Cost-effective strategies can reduce water and energy requirements in China's wastewater treatment by 2035.

Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Nat Commun . 2026 Mar 3;17:3390. doi: 10.1038/s41467-026-70159-y Search in PMC Search in PubMed View in NLM Catalog Add to search Cost-effective strategies can reduce water and energy requirements in China’s wastewater treatment by 2035 Siqi Han Siqi Han 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China Find articles by Siqi Han 1, 2 , Edward R Jones Edward R Jones 3 Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands Find articles by Edward R Jones 3 , Tuo Yin Tuo Yin 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China Find articles by Tuo Yin 1, 2 , Weijie Chen Weijie Chen 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China Find articles by Weijie Chen 1, 2 , Yanhong Wu Yanhong Wu 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China Find articles by Yanhong Wu 1 , En Xie En Xie 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China Find articles by En Xie 1, 2 , Lei Li Lei Li 4 School of Environment and Civil Engineering, Dongguan University of Technology, Guangdong, China Find articles by Lei Li 4 , Yang Xiao Yang Xiao 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China Find articles by Yang Xiao 1 , Jiading Zhang Jiading Zhang 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China Find articles by Jiading Zhang 1 , Xiuzhi Chen Xiuzhi Chen 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 5 Yellow River Research Institute, North China University of Water Resources and Electric Power, Zhengzhou, China Find articles by Xiuzhi Chen 1, 5 , Xiaokaitijiang Kasmu Xiaokaitijiang Kasmu 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 6 Xinjiang Uygur Autonomous Region Water Conservancy Management Station, Xinjiang, China Find articles by Xiaokaitijiang Kasmu 1, 6 , Qiang Zheng Qiang Zheng 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China Find articles by Qiang Zheng 1, 2 , Bo Zhou Bo Zhou 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China Find articles by Bo Zhou 1, 2, ✉ , Yunkai Li Yunkai Li 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China Find articles by Yunkai Li 1, 2, ✉ Author information Article notes Copyright and License information 1 State Key Laboratory of Efficient Utilization of Agricultural Water Resources, College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China 2 Engineering Research Center for Agricultural Water-Saving and Water Resources, Ministry of Education, Beijing, China 3 Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands 4 School of Environment and Civil Engineering, Dongguan University of Technology, Guangdong, China 5 Yellow River Research Institute, North China University of Water Resources and Electric Power, Zhengzhou, China 6 Xinjiang Uygur Autonomous Region Water Conservancy Management Station, Xinjiang, China ✉ Corresponding author. Received 2025 Mar 21; Accepted 2026 Feb 18; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13065982  PMID: 41775719 Abstract Wastewater treatment is a key component in ensuring future water resource security. However, this process itself faces major challenges in water and energy consumption. Reducing these inputs at low cost without compromising wastewater treatment effectiveness is crucial for sustainable development. Here, we assess the water and energy footprint of wastewater treatment in China, using estimated data from 10,124 urban wastewater treatment plants and 90 cases. We show that the water and energy footprints of wastewater treatment in China have nearly tripled from 2009 to 2022. By aligning treatment process selection through multi-objective trade-offs, reductions can be effectively achieved. By 2035, China’s wastewater treatment water footprint and energy footprint could be reduced by 16.1% and 25.6%, respectively, with investments below 8% of total treatment costs, while the removal remains stable. Our findings offer a broadly applicable framework to guide sustainable wastewater management and support progress toward the Sustainable Development Goals. Subject terms: Environmental impact, Socioeconomic scenarios, Sustainability, Hydrology, Water resources China’s growing wastewater sector uses substantial water and energy, straining limited resources. This study shows that optimizing treatment processes nationwide could cut 2035 water and energy footprints by up to 25% at modest extra cost. Introduction Achieving resource conservation requires collaboration across sectors to address challenges related to water and energy 1 , 2 . While the agricultural and manufacturing sectors play crucial roles in water and energy conservation, it is vital to acknowledge and explore the potential contributions of other sectors 3 – 5 . Wastewater treatment, while crucial for water resource recycling 6 and water quality protection 7 , often receives inadequate attention regarding its resource consumption 8 , 9 . Wastewater systems face rising economic and environmental pressures due to aging infrastructure, urbanization trends, emerging contaminants, and competing water demands 10 – 15 . Scientifically evaluating the resource consumption of wastewater treatment under different process application 16 strategies and trade-off multi-objective is imperative for advancing progress toward achieving Sustainable Development Goals (SDGs) 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), and 13 (Climate Action) 17 – 19 . Life cycle assessment (LCA) has been widely used to quantify the environmental impacts of human activities 20 – 23 . WWTPs consume water resources and energy starting from the construction phase of building materials input to the operation phase of materials input, electricity consumption, and to the final demolition phase (Fig. 1a–c ). However, most existing research remains limited to small-scale or plant-level assessments and does not reflect large-scale spatial patterns or regional diversity in treatment practices 24 – 26 . Furthermore, existing studies rarely integrate process-level resource modeling with nationwide empirical data to inform spatially explicit optimization strategies. Fig. 1. Research route and methods. Open in a new tab a – c Life cycle analysis of water and energy consumption in wastewater treatment. d Schematic representation of the research technical roadmap. e Spatial distribution of datasets and case studies of wastewater treatment plants across the country. The provincial boundary delineations were referenced from the China Standard Map Service ( http://bzdt.ch.mnr.gov.cn , GS (2024) 0650). Research on the water and energy pressures associated with regional water treatment remains limited, posing substantial challenges to the development of optimization strategies for wastewater treatment resource management. A comprehensive analysis of the water and energy footprints of continuously operating WWTPs is essential for evaluating water and energy impacts and assessing the resource-saving potential of a country or region’s water treatment processes. Balancing the best policies for water and energy consumption under multiple objectives of wastewater treatment in different regions is also of great importance for advancing sustainable wastewater management, particularly in regions with substantial disparities in infrastructure and environmental conditions 27 , 28 . While life cycle assessment (LCA) and multi-objective optimization have been increasingly applied to wastewater systems, most studies rely on limited case data and overlook the spatial heterogeneity of treatment practices. This study evaluates the water and energy footprints of major wastewater treatment processes through representative case analysis, and links them with the operational profiles of 10,124 urban WWTPs across China. By mapping process-level resource intensities onto region-specific process configurations, we assess the spatiotemporal dynamics of wastewater treatment resource use and identify opportunities for improvement (Fig. 1d, e ). Furthermore, we develop a framework that supports region-specific optimization strategies, accounting for local water quality, infrastructure, and economic conditions. This approach offers a scalable, transferable tool for sustainable, cost-effective wastewater management planning across diverse regional contexts. Results Water and energy footprint intensity of processes of wastewater treatment The water and energy footprints of the eight major processes nationwide were analyzed, covering more than 90% of the total installed wastewater treatment capacity. Figure 2 displays the water and energy footprint intensity of various wastewater treatment processes, while intensities for each treatment process case can be found in Supplementary Information S4 . As shown in Fig. 2a, b , the Biological Filter (BF) process exhibits the lowest mean footprint intensity, at 0.96 kg/m 3 and 0.18 kWh/m 3 , whereas the Continuous Activated Sludge Treatment (CAST) process demonstrates the highest mean footprint intensity at 1.91 kg/m 3 and 0.63 kWh/m 3 , with a difference of 65.9%. The water footprint intensity and energy footprint intensity of the CAST process vary widely across different wastewater treatment plants (WWTPs). The high variability of CAST footprint intensities arises from its widespread use across regions with diverse influent qualities, plant scales, and operational practices. Its modular and flexible design allows broad applicability, thereby leading to greater heterogeneity compared to more standardized processes like Anaerobic-Oxic Process (AO) or Anaerobic-Anoxic-Oxic (A 2 O) 29 . As a biological nitrogen and phosphorus removal process, the A 2 O process has