they will cope with bluish environmental conditions as well. In contrast, life strategies of high fecundity can rely on non-adaptive plasticity for their local adaptation to novel environmental conditions, unless the rate of change is too rapid. A recommended management measure is to guarantee interconnection of isolated populations into metapopulations, such that the supply of useful genetic variation can be increased, and, at the same time, provide them with movement opportunities to follow their preferred niche, when local adaptation becomes problematic. This is particularly important for bluish and reddish climatic conditions, when the rate of change is slow, or for any climatic condition when the level of stress (rate of change) is relatively high.
The unprecedented increase in atmospheric concentrations of carbon dioxide (CO2) and other greenhouse gases (GHG) by anthropogenic activities since the Industrial Revolution impacts on various earth system processes, commonly referred to as `climate change´ (CC). CC faces aquatic ecosystems with extreme abiotic perturbations that potentially alter the interrelations between functional autotrophic and heterotrophic plankton groups. These relations, however, modulate biogeochemical cycling and mediate the functioning of aquatic ecosystems as C sources or sinks to the atmosphere. The aim of this thesis was therefore to investigate how different aspects of CC influence community composition and functioning of pelagic heterotrophic bacteria. These organisms constitute a major component of biogeochemical cycling and largely determine the balance between autotrophic and heterotrophic processes.
Due to the vast amount of potential CC impacts, this thesis focuses on the following two aspects: (1) Increased exchange of CO2 across the atmosphere-water interface and reaction of CO2 with seawater leads to profound shifts in seawater carbonate chemistry, commonly termed as `ocean acidification´ (OA), with consequences for organism physiology and the availability of dissolved inorganic carbon (DIC) in seawater. (2) The increase in atmospheric GHG concentration impacts on the efficiency with which the Earth cools to space, affecting global surface temperature and climate. With ongoing CC, shifts in frequency and severity of episodic weather events, such as storms, are expected that in particular might affect lake ecosystems by disrupting thermal summer stratification. Both aspects of CC were studied at the ecosystem-level in large-volume mesocosm experiments by using the Kiel Off-shore Mesocosms for Future Ocean Simulations (KOSMOS) deployed at different coastal marine locations, and the LakeLab facility in Lake Stechlin.
We evaluated the impact of OA on heterotrophic bacterial metabolism in a brackish coastal ecosystem during low-nutrient summer months in the Baltic Sea. There are several in situ experiments that already assessed potential OA-induced changes in natural plankton communities at diverse spatial and seasonal conditions. However, most studies were performed at high phytoplankton biomass conditions, partly provoked by nutrient amendments. Our study highlights potential OA effects at low-nutrient conditions that are representative for most parts of the ocean and of particular interest in current OA research. The results suggest that during extended periods at low-nutrient concentrations, increasing pCO2 levels indirectly impact the growth balance of heterotrophic bacteria via trophic bacteria-phytoplankton interactions and shift the ecosystem to a more autotrophic system.
Further work investigated how OA affects heterotrophic bacterial dissolved organic matter (DOM) transformation in two mesocsom studies, performed at different nutrient conditions. We observed similar succession patterns for individual compound pools during a phytoplankton bloom and subsequent accumulation of these compounds irrespective of the pCO2 treatment. Our results indicate that OA-induced changes in the dynamics of bacterial DOM transformation and potential impacts on DOM quality are unlikely. In addition, there have been no indications that in dependence of nutrient conditions, different amounts of photosynthetic organic matter are channelled into the more recalcitrant DOM pool. This provides novel insights into the general dynamics of the marine DOM pool.
