- Email: [email protected]
Critical indicators for assessing the contribution of infrastructure projects to coordinated urban–rural development in China
Habitat International 36 (2012) 237e246
Contents lists available at SciVerse ScienceDirect
Habitat International journal homepage: www.elsevier.com/locate/habitatint
Critical indicators for assessing the contribution of infrastructure projects to coordinated urbanerural development in China Liyin Shen a, Shijie Jiang b, Hongping Yuan a, * a b
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China Department of Construction Management, Chongqing University of Science and Technology, Chongqing, China
a b s t r a c t Keywords: Coordinated development Infrastructure projects Indicator Principal component analysis China
It is widely recognized that the coordinated development between urban and rural areas can not only narrow gaps in infrastructure between urban and rural areas, promote balanced development in rural areas in terms of society, economy and environment, but is also an effective way to address issues facing agriculture, rural areas and farmers. In line with this, development of infrastructure projects has been an important means for promoting coordinated urbanerural development in developing countries (such as China). However, there is a lack of indicators that can be used to assess the contribution of infrastructure projects to coordinated urbanerural development. This paper thus attempts to present a set of critical indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development in the particular context of Chongqing, Western China. First, a list of optional indicators that are with potential for assessing the contribution of infrastructure projects to coordinated urbanerural development is presented based on examination on related project feasibility reports, official reports and literature. Then 42 indicators are identified from the optional list through a questionnaire survey. By using the data collected, the relative level of significance of each indicator is derived. Finally, an indicator system consisting of 19 critical indicators is established based on results of principle component analysis. The applicability and significance of the identified indicators for assessing the contribution of infrastructure projects to coordinated urbanerural development are discussed as well. Ó 2011 Elsevier Ltd. All rights reserved.
Introduction Development of rural areas in developing countries is generally lagging behind the development of urban areas as there is no adequate investment in infrastructure and various economic activities in the rural area. In recent years, along with the rapid speed of urbanization, provision of public facilities to rural areas to achieve coordinated development between urban and rural areas has become an imperative issue in many developing countries, such as in China. Coordinated urbanerural development addresses the question of how to coordinate development in both urban and rural areas in terms of economy, environment and society. That is, to coordinate urbanerural development, it should take economic, environmental and social development of urban and rural areas into consideration as a whole. In the meantime, priority should be given to development of rural areas, agriculture and farmers’ living
* Corresponding author. E-mail address: [email protected] (H. Yuan). 0197-3975/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.habitatint.2011.10.003
standard when development plans are made at both the national and regional levels, in order to achieve a common prosperity in both rural and urban areas. Coordinated urbanerural development is an effective path to promote rural area development. Importance of coordinated urbanerural development has been extensively investigated in the literature. For example, Liu (2007) argued that investment in infrastructure projects in rural areas is significant in renovating traditional agriculture, transforming and upgrading countryside industries, introducing modern civilization into rural areas, and improving the living standard of farmers. Particularly, implementation of infrastructure projects in rural areas, such as road and irrigation projects, could largely reduce costs of production and transportation in agriculture and agricultural products, improve the effectiveness and efficiency of economic activities in rural areas, and diminish impacts of natural disaster on farmers. Huang (2006) confirmed that water conservation facilities could to a large extent reduce disasters in agriculture. Furthermore, electric power, communication, water supply and drainage projects in rural areas could promote various industries
238
L. Shen et al. / Habitat International 36 (2012) 237e246
and business activities in these areas, and ultimately contribute to the coordinated urbanerural development (Li, 2002). Education, health, culture and entertainment projects in rural areas could help consolidate the foundation for development of public facilities in rural areas and promote farmers’ education level and life quality. Biogas, sewage and garbage disposal projects could improve the living condition, life quality, as well as the ecology in rural areas (Hu, 2008). Solutions for driving rural area development have been proposed by previous studies. For instance, Li (2007) opined that the solution to “San Nong Problems” (which means issues concerning agriculture, countryside and farmer) in China depends not only on the reform and adjustment of the public facility provision system in rural areas, but more importantly, is largely based on the promotion of coordinated urbanerural development. Having realized the importance and benefits of promoting coordinated urbanerural development, the Chinese government has determined to implement a long-term strategy aiming at coordinating development between urban and rural areas. As an essential component of the strategy, the investment in infrastructure in rural areas will be increased in the following years. At the 16th National Congress of the Communist Party of China (CCP), it was confirmed that “to coordinate urbanerural social and economic development, modernizing agriculture, developing rural economy and increasing farmers’ income are the main means for building a generally well-off society in China” (PLRC, 2005a, p. 465). The scientific outlook on development, which was proposed in the 3rd plenary session of the 16th CCP Central Committee, emphasized the “five coordination”, in which “coordination of urbane rural development” is on the top of agenda (PLRC, 2005b, p. 1066). Comments by CCP Central Committee on the “11th Five-Year Plan”, which was passed on the 15th plenary session of the 16th CCP Central Committee, highlighted the pressing need to proactively promote coordinated urbanerural development (PLRC, 2005b, p. 1066). Later, on the 17th National Congress of the Communist Party of China (CCP), the Chinese President Hu Jintao addressed to “strengthen infrastructure in rural areas and promote coordinated development between urban and rural areas in China”. The No.1 document of CCP Central Committee in 2008 further clarified this strategy, which was to focus on strengthening the construction of agricultural infrastructure, and developing a new mechanism to facilitate coordinated urbanerural growth (Song, 2008). The Chinese President Hu Jintao reiterated the policy of coordinating urbanerural development, promoting socialist new countryside construction, and creating a new pattern where urbanerural economy and society are integrated in the 17th National Congress of CCP (J.T. Hu, 2007). China has been adopting a dual economy system for many years, including urban economy and rural economy. In light of the system, provision of infrastructure is under different systems for urban and rural areas, respectively. Urban infrastructure is mainly financed by the State fiscal budget, while infrastructure investment in rural areas is principally borne by towns, villages, communities and even farmers. The project financing mechanism has biased toward cities, and disadvantaged rural areas in securing funding sources for implementing infrastructure projects. The study by Yang (2005) identified that infrastructure investment in rural areas was of severe scarcity in China, whilst infrastructure investment in urban areas had grown dramatically during the last two decades. In fact, this “dual economy system” has led to segregation between urban and rural areas in infrastructure. The emphasis of the national economy is mostly laid on urban areas, where many industries are concentrated, but provision of infrastructure in rural areas has been largely neglected. Such a gap between urban and rural areas in
China results in many economic and social problems. There is thus an urgent need for the government to figure out effective solutions to the problems. Among the solutions, promotion of coordinated urbanerural development is commonly identified as a key strategy to drive rural area development in an effective manner, as the main purpose of coordinated urbanerural development is narrowing the gap between urban and rural areas, especially in terms of transportation, education, medical facilities, etc. In line with the coordinated urbanerural development strategy proposed by the Chinese government, many local governments typically including Chongqing and Chengdu in Western China, have taken actions to practice the strategy in the past four years. The practical experience suggested that there is no indicator that can be used to effectively assess the contribution of infrastructure projects to coordinated urbanerural development. Without such assessment indicators, the effectiveness of infrastructure investment in rural areas cannot be well appreciated. This presents the pressing need to identify a set of critical indicator for assessing how infrastructure projects could benefit coordinated urbanerural development. Few studies so far have been carried out for studying the particular role of infrastructure investment in coordinated urbane rural development. For example, Chandra and Thompson (2000) explored the quasi-experimental nature of rural interstate highway construction and found that opening new interstate highways would not increase net economic activities in nonmetropolitan regions. This study is mainly focused on the economic perspective. The study by Estache, Foster, and Wodon (2000) argued that for the poor, the most dramatic impact of inadequate infrastructure is the lack of sufficient access to infrastructure for the majority of residents in rural areas. Furthermore, Fu, Hao, Zhu, and An (2006) introduced a method to assess the correlation between urbanerural coordinated development and factors affecting the coordinated development. However, it appears that there is no study available in presenting a set of critical indicators to evaluate the contribution of infrastructure projects to coordinated urbanerural development, from a holistic perspective covering economic, social, and environmental aspects. Therefore, the aim of this paper is to identify a set of critical indicators that can be used to effectively assess the contribution of infrastructure projects to coordinated urbanerural development. Critical indicators for coordinated urbanerural development assessment in this study are identified based on the particular urbanerural development practices of Chongqing city, Western China. In 2007, the Chinese National Development and Reform Commission issued “The Notice on the Establishment of a National Pilot Zone for Overall Reform of Coordinating Urban and Rural in Chongqing and Chengdu” (Zhang, 2007). Currently, Chongqing features in the coexistence of metropolis and extensive rural areas, with the prominent contradiction of dual structure between urban and rural areas. In this regard, Chongqing is a typical microcosm of China if we concern the coordinated urbanerural development. That is, if Chongqing succeeded in exploring a new path for achieving coordinated urbanerural development, it can serve as a representative model and drive the national reform largely. Research methodology A hybrid research methodology is applied to identify a set of indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development. First, an examination is conducted on 100 project feasibility reports concerning various kinds of infrastructure projects to filter indicators adopted in current practices for evaluating the contribution of infrastructure projects to coordinated urbanerural development. Besides,
L. Shen et al. / Habitat International 36 (2012) 237e246
relevant literature and official reports are also examined by using a content analysis approach, which is very helpful for deriving useful information from documentary evidence (Holsti, 1969, pp. 14e20). This finally results in a list of optional indicators. Based on the list, a questionnaire is designed to determine the adequacy and significance of each optional indicator through consulting professionals. Professionals are invited to rank the significance of each indicator based on a nine-point Likert scale, with 9 indicating the most significance and 1 representing the least significance. After that, by using the data collected, the level of significance of each indicator is calculated and the relative significance level of each indicator derived. Finally, an indicator system consisting of a set of critical indicators is established for assessing the contribution of infrastructure projects to coordinated urbane rural development. Results and analyses Formulation of assessment indicators As afore-mentioned, identification of optional indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development is based on a thorough examination of documental materials, including an examination on 100 feasibility reports regarding various infrastructure projects (i.e. 50 highways, 10 bridges and 40 water supply projects) and the other examination on relevant literature and official reports, such as Economic Evaluation Methods and Parameters for Construction Projects (National Development and Reform Commission, 2006), Economic Evaluation Cases of Construction Project (Standard and Norm Institute of the Ministry of Construction, 2006), Quantitative Evaluation of Effects of Infrastructure Investment (Lin & Chen, 2006), and Social Evaluation Guidance for Investment Projects in ChinadProjects Financed by the World Bank and Asian Development Bank (CIECC, 2004). The 100 infrastructure projects are the first batch of projects launched in line with “The Notice on the Establishment of a National Pilot Zone for Overall Reform of Coordinating Urban and Rural in Chongqing and Chengdu” issued by the Chinese National Development and Reform Commission in 2007 (Zhang, 2007). All of them are constructed in rural areas in Chongqing, in order to narrow the gap between urban and rural areas in terms of water supply and transportation. The 46 indicators resulted from the above research activities are tabulated in Table 1. It can be observed from Table 1 that the 46 indicators are categorized in two broad groups, namely, benefit indicators and fairness indicators. The former group concerns economic, social, and environmental and ecological benefit while the latter is related to fairness of investment policy, fairness of investment system, and fairness of investment environment. One principal purpose of China’s policies on coordinated urbanerural development is to provide urban and rural residents with equal opportunities for development, facilitate an appropriate flow and optimized allocation of resources, and strengthen the role of city in leading rural area development. In this way, the gap between rural and urban areas can be ultimately narrowed, and thus they can develop in a balanced and sustainable manner (Ma, 2006). In line with this, the gap of development between cities and villages is considered as the priority when allocating infrastructure investments among various regions in China. Since the dual economy system has led to the imbalanced development between urban and rural areas for a long time, infrastructure investments should have a full role to play as an economic policy to bridge the development gap. Therefore, a policy promoting both fairness and benefit should be designed accordingly (Liu, 2007). It is therefore
239
Table 1 Optional indicators for assessing the contribution of infrastructure projects to coordinated urbanerural development. No.
