Global warming, a result of greenhouse gas (GHG) emissions including carbon dioxide (CO2
), has been given importance by governments worldwide. CO2
emissions from fuel combustion in China, the world’s largest producer of CO2
emissions, totaled 8.5 billion tonnes in 2013, which accounted for 25% of global CO2
]. Beijing is facing mounting pressure to lower its CO2
emissions. Urban areas are the main space for humans to live and work, and with the acceleration of industrialization and urbanization in China, urban areas have become the main areas of economic activity and also the major source of CO2
emissions, accounting for about 85% of the country’s total GHG emissions [2
]. Therefore, research on how to achieve low-carbon urban development has become one of the most important policy issues for the Chinese government.
In the process of China’s urbanization, urban agglomerations have replaced single cities and became the most dynamic areas in Chinese economic development [4
]. As the most complex urban spatial pattern, urban agglomeration is the agglomeration of urban economies in terms of spatial organization and structure. Meanwhile, urban spatial interactions represent the exchange of multiple regional elements, for example, population, resources, technologies, and so on. Urban economic agglomeration not only affects the energy demand and efficiency of each urban unit, but it also influences energy-related CO2
emission levels and their spatial distribution [5
]. In this context, it is necessary to explore the spatial features of CO2
emission at the level of urban agglomeration, which may help in the roll out of efficient policies for coordinated CO2
emission reduction and low-carbon urban development [6
Because China is in a critical period of urbanization, city-level CO2
emission has been a research focus among scholars, both at home and abroad. Some have focused on the evaluation of CO2
emissions in manufacturing [7
], transportation [8
], households [10
], and buildings [12
], and have found that these sectors are the main sources of CO2
emission in urban areas. On the other hand, some scholars have suggested that the driving forces of urban CO2
emission include population [14
], the economy [15
], technology [18
], energy structure [21
], urbanization [22
], and spatial patterns [23
], and have achieved many valuable conclusions. For example, the growing urban population and changing age structure exert positive effects on city-level emissions, while the downsizing of families and expansion of the migrant urban population fuel emissions [25
]; growth in economic activity is the most fundamental contributor to the rising city-level emissions [26
]; advancing technologies, in particular, breakthroughs in energy are the major means of lowering emissions [28
] and rising emissions could be curbed by the adjustment of industrial structure and energy consumption patterns [29
]. Other driving forces involve urbanization and irregular changes in urban spatial patterns [23
]. Several previous studies have used spatial econometric approaches to explore the spatial aggregation of CO2
emissions, and have found that the top Chinese urban emitters are in the south and east coastal regions, while those contributing the least are from the west and middle regions [32
emissions are not only a simple urban environmental problem, but can transmit to neighbouring cities through natural factors and economic mechanisms, such as atmospheric circulation and industrial transfer [3
]. Therefore, a city’s CO2
emissions are not only affected by factors such as its own energy consumption, but also by the surrounding cities. The increasing dependence on the trends in external force has given rise to distinctive spatial associations and aggregations in city-level emissions [36
], and the closer two urban areas are, the higher their dependence and aggregation is [38
]. Urban agglomerations are the spatial carriers of regional economic integration. This means that local city governments should actively take cooperative measures in the process of carbon emission reduction, and to investigate and control the spatial correlation effects of carbon emissions. Unfortunately, few studies have analyzed the effects of urban agglomeration on carbon emissions from the perspective of spatial interactions. Even though the above studies involved the spatial aggregation of CO2
emissions, they did not systematically address whether spatial aggregation is derived from endogenous or exogenous spatial interactions. Moreover, the widely adopted spatial econometric approach excels at quantifying the aggregation of city-level emissions on the basis of spatial factors but fails to reveal their spatial interactions and the spatial effects of urban agglomeration on carbon emissions.
In urban agglomerations, different urban areas can be linked by spatial interactions in many aspects such as society, economic, energy, CO2
emissions, and so on [39
]. The spatial interaction of CO2
emissions between two urban areas may be measured by the point-line interaction structure; however, it is difficult to show the spatial interactions of the whole area. Demonstrating spatial interactions in a network-based way is even more challenging [4
]. Networks can identify the mechanisms of the spatial interactions of CO2
emissions and give insight into the changes in spatial interactions. In order to further understand the urban spatial interactions of CO2
emissions in a comprehensive way, a network approach is needed.
Thus, this paper uses social network analysis (SNA) theory to map the spatial interactions of urban CO2
emissions inside urban agglomerations. SNA is a good tool to investigate the network relations and global structure features between individuals within a system. In urban systems, SNA has been applied to measure population migration [40
], urban structure [43
], urban construction [44
], the urban structure of the ecological relationships [45
], and so on.