A fourth enclosure experiment in oligo-mesotrophic Lake Stechlin assessed the impact of a severe summer storm on lake bacterial communities during thermal stratification by artificially mixing. Mixing disrupted and lowered the thermocline, increasing the upper mixed layer and substantially changed water physical-chemical variables. Deep water entrainment and associated changes in water physical-chemical variables significantly affected relative bacterial abundances for about one week. Afterwards a pronounced cyanobacterial bloom developed in response to mixing which affected community assembly of heterotrophic bacteria. Colonization and mineralization of senescent phytoplankton cells by heterotrophic bacteria largely determined C-sequestration to the sediment. About six weeks after mixing, bacterial communities and measured activity parameters converged to control conditions. As such, summer storms have the potential to affect bacterial communities for a prolonged period during summer stratification. The results highlight effects on community assembly and heterotrophic bacterial metabolism that are associated to entrainment of deep water into the mixed water layer and assess consequences of an episodic disturbance event for the coupling between bacterial metabolism and autochthonous DOM production in large volume clear-water lakes.
Altogether, this doctoral thesis reveales substantial sensitivities of heterotrophic bacterial metabolism and community structure in response to OA and a simulated summer storm event, which should be considered when assessing the impact of climate change on marine and lake ecosystems.
In a changing world facing several direct or indirect anthropogenic challenges the freshwater resources are endangered in quantity and quality. An excessive supply of nutrients, for example, can cause disproportional phytoplankton development and oxygen deficits in large rivers, leading to failure of the aims requested by the Water Framework Directive (WFD). Such problems can be observed in many European river catchments including the Elbe basin, and effective measures for improving water quality status are highly appreciated.
In water resources management and protection, modelling tools can help to understand the dominant nutrient processes and to identify the main sources of nutrient pollution in a watershed. They can be effective instruments for impact assessments investigating the effects of changing climate or socio-economic conditions on the status of surface water bodies, and for testing the usefulness of possible protection measures. Due to the high number of interrelated processes, ecohydrological model approaches containing water quality components are more complex than the pure hydrological ones, and their setup and calibration require more efforts. Such models, including the Soil and Water Integrated Model (SWIM), still need some further development and improvement.
Therefore, this cumulative dissertation focuses on two main objectives: 1) the approach-related objectives aiming in the SWIM model improvement and further development regarding nutrient (nitrogen and phosphorus) process description, and 2) the application-related objectives in meso- to large-scale Elbe river basins to support adaptive river basin management in view of possible future changes. The dissertation is based on five scientific papers published in international journals and dealing with these research questions.
Several adaptations were implemented in the model code to improve the representation of nutrient processes including a simple wetland approach, an extended by ammonium nitrogen cycle in the soils, as well as a detailed in-stream module, simulating algal growth, nutrient transformation processes and oxygen conditions in the river reaches, mainly driven by water temperature and light. Although this new approaches created a highly complex ecohydrological model with a large number of additional calibration parameters and rising uncertainty, the calibration and validation of the SWIM model enhanced by the new approaches in selected subcatchment and the entire Elbe river basin delivered satisfactory to good model results in terms of criteria of fit. Thus, the calibrated and validated model provided a sound base for the assessment of possible future changes and impacts in climate, land use and management in the Elbe river (sub)basin(s).
The new enhanced modelling approach improved the applicability of the SWIM model for the WFD related research questions, where the ability to consider biological water quality components (such as phytoplankton) is important. It additionally enhanced its ability to simulate the behaviour of nutrients coming mainly from point sources (e.g. phosphate phosphorus). Scenario results can be used by decision makers and stakeholders to find and understand future challenges and possible adaptation measures in the Elbe river basin.
This is a cumulative dissertation comprising three original studies (one published, one in revision, one submitted; Effective December 2017) investigating how reptile species in arid Australia respond to various climatic parameters at different spatial scales and analysing the two potential main underlying mechanisms: thermoregulatory behaviour and species interactions. This dissertation combines extensive individual-based field data across trophic levels, selected field experiments, statistical analyses, and predictive modelling techniques. Mechanisms and processes detected in this dissertation can now be used to predict potential future changes in the community of arid-zone lizards. This knowledge will help improving our fundamental understanding of the consequences of global change and thereby prevent biodiversity loss in a vulnerable ecosystem.
