Factors Affecting the Formation of Smart Rural Development in Iran FACTORS AFFECTING THE FORMATION OF SMART RURAL DEVELOPMENT IN IRAN

Physical-spatial expansion of human settlements, especially over the last few decades, thanks to technological advances, has been increasing in a higher speed; therefore, developing an optimum and sustainable model for physical-spatial expansion of human settlements (particularly in the developing world) has created an enormous challenge. To the extent that it has affected not only the policies of physical planning but also socio-economic, and environmental issues of many rural and urban areas. Efforts have been made to counter the negative effects of urban dispersion; the most significant one was “smart growth strategy”. Present study aims to investigate the smart development in rural areas of Iran to present a framework for this strategy, including the principles and factors affecting its development using Fuzzy Analytic Hierarchy Process (FAHP), Accordingly, the relationships between objectives, indicators and sub-indicators and the process of determining the weights of indicators and sub-indicators, and the final score of indicators was examined using fuzzy hierarchical analysis model and experts’ opinions. Results showed the indicator of ‘creative rural economy’ with a weight of 0.534 was the most significant indicator in smart rural development. The indicators of environmental factors and human capital weighting 0.214 and 0.148, respectively, were in the next order. In this research, the author analyses the destructive consequences of rural sprawl in Osceola County over the past three decades. This paper outlined the institutional system of spatial planning in Switzerland. Case studies show that there are currently hardly any instruments available with which to steer land use beyond the local level. It is concluded that incentives for local administrations should be introduced in order to limit urban and rural sprawl. This study aims to use an indicator-based assessment model to evaluate smart growth policies and successful practices. The findings suggest that smart growth policies do not fully encompass the values of sustainability. Volker, et al used the statistical data on housing density to examine the patterns of housing growth, sprawl and its environmental impacts across the US, Midwest, particularly fragmentation of forests. The results show rural and sub-urban sprawl had significant negative effects; however, the type and intensity of these effects were different in rural and sub-urban areas. sustainable?


Introduction
Smart growth by no means is a new notion. In the policies of the European Union it includes policies of innovation, research and education, while in the United States it mainly deals with planning policies to counter the urban dispersion. This is probably due to reflections of different interpretations of the challenges created in Europe and the United States. The overall objective of the smart growth in the United States is about urban planning and construction policy, especially to prevent urban dispersion.
However, in Europe, smart growth mainly deals with policies of innovation, research and Aliakbar Anabestani and Mahdi Javanshiri* education rather than planning (Naldi, et al. 2015). 'Smart growth' and 'smart development' , make up the main part of the ten-year growth strategy of the Europe 2020, in which concepts such as "acting based on local capacities and capabilities in future policies" and emphasis on regional advantages, knowledge and innovation make up its basics (European Commission, 2010a; Barca et al., 2012;Combes and Overman, 2004). If the guidelines of smart growth are appropriately adopted, it can bring about diverse social, economic, and environmental benefits. Smart growth usually supports economic development through increasing economic products and decreasing the costs. Some studies have proved that taking the recommendations of the smart growth reduces the costs of public services such as water, schools, roads and transportation (Qorbani and Naushad, 2008). To adopt approaches of smart development has clear environmental benefits which include, improved air and water quality, protection of special settlements and open spaces, rapid development, protection of ecologically sensitive areas, combined uses, higher access and encouraging people to prefer walking to driving (Litman, 2005).
It should be noted that taking into account the principles of regional planning and local conditions of the study area is among the main pre-conditions of smart development. That is, all areas (both developed and underdeveloped ones), considering their various potentials (and economic conditions, knowledge and capacity of innovation), can move on in the path of smart development (McCann and Ortega-Argiles, 2013). This highly depends on business culture, skills of the workforce, education and training of education institutes, support services, ICT (Information and Computer Technology) and the infrastructure (Thissen et al., 2013;Asheim et al., 2011). However, one should not mix up the strategies of regional development with specialised imposed process of using top-down policies or government planning processes; rather they are entrepreneurial discovery process, as entrepreneurs basically are the ones who identify the potential special strategies relevant to each area, and act using a bottom-up planning process (Naldi et al. 2015). As it was argued, policies of smart growth are introduced based on knowledge, innovation and local differences. The proposed theory is more suitable for urban areas which have access to resources, local and regional knowledge, and also more opportunities to access resources of world knowledge (Vanthillo and Verhetsel, 2012). However, this study seeks to find out how the policies of smart growth are related to various rural areas.
It is worth mentioning, although the rural sprawl in rural areas is much less than that in urban areas and suburbs, rural sprawl has imposed more costs on rural communities which include: degradation of agricultural lands and gardens because of the changes in land-use (Lopez and Hynes, 2003), difficult access of people and inefficiency of sustainable modes of transportation such as walking and cycling due to long distances, increased use of private vehicles and the higher tendency of people to buy cars, higher fuel consumption, decreased social interactions (Fornoff and Cadillac News, 2007) , increased costs for development of the infrastructure and services (Jones et al. 2002, Coupal andSeidl 2003), destruction of the environment and ecosystems (Hansen et al. 2002;Stillwell, 1987;Theobald et al. 1997), decline of watershed and underground waters (Edwards and Abivardi 1998), increased number of floods and their higher intensity in rural areas (Leith and Whitfield, 2000), greater hazards for ecological sustainability and decreased ecosystem services, decreased biodiversity, decreased quality of water and soil, increased pollution and decline in public health (Bourhill, 2005;Tom Daniels, 1999). Accordingly, we may conclude that it is essential to adopt the policies of smart growth in rural areas, and considering the principles of sustainable development, smart development in rural areas is essential.

