Economic resilience assessment and policy interaction of coal resource oriented cities for the low carbon economy based on AI

Deng, Yulin; Jiang, Wanyi; Wang, Zeyu

doi:10.1016/j.resourpol.2023.103522

Cited by 35 publications

(12 citation statements)

References 22 publications

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“…They optimized a blockchain technology network system and constructed a risk management system for online public opinion using smart contracts, enabling the tracing of public opinion through intelligent ledgers and risk association tree technologies, thereby optimizing control measures in the online environment. Deng [ 41 ] bolstered the vitality of resource-based urban economic markets by advocating for public participation mechanisms and increased government policy intervention [ 40 ]. Their research holds significant reference value for promoting urban resource management and economic efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Liu,

Guan,

Wang

et al. 2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Liu,

Guan,

Wang

et al. 2024

Heliyon

Self Cite

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“…This work was helpful to optimize the control measures of network environment and provide an important reference for improving the management level of network public opinion. Deng et al ( 27 ) promoted the mechanism of public participation and enhanced the vitality of the economic market of resource-based cities by increasing policy intervention. This study had important reference value for promoting urban resource management and economic efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health

Wang,

Yuan,

Guan

et al. 2024

Front. Public Health

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IntroductionThis paper aims to explore the intersection of corporate social responsibility (CSR) and public health within the context of digital platforms. Specifically, the paper explores the impact of digital platforms on the sustainable development practices of enterprises, seeking to comprehend how these platforms influence the implementation of environmental protection policies, resource management, and social responsibility initiatives.MethodsTo assess the impact of digital platforms on corporate environmental behavior, we conducted a questionnaire survey targeting employees in private enterprises. This survey aimed to evaluate the relationship between the adoption of digital platforms and the implementation of environmental protection policies and practices.ResultsAnalysis of the survey responses revealed a significant positive correlation between the use of digital platforms and the environmental protection behavior of enterprises (r=0.523;p<0.001), Moreover, the presence of innovative environmental protection technologies on these platforms was found to positively influence the enforcement of environmental policies, with a calculated impact ratio of (a∗b/c=55.31%). An intermediary analysis highlighted that environmental innovation technology plays a mediating role in this process. Additionally, adjustment analysis showed that enterprises of various sizes and industries respond differently to digital platforms, indicating the need for tailored environmental policiesDiscussionThese findings underscore the pivotal role of digital platforms in enhancing CSR efforts and public health by fostering improved environmental practices among corporations. The mediating effect of environmental innovation technologies suggests that digital platforms not only facilitate direct environmental actions but also enhance the efficiency and effectiveness of such initiatives through technological advances. The variability in response by different enterprises points to the importance of customizable strategies in policy formulation. By offering empirical evidence of digital platforms’ potential to advance CSR and public health through environmental initiatives, this paper contributes to the ongoing dialogue on sustainable development goals. It provides practical insights for enterprises and policy implications for governments striving to craft more effective environmental policies and strategies.

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“…This work not only provides technical support for optimizing the dissemination mode of blockchain networks but also contributes to enhancing the current social networking environment. Deng et al [28] conducted an evaluation of the economic development of coal-dependent cities within the context of low-carbon economic growth [28]. They initially analyzed the background of coal-dependent cities and the low-carbon economy.…”

Section: Literature Reviewmentioning

confidence: 99%

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

Tian,

Wang

2024

Water

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This study investigates the prioritization and resource allocation strategies adopted by the coastal local governments of Qingdao, Dalian, and Xiamen in the context of marine regulatory reform aimed at enhancing regulatory efficiency. Data on relevant opinions, departmental requirements, and existing resource allocations were collected through a questionnaire survey. A backpropagation (BP) neural network was then applied to analyze the survey data, prioritize regulatory tasks, and propose resource allocation schemes. The findings demonstrate that integrating machine learning into marine regulation can significantly improve resource utilization efficiency, optimize task execution sequences, and enhance the scientific and refined nature of regulatory work. The BP neural network model exhibited strong predictive capabilities on the training set and demonstrated good generalization abilities on the test set. The performance of the BP neural network model varied slightly across different management levels. For the management level, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. For the supervisory level, these metrics were 81%, 83%, and 78%, respectively. At the employee level, the accuracy, precision, and recall rates were 79%, 81%, and 76%, respectively. These results indicate that the BP neural network model can provide differentiated resource allocation recommendations based on the needs of different management levels. Additionally, the model’s performance was assessed based on the employees’ years of experience. For employees with 0–5 years of experience, the accuracy, precision, and recall rates were 82%, 84%, and 79%, respectively. For those with 5–10 years of experience, the metrics were 83%, 86%, and 80%, respectively. For employees with over 10 years of experience, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. These data further confirm the applicability and effectiveness of the BP neural network model across different experience groups. Thus, the adoption of machine learning technologies for optimizing marine regulatory resources holds significant practical value, aiding in the enhancement of regulatory capacity and effectiveness within coastal local governments.

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Economic resilience assessment and policy interaction of coal resource oriented cities for the low carbon economy based on AI

Cited by 35 publications

References 22 publications

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

The influence of digital platform on the implementation of corporate social responsibility: from the perspective of environmental science development to explore its potential role in public health

Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization

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