In the wake of 21st century, humanity witnessed a phenomenal raise of urban agglomerations as powerhouses for innovation and socioeconomic growth. Driving much of national (and in few instances even global) economy, such a gargantuan raise of cities is also accompanied by subsequent increase in energy, resource consumption and waste generation. Much of anthropogenic transformation of Earth's environment in terms of environmental pollution at local level to planetary scale in the form of climate change is currently taking place in cities. Projected to be crucibles for entire humanity by the end of this century, the ultimate fate of humanity predominantly lies in the hands of technological innovation, urbanites' attitudes towards energy/resource consumption and development pathways undertaken by current and future cities. Considering the unparalleled energy, resource consumption and emissions currently attributed to global cities, this thesis addresses these issues from an efficiency point of view. More specifically, this thesis addresses the influence of population size, density, economic geography and technology in improving urban greenhouse gas (GHG) emission efficiency and identifies the factors leading to improved eco-efficiency in cities. In order to investigate the in uence of these factors in improving emission and resource efficiency in cities, a multitude of freely available datasets were coupled with some novel methodologies and analytical approaches in this thesis.
Merging the well-established Kaya Identity to the recently developed urban scaling laws, an Urban Kaya Relation is developed to identify whether large cities are more emission efficient and the intrinsic factors leading to such (in)efficiency. Applying Urban Kaya Relation to a global dataset of 61 cities in 12 countries, this thesis identifed that large cities in developed regions of the world will bring emission efficiency gains because of the better technologies implemented in these cities to produce and utilize energy consumption while the opposite is the case for cities in developing regions. Large cities in developing countries are less efficient mainly because of their affluence and lack of efficient technologies. Apart from the in uence of population size on emission efficiency, this thesis identified the crucial role played by population density in improving building and on-road transport sector related emission efficiency in cities. This is achieved by applying the City Clustering Algorithm (CCA) on two different gridded land use datasets and a standard emission inventory to attribute these sectoral emissions to all inhabited settlements in the USA. Results show that doubling the population density would entail a reduction in the total CO2 emissions in buildings and on-road sectors typically by at least 42 %. Irrespective of their population size and density, cities are often blamed for their intensive resource consumption that threatens not only local but also global sustainability. This thesis merged the concept of urban metabolism with benchmarking and identified cities which are eco-efficient. These cities enable better socioeconomic conditions while being less burden to the environment. Three environmental burden indicators (annual average NO2 concentration, per capita waste generation and water consumption) and two socioeconomic indicators (GDP per capita and employment ratio) for 88 most populous European cities are considered in this study. Using two different non-parametric ranking methods namely regression residual ranking and Data Envelopment Analysis (DEA), eco-efficient cities and their determining factors are identified. This in-depth analysis revealed that mature cities with well-established economic structures such as Munich, Stockholm and Oslo are eco-efficient. Further, correlations between objective eco-efficiency ranking with each of the indicator rankings and the ranking of urbanites' subjective perception about quality of life are analyzed. This analysis revealed that urbanites' perception about quality of life is not merely confined to the socioeconomic well-being but rather to their combination with lower environmental burden.
In summary, the findings of this dissertation has three general conclusions for improving emission and ecological efficiency in cities. Firstly, large cities in emerging nations face a huge challenge with respect to improving their emission efficiency. The task in front of these cities is threefold: (1) deploying efficient technologies for the generation of electricity and improvement of public transportation to unlock their leap frogging potential, (2) addressing the issue of energy poverty and (3) ensuring that these cities do not develop similar energy consumption patterns with infrastructure lock-in behavior similar to those of cities in developed regions. Secondly, the on-going urban sprawl as a global phenomenon will decrease the emission efficiency within the building and transportation sector. Therefore, local policy makers should identify adequate fiscal and land use policies to curb urban sprawl. Lastly, since mature cities with well-established economic structures are more eco-efficient and urbanites' perception re ects its combination with decreasing environmental burden; there is a need to adopt and implement strategies which enable socioeconomic growth in cities whilst decreasing their environment burden.
More than a billion people rely on water from rivers sourced in High Mountain Asia (HMA), a significant portion of which is derived from snow and glacier melt. Rural communities are heavily dependent on the consistency of runoff, and are highly vulnerable to shifts in their local environment brought on by climate change. Despite this dependence, the impacts of climate change in HMA remain poorly constrained due to poor process understanding, complex terrain, and insufficiently dense in-situ measurements.
