Potential Availability of Wood Biomass from Urban Trees: Implications for the Sustainable Management of Maintenance Yards

Abstract

The current energy situation requires the effective utilization of all available resources, and residual wood biomass from urban forestry may represent an excellent opportunity for increasing the presence of short-range energy sources. In urban forestry management, two main operations can provide large amounts of wood biomass: The felling and pruning of trees. These operations are carried out with two principal techniques that differ in terms of mechanization intensity (i.e., accessing the trees’ crown with mechanized aerial lifts or utilizing ropes—tree-climbing). This study has investigated 18 felling and 15 pruning yards, carried out with aerial lifts (17 yards) or tree-climbing (16 yards), most of them located in the city of Rome (Italy), one of the greenest European capitals. The operations were sampled with time studies, and five elements of work time were measured from the beginning of work to the transport of the residual biomass to the loading point, using centesimal stopwatches and video recording. The total observation time amounted to 152.0 h. The total residual biomass was assessed. The cost calculation for each yard took into account fixed, variable, and labor costs. A set of variables for each yard (including several site characteristics, trees’ size, fuel consumption, carbon dioxide (CO₂) emissions, energy consumption, costs of yards, biomass, and work times) was analyzed. This study can contribute to enhancing tree maintenance sustainability in urban sites and estimating the quantity of residual wood biomass obtainable from urban forestry maintenance in the city of Rome.

1. Introduction

The dramatic urban and landscape transformations of recent decades have resulted in a corresponding growth of the urban population. Currently, more than half of the world’s population (56.2%) live in urban areas—increasingly in highly-populated cities [1]. In Italy, 69.5% of the population is urban [2]. On average, cities generate more than 75% of a country’s Gross Domestic Product (GDP) and therefore the main engines of global economic growth. To operate their activities, cities need an uninterrupted supply of energy. They consume approximately 75% of global primary energy and emit between 50 and 60% of the world’s total greenhouse gases [1]. Therefore, many authors assert that sustainable management of cities is the key point of the global ecological transition and the contrast to climate change.

Urban services are crucial to act on this contrast and improve people’s well-being. Some of these ecological services and benefits are provided by trees [3]. For example, trees reduce the urban heat island effect [4], increase biodiversity [5], ameliorate the microclimate [3], reduce airborne particulate matter [6], uptake and store atmospheric carbon dioxide (CO₂) and other gaseous pollutants [7], reduce stormwater runoff, thus reducing the likelihood of flooding [8], can provide improvements in the rehabilitation of psychiatric patients [9], and act as a noise barrier [10]. However, urban trees may also provide ecosystem disservices, generally recognized as management costs [3]. These management costs include two common operations, pruning and felling [11], which produce considerable amounts of woody residual biomass, having the potential to be utilized as a resource that could be used locally for wood products and biofuels to generate electricity and heat after reducing its volume by chipping [12,13]. Moreover, due to the neutral cycle of CO₂ in the chain, biomass is considered to be a source of sustainable energy.

Regarding the potential urban biomass quantities available and the related costs, data and scientific literature are rather scattered because urban forestry maintenance depends on numerous and variable site-associated factors. Furthermore, even the term ‘‘urban wood waste’’ describes materials coming from very different sources [14]. In this paper, we considered urban wood waste as material from pruning or tree removal operations that would otherwise be discarded in a landfill, and biomasses from other significative sources of the urban landscape, such as lawn clippings or fallen leaves or reclaimed wood, were not considered.

In general, authors agree with the assertion that urban woody biomass is a significant potential source of valuable biomass for energy production [15]. The estimation of available biomass can be based on urban forestry inventories. For example, the annual urban woody biomass loss in the United States equates to approximately 46 million tons of fresh-weight merchantable wood or 7.2 billion board feet of lumber or 16 million cords of fire, for a value ranging between $89 and 786 million depending upon the product derived [16]. Timilsina and coauthors [17] estimated annual urban tree stem and crown biomass removals at 2 tons per hectare. Khudyakova and coauthors [18] estimated the total energy capacity of wood waste in Ekaterinburg (Russia) attaining more than 340 thousand GJ per year. Raud and coauthors [19] calculated that 1152 tons of urban greening waste were produced annually from a single square kilometer of parks and public green areas in Tartu (Estonia). Colucci and coauthors [20] calculated the costs and biomass yield in the city of Potenza (Italy).

