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Article

Simulation and Prediction of Land Use Change and Carbon Emission under Multiple Development Scenarios at the City Level: A Case Study of Xi’an, China

by
Rui Bian
1,
Anzhou Zhao
1,*,
Lidong Zou
2,
Xianfeng Liu
3,
Ruihao Xu
1 and
Ziyang Li
1
1
School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China
2
School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China
3
School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(7), 1079; https://doi.org/10.3390/land13071079
Submission received: 24 June 2024 / Revised: 14 July 2024 / Accepted: 15 July 2024 / Published: 17 July 2024
(This article belongs to the Special Issue Land Use Sustainability from the Viewpoint of Carbon Emission)
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Abstract

:
Studying urban land use and its impact on carbon emissions is crucial for achieving China’s dual carbon goals. This research utilized the Shared Socio-economic Pathways (SSPs) scenarios 126, 245, and 585 from the Sixth International Coupled Model Intercomparison Project (CMIP6), along with a coupled System Dynamics (SD) and Patch-generating Land Use Simulation (PLUS) model and a carbon emission coefficient method to simulate and predict Xi’an’s land use carbon emissions from 2020 to 2040. The results indicate the following: (1) Cultivated and forest lands are the predominant land use types in Xi’an, with cultivated and grassland areas projected to decline under all three SSP scenarios by 2040. The most significant expansion of construction land, primarily at the expense of farmland, is projected under the SSP585 scenario, with an increase of 515.92 km2 by 2040. (2) Land use carbon emissions increased from 414.15 × 104 t in 2000 to 2376.10 × 104 t in 2020, with construction land being the primary source of emissions and forest land serving as the main carbon sink. However, the carbon sink capacity remained low at only 21.38 × 104 t in 2020. (3) Carbon emissions are expected to continue increasing under all scenarios through 2030 and 2040, though at a decreasing rate. The SSP126 scenario predicts the lowest emissions, reaching 9186.00 × 104 t by 2040, while SSP585 predicts the highest at 14,935.00 × 104 t. The findings of this study provide theoretical support for future low-carbon and high-quality urban development strategies.

1. Introduction

The adoption of the 2015 United Nations (UN) Sustainable Development Goals (SDGs) is of great significance for the future sustainable development of the world. Goal 13, “Take urgent action to address climate change and its impacts”, aims to reduce global greenhouse gas emissions and strengthen collaboration on climate-related issues [1]. Carbon emissions are one of the main causes of climate change, and reducing carbon emissions is an effective way to achieve this goal [2]. Since 2000, environmental issues such as extreme weather and global warming, driven by the rising concentration of greenhouse gasses (GHGs) like CO2, have seriously threatened the future survival and development of humanity. This has increasingly captured the attention of countries worldwide. Controlling GHG emissions has become a focal point of global concern [3,4]. As the largest developing nation, China has seen its carbon emissions surge alongside its socio-economic growth, becoming the world’s leading emitter since 2006 [5]. It is extremely detrimental to the achievement of the UN SDGs. Nukala and Mutz believe that post-2015 cities will be the main arenas of the sustainable development struggle [6]. Urban expansion and other land use changes significantly contribute to emissions, accounting for one-third of the global anthropogenic total [7]. Since the reform and opening up, China’s urbanization rate has steadily increased from 17.90% in 1978 to 64.72% in 2021 [8]. The rapid expansion of construction land continues to encroach on green ecological spaces, such as cultivated land, forests, and grasslands, resulting in significant changes in land use carbon emissions. The adoption of the SDGs in cities is a major turning point in the drive towards the achievement of the UN SDGs [6]. Therefore, effectively assessing and predicting urban land use carbon emissions and understanding their impacts are crucial. This analysis provides essential theoretical support and strategic guidance for sustainable urban land use and regional development planning.
Following the introduction of the dual-carbon goal, prediction research has gained prominence, leading to extensive multidisciplinary investigations in this area. Han et al. [9] analyzed Beijing’s land use transitions from 2010 to 2020 under developmental and protective scenarios using the CLUE-S and Markov models, revealing an increasing trend of cultivated land being converted into construction land in mountainous regions. Similarly, Li et al. [10] combined System Dynamics (SD), backpropagation neural network (BPNN), and cellular automata (CA) models to simulate land use dynamics in northern China over the next 30 years. Their results indicated significant land cover changes in agricultural and pastoral zones, particularly in northern and southeastern China. Additionally, Xu et al. [11] integrated the SD and CA models to predict land use and land cover (LULC) changes in typical agricultural and pastoral transition areas in northwestern Shanxi under three future development scenarios. Their findings indicated that these changes are primarily driven by natural and socio-economic factors. Additionally, Sohl et al. [12] analyzed various models’ predictions of future U.S. land use and noted a lack of consistency in projected land use and land cover trends. In terms of carbon emission prediction, Luo et al. [13] developed a spatial simulation model and estimated that peak emissions in Xi’an could reach 60.6 million tons under optimal conditions. Zhang et al. [14] combined the Markov and Patch-generating Land Use Simulation (PLUS) models to predict the carbon emissions of the Wuhan urban agglomeration under scenarios of natural development, economic development, cultivated land protection, and low-carbon development by 2035. Their results showed that the economic development scenario had the highest carbon sources and emissions, while the low-carbon development had the highest carbon sinks. Cao et al. [15] predicted future land use changes and their impacts on terrestrial ecosystem carbon pools along the Silk Road under four Sustainable Development Goal (SDG) scenarios, finding significant differences in land use and carbon stocks across the scenarios. Lastly, Sönke Zaehle et al. [16] used the LPJ-DGVM model to project changes in European carbon stocks from 1990 to 2100, highlighting significant uncertainty in carbon sequestration estimates due to varying climate model projections.
In general, previous studies have significantly advanced the theoretical understanding of land use and carbon emission prediction. However, most studies have primarily focused on land use factors, often using the SD model to analyze land use changes and calculating land use carbon emissions based on the areas of different land types. This approach tends to overlook the interconnections with other factors such as climate, economy, and energy, resulting in a somewhat narrow and fragmented prediction framework. Furthermore, the methodology for setting future projection scenarios typically relies on modifying transfer probabilities based on historical data to create single-year forecasts. This method tends to introduce subjectivity, reducing the credibility of the predictions.
As the starting point of China’s Silk Road and the center of the Guanzhong Plain urban agglomeration, Xi’an is an important economic center in the western region and a key area for implementing the dual-carbon policy [17]. As the sole national central city in northwest China, the future development of Xi’an has an important role in guiding the development direction of northwest China. Studying the city’s future carbon emissions is therefore essential for achieving the dual-carbon target in the western region [18]. The objectives of this study are as follows: (1) to simulate land use changes in Xi’an from 2030 to 2040 using the SD and PLUS models across three development scenarios—SSP126, SSP245, and SSP585; (2) to investigate carbon emissions from land use in Xi’an from 2000 to 2020; and (3) to assess possible carbon emissions from land use under multiple scenarios from 2020 to 2040. The findings will provide a scientific foundation for future low-carbon land use development and support the achievement of dual-carbon goals in Xi’an.

2. Materials and Methods

2.1. Overview of the Study Area

Xi’an City is located in the Guanzhong Basin (107°40′ E–109°40′ E, 33°42′ N–34°45′ N) in the central part of the Yellow River Basin. It borders the Weihe River to the north and the Qinling Mountains to the south, covering a total area of 10,100 km2. Xi’an is the largest city in northwest China and serves as the core city of the Guanzhong Plain urban agglomeration [17] (Figure 1). The city’s topography is higher in the south and lower in the north, with an average elevation of 1027 m. The climate is a warm temperate semi-humid continental monsoon type, with an average annual temperature between 13.0 and 13.7 °C and annual precipitation ranging from 522 to 720 mm [19]. With the construction of the “Belt and Road” initiative, Xi’an has experienced rapid social and economic development, resulting in dramatic urban land expansion. The built-up area of the city increased from 397.35 km2 in 2000 to 2361.20 km2 in 2020 [20].