Optional assessment indicators
1 1.1 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.6 1.1.7 1.2 1.2.1 1.2.2 1.2.3 1.2.4
Benefit indicators Economic benefit IRR (internal rate of return) NPV (net present value) Payback (dynamic) Loan repayment period Return on investment Return on investment before tax (Direct and indirect) benefit-cost ratio of project Social benefit Income level Living standard and quality (expressed by Engel Indicator) Employment level Capability to provide associated facilities (expressed by prevalence percentage) Capability to provide service (expressed by coverage of service points) Culture and education level, hygiene and health level Safety benefit Amount of benefit compensation of project stakeholders and underprivileged groups Mutual adaptability indicator Social risk level (expressed by social risk evaluation value) Environmental and ecological benefit Air pollution indicator (degree) Surface water pollution degree Solid waste pollution degree Noise pollution indicator Landscape impact degree Water and soil loss impact indicator Cultural relic and heritage preservation percentage (value) Energy saving percentage Recycled use percentage of wastes (or wastewater)
1.2.5 1.2.6 1.2.7 1.2.8 1.2.9 1.2.10 1.3 1.3.1 1.3.2 1.3.3 1.3.4 1.3.5 1.3.6 1.3.7 1.3.8 1.3.9 2 2.1 2.1.1 2.1.2 2.1.3 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.3.6 2.3.7 2.3.8 2.3.9 2.3.10 2.3.11 2.3.12
Fairness indicators Fairness of investment policy Preferential treatment of investment policies (for urban or rural areas) Stability of investment policies Support degree of investment policies Fairness of investment system Fairness of investors Fairness of investment decision-making Fairness of financing application reviewing and approval Investment continuity Fairness of investment supervision and administration Fairness of investment environment Fairness of urban and rural natural resources Fairness of urban and rural public resources Fairness of urban and rural energy supply Fairness of urban and rural economy Fairness of urban and rural income distribution Fairness of urban and rural living standard Fairness of urban and rural market Fairness of urban and rural technology Fairness of urban and rural education Fairness of urban and rural employment Fairness of urban and rural social security Fairness of urban and rural law environment
significant for the present study to involve both fairness indicators and benefit indicators in urbanerural coordinated development assessment. The significance of each of these indicators in evaluating the contribution of infrastructure projects to coordinated urbanerural development is examined by using data collected from a questionnaire survey. In the survey, 38 experienced professionals were identified as target respondents, including 10 government officials, 18 researchers, and 10 professionals from companies. Finally 24 valid responses were received, which was summarized in Table 2. In
240
L. Shen et al. / Habitat International 36 (2012) 237e246
Table 2 Profile of experts in the first round questionnaire survey. Sent
Government (10)
Professionals (18)
Company (10)
City level
County level
Research Institutes
Colleges
Enterprise (Finance)
Consulting (Intermediate)
38 100%
6 16%
4 11%
1 3%
17 45%
7 18%
3 8%
Valid response
Government (6)
24 100%
Professionals (10)
Company (8)
City level
County level
Research Institutes
Colleges
Enterprise (Finance)
Consulting (Intermediate)
3 13%
3 13%
1 4%
9 38%
5 21%
3 13%
the survey, respondents were invited to determine whether a particular indicator is significant to be adopted for assessing the contribution of infrastructure projects to coordinated urbanerural development. As a result, 42 indicators which received more than 60% supportive responses were chosen as assessment indicators for further study (see Table 3). Relative significance of assessment indicators Based on the indicators in Table 3, another questionnaire survey was carried out to determine the relative significance of each
indicator. Particularly, professionals were invited to provide their judgment on the relative significance of each indicator based on a nine-point Likert scale, with 9 indicating the most significant and 1 representing the least significant. The respondents were from government departments, research institutes and consulting companies. 200 questionnaires were distributed through email and 125 valid responses were ultimately received, achieving a response rate of 63%. Among them, 30 were from government departments, 45 were from research institutes, and 50 from consulting companies. By using the data collected, statistical analysis was conducted with the aid of the SPSS software package to obtain mean and
Table 3 Indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development. Categories of indicators
Indicators
Code
Economic benefit evaluation
IRR (Internal Rate of Return) NPV (Net Present Value) Payback (dynamic) Loan repayment period EIRR (Economic Internal Rate of Return) ENPV (Economic Net Present Value) (Direct and indirect) cost-benefit ratio of projects Employment status Living standard and quality (expressed by Engel Indicator) Capability to provide associated facilities (expressed by prevalence percentage) Culture and education level, hygiene and health level Safety benefit Amount of benefit compensation of project stakeholders and underprivileged groups Mutual adaptability Social risk level (expressed by social risk evaluation value) Air pollution indicator (degree) Surface water pollution degree Solid waste pollution degree Noise pollution indicator Water and soil loss impact indicator Cultural relic and heritage preservation percentage (value) Energy saving percentage Recycled use percentage of wastes (or wastewater) Preferential treatment of investment policies (for urban or rural areas) Stability of investment policies Support degree of investment policies Fairness of investors Fairness of investment decision-making Fairness of financing application reviewing and approval Investment continuity Fairness of investment supervision and administration Fairness of urban and rural natural resources Fairness of urban and rural public resources Fairness of urban and rural energy supply Fairness of urban and rural economy Fairness of urban and rural income distribution Fairness of urban and rural living standard Fairness of urban and rural technology Fairness of urban and rural education Fairness of urban and rural employment Fairness of urban and rural social security Fairness of urban and rural law environment
X11 X12 X13 X14 X15 X16 X17 X21 X22 X23 X24 X25 X26 X27 X28 X31 X32 X33 X34 X35 X36 X37 X38 X41 X42 X43 X51 X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
Social benefit evaluation
Environmental and ecological benefit evaluation
Fairness of investment policy
Fairness of investment system
Fairness of investment environment
L. Shen et al. / Habitat International 36 (2012) 237e246
variance of the significance of each indicator. The detailed results are tabulated in Table 4. Consistence analysis between response groups Since the data were derived from three different groups, namely, government departments, research institutes, and consulting companies, it is essential to examine the consistence of the responses on the relative significance of indicators between the three groups of respondents. As a commonly recognized technique, ANOVA approach is widely employed to test whether there is significant difference in responses between different categories of respondents in a questionnaire survey (Hair, Anderson, Tatham, & Black, 1995). Thus this method is adopted in the present study. In line with the principles of ANOVA approach, when P-value 0.01, there is an extremely significant difference; when P-value > 0.05, no significant variance exists; while a P-value between 0.01 and 0.05 indicates a significant variance (Hair et al., 1995). Results of ANOVA analysis are shown in Table 5. It can be seen from Table 5 that indicators X14 and X25 have very significant variances between different responding groups. Indicator X14, denoting “loan repayment period”, receives a mean significance value of 6.38 given by consulting companies, 5.23 given
241
by government departments, and 5.87 given by research institutes. On the other hand, X25, an indicator representing “safety benefit”, which is one of the social benefit indicators, is highly ranked by consulting companies, obtaining a mean significance value of 7.56. It is confirmed that there is no significant variance between different responding groups on the other indicators.
Reliability analysis The reliability of the responses was validated through Cronbach’s coefficient, which is a statistical parameter to determine the internal consistency of data. In this study, Cronbach’s coefficient is used to ensure that all responses within a group are consistent. It is usually measured by Cronbach’s a value as follows:
a ¼
P 2! S k 1 P i2 k1 Sx
(1)
where a means the proportion out of the total variance between individual responses, S2i is the variance of the ith indicator by all the respondents, S2x is the variance of all the indicators by all the respondents, and k is the number of indicators. Value of a can range from 0 to 1. The larger the value of a, the better the consistence
Table 4 Mean and SD of assessment indicator significance. Indicator
All (N ¼ 125)
Government (N ¼ 30)
Mean
Std. Deviation
Mean
X11 X12 X13 X14 X15 X16 X17 X21 X22 X23 X24 X25 X26 X27 X28 X31 X32 X33 X34 X35 X36 X37 X38 X41 X42 X43 X51 X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
6.22 6.08 6.37 5.92 5.56 5.54 5.86 6.80 6.82 7.44 6.63 6.82 6.58 6.14 5.98 7.15 7.22 7.07 6.70 6.86 5.99 6.70 6.40 6.61 6.74 6.78 6.18 6.30 6.17 6.66 6.88 5.86 6.54 6.06 5.78 6.18 5.82 5.38 6.43 6.10 6.54 6.42
1.68 1.63 1.89 1.58 1.60 1.57 1.53 1.50 1.61 1.25 1.69 1.77 1.70 1.55 1.81 1.57 1.61 1.62 1.76 1.66 1.96 1.70 1.93 1.45 1.43 1.41 1.64 1.44 1.53 1.78 1.80 1.80 1.51 1.39 1.64 1.64 1.68 1.58 1.89 1.59 1.77 1.88
5.63 5.57 5.97 5.23 4.97 5.07 5.97 6.87 6.57 7.20 6.40 6.60 6.57 6.07 5.53 6.83 6.83 6.70 6.67 6.50 5.70 6.90 6.37 6.87 7.03 7.10 6.27 6.70 6.40 6.37 6.27 5.60 6.30 5.83 5.83 6.20 5.87 5.63 6.43 6.00 6.50 5.80
Professionals (N ¼ 45)
Company (N ¼ 50)
Std. Deviation
Mean
Std. Deviation
Mean
Std. Deviation
1.85 1.59 1.79 1.59 1.59 1.34 1.83 1.48 1.70 1.32 1.63 1.94 1.94 1.66 1.83 1.66 1.72 1.80 1.81 1.66 2.10 1.85 2.04 1.68 1.54 1.32 1.64 1.62 1.83 1.63 2.21 1.55 1.54 1.32 1.62 1.50 1.48 1.63 2.08 1.44 1.68 1.86
