Benchmarking sustainability of urban water infrastructure systems in China

Accepted Manuscript Benchmarking sustainability of urban water infrastructure systems in China Xin Dong, Xinming Du, Ke Li, Siyu Zeng, Brian P. Bledsoe PII:

S0959-6526(17)32036-X

DOI:

10.1016/j.jclepro.2017.09.048

Reference:

JCLP 10549

To appear in:

Journal of Cleaner Production

Received Date: 6 April 2017 Revised Date:

31 August 2017

Accepted Date: 5 September 2017

Please cite this article as: Dong X, Du X, Li K, Zeng S, Bledsoe BP, Benchmarking sustainability of urban water infrastructure systems in China, Journal of Cleaner Production (2017), doi: 10.1016/ j.jclepro.2017.09.048. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Xin Dong 1,2, Xinming Du1, Ke Li3, Siyu Zeng 1,2* Brian P.

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1. School of Environment, Tsinghua University, Beijing, 100084, China

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2. Environmental Simulation and Pollution Control State Key Joint Laboratory,

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School of Environment, Tsinghua University, Beijing 100084, China

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3. College of Engineering, University of Georgia, GA, USA, 30602

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Bledsoe3

Corresponding author: [email protected]

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Benchmarking Sustainability of Urban Water Infrastructure Systems in China

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ABSTRACT: The broad scope and definition of sustainability has perplexed

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assessment of water infrastructure systems, especially for the purpose of directing

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engineering practices when quantified criteria are desired. An input-oriented data

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envelopment analysis (DEA) was improved to benchmark the relative sustainability

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of the water infrastructure of 157 cities in China. The DEA calculates a single

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sustainability score using seven inputs and five outputs that represent the economic,

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resource and environmental dimensions of sustainability. Overall, 69 out of the 157

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sampled systems obtained high sustainability scores. Eight specific efficiency

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ACCEPTED MANUSCRIPT indicators based on individual DEA input to output ratio were evaluated to identify

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the causes of performance differences. Compared to water supply systems, the

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performance of wastewater treatment plants has greater influence on the sustainability

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score of the overall system. For all systems, the sustainability scores are more

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sensitive to sludge production and electricity consumption than capital investment and

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removal efficiency of treatment processes. The DEA provides guidelines to cities for

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setting priorities in order to meet specific sustainability criteria. Statistical analysis

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indicates that the overall sustainability score primarily depends on the system scale,

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meteorological conditions such as air temperature and rainfall, and source water

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quality.

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KEYWORDS: Data envelopment analysis; Urban water infrastructure system;

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Sustainability; Benchmarking; China

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1. INTRODUCTION Reliable and secure water systems are a pre-requisite for the health, prosperity and

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security of a nation. Assessments of water infrastructure sustainability are

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complicated due to the various interconnected factors driving its construction,

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maintenance and operation. Sustainable and resilient water infrastructure development

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requires long-term economic inputs that are controlled by decision making at a variety

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of governance levels.1 The extensive water-energy-pollutant nexus in urban water

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systems increases the complexity of sustainable water infrastructure management.2

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Variation of facility scale, technology adopted, and geographical and meteorological

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conditions further complicate sustainability assessment at regional and national scales.

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This calls for a thorough and systematic benchmark to facilitate large-scale

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comparisons and guide water facilities towards sustainable practices.

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Loucks proposed a sustainability index based on reliability, resilience and

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vulnerability to facilitate the evaluation and comparison of water management

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policies.3 This index is based on the concept of life cycle assessment (LCA), which

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has been applied to identify the main environmental impacts of water infrastructure

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systems.4-7 Cost-benefit analysis (CBA) has also been applied to compare the

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economic feasibility of implementing water infrastructures by defining environmental

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benefits as positive externalities and assigning monetary valuation.8-12 However,

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given the current lack of consensus on the metrics and definitions of sustainability, a

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more quantitative and comprehensive benchmarking approach is necessary for

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assessing systems with varied size and system properties. Originated from production theory in economics, data envelopment analysis (DEA)

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is a nonparametric method to empirically measure the productive efficiency of

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decision making units (DMUs). Unlike other benchmark methods that assume

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particular functional forms, DEA seeks a balanced best practice benchmark based on

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a set of selected metrics, which can be customized based on local priorities.13 By

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forming a “composite” system that produces the most output at specific input levels,

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DEA allows the calculation of an efficient solution for any level of input and output,

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and therefore provides a criterion to evaluate the system performance. The DEA

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approach has been validated for the comparison of water and wastewater plant

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efficiency under a variety of input variables.14,15 More recently, DEA has been

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applied worldwide to analyze the relationship between treatment efficiency and a

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variety of factors such as labor costs, operational and capital cost, energy and water

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consumption, water quality, treatment technology, network length, and so on.16-28 The

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factors ranged across social, environmental, and economy perspectives and suggested

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applicability of DEA as a sustainability benchmarking approach.

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Three DEA studies have been performed for water and wastewater treatment plants

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in China at the province level.29-31 The results are inconsistent because the efficiency

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of water infrastructure system is often determined by constraints at city scale.

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ACCEPTED MANUSCRIPT In order to obtained a benchmark for the water infrastructure at city scale in China,

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in this paper, a novel DEA approach was improved by selecting additional factors

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covering three aspects of sustainability and collecting a comprehensive dataset to

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estimate the sustainability of urban water systems. An inventory of water

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infrastructures was compiled for 657 cities in China. Data from 157 cities were

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chosen to construct decision-making units for data envelopment analysis that

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evaluates the sustainability of urban water infrastructure systems in China at the city

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scale. In contrast to the classic DEA method that only evaluates efficiency, the

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outputs of the novel DEA assessment approach are sustainability scores for each city.