water and energy footprints that are 21.1% and 35.5% higher, respectively, than those of the AO process. Fig. 2. The water footprint and energy footprint intensity of major wastewater treatment processes in China. Open in a new tab a , b Comparison of water and energy footprint intensity in different wastewater treatment processes, box plots show the median as the centre line, the box represents the interquartile range, whiskers extend to 1.5× the interquartile range, and points beyond the whiskers are plotted as outliers each data point represents an independent wastewater treatment plant (WWTP) case, which serves as the independent observational unit for statistical analysis. No technical replicates were used. Sample sizes ( n ) for each process type are reported in the legend. All statistics shown in the boxplots were derived from these independent WWTP cases. c , d Differences in the proportion of water and energy footprints across various stages of wastewater treatment processes. e Intensity of water and energy footprints in different stages of wastewater treatment processes. To further explore the resource performance of emerging technologies, we conducted a comparative life-cycle evaluation of four selected processes—Membrane Bioreactor (MBR), Biological Combined System (BIOCOS), Linpor, and Bardenpho. The results show that Linpor exhibited the lowest water (1.09 kg/m 3 ) and energy footprints (0.22 kWh/m 3 ), indicating its potential as a highly resource-efficient treatment option. BIOCOS also demonstrated relatively low energy consumption ( < 0.57 kWh/m 3 ). These findings provide empirical support for considering alternative technologies in future optimization pathways. Detailed data are provided in Tables S7 and S8 . The water and energy footprint of major processes varies across the construction, operation, and demolition phases of wastewater treatment. The water footprint during the construction and operation phases accounts for nearly 90% of the total, while the water footprint during the demolition phase is almost negligible (Fig. 2c-e ). The construction phase involves a substantial direct water resource input (0.61 kg/m 3 ), comprising 91.5% of the total water footprint for this phase, mainly stemming from on-site processes. This includes processes such as concrete curing and cleaning operations, which, despite being identified as dominant water use contributors in previous studies 30 – 32 , are often underrepresented in LAC inventories. During the operational phase, the water footprint is primarily driven by electricity consumption (0.63 kg/m 3 ), while the demolition phase contributes minimally. The energy footprint is dominated by the operational phase (0.47 kWh/m 3 ), accounting for 89.5% of the total life cycle energy footprint, with direct energy consumption being the main contributor. Spatial Distribution of Water and Energy Footprint Intensity in Wastewater Treatment across China The water and energy footprint intensities of wastewater treatment in China exhibit pronounced regional disparities (Fig. 3a, b ). Central China shows the highest water footprint intensity and energy footprint intensity, closely followed by East China. The predominant processes in these regions include A 2 O and OD. The water and energy footprint intensities in Northeast China (1.42 kg/m 3 and 0.37 kWh/m 3 , respectively) are 7% to 11% lower than those in Central China. At the provincial level, Inner Mongolia, Jiangxi, and Jiangsu exhibit relatively higher water and energy footprint intensities, while Heilongjiang has notably lower intensity, with reductions of approximately 15% and 22% in water and energy footprints, respectively, compared to Inner Mongolia. For spatial visualization of the annual variation in footprint intensity, please see Figs. S4 and S5 . Fig. 3. Spatial distribution and correlations with different factors of national wastewater treatment water footprint (WF) and energy footprint (EF) intensity. Open in a new tab a, b Spatial distribution of wastewater treatment WF and EF intensity at the prefecture-level city scale, calculated based on the proportion of wastewater treatment capacity attributed to different processes within each prefecture-level city. c Correlation analysis between water footprint intensity (WFI), energy footprint intensity (EFI), water footprint per capita (WFP), energy footprint per capita (EFP), and relevant economic, social, and environmental factors. The color scale represents the Spearman correlation coefficient, derived using two-sided Spearman’s rank correlation tests. Blue indicates a positive correlation, and red indicates a negative correlation. An asterisk (*) indicates statistical significance at p < 0.05. The prefecture-level administrative boundary delineations were referenced from the China Standard Map Service ( http://bzdt.ch.mnr.gov.cn ; map approval number GS (2024) 0650). The proportion of groundwater supply and water resources availability per capita are strongly correlated with the intensities of water and energy footprints, as well as the per capita water and energy footprints, highlighting their critical influence on resource utilization patterns (Fig. 3c ). This relationship may stem from increasing marginal pumping costs and stricter conservation policies in groundwater-dependent regions, which drive investment in more efficient treatment technologies and operational upgrades 33 , 34 .On the other hand, regions with higher per capita water availability tend to produce more diluted influent due to abundant domestic water use, resulting in lower pollutant concentrations and greater energy required per unit of pollutant removed. This leads to higher water and energy footprint intensities per unit volume 35 . Wastewater discharge-related factors, such as urban fecal clearance volume per capita and rural sanitary toilet penetration rate, are negatively correlated with water footprint intensity and energy footprint intensity. Systematic fecal waste management and treatment reduce the discharge of untreated pollutants into water bodies, decreasing the need for pollutant removal during wastewater treatment and, consequently, improving water and energy efficiency 36 . Notably, investment in urban reclaimed water utilities per capita shows a significant positive correlation with the energy footprint per capita but no significant correlation with the water footprint per capita. This can be attributed to the fact that such investments often focus on advanced post-treatment technologies (e.g., membrane filtration, UV disinfection), which substantially increase electricity consumption. However, these upgrades do not necessarily alter the amount of water used within the core treatment processes, leading to limited impact on process water consumption 37 . The relationship between per capita water and energy footprints shows a strong correlation. However, there is no significant correlation between footprint intensity and per capita footprint. Spatial and temporal dynamics of water and energy footprint of wastewater treatment in China The total water and energy footprint for wastewater treatment in various provinces exhibited a continuous upward trend (Fig. 4 a, b). The total water and energy footprints increased by 1.6 times from 360.8 × 10 8 kg and 97.5 × 10 8 kWh in 2009 to 928.5 × 10 8 kg and 253.5 × 10 8 kWh in 2022. The A 2 O process accounts for the largest share of the water and energy footprints, rising from 28% in 2009 to 41% in 2022. The OD process, which holds the second-highest share, shows a slight decline over the years. The water and energy footprint intensity and total amount of each province are displayed in Supplementary Information S5 . This upward trend was primarily driven by multiple factors. First, the tightening of national discharge standards since the 12th Five-Year Plan has incentivized the widespread adoption of advanced treatment processes such as A 2 O, which achieve higher pollutant removal at the cost of greater resource input. Second, rapid urbanization has expanded wastewater collection coverage and increased total influent volumes, thereby amplifying total water and energy use. Fig. 4. Temporal and spatial dynamics of national wastewater treatment water and energy footprints. Open in a new tab a , b Evolution of the total water and energy footprints of wastewater treatment in China, illustrated as stacked graphs representing major processes. c – f Spatial distribution of the total water and energy footprints of wastewater treatment in 2009 and 2022. The provincial boundary delineations were referenced from the China Standard Map Service ( http://bzdt.ch.mnr.gov.cn , GS (2024) 0650). The total water and energy footprints of wastewater treatment in Chinese provinces increased markedly, with over half of the provinces experiencing growth of more than double (Fig. 4c–f ). For spatial visualization of the annual variation in water and energy footprints, please see Figs. S6 and S7 . Guizhou exhibited the largest relative increase, with both the water and energy footprints more than tripling. Coastal provinces showed notably high water and energy footprint totals and growth rates. This is mainly due to rapid economic development and population density, which substantially increases the demand for wastewater treatment. From 2009 to 2022, Guangdong ranked first in absolute increases in water and energy footprints, followed by Jiangsu, Shandong, and Zhejiang, each with substantial growth approaching twice their previous levels. For the time-varying water and energy environmental footprints of the top five provinces ranked by wastewater treatment volume, please see Figs. S8 a–e and S9a–e. To better understand the driving forces behind these