6.38 6.31 6.47 5.87 5.58 5.49 5.62 7.00 6.51 7.27 6.38 6.16 6.16 6.31 6.00 7.09 7.29 7.02 6.51 6.67 5.71 6.33 6.38 6.69 6.71 6.84 5.89 6.20 6.11 7.04 7.02 5.64 6.60 6.00 5.82 6.51 6.11 5.33 6.73 6.42 6.93 6.40
1.75 1.81 2.03 1.47 1.66 1.79 1.37 1.43 1.65 1.29 1.67 1.48 1.71 1.41 1.75 1.31 1.41 1.44 1.66 1.55 1.83 1.41 1.60 1.55 1.58 1.40 1.63 1.39 1.48 1.54 1.44 1.54 1.27 1.45 1.56 1.55 1.66 1.57 1.85 1.75 1.74 1.83
6.44 6.18 6.52 6.38 5.90 5.88 6.00 6.58 7.26 7.74 7.00 7.56 6.96 6.02 6.22 7.40 7.40 7.34 6.90 7.24 6.42 6.90 6.44 6.38 6.58 6.54 6.40 6.16 6.08 6.48 7.12 6.20 6.62 6.24 5.70 5.88 5.54 5.26 6.16 5.86 6.22 6.80
1.43 1.45 1.82 1.55 1.49 1.42 1.47 1.58 1.45 1.14 1.70 1.64 1.46 1.61 1.84 1.70 1.70 1.65 1.84 1.71 1.95 1.82 2.16 1.19 1.20 1.46 1.65 1.35 1.38 2.03 1.77 2.11 1.69 1.38 1.75 1.77 1.80 1.58 1.81 1.51 1.82 1.86
242
L. Shen et al. / Habitat International 36 (2012) 237e246
Table 5 Results of ANOVA analysis. Indicator
P-value
Indicator
P-value
Indicator
P-value
Indicator
P-value
Indicator
P-value
X11 X12 X13 X14 X15 X16 X17
0.084 0.132 0.41 0.006 0.04 0.076 0.441
X21 X22 X23 X24 X25 X26 X27 X28
0.384 0.046 0.089 0.138 0 0.068 0.635 0.26
X31 X32 X33 X34 X35 X36 X37 X38
0.279 0.298 0.226 0.561 0.097 0.137 0.203 0.982
X41 X42 X43 X51 X52 X53 X54 X55
0.316 0.387 0.216 0.305 0.223 0.635 0.182 0.097
X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
0.218 0.618 0.425 0.915 0.173 0.253 0.581 0.34 0.214 0.145 0.068
among individual responses, and thus indicates a higher reliability of internal consistency. Previous studies suggested that a a value over 0.8 indicates a very good internal consistency, a value between 0.6 and 0.8 represents a good consistency, whilst a value less than 0.6 indicates a poor internal consistency (Hair et al., 1995). The reliability computation is also conducted by using the SPSS, and detailed results are shown in Table 6 and Table 7. According to the results, it is apparent that Cronbach’s coefficients of all the indicators are greater than 0.8, suggesting that the result of questionnaire survey is of sound quality. Principal components analysis In the questionnaire, 42 indicators dividing into 6 categories were used for assessing the contribution of infrastructure projects to coordinated urbanerural development. However, it seems rather difficult to simultaneously apply these 42 indicators in a single assessment. Furthermore, the importance of each indicator varies from one to another. Therefore, principal components analysis (PCA) approach is adopted in this study to screen out some principal indicators which have a larger influence upon the assessment of infrastructure projects’ contribution to coordinated urbanerural development. For purpose of illustration, this paper mainly takes
“economic benefit indicators” as an example to expound the extracting process of relatively important indicators by means of PCA. KMO test PCA method is one of factor analysis techniques, in which the first several principal components accounting for 85% of the total variance are retained whilst the rest factors can be omitted in further analysis (Chen & Zou, 2009; Li, 2008). According to the principle of PCA, KMO (Kaiser-Meyer-Olkin) test is normally conducted in the first place. KMO test can be used to compare simple correlation coefficient and partial correlation coefficient among variables. KMO test value can range from 0 to 1. When KMO value approaches 1, indicating that the square sum of simple correlation coefficient of all the variables exceeds by far the square sum of their partial correlation coefficient. When a KMO value is closer to 1, indicating that the stronger the correlation among the variables, the more suitable the variables can be used for PCA. When a KMO value approaches to 0, meaning the square sum of simple correlation coefficient of all the variables approaches to 0. When a KMO value is closer to 0, meaning that the weaker the correlation among the variables, the more unsuitable the variables are analyzed through PCA (Lin, 2007).
Table 6 The Cronbach’s alpha of benefit indicators. Indicator
X11 X12 X13 X14 X15 X16 X17 X21 X22 X23 X24 X25 X26 X27 X28 X31 X32 X33 X34 X35 X36 X37 X38
All (N ¼ 125)
Government (N ¼ 30)
Professionals (N ¼ 45)
Company (N ¼ 50)
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
142.63 142.78 142.49 142.94 143.30 143.31 143.00 142.06 142.03 141.42 142.22 142.03 142.28 142.72 142.88 141.70 141.63 141.78 142.15 142.00 142.86 142.16 142.46
450.06 449.27 445.38 444.69 442.55 444.27 450.18 460.60 435.77 448.07 434.92 425.87 436.61 441.40 429.46 427.97 429.12 424.64 423.81 427.50 411.25 423.01 417.83
0.91 0.91 0.91 0.90 0.90 0.90 0.90 0.91 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
137.07 137.13 136.73 137.47 137.73 137.63 136.73 135.83 136.13 135.50 136.30 136.10 136.13 136.63 137.17 135.87 135.87 136.00 136.03 136.20 137.00 135.80 136.33
622.34 609.29 623.86 614.19 606.55 615.62 636.41 624.14 609.64 626.33 611.67 589.13 599.84 599.55 590.63 593.43 596.88 588.83 596.17 595.34 581.10 585.06 581.82
0.94 0.93 0.94 0.93 0.93 0.93 0.94 0.94 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93
140.11 140.18 140.02 140.62 140.91 141.00 140.87 139.49 139.98 139.22 140.11 140.33 140.33 140.18 140.49 139.40 139.20 139.47 139.98 139.82 140.78 140.16 140.11
278.06 280.97 275.84 284.38 275.99 275.59 279.89 288.26 270.93 277.86 275.47 280.09 276.82 281.33 271.03 273.29 274.03 267.98 266.11 268.38 254.81 267.59 263.92
0.84 0.84 0.84 0.84 0.84 0.84 0.84 0.84 0.83 0.83 0.84 0.84 0.84 0.84 0.83 0.83 0.83 0.83 0.83 0.83 0.82 0.83 0.83
148.24 148.50 148.16 148.30 148.78 148.80 148.68 148.10 147.42 146.94 147.68 147.12 147.72 148.66 148.46 147.28 147.28 147.34 147.78 147.44 148.26 147.78 148.24
464.02 464.91 451.48 453.89 458.26 457.06 450.10 470.34 443.88 456.71 435.28 426.92 444.49 444.92 436.87 428.94 428.29 428.56 421.11 433.35 413.22 423.24 413.90
0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.90 0.90 0.91 0.91 0.91 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
L. Shen et al. / Habitat International 36 (2012) 237e246
243
Table 7 The Cronbach’s alpha of fairness indicators. Indicator
X41 X42 X43 X51 X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
All (N ¼ 125)
Government (N ¼ 30)
Professionals (N ¼ 45)
Company (N ¼ 50)
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
112.81 112.68 112.63 113.23 113.11 113.25 112.76 112.54 113.56 112.88 113.36 113.64 113.23 113.59 114.04 112.98 113.32 112.87 113.00
369.33 376.19 371.70 364.84 374.71 367.69 364.06 364.83 369.51 368.77 370.78 363.07 360.15 359.70 368.36 355.69 362.27 356.95 364.02
0.920 0.922 0.920 0.920 0.922 0.920 0.921 0.921 0.923 0.920 0.920 0.919 0.918 0.918 0.921 0.919 0.918 0.918 0.922
112.13 111.97 111.9 112.73 112.3 112.6 112.63 112.73 113.4 112.7 113.17 113.17 112.8 113.13 113.37 112.57 113 112.5 113.2
299.36 311.55 311.54 300.75 311.87 302.52 309.55 283.58 303.83 311.32 314.97 312.28 304.44 309.84 308.10 286.39 304.97 305.64 305.48
0.885 0.890 0.888 0.885 0.891 0.889 0.890 0.883 0.886 0.890 0.890 0.891 0.886 0.889 0.889 0.883 0.885 0.888 0.891
114.33 114.31 114.18 115.13 114.82 114.91 113.98 114.00 115.38 114.42 115.02 115.20 114.51 114.91 115.69 114.29 114.60 114.09 114.62
366.45 366.86 377.97 368.80 367.88 367.72 372.79 385.41 377.33 377.84 372.75 375.30 369.66 363.31 367.04 358.66 357.43 356.31 370.15
0.930 0.930 0.933 0.932 0.929 0.930 0.932 0.935 0.934 0.932 0.931 0.933 0.931 0.930 0.930 0.930 0.929 0.928 0.934
111.84 111.64 111.68 111.82 112.06 112.14 111.74 111.10 112.02 111.60 111.98 112.52 112.34 112.68 112.96 112.06 112.36 112.00 111.42
425.04 434.32 413.41 408.60 429.12 417.18 400.89 405.24 410.96 405.80 412.67 393.32 396.84 398.06 416.28 406.02 412.81 400.25 403.02
0.932 0.935 0.931 0.931 0.935 0.931 0.932 0.931 0.936 0.930 0.930 0.927 0.928 0.929 0.932 0.931 0.931 0.930 0.931
Table 8 Results of KMO and Bartlett’s test. Kaiser-Meyer-Olkin Measure of Sampling Adequacy
0.847
Bartlett’s Test of Sphericity
2.081E3 253 0.000
Approx. Chi-Square df Sig.
Kaiser (1974) suggested the common KMO test evaluation criteria: “a KMO value over 0.9 indicates a strong suitability for PCA; a value of 0.8 means suitability; a value of 0.7 shows a moderate suitability; a value of 0.6 signifies a poor suitability; while a value of 0.5 implies a strong unsuitability”. According to results in Table 8, the KMO value of economic benefit indicators is 0.847, which is greater than 0.8, indicating that the sample is suitable for PCA. Similarly, Bartlett’s test of sphericity produces a concomitant probability of 0.000, which is less than the significance level of 0.05. Therefore, the zero assumption is rejected and it is acceptable for the data to be analyzed by the PCA method. Determination of principal components The explanatory table of total variance is generated after computation (Table 9). It can be seen that the first 4 principal component characteristics and variance accumulation have a contribution percentage of 91.1%, which is greater than 85%. Component matrix is also obtained through PCA on economic benefit indicators, as shown in Table 10. The top row includes 4 principal components as obtained by PCA. The leftmost column
includes 7 economic benefit indicators. The figures in the matrix represent the correlation coefficient between a principal component and its corresponding variables. Expression of principal components The relationship between correlation coefficient in factor loading matrix, denoted by rxi Fj and coefficient vector of PCA, denoted by aij, is calculated via Equation (2)
rxi Fj ¼
qffiffiffiffi
li aij
(2)
where li is the eigenvalue of the ith principal component factor. According to Equation (2), we can get the coefficient vector of PCA through dividing correlation coefficient in factor loading matrix by the corresponding square root of the eigenvalue (Lin & Zhang, 2005). For example, in order to compute the coefficient vector of the first principal component factor denoted as F1, the correlation coefficient of F1, in the first column of factor loading matrix, isffi divided by the square root of the eigenvalue denoted as pffiffiffiffiffiffiffiffiffiffiffiffi li ð 4:123 ¼ 2:031Þ. Hence, F1 can be expressed through the equation below: F1 ¼ 0.402X11 þ 0.391 X12 þ 0.378X13 þ 0.353 X14 þ 0.412X15 þ 0.401 X16 þ 0.295 X17 In line with the same rules, we can get formulas of F2, F3, and F4 accordingly.
