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Article

Decomposition and Decoupling Analysis of Transportation Carbon Emissions in China Using the Generalized Divisia Index Method

1
School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China
2
Academic Affairs, East China Jiaotong University, Nanchang 330013, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(18), 8231; https://doi.org/10.3390/su17188231
Submission received: 12 August 2025 / Revised: 5 September 2025 / Accepted: 9 September 2025 / Published: 12 September 2025

Abstract

The transportation sector is crucial for achieving China’s “dual carbon” strategic goals, yet its emission drivers and decoupling mechanisms exhibit significant provincial heterogeneity that remains underexplored. Existing studies predominantly rely on the LMDI method, which suffers from limitations in handling multiple absolute indicators, and rarely quantify the policy-driven decoupling effort. To address these gaps, this study employs the generalized Divisia index method to decompose transportation carbon emissions across thirty Chinese provinces from 2005 to 2022. Furthermore, we innovatively integrate the Tapio decoupling model with a novel decoupling effort model to assess both the decoupling state and the effectiveness of emission reduction policies. Our key findings reveal that: (1) economic output scale was the primary driver of emission growth, while output carbon intensity was the dominant mitigation factor; (2) driving mechanisms varied considerably across provinces, with 83% of provinces primarily driven by economic scale expansion; (3) the national decoupling state improved from weak to strong decoupling, with 53% of provinces achieving decoupling advancement; and (4) intensity effects were the core driver enabling decoupling efforts, while scale effects represented the primary inhibiting factor. This study provides a robust analytical framework and empirical evidence for formulating differentiated decarbonization strategies across Chinese provinces.

1. Introduction

The rapid increase in greenhouse gas emissions is profoundly disrupting the global climate system, creating critical challenges for sustainable socioeconomic development and ecological equilibrium [1,2]. Carbon dioxide, constituting 76% of aggregate greenhouse emissions, has emerged as the principal driver of climate change [3,4]. In response to the urgent challenge of climate change, a problem that surpasses the capacity of any single nation, the international community has established the multilateral negotiation framework to coordinate emission reduction commitments, aiming to mitigate the “tragedy of the commons” dilemma and curb the escalation of atmospheric carbon concentrations [5]. Within this global governance paradigm, China, as the world’s paramount energy consumer and carbon emitter, has strategically engaged in climate governance through its “dual carbon” strategic goals: achieving carbon emission peak by 2030 and realizing carbon neutrality by 2060. As a critical participant in global climate governance, China’s decarbonization progress will exert pivotal influence on the realization of international sustainable development targets [6]. To operationalize ambitious climate commitments, the Chinese government is systematically advancing deep decarbonization across three major high-emission sectors: industry, construction, and transportation [7].
The transportation sector, as a fundamental, leading, and strategic economic sector that underpins national development [8], has emerged as a critical domain in global climate governance [9]. Globally, transportation contributes over one-third of carbon emissions from end-use sectors, with emissions growing at an average annual rate of 1.71% from 1990 to 2022, second only to the industry sector [10]. In China, rapid industrialization and urbanization have driven transportation carbon emissions to increase at an annual rate of 7.25% during the same period, more than quadruple the global average [11]. This carbon-intensive development model starkly contradicts China’s strategic pursuit of high-quality economic advancement, confronting its transportation sector with a dual challenge: sustaining the rigid demands of economic expansion while adhering to stringent carbon emission constraints [12,13]. Addressing this dilemma requires elucidating the driving mechanisms behind transportation carbon emission patterns and developing actionable pathways to decouple economic development from carbon emissions. Such efforts are crucial for formulating scientifically robust emission reduction strategies, addressing environmental-economic coordination challenges, and accelerating the sector’s transition to low-carbon practices in China [14,15].
Furthermore, transportation carbon emissions in China exhibit pronounced spatial heterogeneity [16], with provincial disparities manifesting not only in emission levels but also in regional responsiveness to carbon governance policies [17]. This heterogeneity underscores the need to systematically investigate the spatially divergent mechanisms governing transportation carbon emission drivers and the decoupling effects at the provincial perspective. Tailored decarbonization strategies should align with provincial socio-economic conditions, resource endowments, and developmental stages to prevent interregional policy coordination failures [18,19]. Consequently, this study focuses on three pivotal research questions.
(1) Driving mechanism identification: Which key factors dominate the dynamic changes in transportation carbon emissions under the perspective of regional heterogeneity? Do their effect intensity and direction exhibit significant spatial differentiation?
(2) Decoupling progress assessment: What spatiotemporal evolution patterns characterize the decoupling status between transportation carbon emissions and economic growth, and what is the magnitude of policy-driven decoupling efforts?
(3) Decoupling contribution analysis: What are the specific contributions of key driving factors to decoupling progression, and how can targeted policy interventions strengthen the decoupling effect?
To address these questions, this study constructed an integrated analytical framework combining decomposition and decoupling analysis to systematically ex-amine transportation carbon emissions across China’s thirty provinces during 2005–2022. The research pathway proceeded as follows: First, a transportation carbon emission decomposition model was established using the generalized Divisia index method (GDIM). This quantified the contribution of each driving factor, thereby revealing spatially heterogeneous mechanisms governing provincial transportation carbon emissions. Second, the Tapio decoupling model was applied to diagnose spatiotemporal decoupling dynamics, and the decoupling effort model was incorporated to evaluate policy-driven emission reduction effects. Finally, GDIM decomposition results were embedded into the decoupling effort framework to quantify factor-specific contributions to decoupling progress, thereby uncovering the intrinsic mechanisms underlying heterogeneous provincial decoupling pathways.
The remainder of this study is structured as follows. Section 2 reviews existing literature on the determinants and decoupling analysis of transportation carbon emissions. Section 3 presents the methodology and data. Section 4 details the empirical results from decomposition and decoupling analysis. Section 5 synthesizes the key conclusions and offers policy recommendations.

