Spatial–Temporal Characteristics and Influencing Factors on Carbon Emissions from Land Use in Suzhou, the World’s Largest Industrial City in China

Abstract

Exploring carbon emissions in Suzhou, a city with a significant heavy industry presence, and understanding the factors that influence these emissions are crucial in achieving China’s dual-carbon goals within the framework of global climate governance. This study utilized land use data and statistical data from 2008 to 2020 in Suzhou. The carbon emission coefficient method was employed to calculate carbon emissions, while GIS technology was used to analyze their temporal and spatial distribution, as well as carbon emission risk. Additionally, the LMDI model was applied to investigate the contribution of influencing factors and TAPIO was used to analyze the decoupling relationship between the main influencing factors and carbon emissions. The study yielded the following findings: (1) From 2008 to 2020, land use changes in all regions of Suzhou are obvious, and there are mutual transformations among different land types. (2) The overall carbon emission in Suzhou showed an upward trend, with a spatial distribution characterized by higher emissions in the northern regions and lower emissions in the southern regions. (3) The risk and pressure index of carbon emission in all regions of Suzhou are too large, and the amount of carbon emission and carbon absorption is seriously out of balance, resulting in an overall carbon imbalance. (4) Among the influencing factors on land use carbon emissions in Suzhou, energy intensity exerted the strongest negative effect, and economic growth showed the strongest positive effect. (5) Decoupling analysis showed that economic growth and carbon emissions are generally shifting towards a strong decoupling and, except for Zhangjiagang, other regions have a good development model. Based on the research findings, this paper proposes specific suggestions for reducing carbon emissions, aiming to provide actionable recommendations for Suzhou and other urban areas in achieving low-carbon and environmentally sustainable cities.

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.

2. Materials and Methods

2.1. Study Area Description

Suzhou (119°55′~121°20′ E, 30°47′~32°02′ N) is located in the southeast of Jiangsu Province, in the heart of China’s Yangtze River Delta and Taihu Lake Plain. The city’s terrain is flat, half of the area consists of plain, and there are many rivers and lakes in the territory [31]. Most of the water surface of Taihu Lake, one of the five major freshwater lakes, is distributed in Suzhou, so it is also known as “Oriental Venice” (Figure 1) [32]. Suzhou experiences a North subtropical humid monsoon climate, characterized by warm and rainy conditions throughout the year, with four distinct seasons. The average monthly precipitation reaches 161 mm, while the annual average temperature stands at 15.7 °C [33]. There are six primary land use categories, among which the construction land area is growing rapidly, corresponding to energy consumption, which is also increasing year by year. Statistical data reveals that Suzhou’s total carbon emissions have reached 200 million tons, accounting for approximately 28% of the province’s emissions and 2% of the national emissions. The total carbon emissions are large, with high intensity, and the pressure for carbon emission reduction is enormous [34].

2.2. Data Sources and Preprocessing

The land use data utilized in this study is sourced from the 30 m resolution land use dataset, produced by the research team led by Professor Yang Jie and Huang Xin from Wuhan University [35]. This data set uses the GEE platform to draw on 30 years of land use data in China through sample selection and testing, which is superior to other data sets in time resolution, and the spatial resolution is as high as 80%, which meets accuracy requirements (https://doi.org/10.5281/zenodo.5816591, accessed on 11 March 2023). According to China’s land use classification system (GB/T21010-2007) combined with the specific conditions of the study area, the land use data were reclassified into six categories: cultivated land, wood land, grassland, water area, unused land and construction land. The energy, population, national economic, and other social statistical data utilized in this study are sourced from authoritative publications such as the Suzhou Statistical Yearbook and China Energy Statistical Yearbook for the period spanning 2009 to 2021. Additionally, various regional social development statistical bulletins have provided valuable data for analysis. The energy carbon emission coefficient comes from the IPCC National Greenhouse Gas Inventory Guide.

