Some higher education organisations have already paid attention to tree planting and maintenance on campus, and undertaken tree surveys to produce reports like “Enhancing the benefits of trees on Campus” from the University of Leeds (Gugan et al., 2019), “Tree Management Strategy” from the University of Sheffield (Winnert and Henderson, 2020), “Tree Trail” of the University of Manchester (2021), “Tree Campus USA” (2008), “Tree Protection Standards” of the University of Kentucky (2017) and “Tree Preservation” of the University of North Texas (2009). But no previous project has wholistically assessed the terrestrial carbon stock of both trees and soils, in the greenspace of a university campus as a function of land cover to assess strategies for institutional carbon off-setting. Referencing our survey, on the campus of Newcastle University, the 0–30 cm topsoil presents a carbon storage per surface area on average 2.5 times higher than tree biomass. In terms of terrestrial carbon augmentation, introducing more trees presents the biggest opportunity as it not only adds the additional biomass carbon of trees, but also augments soil carbon according to our survey results (Table 1).
4.1 Topsoil carbon content and mineral compositions
Across the central campus of Newcastle University, as we expected, topsoil carbon storage is statistically greater in urban woodland than lawned parkland and the suburban sports area. This is a likely consequence of extra organic matter inputs that includes leaves and mulches, and the contribution of shrubs that tend to improve soil carbon content as reported previously (Edmondson et al., 2014). STC values in this study are generally higher than other reports conducted over 0–30 cm turf grass soils with tree cover in urban Melbourne (Livesley et al., 2016), urban green areas in Berlin (Richter et al., 2020), and Helsinki urban parks (Lindén et al., 2020). The soil carbon of lawn with some free-standing trees in the urban campus exceeds the values for sports ground. This could be because most sampling points in urban lawn are in closer proximity to trees (the distance ranged from 3.3 to 15.4 m, average 7.6 m), while on average the distance of soil points to the closest tree in Heaton Sports Ground is 31.6 m (nearest: 8.5 m, farthest: 75.3 m). The presence and abundance of soil microbes (Nacke et al., 2016), and soil chemical properties such as the concentration of metals (Desta et al., 2018), can be importantly influenced by the distance between soil sampling site and the tree trunk, which are all driving factors for soil carbon formation. As explained by Livesley et al. (2016), tree roots not only modify soil compaction and improve nutrient cycling, but enhance organic input to the ground, and strengthen soil carbon content. This discussion might be extended by considering land management practices and other factors, e.g. fertiliser application, frequency of grass cutting, tree ages and recreation of original soil types. Great differences in carbon storage between various land-cover classes have previously been discussed (Lal and Augustin, 2011, Lindén et al., 2020, Pouyat et al., 2002, Richter et al., 2020). In New York City, surveyed soils reported by Pouyat et al. (2002) showed a higher SOC concentration (38%) under low-density built-up lands than in commercial lands. The reasons resulting in this effect may be the differences in management frequency and lack of soil disturbance.
The mineralogy of the soils in this survey is dominated by quartz, with subsidiary kaolinite and feldspar, reflecting the mineralogical composition of the geological parent material (glacial till or alluvium derived from Carboniferous sediments). SIC was reported from all soil samples in this study, and normally the source of SIC should be calcite (CaCO3) (Jorat et al., 2020). However, based on the diffraction patterns in Fig. 2, calcite (CaCO3) is only reported for 2 samples, reflecting the relatively high detection limit for routine X-ray diffraction. The proportion of TIC relative to total carbon is higher for the urban soil samples reported here (24–30%) than has been observed for agricultural soils (e.g. 10%; Wang et al., 2021). This is consistent with the observations reported by Washbourne et al (2015) that carbonation of materials derived from construction and demolition is a rapid process in urban soils, and needs to be recognised as a dynamic and manageable carbon stock.
4.2 Tree growth for different species on the city campus
Amid a total of 67 different tree species, Large-Leaved Lime occurs more frequently (20.1%) than other species found in this survey. Within our dataset, a clear trend of larger DBH, tree height and tree cover area is shown with increasing age classifications. As the first and second largest population in this study, the tree height of mature Large-Leaved Lime (16.58 m) and Sycamore (14.73 m) is lower than the mean value from other 10 British cities (Lime: 18.1 m; Sycamore: 20.7 m) (2 sites in Wales, 6 sites in Southern England, 2 sites in Southern Scotland) (Hand et al., 2019); similarly, the tree heights of mature Norway Maple (11.82 m) and Ash (13.95 m) in central Newcastle are 31% and 33%, respectively, shorter than the trees growing in other British cities (Hand et al., 2019). Again, among semi-mature trees, the mean height of Large-Leaved Lime and Norway Maple summarized by Hand et al. (2019) are still 17% and 29% greater than tree parameters in this report, respectively. In some species, the tree height peaked in the early-mature age classification and remained static into the mature stage (e.g. Sycamore, Swedish Whitebeam), based on tree characteristics measurements (Table S3) (Liepiņš et al., 2016). Generally, Norway Maple and Sycamore from mature age classification in Greater Manchester (Scholz et al., 2016) all express a variably thicker trunk (13–40%) than their counterparts in Newcastle, while fully-grown Lime and Ash from these two cities possess similar features with respect to the diameter of trunk.