HMA's glaciers contain more frozen water than any region outside of the poles. Their extensive retreat is a highly visible and much studied marker of regional and global climate change. However, in many catchments, snow and snowmelt represent a much larger fraction of the yearly water budget than glacial meltwaters. Despite their importance, climate-related changes in HMA's snow resources have not been well studied.
Changes in the volume and distribution of snowpack have complex and extensive impacts on both local and global climates. Eurasian snow cover has been shown to impact the strength and direction of the Indian Summer Monsoon -- which is responsible for much of the precipitation over the Indian Subcontinent -- by modulating earth-surface heating. Shifts in the timing of snowmelt have been shown to limit the productivity of major rangelands, reduce streamflow, modify sediment transport, and impact the spread of vector-borne diseases. However, a large-scale regional study of climate impacts on snow resources had yet to be undertaken.
Passive Microwave (PM) remote sensing is a well-established empirical method of studying snow resources over large areas. Since 1987, there have been consistent daily global PM measurements which can be used to derive an estimate of snow depth, and hence snow-water equivalent (SWE) -- the amount of water stored in snowpack. The SWE estimation algorithms were originally developed for flat and even terrain -- such as the Russian and Canadian Arctic -- and have rarely been used in complex terrain such as HMA.
This dissertation first examines factors present in HMA that could impact the reliability of SWE estimates. Forest cover, absolute snow depth, long-term average wind speeds, and hillslope angle were found to be the strongest controls on SWE measurement reliability. While forest density and snow depth are factors accounted for in modern SWE retrieval algorithms, wind speed and hillslope angle are not. Despite uncertainty in absolute SWE measurements and differences in the magnitude of SWE retrievals between sensors, single-instrument SWE time series were found to be internally consistent and suitable for trend analysis.
Building on this finding, this dissertation tracks changes in SWE across HMA using a statistical decomposition technique. An aggregate decrease in SWE was found (10.6 mm/yr), despite large spatial and seasonal heterogeneities. Winter SWE increased in almost half of HMA, despite general negative trends throughout the rest of the year. The elevation distribution of these negative trends indicates that while changes in SWE have likely impacted glaciers in the region, climate change impacts on these two pieces of the cryosphere are somewhat distinct.
Following the discussion of relative changes in SWE, this dissertation explores changes in the timing of the snowmelt season in HMA using a newly developed algorithm. The algorithm is shown to accurately track the onset and end of the snowmelt season (70% within 5 days of a control dataset, 89% within 10). Using a 29-year time series, changes in the onset, end, and duration of snowmelt are examined. While nearly the entirety of HMA has experienced an earlier end to the snowmelt season, large regions of HMA have seen a later start to the snowmelt season. Snowmelt periods have also decreased in almost all of HMA, indicating that the snowmelt season is generally shortening and ending earlier across HMA.
By examining shifts in both the spatio-temporal distribution of SWE and the timing of the snowmelt season across HMA, we provide a detailed accounting of changes in HMA's snow resources. The overall trend in HMA is towards less SWE storage and a shorter snowmelt season. However, long-term and regional trends conceal distinct seasonal, temporal, and spatial heterogeneity, indicating that changes in snow resources are strongly controlled by local climate and topography, and that inter-annual variability plays a significant role in HMA's snow regime.
Understanding the distribution of species is fundamental for biodiversity conservation, ecosystem management, and increasingly also for climate impact assessment. The presence of a species in a given site depends on physiological limitations (abiotic factors), interactions with other species (biotic factors), migratory or dispersal processes (site accessibility) as well as the continuing
effects of past events, e.g. disturbances (site legacy). Existing approaches to predict species distributions either (i) correlate observed species occurrences with environmental variables describing abiotic limitations, thus ignoring biotic interactions, dispersal and legacy effects (statistical species distribution model, SDM); or (ii) mechanistically model the variety of processes determining species distributions (process-based model, PBM). SDMs are widely used due to their easy applicability and ability to handle varied data qualities. But they fail to reproduce the dynamic response of species distributions to changing conditions. PBMs are expected to be superior in this respect, but they need very specific data unavailable for many species, and are often more complex and require more computational effort. More recently, hybrid models link the two approaches to combine their respective strengths.
