Response of Low Carbon Level to Transportation Efficiency in Megacities: A Case Study of Beijing, China

Abstract

Global warming caused by massive carbon dioxide emissions can lead to a chain of ecological disasters. As one of the main sources of carbon emissions, transportation is of great significance, and the evaluation of its connections with carbon emissions is necessary to achieve “carbon neutrality”. Taking Beijing as an example, this study evaluated traffic efficiency (TE) by utilizing principal component analysis and fuzzy comprehensive evaluation. Using the Tapio decoupling model and coupling coordination degree model, the corresponding relationship between urban low carbon level (LCL) and TE was explored. The results showed the following: (1) The total carbon emission (CE) level exhibited fluctuating variation from increasing to decreasing. The carbon emission intensity (CEI) continued to slow down, and the rapid growth of population density played a key role in low-carbon development. (2) The traffic operations continually showed a positive trend in development. TE increased from a step-like to a slow shape, until it declined in 2020 due to the pandemic. (3) TE and LCL both developed from low coordination to an extreme level of coordination. Per capita carbon emission (CEP) and TE presented an inverted U-shaped curve; meanwhile, with increases in TE, the decline in CEI slowed. In addition, the weak decoupling of TE changed to become strong, due to CE and CEP, and maintained a strong decoupling state from CEI. (4) There is a necessity for the rational planning of land use for transportation infrastructure, the encouragement of a combination of public and private transportation, and the strengthening of the maintenance of the relative infrastructure and the management of traffic behaviors to attain a win–win situation. The results provide a reference for optimizing the traffic structure to achieve “carbon neutrality”.

1. Introduction

Global warming caused by massive carbon dioxide emissions can lead to a chain of reactions that result in ecological disasters [1]. Industry [2], construction [3], transportation [4], and agriculture [5] are the pillars of global carbon emissions [6]. Nearly 25% of global energy-related CO₂ emissions are generated by the transportation sector each year, 75% of which originate from the road traffic sector [7]. China is the largest emitter of carbon in the world, accounting for 28.8% of world energy carbon emissions [8]. In China, transportation is the second largest source of carbon emissions, after the secondary industry, and is the industry with the most rapid growth [9]. Many studies have identified the direct impact of the transportation industry on the deterioration of the ecological environment. The main factor is the consumption of unclean energy in the process, manifested by low transport efficiency (TE) through technology, travel mode, travel distance, and other links, thus leading to higher carbon dioxide emissions [10,11,12]. As a key indicator to measure the orderly development of the urban transportation system, TE can represent the social development process with the goal of socio-economic sustainability [13]. Environmental indicators represented by total carbon emissions (CEs), per capita carbon emissions (CEP), and carbon emission intensity (CEI) can effectively evaluate the low carbon level (LCL) in urban cities, which represents a social development process aimed at ecological sustainability. Therefore, exploring the relationship between LCL and TE can provide a reference for comprehensive sustainable development in China.

For LCL, the current research has mainly focused on the construction of low-carbon cities. One method, which evaluates the issue from the perspectives of the economy, society, and urban planning [14,15], involves a commitment to building an evaluation system for low-carbon cities based on statistical data; the other approach is to directly calculate carbon dioxide emissions based on the ecosystem [16,17], with different calculation methods for diverse scales of research. In large-scale national or provincial studies, scholars have often used statistical data for calculation [18,19,20,21]; however, in small-scale areas of prefecture-level cities and below, nighttime light data are often applied for inversion to obtain more accurate carbon emission data due to difficulty accessing refined statistical data [22,23,24,25]. The definition of TE concentrates on the perspectives of travel and transportation. Costa and Markellos [26] posited that transportation efficiency is the ratio of effective output to corresponding input, reflecting the operating state of the transportation system. Fielding et al. [27] identified that transport efficiency should be considered from both supply and demand perspectives, including the social value of transport services and the externalities they generate. Measurement models are widely adopted in efficiency measurement [28]. Variables such as labor force, facilities, and capital are often considered as inputs of TE [29]; cargo and passenger turnover are often used to measure the output results [30,31]. Moreover, levels of technology and management have also been found to affect TE [32]. Few studies have focused on the relationship between LCL and TE to date; instead, they have focused more on transportation carbon emission efficiency.

As academia has various definitions of TE, studies have been conducted from different perspectives [33,34,35]. Thus far, there have been relatively more studies on the interaction between urbanization and the ecological environment at the macro level [36,37,38]. Urbanization can bring many benefits to residents, but it may also lead to a series of environmental problems [39,40]. On the contrary, changes in the ecological environment also have an impact on human well-being [41]. Most of the literature emphasizes the coupling perspective [42,43] and the use of the coupling coordination degree model to evaluate the interaction intensity and balanced development level among subsystems on the premise of scientific quantification in urban socio-economic subsystems and ecological subsystems [44,45]. The new demands for sustainable development require a more specific and practical perspective to assess the mechanism at the micro level [46], where TE is one of the key links affecting LCL. Although some studies have included environmental indicators in the evaluation system of TE, a large number of studies have proved that there is a complex linear relationship between the environment and traffic systems [47]. At the same time, the influencing factors and contribution degrees of TE in different regions vary and, thus, cannot be generalized. Therefore, the question of how to choose appropriate indicators to perform a systematic TE evaluation needs more discussion, considering the current vague concept of TE. What exactly is TE? How can it be evaluated scientifically and comprehensively? Academia needs a more unified answer. Secondly, previous studies have always taken traffic conditions as the driving factor of economic development to further discuss the impact of social and economic shifts on the ecological environment, but they do not explain the specific relationship between traffic conditions and the ecological environment, which means that the relationship between TE and LCL is not clear. At present, a large number of studies have been carried out on transportation carbon emissions, but there are only a few studies on urban LCL from the perspective of TE. What is the relationship between TE and LCL? What is the response mechanism for both? Sorting out these issues will have far-reaching implications for policy makers to promote carbon-neutral goals and mitigate global climate change.

