- Email: [email protected]
How governance influences the components of sustainable urban development?
Journal Pre-proof How Governance Influences the Components of Sustainable Urban Development?
S. Mostafa Rasoolimanesh, Nurwati Badarulzanan, Aldrin Abdullah, Mohsen Behrang PII:
S0959-6526(19)32853-7
DOI:
https://doi.org/10.1016/j.jclepro.2019.117983
Article Number:
117983
Reference:
JCLP 117983
To appear in:
Journal of Cleaner Production
Received Date:
08 April 2019
Accepted Date:
08 August 2019
Please cite this article as: S. Mostafa Rasoolimanesh, Nurwati Badarulzanan, Aldrin Abdullah, Mohsen Behrang, How Governance Influences the Components of Sustainable Urban Development?, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.117983
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Journal Pre-proof
How Governance Influences the Components of Sustainable Urban Development?
S. Mostafa Rasoolimanesh Faculty of Social Sciences and Leisure Management, Taylor’s University, Subang Jaya, Malaysia Email: [email protected] [email protected]
Nurwati Badarulzanan Housing, Building, and Planning School, Universiti Sains Malaysia, Malaysia Email: [email protected]
Aldrin Abdullah Housing, Building, and Planning School, Universiti Sains Malaysia, Malaysia Email: [email protected]
Mohsen Behrang Housing, Building, and Planning School, Universiti Sains Malaysia, Malaysia Email: [email protected]
Acknowledgment: This study was funded through a research grant from the Ministry of Higher
Education, Malaysia under the Transdisciplinary Research Grant Scheme (TRGS), 2016 (Grant no. 203.PPBGN/67610001). Moreover, we would like to acknowledge that a preliminary version of this paper was presented in the 9th International Conference on PLS and Related Methods (PLS’17), Macau, China.
Journal Pre-proof How Governance Influences the Components of Sustainable Urban Development?
Abstract The objective of this study is to investigate the effects of governance on the three linchpins of sustainable urban development (SUD): social development and inclusion, economic development, and environmental protection. This study relies on secondary data collected by UN-Habitat from 265 cities in developing countries between 2013 and 2016 for the City Prosperity Index program (CPI). In addition, the indicators used to measure the components of SUD were adopted from the CPI. The findings reveal the negative effects of governance on the social development and inclusion, and economic development components of SUD. Moreover, the results of this study demonstrate a positive effect for governance on environmental protection. As such, these findings substantially enrich the SUD literature for which there are few studies having investigated the inter-relationship among SUD components. Moreover, the findings reported here have implications in terms the sustainability of urban development. Keywords: sustainable urban development; governance; economic development; social development and inclusion; environmental protection; partial least squares – structural equation modeling (PLS-SEM) Introduction Three years after the 2012 Rio+20 conference, a proposal was put forward by several UN member states to endorse a series of sustainable development goals (SDGs) for the 2016– 2030 period. These SDGs were later approved by the 70th UN General Assembly in 2015. The eleventh SDG is concerned with sustainable urban development (SUD) or sustainable cities. The objective of this SDG is to “make cities and human settlements inclusive, safe, 1
Journal Pre-proof resilient, and sustainable” (United Nations [UN], 2017). SUD refers to the process of balancing the economic, environmental, and social aspects of urban life (Hassan & Lee, 2015; Sachs, 2012). The concept of SUD has been established in response to the challenges of climate change, rapid population growth, urban poverty, and social-spatial changes, especially in the developing world (Hassan & Lee, 2015; Rasoolimanesh, Badarulzaman, & Jaafar, 2012). In responding to these challenges, the three main linchpins of SUD are economic development, social development and inclusion, and environmental protection (Hassan & Lee, 2015; Sachs, 2012). Sustainable development aims to understand and manage the interactions between the social, economic, and environmental components of urbanism with a view toward creating parity (Sachs, 2015). This balance is achieved through the judicious application of governance at all levels; including the government, private sector, and civil society (Rasoolimanesh et al., 2012; Roy, 2009; Saha & Paterson, 2008). Achieving SUD, however, entails the development of new and innovative approaches to governance that involve the effective participation of stakeholders in transparent processes, responding to the needs of citizens, and achieving economic development while simultaneously being mindful of environmental concerns and the need for social inclusion (Rasoolimanesh et al., 2012; Yigitcanlar & Teriman, 2015). To the best of our knowledge, there is a paucity of studies to investigate empirically the effect of governance on three pillars of urban sustainability (Sachs, 2015). Therefore, the objective of current study is to investigate and examine effects of governance and its predictive capacity with respect to economic development, social development and inclusion, and environmental protection. In current study, we applied the UN-Habitat’s City Prosperity Initiative (CPI) indicators to measure the three linchpins of SUD, as well as governance. The CPI established a set of metrics to assess SUD across six dimensions including economic productivity, public infrastructure, quality of life, social
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Journal Pre-proof inclusion, environmental sustainability, and urban governance, legislation (UN Human Settlements Programme, 2017).
Sustainable Urban Development The idea of sustainability has its origins in the first UN Conference on the Human Environment in Stockholm in 1972 (Whitehead, 2003). Other studies, however, suggest that the concept did not emerge until 1980 and the International Union for Conservation of Nature and Natural Resources’ World Conservation Strategy (Bentivegna et al., 2002; Curwell, Cooper, Deakin, & Symes, 2005). This definition was further refined with the Agenda 21 proposal during the UN Conference on Environment and Development in Rio de Janeiro in 1992, and again in 1996 during the second UN Conference on Human Settlement (i.e., Habitat II) in Istanbul. Participants of Habitat II proposed a renewed focused on the Agenda 21 (LA21) goals of realizing and localizing SUD in urban areas (Tuts, 2012; Whitehead, 2003). Following the Rio+20 conference, a series of SDGs were proposed by UN member states proposed for the 2015–2030 period (UN, 2015). These SDGs were approved during the 70th UN General Assembly. Among the 17 SDGs, the eleventh goal establishes explicit targets for SUD by 2030. This eleventh goal is stated as follows: “Goal 11. Make cities and human settlements inclusive, safe, resilient and sustainable. 11.1 By 2030, ensure access for all to adequate, safe and affordable housing and basic services, and upgrade slums.
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Journal Pre-proof 11.2 By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons. 11.3 By 2030 enhance inclusive and sustainable urbanization and capacities for participatory, integrated and sustainable human settlement planning and management in all countries. 11.4 Strengthen efforts to protect and safeguard the world’s cultural and natural heritage. 11.5 By 2030 significantly reduce the number of deaths and the number of affected people and decrease by y% the economic losses relative to GDP caused by disasters, including water-related disasters, with the focus on protecting the poor and people in vulnerable situations. 11.6 By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality, municipal and other waste management. 11.7 By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, particularly for women and children, older persons and persons with disabilities. 11.a Support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning. 11.b By 2020, increase by x% the number of cities and human settlements adopting and implementing integrated policies and plans towards inclusion, resource efficiency, mitigation and adaptation to climate change, resilience to 4
Journal Pre-proof disasters, develop and implement in line with the forthcoming Hyogo Framework holistic disaster risk management at all levels. 11.c Support least developed countries, including through financial and technical assistance, for sustainable and resilient buildings utilizing local materials”. (UN, 2015) Therefore, with all UN member states having agreed to these SDGs during the 70th UN General Assembly in September 2015, the eleventh goal makes it possible to benchmark member states’ implementation progress of SUD. The UN Conference on Housing and Sustainable Urban Development (i.e., Habitat III)—held in Quito, Ecuador, in October 2016—saw the proposition of the New Urban Agenda (NUA) to facilitate the achievement of SUD by focusing on the eleventh SDG and its associated targets in relation to the other SDGs (UN, 2017b). SUD has become a critical issue in the development of urban policy and management decisions (Dovers, 2005; Hassan & Lee, 2015; Yigitcanlar & Teriman, 2015). Whilst the origins of SUD might be traced back to the 1990s, SUD has recently gained prominence in light of the negative impact human activities have had on the global environment (e.g., climate change) (Hassan & Lee, 2015; Yigitcanlar & Teriman, 2015). Consequently, citizens are increasingly demanding that their governments behave more responsibly with respect to urban planning and development, thus necessitating SUD strategies (Bulkeley & Betsill, 2005; Yigitcanlar & Teriman, 2015). In order to become sustainable, cities in developing countries need to improve their overall economic structures and reduce poverty. Moreover, these developments must be made without damaging the environment or the city’s natural capital, and must promote the inclusion of various social groups, especially the poor (Abu-Ghazalah, 2008; Sachs, 2012). 5
Journal Pre-proof As such, SUD is a dynamic process intended to address a number of economic, social, and environmental concerns (Roy, 2009; Shen et al., 2011). SUD encourages stakeholders to develop “a new large-scale vision to guide the planning agenda for the twenty-first century” (Saha & Paterson, 2008, p. 22). If such a plan, however, is to come to fruition, it will demand governance from all levels of government, the private sector, and civil society (Roy, 2009; Sachs, 2012; Saha & Paterson, 2008). Therefore, three following hypotheses have been identified for this study: H1: Governance has a positive effect on economic development in cities that achieve SUD. H2: Governance has a positive effect on social development and inclusion in cities that achieve SUD. H3: Governance has a positive effect on environmental protection in cities that achieve SUD.
Measurement of SUD pillars Several studies have attempted to identify the indicators to measure and monitor the pillars and components of SUD (Deakin & Reid, 2014; Saha & Paterson, 2008; Yigitcanlar & Teriman, 2015). Saha and Paterson (2008) define economic sustainability in terms of: (a) shrewd growth, including the protection of agricultural zones, the reclamation of brownfields, echo-industrial parks, infill development, and the identification of urban growth boundaries; and (b) the promotion of the local economy and business, including business retention programs, empowerment, and local business incubators. Yigitcanlar and Teriman (2015), on the other hand, are concerned with employment, transport, urban services, and finance. In terms of environmental sustainability, the dominant indicators and components to emerge out of the literature include alternative energy, energy conservation efforts, green building, water and air quality protection, open space preservation, the conservation of the natural 6
Journal Pre-proof environment, urban form, and transportation management (Bentivegna et al., 2002; Bottero, Mondini, & Valle, 2007; Saha & Paterson, 2008; Scipioni, Mazzi, Mason, & Manzardo, 2009; Shen, Jorge Ochoa, Shah, & Zhang, 2011; Singh, Murty, Gupta, & Dikshit, 2009; Walter & Stützel, 2009; Yigitcanlar & Teriman, 2015). Fostering social inclusion and equity includes the development of affordable housing, prevention and intervention programs aimed at addressing homelessness, the establishment of development and investment programs for promoting women and minority-oriented business communities, youth opportunity and development, education, health, security, and increasing the availability of public open areas and services (Bottero et al., 2007; Huang & Rust, 2011; Saha & Paterson, 2008; Walter & Stützel, 2009; Xing, Horner, El-Haram, & Bebbington, 2009; Yigitcanlar & Teriman, 2015). Governance, while not a cornerstone, is nonetheless integral to the development of SUD and involves public participation, transparency, accountability, and empowering minority groups and the poor (Bottero et al., 2007; Conroy, 2006; Rasoolimanesh et al., 2012; Sachs, 2012; Yigitcanlar & Teriman, 2015). Table 1 outlines a number of SUD indicators according to the literature. These indicators can be used for benchmarking and comparative purposes.
