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Evolution, resilience and causes of global petroleum gas trade networks: 1995-2020 被引量:1
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作者 Na Li Yi-Ran Song +1 位作者 Ying Wang Chun-Bao Ge 《Petroleum Science》 SCIE EI CAS CSCD 2024年第5期3656-3674,共19页
Based on the HS 4-digit code trade data in UNCOMTRADE from 1995 to 2020, this paper analyzes the characteristics of the evolution of the global PG trade network using the complex network approach and analyzes the chan... Based on the HS 4-digit code trade data in UNCOMTRADE from 1995 to 2020, this paper analyzes the characteristics of the evolution of the global PG trade network using the complex network approach and analyzes the changes in its resilience at the overall and country levels, respectively. The results illustrated that:(1) The scale of the global PG trade network tends to expand, and the connection is gradually tightened, experiencing a change from a “supply-oriented” to a “supply-and-demand” pattern, in which the U.S., Russia, Qatar, and Australia have gradually replaced Canada, Japan, and Russia to become the core trade status, while OPEC countries such as Qatar, Algeria, and Kuwait mainly rely on PG exports to occupy the core of the global supply, and the trade status of other countries has been dynamically alternating and evolving.(2) The resilience of the global PG trade network is lower than that of the random network and decreases non-linearly with more disrupted countries. Moreover, the impact of the U.S. is more significant than the rest of countries. Simulations using the exponential random graph model(ERGM) model revealed that national GDP, institutional quality, common border and RTA network are the determinants of PG trade network formation, and the positive impact of the four factors not only varies significantly across regions and stages, but also increases with national network status. 展开更多
关键词 Petroleum gas Complex network approach Network resilience Exponential random graph model
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Investigating spatio-temporal characteristics and influencing factors for green energy consumption in China 被引量:1
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作者 Xiaowei Ma Shimei Weng +2 位作者 Jun Zhao Huiling Liu Hongyun Huang 《Geoscience Frontiers》 SCIE CAS CSCD 2024年第3期221-236,共16页
The green transformation of energy consumption is beneficial for promoting green development in China.This study constructed a green energy consumption evaluation index system and measured the green energy consumption... The green transformation of energy consumption is beneficial for promoting green development in China.This study constructed a green energy consumption evaluation index system and measured the green energy consumption levels in 30 provinces of China from 2000 to 2019 using the fuzzy comprehensive evaluation method.This study further employed the spatial Durbin model to examine influencing factors and spillover effects of green energy consumption.The results showed that,temporally,China’s green energy consumption levels had a fluctuating upward trend.While,spatially,the overall levels of green energy consumption in China showed apparent characteristics of“high in the west and low in the east”.In terms of influencing factors,environmental regulations played an important role in promoting green energy consumption in the region,while economic development,opening up,and industrial structure had considerably inhibiting effects.Additionally,economic development,opening up,and industrial structure of neighboring regions showed marked positive spillover effects,while urbanization level and technological innovation showed substantial negative spillover effects.The regional heterogeneity test results showed that environmental regulation and industrial structure rationalization were the important factors for promoting green energy consumption in the eastern region,environmental regulation played an important driving role in the central region,and opening to the outside world and technological innovation helped improve the level of green energy consumption in the western region. 展开更多
关键词 Energy green consumption Spatial and temporal characteristics Influencing factors Spatial Durbin model Regional heterogeneity
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