The urbanization rate in China has shown a sharp increase over the past three decades. The increasing demand for transportation land has contributed to dramatic improvements in the transportation infrastructure. As the political, cultural, economic, scientific, and technological innovation center of China, Beijing has encountered traffic congestion, excessive commuting times, and other low TE problems in the process of development. The prolonged, unreasonable use of transportation land will pose a threat to the ecological environment. This study aims to explore the response relationship between LCL and TE. The results are obtained via the carbon emission measurement method proposed by the IPCC to measure the LCL of Beijing. TE is explored from the following three aspects: input, resource utilization, and externality. Appropriate evaluation indicators are established to build a TE evaluation system for Beijing. Capitalizing on the advantages of principal component analysis (PCA) in data dimension reduction, removal of redundant information, and feature extraction [48,49], we use PCA to calculate the indicator weights. In TE evaluation, because we hope to examine TE from different aspects, the hierarchical evaluation mode of a fuzzy comprehensive evaluation is suitable for this study. That is, the sub-factors are evaluated first, and then the entire system is comprehensively evaluated. This step-by-step approach makes the entire evaluation process clearer [50]. We used it to obtain the TE index of Beijing. Then, we also use the decoupling model and the coupling coordination degree model to analyze the relationship between the two. In theory, we define TE from another perspective and use another method to enrich the empirical literature on the corresponding relationship between TE and LCL. In practice, this study provides insight for policymakers on how to optimize the sustainable development model of transportation and environmental systems.

Therefore, starting from the necessity of reducing carbon emissions in transportation, Beijing is taken as an example to evaluate the urban low-carbon development level and TE during the period 2000–2020 and to obtain the relationship between the two to achieve the following goals: (1) improve the understanding of TE, and establish a reasonable evaluation system at the input and output levels based on the influencing factors of TE; (2) enrich the research content regarding TE’s impact on urban LCL, focusing on the connection between the transportation system and ecological environment, and explore the ecological effects caused by the transportation system from the concept of efficiency; (3) provide new ideas on carbon emission reductions and establish a win–win model of high efficiency and low carbon emissions; and (4) provide a reference to achieve the goal of “carbon neutrality” as soon as possible in the traffic structure optimization path.

2. Materials and Methods

2.1. Study Area

As the capital of China and a municipality directly under the central government, Beijing (39°26′–41°03′ N, 115°25′–117°30′ E) is located in the northwest of the North China Plain, bordering Tianjin in the east and Hebei Province in the west, with jurisdiction over 16 municipal districts (Figure 1). In order to alleviate traffic congestion, Beijing has rapidly developed its rail transit, and by the end of 2020, the length of all lines reached up to 727 km [51]. Beijing has a large and densely distributed population with a high level of urbanization. Its large-scale population and economy lead to significant energy consumption and carbon emissions. From 2017 to 2019, Beijing’s carbon emissions rose from 85 million tons to 88,150 million tons [12].

2.2. Data Sources

The data types used in this study include socio-economic statistics and basic geographic data. The sources of the various types of data are shown in

Table 1. The data sources of this study.

Type	Data Name	Source	Description
Socio-economic statistics	Energy consumption data	China Energy Statistical Yearbook (2001–2021)	CE calculation
	Permanent residential population, GDP, urban road area, bus operation, length of lighting lines, number of accidents, turnover of goods, etc.	Beijing Statistical Yearbook (2001–2021), China Urban Construction Statistical Yearbook, China Transportation Yearbook, Beijing National Economic and Social Development Statistical Bulletin	TE evaluation index calculation, CEP calculation, CEI calculation
Basic terrestrial data	National Land-use Data (2000–2020)	Yang Jie et al. [52]	Calculation of land-use mixing degree

.

2.3. Methods

2.3.1. LCL Evaluation

Since carbon emissions from energy consumption account for 70% of CEs [53], energy carbon emissions in Beijing were used to represent CEs in this study. The inventory calculation method put forward by the Intergovernmental Panel on Climate Change (IPCC) in 2006 is directly based on fuel consumption and has been widely used. In this study, we calculated the carbon emissions in Beijing based on the perspective of energy end-use consumption, referring to the IPCC National Greenhouse Gas Inventory Guide. Considering the variety of sources in the energy balance sheet, eight major fossil fuels, including coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, and natural gas, were selected for calculation, excluding those that are not mainly utilized for combustion. Coal includes raw coal, washed coal, other washed coal, and briquettes. As a secondary energy source, electricity does not produce CO₂ in the terminal consumption, so it was not included in the calculation in this study. The specific calculation method is shown in Formula (1):

$$CE=\sum _{i=1}^n{CE}_i={\sum }_{i=1}^n{E}_i\times {\epsilon }_i\times f_i\times \frac{44}{12}$$

(1)

where $CE$ is the carbon emissions generated by energy consumption (unit: 10,000 tons) and $i$ is the type of energy. $E_i$ is the consumption of different types of energy in tons or billions of cubic meters; ${\epsilon }_i$ is the conversion coefficient of standard coal of different energy sources, whose unit is kg standard coal/kg or kg standard coal/cubic meter; $f_i$ is the carbon emission coefficient of different kinds of energy, whose unit is kg/kg standard coal (

Table 2. Standard coal conversion coefficient and carbon emission coefficient table.