[Table 1 about here]
After the proposition and approval of the New Urban Agenda (NUA) in October 2016, to facilitate and achieve urban sustainability along with the eleventh goal of SDGs (SDGs #11), the UN-Habitat’s City Prosperity Initiative (CPI) was launched in order to measure, and monitor the implementation of NUA and to provide reliable data platform for decisionmakers aiming to achieve SUD (UN Human Settlements Programme, 2017). The CPI established a set of metrics to assess SUD, collecting data from cities around the world each year to inform decision-makers in the development of urban policies, and SUD planning (UN 7
Journal Pre-proof Human Settlements Programme, 2017). These indicators are divided across six dimensions, each corresponding with one of the SUD linchpins (i.e., economic development, social development and inclusion, environmental protection, and governance). These dimensions imply accomplishment, prosperity, thriving conditions, welfare, as well as confidence in the future and opportunities for all. Successful cities it would seem provide a wealth of nonexcludable public goods, thus promoting the pursuit of sustainable policies. Consequently, urban prosperity is thought of in terms of output, the development of public infrastructure, quality of life, social inclusion, environmental sustainability, urban governance, and legislation (UN Human Settlements Programme, 2017). The productivity and infrastructure development dimension pertains to economic development. Productivity involves indicators used to measure cities in regard to residents’ wealth, and the contribution of cities to economic growth, income generation, and the provision of employment and equal opportunities. Infrastructure development refers indicators used to measure the provision of essential urban infrastructure, such as sanitation, clean drinking water, roads, and information and communication technology. Quality of life, equity, and social inclusion dimensions are related to the social development and inclusion pillar of SUD. Quality of life includes indicators used to measure general wellbeing and resident satisfaction. The equity and social inclusion dimension, on the other hand, encompasses indicators used to measure the equitable distribution of benefits, reduce poverty, renovate slums, ensure the rights of minority groups, improve gender equity, and ensure the equitable participation of all in society. The environmental sustainability dimension covers the environmental protection pillar of SUD, and includes indicators used to ensure the protection of the urban environment. The final dimension is urban governance and legislation, which corresponds with the idea of good 8
Journal Pre-proof governance. This includes indicators used to measure the level of urban governance in cities (UN Human Settlements Programme, 2017). Table 2 shows the dimensions, sub-dimensions, and indicators developed under CPI.
[Table 2 about here]
Methodology Data collection process This study investigates the effects of governance on the three linchpins of sustainable development in an urban context. The indicators to measure four main involved constructs in this study (i.e. governance, economic development, social inclusion, and environmental protection) were adopted from the CPI program, which was launched to monitor the implementation of the NUA in Habitat III and to benchmark SUD. These indicators, being composite constructs, thus form the primary measures of the variables in this study. According to the CPI program, economic development is measured by two dimensions; namely economic productivity and infrastructure development. More specifically, the subdimensions of economic productivity (EP) are economic strength, economic agglomeration, and employment; while the sub-dimensions of infrastructure development (EI) are housing infrastructure, social infrastructure, ICT, urban mobility, and urban form. Social development and inclusion are also measured by two dimensions: (a) quality of life, and (b) equity and social inclusion. The sub-dimensions of social quality of life (SQ) are health, education, safety and security, and public space; whereas the sub-dimensions of social equity and inclusion (SE) are economic equity, social inclusion, gender inclusion, and urban diversity. In addition, environmental protection is measured using a single dimension, environmental 9
Journal Pre-proof sustainability (ES), which is inclusive of three sub-dimensions: air quality, waste management, and an energy index. Governance is similarly measured using a single dimension—urban governance and legislation (GOV)—which is comprised of three subdimensions: participation, municipal finance and institutional capacity, and governance of urbanization. As shown in Table 2, a number of indicators developed as part of the CPI program to measure each sub-dimension. This study used secondary data, sources as part of the CPI program, from a sample of 265 cities in developing countries from Africa, Latin America, and Asia. This data was collected between 2013 and 2016, and is available for download from the CPI website (UN Human Settlements Programme, 2017). It should be noted, however, that our analysis relies on CPI data collected and produced for the subdimensions, not the primary indicators. Moreover, not all the sub-dimension data was available for download; therefore, our analysis is restricted to that sub-dimension data that was available for download. Consequently, we measured EP by economic strength, and employment; EI by housing infrastructure, social infrastructure, ICT, and urban mobility; SQ by health, education, and safety and security; SE by equity, social inclusion, and gender inclusion; ES by air quality, and an energy index; and GOV only by participation. In this study, economic and social development, social inclusion, environmental protection, and governance are considered composite–composite second-order constructs. Because these are composite constructs, each of the indicators used measures a different aspect of the construct and the indicators are not exchangeable (Henseler, Hubona, & Ray, 2016). In addition, we adopted the CPI indicators to measure the components of SUD. From the SUD literature, it is clear that the model is likely incomplete and numerous other indicators should be included to ensure a more comprehensive measurement of economic and social development, social inclusion, environmental protection, and governance. However, the CPI program offers only a selected number of these indicators. Therefore, the constructs used in 10
Journal Pre-proof this study are a composite of indicators, the results of which should be interpreted based on the indicators used in each construct. The conceptual framework for this study is shown in Figure 1. [Figure 1 about here] Data analysis This study applies partial least squares – structural equation modeling (PLS-SEM) as a composite-based approach due to complexity of model and inclusion of several composite constructs. In addition, this study uses PLS-SEM and WarpPLS 5.0 software package (Kock, 2015) to perform the analysis because it is well-suited for theory development and predictionoriented studies (Ali et al., 2018; Hair et al., 2017; Rasoolimanesh & Ali, 2018). PLS model assessment involves a dual-phase process (Hair et al., 2017; Rasoolimanesh et al., 2015). The first phase is the measurement model assessment, which involves evaluating the relationships between the constructs and their associated observed indicators; the second phase, on the other hand, involves assessing the structural model, which is concerned with the manner in which the constructs are related to one another (Hair et al., 2017). However, there are various categories of measurement models—reflective, formative, and composite (Henseler et al., 2016; Rasoolimanesh & Ali, 2018)—each with its own criteria for assessment. Each of the constructs in the current study is a composite construct. Ordinarily, composite measurement model assessment would require that the Variance Inflated Factor (VIF), or collinearity between the associated indicators of each construct, be less than 5, and that the outer weight each of the indicators should be significant (Hair et al., 2017; Henseler et al., 2016; Rasoolimanesh et al., 2019b). However, all the main variables in this study— economic and social development, social inclusion, environmental protection, and governance—are second-order composite constructs. Consequently, it is necessary to employ 11
Journal Pre-proof a two-stage approach if we are to establish these second-order composite constructs (Becker, Klein, & Wetzels, 2012; Rasoolimanesh et al., 2019a; Rasoolimanesh, Dahalan, & Jaafar, 2016). The structural model assessment and hypothesis testing rely on three criteria: R2, which is a measure of the endogenous constructs, the path coefficients of each relationship (i.e., hypothesis), and the effect size of each path. While the value of R2 varies somewhat contingent upon the research area, path coefficients need always be significant. Chin (2010) suggests R2 values in the order of 0.67, 0.33, and 0.19 to be considered respectively substantial, moderate, or weak. In addition, values of 0.02, 0.15, and 0.35 are considered small, moderate, and large effect sizes, respectively (Ali et al., 2018; Hair et al., 2017).
Results and Findings Table 3 shows the results of the measurement model assessment for composite first-order constructs before establishing the second-order constructs using two-stage approach. The results show that the VIF values were lower than 2.5, which is perhaps the most conservative threshold for VIF (Kock, 2015). The outer weights seen in Table 3 are for all of the indicators associated with first-order constructs, except for economic equity because its outer weight was not significant. Nonetheless, indicators lacking outer weight significance can be retained on the proviso they maintain a significant outer loading (Hair et al., 2017). Therefore, economic equity can be retained and accepted despite its non-significant outer weight because its associated loading is significant. [Table 3 about here]
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Journal Pre-proof The measurement model assessment results, post the establishment of the second-order constructs, are shown in Table 4. These results indicate that the measurement model is acceptable, the VIF of each indicator being below 2.5, and their outer weights being significant. Moreover, Table 4 shows that the full collinearity of each construct was below 3.3. Full collinearity describes the vertical and lateral collinearity of a construct in relation to other constructs and should be less than 3.3 (Kock & Lynn, 2012). it is reasonable to use full collinearity to assess discriminant validity in the case of models that are inclusive of both formative and/or composite constructs (Rasoolimanesh et al., 2017). [Table 4 about here] The results of assessment of structural model show the R2 values of 0.40, 0.27, and 0.20 for economic development, social development and inclusion, and environmental protection, respectively. Therefore, the R2 values were considered acceptable. In addition, Table 5 shows the significant effects for all three hypotheses. However, the sign of the path coefficients for H1 and H2 differed from what was originally hypothesized. This study hypothesized positive effects for governance on economic and social development, and inclusion; however, the results showed negative effects. Therefore, the results of the current study cannot support either H1 or H2, although support as found for H3. These findings confirm the negative effects of governance on economic development, and social development and inclusion, and a positive effect on environmental protection. The results showed the highest effect size for the effect of governance on economic development, followed by social development and inclusion. The study results showed the lowest effect size for the effect of governance on environmental protection.