Review of Literature
The term 'smart growth', was first introduced by Paris Anglenderning, the mayor of Maryland (from 1994Maryland (from to 2002. Initially, the theory was developed in Canada and the United States and it was proposed in reaction to developments started since the early 1960s. Over 1970s and 1980s, in response to the wide dispersion of cities in these two countries, the smart urban growth theory was gradually developed based on the principles of sustainable development and the compact city, and it was eventually advanced in the form of a theory to sustain the urban spatial form (Feiock et al, 2008;Smartgrowth.org, 2012).
After a brief review of the concept of smart growth as the basis for the concept of smart rural development, it is necessary to review several relevant studies in this field.
Smart growth draws on some principles of development and planning operations, which has created the pattern of land use and effective transportation. This approach includes innumerous strategies whose results include more accessibility, more efficient land use patterns and manifold transportation systems. Various groups support smart growth, Environmental Protection Agency of the US (EPA) and the American Planning Association (APA) are among its main advocates. APA defines smart growth as a combination of planning experiences, regulations and development that make way for optimum use of land through aggregated construction forms, development between the spaces and moderation in parking standards and streets. Its objectives include, reducing uncontrolled development, land reclamation, environmental protection, and creating a desirable neighbourhood (Zarrabi et al., 2011).
Today, many statements of the United Nations and guidelines of non-governmental organisations involved in urban planning seek to promote environmental factors through increasing the movement of pedestrians, reducing air pollution, increasing high-rise buildings, proximity and availability of urban services, removing the need for surface development of the infrastructure and urban services, preventing the destruction of green spaces and belts around cities through applying the ten principles of smart growth and ultimately reaching the ideal of a perfect city De Proprise, 2011). Clark et al., (2006) believe that smart growth is a collection of planning, regulation and development methods in which compact form of the buildings, endogenous development and moderation in standards of streets and parking in which land is used more effectively (Heydari, 2012). Alexander and Tomalty (2002) in an article titled "smart growth and sustainable development" using 13 indicators, investigated the relation between densities and urban development in 26 municipal regions of British Columbia, Canada. They pointed out the relationship between density and the efficiency of the infrastructure and reduced private vehicles' use along with the economic and ecological efficiency (Alexander and Tomalty, 2002: 397). The following are some examples of researches conducted in the field of smart growth, which are summarised in the following Table. However, according to the survey conducted in relation to smart rural development, no such research has been conducted in Iran, and the only research about this subject was conducted by Naldi et al. (2015) in a paper titled "What is rural smart development?". In this paper, they have analysed the conceptual aspects, indicators, smart development practices and its affecting factors, and they have finally identified factors effective in smart rural development. Development, Vol. 37, No. 1, January -March : 2018