Energy Type	Conversion Coefficient of Standard Coal 1 (kgce 3·kg−1)	Carbon Emission Coefficient 2 (kg·kgce−1)
Coal	0.7143	0.7559
Coke	0.9714	0.8550
Crude oil	1.4286	0.5857
Gasoline	1.4714	0.5538
Kerosene	1.4714	0.5714
Diesel oil	1.4571	0.5921
Fuel oil	1.4286	0.6185
Natural gas	1.3300	0.4483

¹ The conversion coefficient of standard coal is derived from the China Energy Statistical Yearbook 2020; ² The carbon emission coefficient is derived from the IPCC Guidelines for National Greenhouse Gas Inventories; ³ kgce = kilogram of coal equivalent.

); 12 and 44 are the atomic mass and molecular mass of carbon and carbon dioxide, respectively.

CEP is the ratio of CEs to the size of the resident population representing the amount of emitted carbon dioxide per capita. CEI refers to the carbon emissions of gross regional production (GDP) and represents the ratio of CEs to GDP, reflecting the relationship between economic development and pollutant emissions. Both are important indexes to evaluate regional sustainable development. The specific calculation methods are shown in Formulas (2) and (3):

$$CEP=CE/P$$

(2)

$$CEI=CE/GDP$$

(3)

where $CEP$ is carbon emissions per capita, whose unit is ton/person; $P$ is the number of permanent residents, and the unit is 10,000; and $CEI$ is carbon emission intensity, and the unit is ton/ten thousand CNY.

2.3.2. Theoretical Framework of TE Evaluation

TE refers to the ratio of effective output and the corresponding input in the urban transport system. The input can be represented by the allocation of public transport resources, while output refers to the consumption generated by residents and the results of the transport service. Given certain inputs and technology, the more effective the output, the higher the level of TE; otherwise, the TE level would be lower. TE indicates whether the layout of the land for urban transportation is reasonable and is an important index for evaluating the traffic status of a city. The TE analyzed in this study comprises three aspects. In terms of investment, an efficient urban transportation system should show a good distribution of transportation resources, where the urban spatial structure determines the layout of transportation resources and is fundamental to improving TE. The imbalance in the urban transport infrastructure can reflect the imbalance between regions [54]. Whether the level of urban transport infrastructure can meet people’s travel needs is crucial, and a developed infrastructure can improve citizens’ happiness. However, the improvement in efficiency contributed by investment is not inevitable, as it also depends on the utility of transportation resources. In terms of resource utilization, the interaction of different elements, such as the spatial structural organization and management level, will also determine the efficiency of the system. In terms of externality, an efficient urban transportation system contributes positive externalities such as closer economic and transportation cooperation, which can be directly reflected in the changes in passenger and freight volumes; otherwise, it will produce negative externalities such as traffic accidents.

According to the existing literature and the development situation in Beijing, and on the basis of scientific, systematic, practical, and comparative evaluation principles, this study investigates the following four aspects: urban spatial structure and traffic structure, urban transport infrastructure level, urban traffic management level, and urban transport structure. This study selects 17 indicators including population density, the number of buses for ${10}^4$ people, the land-use mixing degree, and road network density, as shown in

Table 3. TE evaluation index system of Beijing.

Target Layer	Criterion Layer		Index Layer	Unit
TE	Input	Urban spatial structure and traffic structure	Population density	104 people per square kilometer
			Number of buses	Vehicles per 104 people
			Private car ownership	Trucks
			Taxi operating volume	Vehicles
			Land-use mixing degree	/
		Urban transport infrastructure level	Per capita road area	Square meter per person
			Road network density	Kilometers per square kilometer
			Number of parking lots	/
			Number of streetlights	/
			Lighting circuit length	Kilometers
	Output	Urban traffic management level	Death toll	People per 104 cars
			Injuries	People per 104 cars
			Number of accidents	/
		Urban transport structure	Passenger volume	104 people
			Freight volume	104 tons
			Passenger turnover	104 people
			Freight turnover	104 tons

.

2.3.3. Weight Determination

Principal component analysis (PCA) is a mathematical method that statistically reduces the dimensional number of multiple variables to a lower level. The possible correlation between variables is eliminated via orthogonal transformation, and the original variable information is represented to the furthest extent possible by analyzing fewer principal components. There are certain correlations between elements of an urban traffic system, so this study used the PCA method to determine the weights of each index (

Table 4. The index weights of the Beijing TE evaluation system.

