A Cost Analysis Of Carbon Dioxide Emission Reduction Strategies For New Plants In Michigan's Electric Power Sector

This thesis attempts to find the least-cost strategy to reduce CO2 emission by replacing coal by other energy sources for electricity generation in the context of the proposed EPA’s regulation on CO2 emissions from existing coal-fired power plants. An ARIMA model is built to forecast coal consumption for electricity generation and its CO2 emissions in Michigan from 2016 to 2020. CO2 emission reduction costs are calculated under three emission reduction scenarios- reduction to 17%, 30% and 50% below the 2005 emission level. The impacts of Production Tax Credit (PTC) and the intermittency of renewable energy are also discussed. The results indicate that in most cases natural gas will be the best alternative to coal for electricity generation to realize CO2 reduction goals; if the PTC for wind power will continue after 2015, a natural gas and wind combination approach could be the best strategy based on the least-cost criterion.

Modelling urban public transit users' route choice behaviour: review and outlook

This paper reviews the main development of approaches to modelling urban public transit users’ route choice behaviour from 1960s to the present. The approaches reviewed include the early heuristic studies on finding the least cost transit route and all-or-nothing transit assignment, the bus common line problem and corresponding network representation methods, the disaggregate discrete choice models which are based on random utility maximization assumptions, the deterministic use equilibrium and stochastic user equilibrium transit assignment models, and the recent dynamic transit assignment models using either frequency or schedule based network formulation. In addition to reviewing past outcomes, this paper also gives an outlook into the possible future directions of modelling transit users’ route choice behaviour. Based on the comparison with the development of models for motorists’ route choice and traffic assignment problems in an urban road area, this paper points out that it is rewarding for transit route choice research to draw inspiration from the intellectual outcomes out of the road area. Particularly, in light of the recent advancement of modelling motorists’ complex road route choice behaviour, this paper advocates that the modelling practice of transit users’ route choice should further explore the complexities of the problem.

Multi-objective mission flight planning in civil unmanned aerial systems

Unmanned Aerial Vehicles (UAVs) are emerging as an ideal platform for a wide range of civil applications such as disaster monitoring, atmospheric observation and outback delivery. However, the operation of UAVs is currently restricted to specially segregated regions of airspace outside of the National Airspace System (NAS). Mission Flight Planning (MFP) is an integral part of UAV operation that addresses some of the requirements (such as safety and the rules of the air) of integrating UAVs in the NAS. Automated MFP is a key enabler for a number of UAV operating scenarios as it aids in increasing the level of onboard autonomy. For example, onboard MFP is required to ensure continued conformance with the NAS integration requirements when there is an outage in the communications link. MFP is a motion planning task concerned with finding a path between a designated start waypoint and goal waypoint. This path is described with a sequence of 4 Dimensional (4D) waypoints (three spatial and one time dimension) or equivalently with a sequence of trajectory segments (or tracks). It is necessary to consider the time dimension as the UAV operates in a dynamic environment. Existing methods for generic motion planning, UAV motion planning and general vehicle motion planning cannot adequately address the requirements of MFP. The flight plan needs to optimise for multiple decision objectives including mission safety objectives, the rules of the air and mission efficiency objectives. Online (in-flight) replanning capability is needed as the UAV operates in a large, dynamic and uncertain outdoor environment. This thesis derives a multi-objective 4D search algorithm entitled Multi- Step A* (MSA*) based on the seminal A* search algorithm. MSA* is proven to find the optimal (least cost) path given a variable successor operator (which enables arbitrary track angle and track velocity resolution). Furthermore, it is shown to be of comparable complexity to multi-objective, vector neighbourhood based A* (Vector A*, an extension of A*). A variable successor operator enables the imposition of a multi-resolution lattice structure on the search space (which results in fewer search nodes). Unlike cell decomposition based methods, soundness is guaranteed with multi-resolution MSA*. MSA* is demonstrated through Monte Carlo simulations to be computationally efficient. It is shown that multi-resolution, lattice based MSA* finds paths of equivalent cost (less than 0.5% difference) to Vector A* (the benchmark) in a third of the computation time (on average). This is the first contribution of the research. The second contribution is the discovery of the additive consistency property for planning with multiple decision objectives. Additive consistency ensures that the planner is not biased (which results in a suboptimal path) by ensuring that the cost of traversing a track using one step equals that of traversing the same track using multiple steps. MSA* mitigates uncertainty through online replanning, Multi-Criteria Decision Making (MCDM) and tolerance. Each trajectory segment is modeled with a cell sequence that completely encloses the trajectory segment. The tolerance, measured as the minimum distance between the track and cell boundaries, is the third major contribution. Even though MSA* is demonstrated for UAV MFP, it is extensible to other 4D vehicle motion planning applications. Finally, the research proposes a self-scheduling replanning architecture for MFP. This architecture replicates the decision strategies of human experts to meet the time constraints of online replanning. Based on a feedback loop, the proposed architecture switches between fast, near-optimal planning and optimal planning to minimise the need for hold manoeuvres. The derived MFP framework is original and shown, through extensive verification and validation, to satisfy the requirements of UAV MFP. As MFP is an enabling factor for operation of UAVs in the NAS, the presented work is both original and significant.

