Next Article in Journal
Guidance on the Conceptual Design of Sustainable Product–Service Systems
Previous Article in Journal
The Role of Intermediaries in Supporting Local Low-Carbon Energy Initiatives
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 10
Issue 7
10.3390/su10072451
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Assessment of Social-Economic Risk of Chinese Dual Land Use System Using Fuzzy AHP

by
Hai-Min Lyu
1,2,
Yong-Xia Wu
3,
Jack Shuilong Shen
1,2,* and
An-Nan Zhou
4
1
State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2
Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
3
College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
4
School of Engineering, Royal Melbourne Institute of Technology, Victoria 3001, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(7), 2451; https://doi.org/10.3390/su10072451
Submission received: 5 June 2018 / Revised: 10 July 2018 / Accepted: 11 July 2018 / Published: 13 July 2018
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Browse Figures
Versions Notes

Abstract

:
The Chinese dual land use system (DLUS) has played a crucial role in the industrialization of China since 1950s. However, this dual system caused/causes obstacles in urban development under the new market economic conditions. This paper presents an approach to assess the social-economic risks during urban development in China by integrating the strategic environment assessment (SEA) principle into the fuzzy analytic hierarchy process (AHP) method. In the proposed approach, SEA principles are set as the influencing factors in AHP. Fuzzy AHP is used to assess the relative importance degree of the six principles in SEA. To illustrate the application procedure of the proposed approach, a building collapse incident in Wenzhou is used as a case for the risk analysis. The assessment results show that the index of the manage system has the greatest importance to social-economic risk. The principle of sustainable development (A) and monitoring measures (E) have more importance than the other principles in SEA. It can be concluded that the DLUS in the market management of China may be responsible for building collapse incidents in rural areas. It is suggested that the principles of sustainable development and monitoring measures in SEA should be strictly implemented during urbanization, and it is recommended that the government establish a unified management system and ensure the effective implementation of sustainable urbanization.

1. Introduction

Since the 1950s, China has conducted a land ownership reform in which land ownership was separated into two categories under the land reallocation system: ownership by the country’s population (state-owned urban land) and collective ownership by the working people in a working group (collective-owned rural land) [1,2]. The first category was managed at a national level (usually in urban areas), while the second category was managed within rural areas. Land was owned by the country and small working groups, which formed the so-called urban-rural dual social structure. The dual structure separated the urban and rural areas and restricted land market access. In the urban areas, all land was tightly managed by the government and allocated rent-fee to tenants. In the rural areas, some of the land was managed by rural residents themselves, to which the government did not provide enough management. This system is called the dual land use system (DLUS) in this paper. This land management system sped up the industrialization of China from 1950 to the end of the 1980s. However, the DLUS became inefficient and unsustainable, considering the increasing demand for urban land since the 1990s with the development of market economy conditions.
With the rapid economic development since the 1990s in China, human activities gradually became significant factors in affecting the environment and inducing various hazards. Rapid urbanization stimulates the construction of roads, bridges, and underground transit networks [3,4,5,6,7,8], as well as the development of residential areas. In rural areas, many aged residential buildings were constructed with unauthorized rooftop additions. Urbanization and economic migration of workers from rural to urban areas has changed the natural and residential conditions. Thus, the migration of workers leads to an increase in natural and man-made hazards [9,10,11,12,13,14]. These hazards include building collapses, flooding, land subsidence, water pollution, soil pollution, and slope failures [15,16]. When the factors causing hazards due to human activity are not carefully controlled, catastrophic failures often occur, which has a great impact on the environment and public health.
Strategic Environmental Assessment (SEA) is an effective tool to evaluate the environmental impact of human activity to keep urban development sustainable [14,17]. SEA considers the principles of environmental impact assessment in urban planning and decision-making policy for sustainable development [18,19]. SEA has been adopted by numerous countries to enhance sustainability of development. During the implementation of SEA, different countries should consider their specific circumstances and characteristics. In China, the implementation of SEA should take different ways to achieve sustainable development. It is important to make a social-economic risk assessment for sustainable urban planning.
There are many risk assessment methods, including the analytic hierarchy process (AHP) [20], fuzzy AHP [21,22], the analytic network process (ANP) [23], and gray theory [24]. Fuzzy AHP is an effective approach to deal with uncertain, ambiguous, or imprecise data. Fuzzy AHP can also manage the complexity of assessment systems. This paper employs the method of fuzzy AHP to evaluate social-economic risk integrated with the SEA. The objectives of this paper are (i) to assess the social-economic risk of the dual land use system (DLUS) under market economic conditions and (ii) to propose recommendations for unified management regulation to mitigate the environmental risks of DLUS for the sustainable development of rural areas.

