Time-Space Evolution and Drivers of CO2 Emissions from Land Utilization in Xinjiang from 2000 to 2020

By inergency On Apr 1, 2024

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3.1. Spatio-Temporal Dynamic Analysis of Land Use Change in Xinjiang from 2000 to 2020

The findings of the examination on the fluctuating alteration of land utilization in Xinjiang between 2000 and 2020 are presented in Table 3 and Figure 3. From 2000 to 2010, the study area experienced an overall increase in the area of cropland, grassland, and construction land, while the area of forest land, waters, and unutilized land decreased. Notably, the largest increase was observed in construction land, with a significant rate of change of 83.70% over the 10-year period; the degree of dynamics was 8.37%, increasing at a rate of 2.91 × 10⁴ hm²·a. the area of waters had the biggest decrease, with a rate of change of −35.53% during the 10-year period and a motivation of −3.55%, with a rate of decrease of 14.34 × 10⁴ hm²·a.

From 2010 to 2020, the cropland, waters, and constructed land all showed an increasing trend, and the forest land, grassland, and unutilized land showed a decreasing trend, among which the constructed land had the biggest increase, with a rate of change of 12.66% during the 10-year period. The dynamic degree was 1.27%, increasing at the rate of 0.83 × 10⁴ hm²·a, and the forest land had the largest decrease, with the rate of change over 10 years of −2.56%, and a dynamic degree of −0.26%, decreasing at the rate of 0.58 × 10⁴ hm²·a.

Compared with 2010~2020, the area change of each type of land use during 2000~2010 is large, mainly manifested in the inflow and outflow of grassland and unutilized land; about 12% of unutilized land becomes grassland, and about 23% of grassland becomes unutilized land.

During 2000~2020, the cropland, grassland, and construction land show an increasing trend, and the forest land, waters, and unutilized land show a decreasing trend. Among them, the increase in construction land is the most obvious, with the rate of change of 106.96% over 20 years. The degree of dynamics is 5.35%, increasing at the rate of 1.87 × 10⁴ hm²·a; the decrease in water area is larger, with the rate of change of −33.30% over 20 years, and the degree of dynamics is −1.67%, decreasing at the rate of 6.72 × 10⁴ hm²·a.

Figure 3 and Figure 4 illustrates the changes in land use types in Xinjiang between 2000 and 2020. The most prominent spatial change during this period was the transformation of unutilized land into grassland, primarily occurring in the Altai Mountains, Tianshan Mountains, and Kunlun Mountains.

Additionally, there was a conversion of grassland into cropland and unutilized land, with the majority of the unutilized land conversion zones concentrated in the southern region of Xinjiang, surrounding the Tarim Basin.

The majority of unused land conversion areas are concentrated in specific regions of Aksu, Kashgar, Hotan, Bayangol Mongol Autonomous Prefecture, and Changji Hui Autonomous Prefecture.

Cropland conversion areas are primarily found in the north-central part of Kashgar Region, the north-central part of Aksu, and the southern part of Tacheng Region.

Areas where water is converted to unused land are mainly located in the northern and southern slopes of the Tianshan Mountain range and the western part of the Kunlun Mountain range.

Between 2010 and 2020, the most noticeable change in land use types was the transformation of unused land into grassland. This change was primarily observed in the eastern part of Altay Region, the southeastern part of Changji Hui Autonomous Prefecture, and the southeastern part of Bayangol Mongol Autonomous Prefecture.

Additionally, there was a significant conversion of grassland into cropland, mainly occurring in the northeastern part of Kashgar Region, the central part of Aksu Region, the southern part of Tacheng Region, and the central part of Changji Hui Autonomous Prefecture.

The spatial transformation of land use in Xinjiang between 2000 and 2020 can be summarized as the conversion of unused land into grassland, grassland into cropland, and unused land and water into unused land. The changes in land use during the period from 2000 to 2010 were more significant compared to the period from 2010 to 2020. The areas that remained unchanged in terms of land use were primarily unutilized land, located in the Taklamakan Desert, Gurbantunggut Desert, and Kumutag Desert.

3.2. Analysis of Carbon Emissions from Land Use in Xinjiang from 2000 to 2020

The carbon sources, sinks, and net carbon emissions in Xinjiang from 2000 to 2020 were determined based on Equations (1), (3), and (4). Analysis of Table 4 reveals that there is a direct correlation between the year and net carbon emissions, with a consistent and substantial increase over time. Specifically, the net carbon emissions rose from 7,534,000 t in 2000 to 138,685,100 t in 2020, resulting in a net increase of 131,151,100 t. This represents a staggering 18-fold increase, with the most significant growth occurring between 2010 and 2020, during which the net carbon emissions surged by 109,507,400 t. This translates to an average annual growth rate of 37.53%.

