Tree Schemes

Tree Schemes A Trees Outside Woodland project report January 2025

Contents Contents .................................................................................................................... 2 Executive summary.................................................................................................. 3 Introduction .............................................................................................................. 4 Methodology ............................................................................................................. 5 Trial design ............................................................................................................. 5 Data collection ........................................................................................................ 8 Data analysis .......................................................................................................... 9 Results .................................................................................................................... 14 Survival rates ........................................................................................................ 14 Variables effecting recorded survival rates ........................................................... 15 Cost per tree ......................................................................................................... 20 Scheme size and cost ........................................................................................... 22 Applicant type ....................................................................................................... 23 Reason for planting ............................................................................................... 24 Advice only scheme .............................................................................................. 26 Discussion .............................................................................................................. 27 Effect of scheme type ........................................................................................... 27 Effect of local authority .......................................................................................... 28 Time gap between planting and monitoring .......................................................... 29 Monitoring method ................................................................................................ 29 Applicants and uptake ........................................................................................... 30 Advice only scheme .............................................................................................. 31 Additional benefits and challenges........................................................................ 31 Limitations and opportunities for further research ................................................. 32 Conclusion .............................................................................................................. 34 References .............................................................................................................. 35 Appendix A ............................................................................................................. 36 Appendix B ............................................................................................................. 38 Appendix C ............................................................................................................. 41 Appendix D ............................................................................................................. 42 Acknowledgements................................................................................................ 43

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Executive summary The research programme ‘Trees Outside Woodland’ has undertaken a project investigating the cost effectiveness for tree establishment of different types of tree schemes. The types of schemes trialled were free tree schemes (or tree giveaway schemes), subsidised tree schemes (recipients were charged for 50% of the cost), and advice schemes (advice was given on the funding available for tree planting, both internal and external, but no trees or funding were directly distributed).

Tree schemes, particularly free tree schemes, have been a tool used by organisations to facilitate tree planting. However, there is little evidence of the success of tree schemes in terms of tree survival, and no known evidence on the relative effects of different types of schemes (free or subsidised). Without a good survival rate, trees planted will not realise their potential benefits, and it is therefore important to understand survival to gauge the effectiveness of these schemes.

Across three years and four local authorities, six free tree schemes, three subsidised tree schemes, and two advice only schemes were trialled. Information was collected about the survival of the trees annually, as well as recording capital costs and applicant data. This data has been analysed using statistical modelling to understand the variables that affect tree survival, particularly the scheme type variable.

A significantly higher survival rate was recorded for subsidised trees than free trees (after one year, averages of 88% and 83% respectively). It is possible that this is because recipients made a financial investment (50% of cost) in their trees, which has led to behaviour change of increased ongoing investment and care into their trees. For example, more careful planting, watering, mulching, weeding etc., leading in turn to higher chances of survival through the early establishment years. Whether this trend continues over the slightly longer term will be understood through ongoing survival monitoring being carried out, and an update to this report is planned in 2025.

In the initial few years after planting, trees are most susceptible to mortality, which is apparent in this trial as average survival rates drop from 87% after one year to 59% after two.

The subsidised tree schemes saw a lower capital cost per planted (£1.23) and survived (£1.58) tree after one year than the free tree schemes (£2.20 and £3.59, respectively). This difference is driven firstly by half of the overall cost being met by the recipient, and secondly by the survival rates being higher.

Tree schemes are an effective way for local authorities to distribute trees across their area at a relatively low cost, and to different types of people and organisations. The subsidised tree schemes in this trial have been more cost effective, and shown higher survival, in their first year than the free schemes. Taking steps to increase the expected survival rate of trees through the early establishment period is critical to successfully increasing tree cover. Further research would be required to determine whether these early effects are continued in the longer term, but these early results indicate that subsidised schemes may be better suited to achieving goals of increased tree cover than free tree schemes.

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Introduction Tree schemes involve offering trees, guards, and stakes to the public for free or at a subsidised cost. Often run by local authorities or environmental organisations, tree schemes offer a way of increasing tree cover across many different areas (rural and urban), meaning that the ecosystem services of trees can be delivered over a wide area to a diverse range of people. For example, The Woodland Trust run free tree schemes, delivering tree packs to schools and community groups or offering tree collection points for specific projects. Such free tree schemes have had high uptake, and the range of applicants enables the benefits of these trees to have a wide reach.

Tree schemes can be a way to plant trees at a relatively large scale and have sometimes been used by local authorities keen to meet their tree planting targets, as it can be more straightforward than finding land on which to plant woodland. Beyond the initial effort required to set up and run the scheme, there are no longer term resource requirements for the maintenance of the trees, which typically become the responsibility of the tree recipient. Additionally, running such schemes generates opportunities for local authorities or organisations to engage people on the benefits of trees and tree planting, including residents, community groups, landowners, farmers, schools, and businesses.

However, there has been little data collected on the long-term success of tree schemes in terms of tree survival, as there is often limited opportunity to monitor trees once distributed. Additionally, there is no known evidence on the effects of different levels of subsidy. This tree scheme trial aimed to test how different levels of funding affected tree planting schemes by measuring the level of uptake, number of trees planted, tree survival rates, and cost per tree (distributed and survived).

The trial was delivered by four local authorities over three years, who compared the efficacy of free tree schemes and subsidised tree schemes (where the trees and tree protection were funded at 50%). Schemes were open to private residents, local businesses, schools and any other organisations within that local authority area, to apply for trees to be planted on their land. Advice schemes were also run, where interested parties were given advice on tree planting and existing funding for tree planting, both internal and external, but no trees or funding were provided directly.

The overall aim of the trial was to be able to provide evidence and guidance to organisations that may wish to run tree schemes, to support them in delivering such schemes in a cost-effective way that would enable more trees to survive in the long run. It had been hypothesised, for example, that free tree schemes may have greater uptake, whereas subsidised schemes may have greater buy-in from recipients, leading to greater survival. This trial set out to test these hypotheses and how the different factors balanced out in practice, to understand whether free or subsidised tree schemes may be more likely to lead to greater numbers of surviving trees, and at what financial cost.

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Methodology The trial was designed to gather data from participating local authorities, who would first run a free tree scheme and then a subsidised tree scheme over subsequent planting seasons. The project provided funding for capital costs and the project officer salary, but this trial was set in the real-world context of being delivered by local authorities. Therefore, the method was not prescribed as the pilot had to be conducted within the capacity and limits of the participating local authorities in terms of additional support needs (legal teams etc.), tree suppliers, and resident numbers.

Key research questions 1. How do different levels of funding subsidy for trees impact the uptake of schemes and the number of trees planted outside woods?

2. How do different levels of funding subsidy for trees impact the survival rates of trees planted through tree schemes?

3. How cost effective are tree schemes?

4. How much project officer time and local authority support is needed to run a tree scheme?

Trial design This trial was led by Chichester District Council (CDC) and replicated by Shropshire Council (SC), Kent County Council (KCC), and Norfolk County Council (NCC). Three types of scheme were trialled; advice only schemes, free tree schemes, and subsidised tree schemes (where 50% of the cost was funded by the local authority1).

Each local authority aimed to run a free tree scheme and subsidised tree scheme over subsequent planting seasons so as not to flood the market. The free tree scheme was run before the subsidised scheme to provide a baseline and allow the data collected from both schemes to be compared, particularly in terms of uptake, applicant type, survival rates, and cost. Shropshire had run free tree schemes since 2010, the other local authorities had not run any tree schemes previously.

1 In NCC a small number of orchard trees were subsidised at 10%. See ‘Tree pack development’

below.

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Table 1 sets out which scheme types were delivered in each local authority by year.

Chichester Shropshire Norfolk Kent Year 1 Advice Free Tree N/A N/A Nov 2020 – Only (continued Scheme March 2021 into year 2&3) Year 2 Free Tree Free Tree Free Tree Advice April 2021 Scheme Scheme Scheme Only (continued into March 2022 year 3) Year 3 Subsidised Tree Free Tree Subsidised Tree Free Tree Scheme3 April 2022 Scheme Scheme & Scheme March 2023 Subsidised Tree Scheme2

Free and Subsidised Tree Schemes Tree pack development For the free tree schemes, trees were distributed to recipients in packs as whips, along with free guards and stakes. Tree packs were more feasible for nurseries to supply at scale and more affordable for the local authority to distribute, as opposed to individual trees. Local authority officers designed the tree packs depending on suitability for the local area and availability from their supplier. Packs of up to 30 trees were offered which were either cell grown or bare root, and participants could apply for a maximum of 40 packs each. A variety of tree packs featuring different species were devised for different locations across the local authority, for example; urban, coastal, chalk/clay, Weald and Greensand, and hedgerow packs (examples of the species found in Chichester District Council’s tree packs can be found in appendix A). The composition of the packs were varied across the local authorities.