2.3. Research Methodology

Based on the land use data from 2008 to 2020, this study analyzed the changes in land use quantity by employing the land use dynamic degree and transfer matrix. Additionally, combined with energy consumption data, the carbon emission coefficient method was employed to evaluate total carbon emissions resulting from land use, thus facilitating the analysis of spatial–temporal evolution patterns. Furthermore, the carbon emission risk index and carbon footprint pressure index were adopted to assess carbon emission risks, while the LMDI model was employed to examine the contributions of various influencing factors. The specific methods are as follows.

2.3.1. Land Use Change Analysis

• 1.. Dynamic degree of land use

Dynamic degree of land use refers to the quantity change for a certain type of land use in a certain period of time, mainly reflecting the speed and intensity of change. A higher dynamic degree signifies a more significant alteration in land use type [36,37].

(1)$\mathrm{K}=\frac{U_i-U_j}{U_i}\times \frac{1}{T}\times 100\mathrm{\%}$

where K is the temporal dynamic degree of individual land use within the research period.; if K is positive, the area of this land use type is increasing; if K is negative, it indicates that the area of this land use type is decreasing during the research period; the value of K represents the change rate. $U_i$ and $U_j,$ respectively, denote the area of a specific land type at the beginning and end stages of the study.

• 2.. Land use transfer matrix

The land use transfer matrix is a two-dimensional matrix obtained from the land use change in a certain period of time in the same area [38]. It can clearly reflect the mutual conversion between different land types in the study period, and better analyze the change amount and change trend of land use types. A Sankey diagram can be drawn to visualize the specific situation of land use transfer. The width of the branch part of the Sankey diagram represents the area of land use type transfer.

2.3.2. Carbon Emissions Calculation Method

Land use carbon emissions primarily consist of two components: direct carbon emissions and indirect carbon emissions, which have different sources and calculation methods [11]. Direct carbon emissions are the carbon emissions produced by land as the primary production entity, specifically including cropland, forestland, grassland, water areas, and unused land. Indirect carbon emissions are the carbon emissions resulting from land accommodating human activities, primarily used to estimate carbon emissions from construction land.

• 1.. Direct Carbon Emissions

By constructing a carbon emission estimation model to calculate carbon emissions [15], the direct carbon emission calculation formula is shown in Formula (2).

(2)$C_i=S_i\times N_i$

where $C_i$ denotes carbon emissions of different land use types(t), and positive/negative values represent carbon emission/absorption; $S_i$ is the land use type’s specific area (hm²); and $N_i$ represents the carbon emission coefficient associated with various land types. The carbon emission coefficient is mainly set according to the existing research results and the actual situation of the research area [39], which is shown in Table 1.

• 2.. Indirect Carbon Emissions

Due to the inability to measure carbon emissions of construction land based on area, indirect estimation relies on primary energy consumption data from the study area [15]. The calculation formula for this estimation is presented in Equation (3).

(3)$C_j{=K}_j\times L_j\times M_j$

where $C_j$ is carbon emission for construction land; $K_j$ signifies the carbon emission coefficient for distinct energy sources; $L_j$ denotes the standard coal conversion coefficients pertaining to various energy sources; $M_j$ represents the total consumption of each energy source. Based on the energy consumption data observed in the study area, nine major energy sources are selected, as shown in Table 2. Among these, the standard coal conversion coefficient comes from the China Energy Statistical Yearbook, and the energy carbon emission coefficient comes from the IPCC National Greenhouse Gas Inventory Guidelines.

2.3.3. Carbon Emission Risk Calculation

• 1.. Carbon emission risk index

The carbon emission risk index is an indicator that assesses the risk of carbon emissions by calculating the carbon emissions per unit area. A higher value corresponds to a greater risk [40]. The calculation formula for the index is as follows:

(4)$C_f=\frac{\sum _{i=1}^n{C}_i}{\sum _{i=1}^n{S}_i}$

where $C_f$ represents the land use carbon emission risk index; $C_i$ signifies the carbon emissions of different land use types; and $S_i$ denotes the area of various land use types.

