Assessment of municipal infrastructure development and its critical influencing factors in urban China: A FA and STIRPAT approach
Assessment of municipal infrastructure development and its critical influencing factors in urban China: A FA and STIRPAT approach
Yu Li 0 1 2
Ji Zheng 0 1 2
Fei Li 0 1 2
Xueting Jin 0 1 2
Chen Xu 0 1 2
0 Data Availability Statement: Data of municipal infrastructure are from the China City Statistical Yearbook (2011) , China City Construction Statistical Yearbook (2010) and China Urbanization Survey Report (2011) by China Statistics Press , National Bureau of Statistics of China (Distribution Department , Tel: 86-10-6337 6900). These data can also be found from public China Yearbooks Full-text Database by
1 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences , Beijing , China , 2 University of Chinese Academy of Sciences , Beijing , China
2 Editor: Boris Podobnik, University of Rijeka , CROATIA
Municipal infrastructure is a fundamental facility for the normal operation and development of an urban city and is of significance for the stable progress of sustainable urbanization around the world, especially in developing countries. Based on the municipal infrastructure data of the prefecture-level cities in China, municipal infrastructure development is assessed comprehensively using a FA (factor analysis) model, and then the stochastic model STIRPAT (stochastic impacts by regression on population, affluence and technology) is examined to investigate key factors that influence municipal infrastructure of cities in various stages of urbanization and economy. This study indicates that the municipal infrastructure development in urban China demonstrates typical characteristics of regional differentiation, in line with the economic development pattern. Municipal infrastructure development in cities is primarily influenced by income, industrialization and investment. For China and similar developing countries under transformation, national public investment remains the primary driving force of economy as well as the key influencing factor of municipal infrastructure. Contribution from urbanization and the relative consumption level, and the tertiary industry is still scanty, which is a crux issue for many developing countries under transformation. With economic growth and the transformation requirements, the influence of the conventional factors such as public investment and industrialization on municipal infrastructure development would be expected to decline, meanwhile, other factors like the consumption and tertiary industry driven model and the innovation society can become key contributors to municipal infrastructure sustainability.
Municipal infrastructure is a fundamental facility for the operation and development of an
urban city and is of significant importance for improving the residence environment, city
Funding: This study was supported by the State
Basic Science Key Project of China (No.
2007FY110300), and the National Natural Science
Foundation of China (No. 41271186, 41301642,
41271556). The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
support capability and operation efficiency as well as for the stable progress of sustainable
In recent years, research on municipal infrastructure has achieved substantial progress in
the following areas: 1) assessment of transport infrastructure systems, including the
cost-benefit of urban transport network [
] and its environmental effect [
], reliability [
management mechanism [
6, 10, 11
]; 2) study of urban water supply and drainage systems,
including the influencing factors of the drainage system [
] and its impact on urban river
], and the reliability of water and electric power supply system [
study of urban energy infrastructure, including the requirements for urban energy
] and its impact on the environment [
]; 4) urban environment and sanitation
infrastructure, including the sustainability of municipal environment and sanitation
infrastructure systems [20±23] and its impact on the environment [24±27]; and 5) study of
ecological infrastructure in urban areas, including green area systems and green space in urban areas
[1, 28±30]. On the scale of the whole country or an urban area, researchers have studied the
influence of different factors, such as society, economy, climate, technology, on municipal
infrastructure development and relevant administrative strategies [
6, 11, 31, 32
], and assessing
municipal infrastructure systems and their sustainability has received increasing attention [
]. Overall, much recent literature has focused on the evaluation of municipal infrastructure
construction level, and tended predominantly to address assessing a limited range of
infrastructure types, and often only one type [
2, 4, 34
], involving the factor analysis of risk
assessment for one type of infrastructure , the efficiency assessment [
], ecological system
service and life cycle assessment for the environment effects in an urban area [
20, 21, 23, 36
and hierarchical analysis of infrastructure management . Evaluation of a particular type of
municipal infrastructure can only understand the special municipal infrastructure
construction and its standard. And, municipal infrastructure development and the influences of various
socio-economic factors are complex and systematic. Therefore, infrastructure assessment
systematically is crucial to further understand the municipal infrastructure sustainable
development, especially for China. Overall, comprehensive and multi-index urban infrastructure
assessment on the scale of a region, or a country is relatively scarce, which can be particularly
true for studies on municipal infrastructure construction in developing countries and
emerging economies under transformation. Especially, few of these studies analyzed the question of
accessing urban infrastructure integrally on such an emerging and transforming economy
scale as China's. Thus, the development of urban infrastructure to meet future livelihood
demands is unclear and contested in many countries, including China. Assessing
multipleindex municipal infrastructure development, focusing on its influencing factors based on
samples from multiple cities needs further investigation critically in a large number of developing
countries, including China.
