Introduction

With the advancement of urbanization, the land use around cities undergoes frequent changes. Land use changes affect the carbon emissions and carbon cycle of terrestrial ecosystems, which can either become carbon sources or sink. Conducting research on land use carbon emissions contributes to the low-carbon sustainable development of the economy and society1. Wang analyzed the land use changes and spatial-temporal patterns of carbon emissions in the Pearl River Delta region from 2005 to 2018, highlighting the impact of urban development on carbon emissions in the region2. Lin calculated the carbon emission intensity and per capita land use carbon emissions of 30 provinces in China from 2006 to 2016, explaining the spatio-temporal characteristics3. Xu used information entropy and Tapio models to reveal the spatial-temporal patterns of carbon emissions in the Hubei Urban Agglomeration from 2001 to 2017, identifying their decoupling status. Urban development leads to changes in land use types, resulting in the evolution of carbon emission spatial patterns, affecting not only ecological balance but also balancing human ecological and economic development4. Therefore, exploring the changes in carbon emission patterns caused by land use changes under the background of rapid urbanization has important guiding significance for the formulation of regional energy-saving and carbon reduction policies.

There are many factors influencing land use carbon emissions. Wei scientifically analyzed the influencing factors of carbon emissions in Shanghai using the logarithmic mean index model and evaluated the contribution ratios of various driving factors using the structural decomposition analysis method (SDA)5. Zhao analyzed the uncertainties in carbon accounting principles, driving factors, simulation mechanisms, and their impact on achieving peak targets6. Lin used spatial correlation models to conclude that carbon emissions are influenced not only by land development intensity, urbanization level, and energy consumption structure but also by population policies3. Wei decomposed the factors influencing carbon emissions based on the STIRPAT model and explored the contribution of structural, scale, and technological factors to changes in carbon emissions using regression models7. Cong conducted a time series analysis of carbon emissions and their intensity and identified GDP, technology, and industrial carbon emissions as the main contributing factors8. Li established a factor decomposition model to analyze the role of economic quality, industrial structure, and pollution intensity in reducing carbon emissions9. Li identified through the LMDI model that energy use structure and efficiency, economic development quality, and technological progress level are the three major factors influencing carbon emissions10. Through systematic studies, carbon accounting in cities and the extraction of influencing factors have matured11,12,13,14, providing a solid theoretical basis for further research15,16,17,18. By reviewing the above literature, it can be found that the calculation methods for carbon emissions based on land use type and energy consumption are relatively mature and can effectively measure the carbon emission level of a certain region. In addition, the LMDI decomposition model can analyze the influencing factors of carbon emissions in a relatively complete way, providing a good path for exploring the main contributing factors of regional carbon emissions. Furthermore, there is a paucity of exploratory studies on the carbon emission drivers of large urban areas, particularly those cities that have adopted an eco-economy as their primary development orientation. It is therefore of theoretical and practical significance to take such cities as research objects in order to explore the mechanism of enriching the influencing factors of carbon emissions.

Nanchang is located in the central-northern part of Jiangxi Province in eastern China. In recent years, the city has undergone significant expansion, with initiatives in urban construction, industrial development, ecological protection, and the promotion of standard farmland construction leading to frequent changes in land use patterns19,20. This inevitably results in dynamic changes in the spatial-temporal patterns of carbon emissions21,22. These researches explore and analyze the spatiotemporal pattern evolution of land use carbon emissions under the background of urban development from the perspective of land use change. However, there is a paucity of studies that have conducted a comprehensive analysis of the driving factors over an extended time series. Nanchang, the capital city of Jiangxi Province, a large city of the first type, and the centre of the ecological and economic zone of the Poyang Lake, presents an ideal case study for investigating the spatial and temporal characteristics of its carbon emissions from land use, as well as the underlying driving factors. This can provide valuable insights for the low-carbon development of the region. Using the LMDI decomposition model and combined with the actual situation in the research area, the influencing factors are explored. Based on this, relevant suggestions are proposed for Nanchang City to achieve its development goals of carbon reduction and emission reduction, providing a basis and reference for regional response to the “dual carbon” policy, and ultimately promoting the construction of an ecological civilization city.

