34 results
The Safety, Efficacy, and Expediency of Albuterol Nebulizer Administration by BLS Providers
- Patrick J. Matthews, Douglas R. Ader, Cecelia K. Harrison, Paige J. Ostahowski, Jason T. Nomura
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- Journal:
- Prehospital and Disaster Medicine / Volume 38 / Issue 2 / April 2023
- Published online by Cambridge University Press:
- 01 March 2023, pp. 149-152
- Print publication:
- April 2023
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Introduction:
Many Emergency Medical Service (EMS) systems in the United States restrict albuterol therapy by scope of practice to Advanced Life Support (ALS). The State of Delaware has a two-tiered EMS system in which Basic Life Support (BLS) arrives on scene prior to ALS in the majority of respiratory distress calls.
Study Objective:This study sought to evaluate the safety, efficacy, and expedience of albuterol administration by BLS compared to ALS.
Methods:This retrospective observational study used data collected from July 2015 through January 2017 throughout a State BLS albuterol pilot program. Pilot BLS agencies participated in a training session on the indications and administration of albuterol, and were then authorized to carry and administer nebulized albuterol. Heart rate (HR), respiratory rate (RR), and pulse oximetry (spO2) were obtained before and after albuterol administration by BLS and ALS. The times from BLS arrival to the administration of albuterol by pilot BLS agencies versus ALS were compared. Study encounters required both BLS and ALS response. Data were analyzed using chi-square and t-test as appropriate.
Results:Three hundred eighty-eight (388) incidents were reviewed. One hundred eighty-five (185) patients received albuterol by BLS pilot agencies and 203 patients received albuterol by ALS. Of note, the population treated by ALS was significantly older than the population treated by BLS (61.9 versus 51.6 years; P <.001). A comparison of BLS arrival time to albuterol administration time showed significantly shorter times in the BLS pilot group compared to the ALS group (3.50 minutes versus 8.00 minutes, respectively; P <.001). After albuterol administration, BLS pilot patients showed improvements in HR (P <.01), RR (P <.01), and spO2 (P <.01). Alternately, ALS treatment patients showed improvement in spO2 (P <.01) but not RR (P = .17) or HR (P = 1.00). Review by ALS or hospital staff showed albuterol was indicated in 179 of 185 BLS patients and administered correctly in 100% of these patients.
Conclusion:Patients both received albuterol significantly sooner and showed superior improvements in vital signs when treated by BLS agencies carrying albuterol rather than by BLS agencies who required ALS arrival for albuterol. Two-tiered EMS systems should consider allowing BLS to carry and administer albuterol for safe, effective, and expedient treatment of respiratory distress patients amenable to albuterol therapy.
Reconstructing grains in 3D through 4D Scanning Precession Electron Diffraction
- Patrick Harrison, Xuyang Zhou, Saurabh Mohan Das, Nicola Viganò, Pierre Lhuissier, Michael Herbig, Wolfgang Ludwig, Edgar Rauch
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- Journal:
- Microscopy and Microanalysis / Volume 27 / Issue S1 / August 2021
- Published online by Cambridge University Press:
- 30 July 2021, pp. 2494-2495
- Print publication:
- August 2021
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Contributors
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- By Lenard A. Adler, Pinky Agarwal, Rehan Ahmed, Jagga Rao Alluri, Fawaz Al-Mufti, Samuel Alperin, Michael Amoashiy, Michael Andary, David J. Anschel, Padmaja Aradhya, Vandana Aspen, Esther Baldinger, Jee Bang, George D. Baquis, John J. Barry, Jason J. S. Barton, Julius Bazan, Amanda R. Bedford, Marlene Behrmann, Lourdes Bello-Espinosa, Ajay Berdia, Alan R. Berger, Mark Beyer, Don C. Bienfang, Kevin M. Biglan, Thomas M. Boes, Paul W. Brazis, Jonathan L. Brisman, Jeffrey A. Brown, Scott E. Brown, Ryan R. Byrne, Rina Caprarella, Casey A. Chamberlain, Wan-Tsu W. Chang, Grace M. Charles, Jasvinder Chawla, David Clark, Todd J. Cohen, Joe Colombo, Howard Crystal, Vladimir