302 Vogt et al.: The Costs of Maintaining and Not Maintaining the Urban Forest the four species (Churack et al. 1994). Zillmer et al. (2000) presented an updated productivity tim- ing system for tree climbing training in Milwaukee, Wisconsin, U.S. The application of this technique in other regions would yield robust estimates of costs of pruning municipal tree populations, when combined with tree growth rates, tree population characteris- tics (e.g., tree size and tree species), and labor costs. Utility pruning costs Pruning is performed extensively by utility com- panies and many articles discussing the economics of pruning are from a utility forestry perspective. O’Brien et al. (1992) reported that pruning takes longer for trees under utility wires. The cost of not pruning around utility wires or poles is clear: a lack of tree pruning can result in tree or branch failures or interference with utility lines (e.g., phone, elec- tricity, cable, internet) during storm events, result- ing in costly clean-up, repair, lost customer bill- ing time, safety issues resulting in human injury or death, and more (Medicky 1976; Perry 1977; Dykes 1980; Ulrich 1983; Johnstone 1988; Kuntz et al. 2002; Guikema et al. 2006). Utility pruning, therefore, is frequently examined in the context of “what can be purchased by budget expenditures for tree trimming in terms of reliability” (Perry 1977, p. 157). Concepts like the “minimum permissible clearance” distance between tree branches and util- ity infrastructure (Medicky 1976, p. 56), pole-miles of line maintained (Ulrich 1983), “optimal” main- tenance scheduling algorithms (Kuntz et al. 2002), and other performance criteria [e.g., cost-effective- ness of pruning efforts (David 1979; Holewinski and Johnson 1983); time efficiency (Henning 1990)] in- fuse the utility pruning literature. Ultimately, utility companies have a strong economic incentive (i.e., profit margin) for optimal allocation of resources toward pruning efforts. Avoiding fines for electrical outages also factors into resource allocation. Authors who have most comprehensively exam- ined the costs of deferring utility pruning include Browning and Wiant (1997), Kuntz et al. (2002), and Goodfellow and Kayihan (2013). Browning and Wiant (1997) analyzed the time and costs of pruning utility trees in terms of time per tree as time varies by several factors, including time since last pruning, pre-work clearance distance, branch length, time of pruning, and tree diameter. Results indicate that ©2015 International Society of Arboriculture for each additional year maintenance is deferred past the optimum pruning cycle length, USD $1 saved now will have to be replaced by $1.47–$1.69 of spending four years in the future, and will yield approximately twice the amount of pruning waste for disposal (Browning and Wiant 1997). Kuntz et al. (2002) presented a “quantitative approach to maintenance scheduling” aimed at reducing the cost of maintenance activities and increasing the reliability of utility service (p. 1164). The authors used an optimization problem approach to mini- mize or maximize one of three objective functions with respect to maintenance crew availability: mini- mize total cost of reliability, minimize cost per a given reliability, or maximize reliability for a given cost (Kuntz et al. 2002). The cost of reliability was estimated as customer willingness-to-pay to avoid a power outage; the cost of maintenance efforts was computed as cost per mile of line maintained (Kuntz et al. 2002). Results indicated that computer- optimized maintenance schedules improved the interruption frequency index (a measure of utility system reliability where greater index values indi- cate better performance) by 4%–6.5% over a fixed- interval maintenance schedule (Kuntz et al. 2002). Most recently, Goodfellow and Kayihan (2013) conducted a comprehensive review of the models used in scheduling utility pruning and identified five commonly used models: clearance, cost of deferral, reliability, annual increment, and regu- latory mandate. These authors then presented a probability-based “bow-tie analysis” model that weighs an acceptable level of risk against a desired level of performance in assessing causes and con- sequences of an incident (Goodfellow and Kayihan 2013). In this light, preventative maintenance mea- sures impact the likelihood of an incident occurring (i.e., tree failure and power system interruption), which mitigating maintenance efforts impact the relationship between the incidence and the conse- quence or result that occurs. The authors identified a suite of variables that can be assessed in examin- ing and weighing risk; see Goodman and Kayihan (2013) for the complete literature review and model. Utility rights-of-way also employ chemical means of controlling growth and form of trees near util- ity wires. Several authors in the late 1970s and early 1980s examined chemical growth control (Olenick 1977; Carvell 1975; Domir 1978; Domir and Rob-
November 2015
| Title Name |
Pages |
Delete |
Url |
| Empty |
Search Text Block
Page #page_num
#doc_title
Hi $receivername|$receiveremail,
$sendername|$senderemail wrote these comments for you:
$message
$sendername|$senderemail would like for you to view the following digital edition.
Please click on the page below to be directed to the digital edition:
$thumbnail$pagenum
$link$pagenum
Your form submission was a success. You will be contacted by Washington Gas with follow-up information regarding your request.
This process might take longer please wait
