Domestic trade flourishes with economic development and the spatial separation of production and consumption. Therefore, the prosperity of trade is accompanied by the transfer of pollution from the ...demand side to the supply side, which could potentially worsen the environmental quality of the supply side. Despite a large number of studies on the pollution haven hypothesis in international trade, little attention has been paid to testing the hypothesis in domestic trade. Here, combining a multiregional input-output analysis and a gravity model of trade in China, we provide an empirical test to address this problem for the first time. We also assess the factors affecting the SO
emissions embodied in trade, including population, economic development, coal consumption, distance, and environmental regulations. We found that domestic trade contributed approximate one third of the total SO
emissions in China, and interprovincial transfers of SO
embodied in trade were significantly determined by the population, economic development, coal consumption of the trade pairs, as well as their distance. SO
emission mitigation policies, such as emission reduction target and sulfur dioxide control zone, has a more significant influence on the direct transfer of SO
emission via direct bilateral trade, while their effects were largely offset by indirect trade (through third-party transfers). Our results do not support the pollution haven hypothesis existed in domestic trade in China during 2007-2012. Our paper sets an example and provides a reference for the domestic pollution transfer problem from an econometric perspective. Further attempts on testing pollution haven hypothesis in consideration of various pollutants are still needed to arrive at a robust conclusion.
Tourism is shaped by a wide range of factors and forces, including exogenous ones that have no direct link with the tourism sector. Natural disasters and unexpected events are prime examples of such ...determining factors, as they have profound effects on individuals and society, and as a result have the potential to affect tourism flows considerably. Several theoretical arguments exist why natural disasters and unexpected events could influence tourist destination choices. However, empirical research to confirm the nature and extent of impacts of disasters on tourism is lacking. To address this gap, this paper incorporates a dataset on natural and man-made disaster events into a model of international tourism flows to evaluate the effect of different types of disasters on international arrivals at the national level. Findings provide evidence that the occurrence of different types of event change tourist flows to varying degrees. Although in some cases a positive effect is estimated, in general the impacts are negative, resulting in reduced tourist arrivals following an event. Understanding the relationship between disaster events and tourism is helpful for destination managers who make critical decisions in relation to recovery, reconstruction and marketing.
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•First global evaluation of the impact of disasters on international tourism flows.•Tsunamis, Floods and Volcanic Eruptions constitute negative motivators.•Volcanic Eruptions impact most negatively on international tourism flows.•High economic consequences of a disaster impact negatively on tourism.•In line with theory, some disasters can also positively affect flows.
•We compared the structural characteristics of 18 urban agglomerations in China.•Our framework contained analysis of 3 different scales based on 4 elements’ flows.•Guangdong-Hong Kong-Macao urban ...Agglomeration had the greatest average flow (5.15).•Shandong Peninsula Urban Agglomeration had the best performance in overall network.•Cities with high centrality were in the bidirectional or main spillover subgroups.
Flows of different elements among cities can affect urban organizational structures, leading to specific links between and roles for cities within urban agglomerations. Previous studies have mainly focused on flows produced by specific elements in a single urban agglomeration, and they cannot reveal differences in organizational structures affected by multiple elements among different urban agglomerations. Therefore we selected 18 typical urban agglomerations in China and explored their structural characteristics under comprehensive effects of four elements: economy, resources, population and technology, with gravity model and social network analysis. Guangdong-Hong Kong-Macao Urban Agglomeration had a maximum flow intensity of 5.15, this concentration range was the largest from 0.65 to 3.75, indicating that more elements flowed internally. Shandong Peninsula Urban Agglomeration had the largest network density at 0.412, the lowest efficiency at 0.567, and the lowest hierarchy at 0.000, showing that intercity relationships within SP were the tightest among the18 urban agglomerations. Geographical proximity and topological structure of subgroups proved that connections among subgroups were not complex, and cities of different provinces lacked close interactions. Furthermore, most of the key cities with high centrality indexes were municipalities directly under the Central Government or provincial capitals; these cities played important roles in networks and were distributed into “main spillover” and “bidirectional spillover” subgroups. This paper provides a unified analytical framework aimed at urban agglomerations that accounts for flows of multiple types of elements and explores urban organizational structures, and our results could provide scientific guidance for promoting the coordinated and sustainable development of urban agglomerations.
China faces enormous pressure to reduce carbon emissions. Since the agglomeration and driving effect of urban agglomerations have continued to increase, relying on the network relationship within ...urban agglomerations to coordinate emission reduction becomes an effective way. This paper combines the modified Gravity model and Social Network Analysis method to measure the structure characteristics of carbon emission spatial correlation network of the seven urban agglomerations as a whole and each urban agglomeration in China, analyzes the interaction mechanism between cities and between urban agglomerations, and finally explores the influencing factors of carbon emission spatial correlation through the QAP analysis method. The results are as follows: (1) As for the overall network, overall scale was increasing, but the hierarchical structure had a certain firmness. YRD and PRD urban agglomerations were at the center of the network and received the spillover relationship of MRYR, CC, CP, and HC urban agglomerations. (2) As for the networks of urban agglomerations, the allocation of low-carbon resource elements still needed to be optimized, especially BTH urban agglomeration. Beijing, Shanghai, Nanjing, Wuxi, etc. were at the center of the network. The influencing factors and degree of carbon emission spatial correlation in each urban agglomeration were different.
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•Carbon emission spatial network of 7 urban agglomerations as a whole is analyzed.•The gravity model is modified taking into account economic factors.•The modified Gravity model and SNA are used to the CO2 study in urban agglomeration.•YRD and PRD urban agglomerations were at the center of the overall network.•The hierarchical structure of the overall network had a certain firmness.
