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Climate change and its mitigation is rapidly becoming an item of social concern. Climate change mitigation involves reduction of atmospheric greenhouse gas concentrations through emissions reduction and or sequestration enhancement (collectively called offsets). Many have asked how agriculture and forestry can participate in mitigation efforts. Given that over 80 percent of greenhouse gas emissions arise from the energy sector, the role of agriculture and forestry depends critically on the costs of the offsets they can achieve in comparison with offset costs elsewhere in the economy. A number of researchers have examined the relative offset costs but have generally looked only at producer level costs. However there are also costs incurred when implementing, selling and conveying offset credits to a buyer. Also when commodities are involved like bioenergy feedstocks, the costs of readying these for use in implementing an offset strategy need to be reflected. This generally involves the broadly defined category of transaction costs. This dissertation examines the possible effects of transactions costs and storage costs for bioenergy commodities and how they affect the agriculture and forestry portfolio of mitigation strategies across a range of carbon dioxide equivalent prices. The model is used to simulate the effects with and without transactions and storage costs. Using an agriculture and forestry sector model called FASOMGHG, the dissertation finds that consideration of transactions and storage costs reduces the agricultural contribution total mitigation and changes the desirable portfolio of alternatives. In terms of the portfolio, transactions costs inclusion diminishes the desirability of soil sequestration and forest management while increasing the bioenergy and afforestation role. Storage costs diminish the bioenergy role and favor forest and sequestration items. The results of this study illustrate that transactions and storage costs are important considerations in policy and market design when addressing the reduction of greenhouse gas concentrations in climate change related decision making.

Since the late 1980s, the energy sector in Great Britain has undergone some core changes in its functionality; beginning with the early 1990s privatisation, followed by an increased green ambition, and commencing a transition towards a low-carbon economy. As the British energy sector prepares itself for another major overhaul, it also puts itself at risk for not being sufficiently prepared for the consequences this transition will have on the existing generating capacity, security of supply, and the national electricity market. Upon meeting existing targets, the government of the United Kingdom risks becoming complacent, putting energy regulation to the backseat and focusing on other regulatory tasks, while introducing cuts for thriving renewable and other low-carbon energy generating technologies. The government has implemented a variety of directives, initiatives, and policies that have sometimes been criticised due to their lack of clarity and potential overlap between energy and climate change directives. The government has introduced policies that aim to provide stable short-term solutions. However, a concrete way of resolving the energy trilemma and some of the long-term objectives and more importantly ways of achieving them are yet to be developed. This work builds on analysing each low-carbon technology individually by assessing its past and current state in the British energy mix. By accounting for the changes and progress the technology underwent in its journey towards becoming a part of the energy capacity in Great Britain, its impact on the future wholesale electricity prices is studied. Research covered in this thesis presents an assessment of the existing and incoming low-carbon technologies in Great Britain and their individual and combined impact on the future of British energy economics by studying their implications for the electricity market. The methodological framework presented here uses a cost-minimisation merit order model to provide useful insights for novel methods of electricity production and conventional thermal energy generation to aid with the aftermath of potential inadequate operational and fiscal flexibility. The thesis covers a variety of scenarios differing in renewable and thermal penetration and examines the impact of interconnection, energy storage, and demand side management on the British wholesale electricity prices. The implications of increasing low-carbon capacity in the British energy mix are examined and compared to similar developments across Europe. The analysis highlights that if the optimistic scenarios in terms of green energy installation are followed, there is sufficient energy supply, which results in renewable resources helping to keep the wholesale price of electricity down. However, if the desired capacity targets are not met, the lack of available supply could result in wholesale prices going up, especially in the case of a natural gas price increase. Although initially costly, the modernisation of the British grid leads to a long-term decrease in wholesale electricity prices and provides a greater degree of security of supply and flexibility for all market participants.

Les workflows sont des systèmes typiques traitant le big data. Ces systèmes sont déployés sur des sites géo-distribués pour exploiter des infrastructures cloud existantes et réaliser des expériences à grande échelle. Les données générées par de telles expériences sont considérables et stockées à plusieurs endroits pour être réutilisées. En effet, les systèmes workflow sont composés de tâches collaboratives, présentant de nouveaux besoins en terme de dépendance et d'échange de données intermédiaires pour leur traitement. Cela entraîne de nouveaux problèmes lors de la sélection de données distribuées et de ressources de stockage, de sorte que l'exécution des tâches ou du job s'effectue à temps et que l'utilisation des ressources soit rentable. Par conséquent, cette thèse aborde le problème de gestion des données hébergées dans des centres de données cloud en considérant les exigences des systèmes workflow qui les génèrent. Pour ce faire, le premier problème abordé dans cette thèse traite le comportement d'accès aux données intermédiaires des tâches qui sont exécutées dans un cluster MapReduce-Hadoop. Cette approche développe et explore le modèle de Markov qui utilise la localisation spatiale des blocs et analyse la séquentialité des fichiers spill à travers un modèle de prédiction. Deuxièmement, cette thèse traite le problème de placement de données intermédiaire dans un stockage cloud fédéré en minimisant le coût de stockage. A travers les mécanismes de fédération, nous proposons un algorithme exacte ILP afin d’assister plusieurs centres de données cloud hébergeant les données de dépendances en considérant chaque paire de fichiers. Enfin, un problème plus générique est abordé impliquant deux variantes du problème de placement lié aux dépendances divisibles et entières. L'objectif principal est de minimiser le coût opérationnel en fonction des besoins de dépendances inter et intra-job / The typical cloud big data systems are the workflow-based including MapReduce which has emerged as the paradigm of choice for developing large scale data intensive applications. Data generated by such systems are huge, valuable and stored at multiple geographical locations for reuse. Indeed, workflow systems, composed of jobs using collaborative task-based models, present new dependency and intermediate data exchange needs. This gives rise to new issues when selecting distributed data and storage resources so that the execution of tasks or job is on time, and resource usage-cost-efficient. Furthermore, the performance of the tasks processing is governed by the efficiency of the intermediate data management. In this thesis we tackle the problem of intermediate data management in cloud multi-datacenters by considering the requirements of the workflow applications generating them. For this aim, we design and develop models and algorithms for big data placement problem in the underlying geo-distributed cloud infrastructure so that the data management cost of these applications is minimized. The first addressed problem is the study of the intermediate data access behavior of tasks running in MapReduce-Hadoop cluster. Our approach develops and explores Markov model that uses spatial locality of intermediate data blocks and analyzes spill file sequentiality through a prediction algorithm. Secondly, this thesis deals with storage cost minimization of intermediate data placement in federated cloud storage. Through a federation mechanism, we propose an exact ILP algorithm to assist multiple cloud datacenters hosting the generated intermediate data dependencies of pair of files. The proposed algorithm takes into account scientific user requirements, data dependency and data size. Finally, a more generic problem is addressed in this thesis that involve two variants of the placement problem: splittable and unsplittable intermediate data dependencies. The main goal is to minimize the operational data cost according to inter and intra-job dependencies