Either plastic spiral guards or cardboard guards and stakes/canes and ties were provided with the trees.

Tree packs for the subsidised scheme were of similar composition to the free tree scheme, usually differing only where certain species were not available. In CDC a hedgerow bundle was not included in the free tree scheme but was added to the subsidised scheme following applicant feedback. In some cases, the maximum number of tree packs available per applicant was increased to attract applicants depending on the rate of uptake, in SC there was no maximum. In NCC there was no maximum and larger tree packs of 125 trees were offered as well as smaller ones.

Orchard trees in pots were also offered in the NCC subsidised scheme and were subsidised at only 10% due to the higher cost of the trees compared to bare-root whips.

Procurement

2 Shropshire Council received funding from outside the project to run a free tree scheme in Nov-Dec

2022, just before the subsidised scheme which ran Jan-March 2023. Data from this scheme will be used in this analysis.

3 Kent County Council prioritised running an advice scheme and free tree scheme over a subsidised scheme due to the resources and time available.

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The local authority tendered for a local supplier of trees if a pre-existing agreement with a local nursery wasn’t in place. It was ensured the selected supplier met biosecurity requirements by being registered as Plant Healthy (or in the process of gaining certification) or having an appropriate biosecurity policy reviewed by the project officers. Contracts for the nursery were drawn up by the local authority legal team, and procurement for subsequent subsidised schemes was completed at the same time as the first free tree scheme.

Promotion Schemes were promoted via local authority media channels including social media, local radio, press releases, and local newspapers. They were also promoted through parish councils, community groups, and schools, via emails, meetings, phone calls, and word of mouth. Where a scheme was open for longer, particularly for the subsidised schemes, additional communications resources were necessary and this additional resource requirement was recorded.

Application Application forms and terms and conditions (T&Cs) were developed by the project officers and hosted on the local authority websites. Applications were open to all who had ownership or management control of the proposed planting area, or permission from the landowner to plant. Applications were assessed manually on a first come first served basis to ensure they met the requirements for both the free and subsidised scheme.

Improvements were made to the subsidised tree scheme application process in CDC and NCC following feedback from the free tree scheme applicants, i.e., it was made clearer and easier to navigate, and in NCC the processing of applications became automated.

Payment and legal support For the subsidised tree schemes, payment methods were established for securing applicants' contributions, which varied across the local authorities according to the systems already in place. As applicants were paying a financial contribution to the local authority providing goods, additional legal advice was sought to formalise the T&Cs already in place, and to make additions covering the process of exchanging cash for goods.

Distribution Trees were either delivered by the nursery to the applicants, collected by applicants from the nursery, or collection hubs were set up by the local authority, all between November and March. The method of collection / delivery depended on the capacity of supplier and local authority.

Planting advice When trees were distributed, advice was provided on how best to plant the trees.

This was in the form of a leaflet developed by CDC and adapted by NCC, SC & KCC depending on local conditions and use. The advice covered best methods of planting 7

depending on soil type, how long trees can be stored before planting, how to ‘heel-in’ the trees and how to maintain them.

Monitoring At the point of application, the recipients agreed via a tick box to monitor the survival of their trees by completing a yearly survey and gave permission to be contacted by project officers to check on their trees (solely for research and learning purposes).

Details of scheme applicants/trees supplied were recorded along with survival data.

All recipients were contacted every summer since planting with a tree survival survey. Of those that responded, a random 10% were selected to follow up with a visit from local authority officers to monitor survival rates, to verify survey responses.

Further details are outlined in the data collection section.

Advice Only Scheme The purpose of this scheme was to engage with those across the local authority area looking to plant trees, including landowners, community groups, charities, and residents. Local authority officers identified and monitored available internal and external funding sources and advised interested parties on how to access this funding. Funding sources included (but were not limited to); The Woodland Trust, The Conservation Volunteers, The Tree Council, Trees for Cities, People’s Trust for Endangered Species, Forestry Commission, Environment Agency, Countryside Stewardship, and internal local authority funding schemes.

The stages of the advice only scheme were as follows: 1) Collating resources: Research was conducted into funding sources available across England and compiled into one document, containing details descriptions and links to funding sources.

2) Hosting the information: The information was made available on the local authorities’ websites.

3) Public access to information: The information could either be downloaded directly by interested parties, or they could contact project officers for tailored advice sent via email, phone, or in person.

4) Promotion and engagement: Landowners were informed of opportunities and funding for tree planting outside woodlands – via emails, meetings, word of mouth, and publicising the scheme on council media channels.

5) Monitoring: A monitoring spreadsheet was set up to record engagement and advice given.

Data collection For all free and subsidised tree schemes, data was gathered both before planting via the application process, and after planting via online survey, an example of which can be found in appendix B. The survey was sent between July and September every year. On-site monitoring was conducted by project officers on 10% of the

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survey responses for validation, again between July and September each year after planting.4 Both quantitative and qualitative data was collected through this process.

Quantitative and categorical data This data is used to inform the analysis of the schemes relating to scheme uptake, number of trees planted, tree survival, and cost effectiveness. The data collected were: • • • • • • • •

Applicant details including category type, location, date applied, and pack type(s) applied for, Land use before and after planting, Cost both to applicants and to the local authorities, Tree data including tree packs delivered, number of trees planted, tree species and location, Planting data: Who planted the trees, how long it took, and planting method, Survival data: Number of trees that are healthy, dying, dead, missing and unknown, collected each summer post planting, Maintenance data: Initial preparation of the site, mulch, additional protection, fertiliser, and frequency of watering.

Monitoring of promotional material. All promotion was recorded, and where possible, data on engagement with promotions was collected in the form of click numbers, unique visits, and social media analytics.

Qualitative data collected throughout the schemes This data is used to provide context to inform the discussion of the efficacy and nature of the schemes. This data comprises: • • • •

Applicant reasons for planting (these were collected in free text and then manually categorised into themes).

Information relating to other tree planting schemes running in the local area.

Applicant experiences of the scheme.

Anecdotal evidence from the project officers on their experiences of running the schemes in their local authority, including time spent delivering the trial.

Data analysis The total number of tree pack recipients/plots was 1,222. Of those plots, 643 had some monitoring data recorded. A generalised linear mixed modelling (GLMM) approach was used to investigate the relationships between different fixed and random factors and the recorded survival rates of the trees. These factors included: local authority, time between planting and survival monitoring, scheme type (subsidised or free), applicant type, year planted, and monitoring method.

Data Cleaning Some issues with data quality were experienced, due to the range of people and organisations collecting the data, so the following data cleaning protocol was applied 4 After initially intending to check 50% of sites in person, checks on 10% was deemed more feasible

by the project officers given their time available.

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to ensure the data used was good quality. The total number of survived, dead or missing trees in each data return from an applicant was compared to the number of trees that had been received by the applicant. Where observations exceeded a difference of 20%, these were omitted from the analysis. Thresholds of 20%, 30% and 40% were considered. A 20% threshold was chosen to be the best balance between total responses used in the analysis and confidence in data quality and accuracy.

This leaves a final dataset that contains 𝑁 = 512 observations. These 512 observations are from 408 planting sites; 94 planting sites have two observations; once on-site and once with a survey. 263 planting sites were only monitored using surveys, and 46 were only monitored on-site5. There are 58 observations which are second or third observations made at the same site in different years. For example, where the first observation was made nine months after planting, and a second is made 21 months after planting.

Variables The variables included in the GLMM analysis are summarised in Table 2.

The variable ‘Planted Trees’ is the number of trees given away to individuals and groups who received free or subsidised tree packs. The variable ‘Alive Trees’ is the reported number of trees that have survived at the time of monitoring.

