My dissertation seeks to analyze environmental policy using theoretical, computational, and empirical methods. In the first chapter I develop a Bayesian learning framework for damage functions in integrated assessment models that mimics how modelers have historically updated damage functions. To allow for the model to be solved in a reasonable timeframe I must use sparse grid methods for dynamic programming which are new to climate economics. I also use robust control techniques from the macroeconomics literature to capture concerns that there are errors in integrated assessment models that we will not be able to resolve in a timely fashion. Using these methodological advances, I demonstrate that the convention of updating the calibration of damage functions while maintaining a fixed functional form can backfire and reduce ex-post welfare if the damage function is misspecified like many economists believe. Moreover, accounting for misspecification concerns with robust control can exacerbate the backfire and further reduce ex-post welfare. In my second chapter I analyze the impacts of credit trading under renewable portfolio standards. Specifically, I look at how a change in one state's renewable portfolio standard can propagate through this credit channel and result in reductions in fossil fuel usage in another state. I find that a 1 MWh increase in extra-jurisdictional demand for renewable energy credits leads to a reduction in energy production derived from coal usage by 2 mmbtus and a reduction in CO₂ emissions by 0.285 metric tons. In my last chapter I develop an analytic model for renewable energy credit trading to investigate why states have peculiar trading rules for the credits. I find that, counter to conventional economic wisdom, states may actually not want to engage in credit trading. Credit trading may in fact worsen in state pollution to an extent that completely offsets any gains from trade.

