Urban Intelligence DOI
Manikandan Sathianarayanan, Umut Kırdemir, Alberto Gianoli

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 59 - 90

Опубликована: Окт. 25, 2024

In the rapidly evolving landscape of urbanization, cities face unprecedented challenges due to climate change. As frontline battlegrounds against global warming, urban areas increasingly rely on AI bolster resilience and sustainability. This chapter explores AI's role in shaping resilience, drawing from research real-world applications. revolutionizes modeling, using vast datasets machine learning create predictive models that anticipate mitigate impacts like extreme weather sea-level rise, enabling proactive planning.AI also enhances smart infrastructure resource management, optimizing energy use, water resources, transportation minimize ecological footprints. Through real-time monitoring adaptive systems, can respond environmental changes, ensuring resilience. democratizes decision-making by empowering citizen participation efforts. Open data, cross-sector partnerships, collaborative drive innovation, accelerating transition climate-resilient futures.

Язык: Английский

What factors enable or limit the role of intermediaries in strengthening transformative capacities? Case studies of intermediaries in two Cambodian cities DOI Creative Commons

Fiona Nicole Lord,

Jason Prior, Monique Retamal

и другие.

Urban Transformations, Год журнала: 2025, Номер 7(1)

Опубликована: Янв. 20, 2025

Язык: Английский

Процитировано

0

The nexus of urbanization and renewable energy productivity: implications for sustainable development in developing Asia nations DOI
Shruti Aggarwal, Mantu Kumar Mahalik

International Journal of Energy Sector Management, Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

Purpose This study aims to examine the impact of urbanization and renewable energy productivity on sustainable development in developing Asia economies. Moreover, it investigates potential these economies through lens productivity. Design/methodology/approach Using a balanced panel data set 20 Asian from 2000 2020, this uses goals score as dependent variable. Principal explanatory variables include urban population productivity, with globalization government expenditure control function. diagnostic tests such cross-sectional dependence, unit-root test cointegration ensure robustness. For empirical analysis, pooled mean group autoregressive distributed lag estimation technique is used for both long- short-run dynamics, supplemented by panel-corrected standard errors feasible generalized least squares methods robustness check. Findings The long-run results indicate that significantly enhance development. also identifies significant drivers further identify moderating role thereby helping stimulating recommends policies promote infrastructure, energy-efficient buildings smart cities, while investing technologies systems their integration into plans maintain Originality/value contributes literature highlighting nuanced context urbanization. It underscores synergistic benefits aligning growth initiatives, suggesting strategic fiscal international cooperation essential components advancing

Язык: Английский

Процитировано

0

“Developing governance capacities for regional energy transition: The case of Eindhoven Metropolitan Region” DOI Creative Commons
J. van Dijk, Anna Wieczorek, Josette Gevers

и другие.

Environmental Innovation and Societal Transitions, Год журнала: 2025, Номер 55, С. 100968 - 100968

Опубликована: Фев. 7, 2025

Язык: Английский

Процитировано

0

Shifting from adaptive capacity to transformative capacity: a case study of how Sihanoukville can develop the capacity of urban stakeholders to enable sustainability transformation in sanitation DOI Creative Commons

Fiona Nicole Lord,

Jason Prior, Monique Retamal

и другие.

International Journal of Urban Sustainable Development, Год журнала: 2025, Номер 17(1), С. 103 - 119

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Strengthening transformative capacities for urban sustainability: a case study of waste reform in Battambang, Cambodia DOI Creative Commons

Fiona Nicole Lord,

Monique Retamal, Federico Davila

и другие.

Urban Transformations, Год журнала: 2024, Номер 6(1)

Опубликована: Авг. 13, 2024

Battambang has been identified as an exemplar for sustainability city development in Cambodia due to its success with the introduction of new programs and planning initiatives, which have led recognition clean green status by Cambodian Government Association Southeast Asian Nations (ASEAN). Limited research done date investigate how achieved these results, compared other rapidly urbanising cities Asia. Through lens urban transformations, our identifies capacity strengths gaps preparing initiating city's transformation waste sector, applying transformative capacities framework Wolfram (Cities 51:121-130, 2016). qualitative coding semi-structured interviews document analysis we found that (a) particular provided building blocks capacities, (b) award processes can play a role developing (c) culture innovation relatively stable population political-economy contributed strengthening capacities. This contributes knowledge policy practice on strengthening, it supports phased 'building blocks' approach resource-constrained contexts. • 'Inclusive open governance' 'transformative leadership' were block enabled initiation Battambang's processes. Committed trusted intermediaries long-term place-based expertise played critical roles early Capacities experimentation learning, institutionalisation multi-stakeholder governing reform, next developed. Organisation Wolfram's into more relevant specific phases – preparation, initiation, navigation stabilisation enables tailoring learning reflection context phase transformation.

Язык: Английский

Процитировано

3

Urban Intelligence DOI
Manikandan Sathianarayanan, Umut Kırdemir, Alberto Gianoli

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 59 - 90

Опубликована: Окт. 25, 2024

In the rapidly evolving landscape of urbanization, cities face unprecedented challenges due to climate change. As frontline battlegrounds against global warming, urban areas increasingly rely on AI bolster resilience and sustainability. This chapter explores AI's role in shaping resilience, drawing from research real-world applications. revolutionizes modeling, using vast datasets machine learning create predictive models that anticipate mitigate impacts like extreme weather sea-level rise, enabling proactive planning.AI also enhances smart infrastructure resource management, optimizing energy use, water resources, transportation minimize ecological footprints. Through real-time monitoring adaptive systems, can respond environmental changes, ensuring resilience. democratizes decision-making by empowering citizen participation efforts. Open data, cross-sector partnerships, collaborative drive innovation, accelerating transition climate-resilient futures.

Язык: Английский

Процитировано

2