Results and findings
The results of the analysis showed that in the three areas of Buchanan County (Shahrak, Farhangian and Saman) according to 21 indicators of smart urban growth, the town was in the first place.
The indicators of smart urban growth in the city of Mashhad were reviewed in different municipal regions on the basis of three criteria of compactness, environmental factors and access. It was found that the region 8 had the best structure of smart urban growth.
Smart growth principles and theory were discussed and the theory was introduced as a way to prevent urban dispersion.
The authors discussed the advantages and disadvantages of smart growth. In their paper, the disadvantages included increased density, air pollution, etc. The advantage of this theory included improved chances of transportation, reduced cost of services, etc. This paper introduces the theory of smart growth and suggests the following guidelines to deal with urban dispersion: intensive development, the use of public transportation and the use of lands which already have the infrastructure This study sought to assess the smart growth strategies in 11 areas in small towns and rural communities. For this purpose, a questionnaire was constructed to enable the users to examine the smart growth and identify the gaps in policies and plans.
Researchers in this study sought to evaluate the potential effects of urban sprawl on agricultural land, and presented three scenarios to provide scientific recommendations to guide the development and reduce the negative consequences. (Contd........) In this research, the author analyses the destructive consequences of rural sprawl in Osceola County over the past three decades. This paper outlined the institutional system of spatial planning in Switzerland. Case studies show that there are currently hardly any instruments available with which to steer land use beyond the local level. It is concluded that incentives for local administrations should be introduced in order to limit urban and rural sprawl.
This study aims to use an indicator-based assessment model to evaluate smart growth policies and successful practices. The findings suggest that smart growth policies do not fully encompass the values of sustainability.
Volker, et al used the statistical data on housing density to examine the patterns of housing growth, sprawl and its environmental impacts across the US, Midwest, particularly fragmentation of forests. The results show rural and sub-urban sprawl had significant negative effects; however, the type and intensity of these effects were different in rural and suburban areas.

Theoretical Foundations
Rural Sprawl and its Features : Urban and suburban sprawl are new terms introduced in the past half century in literature of planning, urban planning and policy-making (Engle, 2011). However, it is only a decade that the term "rural sprawl" is used in world literature. Rural sprawl is also known as exurban development ) and rural residential development (Hansen et al., 2002). There is not a universally accepted definition for sprawl, and it has been increasingly complex, ambiguous and evolving. However, the physical features of this type of spatial expansion of settlements include low-density development together with single and large residential areas (usually between one to five acres) which lead to the destruction of open spaces, agricultural land and forests (Lopez and Hynes, 2003).
Although many researchers have focused on urban sprawl (Waldie, 2000), rural sprawl has many larger effects (Weiler and Theobald, 2003;. Density of sprawl in rural areas is much lower than urban and sub-urban areas. Rural Source: The Author's studies, 2016 sprawl is mainly determined by one to five acres' parts. Experts of planning and zoning believe that five acres' parts due to changes in land use and cover, quickly degrade the agricultural lands. Although a small fraction of the rural population (owners of such lands) profit from the sale of their lands, it imposes exorbitant costs on the entire community. Sustainable means of transportation such as using pedestrian and bicycle are now inefficient and difficult to access because of the long distances. Thus, the use of private vehicles and car ownership have become a necessity, more energy is consumed, ecological sustainability is endangered, public health has declined, fertile lands are degraded, and such villages have to pay higher costs to develop their infrastructure and roads (Lopez andHynes, 2003, Fornoff andCadillac News, 2007). In addition, rural sprawl has reduced ecosystem services, destroyed the biodiversity, and the quantity and quality of water and soil are degraded which result in increased pollution (Bourhill, 2005;Tom Daniels, 1999).

