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

Domain: chemistry
Published: 2026-03-03
Editorial outcome: major_revision

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.

Introduction
Achieving resource conservation requires collaboration across sectors to address challenges related to water and energy1,2. 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 sectors3–5. Wastewater treatment, while crucial for water resource recycling6 and water quality protection7 often receives inadequate attention regarding its resource consumption8,9.

Wastewater systems face rising economic and environmental pressures due to aging infrastructure, urbanization trends, emerging contaminants, and competing water demands10–15. Scientifically evaluating the resource consumption of wastewater treatment under different process application16 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 activities20–23. WWTPs consume wa ter resources and energy starting from the construction phase of building materials input to the operation phase of materials input, electricity consumption, to the final demolition phase (Fig. 1Ca-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 practices24–26. Furthermore, existing studies rarely integrate process-level resource modeling with nationwide empirical data to inform spatially explicit optimization strategies.

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 resources management. A comprehensive analysis of the water and energy footprints of continuously operating WWTPs is essential for evaluating the water and energy impacts, as well as assessing the resource-saving potential, within 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 conditions27,28.

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 regionally customized optimization strategies, considering local water quality, infrastructure, and economic conditions. This approach offers a scalable and transferable tool to inform sustainable and cost-effective wastewater management planning under 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 Figure 2a-b, the Biological Filter (BF) process exhibits the lowest mean footprint intensity, at 0.96 kg/m3 and 0.18 kWh/m3, whereas the Continuous Activated Sludge Treatment (CAST) process demonstrates the highest mean footprint intensity at 1.91 kg/m3 and 0.63 kWh/m3, 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 (A2O)29. As a biological nitrogen and phosphorus removal process, the A2O process has water and energy footprint intensities that are 21.1% and 35.5% higher, respectively, compared to those of the AO process.

To further explore the resource performance of emerging technologies, we conducted a comparative life cycle evaluation of four selected emerging processes—Membrane Bioreactor (MBR), Biological Combined System (BIOCOS), Linpor, and Bardenpho. The results show that Linpor exhibited the lowest water (1.09 kg/m3) and energy footprints (0.22 kWh/m3), indicating its potential as a highly resource-efficient treatment option. BIOCOS also demonstrated relatively low energy consumption (<0.57 kWh/m3). These findings provide empirical support for considering alternative technologies in future optimization pathways. Detailed data are provided in Table S7 and S8.

The water and energy footprint of major processes vary 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/m3), 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 studies30–32, are often underrepresented in LAC inventories. During the operational phase, the water footprint is primarily driven by electricity consumption (0.63 kg/m3), while the demolition phase contributes minimally. The energy footprint is dominated by the operational phase (0.47 kWh/m3), 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 A2O and OD. The water and energy footprint intensities in Northeast China (1.42 kg/m3 and 0.37 kWh/m3, 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 have 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 figure S4-S5.

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 upgrades33,34.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 volume35.

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 feces management and treatment reduce the untreated pollutants discharged into water bodies, decreasing the need for pollutant removal during the wastewater treatment phase and consequently improving water and energy efficiency36. 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 consumption37. The relationship between per capita water footprint and energy footprint indicates a strong correlation between the two per capita metrics. 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 360.8×108 kg and 97.5×108 kWh in 2009 to 928.5×108 kg and 253.5×108 kWh in 2022. The A2O process shows the highest share of the water footprint and energy footprint, 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 A2O, 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.

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. 4 c-f). For spatial visualization of the annual variation in water and energy footprints, please see figure S6-S7. Guizhou exhibited the largest relative increase, with both the water and energy footprints more than tripling. Coastal provinces showed notably high totals and growth rates in water and energy footprints. 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 figure S8 a-e and S9 a-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. S10 a-b). The wastewater treatment rate and per capita GDP positively influence the increase in water and energy footprints, while water intensity per unit of GDP has a negative impact. 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 reuse38–40. Meanwhile, technological upgrades in industrial production and municipal infrastructure have reduced per capita wastewater generation41,42. 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 intensity42,43. 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 inevitable10. 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. S12 a-e) and Supplementary data 1.

The S8 scenario (water footprint reduction scenario) reduces costs by over 10% compared to the business as usual (BAU) 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 exceeding 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 the optimal removal 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 footprint, energy footprint, cost, and nitrogen and phosphorus removal rates of future sewage treatment are defined as 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 (Nt1 and Nt2). Compared to the BAU scenario (Fig. 6a-e), both Nt1 and Nt2 scenarios reduce water and energy footprints, with stable TN removal rates and slight improvements in TP removal rates.

Under the Nt2 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%.

During the optimization process, the southeastern coastal and central provinces (such as Guangdong, Henan, Hunan) have experienced marked cost increases, but the removal rates of TN and TP 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 energy footprint of Guangdong decreasing by 16.11% and water footprint of Hunan decreasing by 7.96%. Overall, these provinces have achieved dual benefits of improving environmental quality and reducing 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.

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 China62. 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 based on the design daily treatment capacity of each plant, 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 and 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 footprint and energy footprint under different 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 WWTPs21,24. 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 (Table 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 Figure 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 follow 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 (A2O), 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 Table 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 cases 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.

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 m3 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 WSF associated with chemical use was approximated by converting indirect energy consumption using a coefficient of 2.14 kg/kWh63. This coefficient has also been applied in previous studies as an approximate conversion factor for energy-related water use64. This approach ensures methodological consistency and comparability across process types and regional conditions.

WFI = ∑6 i=1 WFIi （1）
EFI = ∑6 i=1 EFIi （2）

Here, WFI and EFI denote the water and energy footprint intensities for various treatment processes, respectively. WFI1 represents the water footprint intensity of the actual water input during the construction phase of the wastewater treatment plant. WFI2 and EFI1 denote the water and energy footprint intensities during the material acquisition phase for the wastewater treatment plant. WFI3 and EFI2 indicate the water and energy footprint intensities during the construction phase. EFI3 signifies the energy footprint intensity during the transportation phase of construction materials. WFI4 and EFI4 denote the direct water and energy footprint intensities during the operational phase. WFI5 represents the water footprint intensity of chemical agents during the operational phase, while EFI5 denotes the indirect energy footprint intensity during this phase of the wastewater treatment plant. WFI6 and EFI6 refer to the water and energy footprint intensities during the decommissioning phase of the wastewater treatment plant. The units for these water and energy footprint intensities are kg/m3 and kWh/m3, 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:

WFIa = ∑9 j=1 WFIj × Pj （3）
EFIa = ∑9 j=1 EFIj × Pj （4）

Here, WFIa and EFIa 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. WFIj and EFIj denote the water and energy footprint intensities of the 8 major treatment processes plus the “Other” treatment process in equation (1). Pj represents the proportion of the j-th treatment process in the current year. The units for water and energy footprint intensities are kg/m3 and kWh/m3, respectively, while Pj 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 article65. 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 analyses66,67. 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：

WFt = WFt / W1t * W1t / W2t * W2t / W3t * W3t / GDPt * GDPt / Pt * Pt = It * Tt * Gt * Ut * At * Pt (5)
EFt = EFt / W1t * W1t / W2t * W2t / W3t * W3t / GDPt * GDPt / Pt * Pt = It * Tt * Gt * Ut * At * Pt (6)

In the formula, WFt is the total water footprint i in the year of t (t=2009-2017), with a unit of kg; EFt is the total energy footprint (kWh); W1t is the wastewater treatment volume (m3); W2t is the wastewater discharge (m3); W3t is the water consumption (m3); GDPt is the gross domestic product (Chinese yuan); Pt is the total number of permanent residents (billions of people). It is the water footprint intensity (kg/m3); It is Energy footprint intensity (kWh/m3); Tt is the wastewater treatment rate (%); Gt is the wastewater generation rate (%); Ut is the water intensity per unit of GDP (kg/yuan); At is the per capita GDP (yuan/person).

According to the LMDI method, if WF0, EF0 and WFt, EFt 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 (△WF, △EF) can be decomposed into:

△WF = WFt - WF0 = △WFI + △WFT + △WFG + △WFU + △WFA + △WFP （7）
△EF = EFt - EF0 = △EFI + △EFT + △EFG + △EFU + △EFA + △EFP （8）

The impact values of each driving factor on the water and energy footprint are calculated with the following formula:

△WFi = (WFt - WF0) / (lnWFt - lnWF0) * ln(it / i0) （9）
△EFi = (EFt - EF0) / (lnEFt - lnEF0) * ln(it / i0) （10）

In the formula, i0 and it 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 the changes in various driving factors promote the increase of water and energy footprint efficiency of the wastewater treatment plant, 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 203568. 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 the process with better effect. Costs and Removal Rates of Main Wastewater Treatment Processes are provided in table S11. Here we set up two gradients, which are replaced by 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.

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).

Da = (Da0 - DaMin) / (DaMax - DaMin) （11）
Di = (Di0 - DiMin) / (DiMax - DiMin) （12）

Here, Da and Di represent the normalized values of per capita GDP and nitrogen and phosphorus emissions from wastewater, respectively. Da0 and Di0 denote the actual values of per capita GDP and nitrogen and phosphorus emissions from wastewater in different regions. DaMax and DiMax indicate the maximum values of per capita GDP and nitrogen and phosphorus emissions from wastewater across different regions, while DaMin and DiMin 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, A2O, and SBR processes, or (ii) solely promoting CASS.

