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Utility Vegetation Management in North America: Results from a 2024 Utility Forestry Census of Tree Activities & Operations

TABLE OF CONTENTS List of Figures ................................................................................................................................................................ ii List of Tables ................................................................................................................................................................ iii Executive Summary ...................................................................................................................................................... iv Introduction .................................................................................................................................................................. 2 Section 1 – Company Profile .......................................................................................................................................... 5 Section 2 – System Profile ............................................................................................................................................. 7 Section 3 – Personnel and Wages ................................................................................................................................ 12 Section 4 – Budgets ..................................................................................................................................................... 17 Section 5 – Program Attributes .................................................................................................................................... 20 Section 6 – Utility Forest Attributes ............................................................................................................................. 29 Section 7 – Chemical Control ....................................................................................................................................... 31 Conclusions ................................................................................................................................................................. 34 Appendix A: Questionnaire and Results ....................................................................................................................... 37

Suggested Citation: Blair, S. A., Hauer R. J., Johns, M., Walker T., & Miller, R.H. (2026). Utilities & Vegetation Management in North America: Results from a 2024 Utility Forestry Census of Tree Activities & Operations. (Special Publication No. 26-1). Eocene Environmental Group.

LIST OF FIGURES Figure 1-1. Utility ownership structure (n=30). .............................................................................................. 5 Figure 1-2. The combined business structure of utilities overall (n=30). T = Transmission, D = Distribution, G = Generation. ............................................................................................................................................ 5 Figure 2-1. Average right-of-way widths (feet) based on voltage and line type (n=8 to 26). ......................... 8 Figure 2-2. Importance of engineering solutions to utility vegetation management programs (n=27). .......... 8 Figure 2-3. The percentage of vegetation caused outage types by line type (n=3 to 19). ............................. 9 Figure 2-4. Mean annual for vegetation-related events from 2021 to 2023 normalized per thousand-line miles (n=19 to 23). ...................................................................................................................................... 10 Figure 3-1. Education levels and other credentials held by the UVM department head and one level of management below the department head (n=26). ...................................................................................... 12 Figure 3-2. Average years of service within a program by UVM position (n=1 to 24).................................. 14 Figure 3-3. Mean index scores for recruitment difficulty by UVM position (in-house and contract combined) (n=23).......................................................................................................................................................... 15 Figure 3-4. The workers who conduct pre-planning in advance of tree crews (n=23). ................................ 15 Figure 4-1. Average budget allocation by work activity (n=2 to 21). ............................................................ 17 Figure 4-2. Percent of unplanned expenditures by line type and budget adequacy. When estimates of unplanned expenditures were not given, estimates were calculated by summing “Reactive” and “Other” expenditures and dividing by total expenditures. High risk tree programs excluded regularly scheduled cycle work (n= 6 to 20). ............................................................................................................................... 18 Figure 5-1. GIS-based software capabilities used in UVM programs (n=23). ............................................. 20 Figure 5-2. Importance of workload evaluation methods used in UVM (n=23 to 27). .................................. 21 Figure 5-3. Utility relationships with contractor partners (n=29). ................................................................. 22 Figure 5-4. Cycle approaches by utility respondents. Totals exceed 100% due to multiple responses by 24.1% of utilities (n=29)............................................................................................................................... 22 Figure 5-5. Importance of factors for determining appropriate clearance (n=28 to 29). .............................. 24 Figure 5-6. Percent of work type subject to auditing and the entities responsible for auditing (n=11 to 24). .................................................................................................................................................................... 26 Figure 5-7. Importance rankings of cultural control methods used in IVM programs (n= 28). ..................... 27 Figure 6-1. Proportion of utility responses to various utility forest metrics (n=26 to 29). ............................. 29 Figure 7-1. Proportion of responses to various herbicide-related questions (n=15 to 24). .......................... 31 Figure 7-2. Importance ranking of different herbicide application techniques (n=5 to 28). Responses ranking low volume foliar were initially captured in “other” category (n=5). ................................................. 32

LIST OF TABLES Table 2-1. Line distance (miles) of overhead line types reported by utilities (n=29). ..................................... 7 Table 3-1. Annual salary and hourly pay rates by UVM position. ................................................................ 13 Table 5-1. Contract strategy comparison between 2019 (n=70) and 2024 benchmarks (n=30). ................. 22 Table 7-2. Percent of parties responsible for herbicide application (n=21 to 22)......................................... 31

iii

EXECUTIVE SUMMARY Utility Vegetation Management in North America: Results From a 2024 Utility Forestry Census of Tree Activities & Operations report describes the current state of utility forestry tree activities and operations. The intent was to enable industry professionals to compare their program with their peer utilities across North America. The ultimate objective of this benchmark study was to further develop an understanding of utility arboriculture, advance the profession and help utility vegetation managers improve their programs. Further, the study was designed to describe results from the 2023 base year – what utilities are undertaking and accomplishing in the management of vegetation in and near utility corridors. Thirty of 617 utilities responded to a request to participate. Selected utilities were developed using a combined list from Eocene Environmental Group and the Arbor Day Foundation Tree Line USA® database. From that list, 47 addresses were not deliverable, duplicates from merged companies with separate addresses, or companies returning a non-completed survey. As a result, 570 valid utilities were considered eligible to participate, producing a response rate of 5.3%. Questions were designed to quantify the following: • Company profile • System profile • Personnel and wages • Budget • Program attributes • Utility forest attributes • Chemical control • Environmental, social, and governance Appendix A details summary statistics from a 52-page questionnaire mailed between April 9, 2024, and May 13, 2024. Results were tabulated descriptively through mean values, associated variability through the standard error of the mean (SE) statistic, and tests of statistical significance. Respondent perceptions for some questions were ascertained using a five-point Likert scale (e.g., 1 = very unimportant, 2 = unimportant, 3 = neither unimportant nor important, 4 = important, and 5 = very important). Key findings from the report include:

Company Profile •

The average customer base of responding utilities was 768,000, with a range of 11,000 to 5.5 million (n=30).

•

Most (96.7%) responding utilities had distribution systems. Fewer were transmission (60.0%), and 33.3% were transmission, distribution and generation companies.

•

Study participants represented various ownership structures. Investor-owned utilities accounted for 46.7% of respondents, while cooperatives and municipalities accounted for 36.7% and 10.0%, respectively. State and public utility districts combined represented 6.6% of respondents.

System Profile •

Of the participating utilities, the average distribution system consisted of nearly 13,000 overhead line miles with a range of 108 to 82,000 miles. Transmission systems averaged 3,700 line miles, with a range of 28 to 12,400 miles. Sub-transmission systems averaged 3,000 line miles with a range of 3 to 17,300 miles.

•

Respondents reported that vegetation caused outages represent 22.2% (3.4 SE) of all outages. Within the vegetation caused outage category, off-ROW trees account for the greatest proportion on all line types, including 37.4% (8.7 SE) on distribution. Grow-ins, overhang, and branch failure accounted for a small proportion of vegetation causes.

•

Utility definitions of preventable outages vary widely. The proportion of each provided definition ranged from 20% to 47%, while 37% of respondents reported using more than one definition.

Personnel and Wages •

Nearly all responding UVM programs (96.6%) had a department head. Of those department heads, 77.0% held degrees in natural resources or a related field, 69.9% held a B.S., and another 7.2% held an M.S. degree. Utility department heads held the following industry credentials: >> 69.2% ISA Certified Arborists >> 46.2% Certified Utility Specialists >> 23.1% ISA Tree Risk Assessment Qualified (TRAQ)

•

A mean of 375.9 (227.4 SE) workers held roles within a UVM program with a range of 2 to 6,115 people reported. On average, 2.4 (0.18 SE) levels of management were involved in directing the UVM program.

•

Qualified line crew members (non-crew leaders) were ranked as the most difficult positions to fill with a mean index score of 4.06 (contractor) and 3.67 (in-house), in contrast to account heads and department head positions that were less difficult to fill at 2.64 and 3.70, respectively.

•

Most UVM programs pre-plan work (86.7%) in advance of tree crews. Pre-planning is completed by the following roles: >> 49.4% (8.8 SE) contract work planners >> 27.4% (7.4 SE) company employees >> 14.1% (6.3 SE) tree crew members

Budgets •

Most respondents (54.2%) reported that their noncapital annual UVM budgets were, on average, 30.1% (7.6 SE) below need and therefore inadequate for planning, maintenance, removal, education and other UVM program activities.

Program Attributes •

Most utilities (56.7%) used multiple contracting strategies for UVM work. Time and material contracts were used by 90.0% of respondents, although only 26.7% used them exclusively. Unit contracts were used by 50.0% of utilities, but only 6.6% relied on them solely. Hard-price contracts were used by 40.0% of respondents, but only 3.3% used them exclusively.

•

Utilities reported positive relationships with contractors, as mean index scores for all contractor types ranked at least 4.0 on a five-point scale (1=poor and 5=excellent).

•

Cyclical work schedules are overwhelmingly central to UVM (96.7%), primarily consisting of time-based cycles (62.1%), followed next by reliability-based cycles (24.1%). Time-based cycles averaged 4.25 (0.38 SE) years.

•

44.8% of utilities report basing their tree risk assessment program on the Utility Tree Risk Assessment BMP (Goodfellow 2020), four years after its release.

•

At the time of maintenance, utilities reported an average of 16.2% (5.9 SE) of trees in contact with the line and 18.9% (4.4 SE) overhanging the line.

•

Most utilities (76.7%) use GIS-based software to manage their UVM programs. Of those programs, 71.4% are developed by a third-party.

•

Ground surveys were ranked as the most important method (4.26 mean index score) for conducting workload evaluations, followed by satellite imagery (2.87), aerial photos (2.61), and unmanned aerial systems (2.42).

•

Post-work audits were common, with 86.7% of programs reporting the practice. Of those audits, 66.5% (7.8 SE) were conducted by company employees, 30.1% (7.8 SE) by contracted auditors, and 3.4% (2.0 SE) by tree crew members. A combined 71.4% of utilities inspect 100% of their work via windshield and field audits.

•

Among cultural control methods, employing the wire-border zone concept was the only approach ranked as important, with a mean index score of 3.3.

Utility Forest Attributes •

Only 20% of the 29 responding utilities reported on how many trees were on their distribution utility systems, collectively averaging 1.2 million trees (422K SE).

•

A majority of utilities (57.7%) indicated they do not track tree taxonomy in their records. Of those that do, 80% track by common name, 30% by genus, and 10% species.

•

Tree size was reportedly tracked by 55.6% of responding utilities, with 80% categorizing diameter at breast height (DBH) by size category as opposed to taking measurements.

•

Most utilities (77.8%) reported using DBH to define brush, of which size classifications varied, ranging from less than 4” to less than 10”.

Chemical Control •

On average, 10.3% (2.6 SE) of total herbicide application time is spent on notification, with advance notice averaging 63 (18 SE) days, ranging from seven to 365 days.

•

Few respondents (17.4%) require written authorization before herbicide application, 33.3% of which leave buffers when authorization cannot be obtained. Refusals accounted for 11.2% (5.0 SE) of properties encountered.

•

Only 29.3% of respondents reported using glyphosate in herbicide applications.

•

Utilities reported that herbicide application is primarily handled by third-party contractors (42.1%), followed by vegetation contract personnel specifically assigned to herbicide (33.5%), tree crew members (26.6%), and in-house staff (1.1%).

Environmental, Social, and Governance •

The UVM workforce was predominantly white (78.4%) and male (91.6%). Female representation averaged 16.8%. The second most represented ethnicity was Hispanic (13.8%).

•

The UVM workforce exhibited a balanced age distribution primarily concentrated in mid-career stages of 31- to 50-year-olds (54.6%). The 21–30-year age group comprises 26.2% of the workforce, while the earliest (<21) and latest (50<) combine for only 19.1% of the workforce.

•

Diversity spend was required in 78.3% of UVM programs, but only 9.1% reported having spend goals.

•

Overall, 81.8% of utilities were indifferent in their level of satisfaction between diverse and non-diverse contractor performance.

Photo Credit: Todd Walker

vii

Introduction In a 2023 analysis of the urban forestry field, O’Herrin et al. (2023) evaluated the components of a successful modern profession. They detailed how the philosophical foundation of a modern profession is to provide an essential service to society. The safety and reliability of the electric grid, undoubtedly, meet these criteria. They further describe the “tangible core” of a modern profession as a continually expanding, accessible, and specialized body of knowledge. UVM programs share increasing challenges, regulatory pressures, and common objectives that underscore the need for such advancements in knowledge. For example, the USDA and Department of the Interior are working to standardize information-sharing protocols among utilities in response to the growing threat of wildfire risk (Docket No. AD25-16-000), broadening access to knowledge through timely mitigation efforts and lessons learned. The objective of this report is to advance the industry body of knowledge through the lens of industry metrics and practices around program management, budgets, utility forest attributes, and more. In turn, the results of this research can help utility vegetation managers improve their programs and benchmark performance against the industry.

STUDY DESIGN AND METHODS The project was conducted by Eocene Environmental Group (Eocene). A 52-page questionnaire was developed in conjunction with Eocene staff that referenced the “Utility Vegetation Management: The Utility Specialist Certification Study Guide” by Miller & Kempter (2018) and past utility surveys conducted by Cieslewicz & Porter (2010), Porter & Cohn (2014), Porter & Cohn (2016), and Hauer & Miller (2021). These past reports by CNUC, now a part of Eocene, provide a longitudinal connection to this report. Questions were designed to better understand aspects of UVM program management, budgets, personnel and wages, system profiles, chemical control, utility forest attributes, and environmental, social and governance (ESG) metrics. The Institutional Review Board at the University of Wisconsin - Stevens Point evaluated the survey design and classified the project as exempt. In the United States, there are approximately 3,300 electric utilities (U.S. Energy Information Administration, 2019). The sample of utilities used for survey distribution was developed from a combination of U.S. Energy Information Administration data and past CNUC survey records that included utility addresses. Initially, 617 questionnaires were mailed. Of those, 47 were undeliverable by mail. That left 570 remaining utilities, of which 5.3% (30) participated in the study. •

Pre-notification letters were mailed in March of 2024 explaining the objectives of the study and a request to respond to the questionnaire by June 1, 2024.

•

The initial mailing of questionnaires occurred on April 7, 2024.