Smart Growth and Rural Settlements:
In the mid-1990s, the term 'smart growth' appeared in planning and soon became a key term. Whether the term is inherently different from growth management, or the management of growth is debatable (Levey, 2008); however, it has originated from Movement Management (Roberts and Juergensmeyer, 2013;Nelson, 2000).
In fact, smart growth is one of the strategies of regional planning which aims to create regional balance and prevent the destruction of resources in line with the objectives of sustainable development. In other words, 'smart growth' is planning, designing, development and revitalisation of cities, towns, suburbs and rural areas which seek to generate and promote social equality, sense of belonging to a place and community, and conservation of natural resources along with cultural ones. The strategies of smart growth can have substantial benefits for rural communities as it can maintain their history and identity, build more sustainable rural settlements, make way for sustainable economic development, create more affordable housing options and maintain ecological sustainability (Michaud, 2013).
The most important principles of smart growth include:

1.
To limit external expansion of new development on a regular basis to create more compact settlements and preserve open spaces. It could be beneficial by urban growth boundaries of the areas.

2.
To increase population density in areas of new development and existing neighbourhoods; 3.
To provide more mixed land use and suitable pedestrian output to minimise the use of cars for short trips;

4.
To finance new development with its consumers through effective fees rather than jointly paying the costs through community;

5.
To put more emphasis on public transportation to reduce the use of private vehicles; 6. To revitalise older neighbourhoods; 7. To provide affordable housing; 8.
To reduce barriers to encourage developers;

9.
To adopt different rules with regard to aesthetics, street outputs and designs; Accordingly, the rural settlements are part of a local-spatial system, which have experienced uncontrolled growth in recent decades because of changes of internal and external forces and factors. This has posed considerable challenges for rural communities in preserving rural features, and supporting rural economic growth and opportunities. They need a set of tools that can be adjusted to show the diversity of rural communities. Table 2 shows the objectives and strategies of rural smart growth provided by ICMA.

Methodology
This research is an applied one conducted in a descriptive-analytical method. In this study, data were collected using documentary methods and fieldworks. In documentary methods, we drew on statistical records, similar studies conducted in the universities, institutions, scientific journals, various scientific databases on the Internet. The required data about smart development were gathered, and factors affecting the rural smart development were defined in a hierarchal manner. In the fieldworks, the data were collected through questionnaires, interviews and observation. At this stage, in order to make paired comparisons, hierarchies and weighting were conducted based on Fuzzy Analytic Hierarchy Process (FAHP). For weighting of the criteria and sub-criteria, fuzzy pair-wise comparisons were employed which were applied by 46 people of experts and university professors in Iran. They were randomly selected from among experts in the country.
The research data were mainly gathered through fieldworks, questionnaires and interviews. In this study, two types of questionnaires were used. As the initial questionnaire was designed, we consulted with some experts and accordingly made some modifications in several stages. The final questionnaires were finally provided to the participants. The experts were also asked to score the indicators and sub-indicators of rural smart development from 1 (low importance) to 9 (vital importance). The participants were also provided with a sheet of instructions for completing the questionnaires. While the participant was filling out the questionnaires, the researchers were also present in order to remove any probable ambiguities. The data were provided by a total of 16 experts (professors); 92 per cent of the sample were male, 83 per cent were over thirty years' age, 92 per cent had a bachelor degree and 59 per cent had more than ten years of relevant experience. FAHP which was used in the analysis of the research data would be described in next parts. Table 3 shows the indicators and subindicators of the study.

Table 3 : Factors (Indicators and Sub-indicators) Effective in the Formation of Smart Rural Development
Source: Research findings, 2016.

