Research on the structural characteristics and influencing factors of global environmental services trade networks.

The development of the environmental services trade is crucial to achieving climate goals and a green economic transition. Based on environmental services trade data from 2001 to 2019, this work uses the social network analysis (SNA) method to depict the structural features of global environmental services trade networks, empirically testing the influencing mechanism of network evolution based on the quadratic assignment procedure (QAP) model. The results indicate that the global environmental services trade is now in a rebound stable period. The market of the environmental services trade is becoming increasingly diversified, the accessibility and convenience of trade are constantly being enhanced, and there is an obvious core-edge structure in the network. Belgium, Italy, and the Netherlands are at the center of the global environmental services trade network; Greece, as the most prominent trade catch-up country, has gradually become an important "bridge" and "hub." Climate change, geographical distance, and population size are the key factors affecting the global trade network of environmental services; environmental regulation, economic distance, and green technology distance have no significant impact on the development of the global environmental services trade, and language difference is no longer an obstacle to the growth of this trade. The results indicate that countries should develop in-depth transnational trade cooperation in the field of environmental services and do so with a more open attitude. The government should give sufficient policy support to the import and export of environmental services, guaranteeing the development of the environmental services trade.

Spatial-temporal variation characteristics and evolution of the global industrial robot trade: A complex network analysis.

Industrial robots are a strategic future technology and an important part of the development of artificial intelligence, and they are a necessary means for the intelligent transformation of manufacturing industry. Based on global industrial robot trade data from 1998 to 2017, this paper applies the dynamic complex network analysis method to reveal the spatial and temporal variation characteristics and trade status evolution of the global industrial robot trade network. The results show that the global industrial robot network density has steadily increased, and the industrial robot trade has been characterized by 'diversification'. The number of major industrial robot exporters in the world is increasing, and the import market is increasingly diversified. The export market structure is relatively tight, the centrality of the global industrial robot trade network shows a downward trend, and the dissimilarity of the 'core-edge' clusters decreases year by year. The trade status of 'catch-up' countries represented by China has rapidly increased. However, Japan, Germany, and Italy are still in the central position of the industrial robot trade. Moreover, trade of the 'catch-up' countries' is dominated by imports, and exports of industrial robot products are insufficient. Finally, policy suggestions are provided according to the results.