In this thesis, I apply and compare statistical and process-based approaches to predict species distributions, and I discuss their respective limitations, specifically for applications in changing environments. Detailed analyses of SDMs for boreal tree species in Finland reveal that nonclimatic predictors - edaphic properties and biotic interactions - are important limitations at the treeline, contesting the assumption of unrestricted, climatically induced range expansion. While the estimated SDMs are successful within their training data range, spatial and temporal model transfer fails. Mapping and comparing sampled predictor space among data subsets identifies spurious extrapolation as the plausible explanation for limited model transferability. Using these findings, I analyze the limited success of an established PBM (LPJ-GUESS) applied to the same problem. Examination of process representation and parameterization in the PBM identifies implemented processes to adjust (competition between species, disturbance) and missing processes that are crucial in boreal forests (nutrient limitation, forest management). Based on climatic correlations shifting over time, I stress the restricted temporal transferability of bioclimatic limits used in LPJ-GUESS and similar PBMs. By critically assessing the performance of SDM and PBM in this application, I demonstrate the importance of understanding the limitations of the
applied methods.
As a potential solution, I add a novel approach to the repertoire of existing hybrid models. By simulation experiments with an individual-based PBM which reproduces community dynamics resulting from biotic factors, dispersal and legacy effects, I assess the resilience of coastal vegetation to abrupt hydrological changes. According to the results of the resilience analysis, I then modify temporal SDM predictions, thereby transferring relevant process detail from PBM to
SDM. The direction of knowledge transfer from PBM to SDM avoids disadvantages of current hybrid models and increases the applicability of the resulting model in long-term, large-scale applications. A further advantage of the proposed framework is its flexibility, as it is readily extended to other model types, disturbance definitions and response characteristics.
Concluding, I argue that we already have a diverse range of promising modelling tools at hand, which can be refined further. But most importantly, they need to be applied more thoughtfully. Bearing their limitations in mind, combining their strengths and openly reporting underlying assumptions and uncertainties is the way forward.
Der Klimawandel
(2016)
Was ist Gerechtigkeit? Wie könnten gerechte Regelungen aussehen für die Katastrophen und Leiden, die der Klimawandel auslöst bzw. auslösen wird? Diese sind häufig ungerecht, weil sie oft deutlich stärker diejenigen treffen, die am wenigsten zur Klimaveränderung beigetragen haben.
Doch was genau verstehen wir unter dem Schlagwort: ‚Klimawandel‘? Und kann dieser wirklich den Menschen direkt treffen? Ein kurzer naturwissenschaftlicher Abriss klärt hier die wichtigsten Fragen.
Da es sich hierbei um eine philosophische Arbeit handelt, muss zunächst geklärt werden, ob der Mensch überhaupt die Ursache von so etwas sein kann wie z.B. der Klimaerwärmung. Robert Spaemanns These dazu ist, dass der Mensch durch seinen freien Willen mit seinen Einzelhandlungen das Weltgeschehen verändern kann. Hans Jonas fügt dem hinzu, dass wir durch diese Fähigkeit, verantwortlich sind für die gewollten und ungewollten Folgen unserer Handlungen.
Damit wäre aus naturwissenschaftlicher Sicht (1. Teil der Arbeit) und aus philosophischer Sicht (Anfang 2. Teil) geklärt, dass der Mensch mit größter Wahrscheinlichkeit die Ursache des Klimawandels ist und diese Verursachung moralische Konsequenzen für ihn hat.