Criterion Layer	Weight	Index Layer	Weight
Urban spatial structure and traffic structure	0.2984	Population density	0.0613
		Bus capacity of 10,000 people	0.0829
		Private car ownership	0.0628
		Taxi traffic	0.0303
		Land-use mixing degree	0.0611
Urban transportation infrastructure level	0.2964	Per capita road area	0.0636
		Road network density	0.0536
		Number of parking spaces	0.0595
		The number of lamps	0.0600
		Lighting line length	0.0597
Urban traffic management level	0.2204	Death toll per ten thousand cars	0.0654
		Injuries per ten thousand cars	0.0738
		Number of accidents	0.0812
Urban transport structure	0.1848	Passenger volume	0.0306
		Freight volume	0.0292
		Passenger turnover	0.0620
		Turnover of goods	0.0630

).

2.3.4. Fuzzy Comprehensive Evaluation

Fuzzy comprehensive evaluation is a comprehensive evaluation method based on fuzzy mathematics. This study selected this method to evaluate TE in Beijing from 2000 to 2020. Firstly, the comprehensive evaluation factor set is established as follows:

$$U=\left\{u_1,\right.u_2,u_3,\dots ,\left.u_n\right\}$$

(4)

where $u_i$ represents various influencing factors, including urban spatial structure and traffic structure, urban transport infrastructure level, urban traffic management level, and urban transport structure, of TE. Each criterion layer can be divided into the next level of influencing factors as follows:

$$U=\left\{u_{i1},\right.u_{i2},u_{i3},\dots ,\left.u_{in}\right\}$$

(5)

Secondly, the following evaluation scale set for a comprehensive evaluation is established:

$$V=\left\{v_1,\right.v_2,v_3,\dots ,\left.v_n\right\}$$

(6)

In this study, the evaluation scale set was determined as good, stable, poor, and very poor. Thirdly, the evaluation matrix is established. The single factor $u_{ij}$ is evaluated. The membership degree of $u_{ij}$ to the evaluation level $v_i$ is $r_{ij}$, and the single-factor evaluation set of $u_{ij}$ is obtained. The formula is as follows:

$$r_i=\left\{r_{i1},\right.r_{i2},r_{i3},\dots ,\left.r_{in}\right\}$$

(7)

Thus, the total evaluation matrix R is obtained:

$$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \cdots & r_{1n}\\ r_{21}& r_{22}& \cdots & r_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ r_{m1}& r_{m2}& \cdots & r_{mn}\end{array}\right]$$

(8)

where $R$ represents a fuzzy relationship between the evaluation factor set $U$ and the evaluation scale set $V$.

Because of the different importance expressed by the evaluation index in the evaluation system, the factor importance degree is also called weight. Therefore, this study used the PCA method to introduce the weight set, as follows:

$$W=\left\{w_1,\right.w_2,w_3,\dots ,\left.w_n\right\}$$

(9)

Finally, the fuzzy comprehensive evaluation was performed. The fuzzy evaluation mainly includes the following four types of fuzzy operators: (×, ⊕), (∧, ∨), (×, ∨), and (∧, ⊕). The appropriate operators were selected for fuzzy evaluation, and the scores of good, stable, poor, and very poor were 4, 3, 2, and 1, respectively, to obtain the final TE evaluation result.

2.3.5. Tapio Decoupling Model

The initial decoupling theory originated in physics. In 2005, Tapio [55] proposed the decoupling model, which is often used to describe the relationship between economic growth and resource use or environmental pollution. Tapio proposed eight specific relationship types according to the division of the decoupling elasticity coefficient (

Table 5. The decoupling status.

State	Substate	$∆\mathit{E}\mathit{P}$	$∆\mathit{D}\mathit{F}$	$\mathit{D}$
Decoupling	Strong decoupling	<0	>0	<0
	Weak decoupling	>0	>0	0 <$D$< 0.8
	Recessionary decoupling	<0	<0	>1.2
Negative decoupling	Strong negative decoupling	>0	<0	<0
	Weak negative decoupling	<0	<0	0 <$D$ < 0.8
	Expansion negative decoupling	>0	>0	>1.2
Link	Growth link	>0	>0	0.8 <$D$< 1.2
	Recessionary link	<0	<0	0.8 <$D$< 1.2

). In order to explore the sensitivity of carbon dioxide change to TE, this study adopted the Tapio decoupling model to analyze the relationship between TE and LCL and calculated the respective values for TE, CE, CEP, and CEI.

$$D=\frac{∆EP}{∆DF}=\frac{({EP}_s-{EP}_r)/{EP}_r}{({DF}_s-{DF}_r)/{DF}_r}$$

(10)

$D$ is the decoupling elastic coefficient; $∆EP$ is the environmental pressure variable, which is represented by CE, CEP, and CEI in this study. $∆DF$ is the change in the environmental driving force, which is represented by the TE value in this study.

2.3.6. Coupling Coordination Degree Model

Coupling refers to the phenomenon where two or more systems influence each other through functions. According to the coupling and coordination degrees, it can be divided into different coupling stages and coordination development levels (

Table 6. Division of coupling stage.

Coupling Stage	Separation Stage	Antagonistic Stage	Run-in Stage	Coupling Stage
$C$	(0, 0.3]	(0.3, 0.5]	(0.5, 0.8]	(0.8, 1]

and

Table 7. Division of coordinated development level.

