1. Introduction
Excessive carbon emissions contribute significantly to global warming [
1,
2]. Moreover, research has shown the significant impact of land use change on the global carbon budget [
3]. With the rapid advancement of urbanization, drastic changes in land use types have changed the structure and function of surface ecosystems, which further leads to the loss of carbon storage [
4,
5,
6]. Since the 1990s, China has experienced a significant increase in carbon emissions alongside its rapid economic development. Currently, China’s carbon emissions have surpassed those of the United States and rank first in the world, and it is facing huge pressure towards carbon emission reduction [
7]. Given the circumstances, it is of great practical significance to study land use carbon emissions, which can help China to achieve its target of both carbon peak by 2030 and carbon neutrality by 2060 [
8].
In recent years, research on carbon emissions based on land use change has attracted more and more attention from scholars internationally. Research on land use carbon emissions primarily focuses on accounting for carbon emissions and the analysis of influencing factors [
9,
10,
11]. The most crucial aspect is carbon emissions accounting [
12], which serves as the foundation for all research. According to the current research, the mainstream carbon emission calculation method is the carbon emission coefficient method [
13,
14,
15]. Ke et al. used the carbon emission coefficient method to calculate the land use carbon emissions of the megacity Shenzhen [
13]. Q et al. adopted the IPCC method to calculate the total carbon emissions of China’s logistics industry [
14]. Zhang et al. used the carbon emission coefficient method to calculate the carbon emissions of different land use types in the Yellow River Delta region [
15]. At present, the application of the carbon emission coefficient method is very mature, and the calculation is simple and reliable, so this method is selected in this paper.
There are two main types of analysis method for factors affecting land use carbon emissions: index decomposition analysis and structural decomposition analysis [
16,
17,
18]. Shahbaz et al. used the STIRPAT model to study the impact of urbanization on carbon emissions [
17]. Li et al. adopted an approach combining PDA and IDA to analyze the influencing factors on carbon dioxide emissions in China’s provinces [
16]. Xu et al. applied the IDA model to carbon emissions research [
18]. Among these approaches, index decomposition analysis has the advantages of small data requirements, full decomposition, and no residuals [
19]. At present, more and more scholars use LMDI to analyze the factors affecting carbon emissions, and it has become a mainstream choice [
20,
21,
22]. Xu et al. used LMDI to analyze the influencing factors on carbon emissions in the Chang-Zhu-Tan urban agglomeration [
20]. Ren et al. used LMDI to analyze China’s manufacturing carbon emissions [
21]. Xu et al. adopted LMDI to analyze the influencing factors on China’s energy carbon emissions [
22]. Based on the analysis results from the literature above, this paper uses the LMDI model and selects indicators related to energy, economy and population for factor analysis according to the actual situation.
The decoupling theory is a basic theory proposed by the Organization for Economic Co-operation and Development (OECD) to describe the connection between economic growth and resource consumption or environmental pollution [
23]. Nowadays, it has gradually expanded to the environment and other fields [
24,
25]. Much research has focused on the decoupling between economic development and the environment in order to find solutions to mitigate the effects of climate warming. Wu et al. launched a study of the decoupling index between the economy and carbon emissions in 30 Chinese provinces, from which most provinces were found to have strong decoupling [
26]. Li et al. used the Tapio elastic decoupling index to examine the decoupling degree of carbon emission from the construction industry in terms of economic growth [
27]. Liang et al. adopted a wide decoupling method to describe the decoupling state and trend of CFP and economic growth [
28]. At present, the research system for the decoupling analysis of economic development and carbon emissions is relatively complete, and most methods focus on large-scale research, such as that based on provinces. On this basis, this paper selects a smaller research scale for more targeted analysis and proposes a carbon emission reduction plan suitable for each county.
Suzhou is the most economically powerful prefecture-level city in China, and its GDP exceeded the trillion mark in 2011, surpassing a large number of provincial capitals [
29]. At the same time, Suzhou is also a famous city for heavy industry. With the continuous development of manufacturing industry, Suzhou has become a capital for foreign companies, with large industrial zones distributed in each region, and it is China’s top-ranked city in terms of industrial strength. The presence of labor-intensive factories characterized by high pollution and energy consumption has resulted in a significant rise in the latter, increased emissions of pollutants, and intensified pressure for carbon emission reduction, which has had an impact on the livability and urban image of Suzhou [
30]. However, there are relatively few detailed studies on industrial cities at present, which is not conducive to exploring the characteristics of carbon emissions, and it is difficult to carry out city-specific planning and management. Therefore, this paper selects the world’s largest industrial city as a typical case, combining land use data with energy, economic and social development data, using the carbon emission coefficient method to calculate land use carbon emissions and analyze their temporal and spatial evolution. Additionally, in order to measure carbon emission risk and further analyze carbon emission intensity, a carbon emission risk and stress index is introduced. Finally, the LMDI model is used to analyze the contribution value and contribution rate of different influencing factors. At the same time, the factor with the largest contribution and carbon emission is selected for decoupling analysis, so as to better grasp the regional changes. On the basis of the research results, targeted proposals for carbon emission reduction are proposed, hoping to provide ideas for carbon emission reduction planning and energy management in other similar industrial cities.