Ein philosophischer Gerechtigkeitsbegriff wird aus der Kantischen Rechts- und Moralphilosophie entwickelt, weil diese die einzige ist, die dem Menschen überhaupt ein Recht auf Rechte zusprechen kann. Diese entspringt der transzendentalen Freiheitsfähigkeit des Menschen, weshalb jedem das Recht auf Rechte absolut und immer zukommt. Gleichzeitig mündet Kants Philosophie wiederum in dem Freiheitsgedanken, indem Gerechtigkeit nur existiert, wenn alle Menschen gleichermaßen frei sein können.
Was heißt das konkret? Wie könnte Gerechtigkeit in der Realität wirklich umgesetzt werden? Die Realisierung schlägt zwei Grundrichtungen ein. John Rawls und Stefan Gosepath beschäftigen sich u.a. eingehend mit der prozeduralen Gerechtigkeit, was bedeutet, dass gerechte Verfahren gefunden werden, die das gesellschaftliche Zusammenleben regeln. Das leitende Prinzip hierfür ist vor allem: ein Mitbestimmungsrecht aller, so dass sich im Prinzip alle Bürger ihre Gesetze selbst geben und damit frei handeln.
In Bezug auf den Klimawandel steht die zweite Ausrichtung im Vordergrund – die distributive oder auch Verteilungs-Gerechtigkeit. Materielle Güter müssen so aufgeteilt werden, dass auch trotz empirischer Unterschiede alle Menschen als moralische Subjekte anerkannt werden und frei sein können.
Doch sind diese philosophischen Schlussfolgerungen nicht viel zu abstrakt, um auf ein ebenso schwer fassbares und globales Problem wie den Klimawandel angewendet zu werden? Was könnte daher eine Klimagerechtigkeit sein?
Es gibt viele Gerechtigkeitsprinzipien, die vorgeben, eine gerechte Grundlage für die Klimaprobleme zu bieten wie z.B. das Verursacherprinzip, das Fähigkeitsprinzip oder das Grandfathering-Prinzip, bei dem die Hauptverursacher nach wie vor am meisten emittieren dürfen (dieses Prinzip leitete die bisherigen internationalen Verhandlungen).
Das Ziel dieser Arbeit ist, herauszufinden, wie die Klimaprobleme gelöst werden können, so dass für alle Menschen unter allen Umständen die universellen Menschenrechte her- und sichergestellt werden und diese frei und moralisch handeln können.
Die Schlussfolgerung dieser Arbeit ist, dass Kants Gerechtigkeitsbegriff durch eine Kombination des Subsistenzemissions-Rechts, des Greenhouse-Development-Rights-Principles (GDR-Prinzip) und einer internationalen Staatlichkeit durchgesetzt werden könnte.
Durch das Subsistenzemissions-Recht hat jeder Mensch das Recht, so viel Energie zu verbrauchen und damit zusammenhängende Emissionen zu produzieren, dass er ein menschenwürdiges Leben führen kann. Das GDR-Prinzip errechnet den Anteil an der weltweiten Gesamtverantwortung zum Klimaschutz eines jeden Landes oder sogar eines jeden Weltbürgers, indem es die historischen Emissionen (Klimaschuld) zu der jetzigen finanziellen Kapazität des Landes/ des Individuums (Verantwortungsfähigkeit) hinzuaddiert. Die Implementierung von internationalen Gremien wird verteidigt, weil es ein globales, grenzüberschreitendes Problem ist, dessen Effekte und dessen Verantwortung globale Ausmaße haben.
Ein schlagendes Argument für fast alle Klimaschutzmaßnahmen ist, dass sie Synergien aufweisen zu anderen gesellschaftlichen Bereichen aufweisen wie z.B. Gesundheit und Armutsbekämpfung, in denen auch noch um die Durchsetzung unserer Menschenrechte gerungen wird.
Ist dieser Lösungsansatz nicht völlig utopisch?
Dieser Vorschlag stellt für die internationale Gemeinschaft eine große Herausforderung dar, wäre jedoch die einzig gerechte Lösung unserer Klimaprobleme. Des Weiteren wird an dem Kantischen Handlungsgrundsatz festgehalten, dass das ewige Streben auf ideale Ziele hin, die beste Verwirklichung dieser durch menschliche, fehlbare Wesen ist.