After the Reform and Opening-up policy, urbanization in China has experienced rapid
development. During 1980±2014, the proportion of the urban population in China grew from
19% to 54% [
]. With the progress of urbanization, municipal infrastructure has attracted
unprecedented attention in China. In 2013, the Guideline on Boosting Urban Infrastructure
Construction by State Council was published, and municipal infrastructure construction had
achieved rapid progress . In 2015, urban infrastructure investment in China exceeded
around CNY nine trillion [
]. However, compared to developed countries and regions such
as Europe and America, China, as the world's largest developing country and a typical
emerging economy under transformation, still has a relatively low level of overall municipal
infrastructure development, with a still rapid and special growth in the future. Understanding the
determinants of China's municipal infrastructure development is a key to directing future
construction and investment for new projects. Meanwhile, China's experience might also provide
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conceivable references and lessons for other similar countries where large-scale infrastructure
investments and urbanization are being planned, especially such as some developing
Generally, economic growth and its structural change, urban population, technology and
consumption can significantly influence the municipal infrastructure construction as shown
in the literature [
2, 11, 20, 26, 31
]. And it is very meaningful for further improving the
municipal infrastructure to judge its determinants for cities at different urbanization and economic
stages in China and other similar countries. Therefore, a systematic assessment of the
municipal infrastructure sustainable development in China is firstly performed. Then, besides the
above influencing factors, urbanization, industrialization, income and investment, especially
national public investment, would also be especially critical to municipal infrastructure
development in China, with a typical transformation feature [
2, 22, 34, 37
]. Based on the
comprehensive assessment results from the factor analysis (FA) for municipal infrastructure in the
prefecture-level cities (excluding principal capitals) in China in 2010, an optimized Stochastic
Impacts by Regression on Population, Affluence and Technology (STIRPAT) model is
employed to quantitatively examine the key driving forces for municipal infrastructure
development at various stages of urbanization as firstly proposed by Northam  and economic
development. The remaining sections of the article are structured as follows. First, the
methodology of this study is presented, including the FA model, STIRPAT model and index system;
then, China's municipal infrastructure sustainable development is assessed and its influencing
factors are explored; finally, conclusions are presented, and some policies are proposed and
FA and STIRPAT
Since Escofier and Pagès [
] applied a factor analysis (FA) model to multi-variable
dimension reduction, the factor analysis method has been widely applied to the assessment of natural
and human systems. The factor analysis model reassembles original variables to identify
common variable influencing factors and to simplify data. Rotation is applied so that the factor
variable becomes more intuitive and has a clearer meaning [
]. First, this study constructs a
FA (factor analysis) model to assess the overall development of municipal infrastructure. Let
Xi (i = 1, 2, , p) be the original variable of municipal infrastructure development. Then, this
variable is standardized to make the average 0 with a standard deviation of 1. X = (X1, X2, ,
Xp)Â is an observable random vector of overall municipal infrastructure development. F1, F2,
, Fm (m<p) are m factor variables. Components of vector F = (F1, F2, , Fm)Â (m<p) are
independent of each other, and the vector is unobservable. Vector ε = (ε1, ε2, , εm)Â is
independent of F, and E(ε) = 0, the covariance matrix of ε is a diagonal matrix. The components of
ε are also independent of one another. Therefore:
>> X1 a11F1 a12F2 a1mFm ε1
< X2 a21F1 a22F2 a2mFm ε2
Xp ap1x1 ap2x2
The above matrix is the constructed factor model for municipal infrastructure
development. F1, F2, , Fm in the model are common factors (also called main factors) and ε1, ε2, ,
εm are unique factors of Xi (i = 1, 2, , p), which are components of vector X. Unique factors
are independent of one another, and they are independent of all common factors. In the
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model, element aij in matrix A = (aij) is the factor load; matrix A is the factor load matrix. The
factor analysis model for comprehensive municipal infrastructure sustainable development is
the assessment of load matrix A based on the sample data matrix in which the main
component method is employed. The factor score is generated by the regression analysis based on
least square method. The factor score can be calculated on the basis of linear combinations of
the original scores of the surveyed objects weighted with the respective values from the factor
score coefficient matrix.