Dadashev, Sarita B. Dave, Jean Robert Desrouleaux, Richard L. Doty, Robert Duarte, Jeffrey S. Durmer, Christyn M. Edmundson, Eric R. Eggenberger, Steven Ender, Noam Epstein, Alberto J. Espay, Alan B. Ettinger, Niloofar (Nelly) Faghani, Amtul Farheen, Edward Firouztale, Rod Foroozan, Anne L. Foundas, David Elliot Friedman, Deborah I. Friedman, Steven J. Frucht, Oded Gerber, Tal Gilboa, Martin Gizzi, Teneille G. Gofton, Louis J. Goodrich, Malcolm H. Gottesman, Varda Gross-Tsur, Deepak Grover, David A. Gudis, John J. Halperin, Maxim D. Hammer, Andrew R. Harrison, L. Anne Hayman, Galen V. Henderson, Steven Herskovitz, Caitlin Hoffman, Laryssa A. Huryn, Andres M. Kanner, Gary P. Kaplan, Bashar Katirji, Kenneth R. Kaufman, Annie Killoran, Nina Kirz, Gad E. Klein, Danielle G. Koby, Christopher P. Kogut, W. Curt LaFrance, Patrick J.M. Lavin, Susan W. Law, James L. Levenson, Richard B. Lipton, Glenn Lopate, Daniel J. Luciano, Reema Maindiratta, Robert M. Mallery, Georgios Manousakis, Alan Mazurek, Luis J. Mejico, Dragana Micic, Ali Mokhtarzadeh, Walter J. Molofsky, Heather E. Moss, Mark L. Moster, Manpreet Multani, Siddhartha Nadkarni, George C. Newman, Rolla Nuoman, Paul A. Nyquist, Gaia Donata Oggioni, Odi Oguh, Denis Ostrovskiy, Kristina Y. Pao, Juwen Park, Anastas F. Pass, Victoria S. Pelak, Jeffrey Peterson, John Pile-Spellman, Misha L. Pless, Gregory M. Pontone, Aparna M. Prabhu, Michael T. Pulley, Philip Ragone, Prajwal Rajappa, Venkat Ramani, Sindhu Ramchandren, Ritesh A. Ramdhani, Ramses Ribot, Heidi D. Riney, Diana Rojas-Soto, Michael Ronthal, Daniel M. Rosenbaum, David B. Rosenfield, Durga Roy, Michael J. Ruckenstein, Max C. Rudansky, Eva Sahay, Friedhelm Sandbrink, Jade S. Schiffman, Angela Scicutella, Maroun T. Semaan, Robert C. Sergott, Aashit K. Shah, David M. Shaw, Amit M. Shelat, Claire A. Sheldon, Anant M. Shenoy, Yelizaveta Sher, Jessica A. Shields, Tanya Simuni, Rajpaul Singh, Eric E. Smouha, David Solomon, Mehri Songhorian, Steven A. Sparr, Egilius L. H. Spierings, Eve G. Spratt, Beth Stein, S.H. Subramony, Rosa Ana Tang, Cara Tannenbaum, Hakan Tekeli, Amanda J. Thompson, Michael J. Thorpy, Matthew J. Thurtell, Pedro J. Torrico, Ira M. Turner, Scott Uretsky, Ruth H. Walker, Deborah M. Weisbrot, Michael A. Williams, Jacques Winter, Randall J. Wright, Jay Elliot Yasen, Shicong Ye, G. Bryan Young, Huiying Yu, Ryan J. Zehnder
- Edited by Alan B. Ettinger, Albert Einstein College of Medicine, New York, Deborah M. Weisbrot, State University of New York, Stony Brook
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- Book:
- Neurologic Differential Diagnosis
- Published online:
- 05 June 2014
- Print publication:
- 17 April 2014, pp xi-xx
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Contributors
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- By Eric L. Anderson, Dennis Barton, Annette L. Beautrais, O. Joseph Bienvenu, Ashley D. Bone, Curtis Bone, Sharon Bord, Emily Bost-Baxter, Arjun Chanmugam, Michael Clark, J. Raymond DePaulo, Emily Frosch, Angela S. Guarda, James Harrison, Frederick Houts, Lisa S. Hovermale, Geetha Jayaram, Patrick Kelly, Gregory Luke Larkin, Valerie R. Lint, Cynthia Major-Lewis, Catherine A. Marco, Darren Mareiniss, Dave Milzman, Melinda J. Ortmann, Theodosia Paclawskyj, Graham W. Redgrave, Paul P. Rega, Mustapha Saheed, Eric Samstad, Karen Swartz, Dyanne Simpson, Hahn Soe-Lin, Roshni I. Thakore, Glenn Treisman, Patrick Triplett, Crystal Watkins, Holly C. Wilcox
- Edited by Arjun Chanmugam, Patrick Triplett, Gabor Kelen
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- Book:
- Emergency Psychiatry
- Published online:
- 05 May 2013
- Print publication:
- 09 May 2013, pp viii-x
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16 - Multinational corporations and international project appraisal
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
- Published online:
- 14 May 2010
- Print publication:
- 17 October 2002, pp 297-312
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Summary
