Potential Impacts of Subprime Carbon on Australia’s Impending Carbon Market

This paper examines the potential impacts of subprime carbon credits on the impending Australian carbon market. Subprime carbon could potentially be created in carbon offset markets that lack adequate regulation, as projects face risks that can overstate emissions abatement. Recent research suggests that subprime carbon credits will likely cause significant price instability in carbon markets, with some authors drawing parallels to the US market for mortgage backed securities during the subprime mortgage crisis (Chan, 2009). To assess the impacts of subprime carbon credits on the impending Australian carbon market, carbon price fundamentals are examined using a marginal abatement cost curve for the year 2020. The 2020 Australian marginal abatement cost curve is derived using a bottom-up model of the Australian electricity sector, as well as findings by the (DCC, 2009) and (McKinsey, 2008). Impacts are evaluated under several scenarios, which consider different trading scheme limits on the use of offsets; different proportions of offset credits that are subprime; and different emissions reduction targets. The results suggest that subprime carbon credits will always result in overall emissions reductions to be overstated, while sometimes increasing price volatility in the carbon market, depending on the steepness of the marginal abatement cost curve, the proportion of offset credits that are subprime, and the trading schemes limits on the use of offsets. We conclude that carbon markets could benefit significantly from a carbon offsets regulator, which would ensure the environmental and financial integrity of offset credits.Carbon Offsets, Marginal Abatement Cost, Carbon Market Regulation, Subprime Carbon

Turbulent Drag Reduction of polyelectrolyte (DNA) solutions: relation with the elongational viscosity

We report measurements of turbulent drag reduction of two different polyelectrolyte solutions: DNA and hydrolyzed Polyacrylamide. Changing the salt concentration in the solutions allows us to change the flexibility of the polymer chains. For both polymers the amount of drag reduction was found to increase with the flexibility. Rheological studies reveal that the elongational viscosity of the solutions increases simultaneously. Hence we conclude that the elongational viscosity is the pertinent macroscopic quantity to describe the ability of a polymer to cause turbulent drag reduction

Influence of cattle grazing practices on dung beetle (Coleoptera: Scarabaeoidea) communities in the Sandhill rangelands of Central Nebraska

Dung beetles (Coleoptera: Scarabaeoidea) have a significant role in regulating the ecosystem services they provide on rangelands. Colonization of dung piles by dung beetles can help facilitate the decomposition of dung, control dung-breeding pests, and cycle important nutrients into the soil to improve pasture quality. Cattle are grazed on pastures at various stocking densities depending on the type of grazing practice. The influence of grazing practices on dung beetle communities and services remains largely unknown. Our first study investigated dung beetle activity across different cattle grazing practices to determine how grazing might influence dung beetle abundance and diversity. Dung beetle populations were monitored throughout the grazing season on pastures that were grazed under various practices: non-grazed/hay, continuous grazing, low-stocking rotational grazing, and high-stocking (mob) rotational grazing. Results from this study showed significantly higher dung beetle diversity on pastures exposed to rotational grazing practices compared to continuous grazing or no grazing. In some cases, dung beetle abundance and species richness were significantly greater on pastures that were grazed through high-stocking rotational grazing compared to low-stocking rotational or continuous grazing treatments. Based on these data, rotational cattle grazing may favor the colonization of dung beetles on rangeland, regardless of stocking density. Our second study investigated whether dung beetles exhibit preferences for dung from cattle exposed to different grazing practices. Dung from cattle in three separate grazing practices were used to test dung beetle preference: continuous grazing, low-stocking rotational grazing, and high-stocking rotational grazing. Dung beetle abundance was measured as well as the nutrient and physical properties of each dung type. Results of the study revealed no significant differences in dung beetle abundance between dung collected from each grazing practice. Nutritional content, pH, moisture, and dry matter levels also were not significantly different. However, the results indicated varying dung beetle species composition on dung from the continuous versus rotational grazing practices. Overall, cattle grazing practices may not affect dung composition or its influence on dung beetle preferences. Advisors: Jeffrey Bradshaw and Thomas Weisslin

Statistical and image processing techniques for remote sensing in agricultural monitoring and mapping

