Exploring the Relationship Between Municipal Characteristics and Urban Forest Management Grant Success in New York State, USA

Urban Forestry in New York State

New York State (NYS) occupies an area of 141,306 km² (54,559 mi²) and has a population of more than 20 million individuals (US Census Bureau 2025). There are 1,527 municipalities in NYS, and about half are believed to have incorporated areas with public urban forests that may be managed (e.g., planted or maintained). This includes municipalities from New York City, with a population of 8.8 million people, to villages with populations of less than 200 people (DiNapoli 2021). The state has over 264 million urban trees which provide over $1 billion in annual ecosystem benefits and values (Nowak and Greenfield 2018). The economic impact of urban forests in NYS in 2018 was estimated to be $3,515,000,000, accounting for 33,913 full- and part-time jobs (Parajuli et al. 2022).

The NYS Department of Environmental Conservation Urban and Community Forestry Program (NYSDEC UCF) facilitates both pass through grants funded by the USDA Forest Service Urban and Community Forestry Program as well as state-funded and hybrid-funded grants (NYSDEC 2025). One of NYSDEC’s state-funded grants is the Urban and Community Forestry Grant (NYSDEC UCF Grant), underwritten by the Environmental Protection Fund, which aims to “help start, plan and advance strong, self-sustainable UCF programs which enhance the quality of life for all New Yorkers” (NYSDEC 2023). Typically, the grant funds tree planting, tree maintenance, inventories, management plans, and occasionally public education. Municipalities, nonprofit partners, and agencies that advance urban forest management are eligible to apply. In relation to the application, municipalities are divided into two groups (large and small), as the funding legislation requires at least $500,000 to be allocated to projects in large municipalities (population 65,000 or more) (NYS Assembly 2023). The 26 large municipalities are identified in each round’s Request for Applications (RFA). A breakdown of applications and awards for the 4 most recent rounds is included in Table S1 in the Appendix.

Requests for Applications are released alongside workshops that provide technical assistance to guide municipalities through the process and answer questions. Project requirements and scoring guides are included in the RFAs to ensure that applicants understand how they can earn points on their submissions (NYSDEC 2025). Applications are scored and rank-ordered by a panel of NYSDEC employees. Funds are disseminated according to the ranked order until they are exhausted, with worthy projects routinely going unfunded due to resource limitations (G. Van Duyne, personal communication, March 2023).

To advantage municipalities with projects in or benefiting state-designated disadvantaged municipalities, applications were awarded additional points (5% to 7% of the total score, depending on the year) compared to those without. These state designations are either “potential environmental justice area” (PEJA, census blocks, pre-2023) or a “disadvantaged community” (DAC, census tracks, post-2023). Also, applicants who documented environmental, social, or economic need were awarded additional points in some rounds (4% to 8% of the total score, depending on the year).

In addition to supporting disadvantaged municipalities, these grants also supported those that exhibited a commitment to urban forest management by awarding points for criteria that demonstrated that commitment. This included being recognized as a Tree City USA (TCUSA) community (2% or 3%, depending on the year) and receiving application guidance from an International Society of Arboriculture (ISA) Certified Arborist^® (up to 5% depending on the year).

This study aims to illuminate the differences in characteristics and approaches between municipalities that were successful versus unsuccessful in their applications for NYSDEC UCF Grants over the past several rounds. Similarly, attributes of municipalities that applied for NYSDEC UCF Grants will be compared to those that did not apply. In particular, this paper seeks to address the following 3 research questions:

    (1) How do NYS municipalities that receive NYSDEC UCF Grant funding differ from those that apply unsuccessfully and from those that do not apply?

    (2) Is the UCF scoring criteria effective at increasing access to funding for municipalities with target characteristics?

    (3) What barriers prevent NYS municipalities from applying for NYSDEC UCF Grants?