From a carbon source perspective, construction land is the primary contributor to carbon emissions, accounting for over 90% of the total emissions. These emissions have been rapidly increasing over the years. On the other hand, while carbon emissions from cropland have shown a steady increase, their proportion has decreased from 6.9% to 0.97%. This suggests that the role of cropland as a carbon source is gradually diminishing, with construction land emerging as the primary source of carbon emissions from land use.

The carbon sinks in Xinjiang had a consistent fall of approximately 1,098,000 tons between 2000 and 2020. This loss was particularly notable between 2000 and 2010, with a decline rate of 25.31%. Of all the land types, forest land has the highest capacity to absorb carbon, accounting for approximately 40% or more. However, over time, this capacity gradually decreases. This is primarily because around 45% of forest land was converted to grassland between 2000 and 2010, and the overall area of forest land has been decreasing. As a result, the carbon sink capacity has also decreased. The carbon sink of the watershed saw a net decline of 436,000 metric tons during the period from 2000 to 2010. Additionally, approximately 37% of the water resources were converted to unutilized land. The carbon storage capacity of farmland, grassland, and unutilized land remained largely constant.

The spatial distribution of carbon emissions and carbon sinks was determined based on the direct mapping of land use categories. The density of carbon emissions and carbon sinks was then estimated by considering the area of each land use category. The corresponding results can be observed in Figure 5 and Figure 6.

The findings depicted in Figure 5 indicate that during the initial phase of the study, areas with high carbon emissions from land use were primarily concentrated in the central urban regions of Urumqi and other Xinjiang prefectures. Subsequently, these areas gradually expanded towards the outskirts of the urban centers after 2010. In 2010, there was an 83.49% increase in the number of high carbon emission areas compared to 2000.

Notably, these areas were predominantly located in the periphery of the urban centers. Throughout the study period, the carbon emission density of cropland exhibits a fluctuating pattern, initially decreasing and subsequently increasing.

Conversely, the carbon emission density of construction land experiences a significant increase, of 472.15%, over the same period. This leads to the conclusion that the carbon emission resulting from construction land surpasses the contribution of cropland to carbon emission.

Observing the spatial change of the carbon sink density in Xinjiang (Figure 6), the results showed that high carbon-sink land-use patches were mainly distributed in the Altay Mountains, and on the north and south slopes of the Tianshan Mountains and the Kunlun Mountains in Xinjiang. During the study period, there was no significant change in carbon sink density as a whole; the total area of carbon sink patches decreased by 0.27%, and the average carbon sink density decreased by 0.08%.

The spatial-distribution and change trajectories of carbon emissions from land use in Xinjiang from 2000 to 2020 are shown in Figure 7. It can be observed that, in the case of carbon emissions increasing year by year from 2000 to 2020, the spatial-distribution pattern radiating from the central and northern regions to the surrounding cities is presented.

The areas of high carbon emissions and changes in carbon emissions are centrally distributed in the city of Urumqi and the Changji Hui Autonomous Prefecture. Low-value areas are concentrated in the Kizilsu Kirgiz Autonomous Prefecture, Hotan and Kashgar regions.

3.3. Analysis of Carbon Emission Drivers

3.3.1. Data Testing

This work employs the Geographically and Temporally Weighted Regression model (GTWR) to investigate the influential factors behind carbon emissions from land use and their geographical dynamics in Xinjiang. The six categories of factors were initially assessed for covariance and subsequently examined for significance. The results revealed an anomaly, which can be attributed to the excessively high value of the urban development level in Karamay. Upon excluding Karamay City from the analysis, it was observed that the six types of factors significantly influence carbon emissions. Furthermore, the variance inflation factor (VIF) was found to be less than 10, indicating the absence of multicollinearity. Table 5 displays the variance inflation factor and index explanation for each variable.

The GTWR_Beta plug-in, developed by Prof. Bo Huang’s team at the Chinese University of Hong Kong, utilizes the ArcGIS platform to automatically determine the ideal bandwidth and set the spatio-temporal distance-parameter ratio to 1. The regression coefficients of the affecting factors were evaluated and computed. The AIC criterion and the goodness-of-fit R² were chosen as the evaluation metrics for the model’s confidence. The findings may be found in Table 6. Regarding the goodness-of-fit, both R² and the R² adjusted exceeded 0.95, indicating that this model captures well the impact of the explanatory factors on the dependent variables.