Other methods of assessing potential biomass are based on estimation models from dendrometric parameters [21], also using a terrestrial laser scanner and ground-level measurements [22] or satellite remote sensing [23]. Sperandio and coauthors [24], through remote sensing and photointerpretation of different biomass sources, estimated the potential biomass available deriving from urban areas as equal to 4.8 Mg ha⁻¹ on average. Some studies have focused on the estimation of single botanical species, such as Platanus hispanica [25], Morus alba [26], or Pinus pinea [27]. Li and coauthors [28] proposed methods such as artificial neural networks and multiple linear regression for estimating municipal biomass resources.

In this study, site characteristics, mechanization intensity, work time study, energy requirements, and CO₂ emissions were taken into consideration to infer the costs, biomass productivity, and sustainability of felling and pruning yards in urban areas [29,30]. These operations can be carried out using different techniques in terms of mechanization intensity. Yards entail access to the trees’ crown with mechanized aerial lifts or utilizing ropes—so-called tree-climbing—and electric or petrol-powered chainsaws can be used. Numerous other factors, such as the botanical species of trees, the site’s accessibility, the size of plants, etc., affect the characteristics of work sites.

The purposes of this study are to (1) investigate technical and economic features of felling and pruning in an urban environment, (2) evaluate the productivity, costs, CO₂ emissions, and energy consumption of the observed yards, and (3) assess the potential residual biomass to be employed in a cogeneration plant for thermal and electrical energy production.

2. Materials and Methods

2.1. Site and Yards Description

This study investigated 18 felling and 15 pruning yards, carried out either with aerial lifts (17 yards, with a total of 35 trees) or tree-climbing (16 yards, with a total of 16 trees) (

Table 1. Characteristics of work sites and main dendrometric features of trees observed in the studied yards (TC = tree-climbing; AL = aerial lift; D.b.h. is the trunk diameter measured at 1.30 from the ground).

Yard Code	System	Operation	Species	Height [m]	D.b.h. [cm]	Crown Diameter [m]	Site	Distance [m]
AA1	AL	Felling	Pinus pinea	27	100	15	1	10
AA2	AL	Felling	Pinus pinea	25	99	12	1	2
ACI	AL	Pruning	Platanus x acerifolia	17	49	9	4	1
ALB	AL	Pruning	Platanus x acerifolia	21	75	13	4	5
ANA	TC	Felling	Pinus pinea	16	80	11	4	1
BSP	TC	Felling	Pinus pinea	17	56	8	4	2
BUS	AL	Felling	Pinus pinea	22	67	12	2	1
CA1	AL	Felling	Ulmus sp.	15	20	6	3.5	1
CLS	TC	Felling	Pinus pinea	15	42	7	5	1
ENE	AL	Pruning	Pinus pinea	20	79	14	5	1
FAN	AL	Felling	Pinus pinea	20	65	10	4	5
FRT	AL	Felling	Cupressus arizonica	15	49	5	5	1
GRM	AL	Pruning	Cupressus arizonica	15	60	9	5	10
INF	TC	Pruning	Pinus pinea	15	74	12	5	35
ITA	AL	Felling	Pinus pinea	19	71	11	2	1
ITP	AL	Pruning	Pinus pinea	19	63	10	5	1
LAT	AL	Pruning	Pinus pinea	16	72	14	3	1
LEM	TC	Felling	Pinus pinea	11	49	7	2	1
LQR	AL	Felling	Cedrus deodara	20	84	7	3	2
MAC	TC	Pruning	Cedrus libani	22	65	7	4	15
MAG	TC	Pruning	Pinus pinea	22	70	13	4	40
MTC	TC	Pruning	Pinus halepensis	14	57	7	5	35
OLI	AL	Pruning	Celtis australis	18	75	13	5	2
OTT	TC	Pruning	Magnolia grandiflora	15	50	11	5	65
PIG	TC	Felling	Pinus pinea	15	60	9	3	15
QRM	TC	Felling	Pinus pinea	15	70	8	3	5
SMC	TC	Felling	Cupressus sempervirens	12	37	3	5	2
SSB	TC	Pruning	Ailanthus altissima	17	53	9	4	60
TRF	TC	Felling	Laurus nobilis	12	45	6	4	30
TSC	AL	Felling	Eucaliptus sp.	16	70	5	3.5	10
VLR	TC	Pruning	Quercus ilex	17	85	14	5	5
VLS	AL	Felling	Pinus pinea	23	70	16	3	2
VTR	TC	Pruning	Tilia cordata	9	45	6	5	1

). Tree removals were performed by dismantling the tree, i.e., the trunk was cut up into logs that are tied and placed on the ground.

Most of the investigated yards were located in the city of Rome (Italy), one of the greenest European capitals. According to the available inventories, Rome has 4728 ha of green area [31], with a total of 312,583 individual trees present on public roads and in parks, with ten prevailing botanical genera (Pinus, Quercus, Robinia, Platanus, Ligustrum, Tilia, Ulmus, Prunus, Acer, Cupressus) that attain more than 73% of the total tree presence [32]. It is important to point out that data related to trees located in gardens, parks, and private areas are not included in the available inventory.