[Table 5 about here] 13
Journal Pre-proof Discussion The SUD literature describes economic and social development, social inclusion, environmental protection, and governance as the main components of SUD (Sachs, 2012, 2015). However, until now, the inter-relationships between these components has not been clearly understood, thus prompting this attempt to investigate the effect of governance on the three key components of SUD, these components having been identified in previous studies (Rasoolimanesh et al., 2012; Sachs, 2015). The results showed the significant effect of governance on the three main linchpins of SUD, with highest effect being for economic development and the lowest effect: environmental protection. However, the results also identified negative effects for governance on economic and social development, and social inclusion. These findings are inconsistent with the SUD literature (Sachs, 2012, 2015). The current study used the CPI development indicators to measure sub-dimensions, dimensions, and components of SUD based on the availability of data from cities in developing countries, this data being made available view the CPI website. As mentioned earlier, data were not available for all indicators, so a number of indicators were excluded from this study, and the analysis was performed based on the remaining indicators. Also, mentioned earlier, EP was measured by economic strength and employment; EI by housing infrastructure, social infrastructure, ICT, and urban mobility; SQ by health, education, and safety and security; SE by equity, social inclusion, and gender inclusion; ES by air quality, and energy index; and GOV simply by participation and voter turnout. The results, therefore, reveal the effects of participation on economic development, including EP, and EI, social development and inclusion, including SQ and SE; and environmental protection, including ES. The results identified the negative effects of participation on EP (economic productivity) and EI (infrastructure). Therefore, the results demonstrate that developing countries with low levels of economic productivity and infrastructure are more interested in participation. This 14
Journal Pre-proof finding is inconsistent with the literature, which indicates that better governance leads to improved economic productivity and infrastructure (Badarulzanan, Rasoolimanesh, & Abdullah, 2017; Rasoolimanesh et al., 2012; Sachs, 2015). One possible reason for this inconsistent finding is because the only data that was available was for participation (i.e., voter turnout ratio); consequently, governance was measured using this single indicator. Therefore, this effect only describes the effect of the voter turnout ratio on economic productivity and infrastructure, which is assumed negative. Because in the cities with lower productivity and infrastructure the residents and citizens are more interested to participate and improve the productivity and infrastructure by their participation. The current study draws attention to the negative effects of governance on social development and inclusion. However, Table 4 revealed the negative contribution of SQ to establish social development and inclusion, whereas the results showed the positive contribution of SE to social development and inclusion. Therefore, the negative effect of governance (represented by participation and the voter turnout ratio) can be interpreted as positive on SQ and negative on SE. The finding of a positive effect for governance on SQ is consistent with previous studies, suggesting that governance and participation can improve the quality of life of residents and citizens. The finding of a negative effect for governance on SE, however, is inconsistent with the literature. This negative effect of governance on SE might be justified by the fact that the more inequity residents perceive, the more willing they are to participate in the governance of their community. Therefore, the negative effect of governance, which is measured based simply on their participation and voter turnout ratio on social equity and inclusion (SE), makes reasonable sense. The findings also identified the positive effect of GOV on environmental protection or ES. The results revealed that better governance with more participation results in improved environmental sustainability, better air quality, and a better energy index. Consistent with 15
Journal Pre-proof previous studies, this result confirms a positive effect for governance on environmental protection and sustainability (Rasoolimanesh et al., 2012; Sachs, 2015). Moreover, the results indicate that communities with more participation and a higher voter turnout ratio have more concerns about air quality and energy efficiency, suggesting that these measures reflect residents’ efforts to improve these indices.
Conclusion To the best of the author’s knowledge, this is one of the first studies to explore the effects of governance on the three main components of SUD: economic and social development, social inclusion, and environmental protection. Consequently, this study enriches the SUD literature by examining the inter-relationship between the various concepts that form the linchpin of SUD. This assessment relies on data sourced from the CPI program and collected from a number of cities in developing countries. Nonetheless, the CPI program has only made limited data available for a few dimensions, sub-dimensions, and indicators. That we undertook to measure governance, economic and social development, social inclusion, and environmental protection based on such limited data, however, might represent a major shortcoming of the current study. Further studies are necessary to develop a more comprehensive set of indicators with which to measure governance, economic development, social development and inclusion, and environmental protection. Once a complete set of data has been gathered for these indicators, the inter-relationships between constructs should be re-assessed. This represents one the practical implications of this study for UN-Habitat and the CPI program. Despite the development of indicators and a large pool of amassed data, there is insufficient information with which to measure the components SUD and to implement the NUA. Consequently, limitations in relation to the indicators and lack of available data mean that the results of the current study cannot be generalized and more 16
Journal Pre-proof studies necessary to overcome the hurdles. Moreover, this study relied on data from a number of cities in developing countries. This indicates another limitation of the current study and further research is necessary, based not only on more cities from the developing world, but also from the developed countries to test the proposed hypotheses and to form generalizations that might be applied to other cities. References Abu-Ghazalah, S. (2008). The sustainable city development plan for Aqaba, Jordan. Journal of Developing Societies, 24(3), 381–398. https://doi.org/10.1177/0169796X0802400304 Ali, F., Rasoolimanesh, S.M., Sarstedt, M., Ringle, C.M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514–538. Badarulzanan, N., Rasoolimanesh, S. M., & Abdullah, A. (2017, June). Examining the effect of good governance on three pillars of sustainable urban development using partial least squares. Paper presented at the 9th International Conference on PLS and Related Methods (PLS’17), Macau, China. Becker, J. M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLSSEM: guidelines for using reflective-formative type models. Long Range Planning, 45(5), 359-394. Bentivegna, V., Curwell, S., Deakin, M., Lombardi, P., Mitchell, G., & Nijkamp, P. (2002). A vision and methodology for integrated sustainable urban development: BEQUEST. Building Research & Information, 30(2), 83–94. https://doi.org/10.1080/096132102753436468 Bottero, M., Mondini, G., & Valle, M. (2007). The use of the analytic network process for the sustainability assessment of an urban transformation project. In International Conference on Whole Life Urban Sustainability and its Assessment (pp. 27–29). Glasgow, Scotland: SUEMoT. Bulkeley, H., & Betsill, M. (2005). Rethinking sustainable cities: multilevel governance and the “urban” politics of climate change. Environmental Politics, 14(1), 42–63. https://doi.org/10.1080/0964401042000310178 Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655–690). Berlin, Germany: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_29 Conroy, M. M. (2006). Moving the middle ahead: challenges and opportunities of sustainability in Indiana, Kentucky, and Ohio. Journal of Planning Education and Research, 26(1), 18–27. https://doi.org/10.1177/0739456X06289664 17
Journal Pre-proof Curwell, S. R., Cooper, I., Deakin, M., & Symes, M. (Eds.). (2005). Sustainable urban development: The environmental assessment methods (Vol. 2). London: Routledge. Deakin, M., & Reid, A. (2014). Sustainable urban development: use of the environmental assessment methods. Sustainable Cities and Society, 10, 39–48. https://doi.org/10.1016/j.scs.2013.04.002 Dovers, S. (2005). Clarifying the imperative of integration research for sustainable environmental management. Journal of Research Practice, 1(2). Retrieved from https://openresearchrepository.anu.edu.au/bitstream/10440/254/1/Dovers_Clarigying2005.pdf Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Los Angeles, CA: Sage. Hassan, A. M., & Lee, H. (2015). Toward the sustainable development of urban areas: An overview of global trends in trials and policies. Land Use Policy, 48, 199–212. https://doi.org/10.1016/j.landusepol.2015.04.029 Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382 Huang, M.-H., & Rust, R. T. (2011). Sustainability and consumption. Journal of the Academy of Marketing Science, 39(1), 40–54. https://doi.org/10.1007/s11747-010-0193-6 Kock, N. (2015). WarpPLS 5.0: User Manual. Laredo, Texas: ScriptWarp Systems. Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. Journal of the Association of Information Systems, 13(7), 546–580. Labuschagne, C., Brent, A. C., & Van Erck, R. P. (2005). Assessing the sustainability performances of industries. Journal of cleaner production, 13(4), 373-385. Md Noor, S., Rasoolimanesh, S. M., Ganesan, V., & Jaafar, M. (2015). Effective interpretation using various media toward mindfulness: a case study of Malacca. Journal of Heritage Tourism, 10(3), 263–279. https://doi.org/10.1080/1743873X.2015.1005625 Putzhuber, F., & Hasenauer, H. (2010). Deriving sustainability measures using statistical data: a case study from the Eisenwurzen, Austria. Ecological Indicators, 10(1), 32-38. Rasoolimanesh, S. M., Badarulzaman, N., & Jaafar, M. (2012). City development strategies (CDS) and sustainable urbanization in developing world. Procedia - Social and Behavioral Sciences, 36, 623–631. https://doi.org/10.1016/j.sbspro.2012.03.068 Rasoolimanesh, S. M., Jaafar, M., Badarulzaman, N., & Ramayah, T. (2015). Investigating a framework to facilitate the implementation of city development strategy using balanced scorecard. Habitat International, 46, 156-165. Rasoolimanesh, S. M., Dahalan, N., & Jaafar, M. (2016). Tourists’ perceived value and satisfaction in a community-based homestay in the Lenggong Valley World Heritage Site. 18