spatio-temporal trends, we further analyzed the socioeconomic and technological factors influencing total footprints over time. We applied the factor decomposition method to analyze the contribution factors to China wastewater treatment total water footprint and energy footprint (Fig. S10a, b ). Wastewater treatment rates and per capita GDP positively influence increases in water and energy footprints, whereas water intensity per unit of GDP negatively influences them. Among these factors, the reduction in water intensity per unit of GDP emerged as the dominant contributor to footprint reduction. Over time, the role of the wastewater treatment rate in driving footprint increases has grown, reflecting rising public awareness of wastewater treatment and water resource protection. Notably, before 2015, the wastewater generation rate positively contributed to footprint growth, but this trend reversed after 2015. The shift in the contribution of the wastewater generation rate can be attributed to the gradual implementation of policy measures, technological improvements, and economic restructuring. Successive regulations have promoted water conservation, stricter discharge limits, and wastewater reuse 38 – 40 . Meanwhile, technological upgrades in industrial production and municipal infrastructure have reduced per capita wastewater generation 41 , 42 . In addition, the ongoing transition of China’s economy from heavy industry to service-oriented and high value-added sectors has further contributed to declining wastewater generation intensity 42 , 43 . Collectively, these factors explain the shift from a positive to a more constrained or even negative contribution in recent years. Changes in water and energy footprints were primarily driven by wastewater generation and treatment rates, with minimal impact from footprint intensities. Economic development and increased production and consumption demand make the growth in wastewater generation and treatment rates inevitable 10 . Therefore, optimizing technologies to enhance resource efficiency and unlock resource-saving potential is critical. Multi objective trade-off of China’s wastewater treatment The selection of wastewater treatment processes with varying objectives can lead to either synergistic or trade-off effects. This study considers water footprint, energy footprint, cost, nitrogen and phosphorus removal rates, analyzing these dynamics under five scenarios with two gradients (Fig. 5a, b ). The analysis highlights conflicts between different goals. Scenarios targeting the reduction of water and energy footprints align closely, with minimal trade-offs. For instance, the scenario with the lowest water footprint also achieves a minimized energy footprint, while maintaining nitrogen and phosphorus removal efficiencies within a narrow range of variation compared to optimal removal scenarios. For a comparison of absolute values in the scenario analysis, please see Supplementary Information S7 (Fig. S12a–e ) and Supplementary Data 1 . Fig. 5. Changes of water footprint (WF), energy footprint (EF), total phosphorus removal rate (TP), total nitrogen removal rate (TN), and cost in wastewater treatment in China under different target-oriented scenarios at the national and provincial scales. Open in a new tab a national scale; b provincial scales. Based on scenario analysis targeting various objectives, the study identifies five major objectives and two gradients, which are cost reduction (S1 and S2), improve TP removal rate (S3 and S4), improve TN removal rate (S5 and S6), reduce water footprint (S7 and S8) and reduce energy footprint (S9 and S10) aiming to restructure wastewater treatment processes by 2035. The restructuring strategy retains three processes and incorporates an additional process depending on the specific objective. The BAU (Business-As-Usual) scenario represents a set of baseline target parameters derived from the current situation. The five objectives include cost minimization, maximum total phosphorus (TP) removal rate, maximum total nitrogen (TN) removal rate, minimum water footprint, and minimum energy footprint. These objectives are used to evaluate synergies and trade-offs among different targets. Detailed absolute values under the scenario settings are provided in Supplementary Information Fig. S12 . The S8 scenario (water footprint reduction scenario) reduces costs by over 10% compared to the business-as-usual (BAU) scenario, but remains approximately 12% higher than the lowest-cost scenario (S2). Scenarios focused on improving nitrogen and phosphorus removal rates (S3-S6) exhibit water and energy footprints that deviate by 7%-18% and 13%-30%, respectively, from those of two water footprint reduction scenarios, S7 and S8. These scenarios incur the highest costs, averaging about 10% more than the BAU scenario. Notably, the highest nitrogen removal scenario (S6) has costs that exceed the BAU scenario by 17%, and the lowest-cost scenario (S2) by 47%. In contrast, the most economical scenarios (S1 and S2) achieve nitrogen and phosphorus removal rates approximately 10% lower than those of the optimal scenarios. Multi objective optimization of wastewater treatment in China in 2035 By 2035, China aims to establish a systematic, safe, environmentally friendly, and economical framework for wastewater resource utilization. The wastewater treatment capacity of each province in 2035 is predicted based on population changes, and the water and energy footprints, costs, and nitrogen and phosphorus removal rates for future sewage treatment are defined under BAU scenarios, considering the latest process composition in this research. Based on the characteristics of wastewater treatment across different regions in China (Supplementary Information S8 ), various process combinations were identified to evaluate changes in water and energy footprints, costs, and nitrogen-phosphorus removal rates under restructuring scenarios (Nt 1 and Nt 2 ). Compared to the BAU scenario (Fig. 6a–e ), both Nt 1 and Nt 2 scenarios reduce water and energy footprints, with stable TN removal rates and slight improvements in TP removal rates. Under the Nt 2 scenario, water and energy footprints decreased by 16% and 26%, respectively, while costs increased moderately by 6%, and TP removal rates improved by approximately 2%. Fig. 6. Scenario of optimizing wastewater treatment process strategy. Open in a new tab a – e Simulation of total cost, water footprint (WF), energy footprint (EF), total phosphorus (TP) removal rate, and total nitrogen (TN) removal rate for wastewater treatment under the 2035 baseline scenario (BAU) and optimization scenarios (Nt 1 , Nt 2 ). f – j Comparison of the above five elements of wastewater treatment under different scenarios across major provinces in China, collectively accounting for over 90% of the country’s total wastewater treatment capacity. During the optimization process, the southeastern coastal and central provinces (such as Guangdong, Henan, and Hunan) have experienced significant cost increases, but TN and TP removal rates have also substantially improved, which is positive from an environmental governance perspective. Meanwhile, the water footprint and energy footprint of these regions have shown an overall downward trend, with the energy footprint of Guangdong decreasing by 16.11% and the water footprint of Hunan decreasing by 7.96%. Overall, these provinces have achieved dual benefits: improved environmental quality and reduced resource consumption through economic investment. These findings can provide guidance for water and energy conservation, as well as the sustainable development of the wastewater treatment industry in China. Discussion The footprint calculation of the wastewater treatment lifecycle encompasses various stages, and data collection at each stage often involves uncertainties. This can lead to errors in the calculation process, impacting the accuracy of our analysis. This research uses the Data Quality Indicator (DQI) standards to evaluate the data quality of parameters at each stage of wastewater treatment based on five key aspects, with scoring conducted according to the DQI allocation table (Tables S9 and S10 ). Using the original data quality matrix table, the water and energy footprint results of each stage are simulated to determine the distribution type and obtain the uncertainty (U 0 ) of the original data under this distribution type. If U 0 < 10%, the data is deemed to meet the standards (Supplementary Information S6 ). The probability distribution and range values for data at each stage were identified and a Monte Carlo simulation with 50,000 samples and a 95% confidence interval was conducted. Figure S11 (Supplementary Information S6 ) shows that the 95% confidence intervals for the water footprint and energy footprint of wastewater treatment are (0.5960, 2.3698) kg/m 3 and (0.0018, 0.8089) kWh/m 3 , respectively. Both results follow an approximately normal distribution. Uncertainty analysis confirms that the LCA footprint assessment results are accurate and reliable. The reported values are comparable to or slightly higher than those in prior studies, depending on the system boundary and geographic context. For instance, Gu et al. 44 and Singh et al. 45 reported operational energy consumption of 0.3–0.6 kWh/m 3 , which aligns with our CAST and BF ranges. However, unlike those studies, our analysis adopts a full life-cycle perspective, similar to Morera et al. 46 and Rahman et al. 47 , which accounts for construction-phase impacts. The inclusion of construction water use and energy inputs, which are often overlooked, partly explains the higher values observed in certain processes. Moreover, spatial variation in process deployment, plant