Survey—Part 1

To identify municipalities for the online survey examining municipal resources and experiences with the periodic NYSDEC UCF Grant, in 2022 we obtained grant application records from 2016 (Round 13), 2018 (Round 14), and 2019 (Round 15) and identified all Successful and Unsuccessful municipal applicants (see Table S1 in the Appendix for a breakdown of applications). Although NYSDEC allows other agencies and nonprofits to apply for the NYSDEC UCF Grants, we were only interested in municipalities and removed nonmunicipal applications from this study. Over the 3 rounds, the applicant’s status was categorized as either Successful or Unsuccessful. The total number of Successful or Unsuccessful proposals from an individual municipality was not used in Part 1. Instead, municipalities were categorized as Successful if they had at least one funded application in any of the 3 grant rounds (n = 85) and Unsuccessful if they applied in at least one round but had no funded applications (n = 94).

To compare the municipalities that applied to one or more of the studied NYSDEC UCF Grants rounds, a group of municipalities that applied to none of the studied rounds was constructed. This ‘Did Not Apply’ group is made up of 91 municipalities which were randomly selected from a list of municipalities that reported having managed urban forests in a 2021 NYS urban forest program management survey but didn’t appear in the applicant lists for the NYSDEC UCF Grant in 2016, 2018, or 2019 (Hargrave et al. 2024).

In May 2023, all 270 municipalities in the study sample were sent surveys requesting details regarding their experiences with the NYSDEC UCF Grant program over the 2016, 2018, and 2019 grant years. The timing was intentional as applications for the next round were due later in 2023, and we wanted to capture the applicants’ thoughts on their previous application(s) while they were working on their current application.

The three electronic surveys (Successful, Unsuccessful, and Did Not Apply) were deployed through Qualtrics (Qualtrics 2023) via email, one to each group. In accordance with Dillman et al. (2014), a reminder email was sent 9 days after the initial email, and a final reminder was sent 7 days later.

The surveys employed skip logic, whereby respondents were directed to different sets of questions based on their responses. The Successful survey had up to 36 questions; the Unsuccessful survey had up to 20 questions; and the Did Not Apply survey had up to 16 questions (see Appendix). The NYSDEC provided email contacts for the Successful and Unsuccessful groups. Emails for the Did Not Apply group were obtained from the aforementioned survey. The emails for all 3 groups were verified via municipal websites and other public records.

The surveys inquired about staffing, tree board, and partner organization status; the use of a grant writer; whether they celebrated Arbor Day; whether they were recognized as a TCUSA; and about their municipal tree-related budget. Municipalities that applied were asked to provide information on the application’s author(s). Successful municipalities were asked a series of questions about the execution and success of their funded projects and why they believed they had been successful. Unsuccessful applicants were asked why they thought they were unsuccessful, whether they met with NYSDEC staff to debrief, and if they were able to conduct the project without the grant funding. Municipalities in the Unsuccessful and Did Not Apply groups were asked what resources they thought they needed in order to be Successful. Did Not Apply municipalities were also asked if they were aware of the grant program and why they did not apply. All municipalities were asked about their future NYSDEC application plans. We calculated each municipality’s per capita tree- related budget using the 2022 budgeted amount they provided and 2022 US Census population figures (US Census Bureau 2022a, 2022b). Median household income (MHI) was also gleaned from the 2022 US Census. The response rates were: 34 of 94 (36%) Successful applicants; 28 of 85 (33%) Unsuccessful applicants; and 31 of 91 (34%) Did Not Apply municipalities.

Municipal Characteristics—Part 2

To assess the relationship between municipal characteristics and the ability to secure a NYSDEC UCF Grant, we analyzed US Census data and the presence of a PEJA census block or DAC census track for our 3 municipal categories (Successful, Unsuccessful, Did Not Apply). To augment the data, the 2023 NYSDEC UCF Grant (Round 16) applicants were collated in the same manner as outlined in Part 1 and included in the analysis. Subsequently, all municipalities identified as applicants in 2016, 2018, 2019, and 2023 as well as those in the Did Not Apply (2016 to 2019) group, regardless of their response to the survey, were included in the Part 2 analysis. Four of the Did Not Apply municipalities applied for a 2023 grant; thus, that sample size dropped to 87. Successful had 104 municipalities and Unsuccessful had 96, resulting in a combined total of 287. The breakdown of Successful and Unsuccessful applications by year is in Table S1 in the Appendix.

