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County-Level Energy-Related Carbon Emissions and Sustainable Low-Carbon Transition in the Central-Southern Liaoning Urban Agglomeration: Spatiotemporal Evolution and Spatial Spillover Effects

中南遼寧都市圏における郡レベルエネルギー関連炭素排出と持続可能な低炭素移行：空間的時間的進化と空間的波及効果 (AI 翻訳)

Zhenbo Gao, Yanli Sun, Z H Liu, Juan Liu, Yang Yu

Sustainability📚 査読済 / ジャーナル2026-06-11#エネルギー転換Origin: CN対象セクター: cross_sector
DOI: 10.3390/su18126014
原典: https://doi.org/10.3390/su18126014

🤖 gxceed AI 要約

日本語

本論文は、重工業地帯である中南遼寧都市圏の73の郡を対象に、2005年から2024年までのエネルギー関連CO2排出量を再構築し、空間的パターンと駆動要因を分析。空間的自己相関やマルコフ連鎖、空間Durbinモデルを用いた結果、排出量の空間的集中が高まり、高排出隣接郡がさらなる増加圧力に直面することが明らかになった。GDPや人口減少は直接効果を抑制する一方、産業構造とエネルギー消費が排出を促進。差異化された低炭素政策の必要性を示す。

English

This study reconstructs county-level energy-related CO2 emissions for 73 units in the heavy-industrial Central-Southern Liaoning Urban Agglomeration from 2005 to 2024 using socioeconomic data and nighttime light data. Spatial autocorrelation, Markov chains, and a spatial Durbin model reveal increasing spatial clustering of emissions, with high-emission neighbors exerting upward pressure. GDP per capita and population reduce local emissions, while industrial structure and energy consumption increase them. The findings support differentiated low-carbon policies for old industrial regions.

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

日本でも北海道や九州など旧工業地帯のカーボンニュートラル戦略が重要課題であり、本研究の郡スケールでの排出推計手法や空間波及効果の分析枠組みは、日本の自治体レベルの低炭素計画に示唆を与える。ただし、中国のデータ特性や政策環境が異なるため、直接適用には調整が必要。

In the global GX context

This paper provides a granular methodology for reconstructing county-level emissions in heavy-industrial regions, which is globally relevant for regions undergoing industrial transition. The spatial spillover analysis highlights the need for coordinated regional policies, aligning with the European Green Deal's emphasis on just transition and territorial approaches. The techniques could be adapted for industrial clusters in Europe, the US, or Japan.

👥 読者別の含意

🔬研究者:The spatial econometric approach combining DMSP-OLS-like data with STIRPAT offers a replicable method for subnational emission inventories in data-scarce industrial regions.

🏢実務担当者:Regional planners can use the spatial clustering results to prioritize low-carbon interventions in high-emission clusters and consider neighbor effects.

🏛政策担当者:The evidence supports designing differentiated low-carbon policies based on county-level emission characteristics and spatial dependencies in old industrial areas.

📄 Abstract（原文）

For old industrial urban agglomerations, low-carbon planning requires emission information at a finer spatial scale, but county-level energy statistics are often incomplete. This study focuses on the Central-Southern Liaoning Urban Agglomeration, a typical heavy-industrial region in Northeast China. County-level energy-related carbon emissions for 73 units from 2005 to 2024 are reconstructed by combining socioeconomic panel data with harmonized DMSP-OLS-like nighttime light data. On this basis, global and local spatial autocorrelation, Moran scatterplots, Markov and spatial Markov transition matrices, and a spatial STIRPAT-based Spatial Durbin Model are used to examine the spatial pattern, transition process, and driving factors of emissions. The results show that emissions continued to increase during the study period, although the growth rate became slower and no clear regional peak was observed. Moran’s I rose from 0.627 in 2005 to 0.675 in 2024, which means that county-level emissions became more spatially clustered. The traditional Markov matrix shows strong state persistence, with diagonal probabilities ranging from 0.8793 to 0.9852. The spatial Markov results further suggest that counties surrounded by high-emission neighbors face greater pressure to move upward. In the SDM results, the spatial autoregressive coefficient is significant at the 1% level, with rho = 0.537. GDPPC and POP show negative direct effects, SEC increases local emissions but has a negative indirect effect, and PE is positively related to local emissions. Spatially, high-emission counties are mainly distributed around Shenyang, Anshan, Liaoyang, Dalian, and other industrial cores, while eastern ecological counties remain at relatively low emission levels. These findings provide county-scale evidence for differentiated low-carbon governance in old industrial regions.

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