The works were carried out by 9 private companies, formed by expert professional arborists.

Differences in the sites’ ease of access and the simplicity of carrying out the work were recorded. For this last characteristic, a synthetic qualitative variable named “site”, varying from 1 to 5, was created ad hoc. The variable indicates the easiness of the work, mainly in relation to the descending of heavy logs during pruning and felling. A yard scoring “1” implies that the tree was situated close to a busy road, and all the logs must be secured with a rope during their descent, vehicles’ and pedestrians’ passage may eventually be stopped, and so on.

The distance of the biomass loading point from the trees varied from 1 m (complete accessibility to a truck for loading near the tree) to 65 m (the biomass must be transported by hand).

The weight of residual biomass was either estimated based on the volume of cut branches and logs stacked at the loading place or by directly weighing the wood with a hanging electronic weighing balance (Laumas, mod. Dten. 500/1). Conversion tables [33] were used to transform the volume into the fresh weight.

The following values were calculated for the data analyses: (1) Gross time, i.e., the total duration of work per each tree (h tree⁻¹); (2) obtained biomass per tree (Mg tree⁻¹); (3) biomass productivity, i.e., the fresh weight of residual biomass per hour (Mg h⁻¹); (4) hourly cost, i.e., the total cost per hour of work (€ h⁻¹); and (5) total cost per tree (€ tree⁻¹). These variables, the dendrometric data of 51 trees, the characteristics of the site, the hourly fuel consumption (L h⁻¹), the emissions of CO₂ per worked tree (kg tree⁻¹), and the energy consumption per worked tree (MJ tree⁻¹) constituted the base of data analysis (

Table 2. Main parameters obtained for each yard.

Yard Code	Gross Time [h Tree−1]	Biomass per Tree [Mg Tree−1]	Productivity [Mg h−1]	Yard Hourly Cost [€ h−1]	Total Cost [€ Tree−1]	Fuel Consumption [L h−1]	CO2 Emissions [kg Tree−1]	Energy Consumption [MJ Tree−1]
AA1	7.2	15.0	2.1	107.9	779.9	15.0	193.2	3877.3
AA2	4.7	14.0	3.0	157.4	745.9	27.8	251.4	4728.1
ACI	0.6	0.6	1.0	82.7	51.1	6.2	7.3	137.7
ALB	3.7	0.6	0.2	116.7	435.5	11.8	57.2	1575.3
ANA	4.9	4.2	0.9	44.5	219.0	0.6	1.2	92.3
BSP	6.8	4.0	0.6	40.3	271.9	0.1	0.2	33.0
BUS	7.1	5.4	0.8	77.1	546.6	6.8	92.1	1711.4
CA1	1.3	0.5	0.4	110.0	138.8	15.6	43.2	703.7
CLS	1.9	0.8	0.4	35.4	66.6	0.4	0.5	23.0
ENE	3.3	1.4	0.4	54.6	178.9	6.7	49.0	790.0
FAN	0.7	1.6	2.2	78.9	58.0	8.4	13.4	220.6
FRT	1.2	2.0	1.7	72.8	84.2	7.0	17.5	289.8
GRM	7.7	1.6	0.2	69.6	534.3	6.9	115.4	1886.8
INF	3.2	0.9	0.3	46.7	149.1	0.0	0.0	0.0
ITA	1.8	4.2	2.4	62.7	110.9	6.8	26.4	431.8
ITP	0.9	0.4	0.4	52.8	48.8	6.7	13.8	223.5
LAT	1.1	2.1	1.9	86.2	92.9	7.8	17.0	300.5
LEM	2.8	1.6	0.6	57.9	161.5	0.5	0.7	49.3
LQR	8.1	4.2	0.5	88.5	715.0	7.2	121.8	2084.6
MAC	5.1	0.6	0.1	50.1	255.4	1.2	3.4	48.5
MAG	3.6	1.0	0.3	35.1	124.9	1.2	1.9	27.1
MTC	2.9	0.6	0.2	31.7	91.6	0.0	0.0	7.5
OLI	5.4	2.5	0.5	96.7	524.1	7.4	75.8	1433.4
OTT	7.2	1.0	0.1	47.1	338.8	0.0	0.0	9.3
PIG	5.0	1.4	0.3	68.1	341.3	2.0	2.3	87.0
QRM	5.6	3.6	0.6	64.9	363.3	0.3	0.3	56.3
SMC	3.0	5.1	1.7	18.8	56.1	0.1	0.0	6.6
SSB	4.9	0.8	0.2	32.0	157.4	0.0	0.0	12.8
TRF	3.6	4.2	1.2	53.4	192.0	0.2	0.2	23.4
TSC	3.7	1.6	0.4	94.0	347.8	6.9	55.4	908.2
VLR	5.4	0.4	0.1	31.7	169.8	0.0	0.0	0.0
VLS	6.0	5.1	0.8	83.5	503.6	7.3	91.5	1569.4
VTR	2.2	1.9	0.9	31.7	71.0	0.0	0.0	0.0

