Planning Support Systems for Long-Term Climate Resilience: A Critical Review

Krishnan, Supriya; Aydin, Nazli Yonca

doi:10.1007/978-3-030-76059-5_24

Cited by 8 publications

(9 citation statements)

References 44 publications

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“…Planning under climate uncertainty requires combining incremental and structural planning responses 34 across the short-, medium-and long-term. Currently, the temporal horizon planners use are formal planning timeframes; typically spanning between 5 to 20 years.…”

Section: Conceptualising Temporal Urban Dynamicsmentioning

confidence: 99%

TIMEWISE: Temporal Urban Dynamics for Urban Resilience - Lessons from the Global North and South

Krishnan

Aydin

2023

Preprint

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Accelerating climate change and climate-related emergencies require cities to improve resilience across multiple short- and long-lived systems. Urban systems have individual change dynamics that are highly interconnected. However, the aftermath of disruptions is often characterised by myopic decision-making that disregards the long-term implications across systems. In this study, we develop the theoretical foundation for temporal dynamics for urban resilience. (1) We briefly characterise and conceptualise the interplay of major temporal dynamics in urban planning, including endogenous lifecycles and exogenous drivers. (2) We empirically examine temporal dynamics through climate urbanism in regions representing the Global North (GN-Amsterdam, the Netherlands) and Global South (GS-Mumbai, India). Our findings reinforce the need to embed short-term planning goals within long-term resilience visions. To address the lack of systematic planning approaches incorporating temporalities, we present options for temporal planning processes that harmonise the lifecycles of urban systems, adapt to disruptions and align planning horizons toward resilience. We discuss the potential applications of both options in the Global North and South.

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Section: Conceptualising Temporal Urban Dynamicsmentioning

confidence: 99%

TIMEWISE: Temporal Urban Dynamics for Urban Resilience - Lessons from the Global North and South

Krishnan

Aydin

2023

Preprint

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“…Lemmatisation was performed to transform a word to its root form [51]. Special characters that did not contribute to text's meaning were removed to reduce noise for machine learning algorithms [52]. Stop words that did not contribute significantly to meaning in an analysis were removed [53].…”

Section: Text Pre-processing and Parametrizationmentioning

confidence: 99%

What Can Machine Learning Teach Us about Australian Climate Risk Disclosures?

et al. 2022

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There seems to be no agreed taxonomy for climate-related risks. The information in firms’ climate risk disclosures represents a new resource for identifying the priorities and strategies of Australian companies’ management of climate risk. This research surveys 839 companies listed on the Australian Stock Exchange for the presence of climate risk disclosures, identifying 201 disclosures on climate risk. The types of climate risks and the risk management strategies were extracted and evaluated using machine learning. The analysis revealed that Australian firms are focused on acute physical climate risks, followed by market and regulatory risks. The predominant management strategy for these risks was to use a risk reduction approach, rather than avoiding or transferring risk. The analysis showed that key Australian industry sectors, such as materials, banking, insurance, and energy are focusing on different mixtures of risk types, but they are all primarily managing risks through risk-reduction strategies. An underlying driver of climate risk disclosure was composed of the financial implications of climate risk, particularly with respect to acute physical risks. The research showed that emission reductions represent a primary consideration for Australian firms in their disclosures identifying how they are responding to climate risk. Further research using machine learning to evaluate climate risk disclosure should focus on analysing entire climate risk reports for key topics and trends over time.

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“…Regarding the concept of urban resilience in general, several studies have emphasized its multidimensional nature, which encompasses infrastructure, climate, and social resilience, as well as resilience assessment (Carta et al, 2021;Francis and Bekera, 2014;Khalili et al, 2015;Krishnan et al, 2021;Sharifi, 2016). That said, the work of Woolf et al (2016) stands out here, as it investigates resilience-related projects for localized infrastructure, specifically the pilot tests under the Kenya Slum Upgrading Program in Kibera, Nairobi.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A research agenda that integrates the full range of variables and supports choosing appropriate planning responses across multiple infrastructure systems. (Krishnan et al, 2021)…”

Section: Climate Resiliencementioning

confidence: 99%

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

Hassan

Megahed²

2022

A&J

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Introduction:Climate change and global warming are among the greatest challenges facing the world today. A newconcept, known as urban resilience, has been developed in response. There are various approaches to urban resilience. Among them, is the urban energy resilience (UER) approach, which poses a considerable challenge. Machine learning (ML), as an application of artificial intelligence (AI), provides powerful and affordable computing resources, large-scale datamining, advanced algorithms, and real-timemonitoring. However, veryfewstudies have investigated howsuch aspectscan be integrated into urban resilience in general, and UER in particular. Purpose of the study: The study develops an integrative framework that can improve UER, based on ML methods. Methodology: We carried out a bibliometric analysisand a systematic review of UER in accordance with AI concepts, models, and applications. Results:The findings of this study were used to create an integrative framework, based on three hierarchical phases, which effectively addressed the main capabilities of UER, identified its priorities, and shed light on how ML can benefit UER as a whole. Novelty:The framework developed in this study also offers insights in integrating ML methods into UER as strategically as possible, especially in the context of climate change and urban energy systems. This framework can serve as reference for specialistsand decision-makers aiming to expand AI and ML applications to optimize UER.

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Planning Support Systems for Long-Term Climate Resilience: A Critical Review

Cited by 8 publications

References 44 publications

TIMEWISE: Temporal Urban Dynamics for Urban Resilience - Lessons from the Global North and South

TIMEWISE: Temporal Urban Dynamics for Urban Resilience - Lessons from the Global North and South

What Can Machine Learning Teach Us about Australian Climate Risk Disclosures?

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

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