The IPAT (Impact of Population, Affluence and Technology) proposed firstly by Ehrlich
and Holdren  is a classic approach to assessing the impact of human activities. Therein,
IPAT defines the main determinants of environment (I) as population (P), affluence (A, per
capita production) and technology (T), or I = PAT, which has been employed in various
studies, variations and model transformations in related works [46±49]. Although they are useful
heuristic methods, these models are essentially accounting equations, and the test is not
allowed because missing items are determined by known items, which is the most undesirable
]. The relationship between anthropogenic factors and impacts should be
verifiable via socio-ecological and empirical evidence rather than being simply assumed in the
model . To overcome this serious limitation, IPAT was reformulated into the stochastic
model STIRPAT (Stochastic Impacts by Regression on Population, Affluence and
Technology), as the pioneering work of Dietz and Rosa [
The STIRPAT model allows each coefficient to be estimated as a parameter, and each
influencing factor can be decomposed. Based on the above formula, many studies made relevant
improvements according to the respective research characteristics to conduct various studies
on various cases. In this study, based on the characteristics of China's municipal infrastructure
and the relevant typical influencing factors, the STIRPAT model is constructed and employed
to analyze the main influencing factors of municipal infrastructure in China. The
non-proportional impact of variables can be statistically modeled as:
In the actual calculation, the logarithm is performed on both sides of the above formula as
where a is a constant and @ is the error. I represents level of municipal infrastructure
development; P represents urban population; and A stands for per capita GDP, fixed asset investment,
per capita maintenance capital and others; and T represents industrialization level and
industry structure level. Then, b, c, d represents the effects of various influencing factors,
Five thematic infrastructure areas are identified in the literature [
]: energy efficiency, waste
management, sustainable urban transport, water/wastewater, and urban ecosystem
management, and stressed that it's important to integrate among sectors. Herein, based on five major
urban infrastructure systems and ten municipal infrastructure development indexes (per
capita road area, road network density, buses per 10,000 residents, drainage pipe density in
builtup areas, water coverage, gas coverage, per capita gas consumption, green space ratio in
builtup areas, green space coverage in built-up areas and water flush toilet ratio in built-up areas),
factor analysis is performed for the municipal infrastructure sustainable development of the
prefecture-level cities in China in 2010. Because many of the municipal infrastructure
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Per capita GDP
Proportion of tertiary industry output in GDP
Per capita disposable income of urban residents
Total fixed asset investment
Per capita city building and maintenance capital
Water flush toilet ratio in built-up areas
assessment indexes were only published by the national statistics service in 2011 but not in
other years, this study is conducted based on data from 2010 to obtain a more comprehensive
and objective result. However, until now, there are few changes for the whole China's economy
and urbanization model and the municipal infrastructure development model. So, the
implications of empirical analysis and the conclusion still have a certain reference significance for
The urbanization rate here represents the percentage of regional urban population to total
population. The urbanization stages division of various cities are based on the three-stage
urbanization theory proposed by Northam [
]. When urbanization rate is below 30%, it is at
an initial stage of urbanization, in addition, the city is at an acceleration stage when
urbanization rate is 30%-70%, and urbanization rate of above 70% means the city is at stable stage. The
samples of the prefecture-level cities are divided into three groups: initial stage of urbanization
(urbanization rate is below 30%), acceleration stage (urbanization rate is 30%-70%) and stable
stage (urbanization rate is above 70%) as indicated Northam [
]. Because only a small
number of cities in the sample have an urbanization rate over 70%, they are combined with cities in
the acceleration stage of urbanization. This information can be combined with the index of
per capita GDP in all the sampled cities to further divide the sample into some sample
subgroups. The initial analysis shows that two of them, initial stage of urbanization with a low
level of economic development, and acceleration stage of urbanization with a high level of
economic development, are typical and representative sample groups. Therefore, this study
primarily employs the STIRPAT model to analyze these two (Sample A and B).
All of the data are from the China City Statistical Yearbook (2011), China City Construction
Statistical Yearbook (2010) and China Urbanization Survey Report (2011). The variables are
listed in Table 1.
Before conducting the factor analysis of the data for China prefecture-level cities, the KMO
test (Kaiser-Meyer-Olkin Measure of Sampling Adequacy) is first estimated. KMO is the index
to compare the simple correlation coefficient and partial correlation coefficient, and it ranges
from 0 to 1. When KMO is more close to 1, the correlation between variables is stronger, and
the original variables are more suitable for factor analysis. This value is 0.643 at 1% significance
level. Thus, the model is verified, and factor analysis can be performed. Then, the maximum
variance orthogonal rotation method is applied. After rotation, the factors with an eigenvalue
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3 Road and
above 1 are selected. Four common factors are selected, and the total variance is over 65%.