). Furthermore, the CO₂ emissions per Mg of biomass (kg Mg⁻¹) and the energy consumption per Mg of biomass (MJ Mg⁻¹) were calculated.

In each yard, the machinery and equipment used, their power and fuel consumption, and the composition of work teams were recorded.

An analytical method that considered the fixed, variable, and labor costs was applied to evaluate the operating costs of each yard [34].

2.2. Time Work Study

The felling and pruning operations were sampled with time studies. A time study is a structured process of observing and measuring human work with a timing device to determine the time needed for the completion of the work task by an identified worker. The time study procedure entails three steps: (1) Partition of the work into small, easily measurable phases or elements; (2) measurement of those elements; (3) synthesis of those measured phases to obtain the duration required to complete the job [35]. Delays (divided into avoidable and unavoidable) include mechanical delays (breakdowns, tools replacement, all maintenance outside the typical preparation, and so on), operator delays (rest, breaks, physiological, smoke, and phone calls), and organizational and other delays, including waiting for the machinery, interference, survey and planning, and refueling [36].

After preliminary observations aimed at identifying appropriate elements that can be clearly recognized in subsequent events and are of a convenient length for measurement, five main elements (from the beginning of work to the transport of the residual biomass to the collection and loading point) were identified and separated:

(1) “Setting and stacking” (the sum of separate phases, occurring at the beginning and end of the work. It includes, for example, (i) the yard’s preparation, arranging the signage for the safety perimeter, the aerial platform approach to the tree, mounting of friction, wearing personal protective equipment, and dismounting or protecting objects under the tree, i.e., garden lamps; (ii) moving and stacking the residual biomass to the collection point, cleaning of the site, and packing up instruments and tools.

(2) “Cutting” (time that begins when the operator stops to move in the crown and ends when he makes a new movement in the crown; this time includes the use of either manual or motorized cutting equipment).

(3) “Shift” (time of operator’s movement inside the crown, by either using ropes or operating the platform).

(4) “Delay time for avoidable time losses” (DTA).

(5) “Delay time for unavoidable time losses” (DTU).

The time (and the costs) taken to load and transport the obtained biomass outside the work site was not taken into account in this study. The same researcher measured the work time for each operation carried out by an operator during his activity, using centesimal stopwatches and video recording.

2.3. CO₂ Emissions and Energy Consumption

The calculation of CO₂ emissions of every mechanized yard refers to the assessment of the CO₂ emissions generated by fuel consumption, assuming that the quantity of carbon contained in the fuel is completely oxidized into CO₂. The COPERT method proposed by the European Environment Agency [37] was adopted for the calculation, the formula of which is the following:

$$E_{C{O}_2}=44.01\times \frac{FC}{12.01+1.01\times {\tau }_{H:C}}$$

(1)

where E_CO2 is the calculated CO₂ (kg), FC is the fuel consumption (kg), and τ_H:C is the ratio between the number of hydrogen and carbon atoms in the fuel (~1.8 for petrol and ~2.0 for diesel).

Formula (1) was calculated for each piece of machinery employed (such as the aerial lifts) and motorized equipment (mainly chainsaws and blowers), taking into account the duration of the utilization of the machines in the yard.

The energy consumption of machinery was calculated by the following equation:

$$EC=F{C}_h\times LHV$$

(2)

where EC is the energy consumption in the unit time (MJ h⁻¹), FC_h is the hourly fuel consumption (L h⁻¹), and LHV is the low calorific value of fuel (38.85 MJ L⁻¹ for diesel; 32.51 MJ L⁻¹ for petrol; 3.6 MJ kWh⁻¹ for electric). Energy consumption was adjusted in relation to the duration of the utilization of the machine in the yard.

2.4. Statistical Analyses

A set of variables for each yard (including several site characteristics, trees’ size, fuel consumption, CO₂ emissions, energy consumption, cost of the yard, biomass, and work times) was analyzed through a Principal Component Analysis (PCA) in order to group similar yards and indirectly analyze which variable best contributes to differentiate treatments. The PCA was conducted using the software PAST [38].