Coordinated Development Level	Low Degree of Coordination	Moderate Coordination	Highly Coordinated	Extreme Coordination
$D$	(0, 0.3]	(0.3, 0.5]	(0.5, 0.8]	(0.8, 1]

). In this study, the coordinated development and the change in TE and LCL were identified using the coupling degree of the coordination model. The coupling degree was used to characterize the intensity of their interaction, and the coupling degree of coordination represented the level of coordinated development of the two systems. The formulas are as follows:

$$C=2\frac{\sqrt{f\left(T\right)g\left(C\right)}}{f\left(T\right)+g\left(C\right)}$$

(11)

$$T=af\left(T\right)+bg\left(C\right)$$

(12)

$$D=\sqrt{C\times T}$$

(13)

where $C$ is the degree of coupling; $f\left(T\right)$ is the TE change; $g\left(C\right)$ represents the change in LCL, which was represented by CEs, CEP, and CEI in this study; $T$ is the comprehensive coordination degree of TE and LCL; and $a$ and $b$ represent the contribution of TE and LCL, respectively. In this study, $a=b=0.5$ and $D$ is the coupling coordination degree.

3. Results

3.1. LCL Time Evolution

The changes in CEs and their growth rate in Beijing from 2000 to 2020 are shown in Figure 2a. From the perspective of the carbon emission scale, CEs in Beijing show three consecutive trends in stages, with an overall trend of first rising, then stabilizing, and finally rapidly declining. CEs decreased from 103.201 million tons in 2000 to 61.117 million tons in 2020. The first stage was from 2000 to 2007, during which the total volume decreased in 2002, while the overall performance showed a rapid rise. CEs increased by 21.2%, with an average annual growth rate of nearly 3%. The second stage was from 2008 to 2010, during which the change in CEs in Beijing was relatively stable, and showed a slight decline in 2008. After 2010, CEs in Beijing decreased significantly, from 127.639 million tons in 2010 to 61.117 million tons in 2020, with a decrease rate of 52.1%.

To specifically grasp the carbon emission change in Beijing, it is necessary to further analyze the structural characteristics of carbon emissions (Figure 3). It is not difficult to identify that the consumption of coal and coke gradually decreased during the study period. In general, there was a positive correlation between the carbon emission change and CE change in coal resources. The coal resources changed significantly from a dominant position at the beginning of the study period to a low proportion at the end, which can be interpreted as the energy consumption structure in Beijing gradually becoming optimized. On the contrary, with the acceleration of urbanization and the rapid development of the transportation industry in Beijing, the proportions of fuel oil, diesel oil, kerosene, gasoline, and crude oil resources have gradually increased. Among these, crude oil resources have changed from being the second largest source of carbon emissions to the first. As a clean energy source, the proportion of natural gas increased steadily in the late period of this study; however, natural gas does not represent a large share, thus indicating that Beijing still has significant potential to optimize its energy structure.

According to the changes in CEP and the growth rate shown in Figure 2b, the CEP of Beijing principally includes two stages. The first stage was from 2000 to 2006, in which the carbon emissions per capita fluctuated, showing a trend of decreasing initially, then increasing, and finally decreasing slightly. In the second stage, from 2007 to 2020, CEP showed a trend of continuous decline, and the rate of decline gradually stabilized until the outbreak of COVID-19 in 2020 when there was a rapid decline, finally reaching the low point of CEP in the study period. During this period, the proportion of energy carbon emissions from the secondary industry in Beijing decreased significantly, and enterprises with high energy consumption and high pollution were gradually replaced with new industries. In the process of promoting the optimization of the industrial structure, Beijing has continuously reduced its dependence on energy, and the whole society has gradually formed an environmentally friendly low-carbon model.

CEI reflects the extent to which a region’s low-carbon economy has progressed. The CEI of Beijing continued to decline from 3.15 tons/10,000 CNY in 2000 to 0.17 tons/100 million CNY in 2020. The trend of decline gradually slowed down, indicating that Beijing has slowly implemented the low-carbon economic development mode and continuously improved its energy utilization efficiency (Figure 2c). Nevertheless, because there are difficulties in maintaining the early, rapid low-carbon economic development, the relative development mode of energy conservation and emission reductions has gradually entered the bottleneck period.

3.2. TE Time Evolution

As can be seen in Figure 4a, the traffic operation status of Beijing continues to show a positive trend; meanwhile, the inefficient traffic operation has gradually declined with the expansion of the spatial traffic structure, the improvement in management levels, and the construction of infrastructure. The traffic operation status of the whole city is moving steadily towards an efficient mode.

During the study period, the TE index of Beijing was generally improved, which can be divided into three stages (Figure 4b). The first period was from 2000 to 2008. During this period, TE increased in a step-like shape. TE increased continuously and then decreased every three years, with the TE index increasing from 1.12 to 2.84. The second stage was from 2009 to 2019, during which TE increased but at a slow rate overall, from 2.861 in 2009 to 3.661 in 2019. It can be speculated that, in this decade, the traffic resources of Beijing gradually became saturated and approached the equilibrium state of supply and demand. However, in the previous stage, Beijing’s economy developed rapidly with the tertiary industry becoming increasingly mature, and the proportion of industrial structure grew continuously. In the early development stage, the transportation sector received policy support and made obvious progress. In 2020, TE in Beijing decreased compared with the previous year, principally due to the impact of the pandemic, when urban transport activities were restricted by a series of policies [56]. The passenger volume, freight volume, and passenger turnover decreased significantly, which seriously hindered the development of traffic flow and resulted in the stagnation in TE in that year.