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The results can be used to compare relative sustainability among different systems

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and shed light on system and process bottlenecks for achieving sustainability. The

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large dataset allowed statistical analysis to uncover the factors that most influence

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system sustainability.

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2. METHODS

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2.1

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System conceptualization and interpretation of sustainability

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Urban water infrastructure systems are complex with multiple operational

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components and corresponding functions. Four basic elements were identified to

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represent a typical system and its fundamental utility, i.e., water treatment plant

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(WTP), water distribution network, drainage network (combined or separate sewer),

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and wastewater treatment plant (WWTP). In this study, urban water facilities in a city

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were conceptually integrated into one system with four types of elements, which was

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treated as one decision-making unit (DMU) for DEA. Although the definition of sustainability is still debatable, it is commonly agreed

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that it should include three dimensions: economy, environment, and society. The

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metrics for sustainability assessment applied in this study are: life cycle cost for

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capital and operation/maintenance, resource/energy consumption and recovery, and

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environmental impacts.

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energy, resources and environmental assets. Water treatment produces water

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resources but also demands significant investments of energy and resources. While

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wastewater treatment removes contamination and produces new water resources with

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energy and materials input, it also contains abundant energy and has the potential to

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produce sludge for soil improvement.32-34

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2.2 DEA input/output selection and DMUs for assessment

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Water infrastructure can be both a sink and a source of

In accordance with the selected sustainability metrics, seven inputs were chosen as

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inputs for DEA to represent economic costs, energy consumption and environmental

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impacts, as shown in Table 1. The economic costs of the water distribution network

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and sewer network were quantified based on the total length of pipes and unit cost due

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to data availability. Since electricity was the main energy source during the operation

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of the infrastructure system and one of the largest proportions of operation cost, the

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corresponding variables (xec and xew) were selected as the key inputs. Sludge

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production (xsp) was selected to represent the environmental performance of the

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system. Five outputs were selected in the assessment to summarize the function of water

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infrastructure, including two outputs for water resource production and three outputs

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for pollutant removal (Table 1). Three major water quality parameters, the loads of

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chemical oxygen demand (COD), suspended solids (SS), and total nitrogen (TN)

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removed from wastewater, were chosen to depict the environmental impacts of the

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system.

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Information was collected and compiled for water infrastructure systems in 657

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cities from different geographical regions in China. Statistical data for the inputs and

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outputs of the year 2014 were retrieved from the Chinese Urban Construction

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Yearbook, the Urban Water Supply Yearbook, and the Urban Drainage Yearbook.35-37

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Among them, 157 cities were selected for the DEA due to the comprehensiveness and

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representativeness of the available data. The geographical distribution of numbers of

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DMUs were 22, 15, 54, 23, 23, and 20 in the North, Northeast, East, Central, South,

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and Southwest of China, respectively, as listed in Table S1 in Appendix. Sample

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cities are distributed all over the country except for Northwest China; however, 109 of

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157 cities are located in the central and eastern China because most of the Chinese

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population, GDP output and infrastructure construction are concentrated.

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Table 1. Definition of inputs and outputs for DEA Measurement Unit

Input

xic

Fixed assets investment for WTP

103 $

xiw

Fixed assets investment for WWTP

103 $

xlc

Total length of water distribution network

xlw

Total length of sewer network

xec

Electricity consumption for water supply

xew

Electricity

consumption

treatment

for

104 kWh/a

wastewater

104 kWh/a

Sludge production

ton/a

ywc

Clean water supplied

104 m3/a

yww

Wastewater treated

104 m3/a

ypc

COD removed

ton/a

yps

SS removed

ton/a

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xsp

TN removed

ton/a

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km

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2.3 DEA model selection and sensitivity analysis

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There are several types of DEA models. Charnes et al. first established the

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Charnes-Cooper-Rhodes (CCR) model in which the efficient frontier was assumed to

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be a constant return to scale (CRS).38 Banker et al. then removed the CRS assumption

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and proposed the Banker-Charnes-Cooper (BCC) model, which is better suited to

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assessment of complex systems for which the function that governs input and output

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relationship is unknown.39. In this study, the BCC model was selected to perform the

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assessment, in order to take into account various returns to scale in different cities. When a DMU reaches the maximum output given a vector of inputs

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(output-oriented DEA), or uses a minimum of inputs to produce a given output

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(input-oriented DEA), it is placed on the production frontier and thus deemed to be

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sustainable. The input-oriented model provided a series of target values for the

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minimized inputs that deliver appropriate benchmarks to calculate the target

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theoretical performance of unsustainable DMUs. This method was adopted in this

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study.

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The inputs and outputs for the kth DMU are represented by column vectors xk and yk,

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respectively. The dataset consists of the input matrix X7×157 and output matrix Y5×157.

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The basic linear equations for an input-oriented BCC model are as follows.

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min θ, θ,λ

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s.t.

-yk +Yλ≥0 θxk -Xλ≥0 NE'λ=1 λ≥0

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k=1, 2, …, 157

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where θ is the objective function, and operates once for each DMU in the sample; λ is

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a 157×1 vector of constants that locates points on the frontier. NE’ is a 157×1 vector of

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The model attempts to decrease all inputs proportionally when measuring the urban

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water infrastructure system’s sustainability. The sustainability score θ is greater than

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zero but not more than 1. In this study, the model was solved using the software

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MaxDEA. With data entered and BCC model established, MaxDEA can provide

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overall scores and as well as improvement targets of inputs and outputs.