Data regarding municipal characteristics that included population and MHI were obtained from the US Census 5-Year Estimates for 2016, 2018, 2019, and 2023 (US Census Bureau 2016a, 2016b, 2018a, 2018b, 2019a, 2019b, 2023a, 2023b). A shapefile containing NYS PEJA census tracks was downloaded from the NYS GIS Clearinghouse (NYSDEC 2021) and overlaid on shapefiles containing NYS towns, villages, and cities. Municipalities included in this research that contained a portion of a PEJA within their boundaries were marked as PEJA communities in our dataset. The RFAs in 2016, 2018, and 2019 inquired whether the project would impact or benefit a PEJA. In 2023, NYS switched to the DAC designation. The DAC list was obtained from NYS, and municipalities containing a DAC track were recorded (NYS 2024). Metrics were calculated for each round as well as the combination of all 4 rounds.

For both Part 1 and Part 2, summary statistics (e.g., mean, frequency) and statistical significance tests were calculated in R (R Core Team 2025). For nominal data, Chi-square using Fisher’s exact test was used to evaluate the statistical significance of results by municipal characteristics between each pair (i.e., Successful and Unsuccessful, Successful and Did Not Apply, and Unsuccessful and Did Not Apply). Linear regression analyses were used to evaluate the statistical significance of the numerical data. An alpha value of 0.05 and two-sided hypothesis tests are used for assessing statistical significance throughout. For qualitative analyses, the open-ended answers were coded and tallied using Excel (Microsoft, Redmond, WA, USA).

Survey—Part 1

The Successful group had a greater mean population, a higher percentage with a PEJA, a lower MHI, and a lower 2022 per capita budget than the Unsuccessful group, though none of these differences are statistically significant (Table 1). Successful had a significantly higher Population mean (P-value = 0.004) and percentage with a PEJA (P-value = 0.008) than Did Not Apply municipalities. In addition to the lowest population and PEJA, Did Not Apply also had the lowest MHI and per capita tree budgets. Unsuccessful applicants had the highest per capita tree-related budget at $8.56 per person.

Successful municipalities were statistically more likely to celebrate Arbor Day and have been recognized as a TCUSA (91%) than both Unsuccessful (P-value = 0.035 and < 0.009 respectively) and Did Not Apply (P-value < 0.001 and < 0.001 respectively) (Table 1). They were also more likely to have one or more employees with a tree care related degree (36%) or an industry-certified employee (32%), which was statistically different from Did Not Apply (P-value = 0.01 and 0.022, respectively) but not from Unsuccessful. Successful municipalities were more likely to have a tree board than Unsuccessful, although the difference was not significant. However, both groups of applicants, Successful (P-value = 0.004) and Unsuccessful (P-value = 0.03), were significantly more likely to have a tree board than Did Not Apply. There was no difference in the use of a grant writer between Successful and Unsuccessful municipalities.

Successful and Unsuccessful applicants were asked to select the type of person who authored their grant application from a list of possibilities, or to write-in a response, and they could select more than one choice (Table 2). While none of the options were selected at rates which were significantly different between the groups, Successful applicants were more likely to report having used a municipal grant writer and/or an ISA Certified Arborist.

Successful applicants were asked, “How significant or insignificant of an impact do urban forest grants have on your municipal urban forestry program during the years you have had grant funding?” Of respondants, 94% responded that grant funding was “extremely significant” or “somewhat significant” (57% and 37%, respectively). Only 3% reported “neither significant nor insignificant”, and 3% reported “somewhat insignificant”.

Successful applicants were also asked two open-ended questions: “Why do you think your municipality was successful in obtaining a grant?” and “What municipal resources did your municipality have that may have given it an advantage?” Communities attributed their success to having good projects (24%), well-written applications (21%), excellent staff (14%), and being a high-needs community (14%). They considered grant writing staff (26%), good staff support (26%), dedicated volunteers (14%), good political support (11%), and past project success (11%) to be beneficial.