•

Two weeks from the initial mailing, postcard reminders were sent to non-respondents.

•

A second survey was sent to non-respondents on May 13, 2024.

Responses of each utility are kept confidential and are only visible to Eocene research and development staff and the utility. All statistical analyses used JASP Team (2025). Within this report, we define mean as the arithmetic mean, calculated as the sum of values divided by the total valid responses. The range of the values is the lowest reported value to the highest reported value. The standard error of the mean (SE) is used to denote an

estimate of how far the sample mean is likely to be from the population mean. For example, if the current length of a time-based vegetation management cycle is a mean 4.25 (0.38 SE) years, the expected population mean is between 3.87 and 4.63. Within the report, all error bars in a figure represent the standard error. Standard error of the mean is not reported in cases where only one valid response was given. In many cases, we use a five-point scale to ascertain respondents’ opinions of importance (e.g., 1 to 5 scale where 1 = very little and 5 = very much). The frequency of these responses is reported along with a mean index scale that provides central tendency to the responses. In these instances, higher index scores indicate higher importance.

RESULTS This report documents results from the most recent assessment of Utilities & Vegetation Management in North America. Findings in this report document utility baselines for 2023 and are primarily organized by themed sections from the questionnaire. For example, Section I was used to describe attributes of utility companies such as number of customers, business type, and ownership structure. Key outcomes from the questionnaire are presented in each section. Comparisons with Hauer & Miller (2021) refer to benchmark results from 2019 as the base year. Not all findings from the questionnaire are reported. Summary statistics for all questionnaire sections are presented in Appendix A.

Photo Credit: Dakota Workman

Section 1 reports on the attributes of responding utilities. Ascertaining a utility’s customer base provides an understanding of the scope and responsibility of the industry to provide a safe and reliable supply of electricity. A utility’s business type, which characterizes capabilities in generation, transmission, and distribution, was also ascertained. Finally, ownership structure was used to identify if the utility is publicly owned, a cooperative, investor-owned utility, or other.

Ownership Structure

Section 1 – Company Profile State/Provincial

3.3

Public Utility District

3.3

Municipal Cooperative

•

Most utilities (96.7%) provided electric distribution (Figure 1-1). Transmission was also common, at 60.0%, while 40.0% were in the generation business. Further, 89.9% of utilities have a business structure including more than one operation. No respondents were solely in the generation business. Of all respondents, investor-owned utilities comprised 46.7% of the sample (Figure 1-2). Cooperative ownership accounted for 36.7% and municipal utilities were third most common at 10.0%.

46.7

25 Percent

Figure 1-1. Utility ownership structure (n=30).

Combined Structure

•

The total customer base varied among the responding utilities with a mean of 768,213 (257,481 standard error of the mean, SE throughout report) served. The customers ranged from 11,000 to 5,500,000 (n=29). The results indicate the survey included a range of utilities from large-scale investor-owned utilities to smallscale cooperatives.

36.7

Investor-Owned

Key findings include: •

10.0

3.3

D, G

6.7

T, D

23.3

T, D, G

33.3

33.3 0

Percent Figure 1-2. The combined business structure of utilities overall (n=30). T = Transmission, D = Distribution, G = Generation.

Section 2 – System Profile Section 2 investigates factors that profile and characterize respondent systems, such as distance of line managed and number of customers served. These metrics reflect the size and scope of vegetation management. Key findings include: •

Respondents represented a wide range of system line miles. Transmission overhead miles ranged from 28 to 12,369 transmission miles, and sub-transmission ranged from 3 to 17,302 miles (Table 2-1).

•

Distribution lines were the most common type under utility management with a mean of 11,939 (4,735 SE).

•

Nearly three-quarters (72.0%) (7.0 SE) of primary lines were accessible by aerial lift, coinciding with Hauer & Miller (2021) results at 66.8% (3.0 SE).

Table 2-1. Line distance (miles) of overhead line types reported by utilities (n=29). Line Type

Mean (mi)

SE1 (mi)

Transmission

3,667

1,219

28 to 12,369

2,985

1,171

3 to 17,302

Distribution

13,248

4,084

108 to 81,602

Secondary

4,310

3,674

54 to 30,000

Primary2

12,714

4,028

91 to 51,602

Three-phase2

5,634

1,958

62 to 27,074

Single-phase2

7,182

2,304

23 to 34,202

72.0%

7.0

(e.g., 200kV line, subject to FAC-003-4)

Sub-transmission (e.g., local, not subject to FAC-003-4)

Accessible by aerial lift 1

SE=Standard error of the mean throughout report

Primary = Three-phase and Single-phase, means do not equal due to non-reporting of data.

•

Range (mi)

Respondents characterized typical right-of-way (ROW) widths at different voltage and line-type combinations. No strong trends emerged based on transmission structure-types (i.e., single pole versus Hstructures) or voltage (Figure 2-1).

The most recent UVM survey (Hauer & Miller 2021) found that 78.6% of site evaluations included ROW width metrics, enabling managers to plan and achieve proper clearance, and draft vegetation management plans.

Service drops, triplex Service drops open wire Secondary, triplex Secondary open wire Distribution 1 Ø Distribution 3 Ø 45 kV 69KV 115kV single pole 115kV H structure 230kV single pole 230kV steel lattice 230kV H structure 138kV single pole 500kV 345kV H structure 345kV single pole 138kV H structure 345kV steel lattice 765 kV

Line Type

5.5 5.5 7.5 8.1 18.0 22.7 43.1 44.7 54.7 67.4 80.2 80.3 94.3 101.0 106.7 120.8 128.6 129.5 132.1 275.0

100

150

200

250

300

Average ROW Width (ft) Figure 2-1. Average right-of-way widths (feet) based on voltage and line type (n=8 to 26).

Respondents rated the importance of various engineering solutions to UVM. Emphasis was placed on the importance of line reclosers (4.2) and overcurrent strategies (3.8). Underground solutions (3.7) and line relocations (3.5) both approached important (Figure 2-2), an increase from 3.3 and 3.2 in 2019, respectively. Very Unimportant (1) Neither Important or Unimportant (3) Very Important (5)

Engineer Solution

•

Unimportant (2) Important (4)

Index Score

Aerial cable systems Aerial spacer cable Compact construction Covered primary Alley arms Hendrix cable Raising poles Relocating OH lines UG Relocating OH lines UG distribution primary Overcurrent strategy Automatic line reclosers

2.31 2.59 2.70 2.74 2.81 2.88 3.16 3.52 3.54 3.73 3.75 4.15 0%

20%

40%

60%

80%

100%

Percent Figure 2-2. Importance of engineering solutions to utility vegetation management programs (n=27).

•

Responding utilities reported that 22.2% (3.4 SE) of all outages are caused by vegetation, aligning with findings from the 2019 results at 23.2% (2.6 SE).

•

Understanding how vegetation causes an outage provides valuable data to minimize future impacts (Figure 2-3). As a result of ROW widths and maintenance strategies, proportions of vegetation causes vary depending on the line type. For example, transmission lines experience fewer outages because of growth, broken branches, and overhanging limbs because FAC-003 requires minimum vegetation clearance distances (Hauer & Miller 2021). While transmission experiences fewer outages overall, those outages are largely comprised of off-ROW trees (41.7%, 25.0 SE).

•

Outages on distribution lines are also mostly caused by off-ROW trees (37.4%, 8.7 SE), followed by broken branches (28.3%, 5.8 SE), both emphasizing the importance of tree risk assessment in UVM. Interestingly, overhang only accounts for 3.2% of all distribution outages. One possible explanation is the general lack of overhanging vegetation across utility systems. For example, utilities reported that only 18.9% (5.9 SE) of trees overhang the line at the time of maintenance. Depending on how tree caused outage data is collected and structured, it is also possible that some of the outage proportions attributed to broken branches were also overhanging limbs.

Drilling into distribution vegetation outage causes on a large scale is a recognized challenge amongst UVM managers. Outage reporting requirements, definitions and investigation protocols may vary from state to state and utility to utility. Currently, the Utility Arborist Association has formed a working committee with the objective of standardizing tree caused outage terminology, allowing outage statistics to be more clearly interpreted across the UVM industry (Dahle, 2026). 3.7

Broken branches

17.8

Grow-ins

2.0

Failure Type

Uprooting

2.8

Trunk failure

Subtransmission Distribution

7.6 8.0 22.3

12.0 12.3

41.7

Off ROW trees

37.4

Overhang Other

Transmission

28.3

3.2 0.2

54.0

33.3 11.2

20%

40%

60%

80%

Percent of Vegetation Caused Outages Figure 2-3. The percentage of vegetation caused outage types by line type (n=3 to 19).

•

Half of responding utilities (50%, n=22) reported using more than one definition of preventable vegetation caused outages, which together accounted for 33.7% (10.0 SE) of all outages. The most common definition was an outage that would not have occurred if proactive specification clearance had been achieved on schedule (46.7%), followed by “Other” (33.3%), highlighting significant variability in how preventable outages are classified.

Utilities rely on a range of definitions to classify preventable outages. The purpose behind these classifications may be to distinguish maintenance-related gaps and events outside of managerial control,

such as severe weather or human error. At the transmission level, the FAC-003-5 standard treats vegetation encroachment grow-ins as violations, while fall-ins from outside the ROW are generally only reportable. At the distribution level, there is no commonly defined preventable outage definition, yet reliability reporting requirements achieve similar intent. For example, the Pennsylvania Public Utility Commission enforces reliability standards where utilities must maintain performance benchmarks, file maintenance plans, and prevent “unreasonable” interruptions. Failure to meet these expectations may result in investigations and correctional directives, highlighting a potential compliance need for defining preventable outages (52 Pa. Code, 2004). No public utility contacts involving trees were reported by respondents over the past three years, with the exception of arborists not working on behalf of the utility (Figure 2-4). Normalized per 1000-line miles, 0.017 (0.0095 SE, range 0 to 0.16) public arborist contacts occurred per year. Reducing the risk of electric contact involving trees is a prominent responsibility of UVM. Fewer contacts were reported for line clearance arborists at 0.0112 (0.0062 SE, range 0 to 0.10) per year. Respondents reported a mean 0.21 (0.096 SE, 0 to 1.53 range) fires ignited by vegetation annually per 1,000 line miles over a three-year period (Figure 2-4). Fire risk has become increasingly prominent, as power line-caused fires in western North America have cost loss of life and billions of dollars of damage (California Public Utilities Commission, n.d.).

Mean Incidence Rates/Year

•

0.35

0.210

0.3 0.25 0.2 0.15 0.1 0.05 0

0 Public Electrical Contact

0.017

0.011

Arborist Electrical Contact

Utility Arborist Electrical Contact

Ignitions from Vegetation

Vegetation Related Events per 1000 Line Miles Figure 2-4. Mean annual for vegetation-related events from 2021 to 2023 normalized per thousand-line miles (n=19 to 23).

Public safety in utility vegetation management extends beyond reliability concerns to the risk of electrical contact during arboricultural operations. Recent research by Goodfellow & Ball (2022) underscores that arborists working near energized conductors face unique exposures and varying degrees of consequences. They note that largerdiameter branches are both more conductive and difficult to control during rigging operations, highlighting one of the drawbacks to the previously accepted “90-3-9” rule of achieving clearance with few, large-diameter cuts.

Section 3 – Personnel and Wages Section 3 combines questionnaire results from Personnel and Wages and ESG modules. It summarizes UVM demographics, reporting structures, department head titles, staffing levels, pay, and other personnel management variables. This section also reports on the credentials of vegetation program leaders. Key findings include: •

The UVM workforce is predominantly male (91.6%, 3.0 SE) and white (78.4%, 7.7 SE). Hispanic or Latinx comprised the next largest group at 13.8% (6.3 SE).

•

The largest portions of the workforce fell within the age ranges of 21–30 (26.2%, 5.4 SE), 31–40 (27.4%, 3.4 SE), and 41–50 (26.8%, 4.0 SE).

•

UVM programs employed a mean 375.9 (227.4 SE) workers, ranging from 2 to 6,115 individuals. This equates to 14.8 FTEs (3.1 SE) per 1,000 line miles and 3.7 FTEs (0.6 SE) per 10,000 customers.

•

With the exception of management roles, contractors made up the majority of FTEs in a UVM program. For example, respondents reported a mean of 111 tree crew leaders, while utility operations averaged fewer than one, indicating the most field work is performed externally.

•

A mean 54.6% (9.1 SE) of total FTE’s are directly responsible for managing the UVM program, with 2.4 (0.2 SE) levels of management involved in program direction. Most UVM programs have a department head (96.6%), consistent with past benchmark results from 2002, 2006 and 2019 where estimates exceeded 90%.

•

Department heads and UVM managers share similar, yet diverse credentials. The majority possessed the ISA Certified Arborist credential (69% and 73%, respectively), followed by the ISA Utility Specialist credential (46.2% and 53.8%) and a bachelor’s degree in a natural resource–related field (69.2% and 53.8%) (Figure 3-1). Results show an increase in the proportion of department heads holding natural resource-related degrees from 2019 to the present survey, despite historical fluctuations.

Credential

One Level Below Dept. Head UVM Professional Cert

3.8

MS Natural Resources

3.8

ISA BCMA TRAQ Other BS Natural Resources ISA Utility Specialist ISA Certified Arborist

Department Head

7.7 7.7 7.7 23.1 23.1 50.0 41.4 53.8 69.2 53.8 46.2 73.1 69.2

20%

40% Percent

60%

80%

Figure 3-1. Education levels and other credentials held by the UVM department head and one level of management below the department head (n=26).

