A GIS-Based Analysis of the Carbon-Oxygen Balance of Urban Forests in the Southern Mountainous Area of Jinan, China

Abstract

The urban forest is a vital carbon sink base in a city. The carbon-oxygen balance capacity of urban forests affects the urban carbon cycle and urban sustainable development. The forests maintain the carbon-oxygen balance through carbon sequestration and oxygen release (CSOR) processes. The carbon-oxygen balance of urban forests is formed by offsetting the carbon release and oxygen consumption (CROC) process of urban social activities through the CSOR process of forestland. Based on GIS technology, this research used the carbon-oxygen balance model to analyze the CROC and CSOR and study the carbon-oxygen balance of urban forests in the southern mountainous area of Jinan, China. The results of the increase in the carbon-oxygen balance coefficients showed that the carbon-oxygen balance capacity of urban forests showed a decreasing trend, with the decrease in forest area and the increase in fossil energy consumption from 2000 to 2019 in the southern mountainous area of Jinan. To increase the urban carbon-oxygen balance capacity, the city should expand its woodland area to improve the urban forest’s CSOR capacity and adjust the urban energy consumption structure to reduce the CROC of urban social activities.

1. Introduction

Global warming has a significant effect on global ecosystem structure and cyclic processes, which substantially negatively impact the development of human society [1]. The increase in CO₂ in the air leads to global greenhouse and urban heat island effects, which adds to global warming [2,3]. As an essential carbon sink base, forest ecosystems can alleviate the global greenhouse effect by regulating the global carbon-oxygen balance [4,5,6]. Forests maintain the global carbon-oxygen balance through carbon release and oxygen consumption (CROC) processes and carbon sequestration and oxygen release (CSOR) processes [7,8]. Therefore, research on forests’ carbon-oxygen balance capacity can help people understand and grasp the relationship between forest changes and ecosystems to take measures to mitigate greenhouse and urban heat island effects [9].

As the central area of human activities, the sustainable development of cities needs to solve a series of ecological and environmental challenges caused by the massive release of CO₂, such as global warming, climatic anomaly and extreme natural events [2,10,11]. Urban forests are essential to increasing urban carbon sinks and maintaining the urban carbon-oxygen balance [12]. Therefore, the assessment of urban forests can help describe the urban carbon–oxygen balance system and guide urban sustainable development planning.

Previous studies on urban forest carbon-oxygen balance have focused mainly on two aspects (as shown in

Table 1. Previous studies on urban forest carbon-oxygen balance.

Research Direction	Author (s)	Study Area	Date	Method	Result
Carbon sequestration amount of different forest plant species and land-use types	Zheng et al. [13]	New England, USA	2013	Net forest carbon sequestration methodology	Urban forests’ carbon sequestration amount
	Kovacs et al. [14]	New York, USA	2013	National tree benefit calculator model	Three types of trees’ carbon sequestration amount
	Wang et al. [15]	Tianjin, China	2014	Portable photosynthesis test system	CSOR capacity of 25 afforestation trees
	Blair et al. [16]	Sydney, Australian	2017	I-Tree Eco tool	Carbon sink amount of street trees
	She et al. [17]	China	2017	Carbon footprint method	Crops’ carbon sink capacity
	Qiu et al. [18]	China	2020	National forest inventory	Carbon sink of economic, shrubbery and bamboo forests
	Ye and Chuai [19]	China	2022	net ecosystem productivity models	Carbon sink effects of built-up land expansion
	Ghosh et al. [20]	Gangtok, Sikkim	2022	Integrated valuation of ecosystem services and trade-offs method	Carbon sequestration capacity of urban land-use land-cover
Carbon-oxygen balance analyzes of urban forests	Yin et al. [21]	Xiamen, China	2010	Urban carbon-oxygen balance model	Urban CROC measuring
	Zhao et al. [22]	Hangzhou, China	2010	Volume-derived biomass equations	Carbon balance between urban forests and industrial energy
	Lu et al. [23]	Jinan, China	2006	Green equivalent method	Green space’s carbon-oxygen balance pattern
	Tang et al. [24]	Shenyang, China	2019	Carbon-oxygen balance method	Urban carbon-oxygen balance assessment

). The first aspect is quantitative research on the amount of carbon sequestration in different forest plant species and land-use types [13,14,15,16,17,18,19,20]. The other aspect is assessment research on the carbon-oxygen balance of urban forests [21,22,23,24]. These studies analyzed urban forests’ carbon-oxygen balance capacity in relation to human activities and carbon sink capacity. However, these studies lacked a comprehensive study combining the ecological processes of CROC and CSOR in urban forests. In addition, due to the diverse land-use types, scattered distribution and complex environmental structure of the urban forest ecosystem, the assessment results of urban forest carbon-oxygen balance capacity have low precision and high uncertainty under limited forest data [25].