scale, and regional infrastructure conditions across China contributes to footprint diversity 48 . The water footprint of wastewater treatment mainly stems from direct water consumption during construction. In life cycle water footprint studies within the construction industry, nearly 70% of the water footprint stems from direct water use 32 . Indirect water footprints from electricity consumption constitute a notable portion during the operational phase, consistent with previous research findings. The energy footprint of wastewater treatment during construction primarily stems from direct energy use. Electricity is mainly consumed for wastewater purification, sludge recirculation, aeration and mixing for biological treatment, sludge stabilization and treatment, and operation of specialized mechanical equipment 49 . Notably, over 70% of energy consumption is attributed to biological wastewater treatment. Both the CAST and CASS processes are cyclic activated sludge systems, but they differ in specific operational aspects. The CASS process features a continuous influent system and lacks electromagnetic valve control components in the influent pipeline, resulting in less effective nitrogen and phosphorus removal than the CAST process. In contrast, the CAST process uses an intermittent influent process, typically requiring two or more tanks to be used alternately, which increases the complexity of the control system. This complexity contributes to its higher operational energy consumption 50 . The rapid urbanization and industrialization in China have led to a continuous increase in wastewater discharge. Statistics show that the volume of urban wastewater treated in China grew from 16.3 billion cubic meters in 2004 to 61.2 billion cubic meters in 2021, nearly a fourfold increase. The wastewater treatment rate has risen from 45% to 97%. The construction and operation of numerous wastewater treatment facilities have substantially mitigated the extensive environmental pollution caused by wastewater in China. However, the construction and operation of wastewater treatment plants also entail resource consumption. From the direct input of water and energy during construction, the indirect water and energy input of materials, to the production and transportation of materials, wastewater treatment (aeration, mixing, and transportation), and finally the effluent process during the operation phase, water and energy consumption are involved throughout. Green economic development has always been a key part of long-term development planning in China 51 . The recently released 2035 plan outlines specific requirements for wastewater treatment. The plan includes achieving full coverage of urban wastewater networks, implementing differentiated and precise upgrades to treatment standards, promoting centralized, harmless sludge incineration, and achieving a 90% harmless disposal rate for urban sludge. The water resources situation in China is relatively tight, water resources distribution is uneven, imbalances between supply and demand can be prominent, and the problem of water pollution is serious 52 , 53 . In response, the government has implemented stringent water resource management measures to protect and rationally utilize water resources. These measures include setting water usage quotas for various industries to regulate and assess their consumption, which impose strict limits on water utilization across agriculture, industry, domestic life, and the service sector 54 . Addressing this requires reducing water intensity across all industries, abandoning inefficient practices, and improving water-use efficiency. Maximizing output per unit of water will better protect and utilize resources, ensuring future water supply and sustainable national economic and social development 55 . This can help better protect and utilize resources, contributing to a more secure water supply and supporting sustainable national economic and social development. Reusing wastewater has been shown to be a promising approach for alleviating water scarcity in China 56 . Achieving SDG6 (Clean water and sanitation) requires establishing a comprehensive wastewater treatment system that conserves energy, reduces emissions, and meets discharge quality standards 57 . The uneven development across China's regions necessitates the adoption of localized wastewater treatment strategies. Scenario analysis enabled the estimation of potential water and energy footprint savings and changes in other key indicators under various process choices. Our findings suggest that different regions should adopt diverse approaches to address their unique challenges and opportunities. Coastal regions of China, such as Guangdong and Tianjin, have substantial wastewater treatment volumes, resulting in a large water and energy footprint. Given the high level of economic development in these areas, a marked reduction in wastewater generation is unlikely in the near future. From a policy perspective, adopting targeted wastewater treatment processes in these regions is likely a more realistic approach to reducing water and energy consumption, with more pronounced expected effects. For instance, Hainan and Shandong provinces could reduce water consumption by over 36% and energy consumption by over 56%, northwestern regions like Inner Mongolia and Shaanxi have greater potential for technological upgrades to achieve water recycling and wastewater reduction as economic development progresses. Given the marked variation in wastewater quality across regions, it is imperative to select the most suitable treatment processes that balance water and energy footprints with economic costs. Additionally, efforts should be strengthened to optimize the water and energy efficiency of the wastewater treatment processes themselves 58 . To support the provincial scenario optimization, we conducted a comparative assessment of the operational costs associated with mainstream wastewater treatment technologies based on a comprehensive literature review. Domestic studies indicate that processes such as CASS and SBR tend to incur relatively higher operational costs, while others like oxidation ditch and AO are generally more cost-effective 59 , 60 . International comparisons further highlight regional differences—for example, Qi et al. 61 (2020) reported that the operational costs of AO and A 2 O systems in the United States are approximately $0.11/m 3 and $0.13/m 3 , respectively, reflecting variations in energy prices and labor costs. These cost data were used to parameterize the economic assumptions in our scenario modeling, with final values averaged across multiple sources to account for variability in project scale, plant configuration, and local conditions. This ensures the robustness and applicability of our optimization framework. While our optimization analysis demonstrates considerable potential to save resources, the actual implementation of these improvements may face regulatory and institutional challenges. Local governments vary in their capacity to enforce upgraded standards, and current funding mechanisms may not be sufficient to support wide-scale retrofitting of WWTPs. Furthermore, the coordination between environmental authorities and utility operators needs to be strengthened to ensure compliance and long-term sustainability. Therefore, policy support such as targeted subsidies, performance-based incentives, and updated technical guidelines is essential to translate these optimization opportunities into actionable outcomes. The water and energy footprints of wastewater treatment in China and its provinces were evaluated based on data from representative wastewater treatment plants nationwide. Despite its contributions, several limitations warrant further research. The lack of detailed data on specific processes, such as pre-treatment, biological treatment, disinfection, and sludge treatment, restricts the identification of high-energy-consuming stages and the development of targeted optimization strategies. Comprehensive datasets are essential for accurately assessing water and energy footprints at each treatment stage. Moreover, this evaluation primarily focuses on urban wastewater treatment due to data constraints, with scenario simulations conservatively designed around existing processes. Future advancements will require the bold adoption of innovative technologies and strategies to achieve sustainable wastewater management. This study offers valuable insights into resource consumption in wastewater treatment in China. As an integral component of water resource recycling, wastewater treatment facilities demand substantial water resource investments to sustain operations and consume large energy. By 2035, the total water and energy footprints of China’s wastewater treatment sector are projected to reach approximately 70 billion kilograms and 20 billion kilowatt-hours, respectively. Implementing optimized treatment process strategies has the potential to reduce these resource consumptions by up to 25% without compromising the removal efficiency of nitrogen and phosphorus. Notably, this optimization would require an increase in operational costs of less than 8%. These findings provide a robust scientific basis and policy guidance for establishing, by 2035, a systematic, secure, environmentally sustainable, and economically efficient framework for wastewater resource utilization in China. Methods Conceptual framework and technical process This study established the boundaries of wastewater treatment using LCA theory and methods, providing a comprehensive evaluation and analysis of the water and energy footprints of wastewater treatment processes in China 62 . A data set of wastewater treatment