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 Supplementary Data 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 from the China Environmental Statistical Yearbook, published by the Ministry of Ecology and Environment of China, and are publicly accessible via the official website of the Ministry of Ecology and Environment of China (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.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents an analysis of energy and water consumption associated with 10,124 urban wastewater treatment plants (WWTPs) across China. While the dataset is extensive and the topic is relevant to the fields of environmental management and sustainability, the novelty of the study could be more clearly articulated. The current framing largely reads as an application of established methods to a new geographic context. I encourage the authors to revise the introduction to more clearly highlight the unique contributions of this work.\n1. The novelty of the study is not particularly clear. Applying existing methods to a new country alone does not sufficiently establish originality. Furthermore, the introduction is somewhat lengthy and contains repetitive elements. I recommend rewriting it to sharpen the focus on what makes this study distinctive, e.g., scale, methodology integration, or policy relevance.\n2. Lines 116 and 118: Please clarify whether the reported values represent mean or median values. This distinction is important for interpreting central tendencies in highly skewed datasets.\n3. Lines 128: The reported direct water footprint during the construction phase appears unusually high. Could the authors provide an explanation or additional references to support this result? How does the result compare with findings from previous studies?\n4. How do the results presented in Figure 2 compare with those of previous studies in other regions or countries? Are the water and energy intensities of Chinese WWTPs noticeably different, and if so, why?\n5. Lines 136-145: The manuscript notes groundwater supply and population as drivers of spatial variation in WWTP footprints. Please elaborate on the mechanisms behind these associations. How are these variables linked to operational demands or infrastructure development?\n6. Lines 164-171: The observed temporal changes in footprints merit deeper discussion. Are these shifts primarily driven by policy reforms, technological upgrades, or other socioeconomic factors? Providing more context would strengthen the analysis.\n7. The description of the life cycle assessment (LCA) methodology, particularly for the operational phase, is difficult to follow without extensive reference to the supporting information. A clear summary diagram or methodological flowchart in the main text would be helpful. In addition, how were treatment-specific variables (e.g., influent water quality, chemical dosing) incorporated into the LCA calculations?\n8. Please provide justification for using the Logarithmic Mean Divisia Index (LMDI) method over other statistical or decomposition techniques. Why is LMDI particularly suited to this analysis, and how does it contribute to the robustness of the findings?", "reviewer_2": "This is a well written and technically sound manuscript. This work makes an important contribution by framing wastewater treatment as a multi-dimensional optimization challenge. The paper provides a model that can guide and inform infrastructure planning and environmental regulations in other developing countries as well.\nThe research hypothesis is valid, built on a life cycle assessment (LCA) framework, and the topic is relevant to both academic and policy-making audiences. Connecting the objectives to SDGs in introduction section and later in discussion section, provides a meaningful approach. The methods utilized are appropriate, some limitations in data granularity and rural plant representation are acknowledged.\nTypo: Line 121: Anoxic-Toxic should be Anaerobic, Anoxic and Aerobic (or Oxic) (A2O)\nThe results are presented well, figures and tables are well-organized. Interpretations are generally justified, however, some results could be strengthened by including more granular or predictive data for novel technologies.\nThe authors present the limitations of the study and appropriate future work and approaches.\nTo address the questions for the review:\nThe most noteworthy result is the quantified potential to reduce China's wastewater treatment water footprint by 16.1% and energy footprint by 25.6% by 2035 with an investment of less than 8% of the total treatment cost, while not affecting the nutrient removal efficiency.\nThis work will be of significance to the filed because it expands the scope of LCA in wastewater treatment. The study uses data from 10,000+ wastewater treatment plants, a scale that is not commonly processed. Even though the work is not fully original, the scale and real data utilized make it a worthy contribution to the field.\nThe conclusions are mostly supported by the results. One question I have is that, even though the study indicates that there could be substantial cost and resource reductions, the authors did not fully discuss how feasible it would be to implement the potential changes to the existing systems. What are the regulatory or institutional challenges to the potential improvements?\nI did not perceive any flaws in the data analysis. The uncertainty is acknowledged and addressed. One suggestion may be to expand the economic analysis with more specific information (capital cost, operational cost, etc).\nI believe the methodology is sound.\nThere is enough detail, especially with the supplemental materials. A little more clarity on the normalization of data across 10,000+ wastewater treatment plants may allow others to replicate the study better. Similar to my comment above, also a little more detail on the cost assumptions would help.", "reviewer_3": "Dear authors，\nThis manuscript presents a comprehensive assessment of the water and energy footprints associated with wastewater treatment in China, using a life cycle perspective and extensive nationwide data. By integrating scenario simulations, the authors propose optimized, cost-effective pathways for sustainable wastewater treatment by 2035. The study is policy-relevant, well-structured, and supported by substantial supplementary data. There are some issues which need to be addressed.\n1)In line 47, the manuscript mentions that 47 wastewater treatment plants (WWTPs) were selected for analysis. However, according to Supplementary Tables S1–S3, the total number of cases is 90. This discrepancy needs clarification. Moreover, the authors should explain the rationale behind case selection—specifically how these samples reflect geographical, technological, and scale-based diversity, and whether they are representative of national wastewater treatment characteristics.\n2)In lines 119-120, the CAST process is noted as having a wide variation in water and energy footprint intensity. The manuscript should discuss the causes of this variability—e.g., differences in design and operation, regional infrastructure, influent quality, or plant size. Such interpretation is necessary to contextualize the findings and guide technology optimization.\n3)In lines 146-161, the analysis of influencing factors in Figure 3c is limited. For instance, Factor F5 has a statistically significant correlation with both water and energy footprints, but its role is not explained. In addition, while WFP and EFP show strong mutual correlation and similar relationships with other variables, they diverge in relation to Factor F3 (significant correlation with EFP but not WFP). The manuscript should elaborate on the mechanisms or context behind these discrepancies to avoid superficial interpretation of statistical associations.\n4)In lines 153-154, the term “total feces collection” is not consistent with the variable labeled as “urban fecal clearance volume per capita” (F10) in Figure 3. These concepts refer to different metrics (aggregate vs. per capita), and the manuscript should use terminology that accurately reflects the analyzed variable.\n5)In lines 158-160, the authors state that the per capita water and energy footprints indicate a “strong synergy” between water and energy footprint intensity. This interpretation is problematic. First, per capita metrics are distinct from intensity metrics, and such a relationship cannot be inferred directly. Second, the term synergy implies an interaction effect that enhances outcome beyond additive contributions, which is not demonstrated here. If the authors intend to describe a statistical relationship, strong correlation would be more accurate.\n6)In line 251, the DQI scoring table (referred to as Table 1) is not provided in the main manuscript. Please clarify where this table can be found.\n7)In lines 256-262, the Monte Carlo-based uncertainty analysis lacks information about parameter settings, such as distribution types, parameter ranges, or assumptions. These details are essential for assessing the robustness of the findings. Please include a more comprehensive description of simulation settings and justification for key assumptions.\n8)In lines 512–513, while provincial optimization is an important component of the manuscript, the process by which optimal strategies were assigned to each province remains vague. It is strongly recommended that the authors provide one or two detailed case studies (e.g., Guangdong and Inner Mongolia) to demonstrate how optimization scenarios were determined and how trade-offs among cost, footprint, and pollutant removal were balanced.\n9)Line 520 references Supplementary Information S7 for provincial scenario assignments. However, the correct source appears to be Supplementary Information S8. Please revise accordingly.\n10)Figure 1d and some other visual elements are unclear and should be presented in higher resolution for better interpretability.\n11)Table S3 contains inconsistencies (e.g., WWTP #87 is labeled “Liaoning Wastewater Treatment Plant” but assigned to Wuxi, Jiangsu). The authors should carefully verify the geographic accuracy of plant data.\n12)The term “demolition phase” is inconsistently used—appearing also as “dismantling phase” (line 102 and line 384). Consistent terminology should be applied throughout the manuscript and supplement.\n13)The section titled “Methodology for calculating the energy footprint of wastewater treatment” refers to “Tables 2-6 to 2-8,” which are not present in the supplement. Please verify the numbering and presence of these tables.\n14)The sequential order of supplementary tables should be corrected (e.g., Table S7 is followed by Table S10, with S8 and S9 missing)."}, "author_response": "Point-by-point Responses (regular font) to Referees’ Comments (bold font)\nReferee #1:\nThe manuscript presents an analysis of energy and water consumption associated with\n10,124 urban wastewater treatment plants (WWTPs) across China. While the dataset is\nextensive and the topic is relevant to the fields of environmental management and\nsustainability, the novelty of the study could be more clearly articulated. The current\nframing largely reads as an application of established methods to a new geographic context.\nI encourage the authors to revise the introduction to more clearly highlight the unique\ncontributions of this work.\nThank you very much for your insightful comment! Your evaluation and feedback are of great\nsignificance to this paper, and it was very helpful in improving the clarity and positioning of the\nmanuscript. We have made comprehensive revisions based on your specific feedback, which are\ndetailed (point-by-point) below.\nIn particular, we have revised the introduction to more clearly highlight the key innovations of the\nstudy. Rather than assessing each WWTP individually, we evaluated the water and energy\nfootprints of major treatment processes through representative cases. These were then linked to\nnational-level data on the configuration of 10,124 WWTPs to assess regional resource patterns.\nWe’ve also better emphasized that our optimization framework enables region-specific process\nadjustment strategies, tailored to local treatment profiles, infrastructure, and economic conditions.\nWith these revisions, it is our aim to better communicate that our study goes beyond applying\nexisting methods to a new context and instead offers a scalable yet adaptable planning tool with\npractical value.\n1. The novelty of the study is not particularly clear. Applying existing methods to a new\ncountry alone does not sufficiently establish originality. Furthermore, the introduction is\nsomewhat lengthy and contains repetitive elements. I recommend rewriting it to sharpen the\n1\nfocus on what makes this study distinctive, e.g., scale, methodology integration, or policy\nrelevance.\nThank you for this important feedback. We’ve now revised the introduction to address both points\nyou raised. First, we made the structure more concise by removing repetition and tightening the\nnarrative around the research motivation. More importantly, we now explicitly emphasize what\ndistinguishes this study:\n(1) The integration of process-level LCA with national-scale WWTP configuration data (covering\n10,124 plants).\n(2) The use of that data to reveal spatial patterns of water and energy use.\n(3) The development of a region-specific optimization framework that informs scalable yet locally\nadapted policy decisions.