So far in this book, investment projects have been analysed on the understanding that they were domiciled completely within a single country. This approach has allowed the discussion to proceed in the absence of the extra layer of analysis that is required when investments are located outside a firm's home country. In this chapter, we will relax this assumption and investigate the situation where a firm is considering investing in a project in another country.
Multinational corporations (MNC) frequently invest in foreign countries through their subsidiaries established in those foreign countries (also called ‘host countries’). These subsidiaries may be viewed as the MNCs' ‘investment arms’, or ‘business arms’, in host countries.
Multinational corporations' foreign investment analysis is complicated by a variety of factors and risks that are not encountered by purely domestic firms or purely national investments. These complicating factors and risks stem from:
involvement of more than one company – the existence of parent and subsidiary
involvement of more than one country – home (or parent's) country and host (or foreign or subsidiary's) country
tax differentials between home and host countries
requirement to convert funds from one currency to another currency and the associated risks due to unpredictable exchange rate movements
country risk: the host country's political, social, economic and financial risk factors.
The basic concepts, principles and techniques of project analysis still apply to multinational corporations' foreign investments.
2 - Project cash flows
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
- Published online:
- 14 May 2010
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- 17 October 2002, pp 12-36
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Summary
An important part of the capital budgeting process is the estimation of the cash flows associated with the proposed project. Any new project will cause a change in the firm's cash flows. In evaluating an investment proposal, we must consider these expected changes in the firm's cash flows and decide whether or not they add value to the firm. Successful investment decisions will increase the shareholders' wealth through increased cash flows.
Valuing projects by estimating their net present values (NPV) of future cash flows is a means of gaining an idea of their expected addition to shareholder wealth. Correct identification of the relevant cash flows associated with an investment project is one of the most important steps in the calculation of NPV or in the project appraisal. Cash flow is a very simple concept, although it is easily confused with accounting profit or income. Cash flows are simply the dollars received and dollars paid out by the firm at particular points in time.
The focus of project analysis is on cash flows because they easily measure the impact upon the firm's wealth. Profit and loss in financial statements do not always represent the net increase or decrease in cash flows. Cash flows occur at different times and these times are easily identifiable. The timing of flows is particularly important in project analysis. Some of the figures in standard financial statements, such as income statements or profit and loss accounts, may not have a corresponding cash flow effect for the same period; some of their actual cash flows may occur in the future or might already have occurred in the past.
7 - Project analysis under risk
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
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- 14 May 2010
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- 17 October 2002, pp 114-132
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Summary
The previous chapter discussed project analysis under certainty, i.e. in a no-risk situation. In reality, however, the future cash flows of a project are not certain. Cash flows cannot be forecast with absolute accuracy. These are estimates of what is expected in the future, not necessarily what will be realized in the future. Sometimes, even the initial capital outlay can be uncertain and subject to high estimation errors. For example, in 1987 the cost of the Channel Tunnel (between Britain and France) was estimated to be $12 billion, but later this was increased to about $22 billion. The Sydney Opera House is another famous example of a large cost increase over the initial estimate.
