Spatial and Temporal Evolution of Land Use Carbon Emission and Carbon Balance Zoning: Evidence from Xinjiang China

Spatial and Temporal Evolution of Land Use Carbon Emission and Carbon Balance Zoning: Evidence from Xinjiang China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatial and Temporal Evolution of Land Use Carbon Emission and Carbon Balance Zoning: Evidence from Xinjiang China Jiahu Hu, Mengfei Song, Lianwei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6184608/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Land use (LU) change has become one of the primary sources of regional carbon emissions. Investigating the spatiotemporal characteristics and future trends of LU carbon emissions is of great significance for optimizing LU structures, formulating emission reduction policies, and developing a low-carbon economy in the region. Based on data of six LU types and energy consumption from 2000 to 2020 in Xinjiang, this study employs methods such as the LU transfer matrix and land dynamic state to analyze the spatiotemporal changes in LU and carbon emissions in Xinjiang. The results indicate the following: Firstly, from 2000 to 2020, the areas of cropland, forest, water, and impervious in Xinjiang showed an overall trend of continuous expansion, whereas grassland and barren continuously contracted. Secondly, according to the LU transfer matrix, from 2000 to 2020, 90.7% of the grassland was converted to forest, 87.8% of the forest was converted to cropland, 52.7% of the cropland was transformed into barren, 81.7% of the water was converted into barren, and 68.4% of the barren was transformed into cropland. Thirdly, the LU carbon emissions in Xinjiang from 2000 to 2020 exhibited a continuous expansion trend without any sign of mitigation, primarily due to the rapid growth in carbon emissions from impervious, while the carbon emission (absorption) levels from cropland, forest, water, and barren remained relatively stable. Fourth, from 2000 to 2020, the LU carbon emissions of the 14 cities in Xinjiang exhibit an overall pattern of diffusion from Urumqi as the center towards the east and west. Notably, there is a trend of the emission center shifting towards the southwest. Fifthly, in summary the carbon balance zoning in most regions of Xinjiang is progressively transitioning to higher categories. Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Xinjiang Land Use (LU) Carbon Emission Dynamic Change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Land serves as the foundation for human survival and development, with its utilization patterns and carbon emission effects remaining research focal points in fields such as geography, environmental science, and resource management. Beyond providing a space for human production activities, land is also a medium for the growth of flora and fauna. The various natural resources it harbors, including water and mineral resources, are the foundations of human existence [ 1 ][ 2 ]. These resources not only support the economic activities of human societies but also maintain ecological balance and ensure biodiversity [ 3 ][ 4 ]. As the scope of human production activities continues to expand, there have been profound changes in LU practices. The acceleration of urbanization and industrialization has led to the conversion of a significant amount of land originally used for agriculture, forestry, or natural ecosystems into impervious, resulting in increasingly strained land resources. Concurrently, irrational LU practices, such as over-cultivation and deforestation, have not only undermined the natural ecological functions of the land but also triggered a series of ecological and environmental issues, including soil erosion, land desertification, and a reduction in biodiversity. Xinjiang, as the largest provincial administrative region in China, boasts abundant land resources and a diverse range of ecosystems. However, the region's harsh natural environment and relatively weak economic foundation render LU and carbon emissions particularly complex and critical issues. In recent years, the rapid economic development and continuous population growth in Xinjiang have led to profound changes in LU structure and spatial configuration. The reduction in carbon sink areas and the expansion of carbon source areas, caused by these LU changes, pose severe challenges to the ecological environment and sustainable development of Xinjiang. In recent years, with the advancement of tools such as remote sensing and geographic detectors, the acquisition of LU data has become more accurate and convenient. These technological innovations have significantly promoted research in related fields, attracting an increasing number of scholars to engage in the study of LU and its associated impacts. Liu et al. analyzed the basic characteristics and spatial patterns of LU change in China from 1990 to 2010 [ 5 ]. Huang examined the relationship between carbon emissions and LU [ 6 ]. Yang et al. studied the transformation and gradient effects of LU in Shaanxi Province based on LU data [ 7 ]. Liu investigated the impact of LU on ecosystem services in the Yellow River Basin [ 8 ]. In the study of LU carbon emissions, Wei et al. used Geographical Weighted Regression (GWR) and other models to explore the spatiotemporal evolutionary characteristics and influencing factors of LU carbon emissions in Guangzhou [ 9 ]. Wang researched the regional flow of LU change and carbon storage in China based on LU and carbon density data [ 10 ]. Some scholars have focused on a specific type of LU; for example, Patel explored the relationship between LU change and agricultural economic growth in India [ 11 ]. Others have concentrated on the spatial changes of agricultural LU and the carbon emissions of urban LU [ 12 ][ 13 ]. Su studied the net carbon sequestration changes of cropland in Shandong Province [ 14 ]. In research on Xinjiang, comprehensive studies are limited. Zhao et al. studied the impact of LU change on ecosystem service value in Xinjiang [ 15 ]. More scholars have focused on specific regions, such as Li, who used the LU transfer matrix and landscape pattern analysis to study land cover changes in the Aksu area of Xinjiang [ 16 ]. Wang investigated LU changes in the Tarim River Basin [ 17 ]. Existing research on LU and LU carbon emission is extensive, yet studies focusing on the Xinjiang region are limited. As a vital part of the arid area in northwestern China, Xinjiang's unique geographical environment and climatic conditions endow its LU and carbon emission characteristics with distinct regional traits. To gain an in-depth understanding of the spatiotemporal evolution patterns of LU and land carbon emissions in Xinjiang, and to uncover the characteristics and influencing factors of LU and carbon emissions in the region over recent years, this study analyzes the dynamic changes in LU and carbon emissions based on LU data from 14 cities in Xinjiang from 2000 to 2020. The findings contribute to revealing the spatiotemporal evolutionary traits of LU in Xinjiang, offering a scientific basis for the rational utilization and planning of land resources. To gain an in-depth understanding of the temporal and spatial evolution patterns of land carbon emission and carbon balance zoning in Xinjiang, this study analyzes the dynamics of LU change and carbon emission based on LU data from 14 cities in Xinjiang from 2000 to 2020. The findings contribute to revealing the characteristics of LU and carbon emission evolution in the Xinjiang region, thereby providing a scientific basis for the rational utilization and planning of land resources. 2. Study Area and Data 2.1 Study Area Xinjiang encompasses 14 prefectures (cities), covering an area of 1.6649 million square kilometers, approximately one-sixth of China's total area. With a permanent population of 25.98 million, it is in the western part of the arid region in Northwest China (74°41’-96°18’ E, 34°22’-49°33’ N). Situated in the interior of the Eurasian continent, Xinjiang is a vital component of the arid zone in Central Asia. An overview of the study area is provided in Fig. 1 . Xinjiang's topographical pattern is characterized by "three mountains and two basins," namely, the northern Altay Mountains, the central Tianshan Mountains, and the southern Kunlun Mountains, which enclose the Junggar Basin and the Tarim Basin. This configuration gives rise to a unique combination of mountainous, oasis, and desert ecosystems [ 18 ]. Although Xinjiang is rich in land resources, its complex terrain includes various geomorphic types such as high mountains, basins, deserts, and grasslands. Its grassland resources serve as a significant base for animal husbandry. The climate in Xinjiang is complex and variable, with a unique water cycle, rendering the ecological environment extremely vulnerable. In the global context of arid regions, Xinjiang holds unique representativeness. Changes in its ecosystems, water resources, and climatic characteristics not only affect the local ecological environment and economic development but also have a significant impact on global climate change and ecosystem balance. 2.2 Research Data The data used in this study are all derived from public databases. Sources of LU data, energy consumption, and economic statistics by province and city are listed in Table 1 . Table 1 Data Sources Data Name Accuracy Data Source LU Data 30m×30m Wuhan University CLCD Dataset [ 19 ] Administrative Boundary Data - Chinese Academy of Sciences Resource and Environmental Science Data Center Energy Consumption Data - Xinjiang Statistical Yearbook The CLCD dataset from Wuhan University encompasses nine land types. In this study, LU types are consolidated and categorized into six types: Cropland, Forest, Grassland, Water, Impervious, and Barren. 2.3 Research Methodology 2.3.1 Calculation of Land Carbon Emission The carbon emissions from Cropland, Forest, Grassland, Water, and Barren are calculated directly using the carbon emission coefficient method. The carbon emission coefficients for Cropland, Forest, Grassland, Water, and Barren are 0.422, -0.612, -0.021, -0.235 (t/hm²), respectively [ 20 ][ 21 ][ 22 ][ 23 ]. A negative carbon emission coefficient indicates carbon sequestration, while a positive coefficient indicates carbon emission. The calculation method for LU carbon emission is as follows: $$\:\begin{array}{c}{C}_{1}={\sum\:}_{i=1}^{n}{\alpha\:}_{i}{S}_{it}\#（1）\end{array}$$ Among these, \(\:{{\alpha\:}}_{\text{i}}\:\) represents the carbon emission coefficient for different land types, and \(\:{\text{S}}_{\text{i}\text{t}}\) (hm2) represents the area of different land types in various regions. The carbon emission from Impervious is calculated using an indirect method. Drawing on the research of relevant scholars, this study calculates the carbon emission from Impervious based on the energy consumption of raw coal, coke, natural gas, crude oil, gasoline, diesel, kerosene, fuel oil, and liquefied petroleum gas in Xinjiang [ 24 ][ 25 ]. The calculation formula is as follows: $$\:\begin{array}{c}{C}_{2}={\sum\:}_{i=1}^{n}{{\gamma\:}_{i}\beta\:}_{i}{E}_{it}\#（2）\end{array}$$ In the formula, \(\:{{\gamma\:}}_{\text{i}}\:\) represents the conversion coefficient for different types of energy to standard coal. For solid fuels, it is the weight of standard coal required per kilogram of energy consumed, and for gaseous fuels, it is the weight of standard coal required per cubic meter of energy consumed. \(\:{{\beta\:}}_{\text{i}}\) represents the carbon emission coefficient for different types of energy, and \(\:{\text{E}}_{\text{i}\text{t}}\:\) represents the consumption of different types of energy in various regions. The energy consumption is converted into standard coal for calculation. The conversion coefficients and carbon emission coefficients for various types of energy are presented in Table 2 [ 26 ][ 27 ][ 28 ]. Table 2 Conversion Coefficients and Carbon Emission Coefficients for Various Types of Energy Energy Raw Coal Coke Natural Gas Crude Oil Gasoline Diesel Kerosene Fuel Oil Liquefied Petroleum Gas Standard Coal Conversion Coefficient 0.714 0.971 1.33 1.428 1.471 1.457 1.471 1.428 1.714 Carbon Emission Coefficient 0.755 0.855 0.448 0.585 0.553 0.592 0.571 0.618 0.272 2.3.2 Calculation of Land Area by Type This study analyzes the LU data in Xinjiang from 2000 to 2020 by integrating LU transfer matrices and LU dynamics. The principles of LU transfer matrices and LU dynamics are detailed in references [ 29 ][ 30 ]. Additionally, ArcGIS 10.8 is employed to process the reclassified LU raster data for the years 2000, 2004, 2008, 2012, 2016, and 2020. The Raster Calculator tool is used to compute and export the data. 2.3.3 Economic Contribution Rate of LU Carbon Emission The economic contribution rate of carbon emissions indicates the degree to which a region’s carbon emissions influence its economic benefits. The expression formula is as follows [ 31 ]: $$\:\begin{array}{c}ECE=\frac{{\text{G}}_{\text{i}}/\text{G}}{{\text{C}\text{E}}_{\text{i}}/\text{C}\text{E}}\#（3）\end{array}$$ Among them, \(\:{\text{G}}_{\text{i}}\) denotes the GDP of the i region in Xinjiang, \(\:\text{G}\) denotes the total GDP of the 14 regions in Xinjiang. \(\:{\text{C}\text{E}}_{\text{i}}\) denotes the LU carbon emission of the i region in Xinjiang, and \(\:\text{C}\text{E}\) denotes the total LU carbon emission of the 14 regions in Xinjiang. \(\:\text{E}\text{C}\text{E}\) >1 denotes a higher economic contribution rate of carbon emission, meaning a larger contribution to economic benefits. 0< \(\:\text{E}\text{C}\text{E}\) < 1 denotes a lower economic contribution rate of carbon emission, implying a smaller contribution to economic benefits. 2.3.4 Ecological Carrying Coefficient of LU Carbon Sequestration The carbon absorption ecological carrying coefficient measures the strength of a region's carbon sequestration capacity, with the specific expression as follows: $$\:\begin{array}{c}ECS=\frac{{\text{C}\text{S}}_{\text{i}}/\text{C}\text{S}}{{\text{C}\text{E}}_{\text{i}}/\text{C}\text{E}}\#（4）\end{array}$$ Among them, \(\:{\text{C}\text{S}}_{\text{i}}\) denotes the carbon sequestration amount through LU in the i region of Xinjiang, and \(\:\text{C}\text{S}\) denotes the total carbon sequestration amount through LU across the 14 regions of Xinjiang. \(\:\text{E}\text{C}\text{S}\) >1 denotes a higher ecological carrying coefficient of carbon sequestration, suggesting a strong carbon sequestration capacity of the ecosystem. 0 < \(\:\text{E}\text{C}\text{S}\) < 1 denotes a lower ecological carrying coefficient of carbon sequestration, implying a weaker carbon sequestration capacity of the ecosystem. 