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<p>Land use and land cover change (LUCC) intensifies the conflict between regional economic development and ecological protection, driving challenges such as increased carbon emissions and non-point source pollution from nitrogen and phosphorus. Achieving synergistic advancement of economic growth and emission reduction (SEGER) has thus become a critical but inadequately addressed objective in sustainable land use planning. To bridge these gaps, this study develops a novel integrated land use optimization framework specifically targeting the SEGER objective. Its core contribution lies in the coupling of a modified multi-objective optimization algorithm, decision-making method for plans, and a spatially explicit simulation model to reconcile economic and environmental trade-offs. The framework first proposes a Modified Reference Vector Guided Evolutionary Algorithm (M-RVEA), enhancing population initialization and selection mechanisms to better solve high-dimensional problems. The M-RVEA is then applied to optimize the land use structure for SEGER, generating a Pareto front. Subsequently, the Entropy Weight-Technique for Order Preference by Similarity to Ideal Solution (EW-TOPSIS) method impartially selects the optimal allocation scheme, which is finally translated into a spatially explicit 2030 land use pattern via the Cellular Automata-Markov (CA-Markov) model. Applied to the Dongjiang River Basin in southern China, this study compares four scenarios: 2020 baseline, 2030 natural development (ND), 2030 RVEA, and 2030&#xa0;M-RVEA. M-RVEA demonstrates superior convergence and diversity on 3-/4-dimensional test function compared to NSGA-II/III and traditional RVEA. The optimized 2030&#xa0;M-RVEA scenario, compared to 2030 ND scenario, significantly reduces net carbon emissions by 4.39 × 10⁷ tC, nitrogen emissions by 1.67 × 10⁶ kg, and phosphorus emissions by 2.69 × 10⁵ kg, while maintaining economic growth. This is achieved through a strategic reallocation: increasing forest and construction land at the expense of some cultivated and grassland. The results confirm the framework’s effectiveness in generating scientifically-grounded, spatially explicit pathways for synergistic land use management, providing an actionable tool for policymakers aiming to achieve regional sustainable development goals.</p>

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Land use optimization for synergistic economic growth and nitrogen, phosphorus as well as net carbon emissions reduction: a case study of the Dongjiang River Basin

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Yuyin Yang,
  • Yanhu He,
  • Qian Tan,
  • Xiaohong Chen

摘要

Land use and land cover change (LUCC) intensifies the conflict between regional economic development and ecological protection, driving challenges such as increased carbon emissions and non-point source pollution from nitrogen and phosphorus. Achieving synergistic advancement of economic growth and emission reduction (SEGER) has thus become a critical but inadequately addressed objective in sustainable land use planning. To bridge these gaps, this study develops a novel integrated land use optimization framework specifically targeting the SEGER objective. Its core contribution lies in the coupling of a modified multi-objective optimization algorithm, decision-making method for plans, and a spatially explicit simulation model to reconcile economic and environmental trade-offs. The framework first proposes a Modified Reference Vector Guided Evolutionary Algorithm (M-RVEA), enhancing population initialization and selection mechanisms to better solve high-dimensional problems. The M-RVEA is then applied to optimize the land use structure for SEGER, generating a Pareto front. Subsequently, the Entropy Weight-Technique for Order Preference by Similarity to Ideal Solution (EW-TOPSIS) method impartially selects the optimal allocation scheme, which is finally translated into a spatially explicit 2030 land use pattern via the Cellular Automata-Markov (CA-Markov) model. Applied to the Dongjiang River Basin in southern China, this study compares four scenarios: 2020 baseline, 2030 natural development (ND), 2030 RVEA, and 2030 M-RVEA. M-RVEA demonstrates superior convergence and diversity on 3-/4-dimensional test function compared to NSGA-II/III and traditional RVEA. The optimized 2030 M-RVEA scenario, compared to 2030 ND scenario, significantly reduces net carbon emissions by 4.39 × 10⁷ tC, nitrogen emissions by 1.67 × 10⁶ kg, and phosphorus emissions by 2.69 × 10⁵ kg, while maintaining economic growth. This is achieved through a strategic reallocation: increasing forest and construction land at the expense of some cultivated and grassland. The results confirm the framework’s effectiveness in generating scientifically-grounded, spatially explicit pathways for synergistic land use management, providing an actionable tool for policymakers aiming to achieve regional sustainable development goals.