In the previous chapter, one single series – the best estimate of the project's future cash flows – was used to compute the net present value. This series may be viewed as the best estimate of a range of possible outcomes. For example, in Chapters 2 and 6, the Delta Project was considered and its first year's sales were expected to be $345,553. This was the best estimate. But this amount could eventually prove to have been under or over the actual sales that the project generated. This sales forecast was arrived at by estimating the sales units on the basis of past sales and assuming a unit selling price of $0.50. However, the actual selling price might be different to this forecast value.
Index
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
- Published online:
- 14 May 2010
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- 17 October 2002, pp 316-321
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13 - Financial modelling case study in forestry project evaluation
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
- Published online:
- 14 May 2010
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- 17 October 2002, pp 236-250
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Summary
This chapter presents a case study of the structure and application of financial models for the evaluation of investment projects in forestry. Forestry projects provide a good example of capital budgeting in that forestry is a very long-term investment, i.e. one with substantial initial outlays but with main cash inflows typically more than thirty years later. The case study illustrates principles which would apply equally well to other forms of long-term investment, e.g. in mining, energy, tourism or manufacturing.
While one-off models can be developed to evaluate individual forestry projects, the cash outflows and inflows of all forestry projects have much in common, giving rise to the opportunity to develop a generic model which can be used to evaluate a variety of separate investment projects by a variety of users. Generic models are often large and complex, and involve considerable development effort. A generic model has important advantages over a one-off model for investment areas where projects with similar features repeatedly arise. Development costs can be spread over a number of applications or users. Greater expertise can be brought to bear in the design, development and testing of the model, and it is often possible to include a greater range of features or capabilities in the model.
When designing and developing a generic model, a number of important questions need to be addressed: What is the model to be used for?
4 - Forecasting cash flows: qualitative or judgemental techniques
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
- Published online:
- 14 May 2010
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- 17 October 2002, pp 55-73
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Summary
In a perfect world, all cash flows associated with a project would be known with certainty. However, this obviously is not the case, and it is common for estimates of parameters determining cash flows to be derived by a number of techniques. In Chapter 3, the importance of forecasting cash flows was discussed and a number of quantitative means of estimating cash flows were outlined.
Quantitative forecasting techniques can be used when (1) past information about the values being forecast is available, and (2) this information can be quantified. Past information is not, however, always available or relevant. For example, for a new product, there are no data on sales on which to base estimates of future sales. Similarly, past sales of a product might not be relevant if a competitor launches a new product with superior features or performance. In other situations, there is insufficient time to obtain data or use quantitative techniques, or circumstances are changing so rapidly that a statistically based forecast would be of little guidance. Even when statistical techniques are available there is strong evidence that human judgement is the overwhelming choice of managers for forecasting. Further, managers appear to be more comfortable dealing with their own judgements or with those of a colleague, than with forecasts generated via a computer package and lacking transparency. Even when quantitative techniques are used, estimates may be combined with qualitative judgements, or supplemented, reviewed or screened by subjecting them to qualitative judgements.
14 - Property investment analysis
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
- Published online:
- 14 May 2010
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- 17 October 2002, pp 251-273
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Summary
Property (or real estate) is any interest in land and its buildings. Purchasing or leasing a property is a capital budgeting decision that can be evaluated in the ways described in the preceding chapters. In many ways, the decision is no different to acquiring any asset that is expected to provide returns in the future. We apply all the usual principles of capital budgeting but there are some features of property investments and developments that require special care.
There are four main features of properties that distinguish them from most other assets. First, buying a property is an ‘all-or-nothing’ decision with limited choice at any time. Investors must often compromise in their search for the right property and yet the outlay is often the largest made by both private investors and businesses. Properties are not uniform, partial acquisitions are generally impractical and there are sizable transaction costs. Except for property developments, the only way to alter the scale of the investment is to change the financing arrangements.
A second distinction of properties is that each is financed separately, rather than drawing on a pool of company funds. Typically, the financing may be with a mortgage secured over the legal interest of the investor or company. This is one reason why the property and its financing are commonly evaluated together. This is a significant departure from the corporate approach explained in Chapter 2, in which it was pointed out that properties are commonly evaluated by working out their ‘equity’ cash flows.