2. Literature Review

Identifying the key drivers of transportation carbon emissions is a critical prerequisite for formulating effective emission reduction pathways. Existing research has coalesced around two principal methodological paradigms: econometric analysis and decomposition analysis. Econometric analysis primarily aims to identify causal relationships between economic variables and transportation carbon emissions [20]. For example, Saboori et al. [21] employed the fully modified ordinary least squares (FMOLS) cointegration test, revealing a significant bidirectional promoting effect between road transportation carbon emissions and economic growth as well as energy consumption in OECD countries. Liu et al. [22] applied the geographically and temporally weighted regression (GTWR) model, demonstrating significant spatiotemporal heterogeneity in the drivers of provincial transportation carbon emissions across China, with improvements in energy intensity contributing most significantly to emission reductions. Focusing on ASEAN nations, Shabir et al. [23] used a nonlinear autoregressive distributive lag model and found that transport energy consumption exerted a stronger carbon emission effect than foreign direct investment. Li et al. [24] verified the policy effectiveness of the carbon emissions trading system in reducing transport emissions within pilot regions of China using a spatial econometric model. Zhao et al. [25] integrated spatial econometric and GTWR models, revealing that economic development, population size, and road infrastructure drive persistent growth in urban transportation carbon emissions.
While econometric analysis is powerful for identifying causal relationships, it often faces challenges in precisely quantifying the exact arithmetic contribution of individual factors to observed emission changes [26]. Furthermore, although econometric methods (such as the instrumental variables approach) can be employed to address endogeneity in econometric models, establishing a robust causal identification strategy remains a non-trivial challenge in practice [27]. Therefore, decomposition methods are widely employed in the transportation carbon emissions literature to provide an unambiguous accounting of historical driving forces. While SDA demonstrates theoretical rigor, its reliance on non-continuously updated input-output tables has restricted its applicability due to data limitations [28]. In contrast, IDA offers notable advantages in data accessibility, facilitating both time-series analysis and cross-regional comparisons [29]. Among IDA techniques, the logarithmic mean Divisia index (LMDI) method, owing to its capacity to eliminate residual terms and achieve high computational accuracy [30], has become the predominant approach for decomposing transportation carbon emissions.
As shown in Table 1, existing studies employing the LMDI method typically decompose transportation carbon emissions into driving factors such as population scale (POP), economic development (GDP), energy carbon intensity (TEI), energy efficiency (TEE), transportation intensity (TI), energy structure (ES), transportation structure (TS), industrial structure (IS), and per capita GDP (PGDP). Although the LMDI method is widely used, it suffers from two inherent limitations. First, constrained by the multiplicative identity of the Kaya structure, the LMDI can only incorporate a single absolute indicator (e.g., POP or GDP in Table 1), thereby omitting the influence of other critical absolute drivers such as energy consumption and transportation turnover volume [31,32]. Second, the multiplicative factorization, constrained by the underlying identity (e.g., the Kaya identity), means that the estimated effects of the drivers are not statistically independent but are mathematically linked. This structural constraint means that the omission of relevant factors or misspecification of the model can lead to biased conclusions [33]. To comprehensively address these limitations, Vaninsky [34] proposed the GDIM model and has gained widespread adoption in empirical research [35,36].
This approach provides two key theoretical advantages for our study. First, it allows for the simultaneous inclusion of multiple absolute indicators that are crucial for a nuanced understanding of transportation carbon emissions but are inaccessible to the traditional LMDI approach. Second, by employing a system of interconnected identities and leveraging the integral path independence of the Divisia index, GDIM achieves a mathematically robust decomposition where the contributions of all factors sum to the total change without a residual term, enabling the isolation and quantification of each driver’s unique effect, free from the constraint of factor interdependency inherent in the multiplicative form of LMDI. Given that the transportation sector’s emissions are driven by the complex interplay of multiple economic, energy, and transport activity scales, the GDIM model is not merely an alternative but a necessary methodological choice for this study, as it enables a more complete and accurate quantification of the drivers behind provincial transportation carbon emissions in China.
Meanwhile, research on the decoupling effects of transportation carbon emissions constitutes a pivotal issue in transport carbon governance studies [56]. Essentially, decoupling refers to the weakening or breaking of the link between economic growth and environmental pressures. In the context of transportation carbon emissions, it means that economic activity grows while emissions either grow more slowly (relative decoupling) or decline in absolute terms (absolute decoupling) [3]. Decoupling analysis holds significant value as it allows policymakers to evaluate the feasibility of maintaining transportation industry development while reducing carbon emissions. This analysis provides insights into the sector’s potential to reconcile two seemingly conflicting objectives: sustaining economic momentum and achieving deep decarbonization. Thus, the decoupling state has emerged as a key performance indicator for assessing the efficacy of low-carbon transport transition [12,52].
The existing literature has predominantly employed the decoupling model proposed by Tapio [57] to conduct the decoupling analysis of transportation carbon emissions [58,59,60]. While useful, decoupling analysis alone is insufficient for assessing environmental externalities [61] and fails to identify the underlying drivers of changes in the decoupling state [62]. This limitation has led to the development of an integrated analytical framework that combines decoupling analysis with LMDI decomposition techniques. This decomposition-decoupling model quantifies the contributions of various factors to the decoupling elasticity index [56,63,64,65]. However, this model cannot directly evaluate the efficacy of emission reduction efforts, as a decline in emissions may stem from economic downturns rather than effective mitigation measures. Emission reduction is considered effective only if its intensity offsets the emissions increase driven by economic growth [66]. To address this, the “decoupling effort index” has been introduced to pinpoint key drivers of decoupling by excluding emissions attributable to economic expansion [67,68,69,70,71].
Despite significant advancements in understanding the drivers and decoupling of transportation carbon emission, four critical research gaps persist in the existing literature: (1) Most existing studies focus on national or selected regional scales, neglecting the significant spatial heterogeneity in the driving mechanisms and decoupling characteristics of provincial transportation carbon emissions across China. (2) The widely used LMDI method, constrained by the inherent limitations of the Kaya identity, is susceptible to omitted variable bias and may produce skewed conclusions. Although the GDIM theoretically overcomes these limitations, its empirical application in transportation carbon emissions studies remains limited. (3) Existing research rarely integrates decomposition results with decoupling effort model, preventing the quantification of how specific factors contribute to decoupling transportation emissions from economic growth.
To address existing research gaps, this study presents three primary scholarly contributions: (1) This study advances transportation carbon emissions analysis by innovatively adapting the GDIM model, overcoming theoretical constraints of the Kaya identity inherent in conventional LMDI method. Through constructing an integrated decomposition framework incorporating multiple absolute indicators, it provides a robust methodological tool for precisely quantifying and revealing the operational patterns and spatial heterogeneity of key drivers behind provincial transportation carbon emissions. (2) Addressing the fundamental limitation of Tapio decoupling status assessment, this study innovatively integrates the decoupling effort model to isolate growth-induced passive emission increments. This enables scientific quantification of policy-driven decoupling efforts in transportation, establishing an objective metric for evaluating actual policy efficacy. (3) This study establishes a causal analytical framework by embedding GDIM-based decomposition results into the decoupling effort assessment. This systematically quantifies factor-specific contributions to decoupling progress, uncovering the intrinsic mechanisms driving emission–growth decoupling and providing the methodological basis to identify differentiated decoupling pathways.

3. Methodology and Data

3.1. Decomposition Model of Transportation Carbon Emissions

Based on the GDIM decomposition model, the relationship between transportation carbon emissions and its driving factors can be mathematically formulated as Equations (1)–(3).
T C = T G × T C T G = T E × T C T E = T V × T C T V = T G × T C I = T E × T E I = T V × T V I
T V E = T G T V = T C T V / T C T G
T V E = T G T V = T C T V / T C T G
where T C represents the transportation carbon emissions, which can be decomposed into three absolute indicators: economic output scale ( T G ), energy consumption scale ( T E ), and turnover volume scale ( T V ), and five relative indicators: output carbon intensity ( T C I ), energy carbon intensity ( T E I ), transportation carbon intensity ( T V I ), transportation efficiency ( T V E ), and energy intensity ( T E E ), with each driving factors defined in Table 2. Furthermore, Equations (1)–(3) can be transformed into Equations (4)–(8).
T C = T G × T C I
T G × T C I T E × T E I = 0
T G × T C I T V × T V I = 0
T G T V × T V E = 0
T E T G × T E E = 0
Assuming the function T C ( X ) represents the contribution of driving factor X to the change in the transportation carbon emissions, calculating the first-order partial derivatives of each driving factor based on Equations (4)–(8) enables the construction of the Jacobian matrix Φ ( X ) shown in Equation (9), which characterizes the impact of individual decomposition factors on the transportation carbon emissions.
Φ ( X ) = T C I T G T E I T E 0 0 0 0 T C I T G 0 0 T V I T V 0 0 1 0 0 0 T V E 0 T V 0 T E E 0 1 0 0 0 0 T G
Accounting for factor interconnectedness under GDIM principles, Equation (10) can quantify the determinant-specific contributions to the changes in transportation carbon emissions.
Δ T C [ X Φ ] = L T C T ( I Φ X Φ X + ) d X
where L denotes the time interval spanning the base period to the sample period. I and ( ) + represent the identity matrix and generalized inverse operator, respectively. T C denotes the gradient of function T C ( X ) and T C = ( T C I , T G , 0 , 0 , 0 , 0 , 0 , 0 ) T . When Φ ( X ) possesses linearly independent columns, then Φ X + = ( Φ X + Φ X ) 1 Φ X T .
The GDIM model establishes a precise decomposition architecture that enables the change in transportation carbon emissions across Chinese provinces over period Δ T to be systematically decomposed into the sum of eight distinct driving factors. The additive decomposition can be mathematically formalized in Equation (11).
Δ T C | 0 T = Δ T C T G | 0 T + Δ T C T E | 0 T + Δ T C T V | 0 T + Δ T C T C I | 0 T + Δ T C T E I | 0 T + Δ T C T V I | 0 T + Δ T C T V E | 0 T + Δ T C T E E | 0 T
where Δ T C | 0 T denotes the change in transportation carbon emissions over period Δ T , with Δ T C T G | 0 T , Δ T C T E | 0 T , and Δ T C T V | 0 T representing the contributions attributable to the three absolute driving factors, and Δ T C T C I | 0 T , Δ T C T E I | 0 T , Δ T C T V I | 0 T , Δ T C T V E | 0 T , and Δ T C T E E | 0 T quantifying the contributions.