Method and material

Study area

Nanchang(115°27’ E, 28°10’ N ~ 116°35’ E, 29°11’ N), the capital of Jiangxi Province, is located in southeastern China, on the middle and lower reaches of the Yangtze River (Fig. 1). The entire territory encompasses an area of 7,195 km2, annual rainfall is 1,600-1,700 mm. Its topography is predominantly plains, with a relatively flat surface in the southeastern region and rolling hills in the northwestern region. The climate is humid and mild, situated within the subtropical monsoon zone. Over the past 20 years, Nanchang has developed rapidly, with its gross regional product reaching 574.51 billion yuan in 2020.

Fig. 1
figure 1

Geographical location map of Nanchang City.

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Data collection

The remote sensing images of Nanchang City are derived from Landsat 2000, 2010, and 2020 satellite remote sensing images of the geospatial data cloud database (https://www.gscloud.cn/), with a spatial resolution of 30 m. Among them, the city land-5TM remote sensing images in 2000 and 2010, and Landsat-8 OLI remote sensing images in 2020. Moreover, we also revised the interpretation results by comparing them with the overall land use planning of Nanchang City, improving the accuracy of the results. In order to ensure consistency in the analysis of land types, the remote sensing images used in this study were selected to represent a narrower range of seasonal variation, specifically from June to September. Additionally, to minimise the impact of cloud cover on the data, images with less than 10% cloud coverage were prioritised. The annual statistical data of land use survey and the relevant data of land use in the statistical yearbooks of various districts and counties (https://www.jiangxi.gov.cn/col/col48513). Social and economic data come from the Nanchang Statistical Yearbook (https://tjj.nc.gov.cn/ncstjj/tjnj/), Jiangxi Provincial Statistical Yearbook and various government reports and other documents. The energy consumption data came from the energy consumption data recorded in the Jiangxi Statistical Yearbook and Nanchang Statistical Yearbook, and the main energy consumption data of Nanchang City from 2000 to 2020 and the specific energy consumption data of each year were analyzed.

Method

Land use carbon emission calculation formula

Carbon emissions from land use in this study include direct and indirect carbon emissions, of which those from cropland, woodland, grassland, water and unused land can be calculated directly on the basis of previous studies. The formula is as follows23,24,25:

$$Qi = Li \times \delta i$$
(1)

Where: Qi is carbon emissions of various land use types; Li: Area of type i land use; δi: Carbon emission coefficients of different types of land use activities.

The carbon emissions from construction sites are not subject to direct measurement; rather, they are estimated on the basis of indirect indicators, including energy consumption and carbon emission factors. These indicators are guided by the IPCC Guidelines for National Greenhouse Gas Inventories. The calculation formula is as follows5,26:

$$Qc = \sum\nolimits_{i = 1}^{9} {Ei \times NCVi \times \delta i \times ORi}$$
(2)

Where: Qc refers to the carbon emissions of construction land (104 t); Ei refers to the i type of energy consumption (104 t) carried by the construction land; NCVi refers to the average net heating value of the i energy; δi refers to the carbon emission coefficient of the i energy; ORi refers to the oxidation rate of the i energy combustion. In this study, the energy consumption types(i) calculated in this study include raw coal, coke, natural gas, crude oil, gasoline, diesel, fuel oil, kerosene, and electricity.

Analysis of carbon emission factors affecting land use

In this paper, the decomposition model of influencing factors and Kaya identity27,28,29:

$${C^\backprime} = \sum {\frac{Ci}{{Li}}} \times \frac{Li}{L} \times \frac{L}{G} \times \frac{G}{P} \times P$$
(3)

Where, C`: total carbon emissions of each land type (104 t); Ci: carbon emissions of different land types (104 t); Li: area of different land use types (km2); L: the total area of the region (square kilometers); G: total GDP of the region (104 yuan); P: regional population size (104).

Then the total regional carbon emission can be expressed as:

$$fi = \frac{Ci}{{Li}},Si = \frac{Li}{L},I = \frac{L}{G},g = \frac{G}{P}$$
(4)
$${C^\backprime} = \sum {fi \bullet Si \bullet I \bullet g \bullet p}$$
(5)

where fi: carbon emission intensity per unit land; Si: Land use structure; \(\:I\): Land use intensity per unit GDP; g: per capita GDP level; p: Population size status.

