Bilingual
En/Fa
Geographical Research is Published in both Persian and English Full-text.
Volume 38, Issue 2 (2023)
GeoRes 2023, 38(2): 133-141 |
Back to browse issues page
Article Type:
Subject:
History
Received: 2023/01/22 | Accepted: 2023/04/5 | Published: 2023/04/30
Received: 2023/01/22 | Accepted: 2023/04/5 | Published: 2023/04/30
How to cite this article
Alaee R, Rahnama M, Ajzashokouhi M, Forghani A. The Smart Environment Scenarios of Mashhad Metropolis, Iran. GeoRes 2023; 38 (2) :133-141
URL: http://georesearch.ir/article-1-1442-en.html
URL: http://georesearch.ir/article-1-1442-en.html
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rights and permissions
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
1- Department of Geography, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran
2- Department of Technology, Engineering and Mathematics, Faculty of Science, Technology, Engineering and Mathematics (STEM), University of South Australia, Adelaide, Australia
2- Department of Technology, Engineering and Mathematics, Faculty of Science, Technology, Engineering and Mathematics (STEM), University of South Australia, Adelaide, Australia
Full-Text (HTML) (39 Views)
Introduction
Population growth and urbanization are two influential factors shaping individuals’ lifestyles in today’s world. Despite the employment opportunities, residential facilities, and infrastructure that urbanization provides for citizens, it can negatively affect the environment, lifestyle, and urban management [Silva et al., 2018]. The United Nations has predicted that nearly 70% of the world’s population will live in cities by 2050. This risk is greater for cities with dense populations that face numerous urban challenges. Problems such as the expansion of polluted neighborhoods, air pollution, difficulty in accessing drinking water, wastewater, energy resources, traffic congestion, and waste disposal are among the pressing issues of many populous cities worldwide.
To overcome the adverse effects of urbanization under such conditions, the development of smart cities, aimed at addressing urban problems, enhancing efficiency, and introducing new technologies has become a necessity [Toosi et al., 2017]. In many developing countries, however, the smart city concept has remained largely unimplemented. Urban technology projects have often been launched in isolation, without comprehensive and specialized studies, and are rarely integrated with other initiatives.
A smart city is an urban environment that employs information and communication technologies (ICT) to improve the efficiency of urban operations, enhance the quality of services provided to citizens, and ensure that the economic, social, cultural, and sustainability needs of future generations are met [Silva et al., 2018].
Researchers have identified six main dimensions of a smart city: smart economy, smart mobility, smart people, smart governance, smart living, and smart environment [Giffinger et al., 2008]. The significance of urban environments as the primary living spaces of citizens is increasingly emphasized, and their quality requires particular attention. First, they provide the basis for developing various aspects of life, such as health, family, work, and leisure; and second, they accommodate large populations in highly urbanized regions [Van Poll, 2011].
The environmental indicators of smart cities considered in this study, based on the most frequent references in both national and international literature, include:
Air quality has deteriorated compared to previous years, with higher concentrations of particulate matter, CO, and NO₂ reported in 2021 [Mashhad Municipality, 2021]. Annual fuel consumption in Mashhad’s urban transport sector amounts to 774 million liters of gasoline and 730 million liters of diesel, while car ownership has increased by 47% over the past decade. This trend, along with declining use of bicycles, taxis, and buses, significantly contributes to energy consumption, CO₂ emissions, and air pollution [Mashhad Municipality, 2021]. In addition, the absence of a comprehensive sewage system in some districts poses risks of groundwater contamination by domestic wastewater, as evidenced by elevated phosphate (72.2 mg/L) and coliform levels (170 per mL) [Modami et al., 2016].
Consequently, assessing the environmental dimensions of urban life from the perspective of the smart city model and with a foresight-oriented approach appears essential for Mashhad.
Huss and Honton have identified three primary approaches to scenario design, categorized as follows [Huss & Honton, 1987]:
Methodology
This quantitative and survey-based study was conducted in 2022 among experts and specialists in the fields of environment and foresight in Mashhad. A purposive non-random sampling method was applied. To distribute the questionnaires among qualified experts, individuals with postgraduate education and at least 10 years of relevant professional experience were identified.
The data collection instrument was the Cross-Impact Matrix questionnaire. For this purpose, 19 indicators related to the smart environment dimension were identified, and to examine the actual and potential interrelationships among them, a 19×19 cross-impact matrix was employed. The influence of each indicator on others was measured by the sum of its corresponding rows, while its degree of dependence on other indicators was calculated from the sum of its corresponding columns.