2.2. Data Sources

2.2.1. Land Use Data

The land use data of Xi’an City from 2000 to 2020 were acquired from the Resource and Environment Science and Data Centre of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 1 July 2023). This dataset has a spatial resolution of 30 m × 30 m and an overall accuracy of over 90%. For this study, the dataset was reclassified into six primary land categories: cultivated land, forest land, grassland, water, construction land, and unused land.

2.2.2. Data on Land Use Change Drivers

A total of 11 driving factors were selected: population, GDP, Digital Elevation Model (DEM), slope, air temperature, precipitation, distance to primary road, distance to secondary road, distance to railway, distance to highway, and distance to water system. The data for the Qinling ecological function protection zone were used as the restricted conversion area, and the data for the Xi’an city building contour were used and selected as the planned development area. All the data were standardized into raster data for Xi’an City with a 30 m resolution under the CGCS2000_3_Degree_GK_CM_108E projection coordinate system through the operations of projection, masking, and resampling (Table 1).

2.2.3. Socio-Economic and Energy Consumption Data

The socio-economic data required for the SD model of land use carbon emissions primarily encompassed population figures, Gross Domestic Product (GDP), per capita housing space, per capita income, and other statistical indicators of Xi’an from 2000 to 2020. These data were primarily sourced from the Xi’an Statistical Yearbook and the Shaanxi Provincial Statistical Yearbook from 2001 to 2021. Additionally, energy consumption data mainly consisted of records regarding raw coal, crude oil, natural gas, electricity, and urban and rural residential energy consumption in Xi’an from 2000 to 2020. These data were predominantly sourced from the Xi’an Statistical Yearbook, the Shaanxi Provincial Statistical Yearbook, and the China Energy Statistics Yearbook covering the period from 2001 to 2021.
The population and urbanization rate data for future scenarios required for the SD model of land use carbon emissions were obtained from Chen et al. [21], providing gridded population projections for China at a 1 km resolution under different shared socio-economic paths from 2010 to 2100. The GDP data were sourced from Jiang Tong et al. [22], which offers gridded economic change data (0.5° × 0.5°) for 31 provinces in China from 2020 to 2100. For this study, the GDP of Shaanxi Province was used, adjusted according to the ratio of Xi’an’s GDP to Shaanxi Province’s GDP from 2000 to 2020, and combined with Harrisson’s research on future GDP development under different scenarios [23]. The average (0.34), minimum (0.31), and maximum (0.38) ratios from the historical period were selected as conversion ratios under the SSP126, SSP245, and SSP585 scenarios to estimate Xi’an’s GDP from 2020 to 2040. Future temperature and precipitation data (2020–2040) were obtained from the Scenario Model Intercomparison Project (Scenario MIP) in CMIP6 [24] (ESGF MetaGrid (https://www.llnl.gov/), accessed on 26 September 2023). The screening conditions were set according to experimental requirements as follows: Amon_BCC-CSM2-MR_ssp126/ssp245/ssp585_r1i1p1f1_gn_201501-210012 [25,26]. These data were then synthesized and cropped to determine the average annual temperature and precipitation for Xi’an.

2.3. Research Methodology

In this study, an SD model was developed to assess carbon emissions from land use in Xi’an, incorporating data on population, economy, energy, and land use from 2000 to 2020. Once the model’s accuracy was validated, future data on population, GDP, urbanization rate, average annual temperature, and average annual precipitation from 2020 to 2040 were combined to simulate and predict the quantitative changes and carbon emissions of land use categories in Xi’an for both the historical period (2000–2020) and the future period (2020–2040). This analysis was based on three development scenarios: SSP126, SSP245, and SSP585. Utilizing the SD model’s forecasts, land use spatial distribution data were integrated with 11 key driving factors into the PLUS model. This approach enabled mapping the spatial land use patterns in Xi’an for 2030 and 2040 under each scenario, as illustrated in the technical flow chart (Figure 2).

2.3.1. Future Climate Scenarios Based on CMIP6

CMIP6 integrates various scenarios of Shared Socio-economic Pathways and Representative Concentration Pathways to highlight the influence of different socio-economic development patterns on climate change [27]. In order to explore the future development of land use carbon emissions in Xi’an City under different carbon emission intensities, in this study, three scenarios—SSP126, SSP245, and SSP585—were selected from a combination of scenarios [28,29,30].
(1)
SSP126 combines SSP1 and Representative Concentration Pathway 2.6 (RCP2.6) and represents sustainable socio-economic development with low GHG emission levels.
(2)
SSP245 combines SSP2 and RCP4.5 and represents a middle-path scenario of socio-economic development with an intermediate level of GHG emissions.
(3)
SSP585 combines SSP5 and RCP8.5 and represents a rapid socio-economic development scenario characterized by the large-scale use of fossil fuels and high GHG emission levels.

2.3.2. SD Modelling

System Dynamics Modelling, proposed by Professor Forrester [31], analyzes complex systems through a combination of qualitative and quantitative approaches along with feedback loops. Comprising variables, parameters, and functional relationships, the SD model describes complex system structures. The fundamental structure of the SD model is the first-order feedback loop, while the higher-order feedback loops result from combining multiple first-order feedback loops to represent complex system structures [32].
This study constructs a high-order complex system model to simulate and predict carbon emissions from land use changes in Xi’an city through a combination of qualitative and quantitative analyses, integrating various elements including land use categories, energy consumption, socio-economic factors, policies, and institutions. The SD model includes four subsystems: population, economy, energy, and land use, with population change, urbanization rate, GDP, temperature, and precipitation as the primary regulating parameters for future scenario prediction. The model’s time boundary spans from 2000 to 2040 with a step length of 1 year. Firstly, land use data in 2000 and other required parameters are set to simulate the land use structure and carbon emissions in 2020. Subsequently, by comparing the simulated results with the actual data in 2020 to validate the model’s reliability, predictions for land use structure and carbon emissions in 2030 and 2040 are made under the SSP126, SSP245, and SSP585 scenarios. This is achieved by continually adjusting and inputting parameters for different scenarios, with 2020 serving as the starting point.

2.3.3. Measurement of Carbon Emissions from Land Use

The measurement of carbon emission from land use includes both direct and indirect methods [33]. Research has indicated that construction land serves as a carbon source, while crops in cultivated land absorb CO2 but release it over a longer period, resulting in minimal carbon sequestration [34,35,36]. Therefore, this study categorizes cultivated land as a carbon source, while forest land, grassland, water, and unutilized land are considered carbon sinks. Among the six land types, carbon emissions from construction land are indirectly measured, while the other five land types are measured through direct carbon emission measurements.
(1)
Direct carbon emissions from land use
Direct carbon emissions from land use represent the carbon emissions generated during direct land use and are calculated using carbon emission factors specific to each land category. The calculation formula is as follows:
E = ∑ e i = ∑ S i × Q i
where E represents the regional land use carbon emission, ei denotes the carbon emission of a specific land type, Si signifies the area of a specific land type, and Qi indicates the carbon emission coefficient of a specific land type. Based on prior research [37,38,39], the carbon emission coefficients for cultivated land, forest land, grassland, water, and unused land are determined as 0.497, −0.644, −0.021, −0.257, and −0.005 t/hm2·a−1, respectively.
(2)
Indirect carbon emissions from land use
Indirect carbon emissions from land use arise when people use land to engage in socio-economic activities. Therefore, the measurement of carbon emissions from construction land is based on the energy consumed by humans in production and daily life [40]. These emissions primarily result from the combustion of fossil fuels; therefore, this study includes the conversion of crude coal, crude oil, and natural gas into the corresponding major energy sources: coal, oil, and natural gas. In addition, due to the increasing demand for electricity, the energy consumption of electricity is also incorporated into the main energy consumption calculations. The calculation formula is as follows:
E c = ∑ E i × F i
where Ec represents the carbon emission from construction land, Ei denotes the energy consumption, and Fi indicates the energy carbon emission factor. The reference coefficients for converting to standard coal are derived from the China Energy Statistics Yearbook, and the carbon emission coefficients are mainly based on the IPCC Guidelines for National Greenhouse Gas Inventories, supplemented by relevant research results [41,42,43]. The carbon emission coefficients for each energy source are detailed in Table 2.