• 2.. Carbon footprint pressure index

The carbon footprint pressure index is primarily utilized to quantify the extent of human activities’ impact on the natural environment. It represents, as a carbon sink, the ratio of carbon emissions from land use to carbon absorption [41]. The formula for calculating the index is as follows:

(5)$C_z=\frac{C_y}{C_h}$

where $C_z$ represents the carbon footprint pressure index. y indicates the cumulative carbon emissions resulting from land use, and h denotes the cumulative carbon absorption associated with land use. When $C_z$ > 1, it indicates that the total carbon emissions exceed the carbon absorption. At this time, human production activities have a great impact on the natural environment, and the carbon cycle of the ecosystem is disordered and out of balance.

2.3.4. LMDI Model and Tapio Decoupling Model

• 1.. Establishment of LMDI model

This study utilizes the LMDI model based on the Kaya equation to analyze the key factors that influence carbon emissions from land use [42]. The model introduces possible influencing factors for decomposition, and analyzes the contribution value and contribution rate [43,44]. The expression of the LMDI model is given by the following equation

(6)$T=\sum \frac{T}{E}\times \frac{E}{G}\times \frac{G}{P}\times P$

where T is the total land use carbon emission; E is the total energy consumption; G is GDP, gross domestic product; P is the total population. Make

(7)$t=\frac{T}{E};e=\frac{E}{G};g=\frac{G}{P};p=P$

Then the expression can be converted into the product of the above factors, and the specific formula is as follows:

(8)$T=\sum t\times e\times g\times p$

where $∆T_i$ ($i=t, e, g, p$) represents the contribution value of different impact factors, and $D_i$ (i = t, e, g, p) represents the contribution rate of selected impact factors, which are decomposed under LMDI addition and multiplication modes, respectively. The specific calculation process is shown as follows:

(9)$∆T=∆t+∆e+∆g+∆p$

(10)$$\begin{gathered} ∆T_t=\sum \frac{T^t-T^0}{\mathit{ln}{T}^t-\mathit{ln}{T}^0}\times \mathit{ln}\frac{t^t}{t^0}; ∆T_e=\sum \frac{T^t-T^0}{\mathit{ln}{T}^t-\mathit{ln}{T}^0}\times \mathit{ln}\frac{e^t}{e^0}; \\ ∆T_g=\sum \frac{T^t-T^0}{\mathit{ln}{T}^t-\mathit{ln}{T}^0}\times \mathit{ln}\frac{g^t}{g^0}; ∆T_p=\sum \frac{T^t-T^0}{\mathit{ln}{T}^t-\mathit{ln}{T}^0}\times \mathit{ln}\frac{p^t}{p^0}; \end{gathered}$$

(11)$W=\frac{T^t-T^0}{\mathit{ln}{T}^t-\mathit{ln}{T}^0}{ D}_i=\mathit{exp}\left(w∆T_i\right)=\frac{∆T_i}{∆T}$

If the obtained contribution value is greater than 0, it indicates a positive contribution to carbon emissions. Conversely, if the contribution value is less than 0, it signifies inhibition.

• 2.. Establishment of Tapio decoupling analysis model

According to the improved decoupling model proposed by Tapio in 2005 [42], the decoupling relationship between economic development and carbon emissions can be calculated by the following equation [45]:

(12)$D=\frac{∆C\%}{∆G\%}=\frac{(C^t-C^0)/C^0}{(G^t-G^0)/G^0}$

where D is the decoupling indicator, $∆C\mathrm{\%}$ represents changes in carbon emissions in the period 0–t, $∆G\mathrm{\%}$ represents changes in economic growth in the period 0–t, while $C^t$ and $C^0$ are the carbon emissions in year t, and year 0, $G^t$ and $G^0$ are the GDP values in year t and year 0. Different values of D, $∆C\mathrm{\%}$ and $∆G\mathrm{\%}$ correspond to different decoupling states, and there are eight main types, as shown in the following Table 3 [46]. Among them, the best state is strong decoupling, the worst state is strong negative decoupling, and all other states are in between.