Utilities have consistently valued industry credentials. For example, 81.4% of respondents ranked the Certified Arborist credential as important or greater in the 2019 UVM survey. In a review of the urban forestry profession, O’Herrin et al. (2023) emphasizes that credentialing serves as a tool for minimum qualifications for practice, a means of self-regulation, a standard for ethical accountability, and a safeguard to protect the public from unqualified practitioners. They also noted the lack of directly related credentials from a member-serving organization, leading practitioners to borrow credentials from other industries. Although there are no official barriers to entry in UVM, developmental opportunities beyond the ISA Utility Specialist credential are increasing. The Utility Arborist Association and Utility Vegetation Management Association partnered with the University of Wisconsin – Stevens Point to administer the UVM Professional Certification online, which was reportedly held by 7.7% of department heads ( https://www.uwsp.edu/wfc/wisconsin-forestry-center/utilityvegetation-management-certificate). Butte College in California is training utility line clearance arborists, preinspectors, and other job responsibilities in vegetation management (https://butte.edu/business/bus_services/trainingplace/train_as_arborist.php). Law students at Tulane University have the opportunity to engage in a unique Utility Vegetation Management Initiative (https://law.tulane.edu/content/utility-vegetation-management-uvm-initiative). •

Contractor pay increased by position, from groundworker ($19.33) to general foreperson ($35.39) (Table 3-1). In most cases, utility employees earned higher wages than contractors. Reported rates do not include fringe benefits, so total compensation was not ascertained. The average in-house department head salary was $126,288. An in-house position one level below the department head on average made $104,525, and those two levels below averaged $93,267.

Table 3-1. Annual salary and hourly pay rates by UVM position. In-House SE

N-size

Pay USD $1

N-size

Department/Account Head

126,071

5,635

130,000

20,000

Management 1 Level Below

104,525

4,087

82,500

7,500

Management 2 Levels Below

92,165

2,512

92,165

---

35.39

1.83

Management Annual Salary

General Foreperson

Hourly Pay

Contractor

Pay USD $

Position

Crew Leader

44.00

2.52

30.33

1.28

Qualified LCA2

35.33

4.98

27.50

1.16

---

23.94

1.49

Ground Worker

18.00

---

19.33

1.33

Planner QA Specialist

36.82

---

25.92

2.60

Qualified LCA Trainee2

1 2

Pay does not include fringe benefits Line clearance arborist

•

Respondents reported that contractors experience greater turnover than within the utility. On average, the industry sees 32.1% longer tenures at utilities than at contract companies.

•

Higher levels of UVM leadership positions are also the longest tenured in a program with a mean of 14.5 (1.9 SE) years in-house and 12.1 (1.9 SE) years in a contractor role (Figure 3-2).

In a 2009 benchmark of 21 UVM programs, Cieslewicz & Porter (2010) found that contractors were subject to significantly higher turnover than utilities (51% compared to 17%). An earlier benchmark in 2006 identified the highest turnover among ground workers (Cieslewicz & Porter 2010). Contractor

Utility

Ground worker

12.1 14.5 7.0 12.3 5.7 8.7 5.3 7.7 10.1 11.4 7.2 10.9 3.0 6.8 1.5 2.1

Clerical support

10.0

Other

9.0

Department/account head Mgmt 1 below

Position

Mgmt 2 below Planner/QA specialist General foreperson Crew leader

Qualified line clearance members

5 10 15 Average Years of Service

Figure 3-2. Average years of service within a program by UVM position (n=1 to 24).

•

Mean index scores for recruitment difficulty (1=very easy; 5=very difficult) were 3.3 for utility positions and 3.5 for contractor positions. While neither were collectively difficult to recruit for, contractor positions may present a slightly greater challenge attributable to the workforce percentage and higher turnover.

•

Positions responsible for field execution were harder to recruit than managerial and clerical positions (Figure 3-3). The qualified line clearance crew member position was the most difficult to recruit (4.0 mean index score), followed by crew leader (3.8), and general foreperson (3.6). In comparison department heads (utility) and account heads (contractor) combined for an index score of 3.0.

Position

Very Easy (1) Neither Difficult nor Easy (3) Very Difficult (5)

Easy (2) Difficult (4)

Index Score

Qualified tree crew member

3.96

Crew leader

3.77

General foreperson

3.58

Ground worker

3.43

Planner/QA specialist

3.32

Mgmt 2 below

3.30

Mgmt 1 below

3.06

Department/account head

3.04

Clerical support

2.75 0%

20%

40%

60%

80%

100%

Percent Figure 3-3. Mean index scores for recruitment difficulty by UVM position (in-house and contract combined) (n=23).

Other 2

•

UVM work for responding utilities is pre-planned or inspected ahead of tree crews 86.7% of the time, aligning with findings from Hauer & Miller (2021).

Both contract workers (49.4%) and company employees (27.4%) who were not tree crew members performed this task (Figure 3-4). Tree crew members uncommonly performed preplanning or inspection (14.1%).

Other 1

Position

•

1.1 3.9

Tree crew member

14.1

Utility planner

27.4

Contractor planner

49.4 0%

20% 40% Percent

60%

Figure 3-4. The workers who conduct pre-planning in advance of tree crews (n=23).

Results suggest most UVM programs consider pre-planning to be important. Pre-planning is widely used for numerous reasons. First, a single individual identifying work and planning access is more efficient than multiple crew members doing so, which increases idle time of those executing vegetation work. Second, many property owners and other stakeholders expect and deserve time to discuss what is planned on their property. Given their skillset, it may be unrealistic to expect successful customer communication as a secondary tree crew assignment. Finally, vegetation management often interfaces with more customers than any other utility department. That interaction represents the company for better or worse.

Section 4 – Budgets Section 4 reports on UVM budget expenditures overall and by line type, work type, stability and adequacy. Key findings include: •

Respondents provided their total annual non-capital UVM budget. A mean $61M (43M SE) was spent in 2023. The amount spent ranged from $215K to $1.26B. Capital UVM budget expenditures were a mean $4.7M (1.7M SE) and ranged from $20K to $26M.

•

Mean O&M expenditures for distribution were greatest at $71M (52M SE), followed by transmission at $4.0M (1.7M SE), and finally sub-transmission at $3.4M (1.7M SE). Of these expenses, unplanned work accounted for 10.7% (2.2 SE) of distribution spend, 11.5% (7.8 SE) for sub-transmission, and 5.2% (2.1 SE) for transmission.

For transmission and generation owners, FAC-003-05 mandates a defense-in-depth approach to UVM. Compliance with the standard requires annual transmission line inspections (Requirement R6) and completion of annual work plans (Requirement R7). Alternatively, non-compliance is disincentivized through potential fines of up to $1 million per day for each violation, supporting UVM strategies that ultimately reduce the amount of unplanned work. •

Routine work represented the largest UVM budget allocation at approximately 70% for each (Figure 4-1). Quality control was the smallest expenditure for each line type, ranging from 1.3% - 2.1% of spend.

Average Budget Allocation (Percent)

90 80

76.2

73.0

Routine Mid-Cycle

69.6

Herbicide

Reactive

High Risk Tree 27.2

Work Planning

21.7

Quality Control

40 13.7

30 20 10

9.2

13.3 6.3

7.7

10.0 10.7 7.9 2.1

6.5 3.7

5.9 1.3

12.6

2.4

1.5

0 Distribution

Sub-transmission

Transmission

Line Type Figure 4-1. Average budget allocation by work activity (n=2 to 21).

•

Three-quarters (75.0%) of respondents indicated their non-capital VM budget was stable over the past five years, an increase from 53.7% in 2019.

•

Nearly half of responding utilities (54.2%) indicated their budget was not adequate, corresponding with 2019 UVM Survey results of 52.9%. Of these utilities, budgets were a mean 30.1% (7.6 SE) below the identified need, agreeing with 2019 results at 28.9% (4.3 SE).

Utilities with inadequate budgets spent less on unplanned work in their UVM programs (Figure 4-2). On distribution systems, for example, those utilities with inadequate budgets spent 3.8% less on unplanned work than those with adequate budgets.

Percent of Unplanned Spend

•

Adequate Budget

16.2

Inadequate Budget

11.6

10.5

12 10

6.8

8 6 4 2 0

5.3

2.0

7.8

8.1

Transmission

Sub-transmission

Distribution

Total

Line Type Figure 4-2. Percent of unplanned expenditures by line type and budget adequacy. When estimates of unplanned expenditures were not given, estimates were calculated by summing “Reactive” and “Other” expenditures and dividing by total expenditures. High risk tree programs excluded regularly scheduled cycle work (n= 6 to 20).

Section 5 – Program Attributes Section 5 reports on UVM program management tactics employed by utility companies. Inquiries focused on contract strategies, work scheduling, and quality control, among other topics. Questions on technology use were also asked and analyzed. Key findings include:

TECHNOLOGY: •

GIS-based software was regularly used (76.7%) to spatially relate vegetation data and support program management. Of GIS-based work management systems, 71.4% use software provided by a third-party vendor while the remainder use an in-house derived product.

•

Generally, respondents found their software of choice to be at least effective to very effective (68.8%). No trends were observed between the efficacy of in-house versus third-party GIS software.

•

GIS-based software is primarily used for location-specific work history (87.0%) and customer notification records (78.3%) (Figure 5-1). Interfacing with remote sensing was the least reported capability at 17.4%.

GIS-Based Software Capability

Remote sensing interface

17.4

Auto billing

21.7

Timesheet

30.4

Production reports

43.5

Customer complaints

47.8

Report creation

52.2

Refusal records

52.2

Work order development

60.9

Production data collection

65.2

Work order delivery (crew)

69.6

Notification records

78.3

Work history at location

87.0

20%

40% Percent

60%

80%

100%

Figure 5-1. GIS-based software capabilities used in UVM programs (n=23).

•

Respondents rated the importance of various workload assessment methods used on their system (Figure 5-2). Overwhelmingly, ground evaluations were rated with the highest importance with a mean index score of 4.3. Other evaluation methods (unmanned aerial, aerial photos, satellite imagery and LiDAR) were in the range of unimportant to neutral.

Very Unimportant (1) Neither Important or Unimportant (3)

Unimportant (2) Important (4)

Index Score

Evaluation Methods

Very Important (5) Unmanned aerial systems

2.42

LiDAR

2.58

Aerial photos

2.61

Satellite imagery

2.87

Ground evaluations

4.26 0%

20%

40% 60% Percent

80%

100%

Figure 5-2. Importance of workload evaluation methods used in UVM (n=23 to 27).

COMMUNICATIONS: •

Nearly half (46.7%) of utilities track customer complaints. Most complaints are related to worksite tidiness (21.0%), aesthetics (19.5%), and improper notification (15.8%).

•

The least reported customer complaints include rudeness (4.4%), unauthorized herbicide (2.9%), and unauthorized tree removal (2.2%).

Results demonstrate that customers care about clear communication and aesthetics. Past research by Kuhns and Reiter (2007) evaluated perceptions of utility pruning aesthetics and found that most people not only preferred the look of a topped tree, but without an educational brochure believed the practice to be better for the tree than directional pruning. Educational materials positively shifted public attitudes and understanding of natural target pruning. Nearly 20 years later, it is difficult to gauge if and how public sentiment has shifted. While the number of Tree Line USA utilities has remained relatively consistent between 2007 and present, the use of dedicated pre-planners (utility and contracted) has increased from 43.0% in 2006 (Cieslewicz and Porter, 2006) to 76.8% in 2023. Communicating with utility customers up front provides an opportunity to educate, share brochures, and discuss customer concerns (Miller, 2021). Further, more sophisticated recordkeeping practices should enable proactive management of customer communications, complaints, and refusals.

CONTRACT STRUCTURE: This section examines contract strategies for vegetation management work, including pruning, removal and herbicide operations, as well as planning and auditing contract structures. Overall, the contract strategies used are agreeable with Hauer & Miller (2021) (Table 5-1) regarding the percentage of utilities using certain contract strategies. Results suggest that many programs use a variety of contracting strategies, depending on specific needs and circumstances. •

A significant portion of utilities rely on a single contracting approach for vegetation management and planning/auditing activities, 43.4% and 86.9% respectively. Among utilities using only one contract strategy for these functions, time and materials contracts are the predominant model.

•

The average contract duration is 3.1 years (0.2 SE) for VM work and 3.0 years (0.4 SE) for planning and auditing work.

Percent of Utilities UVM Contract Type 2019

2024

Hard Price

31.4%

40.0%

Performance Based

11.4%

16.7%

Time & Material Time and material (71.3%) accounted for the largest proportion of contract Unit Based structures, followed by hard price per circuit (14.5%), and time and material performance based (11.1%).

88.6%

90.0%

50.0%

Respondents also provided information regarding the proportion of VM and planning/auditing work under various contract types. •

Table 5-1. Contract strategy comparison between 2019 (n=70) and 2024 (n=30) benchmarks.

•

VM work contracts are predominantly time and material (50.0%), followed by hard price per circuit (20.6%), and time and material performance based (8.9%). Six varieties of unit cost were reported by 9.8% of respondents. Unit cost per tree, followed by unit cost per pole km/mi were most common.

•

VM work contracts show differences in duration by contract type. Time and material vegetation management contracts average 3.2 years (SE 0.3), which is substantially longer than hard price‑per‑circuit contracts, which average 1.7 years (SE 0.4).

•

Time‑and‑material planning and auditing contracts follow a similar pattern, with an average duration of 3.3 years (SE 0.4).

•

Utilities viewed contractor relationships favorably, with mean index scores at or above 4.0 (Figure 5-3).

Contractor

Poor

Bad

Neither Good nor Bad

Good

Index Score

Excellent

Other

4.00

Veg Mgmt 1

4.07

Veg Mgmt 3

4.08

Veg Mgmt 2

4.21

Preplanner

4.33

Herbicide 1

4.40

Auditor

4.50

Herbicide 2

4.50 0%

20%

40%

60%

80%

100%

Percent Figure 5-3. Utility relationships with contractor partners (n=29).

Relationships between utilities and contractor partners are critical to effective operations. A recent literature review by Cadaval et al. (2024) highlighted top contributors of conflict in urban forest management. Factors for conflict included unaligned goals, poor communication at various levels, and lack of resources. Findings supported conflict-management actions through collaborative processes with all parties involved.

UVM SCHEDULES: •

Respondents overwhelmingly schedule vegetation management work at the circuit level (76.7%). Remaining responses chose “other”, of which 50.0% described scheduling by maintenance shapes, areas or polygons. No utilities reported scheduling by grid. Among utilities scheduling by circuit, 91.3% plan the entire circuit rather than focusing on a specific overcurrent protection zone.