Geographic information system (GIS) technology can provide technical support for the accurate acquisition and evaluation of the urban forest carbon-oxygen balance of urban forest data [26]. With the GIS platform, this study analyzed the carbon-oxygen balance capacity of the urban forest using the urban carbon-oxygen balance model, which could provide a data basis for evaluating and predicting the urban forest carbon-oxygen cycle and the carbon-oxygen balance process [27,28]. The urban carbon-oxygen balance model estimates the annual CSOR and CROC of urban forests based on the forest area obtained from remote sensing, the biomass per unit area and the CSOR and CROC per unit biomass. The results will help us better understand the carbon-oxygen balance in the urban forest ecosystem, providing a carbon data basis for the realization of sustainable urban planning. The results will contribute to our understanding of the urban carbon–oxygen balance system and provide a carbon data basis for sustainable urban planning, with Jinan, China as a case study.

2. Materials and Methods

2.1. Study Area

The southern mountainous area is located south of Jinan city center, with a total area of 571.39 km² (as shown in Figure 1) [29]. The terrain in this area is high in the south and low in the north [29,30]. As the southern mountainous area is in a semihumid continental monsoon climate zone, its average annual temperature is 10–20 °C, and the average annual rainfall is 630–750 mm. The southern mountainous area is a nature reserve of urban forest, including woodland, sparse woodland, arable land and wetland [29]. At present, the simple forest vegetation structure leads to a weak urban carbon-oxygen balance capacity in the urban forests in the southern mountainous area. Therefore, it is urgent to analyze the carbon-oxygen balance capacity of the southern mountainous area to control urban forests’ CSOR to enhance Jinan’s ecological environment.

2.2. Data Sources

The carbon–oxygen balance of urban forests is the balance between the CSOR of urban forest land use and the CROC of urban fossil energy. Hence, the high-resolution satellite images from Landsat-5 and Landsat-8 were used to collect urban forest land-use information and the “Jinan Statistical Yearbook” was used to collect urban fossil energy information [29,31]. The study also included the “Multiple in One Planning for Southern Mountainous Area of Jinan City (2017–2035)” to classify urban forest land-use types [30]. The Landsat satellite images are a series of images obtained from the United States Geological Survey (https://www.usgs.gov/ (accessed on 7 May 2020)) [31]. The data on urban forest land use and urban fossil energy are available on the official Jinan website of the Chinese government (http://www.jinan.gov.cn/ (accessed on 7 May 2020)). The details of relevant data processing are mentioned in the following sections.

2.3. Image Pre-Processing and Remote Sensing Interpretation

The urban forest land-use information was obtained from remote sensing images by image pre-processing and interpretation on the GIS platform. First, the remote sensing images were accurately pre-processed to obtain data on urban forest land use, including geometric correction, image registration, radiometric calibration, atmospheric correction, and image enhancement. Second, unsupervised classification and visual interpretation were performed on the processed images to obtain urban forest land-use maps and remote sensing information GIS-based databases. Finally, raster data of each urban forest land use is taken as the input layer, and the conversion tool of the GIS platform is used to output the vector urban forest land-use map.

2.4. Carbon-Oxygen Balance Method of Urban Forests

The carbon-oxygen balance model of the urban forest was established by studying the carbon-oxygen budget processes of the CROC of urban socioeconomic activities and CSOR of urban forests. In the southern mountainous area, the CROC mainly considers fossil energy consumption in urban social activities, and the CSOR considers the CO₂ absorption and O₂ release of various forest land-use types, such as woodland, sparse woodland, arable land, and wetland [30]. The fossil energy data came from the government’s statistical literature, and the various forest land-use type data came from the data obtained from satellite remote sensing images on the GIS platform [29].