3.2. The GDIM-Based Decoupling Effort Model

The Tapio decoupling model is typically used to measure the ratio of carbon emission growth rate to economic growth rate over period Δ T , thus determining whether economic growth has decoupled from carbon emissions, as mathematically expressed in Equation (12).
φ | 0 T = % Δ T C % Δ T G = ( T C T T C 0 ) T C 0 / ( T G T T G 0 ) T G 0 = Δ T C T C 0 / Δ T G T G 0
where φ | 0 T denotes the decoupling elasticity index, with T C 0 and T C T representing the transportation carbon emissions during the base period and target year T respectively. Similarly, T G 0 and T G T indicate the sector’s gross value added during the base period and target year T , while Δ T G quantifies the change in gross value added over period Δ T relative to the base period. Based on the magnitude of the decoupling elasticity, the decoupling state can be categorized into eight distinct types [57], as shown in Table 3.
However, the decoupling elasticity index cannot capture the impact of government-initiated energy conservation and emission reduction measures on the decoupling progress between economic development and carbon emissions. According to Diakoulaki and Mandaraka [72], excluding the carbon emission changes attributable to the economic growth effect enables an effective assessment of decoupling effort. In this study, “decoupling effort” refers to the direct or indirect measures implemented by the governments in the process of economic development of transportation industry to reduce carbon emissions. Within the GDIM decomposition results, the change in transportation carbon emissions driven by the scale of economic output Δ T C T G can be considered the economic growth effect. Therefore, the change in transportation carbon emissions net of the economic growth effect can be expressed as Equation (13).
Δ Q | 0 T = Δ T C | 0 T Δ T C T G | 0 T = ( Δ T C T E + Δ T C T V + Δ T C T C I + Δ T C T E I + Δ T C T V I + Δ T C T V E + Δ T C T E E ) | 0 T
where Δ Q | 0 T refers to the decoupling effort aimed at reducing transportation carbon emissions over period Δ T , representing the cumulative mitigation potential of the remaining nine factors after systematically excluding the economic growth effect. Meanwhile, a decoupling effort index (DEI) can be further defined as the proportion of the economic growth effect that is offset by carbon emissions reduction efforts [73], as shown in Equation (14).
ϕ | 0 T = Δ Q | 0 T Δ T C T G | 0 T
where ϕ | 0 T denotes the decoupling effort index over period Δ T . When ϕ | 0 T 1 , it indicates strong decoupling efforts. When 0 < ϕ | 0 T < 1 , it indicates weak decoupling efforts. When ϕ | 0 T 0 , it indicates no decoupling efforts. Furthermore, this study integrates Equations (13) and (14) to investigate the driving forces underlying the decoupling process, as formalized in Equation (15).
ϕ | 0 T = Δ Q | 0 T Δ T C T G | 0 T = ( Δ T C T E + Δ T C T V + Δ T C T C I + Δ T C T E I + Δ T C T V I + Δ T C T V E + Δ T C T E E ) | 0 T Δ T C T G | 0 T = ( Δ T C T E Δ T C T G + Δ T C T V Δ T C T G + Δ T C T C I Δ T C T G + Δ T C T E I Δ T C T G + Δ T C T V I Δ T C T G + Δ T C T V E Δ T C T G + Δ T C T E E Δ T C T G ) = ϕ T E + ϕ T V + ϕ T C I + ϕ T E I + ϕ T V I + ϕ T V E + ϕ T E E = ϕ T E + ϕ T V S c a l e   E f f e c t + ϕ T C I + ϕ T E I + ϕ T V I I n t e n s i t y   E f f e c t + ϕ T V E + ϕ T E E E f f i c i e n c y   E f f e c t
where ϕ T E , ϕ T V , ϕ T C I , ϕ T E I , ϕ T V I , ϕ T V E , and ϕ T E E respectively represent the relative contributions of the energy consumption scale, turnover volume scale, output carbon intensity, energy carbon intensity, transportation carbon intensity, transportation efficiency, and energy intensity to the total decoupling effort. Furthermore, the scale effect is defined as the sum of the contributions from the energy consumption scale ϕ T E and the turnover volume scale ϕ T V . The intensity effect is defined as the sum of the contributions from the output carbon intensity ϕ T C I , energy carbon intensity ϕ T E I , and transportation carbon intensity ϕ T V I . The efficiency effect is defined as the sum of the contributions from transportation efficiency ϕ T V E and energy intensity ϕ T E E .

3.3. Data Collection and Curation

Based on data availability constraints, this study examined transportation sectors across thirty provincial administrative regions in mainland China (excluding Tibet Autonomous Region, Hong Kong, Macao, and Taiwan due to incomplete data) for the period 2005–2022. Fundamental energy consumption data were sourced from regional energy balance tables in the China Energy Statistical Yearbook (2006–2023). Provincial transportation carbon emissions were estimated using the IPCC [74] carbon emission coefficient method, accounting for emissions from nine fossil fuel categories: raw coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, liquefied petroleum gas, and natural gas. Additional data were obtained from the China Statistical Yearbook (2006–2023). To ensure comparability across years, all monetary values—specifically, the transportation sector’s value added—were deflated to constant 2005 prices using the tertiary sector value-added index of each province. Meanwhile, turnover volume scale, representing the scale of transportation activity, was measured as the sum of passenger turnover (in passenger–kilometers) and freight turnover (in ton-kilometers). The ratio of freight ton-kilometers to passenger–kilometers for different transport mode are obtained from Song et al. [43]. No missing values were encountered in the dataset for the selected provinces and the study period. Descriptive statistics of related indicators are presented in Table 4.