1 - Capital budgeting: an overview
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
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- 14 May 2010
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- 17 October 2002, pp 1-11
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Summary
Financial management is largely concerned with financing, dividend and investment decisions of the firm with some overall goal in mind. Corporate finance theory has developed around a goal of maximizing the market value of the firm to its shareholders. This is also known as shareholder wealth maximization. Although various objectives or goals are possible in the field of finance, the most widely accepted objective for the firm is to maximize the value of the firm to its owners.
Financing decisions deal with the firm's optimal capital structure in terms of debt and equity. Dividend decisions relate to the form in which returns generated by the firm are passed on to equity-holders. Investment decisions deal with the way funds raised in financial markets are employed in productive activities to achieve the firm's overall goal; in other words, how much should be invested and what assets should be invested in. Throughout this book it is assumed that the objective of the investment or capital budgeting decision is to maximize the market value of the firm to its shareholders. The relationship between the firm's overall goal, financial management and capital budgeting is depicted in Figure 1.1. This self-explanatory chart helps the reader to easily visualize and retain a picture of the capital budgeting function within the broader perspective of corporate finance.
Funds are invested in both short-term and long-term assets. Capital budgeting is primarily concerned with sizable investments in long-term assets.
15 - Forecasting and analysing risks in property investments
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
- Published online:
- 14 May 2010
- Print publication:
- 17 October 2002, pp 274-296
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Summary
Property investments put capital at risk. The outcomes almost always vary from the best estimates that we make when evaluating the property. Forecasting outcomes and gauging the risks of not achieving those outcomes are critical in applying capital budgeting techniques to property decisions. This chapter can therefore be seen as a supplement to Chapter 14 which illustrated the methods of property investment analysis.
The early part of Chapter 15 provides guidelines for forecasting cash flows arising from property investments. There will always be doubts about the reliability of forecasts of property rents, operating expenses and capital values, but what is the alternative to forecasting? Purchasing (or leasing) real estate without forecasting cash flows would require that the company or other investor accept the asking or market price (or rent). The ‘going price’ is derived from other people's opinions of the prospects and worth of the property to them. Thus, however difficult forecasting may be, it is surely better than investing blind. The solution lies in selecting the information and methods of forecasting that are most likely to help in estimating cash flows. The most reliable forecasts for real estate often combine the use of quantitative techniques (Chapter 3) and judgemental techniques (Chapter 4).
The later part of this chapter describes ways in which we can assess the risks of not achieving the investment objectives. Risk analysis is needed because of the uncertainty of the cash flows that we forecast.
10 - Case study in financial modelling and simulation of a forestry investment
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
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- 14 May 2010
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- 17 October 2002, pp 185-203
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Summary
Forestry projects are a form of capital investment with a particularly long time horizon, and as such present an interesting case of capital budgeting. In many countries, plantation forestry has traditionally been the domain of government agencies, e.g. the national Forestry Commission in the UK and the various state forestry services in Australia. Timber was considered to be a critical economic resource but the long production period meant that only governments had the long-term perspective and capacity to enter into forestry investments. This situation has changed markedly in the past fifty years, with probably the bulk of forestry investment in most countries being undertaken by private companies and individuals. The need for the establishment of plantations has been stimulated by decreasing supplies of timber from native forests through unsustainable logging practices, and the withdrawal of large areas from logging because of their being set aside as protected areas, particularly in tropical counties.
An integral component of investment in forestry is the need for financial information about the likely cash flows associated with the establishment, management and final harvest of a plantation. This chapter uses the development of a financial model for forestry investment as a case study of financial modelling. The financial evaluation of forestry projects poses many challenges and this chapter examines the key parameters for forestry appraisal and some of the problems faced by developers of financial models.