Modelling urban public transit users’ route choice behaviour : a review and outlook

Public transport is one of the key promoters of sustainable urban transport. To encourage and increase public transport patronage it is important to investigate the route choice behaviours of urban public transit users. This chapter reviews the main developments of modelling urban public transit users’ route choice behaviours in a historical perspective, from the 1960s to the present time. The approaches re- viewed for this study include the early heuristic studies on finding the least-cost transit route and all-or- nothing transit assignment, the bus common lines problem, the disaggregate discrete choice models, the deterministic and stochastic user equilibrium transit assignment models, and the recent dynamic transit assignment models. This chapter also provides an outlook for the future directions of modelling transit users’ route choice behaviours. Through the comparison with the development of models for motorists’ route choice and traffic assignment problems, this chapter advocates that transit route choice research should draw inspiration from the research outcomes from the road area, and that the modelling practice of transit users’ route choice should further explore the behavioural complexities.

The contribution of psychosocial factors to socioeconomic differences in food purchasing

In developed countries the relationship between socioeconomic position (SEP) and health is unequivocal. Those who are socioeconomically disadvantaged are known to experience higher morbidity and mortality from a range of chronic diet-related conditions compared to those of higher SEP. Socioeconomic inequalities in diet are well established. Compared to their more advantaged counterparts, those of low SEP are consistently found to consume diets less consistent with dietary guidelines (i.e. higher in fat, salt and sugar and lower in fibre, fruit and vegetables). Although the reasons for dietary inequalities remain unclear, understanding how such differences arise is important for the development of strategies to reduce health inequalities. Both environmental (e.g. proximity of supermarkets, price, and availability of foods) and psychosocial (e.g. taste preference, nutrition knowledge) influences are proposed to account for inequalities in food choices. Although in the United States (US), United Kingdom (UK), and parts of Australia, environmental factors are associated with socioeconomic differences in food choices, these factors do not completely account for the observed inequalities. Internationally, this context has prompted calls for further exploration of the role of psychological and social factors in relation to inequalities in food choices. It is this task that forms the primary goal of this PhD research. In the small body of research examining the contribution of psychosocial factors to inequalities in food choices, studies have focussed on food cost concerns, nutrition knowledge or health concerns. These factors are generally found to be influential. However, since a range of psychosocial factors are known determinants of food choices in the general population, it is likely that a range of factors also contribute to inequalities in food choices. Identification of additional psychosocial factors of relevance to inequalities in food choices would provide new opportunities for health promotion, including the adaption of existing strategies. The methodological features of previous research have also hindered the advancement of knowledge in this area and a lack of qualitative studies has resulted in a dearth of descriptive information on this topic. This PhD investigation extends previous research by assessing a range of psychosocial factors in relation to inequalities in food choices using both quantitative and qualitative techniques. Secondary data analyses were undertaken using data obtained from two Brisbane-based studies, the Brisbane Food Study (N=1003, conducted in 2000), and the Sixty Families Study (N=60, conducted in 1998). Both studies involved main household food purchasers completing an interviewer-administered survey within their own home. Data pertaining to food-purchasing, and psychosocial, socioeconomic and demographic characteristics were collected in each study. The mutual goals of both the qualitative and quantitative phases of this investigation were to assess socioeconomic differences in food purchasing and to identify psychosocial factors relevant to any observed differences. The quantitative methods then additionally considered whether the associations examined differed according to the socioeconomic indicator used (i.e. income or education). The qualitative analyses made a unique contribution to this project by generating detailed descriptions of socioeconomic differences in psychosocial factors. Those with lower levels of income and education were found to make food purchasing choices less consistent with dietary guidelines compared to those of high SEP. The psychosocial factors identified as relevant to food-purchasing inequalities were: taste preferences, health concerns, health beliefs, nutrition knowledge, nutrition concerns, weight concerns, nutrition label use, and several other values and beliefs unique to particular socioeconomic groups. Factors more tenuously or inconsistently related to socioeconomic differences in food purchasing were cost concerns, and perceived adequacy of the family diet. Evidence was displayed in both the quantitative and qualitative analyses to suggest that psychosocial factors contribute to inequalities in food purchasing in a collective manner. The quantitative analyses revealed that considerable overlap in the socioeconomic variation in food purchasing was accounted for by key psychosocial factors of importance, including taste preference, nutrition concerns, nutrition knowledge, and health concerns. Consistent with these findings, the qualitative transcripts demonstrated the interplay between such influential psychosocial factors in determining food-purchasing choices. The qualitative analyses found socioeconomic differences in the prioritisation of psychosocial factors in relation to food choices. This is suggestive of complex cultural factors that distinguish advantaged and disadvantaged groups and result in socioeconomically distinct schemas related to health and food choices. Compared to those of high SEP, those of lower SEP were less likely to indicate that health concerns, nutrition concerns, or food labels influenced food choices, and exhibited lower levels of nutrition knowledge. In the absence of health or nutrition-related concerns, taste preferences tended to dominate the food purchasing choices of those of low SEP. Overall, while cost concerns did not appear to be a main determinant of socioeconomic differences in food purchasing, this factor had a dominant influence on the food choices of some of the most disadvantaged respondents included in this research. The findings of this study have several implications for health promotion. The integrated operation of psychosocial factors on food purchasing inequalities indicates that multiple psychosocial factors may be appropriate to target in health promotion. It also seems possible that the inter-relatedness of psychosocial factors would allow health promotion targeting a single psychosocial factor to have a flow-on affect in terms of altering other influential psychosocial factors. This research also suggests that current mass marketing approaches to health promotion may not be effective across all socioeconomic groups due to differences in the priorities and main factors of influence in food purchasing decisions across groups. In addition to the practical recommendations for health promotion, this investigation, through the critique of previous research, and through the substantive study findings, has highlighted important methodological considerations for future research. Of particular note are the recommendations pertaining to the selection of socioeconomic indicators, measurement of relevant constructs, consideration of confounders, and development of an analytical approach. Addressing inequalities in health has been noted as a main objective by many health authorities and governments internationally. It is envisaged that the substantive and methodological findings of this thesis will make a useful contribution towards this important goal.