3. Results

3.1. Spatial and Temporal Characteristics of Land Use Change

In Suzhou, there are six primary land use types, with cropland and water area comprising the largest proportion, approximately 75% of the total area, followed by construction land. Different stages of land use change present different characteristics; this paper uses dynamic degree and transfer matrix for specific analysis.

3.1.1. Dynamic Degree of Land Use

The dynamic degree of land use in Suzhou from 2008 to 2020 is shown in Table 4. During 2008–2012, grassland exhibits the highest dynamic intensity and undergoes the most significant changes. Unused land and construction land also have relatively high dynamic intensities, while water bodies have the lowest dynamic intensity. In terms of the trend in changes, apart from construction land and forest land, the areas of other land types are continuously decreasing. From 2012 to 2016, the highest unused dynamic degree was 26.1%, followed by grassland with a dynamic degree of −18.92%, and water body and cultivated land had the lowest dynamic attitude and the least change. In terms of the trend in changes, except for unused land and construction land, the areas of other land types are continuously decreasing. During 2016–2020, grassland had the highest dynamic intensity and underwent the most significant changes, while cropland had the lowest dynamic intensity and experienced minimal changes. In terms of the trend in changes, woodland, grassland, and water area are continuously decreasing, while the areas of other land types are continuously increasing. From an overall perspective, during the period from 2008 to 2020, grassland demonstrated the highest dynamic intensity at −7.92%, indicating a persistent decline in its area. Simultaneously, construction land exhibited a relatively significant dynamic intensity of 3.69%, suggesting its expansion driven by economic growth and urban development, which leads to a certain reduction in other land types.

3.1.2. Land Use Transfer Matrix

The transition between the various land use types in Suzhou from 2008 to 2020 are illustrated in Figure 2. During the period from 2008 to 2020, cropland experienced the highest total conversion area, amounting to 69,531.84 hm². The primary conversions from cropland were directed towards water and construction land. Following that, water exhibited the second highest conversion area, totaling 42,203.16 hm², with the main conversions being directed towards cropland and construction land. Construction land experienced the largest transfer with an area of 69,578.46 hm², primarily stemming from conversions of cultivated land and water. Following that, cultivated land witnessed a transfer of 39,470.4 hm², mainly originating from conversions of water and woodland. As can be seen from the figure, construction land experienced a substantial inflow, exceeding the outflow, and resulting in the largest net increase in area. Conversely, the inflow of water and cropland was considerably smaller than the outflow, leading to the maximum net decrease in area. The fluctuations in woodland and grassland were relatively minor, while the area of unused land was minimal, resulting in modest inflows and outflows and consequently a small net increase in area. From an environmental protection perspective, attention needs to be paid to the expansion of construction land. Calculations reveal a substantial increase of 69,578.46 hectares in the area of construction land, with approximately 90% (62,913.42 hectares) stemming from the conversion of cropland. This indicates that the accelerated urban development driven by economic growth has led to the expansion of construction land, primarily at the expense of converted cropland.

3.2. Analysis of the Spatial and Temporal Pattern Evolution of Land-Use Carbon Emissions

3.2.1. Time Series Analysis of Carbon Emissions Characteristics in Land Use

According to the calculation formula for land use carbon emissions, land use data and energy consumption data are incorporated to calculate direct and indirect carbon emissions. Then, obtain the carbon emissions of different regions from 2008 to 2020, and calculate the net carbon emissions. The specific calculation results are shown in Table 5.

As can be seen from Table 3, net carbon emissions have increased year by year from 2008 to 2020, with a notable surge from 2008 to 2012, exhibiting a growth rate of 31%. It is worth noting that the carbon emissions associated with construction land followed a similar upward trend, indicating that construction land has a large impact on net carbon emission and a positive correlation between the two. Therefore, construction land can be used as the main entry point to control the increase of carbon emissions. Woodland and water areas are important sources of carbon absorption, while Suzhou includes most of the water area of Taihu Lake. Due to the relatively large area of water, the water area bears the main part of carbon absorption. The continuous expansion of construction land results in a reduction in the area of land categories other than unused land. Consequently, the total carbon absorption capacity decreases. However, considering the relatively small extent of unused land, its influence on carbon emissions is marginal. As a result, there has been a consistent year-on-year increase in total carbon emissions.

