Ambient Noise in the Kitimat Fjord System

Sound is an important medium for communication and marine organisms have evolved to capitalize on the efficiency with which sound energy travels through water. Anthropogenic and natural sound sources contribute to ocean ambient noise, which can interfere with the use of this sensory modality by marine animals. Anthropogenic noise sources have been increasing steadily over recent decades largely due to coastal population growth, increased global transportation, and offshore industrialization. Understanding the potential impacts of anthropogenic noise requires the establishment of ambient acoustic baselines from which to measure change. Establishing baselines, especially in quiet areas still largely unaffected by anthropogenic stressors, is particularly crucial in the face of the expansion of offshore industries, increasing coastal population and growing reliance on the ocean for global transportation. Global demand for liquid natural gas (LNG), catalyzed primarily by a growing Asian market, is expected to increase significantly in the next 20 years. The geographic position of British Columbia relative to these markets, a growing supply of LNG and new technology for extraction and shipping situate British Columbia as a strong competitor in the lucrative market. The LNG industry could have many adverse impacts on these territories and ecosystems. The Kitimat Fjord System is slated for the development of these LNG export facilities increasing shipping traffic for the port and thus increasing ambient noise in the fjord system. The purpose of this study is to 1) quantify the existing sound levels in the area surrounding Gil Island and 2) identify potential source mechanisms in order to provide a baseline study of the acoustic environment in the Kitimat Fjord system prior to potential increases from LNG shipping.

Improving supply chain performance: Real-time demand information and flexible deliveries

In some supply chains, materials are ordered periodically according to local information. This paper investigates how to improve the performance of such a supply chain. Specifically, we consider a serial inventory system in which each stage implements a local reorder interval policy; i.e., each stage orders up to a local basestock level according to a fixed-interval schedule. A fixed cost is incurred for placing an order. Two improvement strategies are considered: (1) expanding the information flow by acquiring real-time demand information and (2) accelerating the material flow via flexible deliveries. The first strategy leads to a reorder interval policy with full information; the second strategy leads to a reorder point policy with local information. Both policies have been studied in the literature. Thus, to assess the benefit of these strategies, we analyze the local reorder interval policy. We develop a bottom-up recursion to evaluate the system cost and provide a method to obtain the optimal policy. A numerical study shows the following: Increasing the flexibility of deliveries lowers costs more than does expanding information flow; the fixed order costs and the system lead times are key drivers that determine the effectiveness of these improvement strategies. In addition, we find that using optimal batch sizes in the reorder point policy and demand rate to infer reorder intervals may lead to significant cost inefficiency. © 2010 INFORMS.

A COMPARISON OF MARKET CLEARING TOOLS IN ELECTRICITY SYSTEMS

To maintain a strict balance between demand and supply in the US power systems, the Independent System Operators (ISOs) schedule power plants and determine electricity prices using a market clearing model. This model determines for each time period and power plant, the times of startup, shutdown, the amount of power production, and the provisioning of spinning and non-spinning power generation reserves, etc. Such a deterministic optimization model takes as input the characteristics of all the generating units such as their power generation installed capacity, ramp rates, minimum up and down time requirements, and marginal costs for production, as well as the forecast of intermittent energy such as wind and solar, along with the minimum reserve requirement of the whole system. This reserve requirement is determined based on the likelihood of outages on the supply side and on the levels of error forecasts in demand and intermittent generation. With increased installed capacity of intermittent renewable energy, determining the appropriate level of reserve requirements has become harder. Stochastic market clearing models have been proposed as an alternative to deterministic market clearing models. Rather than using a fixed reserve targets as an input, stochastic market clearing models take different scenarios of wind power into consideration and determine reserves schedule as output. Using a scaled version of the power generation system of PJM, a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia, and wind scenarios generated from BPA (Bonneville Power Administration) data, this paper explores a comparison of the performance between a stochastic and deterministic model in market clearing. The two models are compared in their ability to contribute to the affordability, reliability and sustainability of the electricity system, measured in terms of total operational costs, load shedding and air emissions. The process of building the models and running for tests indicate that a fair comparison is difficult to obtain due to the multi-dimensional performance metrics considered here, and the difficulty in setting up the parameters of the models in a way that does not advantage or disadvantage one modeling framework. Along these lines, this study explores the effect that model assumptions such as reserve requirements, value of lost load (VOLL) and wind spillage costs have on the comparison of the performance of stochastic vs deterministic market clearing models.

Modeling economy-wide vs sectoral climate policies using combined aggregate-sectoral models

Economic analyses of climate change policies frequently focus on reductions of energy-related carbon dioxide emissions via market-based, economy-wide policies. The current course of environment and energy policy debate in the United States, however, suggests an alternative outcome: sector-based and/or inefficiently designed policies. This paper uses a collection of specialized, sector-based models in conjunction with a computable general equilibrium model of the economy to examine and compare these policies at an aggregate level. We examine the relative cost of different policies designed to achieve the same quantity of emission reductions. We find that excluding a limited number of sectors from an economy-wide policy does not significantly raise costs. Focusing policy solely on the electricity and transportation sectors doubles costs, however, and using non-market policies can raise cost by a factor of ten. These results are driven in part by, and are sensitive to, our modeling of pre-existing tax distortions. Copyright © 2006 by the IAEE. All rights reserved.

