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Climate change is a major threat to biodiversity and ecologists have identified necessary adaptation strategies. However, little research has been conducted so far on the economics of climate adaptation for biodiversity conservation. Three challenges arise from an economic perspective: How to (1) assess the impact of climate change on the cost-effectiveness of conservation, (2) consider the increasing uncertainty, and (3) evaluate conservation policy instruments under climate change. Addressing these challenges provides a thus far largely unexplored perspective on the economics of biodiversity conservation. This perspective relies on novel methodologies and provides policy-relevant insights.
In this thesis, these challenges are addressed in eight articles. Chapter 2 presents a novel economic evaluation framework to assess policy instruments for climate adaptation. Specific criteria are developed, their relevance for different strategies is assessed and suitable instruments identified.
Chapters 3 and 4 have a methodological focus as two climate-ecological-economic (CEE) models are developed. Chapter 3 presents an applied model integrating detailed sub-models able to assess the cost-effective spatio-temporal allocation of conservation measures. In chapter 4, methods from operations research are developed further to identify optimal time series of reserve networks. In both chapters, cost-effective conservation plans are identified in case study applications.
In chapter 5, CEE modelling is applied to examine the role of uncertainties regarding future climatic conditions. It is found that a trade-off between expected performance and robustness emerges in the case study in the future.
In chapters 6 to 8, CEE modelling is used to assess policy instruments under climate change. Chapter 6 examines an agri-environment scheme: cost-effectiveness requires flexibility in adapting the timing of conservation measures due to species’ adaptations and changes in costs. Chapter 7 examines two versions of land purchase: a “no sale” policy which prohibits sales for ecological reasons and a “sale” policy to enhance spatial flexibility for adaptation. A new trade-off is identified: while “no sale” mainly increases habitat permanence of expanding habitat types, “sale” improves the outcome for increasingly threatened habitat types. Chapter 8 is novel in its comparative analysis of two policy instruments considering spatial and management flexibility in a case study. It is found that in the case study, conservation contracts are more cost-effective than land purchase, but that the relative suitability switches when the conservation agency is able to capture producer rents.
Finally, chapter 9 uses the results of chapters 3 and 5 to develop an innovative teaching tool for students to learn about cost-effective biodiversity conservation under climate change.
This dissertation is a compilation of four self-contained research articles that focus on selected subjects in the field of energy economics.
The first article focuses on the competitiveness of offshore wind in mature markets. In this work, we harmonise auction results based on the auction design features. We show that offshore wind power generation can be considered commercially competitive in mature markets without subsidy. Furthermore, once auction results are harmonised, we observe similar expected revenue streams of wind farms across countries. This finding means that different auction designs can fairly reflect the actual costs of developing wind farms and thus translate cost reductions into lower bids.
The second article explores the impacts of uncertainty in integrated electricity and gas system optimization models. We address the trade-off that the energy research community faces on a daily basis, i.e., whether to neglect uncertainty when constructing an energy system model and accept a suboptimal solution or to incorporate uncertainty and increase model complexity. Our research aims to bring a systematic understanding of which parametric uncertainties most substantially affect long-term planning decisions in energy system models.
In the third article, we focus on seasonal flexibility in the European natural gas market. We develop a market optimization model to simulate the operation of the gas market over a long period. This allows us to explore structural trends in market development, which are driven by changing supply and demand fundamentals. Our work contributes to the methodological question of how to measure the contributions of different flexibility options.
Finally, the fourth article investigates the value of Projects of Common Interest—gas infrastructure projects supported by EU public funds—in maintaining gas system resilience amid cold-winter demand spikes and supply shortages. For this purpose, we develop the first application of adaptive robust optimization to gas infrastructure expansion planning. The model endogenously identifies the unfortunate realizations of unknown parameters and suggests the optimal investments strategies to address them. We find that (i) robust solutions point to consistent preferences for specific infrastructure projects, (ii) the real-world construction efforts have been focused on the most promising projects, and (iii) most projects are unlikely to be realized without financial support.
The German Armed Forces provide an operation contingent to support the North Atlantic Treaty Organization (NATO) Response Force (NRF). For this purpose, a „warehouse” containing accommodations, food supplies, medical supplies, and spare parts for the systems has to be available. Such a warehouse is restricted in weight, in order to be quickly movable in an upcoming deployment situation. It should be able to supply the NRF troops for a certain amount of time (e.g., one month) without re-supply from the outside. To ensure optimal use of such a restricted warehouse, we developed the computer program „The OPtimization of a Spare Parts Inventory” (TOPSPIN) to find an optimal mix of spare parts to restore a set of systems to functionality. Each system is composed of several parts, and it can only be used again in the mission if all broken parts are replaced. The failure rate of the individual parts follows a given random distribution, and during deployment it is expected to be higher than in the homeland. Due to the stochastic nature of the problem, we generate scenarios that simulate the actual failure of the parts. The backbone of TOPSPIN is a mixed-integer linear program that determines an optimal, scenario-robust mix of spare parts and is solved using standard state-of-the-art numerical solvers. Using input data provided by the Logistikzentrum, we analyze how many scenarios need to be generated in order to determine reliable solutions. Moreover, we analyze the composition of the warehouse over a variety of different weight restrictions, and we calculate the number of repairable systems as a function of this bound.