\nThese changes aim to clearly communicate that the study goes beyond applying existing methods\nin a new setting—it combines empirical depth with strategic planning relevance. Thanks again for\nyour constructive suggestions. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red):\nINTRODUCTION\nAchieving resource conservation requires collaboration across sectors to address challenges\nrelated to water and energy1,2. While the agricultural and manufacturing sectors play crucial roles\nin water and energy conservation, it is vital to acknowledge and explore the potential contributions\nof other sectors3–5. Wastewater treatment, while crucial for water resource recycling6 and water\nquality protection7 often receives inadequate attention regarding its resource consumption8,9.\nWastewater systems face rising economic and environmental pressures due to aging\ninfrastructure, urbanization trends, emerging contaminants, and competing water demands10–15.\nScientifically evaluating the resource consumption of wastewater treatment under different process\napplication16 strategies and trade-off multi-objective is imperative for advancing progress toward\nachieving Sustainable Development Goals (SDGs) 6 (Clean Water and Sanitation), 7 (Affordable\nand Clean Energy), and 13 (Climate Action)17–19.\nhuman activities20–22. WWTPs consume water resources and energy starting from the construction\nphase of building materials input to the operation phase of materials input, electricity consumption,\nto the final demolition phase (Figure 1). However, most existing research remains limited to small-\nscale or plant-level assessments and does not reflect large-scale spatial patterns or regional\ndiversity in treatment practices23–25. Furthermore, existing studies rarely integrate process-level\nresource modeling with nationwide empirical data to inform spatially explicit optimization\nstrategies.\nResearch on the water and energy pressures associated with regional water treatment remains\nlimited, posing significant challenges to the development of optimization strategies for wastewater\ntreatment resources management. A comprehensive analysis of the water and energy footprints of\ncontinuously operating WWTPs is essential for evaluating the water and energy impacts, as well\nas assessing the resource-saving potential, within a country or region’s water treatment processes.\nBalancing the best policies for water and energy consumption under multiple objectives of\nwastewater treatment in different regions is also of great importance for advancing sustainable\nwastewater management, particularly in regions with substantial disparities in infrastructure and\nenvironmental conditions26,27.\nWhile life cycle assessment (LCA) and multi-objective optimization have been increasingly\napplied to wastewater systems, most studies rely on limited case data and overlook the spatial\nheterogeneity of treatment practices. This study evaluates the water and energy footprints of major\nwastewater treatment processes through representative case analysis, and links them with the\noperational profiles of 10,124 urban WWTPs across China. By mapping process-level resource\nintensities onto region-specific process configurations, we assess the spatiotemporal dynamics of\nwastewater treatment resource use and identify opportunities for improvement. Furthermore, we\ndevelop a framework that supports regionally customized optimization strategies, considering\nlocal water quality, infrastructure, and economic conditions. This approach offers a scalable and\ntransferable tool to inform sustainable and cost-effective wastewater management planning under\ndiverse regional contexts.\n3\n2. Lines 116 and 118: Please clarify whether the reported values represent mean or median\nvalues. This distinction is important for interpreting central tendencies in highly skewed\ndatasets.\nThank you for your observation. The values reported in Lines 116 and 118 represent mean values.\nWe have clarified this in the revised manuscript to avoid ambiguity. Given the size and variability\nof the dataset, we chose to use the mean to reflect the overall central tendency across treatment\nprocesses and to maintain consistency with existing LCA-based studies on wastewater systems.\nPlease see the revised text below:\nRevised original text (The modified or added content is highlighted in red): As shown in Figure\n2a-b, the Biological Filter (BF) process exhibits the lowest footprint intensity (0.96 kg/m³, 0.18\nkWh/m³, mean), whereas the Continuous Activated Sludge Treatment (CAST) process\ndemonstrates the highest footprint intensity (1.91 kg/m³, 0.63 kWh/m³, mean), with a difference\nof 65.9%.\n3. Lines 128: The reported direct water footprint during the construction phase appears\nunusually high. Could the authors provide an explanation or additional references to support\nthis result? How does the result compare with findings from previous studies?\nThank you for your insightful comment. The relatively high direct water footprint observed during\nthe construction phase is mainly attributed to on-site construction activities, such as equipment\ncleaning, concrete curing, and dust control, rather than embedded water in upstream material\nproduction. This distinction is important, as several studies have highlighted that in large-scale\ninfrastructure projects—particularly wastewater treatment plants—the construction site operations\ncan dominate the direct water use within the construction stage.\nFor example, Morera et al. (https://doi.org/10.1016/j.scitotenv.2020.140658) and Nguyen et al.\n(https://doi: 10.1016/j.watres.2017.05.069) both emphasized the environmental burden of the on-\nsite phase in WWTP projects, where activities such as installation and operational testing can\nconsume considerable water. In the building sector, Rivero-Camacho and Madelyn Marrero (2022)\n(Water Footprint of the Life Cycle of Buildings: Case Study in Andalusia, Spain) also showed that\nconstruction-phase water use—not just materials—contributes substantially to the total water\nfootprint. These findings support our conclusion that the elevated direct water footprint during\n4\nconstruction is a result of water-intensive on-site processes, and we have clarified this point in the\nrevised manuscript. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): The construction\nphase involves a substantial direct water resource input (0.61 kg/m³), comprising 91.5% of the\ntotal water footprint for this phase mainly stemming from on-site processes. This includes\nprocesses such as concrete curing and cleaning operations, which, despite being identified as\ndominant water use contributors in previous studies29–31, are often underrepresented in LAC\ninventories.\n4. How do the results presented in Figure 2 compare with those of previous studies in other\nregions or countries? Are the water and energy intensities of Chinese WWTPs noticeably\ndifferent, and if so, why?\nThank you for your thoughtful question. Our study presents full life-cycle water and energy\nfootprints of representative wastewater treatment processes, including construction, operation, and\nend-of-life stages. This distinguishes it from many previous studies that focus solely on the\noperational phase.\nFor example, Gu et al. and Singh et al. only evaluate operational water and energy use, whereas\nRahman et al. and Morera et al. adopt a cradle-to-grave approach. Our reported water footprint\nvalues (0.96–1.91 kg/m³) and energy intensities (0.18–0.63 kWh/m³) are generally consistent with\nor slightly higher than international benchmarks, depending on the process.\nThe observed differences arise from several factors: (1) our inclusion of construction-related water\nand energy use, which is often excluded in other studies; (2) regional disparities in treatment\nconfigurations, plant scale, and infrastructure maturity across China; and (3) broader process\nheterogeneity in a national-scale assessment. We have clarified this comparison and added relevant\nreferences in the discussion of revised manuscript. References See below:\nGu Y, Dong Y, Wang H, et al. Quantification of the water, energy and carbon footprints of\nwastewater treatment plants in China considering a water–energy nexus perspective[J]. Ecological\nindicators, 2016, 60: 402-409.\nmethods[J]. Journal of Environmental Management, 2016, 165: 22-30.\nMorera S, Corominas L, Poch M, et al. Water footprint assessment in wastewater treatment\nplants[J]. Journal of Cleaner Production, 2016, 112: 4741-4748.\nRahman S M, Eckelman M J, Onnis-Hayden A, et al. Life-cycle assessment of advanced nutrient\nremoval technologies for wastewater treatment[J]. Environmental science & technology, 2016,\n50(6): 3020-3030.\nShao L, Chen G Q. Water footprint assessment for wastewater treatment: method, indicator, and\napplication[J]. Environmental science & technology, 2013, 47(14): 7787-7794.\nPlease see the revised text below:\nRevised original text (The modified or added content is highlighted in red): The reported values\nare comparable to or slightly higher than those in prior studies depending on the system boundary\nand geographic context. For instance, Gu et al. 37 and Singh et al. 38 reported operational energy\nconsumption of 0.3–0.6 kWh/m³, which aligns with our CAST and BF ranges. However, unlike\nthose studies, our analysis adopts a full life-cycle perspective, similar to Morera et al. 39 and\nRahman et al. 40, which accounts for construction-phase impacts. The inclusion of construction\nwater use and energy inputs—often overlooked—partly explains the higher values observed in\ncertain processes. Moreover, spatial variation in process deployment, plant scale, and regional\ninfrastructure conditions across China contributes to footprint diversity41.\n5. Lines 136-145: The manuscript notes groundwater supply and population as drivers of\nspatial variation in WWTP footprints. Please elaborate on the mechanisms behind these\nassociations. How are these variables linked to operational demands or infrastructure\ndevelopment?\nThank you very much for the valuable suggestion. This comment has helped us deepen the\ninterpretation of key regional drivers and improve the analytical clarity of our discussion. We have\nelaborated on the mechanisms underlying the observed associations in the revised manuscript.\nSpecifically, regions with a higher proportion of groundwater supply often face higher marginal\nextraction costs and stricter water resource constraints, which incentivize the adoption of advanced,\n6\nresource-efficient wastewater treatment technologies. Conversely, areas with abundant per capita\nwater resources may generate more diluted wastewater, which increases the treatment effort per\nunit of pollutant removed, thereby raising the water and energy footprint intensity per cubic meter.\nWe have clarified these mechanisms in Lines 138–145 of the revised manuscript. Please see the\nrevised text below:\nRevised original text (The modified or added content is highlighted in red): The proportion\nof groundwater supply and water resources availability per capita are strongly correlated with the\nintensities of water and energy footprints, as well as the per capita water and energy footprints,\nhighlighting their critical influence on resource utilization patterns (Figure 3c). This relationship\nmay stem from increasing marginal pumping costs and stricter conservation policies in\ngroundwater-dependent regions, which drive investment in more efficient treatment technologies\nand operational upgrades 32,33.On the other hand, regions with higher per capita water availability\ntend to produce more diluted influent due to abundant domestic water use, resulting in lower\npollutant concentrations and greater energy required per unit of pollutant removed. This leads to\nhigher water and energy footprint intensities per unit volume34.\n6. Lines 164-171: The observed temporal changes in footprints merit deeper discussion. Are\nthese shifts primarily driven by policy reforms, technological upgrades, or other\nsocioeconomic factors? Providing more context would strengthen the analysis.\nThank you for your thoughtful comment. We have revised the manuscript to include additional\ncontextual explanations regarding the temporal trends in total water and energy footprints across\nprovinces. Specifically, we now clarify that the sustained increase in footprints is primarily driven\nby policy tightening (e.g., stricter discharge standards under the 12th Five-Year Plan), which has\npromoted the adoption of resource-intensive treatment processes such as A2O. In parallel, rapid\nurbanization has led to expanded wastewater collection networks and increased influent volumes,\nfurther contributing to total resource use. This qualitative explanation is partially supported by our\nsubsequent quantitative decomposition analysis (Figure S8), which offers complementary insights\ninto the respective contributions of treatment coverage, wastewater generation, and economic\ndevelopment. Please see the revised text below:\nand energy footprint for wastewater treatment in various provinces exhibited a continuous upward\ntrend (Figure 4 a-b). The total water and energy footprints increased significantly by 1.6 times\nfrom 360.8×108 kg and 97.5×108 kWh in 2009 to 928.5×108 kg and 253.5×108 kWh in 2022.