capacity was constructed for the main processes of 10,124 WWTPs nationwide on the “Urban Drainage Statistical Yearbook”, which was used to determine the distribution of treatment process types across provinces. These distributions were derived from each plant's design daily treatment capacity, allowing for a weighted representation of regional technology prevalence. The water footprint intensity and energy footprint intensity of the main wastewater treatment processes were calculated based on the case study data. The spatio-temporal dynamics of water footprint intensity, energy footprint intensity, and total wastewater treatment in each province were analyzed by integrating provincial wastewater treatment data in the “China Environmental Statistics Yearbook”. Further exploration was conducted into the potential economic, social, and environmental factors influencing the water footprint and energy footprint of wastewater treatment in China. Finally, we constructed regulatory scenarios based on process selection for different objectives, evaluated the water and energy footprints under these scenarios, and proposed resource-saving measures and suggestions for wastewater treatment across different regions. LCA boundaries and case collection The LCA boundary for the wastewater treatment process in this paper encompasses the construction, operation, and demolition phases of the WWTPs 21 , 24 . The water footprint intensity and energy footprint intensity in the LCA of wastewater treatment is determined by analyzing various treatment processes. Detailed cases collection and distribution characteristics are provided in the Supplementary Information S1 . The cases analyzed in this study comprise Class Ⅰ complete cases, Class Ⅱ supplemental cases, and Class Ⅲ extended cases (Tables S1 – S3 ). Specific information and calculation methods for these cases can be found in Supplementary Information S1 . To verify the representativeness of the collected cases, we summarized their size distribution, process type proportions, and geographical coverage in Figs. S1 and S2 . The results indicate that the sample broadly reflects the national profile of WWTPs. Assumptions for calculating the water and energy footprint of WWTPs In the absence of specific information, the assumed design service life (life cycle) of the WWTPs is set to 20 years, based on the “ Urban Wastewater Recycling Engineering Design Code (GB 50335-2016) ”. For cases with an original life cycle different from 20 years, the water and energy footprints intensity of the WWTPs is adjusted according to the ratio of the original life cycle to the 20-year life cycle. The engineering construction in all cases adheres to the “ Code for Design of Masonry Structures (GB 50003-2011) ” and the “ Code for Design of Building Foundation (GB 5007-2011) .” Specifically, the plant building thickness is 240 mm, the concrete foundation thickness is 3000 mm, and the steel plate thickness is 9 mm. As the plant specification follows the internal corridor design, the length-to-width ratio is 14:5, according to the “ Standard for Modular Coordination of Industrial Buildings (GB/T 50006-2010) .” We assumed that in all cases, the plant buildings do not include office buildings. The LCA boundary for wastewater treatment includes the construction phase of the WWTPs (encompassing the processing and transportation of building materials and the construction activities), the operation phase, and the demolition phase. During the construction phase. Calculation method for the water and energy footprint of the wastewater treatment process Based on the data from the “Urban Drainage Statistical Yearbook”(2009-2017), the interannual variation characteristics of the proportion of wastewater treatment volumes for different processes have been summarized (Fig. S3 ). The top eight processes with the highest average annual proportions have been identified as the major wastewater treatment processes, including Anaerobic-Anoxic-Oxic (A 2 O), Oxidation Ditch (OD), Conventional Activated Sludge (CAS), Cyclic Activated Sludge System (CASS), Anaerobic-Oxic Process (AO), Sequencing Batch Reactor (SBR), Biological Filter (BF), and Continuous Activated Sludge Treatment (CAST) (Supplementary Information S2 and Tables S4 – S5 ). Based on the LCA boundary established in this research for the wastewater treatment process, the water and energy footprint intensity of the main treatment processes is calculated using the case method. The life cycle assessment framework quantifies the water and energy footprints across construction, operation, and demolition stages of wastewater treatment plants. Stage-specific data and assumptions ranging from empirical case values to literature-based coefficients are integrated to ensure consistent and comparable footprint estimation (Fig. 7 and Table S6 ). The specific calculation process and methods are detailed in Supplementary Information S3 . Fig. 7. Assessment framework and flow. Open in a new tab This figure presents the methodological framework for assessing the water footprint (WF) and energy footprint (EF) of wastewater treatment processes across three main life-cycle stages: build, operation, and demolition. In the build stage, water and energy footprints are calculated based on actual water input, material consumption, and energy use, using coefficient-based methods from literature and national standards. The operation stage accounts for both direct and indirect energy consumption (e.g., aeration and chemical production) and material usage. When direct water-use data for chemicals are unavailable, water–electricity conversion factors from the literature are applied to estimate WF. The demolition stage is assumed to contribute 10% of the build-stage footprint for both water and energy, in accordance with national engineering standards. The total life cycle WF and EF are calculated by summing the footprints of each stage, using a combination of case-based data and conversion coefficients, as shown in the equations on the right. We conducted a life cycle assessment (LCA) of water and energy footprints (WF and EF) for mainstream wastewater treatment processes in China, covering three phases: construction, operation, and decommissioning. The system boundary follows a cradle-to-gate framework, with all results normalized to the treatment of 1 m 3 of municipal wastewater. Data was derived from 90 engineering cases across 47 reports, reflecting variations in technology, region, and plant scale. For the operational phase, EF included both direct electricity consumption and indirect energy embodied in chemical production. Due to limited direct data, the WF associated with chemical use was approximated by converting indirect energy consumption using a coefficient of 2.14 kg/kWh 63 . This coefficient has also been applied in previous studies as an approximate conversion factor for energy-related water use 64 . This approach ensures methodological consistency and comparability across process types and regional conditions. W F I = ∑ i = 1 6 W F I i 1 E F I = ∑ i = 1 6 E F I i 2 Here, WFI and EFI denote the water and energy footprint intensities for various treatment processes, respectively. WFI 1 represents the water footprint intensity of the actual water input during the construction phase of the wastewater treatment plant. WFI 2 and EFI 1 denote the water and energy footprint intensities during the material-acquisition phase of the wastewater treatment plant. WFI 3 and EFI 2 indicate the water and energy footprint intensities during the construction phase. EFI 3 signifies the energy footprint intensity during the transportation phase of construction materials. WFI 4 and EFI 4 denote the direct water and energy footprint intensities during the operational phase. WFI 5 represents the water footprint intensity of chemical agents during the operational phase, while EFI 5 denotes the indirect energy footprint intensity during this phase of the wastewater treatment plant. WFI 6 and EFI 6 refer to the water and energy footprint intensities during the wastewater treatment plant's decommissioning phase. The units for these water and energy footprint intensities are kg/m 3 and kWh/m 3 , respectively. Water and energy footprint intensities of the 8 major processes were estimated through case study data from wastewater treatment plants. The average water and energy footprint intensities of these 8 processes are used as the intensity values for the other processes. The proportion of installed capacity of each treatment process in annual volumes were combined according to the “Urban Drainage Statistical Yearbook” (2009-2017) (Table S5 ), we calculated the annual average water and energy footprint intensities of wastewater treatment processes for each year: W F I a = ∑ j = 1 9 W F I j × P j 3 E F I a = ∑ j = 1 9 E F I j × P j 4 Here, W F I a and E F I a represent the annual average water and energy footprint intensities of wastewater treatment processes, respectively. Index j (1, 2, 3… 9) corresponds to the 8 major treatment processes plus the “Other” treatment process. W F I j and E F I j denote the water and energy footprint intensities of the 8 major treatment processes plus the “Other” treatment process in Eq. ( 1 ). P j represents the proportion of the j-th treatment process in the current year. The units for water and energy footprint intensities are kg/m 3 and kWh/m 3 , respectively, while P j is expressed in %. For the calculation results of the water and energy footprint intensity of each stage of the main process, please refer to Supplementary Data 2 . Driver analysis The Logarithmic Mean Divisia Index (LMDI) method was applied to analyze the driving forces behind the changes in China’s wastewater