2. The Dual Land Use System

2.1. Household Registration System in China

Since the late 1950s, a dual structure system between residents of urban and rural areas gradually formed in China. Each resident has a household registration in this dual structure system, which caused a dichotomy between rural and urban residents. Urban registration residents have advantages such as working in a certain industry. Moreover, there were also differences in the household system between the rural and urban residents. Housing for urban residents was constructed and distributed by the government. Housing for rural residents was constructed with properties for residents themselves on their own rural homestead land under collective ownership. In most situations, the benefits for urban residents were greater than those for rural residents before the 1990s. In many cases, rural residents tried to claim urban residency status. To standardize the status of the household registration system, in 1988, the Chinese Government first announced a “dual structure” policy to separate rural and urban residents into two distinct groups. However, there were three main drawbacks raised after announcing this policy, including (i) curbing economic migration of workers from rural to urban areas, (ii) restrictions on urbanization, and (iii) an increased dichotomy between rural and urban areas [25,26]. The “dual structure” policy distinguished rural resident households from city resident households, restricting population migration to the urban cities [27,28].
However, there were some benefits for rural residents. For example, rural residents won the right to construct houses or buildings on their own rural homestead land without government approval. In the DLUS, when building is conducted in urban areas, the procedure for design and approval should be strictly followed. However, for buildings in rural areas, since the investment came generally from rural residents themselves, the procedure for design and approval was generally not strictly required. Since the 1990s, with the economic boom in China, many factories have been constructed in rural areas, e.g., in coastal regions such as Wenzhou, which encouraged the migration of workers from inland rural areas to coastal rural areas. This population migration provided opportunities for the coastal rural residents to lease their houses to these new migrants from inland provinces. As a result, there are more and more buildings with rooftop additions to meet the demand from new migrants.

2.2. DLUS-Induced Problems

The dual land management system has played a vital role in China’s economic activities. Due to large-scale industrialization, urban land consumption far exceeded expectations and exceeded the total urban land area available. From 2000 to 2010, the urban population increased by 46%, and built-up areas increased by 65%. The rate of urbanization in China increased from 36% in 2000 to 54% in 2013 [29,30]. Accordingly, each one percentage point increase in the rate of urbanization will require 670,000 hectares of extremely limited urban land [1]. To meet the short supply of urban land, city governments began to carry out land acquisition of rural lands. During the land acquisition process, the government acquired rural land for prices far below market rates. However, since the 1980s, according to new land policy, this rural land can be sold only at higher prices than market rates. Due to a lack of reasonable compensation for the acquisition of rural land, rural residents usually do not have any intention of allowing the government to acquire rural land but would instead build residential buildings on their own land. In addition, many residential buildings were developed for lease or sale on these lands, increasing the transaction potential of rural land, particularly in coastal cities.
Thus, under the DLUS, since there is not enough government supervision of construction in rural areas, the quality of buildings constructed by the rural residents is poor. There have been more than 13 collapse incidents from 2009 to 2017 [31,32]. As illustrated by Lyu et al. (2018) [31], the incident locations were mainly located in the coastal area of China. In the past three decades, the coastal area has experienced rapid development. The rural areas have been gradually surrounded by urban areas, which leads to the appearance of “urban villages” (urban area is surrounded by rural area). These collapsed buildings mostly happened in these “urban villages.” Most of the collapsed buildings with rooftop additions were constructed by residents themselves. The construction of the illegal rooftop additions is ascribed to a lack of strict management because of the rural land use policy.

3. Methodology

3.1. Analytic Hierarchy Process

AHP was originally proposed by Saaty [19,20,21]. AHP can evaluate the results both qualitatively and quantitatively. To reflect the influence of each factor, the AHP model is characterized of the object layer, ruler layer, and factor layer. The main steps of AHP are as follows: (1) establishing analytic hierarchy model on the basis of relative factors, (2) establishing assessment matrix with Equation (1), (3) calculating weight coefficients of each factor as shown in Equation (A1) in Appendix, (4) validating consistency ratio (CR < 0.1) of the judgment matrix by Equation (A2) in Appendix, and (5) calculating comprehensive weight coefficient of each factor.
A u = ( a i j ) n × n = ( 1 ⋯ a 1 n ⋮ 1 ⋮ 1 a 1 n ⋯ 1 )  
where Au is the judgment matrix, and aij is the relative value of i factor to j factor, which is range from 1 to 9 and their reciprocals. Based on the judgment matrix, the weight of each factor can be calculated. According to Saaty [19,20,21], if the value of consistent ratio (CR) is less than 0.1, the judgment matrix is reasonable; if the value of CR is greater than 0.1, then the judgment matrix is unreasonable and must be re-determined. The detailed calculation process of the weight and CR is listed in the Appendix.