3. Results Analysis 3.1 Analysis of the Area Changes of Different Land Types in Xinjiang Figures 2 and Table 3 present the changes in cropland, forest, grassland, impervious and barren in the Xinjiang region from 2000 to 2020. Table 3 Changes in Land Area by Type in Xinjiang from 2000 to 2020 Unit: Km² Year Cropland Forest Grassland Impervious Water Barren 2000 61125.813 14614.850 392647.746 1146.668 42736.541 1119453.683 2004 64987.503 15920.085 391326.233 1708.629 45934.304 1111848.548 2008 71374.469 17031.776 393533.697 2688.377 46970.643 1100126.340 2012 81572.405 17300.497 382437.272 3344.243 49196.031 1097874.854 2016 86908.795 17933.183 380176.848 4038.526 50219.639 1092448.310 2020 85996.391 18207.287 375385.863 4947.595 45990.122 1101198.045 Based on the land area data presented in Table 3 , the overall trend shows a continuous increase in cropland, forest, water, and impervious, with a decrease in cropland and water observed in 2020. The areas of grassland and barren exhibit an overall decline during the study period, although there was an increase in barren in 2020. From a natural perspective, the Xinjiang region is ecologically fragile, characterized by sparse vegetation and diverse types of soil erosion with complex interactions. The interplay of water, wind, and frost actions exacerbates the issue of soil erosion. Additionally, the region's climate, marked by frequent winds and scarce rainfall, intensifies the wind erosion. Regarding human factors, the pastoral industry in Xinjiang is well-developed, yet some herding practices employed by pastoralists over the long term have led to excessive depletion of grassland vegetation and severe soil erosion. This has resulted in overgrazing and significant degradation of grasslands, leading to a sharp reduction in grassland area and an escalating conflict between grazing animals and vegetation. Population growth and the inappropriate development and utilization of water and soil resources have also been significant contributors to the destruction of surface vegetation and the intensification of soil erosion. 3.2 Analysis of Dynamic Changes in LU Types in Xinjiang According to the data presented in Table 4 , from 2000 to 2020, cropland, forest, and impervious in Xinjiang generally show an increasing trend over most periods, whereas barren and grassland exhibit a decreasing trend. Specifically, the area of cropland increased during the periods of 2000–2004 and 2012–2016, with change rates of 6.318% and 6.542%, respectively. However, a slight decrease in cropland area was observed from 2016 to 2020, with a change rate of -1.050%. In terms of dynamic rates, the cropland dynamic rates for the periods 2000–2004 and 2012–2016 were 1.579% and 1.635%, respectively, while the dynamic rate was negative (-0.262%) from 2016 to 2020. The area of forest exhibits an overall trend of increase, with a particularly notable rate of change during the period from 2000 to 2004, reaching up to 8.931%. However, in the subsequent time frames, the rate of increase in forest area has gradually slowed down. In terms of dynamic degree, the forest had a relatively high dynamic degree during the periods of 2000–2004 and 2008–2012 (with rates of 2.233% and 0.394% respectively), but a lower dynamic degree during other time periods. The area of grassland shows an overall trend of decrease. In particular, the rate of decline in grassland area was rapid during the periods of 2008–2012 and 2016–2020, with rates of change of -2.820% and − 1.260% respectively. In terms of LU dynamic degree, there was a slight increase in grassland during the 2004–2008 period. However, the dynamic degree was negative and the absolute values were significant during other time frames, indicating a rapid decrease in grassland area. The area of impervious exhibits a marked upward trend throughout the study period, with a relatively high rate of change. Notably, during the 2000–2004 and 2004–2008 intervals, the rates of change reached 49.008% and 57.341%, respectively. This sharp increase reflects the rapid expansion of impervious during these two phases. In terms of dynamicity, the impervious maintained a positive and relatively high level of dynamicity across all time periods, further indicating a swift and continuous increase in their area. The area of barren exhibits an overall increasing trend. However, during the period from 2016 to 2020, the rate of reduction in barren land is relatively rapid, with a change rate of -8.422%. In terms of dynamic degree, the dynamic degree of barren was positive during the four periods from 2000 to 2016, indicating a gradual increase in barren over these periods. In contrast, the dynamic degree turned negative during the 2016–2020 period, signifying a reduction in barren during this time. Table 4 Change Rates and Dynamic Degrees of Various LU Types in Xinjiang Unit: % Period Indicator Cropland Forest Grassland Impervious Barren 2000–2004 Change Rates 6.318 8.931 -0.337 49.008 7.483 Dynamic Degrees 1.579 2.233 -0.084 12.252 1.871 2004–2008 Change Rates 9.828 6.983 0.564 57.341 2.256 Dynamic Degrees 2.457 1.746 0.141 14.335 0.564 2008–2012 Change Rates 14.288 1.578 -2.820 24.396 4.738 Dynamic Degrees 3.572 0.394 -0.705 6.099 1.184 2012–2016 Change Rates 6.542 3.657 -0.591 20.761 2.081 Dynamic Degrees 1.635 0.914 -0.148 5.190 0.520 2016–2020 Change Rates -1.050 1.528 -1.260 22.510 -8.422 Dynamic Degrees -0.262 0.382 -0.315 5.627 -2.106 3.3 The LU Transition Matrixes in Xinjiang The shift matrix for LU changes in Xinjiang from 2000 to 2020 is presented in Table 5 . With respect to the changes in grassland, a total of 315.044 km² of grassland was converted to other land types, of which 285.791 km² was transformed into forest land. This shift may be attributed to the well-developed livestock industry in Xinjiang, where overgrazing could lead to the degradation of pastures. Additionally, climate change, which may result in reduced precipitation, could prevent grasslands from receiving adequate moisture, leading to their gradual deterioration. In terms of forest changes, 8300.434 km² of forest has been converted to other land types, with more than 85% being transformed into cropland. This shift indicates that with the growth of Xinjiang's population and the improvement of living standards, the demand for agricultural products is continuously increasing. The conversion of some forests to cropland aims to meet the societal demand for agricultural products by expanding the area of agricultural production. However, the reduction in forests may also lead to issues such as soil erosion and climate change. With respect to the changes in cropland, a total of 64,255.396 km² of cropland has been converted to other land uses. Nearly half of this converted cropland has become barren, indicating that soil erosion is a significant issue in Xinjiang. Regarding the changes in water, a total of 5982.201 km² of water area has been converted to other land uses, with more than 80% transformed into barren. This indicates a significant degradation of water in Xinjiang, which may be influenced by the natural environment of the region or could be a result of excessive exploitation and utilization by humans leading to water degradation. In terms of the changes in barren, a total of 57,194.844 km² of barren has been converted to other land uses. This indicates that over the past two decades, Xinjiang has experienced rapid economic development and urbanization. Specifically, 39,110.289 km² of barren has been transformed into cropland, demonstrating the effective development and utilization of land resources in Xinjiang, as well as positive efforts and significant achievements in cropland protection. Table 5 Xinjiang LU Transfer Matrix from 2000 to 2020 Unit: km² Grassland Transfer Grassland to Forest Grassland to Cropland Grassland to Water Grassland to Impervious Grassland to Barren Total 285.791 18.616 8.601 1.979 0.057 315.044 Forest Transfer Forest to Grassland Forest to Cropland Forest to Water Forest to Impervious Forest to Barren 80.567 7184.169 216.008 649.868 169.822 8300.434 Cropland Transfer Cropland to Forest Cropland to Grassland Cropland to Water Cropland to Impervious Cropland to Barren 3771.685 23497.036 1176.976 1931.492 33878.207 64255.396 Water Transfer Water to Forest Water to Grassland Water to Cropland Water to Impervious Water to Barren 46.703 237.707 678.128 132.767 4886.896 5982.201 Impervious Transfer Impervious to Forest Impervious to Grassland Impervious to Cropland Impervious to Water Impervious to Barren 0 0.701 2.310 9.325 1.22 13.566 Barren Transfer Barren to Forest Barren to Grassland Barren to Cropland Barren to Water Barren to Impervious 8.527 9149.775 39110.289 7824.871 1098.382 57191.844 3.4 Analysis of the Temporal and Spatial Evolution of the LU Carbon Emissions in Xinjiang The carbon emissions from cropland, forest, grassland, water, and barren in Xinjiang from 2000 to 2020 are obtained through LU data. Subsequently, the indirect carbon emissions from impervious in Xinjiang are calculated based on energy consumption data. This results in the total carbon emissions from LU in Xinjiang from 2000 to 2020 (as shown in Fig. 3 ). Temporally, the net carbon emissions from LU in Xinjiang increased from 16,680.720 thousand tons in 2000 to 142,365.242 thousand tons in 2020, with an annual growth rate exceeding 10%, demonstrating a strong growth trend. In contrast, the total carbon absorption in Xinjiang grew slowly from − 3317.309 thousand tons in 2000 to -3571.915 thousand tons in 2020. Table 6 presents the total carbon emissions from various types of land in Xinjiang from 2000 to 2020. Viewing the total carbon emissions by land type, the emissions from cropland and impervious in Xinjiang have been continuously increasing, with the growth rate of carbon emissions from impervious being particularly rapid. This, to some extent, reflects the rapid economic development and significant urbanization in Xinjiang from 2000 to 2020. The rapid economic development has led to increased industrial, commercial, and residential energy demands. As urbanization accelerates, urban construction and infrastructure continue to improve, expanding the scale of impervious, which has resulted in the rapid growth of carbon emissions from this land type. The carbon absorption of forest, grassland, and water has remained relatively stable during the study period, with forest and water showing a slow growth trend in carbon absorption, while grassland carbon absorption has shown a declining trend. Table 6 Total Land Carbon Emissions by Land Type in Xinjiang from 2000 to 2020 Unit: Thousand Tons Year Cropland Forest Grassland Water Impervious Barren 2000 2575.591 -886.612 -818.461 -1054.961 16628.671 -557.274 2004 2738.292 -966.144 -815.819 -1135.075 28437.542 -553.462 2008 3007.496 -1033.543 -820.518 -1160.722 40564.755 -547.606 2012 3437.275 -1049.646 -797.473 -1214.518 72190.994 -546.478 2016 3662.168 -1088.108 -792.587 -1241.151 99257.303 -543.789 2020 3623.964 -1104.729 -782.756 -1136.366 142313.193 -548.064 Figure 4 illustrates the evolutionary trend of carbon emissions across 14 cities in Xinjiang from 2000 to 2020. Spatially, the main concentration of LU carbon emissions is in the eastern and western regions, with Urumqi at the center. The distribution of carbon emissions in these cities generally corresponds to their economic output, suggesting a strong correlation between economic development and carbon emissions. From 2000 to 2008, Urumqi and Karamay were the primary contributors to LU carbon emissions. As urbanization intensified around Urumqi, LU carbon emissions rose sharply. By 2020, the number of major cities with significant LU carbon emissions in Xinjiang increased from 2 to 6, predominantly in the eastern and western parts of the region. This shift indicates a changing pattern of LU carbon emissions, expanding beyond traditional economic centers to more cities. In terms of the growth rate of LU carbon emissions, both Yili and Hami have experienced a remarkable surge, ascending from the third tier in 2000 to the first tier in 2020, demonstrating a strong growth trend. Geographically, Yili is a pivotal corridor connecting Xinjiang with Central Asia, West Asia, and Europe, offering unique location advantages and serving as an important platform for foreign trade and border cooperation. Hami, situated in the eastern part of Xinjiang, is a key node city on the Silk Road Economic Belt. Its strategic location and convenient transportation make it a pivotal city for Xinjiang to enhance connectivity with inland regions and neighboring countries. With the advancement of the national “Belt and Road” initiative and the rapid development of Xinjiang, both Yili and Hami are set to have broader development prospects. To further analyze the spatial changes in LU carbon emission in Xinjiang, ArcGIS 10.8 software was used to create standard deviation ellipses and shifts in the center of gravity for LU carbon emission from 2000 to 2020 (as shown in Fig. 5 ). The spatial distribution of LU carbon emission in Xinjiang exhibits a significant east-west trend. From 2000 to 2020, the standard deviation ellipses of LU carbon emission in Xinjiang primarily encompass cities such as Urumqi, Changji, and Karamay, which are important economic centers in the region and likely have relatively high levels of LU carbon emission. As indicated in Fig. 5 , the center of gravity of LU carbon emission in Xinjiang shows an overall migration trend from the central-northern part of the region towards the southeast from 2000 to 2020. The regional differences in economic development across Xinjiang are pronounced, with the central-northern areas mainly engaged in traditional agriculture, animal husbandry, and tourism, which consume less energy. In contrast, the southeastern areas are dominated by industry, services, and other sectors with higher energy demands. This difference in industrial structure may lead to changes in LU patterns and carbon emission intensity, thereby influencing the migration of the carbon emission center of gravity in Xinjiang. 