Empirical evidence of urban climate adaptation alignment with sustainable development: Application of LDA

Jin, Saebom; Stokes, G. M.; Hamilton, Clovia

doi:10.1016/j.cities.2023.104254

Cited by 8 publications

(1 citation statement)

References 86 publications

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“…The authors emphasize the necessity of addressing the vulnerabilities of infrastructure systems, such as transportation, energy and water, to effectively cope with the repercussions of climate change, including extreme weather events and rising sea levels. To support their claims, Jin et al (2023) present empirical evidence demonstrating the alignment of urban climate adaptation with sustainable development, using LDA as their methodology. Similarly, Chiu et al (2023) concentrate on the application of unsupervised learning techniques to establish a regional risk model based on the Task Force on Climate-related Financial Disclosures framework.…”

Section: Thematic Areasmentioning

confidence: 95%

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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Purpose From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries. Design/methodology/approach This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development. Findings ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals. Originality/value This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

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Section: Thematic Areasmentioning

confidence: 95%

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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Analysis of policy changes for Yangtze River Basin governance from 1980 to 2022—based on semantic analysis method and punctuated equilibrium theory

Li,

Qin

et al. 2024

FURP

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The governance policies have a prominent impact on the development and regional planning of the Yangtze River Basin, a key region for China to achieve modernization. Taking the public policy documents issued by China’s central government for the Yangtze River Basin from 1980 to 2022 as the research object, this paper introduces the NLP (Natural Language Processing) technology and semantic analysis methods to carry out quantitative research on policies. This study first examines the textual connotations of the policies and the characteristics of the policy changes based on the LDA (Latent Dirichlet Allocation) model to determine how policy changes occur for the governance of the Yangtze River Basin. Using the punctuated equilibrium theory, it then conducts two case studies in which the implications of specific major events are analyzed in depth. Research has shown that the changes in governance policies for the Yangtze River Basin are characterized by gradual changes and intermittent mutation, roughly divided into three stages: the stage of reform and development, the stage of transition, and the stage of prioritizing ecological conservation and green development. Moreover, such changes jointly result from three factors: the major events, the focus of public opinions, and the care of the central government.

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Examining public attitudes and perceptions of waste sorting in China through an urban heterogeneity lens: A social media analysis

Huang,

Long,

Chen

et al. 2023

Resources, Conservation and Recycling

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Empirical evidence of urban climate adaptation alignment with sustainable development: Application of LDA

Cited by 8 publications

References 86 publications

Machine learning for sustainable development: leveraging technology for a greener future

Machine learning for sustainable development: leveraging technology for a greener future

Analysis of policy changes for Yangtze River Basin governance from 1980 to 2022—based on semantic analysis method and punctuated equilibrium theory

Examining public attitudes and perceptions of waste sorting in China through an urban heterogeneity lens: A social media analysis

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