3.2. Fuzzy AHP

Fuzzy AHP theory uses approximate information and uncertainty of human reasoning to generate decisions. Vagueness is always encountered in decision making. Good decision-making models should be able to consider the vagueness of factor. A fuzzy set model is characterized by an objected feature matrix as shown in Equation (2). To consider the relative membership degree of judgment factors, the objected feature matrix should be transformed as relative membership degree matrix as shown in Equation (3).
B p = ( b i j ) = ( b 11 b 12 ⋯ b 1 n b 21 b 22 ⋯ b 2 n ⋮ ⋮ ⋱ ⋮ b m 1 b m 2 ⋯ b m n ) p  
where Bp is the objected feature matrix, and bij is the score of j plan to i factor, which is according to the score by an expert.
R p = ( r i j ) = ( r 11 r 12 ⋯ r 1 n r 21 r 22 ⋯ r 2 n ⋮ ⋮ ⋱ ⋮ r m 1 r m 2 ⋯ r m n ) p  
where Rp is the relative membership degree matrix and rij is the relative membership degree of j plan to i factor. rij can be calculated according to Equations (A3) and (A4) in the Appendix.
Based on the combination of the weight coefficients calculated by the AHP and the relative membership degree matrix, a fuzzy optimization decision model is established using Equation (4).
u j = 1 1 + ( 1 ∑ i = 1 m w i r i j − 1 ) 2  
where uj is the relative membership degree, wi is the weight coefficient calculated by the AHP, rij is the relative membership degree based on the fuzzy theory.

3.3. SEA Principles

SEA evaluates the environmental impacts of policies, plans, and program. This approach can effectively weave sustainability principles into fabric of urban plans. The SEA concept was first proposed by Shepherd and Ortolano (1996) [23] for sustainable urban development. The SEA includes six principles, which are (A) considering sustainability principle systematically, (B) evaluating projected environmental impacts related to policy plans and construction, (C) considering cumulative and correlated impacts, (D) tiering to consider sustainability principles from policies, plans and programs (PPPs) to projects, (E) monitoring and adopting measures to improve environmental management, and (F) perfecting legal and public monitoring mechanisms. Several research projects report that SEA can qualitatively evaluate environment risk more comprehensively than other methods [14,32,33,34]. SEA can incorporate sustainability principles throughout decision making from policies, plans, and programs to the level of projects. The decision making in the SEA is based on the experts’ score for each principle from (A) to (F). This cannot consider the relative degree of importance among different principles. This study integrates SEA principles into fuzzy AHP to assess the relative degree of importance among different principles.

4. Case Analyses with the Proposed Method

The above approach is used to analyze a building collapse incident in Wenzhou City. On 10 October 2016, a severe building collapse incident happened in Wenzhou City, China, which caused 22 deaths and six injuries. Most victims were migrant workers, and this remains a painful lesson for the Chinese Government when considering future reform of land policy. Figure 1 shows the situation at the collapsed site. The group of residential buildings was surrounded by high-rise buildings (see Figure 1a). The collapsed buildings were surcharged with unauthorized rooftop additions (see Figure 1b). Wenzhou City lies on the southeast coast of Zhejiang Province and is underlain by plastic marine clay deposits. Figure 2 shows the topography of Wenzhou City. Based on site investigations, the average elevation of Wenzhou City is 450 m. There are abundant soft marine clay deposits present in regions at low elevation. Figure 3 shows the topography of Lucheng District. As shown in Figure 3, the collapse site is located on a floodplain and is close to the confluence of inland rivers (see Figure 2). The geotechnical conditions require foundation treatment at the locations of the collapse site [35,36,37,38,39,40,41,42,43], which unfortunately was not carried out.
Figure 4 shows the schematic illustration of the collapsed residential buildings. Based on the preliminary investigations, the two-story building had one unauthorized rooftop addition, while each of the six-story buildings had two unauthorized rooftop additions. These unauthorized rooftop additions made a significant contribution to this collapse incident. The underlying marine clay has very low shear strength and is not capable of providing adequate bearing resistance to support buildings or structures. In addition, there were additional high-rise buildings around the collapsed buildings. Differential settlement (δ) was observed at the collapse site [44,45,46,47] and further developed through continuous consolidation resulting from the creep effect [48,49]. Generally, during the construction of any building with more than two floors, ground improvements need to be conducted using techniques such as prefabricated vertical drain and jet-grouting [50,51,52]. Both the effects of unauthorized rooftop additions and the surrounding high-rise buildings contributed to the collapse of the residential buildings.

5. Risk Assessment

5.1. AHP Structure

To assess social-economic risk during urbanization, the conceptual assessment model was established based on the investigation of collapse incidents and social backgrounds. Figure 5 shows the AHP structure for social-economic risk assessment. The established structure was characterized by the technological level (U1), dual social system (U2), and management system (U3). First, these self-conditions of collapsed buildings were considered from the technology level. Second, the external factors induced by social background were considered from the perspective of a dual social system. Third, the management system was implemented for sustainable development. This model was established based on the AHP theory. The SEA principles for social background and management system were considered as influencing factors in the fuzzy AHP model.

5.2. Weight Calculation

To assess the social-economical risk of the environment on the basis of the hierarchy structure (see Figure 5), the judgment matrix of each factor was established. The relative importance of two elements was rated using the nine-scale method. The judgment matrix of index layer (Ui) to object layer (U) can be established according to the AHP method by Saaty [19,20,21]. Similarly, the judgment matrix of factor layer to index layer can also be established. The weight coefficient of each factor can be obtained using Equation (2). Figure 6 shows the comprehensive weight coefficient of each factor in the assessment model.