Table 9 Total variance explained. Component
1 2 3 4 5 6 7
Initial eigenvalues
Extraction Sums of Squared Loadings
Total
% of variance
Cumulative %
Total
% of variance
Cumulative %
4.123 0.987 0.701 0.568 0.324 0.224 0.073
58.905 14.096 10.010 8.114 4.624 3.205 1.046
58.905 73.001 83.011 91.125 95.749 98.954 100.000
4.123 0.987 0.701 0.568
58.905 14.096 10.010 8.114
58.905 73.001 83.011 91.125
Extraction method: principal component analysis.
244
L. Shen et al. / Habitat International 36 (2012) 237e246
As a result, the comprehensive assessment model in this case is:
Table 10 The component matrix. Indicator
F ¼ 0.646*F1þ0.155*F2þ0.11*F3þ0.089*F4
Component
X11 X12 X13 X14 X15 X16 X17
1
2
3
4
0.817 0.795 0.767 0.717 0.837 0.814 0.599
0.293 0.330 0.433 0.190 0.395 0.444 0.464
0.022 0.268 0.064 0.393 0.270 0.296 0.556
0.365 0.236 0.144 0.473 0.143 0.127 0.313
Extraction method: principal component analysis. 4 components extracted.
Table 11 The importance coefficient of assessment indicators. Indicator
X11 X12 X13 X14 X15 X16 X17
Principal component F1
F2
F3
F4
0.260 0.253 0.244 0.228 0.267 0.259 0.191
0.046 0.051 0.067 0.030 0.061 0.069 0.072
0.003 0.035 0.008 0.052 0.035 0.039 0.073
0.043 0.028 0.017 0.056 0.017 0.015 0.037
P 0.260 0.194 0.168 0.194 0.276 0.274 0.373
F2 ¼ 0.295*X11 0.332*X12 0.436*X13 0.191*X14 þ 0.397*X15 þ 0.447*X16 þ 0.467*X17 F3 ¼ 0.026*X11 0.321*X12 þ 0.076*X13 þ 0.469*X14 0.323*X15 0.354*X16 þ 0.664*X17 F4 ¼ 0.484*X11 þ 0.313*X12 0.191*X13 0.628*X14 0.190*X15 0.169*X16 þ 0.416*X17 Determining relevant weightings of the principal components is critical to formulate the comprehensive assessment model. Generally, the weightings can be obtained by calculating the proportion of the corresponding eigenvalue to the cumulative eigenvalues of all selected principal components (Lin & Zhang, 2005; Zhang, 2006), as shown in Equation (3).
F ¼
m X
ðli =ðl1 þ l2 þ . þ lm ÞÞFi
(3)
i¼1
where m is the number of principal components.
(4)
Furthermore, we can get the relation between F and the original variables as shown by the equation below (Table 11): F ¼ 0.260X11 þ 0.194X12 þ 0.168X13 þ 0.184X14 þ 0.276X15 þ 0.274X16 þ 0.373X17 Extraction of the relatively important indicators At last, we respectively use the importance coefficient of each indicator to minus the mean value, which is 0.249. If the result is greater than 0, it means that the indicator is relatively important; while if it is less than 0, it indicates that the indicator is relatively unimportant. It can be seen from Fig. 1 that each of the 4 indicators, including X11, X15, X16, and X17, has an importance coefficient that is greater than the mean value, indicating they are relatively important for evaluating the contribution of infrastructure projects to coordinated urbanerural development. Similarly, PCA are carried out on other indicators. As a result, 19 relatively important indicators are extracted from the total 42 indicators. Based on the indicators identified, an indicator system for evaluation the contribution of infrastructure projects to coordinated urbanerural development is developed, as shown in Fig. 2. Discussions From the analysis above, it can be seen that relatively important indicators in terms of economic benefit assessment include IRR, EIRR, ENPV and cost-benefit ratio of projects. Among them, the latter three indicators are all related to the national economy. This to a large extent shows infrastructure projects in rural areas play an important role in promoting the national economy. While determining the social evaluation indicators, results of PCA suggest 5 relatively important indicators, including employment status, living standard and quality, benefit compensation of project stakeholders and underprivileged groups, mutual adaptability and social risk level. The involvement of the 5 indicators demonstrates the importance of social benefit in assessing the contribution of infrastructure projects to coordinated urbanerural development in China. Furthermore, the latter three indicators, namely, benefit compensation of project stakeholders and underprivileged groups, mutual adaptability and social risk level, are generally the emphasis
Fig. 1. Comparison of the importance of assessment indicators.
L. Shen et al. / Habitat International 36 (2012) 237e246
245
Fig. 2. An indicator system for assessing the contribution of infrastructure projects to coordinated urbanerural development.
when assessing the social contribution of infrastructure investment financed by the World Bank or Asian Development Bank (CIECC, 2004). In terms of environment and eco-efficiency evaluation, 4 indicators are identified, including noise pollution, water and soil loss impact, proportion of cultural relic and heritage protection, and energy saving percentage. It was reported that the most adverse influence on environment and ecology is land loss caused by infrastructure construction (CIECC, 2004). Currently, energy saving percentage is a major criterion for evaluating the performance of infrastructure construction. As for fairness of investment policy, only one indicator is identified, i.e. support degree of investment policies. This indicator to some degree reflects the fact that investment in infrastructure projects relies highly on government policy support and guidance. The investment system involves 2 indicators, i.e. fairness of investors, and fairness of investment supervision and administration. At present, the dual structure development pattern induces a system that discriminates urban areas from rural areas when providing infrastructure facilities, leading to a major difference between urban and rural areas in investment supervision and administration. Thus, in the particular context of urbanerural coordinated development in China, fairness of investment supervision and administration is especially important. Finally, the fairness of investment environment involves 3 indicators, fairness of urban and rural natural resources, fairness of urban and rural public resources, and fairness of urban and rural energy supply. This on one hand reflects the difference between urban and rural areas in terms of land resources, energy resources, and infrastructure investment, and on the other hand reveals the important role of hard environment in attracting infrastructure investment.
Conclusions It is widely acknowledged that infrastructure not only plays an important role in social and economic activities in a country, especially in developing countries, but more importantly, makes a great contribution to coordinated urbanerural development. However, it is found from existing literature that there is a lack of a set of critical indicators that can be used to effectively assess the contribution of infrastructure projects to coordinated urbanerural development. Therefore, this research attempts to fill this gap. Through an examination of 100 project feasibility reports, related official reports and literature, and a questionnaire survey among various professionals, 42 indicators under 6 categories were identified for assessing the contribution of infrastructure projects to coordinated urbanerural development. Furthermore, the PCA approach is applied to further establish an indicator system for assessing the contribution of infrastructure investment to coordinated urbanerural development. The indicator system consists of 19 critical indicators, covering 6 dimensions, namely, economic benefit, social benefit, environmental and ecological benefit, fairness of investment policy, fairness of investment system, and fairness of investment environment. The indicators presented in this paper have been proved to be reasonable and reliable through a series of statistical analyses. The identified indicators can be adopted to assess the contribution of infrastructure projects to coordinated urbanerural development, especially in the stages of feasibility study, project planning and project post-evaluation. Furthermore, the government is suggested to launch related statistical work by referring to the critical
246
L. Shen et al. / Habitat International 36 (2012) 237e246
indicators identified in this study. In this regard, these indicators lay a foundation enabling quantitative assessment of the contribution of infrastructure projects to coordinated urbanerural development. It should be noted that this study is conducted in line with the particular context of China, especially the Chongqing city, which is a state-designated experimental zone for reform in urban and rural infrastructure development. Nevertheless, the findings can be useful reference for other cities when similar research attempts are intended.
References Chandra, A., & Thompson, E. (2000). Does public infrastructure affect economic activity? Evidence from the rural interstate highway system. Regional Science and Urban Economics, 30, 457e490. Chen, C., & Zou, Y. (2009). Intensive explanation of practical examples of commonly use functions and application of SPSS15.0. Beijing: Electronic Industry Press. CIECC (China International Engineering Consulting Corporation). (2004). Social evaluation Guidance for investment projects in China e Projects financed by the World Bank and Asian Development Bank. Beijing: China Planning Press. Estache, A., Foster, V., & Wodon, Q. (2000). Making infrastructure reform work for the poor: policy options based on Latin American experience. Available at http://cdi.mecon.gov.ar/biblio/docelec/dp2163.pdf. Fu, H. Y., Hao, J. M., Zhu, D. J., & An, P. L. (2006). The evaluation on the status of coordinated urban and rural development and analysis on its relevant factors. Journal of Agro-technical Economics, 5, 44e49. Hair, J. F., Anderson, R. W., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall. Holsti, O. R. (1969). Content analysis for the social sciences and humanities. Massachusetts: Addison-Wesley Publishing Company. Hu, J. T. (2007). Report to the seventeenth National Congress of the Communist Party of China: Hold high the great banner of socialism with Chinese characteristics and strive for new victories in building a moderately prosperous society in all. People’s Daily. Retrieved on 25.10.07. Hu, H. Y. (2008). The reforms and the key of system of rural infrastructure. Review of Economic Research, 32, 19e34.
Huang, L. H. (2006). China’s rural infrastructure status, problem and countermeasure research. Contemporary Manager, 21, 1403e1404. Kaiser, H. F. (1974). An indicator of factorial simplicity. Pschometrika, 39, 31e36. Li, C. G. (2002). Theory, purpose and policy of infrastructure construction in countryside. Journal of Hebei Vacation Technical Techers Universty (Social Sciences Edition), 1, 1e7. Li, H. J. (2007). The supply of public goods in rural areas, coordinated urban and rural development and the new rural construction. Southeast Academic Research, 2, 46e53. Li, H. (2008). Statistical analysis software and application experiment. Beijing: Economic Science Press. Lin, Z. Y. (2007). Operation and application of SPSS for multi-variable analysis. Beijing University Press. Lin, X. Y., & Chen, Y. X. (2006). Quantitative evaluation of infrastructure investment effect. Tsinghua University Press and Beijing Jiaotong University Press. Lin, H. M., & Zhang, W. L. (2005). Detailed discrimination of similarities and differences between principal component analysis and factor analysis and SPSS software. Beijing: Statistical Research Press. Liu, A. C. (2007). Analysis on supply mechanism of Chinese rural infrastructure. The Master’s Thesis of Jilin University, April 2007. Ma, Y. J. (2006). Building of villages and towns under coordinated urban and rural development: abroad experience. Special Zone Economy, 5, 41e43. National Development and Reform Commission. (2006). Economic evaluation method and parameter of construction projects (3rd ed.). Beijing: China Planning Press. PLRC (Party Literature Research Centre of the CPC Central Committee). (2005a). The important selected documents since the 16th National Congress: I. Beijing: Central Party Literature Press. PLRC (Party Literature Research Centre of the CPC Central Committee). (2005b). The important documents by selected since the 16 National Congress: II. Beijing: Central Party Literature Press. Song, Z. Y. (2008). Open a new era of coordinated urban and rural development and focus on the five No. 1 document of new century. Available at http://news. xinhuanet.com, Retrieved on 31.01.08. Standard and Norm Institute of the Ministry of Construction. (2006). Economic evaluation cases of construction project. Beijing: China Planning Press. Yang, L. (2005). Public finance rural infrastructure under the framework of the effective supply. Macroeconomics, 10, 56e59. Zhang, W. L. (2006). Application of principal component analysis in weighing of satisfaction degree. Market Research, 6, 18e22. Zhang, X. (2007). The approval of the establishment of a National urban and rural comprehensive reform test zone. Available at www.cctv.com, Retrieved on 20.08.11.