Cieslewicz & Porter (2010) noted that defining cycle length is challenging due to shifting frameworks in scheduling (e.g., toward reliability). They suggested that inspection and planning work may be more cyclical in nature than the execution of vegetation work. Based on 2009 survey results from 32 utilities, they loosely defined a cycle as “how long it takes to manage the whole system one time.” Utilities were asked again about cycle lengths, definitions, and variability. Of 30 respondents, 96.7% reported conducting UVM work cycles. Cycles were defined as completing 100% of lines within a set period (62.1%) or a percentage within a given year (20.0%). Other responses (17.9%) yielded no trends, but referenced patrol rather than work cycles, circuit-specific cycles, and a fixed number of miles maintained each year.

•

About 24.1% of utilities used multiple scheduling approaches. Also, 30.8% of utilities reported using different cycle lengths based on line location or work type. Of those, 63.6% varied between x-urban and rural areas, at 4.3 (0.5 SE) and 5.5 (0.5 SE) years, respectively.

•

Time‑based cycle strategies were cited as the predominant scheduling method by 62.1% of utilities (Figure 5-4), lasting an average of 4.3 years (0.4 SE). In comparison, 88.7% of respondents in 2019 reported using time-based cycles, lasting an average of 4.5 years (0.2 SE).

Basis for Cycle Approach

•

Time Reliability Condition Safety Reactive A.I 0%

25%

50%

75%

Percent Figure 5-4. Cycle approaches by utility respondents. Totals exceed 100% due to multiple responses by 24.1% of utilities (n=29).

Only 6.9% of utilities reported artificial intelligence-based cycles as their primary approach (Figure 5-4). Condition-based (17.2%) and reliability-based (24.1%) comprised the middle. In 2019, 3.1% of utilities described their time-based cycles as reliability-focused but were not asked about condition-based vegetation management.

Although few program schedules rely primarily on remote sensing, its adoption has increased focus on condition-based vegetation management. Because few definitions exist, discerning between reliability, risk and condition-based approaches becomes challenging. The definition of ‘enhanced vegetation management’ in the Vegetation Management Investment Guide alludes to practices that enable shifting away from fixed-cycle vegetation management to the use of reliability and risk-based criteria (U.S. Department of Energy, 2024). The IVM BMP alludes to condition-based vegetation management in that vegetation management plans are adjusted based on variations in environmental and site conditions (Miller, 2021).

CLEARANCE, PRUNING AND IVM: Utilities were asked about the importance of different factors in determining appropriate clearance distances. •

Respondents emphasized the importance of both reliability and risk reduction with mean index scores of 4.6 (important to very important) (Figure 5-5). Less important clearance factors included line location and maintaining tree structure.

Clearance Factors

Very Unimportant Neither Important or Unimportant Very Important

Unimportant Important

Index Score

3.17 3.41 3.43 3.46 3.55 3.69 3.69 3.83 3.83 4.00 4.59 4.62

Line location Facility construction Maintain tree structure Expected weather Number of customers Number of phases Facility priority Tree position to line Site factors Planned cycle length Risk reduction (safety) Reliability 0%

20%

40% 60% Percent

80%

100%

Figure 5-5. Importance of factors for determining appropriate clearance (n=28 to 29).

•

The pruning distance from electrical distribution lines was dependent upon the line type. Pole to pole triplex and open wire secondary lines were pruned to relieve strain and abrasion by 34.5% and 13.8% of distribution utilities, respectively. 6.9% of utilities did not perform any maintenance on secondary and triplex.

•

The mean side clearance for single-phase and three-phase was similar, and together they averaged 10.9 ft (0.6 SE) or 3.3 m (0.2 SE). The only notable deviation was for single-phase distribution wye configurations, which had less average side clearance of 9.3 ft (0.5 SE) or 2.8 m (0.2 SE). Across all distribution configurations, overhang clearances were approximately 1.5 ft (0.5 m) greater than side and under clearances. Under clearances were similar to side clearances at 11.1 ft (0.5 SE) or 3.4 m (0.2 SE) for primary distribution lines.

In 2019, 76.8% of respondents reported using action thresholds in decision-making. Action thresholds are defined in the Integrated Vegetation Management BMP (Miller, 2021) as the level of incompatible plant pressure (i.e., vegetation conditions) where vegetation management should occur to prevent vegetation from reaching tolerance levels. •

For this study, only 35.7% of utilities implement work using action thresholds, and 80.0% of those using action thresholds managed transmission programs, while the remainder managed only distribution and generation programs.

•

At the time of maintenance, 16.2% (4.4 SE) of trees showed signs of contact with conductors and 18.9% (5.9 SE) of trees were over-hanging the line, compared with 2019 survey results at 21.4% (3.5 SE) and 24.6% (3.7 SE), respectively. Respondents were asked to rate the importance of different clearance factors, emphasizing the importance of both reliability and risk reduction with mean index scores of 4.6. Less important clearance factors included line location and maintaining tree structure.

•

Mechanical cutting was used by 65.5% of utilities. Of those, 14 utilities reported the proportion of their UVM budget allocated to mechanical pruning, which averaged 14.1% (4.8 SE) and ranged from 1% to 65%.

The most recent revision to ANSI A300 standards revised Clause 5, Pruning, lifted the requirement that mechanical pruning be limited to remote and rural areas. Kempter (2024) critically outlines and reviews the change - citing safety, workforce limitations, regulatory time constraints, and advances in the equipment as justification. He also goes on to outline some of the concerns such as long-term risk associated with tree injuries and improper site selection for the equipment. •

“Ground to sky” pruning was reportedly used by 53.3% of utilities, implemented in rural areas by 23.1%, along three-phase lines by 38.4%, and in all areas by 38.4% of respondents.

Recent research has evaluated the impacts of overhang removal on tree biomechanics. Although the results were limited to leaf-off conditions for seven days after pruning, Cranmer et al. (2024) established a methodology using ground-based LiDAR and accelerometers to evaluate tree movement in response to different utility pruning practices. During the study period, the researchers found no consistent changes in movement among pruning specifications. Crown diameter to height ratio was the only significant predictor of tree sway frequency. The researchers stated that future work should incorporate leaf-on conditions over a longer study period. Enhanced pruning specifications have also been evaluated against reliability performance. In the Northeastern United States, research by Parent et al. (2019) found significantly fewer fault-type outages where specifications were increased to include overhang removal. Major outages such as broken poles and downed conductors did not change in a statistically meaningful way. Four years prior to this survey, the Utility Tree Risk Assessment (UTRA) Best Management Practices were published (Goodfellow, 2020). •

Adoption of the UTRA BMP was noted by 44.8% of respondents.

As outlined in the UTRA BMP, Level 1 assessments represent a starting point to identify high and extreme risk trees. Trees flagged during the walk-by or drive-by may receive a Level 2 assessment. Recent research by McBride et al. (2026) evaluated the storm performance of Level 1 assessments by comparing risk assessment results with post-storm tree failures on over 2,000 trees. Straight-line wind speeds ranged from 40-59 mph. Assessors correctly classified acceptable risks (i.e., trees that did not fail) 92.5% of the time. However, 92.5% of failed trees were not identified as having elevated risk when assessed three months earlier. While likelihood of failure evaluations anticipate loads under normal conditions, findings reiterate the challenge of preparing a utility urban forest for extreme events.

QUALITY ASSURANCE: Evaluations including quality assurance and quality control (QA/QC) represent a key component of the IVM process. QC activities occur at project-levels to identify and correct issues early, while QA and acceptance are

built upon meeting pre-determined performance indicators such as audit accuracy, clearance distances, or acceptable vegetation height and density. •

Utility respondents confirmed the importance of QA/QC, with 86.7% of programs conducting post-work audits to validate vegetation management activities. Utility personnel were primarily responsible for auditing (66.5%). Contracted auditors also supported QA (30.1%), a responsibility rarely given to tree crew members (3.4%) (Figure 5-6).

•

Utilities reported a mix of audit structures. One hundred percent field audits were used by 60.7% of utilities. Sample-based windshield audits were used by 42.8% of respondents and reviewed 36.7% of completed work, on average. Fewer utilities audited using remote-sensing tools such as LiDAR (7.1%) or satellite imagery (7.1%). The level of quality control varies by work type, with the greatest focus on vegetation management operations (70.9%), followed by herbicide application (51.7%), pre-planning (29.2%), and auditing functions (20.1%). Auditing Entity

Percent QC

Work Activity Subject to QC 90 80 70 60 50 40 30 20 10 0

20.1

51.7

70.9

29.2

66.5

30.1

3.4

Figure 5-6. Percent of work type subject to auditing and the entities responsible for auditing (n=11 to 24).

CULTURAL CONTROL METHODS: Cultural control methods aim to limit the growth of incompatible vegetation on the ROW by promoting compatible land use such as agricultural and recreational areas that still allow for the intended use of the site (i.e., power transmission and delivery). The establishment of compatible vegetation through practices such as seeding and landscaping, or control methods such as targeted grazing, represent various forms of cultural controls. •

Nearly all cultural control methods listed were considered neither important nor unimportant (2.9 or lower), except for wire-border zone establishment (3.3) (Figure 5-7).

•

Utilities with transmission favored cultural control methods to a greater extent than utilities with only distribution or generation. For example, the mean index score for wire-border zone implementation was 3.6 for utilities with transmission and 2.6 for those without. Differences in cover type conversion favorability were low, with 2.9 for utilities with transmission and 2.7 for utilities without.

Logistically, the implementation of certain cultural control methods presents a greater challenge on a distribution system of narrower rights-of-way and a more sporadic configuration. The width of a transmission ROW better lends itself to establishing a transition zone in the ROW edge. Even so, given the maturity of compatible plant establishment across transmission systems, cultural methods remain a valuable tool for managing vegetation on any ROW. While cover type conversion is largely considered a biological control, it can be facilitated through a combination of methods.

Very Unimportant (1)

Unimportant (2)

Neither Important or Unimportant (3)

Important (4)

Index Score

Cultural Control Methods

Very Important (5) Wire-border zone

3.25

Cover type conversion

2.86

Plant/Seed compatibles

2.82

Landscaping

2.71

Prairie restoration

2.29

Agriculture

2.25

Hydroseeding

2.04

Fertilizing

1.96 0%

20%

40% 60% Percent

80%

100%

Figure 5-7. Importance rankings of cultural control methods used in IVM programs (n=28).

Photo Credit: Kinsey Stoker

Section 6 – Utility Forest Attributes For this survey, the utility forest was defined as including any vegetation that could affect electric facilities in its lifetime. The Tree Inventories BMP refers to this as “stocking” - the amount or density of trees within the area of interest from which inference about the population and management decisions can be made (Wiseman et al., 2025). In an urban setting, the authors note that there are no standardized benchmarks for urban forest stocking due to the complexity of factors related to forests surrounding built environments. Only 20% of the 29 responding utilities reported estimates of how many trees are in their distribution utility forest, with an average of roughly 1.2 million trees (422K SE). Trees per mile estimates from respondents were also limited.

•

More respondents tracked the amount of work completed on their system (82.8%) than answered questions about the utility forest. Responses for distribution were more common than for subtransmission and transmission. The distance of line cleared, brush removed, brush treated with herbicide, and trees maintained exhibited substantial variance given the range in sizes of utilities. However, the mean trees per mile worked was 69.3 (21.6 SE).

•

A slight minority of utilities (42.3%) indicated they track tree taxonomy (Figure 6-1). Among those recording taxonomy, 80.0% track by common name, 30.0% by genus and 10.0% by species.

•

Tree size was tracked by 55.6% of responding utilities. Of those tracking tree size, all used Diameter at Breast Height (DBH), with 80.0% categorizing a range rather than direct measurements. Four utilities indicated tracking tree size for removals only. Additionally, 77.7% of utilities reported using DBH to define brush. While a wide range is used, the most frequently occurring maximum DBH was 4 inches (42.9%) consistent with findings by Cieslewicz & Porter (2010). Only 33.3% tracked tree aspect to the electric facility (i.e., side, under, overhang).SECTION

Utility Forest Metrics Tracked

•

Tree aspect to power line

33.3

Tree size

55.6

Tree type

55.6

Amount of work accomplished

82.8

25%

50%

75%

100%

Percent Figure 6-1. Proportion of utility responses to various utility forest metrics (n=26 to 29). 7 – CHEMICAL CONTROL

Section 7 – Chemical Control Section 7 examines several facets of chemical control as they relate to customer notification, glyphosate use, buffers, applicators, tree growth regulators (TGRs) and application methods.

HERBICIDE APPLICATION: Customer notification accounted for 10.3% (2.6 SE) of the total herbicide application time. Advance notice averaged 63 days (18 SE), with a range of 7-365 days (Figure 7-1).

•

Only 17.4% of respondents required written authorization before applying herbicide, with an average of 11.2% (5.0 SE) of landowners refusing herbicide application altogether. When work was refused, 33.3% of utilities provided a surrounding buffer. Forty-five percent of the respondents used a wetland buffer at an average of 32.0 feet (10.2 SE) in width.

•

Glyphosate was used by 29.3% of respondents. Of those respondents, glyphosate accounted for 27.8% (21.0 SE) of total herbicide application, with reported proportions ranging from 1.0% to 90.0%.

Herbicide Characteristic

•

Do you require written authorization? % of application time spent notifying?

17.4 10.3

% of owners refusing herbicide?

11.2

Do you buffer non-authorized properties?

33.3

Do you use wetland buffers?

45.0

Do you use glyphosate?

29.2

10%

20% 30% Percent

40%

50%

Figure 7-1. Proportion of responses to various herbicide-related questions (n=15 to 24).

•

Herbicide application was performed primarily by a third-party specialized applicator company, followed by vegetation contract personnel specifically assigned to herbicide application, tree crew members, and inhouse staff rounding out the bottom (Table 7-1).

Table 7-1. Percent of parties responsible for herbicide application (n=21 to 22). Type of Applicator Mean (%) SE Tree crew member 26.6 8.6 VM contractor specifically assigned to herbicide application

33.5

9.8

Third party herbicide application contractor

42.1

9.9

In-house personnel

1.1

0.7

•

Low volume foliar (reported through responses labelled “Other”) was the most important application method with an index score of 4.8, which was followed by stump treatment at 3.8 (Figure 7-2). Conversely, chemical tree pruning and aerial application were both rated as unimportant (1.7).