5.3. Fuzzy AHP Method

To assess the relative degree of importance of six principles in the SEA, a questionnaire of experts with six principles from the perspectives of technology level, dual social system and management system was conducted to determine the criteria for evaluation. We have consulted 15 experts. Then, according to experts’ scores, we determined the score of each principle in the SEA. It is worth to note that, the scores are ranged from a low value to a high value, but we determined a mean value to evaluate its influence. This mean value was used to guarantee the next calculation. Table 1 tabulates the scores of six principles in the SEA. Based on the scores as listed in Table 1, the fuzzy judgment matrix X can be obtained as Equation (4). Applying Equation (A3) or Equation (A4) in the Appendix, the relative membership degree matrix R can be obtained (here, we use Equation (A3)).
X = ( 5 3 4 6 6 5 7 8 3 4 3 3 9 7 4 5 9 8 )     R = ( 0.6667 0 0.3333 1 1 0.6667 0.8 1 0 0.2 0 0 1 0.6 0 0.2 1 0.8 )

5.4. Assessment Results

According to the results in Figure 6, we can find that the index of the management system plays the most important role in the social-economic risk. The result shows that, from the perspective of the technology level, the factor of rooftop addition was the greatest factor that induced this collapse incident, and the second factor was the geotechnical conditions (soft clay deposits). From the perspective of the dual social system, the factor of the land use system was the greatest risk, followed by the household registration system. From the perspective of the management system, there were four factors of the SEA: (A), (E), (D), and (F) which were relatively higher than the other factors. The results reveal that the poor quality of structures of the collapsed buildings that induced this collapsed incident had significant impacts. To mitigate the risk to the environment, it is recommended that the principle of sustainable development (A) and monitoring measures (E) in the SEA should be strictly implemented in future. This implies that in the future the dual management systems for DLUS should be combined to make a unified management system.
The weight coefficients of six factors of the SEA are as follows:
wi = [(A), (B), (C), (D), (E), (F)] = [0.1313, 0.0569, 0.0364, 0.0839, 0.1222, 0.0420].
According to the weight coefficients wi and the fuzzy judgment matrix R, the optimized relative membership degree uj can be calculated by using Equation (A1) in Appendix.
ui = [0.1866, 0.0099, 0.0002, 0.0174, 0.0947, 0.0043]
Figure 7 shows the rank of the relative membership degree for six principles in the SEA. As shown in Figure 7, the principle of sustainable development (A) was the most significant factor, followed by the principle of monitoring measure (E). The results reveal that, the principles of sustainable development and monitoring measures have critical impacts in the process of urban development. The Wenzhou government should pay more attention to these two factors to mitigate social-economic risk in the future.

6. Discussion

6.1. Implications

The application of fuzzy AHP integrated with SEA principles was conducted to evaluate the social-economic risk during urban development. An assessment index system with three indices and 11 sub-indices was established for social-economic risks. The AHP method was adopted to determine the weight coefficients of factors, and the fuzzy theory was applied to evaluate the relative importance of each principle in the SEA. Subsequently, the comprehensive fuzzy AHP model was used to assess the social-economic risks to the environment. Fuzzy AHP can combine qualitative and quantitative analyses in risk assessment. The integration of fuzzy AHP and SEA can give a comprehensive assessment of social-economic risk in urban development. In comparison with SEA, using AHP can quantitatively evaluate the effective degree among SEA principles, which provides decision makers with a quantitative judgment. According to the assessment results, the management system is the most important factor in urban development. For the six principles in SEA, the factors of sustainable development (A) and monitoring measures (E) are more important than other principles. The social-economic risk assessment results provided an indication of the relative importance of the various factors for urban development. During urban development, many social and environmental problems appeared relevant to the DLUS. For social issues, e.g., collapse incidents in the rural area surrounded by urban areas, the poorly built structure of the collapsed building was the main reason for the incident, which was due to poor management under the DLUS. According to the statistical result by Lyu et al. (2018) [31], there were more than 12 collapse incidents from 2009 to 2016 [31]. To mitigate the risk of future similar incidents, it is recommended that the principles of SEA should be implemented, particularly for the principles of sustainable development (A) and monitoring measures (E). Based on the evaluated results, the government and local authorities should establish a unified management system for both urban and rural regions to mitigate social-economical risk and to keep urban development sustainable.

6.2. Suggestions

During the past four decades, Chinese reform has achieved much success. Land use reform is the inevitable trend in urban development. However, there are many social issues that have arisen during the urbanization process [53,54]. Environmental and social issues caused by the DLUS have promoted the integration of policies between urban and rural area. Figure 8 shows the social-economic relationship as well as reform countermeasure for land-policy to sustainable development. Many environmental and social issues appeared during the process of urbanization demand for sustainable countermeasures. Therefore, in the land-policy reform process, local governments and authorities should pay more attention to establishment of the unified management system. The unified management system means that the urban and rural should obey the same management regulation or law. During renovations in the rural area surround by urban area, the authorities should strengthen the management measures. It is challenging to achieve unifying management between urban and rural areas. The assessment of social-economic risk gives a suggestion for authorities to identify the influencing factors. According to the assessed result of social-economic risk, the principles of sustainable development and monitoring measures in SEA should be emphasized. In addition, the government should accelerate the process of land-policy reform to mitigate social issues and strengthen the regulations on construction (such as banning of illegal rooftop additions and building on untreated soft clay).