2.4. PLUS Modelling

The PLUS model, developed by Liang Xun’s team, is a cellular automata model designed to identify land expansion drivers and predict land use evolution at the patch level using raster data. The following steps are used to simulate land use change [44].

2.4.1. Transfer Matrix

The transfer matrix uses 0 and 1 to indicate whether land classes can be converted into each other, which can reflect the conversion situation for each land class intuitively and effectively. The formula is as follows:
{ P i , k d = 1 > τ , T M c , k = 1   P i , k d = 1 ≤ τ , T M c , k = 0   τ = δ l × R 1
where τ represents the land use growth threshold, δ indicates the attenuation coefficient for the threshold τ (ranging between 0 and 1), R1 denotes a normal distribution with a mean of 1, and TMc,k signifies the transition transfer matrix.

2.4.2. Field Weights

The domain weight can reflect the level of difficulty associated with the expansion of land use types, ranging between 0 and 1. In this study, it is determined based on the percentage of expansion area for each land use type, with the formula as follows:
W i = | Δ S i | | Δ S |
where Wi denotes the domain weight of land category i, ΔSi represents the expansion area of land category i, and ΔS indicates the total expansion area of land categories.

3. Results

3.1. Accuracy Verification

To assess the accuracy of land use changes simulated by the PLUS model, the spatial pattern of land use in Xi’an was simulated for 2020 using data from 2010 and 2020 as the baseline. This involved inputting the development probability map, spatial development planning map, parameters of land demand quantity, and the transfer laws derived from driving factors. A comparison with the actual land use distribution pattern for 2020 revealed a generally consistent spatial distribution between the simulated and actual data (Figure 3). The Kappa coefficient, overall accuracy (OA) coefficient, and Figure of Merit (FOM) were calculated as 0.86, 0.90, and 0.19, respectively. These results indicate that the land use simulation performed by the PLUS model for Xi’an exhibits high accuracy, suggesting that the model is reliable and valid. Consequently, it can be effectively used to predict future changes in land use development.
The SD flow diagram of carbon emissions from land use in Xi’an City is composed of five subsystems (Figure 4). Among them, the blue part is the land use subsystem, the purple part is the economic subsystem, the yellow part is the population subsystem, the green part is the carbon emission subsystem, and the brown part is the energy subsystem. In the Figure 4, the red font variable is the dominant variable set for future scenarios. And the arrows indicate the direction of information flow. The successful operation of the SD model indicates the logical coherence among the parameters of each variable. Comparing the simulated quantities of each land use type in Xi’an in 2020 with the actual quantities confirms the relative errors of cultivated land, forest land, grassland, water, and construction land were −0.1%, 0.00%, −0.02%, 1.16% and 0.31%, relative errors less than 2% (Table 3), a level considered acceptable according to Wang et al. [45,46]. This highlights the effectiveness of the SD model in constructing relationships and its potential for predicting land use quantities and carbon emissions in Xi’an under future scenarios.

3.2. Simulation of Land Use Change in Xi’an under Different Scenarios

Table 4 shows the changing trend in each land type area in Xi’an under different scenarios for 2030 and 2040. In the SSP126 scenario, the cultivated land area in 2030 is recorded at 3308.00 km2, while the grassland area is 2080.00 km2. By 2040, these areas decrease to 3082.00 km2 and 2064.00 km2, respectively, with reductions of 6.83% and 0.78%. Conversely, the forest land and water areas show increasing trends, with the forest land area growing from 3042.00 km2 in 2030 to 3051.00 km2 in 2040 and the water area expanding from 171.80 km2 in 2030 to 182.60 km2 in 2040. Notably, the construction land area experiences significant expansion, increasing by a total of 212.00 km2, with its proportion increasing from 15.00% in 2030 to 17.03% in 2040, reflecting a 2.10% increase. Under the SSP245 scenario, the cultivated land and grassland areas decrease by 443.66 km2 and 43.35 km2 in 2030 and 2040, respectively, with decreases of 4.39% and 0.43%. The forest land area undergoes minimal change, with only a 5.11 km2 increase. The construction land area expands to 1492.00 km2 by 2030 and continues to gradually expand to 1691.00 km2 in 2040, marking an increase of 417.92 km2 over 20 years. Based on the continuous expansion of the water area observed from 2000 to 2020, the simulation predicts a continued slow expansion of the water area in 2030 and 2040 under the SSP245 scenario, following the pattern of the previous two decades. The gradual decrease in cultivated land and forest land areas in the SSP585 scenario is primarily due to the aggressive expansion of construction land. The forest land area decreases to 3002.00 km2 in 2030 and further decreases to 2976.00 km2 by 2040, making SSP585 the only scenario where forest land area diminishes. Construction land shows the most dramatic expansion, being the only land type that increases in area under this scenario. By 2040, construction land expands to 1789.00 km2, an increase of 515.92 km2 (5.11%) compared to 2020. Unused land, which has a small proportion in the study area, is not significant for interpretation. However, it is noteworthy that under the SSP585 scenario, the area of unused land approaches zero by 2040, indicating severe encroachment on unused land in this scenario.
Figure 5 illustrates the land use spatial distribution in Xi’an in 2030 and 2040 under multiple scenarios. Forest land and grassland are predominantly found in the southern and southeastern Qinling mountainous zones, while construction land is concentrated in the central region, and cultivated land is mainly in the central and northern Guanzhong Plains areas. Across the three future scenarios, the change in construction land area within the main urban area of Xi’an in 2030 and 2040 is not significant, but there is a noticeable expansion of small construction land patches in the surrounding areas. Under the SSP126 scenario, the expansion of construction land near the southern Qinling Mountains is limited due to the Qinling Ecological Protection Zone (Figure 5a,d). Conversely, the SSP585 scenario shows significant expansion of construction land, with substantial encroachment on forest land at the periphery (Figure 5c,f). The SSP245 scenario also exhibits construction land expansion, though not as extensive as in the SSP585 scenario (Figure 5b,e). Because forest land and grassland are concentrated in the Qinling Mountains area and are less affected by human activities, changes in these areas are slow. However, from the distribution of forest land around the built-up area of Xi’an, it is evident that in 2030 and 2040, construction land in Xi’an will mainly expand by encroaching on forest and grassland around and within the built-up area (Figure 5). Compared to the SSP245 and SSP585 scenarios, the SSP126 scenario also expands construction land but mainly encroaches on cultivated land rather than carbon sink areas such as forest land, prioritizing ecological and green development.