Ο σκοπός αυτής της διατριβής είναι η υποδειγματοποίηση της επίδρασης της αβεβαιότητας στις αποφάσεις επένδυσης των επιχειρήσεων στην Ευρώπη και στην Ελλάδα. Για να γίνει αυτό, η διατριβή χτίζει ένα δυναμικό υπόδειγμα επένδυσης, όπου οι οικονομικές μεταβλητές, η αβεβαιότητα και η πραγματική επένδυση συνδέονται. Ελλείψει των αποκλίσεων από το νεοκλασικό παράδειγμα, η επένδυση αντιδρά θετικά στην αβεβαιότητα. Εντούτοις, όταν εισάγονται «τριβές» όπως ο ατελής ανταγωνισμός, η μη αναστρεψιμότητα του κεφαλαίου και οι φθίνουσες αποδόσεις κλίμακας, το θετικό πρόσημο της σχέσης επένδυσης-αβεβαιότητας εξασθενεί σταδιακά και τελικά γίνεται αρνητικό. Επιπλέον, αυτή η διατριβή παρέχει εμπειρικά ευρήματα υπέρ της θεωρητικής πρόβλεψης από τους Lee και Shin (2000) ότι η αβεβαιότητα μπορεί να ασκήσει ανομοιόμορφη επίδραση στην επένδυση δεδομένου ότι οι συναρτήσεις παραγωγής των ληπτών αποφάσεων εκθέτουν διαφορετικά μερίδια εργασίας. Εναλλακτικά μέτρα της αναστρεψιμότητας του κεφαλαίου υιοθετούνται, προερχόμενα είτε από την τεχνολογική είτε από τη συναλλακτική φύση της αναστρεψιμότητας. Ο τεχνολογικός ορισμός της αναστρεψιμότητας πηγάζει από τη δυνατότητα να υποκατασταθεί η εργασία με το κεφάλαιο. Από τη συναλλακτική σκοπιά, στατιστικά δεδομένα για δαπάνες σε μεταχειρισμένο κεφάλαιο και δαπάνες σε χρηματοδοτική μίσθωση χρησιμοποιήθηκαν ως έμμεσοι δείκτες του βαθμού αναστρεψιμότητας. Σύμφωνα με τη σχετική βιβλιογραφία, τα εμπειρικά μας αποτελέσματα δεικνύουν ότι η αρνητική επίπτωση της αβεβαιότητας στην επένδυση αυξάνεται μονοτονικά με το βαθμό της μη αναστρεψιμότητας. Η υπάρχουσα βιβλιογραφία που εστιάζει στην επίδραση της αβεβαιότητας στην επένδυση έχει αγνοήσει κατά ένα μεγάλο μέρος την ετερογένεια του κεφαλαίου. Αυτή η διατριβή καλύπτει αυτό το προφανές κενό, έχοντας ως σημείο αναφοράς το γεγονός ότι υπό την παρουσία πολλαπλών κεφαλαιουχικών αγαθών το συνολικό ποσοστό επένδυσης είναι το γινόμενο του εκτατικού και του εντατικού περιθωρίου. Κατόπιν, βασισμένοι στο επιχείρημα ότι η αβεβαιότητα έχει άμεση επίπτωση στο εκτατικό περιθώριο διερευνούμε εμπειρικά τη σχέση τους. Τα κύρια ευρήματά μας δείχνουν ότι η αβεβαιότητα ασκεί σημαντικά αρνητική επίδραση στον αριθμό των κεφαλαιουχικών αγαθών (εκτατικό περιθώριο) που ο λήπτης αποφάσεων αποφασίζει να επενδύσει. Αντίθετα το βάθος της επένδυσης (εκτατικό περιθώριο) βρέθηκε να μην αντιδρά στην αβεβαιότητα. Εφαρμόζουμε την τεχνική GMM των δυναμικών διαχρονικών και διαστρωματικών στοιχείων σε βιομηχανικό επίπεδο για τις περισσότερες από τις ηπειρωτικές ευρωπαϊκές χώρες για τρεις διαφορετικές χρονικές περιόδους, 1987-2002, 1989-2004 και 1995-2003. Επιπλέον, ένα δείγμα χρονικώς επαναλαμβανόμενων διαστρωματικών στοιχείων (ΧΕΔΣ) των ελληνικών βιομηχανιών μεταποίησης αξιοποιείται κατά τη διάρκεια της περιόδου 1993-2001. Τέλος, υιοθετούμε την υπό συνθήκη αβεβαιότητα προερχόμενη από ένα υπόδειγμα της μορφής Pooled-Panel GARCH. / The purpose of this thesis is to model the impact of uncertainty on the investment decisions of firms in Europe and in Greece. To do so, the thesis builds a dynamic investment model, where financial variables, uncertainty and real investment are linked. In the absence of deviations from the neoclassical paradigm investment reacts positively to uncertainty. However, as frictions are introduced such as imperfect competition, irreversibility of capital and decreasing returns-to-scale, the positive sign of the investment-uncertainty relationship gradually dies out and eventually turns negative. Furthermore, this thesis provides empirical evidence in favour of the theoretical prediction by Lee and Shin (2000) that uncertainty may exert a non-uniform impact on investment as decision makers' production functions exhibit differential labour shares. Alternative measures of capital irreversibility are employed, stemming from either the technological or the transactional nature of irreversibility. The technology-based definition views irreversibility in terms of the ability to substitute labour for capital. From a transactions-based concept, data on used capital investment expenditures and leasing penetration rates are employed as indirect indicators for the degree of irreversibility. Consistent with the relevant literature, the empirical results indicate that the negative effect of uncertainty on investment is monotonically increasing with the degree of irreversibility. The extant literature focusing on the impact of uncertainty on investment has largely ignored capital heterogeneity. This thesis fills this apparent gap having as departure point the fact that in the presence of multiple capital goods total investment rate is the product of the extensive and intensive margins. Then building on the argument that uncertainty affects directly the extensive margin we empirically explore their relationship. Our main results indicate that uncertainty exerts a significantly negative impact on the number of capital types (extensive margin) the decision maker decides to invest in. In contrast the depth of investment (intensive margin) is found to be insensitive to uncertainty. We employ a dynamic panel data methodology, the GMM estimation technique, on a panel data set at an industrial level for most of the continental European countries for the periods 1987-2002, 1989-2004 and 1995-2003. In addition, a panel data set of Greek manufacturing industries is also exploited over the period 1993-2001. Finally, conditional volatility is generated in a panel framework applying the Pooled-Panel GARCH method.

No description available.

Soil erosion is a key issue in rangelands, but current approaches for predicting soil erosion are based on research in croplands and may not be appropriate for rangelands. An improved model is needed that accounts for the dominant erosion processes that operate in rangelands rather than croplands. In addition, effective application of such a model of rangeland erosion requires improved methods for assessing both model sensitivity and uncertainty if the model is to be applied confidently in natural resources management.I developed a new equation for calculating the combined rate of splash and sheet erosion (Dss, kg/m2) using existing rainfall-simulation data sets from the western United States that is distinct from that for croplands: Dss = Kss I 1.052q0.592, where Kss is the splash and sheet erosion coefficient, I (m/s) is rainfall intensity, and q (mm/hr) is runoff rate. This equation, which accounts for inter-relationship between I and q, was incorporated into a new model, the Rangeland Hydrology and Erosion Model (RHEM). This new model was better at predicting observed erosion rates than the commonly used, existing soil erosion model Water Erosion Prediction Project (WEPP).New approaches for assessing model uncertainty and sensitivity were developed and applied to the model. The new approach for quantifying localized sensitivity indices, when combined with techniques such as correlation analysis and scatter plots, can be used effectively to compare the sensitivity of different inputs, locate sensitive regions in the parameter space, decompose the dependency of the model response on the input parameters, and identify nonlinear and incorrect relationships in the model. The approach for assessing model predictive uncertainty, called "Dual-Monte-Carlo" (DMC), uses two Monte-Carlo sampling loops to not only calculate predictive uncertainty for one input parameter set, but also examine the predictive uncertainty as a function of model inputs across the full range of parameter space. Both approaches were applied to RHEM and yielded insights into model behavior.Collectively, this research provides an important advance in developing improved predictions of erosion rates in rangelands and simultaneously provides new approaches for model sensitivity and uncertainty analyses that can be applied to other models and disciplines.