4. Results

4.1. Carbon Emissions and Sectoral Value Added in China’s Transportation Sector

4.1.1. Spatiotemporal Distribution of Transportation Carbon Emissions in China

As illustrated in Figure 1, fossil fuel-related carbon emissions from China’s transportation sector increased from 348.33 Mt in 2005 to 637.82 Mt in 2022, with an average annual growth rate of 3.62%. Notably, emissions declined from 736.29 Mt in 2019 to 659.85 Mt in 2020 (a−10.38% year-on-year drop) due to the COVID-19 pandemic. Although a post-pandemic rebound occurred in 2021 (688.06 Mt), 2022 emissions remained 13.37% below the 2019 peak, demonstrating incomplete recovery from the pandemic’s long-term impacts. Structurally, petroleum-derived fuels dominate transportation emissions, with gasoline and diesel oil as primary contributors. Gasoline’s share decreased from 28.61% (2005) to 24.77% (2022), while diesel oil consistently maintained the largest single-source contribution, rising from 46.38% to 49.73%.
This study further employed ArcGIS 10.5 to investigate the spatial distribution patterns of provincial transportation carbon emissions in China using trend surface analysis (results in Figure 2). The trend surface revealed significant regional disparities. Along the east–west axis, transportation carbon emissions consistently exhibited a stable pattern characterized by higher levels in the east and lower levels in the west. This pronounced disparity stems from disparities in regional economic development levels, geographical positioning, resource endowments, and transportation emission-reduction technologies [22,75]. Notably, the top five provinces in average emissions are consistently located in the east: Guangdong (55.22 Mt), Shandong (42.14 Mt), Shanghai (41.24 Mt), Jiangsu (34.25 Mt), and Liaoning (32.71 Mt). Along the north–south axis, emissions showed marginally higher values in southern provinces in 2005, 2010, and 2015, though the spatial gradient remained visually indistinct. Critically, by 2022, the north–south trend surface had undergone a marked transformation, exhibiting a pronounced inverted U-shaped pattern with a substantially amplified south-higher-north gradient. Overall, the spatial distribution underscores the imperative for region-specific mitigation strategies. Tailored policy interventions are urgently required to address evolving regional dynamics and achieve effective decarbonization.

4.1.2. Spatial Heterogeneity in Sectoral Value Added of China’s Transportation Sector

Taking 2005 as the base year, the value added of China’s transportation sector increased from CNY 1066.87 billion to CNY 4408.53 billion during 2005–2022, with an average annual growth rate of 8.70%. This significantly exceeded the growth rate of fossil fuel-related transportation carbon emissions (3.62%) over the same period, indicating a trend of relative decoupling—a divergence between the expansion of economic scale and the growth rate of environmental pressure [76]. Using provincial-level data, a quintile classification method categorized provincial transportation sector value added into five tiers: low development level (CNY 3.19 billion–CNY 29.20 billion), lower-middle development (CNY 29.20 billion–CNY 56.84 billion), middle development level (CNY 56.84 billion–CNY 87.27 billion), upper-middle development level (CNY 87.27 billion–CNY 147.14 billion), and high development level (CNY 147.14 billion–CNY 480.80 billion). Spatial visualization was conducted using ArcGIS 10.5 (as shown in Figure 3). Provinces with upper-middle and high development levels were predominantly concentrated in eastern and central China, particularly along the eastern coast. This spatial pattern was exemplified by the annual averages for leading provinces: Shandong (CNY 270.83 billion), Guangdong (CNY 246.39 billion), Jiangsu (CNY 211.98 billion), Hebei (CNY 192.36 billion), Henan (CNY 167.60 billion), and Shanghai (CNY 147.53 billion). In contrast, provinces with low development level were predominantly clustered in western China, exemplified by Qinghai (CNY 7.54 billion) and Ningxia (CNY 10.36 billion). Notably, the provinces with the lowest carbon emissions—Qinghai (2.95 Mt CO2) and Ningxia (3.18 Mt CO2)—exactly coincide with those exhibiting the lowest industrial value added.
This “high-east, low-west” spatial pattern exhibits a striking resemblance to the spatial distribution of transportation carbon emissions. Statistical analysis confirms a strong and statistically significant positive spatial correlation (Pearson correlation coefficient = 0.758, p < 0.01) between provincial transportation value added and carbon emissions, providing robust evidence of a significant positive spatial association. This finding corroborates from a spatial perspective that economic activity is a primary driver of environmental pressure. It further underscores the need to carefully address potential increases in carbon emissions resulting from transportation growth during regional coordinated development. Critically, achieving absolute decoupling—sustained economic growth coupled with declining carbon emissions—represents a pivotal challenge for the sustainable development of China’s provincial transportation sectors.

4.2. Results of GDIM Decomposition

4.2.1. Drivers of Transportation Carbon Emissions Change at the Mational Level

Based on the GDIM model, this study employed R software (version 4.3.3) to decompose drivers of carbon emissions in China’s transportation sector. As presented in Figure 4 and Figure 5, the annual decomposition results and cumulative contributions (with 2005 as the base year) reveal that absolute indicators—economic output, energy consumption, and turnover volume—exert significant promoting effects on emission growth. Economic output scale emerges as the dominant driver, exhibiting a persistent positive effect across all years with a cumulative contribution of 289.20 Mt CO2. This underscores the pivotal role of economic activity in emission escalation [73,77]. Turnover volume scale ranks second with a cumulative contribution of 208.36 Mt CO2, reflecting the substantial emissions pressure from China’s massive passenger and freight demand, thereby highlighting transportation demand management as a critical mitigation lever [78]. The energy consumption scale contributes 107.44 Mt CO2, attributable to the sector’s entrenched reliance on petroleum derivatives: road, aviation, and water transportation predominantly utilize oil-based fuels, while rail depends on diesel and grid electricity. Although the rapid adoption of electric vehicles in China [10] may reduce direct emissions, the dominance of coal-fired thermal power in electricity generation implies that rising electricity consumption in the transportation sector will significantly increase indirect carbon emissions.
The relative indicators—including output carbon intensity, energy carbon intensity, transportation carbon intensity, transportation efficiency, and energy intensity—suppressed carbon emission growth in China’s transportation sector. During 2005–2022, their cumulative emission reductions totaled 176.93, 4.51, 77.76, 38.71, and 17.60 Mt CO2, respectively.
Notably, output carbon intensity emerged as the dominant mitigator. Since the inclusion of binding energy intensity target in China’s 11th Five-Year Plan initiated the transition toward low-carbon transportation, substantial improvements in carbon productivity (i.e., declining output carbon intensity) have driven significant emission reductions [79]. Nationally, the average output carbon intensity of the transportation sector decreased from 3.3080 tons/104 CNY in 2005 to 1.5398 tons/104 CNY in 2022—a 53.45% reduction that directly contributed to emission mitigation in the sector. The emission mitigation effect of energy carbon intensity stems primarily from optimization in transportation energy structure, exemplified by natural gas consumption growing at an annual rate of 17.60% over the study period. Nevertheless, its contribution remains the weakest among all indicators, suggesting that while progress in energy transition has been made, further structural refinement is imperative. Transportation carbon intensity ranks second in emission reduction efficacy, with national average intensity falling from 0.6335 tons/104 ton-km in 2005 to 0.4559 tons/104 ton-km in 2022, reflecting the efficacy of modal shift policies (e.g., shifts from road to rail/water transportation initiatives). This underscores that modal restructuring in passenger and freight transportation is critical for emission abatement [54].
Concurrently, improvements in transportation efficiency (rising from 0.2010 to 0.3140 CNY/ton-km, 2.67% annual growth) and energy intensity (falling from 1.5663 to 0.7535 tce/104 CNY, 4.21% annual decline) further suppressed emissions. Critically, however, a paradox emerges: despite the sustained decline in energy intensity—a common macro-level indicator of improving energy efficiency—total energy consumption in the transportation sector has increased rather than decreased. This phenomenon suggests that the efficiency gains may have been offset by a rebound effect, wherein improvements in efficiency stimulate additional energy demand. This phenomenon aligns precisely with the energy rebound effect, wherein efficiency gains inadvertently stimulate higher aggregate energy use, thereby undermining emission reduction targets. Empirical studies by Chen et al. [80], Zheng et al. [81], and Ouyang et al. [82] corroborate the presence of a significant rebound effect in China’s transportation sector. This rebound effect occurs because efficiency improvements stimulate additional transport demand, ultimately increasing total energy use. Consequently, mitigating rebound effects represents a critical frontier for enhancing the efficacy of decarbonization policies in China’s transportation sector.