To compare average values among treatments, an ANOVA (Analysis of Variance) parametric test was performed, after checking the distribution of the given variables. Not normally distributed variables (Shapiro–Wilk test, p-value < 0.05) were normalized according to the results of a Box-Cox test procedure, which showed that the best technique for data normalization for the selected variables was logarithmic transformation. A two-way ANOVA was performed to compare the effect of the method of accessing the tree crown (and, consequently, the intensity of mechanization) and the operation (felling or pruning) on nine dependent technical and economic variables (

Table 3. Results of ANOVA.

Parameter	Factors	Probability (p-Values)	Significance (1)
Gross time per tree (h tree−1)	System	0.5726
	Operation	0.608
	System × Operation	0.5176
Biomass per tree (Mg tree−1)	System	0.1680193
	Operation	<0.001	***
	System × Operation	0.9638159
Hourly productivity (Mg h−1)	System	0.006157	**
	Operation	<0.001	***
	System × Operation	0.532674
CO2 emission per tree (kg tree−1)	System	<0.001	***
	Operation	<0.001	***
	System × Operation	<0.001	***
CO2 emission per ton of biomass (kg Mg−1)	System	<0.001	***
	Operation	<0.001	***
	System × Operation	<0.001	***
Biomass cost per ton (Euro_Mg−1)	System	0.91624
	Operation	0.00634	**
	System × Operation	0.61045
Cost per tree (Euro tree−1)	System	0.2472
	Operation	0.3134
	System × Operation	0.5848
Energy consumption per ton of biomass (MJ Mg−1)	System	0.003523	**
	Operation	0.070683	.
	System × Operation	0.042441	*
Energy consumption per tree (MJ tree−1)	System	0.002418	**
	Operation	0.029844	*
	System × Operation	0.042119	*

⁽¹⁾ Significance codes of p-values: 0 ‘***’; 0.001 ‘**’; 0.01 ‘*’: 0.05 ‘.’.

).

To predict the gross time per tree (h tree⁻¹), the cost per tree (€ tree⁻¹), and the productivity (Mg h⁻¹), Multiple Linear Regression (MLR) modelling was used. MLR is a broadly employed method that allows one to predict the outcome of a response variable using several explanatory variables. For this aim, a set of eight independent variables (tree height, trunk diameter, crown diameter, site characteristics, system of work, operation, number of workers, and distance of the stacking point from the tree), which is easy to measure or estimate before the maintenance operations, were employed. For the absence or the presence of specific treatments, a value of 0 or 1 was used [39]. Then a backward stepwise regression was performed to fit the regression model. This procedure entails beginning with all candidate variables and testing the deletion of each variable until no further variables can be eliminated without a statistically insignificant loss of fit. The predictive capacity of the equation is given by the coefficient of determination (R²). The Akaike information criterion (AIC) was calculated in each step of the backward regression to determine which model best fits the data. The variance inflation factor (VIF) of the model’s terms was employed to assess the amount of multicollinearity in the model. The software R [40] was employed for the statistical analysis.

3. Results

The gross work time, residual biomass, and productivity in terms of Mg per hour of all yards are reported in Table 2. Data include yards managed in different ways and with different purposes, and they include both felling and pruning of different tree species.

The two work methods (aerial lift and tree-climbing) required similar gross work time, as well as the two studied operations (pruning and felling) (Figure 1).

As a consequence, the work time duration of the different work segments (Figure 2 and Figure 3) reflects the variability of work conditions in terms of worksite characteristics and yard organization. In the work time study, the total observation time amounted to 152.0 h.

A summary of these data is shown in Figure S1 where the average values of work phase durations are reported according to the different methods of work (aerial lift vs. tree-climbing) and operation (felling vs. pruning), respectively. The yards largely differ in terms of the time needed for completing the given operation, even if the sequence of the work phases and the equipment’s use were similar in all yards.

On average, the gross time was equal to 4.01 h tree⁻¹, with a longer duration in yards using tree-climbing (4.25 h tree⁻¹) than an aerial lift (3.80 h tree⁻¹) and for felling than pruning (4.19 and 3.81 h tree⁻¹, respectively). However, it is interesting to point out that these averages do not differ statistically (Figure 1 and Table 3).

Comparing yards that carried out operations with an aerial lift with yards that used tree-climbing (Figure S1), it was possible to observe that with tree-climbing, the phase of “setting and stacking” was longer than with an aerial lift. This is likely due to the time necessary for adorning the special personal protective equipment, the slings, etc., and to the time for launching the throwline (the first rope used to access the tree’s crown), which often requires several attempts to be successful. Furthermore, the distance between the tree and the loading point is significantly greater in tree-climbing yards than in the aerial lift ones, increasing the total time of the “setting and stacking” phase. Tree climbing is, in fact, the favorite (and frequently the only possible) method to work in sites where access to machines is not possible.