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Due to propagation of uncertainties in the collected data, we performed a sensitivity

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analysis to identify critical external factors and evaluate the validity of the DEA.40

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The sensitivity analysis followed the approach proposed by Charnes et al., which was

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performed by changing the input/output variables in increments of 10% for

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sustainable DMUs while keeping those for unsustainable DMUs constant.41 The seven

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input variables were decreased by 10% yet the five outputs were increased by 10% for

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each iteration. The change of sustainability score was examined to evaluate the

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sensitivity of the inputs and outputs.

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2.4 Exploration of influential factors of system sustainability

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To characterize and explain the sustainability disparities of water infrastructure

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systems, eight efficiency indicators were defined by ratios of a specific input to an

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output to depict the system from the different angels as below.

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IS=xic /ywc ; IT=xiw /yww ; ES=xec /ywc; ET=xew /yww; RC=xew /ypc ; RS=xew /yps; RN=xew /ypn; SW=xsp /yww.

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constraints on the variations of system sustainability scores and specific efficiency

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indicators were explored using K-W test, multiple nonlinear regression, K-S test, and

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M-W test with IBM SPSS 20.0.

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3. RESULTS

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3.1 Characteristics of sample systems

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Figure 1 shows a summary of the range and distribution of the input/output of the

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157 samples. In general, fixed assets investment for WTP was higher than that for

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WWTP, while the length of the distribution network was larger than that of the sewer

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network. The level of electricity consumption in water supply and wastewater

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treatment was quite similar to each other.

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The geographical property of the mean values of the systems followed the

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economic development pattern in China. Systems in the East China had the highest

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mean values of the investment, energy consumption, and quantity of water supply and

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wastewater treatment while the systems in Southwest China had the lowest means.

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Table 2 lists statistics of the eight efficiency indicators. The large variance

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demonstrates technical differences among cities and possible improvement through

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technology exchange or promoting best available practices. The variance of energy

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Figure 1. Inputs and outputs of sample systems

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indicators, and the indicator IT and SW had the least coefficient of variation (44% and

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58%, respectively). It clearly shows that energy conservation is a bottleneck for the

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sustainability of wastewater infrastructure in many cities.

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Table 2. Statistics of eight efficiency indicators Mean

Median

IS ($/m3)

0.87

0.67

0.11

IT ($/m3)

0.64

0.61

0.04

ES (kWh/m3)

0.24

0.20

0.21

ET (kWh/m3)

0.24

0.22

0.18

RC (kWh/kg COD)

1.15

1.00

1.00

RS (kWh/kg SS)

2.15

1.62

3.45

15.86

12.30

15.53

0.15

0.14

0.09

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SW (kg dry sludge/m3)

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Efficiency Indicator

RN (kWh/kg TN)

3.2 Scores and gaps of system sustainability

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The DEA model calculates a sustainability score for the water infrastructure system

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of each city. Statistical results of all cities in detail are shown in Table S1 in Appendix.

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Figure 2 shows a histogram of sustainability score. A total of 69 water infrastructure

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systems had the score of 1.0 and were deemed the best practice, which account for

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approximately 44.0% of all the samples. The mean value of the sustainability scores

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was 0.883 and the standard deviation of the system sustainability was 0.142,

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the underperformed systems scored between 0.6 and 1.0 Nonetheless, distinguished

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disparities existed among the systems, and more than half of water infrastructure

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systems in China show high potential for improving their overall relative performance.

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It should be noted that, due to the nature of DEA method, i.e. multi-objective

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evaluation, as the number of evaluation indicators increases, the discrimination of the

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systems under evaluation is reduced. Therefore, a system with a score of 1 is not an

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optimal solution, but a non-inferior solution. This means that all performances of this

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system is not the best, and this system can be improved in some aspects.

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Figure 2. Distribution of sustainability scores

To make comparisons with water infrastructure systems outside China, three new

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DMUs were added for Spain, German, and USA, respectively, as shown in Table 3.

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The data were from United Nation’s Data, World Bank, US EPA, and literatures.42-47

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According to the data, there was a big difference on the energy, economic and

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environmental performance of water infrastructure system among these four countries.

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To be specific, annual clean water supplied (ywc) and wastewater treated (yww) in

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China were much higher than that in other three countries, which was directly related

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to China’s vast territory, large population and high demand for water supply and

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drainage. Besides, the unit investment in water supply (xic) and drainage (xiw) in China

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were also obviously higher than that in other countries. It seems that China now

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attaches great importance to the construction and economic input of infrastructure,

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and there is much room and possibility for the improvement of China’s urban water

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infrastructure systems. The DEA indicated that the sustainability scores of the 157 cities remained almost

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the same with or without three extra DMUs, while Spain, German, and U.S all got a

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sustainability score of 1.0. This suggests that, despite the technical, management and

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policies differences, national averages of the three countries are at the same level of

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the best practices in China. Meanwhile, the water infrastructure systems with the

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score of 1.0 are comparable to these developed countries. Overall, the best practices

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in China can be used as a benchmark for sustainability. However, as we discussed

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previously, more than half (56%) of the systems need enhancements to achieve

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sustainability goal.