Journal Pre-proof Journal of Hospitality and Tourism Management, 26, 72–81. https://doi.org/10.1016/j.jhtm.2016.01.005 Rasoolimanesh, S. M., Nejati, M., Ramayah, T., Leimee, T., Shafaei, A., & Abd Razak, N. (2017, June). Full collinearity as a new criterion to assess discriminant validity of composite (formative) and reflective measurement models. Paper presented at the 9th International Conference on PLS and Related Methods (PLS’17), Macau, China. Rasoolimanesh, S. M., & Ali, F. (2018). Partial least squares–structural equation modeling in hospitality and tourism. Journal of Hospitality and Tourism Technology, 9(3), 238-248. Rasoolimanesh, S. M., Md Noor, S., Schuberth, F., & Jaafar, M. (2019a). Investigating the effects of tourist engagement on satisfaction and loyalty. The Service Industries Journal, 39(7-8), 559-574. Rasoolimanesh, S.M., Taheri, B., Gannon, M., Vafaei-Zadeh, A., & Hanifah, H. (2019b). Does living in the vicinity of heritage tourism sites influence residents’ perceptions and attitudes?. Journal of Sustainable Tourism, doi: 10.1080/09669582.2019.1618863. Roy, M. (2009). Planning for sustainable urbanisation in fast growing cities: Mitigation and adaptation issues addressed in Dhaka, Bangladesh. Habitat International, 33(3), 276–286. https://doi.org/10.1016/j.habitatint.2008.10.022 Sachs, J. D. (2012). From millennium development goals to sustainable development goals. The Lancet, 379(9832), 2206–2211. https://doi.org/10.1016/S0140-6736(12)60685-0 Sachs, J. D. (2015). The age of sustainable development. New York, NY: Columbia University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=944998 Saha, D., & Paterson, R. G. (2008). Local government efforts to promote the “Three Es” of sustainable development: survey in medium to large cities in the United States. Journal of Planning Education and Research, 28(1), 21–37. https://doi.org/10.1177/0739456X08321803 Scipioni, A., Mazzi, A., Mason, M., & Manzardo, A. (2009). The dashboard of sustainability to measure the local urban sustainable development: the case study of Padua municipality. Ecological Indicators, 9(2), 364–380. https://doi.org/10.1016/j.ecolind.2008.05.002 Shen, L.-Y., Jorge Ochoa, J., Shah, M. N., & Zhang, X. (2011). The application of urban sustainability indicators – A comparison between various practices. Habitat International, 35(1), 17–29. https://doi.org/10.1016/j.habitatint.2010.03.006 Singh, R. K., Murty, H. R., Gupta, S. K., & Dikshit, A. K. (2009). An overview of sustainability assessment methodologies. Ecological Indicators, 9(2), 189–212. https://doi.org/10.1016/j.ecolind.2008.05.011 Tuts, R. (2012). The Regional Institute - Exploring the linkages between Local Agenda 21, good urban governance and urban poverty reduction: lessons from UN-HABITAT’s experience over the past decade. Presented at the International Local Agenda 21 Conference, Rio de Janeiro: The Regional Institute. Retrieved from http://www.regional.org.au/au/soc/2002/1/tuts.htm 19
Journal Pre-proof United Nations. (2015). Goal 11: sustainable development knowledge platform. Retrieved September 18, 2017, from https://sustainabledevelopment.un.org/sdg11 United Nations. (2017a). Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable — SDG Indicators. Retrieved September 18, 2017, from https://unstats.un.org/sdgs/report/2016/goal-11/ United Nations. (2017b). New Urban Agenda. Quito, Ecuador: United Nations. United Nations Human Settlements Programme. (2017). City Prosperity Initiative. Retrieved September 14, 2017, from http://cpi.unhabitat.org/ Walter, C., & Stützel, H. (2009). A new method for assessing the sustainability of land-use systems (ii): evaluating impact indicators. Ecological Economics, 68(5), 1288–1300. https://doi.org/10.1016/j.ecolecon.2008.11.017 Whitehead, M. (2003). (Re)analysing the sustainable city: nature, urbanisation and the regulation of socio-environmental relations in the UK. Urban Studies, 40(7), 1183–1206. https://doi.org/10.1080/0042098032000084550 Wiek, A., & Binder, C. (2005). Solution spaces for decision-making—a sustainability assessment tool for city-regions. Environmental Impact Assessment Review, 25(6), 589-608. Xing, Y., Horner, R. M. W., El-Haram, M. A., & Bebbington, J. (2009). A framework model for assessing sustainability impacts of urban development. Accounting Forum, 33(3), 209– 224. https://doi.org/10.1016/j.accfor.2008.09.003 Yigitcanlar, T., & Teriman, S. (2015). Rethinking sustainable urban development: towards an integrated planning and development process. International Journal of Environmental Science and Technology, 12(1), 341–352. https://doi.org/10.1007/s13762-013-0491-x
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Table 1 Principles and Indicators of Sustainable Urban Development Principles of SUD Environmental
Indicators 1- Alternative energy conservation efforts
References and
energy
2- Green building programs 3- Water quality protection 4- Air pollution
Bentivegna et al., 2002; Bottero et al., 2007; Saha & Paterson, 2008; Scipioni et al., 2009; Shen et al., 2011; Singh et al., 2009; Walter & Stützel, 2009; Yigitcanlar & Teriman, 2015
5- Open space preservation 6- Conservation of natural environment 7- Urban form 8- Transportation management Economic
1- Poverty reduction
Huang & Rust, 2011; Labuschagne et al., 2005; Putzhuber & Hasenauer, 2010; Saha & Paterson, 2008; Scipioni et al., 2009; Wiek & Binder, 2005; Yigitcanlar & Teriman, 2015;
2- Agriculture protection zoning 3- Infill development 4- Urban productivity 5- Urban services 6- Urban growth boundary 7- Financial management 8- Empowerment of local businesses Social Inclusion Equity
and
1- Affordable housing 2- Homeless prevention intervention programs
and
3- Women/minority-oriented business community development and investment programs,
21
Bottero et al., 2007; Huang & Rust, 2011; Saha & Paterson, 2008; Walter & Stützel, 2009; Xing et al., 2009; Yigitcanlar & Teriman, 2015
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1234-
Public participation Transparency, Accountability Empowerment of minority groups and poor people
5- Empowerment of NGOs
22
Bottero et al., 2007; Conroy, 2006; Rasoolimanesh et al., 2012; Sachs, 2012; Yigitcanlar & Teriman, 2015
Table 2 Indicators to Measure SUD under CPI Principles of SUD Environmental (ENV)
Dimensions 1- Environmental Sustainability
Sub-dimensions 1.1- Air quality
Indicators 1.1.1- Number of monitoring stations 1.1.2- PM2.5 concentration 1.1.3- CO2 emissions
1.2- Waste management
1.2.1- Solid waste collection 1.2.2- Waste water treatment 1.2.3- Solid waste recycling share
Economic (ECO)
1- Productivity
1.3- Energy index
1.3.1- Share of renewable energy
1.1- Economic strength
1.1.1- City product per capita 1.1.2- Old age dependency ratio 1.1.3- Mean household income
1.2- Economic agglomeration
1.2.1- Economic density 1.2.2- Economic specialization
1.3- Employment
1.3.1- Unemployment rate 1.3.2- Employment to population ratio 1.3.3- Informal employment
2- Infrastructure Development
2.1- Housing infrastructure
2.1.1- Improved shelter 2.1.2- Access to improved water 2.1.3- Access to improved sanitation 2.1.4- Access to electricity 2.1.5- Sufficient living area 2.1.6- Population density
2.2- Social infrastructure
2.2.1- Physician density 2.2.2- Number of public libraries
2.3- Information and Communication
23
2.3.1- Internet access
Technology (ICT)
2.3.2- Home computer access 2.3.3- Average broadband speed
2.4- Urban mobility
2.4.1- Use of public transport 2.4.2- Average daily travel time 2.4.3- Length of mass transport network 2.4.4- Traffic fatalities 2.4.5- Affordability of transport
2.5- Urban form
2.5.1- Street intersection density 2.5.2- Street density 2.5.3- Land allocated to streets
Social Inclusion and Equity
1.1- Health
1- Quality of Life
1.1.1- Life expectancy at birth 1.1.2- Under-five mortality rate 1.1.3- Vaccination coverage 1.1.4- Maternal mortality
1.2- Education
1.2.1- Literacy rate 1.2.2- Mean years of schooling 1.2.3- Early childhood education 1.2.4- Net enrollment rate in higher education
1.3- Safety and security
1.3.1- Homicide rate 1.3.2- Theft rate
1.4- Public space
1.4.1- Accessibility to open public areas 1.4.2- Green Area per Capita
2- Equity and Inclusion
Social
2.1- Economic equity
2.1.1- Gini coefficient 2.1.2- Poverty rate
2.2- Social inclusion
2.2.1- Slums households 2.2.2- Youth unemployment
2.3- Gender inclusion
24
2.3.1- Equitable secondary school enrollment 2.3.2- Women in local government
2.3.3- Women in local work force Governance
1- Urban Governance and Legislation
2.4- Urban diversity
2.4.1- Land use mix
1.1- Participation
1.1.1- Voter turnout 1.1.2- Access to public information 1.1.3- Civic participation
1.2- Municipal financing institutional capacity
and
2.2.1- Own revenue collection 2.2.2- Days to start a business 2.2.3- Subnational debt 2.2.4- Local expenditure efficiency
1.3- Governance of urbanization
25
1.3.1- Land use efficiency
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Table 3 Assessment of Measurement Model with First-Order Constructs Items Economic Productivity (EP) 1- Economic Strength
Construct type
2- Social Infrastructure 3- ICT 4- Urban Mobility
2- Education 3- Safety and Security
2- Social Inclusion 3- Gender Inclusion
1.115
0.755
<0.01
1.115
0.106
<0.05
1.094
0.645
<0.01
1.157
-0.852
<0.01
1.253
0.314
<0.01
1.174
0.427
<0.01
1.098
-0.526
<0.01
1.119
0.717
<0.01
1.036
0.04
0.254
1.06
0.419
<0.01
1.037
0.466
<0.01
1.03
Composite
1- Air Quality 2- Energy Index Urban Governance and Legislation (GOV)
<0.01
Composite
1- Economic Equity
Environmental Sustainability (ES)
-0.456
Composite
1- Health
Social Equity and Inclusion (SE)
VIF
Composite
1- Housing Infrastructure
Social Quality of Life (SQ)
p-value
Composite
2- Employment Economic Infrastructure (EI)
Weights
0.693
<0.01
1.033
0.607
<0.01
1.033
1.00
<0.001
Composite
1- Participation
26
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Table 4 Assessment of Measurement Model with Second-Order Constructs Items Economic Development
Construct type
Weights
p-value
VIF
Composite
1.913
EP
0.636
<0.01
1.35
EI
0.513
<0.01
1.35
Social Development and Inclusion
Composite
1.485
SQ
-0.637
<0.01
1.014
SE
0.7
<0.01
1.014
Environmental Protection
Composite
1.476 1.00
ES Governance
Full Collinearity
<0.01
------
Composite
1.916 1.00
GOV
27
<0.01
------
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Table 5 Results of Hypothesis Testing Hypothesis
Path Coefficient
p-value
Effect size
Supported NO (Different sign) NO (Different sign) YES
H1
Governance Economic Development
-0.634
<0.01
0.402
H2
Governance Social Development and Inclusion
-0.523
<0.01
0.273
H3
Governance Environmental Protection
0.448
<0.01
0.201
28
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Figure 1. Conceptual framework.
29
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Highlights
1- This study investigates the effects of governance on the three pillars of sustainable urban development (SUD). 2- This study relies on secondary data collected by the City Prosperity Index program (CPI) from 265 cities in developing countries. 3- The indicators used to measure the components of SUD were adopted from the CPI. 4- The findings reveal the negative effects of governance on the economic development, and social development and inclusion. 5- The results indicate a positive effect for governance on environmental protection.