The variable ‘Time Gap’ is calculated by first transforming the month and year the tree packs were given away and the month and year the tree packs were monitored into a number. Then, the number corresponding to when the tree packs were planted is subtracted from the number corresponding to when the tree packs were given away.

The local authority (LA) in which the tree pack recipient is located is represented by a categorical variable and labelled respectively. Most of the observations (42%) come from Norfolk and the least number of observations (11%) come from Shropshire.

The variable ‘Scheme’ is categorical and indicates whether a tree pack recipient received the trees for free (Free) or paid 10-50% (Subsidised). The data used in the analysis is 62% free scheme data and 38% subsidised scheme data.

Different applicant types were issued tree packs, including local authorities, businesses, private landowners, tenant farmers, and other groups like charities, schools, and community groups. The type of applicant is captured as a categorical variable called ‘Applicant Type’.

‘Monitoring Type’ refers to either survey or on-site monitoring. In each local authority, 10% of surveyed monitoring responses were selected randomly for on-site

5 In person monitoring only occurred at sites where a survey response was received. These 46 sites

were also monitored via survey, however their original survey data was lost and superseded by the on-site monitoring responses.

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monitoring. About 73% of all observations are from surveyed values, 27% of observations are from on-site monitoring by local authority officers.

The year the trees were planted/given to recipients is included as a categorical variable ‘Year Planted’. The number of observations from each year are split fairly evenly across 2021, 2022 and 2023.

Finally, the month in which the trees were monitored is included as a categorical variable, ‘Month Monitored’, to capture any seasonal variation in recorded survival rates. Monitoring in July was the most popular (n=205) and monitoring in November was the least popular (n=3) in the dataset used for the regression model.

Table 2: Summary of data used in the GLMM analysis .

Variable Planted Trees Alive Trees Time Gap LA ... Chichester ... Kent ... Norfolk ... Shropshire Scheme ... Free ... Subsidised Applicant Type ... Business … Private Landowner … Resident … School … Parish Council … Community Group … Charity … Tenant Farmer … AONB Partnership … District council … Other Monitoring Type ... On-Site ... Survey Year Planted

Number of observations 512 512 512 512 139 98 217 58 512 315 197 512 12 97 335 10 16 22 5 3 5 1 6 512 132 380 512

Mean

Min. 25th Pctl.

10 20 0 16 1 6 -

75th Pctl.

125 100 9 -

Max.

96 82 8.9 -

Std. Dev. 145 137 5 -

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2000 1950 25 -

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... 2021 ... 2022 ... 2023 Month Monitored June July August September October November

141 172 199 512 69 205 107 94 34 3

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Model A generalised linear mixed model (GLMM) was used to identify the statistical relationships between the recorded survival rates and the set of explanatory variables. A formal mathematical representation of the GLMM is presented in Appendix C. The primary components of a GLMM model are fixed effects and random effects. Fixed effects are factors that stay the same across groups and are usually planned as part of a study, such as the scheme type and monitoring type categorical variables described above. Random effects vary across groups and are typically drawn from a larger population of values, for example in this analysis the individual recipients of tree packs. They can be a source of ‘noise’ in models that needs to be accounted for in the model.

A series of candidate models were estimated in R Studio using the glmer() function in the “lmer” package (Bates et al., 2015). The preferred GLMM model was selected based on the AIC and log-likelihood values, and on a series of post-estimation diagnostic tests. The preferred model includes a linear term for the Time Gap variable and fixed effects for the monitoring type, scheme type, and local authority.

Unique identifiers of programme participants are nested within the LA as a random effect. The model has undergone post-estimation tests using the “DHARMa” package in R Studio and the results are described in the appendix D (Hartig, 2022).

The overall fit of the model is represented by the marginal and conditional 𝑅 2 values.

The marginal 𝑅 2 is a value between zero and one that measures the proportion of variance in the recorded survival rates that can be explained by the time gap variable and fixed effects. The reported value of 0.136 is fairly low. The conditional 𝑅 2 measures the proportion of variance in the recorded survival rates that can be explained by the entire model. The reported value of 0.561 is higher than the marginal 𝑅 2 . This suggests that including the random effects in the GLMM is important in improving the model fit. Post model estimation diagnostic tests are performed to identify whether the model violates any of its assumptions. A detailed look at the post model estimation diagnostic tests is provided in Appendix D and are highlighted here for brevity. Results indicate that the model residuals are normally distributed, there is no under dispersion or over dispersion in the model, and there are no significant outliers. However, the normality assumption of the random effects is violated. Exploration of linear mixed effects and generalized linear mixed effects 12

models suggest that parameter estimates are robust to violations of distributional assumptions of error variance and random effects (Schielzeth et al., 2020).

Therefore, we proceed with interpreting the model results.

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Results In total, nine tree schemes were run by four local authorities (six free and three subsidised schemes). A total of 91,540 trees were distributed through the free tree schemes, and 53,110 in the subsidised schemes. In total, 144,650 trees were distributed to 1,222 applicants through the nine tree schemes.

Table 3. Summary table showing the survey response rates, number of applicants, tree packs distributed and trees planted per scheme.

Local Authority

Scheme type

Scheme year

Monitoring survey response rate 2021

Monitoring survey response rate 2022

Monitoring survey response rate 2023

Number of applicants

Number of tree packs

Number of trees

Chichester

Free

2021/22

N/A

70%

48%

104

429

8,020

Chichester

Subsidised

2022/23

N/A

N/A

79%

48

330

7,475

Kent

Free

2022/23

N/A

N/A

60%

203

874

17,910

Norfolk

Free

2021/22

N/A

66%

47%

121

581

10,890

Norfolk

Subsidised

2022/23

N/A

N/A

57%

288

666

31,495

Shropshire

Free

2020/21

31%

N/A

N/A

96

755

15,100

Shropshire

Free

2021/22

N/A

30%

N/A

121

900

18,000

Shropshire

Free

2022/23

N/A

N/A

35%

120

1,081

21,620

Shropshire

Subsidised

2022/23

N/A

N/A

37%

121

707

14,140

Total

1,222

6,323

144,650

Survival rates Table 4 shows the survival rates from annual monitoring completed up to and including 2023. All local authorities saw a survival rate of over 70% in the first growing season after planting. In the second growing season post planting through free schemes in Chichester and Norfolk, the survival rate saw a drop of 13% and 6% respectively. For the subsidised tree scheme, the minimum survival rate was 87% in the first year of planting. In Chichester and Norfolk, the survival rate was higher for the subsidised tree scheme than the free tree scheme, but for Shropshire the opposite is true (although this was a smaller difference between free and subsidised schemes than for the other local authorities).

Table 4. Table showing the average survival rates per scheme and year, by local authority.

The data for year 1 was collected in the summer following winter planting. The data for year 2 was collected at the same time the following year. Data for Shropshire is currently only available for the schemes run in 2022/23.

Local authority

Chichester Kent Norfolk Shropshire

Survival rate Free First year survival rate 71% 89% 79% 92%

Free Second year survival rate 58% N/A 73% N/A

Subsidised First year survival rate 89% N/A 87% 87% 14

While this is an accurate reflection of the survival rates for the first one to two years, survival rates could be expected to fall in later years, as trees are not considered to be established until at least three years post-planting and they remain vulnerable during this time.

Variables effecting recorded survival rates This section presents the results of the GLMM model for each identified variable.

The model estimation results of the preferred GLMM model are summarised in Table 5. There were 512 observations used in the model estimation that came from 408 sites (𝑁𝑠𝑖𝑡𝑒 = 408) because some sites had multiple survival rate observations.

Results indicate that each of the independent variables Time Gap, Monitoring, Scheme type, and Local Authority are statistically significant at the 5% confidence level except for the fixed effect of local authority 4 (Shropshire). The on-site monitoring, free scheme, and local authority 1 (Chichester) fixed effects are omitted from the results output to avoid extreme multicollinearity in the model.

The estimated parameters are presented in log-odds which makes interpreting the size of the effect on the dependent variable difficult using the estimated coefficients.