The LMDI model can be decomposed into two modes30,31: additive decomposition mode and multiplicative decomposition method. In this paper, two methods are used to calculate the influence value and the influence rate. Therefore, in order to facilitate the study, this paper sets C0 as the initial carbon emission and C` is the carbon emission at the time point of the research object. The change effect of carbon emissions during the study period can be expressed as:

$$\Delta {C^\backprime} = {C^\backprime} - C0 = \Delta {C^\backprime} fi + \Delta {C^\backprime} Si + \Delta {C^\backprime} I + \Delta {C^\backprime} g + \Delta {C^\backprime} p$$
(6)
$$\begin{gathered} \Delta {C^\backprime}fi = \sum {wi \bullet \ln \frac{{f_{i}^{t} }}{{f_{i}^{0} }}} ;\Delta {C^\backprime}Si = \sum {wi \bullet \ln \frac{{S_{i}^{t} }}{{S_{i}^{0} }}} \hfill \\ \Delta {C^\backprime}I = \sum {wi \bullet \ln \frac{{I_{{}}^{t} }}{{S_{{}}^{0} }}} ;\Delta {C^\backprime}g = \sum {wi \bullet \ln \frac{{g_{{}}^{t} }}{{g_{{}}^{0} }}} \hfill \\ \Delta {C^\backprime}p = \sum {wi \bullet \ln \frac{{p_{i}^{t} }}{{p_{{}}^{0} }}} \hfill \\ \end{gathered}$$
(7)
$$\begin{gathered} D = \frac{{{C^\backprime}^{T} }}{{{C^\backprime}^{0} }} = Dfi \bullet DSi \bullet DI \bullet Dg \bullet Dp \hfill \\ Dfi = \exp (W\Delta {C^\backprime}fi);DSi = \exp (W\Delta {C^\backprime}Si);DI = \exp (W\Delta {C^\backprime}I) \hfill \\ Dg = \exp (W\Delta {C^\backprime}g);Dp = \exp (W\Delta {C^\backprime}p);W = \frac{\ln D}{{\Delta C}} \hfill \\ \end{gathered}$$
(8)

Result and analysis

Characteristics of Land Use changes in Nanchang City, 2000–2020

Using land use data from Nanchang City between 2000 and 2020, referencing the national standard for land use types from the third national land survey, and employing ArcGIS 10.8 software, the vectorization of remote sensing images of Nanchang City was conducted to obtain the spatiotemporal distribution of land use in 2000, 2010, and 2020 (Table 1; Fig. 2).

Table 1 Dynamic degree of land use in Nanchang City from 2000 to 2020.
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Over the past 20 years, the area of construction land in Nanchang City has expanded year by year, increasing by 452.31 km2, mainly through the development of unused land and conversion of arable land. From 2000 to 2020, the area of forest land in Nanchang City showed a gradual decrease trend, but with overall minor changes, maintaining a relatively stable area. The water area has increased annually, while grassland and unused land have decreased. The reduced grassland and forest land have mainly been converted into arable land and construction land, and the reduced unused land has been primarily developed into construction land. In general, the land use changes in Nanchang City over 20 years show a significant increase in construction land, some loss in arable land, and minor changes in forest land, grassland, and water bodies, with a considerable amount of unused land being developed.

Fig. 2
figure 2

Land use change map of Nanchang City from 2000 to 2020.

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Analyzing the dynamic degree of land use revealed that the absolute dynamic degree of construction land and arable land from 2000 to 2010 was lower than the dynamic value of construction land from 2010 to 2020, indicating an increasing rate of construction land growth and a rising rate of arable land decrease, which are two values worth attention (Table 1). From 2000 to 2020, arable land, grassland, forest land, and unused land all showed negative values, with grassland and forest land decreasing relatively less in area compared to the fastest reduction in unused land, followed by arable land. On the other hand, construction land and water bodies had positive values, with their areas increasing during this period.

Characteristics of carbon emissions from Land Use in Nanchang City, 2000–2020

The carbon emissions, carbon absorption, and net carbon emissions in Nanchang City in 2000, 2010, and 2020 are shown in Table 2. The change in carbon absorption was minimal, decreasing from 163.13 to 151.82 million tons, possibly due to conflicts between construction land and ecological space. Carbon emissions showed significant changes, with an increase of 244.63 million tons in 2010 compared to 2000. From 2010 to 2020, carbon emissions in Nanchang City exhibited a declining trend, dropping to 654.52 million tons in 2020. The substantial increase in carbon emissions in the initial years of the study area was attributable to the surge in carbon emissions from construction land, which reflected a considerable increase in traditional energy consumption in the area. This phenomenon is evident in the statistical yearbook of the area, as evidenced by an expansion in demand for energy, primarily in the secondary sector. The decrease in carbon emissions indicates that Nanchang City is undergoing a rapid economic transformation, shifting from pursuing economic development in scale to quality, continuously implementing the five major new development concepts, and making strides towards an environmentally friendly society.