Herbicide ApplicationMethod

Very Unimportant (1) Neither Important or Unimportant (3) Very Important (5)

Unimportant (2) Important (4)

Index Score

Chemical tree-pruning

1.68

Aerial application

2.71

High volume foliar

2.85

Basal

2.93

Cut stubble Bare ground treatment

3.08

Stump treatment

3.81

Low volume foliar

4.80 0%

20%

40% 60% Percent

80%

100%

Figure 7-2. Importance ranking of different herbicide application techniques (n=5 to 28). Responses ranking low volume foliar were initially captured in “other” category (n=5).

TREE GROWTH REGULATORS: Respondents were asked if they used TGRs and how important they were to their programs. •

A mere 24.1% of respondents reported using TGRs, with 47.6% answering that TGRs were very unimportant and 38.1% indicating that they were neither unimportant nor important.

•

The 2019 UVM Survey yielded similar results, with just 25.7% of responding utilities indicating that they used TGRs (Hauer & Miller, 2021). The reasons behind the lack of adoption are unclear. TGRs can provide benefits beyond reduced shoot growth and increased time between required pruning, including reduced water stress, resistance to fungal pathogens, and improved aesthetics through greener and more compact foliage (Chaney, 2005).

Conclusions The Utility Vegetation Management in North America: Results from a 2024 Utility Forestry Census of Tree Activities & Operations report outlines key findings regarding utility forestry tree activities and operations across North America. Thirty of 570 utilities responded, which represented over 22 million customers, 40,341 miles of transmission, 53,734 miles of sub-transmission, and 384,191 miles of distribution lines. Respondents answered questions pertaining to multiple domains including company profile, system profile, personnel and wages, budget, program attributes, utility forest attributes, and chemical control. The over-arching aim of this report was to provide actionable material from which utility managers could benchmark their own programs and thereby improve them in various ways. In addition, this study sought to develop a deeper understanding of utility arboriculture so that the industry might advance in knowledge, insight and professionalism. The responding utilities served an average customer base of 768,000 and maintained an average of 13,000 miles of overhead distribution, 3,000 miles of sub-transmission and 3,700 miles of transmission lines. UVM programs were staffed with an average of 375.9 workers, with qualified line crew tree workers being the hardest position to fill (in-house had a mean index score of 3.7 while contractor positions combined had a score of 4.1). An average of 91.6% of the workforce was male and white (mean of 78.4%). In addition, 96.7% of respondents reported that their UVM was cycle-based. Most schedules aligned with time (62.1%) and averaged 4.25 years in length. Over half (54.2%) of respondents reported inadequate non-capital budgets that were 30.1% below need, on average. Finally, we would like to give sincere thanks to the participating utilities that contributed to this research.

References 52 Pa. Code, §§ 57.194-57.197 (2004). https://www.pacodeandbulletin.gov/Display/pacode?file=/secure/pacode/data/052/chapter57/s57.194.ht ml&d=reduce Cadaval, S., Clarke, M., Dinkins, L., Klein, R. W., Roberts, J. W., & Yang, Q. (2024). Why can’t we all just get along? Conflict and collaboration in urban forest management. Arboriculture & Urban Forestry, 50(5), 346–364. https://doi.org/10.48044/jauf.2024.018 California Public Utilities Commission. (n.d.). Wildfire and Wildfire Safety. Retrieved April 9, 2026, from https://www.cpuc.ca.gov/industries-and-topics/wildfires Chaney, W. R. (2005). Growth retardants: A promising tool for managing urban trees. Purdue University Extension. https://www.extension.purdue.edu/extmedia/FNR/FNR-252-W.pdf Cieslewicz, S., & Porter, W. (2010). Utility vegetation management benchmark & industry intelligence. CN Utility Consulting. Cranmer, N., Fahey, R. T., Worthley, T., Witharana, C., Alveshere, B., & Bunce, A. (2024). Tree trimming effects on 3-dimensional crown structure and tree biomechanics: A pilot project. Arboriculture & Urban Forestry, 50(6), 395–413. https://doi.org/10.48044/jauf.2024.020 Dahle, G. (2026, April). Research Committee Update. Utility Arborist Newsline, 17(2), 10–11. U.S. Energy Information Administration. (2019). Investor-owned utilities served 72% of U.S. electricity customers in 2017. https://www.eia.gov/todayinenergy/detail.php?id=40913

Goodfellow, J. (2020). Utility tree risk assessment. International Society of Arboriculture. Goodfellow, J., & Ball, J. (2022). Characterizing the risk of electrical contact to arborists (Nos. 21-UARF-01; Utility Arborists Research Fund, p. 49). TREE Fund. https://treefund.org/wpcontent/uploads/2023/05/Final-Report-Characterizing-the-Risk-of-Electrical-Contact-to-Arborists-TREEFund-Grant-21-UARF-01.pdf Hauer, R. J., & Miller, R. H. (2021). Utilities & vegetation management in north america: Results from a 2019 utility forestry census of tree activities & operations. (p. 69) [Special Publication]. College of Natural Resources. University of Wisconsin - Stevens Point. JASP Team. (2025). JASP (Version 0.95.4) [Computer software]. https://jasp-stats.org/ Kempter, G. (2024). ANSI A300, Clause 5, expands use of mechanical pruning. TCI Magazine. https://tcimag.tcia.org/safety/equipment-safety-mechanization/ansi-a300-clause-5-expands-use-ofmechanical-pruning/ Kuhns, M., & Reiter, D. (2007). Knowledge of and attitudes about utility pruning and how education can help. Arboriculture & Urban Forestry, 33(4), 264–274. https://doi.org/10.48044/jauf.2007.030 McBride, L. W., Klein, R. W., Koeser, A. K., Clarke, M. K., Hauer, R. J., Ward, T., Bull, T., & Harchick, C. (2026). Effectiveness of limited visual risk assessments in predicting urban tree storm failure in Wisconsin, U.S. Urban Forestry & Urban Greening, 119, 129369. https://doi.org/10.1016/j.ufug.2026.129369 Miller, R. H. (2021). Integrated vegetation management (3rd ed.). International Society of Arboriculture. Miller, R. H., & Kempter, G. (2018). Utility arboriculture: The utility specialist certification study guide. International Society of Arboriculture. O’Herrin, K., Bassett, C. G., Day, S. D., Ries, P. D., & Wiseman, P. E. (2023). Borrowed credentials and surrogate professional societies: A critical analysis of the urban forestry profession. Arboriculture & Urban Forestry, 49(3), 107–136. https://doi.org/10.48044/jauf.2023.009 Parent, J. R., Meyer, T. H., Volin, J. C., Fahey, R. T., & Witharana, C. (2019). An analysis of enhanced tree trimming effectiveness on reducing power outages. Journal of Environmental Management, 241, 397– 406. https://doi.org/10.1016/j.jenvman.2019.04.027 Porter, W., & Cohn, N. (2014). Utility vegetation management benchmark & industry intelligence: 2014 distribution update (p. 59). CNUC. Porter, W., & Cohn, N. (2016). Distribution vegetation management benchmark survey results 2016 (p. 95). CNUC. University of Wisconsin - Stevens Point. (n.d.). Professional Utility Vegetation Management Program. Professional Utility Vegetation Management Program. Retrieved April 9, 2026, from https://www.uwsp.edu/wfc/wisconsin-forestry-center/utility-vegetation-management-certificate/ U.S. Department of Energy. (2024). Vegetation Management: Resilience Investment Guide (p. 9). U.S. Department of Energy Grid Deployment Office. https://www.energy.gov/sites/default/files/202411/111524_Vegetation_Management.pdf U.S. Energy Information Administration. (n.d.). Investor-owned utilities served 72% of U.S. electricity customers in 2017. Retrieved April 9, 2026, from www.eia.gov/todayinenergy/detail.php?id=40913 Wiseman, P. E., Endahl, J., & Christensen, B. (2025). Tree inventories (3rd ed.). International Society of Arboriculture.

Appendix A: Questionnaire and Results Thank you for participating in the 2024 Eocene Environmental Group (formerly CNUC) utility vegetation management survey. It is the latest iteration of benchmarking surveys CNUC has conducted since 2002. This survey is intended for the person(s) who can best tell us about your utility operation and utility vegetation management (UVM). Ideally, the person directly responsible for your program will respond; however, any utility employee who can best respond to any question should do so. Answer each question the best you can. If multiple departments/groups are involved with your utility, we ask that the entire program be formulated as one response using this questionnaire. It is important to answer as many applicable questions as possible. Please read each question carefully: Respond to each question with the answer that best fits your utility. An example follows below. 1. Does your utility manage trees and vegetation? (CHECK ONE)

 Yes  No Please try to answer every question that applies to you. If none of the answers provided seem exactly right, choose the one that best reflects what occurs in your utility. If you are unable to answer any question, leaving it blank is acceptable. The survey may take up to 60 to 90 minutes to complete. The data from each participating utility is pooled together and shared, without identifying any responding utility. The intent is to enable industry professionals to compare their program with their other North American utilities. The ultimate objective of this study is to further develop an understanding of utility arboriculture, advance the profession, and help utility vegetation managers improve their programs. The results of the survey will be submitted for publication in Arboriculture and Urban Forestry, the Utility Arborist Newsline, and other periodicals. Each participating utility will be randomly assigned a discreet two-character alpha numeric identifier, to which responses will be exclusively attributed. Codes are changed for each survey to enhance security. Participating utilities will be informed of their individual code. Only the subject utility and the CNUC director of research and development will know the codes assigned to specific respondents. Exceptions will only be made at a participating utility’s written permission or request.

SECTION I – COMPANY PROFILE 1. How many total customers does your company serve? n=29 768,200 (mean), 11,000 to 5,500,000 (range) 2. What type of business is your utility? n=30 (CHECK ALL THAT APPLY) 96.7% Distribution 60.0% Transmission 40.0% Generation

3. What is your ownership structure? n=30 (CHECK ALL THAT APPLY) 36.7% Cooperative 0.0% Federal 46.7% Investor-owned utility 10.0% Municipal utility 3.3% Public utility district 3.3% State/provincial

SECTION II – SYSTEM PROFILE 1. Specify the unit metric used to measure line distance? Units are in (CHECK ONE) km (kilometers) mi (miles) 2. What distance (km or mi) of overhead lines do you have on your system? Enter “0” if none (n=30). Line Type Transmission (subject to FAC-003-5) Sub-transmission (not subject to FAC-003-5) Distribution

Mean (mi)

Range (mi)

SE1 (mi)

3,667

28 to 12,369

1,219

2,985

3 to 17,302

1,171

13,248

108 to 81,602

4,084

Secondary

4,310

54 to 30,000

3,674

Primary2

12,714

91 to 51,602

4,028

Three Phase

5,634

62 to 27,074

1,958

Single Phase

7,182

23 to 34,202

2,304

72.0 (mean, 7.0 SE), 2 to 100 (range) % of primary lines accessible by aerial lift (e.g., truck, backyard, skidder) 1SE = Standard error of the mean throughout report 2Primary = Three-phase and Single-phase

3. What are your typical right-of-way (ROW) widths (ft/m)? n=2 to 26 Line Type Service drops, triplex Service drops open wire Secondary, triplex Secondary open wire Distribution 3 Ø Distribution 1 Ø 45 kV 69KV 115kV single pole 115kV H structure 138kV single pole 138 H structure 230kV single pole 230kV H structure 230 steel lattice 345 single pole 345kV H structure 345kV steel lattice 500kV 765 kV

Mean (ft) 5.46 5.50 7.50 8.06 22.69 18.04 43.13 44.67 54.67 67.44 101.00 129.50 80.20 94.33 80.33 128.57 120.83 132.14 106.67 275.00

13 12 16 16 26 26 8 15 9 9 7 6 5 6 3 7 6 7 3 2

SE (ft) 1.47 1.55 1.49 1.41 2.00 1.68 8.76 6.14 8.35 8.05 33.53 35.86 14.97 8.86 11.26 14.87 13.57 11.85 15.90 25.00

Range (ft) 0 to 20 0 to 20 0 to 20 0 to 20 5 to 40 3 to 30 15 to 80 10 to 100 25 to 100 25 to 100 42 to 300 57 to 300 46 to 125 61 to 125 61 to 100 50 to 150 75 to 150 75 to 150 75 to 125 250 to 300

Important (4)

Very Important (5)

Mean Index Score

Other: n = 2, fiber service, line sensors

Neither Important nor Unimportant (3)

Aerial cable systems Aerial spacer cable Alley arms Automatic line reclosers Compact construction Covered primary Hendrix cable Overcurrent strategy Raising poles Relocating overhead lines Relocating overhead lines underground Undergrounding distribution primary

Unimportant (2)

Solution (% of total & mean)

Very Unimportant (1)

4. How important are the following engineering solutions to your vegetation management program? n=23 to 27

26.9 14.8 11.5 11.5 13.0 18.5 12.5 8.3 8.0 3.8 7.4 0.0 -

26.9 33.3 26.9 0.0 26.1 25.9 16.7 0.0 12.0 11.5 11.1 15.4 -

34.6 33.3 34.6 3.8 39.1 25.9 45.8 20.8 44.0 26.9 18.5 19.2 -

11.5 14.8 23.1 30.8 21.7 22.2 20.8 50.0 28.0 42.3 48.1 42.3 -

0.0 3.7 3.8 53.8 0.0 7.4 4.2 20.8 8.0 15.4 14.8 23.1 -

2.3 2.6 2.8 4.2 2.7 2.7 2.9 3.8 3.2 3.5 3.5 3.7 -

5. Outage Metrics Please provide your outage performance in the following categories for 2023. n=7 to 25 Outage Metric

Mean

Average System Availability Index (ASAI) Customer Average Availability Interruption Index (CAIDI) Momentary Average Interruption Frequency Index (MAIFI) System Average Interruption Duration Index (SAIDI) System Average Interruption Frequency Index (SAIFI)

99.87 349.79 0.87 84.44 0.80

SE 0.11 256.47 0.42 15.19 0.13

6. What percentage of your 2023 outages were caused by vegetation? n=26 22.2% (mean), 3.4 (SE) 7. What percentage of your 2023 vegetation caused outages were preventable? n=14 33.7% (mean), 10.1 (SE) 8. How do you define preventable vegetation caused outages? n=22 Preventable Outage Definition

Mean %

An outage that would not have occurred if proactive specification clearance had achieved on schedule

46.7

An outage caused by vegetation on a circuit that is behind scheduled maintenance

20.0

An outage caused by vegetation that was the subject of a customer call that was not addressed within the required period of time

20.0

An outage caused by vegetation that was identified by internal or contracted vegetationmanagement staff as problematic, but was not address within a required period of time.