•Theoretical foundations for gravity models in the context of tourism are provided.•Gravity models for tourism demand are derived from consumer choice theory.•Gravity models are linked to aggregated ...tourism demand equations.•Estimation procedures for gravity models are suggested.
Neglected by the tourism demand literature for the last decades, gravity models have re-emerged as a way for modeling tourism demand when the role of structural factors on tourism has to be evaluated. From the initial formulation of the gravity model, more sophisticated specifications have been developed including a more complete set of explanatory variables and allowing differentiation between origin and destination countries. In this paper, we propose a theoretical background to the gravity model for bilateral tourism flows derived from the individual utility theory. The issues in distinguishing the recent versions of gravity models from aggregated demand models are shown and the suitability of this methodology when structural factors have to be evaluated and quantified in the context of tourism demand is discussed.
The identification of influential nodes in complex networks has been a topic of immense interest. In most cases, the local approach represented by degree centrality performs well but has limitations ...when dealing with the bridge nodes. In order to solve the problem of being trapped in the locality, researchers have proposed many useful methods. The gravity model is an emerging research direction among them. However, such models have to exhaust the shortest distance between all nodes, which renders them impractical and difficult to run over large graphs. In order to address this issue, we propose a random walk-based gravity model to identify influential spreaders. Our proposed model decreases the time complexity of calculating the shortest distance—a critical step in the conventional gravity models, from O(|V|2) to O(|V|*γ*lr(l-r)), and reduces space complexity of O(|V|2) to O( 2|V|), where 2≪|V| and γ*lr(l-r)≪|V|. Some random walk properties are also investigated to support our model. In order to demonstrate the feasibility of the proposed gravity centrality, we have verified its spreading ability and convergence speed under different random walk strategies. Experimental results indicate that our method performs far better than most gravity models.
•Climate change alters comparative advantages and trading opportunities.•Changes in climate distance is a driver of bilateral trade.•The more the climate conditions diverge overtime, the larger the ...trade.•Productivity is a plausible channel through which climate distance may affect trade.
Climate conditions are sources of comparative advantages and may foster trading opportunities. Through a gravity-type approach we show that overtime variations in climate differences correlate with bilateral trade values. Intuitively, these differences may be associated with diverse productivity levels, a plausible channel through which climate distance affects trade.
The identification of important nodes in complex networks is an area of exciting growth due to its applications across various disciplines like disease control, data mining and network system ...control. Many measures have been proposed to date, but they are either based on the locality of nodes or the global nature of the network. These measures typically use the traditional Euclidean Distance, which only focuses on local static geographic distance between nodes but ignores the dynamic interaction between nodes in real-world networks. Both the static and dynamic information should be considered for the purpose of identifying influential nodes. In order to address this problem, we have proposed an original and novel gravity model with effective distance for identifying influential nodes based on information fusion and multi-level processing. Our method is able to comprehensively consider the global and local information of complex networks, and also utilizes the effective distance to incorporate static and dynamic information. Moreover, the proposed method can help us mine for hidden topological structure of real-world networks for more accurate results. The susceptible infected model, Kendall correlation coefficient and eight existing identification methods are utilized to carry out simulations on twelve different real networks.
To identify influential nodes in real networks, it is essential to note the importance of considering the local and global information in a network. In addition, it is also key to consider the ...dynamic information. Accordingly, the main aim of this paper is to present a new centrality measure based on return random walk and the effective distance gravity model (CRRWG). This new metric increases the relevance of nodes with a dual role: i) at the local level, they are important in their community or cluster, and ii) at the global level, they give cohesion to the network. It has advantages over other traditional models of centrality since it considers the global and local information, as well as the information of the dynamic interaction between the nodes, as recent studies on community-aware centrality measures demonstrate. Thus, the combination of dynamic and static information makes it easier to detect influential nodes in complex networks. To validate the effectiveness of the proposed centrality measure, it is compared with classic measures, such as Degree, Closeness, Betweenness, PageRank, and other measures based on the gravity model, effective distance and community-aware approaches. The experimental results show the effectiveness of CRRWG through a set of experiments on different types of networks.
Climate, conflict and forced migration Abel, Guy J.; Brottrager, Michael; Crespo Cuaresma, Jesus ...
Global environmental change,
January 2019, 2019-01-00, 20190101, Volume:
54
Journal Article
Peer reviewed
Open access
•Growing public interest on climate driver of conflict and migration but evidence is scarce.•Climate change is responsible for increase in asylum seeking through conflict.•Reduced rainfall increases ...conflict which in turn drive outflow of asylum seekers.•Established causal relationship between climate, conflict and asylum seeking only in the recent period.
Despite the lack of robust empirical evidence, a growing number of media reports attempt to link climate change to the ongoing violent conflicts in Syria and other parts of the world, as well as to the migration crisis in Europe. Exploiting bilateral data on asylum seeking applications for 157 countries over the period 2006–2015, we assess the determinants of refugee flows using a gravity model which accounts for endogenous selection in order to examine the causal link between climate, conflict and forced migration. Our results indicate that climatic conditions, by affecting drought severity and the likelihood of armed conflict, played a significant role as an explanatory factor for asylum seeking in the period 2011–2015. The effect of climate on conflict occurrence is particularly relevant for countries in Western Asia in the period 2010–2012 during when many countries were undergoing political transformation. This finding suggests that the impact of climate on conflict and asylum seeking flows is limited to specific time period and contexts.
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