7. Conclusions

This paper applied the fuzzy AHP method integrated with SEA principles to assess the social-economic risk of the Chinese dual land use system. A collapse incident in Wenzhou was used as an example to illustrate the application procedure. According to the analysis, the following conclusions can be drawn.
  • The assessment structure for social-economic risk was established with three indices and 11 sub-indices. The SEA principle was considered as sub-indices in the assessment structure. The AHP method is used to calculate the weights of assessment factors, while fuzzy AHP is used to assess the relative importance degree of the SEA principles. The integration of fuzzy AHP and SEA principles can give a qualitative assessment for social-economic risk in urban development.
  • Assessment results show that the management system is the most important factor in urban development. For the six principles in SEA, the optimized relative importance degrees of the factors of sustainable development (A) and monitoring measures (E) are greater than other principles. Therefore, the principles of sustainable development and monitoring measures in SEA should be strictly implemented during urban development.
  • In the future, it is recommended that the government establish a unified management system for both urban and rural areas that can ensure the effective implementation of sustainable urban development. The supervision capacity of government authorities to prevent unauthorized rooftop additions also needs to be strengthened. Mandatory regulations for construction projects in China, particularly for private self-constructed work undertaken on house-building sites in rural areas, should be strictly carried out.

Author Contributions

This paper represents a result of collaborative teamwork. J.S.S. developed the concept; H.M.L. drafted the manuscript; Y.X.W. provided constructive suggestions and revised the manuscript; A.N.Z. collected the data and revised the manuscript. The four authors contributed equally to this work.

Funding

The research work described herein was funded by the Innovative Research Funding of the Science and Technology Commission of Shanghai Municipality (Grant No. 18DZ1201102). This financial support is gratefully acknowledged.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Supplement for the Calibration Formula in Context

  • The weight of assessment factor in the judgment matrix can be calculated using Equation (A1).
    w i = M i ∑ i = 1 n M i  
    where wi is the weight coefficient of each factor; M i = ∏ j = 1 n a i j n .
  • The value of CR can be calculated using Equation (A2).
    C R = C I R I = ∑ j = 1 n a j C I j ∑ j = 1 n a j C R j  
    where C I = ( λ max − n ) / ( n − 1 ) ; λmax is the largest eigenvalue of judgment matrix; n is the dimension of the judgment matrix.
  • The normalized formula is as follows:
    r i j = b i j − j ∧ b i j j ∨ b i j − j ∧ b i j  
    r i j = j ∨ b i j − b i j j ∨ b i j − j ∧ b i j  
    where bij is the score of j plan to i factor and ∧ and ∨ are represent the minimum and maximum, respectively.