3.2.2. Spatial Analysis of the Characteristics of Land-Use Carbon Emissions

In order to further analyze the regional specificity of carbon emissions, this paper divides Suzhou into different regions for more specific statistical analysis, namely four county-level cities, one municipal district and the urban area. The municipal district is Wujiang District, and the urban area is divided into four districts, Gusu District, Huqiu District, Wuzhong District and Xiangcheng District, according to the statistical standard of the energy data from the Suzhou Statistical Yearbook.

According to Figure 3, it can be observed that carbon emissions have shown an overall increasing trend in recent years across different regions, and there was a substantial growth in all regions from 2008 to 2012, which could be attributed primarily to rapid industrial development, leading to a substantial increase in energy consumption, thus leading to a small peak in carbon emissions. In the following years, the industrial structure was gradually optimized, and the growth rate of carbon emissions slowed down significantly. Some regions even began to experience negative growth.

To visually depict the carbon emission intensity resulting from land use in different districts of Suzhou, we utilized the natural break method to categorize carbon emissions into four levels. This approach enabled us to ascertain the spatial distribution pattern of carbon emissions across Suzhou. As can be seen from Figure 4, the overall carbon emission of Suzhou presents a spatial trend of high carbon emission in the north and low carbon emission in the south. Zhangjiagang is in the heavy carbon emission zone, Changshu is in the medium-high carbon emission zone, and the southern region is in the medium-low carbon emission zone, which is related to the distribution of industrial zones and industrial structure.

It is not difficult to find that Zhangjiagang has been situated in a heavy carbon emission zone, indicating that its carbon emissions have been far ahead of other cities. The main reason may be that Zhangjiagang’s industrial structure is too heavy, there are more metallurgical and chemical factories, and the energy consumption accounts for about half of the total for Suzhou. The next step should capitalize on its port advantages and maximizes the utilization of its strategic location. Simultaneously, there should be a focus on optimizing the industrial structure to enhance industrial value while minimizing energy consumption. We can also see that the carbon emissions of Wujiang District and Kunshan from 2008 to 2020 are at a lower level, which is mainly related to their low energy consumption and large water body area.

3.3. Risk Analysis of Land-Use Carbon Emission

The carbon emission risk index and carbon footprint pressure index can be calculated using Equations (4) and (5), as presented in Table 6. From 2008 to 2012, there was a substantial increase in both the risk index and pressure index for carbon emissions resulting from land use across all districts of Suzhou. Furthermore, during the period from 2012 to 2020, Wujiang District, Changshu, Kunshan, and Zhangjiagang observed a consistent rise in both land use carbon emission risk and carbon footprint pressure. In 2012, the urban area and Taicang City reached their peak risk index values, while during subsequent years, until 2020, there was a steady decline in both carbon emission risk and pressure.

From an overall perspective, Suzhou City exhibits a significant and increasing carbon emission risk associated with land use. It is not difficult to see from Figure 5 that the distribution of carbon emission risk and carbon footprint pressure index follows a trend of higher values in the northeast and lower values in the southwest, with significant variations in index values. The risk of carbon emission is high in the northeast of Suzhou, and Zhangjiagang is the most typical area. The carbon footprint pressure index is consistently much higher than 1 in all regions, indicating that the total carbon emissions within the study area greatly exceed the total carbon absorption. Human production activities have a significant impact on the natural environment, leading to an imbalance in the carbon cycle within the ecosystem. Particularly in Taicang and Zhangjiagang, where the carbon footprint exceeds 2000, there is a severe imbalance between carbon emissions and carbon absorption, resulting in substantial pressure to reduce carbon emissions. It is necessary to implement corresponding measures to protect the ecological environment and reduce carbon emissions.