3.5 Carbon Balance Zoning of LU in Xinjiang 3.5.1 Analysis of the Spatial Pattern of Carbon Balance in LU in Xinjiang Figure 6 presents the locational map of carbon balance in Xinjiang from 2000 to 2020. From 2000 to 2012, regions in Xinjiang that achieved carbon balance were primarily those with weaker economic development, such as Hetian. These areas, characterized by lower population densities and slower expansion of impervious and cropland, had a relatively low energy demand. This resulted in the carbon emission from impervious and cropland being less than the carbon sequestration by forests and grasslands. After 2012, all 14 regions in Xinjiang entered the non-carbon balance zone, indicating an accelerated pace of urban construction in Xinjiang. On one hand, the increase in population density led to the expansion of impervious surfaces and an increased demand for energy, thereby raising carbon emissions from impervious and cropland. On the other hand, due to the expansion of population and economic activities, there was a reduction in forested and grassland areas, leading to a decline in carbon sequestration capacity and disrupting the original state of carbon balance. Therefore, while amidst the process of rapid urban expansion, Xinjiang should comprehensively consider how to balance economic development with environmental protection. On one hand, it is imperative to augment efforts in ecological protection and restoration to rehabilitate and augment the carbon sequestration capabilities of natural ecosystems. On the other hand, the development of a low-carbon economy and circular economy should be considered to improve energy efficiency and reduce carbon emissions. 3.5.2 Carbon Balance Zoning for LU in Xinjiang Utilizing the previously outlined calculation formulas, the ecological carrying capacity coefficients and economic contribution coefficients for the 14 administrative regions in Xinjiang were calculated. Based on these coefficients, the regions were categorized into carbon sink functional areas, low-carbon economic areas, carbon intensity control areas, and high-carbon optimization areas (Fig. 7 ), and the criteria for the functional area classification are outlined in Table 7 . This zoning serves as a reference for future carbon emission reduction policies and development strategies in various regions across Xinjiang. Table 7 Characteristics of Carbon Balance Zones Region Division Division Criteria Division Characteristics Low Carbon Development Zone ECE>1, ECS>1 Both the ecological carrying coefficient and economic contribution coefficient are high. These regions should focus on the application of low-carbon technologies and the reduction of carbon emissions while maintaining economic development. Carbon Intensity Control Zone ECE>1, ECS<1 The ecological carrying coefficient is low, but the economic contribution coefficient is high. These regions need to prioritize the protection and restoration of ecosystems, enhance carbon sequestration capacity, and pay attention to economic transformation. Carbon Sink Functional Zone ECE1 The ecological carrying coefficient is high, while the economic contribution coefficient is low. These regions should optimize the industrial structure, reduce the proportion of high-carbon industries, and promote green and low-carbon development. High Carbon Optimization Zone ECE<1, ECS<1 Both the ecological carrying coefficient and economic contribution coefficient are low. These regions need to optimize the industrial structure and promote green and low-carbon development,advance ecological environmental protection and governance efforts. (1) Low Carbon Development Areas. From 2000 to 2020, Xinjiang has had a total of 6 regions, including Hetian, consistently designated as low-carbon development areas, reflecting an effective balance between the economy and ecological health. These regions, mainly in southern and northwestern Xinjiang, are characterized by extensive agricultural and pastoral activities, large land areas, and low population densities, making them conducive to agricultural production. Their distance from the economic center has curtailed the growth of high-energy, high-emission industries, and facilitating the development of a unique model of ecological agriculture and pastoralism due to their unique geographical location. To be specific, in southern Xinjiang, regions like Hetian use water resources from rivers such as the Tarim to support irrigation and specialty agriculture, thereby creating local agricultural brands. In the northwestern part of Xinjiang, regions like Yili Valley, with its vast grasslands and forests, have developed a balanced agriculture and pastoral economy, promoting both economic and ecological benefits. These regions need to continue to enhance eco-agriculture and pastoralism. With improved technology and management, leveraging abundant natural resources will enhance specialized farming and quality pastoralism. It is essential to protect the environment, preserve biodiversity, and ensure sustainable agriculture. (2) Carbon Intensity Control Zone. These regions exhibit high economic contribution for carbon emissions but exhibit low ecological carrying coefficients. Turpan was the primary area, and then transitioned to Urumqi and Akesu. exhibit high economic contribution to carbon emissions but low ecological carrying capacity. Initially, Turpan was identified as a carbon intensity control area before transitioning to a high-carbon optimization area, reflecting economic challenges and a decline in carbon emission efficiency. Conversely, Urumqi and Akesu shifted from high-carbon to carbon intensity control areas, indicating improved emission efficiency and a move towards sustainable development. These areas should adopt strategies to align economic growth with environmental protection, enhancing carbon emission efficiency and promoting a greener economy.re transitioning to a high-carbon optimization area, reflecting economic challenges and a decline in carbon emission efficiency. Conversely, Urumqi and Akesu shifted from high-carbon to carbon intensity control areas, indicating improved emission efficiency and a move towards sustainable development. These areas should adopt strategies to align economic growth with environmental protection, enhancing carbon emission efficiency and promoting a greener economy. (3) Carbon Sink Functional Zone. These regions feature high ecological carrying coefficients but low economic contribution coefficients for carbon emissions. Initially, areas such as Altay and Hami were classified as carbon sink functional areas. Over time, Altay transitioned to a low-carbon development area, indicating enhanced economic efficiency of carbon emissions through industrial upgrading while preserving the ecological environment. Hami, however, not only failed to optimize its industrial structure and develop low-carbon industries, but the growth of high-pollution and high-consumption industries also impaired the region's ecological carrying coefficient to some extent. These areas should promptly adjust their industrial structures, phase out outdated production capacity, and foster the development of low-carbon and environmentally friendly emerging industries. (4) High Carbon Optimization Zone. Regions in this category have low ecological carrying coefficients and economic contribution coefficients for carbon emissions, often facing significant environmental pressure and transformation challenges during economic development. Initially, these areas primarily included cities in the northeast and west, later becoming more concentrated in the northeast. Early on, regions such as Karamay and Changji were dominant; their resource endowments led to a relatively simplistic industrial structure dominated by industrial and resource-based industries. Over time, cities like Akesu have gradually moved out of the high carbon optimization areas, while some cities in the northeast continue to face substantial environmental pressure and transformation challenges due to difficulties in industrial structure adjustment and transformation. Karamay's economic development is closely linked to the oil and petrochemical industry. On November 28, 2023, the National Development and Reform Commission designated Karamay as one of the first batch of pilot cities for carbon peak, providing a significant opportunity for its green and low-carbon transformation. Regions such as Changji, Turpan, and Hami are also actively promoting industrial transformation, upgrading, and green, low-carbon development. These areas should take proactive measures to drive industrial transformation and upgrading, as well as green and low-carbon development, while also advancing ecological environmental protection and governance efforts. 4. Conclusion and Discussion 4.1 Conclusion (1) From 2000 to 2020, the area of cropland, forest, water bodies, and impervious land in Xinjiang has shown an overall trend of continuous expansion, while grassland and barren land have continuously contracted. Over the 20-year period, the area of cropland in Xinjiang has increased by 40.7%, forest area by 24.6%, water body area by 7.6%, and impervious land area by 331.5%. Conversely, the areas of grassland and barren land have decreased by 4.4% and 1.6%, respectively. (2) From 2000 to 2020, Xinjiang experienced a conversion of 315.044 km² of grassland into other land types, with 90.7% of this conversion being to forest. A total of 8300.434 km² of forest was transformed into other land types, with more than 87.8% of this area being converted to cropland. The conversion of 64255.396 km² of cropland resulted in nearly 52.7% being transformed into non-impervious land uses. An area of 5982.201 km² of water bodies was converted to other land uses, with over 81.7% being transformed into non-impervious land. Additionally, a total of 57194.844 km² of barren land was converted to other land uses, with 68.4% of the barren land being transformed into cropland. (3) From 2000 to 2020, the LU carbon emissions in Xinjiang have shown a trend of continuous expansion without any sign of deceleration. This is primarily due to the excessive growth in carbon emissions from impervious. The carbon emissions (absorption) from cropland, forest, water, and barren have remained relatively stable overall. The net carbon emissions in Xinjiang increased from 16680.720 × 10³ tons in 2000 to 142365.242 × 10³ tons in 2020, with an annual growth rate exceeding 10%, indicating a strong growth trend. The carbon emissions from impervious increased from 16628.671 × 10³ tons in 2000 to 142313.193 × 10³ tons in 2020, with a growth rate exceeding 11%, which is higher than the growth rate of Xinjiang's net carbon emissions. The total carbon absorption in Xinjiang grew slowly from − 3317.309 × 10³ tons in 2000 to -3571.915 × 10³ tons in 2020. (4) From 2000 to 2020, the LU carbon emissions of the 14 cities in Xinjiang display an east-west spatial pattern, characterized by a diffusion from Urumqi as the central point towards both the eastern and western regions. The spatial distribution of these emissions generally aligns with the spatial distribution of the cities' economic output. Notably, Yili and Hami exhibit a significant growth trend in carbon emissions. The migration of the carbon emission center of gravity indicates a trend of LU carbon emissions in Xinjiang shifting towards the southwest. (5) According to carbon balance analysis, the scope of the low-carbon development areas in Xinjiang has been gradually expanding from 2000 to 2020, while the number of high-carbon optimization areas has been decreasing. Specifically, most cities in the southern and eastern parts of Xinjiang have consistently remained within the low-carbon development areas. Urumqi and Akesu are transitioning from high-carbon optimization areas to low-carbon development areas. In contrast, the results for Turpan and Hami indicate the opposite trend. 4.2 Discussion (1) The significant changes in LU types in the Xinjiang region, particularly the substantial increases in cropland, forest, and impervious, as well as the decreases in grassland and barren, are likely driven by a variety of factors. Policy initiatives, population growth, economic development, and ecological environmental demands have all influenced LU changes to varying degrees. Population growth may have contributed to the expansion of cropland area; the acceleration of urbanization has directly led to a significant increase in impervious; and the rapid development of animal husbandry has, to some extent, caused the degradation of grasslands. Concurrently, ecological protection and restoration policies may also have an impact on the changes in forest and grassland. (2) The extensive conversion of grassland to forest may reflect the efforts of the Xinjiang region in ecological environmental protection, such as the implementation of policies like converting farmland to forest and grassland. However, this transformation could also have certain impacts on the local ecosystem, including changes in biodiversity, soil erosion, and degradation of soil quality. Furthermore, the large-scale conversion of forest to cropland, while potentially increasing LU efficiency, may also exert pressure on the ecological environment, such as the reduction of water conservation capacity and the degradation of soil fertility. Therefore, it is necessary to consider the ecological, economic, and social benefits of LU transformation to develop reasonable LU planning. (3) The rapid growth of LU carbon emissions in Xinjiang, particularly the swift increase in emissions from impervious, is noteworthy. The expansion of impervious is often accompanied by extensive infrastructure development, including roads, bridges, residential areas, and factories. These construction activities necessitate a substantial consumption of energy and materials, resulting in significant carbon emissions. To mitigate carbon emissions, a range of measures must be adopted, such as optimizing the energy consumption structure, enhancing energy efficiency, promoting renewable energy sources, and strengthening LU planning and management. Additionally, it is essential to enhance the monitoring and assessment of carbon emissions to promptly understand changes in emission levels. (4) The spatial distribution differences in LU carbon emissions among cities in Xinjiang may be related to factors such as the level of economic development, industrial structure, energy consumption patterns, and types of LU. As the political, economic, and cultural center of Xinjiang, Urumqi has relatively high LU carbon emissions. The rapid growth in carbon emissions in regions like Yili and Hami may be associated with the deepening implementation of the national "Belt and Road" initiative. Therefore, it is necessary to formulate differentiated carbon emission management policies based on the actual conditions of various regions to promote regional coordinated development. (5) Overall, most regions in Xinjiang are progressively moving up in the carbon balance zoning, a trend that reflects the positive advancements made in low-carbon development within the region. On one hand, Xinjiang has introduced a series of policies in recent years aimed at promoting green and low-carbon development. For instance, the government has mandated that the urban and rural construction sectors reach peak carbon emissions before 2030. On the other hand, Xinjiang has actively explored ways to advance green development. For example, through the implementation of the "Ecology Plus" plan, photovoltaic power projects for desert control have been established at the edges of deserts. These projects not only provide residents with clean green electricity but also achieve a triple win in ecological, economic, and social benefits. Regions like Turpan and Hami, however, may rely more on energy-intensive industries and fossil energy consumption, resulting in relatively higher carbon emissions. In the future, Xinjiang needs to continue strengthening efforts in policy guidance, technological innovation, and industrial structure adjustment to drive the province towards a more green and low-carbon development goal. (6) This study has the following limitations: Firstly, due to data availability and spatial analytical capabilities, the research was unable to investigate carbon emissions and their zoning at the county level in Xinjiang. Future scholars with interest in this area may conduct more comprehensive studies to explore aspects not covered in this paper. Secondly, constrained by methodological limitations, the LU carbon emission coefficients employed in this paper were based on previous scholarly research. Carbon emission coefficients can vary slightly across different regions. Interested scholars in the future may delve deeper into the examination of carbon emission coefficients for various regions. Declarations Author Contributions: Data curation, Jiahu Hu; Funding acquisition, Lianwei Zhang; Investigation, Jiahu Hu; Methodology, Jiahu Hu; Project administration, Lianwei Zhang; Resources, Jiahu Hu; Software, Mengfei Song; Supervision, Lianwei Zhang; Writing – original draft, Mengfei Song. Funding : This work is supported by the Major Projects of the Ministry of Education (Grant Number: 22JZDZ021). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data are fully available without restriction. The datasets are taken from several public repository, Wuhan University CLCD Dataset (http://irsip.whu.edu.cn/resources/CLCD.php), Chinese Academy of Sciences Resource and Environmental Science Data Center(http://www.resdc.cn), Xinjiang Statistical Yearbook(https://tjj.xinjiang.gov.cn/tjj/tjfw/list_tjfw.shtml) Conflicts of Interest: The authors declare no conflict of interest. References Li W , Yang C X, Wang L C , et al. National attractive territorial area:A national spatial planning strategy reshaping regional patterns. Journal of Natural Resources 2020, 35, 501-512. Sun X Z , Zhou H L, Xie G D. Ecological Services and Their Values of Chinese Agroecosystem. China Population,Resources and Environment 2007, 17, 55-60. Nagendra H, Reyers B, Lavorel S. Impacts of land change on biodiversity: making the link to ecosystem services. Current Opinion in Environmental Sustainability, 2013, 5, 503-508. Haines-Young R. Land use and biodiversity relationships. Land use policy, 2009, 26, 178-186. Liu J Y , Kuang W H, Zhang Z X, et al. Spatiotemporal characteristics,patterns and causes of land use changes in China since the late 1980s. Acta Geographica Sinica 2014, 69, 3-14. Huang S, Xi F, Chen Y, et al. Land use optimization and simulation of low-carbon-oriented—A case study of Jinhua, China. Land, 2021, 10, 1020. Yang S H, Li L, Ma J D, et al. Land use transitions and its terrain gradient effects based on production-living-ecological spaces in Shaanxi Province during 1990-2020. Arid Zone Research 2024, 41, 1249-1258. Liu B, Pan L, Qi Y, et al. Land use and land cover change in the Yellow River Basin from 1980 to 2015 and its impact on the ecosystem services. Land, 2021, 10, 1080. Wei J C, Mei Z X , Ma J J, et al. Spatiotemporal Evolution and Influencing Factors of Land-Use Carbon Emissions in Guangzhou. Research of Soil and Water Conservation, 2024, 31, 298-307. Wang S, Zhou S, Wu R, et al. Interregional flows of embodied carbon storage associated with land-use change in China. Annals of the American Association of Geographers, 2024, 114, 1526-1545. Patel S K, Verma P, Shankar Singh G. Agricultural growth and land use land cover change in peri-urban India. Environmental monitoring and assessment, 2019, 191, 1-17. Li X, Wu K N, Feng Z, et al. Spatial identification and tradeoff/synergy analysis of multiple agricultural land use system functions at grid scale in Henan Province. Annals of the American Transactions of the Chinese Society of Agricultural Engineering, 2023, 39, 242-252. Pang H Z, Shi R, Cai Z P, et al. Study on Supporting Territorial Spatial Planning through Identification Carbon Emission Sources in Urban Land Use:A Case Study of Nanjing. Modern Urban Research, 2024, 01, 1-7, 15. Su H, Li J K, Li K, et al. Relationship between net carbon sequestration change and cultivated land use benefit of cultivated land use in Shandong Province. Scientia Geographica Sinica, 2024, 44, 864-873. Zhao Y, Zhang Y F, Bo X, et al. Land use change and its impact on ecosystem service value in Xinjiang from 2000 to 2020. Journal of Tianjin Normal University：Natural Science Edition, 2023, 43, 53-60, 80. Li Y, Luo C L, Wu H Q, et al. Land Cover Change in Aksu Region of Xinjiang from 2000 to 2020. Journal of Agricultural Science and Technology, 2023, 26, 172-179. Wang L C, Han H J, Zhang J, et al. Spatio−temporal evolution of land use and human activity intensity in the Tarim River Basin,Xinjiang. Geology in China, 2024, 51, 203-220. Shi Q D, Wang Z, He L M, et al. Landscape classification system based on climate, landform, ecosystem: a case study of Xinjiang area. Acta Ecologica Sinica, 2014, 34, 3359-3367. Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925. Zhang C, Zhao L, Zhang H, et al. Spatial-temporal characteristics of carbon emissions from land use change in Yellow River Delta region, China. Ecological Indicators, 2022, 136, 108623. Hu Y Z, Yu D. Transmission Effect and Prediction of Land Use Carbon Emissions in Urban Agglomeration Around Poyang Lake. Research of Soil and Water Conservation, 2024, 31, 342-353. Sun H, Liang H, Chang X, et al. Land use patterns on carbon emission and spatial association in China. Econ. Geogr, 2015, 35, 154-162. Xu Y, Guo N, Ru K L, et al. Characteristics and optimization strategies of territorial space zone in Fujian Province, China based on carbon neutrality. The Journal of Applied Ecology, 2022, 33, 500-508. Li Y Y, Wei W, Zhou J J. Changes in Land Use Carbon Emissions and Coordinated Zoning in China. Environmental Science, 2023, 44, 1267-1276. Meng H, Zhang X, Du X, et al. Spatiotemporal Heterogeneity of the Characteristics and Influencing Factors of Energy-Consumption-Related Carbon Emissions in Jiangsu Province Based on DMSP-OLS and NPP-VIIRS. Land, 2023, 12, 1369. Li Z Z, Zhu X S, Yang L, et al. Spatial-temporal Evolution Characteristics and Influencing Factors of Carbon Emissions in Yunnan Province Based on Land Use Changes. Bulletin of Soil and Water Conservation, 2023, 43, 297-303, 311. Wei Y R, Chen S L, Yang L, et al. Spatial correlation and carbon balance zoning of land use carbon emissions in Fujian Province. Acta Ecologica Sinica, 2021, 41, 5814-5824. Li Z H, Zhou D M, Jiang J, et al. Spatial and Temporal Evolution Characteristics of Carbon Emission from Land Use and Influencing Factors in Gansu Province. Environmental Science, 2024, 45, 5040-5048. Wang J, Chen Y, Shao X, et al. Land-use changes and policy dimension driving forces in China: Present, trend and future. Land use policy, 2012, 29, 737-749. Wang P, Li R, Liu D, et al. Dynamic characteristics and responses of ecosystem services under land use/land cover change scenarios in the Huangshui River Basin, China. Ecological Indicators, 2022, 144, 109539. Xia S, Yang Y. Spatio-temporal differentiation of carbon budget and carbon compensation zoning in Beijing-Tianjin-Hebei Urban Agglomeration based on the Plan for Major Function-oriented Zones. Acta Geographica Sinica, 2022, 71, 679-696. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6184608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451311367,"identity":"c40c7a07-1931-4f4a-a3e8-5e8875e70435","order_by":0,"name":"Jiahu Hu","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jiahu","middleName":"","lastName":"Hu","suffix":""},{"id":451311368,"identity":"8b8b5754-f3b0-45ce-9ff6-855658d5efb7","order_by":1,"name":"Mengfei Song","email":"","orcid":"","institution":"Shihezi 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2","display":"","copyAsset":false,"role":"figure","size":345026,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in Land Area by Type in Xinjiang from 2000 to 2020\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6184608/v1/a839e7c57cf6973adf938e09.png"},{"id":82072557,"identity":"f6c5250c-a699-407d-8954-93757f6a3e96","added_by":"auto","created_at":"2025-05-06 13:25:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25230,"visible":true,"origin":"","legend":"\u003cp\u003eTotal LU Carbon Emissions in Xinjiang from 2000 to 2020\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6184608/v1/3e6218d752b38545ef2bfc0e.png"},{"id":82073728,"identity":"9a86ad4a-76ae-405a-a020-bbe5222d4b77","added_by":"auto","created_at":"2025-05-06 13:33:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":341271,"visible":true,"origin":"","legend":"\u003cp\u003eThe Spatiotemporal Evolution of LU Carbon Emissions in Xinjiang from 2000 to 2020\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6184608/v1/fa59bd701f518900dc58a069.png"},{"id":82072560,"identity":"39abfd13-b516-448a-863c-bd66485046b8","added_by":"auto","created_at":"2025-05-06 13:25:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":204452,"visible":true,"origin":"","legend":"\u003cp\u003eStandard Deviation Ellipse of Land Carbon Emissions from LU in Xinjiang\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6184608/v1/9c1d801576857be2f8790124.png"},{"id":82072572,"identity":"6f30ec55-101e-4c86-bcd5-bd11a83a5a92","added_by":"auto","created_at":"2025-05-06 13:25:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":197958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of LU Carbon Balance Zones in Xinjiang from 2000 to 2020\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6184608/v1/18763e7685f4ed30d6dc2e14.png"},{"id":82072563,"identity":"a7132815-058a-441b-a8cf-105114787ffa","added_by":"auto","created_at":"2025-05-06 13:25:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":238745,"visible":true,"origin":"","legend":"\u003cp\u003eLU\u003cstrong\u003e Carbon Balance Zoning in Xinjiang from 2000 to 2020\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6184608/v1/6389724c2679fbd24291a320.png"},{"id":93956052,"identity":"1e945fe1-da05-436a-99ae-15cbc37d9403","added_by":"auto","created_at":"2025-10-20 16:09:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2410640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6184608/v1/94d821f4-6428-4ce6-91fc-e9551f77383c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial and Temporal Evolution of Land Use Carbon Emission and Carbon Balance Zoning: Evidence from Xinjiang China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLand serves as the foundation for human survival and development, with its utilization patterns and carbon emission effects remaining research focal points in fields such as geography, environmental science, and resource management. Beyond providing a space for human production activities, land is also a medium for the growth of flora and fauna. The various natural resources it harbors, including water and mineral resources, are the foundations of human existence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These resources not only support the economic activities of human societies but also maintain ecological balance and ensure biodiversity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the scope of human production activities continues to expand, there have been profound changes in LU practices. The acceleration of urbanization and industrialization has led to the conversion of a significant amount of land originally used for agriculture, forestry, or natural ecosystems into impervious, resulting in increasingly strained land resources. Concurrently, irrational LU practices, such as over-cultivation and deforestation, have not only undermined the natural ecological functions of the land but also triggered a series of ecological and environmental issues, including soil erosion, land desertification, and a reduction in biodiversity.\u003c/p\u003e \u003cp\u003eXinjiang, as the largest provincial administrative region in China, boasts abundant land resources and a diverse range of ecosystems. However, the region's harsh natural environment and relatively weak economic foundation render LU and carbon emissions particularly complex and critical issues. In recent years, the rapid economic development and continuous population growth in Xinjiang have led to profound changes in LU structure and spatial configuration. The reduction in carbon sink areas and the expansion of carbon source areas, caused by these LU changes, pose severe challenges to the ecological environment and sustainable development of Xinjiang.