References

  1. Ding, C. Land policy reform in China: Assessment and prospects. Land Use Policy 2003, 20, 109–120. [Google Scholar] [CrossRef]
  2. Yang, X. Analysis on China’s urban-rural integration: The perspective of path-dependence. Open J. Soc. Sci. 2016, 4, 133–140. [Google Scholar] [CrossRef]
  3. The Central People’s Government of the People’s Republic of China (CPGPRC). Provisions on the Quality Supervision and Management of Housing Construction and Municipal Infrastructure Projects the Central People’s Government of the People’s Republic of China. No. 5; 2010. Available online: http://www.gov.cn/gzdt/2010-09/02/content_1694406.html (accessed on 23 April 2017). (In Chinese)
  4. Tan, Y.; Huang, R.; Kang, Z.; Bin, W. Covered semi-top-down excavation of subway station surrounded by closely spaced buildings in downtown Shanghai: Building response. J. Perform. Constr. Facil. ASCE 2016. [Google Scholar] [CrossRef]
  5. Shen, S.L.; Wu, Y.X.; Xu, Y.S.; Hino, T.; Wu, H.N. Evaluation of hydraulic parameters from pumping tests of multi-aquifers with vertical leakage Tianjin. Comput. Geotech. 2015, 68, 196–207. [Google Scholar] [CrossRef]
  6. Shen, S.L.; Wu, Y.X.; Misra, A. Calculation of head difference at two sides of a cut-off barrier during excavation dewatering. Comput. Geotech. 2017, 91, 192–202. [Google Scholar] [CrossRef]
  7. Lyu, H.M.; Sun, W.J.; Shen, S.L.; Arulrajah, A. Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach. Sci. Total Environ. 2018, 626, 1012–1025. [Google Scholar] [CrossRef] [PubMed]
  8. Lyu, H.M.; Shen, J.S.; Arulrajah, A. Assessment of geohazards and preventative countermeasures using AHP incorporated with GIS in Lanzhou, China. Sustainability 2018, 10, 304. [Google Scholar] [CrossRef]
  9. Shen, S.L.; Xu, Y.S. Numerical evaluation of land subsidence induced by groundwater pumping in Shanghai. Can. Geotech. J. 2011, 48, 1378–1392. [Google Scholar] [CrossRef]
  10. Shen, S.L.; Ma, L.; Xu, Y.S.; Yin, Z.Y. Interpretation of increased deformation rate in aquifer IV due to groundwater pumping in Shanghai. Can. Geotech. J. 2013, 50, 1129–1142. [Google Scholar] [CrossRef]
  11. Deng, J.L.; Shen, S.L.; Xu, Y.S. Investigation into pluvial flooding hazards caused by heavy rain and protection measures in Shanghai, China. Nat. Hazards 2016, 83, 1301–1320. [Google Scholar] [CrossRef]
  12. Lyu, H.M.; Wang, G.F.; Cheng, W.C.; Shen, S.L. Tornado hazards on June 23rd in Jiangsu Province, China: Preliminary investigation and analysis. Nat. Hazards 2017, 85, 597–604. [Google Scholar] [CrossRef]
  13. Lyu, H.M.; Wang, G.F.; Shen, J.S.; Lu, L.H.; Wang, G.Q. Analysis and GIS mapping of flooding hazards on 10 May 2016, Guangzhou, China. Water 2016, 8, 447. [Google Scholar] [CrossRef]
  14. Xu, Y.S.; Shen, S.L.; Ren, D.J.; Wu, H.N. Factor analysis of land subsidence in Shanghai: A view based on Strategic Environmental Assessment. Sustainability 2016, 8, 573. [Google Scholar] [CrossRef]
  15. Du, Y.J.; Jiang, N.J.; Liu, S.Y.; Jin, F.; Singh, D.N.; Puppala, A.J. Engineering properties and microstructural characteristics of cement-stabilized zinc-contaminated kaolin. Can. Geotech. J. 2014, 51, 289–302. [Google Scholar] [CrossRef]
  16. Shen, S.L.; Wang, J.P.; Wu, H.N.; Xu, Y.S. Evaluation of hydraulic conductivity for both marine and deltaic deposits based on piezocone testing. Ocean Eng. 2015, 110, 174–182. [Google Scholar] [CrossRef]
  17. Shepherd, A.; Ortolano, L. Strategic environmental assessment for sustainable urban development. Environ. Impact Assess. Rev. 1996, 16, 321–335. [Google Scholar] [CrossRef]
  18. Du, Y.J.; Yang, Y.L.; Fan, R.D.; Wang, F. Effects of phosphate dispersants on the liquid limit, sediment volume and apparent viscosity of clayey soil/calcium-bentonite slurry wall backfills. KSCE J. Civ. Eng. 2016, 20, 670–678. [Google Scholar] [CrossRef]
  19. Lyu, H.M.; Xu, Y.S.; Cheng, W.C.; Arulrajah, A. Flooding hazards across southern China and prospective sustainability measures. Sustainability 2018, 10, 1682. [Google Scholar] [CrossRef]
  20. Saaty, T.L. Decision Making with Dependence and Feedback: The Analytic Network Process; RWS Publication: Pittsburgh, PA, USA, 1996. [Google Scholar]
  21. Saaty, T.L. Decision making—The analytic hierarchy and network processes (AHP/ANP). J. Syst. Sci. Syst. Eng. 2004, 13, 1–35. [Google Scholar] [CrossRef]
  22. Saaty, T.L. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 2008, 1, 83–98. [Google Scholar] [CrossRef]
  23. Dağdeviren, M.; Yüksel, İ.; Kurt, M. A fuzzy analytic network process (ANP) model to identify faulty behavior risk (FBR) in work system. Saf. Sci. 2008, 46, 771–783. [Google Scholar] [CrossRef]