This essentially reflects the actual municipal infrastructure development. Each common
factor's corresponding eigenvalue and variance contribution rate are listed in Table 2. The load
matrix after rotation is listed in Table 3.
Thereafter, the factor structure is analyzed, i.e., the names and definitions of the common
factors. According to Tables 3 and 4, the main component factors are ªeco-environment
factor,º ªsanitation factor,º ªroad and transport factorº and ªresidence infrastructure
construction factor.º The variance percentage of common factor represents the percentage of the
common factor percentage in the total variance. And larger the variance percentage is,
stronger the integrating capacity of the common factor.
Common factor F1 demonstrates a high degree of positive correlation with indexes such as
green space ratio in built-up areas and green space coverage in built-up areas. F1 is defined as
the eco-environment construction factor, whose variance percentage is 20.421%.
Common factor F2 demonstrates a high degree of positive correlation with indexes such as
per capita road area, drainage pipe density in built-up areas and water flush toilet ratio in
built-up areas. Because drainage pipes are normally constructed along roads, in the analysis,
the index of per capita road area is included in common factor 2. Common factor 2 is defined
as the environmental sanitation factor, whose variance percentage is 16.801%.
In formula (1), EFI stands for the level of municipal infrastructure sustainable development;
ωi is the weight of common factor i, which is calculated from the proportion of each common
factor's variance percentage in the cumulative variance percentage; and νi is the score of factor
i in the common factors.
As is shown in Fig 1, municipal infrastructure development in China prefecture-level cities
demonstrates typical characteristics of regional differentiation. That is normally in line with
the distribution pattern of economic growth. Cities with high levels of overall sustainable
development are primarily located in metropolitan areas including the Beijing-Tianjin-Hebei
area, Yangtze River Delta, Pearl River Delta and Wuhan metropolis circle.
In recent years, the relationship between municipal infrastructure development, economic
growth and urbanization has attracted widespread attention [
1, 2, 11, 26
]. However, most
researches primarily focused on the influence of a single factor of urbanization, economic
growth on the municipal infrastructure, mainly in examples of developed countries with
mature economies. Further studies could be needed to understand how various driving factors,
such as population, industry and technology, influence municipal infrastructure sustainable
development of cities at various stages of urbanization and economy, in developing countries,
especially under economic transformation.
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Fig 1. Distribution of municipal infrastructure development in China prefecture-level cities.
In this study, the STIRPAT model is employed to examine and analyze the influencing
factors of municipal infrastructure development in various stages of urbanization and economic
Firstly, we investigate the STIRPAT model for the total sample of the prefecture-level cities
in China. The STIRPAT model is employed to investigate the influencing factors of the
municipal infrastructure standard (EFIadj). The study primarily focuses on the main influencing
variables such as urban population, per capita GDP, industrialization, proportion of tertiary
industry output in GDP, per capita disposable income of urban residents, fixed asset
investment, per capita city building and maintenance capital and urbanization level. In the STIRPAT
model, EFI for the total sample is significantly influenced by BM, IN and FAI, with the
significance of below 0.05. Herein, public investment is indicated to be still the main driving force of
the municipal infrastructure. However, urban population percentage and tertiary industry
cannot have significant effects, different from some developed countries.
Secondly, we mainly investigate the STIRPAT model for the sample group A in the initial
stage of urbanization with a low level of economic development. As is indicated in the Table 5,
EFI for cities in the initial stage of urbanization with a low level of economic development is
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Note: a ***, ** and * denote significance below the 1%, 5% and 10% levels, respectively. UR, P, A, IV, FAI, SV are not significant above 0.1.
primarily influenced by three major variables: IN, BM and IV, where the significances of all of
the coefficients are below 0.01 and the model has a desirable fitting effect. Some variables, such
as per capita GDP, per capita city building and maintenance capital and per capita disposable
income of urban residents, have the positive influence on municipal infrastructure
development of cities in the initial stage of urbanization with a low level of economic development. In
addition, the coefficients show that industrialization contributes more than per capita
disposable income of urban residents and per capita city building and maintenance capital, where
elasticity for IN, BM and IV is 0.056, 0.086 and 0.334, mostly at a 1% level of significance,
respectively, as listed in Table 5. Implicit here is that a 1% increase in IN, BM and IV,
respectively, leads to a respective 0.056%, 0.086% and 0.334% increase in municipal infrastructure
development in sample A cities of China.