26.7

Other: Responses include trees on-ROW (3), growth (1), any tree (1), deferred removal (1), worker caused (1), fall ins from off-ROW (1)

33.3

9. What percentage of your 2023 vegetation-related outages were caused by the following? Percent of outages mean value and (SE) VM Outage Type Broken branches Grow-ins Off right-of-way trees Overhang Trunk failure Uprooting Other

Distribution n=19 28.3 (5.8) 7.6 (2.4) 37.4 (8.7) 3.2 (1.4) 12.3 (4.1) 8.0 (2.2) 11.2 (4.7)

Sub-transmission n=6 17.8 (11.3) 2.0 (1.5) 54.0 (19.9) 0.0 12.0 (7.6) 2.8 (2.8) 0.2 (0.2)

Transmission n=4 3.7 (3.7) 0.0 41.7 (25.0) 0.0 22.3 (22.3) 0.0 8.3 (16.7)

vines (4), palms (2), unknown vegetation (1), tree in ROW (1), hazard tree (1), logger (1)

10. How many members of the public where shocked due to contacting electrical facilities through trees on your system (distribution, sub-transmission, transmission) from 2021 to 2023 (inclusive)? n=19 0 (mean), 0 (SE), 0 to 0 (range): electrical contacts 0 (mean), 0 (SE), 0 to 0 (range) electrical contacts per year per 1,000 miles 11. How many tree workers not employed or contracted on your system for line clearance occurred were shocked due to electrical contact on your system from 2021 to 2023 (inclusive)? n=21 0.24 (mean), 0.12 (SE), 0 to 2 (range): electrical contacts 0.02 (mean), 0.01 (SE), 0 to 0.16 (range): electrical contacts per year per 1,000 miles 12. How many tree workers employed or contracted on your system for line clearance were shocked due to electrical contact on your system from 2021 to 2023 (inclusive)? n=23 0.17 (mean), 0.08 (SE), 0 to 1 (range): electrical contacts 0.01 (mean), 0.01 (SE), 0 to 0.01 (range): electrical contacts per year per 1,000 miles 13. How many fires ignited by vegetation contacting electrical facilities have occurred on your system (distribution and transmission) from 2021 to 2023 (inclusive)? n=19 24.58 (mean), 23.53 (SE), 0 to 448 (range): number of fires 0.21 (mean), 0.01 (SE), 0 to 1.5 (range): number of fires per year per 1,000 miles 14. How many acres/hectares were burned as a result of fires ignited by vegetation contracting electrical facilities on your system from 2021-2023 (inclusive)? n=18 4.11 (mean), 3.88 (SE), 0 to 70 (range): number of acres 1.37 (mean), 1.29 (SE), 0 to 23.3 (range): number of acres per year

SECTION III – PERSONNEL AND WAGES 1. How many total full-time equivalents (FTEs) (both in in-house and contracted) worked in your VM program in 2023? n=27 375.9 (mean), 227.4 (SE), 2 to 6,115 (range) 2. How many levels of management are involved in directing the UVM program? n=30 2.4 (mean), 0.18 (SE), 1 to 5 (range) 3. Do you have a UVM department head? n=30 (CHECK ONE) 96.6% Yes 3.3% No 

(PLEASE GO TO QUESTION 8)

4. What is our UVM department head’s title? n=29 (CHECK ONE) 6.9% 6.9% 0.0%

Arborist Director of Vegetation Management Forester

34.5% 3.4% 0.0% 0.0% 0.0% 3.4% 44.8%

Manager of Vegetation Management Operations Manager Senior Utility Arborist Supervisor of Vegetation Management System Forester Vice President of Vegetation Management Other (13 responses): Manager (7); Director (4); Supervisor (1); VP (1)

5. What credentials does the UVM department head have? n=26 (CHECK ALL THAT APPLY) 69.2% ISA Certified Arborist® 46.2% ISA Certified Arborist Utility Specialist™ 0.0% ISA Board Certified Master Arborist® 23.1% ISA Tree Risk Assessment Qualification 0.0% Project Manager Body of Knowledge (PMBOK®) 0.0% Professional Engineer (PE) 69.2% Bachelor of Science in a natural resource field 7.7% Master of Science in natural resource field 0.0% Ph.D. in natural resource related field 7.7% Utility Vegetation Management Professional Credential 41.4% Other (11 responses): Other degrees (4); State arborist certifications (2); Pesticide applicator (2); Other (3) 6. What is the title of the VM position one level below the department head? n=28 (CHECK ONE) 9.1% 34.8% 17.9% 4.8% 4.8% 12.5% 71.4% Other (7)

Arborist Forester Manager of Vegetation Management Operations Manager Senior Utility Arborist Supervisor of Vegetation Management Other (15 responses): Manager (2); Supervisor (2); Coordinator (2); Utility Forester (2);

7. What credentials does the UVM position one level below the department head have? n = 27 (CHECK ALL THAT APPLY) 73.1% ISA Certified Arborist® 53.8% ISA Certified Arborist Utility Specialist™ 7.7% ISA Board Certified Master Arborist® 23.1% ISA Tree Risk Assessment Qualification 0.0% Project Manager Body of Knowledge (PMBOK®) 0.0% Professional Engineer (PE) 53.8% Bachelor of science in a natural resource field 3.8% Master of science in natural resource field 0.0% PhD in natural resource related field 3.8% Utility Vegetation Management Professional Credential Other (12 responses): Other degrees (4), Experience (2), State arborist credentials (2), Pesticide applicator (2), Drone pilot (2), Other (1)

8. How many in-house UVM FTEs work in your UVM program? Fill in the number of positions and FTEs? (Enter “0” if no position). n=2 to 24 Number of In-house Positions

Number of InHouse FTEs

Department head

1.00

Management 1 level below department head

2.04

2.5

Management 2 levels below department head

6.5

3.7

1.0

0.8

7.2

85.4

0.8

0.6

1.0

0.6

0.0

Clerical support

1.0

20.8

Other (specify)

17.4

25.9

Position Categories (mean values)

General foreperson/supervisor (direct supervision of field personnel) Planners/auditors Tree crew leader (foreperson) Qualified line clearance tree crew members (non-crew leaders) Ground workers

9. How many UVM contractor FTEs work in your UVM program? Fill in the number of positions and Full Time Equivalents – FTEs, 2080 hours base year, enter “0” if no position. n=3 to 22 Position Categories (mean values) Account head (top management position with direct responsibility for work on the contract) Management 1 level below account head Management 2 levels below account head

Number of Contract Positions

Number of Contract FTEs

1.8

1.4

1.3

2.0

2.5

General foreperson/supervisor (direct supervision of field personnel) Planners/auditors

5.4

20.9

14.2

111.7

Tree crew leaders

22.3

111.1

31.1

124.8

12.2

85.5

Clerical support

1.17

Other (specify)

1.0

34.6

Qualified line clearance tree crew members (non-crew leaders) Ground workers

10. What are the 2023 salaries or wages for the following in-house positions? n=1 to 19 Position Categories Department head (salary)

In-house Pay USD mean value and (SE) $126,071 (5,635)

Management 1 level below department head (salary)

$104,525 (4,087)

Management (2 levels below the department head) (salary)

$92,165 (2,512)

General foreperson/supervisor (direct supervision of field personnel)

Crew leader (hourly)

$44.00 (2.52)

Qualified utility arborist (hourly)

$35.33 (4.98)

Qualified utility arborist trainee (hourly)

Ground worker (hourly)

$18.00

Chemical applicator (hourly)

$20.00

Planner Q/A specialist (hourly)

$36.82

Other: (responses: utility forester, data analyst)

$40.10 (5.10)

11. What are the 2023 salaries or wages for the following contracted positions? n=1 to 11 Position Categories Account head (top management position with direct responsibility for work on the contract) (salary) Management 1 level below account head (salary) Management 2 levels below the account head General foreperson/supervisor (direct supervision of field personnel) (salary) General foreperson/supervisor (direct supervision of field personnel) (hourly) Crew leader (hourly)

Contractor Pay USD mean value and (SE) $130,000 (20,000) $82,500 (7,500) $92,165 NA $35.39 (1.83) $30.33 (1.28)

Qualified utility arborist (hourly)

$27.51 (1.16)

Qualified utility arborist trainee (hourly)

$23.94 (1.49)

Ground worker (hourly)

$19.33 (1.33)

Chemical applicator (hourly)

Planner Q/A specialist (hourly)

$25.92 (2.60)

Other: (response: mechanic)

$24.00

12. On average, how long have employees in the following in-house positions worked in your UVM program by the end of 2023? n=1 to 24

In-house Position

Years of Service mean value and (SE)

Department head

14.5 (1.92)

Management 1-level below department head

12.3 (1.88)

Management 2-levels below department head

8.7 (2.28)

General foreperson/supervisor (with direct supervision of field personnel)

11.4 (2.76)

Planners/auditors

7.7 (3.84)

Tree crew leaders

10.9 (3.01)

Qualified line-clearance tree crew members (non-crew leaders)

6.8 (1.53)

Groundworkers

2.1 (0.66)

Clerical support

10.0

Other (specify)

9.0 (3.79)

13. On average, how long have employees in the following contracted positions worked in your UVM program by the end of 2023? n=3 to 13 Contractor Position

Years of service mean value and SE

Account head (top management position with direct responsibility for work on the contract)

12.1 (2.59)

Management 1-level below account head

7.0 (2.35)

Management 2-levels below account head

5.7 (2.19)

General foreperson/supervisor (with direct supervision of field personnel)

10.1 (2.21)

Planners/auditors

5.3 (2.04)

Tree crew leaders

7.2 (2.41)

Qualified line-clearance tree crew members (non-crew leaders)

3.0 (0.62)

Groundworkers

1.5 (0.27)

Clerical support

Other (specify)

Easy

Very easy

Index Score

Management 1 level below department head Management 2 levels below department head General foreperson/supervisor Planners/auditors Tree crew leaders Qualified tree crew member (non-crew leaders) Groundworkers Clerical support Other: 2 responses

Neither difficult nor easy

Department head

Difficult

Position Categories (mean value)

Very difficult

14. How difficult is it to recruit candidates to fill the following in-house positions? n=5 to 23

8.7

13.0

43.5

21.7

13.0

3.17

9.5

19.0

33.3

28.6

9.5

3.10

0.0

15.4

53.8

15.4

3.31

0.0 0.0 0.0

16.7 20.0 33.3

33.3 40.0 16.7

33.3 20.0 16.7

16.7 20.0 33.3

3.50 3.40 3.50

0.0

33.3

16.7

0.0

50.0

3.67

0.0 0.0 -

20.0 0.0 -

20.0 80.0 -

40.0 20.0 -

20.0 0.0 -

3.60 3.20 -

Difficult

Neither difficult nor easy

Easy

Very easy

Index Score

Position Categories Account head (top management position with direct responsibility for work on the contract) Management 1-level below account head Management 2-levels below account head General foreperson/supervisor (with direct supervision of field personnel) Planners/auditors Tree crew leaders Qualified tree crew member (non-crew leaders) Groundworkers Clerical support Other: 1 response

Very difficult

15. How difficult is it to recruit candidates to fill the following contractor positions? n=5 to 20

20.0

40.0

20.0

0.0

2.40

10.0 14.3

20.0 14.3

40.0 14.3

20.0 42.9

10.0 14.3

3.00 3.29

5.6

16.7

22.2

33.3

3.61

14.3 5.0

14.3 10.0

21.4 10.0

28.6 45.0

21.4 30.0

3.29 3.85

5.6

44.4

38.9

4.06

6.3 33.3 -

37.5 33.3 -

0.0 33.3 -

25.0 0.0 -

31.3 0.0 -

3.38 2.00 -

16. What percentage of your total FTE’s are directly responsible for managing your UVM program? n=26 54.6 (mean), 9.1 (SE), 0 to 100 (range) 17. Do you pre-plan or inspect work ahead of tree crews? n=30 (CHECK ONE) 86.7% Yes 13.3% No (PLEASE GO TO SECTION IV - BUDGET) 18. Who conducts your pre-planning or inspection ahead of tree crews? (Please enter “0” if none). n=23 Who Conducts Pre-Planning Company employees Contract work planners Tree crew members Other: (4 responses: general foreperson, dedicated planner)

% of total mean value and (SE) 27.4% (7.4) 49.4% (8.8) 14.1% (6.3) 5.0% (3.3)

SECTION IV – BUDGET 1. What were your total UVM expenditures in 2023 (exclude capital work)? n=29 $61.0M (mean), $43.2M (SE), $215K to $1.3B (range) 2. What were your total capital expenditures in 2023? n=25 $4.7M (mean), $1.7M (SE), $20K to $25.6M (range) 3. Is your non-capital UVM budget adequate to meet current needs as defined in your work plan or your identified annual UVM budget needs? This includes planning, maintenance, removal, inventory, education, etc. n=24 45.8% Yes  (PLEASE GO TO QUESTION 4) 54.2% No 4. How much is your non-capital VM budget below the identified need? n=24 30.1% (mean), 7.6% (SE), 2% to 100% (range) 5. Has your UVM non-capital budget been stable over the past five years? n=28 (CHECK ONE) 75.0% Yes 25.0% No 6. What were your 2023 non-capital program expenditures? n=3 to 24 (Please enter “0” if none.)