3.4. Analysis of Influencing Factors on Carbon Emissions

3.4.1. Results of LMDI Model Decomposition

Using the LMDI factor decomposition model, the carbon emission related data calculated above and the economic and social statistical data in the statistical yearbook are substituted into the equation for decomposition and calculation, and the influence of four factors on carbon emission is obtained: energy structure, energy consumption intensity, economic growth and population scale. The decomposition analysis results of land-use carbon emission factors can be obtained, as shown in Table 7.

According to the findings presented in Table 7, cumulative carbon emissions witnessed a significant increase of 23.3099 million tons between 2008 and 2020. Notably, energy intensity and economic development displayed noteworthy impacts on carbon emissions, characterized by negative and positive contributions, respectively. Moreover, population size exhibited a positive influence on carbon emissions, whereas the relationship between energy structure and carbon emissions showed an initial negative effect followed by a subsequent positive effect (Figure 6a). This result aligns with the carbon emission patterns observed in Suzhou, a rapidly developing city known for its heavy industry sector.

Economic growth has a significant promoting effect on land use carbon emissions in Suzhou, and the cumulative contribution value from 2008 to 2020 has reached 58.647,600 tons. The years 2008–2012 observed the highest contribution values, coinciding with a more than doubling of GDP. This indicates that rapid economic development has led to increased human activities impacting the natural environment and has driven an increase in carbon emissions from land use. Between 2012 and 2020, the influence of economic growth started to decline, mainly because of the economic slowdown, and the government began to focus on technological improvement and environmental protection. The positive impact of population size on carbon emissions is also large and exhibits a significant annual increase. This trend can be attributed to a rise in the number of permanent residents in Suzhou, which witnessed an increase of 1.1458 million individuals from 2008 to 2020, corresponding to a growth rate of nearly 20%. The combination of population growth and the escalating issue of aging has hindered the advancement of low-carbon lifestyles, consequently leading to a continuous rise in carbon emissions.

Energy intensity exerts a notable inhibitory impact on land-use carbon emissions, as evidenced by a cumulative contribution value of −50.2049 million tons from 2008 to 2020. Furthermore, this negative effect demonstrates a steady increase over the years. Energy consumption intensity has witnessed a decline from 0.76 million tons per 100 million yuan in 2008 to 0.37 million tons per 100 million yuan in 2020. This indicates that, as the national GDP improves in terms of economic development, there is also an improvement in energy utilization efficiency attributed to the adoption of new technologies and the optimization of the industrial structure. Consequently, the negative impact of energy intensity on carbon emissions continues to increase. The influence of energy structure on carbon emissions is relatively minor, and the initial negative effect is changed to a positive effect due to increased carbon emissions resulting from the consumption of coal and other energy sources.

Equation (11) is employed to compute the contribution rates of various factors influencing land use carbon emissions (Figure 6b), facilitating a clearer observation of their interrelationships. Economic development and energy intensity exert a substantial impact on carbon emissions. As economic development gradually decelerates, the negative effect of energy intensity on carbon emissions offsets the positive effect brought about by economic development. Notably, from 2016 to 2020, all contributing factors exhibit a significant increase in their contribution rates, while the increment of carbon emissions during this period diminishes. This trend implies that the growth rate of the negative contribution caused by energy intensity surpasses that of the positive contribution conferred by economic development. This reflects a transition toward low-carbon development in the realm of economic and social progress.

3.4.2. Results of Tapio Decoupling Model

From the analysis results of the LMDI model, it can be seen that economic development has the greatest positive contribution to land use carbon emissions in Suzhou. Therefore, conducting TAPIO decoupling analysis to further understand the specific decoupling between economic growth and carbon emissions can help us understand the current development model, which will help to take corresponding carbon emission reduction measures. The results show that there are four main decoupling states in Suzhou for 20082020: Weak decoupling(WD), Strong decoupling(SD), Expansive negative decoupling(END) and Expansive coupling(EC), as shown in Table 8.

