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<p>Accurate forecasting of emission pathways from key sectors, such as logistics, is crucial for climate mitigation in urban areas. Existing frameworks often overlook factor interdependencies and cannot directly forecast the state of decoupling between economic growth and emissions. This study develops an integrated framework that links the Generalized Divisia Index Method with a Particle Swarm Optimization-Back Propagation neural network for the Beijing-Tianjin-Hebei region. This is the first application of the Generalized Divisia Index Method to decompose logistics emissions in this region, overcoming the limitations of factor independence. The neural network directly predicts the decoupling elasticity index. Results from 2012 to 2022 show a precarious ‘weak decoupling’ state, mainly driven by energy consumption and increasingly offset by technological progress. Decomposition reveals significant regional differences: Beijing faces carbon lock-in in its aviation sector, Tianjin leads in structural decarbonization, and Hebei shows efficiency-driven volatility. Critically, forecasts indicate a lingering risk of weak decoupling and a potential slide into negative decoupling by 2027, highlighting the system’s vulnerability to economic fluctuations. This study presents a novel, replicable framework for forecasting decoupling risks, providing spatially differentiated insights for low-carbon logistics and climate resilience.</p>

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An integrated framework for forecasting carbon decoupling pathways in the Beijing-Tianjin-Hebei logistics sector

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • J. Chen,
  • X. Gu,
  • H. Zhao

摘要

Accurate forecasting of emission pathways from key sectors, such as logistics, is crucial for climate mitigation in urban areas. Existing frameworks often overlook factor interdependencies and cannot directly forecast the state of decoupling between economic growth and emissions. This study develops an integrated framework that links the Generalized Divisia Index Method with a Particle Swarm Optimization-Back Propagation neural network for the Beijing-Tianjin-Hebei region. This is the first application of the Generalized Divisia Index Method to decompose logistics emissions in this region, overcoming the limitations of factor independence. The neural network directly predicts the decoupling elasticity index. Results from 2012 to 2022 show a precarious ‘weak decoupling’ state, mainly driven by energy consumption and increasingly offset by technological progress. Decomposition reveals significant regional differences: Beijing faces carbon lock-in in its aviation sector, Tianjin leads in structural decarbonization, and Hebei shows efficiency-driven volatility. Critically, forecasts indicate a lingering risk of weak decoupling and a potential slide into negative decoupling by 2027, highlighting the system’s vulnerability to economic fluctuations. This study presents a novel, replicable framework for forecasting decoupling risks, providing spatially differentiated insights for low-carbon logistics and climate resilience.