\u003c/p\u003e \u003cp\u003eIn recent years, with the advancement of tools such as remote sensing and geographic detectors, the acquisition of LU data has become more accurate and convenient. These technological innovations have significantly promoted research in related fields, attracting an increasing number of scholars to engage in the study of LU and its associated impacts. Liu et al. analyzed the basic characteristics and spatial patterns of LU change in China from 1990 to 2010 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Huang examined the relationship between carbon emissions and LU [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Yang et al. studied the transformation and gradient effects of LU in Shaanxi Province based on LU data [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Liu investigated the impact of LU on ecosystem services in the Yellow River Basin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the study of LU carbon emissions, Wei et al. used Geographical Weighted Regression (GWR) and other models to explore the spatiotemporal evolutionary characteristics and influencing factors of LU carbon emissions in Guangzhou [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Wang researched the regional flow of LU change and carbon storage in China based on LU and carbon density data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Some scholars have focused on a specific type of LU; for example, Patel explored the relationship between LU change and agricultural economic growth in India [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Others have concentrated on the spatial changes of agricultural LU and the carbon emissions of urban LU [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Su studied the net carbon sequestration changes of cropland in Shandong Province [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In research on Xinjiang, comprehensive studies are limited. Zhao et al. studied the impact of LU change on ecosystem service value in Xinjiang [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. More scholars have focused on specific regions, such as Li, who used the LU transfer matrix and landscape pattern analysis to study land cover changes in the Aksu area of Xinjiang [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Wang investigated LU changes in the Tarim River Basin [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting research on LU and LU carbon emission is extensive, yet studies focusing on the Xinjiang region are limited. As a vital part of the arid area in northwestern China, Xinjiang's unique geographical environment and climatic conditions endow its LU and carbon emission characteristics with distinct regional traits. To gain an in-depth understanding of the spatiotemporal evolution patterns of LU and land carbon emissions in Xinjiang, and to uncover the characteristics and influencing factors of LU and carbon emissions in the region over recent years, this study analyzes the dynamic changes in LU and carbon emissions based on LU data from 14 cities in Xinjiang from 2000 to 2020. The findings contribute to revealing the spatiotemporal evolutionary traits of LU in Xinjiang, offering a scientific basis for the rational utilization and planning of land resources. To gain an in-depth understanding of the temporal and spatial evolution patterns of land carbon emission and carbon balance zoning in Xinjiang, this study analyzes the dynamics of LU change and carbon emission based on LU data from 14 cities in Xinjiang from 2000 to 2020. The findings contribute to revealing the characteristics of LU and carbon emission evolution in the Xinjiang region, thereby providing a scientific basis for the rational utilization and planning of land resources.\u003c/p\u003e"},{"header":"2. Study Area and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eXinjiang encompasses 14 prefectures (cities), covering an area of 1.6649\u0026nbsp;million square kilometers, approximately one-sixth of China's total area. With a permanent population of 25.98\u0026nbsp;million, it is in the western part of the arid region in Northwest China (74\u0026deg;41\u0026rsquo;-96\u0026deg;18\u0026rsquo; E, 34\u0026deg;22\u0026rsquo;-49\u0026deg;33\u0026rsquo; N). Situated in the interior of the Eurasian continent, Xinjiang is a vital component of the arid zone in Central Asia. An overview of the study area is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Xinjiang's topographical pattern is characterized by \"three mountains and two basins,\" namely, the northern Altay Mountains, the central Tianshan Mountains, and the southern Kunlun Mountains, which enclose the Junggar Basin and the Tarim Basin. This configuration gives rise to a unique combination of mountainous, oasis, and desert ecosystems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although Xinjiang is rich in land resources, its complex terrain includes various geomorphic types such as high mountains, basins, deserts, and grasslands. Its grassland resources serve as a significant base for animal husbandry. The climate in Xinjiang is complex and variable, with a unique water cycle, rendering the ecological environment extremely vulnerable. In the global context of arid regions, Xinjiang holds unique representativeness. Changes in its ecosystems, water resources, and climatic characteristics not only affect the local ecological environment and economic development but also have a significant impact on global climate change and ecosystem balance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Data\u003c/h2\u003e \u003cp\u003eThe data used in this study are all derived from public databases. Sources of LU data, energy consumption, and economic statistics by province and city are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLU Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30m\u0026times;30m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWuhan University CLCD Dataset [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrative Boundary Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChinese Academy of Sciences Resource and Environmental Science Data Center\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Consumption Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXinjiang Statistical Yearbook\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CLCD dataset from Wuhan University encompasses nine land types. In this study, LU types are consolidated and categorized into six types: Cropland, Forest, Grassland, Water, Impervious, and Barren.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research Methodology\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Calculation of Land Carbon Emission\u003c/h2\u003e \u003cp\u003eThe carbon emissions from Cropland, Forest, Grassland, Water, and Barren are calculated directly using the carbon emission coefficient method. The carbon emission coefficients for Cropland, Forest, Grassland, Water, and Barren are 0.422, -0.612, -0.021, -0.235 (t/hm\u0026sup2;), respectively [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A negative carbon emission coefficient indicates carbon sequestration, while a positive coefficient indicates carbon emission. The calculation method for LU carbon emission is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{C}_{1}={\\sum\\:}_{i=1}^{n}{\\alpha\\:}_{i}{S}_{it}\\#（1）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAmong these, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\alpha\\:}}_{\\text{i}}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the carbon emission coefficient for different land types, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e(hm2) represents the area of different land types in various regions.\u003c/p\u003e \u003cp\u003eThe carbon emission from Impervious is calculated using an indirect method. Drawing on the research of relevant scholars, this study calculates the carbon emission from Impervious based on the energy consumption of raw coal, coke, natural gas, crude oil, gasoline, diesel, kerosene, fuel oil, and liquefied petroleum gas in Xinjiang [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The calculation formula is as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{C}_{2}={\\sum\\:}_{i=1}^{n}{{\\gamma\\:}_{i}\\beta\\:}_{i}{E}_{it}\\#（2）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\gamma\\:}}_{\\text{i}}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the conversion coefficient for different types of energy to standard coal. For solid fuels, it is the weight of standard coal required per kilogram of energy consumed, and for gaseous fuels, it is the weight of standard coal required per cubic meter of energy consumed. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the carbon emission coefficient for different types of energy, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}}_{\\text{i}\\text{t}}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the consumption of different types of energy in various regions. The energy consumption is converted into standard coal for calculation. The conversion coefficients and carbon emission coefficients for various types of energy are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConversion Coefficients and Carbon Emission Coefficients for Various Types of Energy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw Coal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoke\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural Gas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrude Oil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGasoline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiesel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKerosene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFuel Oil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLiquefied Petroleum Gas\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Coal Conversion Coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Emission Coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Calculation of Land Area by Type\u003c/h2\u003e \u003cp\u003eThis study analyzes the LU data in Xinjiang from 2000 to 2020 by integrating LU transfer matrices and LU dynamics. The principles of LU transfer matrices and LU dynamics are detailed in references [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, ArcGIS 10.8 is employed to process the reclassified LU raster data for the years 2000, 2004, 2008, 2012, 2016, and 2020. The Raster Calculator tool is used to compute and export the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Economic Contribution Rate of LU Carbon Emission\u003c/h2\u003e \u003cp\u003eThe economic contribution rate of carbon emissions indicates the degree to which a region\u0026rsquo;s carbon emissions influence its economic benefits. The expression formula is as follows [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}ECE=\\frac{{\\text{G}}_{\\text{i}}/\\text{G}}{{\\text{C}\\text{E}}_{\\text{i}}/\\text{C}\\text{E}}\\#（3）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAmong them, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{G}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e denotes the GDP of the i region in Xinjiang, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{G}\\)\u003c/span\u003e\u003c/span\u003e denotes the total GDP of the 14 regions in Xinjiang.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{E}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e denotes the LU carbon emission of the i region in Xinjiang, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{C}\\text{E}\\)\u003c/span\u003e\u003c/span\u003e denotes the total LU carbon emission of the 14 regions in Xinjiang. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{E}\\text{C}\\text{E}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt;1 denotes a higher economic contribution rate of carbon emission, meaning a larger contribution to economic benefits. 0\u0026lt;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{E}\\text{C}\\text{E}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 1 denotes a lower economic contribution rate of carbon emission, implying a smaller contribution to economic benefits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Ecological Carrying Coefficient of LU Carbon Sequestration\u003c/h2\u003e \u003cp\u003eThe carbon absorption ecological carrying coefficient measures the strength of a region's carbon sequestration capacity, with the specific expression as follows:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}ECS=\\frac{{\\text{C}\\text{S}}_{\\text{i}}/\\text{C}\\text{S}}{{\\text{C}\\text{E}}_{\\text{i}}/\\text{C}\\text{E}}\\#（4）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAmong them,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{S}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003edenotes the carbon sequestration amount through LU in the i region of Xinjiang, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{C}\\text{S}\\)\u003c/span\u003e\u003c/span\u003e denotes the total carbon sequestration amount through LU across the 14 regions of Xinjiang. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{E}\\text{C}\\text{S}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt;1 denotes a higher ecological carrying coefficient of carbon sequestration, suggesting a strong carbon sequestration capacity of the ecosystem. 0 \u0026lt; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{E}\\text{C}\\text{S}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 1 denotes a lower ecological carrying coefficient of carbon sequestration, implying a weaker carbon sequestration capacity of the ecosystem.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results Analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of the Area Changes of Different Land Types in Xinjiang\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the changes in cropland, forest, grassland, impervious and barren in the Xinjiang region from 2000 to 2020.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in Land Area by Type in Xinjiang from 2000 to 2020 Unit: Km\u0026sup2;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImpervious\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBarren\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61125.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14614.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e392647.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1146.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42736.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1119453.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64987.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15920.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e391326.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1708.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45934.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1111848.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71374.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17031.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e393533.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2688.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46970.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1100126.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81572.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17300.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e382437.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3344.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49196.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1097874.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86908.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17933.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e380176.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4038.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50219.