Exploring the multidimensional factors and emergence mechanisms of industrial symbiotic relationships based on machine learning

Wang, Shida; Zhang, Zimeng; Wang, Zhen; Liu, Gang

doi:10.1016/j.jclepro.2022.135169

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…For scholars like Wang, Zhang, Wang, and Liu (2022), in the case of industrial symbiosis, its adoption considers factors determining how it can be implemented at an industrial level. The adoption process considers forming inter-organizational networks intending to create conditions for industrial sustainability and manufacturing.…”

Section: Technology Adoption Theory Perspectives In Industrial Symbiosismentioning

confidence: 99%

Risks associated with the technological adoption of industrial symbiosis: A study of its implementation by geographic regions

Diaz,

Escandon-Barbosa,

Salas-Paramo

2024

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This research investigates the factors influencing the adoption of industrial symbiosis practices, focusing on their relevance in geographical contexts within developing countries associated with the European Economic Community. The study employs geographical weighting analysis as its primary methodology, allowing for a nuanced examination of variable impacts within specific geographic regions. Data is gathered through surveys distributed to companies operating in various high-energy consumption sectors that have previously engaged in industrial symbiosis activities to varying extents. The study’s findings emphasize the crucial role that financial and performance-related risks played in influencing the decision-making process surrounding the implementation of industrial symbiosis. Furthermore, the research highlights the significance of regional dynamics in influencing waste disposal practices and the overall success of industrial symbiosis initiatives. Additionally, the study underscores the impact of social media and networks in disseminating vital information that is instrumental in advancing innovative industrial symbiosis processes. These findings hold practical implications for stakeholders, including policymakers, businesses, and organizations aiming to foster sustainable economic practices. Understanding the influence of risk factors and regional intricacies enables tailoring strategies that can promote industrial symbiosis adoption. Moreover, recognizing the pivotal role of social media and networks in knowledge dissemination can accelerate the adoption of novel practices, facilitating the transition towards greener economies in developed and developing regions associated with the European Economic Community.

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Section: Technology Adoption Theory Perspectives In Industrial Symbiosismentioning

confidence: 99%

Risks associated with the technological adoption of industrial symbiosis: A study of its implementation by geographic regions

Diaz,

Escandon-Barbosa,

Salas-Paramo

2024

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“…Furthermore, MLpowered technologies facilitate the development of smart cities (De Las Heras et al, 2020) and enhance the quality of life for residents (Cai et al, 2022;Khanna and Khanra, 2023). By harnessing the capabilities of ML, companies may address business problems while generating positive environmental and social outcomes (Dong et al, 2023;Wang et al, 2022aWang et al, , 2022bWang et al, , 2022c). An overview of previous similar review studies on the application of ML in sustainability is presented in Table 1.…”

Section: Machine Learningmentioning

confidence: 99%

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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Endogenous phosphorus release from plateau lakes responds significantly to temperature variability over the last 50 years

Yan,

Xia,

Chen

et al. 2024

Journal of Environmental Management

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Exploring the multidimensional factors and emergence mechanisms of industrial symbiotic relationships based on machine learning

Cited by 5 publications

References 48 publications

Risks associated with the technological adoption of industrial symbiosis: A study of its implementation by geographic regions

Risks associated with the technological adoption of industrial symbiosis: A study of its implementation by geographic regions

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

Endogenous phosphorus release from plateau lakes responds significantly to temperature variability over the last 50 years

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