3.3. Regression Results of TE and LCL

In order to further study the correlation between TE and urban LCL, this study conducted correlation analyses between the TE index and CEs, CEP, and CEI in Beijing (

Table 8. The correlation coefficient between TE and LCL.

	Correlation Coefficient	p-Value
CE	−0.424 *	0.056
CEP	−0.848 ***	0.000
CEI	−0.98 ***	0.000

Note: *** and * represent the significance level of 1% and 10%, respectively.

). The results show that the absolute value of the correlation coefficient between TE and CEs is low, from which the relationship between TE and CEs could not be identified. The absolute value of the correlation coefficient between TE, CEP, and CEI is high, with a 1% significance level. Therefore, a negative correlation is inferred between TE, CEP, and CEI. A higher TE would lead to a lower CEP and a lower CEI, indicating that the increase in TE drives the development of urban LCL. As shown in Figure 5, the TE index has a good fitting effect with CEP and CEI, implying a negative correlation to some extent.

From the fitting results, it can be seen that CEs and TE present “W”-shaped curves; however, due to the limited number of samples, the trend in the latter half is unknown, or it may be an inverted “N”-shaped curve. Meanwhile, TE is highly correlated with CEP and CEI according to the correlation analysis results, so the regression analysis can be performed. Therefore, this section focuses on the relationships between TE, CEP, and CEI, and the regression models are as follows:

The regression model of TE and CEP in Beijing:

$$y=-1.0787x^2+3.5303x+4.701$$

(14)

The regression model of TE and CEI in Beijing:

$$y=-2.452ln\left(x\right)+3.3435$$

(15)

CEP and TE present an inverted U-shaped curve, and the peak value is reached when the TE index is 1.64, where CEP is the highest. When the TE index is less than 1.64, TE increases at the cost of emitting more carbon dioxide. With the gradual implementation of technological innovation and related policies, such as increasing the use of new energy sources, TE is no longer improved at the expense of the ecological environment, under which the environment and economy develop together in a sustainable way. CEI and TE can, to some extent, be interpreted as being in a linear correlation, where the relative logarithmic fitting results are better than the linear fitting results. With the increase in TE, CEI gradually decreases. CEI reaches the turning point when the TE index is 2.452. Specifically, when the TE index is less than 2.452, the decreasing rate of CEI is greater than 1; meanwhile, the reduction rate of CEI is less than 1 when the TE index is greater than 2.452.

3.4. Decoupling Analysis of TE and LCL

As can be seen in

Table 9. The decoupling changes in TE and carbon emissions.

Environmental Pressure Variable	2000–2005	2005–2010	2010–2015	2015–2020
CE	Weak decoupling	Weak decoupling	Strong decoupling	Strong decoupling
CEP	Weak decoupling	Strong decoupling	Strong decoupling	Strong decoupling
CEI	Strong decoupling	Strong decoupling	Strong decoupling	Strong decoupling

, the decoupling states of TE with CEs and CEP in Beijing have changed from a weak to a strong status, and CEP has reached a strong decoupling state with TE relatively early. At the same time, TE and CEI in Beijing are in a state of strong decoupling during the entire study period, which changes from −0.51 in the first stage to −4.79 in the fourth, and the absolute value of the decoupling elastic coefficient increases rapidly. It can be seen that TE in Beijing shows an upward trend at each stage; meanwhile, CEs increase in the first two stages and then decrease, and their growth rate is slightly slower than that of TE in the first two stages. Similarly, CEP increases in the first stage and then decreases in the following stage. In the first stage, CEP grows more slowly than TE. The CEI declines throughout the study period. With the increase in TE, the pressure on the ecological environment is reduced, and the LCL of Beijing increases.

3.5. Analysis of Coordinated Development of TE and LCL

The results imply that the urban LCL represented by TE, CEs, CEP, and CEI show a trend of development from low coordination to an extreme level of coordination (Table S1); among them, the coordination degree between CEI and TE increases the fastest, followed by that of CEP and CE.

The coupling degree of LCL represented by TE and CEs can be divided into two periods. From 2000 to 2010, the overall coupling degree of the two presented an inverted “U”-shaped structure; that is, they experienced the antagonism stage, run-in stage, coupling stage, and then regressed to the separation stage. In this 10-year period, the coupling coordination degree also closely showed an inverted “U”-shaped structure, but the coupling degree did not present significant fluctuation and exhibited a low coordination degree in 2010. After 2010, the coupling degree and coordination degree gradually increased, showing rapid growth in the early stage, and then slowing until reaching extreme coordination. The reason is that in the early stage, the traffic expansion introduced by rapid urbanization needs to adapt to the ecological environment, and during this adaptation process, certain resources are consumed. When these consumed resources reach the carrying capacity of the ecological system, the development level of the ecological environment, namely the LCL development of the studied city, is relatively underdeveloped. Improvements in TE will bring economic growth; meanwhile, the lagging development of the ecological environment will restrict the further expansion of urban traffic and affect the stability of the supply and demand structure, further constraining the improvements in TE. Consequently, in order to solve the stagnation in LCL development, more funds are required to invest in the restoration and improvement of the ecological environment so as to promote the coordinated coupling development of TE and LCL at a high level.

