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Spatial Correlation Network of Carbon Emissions in Belt and Road Countries: Social Network Analysis and TERGM (2011–2020)

一带一路諸国における炭素排出の空間的相関ネットワーク：社会ネットワーク分析とTERGM（2011-2020年） (AI 翻訳)

L. -D. Zhang, Meixian Wang, Wenjing Ma, Zuojian Zheng, Hongxian Li, Chunlu Liu

Sustainability📚 査読済 / ジャーナル2026-04-09#climate_policyOrigin: CN
DOI: 10.3390/su18083714
原典: https://doi.org/10.3390/su18083714
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🤖 gxceed AI 要約

日本語

本研究は、54の一带一路（BRI）諸国を対象に、修正重力モデルを用いて炭素排出の空間的相関ネットワークを構築し、社会ネットワーク分析（SNA）と時間指数ランダムグラフモデル（TERGM）を用いてネットワーク構造とその要因を分析した。結果、ネットワークの相互接続性と結束性が高まり、中核国（カタール、イスラエル、インド、中国、UAE）が排出リンクの形成を牽引していることが明らかになった。技術革新と産業構造の最適化は排出リンク形成に正の関連を示し、エネルギー構造と外国投資は負の関連を示した。

English

This study constructs a spatial correlation network of carbon emissions for 54 Belt and Road Initiative (BRI) countries using a modified gravity model, and applies social network analysis (SNA) and Temporal Exponential Random Graph Model (TERGM) to examine network structure and driving factors. Results show increasing interconnectedness and cohesion, with core countries (Qatar, Israel, India, China, UAE) driving emission link formation. Technological innovation and industrial structure optimization are positively associated with link formation, while energy structure and foreign investment are negatively associated.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本はBRIに直接関与しないが、アジア域内の排出ネットワーク構造の理解は、サプライチェーン排出管理や国際的な気候政策連携において示唆を与える。特に、技術革新と産業構造が排出リンクに与える影響は、日本のGX投資判断に参考となる。

In the global GX context

This paper provides empirical evidence on the spatial dynamics of carbon emissions across BRI countries, relevant for global climate governance and cross-border emission reduction strategies. The network analysis offers insights for policymakers and investors in understanding regional interdependencies, though the focus is on macro-level patterns rather than corporate disclosure.

👥 読者別の含意

🔬研究者:Researchers in climate policy and network analysis can leverage the SNA and TERGM methodology to study emission interdependencies in other regions.

🏢実務担当者:Corporate sustainability teams with supply chains in BRI countries can use the findings to identify key emission nodes and assess transition risks.

🏛政策担当者:Policymakers in BRI countries and international bodies can use the network insights to design coordinated emission reduction policies and target core countries.

📄 Abstract（原文）

The countries in the Belt and Road Initiative (BRI) significantly influence global carbon emissions, and the spatial correlation and driving mechanisms of their emissions are crucial for regional emission reduction and global climate governance. This study constructs a carbon emission spatial correlation network, where links represent pairwise spatial correlations derived from a modified gravity model, using data from 54 BRI countries (2011–2020). It applies social network analysis (SNA) to examine the network structure and uses the Temporal Exponential Random Graph Model (TERGM) to identify influencing factors. The main findings are as follows: (1) The BRI carbon emission network has become more interconnected and cohesive, with stronger regional connectivity and reduced inequality. (2) The network shows a core–periphery structure with notable spatial association patterns. Countries like Qatar, Israel, India, China, and the UAE have rapidly established carbon emission links, positioning them at the core due to their high connectivity and influence. (3) The network displays temporal dependence, with reciprocity associated with stronger mutual connections and transitivity associated with more cohesive network structures. Technological innovation and industrial structure optimization are positively associated with the formation of carbon emission connections, while energy structure and foreign investment are negatively associated with it. Economic development and technological innovation are associated with a country’s greater involvement in carbon emission connections, and countries with similar urbanization rates, energy, and industrial structures, but large economic disparities are more likely to form carbon emission associations, reflecting potential complementarities in the network structure.

🔗 Provenance — このレコードを発見したソース

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