However, interpretation of the sign of estimated coefficients is still possible for the statistically significant variables. The positive coefficient for Scheme [Subsidised] suggests that survival rates are higher on average for trees that were funded partially than trees that were given away for free. The negative coefficient for Time Gap suggests that as the number of months between giving away the trees and monitoring the trees increases, the recorded survival rates decrease. A negative value for the Monitoring [Survey] coefficient indicates that recorded survival rates from surveys are lower on average than recorded survival rates from on-site monitoring. The positive coefficients for local authority 2 (Kent) and local authority 3 (Norfolk), indicate that survival rates are higher on average for trees located in these local authorities than in local authority 1 (Chichester).

The odds ratio with confidence intervals are reported in Table 5 to interpret the effect size of each independent variable on recorded survival rates. The interpretation of the odds ratio for each variable indicates the difference in odds of 100% recorded survival compared to the control group. For example, results suggest that the odds of 100% recorded survival from a surveyed observation is 20% lower on average, holding all other variables fixed, than from an on-site monitored observation (i.e., 1 − 0.80 = 0.2). The odds of 100% survival from an observation made from a subsidised tree scheme site is approximately twice as high on average, holding all other variables fixed, compared to an observation from a free-tree scheme site on average (i.e., parameter value = 2.05).

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Table 5: GLMM regression results

Estimated Coefficients (confidence interval) Explanatory Variables Intercept Time Gap Monitoring [Survey] Scheme [Subsidised] LA [2] LA [3] LA [4]

Log odds

Odds ratio

2.56*** (2.16, 2.97) -0.13*** (-0.14, -0.12) -0.23*** (-0.34, -0.11) 0.72** (-0.26, 1.17) 1.61*** (1.03,2.18) 1.01*** (0.52,1.51) 0.55 (-0.14,1.23)

13.00 (8.66, 19.60) 0.88 (0.87, 0.89) 0.80 (0.71, 0.90) 2.05 (1.30, 3.25) 4.99 (2.82, 8.93) 2.75 (1.68, 4.53) 1.73 (0.87, 3.41)

Random Effects 𝜎2 𝜏00 𝐿𝐴:𝑆𝑖𝑡𝑒 ICC 𝑁𝐿𝐴 𝑁𝑆𝑖𝑡𝑒

3.29 3.19 0.49 4 408

Observations Marginal 𝑅 2 /Conditional 𝑅 2

512 0.136/0.561

Note: *** = statistically significant at the 0.1% level. ** = statistically significant at the 1% level. * = statistically significant at the 5% level.

Local authority variable Figure 1 indicates that survival rates are lowest in Chichester and highest in Kent.

The uncertainty in the estimated effect in Shropshire is the largest.

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100 95

Survival Rate (%)

90 85 80 75 70 65 60 Chichester

Kent

Norfolk

Shropshire

LA Figure 1: The effect of the fixed effect Local authority on survival rates. Mean values are the points, and the whiskers are the 5th and 95th percentiles of the effects.

Time Gap variable Figure 2 suggests that there is an initial steady decrease in recorded survival rates over the first 6 months post planting, and then a sharper decrease onwards. The average recorded survival rate across all local authorities after one year is approximately 87%, and the average recorded survival after two years is approximately 57%. This effect is likely present because in the initial few years after planting, trees are most susceptible to mortality. The confidence intervals of the effect of time between planting and monitoring increases over time, which could be partially explained by the fact that the number of observations with higher time gap values is low relative to the number of observations with lower time gap values.

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100 90 80

Survival rate (%)

70 60 50 40 30 20 10 0 1

5 10 15 20 Months between planting date and monitoring date

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Figure 2: Time Gap effect plotted over months 1 to 25. The bars are the average estimated effect, and the whiskers are the 5th and 95th percentiles of the effects.

Monitoring method 27% of observations are from on-site visits from local authority officers, which followed up survey responses. This enables comparison between the two figures from the same site and year. In Figure 3 the black lines are the effect of on-site monitoring relative to survey monitoring. The red line is a 1:1 baseline effect mapping between the x and y axis and is used to accentuate the effect size, which is the difference between the black lines and the red line. Figure 3 shows that higher survival was recorded from on-site visits by local authority officers than from selfassessment surveys completed by the recipients. This is shown by the black lines always being above the red line. For example, a 40% recorded survival rate for an on-site monitored observation translates to approximately 32% recorded survival for a surveyed observation. The magnitude of the effect is represented by the difference between the black lines and the red line. The effect of surveyed versus on-site monitoring is smallest at the extreme values of reported survival rates (i.e., 0% and 99%), which could be an artefact of the possible values being bounded at 0 and 100%.

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100 90

Subsidised tree scheme survival rate (%)

80 70 60 50 40 30 20 10

0 0

10

20

30 40 50 60 70 Free tree scheme survival rate (%)

80

90

Figure 3: Effect of on-site monitoring on recorded survival rates versus surveyed monitoring.

Solid black line is the average effect of on-site monitoring relative to survey monitoring.

Dashed black lines are 5th and 95th percentiles. The red line is a 1:1 baseline effect mapping between the x and y axis and is used to accentuate the effect size - the difference between the black lines and the red line shows the magnitude of the effect of on-site monitoring on recorded survival rates.

Scheme type (subsidised or free) Figure 4 shows that higher survival was generally recorded for subsidised trees than free trees. This is shown by the black lines always being below the red line. The magnitude of the effect of free versus subsidised is larger than the effect of on-site monitoring versus surveyed. For example, a 40% survival rate for a free tree scheme observation translates to approximately 58% survival for a subsidised tree scheme observation. The effect of free versus subsidised tree scheme is smallest at the extreme values of reported survival rates (i.e., 0% and 99%). This could be explained by other factors that are responsible by exceptionally poor survival (i.e., extreme heat, pests and disease) and exceptionally good survival (i.e., the right trees

19

in the right place) and could also be an artefact of the possible values being bounded at 0 and 100%.

100 90

Free tree scheme survival rate (%)

80 70 60 50 40 30 20 10 0 0

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 Subsidised tree scheme survival rate(%)

Figure 4: Effect of free tree scheme on survival rates versus subsidised tree scheme. Solid black line is the average effect of the subsidised tree scheme relative to the free tree scheme. Dashed black lines are 5th and 95th percentiles. The red line is a 1:1 baseline effect mapping between the x and y axis and is used to accentuate the effect size - the difference between the black lines and the red line shows the magnitude of the effect of the free vs subsidized tree schemes on recorded survival rates.

Cost per tree The cost per tree is calculated by the total capital cost to the local authority (which included the cost of the tree, guard, tie and stake) divided by the number of planted trees (represented in Figure 5 in blue), or divided by the number of trees surviving, in summer after planting (represented in orange) or in the following year (represented in grey). The subsidised tree scheme saw a lower cost per planted and survived tree than the free tree scheme.

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Cost per tree across both free and subsidised schemes Cost per planted tree

Cost per survived tree in year 1

Cost per survived tree in year 2

8 7 6

Cost (£)

5

4 3 2 1 0 Free

Subsidised

Figure 5. Chart showing the cost per planted tree and cost to local authority per survived tree for both the free and subsidised scheme. For the free tree scheme where there has been 2 years' worth of monitoring, the cost per survived tree after two years has been included. Outliers have been omitted. Line indicates median, X indicates mean.

Table 6 shows the cost per tree by local authority. There is variation across local authorities of cost per planted tree for both scheme types. For example, in the free tree scheme, Chichester’s planted trees cost almost double those in Shropshire. This is likely due to differences in nursery prices. Again, the cost to the local authority per tree is lower in the subsidised tree scheme than the free scheme, because recipients paid for 50% of the cost of their trees. The cost per survived tree increases in year 2 after planting, due to survival rates falling over time. Therefore, the cost per survived tree can be expected to increase over time for both free and subsidised schemes.

Table 6. Average costs to the local authority per planted and survived tree, for both free and subsidised schemes, with the overall average.

Local authority

Cost per planted tree Free

Chichester Kent Norfolk Shropshire Overall average

£2.91 £1.83 £2.00 £1.50 £2.20

Cost per survived tree Subsidised Free Year 1 £1.40 £5.60 N/A £2.11 £1.35 £3.76 £0.75 £1.63 £1.23 £3.59

Year 2 £6.04 N/A £4.88 N/A £5.53

Subsidised Year 1 £1.57 N/A £1.81 £0.92 £1.58

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Scheme size and cost The number of trees distributed per scheme varies across the local authorities, from about 7,500 to 31,500 per scheme. The total capital cost per scheme also varies across the local authorities, and there does not seem to be a clear relationship between number of trees distributed and total cost of the scheme across the local authorities. This is shown by the lack of clear correlation between the number of trees (crosses) and scheme cost (bars) in Figure 6.

