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<p>Anthropogenic nutrient discharges drive lake eutrophication and can consequently enhance greenhouse gas (GHG) emissions, amplifying climate change. Managing nutrient levels can substantially reduce GHG emissions from lakes, yet the climate benefits and the cost-effectiveness of managing diverse nutrient sources remain insufficiently quantified. To address this limitation, we develop a machine learning-based integrated assessment framework that synthesizes multisource datasets across China, encompassing lake GHG fluxes, trophic status, morphometric parameters, temperature, hydrological conditions and basin-scale anthropogenic nutrient discharges. Here we show that, compared with a high-discharge trajectory, a strategically managed reduction in anthropogenic nutrient loads could lower cumulative GHG emissions by 251–307 TgCO<sub>2</sub> equivalents between 2021 and 2100 under a moderate warming scenario, which is equivalent to avoiding global climate damages valued at US$32–50.1 billion (2020 US$, discounted at 1.5%). A cost–benefit analysis indicates that controlling nutrient discharges from industrial sources is the most cost-effective. This study highlights the necessity of integrating lake nutrient management into global climate strategies and provides a science-based tool for policymakers to optimize both eutrophication control and GHG reduction.</p>

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Climate benefits of lake nutrient management in China

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Feng Zhao,
  • Qirui Wang,
  • Zhao Huang,
  • Wei Zhi,
  • Jiawen Xie,
  • Guangshuo Chai,
  • Rongkun Liu,
  • Xiaoyu Cui,
  • Yindong Tong

摘要

Anthropogenic nutrient discharges drive lake eutrophication and can consequently enhance greenhouse gas (GHG) emissions, amplifying climate change. Managing nutrient levels can substantially reduce GHG emissions from lakes, yet the climate benefits and the cost-effectiveness of managing diverse nutrient sources remain insufficiently quantified. To address this limitation, we develop a machine learning-based integrated assessment framework that synthesizes multisource datasets across China, encompassing lake GHG fluxes, trophic status, morphometric parameters, temperature, hydrological conditions and basin-scale anthropogenic nutrient discharges. Here we show that, compared with a high-discharge trajectory, a strategically managed reduction in anthropogenic nutrient loads could lower cumulative GHG emissions by 251–307 TgCO2 equivalents between 2021 and 2100 under a moderate warming scenario, which is equivalent to avoiding global climate damages valued at US$32–50.1 billion (2020 US$, discounted at 1.5%). A cost–benefit analysis indicates that controlling nutrient discharges from industrial sources is the most cost-effective. This study highlights the necessity of integrating lake nutrient management into global climate strategies and provides a science-based tool for policymakers to optimize both eutrophication control and GHG reduction.