A case study of the Iranian national railway and its absolute capacity expansion using analytical models

Identifying railway capacity is an important task that can identify "in principal" whether the network can handle an intended traffic flow, and whether there is any free capacity left for additional train services. Capacity determination techniques can also be used to identify how best to improve an existing network, and at least cost. In this article an optimization approach has been applied to a case study of the Iran national railway, in order to identify its current capacity and to optimally expand it given a variety of technical conditions. This railway is very important in Iran and will be upgraded extensively in the coming years. Hence the conclusions in this article may help in that endeavor. A sensitivity analysis is recommended to evaluate a wider range of possible scenarios. Hence more useful lower and upper bounds can be provided for the performance of the system

Capacity determination and expansion models for rail networks

This thesis focused upon the development of improved capacity analysis and capacity planning techniques for railways. A number of innovations were made and were tested on a case study of a real national railway. These techniques can reduce the time required to perform decision making activities that planners and managers need to perform. As all railways need to be expanded to meet increasing demands, the presumption that analytical capacity models can be used to identify how best to improve an existing network at least cost, was fully investigated. Track duplication was the mechanism used to expanding a network's capacity, and two variant capacity expansion models were formulated. Another outcome of this thesis is the development and validation of bi objective models for capacity analysis. These models regulate the competition for track access and perform a trade-off analysis. An opportunity to develop more general mulch-objective approaches was identified.

Targeting resource investments to achieve sediment reduction and improved Great Barrier Reef health

Concerns about excessive sediment loads entering the Great Barrier Reef (GBR) lagoon in Australia have led to a focus on improving ground cover in grazing lands. Ground cover has been identified as an important factor in reducing sediment loads, but improving ground cover has been difficult for reef stakeholders in major catchments of the GBR. To provide better information an optimising linear programming model based on paddock scale information in conjunction with land type mapping was developed for the Fitzroy, the largest of the GBR catchments. This identifies at a catchment scale which land types allow the most sediment reduction to be achieved at least cost. The results suggest that from the five land types modelled, the lower productivity land types present the cheapest option for sediment reductions. The study allows more informed decision making for natural resource management organisations to target investments. The analysis highlights the importance of efficient allocation of natural resource management funds in achieving sediment reductions through targeted land type investments. © 2012.