50,000

Total scheme cost per local authority with number of trees distributed per scheme

35,000

45,000 Total cost

Number of trees

30,000

40,000

25,000

35,000

total cost (£)

30,000

Number of trees

20,000

25,000

15,000

20,000 15,000

10,000

10,000 5,000 5,000 -

Free 21/22

Subsidised 22/23

Chichester

Free 22/23 Kent

Free 21/22

Subsidised 22/23

Norfolk

Free 20/21

Free 21/22

Free 22/23

Subsidised 22/23

Shropshire

Figure 6. The total cost and number of trees distributed per scheme. The total cost refers to the cost of the trees, canes and guards, and any costs associated with their delivery. *The free tree scheme in Shropshire for 22/23 was funded externally.

Chichester had smaller scale schemes, and distributed similar amounts of trees across the free and subsidised scheme, though it cost around half the amount in subsidised scheme, as expected. However, it was more expensive for them to run their scheme in relation to how many trees were distributed, compared to other local authorities. This could indicate that running smaller schemes is more expensive per tree due to distribution costs associated with reduced economies of scale.

Chichester also has a plastic free policy meaning that all guards which were offered along with the trees were more expensive cardboard guards rather than plastic spirals.

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Norfolk ran a much larger subsidised scheme than any other local authority, distributing nearly three times as many trees as their free tree scheme, for around twice the overall cost.

Shropshire distributed more trees at a lower cost overall, compared to the other local authorities. In 2023 a free tree scheme and subsidised tree scheme were run consecutively in the same planting season, which may have had an impact on the market for trees. The subsidised tree scheme distributed a similar number of trees as the free tree scheme in 20/21 but for a much lower cost to the local authority.

Applicant type Applicants were asked to self-identify into a category during the application process.

The responses are shown in figure 7.

Proportion of applicant type per scheme 100%

Proportion (%)

80%

60%

40%

20%

0% Free

Subsidised

Business

Charity / Community group

Council

Landowner / farmer

Other

Resident

School

Unknown

Figure 7. Proportions of applicant types by scheme type.

The answer options in the application form differed between the two schemes in Chichester: the landowner/farmer category was removed in the subsidised scheme, so it may be that in Chichester’s subsidised scheme data the landowners/farmers are instead recorded within the resident category. This potentially explains why the resident proportion is increased for subsidised schemes. The question was also made mandatory on the application form for the subsidised tree scheme, meaning there were no ‘unknown’ responses. Taking this into consideration, the spread of applicants across both schemes appears fairly similar.

The subsidised schemes introduced a cost to recipients for their trees. The Index of Multiple Deprivation (IMD) scores of recipient postcodes have been analysed to 23

understand if there is a difference in applicants by this metric between scheme types. The IMD is used by government as an indication of the relative deprivation across the UK and thus local authorities of this trial, by creating scores for areas of between 1,000 and 3,000 people which are ranked from the most deprived (score/rank 1) to least deprived area (score/rank 10) (CDRC, 2019). The range of IMD scores for recipients from both scheme types was between 1 and 10. However, the average IMD of each scheme was between 5.84 to 8.93. Therefore, most applicants, for both scheme types, were in areas with higher (least deprived) IMD scores.

Table 7. Average IMD score for the applicants of the free and subsidised scheme by local authority. The range of IMD percentiles reached per scheme is also shown. Kent has been excluded as they only ran a free tree scheme.

Local Authority Chichester

Free scheme IMD average score 7.04

Norfolk

7.99

Shropshire

5.84

Range of scores (percentiles) Between 5 and 10 Between 1 and 10 Between 4 and 10

Subsidised scheme IMD Range of scores average (percentiles) score 8.93 Between 3 and 10 7.26 Between 1 and 10 5.92 Between 2 and 10

For the subsidised scheme, in all the local authorities the range of applicant IMD scores increased and included recipients in areas with lower (more deprived) scores than there were in the free tree schemes. There was a large increase in average IMD score in Chichester to 8.93. Norfolk, however, saw a drop in average IMD score, though only marginally. Therefore, this analysis does not identify a trend across the local authorities between the two scheme types.

Reason for planting Figure 8 shows that overall, the motivations for tree planting across both schemes were broadly similar.

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Reasons for applying to tree schemes n=864

90

Percentage of applicants

80 70 60 50 40 30 20 10 0 Visual appeal

Supporting Counteract Replace wildlife climate lost trees / change enhance existing hedges

Provide Contribute Connect Improve air Provide for shade / to the existing quality the next shelter / community landscape generation barrier features

Reason for planting Subsidised

Free

Figure 8. Reasons for applying to the free and subsidised scheme, and the proportion of applicants for each reason6.

For both the free and subsidised schemes, supporting wildlife was the most frequently cited reason for planting trees. Subsidised scheme applicants were interested in planting trees to provide shelter, shade or a barrier, and much more likely to plant to improve air quality. Free tree scheme applicants on the other hand were more likely to plant to replace lost trees or enhance existing hedges. They were also more likely than the subsidised applicants to plant to improve visual appeal and for the benefit of future generations. However, broadly, the motivations are similar across both scheme types.

Examples of responses to the question on motivations for planting trees include: “Great scheme and in the present climate we need all the help we can get for planting more trees and creating an enhanced diverse environment.”

“We want to increase the flora and fauna diversity of the site. To benefit the wildlife, environment and residents over future decades.”

“I have a small area at the top of our plot, there are a few trees that are very old and dying. I would like to rejuvenate and extend this area so that the 6 The data for this chart was gained by manually coding the free text responses into categories. Each

free text response may have contained multiple reasons for planting.

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wildlife that pass through might be more inclined to stay and use it for their habitat.”

“The aim is to infill an existing 3 year old hedge and to replace a triangular tree area between two fields that had outgrown its location. The purpose of all the hedging and trees we have planted is primarily as habitat for birds and animals.”

“We wish our planting to connect to the trees in the adjoining fields to help wildlife to thrive and to provide a peaceful area for residents to relax and reconnect with nature.”

Advice only scheme The advice only schemes were run between January 2022 and February 2023 in Kent, and January 2021 and September 2022 in Chichester. There were significantly more enquiries in Chichester with 152, whereas in Kent there were 20 recorded.

Enquiries came through via email and took an average of 18 minutes of local authority officer time, with the majority taking between 5 and 20 minutes. Enquiries which took above 60 minutes included a site visit and were directed to other local authority tree planting grants. Nearly all enquirers were directed to the free tree scheme run in that local authority, both before it was opened and during, as well as receiving the external funding schemes advice document. Additional advice was sometimes given on species and location.

The majority of enquiries came from homeowners or residents, whose enquiries ranged from wanting to know how to plant trees around new build estates to wanting to plant hedgerows in large gardens. Chichester saw more enquires from landowners, farmers and estate managers than Kent.

Proportion of enquirer type by local authority Business

3% 5%

Charity / community group

4%

Chichester n=152 10% 9%

Council

15% 50%

Homeowner / resident Landowner / farmer / estate manager

Kent n=20

65% 22% 5%

Other

7%

Uknown

5%

Figure 9. Proportion of enquirer type by local authority.

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Discussion The tree schemes of this trial, including both free and subsidised, distributed a total of 145,000 trees across the four local authority areas. Not only did the schemes contribute to the local authorities’ tree targets, but the range of applicants across all the schemes indicates how trees and their benefits reached a wide area and range of people.

The schemes were also relatively inexpensive. The initial overall cost for the local authority per tree was relatively low, because the local authorities only needed to pay for the trees, guards and canes, and any additional promotion and guidance, and did not pay for planting labour or fencing. However, to understand the cost effectiveness of the schemes we need to understand tree survival, as this influences the cost per survived tree.

The statistical analysis found four significant variables affecting recorded survival rates: • • • •

Scheme type Local authority Time gap between planting and monitoring Monitoring method

Further monitoring is ongoing to understand the longer-term results, which will be important as the current data has only been gathered up to 2 years after planting and therefore is only indicative of very early tree establishment.