S. Mostafa Rasoolimanesh, Nurwati Badarulzanan, Aldrin Abdullah, Mohsen Behrang PII:
S0959-6526(19)32853-7
DOI:
https://doi.org/10.1016/j.jclepro.2019.117983
Article Number:
117983
Reference:
JCLP 117983
To appear in:
Journal of Cleaner Production
Received Date:
08 April 2019
Accepted Date:
08 August 2019
Please cite this article as: S. Mostafa Rasoolimanesh, Nurwati Badarulzanan, Aldrin Abdullah, Mohsen Behrang, How Governance Influences the Components of Sustainable Urban Development?, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.117983
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
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How Governance Influences the Components of Sustainable Urban Development?
S. Mostafa Rasoolimanesh Faculty of Social Sciences and Leisure Management, Taylor’s University, Subang Jaya, Malaysia Email: [email protected] [email protected]
Nurwati Badarulzanan Housing, Building, and Planning School, Universiti Sains Malaysia, Malaysia Email: [email protected]
Aldrin Abdullah Housing, Building, and Planning School, Universiti Sains Malaysia, Malaysia Email: [email protected]
Mohsen Behrang Housing, Building, and Planning School, Universiti Sains Malaysia, Malaysia Email: [email protected]
Acknowledgment: This study was funded through a research grant from the Ministry of Higher
Education, Malaysia under the Transdisciplinary Research Grant Scheme (TRGS), 2016 (Grant no. 203.PPBGN/67610001). Moreover, we would like to acknowledge that a preliminary version of this paper was presented in the 9th International Conference on PLS and Related Methods (PLS’17), Macau, China.
Journal Pre-proof How Governance Influences the Components of Sustainable Urban Development?
Abstract The objective of this study is to investigate the effects of governance on the three linchpins of sustainable urban development (SUD): social development and inclusion, economic development, and environmental protection. This study relies on secondary data collected by UN-Habitat from 265 cities in developing countries between 2013 and 2016 for the City Prosperity Index program (CPI). In addition, the indicators used to measure the components of SUD were adopted from the CPI. The findings reveal the negative effects of governance on the social development and inclusion, and economic development components of SUD. Moreover, the results of this study demonstrate a positive effect for governance on environmental protection. As such, these findings substantially enrich the SUD literature for which there are few studies having investigated the inter-relationship among SUD components. Moreover, the findings reported here have implications in terms the sustainability of urban development. Keywords: sustainable urban development; governance; economic development; social development and inclusion; environmental protection; partial least squares – structural equation modeling (PLS-SEM) Introduction Three years after the 2012 Rio+20 conference, a proposal was put forward by several UN member states to endorse a series of sustainable development goals (SDGs) for the 2016– 2030 period. These SDGs were later approved by the 70th UN General Assembly in 2015. The eleventh SDG is concerned with sustainable urban development (SUD) or sustainable cities. The objective of this SDG is to “make cities and human settlements inclusive, safe, 1
Journal Pre-proof resilient, and sustainable” (United Nations [UN], 2017). SUD refers to the process of balancing the economic, environmental, and social aspects of urban life (Hassan & Lee, 2015; Sachs, 2012). The concept of SUD has been established in response to the challenges of climate change, rapid population growth, urban poverty, and social-spatial changes, especially in the developing world (Hassan & Lee, 2015; Rasoolimanesh, Badarulzaman, & Jaafar, 2012). In responding to these challenges, the three main linchpins of SUD are economic development, social development and inclusion, and environmental protection (Hassan & Lee, 2015; Sachs, 2012). Sustainable development aims to understand and manage the interactions between the social, economic, and environmental components of urbanism with a view toward creating parity (Sachs, 2015). This balance is achieved through the judicious application of governance at all levels; including the government, private sector, and civil society (Rasoolimanesh et al., 2012; Roy, 2009; Saha & Paterson, 2008). Achieving SUD, however, entails the development of new and innovative approaches to governance that involve the effective participation of stakeholders in transparent processes, responding to the needs of citizens, and achieving economic development while simultaneously being mindful of environmental concerns and the need for social inclusion (Rasoolimanesh et al., 2012; Yigitcanlar & Teriman, 2015). To the best of our knowledge, there is a paucity of studies to investigate empirically the effect of governance on three pillars of urban sustainability (Sachs, 2015). Therefore, the objective of current study is to investigate and examine effects of governance and its predictive capacity with respect to economic development, social development and inclusion, and environmental protection. In current study, we applied the UN-Habitat’s City Prosperity Initiative (CPI) indicators to measure the three linchpins of SUD, as well as governance. The CPI established a set of metrics to assess SUD across six dimensions including economic productivity, public infrastructure, quality of life, social
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Sustainable Urban Development The idea of sustainability has its origins in the first UN Conference on the Human Environment in Stockholm in 1972 (Whitehead, 2003). Other studies, however, suggest that the concept did not emerge until 1980 and the International Union for Conservation of Nature and Natural Resources’ World Conservation Strategy (Bentivegna et al., 2002; Curwell, Cooper, Deakin, & Symes, 2005). This definition was further refined with the Agenda 21 proposal during the UN Conference on Environment and Development in Rio de Janeiro in 1992, and again in 1996 during the second UN Conference on Human Settlement (i.e., Habitat II) in Istanbul. Participants of Habitat II proposed a renewed focused on the Agenda 21 (LA21) goals of realizing and localizing SUD in urban areas (Tuts, 2012; Whitehead, 2003). Following the Rio+20 conference, a series of SDGs were proposed by UN member states proposed for the 2015–2030 period (UN, 2015). These SDGs were approved during the 70th UN General Assembly. Among the 17 SDGs, the eleventh goal establishes explicit targets for SUD by 2030. This eleventh goal is stated as follows: “Goal 11. Make cities and human settlements inclusive, safe, resilient and sustainable. 11.1 By 2030, ensure access for all to adequate, safe and affordable housing and basic services, and upgrade slums.
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Journal Pre-proof 11.2 By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons. 11.3 By 2030 enhance inclusive and sustainable urbanization and capacities for participatory, integrated and sustainable human settlement planning and management in all countries. 11.4 Strengthen efforts to protect and safeguard the world’s cultural and natural heritage. 11.5 By 2030 significantly reduce the number of deaths and the number of affected people and decrease by y% the economic losses relative to GDP caused by disasters, including water-related disasters, with the focus on protecting the poor and people in vulnerable situations. 11.6 By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality, municipal and other waste management. 11.7 By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, particularly for women and children, older persons and persons with disabilities. 11.a Support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning. 11.b By 2020, increase by x% the number of cities and human settlements adopting and implementing integrated policies and plans towards inclusion, resource efficiency, mitigation and adaptation to climate change, resilience to 4
Journal Pre-proof disasters, develop and implement in line with the forthcoming Hyogo Framework holistic disaster risk management at all levels. 11.c Support least developed countries, including through financial and technical assistance, for sustainable and resilient buildings utilizing local materials”. (UN, 2015) Therefore, with all UN member states having agreed to these SDGs during the 70th UN General Assembly in September 2015, the eleventh goal makes it possible to benchmark member states’ implementation progress of SUD. The UN Conference on Housing and Sustainable Urban Development (i.e., Habitat III)—held in Quito, Ecuador, in October 2016—saw the proposition of the New Urban Agenda (NUA) to facilitate the achievement of SUD by focusing on the eleventh SDG and its associated targets in relation to the other SDGs (UN, 2017b). SUD has become a critical issue in the development of urban policy and management decisions (Dovers, 2005; Hassan & Lee, 2015; Yigitcanlar & Teriman, 2015). Whilst the origins of SUD might be traced back to the 1990s, SUD has recently gained prominence in light of the negative impact human activities have had on the global environment (e.g., climate change) (Hassan & Lee, 2015; Yigitcanlar & Teriman, 2015). Consequently, citizens are increasingly demanding that their governments behave more responsibly with respect to urban planning and development, thus necessitating SUD strategies (Bulkeley & Betsill, 2005; Yigitcanlar & Teriman, 2015). In order to become sustainable, cities in developing countries need to improve their overall economic structures and reduce poverty. Moreover, these developments must be made without damaging the environment or the city’s natural capital, and must promote the inclusion of various social groups, especially the poor (Abu-Ghazalah, 2008; Sachs, 2012). 5
Journal Pre-proof As such, SUD is a dynamic process intended to address a number of economic, social, and environmental concerns (Roy, 2009; Shen et al., 2011). SUD encourages stakeholders to develop “a new large-scale vision to guide the planning agenda for the twenty-first century” (Saha & Paterson, 2008, p. 22). If such a plan, however, is to come to fruition, it will demand governance from all levels of government, the private sector, and civil society (Roy, 2009; Sachs, 2012; Saha & Paterson, 2008). Therefore, three following hypotheses have been identified for this study: H1: Governance has a positive effect on economic development in cities that achieve SUD. H2: Governance has a positive effect on social development and inclusion in cities that achieve SUD. H3: Governance has a positive effect on environmental protection in cities that achieve SUD.
Measurement of SUD pillars Several studies have attempted to identify the indicators to measure and monitor the pillars and components of SUD (Deakin & Reid, 2014; Saha & Paterson, 2008; Yigitcanlar & Teriman, 2015). Saha and Paterson (2008) define economic sustainability in terms of: (a) shrewd growth, including the protection of agricultural zones, the reclamation of brownfields, echo-industrial parks, infill development, and the identification of urban growth boundaries; and (b) the promotion of the local economy and business, including business retention programs, empowerment, and local business incubators. Yigitcanlar and Teriman (2015), on the other hand, are concerned with employment, transport, urban services, and finance. In terms of environmental sustainability, the dominant indicators and components to emerge out of the literature include alternative energy, energy conservation efforts, green building, water and air quality protection, open space preservation, the conservation of the natural 6
Journal Pre-proof environment, urban form, and transportation management (Bentivegna et al., 2002; Bottero, Mondini, & Valle, 2007; Saha & Paterson, 2008; Scipioni, Mazzi, Mason, & Manzardo, 2009; Shen, Jorge Ochoa, Shah, & Zhang, 2011; Singh, Murty, Gupta, & Dikshit, 2009; Walter & Stützel, 2009; Yigitcanlar & Teriman, 2015). Fostering social inclusion and equity includes the development of affordable housing, prevention and intervention programs aimed at addressing homelessness, the establishment of development and investment programs for promoting women and minority-oriented business communities, youth opportunity and development, education, health, security, and increasing the availability of public open areas and services (Bottero et al., 2007; Huang & Rust, 2011; Saha & Paterson, 2008; Walter & Stützel, 2009; Xing, Horner, El-Haram, & Bebbington, 2009; Yigitcanlar & Teriman, 2015). Governance, while not a cornerstone, is nonetheless integral to the development of SUD and involves public participation, transparency, accountability, and empowering minority groups and the poor (Bottero et al., 2007; Conroy, 2006; Rasoolimanesh et al., 2012; Sachs, 2012; Yigitcanlar & Teriman, 2015). Table 1 outlines a number of SUD indicators according to the literature. These indicators can be used for benchmarking and comparative purposes.