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1092448.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85996.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18207.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e375385.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4947.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45990.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1101198.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the land area data presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the overall trend shows a continuous increase in cropland, forest, water, and impervious, with a decrease in cropland and water observed in 2020. The areas of grassland and barren exhibit an overall decline during the study period, although there was an increase in barren in 2020. From a natural perspective, the Xinjiang region is ecologically fragile, characterized by sparse vegetation and diverse types of soil erosion with complex interactions. The interplay of water, wind, and frost actions exacerbates the issue of soil erosion. Additionally, the region's climate, marked by frequent winds and scarce rainfall, intensifies the wind erosion. Regarding human factors, the pastoral industry in Xinjiang is well-developed, yet some herding practices employed by pastoralists over the long term have led to excessive depletion of grassland vegetation and severe soil erosion. This has resulted in overgrazing and significant degradation of grasslands, leading to a sharp reduction in grassland area and an escalating conflict between grazing animals and vegetation. Population growth and the inappropriate development and utilization of water and soil resources have also been significant contributors to the destruction of surface vegetation and the intensification of soil erosion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of Dynamic Changes in LU Types in Xinjiang\u003c/h2\u003e \u003cp\u003eAccording to the data presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, from 2000 to 2020, cropland, forest, and impervious in Xinjiang generally show an increasing trend over most periods, whereas barren and grassland exhibit a decreasing trend. Specifically, the area of cropland increased during the periods of 2000\u0026ndash;2004 and 2012\u0026ndash;2016, with change rates of 6.318% and 6.542%, respectively. However, a slight decrease in cropland area was observed from 2016 to 2020, with a change rate of -1.050%. In terms of dynamic rates, the cropland dynamic rates for the periods 2000\u0026ndash;2004 and 2012\u0026ndash;2016 were 1.579% and 1.635%, respectively, while the dynamic rate was negative (-0.262%) from 2016 to 2020.\u003c/p\u003e \u003cp\u003eThe area of forest exhibits an overall trend of increase, with a particularly notable rate of change during the period from 2000 to 2004, reaching up to 8.931%. However, in the subsequent time frames, the rate of increase in forest area has gradually slowed down. In terms of dynamic degree, the forest had a relatively high dynamic degree during the periods of 2000\u0026ndash;2004 and 2008\u0026ndash;2012 (with rates of 2.233% and 0.394% respectively), but a lower dynamic degree during other time periods.\u003c/p\u003e \u003cp\u003eThe area of grassland shows an overall trend of decrease. In particular, the rate of decline in grassland area was rapid during the periods of 2008\u0026ndash;2012 and 2016\u0026ndash;2020, with rates of change of -2.820% and \u0026minus;\u0026thinsp;1.260% respectively. In terms of LU dynamic degree, there was a slight increase in grassland during the 2004\u0026ndash;2008 period. However, the dynamic degree was negative and the absolute values were significant during other time frames, indicating a rapid decrease in grassland area.\u003c/p\u003e \u003cp\u003eThe area of impervious exhibits a marked upward trend throughout the study period, with a relatively high rate of change. Notably, during the 2000\u0026ndash;2004 and 2004\u0026ndash;2008 intervals, the rates of change reached 49.008% and 57.341%, respectively. This sharp increase reflects the rapid expansion of impervious during these two phases. In terms of dynamicity, the impervious maintained a positive and relatively high level of dynamicity across all time periods, further indicating a swift and continuous increase in their area.\u003c/p\u003e \u003cp\u003eThe area of barren exhibits an overall increasing trend. However, during the period from 2016 to 2020, the rate of reduction in barren land is relatively rapid, with a change rate of -8.422%. In terms of dynamic degree, the dynamic degree of barren was positive during the four periods from 2000 to 2016, indicating a gradual increase in barren over these periods. In contrast, the dynamic degree turned negative during the 2016\u0026ndash;2020 period, signifying a reduction in barren during this time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChange Rates and Dynamic Degrees of Various LU Types in Xinjiang Unit: %\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImpervious\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBarren\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange Rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Degrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange Rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Degrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange Rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Degrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange Rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Degrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange Rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Degrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The LU Transition Matrixes in Xinjiang\u003c/h2\u003e \u003cp\u003eThe shift matrix for LU changes in Xinjiang from 2000 to 2020 is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. With respect to the changes in grassland, a total of 315.044 km\u0026sup2; of grassland was converted to other land types, of which 285.791 km\u0026sup2; was transformed into forest land. This shift may be attributed to the well-developed livestock industry in Xinjiang, where overgrazing could lead to the degradation of pastures. Additionally, climate change, which may result in reduced precipitation, could prevent grasslands from receiving adequate moisture, leading to their gradual deterioration.\u003c/p\u003e \u003cp\u003eIn terms of forest changes, 8300.434 km\u0026sup2; of forest has been converted to other land types, with more than 85% being transformed into cropland. This shift indicates that with the growth of Xinjiang's population and the improvement of living standards, the demand for agricultural products is continuously increasing. The conversion of some forests to cropland aims to meet the societal demand for agricultural products by expanding the area of agricultural production. However, the reduction in forests may also lead to issues such as soil erosion and climate change.\u003c/p\u003e \u003cp\u003eWith respect to the changes in cropland, a total of 64,255.396 km\u0026sup2; of cropland has been converted to other land uses. Nearly half of this converted cropland has become barren, indicating that soil erosion is a significant issue in Xinjiang.\u003c/p\u003e \u003cp\u003eRegarding the changes in water, a total of 5982.201 km\u0026sup2; of water area has been converted to other land uses, with more than 80% transformed into barren. This indicates a significant degradation of water in Xinjiang, which may be influenced by the natural environment of the region or could be a result of excessive exploitation and utilization by humans leading to water degradation.\u003c/p\u003e \u003cp\u003eIn terms of the changes in barren, a total of 57,194.844 km\u0026sup2; of barren has been converted to other land uses. This indicates that over the past two decades, Xinjiang has experienced rapid economic development and urbanization. Specifically, 39,110.289 km\u0026sup2; of barren has been transformed into cropland, demonstrating the effective development and utilization of land resources in Xinjiang, as well as positive efforts and significant achievements in cropland protection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eXinjiang LU Transfer Matrix from 2000 to 2020 Unit: km\u0026sup2;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland Transfer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrassland to Forest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrassland to Cropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrassland to Water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrassland to Impervious\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrassland to Barren\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e315.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest Transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest to Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest to Cropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForest to Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eForest to Impervious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eForest to Barren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7184.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e649.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8300.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland Transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCropland to Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCropland to Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCropland to Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCropland to Impervious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCropland to Barren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3771.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23497.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1176.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1931.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33878.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64255.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater to Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater to Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater to Cropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater to Impervious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWater to Barren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e678.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4886.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5982.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpervious Transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpervious to Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpervious to Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImpervious to Cropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImpervious to Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImpervious to Barren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren Transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarren to Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBarren to Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBarren to Cropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarren to Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBarren to Impervious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9149.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39110.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7824.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1098.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57191.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of the Temporal and Spatial Evolution of the LU Carbon Emissions in Xinjiang\u003c/h2\u003e \u003cp\u003eThe carbon emissions from cropland, forest, grassland, water, and barren in Xinjiang from 2000 to 2020 are obtained through LU data. Subsequently, the indirect carbon emissions from impervious in Xinjiang are calculated based on energy consumption data. This results in the total carbon emissions from LU in Xinjiang from 2000 to 2020 (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Temporally, the net carbon emissions from LU in Xinjiang increased from 16,680.720 thousand tons in 2000 to 142,365.242 thousand tons in 2020, with an annual growth rate exceeding 10%, demonstrating a strong growth trend. In contrast, the total carbon absorption in Xinjiang grew slowly from \u0026minus;\u0026thinsp;3317.309 thousand tons in 2000 to -3571.915 thousand tons in 2020.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the total carbon emissions from various types of land in Xinjiang from 2000 to 2020. Viewing the total carbon emissions by land type, the emissions from cropland and impervious in Xinjiang have been continuously increasing, with the growth rate of carbon emissions from impervious being particularly rapid. This, to some extent, reflects the rapid economic development and significant urbanization in Xinjiang from 2000 to 2020. The rapid economic development has led to increased industrial, commercial, and residential energy demands. As urbanization accelerates, urban construction and infrastructure continue to improve, expanding the scale of impervious, which has resulted in the rapid growth of carbon emissions from this land type. The carbon absorption of forest, grassland, and water has remained relatively stable during the study period, with forest and water showing a slow growth trend in carbon absorption, while grassland carbon absorption has shown a declining trend.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal Land Carbon Emissions by Land Type in Xinjiang from 2000 to 2020 Unit: Thousand Tons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImpervious\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBarren\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2575.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-886.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-818.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1054.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16628.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-557.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2738.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-966.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-815.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1135.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28437.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-553.