2.4.1. Carbon Sequestration and Oxygen Release Method

(1)

Carbon sequestration model

The total carbon sequestration amount of the urban forest was obtained by calculating the actual area and carbon sequestration per unit area of forest land use [32]. The calculation formula is as follows:

$$C_s={\displaystyle \sum }_{i=1}^k{C}_i\cdot A_i$$

(1)

where $C_s$ is the annual carbon sequestration of the urban forest (t/yr); $C_i$ is the annual carbon sequestration per unit area of urban forest land-use type i (t/(yr·km²)); and $A_i$ is the total area of urban forest land-use type i (km²).

(2)

Oxygen release model

The total oxygen release amount of urban forests was obtained by calculating the actual area and oxygen release per unit area of four forest land-use types of the urban forest [32]. The calculation formula is as follows:

$$O_r={\displaystyle \sum }_{i=1}^k{O}_i\cdot A_i$$

(2)

where $O_r$ is the annual oxygen release of the urban forest (t/yr); $O_i$ is the annual oxygen release per unit area of urban forest land-use type i (t/(yr·km²)); and $A_i$ is the total area of urban forest land-use type i (km²).

It is worth noting that the carbon sequestration ($C_i$) and oxygen release ($O_i$) of urban forest land-use types in Equations (1) and (2) were estimated by referring to the related literature and the light summation equation, and the specific parameters are shown in

Table 2. Carbon sequestration and oxygen release of urban forest land-use types.

Types of Urban Forest Land Use	Carbon Sequestration or Oxygen Release	Parameter Value (t/(yr·km2))
Woodland	Carbon sequestration ($C_i$)	903
Sparse woodland		493
Arable land		686
Wetland		450
Woodland	Oxygen release ($O_i$)	665
Sparse woodland		363
Arable land		505
Wetland		331

[33].

2.4.2. Carbon Release and Oxygen Consumption Model

(1)

Carbon release model

Fossil energy consumption is the primary carbon release source of urban social activities. In this study area, urban fossil energy comes from coal, gasoline, diesel, and liquefied petroleum gas [32,34]. Therefore, the calculation formula of the carbon release amount is as follows:

$$C_r={\displaystyle \sum }_{m=1}^n{C}_m\cdot B_m$$

(3)

where $C_r$ is the annual direct carbon release (t/yr); C_m is the annual consumption of energy m, which needs to be converted into standard coal consumption; and B_m is the converted carbon release coefficient of energy m.

(2)

Oxygen consumption model

The urban fossil energy oxygen consumption amount is calculated using the following formula [32]:

$$O_c={\displaystyle \sum }_{m=1}^n{C}_m\cdot P_m$$

(4)

where $O_c$ is the annual oxygen consumption of urban combustion material (t/yr); C_m is the annual consumption of energy m, which needs to be converted into standard coal consumption; and $P_m$ is the converted oxygen consumption coefficient of energy m.

The converted carbon release coefficient ($B_m$) and converted oxygen consumption coefficient ($P_m$) in Equations (3) and (4) were estimated by referring to the related literature and the specific parameters are shown in

Table 3. Urban carbon release coefficient and oxygen consumption coefficient.

Project	Convert Standard Coal Coefficient (t/t)	Converted Carbon Release Coefficient (t/t) (${\mathit{B}}_{\mathit{m}}$)	Converted Oxygen Consumption Coefficient (t/t) (${\mathit{P}}_{\mathit{m}}$)
Coal	0.7143	2.492	2.13
Gasoline	1.4714	1.988	3.428
Diesel	1.4571	2.167	3.428
Fuel oil	1.4286	2.219	3.428
Liquefied petroleum gas	1.7143	1.828	3.636

[35,36].

2.4.3. Carbon Release and Oxygen Consumption Model

The actual and potential role of current urban forests in reducing CO₂ from urban socioeconomic development can be assessed by comparing the total amount of urban fossil energy consumption’s CROC with the total amount of urban forests’ CSOR [32,37,38,39]. Therefore, this research constructed the oxygen balance coefficient and carbon balance coefficient to indicate the state of the carbon-oxygen balance of urban forests and assess the current support of urban forest systems for the sustainable development of urban populations and social economies. The assessment model is as follows:

$$B_c=\frac{C_r}{C_s}$$

(5)

$$B_o=\frac{O_c}{O_r}$$

(6)

where $B_c$ and $B_o$ are the carbon balance coefficient and oxygen balance coefficient, respectively; $C_r$ is the annual direct carbon release; $C_s$ is the annual carbon sequestration of urban forest; $O_c$ is the annual oxygen consumption of urban combustion material; and $O_r$ is the annual oxygen release of urban forest.