[Table 1 about here]
After the proposition and approval of the New Urban Agenda (NUA) in October 2016, to facilitate and achieve urban sustainability along with the eleventh goal of SDGs (SDGs #11), the UN-Habitat’s City Prosperity Initiative (CPI) was launched in order to measure, and monitor the implementation of NUA and to provide reliable data platform for decisionmakers aiming to achieve SUD (UN Human Settlements Programme, 2017). The CPI established a set of metrics to assess SUD, collecting data from cities around the world each year to inform decision-makers in the development of urban policies, and SUD planning (UN 7
Journal Pre-proof Human Settlements Programme, 2017). These indicators are divided across six dimensions, each corresponding with one of the SUD linchpins (i.e., economic development, social development and inclusion, environmental protection, and governance). These dimensions imply accomplishment, prosperity, thriving conditions, welfare, as well as confidence in the future and opportunities for all. Successful cities it would seem provide a wealth of nonexcludable public goods, thus promoting the pursuit of sustainable policies. Consequently, urban prosperity is thought of in terms of output, the development of public infrastructure, quality of life, social inclusion, environmental sustainability, urban governance, and legislation (UN Human Settlements Programme, 2017). The productivity and infrastructure development dimension pertains to economic development. Productivity involves indicators used to measure cities in regard to residents’ wealth, and the contribution of cities to economic growth, income generation, and the provision of employment and equal opportunities. Infrastructure development refers indicators used to measure the provision of essential urban infrastructure, such as sanitation, clean drinking water, roads, and information and communication technology. Quality of life, equity, and social inclusion dimensions are related to the social development and inclusion pillar of SUD. Quality of life includes indicators used to measure general wellbeing and resident satisfaction. The equity and social inclusion dimension, on the other hand, encompasses indicators used to measure the equitable distribution of benefits, reduce poverty, renovate slums, ensure the rights of minority groups, improve gender equity, and ensure the equitable participation of all in society. The environmental sustainability dimension covers the environmental protection pillar of SUD, and includes indicators used to ensure the protection of the urban environment. The final dimension is urban governance and legislation, which corresponds with the idea of good 8
Journal Pre-proof governance. This includes indicators used to measure the level of urban governance in cities (UN Human Settlements Programme, 2017). Table 2 shows the dimensions, sub-dimensions, and indicators developed under CPI.
[Table 2 about here]
Methodology Data collection process This study investigates the effects of governance on the three linchpins of sustainable development in an urban context. The indicators to measure four main involved constructs in this study (i.e. governance, economic development, social inclusion, and environmental protection) were adopted from the CPI program, which was launched to monitor the implementation of the NUA in Habitat III and to benchmark SUD. These indicators, being composite constructs, thus form the primary measures of the variables in this study. According to the CPI program, economic development is measured by two dimensions; namely economic productivity and infrastructure development. More specifically, the subdimensions of economic productivity (EP) are economic strength, economic agglomeration, and employment; while the sub-dimensions of infrastructure development (EI) are housing infrastructure, social infrastructure, ICT, urban mobility, and urban form. Social development and inclusion are also measured by two dimensions: (a) quality of life, and (b) equity and social inclusion. The sub-dimensions of social quality of life (SQ) are health, education, safety and security, and public space; whereas the sub-dimensions of social equity and inclusion (SE) are economic equity, social inclusion, gender inclusion, and urban diversity. In addition, environmental protection is measured using a single dimension, environmental 9
Journal Pre-proof sustainability (ES), which is inclusive of three sub-dimensions: air quality, waste management, and an energy index. Governance is similarly measured using a single dimension—urban governance and legislation (GOV)—which is comprised of three subdimensions: participation, municipal finance and institutional capacity, and governance of urbanization. As shown in Table 2, a number of indicators developed as part of the CPI program to measure each sub-dimension. This study used secondary data, sources as part of the CPI program, from a sample of 265 cities in developing countries from Africa, Latin America, and Asia. This data was collected between 2013 and 2016, and is available for download from the CPI website (UN Human Settlements Programme, 2017). It should be noted, however, that our analysis relies on CPI data collected and produced for the subdimensions, not the primary indicators. Moreover, not all the sub-dimension data was available for download; therefore, our analysis is restricted to that sub-dimension data that was available for download. Consequently, we measured EP by economic strength, and employment; EI by housing infrastructure, social infrastructure, ICT, and urban mobility; SQ by health, education, and safety and security; SE by equity, social inclusion, and gender inclusion; ES by air quality, and an energy index; and GOV only by participation. In this study, economic and social development, social inclusion, environmental protection, and governance are considered composite–composite second-order constructs. Because these are composite constructs, each of the indicators used measures a different aspect of the construct and the indicators are not exchangeable (Henseler, Hubona, & Ray, 2016). In addition, we adopted the CPI indicators to measure the components of SUD. From the SUD literature, it is clear that the model is likely incomplete and numerous other indicators should be included to ensure a more comprehensive measurement of economic and social development, social inclusion, environmental protection, and governance. However, the CPI program offers only a selected number of these indicators. Therefore, the constructs used in 10
Journal Pre-proof this study are a composite of indicators, the results of which should be interpreted based on the indicators used in each construct. The conceptual framework for this study is shown in Figure 1. [Figure 1 about here] Data analysis This study applies partial least squares – structural equation modeling (PLS-SEM) as a composite-based approach due to complexity of model and inclusion of several composite constructs. In addition, this study uses PLS-SEM and WarpPLS 5.0 software package (Kock, 2015) to perform the analysis because it is well-suited for theory development and predictionoriented studies (Ali et al., 2018; Hair et al., 2017; Rasoolimanesh & Ali, 2018). PLS model assessment involves a dual-phase process (Hair et al., 2017; Rasoolimanesh et al., 2015). The first phase is the measurement model assessment, which involves evaluating the relationships between the constructs and their associated observed indicators; the second phase, on the other hand, involves assessing the structural model, which is concerned with the manner in which the constructs are related to one another (Hair et al., 2017). However, there are various categories of measurement models—reflective, formative, and composite (Henseler et al., 2016; Rasoolimanesh & Ali, 2018)—each with its own criteria for assessment. Each of the constructs in the current study is a composite construct. Ordinarily, composite measurement model assessment would require that the Variance Inflated Factor (VIF), or collinearity between the associated indicators of each construct, be less than 5, and that the outer weight each of the indicators should be significant (Hair et al., 2017; Henseler et al., 2016; Rasoolimanesh et al., 2019b). However, all the main variables in this study— economic and social development, social inclusion, environmental protection, and governance—are second-order composite constructs. Consequently, it is necessary to employ 11
Journal Pre-proof a two-stage approach if we are to establish these second-order composite constructs (Becker, Klein, & Wetzels, 2012; Rasoolimanesh et al., 2019a; Rasoolimanesh, Dahalan, & Jaafar, 2016). The structural model assessment and hypothesis testing rely on three criteria: R2, which is a measure of the endogenous constructs, the path coefficients of each relationship (i.e., hypothesis), and the effect size of each path. While the value of R2 varies somewhat contingent upon the research area, path coefficients need always be significant. Chin (2010) suggests R2 values in the order of 0.67, 0.33, and 0.19 to be considered respectively substantial, moderate, or weak. In addition, values of 0.02, 0.15, and 0.35 are considered small, moderate, and large effect sizes, respectively (Ali et al., 2018; Hair et al., 2017).
Results and Findings Table 3 shows the results of the measurement model assessment for composite first-order constructs before establishing the second-order constructs using two-stage approach. The results show that the VIF values were lower than 2.5, which is perhaps the most conservative threshold for VIF (Kock, 2015). The outer weights seen in Table 3 are for all of the indicators associated with first-order constructs, except for economic equity because its outer weight was not significant. Nonetheless, indicators lacking outer weight significance can be retained on the proviso they maintain a significant outer loading (Hair et al., 2017). Therefore, economic equity can be retained and accepted despite its non-significant outer weight because its associated loading is significant. [Table 3 about here]
12
Journal Pre-proof The measurement model assessment results, post the establishment of the second-order constructs, are shown in Table 4. These results indicate that the measurement model is acceptable, the VIF of each indicator being below 2.5, and their outer weights being significant. Moreover, Table 4 shows that the full collinearity of each construct was below 3.3. Full collinearity describes the vertical and lateral collinearity of a construct in relation to other constructs and should be less than 3.3 (Kock & Lynn, 2012). it is reasonable to use full collinearity to assess discriminant validity in the case of models that are inclusive of both formative and/or composite constructs (Rasoolimanesh et al., 2017). [Table 4 about here] The results of assessment of structural model show the R2 values of 0.40, 0.27, and 0.20 for economic development, social development and inclusion, and environmental protection, respectively. Therefore, the R2 values were considered acceptable. In addition, Table 5 shows the significant effects for all three hypotheses. However, the sign of the path coefficients for H1 and H2 differed from what was originally hypothesized. This study hypothesized positive effects for governance on economic and social development, and inclusion; however, the results showed negative effects. Therefore, the results of the current study cannot support either H1 or H2, although support as found for H3. These findings confirm the negative effects of governance on economic development, and social development and inclusion, and a positive effect on environmental protection. The results showed the highest effect size for the effect of governance on economic development, followed by social development and inclusion. The study results showed the lowest effect size for the effect of governance on environmental protection.