In contrast to traditional data applications, many real-world scenarios nowadays depend on managing and querying huge volumes of uncertain and incomplete data. This new type of applications emerge, for example, when we integrate data from various sources, analyse social/biological/chemical networks or conduct privacy-preserving data mining.
A very promising concept addressing this new kind of probabilistic data applications has been proposed in the form of probabilistic databases. Here, a tuple only belongs to its table or query answer with a specific likelihood. That probability expresses the uncertainty about the given data or the confidence in the answer. The most challenging task for probabilistic databases is query evaluation. In fact, there are even simple relational queries for which determining the occurrence probability of a single answer tuple is hard for #P.
Lineage formulas constitute the central concept under investigation in this work. In short, the mechanism behind lineage formulas facilitates the representation and evaluation of events of the probability space, which is defined by a probabilistic database. On the basis of lineage formulas, we devise a framework that is designed as a combination of a relational database layer and an additional probabilistic query engine.
In particular, the following three aspects are studied:
(i) an efficient construction of lineage formulas,
(ii) an orthogonal combination of lineage optimization techniques, which are performed within the relational database layer and the probabilistic query engine, and
(iii) effective and compact data structures to represent lineage formulas within a probabilistic query engine.
The developed framework provides a novel lineage construction method that is able to construct nested lineage formulas, to avoid large tuple sets within the relational database layer tuples, and to provide full relational algebra support. In addition, the proposed system completely resolves the conflict between the contradicting query plans optimized for the relational database layer and the probabilistic query engine.
In anthropogenically heavily impacted river catchments, such as the Lusatian river catchments Spree and Schwarze Elster in Germany, the robust assessment of potential impacts of climate change on the regional water resources is of high relevance for water resources management. Large uncertainties inherent in future scenarios may, however, reduce the willingness of regional stakeholders to develop and implement suitable adaptation strategies to climate change.
This thesis proposes the use of an integrated framework consisting of i) an ensemble based modelling approach and ii) the incorporation of measured and simulated meteorological and hydrological trends to consider uncertainties in climate change impact assessments. In addition, land use, as the most responsive catchment characteristic to buffer potential climate change impacts, is considered as one suitable trigger for climate change adaptation.
The ensemble based modelling approach consists of the meteorological output of four climate downscaling approaches (DAs): two dynamical and two statistical. These DAs drive different model configurations of the two conceptually different hydrological models WaSiM ETH and HBV light. The objective of incorporating measured meteorological trends into the analysis was twofold: trends in measured time series can i) be regarded as harbinger for future change and ii) serve as a mean to validate the results of the DAs. In order to evaluate the nature of the trends, both gradual (Mann Kendall test) and step changes (Pettitt test) are considered as well as temporal and spatial correlations in the data. The suitability of land use change as an adaptation strategy to climate change is evaluated in the form of different land use change scenarios: i) extreme scenarios where the entire catchment is parameterised as coniferous forest and uncultivated land and ii) scenarios of changes in crop cultivation and ii) a combination of a change in crop cultivation and forest conversion. As study areas serve three almost natural subcatchments of the Spree and Schwarze Elster (Germany).
The results of the ensemble based climate change impact analysis show that depending on the type (dynamical or statistical) of DA used, opposing trends in precipitation, actual evapotranspiration and discharge are simulated in the scenario period (2031 2060). While the statistical DAs simulate a decrease in future long term annual precipitation, the dynamical DAs simulate a tendency towards increasing precipitation. The trend analysis suggests that measured precipitation has not changed significantly during the period 1961 2006. Therefore, the strong decrease in precipitation simulated by the statistical DAs should be interpreted as a rather dry future scenario. The dynamical DAs, on the other hand, are too wet in the reference period and needed to be statistically bias corrected which destroys the physical consistency between the parameters. Concerning temperature, measured and simulated trends agree on a positive trend. The uncertainty related to the hydrological model within the climate change modelling chain is comparably low when long term averages are considered but increases during low flow events. The proposed framework of combining an ensemble based modelling approach with trend analysis on measurements is a promising approach to gain more confidence into the final results of climate change impact assessments and to obtain an increased process understanding of the interrelation between climate and water resources.
In terms of climate change adaptation, land use alternatives can have a considerable impact on the water balance components as the analysis of the extreme scenarios revealed. The scenarios of changes in crop cultivation in combination with forest conversion show, however, that the impact on the long term annual water balance is comparably low. An intra annual shift in the water balance components can be triggered which makes these scenarios suitable to reduce low flow risks during the summer. Overall, land use change can serve as one part of an integrated climate change adaptation strategy. Such as strategy needs, depending on the severity of the climate change impact, to include other, especially technical measures of water resources management, such as additional water storage, different strategies to manage the existing and new reservoirs. It may also consider additional water transfers from neighbouring, more water rich, river catchments. Regional adaptation planning needs also to consider problems related to water quality which are a consequence of the long term mining activities in the Lusatian river catchments. Last but not least, adaptation strategies should not only consider climate but also other aspects of global change.