The tree-climbing yards showed a significant difference in terms of avoidable delay times, which were longer than using an aerial lift. This is likely caused by the platform operator’s reduced view of the crown, which sometimes can lead to the aerial lift becoming stuck in the branches for a while. Furthermore, in order to place the platform properly, the operator of the aerial lift needs to spend more time communicating with the ground workers than the tree-climber, who tends to work more autonomously.

The comparison between felling and pruning operations (Figure S1) shows substantial differences in the “shifting” work phase only, likely because, during felling, the operator must work along the tree, progressively shifting from the crown to the trunk.

Principal Component Analysis (PCA) was performed to ordinate the yards according to the characteristics that define their variability. According to the analyzed variables (fuel consumption in L tree⁻¹; energy consumption in MJ tree⁻¹; CO₂ emissions in kg tree⁻¹; total cost in € tree⁻¹; biomass production in Mg tree⁻¹; tree height in m; tree diameter in cm; total work time in h tree⁻¹; productivity in Mg h⁻¹; distance of the biomass collection point from the tree in m; site difficulty (score from 1 to 5)), the results of the PCA, summarized in the bi-plot graph of Figure 4, show the yards that can be grouped for similar characteristics. The PCA refers to the first two Principal Components (PC) that explain 74.3% of the total variance, and the loading values of the considered variables are reported in

Table 4. Loading values in each principal component and analyzed variables (major loadings are written in bold).

Variables	PC 1	PC 2
Fuel consumption [L tree−1]	0.38	0.00
Energy consumption [MJ tree−1]	0.38	0.00
CO2 emissions [kg tree−1]	0.38	0.00
Total cost per tree [€ tree−1]	0.35	0.29
Residual biomass per tree [Mg tree−1]	0.34	−0.16
Tree height [m]	0.29	0.06
D.b.h. [cm]	0.28	0.12
Total work time [h tree−1]	0.20	0.56
Biomass hourly productivity [t h−1]	0.19	−0.57
Distance to the collection point [m]	−0.10	0.45
Site difficulty	−0.28	0.20

.

The variables related to the mechanization intensity, all associated with the fuel consumption per each pruned or felled tree, are the “heaviest” variables in the first PC, followed by the total cost per tree and the weight of residual biomass obtained per tree. The second PC (which explains only 16.2% of the variance) is mainly influenced by variables linked to the work time and site characteristics. In general, almost all the yards in tree climbing are separated from the other yards due to their lower fuel consumption (and associated CO₂ emissions and energy consumption), lower cost per tree, and lower obtained biomass. Therefore, it seems possible to individuate the first group of five yards (AA1, AA2, LQR, BUS, and VLS) with a higher degree of mechanization, high fuel and energy consumption and CO₂ emissions, more obtained biomass, and higher cost per tree. All these yards carried out tree felling with an aerial lift. On the opposite side, the second group of yards includes seven out of the eight pruning yards (OTT, SSB, MTC, INF, MAG, VLR, and MAC), which carried out tree-climbing and are characterized by a low (or zero) mechanization intensity, low yield of biomass, and major distances between the stacking/collection point of the residual biomass and the tree. All the other yards are not clearly separated due to the complexity of associated variables.

Multiple Linear Regression (MLR) modelling to predict the gross time per tree (h tree⁻¹), starting from variables easily measurable before the work, did not result in a good model for this dependent variable. This result is likely due to yards’ variability, in terms of associated characteristics, and the difference in terms of botanical species. Considering the 33 yards involved in this study, no relation was found between productivity (in terms of Mg of biomass per hour) and the set of independent variables. However, the predictive MLR model that estimates the cost per tree (€ tree⁻¹) individuated three variables (operation, tree trunk diameter, and the number of workers) significantly related to the independent variables (p < 0.001) and returned an adjusted R² equal to 0.55.

In the case of felling, the biomass obtained is related to the diameter of the trunk and the height of the plant, while the biomass obtained from the trees’ pruning is not related to the size of the trees. In fact, no correlations were observed between the biomass obtained per tree and the measured dendrometric parameters, such as the diameters of the trunk and crown and the tree’s height. This is due to the fact that pruning can be performed with a different intensity, depending on the purpose for which it is carried out (dead branch removal, reshaping of the crown for stability reasons, etc.) and depending on the tree’s vegetative conditions.