After a comprehensive review of national and international literature and validation of the indicators, the content validity of the questionnaire was confirmed by several experts and university professors. Reliability was assessed through a pilot test, in which the questionnaire was distributed twice among 10 respondents from the target group before the main survey, to ensure stability of measurement.
To analyze the relationships between the identified indicators, the software packages MICMAC 6.1.2 and ScenarioWizard 4.31 were employed. In order to identify the most influential drivers of future changes in the main smart environment trends, 30 experts were asked to rate the impact of each parameter on the others on a scale of 0 to 3 (0 = no impact, 1 = low, 2 = medium, 3 = high). The results were then processed using MICMAC structural analysis software [Moulaei & Talebiyan, 2015].
Within the MICMAC methodology, four types of matrices are typically applied: Matrix of Direct Influence (MDI), Matrix of Indirect Influence (MII), Matrix of Potential Direct Influence (MPDI), and Matrix of Potential Indirect Influence (MPII). The process began with the MDI, which included only current relationships among parameters and represented the structural components of the system. Subsequently, the MII was developed by iterative reinforcement (number of rotations) of the direct influence matrix. The MPDI and MPII were generated by assigning corresponding values to those defined in the MDI, capturing both current and potential interrelationships among drivers. In this study, no specific values were defined for the MPDI; therefore, the MDI and MPDI produced identical results.
After completing the cross-impact analysis structure and identifying the main drivers using MICMAC, scenario-building for Mashhad’s smart environment was conducted. Key descriptors were derived from the most influential drivers and coded using the Cross-Impact Balance (CIB) method. These descriptors were organized into matrices and submitted to 20 experts for evaluation. After collecting the completed questionnaires, the data were processed in ScenarioWizard for scenario construction. The evaluation scale ranged from strong reinforcing impact (+3) to strong constraining impact (–3). Descriptor selection for the scenario analysis was based on a combination of influential parameters and risk factors.
Findings
After collecting expert opinions on the mutual influence of smart environmental drivers, a two-dimensional matrix was constructed in which the rows represent the influencing drivers and the columns indicate the influenced drivers.
Strategic or dual parameters that acted as both highly influential and highly influenced were positioned in the northeastern quadrant of the diagram. These parameters were both manipulable and controllable, while simultaneously affecting the dynamics and transformation of the system; in other words, they constituted instability indicators. Parameters located above the diagonal line of this area were labeled as risk parameters, since they had the potential to become key actors, were rapidly affected by changes, and quickly transmitted these changes to dependent parameters. Parameters situated below the diagonal line were identified as target parameters, indicating that manipulating them could guide the system toward evolutionary changes. The status of Mashhad city in terms of smart environmental indicators w:as char:acterized by instability, as the drivers were scattered across the diagram.
Following the distribution and completion of the questionnaires, the data were analyzed using the Scenario Wizard software, resulting in 180 cell judgments.
The possible states of each driver, categorized as favorable, semi-favorable, and unfavorable. In total, 115 possible scenarios were identified, among which 113 were weak and 2 were strong. For the first scenario, the values (A1, B1, C1, D1, E1) were examined, and for the second scenario, the values (A3, B3, C3, D3, E3) were assessed.
The descriptor “development of environmental regulations” had the greatest impact compared to the other descriptors. Furthermore, the descriptor “environmental monitoring and supervision,” due to its position in the upper half of the system, exhibited an influencing property over other descriptors and, given its location, could be identified as a dual descriptor. The three descriptors of “utilization of renewable energies,” “public transportation, cycling, and walking,” and “compact urban development” demonstrated high levels of susceptibility.
Discussion
This study, by identifying the key drivers affecting the smart environment of Mashhad metropolis from the perspective of experts, explored the possible future scenarios for the city. The indicator of “utilization of renewable energies and development of environmental regulations,” with a score of 38, had the greatest influence on other indicators. “Enhancement of awareness and participation,” “development of public transportation, cycling, and walking,” “compact urban development,” and “environmental monitoring and supervision” ranked next in terms of influence. Conversely, the indicators of “household waste generation” (score 24) and “access to green space” (score 25) had the lowest impact on other smart environmental indicators.