4.2.2. Drivers of Transportation Carbon Emissions Change at the Provincial Level

Figure 6 and Figure 7 present the annual decomposition and cumulative contributions of driving factors for transportation carbon emissions across thirty provincial administrative regions during the study period. GDIM decomposition revealed that despite significant heterogeneity in driving mechanisms among provinces, scale effects (encompassing economic output scale and turnover volume scale) accounted for the dominant factor driving carbon emission growth. The emission growth in Guangdong, Fujian, Tianjin, and Hainan was primarily driven by turnover volume scale, with cumulative contributions of 36.69, 11.35, 9.79, and 6.18 Mt, respectively, accounting for 97.65%, 148.98%, 146.57%, and 240.82% of total growth (a contribution rate exceeding 100% indicates that the growth-driven effect of this factor was substantial that it was partially offset by the combined emission-reduction effects of other factors, yet still resulted in a net positive growth in emissions). Qinghai’s emissions were predominantly influenced by energy consumption scale, with a cumulative contribution of 1.53 Mt and contribution rate of 198.90%, attributable to a 12.12% annual increase in energy consumption—from 0.36 Mtce in 2005 to 2.52 Mtce in 2022—the highest nationwide growth rate. For other provinces, economic output scale was the primary driver, with the top seven cumulative contributions observed in Shandong (23.48 Mt), Shanghai (19.84 Mt), Jiangsu (16.79 Mt), Hubei (15.32 Mt), Liaoning (13.14 Mt), Zhejiang (12.67 Mt), and Hunan (11.78 Mt). Collectively, the dual drivers of economic expansion and rising transportation demand fundamentally underpin provincial transportation emission growth. Notably, the COVID-19 pandemic induced a negative contribution from energy consumption scale in most provinces in 2020, with the most pronounced declines in Shandong (−2.88 Mt), Beijing (−2.87 Mt), Guangdong (−2.73 Mt), Hebei (−2.26 Mt), and Hubei (−2.08 Mt). Furthermore, energy carbon intensity exerted a limited promotive effect on emission growth in Guangdong, Hainan, and Sichuan, with cumulative contributions of 0.16 Mt, 0.024 Mt, and 0.016 Mt, respectively. Transportation carbon intensity positively drove emissions growth in Hunan, Liaoning, Guizhou, Heilongjiang, Henan, and Qinghai, with cumulative contributions of 7.77 Mt, 4.61 Mt, 2.88 Mt, 2.49 Mt, 1.02 Mt, and 0.41 Mt, indicating an imperative for these provinces to optimize transportation structures and transition toward cleaner modal shifts.
Regarding emission reduction, transportation carbon intensity emerged as the most significant mitigating factor for Guangdong, Fujian, and Shanxi, achieving cumulative reductions of −30.54 Mt, −5.73 Mt, and −4.05 Mt, respectively. Conversely, Jiangxi, Anhui, and Sichuan exhibited substantially weaker emission reduction effects from transportation carbon intensity, with cumulative contribution rates of only −3.16%, −3.20%, and −3.84%. Transportation efficiency dominated emission reductions in Tianjin, Hainan, and Qinghai, yielding cumulative reductions of −9.09 Mt, −3.05 Mt, and −0.26 Mt, respectively. For the remaining provinces, output carbon intensity served as the primary driver of emission reduction, with the most pronounced contributions from Shandong (−19.77 Mt), Shanghai (−15.71 Mt), Hubei (−9.51 Mt), Liaoning (−9.48 Mt), and Inner Mongolia (−7.86 Mt). Notably, energy carbon intensity was the weakest contributing factor to emission reduction in the vast majority of provinces, accounting for 76.67% of all provinces. Furthermore, although energy intensity contributed to emission reductions in all provinces, its impact remained marginal—particularly for Tianjin, Jiangsu, Liaoning, and Zhejiang, where contribution rates reached only −0.43%, −1.37%, −1.43%, and −1.92%, respectively. Overall, the comparatively weak mitigation effects of energy carbon intensity and energy intensity highlight a critical priority for decarbonizing China’s transportation sector: enhancing energy-related emission reduction mechanisms should become central to future low-carbon transition strategies.

4.2.3. Provincial Contributions to National Transportation Carbon Emissions Change

Drawing upon the research of Liu et al. [73], this study further quantified the contributions of provincial drivers to the national transportation carbon emissions change over the research period, as illustrated in Figure 8. The analysis revealed that economic output scale and turnover volume scale were the two dominant provincial-level drivers of national transportation carbon emissions growth, with aggregate contribution rates of 83.03% and 62.94%, respectively, significantly exceeding the contribution rate of energy consumption scale (30.85%). Specifically, the turnover volume scale in Guangdong (contribution rate: 10.53%) and Shanghai (4.37%), along with the economic output scale in Guangdong (7.45%), Shandong (6.74%), Shanghai (5.70%), and Jiangsu (4.82%), exhibited particularly pronounced contributions to national emissions growth. This underscores a marked regional concentration in China’s transportation carbon emissions trajectory, wherein economically advanced provinces—notably Guangdong, Shanghai, Shandong, and Jiangsu—act as pivotal drivers of national emissions growth due to their substantial economic scale and robust transportation demand. Furthermore, the energy consumption scale in Henan, Jiangsu, and Hunan demonstrated relatively significant contribution rates to national emissions growth among provinces.
Simultaneously, output carbon intensity, energy carbon intensity, and transportation carbon intensity across the vast majority of Chinese provinces, alongside transportation efficiency and energy intensity in all provinces, exerted suppressive effects on national transportation carbon emissions growth. The aggregate contribution rates of provincial output carbon intensity and transportation carbon intensity reached −50.79% and −25.45%, respectively, their absolute magnitudes substantially exceeding those of other mitigating factors. In terms of provincial mitigation contributions, Guangdong’s transportation carbon intensity (−8.77%), along with the output carbon intensity in Guangdong (−6.37%), Shandong (−5.68%), and Shanghai (−4.51%), demonstrated the strongest effects. This stems primarily from these provinces’ inherently high transportation emission baselines (ranking among the top three nationally in average emissions during the study period), which harbor substantial mitigation potential. In contrast, the suppressive impacts of energy carbon intensity, transport efficiency, and energy intensity were comparatively limited, with aggregate contribution rates of merely −1.29%, −11.11%, and −5.05%, respectively. The strongest single-province contributions for these factors came from Jilin (energy carbon intensity, −0.15%), Tianjin (transport efficiency, −2.61%), and Shandong (energy intensity, −0.74%).
Overall, all provinces exerted positive contributions to the growth of national transportation carbon emissions. Among them, Henan, Jiangsu, Hunan, and Sichuan demonstrated the most pronounced net driving effects, with total contribution rates of 7.64%, 7.46%, 6.01%, and 5.37%, respectively, marking them as the most critical provinces influencing the change in national transportation carbon emissions. In contrast, Tianjin, Ningxia, and Hebei exhibited the lowest contributions to the national emission increase, with total contribution rates of merely 0.45%, 0.34%, and 0.10%, respectively. This disparity stems primarily from the relatively stronger influence of emission-reducing drivers within these three provinces, which effectively offset the impact of emissions-increasing drivers.

