Research on the influence mechanism of heterogeneous environmental regulation on the manufacturing equipment industry in Asia-Pacific countries

Zhang, Xiufan; Fan, Deng-Ping

doi:10.1007/s10098-023-02470-x

Cited by 5 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) Environmental regulations aim to reduce the negative impact of manufacturing development on the environment and promote itself transformation. Zhang et al (2023) [10] found that environmental regulation accelerates the transfer of equipment manufacturing industry, but its impact on its international competitiveness presents a "U-shaped" pattern. Xie et al (2023) [11] found technology innovation can be stimulated by combining environmental regulation and other policies to improve capacity utilization in moderate and severe pollution sectors.…”

Section: The Concept Of Urban Advanced Manufacturing Developmentmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

View full text Add to dashboard Cite

With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

show abstract

Section: The Concept Of Urban Advanced Manufacturing Developmentmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

View full text Add to dashboard Cite

show abstract

“…Innovations that help businesses achieve carbon neutrality include smart grids, energy management systems, enhanced insulating materials, and energy‐efficient lighting solutions. These innovations allow organizations to maximize energy utilization and decrease waste (Chen et al, 2022; X. Zhang & Fan, 2023). One of the main causes of greenhouse gas emissions is transportation.…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Artificial intelligence (AI)‐driven strategic business model innovations in small‐ and medium‐sized enterprises. Insights on technological and strategic enablers for carbon neutral businesses

Shaik,

Alshibani,

Jain

et al. 2023

Bus Strat Env

View full text Add to dashboard Cite

This study investigates the enhancement of technological and strategic enablers for carbon‐neutral businesses (CNB) through artificial intelligence (AI)‐driven business model innovation (AIDBMI). Drawing upon the insights gained from the literature review, the study employs structural equation modeling (partial least squares structural equation modeling [PLS‐SEM]) as the methodology to examine the relationships between AIDBMI, technological enablers, strategic enablers, and the attainment of carbon neutrality. The sample size consists of 326 small‐ and medium‐sized enterprises (SMEs) in the United States of America. The findings of this study affirm the significant positive relationships between AIDBMI and both technological and strategic enablers for CNB. The utilization of AI technologies proves to be instrumental in fostering the development and implementation of innovative business models that integrate sustainability practices and address environmental challenges. By leveraging AIDBMI, SMEs can harness technological advancements to adopt energy‐efficient processes, embrace renewable energy solutions, and implement effective carbon reduction strategies. Moreover, the study highlights the critical role of strategic enablers in driving the transition towards carbon neutrality. The alignment of sustainability goals with organizational strategies, stakeholder collaboration, and employee engagement emerge as pivotal factors in enabling SMEs to effectively utilize AIDBMI and leverage technological advancements to achieve carbon neutrality. The implications of this study contribute to the existing literature by highlighting the importance of technological and strategic enablers in creating CNB. By integrating AIDBMI, organizations can drive sustainable transformations, optimize their operations, and align their resource management with sustainable practices. These insights provide valuable guidance for SMEs, policymakers, and researchers seeking to foster sustainable practices, promote carbon neutrality, and contribute to the advancement of a low‐carbon economy.

show abstract