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<p>Land-use change is a decisive factor in climate mitigation, yet remains less examined than economic and energy drivers; this gap, amplified by China’s rapid urbanization, highlights the need to integrate land-use dynamics and uncertainty into emission forecasting, often overlooked by traditional models. This study confronts these gaps with an integrated framework to forecast greenhouse gas emissions by land-use type for the Airport New City in Xixian New Area, China (2021–2035). Land-use scenarios include four model-based pathways using Markov Chain and Goal Programming, plus one pathway from regulatory planning; these are combined with four socioeconomic pathways to form 20 scenarios. A Stochastic Impacts by Regression on Population, Affluence, and Technology with ridge regression (STIRPAT-RR) model linked socioeconomic and land-use factors to emissions, while Monte Carlo simulations quantified probabilistic peak ranges and Random Forest-based Global Sensitivity Analysis identified key drivers. Results show expansion-oriented pathways delay and elevate the emissions peak, while eco- and balance-oriented ones achieve earlier and lower peaks. The earliest peak occurs in 2028.41 ± 2.12 with 352.60 ± 6.20 10,000 tCO<sub>2</sub>e (90% CI), whereas under expansion-oriented pathway, the peak is delayed by 3.68 ± 2.65 years and increases by 64.96%. Agricultural and industrial lands face the highest forecast risks, whereas residential, commercial, and airport lands remain more stable. Economic growth is the dominant long-term driver, with energy intensity gradually gaining importance. These findings highlight land-use allocation as a decisive lever shaping emission trajectories, guiding differentiated and risk-informed low-carbon strategies.</p>

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Integrating land-use change into probabilistic forecasting and sensitivity analysis of urban greenhouse gas emissions

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Wanchen Liu,
  • Zidong Zhang,
  • Long Shi,
  • Hongxing Li,
  • Yafeng Li,
  • Lei Huang,
  • Wufangmin Liu,
  • Chunlei Du,
  • Tianye Liu,
  • Minoru Fujii,
  • Lu Sun

摘要

Land-use change is a decisive factor in climate mitigation, yet remains less examined than economic and energy drivers; this gap, amplified by China’s rapid urbanization, highlights the need to integrate land-use dynamics and uncertainty into emission forecasting, often overlooked by traditional models. This study confronts these gaps with an integrated framework to forecast greenhouse gas emissions by land-use type for the Airport New City in Xixian New Area, China (2021–2035). Land-use scenarios include four model-based pathways using Markov Chain and Goal Programming, plus one pathway from regulatory planning; these are combined with four socioeconomic pathways to form 20 scenarios. A Stochastic Impacts by Regression on Population, Affluence, and Technology with ridge regression (STIRPAT-RR) model linked socioeconomic and land-use factors to emissions, while Monte Carlo simulations quantified probabilistic peak ranges and Random Forest-based Global Sensitivity Analysis identified key drivers. Results show expansion-oriented pathways delay and elevate the emissions peak, while eco- and balance-oriented ones achieve earlier and lower peaks. The earliest peak occurs in 2028.41 ± 2.12 with 352.60 ± 6.20 10,000 tCO2e (90% CI), whereas under expansion-oriented pathway, the peak is delayed by 3.68 ± 2.65 years and increases by 64.96%. Agricultural and industrial lands face the highest forecast risks, whereas residential, commercial, and airport lands remain more stable. Economic growth is the dominant long-term driver, with energy intensity gradually gaining importance. These findings highlight land-use allocation as a decisive lever shaping emission trajectories, guiding differentiated and risk-informed low-carbon strategies.