The indicator of “enhancing public awareness and participation” scored 38 and was the most influenced by other indicators, whereas “the proportion of residential units connected to sewage systems,” with a score of 24, was the least influenced. Furthermore, it can be stated that the two indicators of environmental regulation development and environmental monitoring and supervision were identified as risk drivers in the smart environmental development of Mashhad. These drivers exerted very high levels of both influence and susceptibility on other drivers and, along with the influential drivers, were selected as scenario descriptors.
Mohammadi identified six indicators for assessing the smart environment component, whereas the present study, through reviewing both domestic and international sources, recognized a greater number of indicators in the environmental dimension of smart cities [Mohammadi, 2016]. In another comparative study on the conceptualization of city smartness, three components of people, institutional factors, and infrastructure along with three factors of intelligence, innovation, and integration were considered as key determinants. That study highlights the crucial role of people and institutional factors in the smart development of cities [Moulaei et al., 2016]. Similarly, the findings of the present research emphasized that enhancing citizens’ awareness and participation plays a critical role as a driving force in the smart environmental development of Mashhad metropolis.
The main limitations of this study included the scarcity of domestic sources and prior research on the subject in Iran, the lack of locally adapted indicators for assessing urban environmental smartness in Iranian metropolises, and the absence of reports on environmental initiatives implemented in Iranian cities, especially major metropolises. Based on the findings, several recommendations can be proposed: raising citizens’ awareness of environmental issues by relevant managers, designing programs to foster a culture of participation, encouraging urban managers, executive bodies, and policymakers to recognize the importance of the environment, particularly its smart development, in Mashhad metropolis, and drawing on the experiences of pioneering cities worldwide. For future research, it is recommended to examine the role of stakeholders in the smart environmental development of Mashhad metropolis.
Conclusion
Among the 115 possible scenarios envisioned for Mashhad city, only two demonstrated strong consistency. The first scenario (the “Golden Scenario”) reflects the favorable status of descriptors for the smart environment of Mashhad, while the second scenario (the “Catastrophic Scenario”) illustrates their unfavorable status. Among these, the development of environmental regulations and environmental monitoring and supervision exerted the greatest influence, underscoring the critical importance of these two factors in the smart environmental development of Mashhad. Strengthening these drivers can increase the likelihood of realizing the first scenario while reducing the risk of the second.
Acknowledgments: The authors would like to express their sincere gratitude to all organizations that supported this research, particularly the Mashhad Water and Wastewater Company, the Khorasan Razavi Department of Environmental Protection, and the Mashhad Municipality for providing the necessary data and assisting in the completion of the questionnaires.
Ethical Approval: No ethical issues were reported by the authors.
Conflict of Interest: No conflicts of interest were reported by the authors.
Authors’ Contributions: Alaee R (First Author), Principal Researcher/Introduction Writer (60%); Rahnama MR (Second Author), Methodologist/Discussion Writer (20%); Ajzashokouhi M (Third Author), Assistant Researcher/Statistical Analyst (10%); Farghani A (Fourth Author), Assistant Researcher (10%).
Funding: This article is derived from the doctoral dissertation of the first author, supervised by the second author and advised by the third and fourth authors in the Department of Geography, Ferdowsi University of Mashhad.
Population growth and urbanization are two influential factors shaping individuals’ lifestyles in today’s world. Despite the employment opportunities, residential facilities, and infrastructure that urbanization provides for citizens, it can negatively affect the environment, lifestyle, and urban management [Silva et al., 2018]. The United Nations has predicted that nearly 70% of the world’s population will live in cities by 2050. This risk is greater for cities with dense populations that face numerous urban challenges. Problems such as the expansion of polluted neighborhoods, air pollution, difficulty in accessing drinking water, wastewater, energy resources, traffic congestion, and waste disposal are among the pressing issues of many populous cities worldwide.
To overcome the adverse effects of urbanization under such conditions, the development of smart cities, aimed at addressing urban problems, enhancing efficiency, and introducing new technologies has become a necessity [Toosi et al., 2017]. In many developing countries, however, the smart city concept has remained largely unimplemented. Urban technology projects have often been launched in isolation, without comprehensive and specialized studies, and are rarely integrated with other initiatives.
A smart city is an urban environment that employs information and communication technologies (ICT) to improve the efficiency of urban operations, enhance the quality of services provided to citizens, and ensure that the economic, social, cultural, and sustainability needs of future generations are met [Silva et al., 2018].
Researchers have identified six main dimensions of a smart city: smart economy, smart mobility, smart people, smart governance, smart living, and smart environment [Giffinger et al., 2008]. The significance of urban environments as the primary living spaces of citizens is increasingly emphasized, and their quality requires particular attention. First, they provide the basis for developing various aspects of life, such as health, family, work, and leisure; and second, they accommodate large populations in highly urbanized regions [Van Poll, 2011].