This paper aims to explore the structure and characteristic of spatial interactions of urban CO2 emissions in urban agglomerations. We focused specially on urban agglomeration in Chengdu-Chongqing, Southwest China. Based on the CO2 emission data in 16 sampled urban areas from 2005–2016, we constructed spatial association networks of CO2 emissions and analyzed the structural roles of urban areas in the spatial association networks. Furthermore, we applied a panel regression model to analyze the impacts of the different structural roles of urban areas on CO2 emissions.
4. Conclusions and Policy Implications
This study explored the spatial structure and effects of the association network of CO2 emissions in the Chengdu-Chongqing urban agglomeration through SNA. By using data from the agglomeration from 2005–2016, an association matrix of CO2 emissions was calculated through a modified gravity model. In addition, the association network of the urban agglomeration was constructed. The structure of the association network of CO2 emissions was investigated by relating it to the global network, individual network, and spatial agglomeration. The study was concluded by examining the structural effects of the association network of CO2 emissions. The major findings are as follows:
The global network structure revealed that the network density and association strength of the spatial association network of CO2 emissions in the agglomeration are increasing on a yearly basis, indicating closer CO2 emission connections among cities in the urban agglomeration. From a static perspective, there are still significant differences in inter-city CO2 emissions, providing evidence of the imbalance in the spatial structure of CO2 emissions within the urban agglomeration.
The individual network structure indicates that the out-degrees of Chengdu and Chongqing are considerably higher than those of other cities and their in-degrees. This shows that there is a strong spatial spillover effect in Chengdu and Chongqing. The fact that the out-degrees of these cities are much higher than the in-degrees of other cities illustrates that these cities all benefit from CO2 emissions. Before 2011, only Chengdu and Chongqing had higher than average BC values, but Nanchong and Mianyang were also on the list in 2016. In the sample period, the BC values of Chengdu and Chongqing were in decline, which indicates the occurrence of more links for CO2 emission in the second- and third-tier cities. The “bridge” role of the two core cities weakened. Prior to 2016, the cities with higher than average CC values were Chengdu, Chongqing, Deyang, Mianyang, Neijiang, Leshan, Ziyang, Luzhou, and Yibin. This demonstrates the radiation effect of Chengdu and Chongqing and proves that secondary cities are moving toward the network center.
The spatial agglomeration assessment revealed that the Chengdu-Chongqing urban agglomeration is divided into four subgroups based on geographic location. The first group, located in west Chongqing, contains Chongqing and Dazhou; the second group, located in south Sichuan, contains Suining, Nanchong, and Guang’an; the third group, located in surrounding Chengdu, contains Chengdu, Mianyang, Deyang, Leshan, Ziyang, Ya’an, and Meishan; and finally, the fourth group, located in northeast Sichuan, contains Luzhou, Yibin, Zigong, and Neijiang.
The effect analysis showed that the association network structure of CO2 emissions has a significant influence on the regional CO2 emission intensity and the differences in CO2 emission intensity among cities. The increase in network density not only lowers the regional CO2 emission intensity greatly but can also narrow the differences in inter-city CO2 emission intensity. Meanwhile, increasing node centrality, especially regarding the DC and CC, is beneficial for lowering CO2 emission intensity.
The above research indicates that the success of regional (urban agglomeration) CO2 emission reduction concerns not only individual cities, but the establishment of long-term, coordinated emission reduction mechanisms within the whole region. This should be taken into account during policy making, as explained below.
Firstly, CO2 emissions should be distributed appropriately in the Chengdu-Chongqing urban agglomeration. In order to promote the coordinated development of carbon emission reduction in the urban agglomeration, all regions shall be taken into account during goal setting, which means that the different economic volumes and developing goals of the various cities should be considered, as well as the flow direction and transmission mechanisms of CO2 emission within the urban agglomeration according to the CO2 emission relationships among cities. Thus, the emission reduction plan should incorporate all regions instead of only considering individual needs for development.
Secondly, the association network of CO2 emissions should be optimized in the Chengdu-Chongqing urban agglomeration to take advantage of the core cities with low CO2 emissions, and to promote associations among various cohesive subgroups. The conclusion of the analysis of centrality showed that Chengdu and Chongqing are two core cities in the association network of CO2 emissions, while Mianyang, Deyang, and Nanchong are sub-core cities. To promote the coordinated development of the urban agglomeration, it is important to use the radiation roles of Chengdu and Chongqing and strengthen the influences of sub-core cities. The result of cohesive subgroups indicated that there are more and closer links among subgroups, which should be fully utilized to achieve coordinated emission reduction.
Finally, the promotional roles of the global network structure and individual network structure of CO2 emission should be exerted to lower the regional CO2 emission intensity and narrow the differences in CO2 emission among cities. On one hand, the association network links of CO2 emissions should be increased, which will involve the creation of more channels of CO2 emission flow among cities. On the other hand, the strength of association should be reinforced, which will involve broadening the width of the channel for CO2 emission flow.