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Table 3. Inputs and outputs of three new DMUs at the national level Variable

Unit

China

Spain

German

U.S.

1011 $

3.24

0.11

0.32

1.39

xiw

105km

6.76

1.57

6.95

18.92

xlc

105km

5.20

0.92

4.64

12.61

xlw

1010 $

16.15

0.54

1.62

6.92

xec

106 kWh/a

1.10

3.07

1.14

6.43

xew

105 kWh/a

7.36

36.35

27.37

54.79

xsp

106 ton/a

6.92

5.07

0.20

8.20

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DMU

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xic

Inputs

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Unit

China

Spain

German

U.S.

ywc

109 m3/a

255.06

3.45

4.54

64.78

yww

1010 m3/a

3.80

0.41

1.10

5.52

ypc

107 ton/a

1.01

0.19

0.60

0.31

yps

106 ton/a

4.91

91.05

1.41

7.13

ypn

105 ton/a

9.09

1.58

6.59

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DMU

3.3 Sensitive inputs and outputs for sustainability assessment

Figure 3 shows the result of sensitivity analysis. The “baseline” stands for the result

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of DEA without any inputs or outputs variation. Overall, the benchmark is not

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significantly sensitive to any of the variables alone. The number of systems meet the

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benchmark is more sensitive to the change of inputs than the average sustainability

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score. Variation of sludge production (xsp) is the most sensitive factors of which a 10%

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reduction could lead to the number of best practice systems varies from 69 to 74, an

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increase of approximately 7%. Other significant factors are: yww (wastewater treated),

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xew (electricity consumption for wastewater treatment), yps (SS removed), and ywc

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(water supplied). The average sustainability score is even less sensitive with the

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highest change of only 0.45% when xsp changes by 10%. Considering the variation of

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the inputs and outputs as shown in Figure 1, since the change of assessment results

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was quite lower than the variation of the inputs and outputs, the sustainability scores

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were hardly affected by the data uncertainty and could be used for the benchmarking.

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with inputs or outputs variation by 10%. Results for baseline are marked with the red squares.

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According to the results of the sensitivity analysis, the sustainability of urban water

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infrastructures in China depends more on the performance of the wastewater

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treatment than that of the other three system components. Sludge production was one

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of the sensitive variables in the sustainability assessment. In general, the sludge

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amount is greatly affected by the influent load, treatment technology, and operation

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conditions.48-50 One other significant factor is the energy consumption of WWTP. As

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the wastewater, nutrients, and energy flow coupled in intimate ways during the

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biological processes in WWTPs, it is a challenge as well as an opportunity for

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sustainable water services.

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4. DISCUSSION

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4.1 Improvement priorities for unsustainable systems

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To screen the most important factors for unsustainable performances, the variables

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related to resource inputs for the construction and operation of water infrastructures

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were selected and classified into three categories, which are: fixed assets investment

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(i.e. xic and xiw), network scale (i.e. xlc and xlw), and electricity consumption (i.e. xec

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and xew). For each category, water infrastructure systems were iteratively ranked as

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the input values were changed proportionately. The first 25 percent of systems that

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need the most input reduction were defined to be the “worst performance” in that

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specific category, based on how the necessary improvements were prioritized. The result of the improvement priority for each city is listed in Table S2 in

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Appendix. Eighteen out of the 88 underperforming cities need to prioritize

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improvement in two categories, while the remaining cities need only one. The number

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of systems with improvement priorities in fixed assets investment, network scale, and

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electricity was 39, 34, and 40, respectively. For existing water infrastructures, great

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efforts should be taken to improve and better maintain assets and lowering their

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depreciation to improve the system performance since the fixed asset investment and

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network scale could not be easily changed due to the lock-in effect. For the 40

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systems with a priority of reducing electricity input, both the technical measures for

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the operational optimization of the facilities and the incentive policies focused on

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energy conservation should be implemented.

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4.2 Influential factors for system sustainability

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4.2.1 Urban development scale

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In China, great disparity exists in the social-economic development of different

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cities. Since the urban water infrastructures are constructed to provide the water

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services for the citizens, the population size determines the scale of individual

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facilities and the whole system. Sample cities were classified into five groups

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according to their population scales and the statistics as shown in Table 4.51 It

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indicated that the value of sustainability score would go up with the increasing urban

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population, while the standard deviation of sustainability score would decline. This

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implied that urban water system servicing the city with larger population would have

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a better performance. To further analyze the different performances among different groups, a K-W test

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was conducted to examine the relationship between urban development level and

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system sustainability score. With a confidence level of 90%, the K-W test suggest that

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the sustainability scores were significantly different among cities of different sizes

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(p<0.10). Cities with the higher average populations and water service (amount of

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clean water supplied and wastewater treated) showed higher average scores. Despite

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the ongoing discussion about the centralized versus distributed systems, the scaling

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effect seems to be dominant for the best practices in China. This is mostly due to the

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beneficial scaling effect on capital investment and energy efficiency.52-55 In addition,

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larger cities have more resources and public awareness to ensure utility performance

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and maintenance. In contrast, smaller cities tend to have undesirable performances in

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their water infrastructure systems. More emphasis should be laid on sufficient

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investment, timely maintenance, and consciousness raise in medium and small cities.

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Table 4. Statistics of sustainability scores of sample cities with different population size

Population Sample Group

Water Supplied

Wastewater Treated

Sustainability

(106)

(106m3/a)

(106m3/a)

Score

Size

(106) <2.5

Population

27

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

1.72

0.50

87.9

71.0

60.3

47.62

0.853

0.149

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Group

Water Supplied

Wastewater Treated

Sustainability

(106)

(106m3/a)

(106m3/a)

Score

Size

(106)

Mean

S.D.