4.3. Decoupling Analysis of Transportation Carbon Emissions

4.3.1. Assessment of Decoupling Status Using the Tapio Model

Based on China’s Five-Year Plan (FYP) system, this study divided the research period into four sub-phases: 2005–2010 (the 11th FYP), 2010–2015 (the 12th FYP), 2015–2020 (the 13th FYP), and 2020–2022 (the initial phase of the 14th FYP), to systematically investigate decoupling dynamics between transportation carbon emissions and economic development at national and provincial levels. National-level analysis revealed a consistent trend of improvement: the Tapio decoupling elasticity index declined from 0.7277 (weak decoupling) during the 11th FYP, to 0.4220 (weak decoupling) in the 12th FYP, then to 0.0106 (weak decoupling) in the 13th FYP, and finally transitioned to −0.2605 (strong decoupling) in the initial 14th FYP phase. This evolution stems from progressively strengthening decoupling between industrial growth and carbon emissions. Specifically, the annual average growth rate of transportation sector value-added (11.96%, 8.59%, and 6.63% during the 11th, 12th, and 13th FYPs, respectively) consistently exceeded the corresponding growth rates of transportation carbon emissions (9.20%, 3.98%, and 0.08%), with the growth gap widening from 2.76 percentage points in the 11th FYP to 6.65 percentage points in the 13th FYP. Critically, during the initial phase of the 14th FYP, the sector achieved negative emission growth (−0.68%) despite slowed economic growth (2.24%), signaling its formal entry into the strong decoupling stage. Overall, China’s Tapio decoupling index for transportation carbon emissions exhibits a persistent downward trend, with the decoupling status evolving steadily from weak decoupling toward strong decoupling. This indicates a substantial weakening of the linkage between carbon emissions and economic growth in China’s transportation sector. These findings align with the conclusions of Cai et al. [48], Li et al. [83] and Chen et al. [14].
Figure 9 further illustrates the evolution of decoupling states across Chinese provinces from the 11th FYP period to the initial phase of the 14th FYP, encompassing four categories: expansive negative decoupling, expansive coupling, weak decoupling, and strong decoupling. During the 11th FYP, 56.67% of provinces (17 provinces) exhibited weak decoupling under extensive development patterns, with no province achieving strong decoupling, reflecting deep dependence of transportation expansion on energy consumption. The 12th FYP marked a policy-driven inflection point: while 17 provinces maintained weak decoupling, six provinces (Tianjin, Inner Mongolia, Shandong, Hainan, Sichuan, Shaanxi) achieved strong decoupling (economic growth concurrent with declining emissions), whereas Heilongjiang and Anhui remained in expansive negative decoupling. By the 13th FYP, the implementation of China’s 13th Five-Year Plan for Modern Integrated Transport System Development, which integrated low-carbon transportation as a core indicator, significantly accelerated decoupling. Specifically, provinces exhibiting weak or strong decoupling collectively accounted for 93.33% (28 provinces), with weak decoupling provinces declining to 11 provinces and strong decoupling provinces surging to 17 provinces. In the initial phase of the 14th FYP, provinces achieving strong decoupling accounted for 46.67% (14 provinces), weak decoupling for 30.00% (9 provinces), expansive coupling for 13.33% (4 provinces), and expansive negative decoupling for 10.00% (3 provinces)—with Henan, Guizhou, and Qinghai reflecting a relatively unsustainable development pathway.
Overall, compared to the 11th FYP, provincial decoupling states exhibited positive evolution during the initial 14th FYP phase: 53.33% of provinces shifted to more favorable states, while 33.33% maintained stability. However, Hebei, Heilongjiang, and Guangxi regressed from weak decoupling to expansive coupling, and Henan deteriorated from weak decoupling to expansive negative decoupling. These transitions prioritized attention to low-carbon transition pathways in these provinces to prevent regional disparities in achieving the dual carbon goals.

4.3.2. Decoupling Efforts of Carbon Emission Drivers Based on GDIM Decomposition

To precisely identify the core factors driving the decoupling of economic growth from carbon emissions in the transportation sector and scientifically evaluate the effectiveness of government energy-saving and emission-reduction policies, this study quantified national- and provincial-level decoupling effort indices using the decoupling effort model. The contribution of various driving factors to decoupling was quantified by integrating GDIM decomposition results. National-level analysis (as shown in Table 5) revealed the following:
During the 11th FYP, the DEI (decoupling effort index) was −1.2015, indicating no decoupling effort. This demonstrates that transportation carbon emissions growth driven by economic expansion significantly exceeded policy-induced emission reductions. Specifically, the combined effect of all driving factors was insufficient to offset the carbon emission increases from economic growth. The scale effect (particularly the turnover volume scale) was the dominant factor inhibiting decoupling. Conversely, the intensity effect (especially the transportation carbon intensity and output carbon intensity) and the efficiency effect contributed positively to decoupling, though energy carbon intensity and energy efficiency improvements had limited impacts. In the 12th FYP, the DEI improved to −0.1919 (remaining no decoupling effort), reflecting a weakened inhibitory role of the scale effect and strengthened decoupling contributions from the intensity effect (excluding transportation carbon intensity) and efficiency effect. The DEI surged to 0.9675 during the 13th FYP, reaching a weak decoupling effort level. This indicates that the combined emissions-reduction effects from driving factors could partially offset transportation carbon emissions growth induced by economic expansion. By the initial phase of the 14th FYP, the DEI further increased to 1.9703, demonstrating a strong decoupling effort. Notably, except for turnover volume scale, all other factors effectively contributed to promoting the decoupling process. The evolutionary trajectory of the DEI in China’s transportation sector—progressing from no decoupling effort to strong decoupling effort across four planning periods—demonstrates a substantial enhancement in policy stringency and implementation efficacy.
Figure 10 presents provincial decoupling effort indices and the contribution of driving factors to decoupling. During the 11th FYP, the transportation sector exhibited no decoupling effort across nearly all provinces. This reflects that emissions growth driven by rapid economic expansion significantly outweighed government policy-led emission reductions—a pattern consistent with the characteristic escalation of environmental pressure accompanying economic expansion in early development stages [84,85]. By the 12th FYP, the number of provinces with no decoupling effort decreased from the previous peak to 20, accompanied by structural divergence: five provinces (Tianjin, Inner Mongolia, Shandong, Hainan, and Shaanxi) achieved strong decoupling effort, while another five (Hebei, Shanghai, Hubei, and Yunnan) attained weak decoupling effort. This indicates that enhanced national emission reduction targets began yielding measurable outcomes regionally, though heterogeneity persisted. During the 13th FYP, low-carbon transition accelerated, reducing provinces with no decoupling effort to nine (Jiangsu, Fujian, Jiangxi, Henan, Hubei, Hunan, Sichuan, Yunnan, and Qinghai), while those achieving strong decoupling effort increased to 17. This marked a critical shift from localized breakthroughs to widespread decoupling adoption, significantly strengthening the decoupling trend. However, the initial phase of the 14th FYP exhibited temporary fluctuations: provinces with no decoupling effort rose to 10, and strong decoupling effort provinces declined to 14. This underscores the disruptive impact of exogenous shocks (e.g., post-pandemic economic volatility) on emission reduction continuity, highlighting persistent challenges in policy resilience and long-term transition mechanisms. Overall, the DEI evolution illustrates China’s transportation sector shifting from growth-first development models toward integrated sustainability frameworks that reconcile economic growth with decarbonization goals.
Regarding decoupling contributions of driving factors: During the 11th FYP, the scale effect inhibited decoupling in all provinces. The intensity effect further suppressed decoupling in 9 provinces (Beijing, Tianjin, Shanxi, Fujian, Hainan, Guizhou, Yunnan, Shaanxi, and Qinghai), while the efficiency effect promoted decoupling nationwide but with minimal impact. In the 12th FYP, regional divergence in the scale effect emerged: only Inner Mongolia and Shandong achieved decoupling promotion through simultaneous optimization of energy consumption and turnover volume scale, while the scale effect remained inhibitory elsewhere. The number of provinces where the intensity effect inhibited decoupling expanded to 12. The efficiency effect persisted as a promoter but with limited impact. By the 13th FYP, the structure of decoupling drivers further optimized: the scale effect became promotional in 12 provinces, while the intensity effect’s inhibitory role narrowed to 7 provinces (Liaoning, Jiangxi, Hubei, Hunan, Sichuan, Yunnan, Qinghai). The efficiency effect maintained its universal promotional role yet remained constrained. In the initial phase of the 14th FYP, the scale effect promoted decoupling in only 8 provinces (Beijing, Liaoning, Jilin, Shanghai, Jiangsu, Anhui, Guangdong, and Chongqing), while the intensity effect inhibited merely three provinces (Hebei, Liaoning, and Guizhou). The efficiency effect continued as the most stable decoupling driver.
Collectively, this dynamic reveals three critical patterns: First, the scale effect evolved from universal inhibition to regional divergence, yet the rigid pressure of scale expansion predominantly dictated the inhibitory role of the scale effect. Second, the number of provinces where the intensity effect inhibited decoupling fluctuated but narrowed from nine to three. Third, the efficiency effect consistently promoted decoupling across all periods, albeit with persistently weak contributions. This trajectory underscores that transportation decarbonization cannot rely solely on technological pathways. Achieving substantive decoupling demands a systematic policy intervention framework that synergistically strengthens scale regulation, intensity constraints, and efficiency enhancement.

