Abstract
This paper had two main purposes. One was to estimate annual total aviation CO2 emissions from/among all key urban agglomerations (UAs) in China and its changes patterns from 2007 to 2014. The second one was to visualize the aviation carbon footprints among the UAs by using a chord diagram plot. This study also used Kaya identity to decompose the contribution of potential driving forces behind the aviation CO2 emissions using Kaya identity. Especially, it decomposed factor CO2/gross domestic product (GDP), which is wildly used in Kaya identity analysis, into factor CO2/value-added (VA) and factor VA/GDP. Here, VA represents the tourism value added of the corresponding flights. The main results were: (1) The UAs developed a much bigger and stronger carbon network among themselves. (2) There was also an expanding of the flows to less densely populated or less developed UAs. However, the regional disparity increased significantly. (3) Compared with the driving factor of population, the GDP per capita impacted the emission amount more significantly. Our contribution had two folds. First, it advances current knowledge by fulfilling the research gap between transport emissions and UA relationship. Second, it provides a new approach to visualizing the aviation carbon footprints as well as the relationships among UAs.
Highlights
Living in an age where urban areas expand rapidly and the interaction between cities grows frequently, urban agglomeration (UA) sustainability has become a hot topic for researchers since 1990s
Using the data derived from the 1:1 million basic geographic databases of the National Basic Geographic Information Center of China, we developed Figure 1 to show the geographical location of all UAs, where green blocks refer to UA boundaries, grey ones are provincial boundaries and red circles present major cities
The calculation results show that the total annual CO2 emissions produced by all 19 UAs increased from 24.6 million tons in 2007 to 44 million tons in 2014
Summary
Living in an age where urban areas expand rapidly and the interaction between cities grows frequently, urban agglomeration (UA) sustainability has become a hot topic for researchers since 1990s. According to the World Conference on Transport Research’s project, the ‘Comparative study on Urban Transport and the Environment’, urban low-carbon transport measures could be divided into two categories—‘Strategies’ and ‘Instruments’ (Nagamura et al [6]), and Nakamura and Hayashi [7] summarized these strategies into three components: Avoid, shift and improve, based on the so-called avoid–shift–improve (ASI) framework. These previous studies solely focused on the emissions of a single urban area or UA [8]. To the best of our knowledge, there have been relatively few studies exploring transport emissions and interrelations among UAs
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