The environmental indicators of smart cities considered in this study, based on the most frequent references in both national and international literature, include:
- Air quality (concentrations of CO, NO₂, SO₂, and particulate matter) [Hatzelhoffer et al., 2012; Castelli et al., 2017; Staffans & Horelli, 2014; Miles et al., 2018; Lowe et al., 2013].
- Energy and buildings (per capita electricity consumption, energy efficiency in buildings, and the use of renewable energy sources) [Kanchev et al., 2011; Herzog et al., 2001; Nilssen, 2019; Thornbush & Golubchikov, 2021].
- Waste recycling (average annual household waste production in Mashhad, recycling rates, and per capita urban waste collection by the municipality) [Rybnytska et al., 2018; Anagnostopoulos et al., 2015; Staffans & Horelli, 2014].
- Water and wastewater (percentage of households connected to municipal sewer systems, access to piped drinking water, per capita domestic water consumption, water resource protection, and wastewater collection and treatment) [Sun et al., 2017; Corbett & Mellouli, 2017].
- Green spaces (per capita access to parks and protected areas, and preservation of green space) [Mone, 2015; Corbett & Mellouli, 2017; Hatzelhoffer et al., 2012; Al-Hader et al., 2011].
- Environmental protection management (raising citizen awareness and participation, developing health-related service regulations, and environmental monitoring and control) [Lemos & Agrawal, 2019; Beatley & Newman, 2008; Moghim & Garna, 2019; Gunderson, 2000; Liang et al., 2013; Koontz & Thomas, 2006; Mahdizadeh, 2016; Salehi et al., 2011; Behtash et al., 2013; Hoseini, 2017].
- Urban planning and redevelopment (initiatives promoting compact development, transit-oriented development, cycling, and walking) [Kristiningrum & Kusmo, 2021; Shum & Watanabe, 2017].
- Stockholm (2007): Projections for 2050 anticipated a population increase of 700,000. To achieve the metropolitan region’s development goals, three scenarios were proposed:
- Core story: Emphasizing densification and vertical development, reliance on public transport, walking, and cycling, with reduced car use, fuel consumption, CO, and greenhouse gas emissions.
- Star story: Based on the current star-shaped city structure.
- Scattered story: Characterized by urban sprawl, loss of green structure, increased car ownership, and worsening environmental issues such as fuel consumption, pollution, and land-use changes [Rahnama, 2019].
- Catalonia: With a population of 7,535,000, the region faced severe waste disposal challenges due to a sixfold increase in waste generation between 1970 and 2000. Limited land availability exacerbated the problem. In 2008, the regional parliament adopted an integrated waste management system to achieve a “landfill-free” and eventually “zero waste” territory, in collaboration with public, private, and governmental sectors. Between 2014–2020, initiatives transformed waste into energy products such as biogas, electricity, and heat, while addressing nuclear waste disposal issues [Martinez et al., 2010].
Air quality has deteriorated compared to previous years, with higher concentrations of particulate matter, CO, and NO₂ reported in 2021 [Mashhad Municipality, 2021]. Annual fuel consumption in Mashhad’s urban transport sector amounts to 774 million liters of gasoline and 730 million liters of diesel, while car ownership has increased by 47% over the past decade. This trend, along with declining use of bicycles, taxis, and buses, significantly contributes to energy consumption, CO₂ emissions, and air pollution [Mashhad Municipality, 2021]. In addition, the absence of a comprehensive sewage system in some districts poses risks of groundwater contamination by domestic wastewater, as evidenced by elevated phosphate (72.2 mg/L) and coliform levels (170 per mL) [Modami et al., 2016].
Consequently, assessing the environmental dimensions of urban life from the perspective of the smart city model and with a foresight-oriented approach appears essential for Mashhad.
Huss and Honton have identified three primary approaches to scenario design, categorized as follows [Huss & Honton, 1987]:
- Intuitive logic: Based on complex interrelations among economic, political, technological, social, environmental, and resource factors. Key drivers are classified by importance and uncertainty, with emphasis on factors of high importance and low uncertainty.
- Trend impact analysis: Introduced by Schwartz, this method begins with a list of parameters and future trends, divided into three categories: trends with certain impacts, trends with uncertain impacts, and trends with unknown impacts.