Mean

S.D.

Mean

S.D.

Mean

S.D.

46.18

0.873

0.152

45

3.21

0.45

97.0

69.4

66.3

4.0-7.0

47

5.32

0.83

197.9

138.3

146.7

7.0-10.0

26

7.91

0.73

363.2

332.2

274.9

>10.0

12

14.48

6.11

1076.5

949.3

824.9

4.2.2 Regional meteorological conditions

107.78

0.878

0.137

224.2

0.905

0.134

0.964

0.098

SC

2.5-4.0

634.9

M AN U

285

Population

RI PT

Population Sample

Previous studies found that meteorological factors could influence the performance

287

of water system, and temperature and rainfall are considered as the common factors

288

with great impacts on the performance.28 Therefore, it is expected that systems in

289

different regions of China would behave differently. The statistics of the sustainability

290

scores of systems in six geographic regions are shown in Table 5.51 It is shown that

291

the natural conditions in these six regions have great differences. Because of the

292

humid climate, the average annual rainfall of east and south regions are over 1000mm,

293

while the value in north region is only 557 mm. Distinction also exists in the air

294

temperature and the largest gap among six regions is 12.5 °C. Various meteorological

295

conditions would have effect on the construction and operation of urban water

296

infrastructure, and thus give rise to distinctive system performances among cities.

AC C

EP

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286

297

K-W test was performed to compare the scores among six regions and the result

298

indicated that the spatial difference in the sustainability level of urban water

19

ACCEPTED MANUSCRIPT infrastructures in China was significant (p<0.10). Sample systems in Southwest China

300

had the highest mean score (0.955) and lowest proportion of underperformed system

301

(35%), while those in Northeast China performed worst on average. Although water

302

infrastructures in East China tended to adopt more advanced technologies due to the

303

higher economic affordability, their sustainability scores were comparatively lower. It

304

was indicated that overall economic condition of a city is not directly tied to its

305

sustainable development. At the provincial level, Jiangsu Province and Anhui

306

Province in East China and Liaoning Province in Northeast China had the relatively

307

higher percentages of the unsustainable systems, which were 80.0%, 77.8%, and

308

77.8%, respectively. Sichuan Province in Southwest China had a quite high ratio of

309

the sustainable systems, which was 72.7%.

SC

M AN U

Sample

Sustainability

Percentage of

Size

Score

Underperformed

Average Annual Air

Annual

System

Temperature (°C)

Rainfall (mm)

EP

Region

TE D

Table 5. Statistics of sustainability scores and meteorological conditions of different regions City with unsustainable system

AC C

310

RI PT

299

North

22

0.862

0.152

68%

11.9

2.7

557

190

Northeast

15

0.841

0.181

60%

8.6

4.1

868

223

East

54

0.862

0.154

61%

15.7

3.8

1004

374

Central

23

0.917

0.098

48%

16.0

1.9

814

437

South

23

0.886

0.137

57%

21.1

3.8

1555

545

Mean

S.D.

20

Mean

S.D.

Mean

S.D.

ACCEPTED MANUSCRIPT

Southwest

Sample

Sustainability

Percentage of

Size

Score

Underperformed

Average Annual Air

Annual

System

Temperature (°C)

Rainfall (mm)

20

Mean

S.D.

0.955

0.079

City with unsustainable system

Mean 35%

15.9

S.D.

Mean

S.D.

RI PT

Region

3.5

983

236

To characterize the regional difference of the system sustainability in China, a

312

multiple nonlinear regression model (p<0.10) was generated, with all the

313

unsustainable systems of 88 cities as samples, shown as below. The reciprocal of

314

sustainability score (1/θ) was treated as the dependent variable, while the average

315

annual air temperature (T, °C) and the annual rainfall (P, mm) of the corresponding

316

city were selected as independent variables. At the significance level of 0.10, both of

317

the explanatory variables were significant. As the representative variables of

318

meteorological conditions, the statistics of average air temperature and annual rainfall

319

of cities with unsustainable systems are given in Table 5 as well.

M AN U

TE D

EP 1 θ

=-1.433×10-5 ×T3 +0.138×lnP+0.433

(3)

AC C

320

SC

311

321

The estimated coefficient of the temperature was positive, while that of the annual

322

rainfall was negative. It seems that cities with higher temperature and lower rainfall

323

tend to obtain higher system sustainability, which is in accordance with the better

324

performance in Southwest and Central China. Low temperature may interfere the

325

biological treatment process in WWTP, which is a significant factor for sustainability

326

score as discovered in the previous section. Frequent heavy rainfall may create surges

21

ACCEPTED MANUSCRIPT 327

of quantity and drastic change in quality of water entering into the treatment facility

328

and increase the difficulty of system design and management.

329

4.2.3 Environmental constraints Apart from socio-economic and meteorological factors, environmental constraints

331

such as the quality of drinking water source and the discharge standards of WWTP

332

effluent may also be potential influential factors for the system sustainability.