Effect of scheme type Understanding how the scheme type affected tree survival was the central aim of this trial. Results show that survival rates of subsidised trees are higher than free trees.

Overall, this suggests that where applicants contributed financially, their trees were more likely to survive. This could be because having paid for their trees, they might be more invested in looking after them. This is a notable finding, especially considering that the subsidised tree schemes were also lower cost to the local authority, due to 50% of the tree and tree protection costs being paid for by recipients.

This indicates that subsidised tree schemes are more cost effective for local authorities in two ways, as they allow local authorities to get more trees planted for the same cost as a free tree scheme and also increase the likelihood of survival. At the scale of this project, where a total of 75,620 trees were planted in free schemes (for which there is available survival data), this increase in survival roughly equates to 6,000 additional trees that would potentially have survived if a subsidised scheme had been used. If such a scheme was expanded beyond 4 local authorities, a very large number of additional trees could survive their first two years by using subsidised rather than a free schemes.

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However, compared to the free tree scheme, there was an additional resource burden on the local authorities when running a subsidised tree scheme. For example, the finance and legal teams were required to set up payment systems and draw up terms and conditions. In some local authorities the subsidised scheme trees took longer to distribute and additional promotion was required, incurring an additional resource cost met by the local authority comms and digital teams. This, and all other internal costs, have not been reflected in the cost analysis as this was outside the scope of the project.

That the scheme type, or financial contribution by recipients, has a statistically significant effect on survival could indicate a behaviour change in how people look after their trees. There were other behaviour or attitudinal differences observed by the project officers in the subsidised tree recipients compared to the free tree recipients: •

•

• •

Collection: For all the free tree schemes, some applicants pulled out last minute or failed to collect their trees (these trees were then redistributed). By contrast, in the subsidised tree scheme there were instances where applicants changed their minds about the trees for which they had already paid, either no longer wanting them or wanting to change their tree pack.

Ownership: In the free tree scheme, it was reported anecdotally that applicants passed around the trees from their tree packs amongst their friends and families.

Choice: In the subsidised tree scheme there were instances where applicants wanted more choice over the size and species of the trees in the packs.

Motivations: The motivations for planting also varied slightly at the point of application. The subsidised scheme applicants tended to want the trees to fulfil additional purposes, such as providing shade or improving the air quality.

These behaviours combined suggest that where there is applicant buy-in, there is higher demand and ownership over the trees. The subsidised scheme seems to have been viewed as a way of purchasing trees as opposed to getting access to trees more opportunistically. This attitude may have contributed to higher survival.

Effect of local authority The results show that recorded tree survival is affected by which local authority ran the scheme. However, there is no evidence to suggest what is driving this difference in survival across the local authorities. This project had a focus on understanding how local authorities could deliver these approaches to tree planting in a real-world context and variability was expected. For example, the trees were procured from local nurseries with different terms, and the start and end date could be adapted depending on uptake. Other differences between local authorities that may have affected survival include local climate and geography, local authority size, and tree pack composition, which were specifically designed for the local area and were adapted to meet local demand. The tree growing method (i.e., either bare root or cell grown) also varied within and across local authorities depending on species and availability. The advice distributed with the trees also varied slightly across the local 28

authorities to suit the local area but was the same within each local authority for both scheme types. The fact that the local authority variable has a significant effect on recorded survival rates indicates that standardised survival rates and costs are unlikely to be realistic. However, it is notable that in the subsidised tree schemes, the survival rates were more consistent across the local authorities than in the free tree schemes.

Time gap between planting and monitoring The modelling shows that the time between planting and when the trees were monitored had a statistically significant effect on the recorded survival rate. Over the first two years post-planting, recorded survival rates declined in an accelerating pattern. This represents what is generally considered to be the most challenging period of a newly-planted tree’s life, and mortality would be expected to start to level off as the time increases beyond 25 months. As data continues to be gathered over the next few years, it will give greater insight into the shape of the mortality curve and a clearer indication of how many of the trees planted could establish as mature trees.

This trial’s results mirror the widely held views in the sector: that survival rates of trees planted through tree schemes are expected to trend towards 50% survival in the first two years. This reflects what is already known about the importance of supporting tree establishment in early years for long-term tree survival, as it’s in these years that most deaths occur before survival rates level out. A more rapid levelling off of mortality rates, at a higher rate of survival, would indicate a more costeffective scheme, so finding methods of increasing early survival rates is key.

Monitoring method It is assumed that the local authority officers who monitored the trees in person would be more accurate given their expertise. On-site monitoring recorded survival rates of up to 5.6% higher than those reported by the recipient survey. This is contrary to expectations that recipients might not fully disclose or record the number of trees that did not survive. This could be because local authority officers identified damaged or poorly performing trees as alive, or because they conducted monitoring later in the season than the survey responses were collected, meaning that trees might have changed in appearance and become more easily identifiable (for example, re-flushing in the autumn following earlier leaf loss in the summer 2022 drought was observed in some cases).

Overall, survey monitoring conducted by recipients tended to be conservative.

However, the effect is modest, suggesting that survey monitoring provides a reasonable level of accuracy and avoids the additional cost of in-person monitoring.

It is important to note that data collected is self-selecting, and it is possible that recipients that are seeing better survival of their trees, or that are more engaged with the trees, are more heavily represented in responses.

There were, however, challenges with obtaining monitoring data through the recipient survey and the quality of the data received, and it required significant levels 29

of cleaning before analysis. The project received responses from around half the recipients, but around 33% of that data was unusable for analysis. A major reason for this was the recipients recorded either significantly more or significantly less total trees (alive, dead, and missing) than had in fact been distributed to the recipient.

Project officers surmised that recipients would have either not planted all their trees, or also obtained trees from elsewhere and planted them all together without keeping track of which trees came from which source, and subsequently included more or less trees in their survey than they were actually given through this trial.

Over time, the recipients were less likely to respond to monitoring requests which is reflected by the decreasing response rates in table 3. This indicates that further monitoring data from this trial, though crucial for further analysis, may become increasingly difficult to obtain.

Applicants and uptake Across both scheme types and all local authorities, there was no clear difference in proportions of applicant type. This is a surprising find, as it was expected that charities, community groups and schools may have had less interest in subsidised tree schemes. However, the community groups, councils, and businesses that applied to the free tree schemes tended to apply for more trees, whereas in the subsidised schemes residents and landowners applied for more trees. The results also indicated that applicant type had no statistically significant effect on survival.

This again presented a surprise, as it was hypothesised that different applicant types have different available time, money, and knowledge that could impact survival.

The main motivation for planting across both scheme type and applicant type was to support wildlife and benefit biodiversity. This might suggest that the schemes engaged an audience who already had an interest in planting for nature, or that the schemes themselves generated an interest in planting for nature. Possibly this finding was a result of tree pack composition, which was almost entirely native species and a large proportion of hedgerow species. In any case, both free and subsidised schemes could be a simple way for local authorities or organisations to target nature-interested people to plant trees, which span different types of individuals and groups in the local area.

There were some slight differences in motivations between the two schemes. For example, applicants to subsidised schemes were more likely to plant for shelter, which could suggest that if people are financially contributing towards the cost of the tree, they may be more likely to want some use or additional benefit from them, or that they better understand the range of benefits trees can provide.

There was appetite for both scheme types, though the extent of this varied across the local authorities. All the local authorities reported that trees took less time to allocate through the free tree scheme than the subsidised tree scheme.

Chichester is the only district council participating in the trial, meaning that they cater to a smaller population of nearly 125,000 compared to 1.5 million, 916,000 and 323,000 in Kent, Norfolk and Shropshire respectively (ONS, 2021). For local 30

authorities with larger population sizes, the tree schemes were fully subscribed more quickly, perhaps because proportionally there were fewer trees on offer. The size of the tree scheme also has an impact on cost efficiency, as smaller schemes may be more expensive per tree to administer and distribute. In Chichester, it took more advertising to distribute subsidised trees.

Where both schemes were run in consecutive or the same years, it was anecdotally reported that the presence of a free tree scheme in the previous year did not deter applicants. In Shropshire four tree schemes were run over three consecutive years, and both the number of applicants and trees distributed remained at similar levels.

This suggests that there was an appetite for trees across all the local authorities and having to pay 50% of the cost did not significantly deter this appetite.