- Cross-impact analysis: Focuses on understanding how trends influence one another and the interrelations among system parameters, also referred to as “structural analysis.”
Methodology
This quantitative and survey-based study was conducted in 2022 among experts and specialists in the fields of environment and foresight in Mashhad. A purposive non-random sampling method was applied. To distribute the questionnaires among qualified experts, individuals with postgraduate education and at least 10 years of relevant professional experience were identified.
The data collection instrument was the Cross-Impact Matrix questionnaire. For this purpose, 19 indicators related to the smart environment dimension were identified, and to examine the actual and potential interrelationships among them, a 19×19 cross-impact matrix was employed. The influence of each indicator on others was measured by the sum of its corresponding rows, while its degree of dependence on other indicators was calculated from the sum of its corresponding columns.
After a comprehensive review of national and international literature and validation of the indicators, the content validity of the questionnaire was confirmed by several experts and university professors. Reliability was assessed through a pilot test, in which the questionnaire was distributed twice among 10 respondents from the target group before the main survey, to ensure stability of measurement.
To analyze the relationships between the identified indicators, the software packages MICMAC 6.1.2 and ScenarioWizard 4.31 were employed. In order to identify the most influential drivers of future changes in the main smart environment trends, 30 experts were asked to rate the impact of each parameter on the others on a scale of 0 to 3 (0 = no impact, 1 = low, 2 = medium, 3 = high). The results were then processed using MICMAC structural analysis software [Moulaei & Talebiyan, 2015].
Within the MICMAC methodology, four types of matrices are typically applied: Matrix of Direct Influence (MDI), Matrix of Indirect Influence (MII), Matrix of Potential Direct Influence (MPDI), and Matrix of Potential Indirect Influence (MPII). The process began with the MDI, which included only current relationships among parameters and represented the structural components of the system. Subsequently, the MII was developed by iterative reinforcement (number of rotations) of the direct influence matrix. The MPDI and MPII were generated by assigning corresponding values to those defined in the MDI, capturing both current and potential interrelationships among drivers. In this study, no specific values were defined for the MPDI; therefore, the MDI and MPDI produced identical results.
After completing the cross-impact analysis structure and identifying the main drivers using MICMAC, scenario-building for Mashhad’s smart environment was conducted. Key descriptors were derived from the most influential drivers and coded using the Cross-Impact Balance (CIB) method. These descriptors were organized into matrices and submitted to 20 experts for evaluation. After collecting the completed questionnaires, the data were processed in ScenarioWizard for scenario construction. The evaluation scale ranged from strong reinforcing impact (+3) to strong constraining impact (–3). Descriptor selection for the scenario analysis was based on a combination of influential parameters and risk factors.
Findings
After collecting expert opinions on the mutual influence of smart environmental drivers, a two-dimensional matrix was constructed in which the rows represent the influencing drivers and the columns indicate the influenced drivers.
Strategic or dual parameters that acted as both highly influential and highly influenced were positioned in the northeastern quadrant of the diagram. These parameters were both manipulable and controllable, while simultaneously affecting the dynamics and transformation of the system; in other words, they constituted instability indicators. Parameters located above the diagonal line of this area were labeled as risk parameters, since they had the potential to become key actors, were rapidly affected by changes, and quickly transmitted these changes to dependent parameters. Parameters situated below the diagonal line were identified as target parameters, indicating that manipulating them could guide the system toward evolutionary changes. The status of Mashhad city in terms of smart environmental indicators w:as char:acterized by instability, as the drivers were scattered across the diagram.
Following the distribution and completion of the questionnaires, the data were analyzed using the Scenario Wizard software, resulting in 180 cell judgments.
The possible states of each driver, categorized as favorable, semi-favorable, and unfavorable. In total, 115 possible scenarios were identified, among which 113 were weak and 2 were strong. For the first scenario, the values (A1, B1, C1, D1, E1) were examined, and for the second scenario, the values (A3, B3, C3, D3, E3) were assessed.
The descriptor “development of environmental regulations” had the greatest impact compared to the other descriptors. Furthermore, the descriptor “environmental monitoring and supervision,” due to its position in the upper half of the system, exhibited an influencing property over other descriptors and, given its location, could be identified as a dual descriptor. The three descriptors of “utilization of renewable energies,” “public transportation, cycling, and walking,” and “compact urban development” demonstrated high levels of susceptibility.