This study integrated and applied effective communication concepts to highlight landscape as both medium and method to improve land use decisions, in the face of uncertainty, such as that posed by global climate change. Grounded theory guided the emergence of a communication model to illustrate impacts of land use scenarios in a study area. Scenarios incorporated socioeconomic trends and biophysical data, including localized climate projections and relevant audience traits assumed from prior assessments. Scenario implications were analyzed by comparing their ecological service values; the communication model’s effectiveness was evaluated against principles derived from the literature. Results suggest that a communication framework grounded in landscape can improve comprehension of environmental and human needs; however, further testing is needed. This framework can help enable broader landscape understanding through shared experience and engagement. Enhancing communication channels in this way is required as increasingly complex environmental problems demand more collaborative and communal solutions. / Landscape Architecture Canada Foundation

Depletion calculations for nuclear reactors model the dynamic coupling between the material composition and neutron flux and help predict reactor performance and safety characteristics. In order to be trusted as reliable predictive tools and inputs to licensing and operational decisions, the simulations must include an accurate and holistic quantification of errors and uncertainties in its outputs. Uncertainty quantification is a formidable challenge in large, realistic reactor models because of the large number of unknowns and myriad sources of uncertainty and error. We present a framework for performing efficient uncertainty quantification in depletion problems using an adjoint approach, with emphasis on high-fidelity calculations using advanced massively parallel computing architectures. This approach calls for a solution to two systems of equations: (a) the forward, engineering system that models the reactor, and (b) the adjoint system, which is mathematically related to but different from the forward system. We use the solutions of these systems to produce sensitivity and error estimates at a cost that does not grow rapidly with the number of uncertain inputs. We present the framework in a general fashion and apply it to both the source-driven and k-eigenvalue forms of the depletion equations. We describe the implementation and verification of solvers for the forward and ad- joint equations in the PDT code, and we test the algorithms on realistic reactor analysis problems. We demonstrate a new approach for reducing the memory and I/O demands on the host machine, which can be overwhelming for typical adjoint algorithms. Our conclusion is that adjoint depletion calculations using full transport solutions are not only computationally tractable, they are the most attractive option for performing uncertainty quantification on high-fidelity reactor analysis problems.

The subject of this dissertation is the calculation and visualization of intraoperative measurement uncertainty in computer-assisted surgical procedures. Error is the difference between the observed or measured value and the true value (called ground-truth) of a quantity. Uncertainty is the unknown difference between the measured and true values, and exists in the absence of knowledge of ground truth. If one has an algorithm for computing the ground truth then one can get an accurate estimate of error. However, in computer-assisted surgery, the ground truth is often unknown. The introduction of error to surgical procedures is inevitable: it cannot be avoided by simply taking very careful measurements, providing more accurate algorithms, or by improving instrument calibration. One can only reduce errors as much as reasonably possible, calculate a reliable estimate of the uncertainty, and provide a meaningful way to convey this uncertainty information to clinicians. In this dissertation, I demonstrate that the visualization of registration uncertainty improves surgical navigation and that real-time computation of intraoperative measurement uncertainty is possible. In an extensive user study of surgeons and surgical residents, I compare methods of visualizing intraoperative uncertainty and determine that there are several methods of effectively conveying uncertainty in surgical navigation. / Thesis (Ph.D, Computing) -- Queen's University, 2010-01-25 16:33:26.755

The objective of this research was to determine the impact of climate change on the Churchill River basin and perform analysis on uncertainty related to this impact. Three hydrological models were used to determine this impact and were calibrated to approximately equivalent levels of efficiency. These include WATFLOODTM, a semi-physically based, distributed model; HBV-EC, a semidistributed, conceptual model; and HMETS, a lumped, conceptual model. These models achieved Nash-Sutcliffe calibration values ranging from 0.51 to 0.71. Climate change simulations indicated that the average of simulations predict a small increase in flow for the 2050s and a slight decrease for the 2080s. Each hydrological model predicted earlier freshets and a shift in timing of low flow events. Uncertainty analysis indicated that the chief contributor of uncertainty was the selection of GCM followed by hydrological model with less significant sources of uncertainty being parameterization of the hydrological model and selection of emissions scenario.