During the study period, LCL and TE remained in the coupling stage for a long time, despite the LCL coupling degree characterized by TE and CEP being lower than 0.8 during 2004–2008, indicating that LCL characterized by CEP and TE has maintained a high degree of consistency. From 2004 to 2008, the coupling coordination degree of TE and LCL was low. After 2008, the progress in urban LCL was emphasized, together with the strengthening of urban transportation construction, aiming to promote their moderate coordination development to a significant degree.

The coupling degrees of LCL and TE represented by CEI were all above 0.8 during the study period, indicating an all-time coupling state. In terms of the coupling coordination degree, there were three stages in the increase in deceleration. Efficient TE is conducive to improving residents’ travel intentions. Under the reasonable resource utilization and management of the urban transportation system, the TE will be improved to form a positive feedback response between the systems. Rapid social and economic activities provide financial and policy support for the progress of urban LCL and promote improvements in the coordination level of these two factors.

4. Discussion

4.1. Analysis of Evolution Stage

Based on the results, the study period can be roughly divided into two stages with 2008 as the cut-off point. On 13 July 2001, China won the right to host the 2008 Summer Olympic Games. After that, until around 2007, the success of the Olympic bid caused the corresponding industries to accelerate the pace of construction, and the amount of energy consumption also rapidly increased. At this stage, Beijing’s industrialization was swiftly progressing, with a significant demand for energy from all industries [57]; this also accelerated the unilateral development of the social economy. However, as an energy-intensive industrial sector, heavy industry led to a significant amount of carbon emissions [58]. During the 2008 Beijing Olympics, hydrogen fuel cell buses served the marathon spectators. Unlike conventional fossil-fuel-powered vehicles, hydrogen fuel cell vehicles only emit water vapor, which is low pollution, and their materials can be recycled after being scrapped. Moreover, in order to ensure smooth traffic conditions and good air quality during the Beijing Olympics and the Paralympic Games, the city formulated and implemented a no-license travel policy. The results showed that during the Olympic Games, the air pollution index was 36% lower than the average level of the previous 8 years, which significantly reduced the environmental PM2.5 index [59]. After 2008, based on the remarkable results achieved by the implementation of policies during the Beijing Olympic Games, Beijing began to pay attention to the concept of green and sustainable environmental protection, and the government strengthened the emphasis on energy conservation and emission reductions through the introduction of a series of corresponding environmental protection policies [60,61,62]. For example, the traffic control policy was issued in 2009, as well as the Notice of Beijing Municipal People’s Government on the Implementation of Regional Traffic Restriction Management Measures on Weekday Peak Periods. In 2010, in order to rapidly popularize new energy vehicles, the Chinese government encouraged consumers to buy electric and hybrid vehicles through subsidies, free license plates, and other measures. Due to the lagging effect of policy implementation, Beijing has achieved the initial outcomes in both energy conservation and emission reductions since 2010, with a significant reduction in CEs. At the same stage, the coordinated development level of LCL and TE was also continually improving, which was inseparable from the policy support and technological innovation. In addition, China has gradually established a carbon emission trading market since 2017, covering industries, including related enterprises, in the transport sector, so a large number of enterprises have begun to proactively reduce carbon emissions. Since the beginning of 2000, China has gradually introduced different stages of vehicle emission standards, from the initial National I standard to the current National VI standard, prompting car manufacturers to adopt cleaner engines and exhaust treatment technology, thereby reducing pollutant emissions. Under the comprehensive effect of various aspects of policy guidance, the coordinated development of Beijing’s traffic and environmental systems has been promoted, which provides evidence for the research results of this paper.

4.2. Ideal Coupling Coordination Mechanism

TE and LCL interact with each other, forming a complex system in which the city manager plays an intermediate role (Figure 6). City managers invest in technology, structure, and facilities of traffic subsystems to produce corresponding outputs, such as the traffic service level and passenger flow. This link shows the level of TE, which directly produces carbon dioxide and indirectly affects the urban LCL development through travel distance, travel times, fuel, and emission standards. At present, the development of new energy vehicles has received widespread attention. Compared with fuel vehicles, new energy vehicles use the power system to generate electricity, and their carbon dioxide emissions can be reduced by up to 75% during their service life [63], which means these vehicles are favored by more people. Compared with the previous fuel transportation system, a new transportation system with a high proportion of electrified vehicles not only shows higher travel efficiency [64,65] but also shows significant potential in reducing carbon dioxide emissions and improving the low-carbon levels in the city. In theory, a fully electrified transport system would produce no CO₂ emissions during travel. This plays a key role in improving the decoupling level of TE and LCL. In 2009, the Beijing municipal government launched a corresponding action, not only through limiting the number of local car purchases but also through the provision of more favorable conditions for new energy vehicles. Ideally, efficient TE will contribute to the improvement in urban LCL, which will satisfy our expectations for low-carbon cities. If TE has a significant inhibitory effect on LCL, the city manager will receive that feedback and further adjust the traffic subsystem, and make efforts to balance the relationship between input and output to obtain a higher TE. At the same time, direct control of urban LCL should be carried out to jointly promote urban LCL development through both direct and indirect approaches. We hope a spiraling urban sustainable development model can be formed between the transportation and the environmental subsystem, thus forming a new state with a higher level of coupling coordination.