Discussion
This study, by identifying the key drivers affecting the smart environment of Mashhad metropolis from the perspective of experts, explored the possible future scenarios for the city. The indicator of “utilization of renewable energies and development of environmental regulations,” with a score of 38, had the greatest influence on other indicators. “Enhancement of awareness and participation,” “development of public transportation, cycling, and walking,” “compact urban development,” and “environmental monitoring and supervision” ranked next in terms of influence. Conversely, the indicators of “household waste generation” (score 24) and “access to green space” (score 25) had the lowest impact on other smart environmental indicators.
The indicator of “enhancing public awareness and participation” scored 38 and was the most influenced by other indicators, whereas “the proportion of residential units connected to sewage systems,” with a score of 24, was the least influenced. Furthermore, it can be stated that the two indicators of environmental regulation development and environmental monitoring and supervision were identified as risk drivers in the smart environmental development of Mashhad. These drivers exerted very high levels of both influence and susceptibility on other drivers and, along with the influential drivers, were selected as scenario descriptors.
Mohammadi identified six indicators for assessing the smart environment component, whereas the present study, through reviewing both domestic and international sources, recognized a greater number of indicators in the environmental dimension of smart cities [Mohammadi, 2016]. In another comparative study on the conceptualization of city smartness, three components of people, institutional factors, and infrastructure along with three factors of intelligence, innovation, and integration were considered as key determinants. That study highlights the crucial role of people and institutional factors in the smart development of cities [Moulaei et al., 2016]. Similarly, the findings of the present research emphasized that enhancing citizens’ awareness and participation plays a critical role as a driving force in the smart environmental development of Mashhad metropolis.
The main limitations of this study included the scarcity of domestic sources and prior research on the subject in Iran, the lack of locally adapted indicators for assessing urban environmental smartness in Iranian metropolises, and the absence of reports on environmental initiatives implemented in Iranian cities, especially major metropolises. Based on the findings, several recommendations can be proposed: raising citizens’ awareness of environmental issues by relevant managers, designing programs to foster a culture of participation, encouraging urban managers, executive bodies, and policymakers to recognize the importance of the environment, particularly its smart development, in Mashhad metropolis, and drawing on the experiences of pioneering cities worldwide. For future research, it is recommended to examine the role of stakeholders in the smart environmental development of Mashhad metropolis.
Conclusion
Among the 115 possible scenarios envisioned for Mashhad city, only two demonstrated strong consistency. The first scenario (the “Golden Scenario”) reflects the favorable status of descriptors for the smart environment of Mashhad, while the second scenario (the “Catastrophic Scenario”) illustrates their unfavorable status. Among these, the development of environmental regulations and environmental monitoring and supervision exerted the greatest influence, underscoring the critical importance of these two factors in the smart environmental development of Mashhad. Strengthening these drivers can increase the likelihood of realizing the first scenario while reducing the risk of the second.
Acknowledgments: The authors would like to express their sincere gratitude to all organizations that supported this research, particularly the Mashhad Water and Wastewater Company, the Khorasan Razavi Department of Environmental Protection, and the Mashhad Municipality for providing the necessary data and assisting in the completion of the questionnaires.
Ethical Approval: No ethical issues were reported by the authors.
Conflict of Interest: No conflicts of interest were reported by the authors.
Authors’ Contributions: Alaee R (First Author), Principal Researcher/Introduction Writer (60%); Rahnama MR (Second Author), Methodologist/Discussion Writer (20%); Ajzashokouhi M (Third Author), Assistant Researcher/Statistical Analyst (10%); Farghani A (Fourth Author), Assistant Researcher (10%).
Funding: This article is derived from the doctoral dissertation of the first author, supervised by the second author and advised by the third and fourth authors in the Department of Geography, Ferdowsi University of Mashhad.