Thirdly, we proceed to the STIRPAT model for the sample group B in the acceleration stage
of urbanization with a high level of economic development. EFI for cities with a high level of
urbanization and economy is primarily influenced by two variables: IN and BM. The
significance of each coefficient is below 0.01 indicating a high level of significance. The municipal
infrastructure development of cities in the acceleration stage of urbanization with a high level
of economic development is significantly influenced by per capita disposable income of urban
residents and per capita city building and maintenance capital in a positive way. In addition,
the per capita disposable income of urban residents contributes more than per capita city
building and maintenance capital, where elasticity for IN and BM is 0.232 and 0.048, at a 1%
level of significance, respectively, as listed in Table 6. Implicit here is that a 1% increase in IN
and BM, respectively, leads to a respective 0.232% and 0.048% increase in municipal
infrastructure development in sample B cities of China.
Conclusions and discussion
In the present study, based on the data of the prefecture-level cities in China, a FA analysis is
performed to systematically assess municipal infrastructure sustainable development, and then
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a STIRPAT model is used to investigate the key influencing factors. The results indicate that
municipal infrastructure in China has the typical characteristics of regional differentiation, in
line with its economic development pattern, that is primarily influenced by income,
industrialization and public investment. Municipal infrastructure development of cities in the initial
stage of urbanization with a low level of economic development is significantly influenced by
disposable income of urban residents, industrialization and city construction investment in a
positive way, whereas in the acceleration stage of urbanization with a high level of economic
development, it is significantly influenced by income of urban residents and city construction
investment. China is a typical emerging economy and has displayed the phasic characteristics
of a ªcatching-up economyº. The traditional growth model of investment and export,
especially the former, are always the engines of China's economic growth, since the economic
take-off after the reform policy, and the city construction investment is critically influencing
urbanization and the municipal infrastructure development, as the results of the empirical
In cities with a low level of urbanization and economy, municipal infrastructure
development is significantly driven by disposable income of urban residents, proportion of secondary
industry output in GDP and city building and maintenance capital in a positive way. When
cities are in the initial and intermediate stages of urbanization, economic growth and
industrialization are still the most significant influencing factors of municipal infrastructure. For
primary development stage in developing and backward countries, industrial growth would
critically influence municipal infrastructure development. Industrialization is a significant
indicator on municipal infrastructure, especially applicable to that in the initial stages of
urbanization with an economic boom, where secondary industry development in a city
remains crucial. Moreover, even not in cities with a low level of urbanization and a high level
of economy but also in cities with a high level of urbanization and economy, municipal
infrastructure is still closely related to city construction investment and fixed asset investment. In
China and other similar developing countries under transformation, public investment is still
the primary driving force of national economic growth as well as the key influencing factor for
urbanization. And, the income growth and public investment indicators remain the key
constraint factors for municipal infrastructure sustainable development. However, urban
population related consumption and tertiary industry still have limited effects in this country [
And, the contribution of consumption to the municipal development is still so scant, while
investment, especially national public investment, remains a key contributor to infrastructure
sustainable development, which is the crux of the matter encountered by China and similar
developing countries under transformation. On the whole, China's urbanization relative
consumption level, and the tertiary industry are less significantly related with the municipal
infrastructure than some developed countries [
]. Expectedly, the influence of the conventional
national public service model and the investment driven and industrialization model on
urbanization and municipal infrastructure development would continue to decrease, with
secondary industry's influence on municipal infrastructure development dropping off
accordingly, while other relevant factors such as the consumption and tertiary industry driven model
and the innovation society would be key to municipal infrastructure sustainable development,
although for transforming countries it exists an obstacle to transform dominated public
investment to consumption and private investment, with deep-rooted structural imbalance of
difficult adjustment. Government decentralization progressively and increasing non-government
economy are clearly needed to move forward firmly.
This study has provided a general overview of municipal infrastructure development in
urban China, which is among the most challenging in the world due to the scale of population
and economic activity and its transformation involved. Although a further investigation is
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clearly needed before any unquestionable conclusion can be drawn, especially comparative
and a more detailed study of municipal infrastructure based on more panel data of China and
other developing countries, deriving the quantitative estimates of the sustainable development
and its likely critical influencing factors is helpful to advance decision debate. China's
experiences can also provide values for municipal infrastructure development in other developing
Fifty-two prefecture-level cities in the sample A are in the initial stage of urbanization with a
low level of economic development: Hebei ProvinceÐHengshui and Xingtai; Shanxi Province
ÐLvliang, Linfen, Yuncheng and Xinzhou; Inner Mongolia Autonomous RegionÐUlanqab;
Heilongjiang ProvinceÐSuihua; Anhui ProvinceÐBengbu, Chuzhou, Chizhou, Chaohu,
Xuancheng, Suzhou and Buyang; Jiangxi ProvinceÐGanzhou, Shangrao, Ji'an, Fuzhou and
Xuanchun; Fujian ProvinceÐPutian; Shangdong ProvinceÐHeze; Henan ProvinceÐAnyang,
Xinxiang, Jiaozuo, Luohe, Zhumadian, Nanyang, Zhoukou, Xinyang and Shangqiu; Hubei
ProvinceÐHuanggang, Xianning, Xiaogan and Jingmen; Hunan ProvinceÐShaoyang;
Guangdong ProvinceÐMeizhou; Guangxi Zhuang Autonomous RegionÐHechi, Yulin and
Qinzhou; Sichuan ProvinceÐLuzhou, Ya'an, Neijiang, Nanchong and Suining; Yunnan
ProvinceÐZhaotong and Baoshan; Guizhou ProvinceÐZunyi; Shaanxi ProvinceÐHanzhong;
Gansu ProvinceÐPingliang and Tianshui; and Ningxia Hui Autonomous RegionÐGuyuan.