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3007.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1033.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-820.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1160.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40564.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-547.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3437.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1049.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-797.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1214.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72190.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-546.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3662.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1088.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-792.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1241.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99257.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-543.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3623.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1104.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-782.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1136.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142313.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-548.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the evolutionary trend of carbon emissions across 14 cities in Xinjiang from 2000 to 2020. Spatially, the main concentration of LU carbon emissions is in the eastern and western regions, with Urumqi at the center. The distribution of carbon emissions in these cities generally corresponds to their economic output, suggesting a strong correlation between economic development and carbon emissions. From 2000 to 2008, Urumqi and Karamay were the primary contributors to LU carbon emissions. As urbanization intensified around Urumqi, LU carbon emissions rose sharply. By 2020, the number of major cities with significant LU carbon emissions in Xinjiang increased from 2 to 6, predominantly in the eastern and western parts of the region. This shift indicates a changing pattern of LU carbon emissions, expanding beyond traditional economic centers to more cities.\u003c/p\u003e \u003cp\u003eIn terms of the growth rate of LU carbon emissions, both Yili and Hami have experienced a remarkable surge, ascending from the third tier in 2000 to the first tier in 2020, demonstrating a strong growth trend. Geographically, Yili is a pivotal corridor connecting Xinjiang with Central Asia, West Asia, and Europe, offering unique location advantages and serving as an important platform for foreign trade and border cooperation. Hami, situated in the eastern part of Xinjiang, is a key node city on the Silk Road Economic Belt. Its strategic location and convenient transportation make it a pivotal city for Xinjiang to enhance connectivity with inland regions and neighboring countries. With the advancement of the national \u0026ldquo;Belt and Road\u0026rdquo; initiative and the rapid development of Xinjiang, both Yili and Hami are set to have broader development prospects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further analyze the spatial changes in LU carbon emission in Xinjiang, ArcGIS 10.8 software was used to create standard deviation ellipses and shifts in the center of gravity for LU carbon emission from 2000 to 2020 (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The spatial distribution of LU carbon emission in Xinjiang exhibits a significant east-west trend. From 2000 to 2020, the standard deviation ellipses of LU carbon emission in Xinjiang primarily encompass cities such as Urumqi, Changji, and Karamay, which are important economic centers in the region and likely have relatively high levels of LU carbon emission.\u003c/p\u003e \u003cp\u003eAs indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the center of gravity of LU carbon emission in Xinjiang shows an overall migration trend from the central-northern part of the region towards the southeast from 2000 to 2020. The regional differences in economic development across Xinjiang are pronounced, with the central-northern areas mainly engaged in traditional agriculture, animal husbandry, and tourism, which consume less energy. In contrast, the southeastern areas are dominated by industry, services, and other sectors with higher energy demands. This difference in industrial structure may lead to changes in LU patterns and carbon emission intensity, thereby influencing the migration of the carbon emission center of gravity in Xinjiang.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Carbon Balance Zoning of LU in Xinjiang\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Analysis of the Spatial Pattern of Carbon Balance in LU in Xinjiang\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the locational map of carbon balance in Xinjiang from 2000 to 2020. From 2000 to 2012, regions in Xinjiang that achieved carbon balance were primarily those with weaker economic development, such as Hetian. These areas, characterized by lower population densities and slower expansion of impervious and cropland, had a relatively low energy demand. This resulted in the carbon emission from impervious and cropland being less than the carbon sequestration by forests and grasslands.\u003c/p\u003e \u003cp\u003eAfter 2012, all 14 regions in Xinjiang entered the non-carbon balance zone, indicating an accelerated pace of urban construction in Xinjiang. On one hand, the increase in population density led to the expansion of impervious surfaces and an increased demand for energy, thereby raising carbon emissions from impervious and cropland. On the other hand, due to the expansion of population and economic activities, there was a reduction in forested and grassland areas, leading to a decline in carbon sequestration capacity and disrupting the original state of carbon balance.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTherefore, while amidst the process of rapid urban expansion, Xinjiang should comprehensively consider how to balance economic development with environmental protection. On one hand, it is imperative to augment efforts in ecological protection and restoration to rehabilitate and augment the carbon sequestration capabilities of natural ecosystems. On the other hand, the development of a low-carbon economy and circular economy should be considered to improve energy efficiency and reduce carbon emissions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Carbon Balance Zoning for LU in Xinjiang\u003c/h2\u003e \u003cp\u003eUtilizing the previously outlined calculation formulas, the ecological carrying capacity coefficients and economic contribution coefficients for the 14 administrative regions in Xinjiang were calculated. Based on these coefficients, the regions were categorized into carbon sink functional areas, low-carbon economic areas, carbon intensity control areas, and high-carbon optimization areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), and the criteria for the functional area classification are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. This zoning serves as a reference for future carbon emission reduction policies and development strategies in various regions across Xinjiang.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Carbon Balance Zones\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion Division\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivision Criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDivision Characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Carbon Development Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECE\u0026gt;1, ECS\u0026gt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth the ecological carrying coefficient and economic contribution coefficient are high. These regions should focus on the application of low-carbon technologies and the reduction of carbon emissions while maintaining economic development.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Intensity Control Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECE\u0026gt;1, ECS\u0026lt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ecological carrying coefficient is low, but the economic contribution coefficient is high. These regions need to prioritize the protection and restoration of ecosystems, enhance carbon sequestration capacity, and pay attention to economic transformation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Sink Functional Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECE\u0026lt;1, ECS\u0026gt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe ecological carrying coefficient is high, while the economic contribution coefficient is low. These regions should optimize the industrial structure, reduce the proportion of high-carbon industries, and promote green and low-carbon development.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Carbon Optimization Zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECE\u0026lt;1, ECS\u0026lt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth the ecological carrying coefficient and economic contribution coefficient are low. These regions need to optimize the industrial structure and promote green and low-carbon development,advance ecological environmental protection and governance efforts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(1) Low Carbon Development Areas. From 2000 to 2020, Xinjiang has had a total of 6 regions, including Hetian, consistently designated as low-carbon development areas, reflecting an effective balance between the economy and ecological health. These regions, mainly in southern and northwestern Xinjiang, are characterized by extensive agricultural and pastoral activities, large land areas, and low population densities, making them conducive to agricultural production. Their distance from the economic center has curtailed the growth of high-energy, high-emission industries, and facilitating the development of a unique model of ecological agriculture and pastoralism due to their unique geographical location. To be specific, in southern Xinjiang, regions like Hetian use water resources from rivers such as the Tarim to support irrigation and specialty agriculture, thereby creating local agricultural brands. In the northwestern part of Xinjiang, regions like Yili Valley, with its vast grasslands and forests, have developed a balanced agriculture and pastoral economy, promoting both economic and ecological benefits. These regions need to continue to enhance eco-agriculture and pastoralism. With improved technology and management, leveraging abundant natural resources will enhance specialized farming and quality pastoralism. It is essential to protect the environment, preserve biodiversity, and ensure sustainable agriculture.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(2) Carbon Intensity Control Zone. These regions exhibit high economic contribution for carbon emissions but exhibit low ecological carrying coefficients. Turpan was the primary area, and then transitioned to Urumqi and Akesu. exhibit high economic contribution to carbon emissions but low ecological carrying capacity. Initially, Turpan was identified as a carbon intensity control area before transitioning to a high-carbon optimization area, reflecting economic challenges and a decline in carbon emission efficiency. Conversely, Urumqi and Akesu shifted from high-carbon to carbon intensity control areas, indicating improved emission efficiency and a move towards sustainable development. These areas should adopt strategies to align economic growth with environmental protection, enhancing carbon emission efficiency and promoting a greener economy.re transitioning to a high-carbon optimization area, reflecting economic challenges and a decline in carbon emission efficiency. Conversely, Urumqi and Akesu shifted from high-carbon to carbon intensity control areas, indicating improved emission efficiency and a move towards sustainable development. These areas should adopt strategies to align economic growth with environmental protection, enhancing carbon emission efficiency and promoting a greener economy.\u003c/p\u003e\u003cp\u003e(3) Carbon Sink Functional Zone. These regions feature high ecological carrying coefficients but low economic contribution coefficients for carbon emissions. Initially, areas such as Altay and Hami were classified as carbon sink functional areas. Over time, Altay transitioned to a low-carbon development area, indicating enhanced economic efficiency of carbon emissions through industrial upgrading while preserving the ecological environment. Hami, however, not only failed to optimize its industrial structure and develop low-carbon industries, but the growth of high-pollution and high-consumption industries also impaired the region's ecological carrying coefficient to some extent. These areas should promptly adjust their industrial structures, phase out outdated production capacity, and foster the development of low-carbon and environmentally friendly emerging industries.\u003c/p\u003e\u003cp\u003e(4) High Carbon Optimization Zone. Regions in this category have low ecological carrying coefficients and economic contribution coefficients for carbon emissions, often facing significant environmental pressure and transformation challenges during economic development. Initially, these areas primarily included cities in the northeast and west, later becoming more concentrated in the northeast. Early on, regions such as Karamay and Changji were dominant; their resource endowments led to a relatively simplistic industrial structure dominated by industrial and resource-based industries. Over time, cities like Akesu have gradually moved out of the high carbon optimization areas, while some cities in the northeast continue to face substantial environmental pressure and transformation challenges due to difficulties in industrial structure adjustment and transformation. Karamay's economic development is closely linked to the oil and petrochemical industry. On November 28, 2023, the National Development and Reform Commission designated Karamay as one of the first batch of pilot cities for carbon peak, providing a significant opportunity for its green and low-carbon transformation. Regions such as Changji, Turpan, and Hami are also actively promoting industrial transformation, upgrading, and green, low-carbon development. These areas should take proactive measures to drive industrial transformation and upgrading, as well as green and low-carbon development, while also advancing ecological environmental protection and governance efforts.