Keywords:
References
1. Al-Hader M, Rodzi A (2009). The smart city infrastructure development and monitoring. Theoretical and Empirical Researches in Urban Management. 4(2):87-94. [Link]
2. Anagnostopoulos T, Kolomvatsos K, Anagnostopoulos C, Zaslavsky A, Hadjiefthymiades S (2015). Assessing dynamic models for high priority waste collection in smart cities. Systems and Software. 110:178-192. [Link] [DOI:10.1016/j.jss.2015.08.049]
3. Beatley T, Newman P (2008). Green Urbanism Down Under: Learning from Sustainable Communities in Australia. 1st Edition. Washington DC: Island Press. [Link]
4. Behtash M, Keinejad MA, Pirbabaei MT, Asgari A (2013). Evaluation and analysis of dimensions and components and components of Tabriz metropolis resiliency. HONAR-HAYE-ZIBA MEMARI-VA-SHAHRSAZI. 18(3):33-42. [Persian] [Link]
5. Castelli M, Goncalyes I, Trujillo L, Popovic A (2017). An evolutionary system for ozone concentration forecasting. Information Systems Frontiers. 19:1123-1132. [Link] [DOI:10.1007/s10796-016-9706-2]
6. Corbett J, Mellouli S (2017). Winning the SDG battle in cities: How an integrated information ecosystem can contribute to the achievement of the 2030 sustainable development goals. Information System Journal. 27(4):427-461. [Link] [DOI:10.1111/isj.12138]
7. Giffinger R, Kramar H, Haindl G (2008). The role of rankings in growing city competition. Proceedings of XI EURA Conference; 2008 Oct 9-11; Milan, Italy. Dortmund: European Urban Research Associations. [Link]
8. Gunderson LH (2000). Ecological resilience- in theory and application. Annual Reviews of Ecology and Systematics. 31:425-439. [Link] [DOI:10.1146/annurev.ecolsys.31.1.425]
9. Hatzelhoffer L, Humboldt K, Lobeck M, Wiegandt CC (2012). Smart City in Practice: Converting Innovation Ideas into Reality. Berlin: JOVIS Verlag. [Link]
10. Herzog AV, Lipman TE, Kammen DM (2001). Renewable energy sources. In: EOLSS, editor. Theory and practices for energy education, training, regulation and standards. Abu Dhabi: EOLSS Publishers Co. [Link]
11. Hoseini M (2017). Explaining the Green City pattern with scenario approach on the horizon 2026 on Mashhad [dissertation]. Mashhad: Department of Geography, Faculty of Literature and Humanities, Ferdowsi University of Mashhad. [Persian] [Link]
12. Huss W, Honton E (1987). Scenario Planning- What Style Should you use? Long Range Planning. 20(4):21-29. [Link] [DOI:10.1016/0024-6301(87)90152-X]
13. Kanchev H, Lu D, Colas F, Lazarov V, Francois B (2011). Energy management and operational planning of a micro-grid with a PV-based active generator for smart grid applications. IEEE Transactions on Industrial Electronics. 58(10):4583-4592. [Link] [DOI:10.1109/TIE.2011.2119451]
14. Karbasi A, Sayadi Ch (2015). Analysis and strategic planning of urban waste in Mashhad city in order to protect the environment. Proceedings of the 6th National Conference on Urban Planning and Management with Emphasis on the Elements of Islamic City; 2014 Nov 12-13; Mashhad, Iran. [Persian] [Link]
15. Koontz TM, Thomas CW (2006). What do we know and need to know about the environmental outcomes of collaborative management? Public Administration Review. 66(s1):111-121. [Link] [DOI:10.1111/j.1540-6210.2006.00671.x]
16. Kristiningrum E, Kusumo H (2021). Indicators of Smart City Using SNI ISO 37122:2019. IOP Conference Series: Materials Science and Engineering. 1096(1):012013. [Link] [DOI:10.1088/1757-899X/1096/1/012013]
17. Lemos M, Agrawal A (2006). Environmental Governance. Annual Review of Environment and Resources. 31:297-325. [Link] [DOI:10.1146/annurev.energy.31.042605.135621]
18. Liang S, Xu M, Suh S, Tan RR (2013). Unintended environmental consequences and cobenefits of economic restructuring. Environmental Sciences Technology. 47(22):12894-12902. [Link] [DOI:10.1021/es402458u]
19. Lindgren M, Bondhold H (2019). Scenario planning: The link between future and strategy. Tatar A, translator. Tehran: Educational and Research Institute of Defense Industry Technology, Science and Defense Future Research Center. [Persian] [Link]
20. Lowe M, Whitzman C, Badland H, Davern M, Hes D, Aye L, et al (2013). Livable, healthy, sustainable; what are the key indicators for Melbourne neighborhoods? (Research paper 1). Melbourne: Place, Health and Livability Research Program. [Link]