For Silver Birch, on average, the trees in Newcastle are shorter than 22-year old trees in Finland, Estonia, Latvia and Russia; whereas, regarding DBH, the Newcastle group is greater than these four Baltic groups (Viherä-Aarnio and Velling, 2017). Comparing with the study of 7,768 Silver Birch in southern Finland (Kilpeläinen et al., 2011), Silver Birch in our survey are 2–3 cm larger in diameter and 6–7 metres shorter in height. With respect to urban Ash in Newcastle, Latvia Ash from the 80–100 years-old group (Liepiņš et al., 2016) has an up to 14 cm smaller average DBH; conversely, relating to mean height. Ash in Latvia is almost double the height of trees in Newcastle (Liepiņš et al., 2016).
Mean environmental temperature, precipitation, sunlight time and air moisture, affected by differences in climate, are all vital factors for tree growth and the development of different tree dimensions (Hand et al., 2019). The reasons for the different DBH values from the same tree species between Newcastle and other European cities may lie in the tree’s ability to adapt to different photoperiodic conditions caused by latitude, which in turn could explain the trunk diameter variation between Newcastle and cities in more southern regions of the UK (Hand et al., 2019, Scholz et al., 2016). The decreasing tree stem height when exposed to stronger winds seems to hold true (Kronfuss and Havranek, 1999), which may thus be related to the occurrence of taller trees in other England cities compared to Newcastle (Hand et al., 2019, Scholz et al., 2016). Although wind speed was not measured in this report, a comparable dataset can be referenced (Weather Spark): average wind speeds in London and Manchester show a 6–15% and 5–13% weaker pattern than Newcastle, respectively. High wind speed hampers the growth of tree height: not just by escalating the risks of falling down or the loss of branches, but also by cooling of air, soil, leaves and meristems which are potential drawbacks during the vegetation period (Kronfuss and Havranek, 1999). It should be emphasized that tree distribution in our project is not dense because most of the trees are planted along pathways and roads where more open spaces are provided, which also means each tree is less protected by its neighbours from strong winds.
4.3 The capacity of carbon capture for different tree species
The amounts of carbon stored by 490 trees calculated in this study by using allometric biomass equations is 223 tonnes. In Newcastle, urban tree carbon storage averages 76.6 tonnes per hectare canopy. Carbon storages in our urban campus survey varied substantially among the eight largest number of tree species from 2.94 to 14.67 kg·m− 2, which influences local ecosystem functions (Lal and Augustin, 2011, Nowak et al., 2013), and can inform tree species selection for carbon accumulation (Burton et al., 2021, Edmondson et al., 2014, Ennos et al., 2020, Hand et al., 2019). In our analysis, some trees which are not of the main populations are likely to store larger quantities of carbon, such as the carbon storage per m2 of tree cover from Ash and Swedish Whitebeam (Fig. 4). This is because tree tissues (root, stem, branch, foliage, etc) of these species may have a higher carbon density (Widagdo et al., 2021), and probably these trees also face a less suppressed growth condition caused by impervious surfaces and local climate (Richter et al., 2020).
Furthermore, tree age classifications play an imperative role in carbon stock outcome. The research from ourselves and Hand et al. (2019) demonstrated that carbon storage of newly planted trees is comparatively less than that of fully established trees in urban areas. For the eight largest population species modelled in our work, carbon stock increases with each successive age classification, slowly in some species (e.g. Norway Maple) but faster in others (e.g. Silver Birch) (Table 3). Carbon stock varies not just due to different tree species, but also the climate features of sampling sites. Greater tree carbon stock values were found in other British cities (Hand et al., 2019) than the value in Newcastle, which probably occurs as most cities surveyed in that study are in more southerly, sunnier and warmer locations with relatively more sunlight, compared with north-eastern England, which benefits enzyme activity and provides more time for photosynthesis (Hand et al., 2019). One noted point is that the choice of allometric biomass formulas can lead to a diverse range of results when evaluating carbon storage performance in vegetation, despite inputting the same dimensions (Lal and Augustin, 2011). By using three sets of biomass equations, Vorster et al. (2020) demonstrated a substantial uncertainty of up to 75% for the estimated biomass of three tree species. Zhou et al. (2015) suggested that most biomass equations were developed based on forests, probably causing a disparity on estimating carbon stock of free-standing trees, like individual trees on the Newcastle University campus.
