Accounting for interactions between Sustainable Development Goals is essential for water pollution control in China

The Sustainable Development Goals are interconnected and actions toward one goal can create synergies or tradeoffs with others. In China, nutrient-driven water pollution adversely affects human health and ecosystems in rivers and coastal waters, challenging the achievement of SDG 6 (Clean Water and Sanitation) and SDG 14 (Life Below Water). The study aims to identify and assess interactions between SDGs relevant to nutrient pollution and to explore future scenarios that promote synergies and mitigate tradeoffs, focusing on agriculture, sewage, food consumption, and climate mitigation. The overarching research question is how accounting for SDG interactions can improve the effectiveness of water pollution control in China while supporting broader sustainable development objectives.

Prior studies document rising nutrient pollution globally and in China, with significant societal and ecological impacts. Between 2000 and 2012, 28% of Chinese groundwater samples exceeded the WHO nitrate standard (10 mg N L−1). In 2010, 36% of river sections and 40% of major lakes in China failed to meet drinking water source quality criteria. From 2006 to 2012, around 500 harmful algal bloom events occurred in China’s coastal seas. Socioeconomic development and climate change are expected to exacerbate nutrient pollution. China is off track on many SDGs due to rapid population growth, slow progress toward sustainable production and consumption, and COVID-19 impacts. While frameworks exist to map SDG interactions, understanding remains limited, especially in the context of water quality. Previous modeling work (e.g., MARINA 1.0/2.0) has quantified nutrient fluxes to Chinese seas and highlighted challenges of controlling eutrophication under global change, motivating integrated assessments of SDG interactions.

The analysis proceeded in several steps. 1) Identification of SDG targets relevant to nutrient pollution: Using literature review and expert judgment, the authors identified 51 targets across all 17 SDGs as relevant to nutrient pollution in Chinese rivers and coastal waters, classifying them by relevance (high, moderate, low) based on whether they address direct sources, impacts/resilience, or indirect technological/social/economic interventions. 2) Assessment of interactions: Using the seven-point-scale framework of Griggs et al. (−3 cancelling to +3 indivisible), the team scored synergies (positive) and tradeoffs (negative) between targets of SDG 6 and 14 and other SDGs, noting directionality (unidirectional vs bidirectional). Explanations for interactions are provided in supplementary materials. 3) Scenario development: One baseline (SSP5-RCP8.5 for 2050) and five alternative scenarios were developed to reduce water pollution while leveraging synergies and avoiding tradeoffs among SDGs 6, 14, 2 (agriculture), 11 (cities), 12 (consumption/production), and 13 (climate). Scenarios: SE (all wastewater connected; N removal 80%, P removal 90%); AG (balanced fertilization; improved animal feeding/genetics; all manure treated/recycled; 12% lower N and P excretion than baseline); AG+SE (combination of AG and SE); AG+SE+SFC (Chinese dietary guidelines, 20% less food waste, 20% lower crop and animal production requirements, improved manure redistribution/treatment, 50% lower atmospheric N deposition vs baseline); AG+SE+SFC+CLI (adds climate mitigation consistent with RCP2.6 affecting hydrology to reduce nutrient export). 4) Modeling: The MARINA 2.0 model quantified TDN and TDP inputs to rivers and exports to seas for six major Chinese basins (Liao, Hai, Yellow, Huai, Yangtze, Pearl), resolving DIN, DON, DIP, DOP, and accounting for diffuse and point sources and in-river retention. Indicators: For SDG 6, compliance with China’s grade III standard was assessed using modeled DIN and TDP concentrations at subbasin outlets relative to thresholds (NH3 proxy via DIN <1.0 mg N/L; TP proxy via TDP <0.2 mg P/L). For SDG 14, the Indicator for Coastal Eutrophication Potential (ICEP) was calculated from modeled N, P, and Si loads using Redfield ratios. 5) Model evaluation and sensitivity analysis: MARINA 2.0 performance showed R2=0.85, NSE=0.72, RSR=0.53. Sensitivity analysis varied 12 key inputs by ±10% under the AG+SE+SFC+CLI scenario; outputs were most sensitive to sewage connection rates, treatment removal fractions, and animal manure nutrient inputs, underscoring their importance for policy effectiveness.