3.3. Land Use Carbon Emission Projections

3.3.1. Modelling of Carbon Emissions from Land Use from 2000 to 2020

The simulation results according to the SD model are presented in Table 5. Carbon emissions from land use in Xi’an continuously increased from 2000 to 2020, with net carbon emissions rising from 393.96 × 104 t to 2355.72 × 104 t over the 20-year period, with a total increase of 1961.76 × 104 t. The source of carbon emissions increased substantially during this time, from 414.15 × 104 t in 2000 to 2376.10 × 104 t in 2020. Construction land was the main carbon source, contributing 95.94%, 98.71%, and 99.37% in 2000, 2010, and 2020, respectively. As another source of carbon emissions, the contribution rate of cultivated land was relatively low and showed a gradual decline, decreasing from 4.06% in 2000 to 0.63% in 2020. Forest land, grassland, water, and unused land acted as carbon sinks, although their overall carbon sequestration was low. There was a slight upward trend from 2000 to 2020, with carbon sinks increasing from 20.19 × 104 t to 21.38 × 104 t, reflecting the small changes in the area of carbon sinks in Xi’an over the past 20 years. Forest land, the main carbon sink, increased from 19.43 × 104 t in 2000 to 19.52 × 104 t in 2020, but its contribution rate to the carbon sink decreased from 96.24% to 95.78%, maintaining an overall contribution rate of around 96%. The carbon sink of grassland changed minimally, slightly decreasing. With the expansion of water areas over the 20 years, the carbon sink increased from 0.31 × 104 t in 2000 to 0.42 × 104 t in 2020, and its contribution rate increased from 1.54% to 2.06%. Unused land had relatively weak carbon sequestration ability, with its carbon sink contribution remaining stable.

3.3.2. Projections of Carbon Emissions from Land Use under Multiple Scenarios

The results of the SD model prediction are shown in Figure 6. Under the SSP126 scenario, the carbon emission from cultivated land in 2040 is 13.01 × 104 t, a decrease of 1.89 × 104 t compared to 2020. The carbon emission contribution rate decreases from 0.62% in 2020 to 0.14% in 2040 (Figure 6a). The carbon emission from construction land in 2040 is 9186.00 × 104 t, an increase of 6824.80 × 104 t over 20 years, representing a 281.68% increase (Figure 6e). Compared to the 494.24% increase in carbon emissions from construction land from 2000 to 2020, the SSP126 scenario shows a significant reduction in the carbon emission growth rate from 2020 to 2040, with a decrease of 212.56%. By 2040, the total carbon sinks amount to 20.55 × 104 t, with the forest land’s carbon sink being 19.65 × 104 t (Figure 6b), an increase of 0.14 × 104 t compared to 2020. The grassland carbon sink slightly decreases from 0.44 × 104 t in 2020 to 0.43 × 104 t in 2040, a total decrease of 0.01 × 104 t (Figure 6c). The water carbon sink increases to 0.47 × 104 t in 2040 due to area expansion (Figure 6d). In 2040, the total net carbon emissions reach 9178.46 × 104 t (Figure 6f). Under the SSP245 scenario, the carbon emission from cultivated land in 2040 is 13.30 × 104 t, a decrease of 1.60 × 104 t compared to 2020. The carbon emission contribution rate decreases from 0.62% in 2020 to 0.11% in 2040 (Figure 6a). The carbon emission from construction land in 2040 is 11,672.00 × 104 t, an increase of 9270.50 × 104 t over 20 years, representing an increase of 386.03% (Figure 6e). Compared to the 494.24% increase in carbon emissions from construction land from 2000 to 2020, the SSP245 scenario shows less mitigation than the SSP126 scenario, with a reduction in the carbon emission growth rate from 2020 to 2040 by 108.21%. By 2040, the total carbon sinks amount to 20.45 × 104 t, with the forest land’s carbon sink being 19.58 × 104 t (Figure 6b), an increase of 0.0007 Mt compared to 2020. The trend in the grassland carbon sink is similar to that of the SSP126 scenario, showing a gradual decrease (Figure 6c). The water carbon sink experiences a small decrease from 2025 to 2030, then recovers and gradually increases from 2030 to 2035 (Figure 6d). In 2040, the total net carbon emissions reach 11,664.85 × 104 t (Figure 6f). Under the SSP585 scenario, the carbon emission from cultivated land in 2040 is 13.41 × 104 t (Figure 6a), a decrease of 0.63% compared to 2020. The carbon emission from construction land in 2040 is 14,935.00 × 104 t, which increases by 12,563.70 × 104 t over 20 years, representing a dramatic increase of 529.82% (Figure 6e). Compared with the increase in carbon emissions from 2000 to 2020, the carbon emissions under the SSP585 scenario show an increase of 35.58%. In 2040, the total carbon sinks amount to 19.99 × 104 t, with decreases in the water and forest land carbon sinks of 0.35 × 104 t and 0.03 × 104 t (Figure 6b,d). In 2040, the total net carbon emissions reach 14,928.44 × 104 t (Figure 6f). Across all three scenarios, the variation in carbon sink is minimal, which can be attributed to the limited changes in land area and the low carbon emission coefficient.
Overall, SSP585 stands out as the scenario with the highest emissions and the most drastic growth rate among the three development scenarios. Under the three scenarios, the overall carbon emissions from the carbon source are SSP585 > SSP245 > SSP126. While farmland’s carbon emissions are relatively low, the contribution of cultivated land to carbon emissions follows the sequence SSP126 > SSP245 > SSP585. Changes in carbon sinks for forest land, grassland, water, and unused land are relatively small, with forest land exhibiting the most significant change in the carbon sink amount. SSP126 represents a low-emission scenario with the lowest simulated carbon emissions, while SSP585 represents a high-emission scenario with the highest simulated carbon emissions (Figure 6f).

4. Discussion

4.1. Reasons for Changes in Land Use and Carbon Emissions under Different Scenarios

This study utilizes the SD-PLUS model to explore the land use development and carbon emissions of Xi’an from 2000 to 2040 under three future development scenarios: SSP126, SSP245, and SSP585. Notably, significant changes occur in the areas of cultivated land and construction land under these scenarios (Figure 5). The analysis shows that the construction land expansion is primarily driven by economic factors, whereas changes in cultivated land are closely associated with population dynamics (Figure 4). Moreover, construction land expansion typically occurs at the expense of cultivated areas (Figure 5). Influenced by terrain distribution, changes in forest land area are concentrated in the northern and central regions of Xi’an. Among the three scenarios, the SSP126 scenario, characterized by ecological concepts and favorable climatic conditions, encourages the continuous expansion of forest lands and water bodies. However, the grassland area shows a decreasing trend across all three scenarios, mainly due to the expansion of cultivated land and forest land [47,48].
Carbon emissions from land use in Xi’an surpass carbon sinks, resulting in net carbon emissions following a similar trend to carbon emissions alone, aligning with findings from Wang’s study [48]. As the dominant carbon source, emissions from construction land are mainly affected by fossil energy consumption. In the SSP126 scenario, characterized by ecological and high-quality economic development, carbon emissions are minimized, while carbon sinks, notably forest land and water, continue to increase (Figure 6). Conversely, under the SSP585 scenario, high energy consumption and rapid construction land expansion lead to the dramatic increase in carbon emissions, coupled with a simultaneous reduction in forest land, grassland, and water areas, leading to a continuous decline in carbon sinks. These findings indicate that changes in the area of each land use category are merely surface-level explanations for carbon emission changes, with underlying human activities reflecting different development concepts driving the actual changes in carbon emissions [49,50].