3. Results

3.1. Urban Forest Land-Use Change

As the main covered area of the urban forest in Jinan, the southern mountainous area’s forest land uses include woodland, sparse woodland, arable land and wetland, and they maintain the urban carbon–oxygen balance [29,30]. Since the annual CSOR of different land-use types of urban forests are different, a change of forest land-use type and structure in urban forests will affect the carbon–oxygen balance capacity of urban forests [36,39]. According to the distribution maps of urban forest land use (Figure 2), the patches of different urban forest land-use types are scattered and fragmented. The areas of forest land-use patches were quite different, and the descending order of forest patches was woodland, sparse woodland, arable land and wetland. From 2000 to 2019, the area of the woodland area increased by 19.92 km², and the woodland and arable areas decreased by 18.61 km² and 7.76 km², respectively. The total forestland area and land utilization rate showed a decreasing trend (from 97.57% to 96.45%) (as shown in

Table 4. Urban forest land-use data in the southern mountainous area in 2000, 2010 and 2019.

Land-Use Types	2000		2010		2019
	Land Area (km2)	Proportion	Land Area (km2)	Proportion	Land Area (km2)	Proportion
Woodland	252.12	44.12%	266.41	46.62%	272.04	47.61%
Sparse woodland	124.96	21.87%	111.2	19.46%	106.35	18.61%
Arable land	175.55	30.72%	169.98	29.75%	167.79	29.37%
Wetland	4.87	0.85%	4.97	0.87%	4.91	0.86%
Total	557.5	97.57%	552.55	96.7%	551.09	96.45%

). This result indicates that the increase in woodland was not balanced with the decrease in other forest land-use types, which led to a decrease in the carbon-oxygen balance capacity in the southern mountainous area from 2010 to 2019.

3.2. Carbon Sequestration and Oxygen Release of Urban Forests

The CSORs of urban forests in the southern mountainous area in 2000, 2010 and 2019 were estimated using the total carbon sequestration and oxygen release from each urban forest land use (

Table 5. Urban forests’ CSOR in 2000, 2010 and 2019.

Land-Use Types	CSOR * in 2000		CSOR in 2010		CSOR in 2019
	Carbon Sequestration (104t)	Oxygen Release (104t)	Carbon Sequestration (104t)	Oxygen Release (104t)	Carbon Sequestration (104t)	Oxygen Release (104t)
Woodland	22.77	16.77	24.06	17.72	24.57	18.09
Sparse woodland	6.16	4.54	5.48	4.04	5.24	3.86
Arable land	12.04	8.87	11.66	8.58	11.51	8.47
Wetland	0.22	0.16	0.22	0.16	0.22	0.16
Total	41.19	30.34	41.42	30.50	41.54	30.58

* The CSOR is defined as carbon sequestration and oxygen release.

). From 2000 to 2019, the amount of carbon sequestration in the southern mountainous area increased from 41.19 × 10⁴ t to 41.54 × 10⁴ t, and the amount of oxygen release increased from 30.34 × 10⁴ t to 30.58 × 10⁴ t. The increase in CSOR in woodland compensated for the decrease in CSOR in sparse woodland and arable land.

3.3. Carbon Release and Oxygen Consumption

The total area of Jinan is 10,244 km², and the area of the southern mountainous area is 571.39 km². According to the principle of equal area distribution, the fossil energy consumption of the southern mountainous area, which accounts for 5.58% of Jinan’s area, should come from 5.58% of the annual fossil energy consumption of Jinan, China. The CROCs of the southern mountainous area in 2000, 2010 and 2019 were estimated using the total carbon release and oxygen consumption from each fossil energy consumption (

Table 6. Southern mountainous area’s CROC in 2000, 2010 and 2019.