References
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
- Published online:
- 14 May 2010
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- 17 October 2002, pp 313-315
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List of figures
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
- Published online:
- 14 May 2010
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- 17 October 2002, pp xiii-xiii
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8 - Sensitivity and break-even analysis
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
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- 14 May 2010
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- 17 October 2002, pp 133-152
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Summary
In Chapter 7, two methods of dealing with risk in investment analysis were covered. These focused on capturing risk by using an appropriate discount rate, or by employing a certainty equivalent coefficient. The risk-adjusted discount rate method sought to find a discount rate that reflected the comparative risk of the project. The presumption in this type of risk measurement is that the higher the risk, the higher the required rate of return. The certainty equivalent method sought to convert all uncertain future cash flows to their equivalent amounts to be received with certainty, and then to discount these flows at the risk-free rate.
This chapter introduces two mechanical methods for analysing projects under risk. The aim of these methods is to discover which variables (or parameters) have the greatest impact on the project's outcome. The first method is known as sensitivity analysis. In this process, individual forecasted variables are progressively stepped through their pessimistic, most likely and optimistic levels, to determine which variables cause the largest shifts in the project's net present value. For example, management may wish to know whether optimistic or pessimistic unit sales prices have greater impacts than optimistic and pessimistic values of sales growth rates.
The second method is known as break-even analysis. This process determines how low an income variable can fall, or how high a cost variable can rise, before the project breaks even at a net present value of zero.
9 - Simulation concepts and methods
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Capital Budgeting
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- 14 May 2010
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- 17 October 2002, pp 153-184
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Summary
The term ‘simulation’ is widely used nowadays, and most people have their own view of its meaning. In general, to ‘simulate’ means to mimic or capture the essence of something, without attaining reality. In management applications, simulation typically involves developing a model of a business or economic system, and then performing experiments using this model to predict how the real system would behave under a range of management policies. In that financial models have been used repeatedly in earlier chapters, the importance of modelling will come as no surprise here. But when discussing simulation, attention to aspects of modelling becomes even more important since simulation models are often highly complex representations of business systems.
While many quantitative techniques take a well-recognized form, simulation differs in its great flexibility, variety of applications and variations in form. These features, while highly valuable for modelling complex business systems, make this a difficult methodology to explain and to comprehend. In fact, simulation has been described as ‘more art than science’. Proficiency with this technique cannot readily be gained in the classroom. Considerable hands-on experience from repeatedly designing, developing and performing experiments with a number of different models is also necessary. But even for readers who will not be engaged in developing complex models, an understanding of simulation concepts is indispensable because of the widespread use of this methodology.
The financial models encountered in earlier chapters, typically developed on a spread-sheet, may be regarded as a form of simulation.
6 - Project analysis under certainty
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
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- 14 May 2010
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- 17 October 2002, pp 91-113
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Summary
In the previous chapters, we have discussed the identification and estimation of project cash flows and illustrated the mathematical formulae essential for project evaluation. This chapter now uses these elements for investment analysis. There are two groups of project evaluation techniques: discounted cash flow (DCF) analysis and non-discounted cash flow (NDCF) analysis. The first group includes the net present value (NPV) and the internal rate of return (IRR). The second group includes the payback period (PP) and the accounting rate of return (ARR).
Generally, DCF analysis is preferred to NDCF analysis. Within DCF analysis, the theoretical and practical strengths of NPV and IRR differ. Theoretically, the NPV approach to project evaluation is superior to that of IRR. The NPV technique discounts all future project cash flows to the present day to see whether there is a net benefit or loss to the firm from investing in the project. If the NPV is positive, then the project will increase the wealth of the firm. If it is zero, then the project will return only the required rate of return, and will not increase the firm's wealth. If the NPV is negative, then the project will decrease the value of the firm and should be avoided.
In spite of the theoretical superiority of the NPV technique, project analysts and decision-makers sometimes prefer to use the IRR criterion. The preference for IRR is attributable to the general familiarity of managers and other business people with rates of return rather than with actual dollar returns (values).
Contents
- Don Dayananda, Central Queensland University, Richard Irons, Central Queensland University, Steve Harrison, University of Queensland, John Herbohn, University of Queensland, Patrick Rowland, Curtin University of Technology, Perth
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- Book:
- Capital Budgeting
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- 14 May 2010
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- 17 October 2002, pp v-xii
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