Potential and economics of forestry options for carbon sequestration in India

There is a need to understand the carbon (C) sequestration potential of the forestry option and its financial implications for each country.In India the C emissions from deforestation are estimated to be nearly offset by C sequestration in forests under succession and tree plantations. India has nearly succeeded in stabilizing the area under forests and has adequate forest conservation strategies. Biomass demands for softwood, hardwood and firewood are estimated to double or treble by the year 2020. A set of forestry options were developed to meet the projected biomass needs, and keeping in mind the features of land categories available, three scenarios were developed: potential; demand-driven; and programme-driven scenarios. Adoption of the demand-driven scenario, targeted at meeting the projected biomass needs, is estimated to sequester 78 Mt of C annually after accounting for all emissions resulting from clearfelling and end use of biomass. The demand-driven scenario is estimated to offset 50% of national C emission at 1990 level. The cost per t of C sequestered for forestry options is lower than the energy options considered. The annual investment required for implementing the demand-driven scenario is estimated to be US$ 2.1 billion for six years and is shown to be feasible. Among forestry options, the ranking based on investment cost per t of C sequestered from least cost to highest cost is; natural regeneration-agro-forestry-enhanced natural regeneration (< US$ 2.5/t C)-timber-community-softwood forestry (US$ 3.3 to 7.3 per t of C).

Decentralized Sustainable Energy Planning of Tumkur District, India

Present work shows the feasibility of decentralized energy options for the Tumkur district in India. Decentralized energy planning (DEP) involves scaling down energy planning to subnational or regional scales. The important aspect of the energy planning at decentralized level would be to prepare an area-based DEP to meet energy needs and development of alternate energy sources at least-cost to the economy and environment. The geographical coverage and scale reflects the level at which the analysis takes place, which is an important factor in determining the structure of models. In the present work, DEP modeling under different scenarios has been carried out for Tumkur district of India for the year 2020. DEP model is suitably scaled for obtaining the optimal mix of energy resources and technologies using a computer-based goal programming technique. The rural areas of the Tumkur district have different energy needs. Results show that electricity needs can be met by biomass gasifier technology, using biomass feedstock produced by allocating only 12% of the wasteland in the district at 8 t/ha/yr of biomass productivity. Surplus electricity can be produced by adopting the option of biomass power generation from energy plantations. The surplus electricity generated can be supplied to the grid. The sustainable development scenario is a least cost scenario apart from promoting self-reliance, local employment, and environmental benefits. (C) 2010 American Institute of Chemical Engineers Environ Prog, 30: 248-258, 2011

Impact of Energy Expenditure Rate Constraints on the performance of Multihop Wireless Mesh Networks

In this article we study the problem of joint congestion control, routing and MAC layer scheduling in multi-hop wireless mesh network, where the nodes in the network are subjected to maximum energy expenditure rates. We model link contention in the wireless network using the contention graph and we model energy expenditure rate constraint of nodes using the energy expenditure rate matrix. We formulate the problem as an aggregate utility maximization problem and apply duality theory in order to decompose the problem into two sub-problems namely, network layer routing and congestion control problem and MAC layer scheduling problem. The source adjusts its rate based on the cost of the least cost path to the destination where the cost of the path includes not only the prices of the links in it but also the prices associated with the nodes on the path. The MAC layer scheduling of the links is carried out based on the prices of the links. We study the e�ects of energy expenditure rate constraints of the nodes on the optimal throughput of the network.

Economic tradeoffs in mitigation, due to different atmospheric lifetimes of CO2 and black carbon

Tradeoffs are examined between mitigating black carbon (BC) and carbon dioxide (CO2) for limiting peak global mean warming, using the following set of methods. A two-box climate model is used to simulate temperatures of the atmosphere and ocean for different rates of mitigation. Mitigation rates for BC and CO2 are characterized by respective timescales for e-folding reduction in emissions intensity of gross global product. There are respective emissions models that force the box model. Lastly there is a simple economics model, with cost of mitigation varying inversely with emission intensity. Constant mitigation timescale corresponds to mitigation at a constant annual rate, for example an e-folding timescale of 40 years corresponds to 2.5% reduction each year. Discounted present cost depends only on respective mitigation timescale and respective mitigation cost at present levels of emission intensity. Least-cost mitigation is posed as choosing respective e-folding timescales, to minimize total mitigation cost under a temperature constraint (e.g. within 2 degrees C above preindustrial). Peak warming is more sensitive to mitigation timescale for CO2 than for BC. Therefore rapid mitigation of CO2 emission intensity is essential to limiting peak warming, but simultaneous mitigation of BC can reduce total mitigation expenditure. (c) 2015 Elsevier B.V. All rights reserved.