4.2. Impact of Land Use Change on Carbon Emissions

Carbon emissions resulting from land use activities in Xi’an are significantly influenced by changes in the area of each land category, aligning with the results of Luo’s study [13]. Over the period from 2000 to 2020, carbon emissions from land use in Xi’an show an upward trend, with emissions from carbon source land categories surpassing those from carbon sink land categories (Figure 7a), resulting in net carbon emissions essentially tracking carbon emissions from carbon sources alone. This pattern is evident as the establishment of the Xi’an International Metropolitan Area facilitated rapid economic growth and increased energy consumption, driving a notable increase in construction land from 829.93 km2 in 2000 to 1273.08 km2 in 2020. Consequently, carbon emissions surged by 494.24% from 397.35 × 104 t in 2000 to 2361.20 × 104 t in 2020 (Figure 7a,d). Conversely, the area of cultivated land decreased from 3984.56 km2 to 3533.66 km2, resulting in a corresponding 11.31% decline in carbon emissions from 16.80 × 104 t to 14.90 × 104 t, reflecting trends observed by Shao [51] regarding extensive land development in Xi’an. This underscores the urgent need for stringent forest protection policies to enhance ecosystem service values (ESVs). Notably, the impact of construction land on carbon emissions is significantly higher than that of cultivated land, highlighting the imperative to strictly adhere to cultivated land protection guidelines and restrict the continuous expansion of construction land. In terms of carbon sinks, the slight expansion of forest land and water area resulted in a slight increase of 0.19 × 104 t in carbon sinks over the 20-year period.
The simulation results reveal that by 2030, carbon emissions under the SSP126, SSP245, and SSP585 scenarios will amount to 4693.76 × 104 t, 5304.70 × 104 t, and 5943.47 × 104 t, respectively, with corresponding net carbon emissions of 4673.29 × 104 t, 5284.29 × 104 t, and 5923.31 × 104 t, respectively. Notably, the proportion of carbon sinks under these scenarios remains minimal, exerting limited influence on the overall net carbon emissions of Xi’an. Construction land is the primary contributor to carbon emissions (Figure 7b,d). In 2040, the area of construction land and corresponding carbon emissions under the SSP126, SSP245, and SSP585 scenarios significantly vary, with construction land covering 1721.00 km2, 1683.00 km2, and 1789.00 km2 and carbon emissions amounting to 9186.00 × 104 t, 11,672.00 × 104 t, and 14,935.00 × 104 t, respectively, while net carbon emissions reach 9178.46 × 104 t, 11,664.85 × 104 t, and 14,928.44 × 104 t, respectively (Figure 7d). SSP585, characterized by high emissions and energy consumption, exhibits the highest carbon emissions among the scenarios due to substantial expansion of construction land, heavy reliance on fossil energy for economic development, and significant encroachment on forest land and other carbon sinks. In contrast, SSP126, representing a low-emission, low energy consumption scenario, demonstrates the least carbon emissions, reflecting its emphasis on energy conservation, emission reduction, and low-carbon development. SSP245, positioned as a medium-emission scenario, presents the lowest carbon emissions. However, its construction land area expansion and lack of a green low-emission development plan position it between SSP585 and SSP126 (Figure 7c,d).

4.3. Future Perspectives

Accurate estimations of carbon emissions and sinks are crucial for achieving the Two-Carbon Goal and SDG 13. Land use carbon emissions constitute a prevailing and crucial topic in contemporary research. Forests cover one-third of the United States and offset approximately 15% of the nation’s annual carbon emissions [52]. Marvin et al. [53] found that forests will account for 85% of carbon reduction. India’s relatively rapid economic growth is mainly based on the consumption of fossil fuels caused by human activities on construction land, which is also a major source of greenhouse gasses in the country. Agroforestry will be one of the significant factors in achieving sustainable development in the country [54]. Rong et al.’s [55] research on China’s land use carbon emission forecast indicates that construction land carbon emission is the primary carbon source, and land use carbon emission under the ecological protection scenario will be lower than that under the natural development scenario. The results of this study are consistent with those of the aforementioned research on land use carbon emissions. Forest land and construction land are the main land types that, respectively, influence the increase and decrease in land use carbon emissions. The extensive use of fossil fuels and the continuous expansion of construction land are the main reasons leading to the continuous increase in land use carbon emissions in Xi’an. Therefore, in future development, Xi’an city should rationally adjust the structure of land use, restrict the arbitrary expansion of construction land, and efficiently utilize the expanded construction land. In terms of economic development and human production and life, it should reduce the use of fossil energy, advocate clean energy to replace the existing consumption of high-emission fossil energy, and promote the dominant position of non-fossil energy in energy consumption. Forest land, as the leading carbon sink type, benefits from the Qinling Forest Ecological Protection Area in the south of Xi’an. Therefore, special attention should be devoted to the protection of the Qinling Mountains ecological zone in the south of Xi’an in future development, and the carbon sink function of forest land should be exploited to a great extent.
Most previous studies have focused on individual aspects such as carbon emissions or land use change and have lacked simulation projections for multiple future scenarios [37,40]. This study, however, combines these two aspects by employing a carbon-emission-based SD model that incorporates population, urbanization rate, GDP, temperature, and precipitation data under different development scenarios with different emission intensities. Additionally, the PLUS model incorporates the development restriction zone for ecological protection and urban development zones constructed by urban building contours. This comprehensive approach ensures that this study’s results are more reliable and reasonable. Therefore, compared to previous studies, this study addresses their limitations by integrating both carbon emissions and land use changes. It simulates and predicts land use changes in Xi’an under different future development scenarios, providing comprehensive and reliable scientific support for the city’s future development.
This study simulates and predicts carbon emissions from land use for 2030 and 2040 based on data from 2000 to 2020. Future studies could further extend this work to predict carbon emissions over a long period. The scenarios SSP126, SSP245, and SSP585 were selected to represent possible future developments. In addition, the relationships between factors in the SD model and the constructed function formulas need further optimization, and subsequent studies can further improve them.

5. Conclusions

This study employs the SD-PLUS model combined with scenario emission–carbon emission (SE-CE) analyses to simulate future land use and carbon emissions in Xi’an under various SSP scenarios. From 2020 to 2040, the areas of cultivated land and grassland are projected to gradually decrease, while construction land will continue to expand. Under the SSP126 scenario, the cultivated land area is expected to decrease to 3308.00 km2 by 2030 and further to 3082.00 km2 by 2040. In the SSP245 scenario, forest area shows minimal change, with a slight increase of 5.11 km2. Conversely, the SSP585 scenario anticipates the significant expansion of construction land, totaling an increase of 515.92 km2. From 2000 to 2020, carbon emissions from land use in Xi’an increased by a total of 1961.76 × 104 t. Construction land continues to be the predominant source of carbon emissions, while forest land remains the primary carbon sink. It demonstrates that the carbon emission from land use in Xi’an is on the path to attaining the goal of carbon peaking and carbon neutrality. In the SSP126 scenario, the carbon emission is the lowest. Under the SSP585 scenario, carbon emissions increase sharply. This indicates that the ecological development approach can effectively achieve carbon emission reduction. The future development of Xi’an should take ecological low-carbon as the leading factor, vigorously enhance the protection of the Qinling Mountains ecological zone in the south of Xi’an, and contribute to China’s achievement of the “double carbon” goal and sustainable development.

Author Contributions

Conceptualization, R.B. and A.Z.; methodology, X.L. and Z.L.; software, R.X.; validation, R.B., A.Z. and X.L.; formal analysis, R.B.; writing—original draft preparation, R.B.; writing—review and editing, A.Z. and L.Z.; visualization, R.B and R.X.; supervision, L.Z and X.L.; project administration, A.Z.; funding acquisition, A.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China (42171212); Natural Science Foundation of Hebei Province (D2022402030).

Data Availability Statement

Dataset available on request from the authors.