Project	Year	Jinan’s Consumption (104t)	Consumption (104t)	Converted Consumption (104t)	CROC *
					Carbon Release (104t)	Percentage	Oxygen Consumption (104t)	Percentage
Coal	2000	361.11	20.15	14.39	35.87	72.06%	30.66	57.39%
	2010	1091.22	60.89	43.49	108.38	73.42%	92.63	59.19%
	2019	1237.10	69.03	49.31	122.87	72.32%	105.02	58.21%
Gasoline	2000	16.31	0.91	1.32	2.63	5.28%	4.53	8.48%
	2010	48.39	2.70	3.93	7.81	5.29%	13.47	8.61%
	2019	41.58	2.32	3.38	6.72	3.95%	11.58	6.42%
Diesel	2000	38.53	2.15	3.13	6.79	13.64%	10.74	20.10%
	2010	86.38	4.82	7.02	15.21	10.31%	24.07	15.38%
	2019	75.99	4.24	6.18	13.38	7.88%	21.17	11.73%
Fuel oil	2000	18.46	1.03	1.47	3.26	6.54%	5.03	9.42%
	2010	75.27	4.20	6.00	13.31	9.01%	20.56	13.14%
	2019	138.89	7.75	11.08	24.58	14.47%	37.96	21.04%
Liquefied petroleum gas	2000	7.17	0.40	0.68	1.24	2.49%	2.47	4.62%
	2010	16.67	0.93	1.59	2.90	1.97%	5.78	3.69%
	2019	13.44	0.75	1.29	2.35	1.38%	4.67	2.59%
Total	2000	441.40	24.63	20.99	49.78	100.00%	53.42	100.00%
	2010	1317.56	73.52	62.03	147.61	100.00%	156.51	100.00%
	2019	1506.99	84.09	71.22	169.89	100.00%	180.41	100.00%

* The CROC is defined as carbon release and oxygen consumption.

). In the context of urban rapid development, energy combustion consumption shows an increasing trend (from 441.4 × 10⁴ t to 1506.99 × 10⁴ t). Coal is the main type of fossil energy that emits carbon in China [40]. In the southern mountainous area, coal is the most significant type of energy consumption, with carbon release and oxygen consumption values above 72% and 57%, respectively (Table 6). From 2000 to 2019, the CROC of coal and fuel oil increased continuously, and the CROC of gasoline, diesel and liquefied petroleum gas first increased and then decreased. These changes were mainly due to the urban adjustment of the energy structure and environmental protection [29].

Moreover, from 2000 to 2019, the amount of carbon release in the southern mountainous area increased from 49.78 × 10⁴ t to 169.89 × 10⁴ t, and the amount of oxygen consumption increased from 53.42 × 10⁴ t to 180.41 × 10⁴ t. According to Table 6, the increase of coal consumption was the most significant factor causing the increase of CROC in the southern mountains.

3.4. Carbon-Oxygen Balance Capacity of Urban Forests

The carbon-oxygen balance coefficients of the urban forests in the southern mountainous area in 2000, 2010 and 2019 were estimated using the total urban forest CSOR and southern mountainous area’s CROC (

Table 7. Carbon-oxygen balance coefficient of the urban forest in the southern mountainous area.

Year	Carbon Balance Coefficient	Oxygen Balance Coefficient
2000	1.21	1.76
2010	3.56	5.13
2019	4.09	5.90

). The calculation results showed that the carbon balance coefficients and oxygen balance coefficients of the southern mountainous area from 2000 to 2019 increased from 1.21 to 4.09 and from 1.76 to 5.9, respectively. This means that the carbon produced by urban social activities was 4.09 times the amount of carbon sequestered in urban forests, and the urban oxygen consumption was 5.9 times the amount of fresh oxygen supplied by the forest by 2019.

4. Discussion

Based on the demand for CROC in the southern mountainous area, the carbon sequestration and oxygen release amounts of the urban forests in 2019 should be 169.89 × 10⁴ t and 180.41 × 10⁴ t, respectively, and the carbon sequestration and oxygen release amounts of the urban forests were 41.54 × 10⁴ t and 30.58 × 10⁴ t, respectively. Therefore, the amount of carbon sequestration and oxygen release amounts in the southern mountainous area accounted for 24.45% and 16.95% of the total amount, respectively. Under the background of the demand for urban CSOR in 2019 (Table 5), the urban forest’s carbon sequestration and oxygen release amount needed to increase by 128.35 × 10⁴ t and 149.83 × 10⁴ t, respectively. This result shows that with urban development, the increase in forest in the southern mountainous area was not balanced with the urban fossil energy consumption, and the carbon-oxygen balance capacity in the southern mountainous area decreased continuously from 2010 to 2019. Therefore, it is necessary to increase the urban forest area to meet the demand of urban CSOR. Moreover, extreme events enhanced by climate change will lead to tree death in urban forests by 2050 [41]. Therefore, under the background of extreme climates, the city should maintain the number of trees in urban forests while expanding the area of urban forests.