SC

RI PT

330

To investigate the impact of the quality of source water, urban infrastructure

334

systems were classified into two groups based on the quality compliance of the

335

corresponding city water source to the National Environmental Quality Standards for

336

Surface

337

(GB/T14848-93) in China.56,57 There were 23 cities with substandard source water, of

338

which the mean value and the standard deviation of the sustainability score were

339

0.837 and 0.146, respectively. The other 134 cities with good quality of source water

340

had an average score of 0.892 with the standard deviation of 0.140. By both K-S test

341

and M-W test, it was found that the system sustainability was significantly different

342

between these two groups (p<0.10). The better the quality of source water, the higher

343

the sustainability score. This analysis agreed with the importance of source water

344

management on the sustainability of the overall water system.58,59

(GB3838-2002)

and

Quality

Standard

for

Ground

Water

AC C

EP

TE D

Water

M AN U

333

345

When it comes to the impact of the discharge standards of WWTP, hypothesis tests

346

were also conducted to explore whether the standard can be a guide and supervision

347

tool to improve system sustainability. The results showed that no significant

22

ACCEPTED MANUSCRIPT difference in the sustainability scores among systems complying with different

349

requirements on WWTP effluents. It may be due to the less determinant role of the

350

output indicators of pollutants removed in this sustainability quantification, as is

351

shown in the result of sensitivity analysis.

352

4.3 Sustainability-based benchmarking for water infrastructures

RI PT

348

Benchmarking is a useful management tool to support the system improvement in a

354

cost-effective way.60-62 The distributions of eight efficiency indicators of the

355

sustainable systems (i.e. score=1.0) and the unsustainable systems (i.e. score<1.0)

356

were shown in Figure 4, indicating the detailed system performance gaps. To further

357

demonstrate the utility of benchmarking, K-S tests were conducted. All efficiency

358

indicators were significantly different between two groups (p<0.10), making

359

themselves quite useful for the deep understanding and further benchmarking of the

360

system behavior in terms of the sustainability enhancement.

M AN U

TE D

EP

361

SC

353

Figure 4. Differences of efficiency indicators between sustainable and unsustainable systems

363

Based on the 25th percentile of each indicator of sustainable systems as an example,

364

a series of efficiency thresholds could be formulated: IS, 0.33 $/m3; IT, 0.37 $/m3; ES,

365

0.08 kWh/m3; ET, 0.16 kWh/m3; RC, 0.60 kWh/kgCOD; RS, 0.81 kWh/kgSS; RN,

366

6.96 kWh/kgTN; and SW, 0.10 kg Sludge/m3. This means that to construct and

367

operate such an ideal example system in a city to supply clean water of 1.0×108 m3

368

and treat wastewater of 7.5×107 m3 per year (the average ratio of wastewater treated

AC C

362

23

ACCEPTED MANUSCRIPT to clean water supplied in China was 0.75 of the case study year) would require the

370

investment of 6.0×107 $ for the fixed assets in total and the electricity of 2.0×106 kWh

371

per year, while the system would remove 2.0×104, 1.5×104, and 1.7×103 ton COD, SS,

372

and TN and produce 7.5×103 ton dry sludge per year. This hypothetical system scored

373

1.0 with the DEA model.

RI PT

369

375

SC

374

5. CONCLUSION

In the framework of data envelopment analysis, this paper evaluated the relative

377

sustainability of urban water infrastructure systems in China to serve as a

378

benchmarking. The assessment at the city scale was unique and meaningful, which

379

supports the exploration of more system-dependent information for further

380

improvement. Although the development of urban water infrastructure in China is not

381

extremely unbalanced, disparities exist among cities. The most determinate inputs for

382

the sustainability differentiation

383

consumption, which revealed common bottlenecks in the performance improvement

384

of urban water infrastructure systems in China. More cost effective utilization of

385

energy sources and cautious handling of sludge produced are priorities for the

386

continuous enhancement of system sustainability, which is highlighted in this study

387

and meaningful for other developing cities in assessing the current and potential

388

performance of their urban water infrastructures.

TE D

M AN U

376

and

electricity

AC C

EP

include sludge production

24

ACCEPTED MANUSCRIPT It was confirmed that the system scale, meteorological conditions of the city, and

390

environmental constraints on the system operation regulate the system sustainability.

391

Although some natural constraints cannot be easily changed such as the ambient

392

temperature, the results are applicable for policy makers to identify management

393

weaknesses and facilitate the system design and operation on the basis of a

394

comprehensive investigation. Better designs that fully utilize the scale effect of

395

infrastructure systems, improved water source protection, and more effective

396

operation of the system including energy conservation should be encouraged, as

397

required to improve the system sustainability.

398

M AN U

SC

RI PT

389

ACKNOWLEDGEMENTS

400

This work was supported by the National Natural Science Foundation of China

401

(51308320 & 71473148).

TE D

399

404

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568

APPENDIX

573

Some statistical results of sustainability scores from DEA models, as well as the

574

geographical distribution of Chinese cities in China, are provided in Table S1.

575

Detailed results of the improvement priority for each city are in Table S2.