4.3. Policy Suggestions

The interaction between the four criterion-layer factors of TE and LCL is further discussed in this section. According to the correlation results (Figure 7), all four factors and LCL show promotional effects. Among the interactions between CEs and the four factors, except for the urban traffic management level, the order of the correlations between the other three types and CEs are as follows: urban transport structure > urban spatial structure and traffic structure > urban transport infrastructure level. In terms of the correlations between the four factors and CEI, the order is as follows: urban spatial structure and traffic structure > urban transport structure > urban transport infrastructure level > urban traffic management level. The performance of the correlations with CEI is as follows: urban traffic management level > urban spatial structure and traffic structure > urban transport infrastructure level > urban transport structure. The performance of urban spatial structure and traffic structure was found to be the most prominent, which has a strong inhibitory effect on CEP and CEI whose correlation coefficients are both greater than 0.9. This is because the spatial layout of urban land determines the choice of traffic source, traffic flow, and transportation mode. The reasonable planning of urban transportation land by city managers can balance the contradiction between the supply and demand of transportation resources, effectively alleviating the current problems such as traffic congestion and residents’ long commute times, and reducing unnecessary pollution exposure. At the same time, the urban transport structure affects the level of TE through the operation speed, safety level, and input cost manifested in different levels of public and private transport, affecting the urban LCL level. The efficient combination of public and private transport would promote the stability of the urban transport structure and achieve a win–win situation of high efficiency and low carbon emissions.

In summary, the following urban management suggestions are put forward. Firstly, reasonable planning of the urban traffic land is necessary by making the layout of various traffic facilities and roads more reasonable to reduce road congestion. Secondly, the government needs to adjust the traffic structure by encouraging an efficient combination of public and private transport and strengthening the priority strategy of public transport [12]. Although the density of Beijing’s transport network is relatively high, there is still space for improvement in the quality of public transport services. Therefore, it is necessary to constantly improve the facilities and services of public transport so that more citizens will be willing to use public transport. In addition, low-carbon transport modes should be actively promoted, such as walking and cycling, to advocate the concept of green travel. Finally, the maintenance and updating of the transport infrastructure should be strengthened to improve the efficiency and service quality of transport facilities. At the same time, the management and supervision of urban traffic behaviors should be strengthened, such as jettisoning illegal parking and other traffic disorders, so that citizens are more aware of compliance with traffic laws and regulations and the optimization of orderly traffic.

4.4. Limits of Research

In order to establish a more adequately described urban sustainable development system, there should be more refined data to further characterize LCL and TE. Focusing on the use of statistical data makes the data type too simple; moreover, the evaluation index is incomplete due to the data availability, as well as the inaccurate descriptions of the traffic conditions and spatial characteristics of LCL in Beijing. Since the carbon emissions in energy consumption account for 70% of CEs, this study used the energy carbon emissions of Beijing to represent CEs, which may lead to uncertain results. On the basis of a wider range of data types, refining the index system, expanding the temporal and spatial variation analyses, and strengthening accurate CE accounting will be the main directions of future research. It is worth mentioning that our accounting object for TE was the urban road system, and urban rail transit and other emerging shared travel services were not included. However, many studies have shown that both rail transit [66] and shared travel services [67] are of great significance in improving travel efficiency and the urban low-carbon level, so as to help realize a virtuous cycle between the development mode of the transport and environmental system. However, due to data limitations, we did not extensively discuss these public services, which we recognize as a shortcoming of this study, and in future studies, this will be further improved.

5. Conclusions

Taking Beijing as the object, this study researched the respective changes in urban LCL and TE during 2000–2020, analyzed the relationship and coupling coordination between them, and discussed the response mechanism, thereby providing empirical support for the clarification of the role of TE on urban LCL. The following conclusions are drawn:

(1)

During the study period, CEs in Beijing showed a trend of rising, stabilizing, and finally rapidly declining, in which the dominant role of coal resources in carbon emission structure was gradually replaced with crude oil resources. From 2007 to 2010, the population density increased rapidly, which played a significant role in the low-carbon development of Beijing. CEI in Beijing continued to decline during the period studied, and its trend gradually stabilized after 2015.

(2)

The urban spatial structure and traffic structure, as well as the level of the urban transport infrastructure, were the main influencing factors of TE. The traffic operation conditions of Beijing consistently showed a positive trend in development; meanwhile, inadequate traffic operation conditions gradually declined with the expansion of space structure, the improvements in management levels, and the construction of infrastructure. TE increased from a step-like to a slower trend and then decreased in 2020 due to the COVID-19 pandemic.

(3)

TE was strongly correlated with CEP and CEI. CEP and TE showed an inverted U-shaped curve, and CEP reached its peak when the TE index was 1.64. With the increase in TE, CEI decreased, and the rate decreased gradually. During the study period, TE, CEs, and CEP in Beijing changed from weak decoupling to a strong level, and CEP reached strong decoupling with TE earlier. TE and CEI were always in a state of strong decoupling. After the adaptation, stress, and drive of rapid urbanization and the ecological environment, the urban LCL represented by TE, CEs, CEP, and CEI in Beijing showed a shift from low coordination to an extreme degree of coordination. The coupling coordination degree of CEI and TE increased fastest, followed by that of CEP and CE.

(4)

In order to achieve a win–win situation of efficient transportation and low-carbon development, Beijing should rationally project transportation land, encourage the combination of public and private transportation, actively promote low-carbon transportation modes, strengthen the maintenance and updating of the transportation infrastructure, and improve the management and supervision of residents’ transportation behaviors.