Acknowledgments

We are grateful for the Xi’an Construction Outline Zone data support from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/, accessed on 17 January 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Rashid Khan, H.U.; Nassani, A.A.; Aldakhil, A.M.; Qazi Abro, M.M.; Islam, T.; Zaman, K. Pro-Poor Growth and Sustainable Development Framework: Evidence from Two Step GMM Estimator. J. Clean. Prod. 2019, 206, 767–784. [Google Scholar] [CrossRef]
  2. Yang, W.; Feng, L.; Wang, Z.; Fan, X. Carbon Emissions and National Sustainable Development Goals Coupling Coordination Degree Study from a Global Perspective: Characteristics, Heterogeneity, and Spatial Effects. Sustainability 2023, 15, 9070. [Google Scholar] [CrossRef]
  3. Bauer, N.; Keller, D.; Garbe, J.; Karstens, K.; Piontek, F.; Von Bloh, W.; Thiery, W.; Zeitz, M.; Mengel, M.; Strefler, J.; et al. Exploring Risks and Benefits of Overshooting a 1.5 °C Carbon Budget over Space and Time. Environ. Res. Lett. 2023, 18, 54015. [Google Scholar] [CrossRef]
  4. Cox, P.M.; Betts, R.A.; Jones, C.D.; Spall, S.A.; Totterdell, I.J. Acceleration of Global Warming Due to Carbon-Cycle Feedbacks in a Coupled Climate Model. Nature 2000, 408, 184–187. [Google Scholar] [CrossRef] [PubMed]
  5. Mi, Z.; Meng, J.; Guan, D.; Shan, Y.; Song, M.; Wei, Y.-M.; Liu, Z.; Hubacek, K. Chinese CO2 Emission Flows Have Reversed since the Global Financial Crisis. Nat. Commun. 2017, 8, 1712. [Google Scholar] [CrossRef] [PubMed]
  6. Kalfas, D.; Kalogiannidis, S.; Chatzitheodoridis, F.; Toska, E. Urbanization and Land Use Planning for Achieving the Sustainable Development Goals (SDGs): A Case Study of Greece. Urban Sci. 2023, 7, 43. [Google Scholar] [CrossRef]
  7. Hong, C.; Zhao, H.; Qin, Y.; Burney, J.A.; Pongratz, J.; Hartung, K.; Liu, Y.; Moore, F.C.; Jackson, R.B.; Zhang, Q.; et al. Land-Use Emissions Embodied in International Trade. Science 2022, 376, 597–603. [Google Scholar] [CrossRef] [PubMed]
  8. Huang, Y.; Jiang, X.; Chen, Y. Analysis of the Spatial-Temporal Evolution of Urbanization Quality in Zhejiang Province, China. Int. J. Environ. Res. Public Health 2023, 20, 4093. [Google Scholar] [CrossRef] [PubMed]
  9. Han, H.; Yang, C.; Song, J. Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China. Sustainability 2015, 7, 4260–4279. [Google Scholar] [CrossRef]
  10. Li, Y.; He, C. Scenario Simulation and Forecast of Land Use/Cover in Northern China. Chin. Sci. Bull. 2008, 53, 1401–1412. [Google Scholar] [CrossRef]
  11. Xu, X.; Du, Z.; Zhang, H. Integrating the System Dynamic and Cellular Automata Models to Predict Land Use and Land Cover Change. Int. J. Appl. Earth. Obs. 2016, 52, 568–579. [Google Scholar] [CrossRef]
  12. Sohl, T.L.; Wimberly, M.C.; Radeloff, V.C.; Theobald, D.M.; Sleeter, B.M. Divergent Projections of Future Land Use in the United States Arising from Different Models and Scenarios. Ecol. Model. 2016, 337, 281–297. [Google Scholar] [CrossRef]
  13. Luo, H.; Li, Y.; Gao, X.; Meng, X.; Yang, X.; Yan, J. Carbon Emission Prediction Model of Prefecture-Level Administrative Region: A Land-Use-Based Case Study of Xi’an City, China. Appl. Energy 2023, 348, 121488. [Google Scholar] [CrossRef]
  14. Zhang, J.; Zhang, C.; Dong, H.; Zhang, L.; He, S. Spatial–Temporal Change Analysis and Multi-Scenario Simulation Prediction of Land-Use Carbon Emissions in the Wuhan Urban Agglomeration, China. Sustainability 2023, 15, 11021. [Google Scholar] [CrossRef]
  15. Cao, M.; Tian, Y.; Wu, K.; Chen, M.; Chen, Y.; Hu, X.; Sun, Z.; Zuo, L.; Lin, J.; Luo, L.; et al. Future Land-Use Change and Its Impact on Terrestrial Ecosystem Carbon Pool Evolution along the Silk Road under SDG Scenarios. Sci. Bull. 2023, 68, 740–749. [Google Scholar] [CrossRef] [PubMed]
  16. Zaehle, S.; Bondeau, A.; Carter, T.R.; Cramer, W.; Erhard, M.; Prentice, I.C.; Reginster, I.; Rounsevell, M.D.A.; Sitch, S.; Smith, B.; et al. Projected Changes in Terrestrial Carbon Storage in Europe under Climate and Land-Use Change, 1990–2100. Ecosystems 2007, 10, 380–401. [Google Scholar] [CrossRef]
  17. Zhang, P.; Liu, L.; Yang, L.; Zhao, J.; Li, Y.; Qi, Y.; Ma, X.; Cao, L. Exploring the Response of Ecosystem Service Value to Land Use Changes under Multiple Scenarios Coupling a Mixed-Cell Cellular Automata Model and System Dynamics Model in Xi’an, China. Ecol. Indic. 2023, 147, 110009. [Google Scholar] [CrossRef]
  18. Fang, C.; Yu, D. China’s Urban Agglomerations, 1st ed.; Springer Geography; Springer: Singapore; Science Press: Beijing, China, 2020; ISBN 9789811515507. [Google Scholar]
  19. Feng, D.; Bao, W.; Fu, M.; Zhang, M.; Sun, Y. Current and Future Land Use Characters of a National Central City in Eco-Fragile Region—A Case Study in Xi’an City Based on FLUS Model. Land 2021, 10, 286. [Google Scholar] [CrossRef]
  20. Yuan, B.; Zhou, L.; Hu, F.; Zhang, Q. Diurnal Dynamics of Heat Exposure in Xi’an: A Perspective from Local Climate Zone. Build Environ. 2022, 222, 109400. [Google Scholar] [CrossRef]
  21. Chen, Y.; Guo, F.; Wang, J.; Cai, W.; Wang, C.; Wang, K. Provincial and Gridded Population Projection for China under Shared Socioeconomic Pathways from 2010 to 2100. Sci. Data 2020, 7, 83. [Google Scholar] [CrossRef]
  22. Jiang, T.; Zhao, J.; Cao, L.-G.; Wang, Y.-J.; Su, B.-D.; Jing, C.; Wang, R.; Gao, C. Projection of National and Provincial Economy under the Shared Socioeconomic Pathways in China. Adv. Clim. Chang. Res. 2018, 14, 50. [Google Scholar]
  23. Harrisson, T. Explainer: How ‘Shared Socioeconomic Pathways’ Explore Future Climate Change. Available online: https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change/ (accessed on 24 May 2024).
  24. O’Neill, B.C.; Tebaldi, C.; van Vuuren, D.P.; Eyring, V.; Friedlingstein, P.; Hurtt, G.; Knutti, R.; Kriegler, E.; Lamarque, J.-F.; Lowe, J.; et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 2016, 9, 3461–3482. [Google Scholar] [CrossRef]
  25. Gidden, M.J.; Riahi, K.; Smith, S.J.; Fujimori, S.; Luderer, G.; Kriegler, E.; van Vuuren, D.P.; van den Berg, M.; Feng, L.; Klein, D.; et al. Global Emissions Pathways under Different Socioeconomic Scenarios for Use in CMIP6: A Dataset of Harmonized Emissions Trajectories through the End of the Century. Geosci. Model Dev. 2019, 12, 1443–1475. [Google Scholar] [CrossRef]