Throughout most of history, increasing agricultural production has been largely driven by expanded land use, and – especially in the 19th and 20th century – by technological innovation in breeding, genetics and agrochemistry as well as intensification through mechanization and industrialization. More recently, information technology, digitalization and automation have started to play a more significant role in achieving higher productivity with lower environmental impact and reduced use of resources. This includes two trends on opposite scales: precision farming applying detailed observations on sub-field level to support local management, and large-scale agricultural monitoring observing regional patterns in plant health and crop productivity to help manage macroeconomic and environmental trends. In both contexts, remote sensing imagery plays a crucial role that is growing due to decreasing costs and increasing accessibility of both data and means of processing and analysis. The large archives of free imagery with global coverage, can be expected to further increase adoption of remote sensing techniques in coming years. This thesis addresses multiple aspects of remote sensing in agriculture by presenting new techniques in three distinct research topics: (1) remote sensing data assimilation in dynamic crop models; (2) agricultural field boundary detection from remote sensing observations; and (3) contour extraction and field polygon creation from remote sensing imagery. These key objectives are achieved through combining methods of probability analysis, uncertainty quantification, evolutionary learning and swarm intelligence, graph theory, image processing, deep learning and feature extraction. Four new techniques have been developed. Firstly, a new data assimilation technique based on statistical distance metrics and probability distribution analysis to achieve a flexible representation of model- and measurement-related uncertainties. Secondly, a method for detecting boundaries of agricultural fields based on remote sensing observations designed to only rely on image-based information in multi-temporal imagery. Thirdly, an improved boundary detection approach based on deep learning techniques and a variety of image features. Fourthly, a new active contours method called Graph-based Growing Contours (GGC) that allows automatized extractionof complex boundary networks from imagery. The new approaches are tested and evaluated on multiple study areas in the states of Schleswig-Holstein, Niedersachsen and Sachsen-Anhalt, Germany, based on combine harvester measurements, cadastral data and manual mappings. All methods were designed with flexibility and applicability in mind. They proved to perform similarly or better than other existing methods and showed potential for large-scale application and their synergetic use. Thanks to low data requirements and flexible use of inputs, their application is neither constrained to the specific applications presented here nor the use of a specific type of sensor or imagery. This flexibility, in theory, enables their use even outside of the field of remote sensing.Landwirtschaftliche Produktivitätssteigerung wurde historisch hauptsächlich durch Erschließung neuer Anbauflächen und später, insbesondere im 19. und 20. Jahrhundert, durch technologische Innovation in Züchtung, Genetik und Agrarchemie sowie Intensivierung in Form von Mechanisierung und Industrialisierung erreicht. In jüngerer Vergangenheit spielen jedoch Informationstechnologie, Digitalisierung und Automatisierung zunehmend eine größere Rolle, um die Produktivität bei reduziertem Umwelteinfluss und Ressourcennutzung weiter zu steigern. Daraus folgen zwei entgegengesetzte Trends: Zum einen Precision Farming, das mithilfe von Detailbeobachtungen die lokale Feldarbeit unterstützt, und zum anderen großskalige landwirtschaftliche Beobachtung von Bestands- und Ertragsmustern zur Analyse makroökonomischer und ökologischer Trends. In beiden Fällen spielen Fernerkundungsdaten eine entscheidende Rolle und gewinnen dank sinkender Kosten und zunehmender Verfügbarkeit, sowohl der Daten als auch der Möglichkeiten zu ihrer Verarbeitung und Analyse, weiter an Bedeutung. Die Verfügbarkeit großer, freier Archive von globaler Abdeckung werden in den kommenden Jahren voraussichtlich zu einer zunehmenden Verwendung führen. Diese Dissertation behandelt mehrere Aspekte der Fernerkundungsanwendung in der Landwirtschaft und präsentiert neue Methoden zu drei Themenbereichen: (1) Assimilation von Fernerkundungsdaten in dynamischen Agrarmodellen; (2) Erkennung von landwirtschaftlichen Feldgrenzen auf Basis von Fernerkundungsbeobachtungen; und (3) Konturextraktion und Erstellung von Polygonen aus Fernerkundungsaufnahmen. Zur Bearbeitung dieser Zielsetzungen werden verschiedene Techniken aus der Wahrscheinlichkeitsanalyse, Unsicherheitsquantifizierung, dem evolutionären Lernen und der Schwarmintelligenz, der Graphentheorie, dem Bereich der Bildverarbeitung, Deep Learning und Feature-Extraktion kombiniert. Es werden vier neue Methoden vorgestellt. Erstens, eine neue Methode zur Datenassimilation basierend auf statistischen Distanzmaßen und Wahrscheinlichkeitsverteilungen zur flexiblen Abbildung von Modell- und Messungenauigkeiten. Zweitens, eine neue Technik zur Erkennung von Feldgrenzen, ausschließlich auf Basis von Bildinformationen aus multi-temporalen Fernerkundungsdaten. Drittens, eine verbesserte Feldgrenzenerkennung basierend auf Deep Learning Methoden und verschiedener Bildmerkmale. Viertens, eine neue Aktive Kontur Methode namens Graph-based Growing Contours (GGC), die es erlaubt, komplexe Netzwerke von Konturen aus Bildern zu extrahieren. Alle neuen Ansätze werden getestet und evaluiert anhand von Mähdreschermessungen, Katasterdaten und manuellen Kartierungen in verschiedenen Testregionen in den Bundesländern Schleswig-Holstein, Niedersachsen und Sachsen-Anhalt. Alle vorgestellten Methoden sind auf Flexibilität und Anwendbarkeit ausgelegt. Im Vergleich zu anderen Methoden zeigten sie vergleichbare oder bessere Ergebnisse und verdeutlichten das Potenzial zur großskaligen Anwendung sowie kombinierter Verwendung. Dank der geringen Anforderungen und der flexiblen Verwendung verschiedener Eingangsdaten ist die Nutzung nicht nur auf die hier beschriebenen Anwendungen oder bestimmte Sensoren und Bilddaten beschränkt. Diese Flexibilität erlaubt theoretisch eine breite Anwendung, auch außerhalb der Fernerkundung