Sixty-one prefecture-level cities in the sample B are in the acceleration stage of urbanization
with a high level of economic development: Hebei ProvinceÐTangshan, Qinhuangdao,
Langfang and Zhangjiakou; Shanxi ProvinceÐYangquan; Inner Mongolia Autonomous RegionÐ
Ordos, Baotou, Wuhai and Tongliao; Liaoning ProvinceÐDalian, Panjin, Anshan, Benxi,
Yingkou, Fushun and Jinzhou; Jilin ProvinceÐJilin and Tonghua; Heilongjiang ProvinceÐ
Daqing and Qitaihe; Jiangsu ProvinceÐSuzhou, Wuxi, Changzhou, Zhenjiang, Nantong,
Lianyungang and Yancheng; Zhejiang ProvinceÐNingbo, Shaoxing, Jiaxing, Zhoushan and
Huzhou; Anhui ProvinceÐMa'anshan, Tongling and Wuhu; Fujian ProvinceÐXiamen and
Sanming; Shangdong ProvinceÐDongying, Qingdao, Yantai, Zibo, Weihai, Rizhao, Dezhou,
Jining, Binzhou, Weifang and Tai'an; Hubei ProvinceÐYichang; Guangdong ProvinceÐ
Dongguan, Foshan and Zhanjiang; Guangxi Zhuang Autonomous RegionÐLiuzhou; Hainan
ProvinceÐSanya; Shaanxi ProvinceÐXianyang and Baoji; Gansu ProvinceÐJiayuguan;
Jiangxi ProvinceÐXinyu and Pingxiang; and Ningxia Hui Autonomous RegionÐShizuishan.
Conceptualization: Yu Li, Fei Li.
Data curation: Ji Zheng, Xueting Jin, Chen Xu.
Formal analysis: Ji Zheng, Chen Xu.
Investigation: Yu Li, Fei Li, Xueting Jin.
Methodology: Fei Li.
Project administration: Yu Li, Fei Li.
Software: Yu Li, Ji Zheng, Fei Li, Xueting Jin, Chen Xu.
Supervision: Yu Li.
Validation: Yu Li, Fei Li.
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Writing ± original draft: Yu Li, Ji Zheng, Fei Li, Xueting Jin, Chen Xu.
Writing ± review & editing: Yu Li, Fei Li.
12 / 14
Wendel H E W, Downs J A, Mihelcic J R. Assessing Equitable Access to Urban Green Space: The Role
of Engineered Water Infrastructure. Environmental Science & Technology 2011; 45(16): 6728±6734.
13 / 14
1. Tzoulas K , Korpela K , Venn S , Yli-Pelkonen V , Kazmierczak A , Niemela J , et al. Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review . Landscape and Urban Planning 2007 ; 81 ( 3 ): 167 ± 178 .
2. Yan S H , Li Y , Mao Q , Dong S C. The Urban Municipal Infrastructure Construction Status, Problem and Countermeasure of China . Urban Development Studies 2012 ; 19 ( 5 ): 28 ± 33 .
3. Bensassi S. Marquez-Ramos L , Martinez-Zarzoso I , Suarez-Burguet C . Relationship between logistics infrastructure and trade: Evidence from Spanish regional exports . Transportation Research Part A 2015 ; 72 : 47 ± 61 .
Wu X H , Li X P . Urban and rural municipal infrastructure planning . Beijing: China Architecture & Building Press; 2016 .
5. Carr R I. Construction Congestion Cost (CO3) basic model . Journal of Construction Engineering and Management-Asce 2000 ; 126 ( 2 ): 105 ± 113 .