No description available.

There are existing standards and guidelines for the effective management of infrastructure through infrastructure asset management planning (IAM). However, few if any of these standards explicitly address the financial implications associated with the uncertainty that underlies the risk associated with service provision. Without credibly quantifying the potential implications of this network variable uncertainty (i.e. an extreme weather event that affects the performance and costs of many segments within the study network, or the introduction of a new technology that may impact the network cost estimates) infrastructure management systems may actually regularly and significantly over or under estimate the actual financial requirements required to provide services. Therefore, financial projections may actually include a systematic bias. It was hypothesized that a model could be developed that quantifies and communicates the financial implications of network variable uncertainty within the IAM context. A model was developed to demonstrate how network variable uncertainty could be included in financial planning for infrastructure networks. The model was able to: (1) be applied to various types of infrastructure networks, (2) incorporate network variable uncertainty, (3) compare alternatives and scenarios, and (4) support effective communication of results. The outputs of the model were the average network annual worth (AW) and network present worth (PW). These outputs, along with tornado plots, risks curves, level of service dashboards, and existing budget levels, were used to communicate the impacts of the network variable uncertainty on the financial projections. The model was developed using Excel tools linked to DPL software to utilize probabilistic methods. The Life Cycle Cost (LCC) portion of the model was successfully verified against an existing infrastructure costing tool, the Land and Infrastructure Resiliency Assessment (LIRA) tool developed by the Agri-Environmental Services Branch of Agriculture and Agri-Food Canada. The impact of the network variable uncertainty within the variables was also quantified in terms of levels of service provided by the organization. The developed model was first applied to a hypothetical twelve segment road network for illustrative purposes. For the hypothetical road network there were four events, representing network variable uncertainty, that were considered. These decisions or events included the: (1) decision to implement a new technology, (2) event of changing standards, (3) event of increased material costs, and (4) occurrence of an extreme rainfall event. The hypothetical network illustrated that if the defined decisions or events occurred then the expected network AW would increase by 41%. The impacts of decisions or events on the hypothetical network levels of service, stemming from network variable uncertainty, were also considered. The measured levels of service for the hypothetical network included the network financial sustainability indicator (an indicator reflecting the network current budget divided by the network annual worth as a percentage) and the frequency of blading of the roads. The model was next applied to a case study using the Town of Shellbrook sanitary main network. The Town has a large quantity of aging mains which were constructed in the 1960’s and are expected to require renewal in the near term. The network variable uncertainty for the case study resulted from the potential decision to implement a new trenchless technology for the renewal of sanitary mains. The new technology was expected to decrease the renewal costs. However, there was uncertainty as to what percentage of the sanitary mains would be found to be suitable for the new technology. Using the model it was determined that if the decision was made to implement the new technology, there would be an expected reduction of 17% in the network AW. The levels of service that were used for the Shellbrook case study were the network financial sustainability indicator (annual budget / network AW) and the meeting of standards set by regulating bodies. It was determined that the network financial sustainability indicator was sensitive to the decision to implement the trenchless technology, while the meeting of regulating bodies was not. If the decision was made to implement the new technology the network sustainability indicator would be expected to increase from 28% (if the new technology was not implemented) to 34% (if the new technology were implemented). The model was finally applied to a case study looking at the RM of Wilton gravel road network. The network variable uncertainty for this case study resulted from the potential increase in gravel material costs. The network variable uncertainty represented the magnitude of the annual increase in gravel costs. Given the event of increasing gravel costs the expected network AW would increase by 14%. The levels of service indicators used for the RM of Wilton case study were the network financial sustainability indicator and the frequency of blading. It was determined that the network financial sustainability indicator was sensitive to the event (increasing gravel costs), while the frequency of blading was not directly impacted (although it may be indirectly impacted). If the event of increasing gravel costs were to occur then the network financial sustainability indicator would be expected to decrease from 59% (if gravel costs did not increase) to 52% (if gravel costs did increase). This research proved that the hypothesis was correct, and that a model could be developed that quantified and communicated the financial implications and level of service impacts of network variable uncertainty for IAM planning. This research illustrated and quantified that IAM planning without accounting for network variable uncertainty, such as: (1) changing technology, (2) changing standards, (3) increasing material costs, and (4) extreme weather events, managers may introduce a systematic bias into long term planning. Network variable uncertainty can significantly impact the projected expenditures required for the long term provision of services. Infrastructure managers and decision makers need to manage infrastructure in a sustainable way over the long term in the face of uncertainty. It is necessary that decision makers have information regarding the impacts of network variable uncertainty on both LCCs and levels of service to make fully informed decision.