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Global renewable energy trade network: patterns and determinants.

Lianyue Feng1, Bixia Chen2, Gang Wu3

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Global renewable energy trade networks expanded from 2000-2018, showing complex community structures and key economic drivers. Key determinants include geographic proximity, economic factors, and trade agreements, highlighting the interconnectedness of the renewable energy market.

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ERGMRenewable energyTERGMTrade network

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Area of Science:

  • International Economics
  • Complex Network Analysis
  • Renewable Energy Markets

Background:

  • The global trade of renewable energy products is crucial for economic development and international relations.
  • Previous research has primarily focused on bilateral trade, leaving a gap in understanding industry-wide trade patterns and evolution.
  • A comprehensive analysis of global renewable energy trade networks (GRETNs) is needed to identify structural characteristics and influential factors.

Purpose of the Study:

  • To construct and analyze global renewable energy trade networks (GRETNs) from 2000 to 2018.
  • To explore the evolving patterns, community structures, and core economies within the GRETNs.
  • To identify the key determinants shaping the global renewable energy product trade.

Main Methods:

  • Utilized bilateral trade data to construct global renewable energy trade networks (GRETNs).
  • Applied complex network analysis, Exponential Random Graph Models (ERGM), and Temporal Exponential Random Graph Models (TERGM) for pattern exploration.
  • Analyzed network expansion, community structures, core-periphery dynamics, and trade relationship determinants.

Main Results:

  • GRETNs exhibited significant expansion and complex characteristics, including small-world properties, reciprocity, degree disassortativity, and export volume heterogeneity.
  • Identified four distinct communities within the GRETNs, with dynamic fluctuations in their patterns over time.
  • North America, Europe, and Asia emerged as dominant economic regions, with the USA, Germany, and China as central trading hubs. Endogenous structures like reciprocity and structural embeddedness were key drivers.

Conclusions:

  • The global renewable energy trade is characterized by increasing interconnectedness, dominated by specific economic powers, and influenced by both network structures and external factors.
  • Geographic proximity, participation in trade organizations (APEC, WTO), similar economic development levels (GDP, urbanization), and historical/political ties significantly predict trade relationships.
  • The findings underscore the importance of understanding network dynamics and socio-economic factors for future renewable energy trade policies and global energy transitions.