Advice only scheme Through the advice only scheme, the project officers were able to engage with the public on tree planting in the area, as demonstrated by the enquiries received. It is notable that Chichester received proportionally far more enquiries than Kent when considering their relative population sizes. Though the advice scheme signposted to several different tree planting schemes or funding options, project officers reported that it was also an effective way of promoting not only the free or subsidised tree schemes, but also other funding available for tree planting within the local authority.

Researching the available funding and publishing the advice page online required initial time and resource from the local authorities. It was not too intensive, and could be a simple, low-cost method of potentially encouraging local tree planting. It was considered by the project officers that the subsequent average time of 18 minutes per enquiry was manageable. It seemed to be an effective use of time and resource to promote tree planting through local authority schemes.

However, it is impossible to assess how the scheme influenced the planting and survival of trees, as no further data was gathered beyond the point of enquiry. It is therefore unknown how cost-effective the advice only scheme was in terms of increasing tree cover in the local area. It is also difficult to determine from the data available how effective the advice scheme was in reaching those who would benefit from it in the local area. This is because the number of enquiries may not be indicative of the effectiveness of the advice page, particularly when some email enquiries were unnecessary given the information available on the websites.

Additional benefits and challenges There were additional benefits to running tree schemes reported by the project officers. Both types of tree schemes were positively received by the public and tree collection days offered an opportunity to engage with the public on wider environmental issues. The tree schemes were also a way for the local authority to oversee supply of trees that were planted in the local area. For example, sourcing the trees from a biosecure supplier ensured the trees planted had a lower risk of spreading pests and disease, and may have reduced the chance of applicants purchasing the trees themselves from less biosecure sources.

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There were also some challenges experienced by the local authorities in setting up and running their tree schemes. For example, some local authorities had difficulty procuring a suitable nursery: Chichester only had one nursery respond to the tender.

There was a limited number of nurseries in the area who were able to provide the mix of species at the required size, and with the required biosecurity policy.

In Norfolk, there was reportedly a risk posed by the schemes to local community tree nurseries. As the council procured a large number of trees from commercial nurseries, which were distributed to local people and groups for free or at low cost, there was potential for this to undermine the markets of community tree nurseries where people might otherwise go for free or low-cost trees.

Limitations and opportunities for further research Weather conditions The summer of 2022, the year in which free tree schemes were run in Chichester, Norfolk and Shropshire, experienced temperatures of up to 40 degrees and long periods of drought. These are challenging conditions for newly planted trees to establish. By comparison, 2023, when the subsided schemes were run (as well as free schemes in Kent and Shropshire), did not experience such hot dry weather.

However, the GLMM did not find a statistically significant effect of year planted on survival, which indicates that the difference in weather between planting years may not have significantly affected survival. The effects of the other variables outlined in the results and discussion (such as scheme type) were shown to be greater and more significant.

However, this finding could change with a larger data set over more years and locations as it is likely that weather conditions do impact survival in some capacity, though the extent of this is not known from this data. Longer-term monitoring could allow for this analysis.

Only one level of subsidy The results have shown there is a statistically significant effect of subsidy on survival. However, the project mainly trialled one level of subsidy where the trees cost 50% to the recipients. Though Norfolk did offer orchard trees with a 10% subsidy, it is impossible to understand what effect this had on survival compared to those which were subsidised at 50%. This is because it only occurred in one local authority, and the trees themselves were not comparable to the whips distributed through the subsidised schemes due to their different species, size, and care requirements. Additionally, recipients may have had attitudinal differences to orchard trees, which could drive changes in their behaviour when looking after their trees.

Different levels of subsidy (for example, charging recipients for 25% or 75% of the cost) could have different effects on the uptake to the scheme and also the survival of the trees. This may be an option for further testing, particularly if there are instances where the budget for a tree scheme is limited.

Lack of time-cost data 32

The results from this project found that subsidised tree schemes are more cost effective as they are cheaper per tree due to the 50% cost-recovery from the recipient and increased the likelihood of tree survival in the first two years of planting.

However, anecdotal data from the project officers also indicate that there is an increased resource burden that is absorbed by the local authority, as additional support is required from the legal, finance and comms teams when setting up and running a subsidised tree scheme. This data was not formally recorded, so the extent of this burden is unknown, both in terms of time and money. The cost results are therefore only considering capital costs. However, the participating local authorities considered it unlikely that the additional resource requirements of a subsidised tree scheme will outweigh the benefits of reduced cost per established tree.

It is likely that the extent of the burden will vary across local authorities depending on internal structures and processes already in place. For example, local authority officers felt that running a free scheme first meant that there were existing processes and knowledge that eased the workload of the subsequent subsidised scheme.

Unknown relationship between tree pack type, survival, and applicant The results from the trial do not yield information on how the pack types (or tree species) interact with survival. This was because the tree packs varied between the local authorities in terms of species composition, source and growing method. The tree packs offered were unique to each local authority and therefore would be highly correlated to the local authority fixed effects, so they were not included in the model to avoid issues around multicollinearity. Due to this variability, it is also difficult to understand the relationship between applicant type and tree pack type, and whether this could have an impact on overall survival rates.

Other effects on survival The pilot set out to explore how the level of subsidy effected tree survival in these schemes. The effects of other variables were considered in order to contextualise the results. However, whilst outside the scope of this research, these and other variables are worth exploring further in terms of how they affect tree survival, to inform future tree schemes. For example, future research could investigate how the growing method of trees (bare root or cell grown) effects survival.

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Conclusion The results from this trial show that subsidised tree schemes are a cost-effective way for local authorities to distribute trees while securing higher survival rates than free tree schemes. Subsidised tree schemes had a lower capital cost per established tree (£1.58) compared to free tree schemes (£3.59) in the first year of this trial, due to the better survival rate of trees distributed under this model. Additionally, by requiring recipients to contribute 50% of the cost of trees, stakes, and guards, subsidised schemes reduced the financial burden on local authorities and offered a good option for running a tree scheme with a lower capital budget.

The fact that survival rates were higher in the subsidised tree schemes could be due to behavioural differences in recipients. For example, because recipients have paid a part of the cost, they may be more invested in their survival and take better care of their trees. This hypothesis is supported by the fact that fewer participants failed to collect their trees from the subsidised tree schemes than the free schemes.

While subsidised tree schemes have proved more economical and effective in the first two years of this trial, it is important to note that they may require additional resources to manage, such as legal and communications advice. Outcomes may also vary depending on factors such as tree procurement costs and other variables that could affect survival. Therefore, while the figures illustrate the differences between the two types of schemes, results and costs will differ when implementing future tree schemes.

The data, predominantly from free schemes, shows around a 60% average survival rate for trees across all schemes after two years. While data collection to understand long-term survival trends is ongoing, early findings suggest trees distributed through subsidised schemes have significantly higher early survival rates. This advantage is likely to translate into higher long-term survival since tree mortality is highest in the early years, although further research would be required to confirm this. Enhancing survival during this critical period is key to successfully increasing tree cover.

Therefore, organisations aiming to increase tree cover should consider whether subsidised schemes may be better suited to achieve their goals.

The average survival rate after two years of 60% means 40% of the trees distributed have died, which is unsurprising given the limited oversight of planting locations, methods, and aftercare. However, the approach appears to remain cost-effective for local authorities, in terms of cost per survived tree, as it avoids planting labour costs while offering additional benefits. Tree schemes are popular among diverse groups – farmers, residents, businesses, schools, and charities - provide opportunities for community engagement in environmental projects, and distribute trees across varied landscapes, spreading their benefits. In our results, we did not find that applicants differed significantly across free and subsidised schemes, however more research would need to be done into whether the cost to the applicant associated with the latter would prevent access to some demographics.