Thermodynamic simulation of solar thermal power stations with liquid salt as heat transfer fluid

The Gemasolar solar power tower plant uses molten salt as heat transfer fluid and is therefore the first commercial project to apply this technology. Current research and development in line focusing systems is concentrated on transferring this proved salt technology to solar thermal power stations with parabolic trough collectors. This thesis identifies the economic performance of such a power station. To that end, a transient thermodynamic model is implemented into the commercial software tool EBSILON®Professional. The system behavior of the solar field is modeled in transient and pseudo transient mode for the power block. The model discretized one year into 10 minute intervals in order to calculate the levelized electric costs (LECs) for a 125 MWe reference plant with live steam parameters of 150 bar and 510 °C. The solar field layout is assumed to be a 2 H layout with 352collector loops, each consisting of four Eurotrough ET150 collectors (solar multiple of 2.233). The storage system is able to feed the steam generator for 10 hours. Three different years with different annual averaged direct normal irradiation averaged annual sums were compared. Given the 90% confidence interval (50% ± 45%), the LECs are between 0.117 and 0.190 €/kWhe (2659 kWh/m²/y), 0.136 and 0.221 €/kWhe (2300 kWh/m²/y), and 0.149 and 0.243 €/kWhe (2095 kWh/m²/y). The mode LECs are 0.149 €/kWhe, 0.172 €/kWhe, and 0.190 €/kWhe

Causal Analysis of Fatal Trenching Accidents

A study was performed by the Construction Industry Research and Policy Center at The University of Tennessee, Knoxville to identify causation for U.S. trench collapse fatalities in the construction industry that occurred during the years 1997-1999. Of the 1217 fatality case files analyzed, 44 were categorized as trench collapse fatalities. The 44 trench collapse case files were analyzed and the contributed factors of the fatalities were identified in an effort to determine the causation of collapses. The results of the study showed a large number of trenches without any type of protective devices being used. The findings of the fatal trench collapse investigation events suggest that fatal events might have been prevented if there was compliance with OSHA regulations for protective devices in the trenches, training of employees, and having an OSHA trained competent person on site