[Table 5 about here] 13
Journal Pre-proof Discussion The SUD literature describes economic and social development, social inclusion, environmental protection, and governance as the main components of SUD (Sachs, 2012, 2015). However, until now, the inter-relationships between these components has not been clearly understood, thus prompting this attempt to investigate the effect of governance on the three key components of SUD, these components having been identified in previous studies (Rasoolimanesh et al., 2012; Sachs, 2015). The results showed the significant effect of governance on the three main linchpins of SUD, with highest effect being for economic development and the lowest effect: environmental protection. However, the results also identified negative effects for governance on economic and social development, and social inclusion. These findings are inconsistent with the SUD literature (Sachs, 2012, 2015). The current study used the CPI development indicators to measure sub-dimensions, dimensions, and components of SUD based on the availability of data from cities in developing countries, this data being made available view the CPI website. As mentioned earlier, data were not available for all indicators, so a number of indicators were excluded from this study, and the analysis was performed based on the remaining indicators. Also, mentioned earlier, EP was measured by economic strength and employment; EI by housing infrastructure, social infrastructure, ICT, and urban mobility; SQ by health, education, and safety and security; SE by equity, social inclusion, and gender inclusion; ES by air quality, and energy index; and GOV simply by participation and voter turnout. The results, therefore, reveal the effects of participation on economic development, including EP, and EI, social development and inclusion, including SQ and SE; and environmental protection, including ES. The results identified the negative effects of participation on EP (economic productivity) and EI (infrastructure). Therefore, the results demonstrate that developing countries with low levels of economic productivity and infrastructure are more interested in participation. This 14
Journal Pre-proof finding is inconsistent with the literature, which indicates that better governance leads to improved economic productivity and infrastructure (Badarulzanan, Rasoolimanesh, & Abdullah, 2017; Rasoolimanesh et al., 2012; Sachs, 2015). One possible reason for this inconsistent finding is because the only data that was available was for participation (i.e., voter turnout ratio); consequently, governance was measured using this single indicator. Therefore, this effect only describes the effect of the voter turnout ratio on economic productivity and infrastructure, which is assumed negative. Because in the cities with lower productivity and infrastructure the residents and citizens are more interested to participate and improve the productivity and infrastructure by their participation. The current study draws attention to the negative effects of governance on social development and inclusion. However, Table 4 revealed the negative contribution of SQ to establish social development and inclusion, whereas the results showed the positive contribution of SE to social development and inclusion. Therefore, the negative effect of governance (represented by participation and the voter turnout ratio) can be interpreted as positive on SQ and negative on SE. The finding of a positive effect for governance on SQ is consistent with previous studies, suggesting that governance and participation can improve the quality of life of residents and citizens. The finding of a negative effect for governance on SE, however, is inconsistent with the literature. This negative effect of governance on SE might be justified by the fact that the more inequity residents perceive, the more willing they are to participate in the governance of their community. Therefore, the negative effect of governance, which is measured based simply on their participation and voter turnout ratio on social equity and inclusion (SE), makes reasonable sense. The findings also identified the positive effect of GOV on environmental protection or ES. The results revealed that better governance with more participation results in improved environmental sustainability, better air quality, and a better energy index. Consistent with 15
Journal Pre-proof previous studies, this result confirms a positive effect for governance on environmental protection and sustainability (Rasoolimanesh et al., 2012; Sachs, 2015). Moreover, the results indicate that communities with more participation and a higher voter turnout ratio have more concerns about air quality and energy efficiency, suggesting that these measures reflect residents’ efforts to improve these indices.
Conclusion To the best of the author’s knowledge, this is one of the first studies to explore the effects of governance on the three main components of SUD: economic and social development, social inclusion, and environmental protection. Consequently, this study enriches the SUD literature by examining the inter-relationship between the various concepts that form the linchpin of SUD. This assessment relies on data sourced from the CPI program and collected from a number of cities in developing countries. Nonetheless, the CPI program has only made limited data available for a few dimensions, sub-dimensions, and indicators. That we undertook to measure governance, economic and social development, social inclusion, and environmental protection based on such limited data, however, might represent a major shortcoming of the current study. Further studies are necessary to develop a more comprehensive set of indicators with which to measure governance, economic development, social development and inclusion, and environmental protection. Once a complete set of data has been gathered for these indicators, the inter-relationships between constructs should be re-assessed. This represents one the practical implications of this study for UN-Habitat and the CPI program. Despite the development of indicators and a large pool of amassed data, there is insufficient information with which to measure the components SUD and to implement the NUA. Consequently, limitations in relation to the indicators and lack of available data mean that the results of the current study cannot be generalized and more 16
Journal Pre-proof studies necessary to overcome the hurdles. Moreover, this study relied on data from a number of cities in developing countries. This indicates another limitation of the current study and further research is necessary, based not only on more cities from the developing world, but also from the developed countries to test the proposed hypotheses and to form generalizations that might be applied to other cities. References Abu-Ghazalah, S. (2008). The sustainable city development plan for Aqaba, Jordan. Journal of Developing Societies, 24(3), 381–398. https://doi.org/10.1177/0169796X0802400304 Ali, F., Rasoolimanesh, S.M., Sarstedt, M., Ringle, C.M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514–538. Badarulzanan, N., Rasoolimanesh, S. M., & Abdullah, A. (2017, June). Examining the effect of good governance on three pillars of sustainable urban development using partial least squares. Paper presented at the 9th International Conference on PLS and Related Methods (PLS’17), Macau, China. Becker, J. M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLSSEM: guidelines for using reflective-formative type models. Long Range Planning, 45(5), 359-394. Bentivegna, V., Curwell, S., Deakin, M., Lombardi, P., Mitchell, G., & Nijkamp, P. (2002). A vision and methodology for integrated sustainable urban development: BEQUEST. Building Research & Information, 30(2), 83–94. https://doi.org/10.1080/096132102753436468 Bottero, M., Mondini, G., & Valle, M. (2007). The use of the analytic network process for the sustainability assessment of an urban transformation project. In International Conference on Whole Life Urban Sustainability and its Assessment (pp. 27–29). Glasgow, Scotland: SUEMoT. Bulkeley, H., & Betsill, M. (2005). Rethinking sustainable cities: multilevel governance and the “urban” politics of climate change. Environmental Politics, 14(1), 42–63. https://doi.org/10.1080/0964401042000310178 Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655–690). Berlin, Germany: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_29 Conroy, M. M. (2006). Moving the middle ahead: challenges and opportunities of sustainability in Indiana, Kentucky, and Ohio. Journal of Planning Education and Research, 26(1), 18–27. https://doi.org/10.1177/0739456X06289664 17
Journal Pre-proof Curwell, S. R., Cooper, I., Deakin, M., & Symes, M. (Eds.). (2005). Sustainable urban development: The environmental assessment methods (Vol. 2). London: Routledge. Deakin, M., & Reid, A. (2014). Sustainable urban development: use of the environmental assessment methods. Sustainable Cities and Society, 10, 39–48. https://doi.org/10.1016/j.scs.2013.04.002 Dovers, S. (2005). Clarifying the imperative of integration research for sustainable environmental management. Journal of Research Practice, 1(2). Retrieved from https://openresearchrepository.anu.edu.au/bitstream/10440/254/1/Dovers_Clarigying2005.pdf Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Los Angeles, CA: Sage. Hassan, A. M., & Lee, H. (2015). Toward the sustainable development of urban areas: An overview of global trends in trials and policies. Land Use Policy, 48, 199–212. https://doi.org/10.1016/j.landusepol.2015.04.029 Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382 Huang, M.-H., & Rust, R. T. (2011). Sustainability and consumption. Journal of the Academy of Marketing Science, 39(1), 40–54. https://doi.org/10.1007/s11747-010-0193-6 Kock, N. (2015). WarpPLS 5.0: User Manual. Laredo, Texas: ScriptWarp Systems. Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. Journal of the Association of Information Systems, 13(7), 546–580. Labuschagne, C., Brent, A. C., & Van Erck, R. P. (2005). Assessing the sustainability performances of industries. Journal of cleaner production, 13(4), 373-385. Md Noor, S., Rasoolimanesh, S. M., Ganesan, V., & Jaafar, M. (2015). Effective interpretation using various media toward mindfulness: a case study of Malacca. Journal of Heritage Tourism, 10(3), 263–279. https://doi.org/10.1080/1743873X.2015.1005625 Putzhuber, F., & Hasenauer, H. (2010). Deriving sustainability measures using statistical data: a case study from the Eisenwurzen, Austria. Ecological Indicators, 10(1), 32-38. Rasoolimanesh, S. M., Badarulzaman, N., & Jaafar, M. (2012). City development strategies (CDS) and sustainable urbanization in developing world. Procedia - Social and Behavioral Sciences, 36, 623–631. https://doi.org/10.1016/j.sbspro.2012.03.068 Rasoolimanesh, S. M., Jaafar, M., Badarulzaman, N., & Ramayah, T. (2015). Investigating a framework to facilitate the implementation of city development strategy using balanced scorecard. Habitat International, 46, 156-165. Rasoolimanesh, S. M., Dahalan, N., & Jaafar, M. (2016). Tourists’ perceived value and satisfaction in a community-based homestay in the Lenggong Valley World Heritage Site. 18