33

ACCEPTED MANUSCRIPT Table S1. Sustainability score of urban water infrastructure systems in Chinese cities

Province

DMU

Score

North

Beijing

Beijing

1

Tianjin

Tianjin

0.954

Hebei

Shijiazhuang

1

Hebei

Tangshan

0.906

Hebei

Qinhuangdao

0.811

Hebei

Handan

Hebei

Xingtai

Hebei

Baoding

Hebei

Zhangjiakou

Hebei

Chengde

Hebei

Cangzhou

1

Hebei

Langfang

0.792

Hebei

Hengshui

1

Taiyuan

0.876

Datong

0.790

Yangquan

0.641

Jincheng

0.608

Shuozhou

1

Xinzhou

0.977

Yuncheng

0.995

Shanxi

Lvliang

1

Inner Mongolia

Bayannaoer

1

Liaoning

Shenyang

1

Liaoning

Dalian

0.711

Liaoning

Benxi

1

Liaoning

Yinkou

0.628

Liaoning

Fuxin

0.793

Liaoning

Liaoyang

0.560

Liaoning

Panjin

0.917

Liaoning

Tieling

0.624

Liaoning

Huludao

0.543

Jilin

Changchun

0.840

Jilin

Jilin

1

Jilin

Siping

1

Jilin

Liaoyuan

1

Jilin

Songyuan

0.993

Heilongjiang

Harbin

1

Shanghai

Shanghai

1

Jiangsu

Changzhou

0.468

Shanxi Shanxi Shanxi Shanxi Shanxi

TE D

Shanxi

EP

Northeast

East

0.709

0.597

0.657

34

0.980

0.671

SC

M AN U

Shanxi

RI PT

Region

AC C

576 577

ACCEPTED MANUSCRIPT Nantong

0.700

Jiangsu

Yancheng

0.924

Jiangsu

Yangzhou

1

Jiangsu

Zhenjiang

0.743

Zhejiang

Hangzhou

1

Zhejiang

Ningbo

0.995

Zhejiang

Wenzhou

1

Zhejiang

Jiaxing

0.722

Zhejiang

Shaoxing

Zhejiang

Jinhua

Zhejiang

Quzhou

Zhejiang

Taizhou

Zhejiang

Lishui

Anhui

Hefei

Anhui

Wuhu

Anhui

Huaibei

0.597

0.804

SC

1

1

0.8937

1

Huangshan

0.843

Fuyang

0.617

Suzhou

0.742

Chuzhou

0.855

Lu'an

0.984

Xuancheng

0.809

Fuzhou

0.772

Fujian

Xiamen

0.682

Fujian

Sanming

1

Fujian

Quanzhou

1

Fujian

Zhangzhou

0.618

Fujian

Nanping

0.896

Fujian

Longyan

1

Fujian

Ningde

1

Jiangxi

Nanchang

1

Jiangxi

Jingdezhen

1

Jiangxi

Jiujiang

0.780

Jiangxi

Xinyu

0.743

Jiangxi

Fuzhou

0.962

Jiangxi

Ji'an

0.704

Shandong

Jinan

0.822

Shandong

Qingdao

0.735

Shandong

Zibo

1

Shandong

Zaozhuang

0.720

Shandong

Yantai

1

Shandong

Weifang

1

Anhui Anhui Anhui Anhui Anhui

EP

TE D

Fujian

AC C

1

1

M AN U

Anhui

RI PT

Jiangsu

35

ACCEPTED MANUSCRIPT 0.809

Shandong

Tai'an

0.715

Shandong

Dezhou

1

Shandong

Weihai

0.970

Shandong

Liaocheng

1

Shandong

Rizhao

0.471

Shandong

Laiwu

0.893

Shandong

Linyi

0.952

Shandong

Binzhou

Shandong

Heze

Henan

Zhengzhou

Henan

Kaifeng

Henan

Luoyang

Henan

Pingdingshan

0.810

Henan

Anyang

1

Henan

Hebi

Henan

1

0.922

0.838

SC

0.824

0.786

0.902

Jiaozuo

0.855

Puyang

1

Xuchang

1

Luohe

1

Sanmenxia

1

Shangqiu

1

Henan

Nanyang

1

Henan

Xinyang

0.787

Henan

Zhoukou

1

Henan

Zhumadian

1

Hunan

Changsha

1

Hunan

Zhuzhou

0.825

Hunan

Xiangtan

0.836

Hunan

Huaihua

0.696

Hunan

Loudi

1

Hunan

Hengyang

1

Guangdong

Guangzhou

1

Guangdong

Shaoguan

0.952

Guangdong

Shenzhen

1

Guangdong

Zhuhai

0.769

Guangdong

Shantou

0.740

Guangdong

Foshan

1

Guangdong

Jiangmen

0.995

Guangdong

Zhanjiang

0.787

Guangdong

Huizhou

0.773

Henan Henan Henan Henan

EP

TE D

Henan

AC C

0.623

Xinxiang

Henan

South

RI PT

Jining

M AN U

Central

Shandong

36

ACCEPTED MANUSCRIPT 0.658

Guangdong

Zhaoqing

0.753

Guangdong

Chaozhou

1

Guangdong

Dongguan

1

Guangdong

Zhongshan

1

Guangdong

Heyuan

1

Guangdong

Yangjiang

1

Guangdong

Qingyuan

0.560

Guangxi

Nanning

Guangxi

Guilin

Guangxi

Hechi

Hainan

Haikou

Hainan

Sanya

Hainan

Sansha

Chongqing

Chongqing

1

Sichuan

Chengdu

1

Sichuan

1

0.962

0.739

SC

0.832

0.857

1

Panzhihua

1

Deyang

0.719

Suining

1

Neijiang

0.857

Leshan

1

Dazhou

0.984

Sichuan

Guang'an

1

Sichuan

Meishan

1

Sichuan

Bazhong

1

Guizhou

Guiyang

0.839

Guizhou

Liupanshui

1

Guizhou

Zunyi

0.938

Guizhou

Tongren

0.851

Guizhou

Bijie

1

Yunan

Kunming

1

Yunan

Yuxi

0.922

Yunan

Qujing

1

Sichuan Sichuan Sichuan Sichuan

EP

TE D

Sichuan

AC C

1

Zigong

Sichuan

578 579

RI PT

Maoming

M AN U

Southwest

Guangdong

37

ACCEPTED MANUSCRIPT

Anyang

Fixed assets investment

Anshan

Fixed assets investment

Beijing

Fixed assets investment

Cangzhou

Fixed assets investment

Chengdu

Fixed assets investment

Dalian

Fixed assets investment

Dongguan

Fixed assets investment

Foshan

Fixed assets investment

Fuzhou

Fixed assets investment

Guangzhou

Fixed assets investment

Guiyang

Fixed assets investment

Harbin

Fixed assets investment

Hangzhou

Fixed assets investment

Hefei