6. Topalovic P , Carter J , Topalvoic M , Krantzberg G . Light Rail Transit in Hamilton: Health, Environmental and Economic Impact Analysis . Social Indicators Research 2012 ; 108 ( 2 ): 329 ± 350 .
7. Forman R T. Estimate of the area affected ecologically by the road system in the United States . Conservation Biology 2000 ; 14 ( 1 ): 31 ± 35 .
8. Andrade C E S , D'Agosto M A. The Role of rail transit systems in reducing energy and carbon dioxide emissions . Sustainability 2016 ; 8 : 1± 16 .
9. Clark S , Watling D. Modelling network travel time reliability under stochastic demand . Transportation Research Part B: Methodological 2005 ; 39 ( 2 ): 119 ± 140 .
10. Sid G , Jakkapan J . Identifying and assessing the critical risk factors in an underground rail project in Thailand: a factor analysis approach . International Journal of Project Management 2004 ; 22 ( 8 ): 633 ± 643 .
11. Upadhyaya J K , Biswas N , Tam E. A review of infrastructure challenges: assessing storm water system sustainability . Canadian Journal of Civil Engineering 2014 ; 41 ( 6 ): 483 ± 492 .
12. Van Riel W , Langeveld J , Herder P , Clemens F. Information Use in Dutch Sewer Asset Management . In: Proceedings of the 7th World Congress on Engineering Asset Management (Wceam 2012 ); 2015 . p. 615 ± 624 .
13. Hatt B E , Fletcher T D , Walsh C J , Taylor S L. The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams . Environmental Management 2004 ; 34 ( 1 ): 112 ± 124 . https://doi.org/10.1007/s00267-004 -0221-8 PMID: 15383877
14. Freni G , Mannina G , Viviani G . Assessment of the integrated urban water quality model complexity through identifiability analysis . Water Research 2011 ; 45 ( 1 ): 37 ± 50 . https://doi.org/10.1016/j.watres. 2010 . 08 .004 PMID: 20732705
15. Avritzer A , Carnevali L , Ghasemieh H , Happe L , Havekort B R , Koziolek A , et al. Survivability Evaluation of Gas, Water and Electricity Infrastructures . Electronic Notes in Theoretical Computer Science 2015 ; 310 : 5± 25 .
16. Ma X , Xue X B , GonzaÂlez-MejÂõa A , Garland J , Cashdollar J . Sustainable water systems for the city of tomorrow- a conceptual framework . Sustainability 2015 ; 7 ( 9 ): 12071 ± 12105 .
17. Yeo I A , Yoon S H , Yee J J. Development of an urban energy demand forecasting system to support environmentally friendly urban planning . Applied Energy 2013 ; 110 : 304 ± 317 .
18. Futcher J A. , Mills G. The role of urban form as an energy management parameter . Energy Policy 2013 ; 53 : 218 ± 228 .
19. Dunn S. Hydrogen Futures : Toward a Sustainable Energy System . International Journal of Hydrogen Energy 2002 ; 27 ( 3 ): 235 ± 264 .
20. Joardar S D. Carrying capacities and standards as bases towards urban infrastructure planning in India: A case of urban water supply and sanitation . Habitat International 22 ( 3 ): 327 ± 337 .
21. Schaffler A. , Swilling M. , 2013 . Valuing green infrastructure in an urban environment under pressureÐ The Johannesburg case . Ecological Economics 1998 ; 86 : 246 ± 257 .
22. Chen X P , Pang J X , Zhang Z L , Li H J . Sustainability assessment of solid waste management in China . Sustainability 2014 ; 6 , 9268 ± 9281 .
23. Rigamonti L , Sterpi I , Grosso M. Integrated municipal waste management systems: An indicator to assess their environmental and economic sustainability . Ecological Indicators 2016 ; 60 : 1 ± 7 .
24. Henriques J J , Louis G E. A decision model for selecting sustainable drinking water supply and greywater reuse systems for developing communities with a case study in Cimahi, Indonesia . Journal of Environmental Management 2011 ; 92 ( 1 ): 214 ± 222 . https://doi.org/10.1016/j.jenvman. 2010 . 09 .016 PMID: 20888682
25. Galvis A , Zarnbrano D A , van der Steen N P , Gijzen H J . Evaluation of pollution prevention options in the municipal water cycle . Journal of Cleaner Production 2014 ; 66 : 599 ± 609 .
26. Brands E. Prospects and challenges for sustainable sanitation in developed nations: a critical review . Environmental Reviews 2014 ; 22 ( 4 ): 346 ± 363 .