\nThe A O process shows the highest share of the water footprint and energy footprint, rising from\n2\n28% in 2009 to 41% in 2022. The OD process, which holds the second-highest share, shows a\nslight decline over the years. The water and energy footprint intensity and total amount of each\nprovince are displayed in Supplementary Information S5. This upward rend was primarily driven\nby multiple factors. First, the tightening of national discharge standards since the 12th Five-Year\nPlan has incentivized the widespread adoption of advanced treatment processes such as A O,\n2\nwhich achieve higher pollutant removal at the cost of greater resource input. Second, rapid\nurbanization has expanded wastewater collection coverage and increased total influent volumes,\nthereby amplifying total water and energy use.\nThe total water and energy footprints of wastewater treatment in Chinese provinces increased\nsignificantly, with over half of the provinces experiencing growth of more than double. Guizhou\nexhibited the largest relative increase, with both the water and energy footprints more than tripling.\nCoastal provinces showed notably high totals and growth rates in water and energy footprints. This\nis mainly due to rapid economic development and population density, which significantly increase\nthe demand for wastewater treatment. From 2009 to 2022, Guangdong ranked first in absolute\nincreases in water and energy footprints, followed by Jiangsu, Shandong, and Zhejiang, each with\nsubstantial growth approaching twice their previous levels. To better understand the driving forces\nbehind these spatiotemporal trends, we further analyzed the socioeconomic and technological\nfactors influencing total footprints over time.\n7. The description of the life cycle assessment (LCA) methodology, particularly for the\noperational phase, is difficult to follow without extensive reference to the supporting\ninformation. A clear summary diagram or methodological flowchart in the main text would\nbe helpful. In addition, how were treatment-specific variables (e.g., influent water quality,\nchemical dosing) incorporated into the LCA calculations?\nThank you very much for this thoughtful and constructive comment. We appreciate your emphasis\non the clarity and transparency of the life cycle assessment (LCA) methodology, especially in\n8\nrelation to the operational phase and the incorporation of treatment-specific variables. To improve\nthe readability and accessibility of our LCA framework, we have now added a methodological\nflowchart to the main text (see Figure 7), which provides a clear visual summary of the assessment\nboundaries, calculation stages (construction, operation, demolition), and the method used at each\nstage. This complements the detailed tables and explanations provided in the Supplementary\nInformation.\nRegarding the incorporation of treatment-specific variables, such as influent water quality and\nchemical dosing, our approach was to rely on a comprehensive dataset of 90 real-world wastewater\ntreatment cases extracted from 47 reports and publications.\nSpecifically, direct and indirect energy consumption during the operational phase was derived\nfrom documented case data. Water footprint contributions from chemical use were estimated using\nan energy-to-water conversion factor (2.14 kg/kWh) when direct water use data for chemicals was\nunavailable.\nWhile we acknowledge that detailed influent quality data (e.g., COD, TN, TP) was not always\navailable, we addressed variability through Monte Carlo simulations, incorporating uncertainty\nranges from the collected case statistics. We have clarified these methodological points in the\nrevised Methods section.\nWe hope these revisions address your concern and make the methodological framework more\ntransparent and reproducible. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): Calculation of\nWater and Energy Footprint Intensity and Annual Average Intensity for Different\nWastewater Treatment Processes. Based on the LCA boundary established in this research for\nthe wastewater treatment process, the water and energy footprint intensity of the main treatment\nprocesses is calculated using cases method. The life cycle assessment framework quantifies the\nwater and energy footprints across construction, operation, and demolition stages of wastewater\ntreatment plants. Stage-specific data and assumptions—ranging from empirical case values to\nliterature-based coefficients—are integrated to ensure consistent and comparable footprint\nestimation (Figure 7). The specific calculation process and methods detailed in Supplementary\nInformation S3.\nmainstream wastewater treatment processes in China, covering three phases: construction,\noperation, and decommissioning. The system boundary follows a cradle-to-gate framework, with\nall results normalized to the treatment of 1 m³ of municipal wastewater. Data were derived from\n90 engineering cases across 47 reports, reflecting variations in technology, region, and plant scale.\nFor the operational phase, the EF included both direct electricity consumption and indirect energy\nembodied in chemical production. Due to limited direct data, the WF associated with chemical use\nwas approximated by converting indirect energy consumption using a coefficient of 2.14 kg/kWh.\nThis approach ensures methodological consistency and comparability across process types and\nregional conditions.\n𝑊𝐹𝐼 = ∑6 𝑊𝐹𝐼 （1）\n𝑖=1 𝑖\n𝐸𝐹𝐼 = ∑6 𝐸𝐹𝐼 （2）\n𝑖=1 𝑖\nHere, WFI and EFI denote the water and energy footprint intensities for various treatment\nprocesses, respectively. WFI represents the water footprint intensity of the actual water input\n1\nduring the construction phase of the wastewater treatment plant. WFI and EFI denote the water\n2 1\nand energy footprint intensities during the material acquisition phase for the wastewater treatment\nplant. WFI and EFI indicate the water and energy footprint intensities during the construction\n3 2\nphase. EFI signifies the energy footprint intensity during the transportation phase of construction\n3\nmaterials. WFI and EFI denote the direct water and energy footprint intensities during the\n4 4\noperational phase. WFI represents the water footprint intensity of chemical agents during the\n5\noperational phase, while EFI denotes the indirect energy footprint intensity during this phase of\n5\nthe wastewater treatment plant. WFI and EFI refer to the water and energy footprint intensities\n6 6\nduring the decommissioning phase of the wastewater treatment plant. The units for these water\nand energy footprint intensities are kg/m³ and kWh/m³, respectively.\nwater footprint (WF) and energy footprint (EF) of wastewater treatment processes across three main life cycle stages:\nbuild, operation, and demolition. In the build stage, water and energy footprints are calculated based on actual water\ninput, material consumption, and energy use, using coefficient-based methods from literature and national standards.\nThe operation stage accounts for both direct and indirect energy consumption (e.g., aeration and chemical production)\nand material usage. When direct water use data for chemicals are unavailable, water–electricity conversion factors\nfrom literature are applied to estimate WF. The demolition stage is assumed to contribute 10% of the build-stage\nfootprint for both water and energy, based on national engineering standards. The total life cycle WF and EF are\ncalculated by summing the footprints of each stage, using a combination of case-based data and conversion coefficients,\nas shown in the equations on the right.\n8. Please provide justification for using the Logarithmic Mean Divisia Index (LMDI) method\nover other statistical or decomposition techniques. Why is LMDI particularly suited to this\nanalysis, and how does it contribute to the robustness of the findings?\nThank you for your insightful comment. We have added a justification for our use of the\nLogarithmic Mean Divisia Index (LMDI) method in the revised manuscript. Specifically, the\nLMDI approach was selected due to its well-recognized advantages in decomposition analysis,\nincluding its completeness (no residual term), additivity, and ability to handle zero or near-zero\nvalues without generating undefined results. These properties make it particularly suitable for our\n11\nstudy, which involves multi-year, cross-provincial data with varying magnitudes. Importantly, the\nLMDI method enhances the robustness of our findings by enabling consistent and interpretable\nattribution of water and energy footprint changes to their driving factors. The corresponding\nexplanation has been included in method of the revised manuscript. Please see the revised text\nbelow:\nRevised original text (The modified or added content is highlighted in red): The Logarithmic Mean\nDivisia Index (LMDI) method was applied to analyze the driving forces behind the changes in\nChina’s wastewater treatment water and energy footprints in this article56. The LMDI method was\nchosen for its completeness (no residual term), additivity, and suitability for handling zero or near-\nzero values. These properties ensure robust, consistent, and residual-free decomposition of water\nand energy footprint drivers across provinces and years, thereby enhancing the reliability of\ncomparative and temporal analyses57,58.\nReviewer #2:\n1. This is a well written and technically sound manuscript. This work makes an important\ncontribution by framing wastewater treatment as a multi-dimensional optimization\nchallenge. The paper provides a model that can guide and inform infrastructure planning\nand environmental regulations in other developing countries as well.\nWe sincerely appreciate your positive and encouraging feedback. We are grateful that you found\nthe manuscript well-written and technically sound, and that you recognized the significance of our\ncontribution in framing wastewater treatment as a multi-dimensional optimization problem. Your\nacknowledgment of the model's broader applicability to infrastructure planning and environmental\nregulation in other developing contexts is especially meaningful to us. We hope our work can\nindeed support sustainable decision-making beyond the case of China.\n2. The research hypothesis is valid, built on a life cycle assessment (LCA) framework, and\nthe topic is relevant to both academic and policy-making audiences. Connecting the\nobjectives to SDGs in introduction section and later in discussion section, provides a\nmeaningful approach. The methods utilized are appropriate, some limitations in data\ngranularity and rural plant representation are acknowledged.\nthe policy relevance of the topic, and the integration of SDG objectives throughout the manuscript.\nWe are also grateful for your acknowledgment of our methodological approach and the discussion\nof data-related limitations. Your recognition is encouraging and affirms the value of our work.\n3. Typo: Line 121: Anoxic-Toxic should be Anaerobic, Anoxic and Aerobic (A2O)\nThank you for pointing out this typographical error. We have corrected “Anoxic-Toxic” to\n“Anaerobic, Anoxic and Aerobic (A O)” in Line 121 to accurately describe the treatment process.\nRevised original text (The modified or added content is highlighted in red): The high variability\nof CAST footprint intensities arises from its widespread use across regions with diverse influent\nqualities, plant scales, and operational practices. Its modular and flexible design allows broad\napplicability, thereby leading to greater heterogeneity compared to more standardized processes\nlike Anaerobic-Oxic Process (AO) or Anaerobic-Anoxic-Oxic (A O)28. As a biological nitrogen\n2\nand phosphorus removal process, the A O process has water and energy footprint intensities that\n2\nare 21.1% and 35.5% higher, respectively, compared to those of the AO process.\n4. The results are presented well, figures and tables are well-organized. Interpretations are\ngenerally justified, however, some results could be strengthened by including more granular\nor predictive data for novel technologies.\nThank you for this insightful and constructive suggestion. We fully agree that incorporating more\ngranular analysis of emerging technologies can strengthen the depth and forward-looking\nrelevance of our results. In response, we have added a dedicated comparative assessment of four\nrepresentative novel technologies—Membrane Bioreactor (MBR), Biological Combined System\n(BIOCOS), Linpor process, and the Bardenpho process—based on our life cycle evaluation results.\nThese technologies were not included in the mainstream process analysis presented earlier in the\nmanuscript, and they represent innovations in the field of wastewater treatment. Their inclusion\nnot only addresses the need for broader technological representation but also helps contextualize\nthe relative performance of conventional versus novel systems.\nreference value for future technology optimization and policy-making. We sincerely appreciate\nthe reviewer’s thoughtful recommendation, which has significantly improved the completeness\nand applicability of our analysis. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): The water and energy\nfootprints of the eight major processes nationwide were analyzed, covering more than 90% of the\ntotal installed wastewater treatment capacity. Figure 2 displays the water and energy footprint\nintensity of various wastewater treatment processes, while intensities for each treatment process\ncase can be found in Supplementary Information S4. As shown in Figure 2a-b, the Biological\nFilter (BF) process exhibits the lowest footprint intensity (0.96 kg/m³, 0.18 kWh/m³, mean),\nwhereas the Continuous Activated Sludge Treatment (CAST) process demonstrates the highest\nfootprint intensity (1.91 kg/m³, 0.63 kWh/m³, mean), with a difference of 65.9%. The water\nfootprint intensity and energy footprint intensity of the CAST process vary widely across different\nwastewater treatment plants (WWTPs). The high variability of CAST footprint intensities arises\nfrom its widespread use across regions with diverse influent qualities, plant scales, and operational\npractices. Its modular and flexible design allows broad applicability, thereby leading to greater\nheterogeneity compared to more standardized processes like Anaerobic-Oxic Process (AO) or\nAnaerobic-Anoxic-Oxic (A O)28. As a biological nitrogen and phosphorus removal process, the\n2\nrespectively, compared to those of the AO process.\nTo further explore the resource performance of emerging technologies, we conducted a\ncomparative life cycle evaluation of four selected novel processes—Membrane Bioreactor (MBR),\nBiological Combined System (BIOCOS), Linpor, and Bardenpho. The results show that Linpor\nexhibited the lowest water (1.09 kg/m³) and energy footprints (0.22 kWh/m³), indicating its\npotential as a highly resource-efficient treatment option. BIOCOS also demonstrated relatively\nlow energy consumption (<0.57 kWh/m³). These findings provide empirical support for\nconsidering novel technologies in future optimization pathways. Detailed data are provided in\nSupplementary Table S7.\nand demolition phases of wastewater treatment. The water footprint during the construction and\noperation phases accounts for nearly 90% of the total, while the water footprint during the\ndemolition phase is almost negligible (Figure 2c-e). The construction phase involves a substantial\ndirect water resource input (0.61 kg/m³), comprising 91.5% of the total water footprint for this\nphase mainly stemming from on-site processes. This includes processes such as concrete curing\nand cleaning operations, which, despite being identified as dominant water use contributors in\nprevious studies29–31, are often underrepresented in LAC inventories. During the operational phase,\nthe water footprint is primarily driven by electricity consumption (0.63 kg/m³), while the\ndemolition phase contributes minimally. The energy footprint is dominated by the operational\nphase (0.47 kWh/m³), accounting for 89.5% of the total lifecycle energy footprint, with direct\nenergy consumption being the main contributor.\n5. The authors present the limitations of the study and appropriate future work and\napproaches.\nThank you very much for your positive comment. We appreciate your recognition of our effort to\nclarify the limitations of the study and outline potential directions for future work. We believe that\nacknowledging current gaps and proposing practical solutions is essential for enhancing the\napplicability and impact of our findings.\nTo address the questions for the review:\n7. The most noteworthy result is the quantified potential to reduce China's wastewater\ntreatment water footprint by 16.1% and energy footprint by 25.6% by 2035 with an\ninvestment of less than 8% of the total treatment cost, while not affecting the nutrient\nremoval efficiency.\nThank you for highlighting this key result. We agree that the quantified potential for reducing\nwater and energy footprints by 16.1% and 25.6%, respectively, with minimal cost increase and no\ncompromise in nutrient removal efficiency, is one of the most significant contributions of our study.\nWe believe this finding provides strong policy relevance and practical value for guiding cost-\neffective and sustainable upgrades in wastewater treatment infrastructure. This result underscores\n15\nthe feasibility of aligning environmental and economic goals, which is particularly important for\nplanning in resource-constrained regions.\n8. This work will be of significance to the filed because it expands the scope of LCA in\nwastewater treatment. The study uses data from 10,000+ wastewater treatment plants, a\nscale that is not commonly processed. Even though the work is not fully original, the scale\nand real data utilized make it a worthy contribution to the field.\nThank you for your positive evaluation of our work. We sincerely appreciate your recognition of\nthe study’s contribution through the expansion of LCA to a national scale and the integration of\ndata from over 10,000 WWTPs. While we acknowledge that the methodological framework builds\nupon existing approaches, we believe that the unprecedented data scale and spatial coverage offer\nvaluable new insights into wastewater treatment sustainability. We are encouraged by your view\nthat these aspects make the study a meaningful contribution to the field. The integration of large-\nscale empirical data into life cycle and optimization analysis remains relatively rare, and we hope\nthis work will help advance more data-informed infrastructure planning and policy design.\n9. The conclusions are mostly supported by the results. One question I have is that, even\nthough the study indicates that there could be substantial cost and resource reductions, the\nauthors did not fully discuss how feasible it would be to implement the potential changes to\nthe existing systems. What are the regulatory or institutional challenges to the potential\nimprovements?\nThank you for this insightful comment. We agree that assessing the feasibility of implementing\nthe proposed optimization measures is essential for practical relevance. In the revised manuscript,\nwe have added a brief discussion on regulatory and institutional challenges in the Discussion\nsection. Specifically, we mention the heterogeneity in local governance capacities, differences in\ntechnical standards across provinces, and the need for stronger incentives and funding mechanisms\nto support the adoption of advanced treatment technologies. These additions help contextualize the\npotential barriers to implementation and emphasize the importance of aligning technological\nimprovements with policy frameworks. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): While our optimization\nanalysis demonstrates significant resource-saving potential, the actual implementation of such\n16\nimprovements may face regulatory and institutional challenges. Local governments vary in their\ncapacity to enforce upgraded standards, and current funding mechanisms may not be sufficient to\nsupport wide-scale retrofitting of WWTPs. Furthermore, the coordination between environmental\nauthorities and utility operators needs to be strengthened to ensure compliance and long-term\nsustainability. Therefore, policy support such as targeted subsidies, performance-based incentives,\nand updated technical guidelines is essential to translate these optimization opportunities into\nactionable outcomes.\n10. I did not perceive any flaws in the data analysis. The uncertainty is acknowledged and\naddressed. One suggestion may be to expand the economic analysis with more specific\ninformation (capital cost, operational cost, etc).\nThank you very much for your positive assessment of our data analysis and uncertainty treatment.\nWe appreciate your suggestion to expand the economic analysis with more specific information.\nIn response, we have included a more detailed description of the operational cost assumptions used\nin our scenario modeling, supported by values from multiple literature sources. Additionally, we\nhave clarified the treatment of economic variability by averaging values across different project\nscales and locations. These additions are now included in the discussion of revised manuscript,\nand we believe they enhance the transparency and reproducibility of our analysis. Thank you again\nfor your constructive input, which helped improve the robustness of our study. Please see the\nrevised text below:\nRevised original text (The modified or added content is highlighted in red): To support the\nprovincial scenario optimization, we conducted a comparative assessment of the operational costs\nassociated with mainstream wastewater treatment technologies based on a comprehensive\nliterature review. Domestic studies indicate that processes such as CASS and SBR tend to incur\nrelatively higher operational costs, while others like oxidation ditch and AO are generally more\ncost-effective52,53. International comparisons further highlight regional differences—for example,\nQi et al54. (2020) reported that the operational costs of AO and A O systems in the United States\n2\nare approximately $0.11/m³ and $0.13/m³, respectively, reflecting variations in energy prices and\nlabor costs. These cost data were used to parameterize the economic assumptions in our scenario\nmodeling, with final values averaged across multiple sources to account for variability in project\n17\nscale, plant configuration, and local conditions. This ensures the robustness and applicability of\nour optimization framework.\n11. I believe the methodology is sound.\nThank you for your positive assessment. We appreciate your recognition of the soundness of our\nmethodology, which strengthens the overall credibility of the study.\n12. There is enough detail, especially with the supplemental materials. A little more clarity\non the normalization of data across 10,000+ wastewater treatment plants may allow others\nto replicate the study better. Similar to my comment above, also a little more detail on the\ncost assumptions would help.\nThank you very much for this helpful suggestion. In response, we have added a clarification on\nhow the dataset of over 10,000 wastewater treatment plants was used to determine the provincial\ndistribution of treatment technologies, based on each plant’s design daily treatment capacity (see\nMethod). Regarding cost-related data, we have further elaborated that the unit costs and TN/TP\nremoval rates were collected and synthesized from multiple literature sources, and the final values\nused in our analysis were derived by averaging across these references. Detailed values and\nreferences are provided in Supplementary Tables S11. As we mentioned above, we also included\ncost analysis and discussion in the discussion. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): This study established\nthe boundaries of wastewater treatment using LCA theory and methods, providing a\ncomprehensive evaluation and analysis of the water and energy footprints of wastewater treatment\nprocesses in China55. A data set of wastewater treatment capacity was constructed for the main\nprocesses of 10,124 WWTPs nationwide on the “Urban Drainage Statistical Yearbook”, which\nwas used to determine the distribution of treatment process types across provinces. These\ndistributions were derived based on the design daily treatment capacity of each plant, allowing for\na weighted representation of regional technology prevalence. The water footprint intensity and\nenergy footprint intensity of the main wastewater treatment processes were calculated based on\nthe case study data. The spatiotemporal dynamics of water footprint intensity and energy footprint\nintensity and total wastewater treatment in each province were analyzed by integrating provincial\nwastewater treatment data in the “China Environmental Statistics Yearbook”. Further exploration\n18\nwas conducted into the potential economic, social, and environmental factors influencing the water\nfootprint and energy footprint of wastewater treatment in China. Finally, we constructed regulatory\nscenarios based on process selection for different objectives, evaluated the water footprint and\nenergy footprint under different scenarios, and proposed resource-saving measures and\nsuggestions for wastewater treatment across different regions.\nTable S11. Main Wastewater Treatment Processes: Costs and Removal Rates\nA O OD AO CAS CASS SBR BF CAST Other\n2\n1.21 1.06 1.06 0.95 2.87 1.35 0.74 2.64\n1.19 1.43 2.05 1.96 0.7 1.55 0.37 0.79\n0.65 0.45 0.43 1.44 0.27 0.75 0.58 1.01\nCost\n0.44 0.47 0.4 1.00\n(yuan/\n0.65 0.55 0.56 1.22\nm³)\n0.45 0.93 1.01\n1.08 0.77\n0.76\nAverage 1.02 0.79 0.92 1.01 1.28 1.22 0.78 1.48 1.06\n67.06 67.45 67.84 66.27 72.99 67.45 75.40 45.40\n93.60 55.00 55.00 76.10 81.0 75.00 30.00 53.10\n76.91 85.00 70.80 78.1 48.00 80.00 62.90\n60.00 85.00 70.00 83.00 67.00\nTN\n71.90 85.00 85.00 85.00\nremoval\n71.50\nrate (%) 64.70\n63.90\n62.30\n80.00\n85.00\nAverage 72.44 69.15 69.66 71.19 77.42 71.69 61.80 62.68 69.50\n85.59 81.76 83.82 82.35 98.38 82.94 76.00 90.00\nTP\n91.90 69.00 90.00 95.9 79.00 73.75 86.83 97.00\nremoval\n60.00 75.00 72.30 85.00 83.00 77.8 76.9\nrate (%) 86.00 90.00 85.00 80.00 85.00\n90.00\nAverage 82.70 75.25 84.03 89.13 87.46 81.17 80.16 87.23 83.39\noperational costs associated with mainstream wastewater treatment technologies based on a\ncomprehensive literature review. Domestic studies indicate that processes such as CASS and SBR\ntend to incur relatively higher operational costs, while others like oxidation ditch and AO are\ngenerally more cost-effective52,53. International comparisons further highlight regional\ndifferences—for example, Qi et al54. (2020) reported that the operational costs of AO and A O\n2\nsystems in the United States are approximately $0.11/m³ and $0.13/m³, respectively, reflecting\nvariations in energy prices and labor costs. These cost data were used to parameterize the economic\nassumptions in our scenario modeling, with final values averaged across multiple sources to\naccount for variability in project scale, plant configuration, and local conditions. This ensures the\nrobustness and applicability of our optimization framework.