Decomposition and Decoupling Analysis of Transportation Carbon Emissions in China Using the Generalized Divisia Index Method
Abstract
1. Introduction
2. Literature Review
3. Methodology and Data
3.1. Decomposition Model of Transportation Carbon Emissions
3.2. The GDIM-Based Decoupling Effort Model
3.3. Data Collection and Curation
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
4.1.2. Spatial Heterogeneity in Sectoral Value Added of China’s Transportation Sector
4.2. Results of GDIM Decomposition
4.2.1. Drivers of Transportation Carbon Emissions Change at the Mational Level
4.2.2. Drivers of Transportation Carbon Emissions Change at the Provincial Level
4.2.3. Provincial Contributions to National Transportation Carbon Emissions Change
4.3. Decoupling Analysis of Transportation Carbon Emissions
4.3.1. Assessment of Decoupling Status Using the Tapio Model
4.3.2. Decoupling Efforts of Carbon Emission Drivers Based on GDIM Decomposition
5. Conclusions and Policy Recommendations
5.1. Conclusions
- (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
- (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
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author(s) | Analysis Area | Absolute Factos | Relative Factors | |||||||
---|---|---|---|---|---|---|---|---|---|---|
POP | GDP | TEI | TEE | TI | ES | TS | IS | PGDP | ||
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 | √ | √ | √ | √ |
Indicator | Definition | Meaning | |
---|---|---|---|
Absoulte Indicators | TG | Economic output scale | Value added in the transportation sector |
TE | Energy consumption scale | Fossil fuel consumption in the transportation sector | |
TV | Turnover volume scale | Sum of passenger–kilometers and freight ton–kilometers | |
Relative Indicators | TCI = TC/TG | Output carbon intensity | Carbon emissions per value added |
TEI = TC/TE | Energy carbon intensity | Carbon emissions per unit of energy consumption | |
TVI = TC/TV | Transportation carbon intensity | Carbon emissions per unit of turnover volume | |
TVE = TG/TV | Transportation efficiency | Value added per unit of turnover volume | |
TEE = TE/TG | Energy intensity | Energy consumption per value added |
Classification | ||||
---|---|---|---|---|
Decoupling | SD (Strong decoupling) | |||
WD (Weak decoupling) | ||||
RD (Recessive decoupling) | ||||
Coupling | EC (Expansive decoupling) | |||
RC (Recessive decoupling) | ||||
Negative Decoupling | SND (Strong negative decoupling) | |||
WND (Weak negative decoupling) | ||||
END (Expansive negative decoupling) |
Variables | Unit | Max | Min | Medium | Mean | SD | Observations |
---|---|---|---|---|---|---|---|
TC | Million tons (Mt) | 70.12 | 0.77 | 17.13 | 19.61 | 12.94 | 540 |
TG | Mllion tons of coal equivalent (Mtce) | 4807.97 | 31.88 | 763.23 | 985.95 | 828.66 | 540 |
TE | CNY 100 million | 33.67 | 0.36 | 8.28 | 9.40 | 6.20 | 540 |
TV | 100 million ton-km | 34,164.06 | 168.21 | 3521.07 | 5445.95 | 5620.77 | 540 |
TCI | Tons/104 CNY | 6.82 | 0.38 | 2.34 | 2.53 | 1.14 | 540 |
TEI | Tons/Tce | 2.32 | 1.95 | 2.08 | 2.09 | 0.05 | 540 |
TVI | Tons/104 ton-km | 2.35 | 0.04 | 0.45 | 0.55 | 0.38 | 540 |
TVE | CNY/ton-km | 1.41 | 0.02 | 0.20 | 0.24 | 0.17 | 540 |
TEE | Tce/104 CNY | 3.24 | 0.18 | 1.11 | 1.21 | 0.54 | 540 |
2005–2010 | 2010–2015 | 2015–2020 | 2020–2022 | |
---|---|---|---|---|
−1.2015 | −0.1919 | 0.9675 | 1.9703 | |
−0.7524 | −0.4292 | −0.0714 | 0.2671 | |
−1.1433 | −0.4955 | −0.3816 | −1.2750 | |
0.2410 | 0.5280 | 0.9267 | 1.2708 | |
0.0073 | 0.0053 | 0.0368 | 0.0156 | |
0.2821 | −0.0213 | 0.2534 | 1.5221 | |
0.1317 | 0.1544 | 0.1313 | 0.0626 | |
0.0321 | 0.0664 | 0.0722 | 0.1071 |
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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
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
Chicago/Turabian StylePeng, 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 StylePeng, 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