| 摘要: |
| 针对各地公路碳排放的空间差异与关联特征，制定合理的低碳策略，实现各地协同碳减排，是行业可持续发展的热点问题。以江西省为研究对象，采用空间自相关、社会网络分析和引力模型等分析11地市公路行业碳排放的空间差异与关联，识别社会网络的结构特征及其演变趋势，明确不同地市在网络中的地位和作用，结果显示：（1）公路货运是公路行业碳排放的最主要来源；（2）江西省公路行业碳排放整体上呈现空间随机分布，不存在全局的空间聚集性，且局部空间聚集特征不断变化；（3）公路行业碳排放网络逐渐从南昌、新余双中心结构演变为南昌为主的单中心结构，网络的协同作用整体呈减弱趋势，核心地市对外围地市的影响力和带动作用不足。 |
| 关键词: 公路 碳排放 社会网络分析 协同碳减排 |
| DOI：10.7515/JEE222064 |
| CSTR：32259.14.JEE222064 |
| 分类号: |
| 基金项目:国家自然科学基金项目（41961048）；2022 年度科技智库青年人才计划（20220615ZZ07110415）；江西省交
通运输厅科技项目（2018R0017，2019C0006） |
| 英文基金项目:National Natural Science Foundation of China (41961048); Youth Talent Program of Science and Technology Think Tanks in 2022 (20220615ZZ07110415); Science and Technology Projects of Department of Transportation of Jiangxi Province (2018R0017, 2019C0006) |
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| Spatiotemporal differentiation and correlation analysis of highway carbon emissions in Jiangxi based on social network analysis |
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TAN Yetuo, ZHAO Hong, LUO Zulin, SHEN Jiacheng, SUN Binfeng
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1. Jiangxi Port Group Co., Ltd., Nanchang 330038, China
2. Jiangxi Transportation Institute Co., Ltd., Nanchang 330200, China
3. Shihutang Navigation and Power Hub Branch, Jiangxi Port and Navigation Construction Investment Group Co., Ltd., Ji’an 343732, China
4. Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
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| Abstract: |
| Background, aim, and scope Climate change presents a global environmental challenge that requires the effective control of atmospheric CO2 and other greenhouse gas concentrations. Energy conservation and CO2 emission reduction are the shared responsibilities of the international community. The transportation industry is a significant contributor to global carbon emissions, accounting for approximately 24% of total carbon emissions, and as such, it has a pivotal role in achieving emission reduction targets. Understanding the spatial differences and correlations between cities and their spatial structures can facilitate the formulation of practical and comprehensive carbon emission reduction measures, leading to synergistic emission reduction in the region. Most existing research on CO2 emissions has primarily focused on regional-scale spatial distribution, with limited attention paid to inter-regional linkages with regard to transportation emissions. Accurately portraying the spatial differences and correlation characteristics of transport carbon emissions, as well as clarifying the position and role of each region in regionally coordinated carbon emission reduction, holds great theoretical significance and application value. Materials and methods In this paper, passenger and freight turnover volume was used to calculate highway traffic carbon emissions in 11 cities in Jiangxi based on data from 2010 to 2019. Subsequently, spatial autocorrelation, correlation analysis, social network analysis, and a gravity model were used to analyze the spatial and temporal differences in highway traffic carbon emissions, identify the characteristics of the social network structure and the evolution trend, and illustrate the position and role of the cities in the network. Results From 2010 to 2019, the proportion of freight carbon emissions in Ganzhou and Ji’an gradually decreased, while the proportion of passenger carbon emissions increased. In Nanchang and Yichun, the proportion of freight carbon emissions increased, while the proportion of passenger carbon emissions decreased, and the overall highway carbon emissions in all cities increased. The spatial autocorrelation analysis showed that there was no significant correlation between the 11 cities, and highway carbon emissions had a spatially random distribution. The overall spatial correlation of highway carbon emissions in different cities was weak, and research units with no significant correlation accounted for a large proportion. The social network analysis showed that the overall network synergistic effect of various cities in Jiangxi decreased and then increased from 2010 to 2019. Nanchang was always the core city of the network. Discussion With regard to spatial agglomeration, there was no significant spatial autocorrelation across the 11 cities, and road transportation carbon emissions in each city presented a spatially random distribution on the whole. Further correlation analysis also showed that there was no general correlation between neighboring cities. This spatial relationship is not conducive to coordinated carbon emission reduction across various cities. To further explore the spatial correlation of road transportation carbon emissions among different regions, social network analysis was used to identify the relationship and interaction between different cities. The results showed that the highway carbon emission network gradually evolved from a Nanchang—Xinyu “two-center” structure to a Nanchang “single-center” structure. The influence and driving effect of central cities on peripheral cities was reduced. Nanchang had an exemplary industrial structure and a low carbon emission intensity. It was associated with all the peripheral cities and had a strong driving effect on the carbon emissions of peripheral cities. However, the lack of connection between the other core cities and peripheral cities increased the freight distance, leading to an increase in road carbon emissions. Adjusting the industrial layout, promoting the correlation between core cities and peripheral cities, and improving the overall collaborative effect of the network are key factors for future carbon emission reduction in the transportation sector. Identifying the correlation and differentiation characteristics of highway carbon emissions among different cities, optimizing the structure and composition of highway transportation in Jiangxi, will contribute to achieving carbon reduction in the transportation industry in Jiangxi. Conclusions (1) Road freight transportation is the main source of highway carbon emissions. (2) The distribution of highway carbon emissions among 11 cities indicated a spatially random distribution rather than a global spatial aggregation. The local spatial aggregation characteristics were constantly changing. (3) The highway carbon emission network changed from a two-center network structure with Nanchang and Xinyu as the center to a single-center network structure with Nanchang as the center. The synergistic effect of the network tended to weaken, and the influence and driving effect of the core cities on the peripheral cities was limited. Recommendations and perspectives An investigation of synergistic CO2 mitigation effects could reflect how the cities’ network structures can reduce or drive regional CO2 emission. Adjusting the industrial layout and promoting connection between core cities and peripheral cities could improve the overall synergistic effect of the network, and are the key points to consider for carbon emission reduction in the future. |
| Key words: highway carbon emission social network analysis synergistic CO2 reduction |