3.3.2. Analysis of Temporal Heterogeneity of Regression Coefficients of Factors Affecting Carbon Emissions from Land Use

The regression coefficients of the factors affecting carbon emissions in each state of Xinjiang were plotted as box plots according to the year (Figure 8 and Figure 9) to explore the temporal heterogeneity.

During the study period, there was a consistent increase in carbon emissions in Xinjiang as a result of urbanization. The outcome is the conversion of a limited portion of agricultural and ecological land, including forests, grasslands, and unused land, into construction land. This conversion diminishes the capacity of these areas to absorb carbon, resulting in a reduction of carbon sinks.

The amount of economic development throughout time has a large and positive impact on carbon emissions, much like the level of urbanization.

The level of population concentration has a direct correlation with carbon emissions. Over time, most regions have experienced an increase in population concentration coefficients.

The degree of technology has a predominantly beneficial impact on carbon emissions, leading to a progressive increase over the course of the study period.

The ratio of developed land has a predominantly favorable impact on carbon emissions, exhibiting a consistent pattern of gradual growth over time.

Throughout the study period, there was an observed pattern of carbon emissions being influenced by the industrial structure, with an initial upward trend followed by a subsequent increase.

3.3.3. Analysis of Spatial Heterogeneity of Regression Coefficients of Factors Affecting Carbon Emissions from Land Use

The regression coefficients of the influencing factors were graphically represented using ArcGIS, resulting in spatial- and temporal-distribution maps for the six categories of influencing factors.

The findings indicate that there is a correlation between the degree of urbanization and carbon emissions, as depicted in Figure 10. Specifically, the data reveal a trend of higher carbon emissions in the southwest region and lower emissions in the middle east region.

The primary factor contributing to carbon emissions in Kizilsu Kirgiz Autonomous Prefecture, and Kashgar and Hotan regions is the amount of urbanization, whereas Urumqi and Changji Hui Autonomous Prefecture have comparatively lower levels of urbanization and thus lesser contributions to carbon emissions. The correlation coefficients of most cities decreased over time, transitioning from positive to negative.

2.: Industrial structure

The regression coefficients of industrial structure (Figure 11) exhibit a pattern of high values on both ends and low values in the middle from 2000 to 2010. In 2020, there is a distinct pattern of high values in the western region and low values in the eastern region.

The regression coefficients for Urumqi, the capital of Xinjiang Province, and the adjacent Changji Hui Autonomous Prefecture are lower. The Aksu and Hami regions are primarily affected by the industrial structure, with the Turpan and Bayangol Mongol Autonomous Prefectures following suit.

3.: Economic development level

The correlation between the degree of economic development and carbon emissions (Figure 12) demonstrates a gradual increase from the western regions to the eastern regions. The Bayangol Mongol Autonomous Prefecture has the most significant influence on carbon emissions in terms of economic development, followed by Urumqi and Changji Hui Autonomous Prefecture.

4.: Population agglomeration

The relationship between the degree of population concentration and carbon emissions, as depicted in Figure 13, exhibits a pattern of high values in the east and low values in the west.

The Altay region is most affected by population agglomeration, followed by the Turpan and Hami regions. With the exception of Kashgar, Hotan, Aksu and Kizilsu Kirgiz Autonomous Prefecture, where the regression coefficients decrease annually, the regression coefficients of the remaining prefectures increase as the years progress.

5.: Technology level

The relationship between technological level and carbon emissions (Figure 14) has high values on both ends and low values in the middle. The carbon emissions in Kashgar are mostly influenced by the level of technology, with Kizilsu Kirgiz Autonomous Prefecture following closely behind.

The emissions are comparatively lower in Aksu and Hotan. The coefficients of most cities have been gradually increasing over time. The regression coefficients for Kashgar and Kizilsu Kirgiz Autonomous Prefectures have changed from negative to positive.

6.: Proportion of construction land

The relationship between the proportion of construction land and carbon emissions (Figure 15) demonstrates a clear geographical pattern, with lower levels observed in the western regions and higher levels in the eastern regions.

The allocation of land for construction has the most significant influence on carbon emissions in the Altay region, with the Tacheng region and Changji Hui Autonomous Prefecture following suit. At the beginning of the study, construction land had a negative effect on carbon emissions in Aksu, Ili Kazakh Autonomous Prefecture, and the Hotan region.

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