Future requirements for and supply of ophthalmologists for an aging population in Singapore.

BACKGROUND: Singapore's population, as that of many other countries, is aging; this is likely to lead to an increase in eye diseases and the demand for eye care. Since ophthalmologist training is long and expensive, early planning is essential. This paper forecasts workforce and training requirements for Singapore up to the year 2040 under several plausible future scenarios. METHODS: The Singapore Eye Care Workforce Model was created as a continuous time compartment model with explicit workforce stocks using system dynamics. The model has three modules: prevalence of eye disease, demand, and workforce requirements. The model is used to simulate the prevalence of eye diseases, patient visits, and workforce requirements for the public sector under different scenarios in order to determine training requirements. RESULTS: Four scenarios were constructed. Under the baseline business-as-usual scenario, the required number of ophthalmologists is projected to increase by 117% from 2015 to 2040. Under the current policy scenario (assuming an increase of service uptake due to increased awareness, availability, and accessibility of eye care services), the increase will be 175%, while under the new model of care scenario (considering the additional effect of providing some services by non-ophthalmologists) the increase will only be 150%. The moderated workload scenario (assuming in addition a reduction of the clinical workload) projects an increase in the required number of ophthalmologists of 192% by 2040. Considering the uncertainties in the projected demand for eye care services, under the business-as-usual scenario, a residency intake of 8-22 residents per year is required, 17-21 under the current policy scenario, 14-18 under the new model of care scenario, and, under the moderated workload scenario, an intake of 18-23 residents per year is required. CONCLUSIONS: The results show that under all scenarios considered, Singapore's aging and growing population will result in an almost doubling of the number of Singaporeans with eye conditions, a significant increase in public sector eye care demand and, consequently, a greater requirement for ophthalmologists.

Dynamic Life Cycle Assessment Modeling Approaches for Transboundary Energy Feedstocks

The rise of the twenty-first century has seen the further increase in the industrialization of Earth’s resources, as society aims to meet the needs of a growing population while still protecting our environmental and natural resources. The advent of the industrial bioeconomy – which encompasses the production of renewable biological resources and their conversion into food, feed, and bio-based products – is seen as an important step in transition towards sustainable development and away from fossil fuels. One sector of the industrial bioeconomy which is rapidly being expanded is the use of biobased feedstocks in electricity production as an alternative to coal, especially in the European Union.

As bioeconomy policies and objectives increasingly appear on political agendas, there is a growing need to quantify the impacts of transitioning from fossil fuel-based feedstocks to renewable biological feedstocks. Specifically, there is a growing need to conduct a systems analysis and potential risks of increasing the industrial bioeconomy, given that the flows within it are inextricably linked. Furthermore, greater analysis is needed into the consequences of shifting from fossil fuels to renewable feedstocks, in part through the use of life cycle assessment modeling to analyze impacts along the entire value chain.

To assess the emerging nature of the industrial bioeconomy, three objectives are addressed: (1) quantify the global industrial bioeconomy, linking the use of primary resources with the ultimate end product; (2) quantify the impacts of the expaning wood pellet energy export market of the Southeastern United States; (3) conduct a comparative life cycle assessment, incorporating the use of dynamic life cycle assessment, of replacing coal-fired electricity generation in the United Kingdom with wood pellets that are produced in the Southeastern United States.

To quantify the emergent industrial bioeconomy, an empirical analysis was undertaken. Existing databases from multiple domestic and international agencies was aggregated and analyzed in Microsoft Excel to produce a harmonized dataset of the bioeconomy. First-person interviews, existing academic literature, and industry reports were then utilized to delineate the various intermediate and end use flows within the bioeconomy. The results indicate that within a decade, the industrial use of agriculture has risen ten percent, given increases in the production of bioenergy and bioproducts. The underlying resources supporting the emergent bioeconomy (i.e., land, water, and fertilizer use) were also quantified and included in the database.

Following the quantification of the existing bioeconomy, an in-depth analysis of the bioenergy sector was conducted. Specifically, the focus was on quantifying the impacts of the emergent wood pellet export sector that has rapidly developed in recent years in the Southeastern United States. A cradle-to-gate life cycle assessment was conducted in order to quantify supply chain impacts from two wood pellet production scenarios: roundwood and sawmill residues. For reach of the nine impact categories assessed, wood pellet production from sawmill residues resulted in higher values, ranging from 10-31% higher.

The analysis of the wood pellet sector was then expanded to include the full life cycle (i.e., cradle-to-grave). In doing to, the combustion of biogenic carbon and the subsequent timing of emissions were assessed by incorporating dynamic life cycle assessment modeling. Assuming immediate carbon neutrality of the biomass, the results indicated an 86% reduction in global warming potential when utilizing wood pellets as compared to coal for electricity production in the United Kingdom. When incorporating the timing of emissions, wood pellets equated to a 75% or 96% reduction in carbon dioxide emissions, depending upon whether the forestry feedstock was considered to be harvested or planted in year one, respectively.

Finally, a policy analysis of renewable energy in the United States was conducted. Existing coal-fired power plants in the Southeastern United States were assessed in terms of incorporating the co-firing of wood pellets. Co-firing wood pellets with coal in existing Southeastern United States power stations would result in a nine percent reduction in global warming potential.