  26. Wei, W.; Wang, J.; Wang, X.; Yan, P.; Xie, B.; Zhou, J.; Liu, T.; Lu, D. The Response of Global Terrestrial Water Storage to Drought Based on Multiple Climate Scenarios. Atmos. Res. 2024, 303, 107331. [Google Scholar] [CrossRef]
  27. Cook, B.; Mankin, J.; Marvel, K.; Williams, A.; Smerdon, J.; Anchukaitis, K. Twenty-First Century Drought Projections in the CMIP6 Forcing Scenarios. Earth’s Future 2020, 8, e2019EF001461. [Google Scholar] [CrossRef]
  28. Cai, G.; Xiong, J.; Wen, L.; Weng, A.; Lin, Y.; Li, B. Predicting the Ecosystem Service Values and Constructing Ecological Security Patterns in Future Changing Land Use Patterns. Ecol. Indic. 2023, 154, 110787. [Google Scholar] [CrossRef]
  29. Luo, P.; Wang, X.; Zhang, L.; Mohd Arif Zainol, M.R.R.; Duan, W.; Hu, M.; Guo, B.; Zhang, Y.; Wang, Y.; Nover, D. Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change. Remote Sens. 2023, 15, 5778. [Google Scholar] [CrossRef]
  30. Tian, L.; Tao, Y.; Fu, W.; Li, T.; Ren, F.; Li, M. Dynamic Simulation of Land Use/Cover Change and Assessment of Forest Ecosystem Carbon Storage under Climate Change Scenarios in Guangdong Province, China. Remote Sens. 2022, 14, 2330. [Google Scholar] [CrossRef]
  31. Schwaninger, M.; Hamann, T.K. Theory-Building with System Dynamics: Principles and Practices. In Computer Aided Systems Theory—EUROCAST 2005; Moreno Díaz, R., Pichler, F., Quesada Arencibia, A., Eds.; Springer: Berlin/Heidelberg, China, 2005; pp. 56–62. [Google Scholar]
  32. Shen, Q.; Chen, Q.; Tang, B.; Yeung, S.; Hu, Y.; Cheung, G. A System Dynamics Model for the Sustainable Land Use Planning and Development. Habitat Int. 2009, 33, 15–25. [Google Scholar] [CrossRef]
  33. Hong, C.; Burney, J.A.; Pongratz, J.; Nabel, J.E.M.S.; Mueller, N.D.; Jackson, R.B.; Davis, S.J. Global and Regional Drivers of Land-Use Emissions in 1961-2017. Nature 2021, 589, 554–561. [Google Scholar] [CrossRef]
  34. Li, Y.-Y.; Wei, W.; Zhou, J.-J.; Hao, R.-J.; Chen, D.-B. Changes in Land Use Carbon Emissions and Coordinated Zoning in China. Huan Jing Ke Xue 2023, 44, 1267–1276. [Google Scholar] [PubMed]
  35. Yu, Z.; Chen, L.; Tong, H.; Chen, L.; Zhang, T.; Li, L.; Yuan, L.; Xiao, J.; Wu, R.; Bai, L.; et al. Spatial Correlations of Land-Use Carbon Emissions in the Yangtze River Delta Region: A Perspective from Social Network Analysis. Ecol. Indic. 2022, 142, 109147. [Google Scholar] [CrossRef]
  36. Zhao, Y.; Ma, S.; Fan, J.; Cai, Y. Examining the Effects of Land Use on Carbon Emissions: Evidence from Pearl River Delta. Int. J. Environ. Res. Public Health 2021, 18, 3623. [Google Scholar] [CrossRef]
  37. Lai, L.; Huang, X.; Yang, H.; Chuai, X.; Zhang, M.; Zhong, T.; Chen, Z.; Chen, Y.; Wang, X.; Thompson, J.R. Carbon Emissions from Land-Use Change and Management in China between 1990 and 2010. Sci. Adv. 2016, 2, e1601063. [Google Scholar] [CrossRef]
  38. Li, Y.; Zhan, J.; Liu, Y.; Zhang, F.; Zhang, M. Response of Ecosystem Services to Land Use and Cover Change: A Case Study in Chengdu City. Resour. Conserv. Recycl. 2018, 132, 291–300. [Google Scholar] [CrossRef]
  39. Zhu, E.; Deng, J.; Zhou, M.; Gan, M.; Jiang, R.; Wang, K.; Shahtahmassebi, A. Carbon Emissions Induced by Land-Use and Land-Cover Change from 1970 to 2010 in Zhejiang, China. Sci. Total Environ. 2019, 646, 930–939. [Google Scholar] [CrossRef]
  40. Zhou, Y.; Chen, M.; Tang, Z.; Mei, Z. Urbanization, Land Use Change, and Carbon Emissions: Quantitative Assessments for City-Level Carbon Emissions in Beijing-Tianjin-Hebei Region. Sustain. Cities Soc. 2021, 66, 102701. [Google Scholar] [CrossRef]
  41. Su, K.; Wei, D.; Lin, W. Influencing Factors and Spatial Patterns of Energy-Related Carbon Emissions at the City-Scale in Fujian Province, Southeastern China. J. Clean. Prod. 2020, 244, 118840. [Google Scholar] [CrossRef]
  42. Yu, H.; Yang, Y.; Li, B.; Liu, B.; Guo, Y.; Wang, Y.; Guo, Z.; Meng, R. Research on the Community Electric Carbon Emission Prediction Considering the Dynamic Emission Coefficient of Power System. Sci. Rep. 2023, 13, 5568. [Google Scholar] [CrossRef]
  43. Zheng, H.; Gao, X.; Sun, Q.; Han, X.; Wang, Z. The Impact of Regional Industrial Structure Differences on Carbon Emission Differences in China: An Evolutionary Perspective. J. Clean. Prod. 2020, 257, 120506. [Google Scholar] [CrossRef]
  44. Liang, X.; Guan, Q.; Clarke, K.C.; Liu, S.; Wang, B.; Yao, Y. Understanding the Drivers of Sustainable Land Expansion Using a Patch-Generating Land Use Simulation (PLUS) Model: A Case Study in Wuhan, China. Comput. Environ. Urban Syst. 2021, 85, 101569. [Google Scholar] [CrossRef]
  45. Wang, Y.; Shen, J.; Yan, W.; Chen, C. Backcasting Approach with Multi-Scenario Simulation for Assessing Effects of Land Use Policy Using GeoSOS-FLUS Software. MethodsX 2019, 6, 1384–1397. [Google Scholar] [CrossRef] [PubMed]
  46. Yonaba, R.; Koïta, M.; Mounirou, L.A.; Tazen, F.; Queloz, P.; Biaou, A.C.; Niang, D.; Zouré, C.; Karambiri, H.; Yacouba, H. Spatial and Transient Modelling of Land Use/Land Cover (LULC) Dynamics in a Sahelian Landscape under Semi-Arid Climate in Northern Burkina Faso. Land Use Policy 2021, 103, 105305. [Google Scholar] [CrossRef]
  47. Wang, L.; Anna, H.; Zhang, L.; Xiao, Y.; Wang, Y.; Xiao, Y.; Liu, J.; Ouyang, Z. Spatial and Temporal Changes of Arable Land Driven by Urbanization and Ecological Restoration in China. Chin. Geogr. Sci. 2019, 29, 809–819. [Google Scholar] [CrossRef]
  48. Wang, Z.; Li, X.; Mao, Y.; Li, L.; Wang, X.; Lin, Q. Dynamic Simulation of Land Use Change and Assessment of Carbon Storage Based on Climate Change Scenarios at the City Level: A Case Study of Bortala, China. Ecol. Indic. 2022, 134, 108499. [Google Scholar] [CrossRef]
  49. Alexiadis, A. Global Warming and Human Activity: A Model for Studying the Potential Instability of the Carbon Dioxide/Temperature Feedback Mechanism. Ecol. Modell. 2007, 203, 243–256. [Google Scholar] [CrossRef]
  50. Pongratz, J.; Reick, C.H.; Raddatz, T.; Claussen, M. Effects of Anthropogenic Land Cover Change on the Carbon Cycle of the Last Millennium. Glob. Biogeochem. Cycles 2009, 23, 2009GB003488. [Google Scholar] [CrossRef]
  51. Shao, Y.; Yuan, X.; Ma, C.; Ma, R.; Ren, Z. Quantifying the Spatial Association between Land Use Change and Ecosystem Services Value: A Case Study in Xi’an, China. Sustainability 2020, 12, 4449. [Google Scholar] [CrossRef]
  52. Liu, L.; Qu, J.; Gao, F.; Maraseni, T.N.; Wang, S.; Aryal, S.; Zhang, Z.; Wu, R. Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives. Land 2024, 13, 279. [Google Scholar] [CrossRef]