\nReviewer #3:\nThis manuscript presents a comprehensive assessment of the water and energy footprints\nassociated with wastewater treatment in China, using a life cycle perspective and extensive\nnationwide data. By integrating scenario simulations, the authors propose optimized, cost-\neffective pathways for sustainable wastewater treatment by 2035. The study is policy-\nrelevant, well-structured, and supported by substantial supplementary data. There are some\nissues which need to be addressed.\nThank you very much for your positive evaluation of our manuscript. We are pleased that you\nfound the study to be well-structured, policy-relevant, and supported by robust national data and\nscenario simulations. We appreciate your recognition of the comprehensive life cycle perspective\nand the contribution it may offer to sustainable wastewater management. We have carefully\nreviewed and addressed all the specific issues you raised in the following point-by-point responses.\nThe manuscript has been revised accordingly to improve clarity, methodological transparency, and\npolicy relevance. We sincerely thank you again for your constructive feedback, which has helped\nus improve the quality of our work.\n1. In line 47, the manuscript mentions that 47 wastewater treatment plants (WWTPs) were\nselected for analysis. However, according to Supplementary Tables S1–S3, the total number\nof cases is 90. This discrepancy needs clarification. Moreover, the authors should explain the\n20\nrationale behind case selection—specifically how these samples reflect geographical,\ntechnological, and scale-based diversity, and whether they are representative of national\nwastewater treatment characteristics.\nThank you for pointing out this important clarification. The discrepancy was due to a misstatement\nin our original manuscript. As correctly reflected in Supplementary Tables S1–S3, a total of 90\nwastewater treatment cases were included in our analysis. These cases were extracted from 47\npublished studies and technical reports, not 47 cases as mistakenly written. We have revised the\nmanuscript accordingly to avoid confusion. To ensure representativeness, the selected cases cover\na broad geographic distribution across China and encompass a diverse range of treatment\ntechnologies and plant capacities. This diversity allows us to capture variations in operational\nperformance, cost, and environmental footprint under different regional and technological contexts.\nWe believe this provides a robust basis for subsequent footprint calculations and scenario modeling.\n2. In lines 119-120, the CAST process is noted as having a wide variation in water and energy\nfootprint intensity. The manuscript should discuss the causes of this variability—e.g.,\ndifferences in design and operation, regional infrastructure, influent quality, or plant size.\nSuch interpretation is necessary to contextualize the findings and guide technology\noptimization.\nThank you for pointing out the need to explain the variability specific to CAST. This is a valuable\ncomment that enhances the interpretation and policy relevance of our findings. We have revised\nthe manuscript to clarify that the high variability of CAST footprint intensities arises from its\nwidespread use across regions with diverse influent qualities, plant scales, and operational\npractices. Its modular and flexible design allows broad applicability, thereby leading to greater\nheterogeneity compared to more standardized processes like AO or A O. It supports our\n2\nconclusion that such operational inconsistencies contribute to the wide variation in CAST-related\nwater and energy footprint intensities. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): The water and energy\nfootprints of the eight major processes nationwide were analyzed, covering more than 90% of the\ntotal installed wastewater treatment capacity. Figure 2 displays the water and energy footprint\nintensity of various wastewater treatment processes, while intensities for each treatment process\n21\ncase can be found in Supplementary Information S4. As shown in Figure 2a-b, the Biological\nFilter (BF) process exhibits the lowest footprint intensity (0.96 kg/m³, 0.18 kWh/m³, mean),\nwhereas the Continuous Activated Sludge Treatment (CAST) process demonstrates the highest\nfootprint intensity (1.91 kg/m³, 0.63 kWh/m³, mean), with a difference of 65.9%. The water\nfootprint intensity and energy footprint intensity of the CAST process vary widely across different\nwastewater treatment plants (WWTPs). The high variability of CAST footprint intensities arises\nfrom its widespread use across regions with diverse influent qualities, plant scales, and operational\npractices. Its modular and flexible design allows broad applicability, thereby leading to greater\nheterogeneity compared to more standardized processes like Anaerobic-Oxic Process (AO) or\nAnaerobic-Anoxic-Oxic (A O)28.\n2\n3. In lines 146-161, the analysis of influencing factors in Figure 3c is limited. For instance,\nFactor F5 has a statistically significant correlation with both water and energy footprints,\nbut its role is not explained. In addition, while WFP and EFP show strong mutual correlation\nand similar relationships with other variables, they diverge in relation to Factor F3\n(significant correlation with EFP but not WFP). The manuscript should elaborate on the\nmechanisms or context behind these discrepancies to avoid superficial interpretation of\nstatistical associations.\nThank you for this insightful comment. We agree that further elaboration on the mechanisms\nbehind the observed correlations in Figure 3c is essential for a more robust interpretation. This\nsuggestion has greatly improved the clarity and policy relevance of our findings. In response, we\nhave expanded the explanatory in the result, particularly regarding the divergent effects of Factor\nF3 (per capita investment in urban reclaimed water utilities) on water and energy footprints. It also\ncontains some of the mechanisms underlying the results of other interesting correlations. Please\nsee the revised text below:\nRevised original text (The modified or added content is highlighted in red): The proportion of\ngroundwater supply and water resources availability per capita are strongly correlated with the\nintensities of water and energy footprints, as well as the per capita water and energy footprints,\nhighlighting their critical influence on resource utilization patterns (Figure 3c). This relationship\nmay stem from increasing marginal pumping costs and stricter conservation policies in\ngroundwater-dependent regions, which drive investment in more efficient treatment technologies\n22\nand operational upgrades 32,33.On the other hand, regions with higher per capita water availability\ntend to produce more diluted influent due to abundant domestic water use, resulting in lower\npollutant concentrations and greater energy required per unit of pollutant removed. This leads to\nhigher water and energy footprint intensities per unit volume34.\nWastewater discharge-related factors, such as urban fecal clearance volume per capita and rural\nsanitary toilet penetration rate, are negatively correlated with water footprint intensity and energy\nfootprint intensity. Systematic feces management and treatment reduce the untreated pollutants\ndischarged into water bodies, decreasing the need for pollutant removal during the wastewater\ntreatment phase and consequently improving water and energy efficiency35. Notably, investment\nin urban reclaimed water utilities per capita shows a significant positive correlation with the energy\nfootprint per capita but no significant correlation with the water footprint per capita. This can be\nattributed to the fact that such investments often focus on advanced post-treatment technologies\n(e.g., membrane filtration, UV disinfection), which substantially increase electricity consumption.\nHowever, these upgrades do not necessarily alter the amount of water used within the core\ntreatment processes, leading to limited impact on process water consumption36. The relationship\nbetween per capita water footprint and energy footprint indicates a strong correlation between the\ntwo per capita metrics. However, there is no significant correlation between footprint intensity and\nper capita footprint.\n4. In lines 153-154, the term “total feces collection” is not consistent with the variable labeled\nas “urban fecal clearance volume per capita” (F10) in Figure 3. These concepts refer to\ndifferent metrics (aggregate vs. per capita), and the manuscript should use terminology that\naccurately reflects the analyzed variable.\nThank you for pointing out this inconsistency in terminology. We appreciate your careful reading,\nwhich has helped improve the clarity of our manuscript. In response, we have revised the text to\nensure alignment between the descriptive language in the main text and the variable label used in\nFigure 3. Specifically, we now refer to “urban fecal clearance volume per capita” (F10) throughout\nthe relevant discussion to accurately reflect the analyzed variable. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): Wastewater discharge-\nrelated factors, such as urban fecal clearance volume per capita and rural sanitary toilet penetration\n23\nrate, are negatively correlated with water footprint intensity and energy footprint intensity.\nSystematic feces management and treatment reduce the untreated pollutants discharged into water\nbodies, decreasing the need for pollutant removal during the wastewater treatment phase and\nconsequently improving water and energy efficiency35.\n5. In lines 158-160, the authors state that the per capita water and energy footprints indicate\na “strong synergy” between water and energy footprint intensity. This interpretation is\nproblematic. First, per capita metrics are distinct from intensity metrics, and such a\nrelationship cannot be inferred directly. Second, the term synergy implies an interaction\neffect that enhances outcome beyond additive contributions, which is not demonstrated here.\nIf the authors intend to describe a statistical relationship, strong correlation would be more\naccurate.\nThank you for this insightful comment. We agree that the original wording was imprecise. The use\nof the term “synergy” may indeed have overstated the observed relationship. We have revised the\nsentence to more accurately describe the statistical association between per capita water and energy\nfootprints, replacing “synergy” with “strong correlation.” Additionally, we clarified the distinction\nbetween per capita metrics and intensity metrics to avoid any misinterpretation. Please see the\nrevised text below:\nRevised original text (The modified or added content is highlighted in red): The relationship\nbetween per capita water footprint and energy footprint indicates a strong correlation between the\ntwo per capita metrics. However, there is no significant correlation between footprint intensity and\nper capita footprint.\n6. In line 251, the DQI scoring table (referred to as Table 1) is not provided in the main\nmanuscript. Please clarify where this table can be found.\nThank you for pointing this out. We apologize for the mislabeling. The correct reference should\nbe Tables S10–S11 in the Supplementary Information, which provide the DQI scoring details. We\nhave corrected the citation in the revised manuscript accordingly. Please see the revised text below:\nRevised original text (The modified or added content is highlighted in red): This research uses the\nData Quality Indicator (DQI) standards to evaluate the data quality of parameters at each stage of\n24\nwastewater treatment based on five key aspects, with scoring conducted according to the DQI\nallocation table (Table S9~S10).\n7. In lines 256-262, the Monte Carlo-based uncertainty analysis lacks information about\nparameter settings, such as distribution types, parameter ranges, or assumptions. These\ndetails are essential for assessing the robustness of the findings. Please include a more\ncomprehensive description of simulation settings and justification for key assumptions.\nThank you for this valuable comment. In our Monte Carlo simulation, we assumed that the water\n(and energy) footprint of each life cycle stage follows a normal distribution, based on the statistical\ncharacteristics (mean and standard deviation) calculated from the 90 collected cases. These\ndistributions were used to reflect the variability and uncertainty inherent in real-world wastewater\ntreatment operations.\nTo estimate the total life cycle footprint, we defined it as the sum of the stage-specific footprints,\nand Oracle Crystal Ball was employed to randomly sample values from each stage’s defined\ndistribution. A total of 50,000 iterations were performed to generate the distribution of predicted\nlife cycle water and energy footprints.\nThis simulation helps evaluate the robustness of our footprint estimates under input uncertainty.