Journal Pre-proof Journal of Hospitality and Tourism Management, 26, 72–81. https://doi.org/10.1016/j.jhtm.2016.01.005 Rasoolimanesh, S. M., Nejati, M., Ramayah, T., Leimee, T., Shafaei, A., & Abd Razak, N. (2017, June). Full collinearity as a new criterion to assess discriminant validity of composite (formative) and reflective measurement models. Paper presented at the 9th International Conference on PLS and Related Methods (PLS’17), Macau, China. Rasoolimanesh, S. M., & Ali, F. (2018). Partial least squares–structural equation modeling in hospitality and tourism. Journal of Hospitality and Tourism Technology, 9(3), 238-248. Rasoolimanesh, S. M., Md Noor, S., Schuberth, F., & Jaafar, M. (2019a). Investigating the effects of tourist engagement on satisfaction and loyalty. The Service Industries Journal, 39(7-8), 559-574. Rasoolimanesh, S.M., Taheri, B., Gannon, M., Vafaei-Zadeh, A., & Hanifah, H. (2019b). Does living in the vicinity of heritage tourism sites influence residents’ perceptions and attitudes?. Journal of Sustainable Tourism, doi: 10.1080/09669582.2019.1618863. Roy, M. (2009). Planning for sustainable urbanisation in fast growing cities: Mitigation and adaptation issues addressed in Dhaka, Bangladesh. Habitat International, 33(3), 276–286. https://doi.org/10.1016/j.habitatint.2008.10.022 Sachs, J. D. (2012). From millennium development goals to sustainable development goals. The Lancet, 379(9832), 2206–2211. https://doi.org/10.1016/S0140-6736(12)60685-0 Sachs, J. D. (2015). The age of sustainable development. New York, NY: Columbia University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=944998 Saha, D., & Paterson, R. G. (2008). Local government efforts to promote the “Three Es” of sustainable development: survey in medium to large cities in the United States. Journal of Planning Education and Research, 28(1), 21–37. https://doi.org/10.1177/0739456X08321803 Scipioni, A., Mazzi, A., Mason, M., & Manzardo, A. (2009). The dashboard of sustainability to measure the local urban sustainable development: the case study of Padua municipality. Ecological Indicators, 9(2), 364–380. https://doi.org/10.1016/j.ecolind.2008.05.002 Shen, L.-Y., Jorge Ochoa, J., Shah, M. N., & Zhang, X. (2011). The application of urban sustainability indicators – A comparison between various practices. Habitat International, 35(1), 17–29. https://doi.org/10.1016/j.habitatint.2010.03.006 Singh, R. K., Murty, H. R., Gupta, S. K., & Dikshit, A. K. (2009). An overview of sustainability assessment methodologies. Ecological Indicators, 9(2), 189–212. https://doi.org/10.1016/j.ecolind.2008.05.011 Tuts, R. (2012). The Regional Institute - Exploring the linkages between Local Agenda 21, good urban governance and urban poverty reduction: lessons from UN-HABITAT’s experience over the past decade. Presented at the International Local Agenda 21 Conference, Rio de Janeiro: The Regional Institute. Retrieved from http://www.regional.org.au/au/soc/2002/1/tuts.htm 19
Journal Pre-proof United Nations. (2015). Goal 11: sustainable development knowledge platform. Retrieved September 18, 2017, from https://sustainabledevelopment.un.org/sdg11 United Nations. (2017a). Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable — SDG Indicators. Retrieved September 18, 2017, from https://unstats.un.org/sdgs/report/2016/goal-11/ United Nations. (2017b). New Urban Agenda. Quito, Ecuador: United Nations. United Nations Human Settlements Programme. (2017). City Prosperity Initiative. Retrieved September 14, 2017, from http://cpi.unhabitat.org/ Walter, C., & Stützel, H. (2009). A new method for assessing the sustainability of land-use systems (ii): evaluating impact indicators. Ecological Economics, 68(5), 1288–1300. https://doi.org/10.1016/j.ecolecon.2008.11.017 Whitehead, M. (2003). (Re)analysing the sustainable city: nature, urbanisation and the regulation of socio-environmental relations in the UK. Urban Studies, 40(7), 1183–1206. https://doi.org/10.1080/0042098032000084550 Wiek, A., & Binder, C. (2005). Solution spaces for decision-making—a sustainability assessment tool for city-regions. Environmental Impact Assessment Review, 25(6), 589-608. Xing, Y., Horner, R. M. W., El-Haram, M. A., & Bebbington, J. (2009). A framework model for assessing sustainability impacts of urban development. Accounting Forum, 33(3), 209– 224. https://doi.org/10.1016/j.accfor.2008.09.003 Yigitcanlar, T., & Teriman, S. (2015). Rethinking sustainable urban development: towards an integrated planning and development process. International Journal of Environmental Science and Technology, 12(1), 341–352. https://doi.org/10.1007/s13762-013-0491-x
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Table 1 Principles and Indicators of Sustainable Urban Development Principles of SUD Environmental
Indicators 1- Alternative energy conservation efforts
References and
energy
2- Green building programs 3- Water quality protection 4- Air pollution
Bentivegna et al., 2002; Bottero et al., 2007; Saha & Paterson, 2008; Scipioni et al., 2009; Shen et al., 2011; Singh et al., 2009; Walter & Stützel, 2009; Yigitcanlar & Teriman, 2015
5- Open space preservation 6- Conservation of natural environment 7- Urban form 8- Transportation management Economic
1- Poverty reduction
Huang & Rust, 2011; Labuschagne et al., 2005; Putzhuber & Hasenauer, 2010; Saha & Paterson, 2008; Scipioni et al., 2009; Wiek & Binder, 2005; Yigitcanlar & Teriman, 2015;
2- Agriculture protection zoning 3- Infill development 4- Urban productivity 5- Urban services 6- Urban growth boundary 7- Financial management 8- Empowerment of local businesses Social Inclusion Equity
and
1- Affordable housing 2- Homeless prevention intervention programs
and
3- Women/minority-oriented business community development and investment programs,
21
Bottero et al., 2007; Huang & Rust, 2011; Saha & Paterson, 2008; Walter & Stützel, 2009; Xing et al., 2009; Yigitcanlar & Teriman, 2015
Journal Pre-proof 4- Youth opportunity and development, 5- Education, 6- Health, 7- Security 8- Availability of local public open areas and services Governance
1234-
Public participation Transparency, Accountability Empowerment of minority groups and poor people
5- Empowerment of NGOs
22
Bottero et al., 2007; Conroy, 2006; Rasoolimanesh et al., 2012; Sachs, 2012; Yigitcanlar & Teriman, 2015
Table 2 Indicators to Measure SUD under CPI Principles of SUD Environmental (ENV)
Dimensions 1- Environmental Sustainability
Sub-dimensions 1.1- Air quality
Indicators 1.1.1- Number of monitoring stations 1.1.2- PM2.5 concentration 1.1.3- CO2 emissions
1.2- Waste management
1.2.1- Solid waste collection 1.2.2- Waste water treatment 1.2.3- Solid waste recycling share
Economic (ECO)
1- Productivity
1.3- Energy index
1.3.1- Share of renewable energy
1.1- Economic strength
1.1.1- City product per capita 1.1.2- Old age dependency ratio 1.1.3- Mean household income
1.2- Economic agglomeration
1.2.1- Economic density 1.2.2- Economic specialization
1.3- Employment
1.3.1- Unemployment rate 1.3.2- Employment to population ratio 1.3.3- Informal employment
2- Infrastructure Development
2.1- Housing infrastructure
2.1.1- Improved shelter 2.1.2- Access to improved water 2.1.3- Access to improved sanitation 2.1.4- Access to electricity 2.1.5- Sufficient living area 2.1.6- Population density
2.2- Social infrastructure
2.2.1- Physician density 2.2.2- Number of public libraries
2.3- Information and Communication
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2.3.1- Internet access
Technology (ICT)
2.3.2- Home computer access 2.3.3- Average broadband speed
2.4- Urban mobility
2.4.1- Use of public transport 2.4.2- Average daily travel time 2.4.3- Length of mass transport network 2.4.4- Traffic fatalities 2.4.5- Affordability of transport
2.5- Urban form
2.5.1- Street intersection density 2.5.2- Street density 2.5.3- Land allocated to streets
Social Inclusion and Equity
1.1- Health
1- Quality of Life
1.1.1- Life expectancy at birth 1.1.2- Under-five mortality rate 1.1.3- Vaccination coverage 1.1.4- Maternal mortality
1.2- Education
1.2.1- Literacy rate 1.2.2- Mean years of schooling 1.2.3- Early childhood education 1.2.4- Net enrollment rate in higher education
1.3- Safety and security
1.3.1- Homicide rate 1.3.2- Theft rate
1.4- Public space
1.4.1- Accessibility to open public areas 1.4.2- Green Area per Capita
2- Equity and Inclusion
Social
2.1- Economic equity
2.1.1- Gini coefficient 2.1.2- Poverty rate
2.2- Social inclusion
2.2.1- Slums households 2.2.2- Youth unemployment
2.3- Gender inclusion
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2.3.1- Equitable secondary school enrollment 2.3.2- Women in local government
2.3.3- Women in local work force Governance
1- Urban Governance and Legislation
2.4- Urban diversity
2.4.1- Land use mix
1.1- Participation
1.1.1- Voter turnout 1.1.2- Access to public information 1.1.3- Civic participation
1.2- Municipal financing institutional capacity
and
2.2.1- Own revenue collection 2.2.2- Days to start a business 2.2.3- Subnational debt 2.2.4- Local expenditure efficiency
1.3- Governance of urbanization
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1.3.1- Land use efficiency
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Table 3 Assessment of Measurement Model with First-Order Constructs Items Economic Productivity (EP) 1- Economic Strength
Construct type
2- Social Infrastructure 3- ICT 4- Urban Mobility
2- Education 3- Safety and Security
2- Social Inclusion 3- Gender Inclusion
1.115
0.755
<0.01
1.115
0.106
<0.05
1.094
0.645
<0.01
1.157
-0.852
<0.01
1.253
0.314
<0.01
1.174
0.427
<0.01
1.098
-0.526
<0.01
1.119
0.717
<0.01
1.036
0.04
0.254
1.06
0.419
<0.01
1.037
0.466
<0.01
1.03
Composite
1- Air Quality 2- Energy Index Urban Governance and Legislation (GOV)
<0.01
Composite
1- Economic Equity
Environmental Sustainability (ES)
-0.456
Composite
1- Health
Social Equity and Inclusion (SE)
VIF
Composite
1- Housing Infrastructure
Social Quality of Life (SQ)
p-value
Composite
2- Employment Economic Infrastructure (EI)
Weights
0.693
<0.01
1.033
0.607
<0.01
1.033
1.00
<0.001
Composite
1- Participation
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Journal Pre-proof
Table 4 Assessment of Measurement Model with Second-Order Constructs Items Economic Development
Construct type
Weights
p-value
VIF
Composite
1.913
EP
0.636
<0.01
1.35
EI
0.513
<0.01
1.35
Social Development and Inclusion
Composite
1.485
SQ
-0.637
<0.01
1.014
SE
0.7
<0.01
1.014
Environmental Protection
Composite
1.476 1.00
ES Governance
Full Collinearity
<0.01
------
Composite
1.916 1.00
GOV
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<0.01
------
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Table 5 Results of Hypothesis Testing Hypothesis
Path Coefficient
p-value
Effect size
Supported NO (Different sign) NO (Different sign) YES
H1
Governance Economic Development
-0.634
<0.01
0.402
H2
Governance Social Development and Inclusion
-0.523
<0.01
0.273
H3
Governance Environmental Protection
0.448
<0.01
0.201
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Figure 1. Conceptual framework.
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Highlights
1- This study investigates the effects of governance on the three pillars of sustainable urban development (SUD). 2- This study relies on secondary data collected by the City Prosperity Index program (CPI) from 265 cities in developing countries. 3- The indicators used to measure the components of SUD were adopted from the CPI. 4- The findings reveal the negative effects of governance on the economic development, and social development and inclusion. 5- The results indicate a positive effect for governance on environmental protection.