Fixed assets investment

Heyuan

Fixed assets investment

Jinan

Fixed assets investment

Jinhua

Fixed assets investment

Kunming

Fixed assets investment

Langfang

Fixed assets investment

Nantong

Fixed assets investment

Qingdao Qujing Shaoxing

Fixed assets investment

Fixed assets investment

Fixed assets investment Fixed assets investment Fixed assets investment

EP

Shenzhen

TE D

Ningbo

SC

Priority

M AN U

DMU

RI PT

Table S2. Different priority of input reduction for each city to verify sustainability criteria

Taizhou

Fixed assets investment

Tianjin

Fixed assets investment

Xiangtan

Fixed assets investment

Yantai

Fixed assets investment

Yunfu

Fixed assets investment

Changchun

Fixed assets investment

Zhenjiang

Fixed assets investment

Zhengzhou

Fixed assets investment

Zhongshan

Fixed assets investment

Chongqing

Fixed assets investment

Bayannaoer

Network scale

Chaozhou

Network scale

Guang'an

Network scale

Hebi

Network scale

AC C

580 581

38

ACCEPTED MANUSCRIPT Network scale

Huangshan

Network scale

Liaoyang

Network scale

Linfen

Network scale

Liupanshui

Network scale

Sansha

Network scale

Shanwei

Network scale

Shizuishan

Network scale

Suining

Network scale

Wuzhong

Network scale

Xinyu

Network scale

Suzhou

Network scale

Ya'an

Network scale

Yuncheng

Network scale

Zhangzhou

Network scale

Anqing

Electricity consumption

Anshun

Electricity consumption

Baishan

Electricity consumption

Bijie

Electricity consumption

Binzhou

Electricity consumption

Chaoyang

Electricity consumption

Fuzhou

Electricity consumption

Fuxin

Electricity consumption

Heze Hengshui Liaocheng

SC

M AN U

Electricity consumption Electricity consumption

Electricity consumption

Electricity consumption Electricity consumption

EP

Longyan

TE D

Guilin

RI PT

Huaihua

Electricity consumption

Nanping

Electricity consumption

Ningde

Electricity consumption

Pingxiang

Electricity consumption

Puyang

Electricity consumption

Siping

Electricity consumption

Suihua

Electricity consumption

Tonghua

Electricity consumption

Zigong

Electricity consumption

Zunyi

Electricity consumption

Pingdingshan

Fixed assets investment & Network scale

Changzhou

Fixed assets investment & Electricity consumption

Nanchang

Fixed assets investment & Electricity consumption

Quanzhou

Fixed assets investment & Electricity consumption

AC C

Lvliang

39

ACCEPTED MANUSCRIPT Network scale & Electricity consumption

Guyuan

Network scale & Electricity consumption

Guigang

Network scale & Electricity consumption

Hechi

Network scale & Electricity consumption

Huaibei

Network scale & Electricity consumption

Ji'an

Network scale & Electricity consumption

Jieyang

Network scale & Electricity consumption

Laibin

Network scale & Electricity consumption

Laiwu

Network scale & Electricity consumption

Lishui

Network scale & Electricity consumption

Lu'an

Network scale & Electricity consumption

Neijiang

Network scale & Electricity consumption

Sanming

Network scale & Electricity consumption

Zhangjiajie

Network scale & Electricity consumption

SC

RI PT

Chengde

AC C

EP

TE D

M AN U

582

40

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

Figure 1. Inputs and outputs of sample systems

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

Figure 2. Distribution of sustainability scores

ACCEPTED MANUSCRIPT

yps 72

baseline 69

xic 69

ypc 72

yps 0.886

xiw 72

ypn 0.885 baseline 0.883 xic 0.884

ypc 0.885

xlc 69 xlw 71

yww 73

xec 71

yww 0.888

xew 72

xsp 0.887

xlw 0.885

xew 0.887

SC

xsp 74

xlc 0.884

xec 0.884

ywc 0.886

ywc 72

xiw 0.885

RI PT

ypn 71

AC C

EP

TE D

M AN U

Figure 3. Change of the number of sustainable DMUs (left) and average sustainability score (right) with inputs or outputs variation by 10%. Results for baseline are marked with the red squares

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

Figure 4. Differences of efficiency indicators between sustainable and unsustainable systems

ACCEPTED MANUSCRIPT Highlights




DEA is used for benchmarking sustainability of urban water infrastructure in China. 69 out of the 157 sampled systems obtain high sustainability scores.




Performance of WWTPs has greater influence on the sustainability score.




Sustainability score is sensitive to sludge production and electricity consumption.




Guidelines are provided to cities in order to meet specific sustainability.

AC C

EP

TE D

M AN U

SC

RI PT