Unsuccessful applicants were asked to select the reason(s) they thought their project was unsuccessful from a list of options or to provide their own response. Of respondants, 54% indicated they did not know why they were unsuccessful; 15% thought it might be because their project lacked sufficient quality; and 12% indicated a lack of TCUSA status. Four percent felt their unsuccess was due to not having an ISA Certified Arborist review their application. “Other” responses referenced a lack of staff or grant writers and issues preparing or submitting the application.

Applicants who were not funded were offered a meeting with the NYSDEC UCF staff to review their proposals. Of the Unsuccessful respondents, 35% took advantage of that review, 35% did not, and the remaining were unsure. All of those who met with NYSDEC staff found it helpful.

Even though their NYSDEC UCF Grant application was unfunded, 23% were still able to complete their project. These municipalities indicated they used internal funds instead of additional outside dollars, and 67% were able to execute the project at its original scale, while the other 33% scaled their projects down.

Did Not Apply municipalities were asked if they were aware of the NYSDEC UCF Grants program before receiving notice of the survey; only 43% responded affirmatively (Table 3). They were then asked why they hadn’t applied for a NYSDEC UCF Grant in the past; their top responses were that they “didn’t have someone to write the grant”; “didn’t know where to get help with the application”; and “didn’t have staff to manage the grant” (Table 3). Unlike the Successful and Unsuccessful groups, this group did indicate the need for political and/or public support, albeit at low rates (6% and 3%, respectively). “Other” responses included a lack of time and a lack of matching dollars (as required by some of the project types), while others indicated that they had access to other sources of funds. One respondent stated that they didn’t apply since they were not part of a DAC, and they did not think it was worth the time to apply.

Unsuccessful and Did Not Apply municipalities were asked which resources they felt they would need in order to successfully apply for a NYSDEC UCF Grant in the future (Table 4). Both groups’ highest response was that they needed technical assistance. Additional highly rated responses for Unsuccessful municipalities were employee training and staff to implement the project. Did Not Apply municipalities reported needing assistance identifying a project and a grants manager. Political support, volunteer training, and public support had the lowest average response rates among these groups. “Other” responses echoed the need for staff and for more substantial projects.

Scoring Points

The New York State Department of Environmental Conservation UCF attempts to elevate application scores for projects benefiting disadvantaged communities by awarding them extra scoring points for projects benefiting a PEJA. More Successful municipalities contained a PEJA than Unsuccessful ones, although the difference was not significant. Data on MHI and per-capita tree budgets were used to evaluate a municipality’s connection to “documented economic need”. While Unsuccessful municipalities had both a higher MHI and per-capita tree budget, these differences are not significant.

Municipalities could also receive points for having TCUSA recognition or by having an ISA Certified Arborist provide professional guidance on the proposal. To infer the impact of these categories for extra points on success, we analyzed responses to related questions. Successful municipalities were significantly more likely to be recognized by TCUSA than Unsuccessful municipalities (P-value < 0.009). However, while Successful respondents indicated higher rates of ISA- or TCIA-certified employees (as well as access to an ISA Certified Arborist when authoring their applications) compared to Unsuccessful, neither of these differences was significant.

Successful Grant Project Outcomes

Successful applicants were asked a series of questions about the success of their projects based on the type of project they conducted: tree planting (36%), tree inventory (39%), management plan (33%), or maintenance project (24%); one-third of the municipalities had combination projects, and as such the percentages sum to over 100%. All applicants reported completing their projects on time, with 87% meeting all their goals and 13% meeting most of them. When asked who carried out the projects, 79% used municipal staff, 55% used community volunteers, 52% used professional contractors, 52% used ISA Certified Arborist (required for the tree inventory and management plan projects), 36% used community partners, and 18% used elected officials.

Of the municipalities that planted trees, 83% reported that those trees were cared for (watered, mulched, etc.) after the project. Additionally, all relayed a survival rate of at least 75%, and 80% reported a survival rate of at least 90%.

Of the municipalities that conducted an inventory, 92% agreed that it had been useful, and 8% replied that the inventory was “somewhat” useful. In addition to the municipalities that conducted an inventory, those that conducted tree maintenance were also asked if they had updated their inventory since the project’s completion. In total, 67% indicated that the inventory had been kept up to date. Those without updated inventories selected “lack of staff time” (85%), “lack of staff training” (57%), and “no funds to subscribe to the software” (42%) as the reasons.