  53. Marvin, D.; Sleeter, B.; Cameron, D.; Nelson, E.; Plantinga, A. Natural Climate Solutions Provide Robust Carbon Mitigation Capacity under Future Climate Change Scenarios. Sci. Rep. 2023, 13, 19008. [Google Scholar] [CrossRef]
  54. Liu, M.; Chen, Y.; Chen, K.; Chen, Y. Progress and Hotspots of Research on Land-Use Carbon Emissions: A Global Perspective. Sustainability 2023, 15, 7245. [Google Scholar] [CrossRef]
  55. Rong, T.; Zhang, P.; Zhu, H.; Jiang, L.; Li, Y.; Liu, Z. Spatial Correlation Evolution and Prediction Scenario of Land Use Carbon Emissions in China. Ecol. Inform. 2022, 71, 101802. [Google Scholar] [CrossRef]
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Figure 1. Schematic overview of the study area.
Figure 1. Schematic overview of the study area.
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Figure 2. Technical flow chart.
Figure 2. Technical flow chart.
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Figure 3. Comparison of actual land use distribution and modelled land use distribution in Xi’an in 2020.
Figure 3. Comparison of actual land use distribution and modelled land use distribution in Xi’an in 2020.
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Figure 4. SD flow diagram of carbon emissions from land use in Xi’an City.
Figure 4. SD flow diagram of carbon emissions from land use in Xi’an City.
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Figure 5. Spatial distribution of multi-scenario land use in Xi’an in 2030 and 2040.
Figure 5. Spatial distribution of multi-scenario land use in Xi’an in 2030 and 2040.
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Figure 6. Carbon emissions by category in 2040 under multiple scenarios.
Figure 6. Carbon emissions by category in 2040 under multiple scenarios.
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Figure 7. Changes in land use area and carbon emissions in Xi’an, 2000–2040. In (d) Carbon emissions, (a–c) on the X-axis correspond to SSP126, SSP245 and SSP585 scenarios.
Figure 7. Changes in land use area and carbon emissions in Xi’an, 2000–2040. In (d) Carbon emissions, (a–c) on the X-axis correspond to SSP126, SSP245 and SSP585 scenarios.
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Table 1. Data sources and descriptions.
Table 1. Data sources and descriptions.
Data TypeData NameYearData Sources
Land use dataLand use2000, 2010 and 2020Centre for Resource and Environmental Sciences and Data, Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 1 July 2023)
Driving factorDemographic2019Centre for Resource and Environmental Sciences and Data, Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 5 July 2023)
GDP2019
Temperatures2020National Earth System Science Data Centre (https://www.geodata.cn/, accessed on 5 July 2023)
Precipitation (meteorology)2020
DEM-Geospatial data cloud GDEMV3.30M resolution digital elevation data
(http://www.gscloud.cn/home, accessed on 5 July 2023)
Elevation-Derived from slope analysis based on DEM
Distance to railway2020Open street map (https://www.openhistoricalmap.org/, accessed on 7 July 2023)
Distance to motorway2020
Distance to primary roads2020
Distance to secondary roads2020
Distance to river2020
Restricted conversion areaQinling Mountain Ecological Function Reserve2020Centre for Resource and Environmental Sciences and Data, Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 10 September 2023)
Reclassification using ArcGIS with restricted areas set to 0 and unrestricted areas set to 1
Development areaXi’an Construction Outline Zone2020National Tibetan Plateau Science Data Centre (https://www.tpdc.ac.cn, accessed on 1 September 2023)
Reclassification using ArcGIS, with development areas set to 2 and non-development areas set to 1
Table 2. Table of energy carbon emission factors.
Table 2. Table of energy carbon emission factors.
Type of EnergyStandard Coal Reference Factor (kgce/kg)Carbon Emission Factor (tC/tce)
Raw coal0.71430.7559
Crude oil1.42860.5857
Petroleum1.21430.4483
Electrical power0.34500.2720
Table 3. Simulation results of the demand for each category in Xi’an in 2020.
Table 3. Simulation results of the demand for each category in Xi’an in 2020.
ResultsCultivated Land/km2Forest Land/km2Grass Land/km2Water/km2Construction Land/km2Unused Land/km2
Actual value3533.663029.892102.35162.221273.084.51
Analogue value3530.003030.002102.00164.101277.002.27
Relative error−0.10%0.00%−0.02%1.16%0.31%−49.66%
Table 4. Area of each category under different scenarios for Xi’an in 2040 (km2).
Table 4. Area of each category under different scenarios for Xi’an in 2040 (km2).
Land Use TypeSSP126SSP245SSP585
203020402030204020302040
Cultivated land3308.003082.003342.003153.003357.003178.00
Forest land3042.003051.003035.013040.123002.002976.00
Grassland2080.002064.002068.002041.002057.002014.00
Water171.80182.60165.70173.70154.10149.00
Construction land1509.001721.001492.001691.001529.001789.00
Unused land−4.854.843.277.717.060.68
Table 5. Carbon emissions by category, 2000–2020 (×104 t).
Table 5. Carbon emissions by category, 2000–2020 (×104 t).
Carbon Footprint200020102020
Carbon sourceCultivated land16.8015.8514.90
Construction land397.351216.302361.20
Total414.151232.152376.10
Carbon sinksForest land19.4319.4519.52
Grassland0.450.450.44
Water0.310.350.42
Unused land0.000.000.00
Total20.1920.2521.38
Net carbon emissions393.961211.902355.72
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Bian, R.; Zhao, A.; Zou, L.; Liu, X.; Xu, R.; Li, Z. Simulation and Prediction of Land Use Change and Carbon Emission under Multiple Development Scenarios at the City Level: A Case Study of Xi’an, China. Land 2024, 13, 1079. https://doi.org/10.3390/land13071079

AMA Style

Bian R, Zhao A, Zou L, Liu X, Xu R, Li Z. Simulation and Prediction of Land Use Change and Carbon Emission under Multiple Development Scenarios at the City Level: A Case Study of Xi’an, China. Land. 2024; 13(7):1079. https://doi.org/10.3390/land13071079

Chicago/Turabian Style

Bian, Rui, Anzhou Zhao, Lidong Zou, Xianfeng Liu, Ruihao Xu, and Ziyang Li. 2024. "Simulation and Prediction of Land Use Change and Carbon Emission under Multiple Development Scenarios at the City Level: A Case Study of Xi’an, China" Land 13, no. 7: 1079. https://doi.org/10.3390/land13071079

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