- Relevance mapping: 51 SDG targets across all 17 SDGs are relevant to nutrient pollution; SDG 6 and SDG 14 are central, with SDGs 2, 11, 12, and 13 strongly related via drivers (agriculture, urbanization, consumption, climate). - Interactions inventory: 319 interactions between SDGs 6 and 14 and other SDGs were identified: 286 synergies and 33 tradeoffs. Among SDGs 2, 11, 12, 13, there were 82 synergies and 22 tradeoffs with SDGs 6/14. Additional SDGs provided 194 synergies and 10 tradeoffs. Tradeoffs often arise between improving water quality and expanding agricultural production (SDG 2) or urban services (SDG 11); SDG 12 (responsible consumption/production) can counterbalance these via efficiency and waste reduction. - Baseline pollution levels (2050, SSP5-RCP8.5): Total inputs to rivers were projected at 18.6 Tg TDN and 1.3 Tg TDP; exports to seas at 4.3 Tg TDN and 0.4 Tg TDP. Major sources include direct manure discharges, synthetic fertilizers, and human waste (treated/untreated). - Scenario outcomes: The AG+SE+SFC+CLI scenario yielded the lowest pollution levels. Relative to baseline, TDN and TDP inputs to rivers decreased by 64% and 90%, respectively; river exports decreased by 68% (TDN) and 91% (TDP). - SDG 6 indicator: In 2012, 23 subbasin outlets of six rivers exceeded grade III standards for either DIN or TDP. Under SSP5-RCP8.5 in 2050, many subbasins remain noncompliant. Under AG+SE+SFC+CLI, all outlets meet grade III for TDP, and seven outlets meet grade III for DIN; reducing nitrogen pollution remains more challenging than phosphorus due to additional N sources and treatment constraints. - SDG 14 indicator (ICEP): Positive ICEP values in 2012 and in 2050 baseline indicate high coastal eutrophication potential at all river mouths (e.g., in 2050 baseline, ICEP ranges roughly from 2 to 45 kg C-eq km−2 day−1 across rivers). Alternative scenarios lower ICEP; AG+SE+SFC+CLI produces negative ICEP for Huai, Yangtze, and Pearl (low eutrophication potential), while Liao, Hai, and Yellow (to Bohai Gulf) may retain positive ICEP. - Co-benefits: Measures improving agriculture nutrient use efficiency, sewage treatment, sustainable diets/waste reduction, and climate mitigation simultaneously advance SDGs 6 and 14 and support SDGs 2, 11, 12, and 13. Additional benefits include reduced atmospheric N deposition and potential biodiversity gains (SDG 15), and improved health (SDG 3) and poverty reduction via better environmental quality (SDG 1).

Accounting for SDG interactions is crucial for designing effective water pollution control strategies. The seven-point-scale scoring illuminated where synergies can be harnessed (e.g., SDG 12 efficiency and waste reduction reinforcing SDGs 6 and 14) and where tradeoffs should be avoided or mitigated (e.g., between agricultural expansion and water quality). Quantitative scenario analysis with MARINA 2.0 demonstrated that combining improved nutrient management in agriculture, universal high-efficiency sewage treatment, sustainable food consumption patterns, and climate mitigation can substantially reduce riverine nutrient inputs and coastal eutrophication potential by mid-century. The findings emphasize policy coherence: interventions focused on a single sector risk shifting pollution between media or regions (e.g., relocating livestock can reduce local water pollution but increase air emissions and biodiversity risks elsewhere). The study also highlights the importance of appropriate indicators for SDGs 6 and 14; reliance solely on NH3 or annual ICEP may miss other nutrient forms or seasonal dynamics. While many interactions are broadly applicable, the direction and strength of some are context-specific to China’s current agricultural practices, urbanization, and policy landscape. Overall, integrating SDG interactions into planning can help achieve multiple goals simultaneously and avoid unintended consequences.

This work systematically identifies and quantifies interactions among SDGs relevant to nutrient pollution in China and demonstrates, via scenario modeling, that policies designed to leverage synergies and avoid tradeoffs can markedly improve river and coastal water quality while advancing multiple SDGs. The combined scenario (AG+SE+SFC+CLI) is most effective, suggesting that coordinated action across agriculture, wastewater management, consumption patterns, and climate mitigation is needed to meet SDGs 6 and 14 and deliver co-benefits for SDGs 2, 11, 12, 13, and others. Future research should develop integrated modeling frameworks spanning food–water–energy–economy systems to quantify multi-SDG outcomes under consistent assumptions, refine context-appropriate indicators (including broader nutrient forms and seasonal dynamics), and evaluate the feasibility and equity implications of proposed measures across regions and sectors.

- Interaction scoring involves expert judgment and interpretation; results are transparent but inherently subjective and may evolve with new evidence or differ by assessor. - Many interaction scores are context-specific to China’s conditions; they may differ in other regions and require localization. - Modeling uncertainties stem from assumptions about future socioeconomic pathways, technology adoption, and implementation to full technical potential. - Sensitivity analysis indicates notable sensitivity to sewage connection rates, treatment removal efficiencies, and manure nutrient inputs, highlighting potential implementation risks. - Indicators used have constraints: Chinese grade III focuses on NH3 and TP (approximated here by DIN and TDP), potentially overlooking other nutrient forms; ICEP is annual and does not capture seasonality, so negative values do not guarantee absence of harmful algal blooms. - The scenario co-benefits for non-water SDGs were assessed conceptually based on assumptions rather than through a fully integrated multi-sector modeling framework.