Management plans were reported to be useful by 82% of the municipalities that wrote one, with the other 18% reporting they were somewhat useful; none reported that the plans were not useful. Of those that elaborated on the usefulness of their plans, 50% obtained additional grants, 38% conducted successful planting efforts, 38% improved maintenance and reduced risk, and 25% improved their tree-related budget and budget planning.

Municipatlities were asked about the obstacles they encountered while executing their projects. Responses varied by the type of project, but emergent themes of importance related to limitations on public gatherings during COVID-19 (i.e., volunteer events or public meetings) (27%), a lack of contractors (15%), a lack of staff time to dedicate to the project (12%), and a lack of employee skills or equipment (6%). Many indicated they had no obstacles, and interestingly, none chose “lack of political support” or noted that it was an obstacle.

Municipal Characteristics—Part 2

The community characteristics data for the municipalities that were Successful at least once across the 2016, 2018, 2019, and 2023 NYSDEC UCF Grant rounds, those that were never successful (Unsuccessful), and the remaining Did Not Apply are compiled in Table 5 along with the pairwise statistical comparisons between groups. Successful municipalities were, on average, larger and more likely to contain a PEJA (71%) or DAC (42%) than Unsuccessful and Did Not Apply. Did Not Apply municipalities were significantly less likely to contain a PEJA (33%) or DAC (25%) than Successful (P-value < 0.001, P-value = 0.013, respectively) and significantly less likely to contain a PEJA than Unsuccessful (P-value < 0.001).

After the cross-round aggregates were analyzed, we examined population size, MHI, and PEJA/DAC for each round separately, including only the municipalities that specifically applied to the round. The Did Not Apply municipalities remained constant through the 4 rounds, but the contemporaneous population size, MHI, and PEJA/DAC status changed across rounds. These single-round analyses revealed unexpected results (Figure 1).

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Figure 1.

Community characteristics of mean population, median household income, and PEJA/DAC percentage by grant round year for Successful, Unsuccessful, and Did Not Apply municipalities.

In 2016, the population of Successful municipalities was significantly larger than Unsuccessful (P-value = 0.032) and Did Not Apply (P-value = 0.015) and significantly more likely than Unsuccessful (P-value = 0.024) and Did Not Apply (P-value < 0.001) to contain a PEJA. Unsuccessful was also significantly more likely to contain a PEJA than Did Not Apply (P-value < 0.001). There were no statistical differences in MHI values between groups.

In 2018, Successful municipalities were significantly larger than Did Not Apply (P-value = 0.046). Unsuccessful municipalities had an MHI that was significantly higher than Successful (P-value = 0.046) and Did Not Apply (P-value = 0.013). Both Successful and Unsuccessful were significantly more likely to contain a PEJA than Did Not Apply (P-value < 0.001 and P-value = 0.001, respectively).

In 2019, the Did Not Apply population and PEJA percentage were significantly lower than those of both the Successful (P-value < 0.001 and P-value < 0.001, respectively) and Unsuccessful (P-value < 0.001 and P-value < 0.001, respectively) groups. There were no significant differences between the groups’ MHI. Interestingly, although not statistically significant, in 2019 the percentage of Successful municipalities with a PEJA (69%) is slightly lower than that of Unsuccessful municipalities (70%).

Lastly, in 2023, we observe the same pattern for the DAC percentage as we did for PEJA in 2019, with Unsuccessful having the highest rate at 43% and Successful slightly lower at 42%, although there is no statistically significant difference among the groups. Additionally, as in 2019, the population of Did Not Apply was significantly lower than Successful and Unsuccessful (P-value < 0.001 and P-value < 0.001, respectively), and there was no significant difference among MHI.

A point of note: the DAC qualifying criteria differ from those used to determine PEJA status. Of the 159 municipalities in this study with a PEJA, only 83 contained a DAC. Furthermore, 12 additional municipalities without a PEJA contained a DAC, for a total of 95.