  24. Liu, H.C.; Liu, L.; Bian, Q.H.; Lin, Q.L.; Dong, N.; Xu, P.C. Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Syst. Appl. 2011, 38, 4403–4415. [Google Scholar] [CrossRef]
  25. The Propriety Law of the People’s Republic of China. 2016. Available online: http://laodongfa.yjbys.com/xin/414131.html (accessed on 31 October 2016). (In Chinese).
  26. Decision on Collection of Collective-Owned Rural Land Parcel in Lucheng District. Office of Housing Management of Municipal Government in Lucheng District, Wenzhou City No. 18. 2013. Available online: http://www.lczsb.com/Art/Art_15/Art_15_412.aspx (accessed on 16 November 2016). (In Chinese).
  27. Detailed Rules for the Implementation of Encouraging Local Villagers Concede Homestead to the Rural Collective Economic Organizations. Decree of Shanghai Municipal People’s Government No. 16. 2003. Available online: http://www.teamlaw.cn/FCJZ/2674.html (accessed on 31 October 2016). (In Chinese).
  28. Principle of Suzhou Municipality on the Administration of Homestead. Decree of Suzhou Municipal People’s Government No. 66. 2003. Available online: http://www.chinalawedu.com/news/1200/22016.htm (accessed on 31 October 2016). (In Chinese).
  29. Tan, X.; Altrock, U. Struggling for an adaptive strategy? Discourse analysis of urban regeneration processes–A case study of Enning Road in Guangzhou City. Habitat Int. 2016, 56, 245–257. [Google Scholar] [CrossRef]
  30. Zhong, T.; Chen, Y.; Huang, X. Impact of land revenue on the urban land growth toward decreasing population density in Jiangsu Province, China. Habitat Int. 2016, 58, 34–41. [Google Scholar] [CrossRef]
  31. Lyu, H.M.; Cheng, W.C.; Shen, J.S.; Arulrajah, A. Investigation of collapsed building incidents on soft marine deposit: Both from social and technical perspectives. Land 2018, 7, 20. [Google Scholar] [CrossRef]
  32. Therivel, R. Systems of strategic environmental assessment. Environ. Impact Assess. Rev. 1993, 13, 145–168. [Google Scholar] [CrossRef]
  33. Noble, B.F. The Canadian experience with SEA and sustainability. Environ. Impact Assess. Rev. 2002, 22, 3–16. [Google Scholar] [CrossRef]
  34. Fischer, T.B. Strategic environmental assessment in post-modern times. Environ. Impact Assess. Rev. 2003, 23, 155–170. [Google Scholar] [CrossRef]
  35. Yin, Z.Y.; Zhu, Q.Y.; Yin, J.H.; Ni, Q. Stress relaxation coefficient and formulation for soft soils. Géotech. Lett. 2014, 4, 45–51. [Google Scholar] [CrossRef] [Green Version]
  36. Yin, Z.Y.; Jin, Y.F.; Shen, S.L.; Huang, H.W. An efficient optimization method for identifying parameters of soft structured clay by an enhanced genetic algorithm and elastic–viscoplastic model. Acta Geotech. 2016, 1–19. [Google Scholar] [CrossRef]
  37. Wu, H.N.; Shen, S.L.; Ma, L.; Yin, Z.Y.; Horpibulsuk, S. Evaluation of the strength increase of marine clay under staged embankment loading: A case study. Mar. Georesour. Geotechnol. 2015, 33, 532–541. [Google Scholar] [CrossRef]
  38. Xu, Y.S.; Shen, S.L.; Lai, Y.; Zhou, A.N. Design of Sponge City: Lessons learnt from an ancient drainage system in Ganzhou, China. J. Hydrol. 2018, 563, 900–908. [Google Scholar] [CrossRef]
  39. Ni, J.C.; Cheng, W.C. Monitoring and modeling grout efficiency of lifting structure in soft clay. Int. J. Geomech. 2010, 10, 223–229. [Google Scholar] [CrossRef]
  40. Hong, Z.S.; Zeng, L.L.; Cui, Y.J.; Cai, Y.Q.; Lin, C. Compression behaviour of natural and reconstituted clays. Géotechnique 2012, 62, 291–301. [Google Scholar] [CrossRef] [Green Version]
  41. Hong, Z.S.; Bian, X.; Cui, Y.J.; Gao, Y.F.; Zeng, L.L. Effect of initial water content on undrained shear behaviour of reconstituted clays. Géotechnique 2013, 63, 441–450. [Google Scholar] [CrossRef]
  42. Wu, Y.X.; Shen, S.L.; Yuan, D.J. Characteristics of dewatering induced drawdown curve under blocking effect of retaining wall in aquifer. J. Hydrol. 2016, 539, 554–566. [Google Scholar] [CrossRef]
  43. Wu, Y.X.; Shen, J.S.; Cheng, W.C.; Hino, T. Semi-analytical solution to pumping test data with barrier, wellbore storage, and partial penetration effects. Eng. Geol. 2017, 226, 44–51. [Google Scholar] [CrossRef]
  44. Yin, Z.Y.; Karstunen, M.; Chang, C.S.; Koskinen, M.; Lojander, M. Modeling time-dependent behavior of soft sensitive clay. J. Geotech. Geoenviron. Eng. 2011, 137, 1103–1113. [Google Scholar] [CrossRef]
  45. Yin, Z.Y.; Yin, J.H.; Huang, H.W. Rate-dependent and long-term yield stress and strength of soft Wenzhou marine clay: Experiments and modeling. Mar. Georesour. Geotechnol. 2015, 33, 79–91. [Google Scholar] [CrossRef]