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion and Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Conclusion\u003c/h2\u003e \u003cp\u003e(1) From 2000 to 2020, the area of cropland, forest, water bodies, and impervious land in Xinjiang has shown an overall trend of continuous expansion, while grassland and barren land have continuously contracted. Over the 20-year period, the area of cropland in Xinjiang has increased by 40.7%, forest area by 24.6%, water body area by 7.6%, and impervious land area by 331.5%. Conversely, the areas of grassland and barren land have decreased by 4.4% and 1.6%, respectively.\u003c/p\u003e \u003cp\u003e(2) From 2000 to 2020, Xinjiang experienced a conversion of 315.044 km\u0026sup2; of grassland into other land types, with 90.7% of this conversion being to forest. A total of 8300.434 km\u0026sup2; of forest was transformed into other land types, with more than 87.8% of this area being converted to cropland. The conversion of 64255.396 km\u0026sup2; of cropland resulted in nearly 52.7% being transformed into non-impervious land uses. An area of 5982.201 km\u0026sup2; of water bodies was converted to other land uses, with over 81.7% being transformed into non-impervious land. Additionally, a total of 57194.844 km\u0026sup2; of barren land was converted to other land uses, with 68.4% of the barren land being transformed into cropland.\u003c/p\u003e \u003cp\u003e(3) From 2000 to 2020, the LU carbon emissions in Xinjiang have shown a trend of continuous expansion without any sign of deceleration. This is primarily due to the excessive growth in carbon emissions from impervious. The carbon emissions (absorption) from cropland, forest, water, and barren have remained relatively stable overall. The net carbon emissions in Xinjiang increased from 16680.720 \u0026times; 10\u0026sup3; tons in 2000 to 142365.242 \u0026times; 10\u0026sup3; tons in 2020, with an annual growth rate exceeding 10%, indicating a strong growth trend. The carbon emissions from impervious increased from 16628.671 \u0026times; 10\u0026sup3; tons in 2000 to 142313.193 \u0026times; 10\u0026sup3; tons in 2020, with a growth rate exceeding 11%, which is higher than the growth rate of Xinjiang's net carbon emissions. The total carbon absorption in Xinjiang grew slowly from \u0026minus;\u0026thinsp;3317.309 \u0026times; 10\u0026sup3; tons in 2000 to -3571.915 \u0026times; 10\u0026sup3; tons in 2020.\u003c/p\u003e \u003cp\u003e(4) From 2000 to 2020, the LU carbon emissions of the 14 cities in Xinjiang display an east-west spatial pattern, characterized by a diffusion from Urumqi as the central point towards both the eastern and western regions. The spatial distribution of these emissions generally aligns with the spatial distribution of the cities' economic output. Notably, Yili and Hami exhibit a significant growth trend in carbon emissions. The migration of the carbon emission center of gravity indicates a trend of LU carbon emissions in Xinjiang shifting towards the southwest.\u003c/p\u003e \u003cp\u003e(5) According to carbon balance analysis, the scope of the low-carbon development areas in Xinjiang has been gradually expanding from 2000 to 2020, while the number of high-carbon optimization areas has been decreasing. Specifically, most cities in the southern and eastern parts of Xinjiang have consistently remained within the low-carbon development areas. Urumqi and Akesu are transitioning from high-carbon optimization areas to low-carbon development areas. In contrast, the results for Turpan and Hami indicate the opposite trend.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Discussion\u003c/h2\u003e \u003cp\u003e(1) The significant changes in LU types in the Xinjiang region, particularly the substantial increases in cropland, forest, and impervious, as well as the decreases in grassland and barren, are likely driven by a variety of factors. Policy initiatives, population growth, economic development, and ecological environmental demands have all influenced LU changes to varying degrees. Population growth may have contributed to the expansion of cropland area; the acceleration of urbanization has directly led to a significant increase in impervious; and the rapid development of animal husbandry has, to some extent, caused the degradation of grasslands. Concurrently, ecological protection and restoration policies may also have an impact on the changes in forest and grassland.\u003c/p\u003e \u003cp\u003e(2) The extensive conversion of grassland to forest may reflect the efforts of the Xinjiang region in ecological environmental protection, such as the implementation of policies like converting farmland to forest and grassland. However, this transformation could also have certain impacts on the local ecosystem, including changes in biodiversity, soil erosion, and degradation of soil quality. Furthermore, the large-scale conversion of forest to cropland, while potentially increasing LU efficiency, may also exert pressure on the ecological environment, such as the reduction of water conservation capacity and the degradation of soil fertility. Therefore, it is necessary to consider the ecological, economic, and social benefits of LU transformation to develop reasonable LU planning.\u003c/p\u003e \u003cp\u003e(3) The rapid growth of LU carbon emissions in Xinjiang, particularly the swift increase in emissions from impervious, is noteworthy. The expansion of impervious is often accompanied by extensive infrastructure development, including roads, bridges, residential areas, and factories. These construction activities necessitate a substantial consumption of energy and materials, resulting in significant carbon emissions. To mitigate carbon emissions, a range of measures must be adopted, such as optimizing the energy consumption structure, enhancing energy efficiency, promoting renewable energy sources, and strengthening LU planning and management. Additionally, it is essential to enhance the monitoring and assessment of carbon emissions to promptly understand changes in emission levels.\u003c/p\u003e \u003cp\u003e(4) The spatial distribution differences in LU carbon emissions among cities in Xinjiang may be related to factors such as the level of economic development, industrial structure, energy consumption patterns, and types of LU. As the political, economic, and cultural center of Xinjiang, Urumqi has relatively high LU carbon emissions. The rapid growth in carbon emissions in regions like Yili and Hami may be associated with the deepening implementation of the national \"Belt and Road\" initiative. Therefore, it is necessary to formulate differentiated carbon emission management policies based on the actual conditions of various regions to promote regional coordinated development.\u003c/p\u003e \u003cp\u003e(5) Overall, most regions in Xinjiang are progressively moving up in the carbon balance zoning, a trend that reflects the positive advancements made in low-carbon development within the region. On one hand, Xinjiang has introduced a series of policies in recent years aimed at promoting green and low-carbon development. For instance, the government has mandated that the urban and rural construction sectors reach peak carbon emissions before 2030. On the other hand, Xinjiang has actively explored ways to advance green development. For example, through the implementation of the \"Ecology Plus\" plan, photovoltaic power projects for desert control have been established at the edges of deserts. These projects not only provide residents with clean green electricity but also achieve a triple win in ecological, economic, and social benefits. Regions like Turpan and Hami, however, may rely more on energy-intensive industries and fossil energy consumption, resulting in relatively higher carbon emissions. In the future, Xinjiang needs to continue strengthening efforts in policy guidance, technological innovation, and industrial structure adjustment to drive the province towards a more green and low-carbon development goal.\u003c/p\u003e \u003cp\u003e(6) This study has the following limitations: Firstly, due to data availability and spatial analytical capabilities, the research was unable to investigate carbon emissions and their zoning at the county level in Xinjiang. Future scholars with interest in this area may conduct more comprehensive studies to explore aspects not covered in this paper. Secondly, constrained by methodological limitations, the LU carbon emission coefficients employed in this paper were based on previous scholarly research. Carbon emission coefficients can vary slightly across different regions. Interested scholars in the future may delve deeper into the examination of carbon emission coefficients for various regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions: \u003c/strong\u003eData curation, Jiahu Hu; Funding acquisition, Lianwei Zhang; Investigation, Jiahu Hu; Methodology, Jiahu Hu; Project administration, Lianwei Zhang; Resources, Jiahu Hu; Software, Mengfei Song; Supervision, Lianwei Zhang; Writing \u0026ndash; original draft, Mengfei Song.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;This work is supported by the Major Projects of the Ministry of Education (Grant Number: 22JZDZ021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement: \u003c/strong\u003eAll data are fully available without restriction. The datasets are taken from several public repository,\u0026nbsp;Wuhan University CLCD Dataset\u0026nbsp;(http://irsip.whu.edu.cn/resources/CLCD.php), Chinese Academy of Sciences Resource and Environmental Science Data Center(http://www.resdc.cn), Xinjiang Statistical Yearbook(https://tjj.xinjiang.gov.cn/tjj/tjfw/list_tjfw.shtml)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest: \u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLi W , Yang C X, Wang L C , et al. National attractive territorial area:A national spatial planning strategy reshaping regional patterns. Journal of Natural Resources 2020, 35, 501-512.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSun X Z , Zhou H L, Xie G D. Ecological Services and Their Values of Chinese Agroecosystem. China Population,Resources and Environment 2007, 17, 55-60.\u003c/li\u003e\n \u003cli\u003eNagendra H, Reyers B, Lavorel S. 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Acta Geographica Sinica, 2022, 71, 679-696.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Xinjiang, Land Use (LU), Carbon Emission, Dynamic Change","lastPublishedDoi":"10.21203/rs.3.rs-6184608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6184608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand use (LU) change has become one of the primary sources of regional carbon emissions. Investigating the spatiotemporal characteristics and future trends of LU carbon emissions is of great significance for optimizing LU structures, formulating emission reduction policies, and developing a low-carbon economy in the region. Based on data of six LU types and energy consumption from 2000 to 2020 in Xinjiang, this study employs methods such as the LU transfer matrix and land dynamic state to analyze the spatiotemporal changes in LU and carbon emissions in Xinjiang. The results indicate the following: Firstly, from 2000 to 2020, the areas of cropland, forest, water, and impervious in Xinjiang showed an overall trend of continuous expansion, whereas grassland and barren continuously contracted. Secondly, according to the LU transfer matrix, from 2000 to 2020, 90.7% of the grassland was converted to forest, 87.8% of the forest was converted to cropland, 52.7% of the cropland was transformed into barren, 81.7% of the water was converted into barren, and 68.4% of the barren was transformed into cropland. Thirdly, the LU carbon emissions in Xinjiang from 2000 to 2020 exhibited a continuous expansion trend without any sign of mitigation, primarily due to the rapid growth in carbon emissions from impervious, while the carbon emission (absorption) levels from cropland, forest, water, and barren remained relatively stable. Fourth, from 2000 to 2020, the LU carbon emissions of the 14 cities in Xinjiang exhibit an overall pattern of diffusion from Urumqi as the center towards the east and west. Notably, there is a trend of the emission center shifting towards the southwest. Fifthly, in summary the carbon balance zoning in most regions of Xinjiang is progressively transitioning to higher categories.\u003c/p\u003e","manuscriptTitle":"Spatial and Temporal Evolution of Land Use Carbon Emission and Carbon Balance Zoning: Evidence from Xinjiang China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 13:25:16","doi":"10.21203/rs.3.rs-6184608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T08:41:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-12T08:21:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T07:46:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85228776406738868628012279066675953807","date":"2025-07-04T09:22:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46816348349574858570994867037551968279","date":"2025-07-02T18:16:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241176922886346865789834724059336971853","date":"2025-05-22T02:49:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T09:21:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68900788496206794644402266465118214827","date":"2025-05-03T01:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T14:19:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-21T07:48:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-17T18:12:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-17T05:13:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-08T14:18:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ec55961-2150-4fa2-93e7-d53047a0ebea","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48016436,"name":"Earth and environmental sciences/Ecology"},{"id":48016437,"name":"Earth and environmental sciences/Environmental sciences"},{"id":48016438,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-10-20T16:03:11+00:00","versionOfRecord":{"articleIdentity":"rs-6184608","link":"https://doi.org/10.1038/s41598-025-19475-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-13 15:57:10","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2025-05-06 13:25:16","video":"","vorDoi":"10.1038/s41598-025-19475-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-19475-9","workflowStages":[]},"version":"v1","identity":"rs-6184608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6184608","identity":"rs-6184608","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}