Even though the trees obtained using an aerial lift provided more biomass (3.7 Mg tree⁻¹) than the trees obtained from tree-climbing (2.0 Mg tree⁻¹), it is interesting to point out that this difference was not statistically significant. Therefore, the amount of obtained biomass per tree does not clearly differ according to the techniques employed (i.e., aerial lift or tree-climbing) (Figure 5 and Table 3).

In contrast, the productivity parameter depends both on the operation and on the adopted system (Figure 5). The productivity of yards utilizing an aerial lift is approximately double that of tree-climbing yards (1.11 and 0.52 Mg h⁻¹, respectively) and it is greater in felling yards (1.14 Mg h⁻¹) than in tree-climbing yards (0.45 Mg h⁻¹).

Because of the different intensities of mechanizations, the observed yards clearly differed in terms of energy consumption and the quantity of CO₂ emitted. These parameters varied according to the system employed and the operation method. The yards employing felling required an average of 938 MJ tree⁻¹ while pruning operations required 430 MJ tree⁻¹. However, unitary energy consumption for produced biomass was higher in pruning yards (402 MJ Mg⁻¹) than in felling yards (230 MJ Mg⁻¹). CO₂ emissions follow a similar pattern, showing that the yards using felling carried out with an aerial lift emitted the maximum quantity of CO₂ (90.5 kg tree⁻¹) but an Mg of obtained biomass produced less CO₂ (23.3 kg Mg⁻¹) than am Mg obtained with pruning (41.6 kg Mg⁻¹) (Figure 6 and Table 3).

Regarding the costs (Euros per tree), similarly to the work duration (gross time per tree), a statistically significant difference between the yards that employed an aerial lift or tree-climbing was not observed, even if the use of an aerial lift is more expensive (346.84 Euros per tree) than tree-climbing (189.34 Euros per tree). The felling operations entail a total cost of 316.78 Euros per tree compared to 214.91 Euros for pruning. It is likely that the differences between averages were not statistically significant because of the wide variation among yards. The biomass is significantly more expensive when it is obtained by pruning (235.4 Euros per Mg) than by felling (98.0 Euros per Mg), but no difference appeared for the work system (Figure 7 and Table 3).

In the observed yards, the obtained biomass ranged from 0.9 to 1.3 Mg per tree (average 1.1 Mg tree⁻¹) in the case of pruning and from 3.1 to 5.4 Mg tree⁻¹ (average 4.4 Mg tree⁻¹) in the case of felling.

The city administration also provides the number of trees that have died and been pruned per year, starting from 2012. According to these figures, the average yearly tree mortality in Rome in the last eight years (2012–2020) was equal to 0.43%, and approximately 1.54% of trees were pruned each year (which corresponds to a pruning return time of approximately 65 years). In

Table 5. Potential biomass production (Mg) with different yearly mortality and tree pruning intensities in the city of Rome.

		Yearly Tree Mortality
		0.5%	1.0%	1.5%	2.0%	2.5%
Pruning cycle years	19	15,792	22,604	29,417	36,229	43,042
	16	17,475	24,288	31,100	37,913	44,725
	13	19,936	26,749	33,561	40,373	47,186
	10	23,873	30,686	37,498	44,311	51,123
	7	31,185	37,997	44,810	51,622	58,435

, the potential obtainable biomass was calculated assuming different rates of tree mortality (from 0.5 to 3.0%) and pruning return time (from 7 to 19 years). Regarding pruning, it was assumed that only half of all trees are being regularly pruned (those growing along streets). Assuming a 2% tree mortality rate [41,42] and a 10-year cycle of pruning, it was discovered that the urban trees of Rome could provide 27,250 Mg of fresh biomass per year from felling and 17,061 Mg per year from pruning. Considering a wood moisture content of approximately 45–50%, this quantity could feed a biomass plant for local district heating producing approximately 80,000–90,000 MWh_t or power a biomass cogeneration plant producing approximately 23,000–26,000 MWh_e and 35,000–45,000 MWh_t.

4. Discussion

The residual wood biomass from urban trees can increase the availability of renewable sources of bioenergy in local bioeconomies, a sector that is still understudied [43]. In urban forestry management, tree removal and pruning provide large amounts of wood biomass that could contribute to other available renewable sources and energy supply systems in operation (e.g., district heating and local cogeneration systems), providing a cost-effective renewable resource, also improving the circular economy on a local scale [44]. In this paper, we investigated these operations to determine their technical and economic features, potential productivity, and sustainability.

The time analysis showed various technical features of urban tree maintenance operations. In general, the observed yards showed a similar organization in terms of the work phase sequence and use of equipment. This allowed us to utilize the same time work elements for collecting the working time data and comparing the different methods employed for felling and pruning urban trees.