treatment water and energy footprints in this article 65 . The LMDI method was chosen for its completeness (no residual term), additivity, and suitability for handling zero or near-zero values. These properties ensure robust, consistent, and residual-free decomposition of water and energy footprint drivers across provinces and years, thereby enhancing the reliability of comparative and temporal analyses 66 , 67 . The water and energy footprints were decomposed into six factors: footprint intensity (I), wastewater treatment rate (T), wastewater generation rate (G), water intensity per unit GDP (U), per capita GDP (A), and population size (P). LMDI models for water, energy, and carbon footprints were constructed, with the specific formula as follows: W F t = W F t W 1 t W 1 t W 2 t W 2 t W 3 t W 3 t G D P t G D P t P t P t = I w t T t G t U t A t P t 5 E F t = E F t W 1 t W 1 t W 2 t W 2 t W 3 t W 3 t G D P t G D P t P t P t = I E t T t G t U t A t P t 6 In the formula, W F t is the total water footprint i in the year of t (t = 2009-2017), with a unit of kg; E F t is the total energy footprint (kWh); W 1 t is the wastewater treatment volume (m 3 ); W 2 t is the wastewater discharge (m 3 ); W 3 t is the water consumption (m 3 ); G D P t is the gross domestic product (Chinese yuan); P t is the total number of permanent residents (billions of people). I w t is the water footprint intensity (kg/m 3 ); I E t is Energy footprint intensity (kWh/m 3 ); T t is the wastewater treatment rate (%); G t is the wastewater generation rate (%); U t is the water intensity per unit of GDP (kg/yuan); A t is the per capita GDP (yuan/person). According to the LMDI method, if W F 0 , E F 0 and W F t , E F t respectively represent the water and energy footprints of the base period and t period, then the changes in water and footprint emissions from the base period to t period ( Δ W F , Δ E F ) can be decomposed into: Δ W F = W F t − W F 0 = Δ W F I + Δ W F T Δ W F G + Δ W F U + Δ W F A + Δ W F P 7 Δ E F = E F t − E F 0 = Δ E F I + Δ E F T Δ E F G + Δ E F U + Δ E F A + Δ E F P 8 The impact values of each driving factor on the water and energy footprint are calculated with the following formula: Δ W F i = W F t − W F 0 ln W F t − ln W F 0 ln ( i t i 0 ) 9 Δ E F i = E F t − E F 0 ln E F t − ln E F 0 ln ( i t i 0 ) 10 In the formula, i 0 and i t are the six driving factors of the base year and the t year, respectively. When the impact value of the driving effect is positive, it indicates that changes in various driving factors promote an increase in the wastewater treatment plant's water and energy footprint efficiency, which is called a positive driving effect. If the contribution value of the driving effect is negative, it is called a negative driving effect. Scenario simulation Based on the application of major wastewater treatment processes in various provinces in 2017, as well as the changes in the treatment capacity of major wastewater treatment processes in each province based on population changes in 2035 68 . We set up five scenarios aiming at different effects. The effects here refer to the goals of the most economical, the best nitrogen and phosphorus removal effect, and the lowest water energy resource consumption. The measures adopted in different scenarios are to replace the inferior process with a process with better effects. Costs and Removal Rates of Main Wastewater Treatment Processes are provided in Table S11 . Here, we set up two gradients, replacing them with the process with the best effect (Top 1) and the first three processes with the best effect (Top 3). This analysis aims to explore the trade-offs and synergies across different aspects when adopting various wastewater treatment technologies (Fig. S12 ). The specific scenarios are shown in Table 1 . Table 1. Scenarios of Wastewater Treatment Process Combinations Target Top3 Top1 Maintain the structure of wastewater treatment process (BAU) —— —— Cost reduction S 1 S 2 Improve TP removal rate S 3 S 4 Improve TN removal rate S 5 S 6 Reduce water footprint S 7 S 8 Reduce energy footprint S 9 S 10 The optimal scenario process combination for each region Nt 1 Nt 2 Open in a new tab At the same time, considering the economic and water quality conditions of each region, the optimal process selection method for each province was simulated (Table S12 ). To reflect the distribution of nitrogen and phosphorus emissions from wastewater in various regions of China, 31 provinces across the country were divided into four quadrants (Fig. S13 ). The horizontal axis represents the per capita Gross Domestic Product (GDP per capita) of each region, while the vertical axis denotes nitrogen and phosphorus emissions from wastewater in each region, both normalized using the min-max scaling method. Based on the wastewater quality, economic development, and current mainstream wastewater treatment processes in each region, optimization scenarios are set for each province (Supplementary Information S8 ). D M = D a 0 − D a M i n D a M a x − D a M i n 11 D N = D i 0 − D i M i n D i M a x − D i M i n 12 Here, D M and D N represent the normalized values of per capita GDP and nitrogen and phosphorus emissions from wastewater, respectively. D a 0 and D i 0 denote the actual values of per capita GDP and nitrogen and phosphorus emissions from wastewater in different regions. D a M a x and D i M a x indicate the maximum values of per capita GDP and nitrogen and phosphorus emissions from wastewater across different regions, while D a M i n and D i M i n denote the minimum values of per capita GDP and nitrogen and phosphorus emissions from wastewater across different regions. To design region-specific optimization pathways for wastewater treatment systems, we developed a decision framework based on each province’s economic development level, wastewater generation characteristics, and pollutant discharge intensity. In this way, our scenario design aims to reflect local constraints and capacities, balancing environmental goals with realistic implementation feasibility. Provinces were classified into different strategy groups by jointly considering total nitrogen and phosphorus pollution levels from wastewater, existing treatment process structures, and per capita GDP. For provinces with relatively low pollutant loads and limited economic capacity, two cost-sensitive optimization scenarios were constructed: one promoting a mix of three mainstream low-cost technologies, and the other promoting only the most affordable process currently in use. Conversely, for provinces with higher pollutant discharge and stronger fiscal capacity, we proposed the adoption of enhanced treatment technologies with stronger removal performance. For instance, Guangdong Province, characterized by high pollution loads and strong economic conditions, was assigned two optimization options: (i) a combination of CASS, A 2 O, and SBR processes, or (ii) solely promoting CASS. Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Supplementary information Supplementary Information (3.2MB, pdf) 41467_2026_70159_MOESM2_ESM.pdf (5.9KB, pdf) Description of Additional Supplementary Files Supplementary Data 1 (34.9KB, xlsx) Supplementary Data 2 (13.5KB, xlsx) Reporting Summary (2.1MB, pdf) Transparent Peer Review file (2.6MB, pdf) Acknowledgements The authors are grateful for financial supports from the State Key Laboratory of Efficient Utilization of Agricultural Water Resources, Pinduoduo-China Agricultural University Research Fund (PC2023A02002, Y. L.), National Natural Science Foundation of China (52339004, Y. L., 52079139, B. Z.), the 2115 Talent Development Program of China Agricultural University (00109023, Y. L.), and European Research Council (ERC) under the European Union’s Horizon Europe research and innovation program (grant agreement 101039426, B-WEX). Author contributions Yunkai Li and B. Z. designed the research. S. H. and B. Z. wrote the manuscript. S.H., B.Z., and Y.W. analyzed the data. Yunkai Li, E.J., T.Y., W.C., X.C., X.K., J.Z., E.X., Y.X., Q.Z., and L.L. provided comments on the manuscript. All authors reviewed the manuscript. Peer review Peer review information Nature Communications thanks Changqing Xu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available. Data availability The processed data generated in this study, including provincial- and national-scale water and energy footprints, process-level footprint intensities, and scenario simulation outputs, are provided in Supplementary Data 1 and 2 . The wastewater treatment plant (WWTP) process composition, treatment capacity, and technology distribution data used in this study are available under restricted access, as they are part of a paid, print-based statistical publication (Urban Drainage Statistical Yearbook, 2009–2017) published by the Ministry of Housing and Urban-Rural Development of China. Access to this data can be obtained through institutional purchase of the yearbooks or by contacting the publisher. The provincial wastewater discharge data used in this study are available in the China Environmental Statistical Yearbook, published by the Ministry of Ecology and Environment of China, and are publicly accessible on the Ministry's official website ( https://www.mee.gov.cn/ ). Socioeconomic data used in this study, including population size, gross domestic product (GDP), and per capita GDP, are available from the China Statistical Yearbook, published by the National Bureau of Statistics of China, and are publicly accessible via the National Bureau of Statistics data portal ( https://www.stats.gov.cn/sj/ndsj/ ). Engineering case data used to estimate process-level water and energy footprints were compiled from published wastewater treatment plant case studies