On the whole, Suzhou City showed a weak decoupling state from 2008 to 2020, which shows that, in the past 13 years, with the development of the economy, the carbon emissions for land use have also been increasing, but the growth rate is slower than the GDP growth rate. From 2008 to 2012, the growth rate of GDP was 73%, while the growth rate of carbon emissions was 31% in the same period. During 2012–2016, the growth rate of GDP was 23%, while the growth rate of carbon emissions was 7.3%. From 2016 to 2020, the growth rate of GDP was 20%, while the growth rate of carbon emissions was only 2%. It is not difficult to see that the growth rate of carbon emissions is becoming slower and slower, which is related to the country’s introduction of a series of emission reduction policies and plans. The decoupling index is becoming smaller and smaller, which shows that Suzhou’s low-carbon economy has initially achieved results. Next, the energy structure should be further optimized to achieve strong decoupling status.

The state of decoupling in different regions of Suzhou City in different periods is different, and there are four main states.

During 2008–2012, all areas of the city showed a weak decoupling state. However, from 2012 to 2016, the development of Changshu City experienced a big problem, which was manifested as a state of expansive negative decoupling, in which the increase rate in carbon emissions was greater than the speed of economic development. Compared with 2008–2012, the effect of carbon emission reduction has declined. At the same time, urban areas have changed from weak decoupling to strong decoupling. This is an ideal state, which shows that economic development is rapid and environmental pollution has reduced. Except for Changshu and urban areas, other areas still maintain a weak decoupling state. From 2016 to 2020, urban areas Changshu and Taicang have reached a state of strong decoupling, while Wujiang and Kunshan are still in a state of weak decoupling. It is worth mentioning that Zhangjiagang is in a state of expansive coupling, which shows that carbon emissions and economic growth in this region are almost the same, but this state still needs to pay attention to the issue of carbon emissions from economic development.

In this study, firstly the carbon emission for land use was calculated by using the carbon emission coefficient method. Then, the temporal and spatial distribution of carbon emissions was analyzed, and it was found that the distribution was high in the north and low in the south. Next, the carbon emission risk index and carbon footprint pressure index were calculated, and it was found that the risk of carbon emission in all parts of Suzhou was relatively high, and the ecological imbalance was serious. Finally, the LMDI model was used to analyze the influencing factors, and the decoupling analysis of economic development and carbon emissions, which had the greatest positive contribution, found a weak decoupling state, gradually approaching strong decoupling.

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.

Author Contributions

Conceptualization, Y.H. and X.G.; methodology, Y.H.; writing—original draft preparation, Y.H.; writing—review and editing, X.G.; visualization, Y.H.; supervision, X.G.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Overview of the study area.

Figure 2. Sankey diagram of land use transfer in Suzhou City from 2008 to 2020.

Figure 3. Carbon emission in different areas of Suzhou from 2008 to 2020.

Figure 4. Spatial pattern of carbon emissions for Suzhou from 2008 to 2020.

Figure 5. Statistics chart for carbon emission risk index and carbon footprint pressure index. (a) Visualization of carbon emission risk index during 2008–2020; (b) Carbon emission risk index during 2008–2020; (c) Visualization of carbon footprint pressure index during 2008–2020; (d) Carbon footprint pressure index during 2008–2020.

Figure 6. The results of the LMDI factor decomposition for land use carbon emissions in the study area (104 t). (a) Contribution value of each impact factor from 2008 to 2020; (b) contribution rates of each impact factor from 2008 to 2020.

Table 1

Carbon emission coefficient for land-use types (t/hm²).

Land Type	Factor of Carbon Emissions
Cropland	0.422
Woodland	−0.644
Grassland	−0.021
Water	−0.245
Unused Land	−0.005

Table 2

The conversion factors and carbon emissions factors.