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2020; 11: 1141–1152. https://doi.org/10.1111/2041-210X.13434

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Appendix A Chichester District Council tree pack species Coastal main pack: • Pedunculate oak, Quercus robur • Holm oak, Quercus ilex • Alder, Alnus glutinosa • Field maple, Acer campestre • Hawthorn, Crataegus monogyna Coastal substitutes • Norway maple, Acer platanoides • Sallow/goat willow, Salix caprea Chalk / Clay Cap main pack: • Hazel, Corylus avellana • Pedunculate oak, Quercus robur • Field maple, Acer campestre • Hawthorn, Crataegus monogyna • Yew, Taxus baccata Chalk / clay substitutes: • Whitebeam, Sorbus aria • Purging Buckthorn, Rhamnus cathartica • Wayfaring tree, Viburnum lantana • Guelder rose, Viburnum opulus • Spindle, Euonymus europaeus • Dogwood, Cornus sanguinea • Juniper, Juniperus communis • Common beech, Fagus sylvatica Wealden greensand main pack: • Pedunculate oak, Quercus robur • Small-leaved lime, Tilia cordata • Hazel, Corylus avellana • Rowan, Sorbus aucuparia • Scots pine, Pinus sylvestris Wealden greensand substitutes: • Hawthorn, Crataegus monogyna • Sallow / goat willow, Salix caprea • Broom, Cytisus scoparius Clay main pack: • Pedunculate oak, Quercus robur • Small-leaved lime, Tilia cordata • Field maple, Acer campestre • Rowan, Sorbus aucuparia • Hornbeam, Carpinus betulus Clay substitutes: 36

• • • • • • •

Wild cherry, Prunus avium Wild service tree, Sorbus torminalis Crab apple, Malus sylvestris Downy birch, Betula pubescens Hawthorn, Crataegus monogyna Hazel, Corylus avellana Alder buckthorn, Frangula alnus

Urban / gardens main pack: • Field maple, Acer campestre • Rowan, Sorbus aucuparia • Hawthorn, Crataegus monogyna • Callery pear, Pyrus calleryana Chanticleer • Snowy Mespil, Amelanchier lamarckii Urban / gardens substitutes: • Strawberry tree, Arbutus unedo • Maidenhair tree, Ginkgo biloba (male) • Judas tree, Cercis siliquastrum • Sweet gum, Liquidambar styraciflua • Pride of India, Koelreuteria paniculata

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Appendix B Example of tree scheme online monitoring survey.

Planting When did you plant your trees? If you planted over several days please just select the first day of planting (required) [date selection] Did you plant in the same location as identified in your application form? (required) -

Yes

-

No

Did you plant the trees alone or did you get help? (required) -

Alone

-

As a group

-

Paid for a contractor to undertake the planting

[If group or contractor selected] How many people were involved in the planting? (required) How long did it take to plant the trees? (Approximate number of hours) What method of planting did you use to plant your trees?

-

Notch planting

-

Turf planting

-

Mound and ridge planting

-

Other

Survival Please note, you won't be penalised for any trees that have not survived. Knowing how many trees have survived or not will help us understand how successful the trial has been, and help to inform future planting programmes.

Please count how many trees are: -

Healthy [The tree is tree alive, growing (and looking characteristic of the species)]

-

Dying [A percentage of leaves are not present or leaves are yellowing/discoloured/browning/crispy.]

-

Dead [No leaves, no visible growth] 38

-

Missing [Tree not present/gap in planting/spiral guard without a tree.]

-

Unknown [Unsure about condition of tree. Please take a picture and tag it for review and validation, you can upload these photos at the end of this form]

If your trees have not survived do you know why?

-

Lack of water

-

Vandalism

-

Eaten by wildlife (deer, rabbits)

-

Other

Maintenance -

Did you do any initial preparation of your site before or during planting? (please select all that apply) [tick box options]

Weeding Watering Clearing the planting site of debris Other (please specify) -

Since planting your trees have you used anything additional to enhance/support them? (please select all that apply)

Mulch [tick box options] [if selected] please add additional details Weed mats [tick box options] [if selected include a pop up] please add additional details Mycorrhizal additive [tick box options] [if selected include a pop up] please add additional details Pesticides/herbicides [tick box options] [if selected include a pop up] please add additional details Fertiliser [tick box options] [if selected include a pop up] please add additional details Further fencing – such as rabbit or deer fencing [tick box options] [if selected include a pop up] please add additional details Other (please specify) Have you watered your trees since you planted them?

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-

Yes

-

No

-

If yes how many times [this needs to be a quantitative response – where people can only input a number]

How do you plan to care for your trees in the future? (please select all that apply) [tick box options] -

Watering during dry periods

-

Weeding around the base of the trees

-

Adding mulch

-

Using fertilisers, pesticides, herbicides or other additives to support the growth of your trees

-

Other (please specify)

Do you feel that the trees have improved the quality of your site, if so how? [free form box] Photos Please upload some photos of your trees. If the survival of any of your trees is unknown please upload a photo, label it as "unknown survival" and we can help you identify how healthy they are.

Photos should be in GIF, JPEG, PNG, PDF or Word format and less than 5MB in size. You can upload up to 5 photos.

Would you be happy for Chichester District Council to use these photos in external communications?

Additional comments The trees through this scheme were subsidised at 50%. The average cost for applicants in Chichester District for one tree was £1.25. In a future scheme if the trees were subsidised at 25% would you apply?

Yes No Maybe [comment] Do you have any further comments you would like to add about the scheme, or your trees?

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Appendix C Formal mathematical representation of a GLMM The general specification of a GLMM is: 𝜋 = 𝑋𝛽 + 𝑍𝑢 Where, 𝑔(𝐸(𝑦)) = 𝜋 𝑦 = ℎ(𝜋) + 𝜀 The conditional expectation of the dependent variable (𝐸(𝑦)) is transformed using a link function (𝑔(. )). The link function (and corresponding inverse link function, ℎ(. )) used in this analysis is a logit link function, which is: 𝑔(. ) = 𝑙𝑛 ( ℎ(. ) =

𝑦 ) 1−𝑦

𝑒 (.)

1 + 𝑒 (.)

The dependent variable itself (𝑦) is the proportion of alive trees to planted trees – the survival rate – and can be calculated by using the inverse of the link function(ℎ(. )) on the transformed conditional expectation of the dependent variable (𝜋) and adding the error component, 𝜀. The transformed conditional expectation of the dependent variable is a function of a 𝑁 × 𝑝 matrix of predictor variables (𝑋), a 𝑝 × 1 vector of estimated fixed effects coefficients (𝛽), a 𝑁 × 𝑞 matix of random effects, and a 𝑞 × 1 matrix of estimated random effects coefficients. The interpretation of 𝑁 is the number of survival rate observations in our data so 𝑁 = 512. The value of 𝑝 is the number of predictor variables, and the value of 𝑞 is the number of random effects.

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Appendix D Post model estimation diagnostic tests Figure A.1a presents the p-values from a series of diagnostic tests and QQ-plot of the model residuals using the “DHARMa” package in R (Hartig, 2022). The dispersion parameter is 1.1301 with a p-value of 0.064 and suggests that there is no significant dispersion. A Kolmogorov-Smirnov test suggests that the distribution of model residuals does significantly deviate from normal. The outlier test reports a pvalue of 0.0.00896 which indicates that simulated values from the model are statistically significantly higher or lower than the observed data. However, performing a bootstrapped test revealed there to be 4 outliers and a p-value of 0.4, which suggests that outliers are not significant. Figure A.1b presents a QQ-plot of the random effects estimated by the GLMM model. A visual inspection reveals that the data does not come from a normal distribution. A Shapiro-Wilks test statistic value of 0.98047 with p-value of <0.001 of suggest that the data differs significantly from a normal distribution.

Figure A1: a. QQ plot and test statistics of model residuals (left) and b. QQ plot of model random effects (right).

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Acknowledgements This report has been developed by a partnership of Natural England, Defra, The Tree Council, and Chichester District Council, with Fera Science, as part of the Trees Outside Woodlands research programme. It has been prepared with assistance from Kent County Council, Norfolk County Council, and Shropshire Council.

The Trees Outside Woodland programme is developing innovative and sustainable new ways to increase tree cover to address both climate and ecological emergencies. The £4.8m, five-year programme is funded by HM Government and delivered in partnership by The Tree Council, Natural England, the Department for Environment, Food & Rural Affairs with five local councils.

This publication is available at treecouncil.org.uk and is published under the Open Government Licence v3.0 Project code: Shared Outcomes Fund 30238 Citation: Natural England, Defra, The Tree Council, Chichester District Council, Fera Science, 2025. Tree Schemes: A Trees Outside Woodlands project report. Tree Council, London.

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