Total Expenditures ($ by Category)

UVM Expenditure Category

Unplanned Expenditures (% of Category) mean value and (SE)

Transmission

$3,956,000 (1.7M)

21.3 (15.8)

Sub-transmission

$3,372,200 ($1.7M)

7.6 (1.8)

Distribution

$70,865,000 (52.1M)

26.7 (12.1)

Total

$22,191,000 (8.2 M)

37.8 (13.9)

7. What were your 2023 distribution expenditures in the following categories? n=5 to 24 Distribution

2023 Annual Expenditures (USD) mean value and (SE)

Routine scheduled (cycle) work

$36,897,859 (22.7M)

Mid-cycle work

$4,563,108 (3.9M)

Herbicide crew (apart from cut stump and incidental tree crew application)

$384,365 (200K)

Reactive

$1,279,981 (499K)

High risk tree removal (apart from scheduled cycle work)

$1,186,241 (611K)

Work planning

$16,686,778 (15.3M)

Quality control

$8,208,363 (7.8M)

Other

$62,890,958 (60.2M)

Total

$65,243,486 (47.8M)

8. What were your 2023 sub-transmission expenditures in the following categories? n=1 to 7 Sub-transmission

2023 Annual Expenditures (USD) mean value and (SE)

Routinely scheduled (cycle) work

$2,616,314 (1.1M)

Mid-cycle work

$400,190 (351K)

Herbicide crew (apart from cut stump and incidental tree crew application)

$604,790 (358K)

Reactive

$88,763 (65K)

High risk tree removal (apart from scheduled cycle work)

$126,070 (106K)

Work planning

$246,374 (103K)

Quality control

$88,806 (6.2K)

Other

$620,141

Total

$3,566,496 (1.6M)

9. What were your 2023 transmission expenditures in the following categories? n=3 to 9 Transmission

2023 Annual Expenditures (USD) mean value and (SE)

Routinely scheduled (cycle) work

$2,449,347 (1.0M)

Mid-cycle work

$364,810 (319k)

Herbicide crew (apart from cut stump and incidental tree crew application)

$608,551 (355K)

Reactive

$1,379,995 (1.3M)

High risk tree removal (apart from scheduled cycle work)

$341,199 (199K)

Work planning

$339,636 (62.1K)

Quality control

$115,439 (55.7K)

Other

$989,083 (797K)

Total

$4,611,098 (1.8M)

SECTION V – PROGRAM ATTRIBUTES 1. Do you use GIS-based software to manage your program? n=30 (CHECK ONE) 76.7% 23.3%

Yes No 

(PLEASE GO TO QUESTION 5)

2. Is it in-house or third-party? n=14 28.6% 71.4%

In-house Third-party

3. How effective do you find your GIS-based software n=16 6.3% 0.0% 25.0% 43.8% 25.0%

Very ineffective Ineffective Neither ineffective nor effective Effective Very effective

4. What capability does your GIS software have? n=23 78.3% 47.8% 52.2% 87.0% 60.9% 69.6% 65.2% 45.5% 30.4% 21.7% 52.2%

Customer notification records Customer complaint records Refusal process records Work history at a location Detailed work order development Electronic work order delivery for tree crew Production data collection Production reporting to management Time sheet Automatic billing Report creation

17.4% 17.4%

Remote sensing interface Other

5. Do you track customer complaints? n=30 53.3% 46.7%

No Yes 

What percent are attributable to the following causes? n=13 to 14

Aesthetics Applied herbicide without authorization Crew didn’t arrive at the agreed date or time Improper notification Property damage

Percentage mean value and (SE) 19.5 (4.7) 2.9 (1.6) 5.5 (3.3) 15.8 (4.0) 11.3 (3.1)

Property was left untidy

21.0 (5.4)

Removed tree(s) without authorization Rude crew members or planners

2.2 (1.1) 4.4 (2.0)

Other

10.0 (4.0)

Complaint

6. What contracting types do you use in your UVM program? n=30 (CHECK ALL THAT APPLY) 40.0% Hard price 16.7% Performance-based 90.0% Time and material 50.0% Unit 0.0% Other 7. How is your VM contract structured? n=30 % of work under contract Contract duration type (n=30) (years) (n=0 to 20) mean values and (SE) Hard price per circuit 20.6 (6.2) 1.7 (0.4) Hard price per management unit (e.g., district) 8.5 (4.8) 2.5 (1.5) Unit cost per grid (polygon) 0.2 (0.2) 3.0 Hard price per system 0 (0) NA Unit cost per km or mi of pole km/mi 3.2 (3.2) 5.0 Unit cost per span 0.6 (0.4) 3.0 (2.0) Unit cost per tree 4.5 (3.2) 3.25 (0.75) Unit cost per mile/km 1.3 (1.3) NA Time and material 50 (7) 3.2 (0.3) Time and material performance-based 8.9 (4.9) 3 (0.8) Other (specify) 2.2 (1.7) 3 3.1 years Average Duration

VM contract structure

8. How are your pre-planning or quality control (auditing) contracts structured?

VM pre-planning and auditing contract structure

% of work under contract type n=18 to 20

Contract duration (years) n= 0 to 14

mean values and (SE) Hard price per circuit

14.5 (7.5)

1.0

Hard price per management unit (e.g., operations district)

5.5 (5.5)

1.0

Unit cost per grid (polygon)

0 (0)

Hard price per system

0 (0)

Unit cost per km or mi of pole km/mi

0 (0)

Unit cost per span

0 (0)

Unit cost per tree

0 (0)

Unit cost per mile/km

0 (0)

Time and material

71.3 (9.8)

3.3 (0.4)

Time and material performance-based

11.1 (7.6)

5.0

Other (specify)

5.3 (5.3)

NA 2.6 years

Average Duration

Bad

Neither good nor bad

Pre-planner

0.0

0.1

0.6

0.4

4.33

Vegetation Management 1

0.0

0.1

0.6

0.3

4.07

Vegetation Management 2

0.0

0.1

0.6

0.3

4.21

Vegetation Management 3

0.0

0.1

0.5

0.3

4.08

Herbicide 1

0.0

0.1

0.5

4.40

Herbicide 2

0.0

0.2

0.7

4.50

Auditor

0.0

0.5

4.50

Other

0.0

1.0

0.0

4.00

Good

Contractor (% of total & mean)

Poor

Excellent

Mean Index Score

9. Please describe your relationship with your VM contractor(s). n=2 to 29

10. How is your distribution work scheduled? n=30 (CHECK ONE) 0.0% 76.7%

Grid Circuit 

91.3% Entire circuit 13.0% By overcurrent protection zone >100% as some utilities reported both 23.3%

Other (7 responses): Polygon (3); Substation (2); Other (2)

11. Do you conduct UVM work cycles? n=30 (CHECK ONE) 3.3% No 96.7% Yes  12. What best describes your predominate approach for cycles? n=29 6.9% 17.2% 0.0% 6.9% 24.1% 13.8% 62.1%

Artificial intelligence-based Condition-based Just in time Reactive Reliability-based Safety-based Time-based 

12a. How long (years)? n=18 4.25 (mean), 0.38 (SE), 1.0 to 9.0 (range) 13. How do you define cycles? n=29 62.1%

100% of distribution primary lines within set period of time

20.7% A percentage of distribution primary lines in a given year (i.e., one-third of the pole miles/kms a year) would make three-year cycle, regardless of whether all the line was worked within the time period. 17.2%

Other (5 responses, no central theme, variety of answers)

. 14. Does your cycle vary by distribution location or work type? n=26 69.2% No 30.8% Yes 

What is the cycle for the following distribution locations or work types. If variable by design, enter the range)? n=1 to 12

Location/work type

Cycle Length (Years) mean value and (SE) Actual Desired

X-urban areas

4.3 (0.5)

4.5 (0.3)

Rural areas

5.5 (0.5)

5.0 (0.3)

5.0

5.3 (0.6)

Brush mastication (mowing)

4.5 (0.6)

5.2 (0.5)

Herbicide

4.1 (0.4)

4.6 (0.5)

Three-phase

6.0 (2.1)

4.8 (0.8)

Single phase

6.5 (3.5)

4.8 (1)

Other (specify)

8.3 (2.7)

7.0

Side

15. Do you utilize action thresholds to schedule your work? n=28 64.3% No 35.7% Yes  What are your action thresholds in the following categories (if you do not have action thresholds in a category, leave blank)? n=0 to 5 Line type CATV Secondary service triplex Secondary service open wire Pole to pole secondary triplex Pole to pole secondary open wire Distribution 45 kV sub-transmission 69 kV sub-transmission 115 kV sub-transmission 115 kV transmission 138 kV sub-transmission 138 kV transmission 230 kV transmission 345 kV transmission 500 kV transmission 765 kV transmission

Action threshold feet from line (mean value and (SE)) NA 5.0 5.0 5.0 (0) 5.0 6.0 (2.8) 10.5 (7.3) 7.9 (2.9) 8.8 (6.3) 8.1 (2.6) 18.0 (3.0) 21.0 13.9 (3.6) 17.7 (2.7) 17.5 (7.5) 25.0

16. Please specify your distribution primary clearances. (Check the NM box if no maintenance, check the CO box if clear only to relieve abrasion or deflection, or enter the applicable distance for side, under, and overhang clearances.) n=0 to 17 Units are in (CHECK ONE) ft (ft) m (meters) Line Type3,4

Clearance Distance (ft) mean value and (SE) NM1

CO2

Side

Under

Overhang

6.9%

13.8%

6.3 (1.4)

7.1 (1.6)

7.9 (2.0)

6.9%

3.4%

5.2 (1.0)

5.4 (1.1)

7.4 (1.9)

Triplex dist. pole to pole sec.

6.9%

34.5%

8.7 (2.7)

8.0 (2.9)

10.7 (3.1)

Triplex dist. sec. underbuilt on primary lines

6.9%

8.4 (2.1)

7.8 (2.7)

9.1 (2.8)

Single-phase dist. wye

9.3 (0.5)

11.0 (1.0)

12.1 (0.9)

Single-phase dist. delta

11.4 (1.3)

11.1 (1.2)

12.3 (0.9)

Three-phase distribution wye

11.4 (1.5)

11.6 (0.9)

12.6 (1.0)

Three-phase distribution delta

11.8 (1.7)

10.8 (0.9)

12.9 (1.0)

46 kV transmission

18.8 (3.8)

16.7 (4.4)

25.0

69 kV transmission

18.8 (2.5)

14.0 (2.4)

115 kV transmission

16.1 (2.9)

16.9 (2.8)

138 kV transmission

22.2 (2.0)

18.2 (2.1)

230 kV transmission

24.7 (6.3)

24.5 (6.3)

345 kV transmission

41.9 (7.2)

33.9 (8.5)

500 kV transmission

29.8 (6.3)

28.8 (5.5)

Open wire dist. pole to pole sec. Open wire dist. sec. underbuilt on primary lines

1NM = No maintenance; percent of valid responses of utilities with distribution 2CO = Clear only to relieve abrasion or deflection; percent of valid responses of utilities with distribution 3dist = distribution 4sec = secondary

17. What was the tree condition at the time of maintenance? n=18 (Please enter “0” if none.) 17a. Percent of trees with desiccated leaves (contact with line) 16.2% (mean), 4.4 (SE), 0 to 60 (range)

17b. Percent of trees over-hanging line 18.9% (mean), 5.9 (SE), 0 to 75 (range)

Consideration

Very Unimportant (1)

Unimportant (2)

Neither Important nor Unimportant (3)

Important (4)

Very Important (5)

Mean Index Score

18. Rate the importance of the following considerations in determining the clearance distances. n=28 to 29

Maintaining tree structure

14.3

0.0

25.0

50.0

10.7

3.4

Reliability

3.4

0.0

24.1

72.4

4.6

Risk reduction (safety)

3.4

0.0

27.6

69.0

4.6

Expected weather in the vicinity

7.1

0.0

39.3

46.4

7.1

3.5

Facility construction

3.4

6.9

48.3

27.6

13.8

3.4

Facility priority

3.4

34.5

37.9

20.7

3.7

Line location (e.g., urban, rural)

6.9

13.8

44.8

24.1

10.3

3.2

Number of customers served

3.4

13.8

31.0

27.6

24.1

3.6

Planned cycle length

6.9

3.4

17.2

27.6

44.8

4.0

Site factors (tree type, terrain, cultural)

3.4

0.0

24.1

55.2

17.2

3.8

Tree positions relative to the line

3.4

0.0

17.2

69.0

10.3

3.8

Three or single-phase lines

3.4

27.6

51.7

13.8

3.7

19. Do you clear “ground to sky” on the wire side of trees? n=29 (CHECK ONE) 46.7% Yes 53.3% No  (PLEASE GO TO QUESTION 20) (CHECK ALL THAT APPLY) n=13 23.1% Rural locations 38.5% Three-phase lines 23.0% To the first protective device

38.5% All locations 13.8%% Other (4 responses: transmission, sub transmission, 34k, exclude yards)

Very Unimportant (1)

Unimportant (2)

Neither Important nor Unimportant (3)

Important (4)

Very Important (5)

Mean Index Score

20. Rate the importance of the following pruning objectives to your UVM program. n=29

Clearance

27.6

72.4

4.7

Maintaining tree structure

34.5

55.2

10.3

3.8

Reliability

3.4

10.3

86.2

4.8

Risk reduction (safety)

27.6

72.4

4.7

Pruning Objectives

Very Unimportant (1)

Unimportant (2)

Neither Important nor Unimportant (3)

Important (4)

Very Important (5)

Mean Index Score

21. How important are the following standards, best practices and resources to your UVM pruning specifications? n=27

ANSI A300 - Part 1

0.0

11.1

37.0

51.9

4.4

ISA Utility Pruning of Trees Best Management Practices

0.0

14.8

55.6

29.6

4.1

Pruning of Trees Near Electric Utility Lines: A Field Pocket Guide for Qualified LineClearance Tree Workers

3.7

0.0

40.7

48.1

7.4

3.6

Other: (6 responses: ISA IVM BMP, UTRA BMP, Cal Fire Prevention Field Guide)

0.0

33.3

66.7

4.7

Standards

22. Do you track the amount of work accomplished on your system? n=29 (CHECK ONE) 17.2% No 82.8% Yes →

22a. Please provide your productivity for 2023 2023 distribution data mean value and (SE) n= 2 to 18

Work Metric Area (ft2/m2) of saplings (brush) removed Area (ft2/m2) of saplings (brush) treated with herbicide Distance (pole km/mi) of line cleared Trees in contact with the line at the time of maintenance Trees per mile/km worked Trees pruned

3.9M (3.4M) 38.7M (15.4M) 1,263 (535) 5,100 (100) 69 (22) 109.8K (50,629)

Trees removed

25,463 (17,527) Other: (11 responses: TGR treated trees; spans worked; total overhead mileage; mechanically linear feet; stems of brush; total trees touched; resiliency program removals) 2023 sub-transmission data mean value and (SE) n=0 to 5