reported in peer-reviewed literature. The full list of literature sources and extracted parameters is provided in Supplementary Information Section S1. Cost data and total nitrogen (TN) and total phosphorus (TP) removal efficiency data for major wastewater treatment technologies used in the scenario analysis were derived from published literature sources and are provided in Supplementary Information Table S11 , with all literature sources listed therein. National technical standards and design codes used in this study to define wastewater treatment plant lifetime, structural parameters, and life cycle assumptions (including GB 50335-2016, GB 50003-2011, GB 50007-2011, and GB/T 50006-2010) are officially issued Chinese national standards and can be queried via the National Public Service Platform for Standards Information ( https://std.samr.gov.cn/ ) and the official website of the Ministry of Housing and Urban-Rural Development of the People’s Republic of China ( https://www.mohurd.gov.cn/ ). Population projection data used in this study are available from the dataset “Provincial and gridded population projection for China under shared socioeconomic pathways (2010-2100)” published by Chen et al. (2020) in Scientific Data. Code availability No custom code was developed for this study. All analyses were performed using standard functions in R (version 4.3.3), Python (version 3.11.7), and Origin, without the use of bespoke algorithms. Therefore, no code is required to reproduce the results. Competing interests The authors declare no competing interests Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Bo Zhou, Email: [email protected]. Yunkai Li, Email: [email protected]. Supplementary information The online version contains supplementary material available at 10.1038/s41467-026-70159-y. References 1. Gozini, H., Zahraie, B. & Ravar, Z. System dynamics modeling of water–energy nexus for resource-saving policy assessment. Int. J. Environ. Res. 15 , 349–367 (2021). [ Google Scholar ] 2. Dai, J. et al. Water-energy nexus: A review of methods and tools for macro-assessment. Appl. Energy 210 , 393–408 (2018). [ Google Scholar ] 3. Pereira, L. S. Water, agriculture and food: challenges and issues. Water Resour. Manag. 31 , 2985–2999 (2017). [ Google Scholar ] 4. Fysikopoulos, A., Pastras, G., Alexopoulos, T. & Chryssolouris, G. On a generalized approach to manufacturing energy efficiency. Int. J. Adv. Manuf. Technol. 73 , 1437–1452 (2014). [ Google Scholar ] 5. Arora, M., Nair, S., Malano, H. & George, B. A combined simulation-optimisation modelling framework for assessing the energy use of urban water systems. J. Environ. Manage. 274 , 111166 (2020). [ DOI ] [ PubMed ] [ Google Scholar ] 6. Ren, Z. J. & Umble, A. K. Recover wastewater resources locally. Nature 529 , 25–25 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 7. Jones, E. R. et al. Current wastewater treatment targets are insufficient to protect surface water quality. Commun. Earth Environ. 3 , 221 (2022). [ Google Scholar ] 8. Du, W.-J. et al. Spatiotemporal pattern of greenhouse gas emissions in China’s wastewater sector and pathways towards carbon neutrality. Nat. Water 1 , 166–175 (2023). [ Google Scholar ] 9. Magni, M., Jones, E. R., Bierkens, M. F. P. & Van Vliet, M. T. H. Global energy consumption of water treatment technologies. Water Res 277 , 123245 (2025). [ DOI ] [ PubMed ] [ Google Scholar ] 10. Cosgrove, W. J. & Loucks, D. P. Water management: Current and future challenges and research directions. Water Resour. Res. 51 , 4823–4839 (2015). [ Google Scholar ] 11. OECD. Water quality and agriculture: meeting the policy challenge. OECD Studies on Water. OECD Publishing , Paris . (2012). 12. Zhan, J. et al. Potential pathogenic microorganisms in rural wastewater treatment process: Succession characteristics, concentration variation, source exploration, and risk assessment. Water Res 254 , 121359 (2024). [ DOI ] [ PubMed ] [ Google Scholar ] 13. Jones, E. R., Bierkens, M. F. P. & Van Vliet, M. T. H. Current and future global water scarcity intensifies when accounting for surface water quality. Nat. Clim. Change 14 , 629–635 (2024). [ Google Scholar ] 14. Liu, L., Zhou, X., Dueñas-Osorio, L., Stadler, L. & Li, Q. Hybrid wastewater treatment and reuse enhances urban water system resilience to disruptive incidents. Nat. Water 1 , 1048–1058 (2023). [ Google Scholar ] 15. Bahadir, M. Water and wastewater management: global problems and measures. springer (2022). 16. Larsen, T. A., Hoffmann, S., Lüthi, C., Truffer, B. & Maurer, M. Emerging solutions to the water challenges of an urbanizing world. Science 352 , 928–933 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 17. Agbaitoro, G. A. & Oyibo, K. I. Realizing the United Nations Sustainable Development Goals 7 and 13 in sub-Saharan Africa by 2030: synergizing energy and climate justice perspectives. J. World Energy Law Bus. 15 , 223–235 (2022). [ Google Scholar ] 18. Fehri, R., Khlifi, S. & Vanclooster, M. Disaggregating SDG-6 water stress indicator at different spatial and temporal scales in Tunisia. Sci. Total Environ. 694 , 133766 (2019). [ DOI ] [ PubMed ] [ Google Scholar ] 19. Allen, C., Reid, M., Thwaites, J., Glover, R. & Kestin, T. Assessing national progress and priorities for the Sustainable Development Goals (SDGs): experience from Australia. Sustain. Sci. 15 , 521–538 (2020). [ Google Scholar ] 20. Benetto, E., Nguyen, D., Lohmann, T., Schmitt, B. & Schosseler, P. Life cycle assessment of ecological sanitation system for small-scale wastewater treatment. Sci. Total Environ. 407 , 1506–1516 (2009). [ DOI ] [ PubMed ] [ Google Scholar ] 21. Byrne, D. M., Lohman, H. A. C., Cook, S. M., Peters, G. M. & Guest, J. S. Life cycle assessment (LCA) of urban water infrastructure: emerging approaches to balance objectives and inform comprehensive decision-making. Environ. Sci. Water Res. Technol. 3 , 1002–1014 (2017). [ Google Scholar ] 22. Frischknecht, R., Stolz, P. & Tschümperlin, L. National environmental footprints and planetary boundaries: from methodology to policy implementation 59th LCA forum, Swiss Federal Institute of Technology, Zürich, June 12, 2015. Int. J. Life Cycle Assess. 21 , 601–605 (2016). [ Google Scholar ] 23. Corominas, L. et al. The application of life cycle assessment (LCA) to wastewater treatment: A best practice guide and critical review. Water Res 184 , 116058 (2020). [ DOI ] [ PubMed ] [ Google Scholar ] 24. Beavis, P. & Lundie, S. Integrated environmental assessment of tertiary and residuals treatment-LCA in the wastewater industry. Water Sci. Technol. 47 , 109–116 (2003). [ PubMed ] [ Google Scholar ] 25. Zhang, Y. et al. Wastewater treatment technology selection under various influent conditions and effluent standards based on life cycle assessment. Resour. Conserv. Recycl. 154 , 104562 (2020). [ Google Scholar ] 26. Loubet, P., Roux, P., Loiseau, E. & Bellon-Maurel, V. Life cycle assessments of urban water systems: A comparative analysis of selected peer-reviewed literature. Water Res 67 , 187–202 (2014). [ DOI ] [ PubMed ] [ Google Scholar ] 27. Bringezu, S. et al. Environmental and socioeconomic footprints of the German bioeconomy. Nat. Sustain. 4 , 775–783 (2021). [ Google Scholar ] 28. Bleischwitz, R. et al. Resource nexus perspectives towards the United Nations Sustainable Development Goals. Nat. Sustain. 1 , 737–743 (2018). [ Google Scholar ] 29. Liu, C., Qian, K. & Li, Y. Diagnostic method for enhancing nitrogen and phosphorus removal in cyclic activated sludge technology (cast) process wastewater treatment plant. Water 14 , 2253 (2022). [ Google Scholar ] 30. Morera, S., Corominas, L., Rigola, M., Poch, M. & Comas, J. Using a detailed inventory of a large wastewater treatment plant to estimate the relative importance of construction to the overall environmental impacts. Water Res 122 , 614–623 (2017). [ DOI ] [ PubMed ] [ Google Scholar ] 31. Nguyen, T. K. L. et al. Contribution of the construction phase to environmental impacts of the wastewater treatment plant. Sci. Total Environ. 743 , 140658 (2020). [ DOI ] [ PubMed ] [ Google Scholar ] 32. Rivero-Camacho, C. & Marrero, M. Water Footprint of the Life Cycle of Buildings: Case Study in Andalusia, Spain. in Advances of Footprint Family for Sustainable Energy and Industrial Systems (ed. Ren, J.) 135–165 (Springer International Publishing, Cham, 2022). 10.1007/978-3-030-76441-8_7. 33. Fan, J.-L., Kong, L.-S., Wang, H. & Zhang, X. A water-energy nexus review from the perspective of urban metabolism. Ecol. Model. 392 , 128–136 (2019). [ Google Scholar ] 34. Turner, S. W. D., Hejazi, M., Yonkofski, C., Kim, S. H. & Kyle, P. Influence of groundwater extraction costs and resource depletion limits on simulated global nonrenewable water withdrawals over the twenty-first century. Earths Future 7 , 123–135 (2019). [ Google Scholar ] 35. Zou, L. et al. Characteristic and correlation analysis of influent and energy consumption of wastewater treatment plants in Taihu Basin. Front. Environ. Sci. Eng. 13 , 83 (2019). [ Google Scholar ] 36. Penakalapati, G. et al. Exposure to animal feces and human health: a systematic review and proposed research priorities. Environ. Sci. Technol. 51 , 11537–11552 (2017). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Tow, E. W. et al. Modeling the energy consumption of potable water reuse schemes. Water Res. X 13 , 100126 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Zhang, P., Qin, C., Yu, L., Yang, L. & Lu, L. A new policy of water resources and environmental regulation in China. Int. J. Environ. Res. Public. Health 20 , 2556 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Lyu, S., Chen, W., Zhang, W., Fan, Y. & Jiao, W. Wastewater reclamation and reuse in China: Opportunities and challenges. J. Environ. Sci. 39 , 86–96 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 40. An, Y., Zhang, L. & Adom, P. K. Economics of wastewater management in China’s industry. Environ. Dev. Econ. 24 , 457–478 (2019). [ Google Scholar ] 41. Wang, D. et al. Impacts of water conservation, wastewater treatment, and reuse on water quantity and quality stress mitigation in China. J. Ind. Ecol. 29 , 777–793 (2025). [ Google Scholar ] 42. Cheng, Y., Lu, L., Shao, T., Shen, M. & Jin, L. Decomposition analysis of factors affecting changes in industrial wastewater emission intensity in China: based on an SSBM-GMI approach. Int. J. Environ. Res. Public. Health 15 , 2779 (2018). [ DOI ] [ PMC free article ] [ PubMed ] 43. Jin, W., Gao, S., Li, W. & Luo, J. The impacts of technological progress and industrial structure distortion on water use intensity in China. Water Resour. Econ. 47 , 100247 (2024). [ Google Scholar ] 44. Gu, Y. et al. Quantification of the water, energy and carbon footprints of wastewater treatment plants in China considering a water–energy nexus perspective. Ecol. Indic. 60 , 402–409 (2016). [ Google Scholar ] 45. Singh, P., Kansal, A. & Carliell-Marquet, C. Energy and carbon footprints of sewage treatment methods. J. Environ. Manage. 165 , 22–30 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 46. Morera, S., Corominas, L. L., Poch, M., Aldaya, M. M. & Comas, J. Water footprint assessment in wastewater treatment plants. J. Clean. Prod. 112 , 4741–4748 (2016). [ Google Scholar ] 47. Rahman, S. M., Eckelman, M. J., Onnis-Hayden, A. & Gu, A. Z. Life-cycle assessment of advanced nutrient removal technologies for wastewater treatment. Environ. Sci. Technol. 50 , 3020–3030 (2016). [ DOI ] [ PubMed ] [ Google Scholar ] 48. Shao, L. & Chen, G. Q. Water footprint assessment for wastewater treatment: method, indicator, and application. Environ. Sci. Technol. 47 , 7787–7794 (2013). [ DOI ] [ PubMed ] [ Google Scholar ] 49. Turovskiy, I. S. & Mathai, P. K. Wastewater Sludge Processing. John Wiley & Sons , Hoboken (2006). 50. Dubey, P. A. Industrial Wastewater Treatment. PHI Learning , New Delhi (2017). 51. Honggui, G. The Multi—game Research on Green Economic Development in China. China Popul. Resour. Environ. 22 , 13–18 (2012). [ Google Scholar ] 52. Liu, X. et al. Assessing transboundary impacts of energy-driven water footprint on scarce water resources in China: catchments under stress and mitigation options. Environ. Sci. Technol. 57 , 9639–9652 (2023). [ DOI ] [ PubMed ] [ Google Scholar ] 53. Jones, E. R. et al. DynQual v1.0: a high-resolution global surface water quality model. Geosci. Model Dev. 16 , 4481–4500 (2023). [ Google Scholar ] 54. Peng, Q. et al. Improving ecological protection redline from the perspective of water quality protection: A case study in the Tianmu Lake watershed. China. Ecol. Indic. 166 , 112445 (2024). [ Google Scholar ] 55. Xu, Z. et al. Assessing progress towards sustainable development over space and time. Nature 577 , 74–78 (2020). [ DOI ] [ PubMed ] [ Google Scholar ] 56. Van Vliet, M. T. H. et al. Global water scarcity including surface water quality and expansions of clean water technologies. Environ. Res. Lett. 16 , 024020 (2021). [ Google Scholar ] 57. Liu, J. & Yang, W. Water sustainability for China and beyond. Science 337 , 649–650 (2012). [ DOI ] [ PubMed ] [ Google Scholar ] 58. Ovink, H., Rahimzoda, S., Cullman, J. & Imperiale, A. J. The UN 2023 Water Conference and pathways towards sustainability transformation for a water-secure world. Nat. Water 1 , 212–215 (2023). [ Google Scholar ] 59. Long, S. et al. Pollution control and cost analysis of wastewater treatment at industrial parks in Taihu and Haihe water basins, China. J. Clean. Prod. 172 , 2435–2442 (2018). [ Google Scholar ] 60. Long, S. et al. A Monte Carlo-based integrated model to optimize the cost and pollution reduction in wastewater treatment processes in a typical comprehensive industrial park in China. Sci. Total Environ. 647 , 1–10 (2019). [ DOI ] [ PubMed ] [ Google Scholar ] 61. Qi, M. et al. Pollution reduction and operating cost analysis of municipal wastewater treatment in China and implication for future wastewater management. J. Clean. Prod. 253 , 120003 (2020). [ Google Scholar ] 62. Guinée, J. B. et al. Life cycle assessment: past, present, and future. Environ. Sci. Technol. 45 , 90–96 (2011). [ DOI ] [ PubMed ] [ Google Scholar ] 63. Chai, L., Liao, X., Yang, L. & Yan, X. Assessing life cycle water use and pollution of coal-fired power generation in China using input-output analysis. Appl. Energy 231 , 951–958 (2018). [ Google Scholar ] 64. Mardi, N. H., Ean, L. W., Malek, M. A., Chua, K. H. & Ahmed, A. N. Operational blue water footprint and water deficit assessment of coal-fired power plants: case study in Malaysia. Environ. Sci. Eur. 35 , 52 (2023). [ Google Scholar ] 65. Cansino, J. M., Sánchez-Braza, A. & Rodríguez-Arévalo, M. L. Driving forces of Spain׳s CO 2 emissions: A LMDI decomposition approach. Renew. Sustain. Energy Rev. 48 , 749–759 (2015). [ Google Scholar ] 66. Ang, B. W. The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33 , 867–871 (2005). [ Google Scholar ] 67. Du, G., Sun, C., Ouyang, X. & Zhang, C. A decomposition analysis of energy-related CO2 emissions in Chinese six high-energy intensive industries. J. Clean. Prod. 184 , 1102–1112 (2018). [ Google Scholar ] 68. Chen, Y. et al. Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. Sci Data 7 , 83 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Information (3.2MB, pdf) 41467_2026_70159_MOESM2_ESM.pdf (5.9KB, pdf) Description of Additional Supplementary Files Supplementary Data 1 (34.9KB, xlsx) Supplementary Data 2 (13.5KB, xlsx) Reporting Summary (2.1MB, pdf) Transparent Peer Review file (2.6MB, pdf) Data Availability Statement The processed data generated in this study, including provincial- and national-scale water and energy footprints, process-level footprint intensities, and scenario simulation outputs, are provided in Supplementary Data 1 and 2 . The wastewater treatment plant (WWTP) process composition, treatment capacity, and technology distribution data used in this study are available under restricted access, as they are part of a paid, print-based statistical publication (Urban Drainage Statistical Yearbook, 2009–2017) published by the Ministry of Housing and Urban-Rural Development of China. Access to this data can be obtained through institutional purchase of the yearbooks or by contacting the publisher. The provincial wastewater discharge data used in this study are available in the China Environmental Statistical Yearbook, published by the Ministry of Ecology and Environment of China, and are publicly accessible on the Ministry's official website ( https://www.mee.gov.cn/ ). Socioeconomic data used in this study, including population size, gross domestic product (GDP), and per capita GDP, are available from the China Statistical Yearbook, published by the National Bureau of Statistics of China, and are publicly accessible via the National Bureau of Statistics data portal ( https://www.stats.gov.cn/sj/ndsj/ ). Engineering case data used to estimate process-level water and energy footprints were compiled from published wastewater treatment plant case studies reported in peer-reviewed literature. The full list of literature sources and extracted parameters is provided in Supplementary Information Section S1. Cost data and total nitrogen (TN) and total phosphorus (TP) removal efficiency data for major wastewater treatment technologies used in the scenario analysis were derived from published literature sources and are provided in Supplementary Information Table S11 , with all literature sources listed therein. National technical standards and design codes used in this study to define wastewater treatment plant lifetime, structural parameters, and life cycle assumptions (including GB 50335-2016, GB 50003-2011, GB 50007-2011, and GB/T 50006-2010) are officially issued Chinese national standards and can be queried via the National Public Service Platform for Standards Information ( https://std.samr.gov.cn/ ) and the official website of the Ministry of Housing and Urban-Rural Development of the People’s Republic of China ( https://www.mohurd.gov.cn/ ). Population projection data used in this study are available from the dataset “Provincial and gridded population projection for China under shared socioeconomic pathways (2010-2100)” published by Chen et al. (2020) in Scientific Data. No custom code was developed for this study. All analyses were performed using standard functions in R (version 4.3.3), Python (version 3.11.7), and Origin, without the use of bespoke algorithms. Therefore, no code is required to reproduce the results. Articles from Nature Communications are provided here courtesy of Nature Publishing Group ACTIONS View on publisher site PDF (3.8 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• On dedicated institutions and funding to support disaster risk reduction: Reflections on local instrumentation in Chile.#659900
• Self management strategies for hypertensive patients on treatment in selected clinics at Ga-Dikgale, Capricorn District, Limpopo, South Africa.#659901
• From integration to use: a qualitative study of barriers and facilitators to postpartum family planning use in northeast Ethiopia—a multistakeholder perspective.#659902
• Universal Health Coverage in Pakistan: Challenges, Reforms, and the Road to 2030.#659903
• Association of social restrictions with fear of cancer recurrence in hematopoietic stem cell transplantation patients: an exploratory analysis of the potential chain mediation role of illness perception and cognitive emotion regulation.#659904