4. Discussion
In response to the prevailing global warming trend, China, taking into account its national circumstances, has set forth the objective of “dual carbon”, actively shouldering its responsibilities as a major nation in addressing the array of challenges posed by climate change [
47]. The relationship between land use and the carbon cycle is inseparable, so it is of great significance to study the carbon emissions caused by land use type change [
3]. At present, the calculation of land use carbon emissions and research on influencing factors are relatively mature, most of them focusing on the provincial and national levels to analyze and deal with carbon emissions from an overall perspective [
14,
16,
21,
24]. However, there are relatively few studies on specific types of cities, a situation which cannot accurately reflect the distribution of carbon emissions, and making it difficult to carry out targeted urban planning and management. Industrialized cities are the hardest-hit areas for carbon emissions in the country [
48]. Suzhou, a heavy industry city, is selected as the study area in this paper, which is highly representative in terms of economy and industrial development [
49]. It is hoped that this study can provide a developmental example for other industrial cities and contribute to the realization of carbon emission reduction at the national level.
This paper uses the carbon emission coefficient method to estimate the carbon emission of Suzhou City from 2008 to 2020, analyzes the temporal and spatial evolution characteristics of carbon emission and, especially, estimates the carbon emission risk and pressure index. Then, the LMDI model was used to analyze the influencing factors on land use carbon emissions, and decoupling analysis of the main influencing factors was carried out. Based on the study findings, the carbon emissions associated with land use in Suzhou exhibited an upward trajectory throughout the study period, displaying a spatial discrepancy characterized by higher levels in the northern regions and lower levels in the southern regions. This trend can be attributed to the distribution of land use types, energy consumption patterns, and industrial structure [
48]. In recent years, with the advancement of urbanization, the area of construction land in Suzhou has increased rapidly, while there has been a notable increase in energy consumption. Meanwhile, the area of water and forest land, which play a crucial role in carbon absorption, has been progressively diminishing. Consequently, carbon emissions have been steadily on the rise. Among the influencing factors, economic development has the strongest positive effect, and the city is developing towards strong decoupling.
This study puts forward the following suggestions: Recommend adherence to the national policy of “returning farmland to forest and grassland” as a means to enhance carbon absorption rates and optimize the ecological environment; Strengthen lake protection and engage in afforestation initiatives, make full use of urban unused land to optimize carbon adsorption capacity; Minimize the energy consumption associated with logistics, for which it is essential to implement a well-organized and strategic approach in the overall planning of construction land, including scale and location, which ensures the effects of mutual proximity and convenience; Efforts should be made to optimize the energy structure and industrial composition by reducing reliance on high-energy consumption disposable energy sources, promoting the development and utilization of clean energy, and implementing rational closures of high-energy-consuming factories. It is hoped that the research on Suzhou can provide direction for the low-carbon development of other industrial cities and promote a low-carbon lifestyle around the world. There are still some deficiencies in this paper. The prediction of land use carbon emissions is not carried out, which could adjust the policy in a more timely manner to reduce carbon emissions [
50]. Therefore, we consider adding this to the follow-up to ensure more in-depth and meaningful research.
5. Conclusions
This paper selects Suzhou, the largest industrial city in the world, to analyze the spatial and temporal distribution of carbon emissions and its influencing factors. The following conclusions can be drawn:
Between 2008 and 2020, Suzhou experienced significant changes in land use, with reductions observed in various land types except for construction land and unused land. Notably, water bodies and cultivated land exhibited substantial decreases. During the period, a total of 116,527.05 hm2 of land was transformed, among which construction land was transformed into a large area and showed rapid expansion, mainly from the conversion of cultivated land and water body, with a net increase of 68,751.99 hm2.
Throughout the study period, Suzhou witnessed a consistent increase in net carbon emissions, resulting in a total growth of 23,309,900 tons. The rate of net carbon emissions displayed rapid acceleration between 2008 and 2012, followed by a deceleration. In all land use types, construction land was the largest source of carbon emissions, while water area was the largest source of carbon absorption. In terms of space, the performance is “high in the north and low in the south”, and the high and heavy carbon emissions are concentrated in the northern region, while the southern region is mainly a middle and low carbon emission area.
The risk and pressure index for land use carbon emission in all regions of Suzhou is high, and the carbon emission risk index exhibited a pattern of ‘High in the northeast and low in the southwest’. We discovered that there was an imbalanced ratio between carbon sources and carbon sinks, i.e., carbon emissions outweigh carbon absorption. What needs to be paid attention to is that human production and life have seriously affected the balance of the ecosystem, especially the carbon balance.
Energy intensity and economic development exert the most significant influence on land use carbon emissions. Energy intensity exhibits a strong inhibitory effect, effectively limiting the growth of carbon emissions. While economic growth plays a role in promoting carbon emissions, the contribution rate is decreasing year by year. The promoting effect of population scale is increasing year by year, showing a stronger and stronger positive effect. The energy structure is changing from the initial negative effect to a positive effect, but compared with other factors the impact is small. As a result, the total amount of carbon emissions is increasing, but at a decreasing rate.
The results of the decoupling analysis show that the city was in a weak decoupling state from 2008 to 2020, which means that the growth rate of carbon emissions is slightly lower than the economic growth rate. From the perspective of counties, all regions except Zhangjiagang have reached or are shifting to a state of strong decoupling. Zhangjiagang is currently in a state of expansive coupling, and its heavy industry is relatively developed. It needs to further optimize its energy structure and strengthen technological innovation.
The whole study includes the temporal and spatial distribution of carbon emissions, carbon emission risks, influencing factors, and decoupling analysis. This paper takes the county as the minimum research scale, selects Suzhou, a representative heavy industrial city among industrial cities, conducts a detailed analysis, and puts forward constructive suggestions, such as improving the energy structure and enhancing technological innovation, so as to provide direction for other industrial cities in order to realize low-carbon cities.