82.3M (76.8M) 93.7M (86.8M) 318 (213) 0 33 6,833 (6,126) 8,448 (8,199)

Other: (0 responses) 2023 transmission data mean value and (SE) n=0 to 4

65,900 NA 839 (460) NA 12 1,215 (285)

Trees removed

2,413 (1,148) Other: (2 responses: total trees worked; acres by density treated with herbicide) 23. Does your VM program conduct post work audits (quality control)? n=30 (CHECK ONE) 13.3% No 86.7% Yes →

23a. Who conducted your post work audits in 2023? n=23

Company employees

% of Total mean value and (SE) 66.5 (7.8)

Contracted auditors

30.1 (7.8)

Tree crew members

3.4 (2.0)

Other

0.0

Employer

24. How is your quality assurance structured? (select all that apply) n=28 10.7% 42.9% 60.7% 7.1% 0.0% 0.0% 7.1% 0.0%

100% windshield audit Sample windshield audit → 36.2% average sample audited 100% field audit LiDAR Photogrammetry Ground-based video Satellite imagery Artificial intelligence

25. What percentage of your distribution work is subject to quality control? n=11 to 24

Auditing

Percent mean value and (SE) 20.1 (8.6)

Herbicide

51.7 (9.3)

Vegetation management

70.9 (7.1)

Pre-planning

29.2 (9.0)

Other (______________)

Work type

26. What percentage of your work is audited by the following? n=10 to 26

Contractor that performed the work

Percent mean value and (SE) 61.9 (12.9)

Third parting auditing contractor

75.0 (8.8)

Utility employees

63.7 (7.8)

Other (_____________)

Auditing entity

Mean Index Score

Very Important (5)

Important (4)

Neither Important nor Unimportant (3)

Unimportant (2)

Assessment Methods

Very Unimportant (1)

27. How important are the following methods in conducting workload evaluations? n=23 to 27

Aerial photos

26.1

39.1

17.4

4.3

2.61

Ground evaluations

11.1

3.7

7.4

3.7

74.1

4.26

LiDAR

29.2

12.5

33.3

20.8

4.2

2.58

Satellite imagery

21.7

8.7

34.8

30.4

4.3

2.87

Unmanned aerial systems (UAS)

29.2

16.7

37.5

16.7

0.0

2.42

Other: (3 responses: ground imagery, historical data)

33.3

0.0

66.7

0.0

3.00

28. Does your program have a dedicated tree risk assessment program based in the ISA tree risk assessment best management practices (Goodfellow 2020)? n=29 (CHECK ONE) 44.8% Yes 55.2% No

Neither Important nor Unimportant (3)

Mean Index Score

Aerial application

64.3

10.7

21.4

0.0

3.6

1.7

Bare ground treatment

30.8

0.0

23.1

3.1

Basal

29.6

0.0

25.9

44.4

0.0

2.9

Chemical tree-pruning

64.3

10.7

17.9

7.1

0.0

1.7

Cut stubble

28.6

10.7

7.1

46.4

7.1

2.9

High volume foliar

32.1

10.7

25.0

17.9

14.3

2.7

Stump treatment

19.2

0.0

11.5

19.2

50.0

3.8

Trunk injection

53.8

19.2

23.1

3.8

0.0

1.8

Other: (5 responses: low volume foliar)

0.0

33.3

66.7

4.7

IVM Treatment Options

Important (4)

Very Important (5)

Unimportant (2)

Very Unimportant (1)

29. Rate the importance of the following herbicide treatment options to your program (1 = lowest to 5 = highest). n=26 to 28

30. Does your program employ mechanical cutting (e.g., machines or helicopters) for line clearance? n=29 (CHECK ONE) 65.5% Yes  % of total UVM budget  14.1 (mean), 4.8 (SE), (n=14) 34.5% No 31. Do you use tree growth regulators (TGRs)? n=29 (CHECK ONE) 24.1% Yes 75.9% No  (PLEASE GO TO QUESTION 26) 32. How important are TGRs to your program? n=21 47.6% 4.8% 38.1% 4.8% 4.8%

Very unimportant Somewhat unimportant Neither unimportant nor important Somewhat important Very important

Very Unimportant (1)

Unimportant (2)

Neither Important nor Unimportant (3)

Important (4)

Very Important (5)

Mean Index Score

33. How important are the following cultural control methods to your program? n=28

Cover type conversion

25.0

3.6

35.7

32.1

3.6

2.9

Hydroseeding

46.4

10.7

35.7

7.1

0.0

2.0

Fertilizing

46.4

14.3

35.7

3.6

0.0

2.0

Growing agricultural crops

39.3

7.1

42.9

10.7

0.0

2.3

Landscaping

28.6

7.1

28.6

35.7

0.0

2.7

Planting or seed compatible plants

32.1

3.6

28.6

21.4

14.3

2.8

Prairie restoration

39.3

14.3

32.1

7.1

2.3

Wire-border zone

25.0

0.0

17.9

39.3

17.9

3.3

Cultural Control Methods

Other: (1 response: landowner use)

SECTION VI – UTILITY FOREST ATTRIBUTES 1. How many trees are in your distribution utility forest? n=6 1.2M (mean), 422K (SE) 2. How many trees are in your sub-transmission utility forest? n=1

106k (mean) 3. How many trees are in your transmission utility forest? n=1 44k (mean) 4. How many total trees are in your utility forest? n=1 1.2M (mean), 903K (SE) 5. What is the average number of trees per mile on your distribution primary system? n=9 318.3 (mean), 192.4 (SE) 6. What is the average number of trees per mile on your sub-transmission system? n=3 188.7 (mean), 166.3 (SE) 7. What is the average number of trees per mile on your transmission system? n=2 263.5 (mean), 256.5 (SE) 8. What is average number of trees per mile across your entire system n=3 217.3 (mean), 152.8 (SE) 9. Do you track tree type? n=26 (CHECK ONE) 57.7% No 42.3% Yes → 0.0% 80.0% 0.0% 30.0% 10.0% 0.0%

By phyla Common name Family Genus Species Other

10. Do you track aspect to the power line? n=27 (CHECK ONE) 66.7% No 33.3% Yes → 88.9% 100.0% 100.0% 22.2%

Overhang Side Under Other

11. Do you track tree size? n=27 (CHECK ONE) 44.4% No 55.6% Yes → 100% 80%

DBH Height

11a. Do you use exact measurement or size categories? n=15 (CHECK ONE) 20.0% Exact 80.0% Categories → Height (no responses) DBH (n = 9) →

DBH Start (inches) mean and (SE) 5.1 (0.6)

DBH Start (inches) mean 4

9.6 (0.6)

15.6 (1.1)

23.9 (1.2)

25.0 (1.0)

DBH Class

12. What is your definition of brush? n=27 77.8% of utilities reported using DBH to define brush with a mode of 4” DBH and mean 5.4” DBH 42.9% 4.8% 38.1 % 4.8% 4.8%

< 4” < 5” < 6” < 8” < 10”

11.1% of utilities reported using height to define brush with a mode of 5’ and a mean of 6.7’ 13. What work did you accomplish on your distribution system in 2023? n=23 (CHECK ALL THAT APPLY) Trees pruned Trees removed Trees treated with TGRs Brush removed Brush treated with herbicide Other

91% 96% 22% 61% 48% 9%

14. What work did you accomplish on your sub-transmission system in 2023? n=11 (CHECK ALL THAT APPLY) Trees pruned Trees removed Trees treated with TGRs Brush removed Brush treated with herbicide Other

91% 91% 9% 55% 64% 0%

15. What work did you accomplish on your transmission system in 2023? n=9

(CHECK ALL THAT APPLY) Trees pruned Trees removed Trees treated with TGRs Brush removed Brush treated with herbicide Other

89% 89% 11% 44% 67% 11%

SECTION VI – CHEMICAL CONTROL 1. Herbicide notification: What percentage of total herbicide application time is spent in notification? n=14 10.3% (mean), 2.6 (SE), 0 to 30 (range) What percentage of property owners/land managers refuse to allow herbicide application? n=15 11.2% (mean), 5.0 (SE), 0 to 75 (range) How far (days) in advance do you notify herbicide work? n=20 63.0 (mean), 17.8 (SE), 7 to 365 (range) 2. Do you require written authorization before you apply herbicide on a property? n=23 (CHECK ONE) No82.6% Yes 17.4% 3. Do you leave a buffer from properties that have not provided written authorization? n=15 (CHECK ONE) No66.7% Yes 33.3% → 200’ (mean), n =1 4. Do you have a wetland buffer? n=20 (CHECK ONE) No Yes

55.0% 45.0% → 32.0’ (mean), 10.2 (SE), 0 to 60 (range), n=5

5. Do you use glyphosate? n=24 (CHECK ONE) No70.8% Yes 29.3% → If so, glyphosate accounts for 27.8% (21.0 SE) of total herbicide applications with a range of 1 to 90 percent (n=4).

6. Who applies herbicide in what proportion? n= 21 to 22 Applicator

% of herbicide application

Tree crew members

26.6% (8.6)

VM contract personnel specifically assigned to herbicide application

33.5% (9.8)

Third party herbicide application contractor

42.1% (9.9)

In-house personnel

1.1% (0.7)

SECTION VII – ENVIRONMENT SOCIAL AND GOVERNANCE 1. What percentage of your workforce is female in 2023? n=17 16.8% (mean), 6.6 (SE), 0 to 100 (range) 2. What percentage of your workforce is male in 2023? n=21 91.6% (mean), 3.0 (SE), 60 to 100 (range) 3. What percentage of your workforce is nonbinary in 2023? n=8 0% (mean), 0 (SE), 0 to 0 (range) 4. Age distribution. What percentage of your workforce falls into the following categories? n=29 Age category (years) <21 21-30 31-40 41-50 51-60 61-65 65<

Percent of workforce mean (SE) 2.3% (1.1) 26.2% (5.4) 27.4% (3.4) 26.8% (4.0) 14.0% (4.9) 2.6% 0.2% (0.2)

5. Ethnic distribution. Percentage of your workforce that falls into the following categories? n=16 Ethnicity American Indian or Alaskan native Asian Black Hispanic or Latinx Native Hawaiian or other Pacific Islander Multiple races White

Percent mean (SE) 2.2% (1.5) 0.0% (0.0) 0.4% (0.3) 13.8% (6.3) 0.5% (0.5) 4.4% (3.3%) 78.4% (7.7)

6. Do you require diversity spend? n=23 (CHECK ONE) No21.7% Yes 78.3% 7. Do you have a goal for percent diversity spend? n=22 (CHECK ONE) No Yes

90.9% 9.1%

8. What is your 2023 diversity spending goal? NA 9. What do you expect the percent diversity spend to be in 5 years? NA 10. What do you expect the percent density spend to be in 10 years? NA 11. Does the requirement to use diverse contracts inflate rates? n=4 No Yes

100% 0%

12. How satisfied are you with the performance of your diversity contractors compared to non-diversity contractors? n=11 Very dissatisfied Dissatisfied Neither satisfied nor dissatisfied Satisfied Very satisfied

0% 18.2% 81.8% 0% 0%

SECTION VIII - DEFINITIONS Action threshold: a level of incompatible plant pressure (e.g., species, density, height, location or condition) where vegetation management control methods should occur to prevent conditions from reaching the tolerance levels (Miller 2021). ASAI: Average service availability index. The total number of customer electric service hours available during a given time period ÷ total demanded customer electric service hours. CAIDI: Customer Average Interruptions Duration Index. Total duration of customer interruption ÷ the total number of electric service interruptions. Delta construction: Electric distribution configuration with three phases, but no common neutral wire. There is no fixed difference between phase-to-phase and phase-to-ground voltages (Miller and Kempter 2018). Full time equivalent (FTEs): 2,080 labor hours a year. MAIFI: Momentary Average Interruption Frequency Index. It includes only transient electric service interruptions: total momentary electric service interruptions ÷ the total number of electric customers served. Qualified line-clearance arborist: an individual who, through related training and on-the-job experience, is familiar with the equipment and hazards in line clearance and had demonstrated the ability to perform the special techniques involved. This individual may or may not currently be employed by a line-clearance contractor (Miller and Kempter 2018). Qualified line-clearance arborist trainee: an individual underdoing line-clearance training under the direct supervision of a qualified line-clearance. In the course of such training, the trainee becomes familiar with the equipment and hazards in line clearance and demonstrates ability in the performance of the special techniques involved (ANSI 2017). Quality assurance: the ability of a process to produce or deliver a quality product or service. It is process or strategically focused. Quality control: the ability to detect problems with a product or service. It is tactically focused (Weber and Wallace 2007). Remote areas: Less than 5 customers per circuit km/mile. Rural areas: approximately 5-25 customers per circuit km/mile. Side orientation: trees with the trunk located to the side of electrical wires. Overhang: Branches overhanging power lines. SAIDI: System Average Interruption duration Index. Total duration of customer interruptions ÷ total number of customers (Miller and Kempter 2018). SAIFI: Average Interruption Frequency Index. Total number of customer interruptions ÷ total number of customers (Miller and Kempter 2018). Sub-transmission: Lines energized above 45 kV not subject to FAC-003-4. Suburban: approximately 25-50 customers per circuit km/mi.

Tolerance level: maximum incompatible plant pressures (species, density, height, location or condition) allowable before unacceptable consequences develop. Transmission: Subject to FAC-003-5: 1). Overhead transmission lines operated at 200kV or higher. 2). Overhead transmission lines operated at lower than 200kV that if lost or degraded are expected to result in instances of instability, cascading or uncontrolled separation that adversely impacts the reliability of the Bulk Electric System for a planning event. 3). Overhead transmission lines operated below 200kV identified as an element of a Major Western Electricity Coordinating Council WECC Transfer Path in the Bulk Electric System by WECC. 4). Overhead transmission lines meeting any of the criteria 1-3 located outside the fenced area of the switchyard, station or substation and any portion of the span of the transmission line that is crossing the substation fence. Under orientation: trees with trunks located directly under the wires. Urban: more than 50 customers per circuit km/mi. Utility Forest: vegetation comprised of species that at any time in their life could affect electrical facilities. Wye construction: distribution construction consisting of three phases and a grounded or neutral wire. The phases have polarity, creating a voltage differential among them (Miller and Kempter 2018). X urban: between 15 and 35 customers per circuit km/mi.