21. Mahdizadeh W (2016). The level of resilience of Sanandaj city in the environmental dimension. Proceedings of the First International Conference on Urban Economy, 2016 May 18: Tehran, Iran. [Persian]. [Link]
22. Mashhad Municipality (2021). Urban environmental pollutants monitoring in Mashhad (Comprehensive report). Mashhad: Department of Environment. [Persian] [Link]
23. Martinez Blanco J, Colón J, Gabarrell X, Font X, Sánchez A, Artola A, et al (2010). The use of life cycle assessment for the comparison of bio waste composting at home and full scale. Waste Management. 30(6):983‐994. [Link] [DOI:10.1016/j.wasman.2010.02.023]
24. Miles A, Zaslavsky A, Browne C (2018). IoT-based decision support system for monitoring and mitigating atmospheric pollution in smart cities. Journal of Decision Systems. 27:56-67. [Link] [DOI:10.1080/12460125.2018.1468696]
25. Modami M, Hedayati H, Dadkhah A (2016). Water pollution in Iran from urban development perspective. Proceedings of the 1st International Comprehensive Environmental Conference; 2016 Feb 20: Tehran, Iran. Tehran: Iran's Development Conference Center. [Persian] [Link]
26. Mohammadi Gh (2016). Explaining the smart city model in Mashhad metropolis based on sustainable development [dissertation]. Mashhad: Department of Geography, Ferdowsi University of Mashhad. [Persian] [Link]
27. Moulaei MM, Talebian H (2014). Future research of Iran's issues with structural analysis method. Majles and Strategy Quarterly Journal. 86:5-32. [Persian] [Link]
28. Moulaei MM, Shah Hosseini G, Dabaghchi S (2016). Explanation and analyzing how to make smart cities in the context of the influencing components and key factors. NAQSHEJAHAN. 6(3):75-93. [Persian] [Link]
29. Mone G (2015). The New Smart Cities. Communications of the ACM. 58(7):20-21. [Link] [DOI:10.1145/2771297]
30. Moghim S, Garna RK (2019). Counties' classification by environmental resilience. Journal of Environmental Management. 230:345-354. [Link] [DOI:10.1016/j.jenvman.2018.09.090]
31. Nilssen M (2019). To the smart city and beyond? Developing a typology of smart urban innovation. Technological Forecasting & Social Change. 142:98-104. [Link] [DOI:10.1016/j.techfore.2018.07.060]
32. Rahnama MR (2019). Status of environmental issues in the global metropolises' perspective. Political Spatial Planning Journal. 1(3):147-154. [Persian] [Link]
33. Rybnytska O, Burstein F, Rybin AV, Zaslavsky A (2018). Decision support for optimizing waste management. Journal of Decision Systems. 27:67-78. [Link] [DOI:10.1080/12460125.2018.1464312]
34. Salehi E, Aghababaei MT, Sarmadi H, Farzad Behtash MR (2011). Considering the environment resiliency by use of cause model. Journal of Environmental Studies. 37(59):99-112. [Persian] [Link]
35. Shum K, Watanabe C (2017). From compact city to smart city: A sustainability science & synergy perspective. Journal of Environmental Science and Engineering. A6:200-208. [Link] [DOI:10.17265/2162-5298/2017.04.004]
36. Silva BN, Khan M, Han K. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society. 38:697-713. [Link] [DOI:10.1016/j.scs.2018.01.053]
37. Staffans A, Horelli L (2014). Expanded Urban Planning as a vehicle for understanding and shaping smart, Livable Cities. Journal of Community Informatics. 10(3): Unknown pages. [Link] [DOI:10.15353/joci.v10i3.3439]
38. Sun M, Wang Y, Strbac G, Kang C (2019). Probabilistic Peak Load Estimation in Smart Cities Using Smart Meter Data. IEEE Transactions on Industrial Electronics. 66(2):1608-1618. [Link] [DOI:10.1109/TIE.2018.2803732]
39. Thornbush M, Golubchikov O (2021). Smart energy cities: The evolution of the city energy- sustainability nexus. Environmental Development. 39:100626. [Link] [DOI:10.1016/j.envdev.2021.100626]
40. Toosi R, Samiei M, Movahedi M (2017). Infrastructure and necessity of moving towards a smart city considering the activities of Mashhad Municipality. Proceedings of the 1st National Smart City Conference, 2016 May 18: Qom, Iran. [Persian] [Link]
41. Van Poll R (2011). Approaches and methods for measuring the quality of the city's residential environment. 1st Edition. Rafeiyan M, Molodi J, translators. Tehran: Azarakhsh Publications. [Persian] [Link]
42. Yazdan Dad H, Sadegh Z (2011). Investigation of landfill leachate treatment methods in Mashhad. Proceedings of the First National Conference on Sustainable Urban Development, 2011 Mar 9: Gilan, Iran. [Persian] [Link]