In urban work sites, numerous (and often unexpected) factors are involved in defining the work time. For example, the yards vary in (1) the distance between the tree and the loading point where the residual wood is stacked; (2) the accessibility of the site to machinery; (3) the occurrence of services around the tree (such as traffic lights, lamps, fences, etc.); (4) the presence of busy roads, etc. In several cases, delays are caused by parked vehicles that need to be moved for safety reasons, or due to interference from residents, bystanders, tree owners, etc. In addition, the personal ability and fitness of the operator may affect the working time. All these factors make it difficult to generalize the obtained results to obtain a common model of urban tree maintenance. However, some considerations are possible. Firstly, the two work methods (aerial lifts and tree-climbing) required similar gross work times, as well as the two studied operations (pruning and felling) (Figure 1). Consequently, the choice of the work method can be based on factors not directly related to the work time. An MLR analysis of the cost resulted in a predictive model, which considered three regressors (the number of operators, operation, and trunk diameter) that determine the final cost. However, the two methods clearly differ in their sustainability, due to the intensity of mechanization of aerial lift yards. In fact, with tree-climbing essentially being manual work, it produces low emissions of CO₂ and has low energy demand.

In this study, an attempt at modeling the yards’ productivity (in terms of tons obtained per hour) and the gross work time by means of an MLR analysis was unsuccessful, despite the fact that when similar models were applied to a set of yards associated with the operations of a single species (Pinus pinea), the obtained MLR models appeared reliable and significant [27]. Further and more in-depth analyses are needed to consider this variability in the models.

The systematic review of the studies on the subject by Vogt et al. pointed out [29] that only 15.6% of the papers included the costs of maintenance and management of urban trees. The most discussed topics regarded environmental aspects, while the analysis of the explicit costs for the pruning/felling is treated in more general terms with reference to the municipal budget expenses for urban green management [45]. The costs of urban tree pruning represent, by definition, the most expensive maintenance operation in the context of urban green management programs [45,46]. According to Kielbaso [47], this operation represented 28–30% of the municipal tree care budgets of US cities. Tschantz and Sacamano [48] reported average pruning costs of $130.04 per tree, values compatible with the average values reported in this paper (€ 215 per tree), considering the different time references.

Regarding the availability of residual woody biomass, since the existing inventory of urban trees present in the city of Rome is not complete, only a rough estimation of the potentiality of biomass can be attempted. In this context, developments in urban forest research methods that can offer more accurate and efficient methods for quantifying and estimating urban trees and biomass are highly desirable [49].

In this study, it was calculated that the urban tree maintenance activities can provide a total amount of approximately 44,300 Mg per year, corresponding to 9.4 Mg ha⁻¹ of green area. This quantity is comparable to an estimation made in the city of Tartu [19] where Raud and coauthors assessed a potential production of 11.5 Mg ha⁻¹, and higher than 4.8 Mg ha⁻¹ from Sperandio and coauthors [24]. Considering that the municipality of Rome is equal to 1285 km², the estimated biomass (0.34 Mg ha⁻¹) is lower than estimates made in cities in the U.S.A. by MacFarlane [15] (0.8 Mg ha⁻¹), Nowak and coauthors [16] (1.2 Mg ha⁻¹), and Timilsina and coauthors [17] (2.0 Mg ha⁻¹).

5. Conclusions

In conclusion, the choice between using an aerial lift or climbing with ropes to access the trees’ crowns for maintenance depends on several factors. For example, tree-climbing is often the only possibility in urban sites without access to aerial platforms, but it requires qualified and skilled personnel. At the same time, aerial lifts appear to be the best choice when trees are not easily accessible by climbers (i.e., hazardous trees with stability problems) and when trees are aligned in rows. Furthermore, the cost of the two methods differs, but this aspect is not generally applicable because of the high variability of the total cost of each yard (especially among yards employing felling with an aerial lift).

From an economic point of view, it was shown that tree-climbing was less expensive because the method entails a low level of mechanization, even if this difference was not statistically significant. However, tree-climbing is clearly more sustainable from an environmental perspective because the CO₂ emitted and the energy consumption were markedly lower than yards that employed an aerial lift.

As for the potential biomass available from urban trees in the city of Rome, it is certainly an aspect to be investigated and taken into greater consideration with respect to the current orientation of the municipal administration, which appears not particularly attentive to the exploitation enhancement of this residual biomass. The latter approach, in fact, only provides for the expensive disposal of biomass in specialized landfills, thus worsening the overall economic balance of the management of the urban green in the city. The comparison of our results with previous research should only be speculative, because numerous and variable factors (tree species, geographical and environmental conditions, scale of estimation—local, regional, and national—techniques, and the organization of biomass exploitation) are considered in each study, making any comparison very complicated.