Tree health issues, including cavities, dead or dying branches (Boa, 2003), winter burn, fungal diseases, infestation (International Society of Arboriculture, 2020), and soil impaction (Sanders et al., 2013), importantly affect whether the trees can perform well for carbon storage. The occurrence, frequency, and out-break scale of tree health problems are combined consequences of inappropriate planting locations, wrong tree species choice, and lack of adequate planning and maintenance. For example Limoges et al. (2018) have shown that 28.54% of total tree growth condition was attributed to variables associated with street levels, geographic orientation (tree position in relation to the street), type of location, or presence of an obstruction, while 65.51% of the variation was led by tree species choice. Additionally, some urban locations are characterized as impervious and so not suitable for particular trees (Morani et al., 2011). Studying 45,500 trees across cities in the USA, Sanders et al. (2013) found significantly larger tree DBH for planting strips and non-limited soil, compared with tree pits. Conversely, the damage to infrastructure like impermeable pavements, roads and kerbs containing drainage systems caused by trees is considerable (Scholz et al., 2016), and another important consideration to take into account when selecting tree species. Land use history is another key factor influencing the appearance of trees (Heusinkvelt, 2016).
4.4 Carbon stock potential of urban greenspace
Many local authorities nowadays pursue a larger urban tree cover (City of Durham, Plymouth City Council, Newcastle City Council, 2019), but as scenarios 1–4 in Table S5-8 in SI show, the possibility of offsetting significant parts of annual carbon emissions at an institutional or city-scale is limited by the current availability of urban greenspace resources. Therefore, the involvement of rural areas through climate partnerships may become necessary to achieve net-zero targets of city institutions (Gebre and Gebremedhin, 2019). Previous work (Wang et al., 2021) showed that land use change at two research farms managed by Newcastle University could make a much more substantial contribution towards offsetting institutional carbon emissions (up to 50%), than the urban campus greenspace analysed in this study (up to 1%). Similarly, city councils could seek assistance with carbon offsetting from rural partners. In return for assisting city councils with carbon abatement by planting trees or restoring peatlands, rural councils could benefit from ecosystem service payments and city council expertise to improve the rural provision of transport services and infrastructures, the upgrade of healthcare and education facilities, etc.
Multiple ecosystem services operated by urban trees have been positively mentioned (De Villiers et al., 2014, Hand et al., 2019, Jenkins et al., 2003, Lindén et al., 2020). For instance, street trees reduce glare reflected from the pavement, mediate a regional urban heat island, provide shade and local cooling (Landscape Architects- Bangkok, 2018), reduce air pollution (Hand et al., 2019), and beautify cities (Newcastle City Council, 2019); conifers can form a windbreak or protect residential privacy because the needle-leaf densely grows from the bottom of the conifer stem and is evergreen (Green, 2017, Lindensmith, 2013); broadleaved trees lose leaves in the fall, which improves ground heat intake from the winter sun (International Society of Arboriculture, 2020, Nowak et al., 2013, Spengler and Ellis, 2019). Despite diverse attractions for tree plantation, considerations related to the increase of tree numbers on campus still should be balanced with other business interests (Table 5).
The limited availability of urban greenspace resources for carbon offsetting also highlights the importance and necessity of using diverse nature-based approaches (Edmondson et al., 2014, Lal and Augustin, 2011). For instance, biochar (the product of organic biomass combusted in a no or limited oxygen pyrolysis environment), as a soil amendment, potentially enhances carbon especially when using cuttings from the maintenance of urban trees or dead woods (Lal and Augustin, 2011). Furthermore, opportunities for managing soil inorganic carbon should be emphasized, as soil inorganic carbon is 24–30% of soil total carbon according to the present study. Urban brownfield land, where areas have previously been used for industrial or commercial activity and become vegetated after demolition (albeit temporarily), with or without a specific design, can promote the soil’s inorganic C sink. Following the observed accumulation of 23 tonnes·ha− 1·yr− 1 inorganic carbon at a demolition site (Washbourne et al., 2015), an accumulation of topsoil inorganic carbon of 16 tonnes·ha− 1·yr− 1 has been reported across 20 brownfield sites in northern England, largely because of calcite precipitation, which emphasizes the importance of soil carbonation to remove CO2 (Jorat et al., 2020). The limits to what can be achieved with nature-based carbon off-setting in urban greenspace also emphasizes the need to substantially reduce emissions when building a green city, such as switching to renewable energy systems, popularizing low carbon transport infrastructures (Newcastle City Council, 2020), deploying eco-homes (Pickerill, 2017), eco-driving and eco-charging (Ortega-Cabezas et al., 2021).