  46. Yamin, L.E.; Hurtado, A.; Rincon, R.; Dorado, J.F.; Reyes, J.C. Probabilistic seismic vulnerability assessment of buildings in terms of economic losses. Eng. Struct. 2017, 138, 308–323. [Google Scholar] [CrossRef]
  47. Seitz, K.F.; Grabe, J. Three-dimensional topology optimization for geotechnical foundations in granular soil. Comput. Geotech. 2016, 80, 41–48. [Google Scholar] [CrossRef]
  48. Jin, Y.F.; Yin, Z.Y.; Shen, S.L.; Hicher, P.Y. Selection of sand models and identification of parameters using an enhanced genetic algorithm. Int. J. Numer. Anal. Methods Geomech. 2015, 40, 1219–1240. [Google Scholar] [CrossRef]
  49. Jin, Y.F.; Yin, Z.Y.; Shen, S.L.; Zhang, D.M. A new hybrid real-coded genetic algorithm and its application to parameters identification of soils. Inverse Probl. Sci. Eng. 2016. [Google Scholar] [CrossRef]
  50. Shen, S.L.; Wang, Z.F.; Yang, J.; Ho, E.C. Generalized approach for prediction of jet grout column diameter. J. Geotech. Geoenviron. Eng. 2013, 139, 2060–2069. [Google Scholar] [CrossRef]
  51. Shen, S.L.; Wang, Z.F.; Cheng, W.C. Estimation of lateral displacement induced by jet grouting in clayey soils. Geotech. ICE 2017, 67, 621–630. [Google Scholar] [CrossRef]
  52. Shen, S.L.; Cui, Q.L.; Ho, E.C.; Xu, Y.S. Ground response to multiple parallel microtunneling operations in cemented silty clay and sand. J. Geotech. Geoenviron. Eng. 2016, 142, 04016001. [Google Scholar] [CrossRef]
  53. Tang, Y. Urban land use in China: Policy issues and options. Land Use Policy 1989, 6, 53–63. [Google Scholar] [CrossRef]
  54. Wong, K.K.; Zhao, X.B. The influence of bureaucratic behavior on land apportionment in China: The informal process. Environ. Plan. C Gov. Policy 1999, 17, 113–125. [Google Scholar] [CrossRef]
Sustainability 10 02451 g001 550
Figure 1. Photos of the four collapsed residential buildings: (a) high-rise buildings in the vicinity of the site; (b) an illegal rooftop addition.
Figure 1. Photos of the four collapsed residential buildings: (a) high-rise buildings in the vicinity of the site; (b) an illegal rooftop addition.
Sustainability 10 02451 g001
Sustainability 10 02451 g002 550
Figure 2. Topography of Wenzhou City.
Figure 2. Topography of Wenzhou City.
Sustainability 10 02451 g002
Sustainability 10 02451 g003 550
Figure 3. Topography of Lucheng District.
Figure 3. Topography of Lucheng District.
Sustainability 10 02451 g003
Sustainability 10 02451 g004 550
Figure 4. Schematic illustrations of the residential buildings in this collapse incident.
Figure 4. Schematic illustrations of the residential buildings in this collapse incident.
Sustainability 10 02451 g004
Sustainability 10 02451 g005 550
Figure 5. Analytic hierarchy process (AHP) structure for social-economic risk assessment.
Figure 5. Analytic hierarchy process (AHP) structure for social-economic risk assessment.
Sustainability 10 02451 g005
Sustainability 10 02451 g006 550
Figure 6. Comprehensive weight coefficients of assessment factors.
Figure 6. Comprehensive weight coefficients of assessment factors.
Sustainability 10 02451 g006
Sustainability 10 02451 g007 550
Figure 7. Rank of the relative membership degree for six principles in strategic environment assessment (SEA).
Figure 7. Rank of the relative membership degree for six principles in strategic environment assessment (SEA).
Sustainability 10 02451 g007
Sustainability 10 02451 g008 550
Figure 8. Framework of social-economic relationship and reform countermeasures.
Figure 8. Framework of social-economic relationship and reform countermeasures.
Sustainability 10 02451 g008
Table
Table 1. Scores of six principles based on experts.
Table 1. Scores of six principles based on experts.
SEAIndex (Score Out of 10)
Technology LevelDual Social SystemManagement System
A (U32)579
B (U23)387
C (U24)434
D (U33)645
E (U31)639
F (U34)538

Share and Cite

MDPI and ACS Style

Lyu, H.-M.; Wu, Y.-X.; Shen, J.S.; Zhou, A.-N. Assessment of Social-Economic Risk of Chinese Dual Land Use System Using Fuzzy AHP. Sustainability 2018, 10, 2451. https://doi.org/10.3390/su10072451

AMA Style

Lyu H-M, Wu Y-X, Shen JS, Zhou A-N. Assessment of Social-Economic Risk of Chinese Dual Land Use System Using Fuzzy AHP. Sustainability. 2018; 10(7):2451. https://doi.org/10.3390/su10072451

Chicago/Turabian Style

Lyu, Hai-Min, Yong-Xia Wu, Jack Shuilong Shen, and An-Nan Zhou. 2018. "Assessment of Social-Economic Risk of Chinese Dual Land Use System Using Fuzzy AHP" Sustainability 10, no. 7: 2451. https://doi.org/10.3390/su10072451

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop