Infrastructure Engineering - Theses
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ItemEnhancing the Sustainable Development Goals (SDGs): Integrating Resilience and Sustainability at the Local Level( 2022)In recent decades, urbanisation and population explosion have adversely affected communities’ resilience and sustainability. This problem is more prevalent in developing countries, which are among the most vulnerable communities left behind by most planning and development practices. Global efforts have been made to manage urbanisation in order to improve resilience to ongoing changes and sustainability of development and accordingly develop a framework to benchmark the two concepts. Even so, there is still a significant gap in the present knowledge at the intersection of resilience and sustainability, as well as the lack of a commonly accepted framework for measuring resilience itself. This prevents us from developing an integrated and commonly accepted framework for measuring resilience. The United Nations Sustainable Development Goals (SDGs) as a widely applied framework to measure the level of sustainable development at the country level has the potential to remove this discrepancy by considering the integration of resilience and measurement of sustainability. As elaborated in Chapter 1 (section 1.1), the framework is criticised for lacking a thorough measurement of resilience being more focused on sustainability. Thinking of the SDGs beyond “using the framework as the best possible approach” or “considering the SDGs as a failure because of its existing weaknesses”, this thesis acknowledges decades of work by the UN and many countries to build the SDGs framework and its worldwide application and at the same time the existence of some gaps and the opportunities for improvement. This research was thus intended to address one of the major criticisms of the Sustainable Development Goals, namely the lack of comprehensiveness in measuring resilience, as well as addressing shortfalls in current knowledge at the nexus of resilience and sustainability. In order to integrate a more comprehensive resilience measurement into the SDGs, this research looked at the possibilities for adding a more comprehensive set of resilience indicators into the SDGs. Besides, since the level of community resilience greatly depends on local characteristics, a methodological workflow for the SDGs’ localisation was urgently required. Hence, this research aimed to: firstly, integrate the quantification and measurement of sustainability and resilience in the context of the SDGs; and secondly, develop a methodological workflow to advance the approach to fit for purpose at the local level. To achieve the research aim, firstly, resilience dimensions and quantification have been conceptualised and a framework of baseline resilience indicators has been developed. Later, referring to the baseline indicators to measure each of the resilience dimensions, the aspects of resilience that have been overlooked among the SDGs have been defined. Accordingly, a pool of resilience indicators has been suggested to be integrated into the SDGs. Next, a localised workflow has been created, tested and evaluated to incorporate additional indicators that significantly contribute to the local SDG Index in the area of interest. The workflow for integrating resilience and sustainability in the SDGs at a local level contains three steps: 1) Assessing the pool of resilience and the SDGs indicators for data availability and application at the local level; 2) Aligning the indicators to with the local context if required; and 3) Analysing the significance of the indicators in measuring resilience/sustainability at the local level applying the Exploratory Regression method. To evaluate the developed workflow, the hypothesis of the research - that adding resilience indicators will enhance the effectiveness of the SDGs in measuring resilience at the local level - was tested. To implement the developed workflow, one of the major challenges in the SDGs utilisation is data availability and particularly in informal settlements, Jakarta, the capital city of Indonesia, with a big share of informal settlements has been selected as the case study. For testing the workflow, after executing steps one and two, each of the indicators and accordingly the local SDG Index has been calculated for each of the 286 sub-districts of Jakarta. As indicated, one main obstacle has always been data availability especially when it comes to informal settlements. Here, a novel method is proposed and used to generate the missing data on the location of these settlements applying machine learning (ML) techniques on the available spatial and statistical open data. Finally, the third step has been conducted and selected indicators have later been used to test the hypothesis of this research through developing the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. Results from the validation that support the hypothesis prove the effectiveness of the proposed workflow for the integration of resilience and sustainability at the local level in the SDGs. This research significantly contributes to the present knowledge about resilience and its quantification by conceptualising its dimensions, resolving the inconsistency in the terminology employed in this subject. As well as filling the gap of a common framework to measure resilience. The research also proposes resolutions for enhancing the Sustainable Development Goals as a globally adopted framework for measuring and monitoring sustainability. It does this by addressing the main criticism of the SDGs, which is the limitation in measuring resilience, and developing a workflow for SDGs’ localisation. In contrast to the current approaches for SDGs’ localisation that are mainly qualitative (see Chapter 8), the proposed method presents a quantitative robust workflow that can be followed in different cities at various jurisdictional levels. This research also presents a sample solution for coping with data availability issue while also proposing a novel method for the detection of informal settlements using freely accessible data. Furthermore, this research significantly contributes to the present knowledge in the domain of resilience and its quantification by conceptualising resilience dimensions, resolving inconsistencies in the subject’s terminology, and proposing a resolution for filling the gap of a common framework to measure resilience while also addressing a major criticism against the SDGs that is limitation in measuring resilience.