Phylogeography and non-invasive landscape genetics of the European pine marten ("martes martes" L. 1758) : insights into ancient and contemporary processes shaping genetic variation

283 p. : graf., map.

Evaluation of a new fishing pot trap (Lege) in River Rima, north western Nigeria

Based on the findings and diagnostic survey, a new fishing trap, christened Lege trap was designed and fabricated, and the performance evaluated. The 8-valve Lege trap was assessed concurrently with Malian and Ndurutu traps by the fishermen in the study area. The experiment was conducted in a randomized complete block design with one factor each replicated three times. Data collected on fish diversity number, biomass and size were subjected to descriptive statistics and analysis of variance. The results showed that 22 fish species belonging to thirteen families were caught. The prototype (Lege) trap recorded higher species diversity index (0.90) than the Malian (0.50) and Ndurutu (0.50) traps. The Lege trap also accounted for the largest number (55%) and biomass (63%) of fish caught which was significantly (P<0.05) higher than those of the Malian and Nduutu traps were. The mean length (15.03~c5.70cm), weight (60.43~c48.61g) and girth (4.77~c1.65cm) of fishes caught in the Lege trap were also significantly (P0.05) higher than those caught in the other two traps. These results demonstrated better performance of the new trap than the two conventional traps, even though the sizes of some of the fish species caught in all the traps were below those allowed by the Sokoto State Fisheries Edict where the study was conducted. Therefore, since it is desirable to develop conservation-oriented trap at a least cost, it is necessary to research further on the number of valves and mesh size of the new trap

An economic air pollution control model-application : photochemical smog in Los Angeles County in 1975

An economic air pollution control model, which determines the least cost of reaching various air quality levels, is formulated. The model takes the form of a general, nonlinear, mathematical programming problem. Primary contaminant emission levels are the independent variables. The objective function is the cost of attaining various emission levels and is to be minimized subject to constraints that given air quality levels be attained.

The model is applied to a simplified statement of the photochemical smog problem in Los Angeles County in 1975 with emissions specified by a two-dimensional vector, total reactive hydrocarbon, (RHC), and nitrogen oxide, (NO_x), emissions. Air quality, also two-dimensional, is measured by the expected number of days per year that nitrogen dioxide, (NO₂), and mid-day ozone, (O₃), exceed standards in Central Los Angeles.

The minimum cost of reaching various emission levels is found by a linear programming model. The base or "uncontrolled" emission levels are those that will exist in 1975 with the present new car control program and with the degree of stationary source control existing in 1971. Controls, basically "add-on devices", are considered here for used cars, aircraft, and existing stationary sources. It is found that with these added controls, Los Angeles County emission levels [(1300 tons/day RHC, 1000 tons /day NO_x) in 1969] and [(670 tons/day RHC, 790 tons/day NO_x) at the base 1975 level], can be reduced to 260 tons/day RHC (minimum RHC program) and 460 tons/day NO_x (minimum NO_x program).

"Phenomenological" or statistical air quality models provide the relationship between air quality and emissions. These models estimate the relationship by using atmospheric monitoring data taken at one (yearly) emission level and by using certain simple physical assumptions, (e. g., that emissions are reduced proportionately at all points in space and time). For NO₂, (concentrations assumed proportional to NO_x emissions), it is found that standard violations in Central Los Angeles, (55 in 1969), can be reduced to 25, 5, and 0 days per year by controlling emissions to 800, 550, and 300 tons /day, respectively. A probabilistic model reveals that RHC control is much more effective than NO_x control in reducing Central Los Angeles ozone. The 150 days per year ozone violations in 1969 can be reduced to 75, 30, 10, and 0 days per year by abating RHC emissions to 700, 450, 300, and 150 tons/day, respectively, (at the 1969 NO_x emission level).

The control cost-emission level and air quality-emission level relationships are combined in a graphical solution of the complete model to find the cost of various air quality levels. Best possible air quality levels with the controls considered here are 8 O₃ and 10 NO₂ violations per year (minimum ozone program) or 25 O₃ and 3 NO₂ violations per year (minimum NO₂ program) with an annualized cost of $230,000,000 (above the estimated $150,000,000 per year for the new car control program for Los Angeles County motor vehicles in 1975).