Contents lists available at SciVerse ScienceDirect
Habitat International journal homepage: www.elsevier.com/locate/habitatint
Critical indicators for assessing the contribution of infrastructure projects to coordinated urbanerural development in China Liyin Shen a, Shijie Jiang b, Hongping Yuan a, * a b
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China Department of Construction Management, Chongqing University of Science and Technology, Chongqing, China
a b s t r a c t Keywords: Coordinated development Infrastructure projects Indicator Principal component analysis China
It is widely recognized that the coordinated development between urban and rural areas can not only narrow gaps in infrastructure between urban and rural areas, promote balanced development in rural areas in terms of society, economy and environment, but is also an effective way to address issues facing agriculture, rural areas and farmers. In line with this, development of infrastructure projects has been an important means for promoting coordinated urbanerural development in developing countries (such as China). However, there is a lack of indicators that can be used to assess the contribution of infrastructure projects to coordinated urbanerural development. This paper thus attempts to present a set of critical indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development in the particular context of Chongqing, Western China. First, a list of optional indicators that are with potential for assessing the contribution of infrastructure projects to coordinated urbanerural development is presented based on examination on related project feasibility reports, official reports and literature. Then 42 indicators are identified from the optional list through a questionnaire survey. By using the data collected, the relative level of significance of each indicator is derived. Finally, an indicator system consisting of 19 critical indicators is established based on results of principle component analysis. The applicability and significance of the identified indicators for assessing the contribution of infrastructure projects to coordinated urbanerural development are discussed as well. Ó 2011 Elsevier Ltd. All rights reserved.
Introduction Development of rural areas in developing countries is generally lagging behind the development of urban areas as there is no adequate investment in infrastructure and various economic activities in the rural area. In recent years, along with the rapid speed of urbanization, provision of public facilities to rural areas to achieve coordinated development between urban and rural areas has become an imperative issue in many developing countries, such as in China. Coordinated urbanerural development addresses the question of how to coordinate development in both urban and rural areas in terms of economy, environment and society. That is, to coordinate urbanerural development, it should take economic, environmental and social development of urban and rural areas into consideration as a whole. In the meantime, priority should be given to development of rural areas, agriculture and farmers’ living
* Corresponding author. E-mail address: [email protected] (H. Yuan). 0197-3975/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.habitatint.2011.10.003
standard when development plans are made at both the national and regional levels, in order to achieve a common prosperity in both rural and urban areas. Coordinated urbanerural development is an effective path to promote rural area development. Importance of coordinated urbanerural development has been extensively investigated in the literature. For example, Liu (2007) argued that investment in infrastructure projects in rural areas is significant in renovating traditional agriculture, transforming and upgrading countryside industries, introducing modern civilization into rural areas, and improving the living standard of farmers. Particularly, implementation of infrastructure projects in rural areas, such as road and irrigation projects, could largely reduce costs of production and transportation in agriculture and agricultural products, improve the effectiveness and efficiency of economic activities in rural areas, and diminish impacts of natural disaster on farmers. Huang (2006) confirmed that water conservation facilities could to a large extent reduce disasters in agriculture. Furthermore, electric power, communication, water supply and drainage projects in rural areas could promote various industries
238
L. Shen et al. / Habitat International 36 (2012) 237e246
and business activities in these areas, and ultimately contribute to the coordinated urbanerural development (Li, 2002). Education, health, culture and entertainment projects in rural areas could help consolidate the foundation for development of public facilities in rural areas and promote farmers’ education level and life quality. Biogas, sewage and garbage disposal projects could improve the living condition, life quality, as well as the ecology in rural areas (Hu, 2008). Solutions for driving rural area development have been proposed by previous studies. For instance, Li (2007) opined that the solution to “San Nong Problems” (which means issues concerning agriculture, countryside and farmer) in China depends not only on the reform and adjustment of the public facility provision system in rural areas, but more importantly, is largely based on the promotion of coordinated urbanerural development. Having realized the importance and benefits of promoting coordinated urbanerural development, the Chinese government has determined to implement a long-term strategy aiming at coordinating development between urban and rural areas. As an essential component of the strategy, the investment in infrastructure in rural areas will be increased in the following years. At the 16th National Congress of the Communist Party of China (CCP), it was confirmed that “to coordinate urbanerural social and economic development, modernizing agriculture, developing rural economy and increasing farmers’ income are the main means for building a generally well-off society in China” (PLRC, 2005a, p. 465). The scientific outlook on development, which was proposed in the 3rd plenary session of the 16th CCP Central Committee, emphasized the “five coordination”, in which “coordination of urbane rural development” is on the top of agenda (PLRC, 2005b, p. 1066). Comments by CCP Central Committee on the “11th Five-Year Plan”, which was passed on the 15th plenary session of the 16th CCP Central Committee, highlighted the pressing need to proactively promote coordinated urbanerural development (PLRC, 2005b, p. 1066). Later, on the 17th National Congress of the Communist Party of China (CCP), the Chinese President Hu Jintao addressed to “strengthen infrastructure in rural areas and promote coordinated development between urban and rural areas in China”. The No.1 document of CCP Central Committee in 2008 further clarified this strategy, which was to focus on strengthening the construction of agricultural infrastructure, and developing a new mechanism to facilitate coordinated urbanerural growth (Song, 2008). The Chinese President Hu Jintao reiterated the policy of coordinating urbanerural development, promoting socialist new countryside construction, and creating a new pattern where urbanerural economy and society are integrated in the 17th National Congress of CCP (J.T. Hu, 2007). China has been adopting a dual economy system for many years, including urban economy and rural economy. In light of the system, provision of infrastructure is under different systems for urban and rural areas, respectively. Urban infrastructure is mainly financed by the State fiscal budget, while infrastructure investment in rural areas is principally borne by towns, villages, communities and even farmers. The project financing mechanism has biased toward cities, and disadvantaged rural areas in securing funding sources for implementing infrastructure projects. The study by Yang (2005) identified that infrastructure investment in rural areas was of severe scarcity in China, whilst infrastructure investment in urban areas had grown dramatically during the last two decades. In fact, this “dual economy system” has led to segregation between urban and rural areas in infrastructure. The emphasis of the national economy is mostly laid on urban areas, where many industries are concentrated, but provision of infrastructure in rural areas has been largely neglected. Such a gap between urban and rural areas in
China results in many economic and social problems. There is thus an urgent need for the government to figure out effective solutions to the problems. Among the solutions, promotion of coordinated urbanerural development is commonly identified as a key strategy to drive rural area development in an effective manner, as the main purpose of coordinated urbanerural development is narrowing the gap between urban and rural areas, especially in terms of transportation, education, medical facilities, etc. In line with the coordinated urbanerural development strategy proposed by the Chinese government, many local governments typically including Chongqing and Chengdu in Western China, have taken actions to practice the strategy in the past four years. The practical experience suggested that there is no indicator that can be used to effectively assess the contribution of infrastructure projects to coordinated urbanerural development. Without such assessment indicators, the effectiveness of infrastructure investment in rural areas cannot be well appreciated. This presents the pressing need to identify a set of critical indicator for assessing how infrastructure projects could benefit coordinated urbanerural development. Few studies so far have been carried out for studying the particular role of infrastructure investment in coordinated urbane rural development. For example, Chandra and Thompson (2000) explored the quasi-experimental nature of rural interstate highway construction and found that opening new interstate highways would not increase net economic activities in nonmetropolitan regions. This study is mainly focused on the economic perspective. The study by Estache, Foster, and Wodon (2000) argued that for the poor, the most dramatic impact of inadequate infrastructure is the lack of sufficient access to infrastructure for the majority of residents in rural areas. Furthermore, Fu, Hao, Zhu, and An (2006) introduced a method to assess the correlation between urbanerural coordinated development and factors affecting the coordinated development. However, it appears that there is no study available in presenting a set of critical indicators to evaluate the contribution of infrastructure projects to coordinated urbanerural development, from a holistic perspective covering economic, social, and environmental aspects. Therefore, the aim of this paper is to identify a set of critical indicators that can be used to effectively assess the contribution of infrastructure projects to coordinated urbanerural development. Critical indicators for coordinated urbanerural development assessment in this study are identified based on the particular urbanerural development practices of Chongqing city, Western China. In 2007, the Chinese National Development and Reform Commission issued “The Notice on the Establishment of a National Pilot Zone for Overall Reform of Coordinating Urban and Rural in Chongqing and Chengdu” (Zhang, 2007). Currently, Chongqing features in the coexistence of metropolis and extensive rural areas, with the prominent contradiction of dual structure between urban and rural areas. In this regard, Chongqing is a typical microcosm of China if we concern the coordinated urbanerural development. That is, if Chongqing succeeded in exploring a new path for achieving coordinated urbanerural development, it can serve as a representative model and drive the national reform largely. Research methodology A hybrid research methodology is applied to identify a set of indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development. First, an examination is conducted on 100 project feasibility reports concerning various kinds of infrastructure projects to filter indicators adopted in current practices for evaluating the contribution of infrastructure projects to coordinated urbanerural development. Besides,
L. Shen et al. / Habitat International 36 (2012) 237e246
relevant literature and official reports are also examined by using a content analysis approach, which is very helpful for deriving useful information from documentary evidence (Holsti, 1969, pp. 14e20). This finally results in a list of optional indicators. Based on the list, a questionnaire is designed to determine the adequacy and significance of each optional indicator through consulting professionals. Professionals are invited to rank the significance of each indicator based on a nine-point Likert scale, with 9 indicating the most significance and 1 representing the least significance. After that, by using the data collected, the level of significance of each indicator is calculated and the relative significance level of each indicator derived. Finally, an indicator system consisting of a set of critical indicators is established for assessing the contribution of infrastructure projects to coordinated urbane rural development. Results and analyses Formulation of assessment indicators As afore-mentioned, identification of optional indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development is based on a thorough examination of documental materials, including an examination on 100 feasibility reports regarding various infrastructure projects (i.e. 50 highways, 10 bridges and 40 water supply projects) and the other examination on relevant literature and official reports, such as Economic Evaluation Methods and Parameters for Construction Projects (National Development and Reform Commission, 2006), Economic Evaluation Cases of Construction Project (Standard and Norm Institute of the Ministry of Construction, 2006), Quantitative Evaluation of Effects of Infrastructure Investment (Lin & Chen, 2006), and Social Evaluation Guidance for Investment Projects in ChinadProjects Financed by the World Bank and Asian Development Bank (CIECC, 2004). The 100 infrastructure projects are the first batch of projects launched in line with “The Notice on the Establishment of a National Pilot Zone for Overall Reform of Coordinating Urban and Rural in Chongqing and Chengdu” issued by the Chinese National Development and Reform Commission in 2007 (Zhang, 2007). All of them are constructed in rural areas in Chongqing, in order to narrow the gap between urban and rural areas in terms of water supply and transportation. The 46 indicators resulted from the above research activities are tabulated in Table 1. It can be observed from Table 1 that the 46 indicators are categorized in two broad groups, namely, benefit indicators and fairness indicators. The former group concerns economic, social, and environmental and ecological benefit while the latter is related to fairness of investment policy, fairness of investment system, and fairness of investment environment. One principal purpose of China’s policies on coordinated urbanerural development is to provide urban and rural residents with equal opportunities for development, facilitate an appropriate flow and optimized allocation of resources, and strengthen the role of city in leading rural area development. In this way, the gap between rural and urban areas can be ultimately narrowed, and thus they can develop in a balanced and sustainable manner (Ma, 2006). In line with this, the gap of development between cities and villages is considered as the priority when allocating infrastructure investments among various regions in China. Since the dual economy system has led to the imbalanced development between urban and rural areas for a long time, infrastructure investments should have a full role to play as an economic policy to bridge the development gap. Therefore, a policy promoting both fairness and benefit should be designed accordingly (Liu, 2007). It is therefore
239
Table 1 Optional indicators for assessing the contribution of infrastructure projects to coordinated urbanerural development. No.