Energy Type	Standard Coal Conversion Factors	Carbon Emissions Factors (t/t)
Raw Coal	0.7143 kg/kg	0.7559
Cleaned Coal	0.9000 kg/kg	0.7559
Coke	0.9714 kg/kg	0.8550
Coke Oven Gas	0.5325 kg/m3	0.3548
Natural Gas	1.2143 kg/m3	0.4483
Gasoline	1.4714 kg/kg	0.5538
Diesel	1.4571 kg/kg	0.5921
Fuel Oil	1.4286 kg/kg	0.6185
Electricity	0.1229 kg/kwh	2.5255

Table 3

Classification criteria for decoupling status of Tapio decoupling model.

Status		∆C%	∆G%	D
Decoupling	Strong decoupling	<0	>0	<0
	Weak decoupling	>0	>0	0 < D < 0.8
	Recessive decoupling	<0	<0	>1.2
Negative decoupling	Strong negative decoupling	>0	>0	<0
	Weak negative decoupling	<0	<0	0 < D < 0.8
	Expansive negative decoupling	>0	>0	>1.2
Coupling	Expansive coupling	>0	>0	0.8 < D <1.2
	Recessive coupling	<0	<0	0.8 < D <1.2

Table 4

Dynamic change in land use type.

Land Use Type	Dynamic Degree/%
	2008–2012	2012–2016	2016–2020	2008–2020
Cropland	−1.38	−0.80	0.19	−0.63
woodland	2.35	−2.41	−1.94	−0.70
grassland	−18.32	−18.92	−21.39	−7.92
water	−0.91	−0.88	−1.24	−0.93
Unused land	−5.30	26.05	1.75	5.77
Construction land	5.25	3.35	1.62	3.69

Table 5

Calculation results for carbon emissions in different periods.

Carbon Emissions	2008	2012	2016	2020
Cropland	161,403.95	152,516.36	147,617.06	148,718.02
Woodland	−10,750.25	−11,760.32	−10,628.07	−9803.35
Grassland	−10.67	−2.85	−0.69	−0.10
Water	−77,304.08	−74,495.13	−71,870.03	−68,306.00
Unused land	−0.07	−0.05	−0.11	−0.12
Construction Land	53,872,097.44	70,570,697.85	75,730,995.65	77,184,760.80
Net Emissions	53,945,436.33	70,636,955.85	75,796,113.8	77,255,369.25

Table 6

Carbon emission risk index and carbon footprint pressure index.

Area.	Carbon Emission Risk Index				Carbon Footprint Pressure Index
	2008	2012	2016	2020	2008	2012	2016	2020
Urban area	21.39	33.76	33.18	28.76	126.62	202.99	205.51	182.58
Wujiang	37.15	49.05	52.99	56.73	477.59	609.36	686.28	864.95
Changshu	66.96	81.35	108.93	105.57	1240.24	1579.90	2320.66	2534.89
Kunshan	46.09	55.09	60.83	64.02	942.37	1294.40	1670.98	2142.45
Taicang	111.59	133.25	121.17	108.10	2258.09	2734.90	2574.21	2384.90
Zhangjiagang	204.64	271.22	289.39	326.76	4116.23	5556.88	6450.55	7664.75

Table 7

LMDI factor decomposition results for contribution value during 2008–2020.

Year	Energy Structure	EnergyIntensity	Economic Growth	Population Scale	Combined Effect
2008–2012	−35.18	−1472.39	3001.73	174.99	1669.15
2012–2016	157.30	−1509.48	1532.57	335.53	515.92
2016–2020	142.00	−2038.62	1330.45	712.08	145.93
Cumulative	264.13	−5020.49	5864.76	1222.60	2330.99

Table 8

The status of decoupling in different areas of Suzhou.

Year	Urban Area	Wujiang	Changshu	Zhangjiagang	Kunshan	Taicang	Suzhou
2008–2012	0.78	0.42	0.34	0.51	0.24	0.24	0.42
	WD	WD	WD	WD	WD	WD	WD
2012–2016	−0.05	0.35	2.62	0.52	0.65	−0.43	0.32
	SD	WD	END	WD	WD	SD	WD
2016–2020	−0.79	0.31	−0.26	0.81	0.15	−0.54	0.09
	SD	WD	SD	EC	WD	SD	WD

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