Table 2 Carbon emissions and carbon absorption of various types of land use in Nanchang City (104t).
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Carbon Emission Intensity and Change Characteristics of Nanchang City from 2000 to 2020, and its calculation results are shown in Table 3. As evidenced by the data presented in the table, the situation and changes in carbon emission intensity in Nanchang City from 2000 (0.30 t per 104 yuan) to 2010 (0.33 t per 104 yuan) showed an increasing trend with minor fluctuations. However, from 2010 to 2020, a decreasing trend with significant fluctuations was observed. This shift can be attributed to Nanchang City’s economy experiencing rapid growth from 2000 to 2010, leading to extensive expansion of construction land, occupying significant agricultural land and ecological conservation space. Subsequently, with increased emphasis on environmental and ecological protection by the national and local governments after 2010, coupled with the implementation of a series of environmental policies, Nanchang City has continuously developed the tertiary industry and emerging high-tech industries like VR, actively adjusting its industrial structure. The decreasing dependency of economic development on energy leads to a more sustainable economic growth. While ensuring steady economic growth, reducing carbon emissions is also meaningful.

Table 3 Carbon emission value and intensity table of energy consumption in Nanchang City.
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Spatial distribution of carbon emissions from land use in Nanchang City

According to the land use type map and statistical yearbook data, the net carbon emissions of each district and county in Nanchang City can be calculated. The specific data is shown in (Table 4).The carbon emissions of the districts and counties in Nanchang have been classified into five levels according to the net carbon emissions of the districts and counties in Nanchang(Fig. 3). Level 1 is defined as 0-500,000 t, level 2 as 500,000–1,000,000 t, level 3 as 1,00–1,500,000 t, level 4 as 1,500,000–2,000,000 t, and level 5 as greater than 2,000,000 t. The overall trend of net carbon emissions among districts and counties in Nanchang City from 2000 to 2020 shows a parabolic pattern of increase followed by decrease. The district with the highest net carbon emissions is Qingshanhu District, followed by Nanchang County, both falling into level 4. Other areas have lower net carbon emissions, categorized into level 1.

Fig. 3
figure 3

Net carbon emissions of Nanchang City.

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In 2010, there were regional variations in carbon emissions from land use among districts and counties in Nanchang City, with a general increase compared to 2000 due to the rapid development of the secondary industry. By 2020, there were still regional differences in net carbon emissions among districts and counties in Nanchang City, with an overall decreasing trend attributed to the increasing environmental awareness, technological advancements, and adjustments in economic structure (this can be found in the statistical data, i.e. a significant decrease in the share of secondary sector output in Nanchang from 2010 to 2020, which is accompanied by a significant increase in tertiary sector output). On the whole, Qingshanhu District remained the area with the highest carbon emissions, followed by several districts falling into level 2. The districts of Wanli, Anyi, Xinjian, and Jinxian are all at level 1 with relatively low net carbon emissions.

Table 4 Regional net carbon emissions of Nanchang from 2000 to 2020 (104t).
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In order to analyze more comprehensively the regional spatial change characteristics of carbon emissions, the carbon emission intensity is divided into six levels based on the carbon emissions in the three major years, where the higher the level the higher the carbon emission intensity, which are as follows: 0.01–0.05 is level 1, 0.05–0.1 is level 2, 0.1–0.5 is level 3, 0.5–1 is level 4, 1-1.5 is level 5, and above 1.5 is level 6. The division of levels can be seen in Table 5; Fig. 4.

Fig. 4
figure 4

Graded variation of carbon emission intensity in Nanchang City.

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It can be observed that there are significant regional differences in carbon emission intensity among the districts and counties of Nanchang in the year 2000. Wanli, Anyi, and Jinxian fall within the level six range of carbon emission intensity, with intensities of 1.59 tons/104 yuan, 1.87 tons/104 yuan, and 1.56 t/104 yuan respectively, indicating relatively high carbon emission intensity. Xihu, Qingshanhu, Xinjian, and Nanchang counties fall within the level five range, with intensities of 1.01,1.02, 1.13, and 1.03 (t /104 yuan) respectively. Donghu and Qingyunpu are at level four, with intensities of 0.99 (t/104 yuan) each. According to Fig. 4; Table 5, in 2010, Xihu district had the lowest carbon emission intensity in Nanchang at 0.13 (t/104 yuan), while Anyi county had the highest at 1.35 (t/104 yuan). Donghu, Xihu, and Qingyunpu districts were at level three in terms of carbon emission intensity, while Honggutan, Qingshanhu, and Nanchang counties were at level four, and Wanli, Xinjian, Anyi, and Jinxian counties were at level five.