\nWe have now included this methodological detail in the revised Supplementary Information\n(Section S6) and clarified our assumptions and modeling settings as recommended. Please see the\nrevised text below:\nRevised original text (The modified or added content is highlighted in red): To evaluate the\nrobustness of life cycle water and energy footprint estimates, a Monte Carlo simulation was\nconducted using Oracle Crystal Ball. In this simulation, the water and energy footprints of each\nlife cycle stage (construction, operation, and decommissioning) were assumed to follow normal\ndistributions, with parameters (mean and standard deviation) derived from the 90 collected\nwastewater treatment plant (WWTP) cases.\nFor each iteration, the total life cycle footprint was calculated as the sum of the randomly sampled\nvalues for all three stages. This simulation was repeated 50,000 times to generate a probability\ndistribution of possible life cycle footprint outcomes. The assumption of normality was adopted\ngiven the central limit characteristics of the aggregated case data and is widely applied in similar\n25\nenvironmental modeling studies. This approach allowed us to quantitatively assess the potential\nvariability in footprint estimates stemming from stage-specific uncertainties, thereby enhancing\nthe reliability and interpretability of the overall results.\n8. In lines 512–513, while provincial optimization is an important component of the\nmanuscript, the process by which optimal strategies were assigned to each province remains\nvague. It is strongly recommended that the authors provide one or two detailed case studies\n(e.g., Guangdong and Inner Mongolia) to demonstrate how optimization scenarios were\ndetermined and how trade-offs among cost, footprint, and pollutant removal were balanced.\nThank you very much for this insightful and constructive comment. We greatly appreciate your\nrecommendation to include detailed examples and better elaborate the rationale behind the\nprovincial optimization strategies. Your suggestion has significantly improved the clarity, policy\nrelevance, and replicability of our analysis.\nWe have now revised the manuscript to provide a more transparent explanation of our optimization\napproach, along with two representative provincial case studies—Guangdong and Inner\nMongolia—to demonstrate how optimal treatment portfolios were determined and how trade-offs\namong cost, footprint, and pollutant removal were addressed.\nOur core principle in scenario optimization is to develop region-specific strategies based on\ndifferences in provincial economic development levels, wastewater discharge characteristics, and\npollutant loads. The goal is to achieve a balance between environmental performance and\neconomic affordability in wastewater treatment system design.\nSpecifically, we evaluated each province’s total nitrogen and phosphorus pollution intensity from\nwastewater, existing treatment technology structure, and economic indicators such as per capita\nGDP. Recognizing the heterogeneous pollution control demands and fiscal capacities across\nregions, we proposed customized optimization pathways.\nFor provinces with relatively low pollution loads or smaller wastewater discharge volumes, and\nwith limited economic capacity, we recommend retaining and expanding the use of existing\nmainstream technologies that are less costly, in order to minimize technology replacement costs.\nTwo levels of optimization were designed:\n26\n- A combined promotion scenario using three cost-effective technologies.\n- A minimalist scenario promoting the single most affordable process.\nThese options aim to maximize resource efficiency while maintaining practicality.\nFor provinces with high pollutant discharge loads and stronger fiscal capacity, we encourage the\nuse of advanced or enhanced technologies with stronger removal performance, to achieve more\nambitious environmental goals. For example, in Guangdong Province, which has both high\ndischarge volumes and strong economic development, we proposed two optimization scenarios:\n- Combining CASS, A O, and SBR\n2\nprocesses.\n- Using only the CASS process, which offers strong removal performance and is widely\nimplemented in the region.\nInner Mongolia represents a special case. According to our current data, OD (oxidation ditch) is\nthe only widely implemented process in the region. Based on our established decision rules, both\noptimization scenarios for Inner Mongolia retain and promote the OD process, highlighting the\ntechnological homogeneity and limited optimization flexibility in some regions.\nThis framework is developed based on the following considerations:\n- It integrates multidimensional criteria to guide region-specific decision-making.\n- It introduces dual optimization pathways (cost-oriented vs. performance-oriented) to\nenhance policy flexibility.\n- It is grounded in real-world technical and economic constraints, avoiding idealized\nassumptions and supporting practical implementation.\nWe also acknowledge several limitations in the current study. For instance, the concentration or\nabsence of process data in some provinces may restrict the adaptability of optimization. Moreover,\nwe did not incorporate forward-looking variables such as technology evolution, fiscal investment\nchanges, or future policy shifts. In future work, we plan to incorporate dynamic optimization\nmodels or multi-criteria decision analysis frameworks to improve the robustness, flexibility, and\npolicy relevance of regional technology planning. Please see the revised text below:\n𝑀\n𝐷 represent the normalized values of per capita GDP and nitrogen and phosphorus emissions\n𝑁\nfrom wastewater, respectively. 𝐷 and 𝐷 denote the actual values of per capita GDP and\n𝑎0 𝑖0\nnitrogen and phosphorus emissions from wastewater in different regions. 𝐷 and 𝐷\n𝑎𝑀𝑎𝑥 𝑖𝑀𝑎𝑥\nindicate the maximum values of per capita GDP and nitrogen and phosphorus emissions from\nwastewater across different regions, while 𝐷 and 𝐷 denote the minimum values of per\n𝑎𝑀𝑖𝑛 𝑖𝑀𝑖𝑛\ncapita GDP and nitrogen and phosphorus emissions from wastewater across different regions.\nTo design region-specific optimization pathways for wastewater treatment systems, we\ndeveloped a decision framework based on each province’s economic development level,\nwastewater generation characteristics, and pollutant discharge intensity. In this way, our scenario\ndesign aims to reflect local constraints and capacities, balancing environmental goals with realistic\nimplementation feasibility.\nProvinces were classified into different strategy groups by jointly considering total nitrogen\nand phosphorus pollution levels from wastewater, existing treatment process structures, and per\ncapita GDP. For provinces with relatively low pollutant loads and limited economic capacity, two\ncost-sensitive optimization scenarios were constructed: one promoting a mix of three mainstream\nlow-cost technologies, and the other promoting only the most affordable process currently in use.\nConversely, for provinces with higher pollutant discharge and stronger fiscal capacity, we\nproposed the adoption of enhanced treatment technologies with stronger removal performance.\nFor instance, Guangdong Province, characterized by high pollution loads and strong economic\nconditions, was assigned two optimization options: (i) a combination of CASS, A O, and SBR\n2\nprocesses, or (ii) solely promoting CASS. A more detailed overview of our strategy is presented\nin Table S12.\n9. Line 520 references Supplementary Information S7 for provincial scenario assignments.\nHowever, the correct source appears to be Supplementary Information S8. Please revise\naccordingly.\nThank you for pointing out the discrepancy. We have corrected the reference from Supplementary\nInformation S7 to S8 in the methods to ensure accuracy and clarity. Please see the revised text\nbelow:\n28\n10. Figure 1d and some other visual elements are unclear and should be presented in higher\nresolution for better interpretability.\nThank you very much for pointing this out. We fully agree that the clarity of visual elements is\nessential for effective communication of results. In response, we have replaced Figure 1d and other\nrelevant figures with higher-resolution versions in the revised manuscript to improve visibility,\nespecially for key labels, legends, data points and font size. We hope this enhancement facilitates\nbetter understanding and interpretation of the presented results. Please see the revise figure:\n29\n[Figure Redacted]\n30\n11. Table S3 contains inconsistencies (e.g., WWTP #87 is labeled “Liaoning Wastewater\nTreatment Plant” but assigned to Wuxi, Jiangsu). The authors should carefully verify the\ngeographic accuracy of plant data.\nThank you very much for your careful review and for identifying this inconsistency. We sincerely\napologize for the oversight and appreciate your attention to detail. We have thoroughly rechecked\nthe geographic assignments in Table S3 and corrected the mislabeled entries, including WWTP\n#87. The updated table has been included in the revised Supplementary Information. We are\ngrateful for your meticulous feedback, which helped improve the accuracy and reliability of our\ndataset.\n12. The term “demolition phase” is inconsistently used—appearing also as “dismantling\nphase” (line 102 and line 384). Consistent terminology should be applied throughout the\nmanuscript and supplement.\nThank you for pointing out this inconsistency. We appreciate your careful reading. We have now\nstandardized the terminology throughout the manuscript and Supplementary Information,\nconsistently using the term “demolition phase” to avoid confusion and ensure clarity. Please see\nthe revised text below:\n13. The section titled “Methodology for calculating the energy footprint of wastewater\ntreatment” refers to “Tables 2-6 to 2-8,” which are not present in the supplement. Please\nverify the numbering and presence of these tables.\nThank you for your observation. We apologize for the confusion caused by the incorrect table\nreference. The correct reference should be Table S6 in the Supplementary Information, which\ncontains the relevant data used for calculating the energy footprint. We have revised the text\naccordingly to ensure consistency and clarity. Please see the revised text below:\nRevised original text: During the production phase of building materials (EFI1), inputs primarily\ninclude cement, steel, and other construction materials, all of which contribute to the energy\nfootprint. Most of the energy consumption for these building materials is directly sourced from\nliterature references in Tables S6. For cases where energy consumption data are not provided, the\nresource consumption quantities for each stage, such as materials, can be multiplied by respective\nenergy footprint coefficients for aggregation and calculation.\n31\n14. The sequential order of supplementary tables should be corrected (e.g., Table S7 is\nfollowed by Table S10, with S8 and S9 missing).\nThank you for your careful review. We appreciate you pointing out the inconsistency in the\nnumbering of the supplementary tables. We have corrected the sequence and ensured that Tables\nS8 and S9 are now properly included and labeled to maintain logical order and completeness in\nthe supplementary material.\nReferee #1:\nThe manuscript has been substantially improved after revision. A few additional\ncomments:\n1. Re: original Comment 2: It would be clearer to indicate the “mean” value directly within\nthe text rather than placing it in parentheses.\nThank you for your helpful suggestion. We have revised the manuscript accordingly by directly\nstating that the reported values represent mean intensities within the text, instead of indicating\n“mean” in parentheses. For example, we now write “… the BF process exhibits the lowest mean\nfootprint intensity, at 0.96 kg/m3 and 0.18 kWh/m3 …” This change has been applied consistently\nthroughout the manuscript for clarity.\n2. Re: original Comment 4: The cited papers appear to be somewhat outdated. There is a\nmore recent and comprehensive review on wastewater LCAs that could be\nreferenced: https://www.sciencedirect.com/science/article/pii/S0043135420305959\nThank you for this valuable suggestion. In the revised manuscript, we have added the\nrecommended reference (Corominas et al., 2020, Water Research) to update the literature\nbackground on wastewater LCAs. This inclusion strengthens the discussion by incorporating a\nmore recent and comprehensive review.\nDear editor and authors,\nHaving read the authors’ responses and the revised manuscript, I would like to thank the\nauthors for their efforts in addressing the comments raised during the previous round of\nreview. Nevertheless, there remain a few issues that need to be addressed. I elaborate on\nthese aspects below, which form the basis of my current assessment.\n1. While the authors have explained the geographical and technological diversity of the cases\nin the revised manuscript, the current discussion still relies on subjective judgment. It is\nrecommended to provide statistical comparisons of the 90 cases in terms of size distribution,\nprocess type proportions, and geographical coverage, and to present these results graphically\nso that readers can clearly assess whether the sample adequately represents the overall\ncharacteristics of wastewater treatment plants nationwide.