27. Bouabid A , Louis G E . Capacity factor analysis for evaluating water and sanitation infrastructure choices for developing communities . Journal of Environmental Management 2015 ; 161 : 335 ± 343 . https://doi. org/10.1016/j.jenvman. 2015 . 07 .012 PMID: 26203872
28. Bulleri F , Chapman M G. The introduction of coastal infrastructure as a driver of change in marine environments . Landscape and Urban Planning 2010 ; 47 ( 1 ): 26 ± 35 .
30. Rostami R , Lamit H , Khoshnava S M , R ostami R , Rosley M S F. Sustainable cities and the contribution of historical urban green spaces . Sustainability 2015 ; 7 : 13290 ± 13316 .
31. Rudrab P P , Mak B A , John H H. Economic growth, development of telecommunications infrastructure, and financial development in Asia, 1991 ± 2012 . The Quarterly Review of Economics and Finance 2016 ; 59 : 25 ± 38 .
32. Matthew J C, Allouche E N , Sterling R L. Social cost impact assessment of pipeline infrastructure projects . Environmental Impact Assessment Review 2015 ; 50 : 196 ± 202 .
33. Ben A S , Eling M. Common risk factors of infrastructure investments . Energy Economics 2015 ; 49 : 257 ± 273 .
34. Liu Q Q , Wang S J , Zhang W Z , Li J M , Zhao Y B , Li W. China's municipal public infrastructure: Estimating construction levels and investment efficiency using the entropy method and a DEA model . Habitat International 2017 ; 64 : 59 ± 70 .
35. Sara B , Leonardo C , Michele O. Measuring Transport Systems Efficiency Under Uncertainty by Fuzzy Sets Theory Based Data Envelopment Analysis: Theoretical and Practical Comparison with Traditional DEA Model . Transportation Research Procedia 2015 ; 5 : 186 ± 200 .
36. Herva M , Neto B , Roca E . Environmental assessment of the integrated municipal solid waste management system in Porto (Portugal) . Journal of cleaner production 2014 ; 70 : 183 ± 193 .
37. Yang J K , Lee C G , Jeon J H , Lee H K. Selection and Management Factor Analysis of Urban Infrastructure for U-City Construction . KSCE Journal of Civil Engineering 2013 ; 17 ( 7 ): 1637 ± 1643 .
38. National Bureau of Statistics of China. China Statistical Yearbook. Beijing: China Statistics Press; 1999 .
39. National Bureau of Statistics of China. China Statistical Yearbook. Beijing: China Statistics Press; 2015 .
40. Northam R M. Urban Geography . New York: J. Wiley Sons; 1975 .
41. Escofier B , Pages J . Multiple factor analysis (AFMULT package) . Computational Statistics & Data Analysis 1994 ; 18 ( 1 ): 121 ± 140 .
42. Escofier B , Pages J. Analyses Factorielles Simples et Multiples, ( 3rd .) Dunod, Paris, France; 1998 .
43. Paatero P. The multilinear engineÐA table-driven, least squares program for solving multi-linear problems, including the n-way parallel factor analysis model . Journal of Computational and Graphical Statistics 1999 ; 8 ( 4 ): 854 ± 888 .
44. Johnson R A , Bhattacharyya G K. Statistics : Principles and Methods 7th Edition , Wiley; 2014 .
Ehrlich P R , Holdren J P. Impact of population growth . Science 1971 ; 171 : 3977 .
46. Dietz T , Rosa E A . Rethinking the environmental impacts of population, affluence and technology . Human Ecology Review 1994 ; 1 ( 2 ): 277 ± 300 .
47. York R , Rosa E A , Dietz T. Bridging environmental science with environmental policy: plasticity of population, affluence and technology . Social Science Quarterly 2002 ; 83 ( 1 ): 18 ± 34 .
48. Lin S F , Zhao D T , Marinova D. Analysis of the environmental impact of China based on STIRPAT model . Environmental Impact Assessment Review 2009 ; 29 ( 6 ): 341 ± 347 .
49. Shao S , Yang L L , Yu M B , Yu M L. Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China) 1994±2009 . Energy Policy 2011 ; 39 ( 10 ): 6476 ± 6494 .
50. York R , Rosa E A , Dietz T. STIRPAT , IPAT and IMPACT: analytic tools for unpacking the driving forces of environmental impacts . Ecological Economics 2003 ; 46 ( 3 ): 351 ± 365 .
51. UNEP (United Nations Environment Programme ) . Sustainable resource efficient cities: Making it happen . Paris, France: UNEP Division of Technology, Industry and Economics . 2012 .
52. Lu D D , Fan J . Regional development in China: 2050 . Beijing: Science Press; 2009 .