Moreover, different forest land-use types have different abilities of carbon sequestration and oxygen release [37]. According to the result of this study and related research, it was found that the carbon-oxygen balance ability of different forest land-use types was woodland > sparse woodland > arable land > wetland [26,33]. However, Figure 2 and Table 4 show that the woodland area in the southern mountainous area was only 44–48% of the study area, and the fragmentation of woodland’s distributed patches was relatively high, which cannot provide more CSOR. Therefore, woodlands with a high CSOR capacity should be selected when increasing urban forest area.

It is worth noting that the growth of the carbon balance coefficient (1.21 to 4.09) was less than that of the oxygen balance coefficient (1.76 to 5.90). This may be because urban planning in the southern mountainous area pays more attention to carbon emission limitation and ignores oxygen consumption control [42]. The results showed that the carbon balance capacity of the urban forest was more substantial than the oxygen balance capacity in the study area. This result is consistent with the research results of Chen, Shan and Chen [36] on the carbon-oxygen balance capacity in Wuhan, China. However, the carbon-oxygen balance capacity of the urban forest was mainly studied in terms of the carbon balance capacity, and the oxygen balance capacity of the urban forest needs further study.

While we have assessed the carbon–oxygen balance capacity of urban forests in southern mountains area, there are some limitations in this research. First, the satellite data from 2000, 2010 and 2019 were used to estimate the total carbon sequestration and oxygen release in urban forests. However, as changes in urban forests’ CSOR at other times were not considered, the research may have produced calculation errors in urban forests’ annual CSOR. Second, although Landsat high-definition satellites were used to determine the area of urban forest land use, the 30 m × 30 m resolution may not fully identify land-use types [31]. A few other land-use types within an urban forest land-use unit in the satellite images may be ignored. Finally, the carbon–oxygen balance assessment of urban forests based on forest land-use types may not be able to comprehensively consider the differences in the CSOR capacity of specific forest tree species. Therefore, our method may not fully reflect the contribution of different tree species to the carbon–oxygen balance of urban forests. In summary, with the development of Jinan, the carbon-oxygen balance coefficients in the southern mountainous area increased from 2000 to 2019. This result indicates that the carbon-oxygen balance in the southern mountainous area is difficult to achieve with the urban forest system alone. Therefore, to decrease the urban carbon-oxygen balance coefficient, Jinan should expand its area of woodland to improve the urban forest’s CSOR capacity and adjust the urban energy consumption structure to reduce the CSOR demand of urban forests.

5. Conclusions

This research assessed the carbon-oxygen balance capacity of urban forests by using Landsat satellite remote sensing images and GIS platforms in the southern mountainous area of Jinan in 2000, 2010, and 2019. Based on a carbon-oxygen balance model, this research quantitatively analyzed the carbon-oxygen balance capacity of urban forests in the southern mountainous area. The results show the following:

(1) The CSOR to CROC in the southern mountainous area were unbalanced. It is necessary to improve the carbon sequestration process by expanding the area of urban forests. Jinan should increase its forest area to absorb urban carbon emissions. For example, 1421.37 km² of woodland would be needed to absorb all the carbon emissions of the southern mountainous area in 2019.

(2) From 2000 to 2019, the CSOR of urban forests continued to increase despite a decrease in urban forest area from 557.5 km² to 551.09 km², as the increase in the CSOR of woodland areas compensated for the decrease in the CSOR of other forest land use types. Therefore, increasing the woodland area is a critical way to achieve carbon-oxygen balance in the city.

(3) With urban development, the increase in fossil energy consumption (from 441.4 × 10⁴ t to 1506.99 × 10⁴ t) contributes to the upward trend of the oxygen balance coefficient (from 1.21 to 4.09) and carbon balance coefficient (from 1.76 to 5.90) of the southern mountainous area significant from 2000 to 2019. The CROC of coal combustion accounted for the highest proportion in Jinan. Therefore, in addition to utilizing the carbon-oxygen balance capacity of urban forests in the southern mountainous area, Jinan needs to adjust its industrial and energy structure at a macro level to reduce the demand for CSOR from urban forests.