5. Conclusions and Policy Recommendations

5.1. Conclusions

As one of China’s fastest-emitting sectors, the transportation industry’s decarbonization trajectory critically determines the nation’s achievement of its dual-carbon strategic goals. This study decomposed carbon emission drivers via the GDIM model, identified decoupling states and driver contribution mechanisms through the Tapio decoupling model and decoupling effort model, and forecasted provincial decoupling effort indices using machine learning algorithms. The principal conclusions are as follows:
(1)
Transportation fossil fuel-related carbon emissions rose from 348.33 Mt (2005) to 637.82 Mt (2022). GDIM decomposition revealed economic output scale as the predominant growth driver, with turnover scale and energy consumption scale as secondary contributors. Conversely, output carbon intensity and transportation carbon intensity emerged as critical mitigation factors. Although both energy carbon intensity and energy intensity effects reduced emissions, contributions remained modest—indicating substantial deep decarbonization potential.
(2)
Provincial carbon emission driving mechanisms exhibited substantial heterogeneity. Economic output scale dominated emission growth in 83.33% of provinces, turnover volume scale dominated in Guangdong, Fujian, Tianjin, and Hainan, while energy consumption scale was the leading driver in Qinghai. On the emission reduction side, the output carbon intensity served as the primary mitigating factor in 80.00% of provinces, while transportation carbon intensity was pivotal in Guangdong, Fujian, and Shanxi.
(3)
Provincial contributions to national emission growth identified economic output scale and turnover scale as the two core provincial drivers. All provinces positively contributed to national transportation emission growth, with Henan, Jiangsu, Hunan, and Sichuan exhibiting the strongest net driving effects. Tianjin, Ningxia, and Hebei showed comparatively lower contributions.
(4)
Tapio decoupling assessment indicates a systemic transition in the national emissions-growth relationship: from weak decoupling during the 11th FYP to strong decoupling by the early 14th FYP. Provincial decoupling patterns improved overall, shifting from initial weak decoupling dominance to a marked rise in strong decoupling provinces. Notably, 53.33% of provinces achieved decoupling status upgrading. However, regional disparities persist, with several provinces persisting in undesirable decoupling states.
(5)
Decoupling effort assessment demonstrated sustained progression from no decoupling effort to strong decoupling effort. Factor analysis revealed intensity effects as the primary enablers of decoupling, while scale effects constituted the main inhibitors. Provincially, scale effects evolved from uniformly inhibitory to regionally heterogeneous, the proportion of provinces where intensity effects acted as inhibitors narrowed substantially, and efficiency effects maintained stable but limited positive contributions.

5.2. Policy Recommendations

Based on the current landscape of China’s low-carbon transportation development and the empirical findings above, this paper proposes the following policy recommendations to foster a synergistic low-carbon development model that advances both environmental and economic objectives in the transportation sector.
(1)
Given that economic output scale and turnover volume scale constitute primary drivers of emission growth and inhibitors of decoupling progress, transportation structure should be systematically optimized while economic efficiency is safeguarded. Priorities include enhancing railway and waterway infrastructure accessibility and convenience, advancing multimodal transportation development, accelerating the modal shift from road to rail and road to water for bulk cargo and medium-to-long-distance transportation, and facilitating green transformation in passenger travel structures. For instance: Guangdong, Fujian, Tianjin, and Hainan, where turnover volume is the dominant growth driver, should prioritize freight structure optimization by promoting rail-water intermodal transport and restricting road freight growth in key corridors. Eastern provinces (e.g., Jiangsu, Shandong, Guangdong) should establish carbon emission caps linked to economic output to prevent scale-driven emission rebounds. Moreover, significant regional disparities exist in provincial contributions to national transportation carbon emissions growth, necessitating the establishment of an equitable regional responsibility-sharing mechanism for emissions reduction.
(2)
As output carbon intensity and transportation carbon intensity represent pivotal drivers of emission reduction and critical decoupling factors, integrating output carbon intensity with the existing transportation carbon intensity constraints into national low-carbon transportation targets is essential. Six provinces—Hunan, Liaoning, Guizhou, Heilongjiang, Henan, and Qinghai—require prioritized interventions to accelerate transitions toward cleaner transportation modes and substantially reduce transportation carbon intensity.
(3)
Considering the limited emission-reduction contributions and insufficient decoupling contributions of energy carbon intensity and energy intensity, energy transition in China’s transportation sector requires three coordinated actions: First, increase non-fossil fuel penetration while concurrently decarbonizing power grids to mitigate indirect emissions from high-carbon-intensity electricity sources; second, promote widespread adoption of low-carbon transport equipment and energy efficiency enhancement technologies; third, design coordinated policy instruments to circumvent the energy rebound effect, ensuring energy efficiency gains translate into tangible carbon reductions.
(4)
In light of 53.33% of provinces achieving decoupling status upgrading amid persistent divergence in regional decoupling performance, regionally differentiated decoupling pathways should be implemented. Province-specific decoupling roadmaps should target expansive negative decoupling provinces (Henan, Guizhou, Qinghai), while adaptive policy evaluation mechanisms are needed for provinces experiencing decoupling regression (Hebei, Heilongjiang, Guangxi). Critically, best practices from strong decoupling provinces (e.g., Beijing, Shanghai, Jiangsu, Guangdong) should be systematically replicated to catalyze systemic decoupling, scaling localized breakthroughs into spatially balanced development.
(5)
Given that advancing decoupling efforts from “no decoupling effort” to “strong decoupling effort” requires integrated coordination of multiple factors, policymakers should avoid over-reliance on single technological pathways and establish a multi-dimensional policy synergy framework. This entails integrating scale regulation, intensity constraints, and efficiency enhancement into a unified policy framework. Tailored policy mixes should be designed for provinces at varying development stages to ensure complementary effects among policy instruments, thereby systematically achieving deep decoupling between economic growth and carbon emissions in the transportation sector.

5.3. Research Limitations and Future Outlook

Inevitably, this study has limitations requiring future research attention. First, the decomposition analysis employed three absolute indicators and five relative ones, excluding some factors such as technological scale and investment scale. Subsequent work should integrate these elements into the GDIM framework to comprehensively elucidate China’s transportation carbon emission driving mechanisms. Second, this study is methodologically constrained to retrospective analysis of historical data, lacking prospective forecasting capability for provincial decoupling trajectories between transportation emissions and economic growth. Subsequent research could extend this analytical framework to project spatiotemporal decoupling dynamics under carbon peaking scenarios, thereby quantifying provincial-scale decoupling potential. Third, while this study identifies the existence of a rebound effect—where improvements in energy efficiency may lead to increased energy consumption and thus counteract emission reductions—it does not quantitatively measure the magnitude of this effect due to methodological constraints and data availability. Future research should employ econometric techniques or scenario-based counterfactual analysis to quantify the rebound effect, thereby providing more robust support for crafting effective energy efficiency policies. Fourth, the GDIM model is applied to each province individually, treating them as closed systems. This approach does not account for spatial spillover effects, such as interprovincial transportation flows or the cross-border impact of policy implementations in neighboring regions. These external factors could influence a province’s emission trajectory but are beyond the capture of the current methodological framework. Future studies could address this gap by integrating spatial econometric models with the decomposition results to uncover the interplay between internal drivers and external spatial dependencies.