Table 5 Regional carbon emission intensity of Nanchang City in 2000, 2010 and 2020 (t/104 yuan).
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In 2020, Xihu district had the lowest carbon emission intensity among the districts and counties of Nanchang at 0.02 (t/104 yuan), while Jinxian county had the highest at 0.71 (t/104 yuan). Xihu and Qingshanhu districts were at level one, Donghu district was at level two, Qingyunpu and Wanli districts were at level three, and Xinjian, Anyi, and Jinxian counties were at level four. It is evident that in 2020 compared to 2010, the carbon emission intensity of various regions has decreased.

In conclusion, from 2000 to 2020, there have been significant regional differences in carbon emission intensity in Nanchang, with the central urban areas with more economic activities having lower carbon emission intensity than the surrounding less developed districts and counties. Over time, Nanchang’s carbon emission intensity has shown an overall decreasing trend, mainly due to the city’s economic and environmental protection development.

Analysis of factors influencing carbon emissions from land use in Nanchang City

Based on the calculated carbon emissions from various land use types in Nanchang City in the years 2000, 2010, and 2020, combined with land data, economic population, and energy usage data. By using the LMDI decomposition model to combine multiplication and addition calculations, the contribution of various driving factors to carbon emissions from land use in Nanchang City can be obtained. The results are shown in Table 6. From 2000 to 2010, there was a positive driving effect on the change in carbon emissions in Nanchang City, where land structure, GDP, carbon emissions based on land, and population size all moved in the same direction, while land efficiency acted as a suppressor. The highest positive driving force was per capita GDP, with a contribution value of 1265.37 million t and a contribution rate of 0.55. The contribution value of land efficiency was 1692.46 million t, with a contribution rate of 0.41. During this period, land carbon emissions had a suppressing effect due to negative effects. The overall contribution value of the suppressing factors was 1691.85 million t, with the largest contribution value for land use carbon emissions per unit area being 1032.94 million t, with a contribution rate of 0.52. Different factors have varying effects on net carbon emissions over different periods, and the potential impact directions can also differ.

Table 6 Factor decomposition results of carbon emission in Nanchang City (104t).
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Looking at the intensity of carbon emissions from land use per unit area, from 2000 to 2010, the contribution value and rate of carbon emissions intensity from land use per unit area in Nanchang City showed a positive effect, while from 2010 to 2020, there was a significant decrease trend, promoting the optimization of land use structure and layout. Both from 2000 to 2010 and from 2010 to 2020, land use structure showed a driving effect on net carbon emissions, with the highest contribution value reaching 311.77 million t. Land use efficiency had a certain suppressing effect on net carbon emissions from land use in Nanchang City, contributing a total of 2351.37 million t over 20 years. Per capita GDP had a positive net carbon emissions contribution over the past 20 years, contributing a total of 2055.43 million t, highlighting the importance of this factor in carbon emission reduction efforts. However, the contribution values and rates from 2010 to 2020 decreased compared to 2000 to 2010, indicating a reduced dependency of economic development on energy, signifying a transition to high-quality development. Population size had a total contribution value of 281.42 million t on net carbon emissions over the study period, consistently showing a positive effect with minor fluctuations. Its role in carbon reduction cannot be overlooked, emphasizing the importance of controlling the population size in Nanchang City to achieve dual carbon goals.

From 2000 to 2010, the five major driving factors with a driving effect were land structure, land efficiency, and population size, with per capita GDP being the most significant driving factor contributing 55%. Land use efficiency acted as a suppressor on carbon emissions, with the value of carbon absorption by land use efficiency being 1692.46 million t, contributing 41%. From 2010 to 2020, only carbon emissions per unit area of land use had a suppressing effect, with a total contribution value of 1691.85 million t, where the contribution value of carbon emissions per unit area of land use accounted for 52%. The total contribution value of driving factors summed up to 1283.88 million t.