Fuzzy Analytical Hierarchy Process:
Common AHP needs accurate judgements. However, due to the complexity and uncertainty involved in real-world issues, sometimes it is unrealistic or even impossible to make precise comparisons (Khorshid and Qaneh, 2009). Therefore, a good decision-making model must have tolerance for ambiguity, because the fuzziness and ambiguity are the common characteristics of decisionmaking problems. As decision-makers often give uncertain answers rather than methods and precise figures (Haq-shenas, et al., 2007), it was advised to use fuzzy data for decision-making and desirability evaluation rather than classic methods and conclusive data. Membership functions of fuzzy data are described with triangular, trapezoidal numbers, etc. Fuzzy AHP using Saaty AHP combined with fuzzy set theory was developed (Khosrovanjam et al., 2013). In these methods, the fuzzy and hierarchy concepts are used in a combined manner. And to select an option and confirm the problems through integration of concepts, fuzzy sets and analytical hierarchical process were designed (Perçin, 2008). Considering the disadvantages of the Chang extended techniques, in this study the improved algorithm technique is used. Improved AHP Fuzzy algorithm follows the basics of AHP technique, and operates in a fuzzy approach and includes the following steps: 1) Draw a hierarchical graph; 2) Define fuzzy numbers for pair-wise comparisons (Table 3); Extremely strong 9,9,9 1/9,1/9,1/9 Extremely strong to very strong 7,8,9 1/7,1/8,1/9 Very strong 6,7,8 1/6,1/7,1/8 Strong to very strong 5,6,7 1/5,1/6,1/7 Strong 4,5,6 1/4,1/5,1/6 Moderately strong to strong 3,4,5 1/3,1/4,1/5 Moderately strong 2,3,4 1/2,1/3,1/4 Equally strong to moderately strong 1,2,3 1,1/2,1/3 Equally strong 1,1,1 1,1,1 3) By selecting the desired fuzzy scale, the gathered data are put in a pair-wise comparison matrix. If there is more than one expert, the geometric mean is used for integration of expert opinions.

4)
In the pair-wise comparison matrix obtained from the integration of experts' opinions, calculate the geometric mean of each row.

5)
Calculate the total fuzzy preferences of the elements:

6)
Reverse the total calculated preferences:

7)
By multiplying the geometric mean of each row by reciprocal of total column preferences, the final fuzzy weight will be obtained.

8)
x i max method is used for defuzzification:

9)
Normalise the obtained weights based on linear normalisation method.

Results and Discussion
According to exploratory studies, six categories of factors in environmental, economic, creative rural economy, physical, socio-cultural and human capital fields in rural areas affect smart development. To determine the weight of the indicators and based on the statements of experts, the following steps were taken. First, the comments of the participants about indicators and sub-indicators of the study collected based on a nine-point scale, were converted to triangular fuzzy numbers. Verbal scale for determining the weight of relevant indicators are shown in Table 5. Journal of Rural Development, Vol. 37, No. 1, January -March : 2018 In the next stage, in the pair-wise comparison matrix of the integration of expert opinions, the geometric mean of each row is calculated. Then the sum of fuzzy preferences of elements are calculated. The following Table  shows the results of this stage on the main indicators of the study. Then, the sum of preferences should be reversed, and the reciprocal of sum value of preferences' column should be multiplied by geometric mean of each row (Table 7). The following Table shows the value of multiplication of the geometric mean by reciprocal of sum of each row. At the end, based on the following relationships, defuzzification process is performed and the final matrix is obtained. Then, the weight of each indicator is determined, which measures the maximum amount in each indicator (row). Weights are normalised using linear normalisation method. X 1 max = L + M +U÷ 3= (0.026 + 0.022 + 0.018) ÷ 3 = 0.02176 X 2 max = L + 2M +U÷ 4= (0.026 + (2 × 0.022) + 0.018) ÷ 4 = 0.02174 X 3 max = L + 4M +U÷ 6= (0.026 + (4 × 0.022) + 0.018) ÷ 6 = 0.02172 Journal of Rural Development, Vol. 37, No. 1, January -March : 2018 According to the results (Table 8)   Journal of Rural Development, Vol. 37, No. 1, January -March : 2018 Economic Factors: Among the economic factors effective in the formation of smart rural development, the following indicators were identified: 'to reduce the cost of service, to reduce the costs of making infrastructure services, to reduce the burden on rural communities, to increase the percentage of the employed population aged ten and more, to create better and more job opportunities, to move towards creating self-sufficient communities' . The indicator of 'to move towards creating selfsufficient communities' with the coefficient of 35.2 per cent, had the greatest effect, and the indicator of 'to reduce the costs of making infrastructure services' with the coefficient of 4.85 per cent, had the least impact on the formation of smart rural development.