\nThank you very much for this constructive suggestion. We agree that quantitative and graphical\nevidence would provide a more objective basis for demonstrating the representativeness of our\ncase dataset. In the revised manuscript, we have now summarized the size distribution, process\ntype proportions, and geographical coverage of the case studies, and presented the results in\ngraphical form (see Supplementary Figure S1, S2). As shown, the distribution of the selected cases\nbroadly reflects the national profile of China WWTPs in 2017 in terms of plant scale, technology\ntype, and regional coverage, thereby supporting the representativeness of the sample set. The\nrevisions to the main text and supplementary information are listed below:\nTo verify the representativeness of the collected cases, we summarized their size distribution,\nprocess type proportions, and geographical coverage in Supplementary Figure S1 and S2. The\nresults indicate that the sample broadly reflects the national profile of WWTPs.\nFigure S2. Scale, Process Type, and Geographical Distribution of China wastewater treatment\nplants\n2. Although the LMDI analysis clearly quantifies the contributions of each driving factor,\nthe explanation for the notable shift in the contribution of the wastewater generation rate\nfrom positive to negative around 2015 remains superficial. It is recommended to provide a\nmore detailed explanation by linking this turning point to relevant policy changes,\ntechnological developments, and economic restructuring.\nThank you for this valuable suggestion. We agree that a deeper explanation of the changing role\nof the wastewater generation rate would improve the discussion. In the revised manuscript, we\n3\nhave clarified that wastewater generation rate indeed exerted a positive effect on water and energy\nconsumption during the study period. This addition provides a clearer context for understanding\nthe decomposition results. The revision to the main text is listed below:\nThe shift in the contribution of the wastewater generation rate can be attributed to the gradual\nimplementation of policy measures, technological improvements, and economic restructuring.\nSuccessive regulations have promoted water conservation, stricter discharge limits, and\nwastewater reuse38–40. Meanwhile, technological upgrades in industrial production and municipal\ninfrastructure have reduced per capita wastewater generation41,42. In addition, the ongoing\ntransition of China’s economy from heavy industry to service-oriented and high value-added\nsectors has further contributed to declining wastewater generation intensity42,43. Collectively, these\nfactors explain the shift from a positive to a more constrained or even negative contribution in\nrecent years.\n3. In the Method section, the conversion factor for water consumption associated with\nchemicals (2.14 m³/GJ) lacks a clear source. The authors are encouraged to provide the\ncorresponding literature reference and to specify the applicable conditions and underlying\nassumptions, in order to justify the appropriateness of its use.\nWe appreciate this helpful suggestion. In the revised manuscript, we have corrected the unit of the\nconversion factor to 2.14 kg/kWh and added the corresponding reference (Chai et al., 2018). This\ncoefficient originates from the reported average blue water footprint of thermal electricity (2.14\nL/kWh ≈ 2.14 kg/kWh) and has also been applied in previous studies as an approximate\nconversion factor for energy-related water use. This clarification ensures transparency and\nmethodological consistency in our analysis. The revision to the main text is listed below:\nFor the operational phase, the EF included both direct electricity consumption and indirect energy\nembodied in chemical production. Due to limited direct data, the WF associated with chemical use\nwas approximated by converting indirect energy consumption using a coefficient of 2.14 kg/kWh63.\nThis coefficient has also been applied in previous studies as an approximate conversion factor for\nenergy-related water use64. This approach ensures methodological consistency and comparability\nacross process types and regional conditions.\n4\n4. Several figures in the manuscript lack essential cartographic components. For example,\nFigure 3a–b, Figure 4c–f, and Supplementary Figure S2 do not include a north arrow. For\nmaps involving spatial distribution, a north arrow is a necessary element, and its absence\nmay affect readers’ spatial orientation and interpretation. It is recommended to add a north\narrow and other basic map elements to all map-based figures.\nThank you for this careful observation. In the revised manuscript, we have added north arrows and\nother basic cartographic elements to all map-based figures to improve clarity and readability. The\nrevision to the main text and SI is listed below:\n5\n[Figure Redacted]\nFigure 3 Spatial distribution and correlations with different factors of national wastew ater\ntreatment water footprint (a) and energy footprint (b) intensity. Spatial distribution of\nwastewater treatment intensity at the prefecture-level city scale, calculated based on the\nproportion of wastewater treatment capacity attributed to different processes within each\nprefecture-level city. c, Correlation analysis between water footprint intensity (WFI), energy\nfootprint intensity (EFI), water footprint per capita (WFP), energy footprint per capita (EFP) and\nrelevant economic, social, and environmental factors. The color scale represents the Spearman\ncorrelation coefficient, with blue indicating a positive correlation and red indicating a negative\ncorrelation. An asterisk (*) denotes statistical significance at the 0.05 level (P < 0.05).\n6\n[Figure Redacted]\nFigure 4 Temporal and spatial dynamics of national wastewater treatment water and energy\nfootprints. a-b, Evolution of the total water and energy footprints of wastewater treatment in\nChina, illustrated as stacked graphs representing major processes. c-f, Spatial distribution of the\ntotal water and energy footprints of wastewater treatment in 2009 and 2022.\n7\n[Figure Redacted]\nFigure S4. Spatial distribution of wastewater treatment water footprint intensity by province\n(kg/m³)\nReferee #3:\nThe manuscript has been substantially improved after revision. A few additional\ncomments:\n1. Having read the revised manuscript, I appreciate the authors’ thorough revisions and\nclear improvements. The paper now meets the publication standard. I have only one minor\nissue: in the caption of Figure 5, the phrase “reduce water footprint (S7 and S8) and reduce\nwater footprint (S9 and S10)” should read “reduce water footprint (S7 and S8) and reduce\nenergy footprint (S9 and S10).” After this small correction, the manuscript can be accepted\nfor publication.\nThank you very much for your positive evaluation and for your careful reading of our revised\nmanuscript. We sincerely appreciate your insightful comment regarding the caption of Figure 5.\nWe have corrected the phrase, changing “reduce water footprint (S7 and S8) and reduce water\nfootprint (S9 and S10)” to “reduce water footprint (S7 and S8) and reduce energy footprint (S9\nand S10).”\ntotal nitrogen removal rate (TN), and cost in wastewater treatment in China under different target-oriented\nscenarios at the national and provincial scales. Based on scenario analysis targeting various objectives, the study\nidentifies five major objectives and two gradients, which are cost reduction (S1 and S2), improve TP\nremoval rate (S3 and S4), improve TN removal rate (S5 and S6), reduce water footprint (S7 and\nS8) and reduce energy footprint (S9 and S10) aiming to restructure wastewater treatment processes by\n2035. The restructuring strategy retains three processes and incorporates an additional process depending on the\nspecific objective. The BAU (Business-As-Usual) scenario represents a set of baseline target parameters derived\nfrom the current situation. The five objectives include cost minimization, maximum total phosphorus (TP) removal\nrate, maximum total nitrogen (TN) removal rate, minimum water footprint, and minimum energy footprint. These\nobjectives are used to evaluate synergies and trade-offs among different targets. Detailed absolute values under the\nscenario settings are provided in Supplementary Information Figure S12.\n2"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript has been substantially improved after revision. A few additional comments:\n1. Re: original Comment 2: It would be clearer to indicate the “mean” value directly within the text rather than placing it in parentheses.\n2. Re: original Comment 4: The cited papers appear to be somewhat outdated. There is a more recent and comprehensive review on wastewater LCAs that could be referenced:\nhttps://www.sciencedirect.com/science/article/pii/S0043135420305959", "reviewer_3": "Dear editor and authors,\nHaving read the authors’ responses and the revised manuscript, I would like to thank the authors for their efforts in addressing the comments raised during the previous round of review. Nevertheless, there remain a few issues that need to be addressed. I elaborate on these aspects below, which form the basis of my current assessment.\n1) While the authors have explained the geographical and technological diversity of the cases in the revised manuscript, the current discussion still relies on subjective judgment. It is recommended to provide statistical comparisons of the 90 cases in terms of size distribution, process type proportions, and geographical coverage, and to present these results graphically so that readers can clearly assess whether the sample adequately represents the overall characteristics of wastewater treatment plants nationwide.\n2) Although the LMDI analysis clearly quantifies the contributions of each driving factor, the explanation for the notable shift in the contribution of the wastewater generation rate from positive to negative around 2015 remains superficial. It is recommended to provide a more detailed explanation by linking this turning point to relevant policy changes, technological developments, and economic restructuring.\n3) In the Method section, the conversion factor for water consumption associated with chemicals (2.14 m³/GJ) lacks a clear source. The authors are encouraged to provide the corresponding literature reference and to specify the applicable conditions and underlying assumptions, in order to justify the appropriateness of its use.\n4) Several figures in the manuscript lack essential cartographic components. For example, Figure 3a–b, Figure 4c–f, and Supplementary Figure S2 do not include a north arrow. For maps involving spatial distribution, a north arrow is a necessary element, and its absence may affect readers’ spatial orientation and interpretation. It is recommended to add a north arrow and other basic map elements to all map-based figures."}, "author_response": "Point-by-point Responses (regular font) to Referees’ Comments (bold font)\nReferee #3:\nThe manuscript has been substantially improved after revision. A few additional\ncomments:\n1. Having read the revised manuscript, I appreciate the authors’ thorough revisions and\nclear improvements. The paper now meets the publication standard. I have only one minor\nissue: in the caption of Figure 5, the phrase “reduce water footprint (S7 and S8) and reduce\nwater footprint (S9 and S10)” should read “reduce water footprint (S7 and S8) and reduce\nenergy footprint (S9 and S10).” After this small correction, the manuscript can be accepted\nfor publication.\nThank you very much for your positive evaluation and for your careful reading of our revised\nmanuscript. We sincerely appreciate your insightful comment regarding the caption of Figure 5.\nWe have corrected the phrase, changing “reduce water footprint (S7 and S8) and reduce water\nfootprint (S9 and S10)” to “reduce water footprint (S7 and S8) and reduce energy footprint (S9\nand S10).”\ntotal nitrogen removal rate (TN), and cost in wastewater treatment in China under different target-oriented\nscenarios at the national and provincial scales. Based on scenario analysis targeting various objectives, the study\nidentifies five major objectives and two gradients, which are cost reduction (S1 and S2), improve TP\nremoval rate (S3 and S4), improve TN removal rate (S5 and S6), reduce water footprint (S7 and\nS8) and reduce energy footprint (S9 and S10) aiming to restructure wastewater treatment processes by\n2035. The restructuring strategy retains three processes and incorporates an additional process depending on the\nspecific objective. The BAU (Business-As-Usual) scenario represents a set of baseline target parameters derived\nfrom the current situation. The five objectives include cost minimization, maximum total phosphorus (TP) removal\nrate, maximum total nitrogen (TN) removal rate, minimum water footprint, and minimum energy footprint. These\nobjectives are used to evaluate synergies and trade-offs among different targets. Detailed absolute values under the\nscenario settings are provided in Supplementary Information Figure S12.\n2"}, {"round": 3, "reviewer_comments": {"reviewer_1": "I do not have any additional comments on the paper.", "reviewer_3": "Having read the revised manuscript, I appreciate the authors’ thorough revisions and clear improvements. The paper now meets the publication standard. I have only one minor issue: in the caption of Figure 5, the phrase “reduce water footprint (S7 and S8) and reduce water footprint (S9 and S10)” should read “reduce water footprint (S7 and S8) and reduce energy footprint (S9 and S10).” After this small correction, the manuscript can be accepted for publication."}, "author_response": ""}]

Decision letter