Discussion

This research is based on land use data and uses direct and indirect carbon emission calculation methods to calculate land use carbon emissions in Nanchang City from 2000 to 2020. The carbon emission pattern and characteristics of Nanchang City in the past 20 years were analyzed, and the LMDI decomposition model was used to explore the influencing factors of carbon emissions in the region. From the results, it can be seen that Nanchang City has experienced a significant degree of land type replacement in the past 20 years, especially a significant expansion of construction land, which is an inevitable trend in the development of densely populated areas24. Secondly, in terms of carbon emissions, the study area has experienced a curvilinear pattern of first increasing and then gradually decreasing. According to similar studies, it can be concluded that the increased use of cleaner energy in the region and strict regulation of high carbon emission industries are the main incentives. In addition, from the perspective of spatial distribution, the carbon emission intensity of urban areas is lower than that of surrounding counties, which is similar to lü’s research32. This indicates that the technological level of the central urban area is higher and the industrial structure is more reasonable, resulting in less carbon emissions per unit of GDP. From a long-term development perspective, this is a good sign, meaning that other regions can learn from the experience of this region to better achieve high-quality and high-level green development in the area. Finally, from the performance of various influencing factors, the improvement of land use efficiency and the degree of intensive conservation can significantly reduce carbon emissions28, which can suppress some of the surge in carbon emissions caused by large-scale population growth. Therefore, from this perspective, optimizing and upgrading the industrial structure, expanding the proportion of clean energy, and highly intensive and economical land use are the necessary paths for the region to achieve green and low-carbon development. The government should focus on these points when formulating relevant policies.

Although this study provides a comprehensive analysis and exploration of the spatiotemporal evolution characteristics and influencing factors of land use carbon emissions in Nanchang City over the past 20 years, there are still certain shortcomings. Firstly, the calculation of carbon emissions in this article, especially in the calculation of carbon emissions from construction land, is based on the main energy consumption of the region. The carbon emissions from construction land within a region are influenced by various factors, and the differences between construction land are also significant. Therefore, more comprehensive and scientific calculation methods are needed in the future. In addition, in the exploration of influencing factors, the LMDI model can indeed decompose the target quantity well, thereby obtaining residual free influencing factors. However, the decomposition process inevitably has a certain subjectivity, and the number of selected influencing factors is also relatively small. Therefore, it is necessary to comprehensively select influencing factors based on regional characteristics in the future. Finally, although this study analyzed the carbon emission pattern in the research area over a 20-year period, the time span was 10 years, and the research accuracy was somewhat insufficient. Further exploration can be conducted from a more detailed perspective.

Conclusion

This study based on changes in the spatial-temporal pattern of urban carbon emissions caused by urban expansion, explores the influencing factors of the spatial-temporal pattern of carbon emissions in urban development, taking Nanchang City as an example. The research findings are as follows: (1) From 2000 to 2020, the construction land area in Nanchang City showed a significant increase trend and the water area shows a slight increase, while arable land and forest land areas decreased, and unused land decreased sharply. From 2000 to 2010, the construction land area increased by a total of 111.93 km2, while the arable land area decreased by 101.78 km2; between 2010 and 2020, the arable land area decreased by 284.22 km2, and the construction land area increased by 340.38 km2. (2) In 2010 compared to 2000, carbon emissions in Nanchang City increased by 244.63 million t; from 2010 to 2020, carbon emissions in Nanchang City showed a decreasing trend, reaching 654.52 million t in 2020. From 2000 to 2010, the carbon emission intensity in Nanchang City increased from 0.31 t/104 yuan to 0.33 t/104 yuan, and by 2020, it decreased to 0.13 t/104 yuan. Spatially, the net carbon emissions of various districts in Nanchang City showed a pattern of first increasing and then decreasing, with the overall net carbon emissions and carbon emission intensity in the central urban area being smaller than in the surrounding counties. (3)Land use efficiency and intensity exhibit a negative inhibitory effect on land use carbon emissions, while population size, GDP per capita, and land use structure have always had a promoting effect, making them the main factors influencing land use carbon emissions. Adjusting and optimizing energy consumption structure from the perspective of supply and demand, promoting industrial structure adjustment and upgrading from the aspects of enterprise production, technological research and development input, and establishing a complete industrial chain, improving urban development policies related to land use layout and structure, controlling population size reasonably, and advocating a low-carbon lifestyle are beneficial for promoting coordinated urban development, urban low-carbon development, and ecological civilization construction.