Factors Related to Creative Rural Economy:
Among the factors related to creative rural economy which are effective in the formation of smart rural development, the following indicators were identified: 'investment in research and development, increased number of highly educated and creative people, to encourage innovation in economic activities, to establish companies and NGOs, to enhance entrepreneurial spirit, to launch and promote new local businesses, access to local markets, presence of relevant industrial activities, and empirical knowledge' ( Table 11). The indicator of 'investment in research and development' with the coefficient of 25.6 per cent, had the greatest effect, and the indicator of 'access to local markets' with the coefficient of 2.41 per cent, had the least impact on the formation of smart rural development.
with the coefficient of 35.2 per cent, had the greatest effect, and the indicator of 'gradation of plots of land' with the coefficient of 4.58 per cent, had the least impact on the formation of smart rural development. Physical Factors: Among the physical factors effective in the formation of smart rural development, the indicators shown in Table 12 were identified. The indicator of 'to encourage the endogenous development (compactness)'

Socio-Cultural Factors:
Among the socio-cultural factors effective in the formation of smart rural development, the indicators shown in Table 13 were identified. The indicator of 'to improve the quality of life and social security' with the coefficient of 35.9 per cent, had the greatest effect, and the indicator of 'population density' with the coefficient of 8.22 per cent, had the least impact on the formation of smart rural development.
Human Capital: Among the human capital factors effective in the formation of smart rural development, the following indicators were identified: 'the percentage of students in various grades, access to higher education institutions, the number of people with higher education, to develop mutual relations between rural areas and other areas, and to develop the infrastructure for  In the end, as the relative weight of indicators and sub-indicators is obtained, the final or normalised weight of sub-indicators are calculated relative to each other. For this purpose, the weight of main indicators is multiplied by relative weight of indicators related to that indicator. The normalised weight of sub-indicators and priority of their significance in formation of the rural smart development is provided in the Table below.

Conclusions
Local-spatial systems are resultant of some forces, and internal and external factors. In fact, the phenomenon of smart growth in rural areas is the result of some factors and forces of environmental, economic, creative rural economy, socio-cultural, physical and human capital factors. These factors and forces operate in a dialectical manner. In fact, we will not be able to analyse the phenomenon of dispersion, unless all these factors and forces are simultaneously taken into account.
Results of this study showed, according to the experts, the indicator of creative rural economy with a weight of 0.534 is the most To move towards creating self-sufficient communities (0.0486),
All geographical phenomena are rulegoverned the same as systems, as a result they also act systematically. Therefore, the science of geography which focuses on the study of these phenomena, in practice deals with 'geographical systems' or 'spatial systems' . Modern geography, emphasising on the identification of new spatial systems, acts as an applied and resourceful science, and claims to have been organising spatial fields, with the aim of promoting development and well-being of human communities in various scales.
What we did in this study, was to present a number of potential indicators and measures of smart growth and its effective factors. The availability of these indicators and measures at various areas is quite different. Some studies should be conducted about the prerequisites and potentials necessary for the smart rural development, so that they can theoretically and experimentally reveal more details about the rural smart growth and its effective factors. No appropriate approach is adopted, unless components of this system are fully known. Smart growth presented in the form of spatial and regional planning to achieve sustainable development has recently received attention in planning circles. Smart growth somehow seeks to create livable communities based on its own principles, strategies and policy-making. It is advisable to draw on these principles and various outlooks to formulate strategies for efficiently developing human settlements.