Optional assessment indicators
1 1.1 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.6 1.1.7 1.2 1.2.1 1.2.2 1.2.3 1.2.4
Benefit indicators Economic benefit IRR (internal rate of return) NPV (net present value) Payback (dynamic) Loan repayment period Return on investment Return on investment before tax (Direct and indirect) benefit-cost ratio of project Social benefit Income level Living standard and quality (expressed by Engel Indicator) Employment level Capability to provide associated facilities (expressed by prevalence percentage) Capability to provide service (expressed by coverage of service points) Culture and education level, hygiene and health level Safety benefit Amount of benefit compensation of project stakeholders and underprivileged groups Mutual adaptability indicator Social risk level (expressed by social risk evaluation value) Environmental and ecological benefit Air pollution indicator (degree) Surface water pollution degree Solid waste pollution degree Noise pollution indicator Landscape impact degree Water and soil loss impact indicator Cultural relic and heritage preservation percentage (value) Energy saving percentage Recycled use percentage of wastes (or wastewater)
1.2.5 1.2.6 1.2.7 1.2.8 1.2.9 1.2.10 1.3 1.3.1 1.3.2 1.3.3 1.3.4 1.3.5 1.3.6 1.3.7 1.3.8 1.3.9 2 2.1 2.1.1 2.1.2 2.1.3 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.3.6 2.3.7 2.3.8 2.3.9 2.3.10 2.3.11 2.3.12
Fairness indicators Fairness of investment policy Preferential treatment of investment policies (for urban or rural areas) Stability of investment policies Support degree of investment policies Fairness of investment system Fairness of investors Fairness of investment decision-making Fairness of financing application reviewing and approval Investment continuity Fairness of investment supervision and administration Fairness of investment environment Fairness of urban and rural natural resources Fairness of urban and rural public resources Fairness of urban and rural energy supply Fairness of urban and rural economy Fairness of urban and rural income distribution Fairness of urban and rural living standard Fairness of urban and rural market Fairness of urban and rural technology Fairness of urban and rural education Fairness of urban and rural employment Fairness of urban and rural social security Fairness of urban and rural law environment
significant for the present study to involve both fairness indicators and benefit indicators in urbanerural coordinated development assessment. The significance of each of these indicators in evaluating the contribution of infrastructure projects to coordinated urbanerural development is examined by using data collected from a questionnaire survey. In the survey, 38 experienced professionals were identified as target respondents, including 10 government officials, 18 researchers, and 10 professionals from companies. Finally 24 valid responses were received, which was summarized in Table 2. In
240
L. Shen et al. / Habitat International 36 (2012) 237e246
Table 2 Profile of experts in the first round questionnaire survey. Sent
Government (10)
Professionals (18)
Company (10)
City level
County level
Research Institutes
Colleges
Enterprise (Finance)
Consulting (Intermediate)
38 100%
6 16%
4 11%
1 3%
17 45%
7 18%
3 8%
Valid response
Government (6)
24 100%
Professionals (10)
Company (8)
City level
County level
Research Institutes
Colleges
Enterprise (Finance)
Consulting (Intermediate)
3 13%
3 13%
1 4%
9 38%
5 21%
3 13%
the survey, respondents were invited to determine whether a particular indicator is significant to be adopted for assessing the contribution of infrastructure projects to coordinated urbanerural development. As a result, 42 indicators which received more than 60% supportive responses were chosen as assessment indicators for further study (see Table 3). Relative significance of assessment indicators Based on the indicators in Table 3, another questionnaire survey was carried out to determine the relative significance of each
indicator. Particularly, professionals were invited to provide their judgment on the relative significance of each indicator based on a nine-point Likert scale, with 9 indicating the most significant and 1 representing the least significant. The respondents were from government departments, research institutes and consulting companies. 200 questionnaires were distributed through email and 125 valid responses were ultimately received, achieving a response rate of 63%. Among them, 30 were from government departments, 45 were from research institutes, and 50 from consulting companies. By using the data collected, statistical analysis was conducted with the aid of the SPSS software package to obtain mean and
Table 3 Indicators for evaluating the contribution of infrastructure projects to coordinated urbanerural development. Categories of indicators
Indicators
Code
Economic benefit evaluation
IRR (Internal Rate of Return) NPV (Net Present Value) Payback (dynamic) Loan repayment period EIRR (Economic Internal Rate of Return) ENPV (Economic Net Present Value) (Direct and indirect) cost-benefit ratio of projects Employment status Living standard and quality (expressed by Engel Indicator) Capability to provide associated facilities (expressed by prevalence percentage) Culture and education level, hygiene and health level Safety benefit Amount of benefit compensation of project stakeholders and underprivileged groups Mutual adaptability Social risk level (expressed by social risk evaluation value) Air pollution indicator (degree) Surface water pollution degree Solid waste pollution degree Noise pollution indicator Water and soil loss impact indicator Cultural relic and heritage preservation percentage (value) Energy saving percentage Recycled use percentage of wastes (or wastewater) Preferential treatment of investment policies (for urban or rural areas) Stability of investment policies Support degree of investment policies Fairness of investors Fairness of investment decision-making Fairness of financing application reviewing and approval Investment continuity Fairness of investment supervision and administration Fairness of urban and rural natural resources Fairness of urban and rural public resources Fairness of urban and rural energy supply Fairness of urban and rural economy Fairness of urban and rural income distribution Fairness of urban and rural living standard Fairness of urban and rural technology Fairness of urban and rural education Fairness of urban and rural employment Fairness of urban and rural social security Fairness of urban and rural law environment
X11 X12 X13 X14 X15 X16 X17 X21 X22 X23 X24 X25 X26 X27 X28 X31 X32 X33 X34 X35 X36 X37 X38 X41 X42 X43 X51 X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
Social benefit evaluation
Environmental and ecological benefit evaluation
Fairness of investment policy
Fairness of investment system
Fairness of investment environment
L. Shen et al. / Habitat International 36 (2012) 237e246
variance of the significance of each indicator. The detailed results are tabulated in Table 4. Consistence analysis between response groups Since the data were derived from three different groups, namely, government departments, research institutes, and consulting companies, it is essential to examine the consistence of the responses on the relative significance of indicators between the three groups of respondents. As a commonly recognized technique, ANOVA approach is widely employed to test whether there is significant difference in responses between different categories of respondents in a questionnaire survey (Hair, Anderson, Tatham, & Black, 1995). Thus this method is adopted in the present study. In line with the principles of ANOVA approach, when P-value 0.01, there is an extremely significant difference; when P-value > 0.05, no significant variance exists; while a P-value between 0.01 and 0.05 indicates a significant variance (Hair et al., 1995). Results of ANOVA analysis are shown in Table 5. It can be seen from Table 5 that indicators X14 and X25 have very significant variances between different responding groups. Indicator X14, denoting “loan repayment period”, receives a mean significance value of 6.38 given by consulting companies, 5.23 given
241
by government departments, and 5.87 given by research institutes. On the other hand, X25, an indicator representing “safety benefit”, which is one of the social benefit indicators, is highly ranked by consulting companies, obtaining a mean significance value of 7.56. It is confirmed that there is no significant variance between different responding groups on the other indicators.
Reliability analysis The reliability of the responses was validated through Cronbach’s coefficient, which is a statistical parameter to determine the internal consistency of data. In this study, Cronbach’s coefficient is used to ensure that all responses within a group are consistent. It is usually measured by Cronbach’s a value as follows:
a ¼
P 2! S k 1 P i2 k1 Sx
(1)
where a means the proportion out of the total variance between individual responses, S2i is the variance of the ith indicator by all the respondents, S2x is the variance of all the indicators by all the respondents, and k is the number of indicators. Value of a can range from 0 to 1. The larger the value of a, the better the consistence
Table 4 Mean and SD of assessment indicator significance. Indicator
All (N ¼ 125)
Government (N ¼ 30)
Mean
Std. Deviation
Mean
X11 X12 X13 X14 X15 X16 X17 X21 X22 X23 X24 X25 X26 X27 X28 X31 X32 X33 X34 X35 X36 X37 X38 X41 X42 X43 X51 X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
6.22 6.08 6.37 5.92 5.56 5.54 5.86 6.80 6.82 7.44 6.63 6.82 6.58 6.14 5.98 7.15 7.22 7.07 6.70 6.86 5.99 6.70 6.40 6.61 6.74 6.78 6.18 6.30 6.17 6.66 6.88 5.86 6.54 6.06 5.78 6.18 5.82 5.38 6.43 6.10 6.54 6.42
1.68 1.63 1.89 1.58 1.60 1.57 1.53 1.50 1.61 1.25 1.69 1.77 1.70 1.55 1.81 1.57 1.61 1.62 1.76 1.66 1.96 1.70 1.93 1.45 1.43 1.41 1.64 1.44 1.53 1.78 1.80 1.80 1.51 1.39 1.64 1.64 1.68 1.58 1.89 1.59 1.77 1.88
5.63 5.57 5.97 5.23 4.97 5.07 5.97 6.87 6.57 7.20 6.40 6.60 6.57 6.07 5.53 6.83 6.83 6.70 6.67 6.50 5.70 6.90 6.37 6.87 7.03 7.10 6.27 6.70 6.40 6.37 6.27 5.60 6.30 5.83 5.83 6.20 5.87 5.63 6.43 6.00 6.50 5.80
Professionals (N ¼ 45)
Company (N ¼ 50)
Std. Deviation
Mean
Std. Deviation
Mean
Std. Deviation
1.85 1.59 1.79 1.59 1.59 1.34 1.83 1.48 1.70 1.32 1.63 1.94 1.94 1.66 1.83 1.66 1.72 1.80 1.81 1.66 2.10 1.85 2.04 1.68 1.54 1.32 1.64 1.62 1.83 1.63 2.21 1.55 1.54 1.32 1.62 1.50 1.48 1.63 2.08 1.44 1.68 1.86
6.38 6.31 6.47 5.87 5.58 5.49 5.62 7.00 6.51 7.27 6.38 6.16 6.16 6.31 6.00 7.09 7.29 7.02 6.51 6.67 5.71 6.33 6.38 6.69 6.71 6.84 5.89 6.20 6.11 7.04 7.02 5.64 6.60 6.00 5.82 6.51 6.11 5.33 6.73 6.42 6.93 6.40
1.75 1.81 2.03 1.47 1.66 1.79 1.37 1.43 1.65 1.29 1.67 1.48 1.71 1.41 1.75 1.31 1.41 1.44 1.66 1.55 1.83 1.41 1.60 1.55 1.58 1.40 1.63 1.39 1.48 1.54 1.44 1.54 1.27 1.45 1.56 1.55 1.66 1.57 1.85 1.75 1.74 1.83
6.44 6.18 6.52 6.38 5.90 5.88 6.00 6.58 7.26 7.74 7.00 7.56 6.96 6.02 6.22 7.40 7.40 7.34 6.90 7.24 6.42 6.90 6.44 6.38 6.58 6.54 6.40 6.16 6.08 6.48 7.12 6.20 6.62 6.24 5.70 5.88 5.54 5.26 6.16 5.86 6.22 6.80
1.43 1.45 1.82 1.55 1.49 1.42 1.47 1.58 1.45 1.14 1.70 1.64 1.46 1.61 1.84 1.70 1.70 1.65 1.84 1.71 1.95 1.82 2.16 1.19 1.20 1.46 1.65 1.35 1.38 2.03 1.77 2.11 1.69 1.38 1.75 1.77 1.80 1.58 1.81 1.51 1.82 1.86
242
L. Shen et al. / Habitat International 36 (2012) 237e246
Table 5 Results of ANOVA analysis. Indicator
P-value
Indicator
P-value
Indicator
P-value
Indicator
P-value
Indicator
P-value
X11 X12 X13 X14 X15 X16 X17
0.084 0.132 0.41 0.006 0.04 0.076 0.441
X21 X22 X23 X24 X25 X26 X27 X28
0.384 0.046 0.089 0.138 0 0.068 0.635 0.26
X31 X32 X33 X34 X35 X36 X37 X38
0.279 0.298 0.226 0.561 0.097 0.137 0.203 0.982
X41 X42 X43 X51 X52 X53 X54 X55
0.316 0.387 0.216 0.305 0.223 0.635 0.182 0.097
X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
0.218 0.618 0.425 0.915 0.173 0.253 0.581 0.34 0.214 0.145 0.068
among individual responses, and thus indicates a higher reliability of internal consistency. Previous studies suggested that a a value over 0.8 indicates a very good internal consistency, a value between 0.6 and 0.8 represents a good consistency, whilst a value less than 0.6 indicates a poor internal consistency (Hair et al., 1995). The reliability computation is also conducted by using the SPSS, and detailed results are shown in Table 6 and Table 7. According to the results, it is apparent that Cronbach’s coefficients of all the indicators are greater than 0.8, suggesting that the result of questionnaire survey is of sound quality. Principal components analysis In the questionnaire, 42 indicators dividing into 6 categories were used for assessing the contribution of infrastructure projects to coordinated urbanerural development. However, it seems rather difficult to simultaneously apply these 42 indicators in a single assessment. Furthermore, the importance of each indicator varies from one to another. Therefore, principal components analysis (PCA) approach is adopted in this study to screen out some principal indicators which have a larger influence upon the assessment of infrastructure projects’ contribution to coordinated urbanerural development. For purpose of illustration, this paper mainly takes
“economic benefit indicators” as an example to expound the extracting process of relatively important indicators by means of PCA. KMO test PCA method is one of factor analysis techniques, in which the first several principal components accounting for 85% of the total variance are retained whilst the rest factors can be omitted in further analysis (Chen & Zou, 2009; Li, 2008). According to the principle of PCA, KMO (Kaiser-Meyer-Olkin) test is normally conducted in the first place. KMO test can be used to compare simple correlation coefficient and partial correlation coefficient among variables. KMO test value can range from 0 to 1. When KMO value approaches 1, indicating that the square sum of simple correlation coefficient of all the variables exceeds by far the square sum of their partial correlation coefficient. When a KMO value is closer to 1, indicating that the stronger the correlation among the variables, the more suitable the variables can be used for PCA. When a KMO value approaches to 0, meaning the square sum of simple correlation coefficient of all the variables approaches to 0. When a KMO value is closer to 0, meaning that the weaker the correlation among the variables, the more unsuitable the variables are analyzed through PCA (Lin, 2007).
Table 6 The Cronbach’s alpha of benefit indicators. Indicator
X11 X12 X13 X14 X15 X16 X17 X21 X22 X23 X24 X25 X26 X27 X28 X31 X32 X33 X34 X35 X36 X37 X38
All (N ¼ 125)
Government (N ¼ 30)
Professionals (N ¼ 45)
Company (N ¼ 50)
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
142.63 142.78 142.49 142.94 143.30 143.31 143.00 142.06 142.03 141.42 142.22 142.03 142.28 142.72 142.88 141.70 141.63 141.78 142.15 142.00 142.86 142.16 142.46
450.06 449.27 445.38 444.69 442.55 444.27 450.18 460.60 435.77 448.07 434.92 425.87 436.61 441.40 429.46 427.97 429.12 424.64 423.81 427.50 411.25 423.01 417.83
0.91 0.91 0.91 0.90 0.90 0.90 0.90 0.91 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
137.07 137.13 136.73 137.47 137.73 137.63 136.73 135.83 136.13 135.50 136.30 136.10 136.13 136.63 137.17 135.87 135.87 136.00 136.03 136.20 137.00 135.80 136.33
622.34 609.29 623.86 614.19 606.55 615.62 636.41 624.14 609.64 626.33 611.67 589.13 599.84 599.55 590.63 593.43 596.88 588.83 596.17 595.34 581.10 585.06 581.82
0.94 0.93 0.94 0.93 0.93 0.93 0.94 0.94 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93
140.11 140.18 140.02 140.62 140.91 141.00 140.87 139.49 139.98 139.22 140.11 140.33 140.33 140.18 140.49 139.40 139.20 139.47 139.98 139.82 140.78 140.16 140.11
278.06 280.97 275.84 284.38 275.99 275.59 279.89 288.26 270.93 277.86 275.47 280.09 276.82 281.33 271.03 273.29 274.03 267.98 266.11 268.38 254.81 267.59 263.92
0.84 0.84 0.84 0.84 0.84 0.84 0.84 0.84 0.83 0.83 0.84 0.84 0.84 0.84 0.83 0.83 0.83 0.83 0.83 0.83 0.82 0.83 0.83
148.24 148.50 148.16 148.30 148.78 148.80 148.68 148.10 147.42 146.94 147.68 147.12 147.72 148.66 148.46 147.28 147.28 147.34 147.78 147.44 148.26 147.78 148.24
464.02 464.91 451.48 453.89 458.26 457.06 450.10 470.34 443.88 456.71 435.28 426.92 444.49 444.92 436.87 428.94 428.29 428.56 421.11 433.35 413.22 423.24 413.90
0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.90 0.90 0.91 0.91 0.91 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
L. Shen et al. / Habitat International 36 (2012) 237e246
243
Table 7 The Cronbach’s alpha of fairness indicators. Indicator
X41 X42 X43 X51 X52 X53 X54 X55 X61 X62 X63 X64 X65 X66 X67 X68 X69 X610 X611
All (N ¼ 125)
Government (N ¼ 30)
Professionals (N ¼ 45)
Company (N ¼ 50)
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
Mean if Item Deleted
Variance if Item Deleted
Alpha if Item Deleted
112.81 112.68 112.63 113.23 113.11 113.25 112.76 112.54 113.56 112.88 113.36 113.64 113.23 113.59 114.04 112.98 113.32 112.87 113.00
369.33 376.19 371.70 364.84 374.71 367.69 364.06 364.83 369.51 368.77 370.78 363.07 360.15 359.70 368.36 355.69 362.27 356.95 364.02
0.920 0.922 0.920 0.920 0.922 0.920 0.921 0.921 0.923 0.920 0.920 0.919 0.918 0.918 0.921 0.919 0.918 0.918 0.922
112.13 111.97 111.9 112.73 112.3 112.6 112.63 112.73 113.4 112.7 113.17 113.17 112.8 113.13 113.37 112.57 113 112.5 113.2
299.36 311.55 311.54 300.75 311.87 302.52 309.55 283.58 303.83 311.32 314.97 312.28 304.44 309.84 308.10 286.39 304.97 305.64 305.48
0.885 0.890 0.888 0.885 0.891 0.889 0.890 0.883 0.886 0.890 0.890 0.891 0.886 0.889 0.889 0.883 0.885 0.888 0.891
114.33 114.31 114.18 115.13 114.82 114.91 113.98 114.00 115.38 114.42 115.02 115.20 114.51 114.91 115.69 114.29 114.60 114.09 114.62
366.45 366.86 377.97 368.80 367.88 367.72 372.79 385.41 377.33 377.84 372.75 375.30 369.66 363.31 367.04 358.66 357.43 356.31 370.15
0.930 0.930 0.933 0.932 0.929 0.930 0.932 0.935 0.934 0.932 0.931 0.933 0.931 0.930 0.930 0.930 0.929 0.928 0.934
111.84 111.64 111.68 111.82 112.06 112.14 111.74 111.10 112.02 111.60 111.98 112.52 112.34 112.68 112.96 112.06 112.36 112.00 111.42
425.04 434.32 413.41 408.60 429.12 417.18 400.89 405.24 410.96 405.80 412.67 393.32 396.84 398.06 416.28 406.02 412.81 400.25 403.02
0.932 0.935 0.931 0.931 0.935 0.931 0.932 0.931 0.936 0.930 0.930 0.927 0.928 0.929 0.932 0.931 0.931 0.930 0.931
Table 8 Results of KMO and Bartlett’s test. Kaiser-Meyer-Olkin Measure of Sampling Adequacy
0.847
Bartlett’s Test of Sphericity
2.081E3 253 0.000
Approx. Chi-Square df Sig.
Kaiser (1974) suggested the common KMO test evaluation criteria: “a KMO value over 0.9 indicates a strong suitability for PCA; a value of 0.8 means suitability; a value of 0.7 shows a moderate suitability; a value of 0.6 signifies a poor suitability; while a value of 0.5 implies a strong unsuitability”. According to results in Table 8, the KMO value of economic benefit indicators is 0.847, which is greater than 0.8, indicating that the sample is suitable for PCA. Similarly, Bartlett’s test of sphericity produces a concomitant probability of 0.000, which is less than the significance level of 0.05. Therefore, the zero assumption is rejected and it is acceptable for the data to be analyzed by the PCA method. Determination of principal components The explanatory table of total variance is generated after computation (Table 9). It can be seen that the first 4 principal component characteristics and variance accumulation have a contribution percentage of 91.1%, which is greater than 85%. Component matrix is also obtained through PCA on economic benefit indicators, as shown in Table 10. The top row includes 4 principal components as obtained by PCA. The leftmost column
includes 7 economic benefit indicators. The figures in the matrix represent the correlation coefficient between a principal component and its corresponding variables. Expression of principal components The relationship between correlation coefficient in factor loading matrix, denoted by rxi Fj and coefficient vector of PCA, denoted by aij, is calculated via Equation (2)
rxi Fj ¼
qffiffiffiffi
li aij
(2)
where li is the eigenvalue of the ith principal component factor. According to Equation (2), we can get the coefficient vector of PCA through dividing correlation coefficient in factor loading matrix by the corresponding square root of the eigenvalue (Lin & Zhang, 2005). For example, in order to compute the coefficient vector of the first principal component factor denoted as F1, the correlation coefficient of F1, in the first column of factor loading matrix, isffi divided by the square root of the eigenvalue denoted as pffiffiffiffiffiffiffiffiffiffiffiffi li ð 4:123 ¼ 2:031Þ. Hence, F1 can be expressed through the equation below: F1 ¼ 0.402X11 þ 0.391 X12 þ 0.378X13 þ 0.353 X14 þ 0.412X15 þ 0.401 X16 þ 0.295 X17 In line with the same rules, we can get formulas of F2, F3, and F4 accordingly.
Table 9 Total variance explained. Component
1 2 3 4 5 6 7
Initial eigenvalues
Extraction Sums of Squared Loadings
Total
% of variance
Cumulative %
Total
% of variance
Cumulative %
4.123 0.987 0.701 0.568 0.324 0.224 0.073
58.905 14.096 10.010 8.114 4.624 3.205 1.046
58.905 73.001 83.011 91.125 95.749 98.954 100.000
4.123 0.987 0.701 0.568
58.905 14.096 10.010 8.114
58.905 73.001 83.011 91.125
Extraction method: principal component analysis.
244
L. Shen et al. / Habitat International 36 (2012) 237e246
As a result, the comprehensive assessment model in this case is:
Table 10 The component matrix. Indicator
F ¼ 0.646*F1þ0.155*F2þ0.11*F3þ0.089*F4
Component
X11 X12 X13 X14 X15 X16 X17
1
2
3
4
0.817 0.795 0.767 0.717 0.837 0.814 0.599
0.293 0.330 0.433 0.190 0.395 0.444 0.464
0.022 0.268 0.064 0.393 0.270 0.296 0.556
0.365 0.236 0.144 0.473 0.143 0.127 0.313
Extraction method: principal component analysis. 4 components extracted.
Table 11 The importance coefficient of assessment indicators. Indicator
X11 X12 X13 X14 X15 X16 X17
Principal component F1
F2
F3
F4
0.260 0.253 0.244 0.228 0.267 0.259 0.191
0.046 0.051 0.067 0.030 0.061 0.069 0.072
0.003 0.035 0.008 0.052 0.035 0.039 0.073
0.043 0.028 0.017 0.056 0.017 0.015 0.037
P 0.260 0.194 0.168 0.194 0.276 0.274 0.373
F2 ¼ 0.295*X11 0.332*X12 0.436*X13 0.191*X14 þ 0.397*X15 þ 0.447*X16 þ 0.467*X17 F3 ¼ 0.026*X11 0.321*X12 þ 0.076*X13 þ 0.469*X14 0.323*X15 0.354*X16 þ 0.664*X17 F4 ¼ 0.484*X11 þ 0.313*X12 0.191*X13 0.628*X14 0.190*X15 0.169*X16 þ 0.416*X17 Determining relevant weightings of the principal components is critical to formulate the comprehensive assessment model. Generally, the weightings can be obtained by calculating the proportion of the corresponding eigenvalue to the cumulative eigenvalues of all selected principal components (Lin & Zhang, 2005; Zhang, 2006), as shown in Equation (3).
F ¼
m X
ðli =ðl1 þ l2 þ . þ lm ÞÞFi
(3)
i¼1
where m is the number of principal components.
(4)
Furthermore, we can get the relation between F and the original variables as shown by the equation below (Table 11): F ¼ 0.260X11 þ 0.194X12 þ 0.168X13 þ 0.184X14 þ 0.276X15 þ 0.274X16 þ 0.373X17 Extraction of the relatively important indicators At last, we respectively use the importance coefficient of each indicator to minus the mean value, which is 0.249. If the result is greater than 0, it means that the indicator is relatively important; while if it is less than 0, it indicates that the indicator is relatively unimportant. It can be seen from Fig. 1 that each of the 4 indicators, including X11, X15, X16, and X17, has an importance coefficient that is greater than the mean value, indicating they are relatively important for evaluating the contribution of infrastructure projects to coordinated urbanerural development. Similarly, PCA are carried out on other indicators. As a result, 19 relatively important indicators are extracted from the total 42 indicators. Based on the indicators identified, an indicator system for evaluation the contribution of infrastructure projects to coordinated urbanerural development is developed, as shown in Fig. 2. Discussions From the analysis above, it can be seen that relatively important indicators in terms of economic benefit assessment include IRR, EIRR, ENPV and cost-benefit ratio of projects. Among them, the latter three indicators are all related to the national economy. This to a large extent shows infrastructure projects in rural areas play an important role in promoting the national economy. While determining the social evaluation indicators, results of PCA suggest 5 relatively important indicators, including employment status, living standard and quality, benefit compensation of project stakeholders and underprivileged groups, mutual adaptability and social risk level. The involvement of the 5 indicators demonstrates the importance of social benefit in assessing the contribution of infrastructure projects to coordinated urbanerural development in China. Furthermore, the latter three indicators, namely, benefit compensation of project stakeholders and underprivileged groups, mutual adaptability and social risk level, are generally the emphasis
Fig. 1. Comparison of the importance of assessment indicators.
L. Shen et al. / Habitat International 36 (2012) 237e246
245
Fig. 2. An indicator system for assessing the contribution of infrastructure projects to coordinated urbanerural development.
when assessing the social contribution of infrastructure investment financed by the World Bank or Asian Development Bank (CIECC, 2004). In terms of environment and eco-efficiency evaluation, 4 indicators are identified, including noise pollution, water and soil loss impact, proportion of cultural relic and heritage protection, and energy saving percentage. It was reported that the most adverse influence on environment and ecology is land loss caused by infrastructure construction (CIECC, 2004). Currently, energy saving percentage is a major criterion for evaluating the performance of infrastructure construction. As for fairness of investment policy, only one indicator is identified, i.e. support degree of investment policies. This indicator to some degree reflects the fact that investment in infrastructure projects relies highly on government policy support and guidance. The investment system involves 2 indicators, i.e. fairness of investors, and fairness of investment supervision and administration. At present, the dual structure development pattern induces a system that discriminates urban areas from rural areas when providing infrastructure facilities, leading to a major difference between urban and rural areas in investment supervision and administration. Thus, in the particular context of urbanerural coordinated development in China, fairness of investment supervision and administration is especially important. Finally, the fairness of investment environment involves 3 indicators, fairness of urban and rural natural resources, fairness of urban and rural public resources, and fairness of urban and rural energy supply. This on one hand reflects the difference between urban and rural areas in terms of land resources, energy resources, and infrastructure investment, and on the other hand reveals the important role of hard environment in attracting infrastructure investment.
Conclusions It is widely acknowledged that infrastructure not only plays an important role in social and economic activities in a country, especially in developing countries, but more importantly, makes a great contribution to coordinated urbanerural development. However, it is found from existing literature that there is a lack of a set of critical indicators that can be used to effectively assess the contribution of infrastructure projects to coordinated urbanerural development. Therefore, this research attempts to fill this gap. Through an examination of 100 project feasibility reports, related official reports and literature, and a questionnaire survey among various professionals, 42 indicators under 6 categories were identified for assessing the contribution of infrastructure projects to coordinated urbanerural development. Furthermore, the PCA approach is applied to further establish an indicator system for assessing the contribution of infrastructure investment to coordinated urbanerural development. The indicator system consists of 19 critical indicators, covering 6 dimensions, namely, economic benefit, social benefit, environmental and ecological benefit, fairness of investment policy, fairness of investment system, and fairness of investment environment. The indicators presented in this paper have been proved to be reasonable and reliable through a series of statistical analyses. The identified indicators can be adopted to assess the contribution of infrastructure projects to coordinated urbanerural development, especially in the stages of feasibility study, project planning and project post-evaluation. Furthermore, the government is suggested to launch related statistical work by referring to the critical
246
L. Shen et al. / Habitat International 36 (2012) 237e246
indicators identified in this study. In this regard, these indicators lay a foundation enabling quantitative assessment of the contribution of infrastructure projects to coordinated urbanerural development. It should be noted that this study is conducted in line with the particular context of China, especially the Chongqing city, which is a state-designated experimental zone for reform in urban and rural infrastructure development. Nevertheless, the findings can be useful reference for other cities when similar research attempts are intended.
References Chandra, A., & Thompson, E. (2000). Does public infrastructure affect economic activity? Evidence from the rural interstate highway system. Regional Science and Urban Economics, 30, 457e490. Chen, C., & Zou, Y. (2009). Intensive explanation of practical examples of commonly use functions and application of SPSS15.0. Beijing: Electronic Industry Press. CIECC (China International Engineering Consulting Corporation). (2004). Social evaluation Guidance for investment projects in China e Projects financed by the World Bank and Asian Development Bank. Beijing: China Planning Press. Estache, A., Foster, V., & Wodon, Q. (2000). Making infrastructure reform work for the poor: policy options based on Latin American experience. Available at http://cdi.mecon.gov.ar/biblio/docelec/dp2163.pdf. Fu, H. Y., Hao, J. M., Zhu, D. J., & An, P. L. (2006). The evaluation on the status of coordinated urban and rural development and analysis on its relevant factors. Journal of Agro-technical Economics, 5, 44e49. Hair, J. F., Anderson, R. W., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall. Holsti, O. R. (1969). Content analysis for the social sciences and humanities. Massachusetts: Addison-Wesley Publishing Company. Hu, J. T. (2007). Report to the seventeenth National Congress of the Communist Party of China: Hold high the great banner of socialism with Chinese characteristics and strive for new victories in building a moderately prosperous society in all. People’s Daily. Retrieved on 25.10.07. Hu, H. Y. (2008). The reforms and the key of system of rural infrastructure. Review of Economic Research, 32, 19e34.
Huang, L. H. (2006). China’s rural infrastructure status, problem and countermeasure research. Contemporary Manager, 21, 1403e1404. Kaiser, H. F. (1974). An indicator of factorial simplicity. Pschometrika, 39, 31e36. Li, C. G. (2002). Theory, purpose and policy of infrastructure construction in countryside. Journal of Hebei Vacation Technical Techers Universty (Social Sciences Edition), 1, 1e7. Li, H. J. (2007). The supply of public goods in rural areas, coordinated urban and rural development and the new rural construction. Southeast Academic Research, 2, 46e53. Li, H. (2008). Statistical analysis software and application experiment. Beijing: Economic Science Press. Lin, Z. Y. (2007). Operation and application of SPSS for multi-variable analysis. Beijing University Press. Lin, X. Y., & Chen, Y. X. (2006). Quantitative evaluation of infrastructure investment effect. Tsinghua University Press and Beijing Jiaotong University Press. Lin, H. M., & Zhang, W. L. (2005). Detailed discrimination of similarities and differences between principal component analysis and factor analysis and SPSS software. Beijing: Statistical Research Press. Liu, A. C. (2007). Analysis on supply mechanism of Chinese rural infrastructure. The Master’s Thesis of Jilin University, April 2007. Ma, Y. J. (2006). Building of villages and towns under coordinated urban and rural development: abroad experience. Special Zone Economy, 5, 41e43. National Development and Reform Commission. (2006). Economic evaluation method and parameter of construction projects (3rd ed.). Beijing: China Planning Press. PLRC (Party Literature Research Centre of the CPC Central Committee). (2005a). The important selected documents since the 16th National Congress: I. Beijing: Central Party Literature Press. PLRC (Party Literature Research Centre of the CPC Central Committee). (2005b). The important documents by selected since the 16 National Congress: II. Beijing: Central Party Literature Press. Song, Z. Y. (2008). Open a new era of coordinated urban and rural development and focus on the five No. 1 document of new century. Available at http://news. xinhuanet.com, Retrieved on 31.01.08. Standard and Norm Institute of the Ministry of Construction. (2006). Economic evaluation cases of construction project. Beijing: China Planning Press. Yang, L. (2005). Public finance rural infrastructure under the framework of the effective supply. Macroeconomics, 10, 56e59. Zhang, W. L. (2006). Application of principal component analysis in weighing of satisfaction degree. Market Research, 6, 18e22. Zhang, X. (2007). The approval of the establishment of a National urban and rural comprehensive reform test zone. Available at www.cctv.com, Retrieved on 20.08.11.