Author Contributions

Conceptualization, Z.P.; methodology: Z.P. and M.L.; software, Z.P.; data curation, M.L.; writing—original draft preparation, Z.P. and M.L.; writing—review and editing, M.L.; supervision, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Education Humanities and Social Sciences Research Project (grant number 21YJC790092).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data can be obtained from the corresponding author upon a reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Carbon emissions and structural composition of China’s transportation sector in 2005 and 2022.
Figure 1. Carbon emissions and structural composition of China’s transportation sector in 2005 and 2022.
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Figure 2. Trends in the spatial distribution of China’s carbon emissions.
Figure 2. Trends in the spatial distribution of China’s carbon emissions.
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Figure 3. Spatial evolution patterns in sectoral value added of China’s transportation sector.
Figure 3. Spatial evolution patterns in sectoral value added of China’s transportation sector.
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Figure 4. Annual decomposition results of transportation carbon emissions at the national level.
Figure 4. Annual decomposition results of transportation carbon emissions at the national level.
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Figure 5. Cumulative contribution of driving factors at the national level.
Figure 5. Cumulative contribution of driving factors at the national level.
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Figure 6. Annual decomposition results of transportation carbon emissions by province.
Figure 6. Annual decomposition results of transportation carbon emissions by province.
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Figure 7. Cumulative contribution of driving factors by Province.
Figure 7. Cumulative contribution of driving factors by Province.
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Figure 8. Relative contribution rates of provincial driving factors to national transportation carbon emissions change.
Figure 8. Relative contribution rates of provincial driving factors to national transportation carbon emissions change.
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Figure 9. Provincial assessment of transportation carbon emissions decoupling dynamics across four phases.
Figure 9. Provincial assessment of transportation carbon emissions decoupling dynamics across four phases.
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Figure 10. Provincial-level decoupling effort index and decoupling contributions of driving factors.
Figure 10. Provincial-level decoupling effort index and decoupling contributions of driving factors.
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Table 1. Decomposition of transport carbon emission using the LMDI method.
Table 1. Decomposition of transport carbon emission using the LMDI method.
Author(s)Analysis AreaAbsolute FactosRelative Factors
POPGDPTEITEETIESTSISPGDP
Solaymani [9]Global
Engo [37]Cameroon
Kim [38]Korea
Zhu and Du [39]Asia-Pacific
Bai, et al. [40]China
Guo and Meng [41]Beijing-Tianjin-Hebei
Zhang, et al. [42]China
Song, et al. [43]China
Raza and Lin [44]Pakistan
Wang, et al. [45]Eurasian logistics corridor
Zhu, et al. [46]China
Hossain, et al. [47]Bangladesh
Cai, et al. [48]China
Jain and Rankavat [49]India
Dolge, et al. [50]European Union
Zhang and Li [51]Yangtze River Basin
Zhang, et al. [52]China
Gu, et al. [53]China
Guan and Huang [54]Yangtze River Basin
Al-lami and Torok [55]Central Europe
“√” indicates that the corresponding factor is considered in the referenced study.
Table 2. The definitions of eight driving factors in GDIM decomposition model.
Table 2. The definitions of eight driving factors in GDIM decomposition model.
IndicatorDefinitionMeaning
Absoulte IndicatorsTGEconomic output scaleValue added in the transportation sector
TEEnergy consumption scaleFossil fuel consumption in the transportation sector
TVTurnover volume scaleSum of passenger–kilometers and freight ton–kilometers
Relative IndicatorsTCI = TC/TGOutput carbon intensityCarbon emissions per value added
TEI = TC/TEEnergy carbon intensityCarbon emissions per unit of energy consumption
TVI = TC/TVTransportation carbon intensityCarbon emissions per unit of turnover volume
TVE = TG/TVTransportation efficiencyValue added per unit of turnover volume
TEE = TE/TGEnergy intensityEnergy consumption per value added
Table 3. Classification of Tapio decoupling state.
Table 3. Classification of Tapio decoupling state.
Classification Δ T C / T C 0 Δ T G / T G 0 φ
DecouplingSD (Strong decoupling) < 0 > 0 φ < 0
WD (Weak decoupling) > 0 > 0 0 < φ < 0.8
RD (Recessive decoupling) < 0 < 0 φ > 1.2
CouplingEC (Expansive decoupling) > 0 > 0 0.8 < φ < 1.2
RC (Recessive decoupling) < 0 < 0 0.8 < φ < 1.2
Negative
Decoupling
SND (Strong negative decoupling) > 0 < 0 φ < 0
WND (Weak negative decoupling) < 0 < 0 0 < φ < 0.8
END (Expansive negative decoupling) > 0 > 0 φ > 1.2
Table 4. Descriptive statistics of the related indicators.
Table 4. Descriptive statistics of the related indicators.
VariablesUnitMaxMinMediumMeanSDObservations
TCMillion tons (Mt)70.120.7717.1319.6112.94540
TGMllion tons of coal equivalent (Mtce)4807.9731.88763.23985.95828.66 540
TECNY 100 million33.670.368.289.406.20 540
TV100 million ton-km34,164.06168.213521.075445.955620.77 540
TCITons/104 CNY6.820.382.342.531.14540
TEITons/Tce2.321.952.082.090.05540
TVITons/104 ton-km2.350.040.450.550.38540
TVECNY/ton-km1.410.020.200.240.17540
TEETce/104 CNY3.240.181.111.210.54540
Table 5. National-level decoupling effort index and decoupling contributions of driving factors.
Table 5. National-level decoupling effort index and decoupling contributions of driving factors.
2005–20102010–20152015–20202020–2022
ϕ −1.2015−0.19190.96751.9703
ϕ T E −0.7524−0.4292−0.07140.2671
ϕ T V −1.1433−0.4955−0.3816−1.2750
ϕ T C I 0.24100.52800.92671.2708
ϕ T E I 0.00730.00530.03680.0156
ϕ T V I 0.2821−0.02130.25341.5221
ϕ T V E 0.13170.15440.13130.0626
ϕ T E E 0.03210.06640.07220.1071
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Peng, Z.; Li, M. Decomposition and Decoupling Analysis of Transportation Carbon Emissions in China Using the Generalized Divisia Index Method. Sustainability 2025, 17, 8231. https://doi.org/10.3390/su17188231

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Peng Z, Li M. Decomposition and Decoupling Analysis of Transportation Carbon Emissions in China Using the Generalized Divisia Index Method. Sustainability. 2025; 17(18):8231. https://doi.org/10.3390/su17188231

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Peng, Zhimin, and Miao Li. 2025. "Decomposition and Decoupling Analysis of Transportation Carbon Emissions in China Using the Generalized Divisia Index Method" Sustainability 17, no. 18: 8231. https://doi.org/10.3390/su17188231

APA Style

Peng, Z., & Li, M. (2025). Decomposition and Decoupling Analysis of Transportation Carbon Emissions in China Using the Generalized Divisia Index Method. Sustainability, 17(18), 8231. https://doi.org/10.3390/su17188231

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