Abstract:Adequate government environmental supervision is the key to promoting green innovation which is an essential driver of green development. In environmental decentralization, an analysis of the mechanism of the inherent influence of central and local supervision on green innovation may prove to be of practical importance. The paper selects data from heavily polluting enterprises in A-shares in China from 2013 to 2019 using fixed-effects models, moderating effect models, heterogeneity tests, and other research me… Show more
“…In terms of the factors influencing green innovation, scholars focus on the effects of different factors such as regulation, economy, structure, and foreign openness on the efficiency of relevant green innovation. For example, the study conducted by Yang et al (2023) provides further evidence for the application of the "Porter hypothesis" in China. They found that both central and local environmental regulations significantly promote green innovation in heavily polluting enterprises, with central environmental regulation enhancing the effectiveness of local environmental regulation in driving green innovation.…”
Green innovation has emerged as a crucial driver for advancing green and high-quality development. Exploring the evolutionary patterns of green innovation efficiency is crucial for achieving the “dual carbon” goals and realizing the benefits of both economic growth and environmental sustainability under the framework of new development concepts. This study employs the network SBM-DEA model under meta-Frontier and group-Frontier. Additionally, it considers the GML index and Moran’s I to conduct a comprehensive analysis of the evolving efficiency of green innovation in Chinese provinces from 2008 to 2020, then uses the Tobit regression model to verify the influencing indicators for green innovation efficiency. The examination covers various aspects, including the stage of green innovation, the diversity of technology accumulation, the comparability of pre- and post-development, the spillover effects in geographical space, and the diversity of influencing factors. The research findings indicate the following: 1) The group division exhibit a high level of geographical correlation, and the efficiency of green innovation in the two-stage and network displays heterogeneity across distinct frontiers. The efficiency loss in the Green Achievement Transformation stage is bigger than that in the Green Technology Research and Development stage. 2) There is an overall increase in green innovation efficiency of each type during most years, and the spatial correlation and stability of the two-stage and network green innovation efficiency have improved year by year. Provinces with higher Green Innovation Environment Composite Index have the highest concentration of “high-high” efficiency agglomeration. 3) Environmental regulation intensity, factor endowment, property rights structure, foreign direct investment and energy consumption have varying degrees of constraints on green innovation, and the regional economic development level can significantly improve the efficiency of various green innovations. Finally, this paper provides some suggestions, including stimulating innovation vitality, formulating differentiated policies, strengthening regional innovation collaboration, and mobilizing resources from various stakeholders. These recommendations aim to provide guidance and reference for promoting green innovation and achieving sustainable development in different regions.
“…In terms of the factors influencing green innovation, scholars focus on the effects of different factors such as regulation, economy, structure, and foreign openness on the efficiency of relevant green innovation. For example, the study conducted by Yang et al (2023) provides further evidence for the application of the "Porter hypothesis" in China. They found that both central and local environmental regulations significantly promote green innovation in heavily polluting enterprises, with central environmental regulation enhancing the effectiveness of local environmental regulation in driving green innovation.…”
Green innovation has emerged as a crucial driver for advancing green and high-quality development. Exploring the evolutionary patterns of green innovation efficiency is crucial for achieving the “dual carbon” goals and realizing the benefits of both economic growth and environmental sustainability under the framework of new development concepts. This study employs the network SBM-DEA model under meta-Frontier and group-Frontier. Additionally, it considers the GML index and Moran’s I to conduct a comprehensive analysis of the evolving efficiency of green innovation in Chinese provinces from 2008 to 2020, then uses the Tobit regression model to verify the influencing indicators for green innovation efficiency. The examination covers various aspects, including the stage of green innovation, the diversity of technology accumulation, the comparability of pre- and post-development, the spillover effects in geographical space, and the diversity of influencing factors. The research findings indicate the following: 1) The group division exhibit a high level of geographical correlation, and the efficiency of green innovation in the two-stage and network displays heterogeneity across distinct frontiers. The efficiency loss in the Green Achievement Transformation stage is bigger than that in the Green Technology Research and Development stage. 2) There is an overall increase in green innovation efficiency of each type during most years, and the spatial correlation and stability of the two-stage and network green innovation efficiency have improved year by year. Provinces with higher Green Innovation Environment Composite Index have the highest concentration of “high-high” efficiency agglomeration. 3) Environmental regulation intensity, factor endowment, property rights structure, foreign direct investment and energy consumption have varying degrees of constraints on green innovation, and the regional economic development level can significantly improve the efficiency of various green innovations. Finally, this paper provides some suggestions, including stimulating innovation vitality, formulating differentiated policies, strengthening regional innovation collaboration, and mobilizing resources from various stakeholders. These recommendations aim to provide guidance and reference for promoting green innovation and achieving sustainable development in different regions.
“…Certain scholars propose that design professionals, alongside engineering technical capabilities, should also possess skills in user analysis, market insights, and corporate innovation [22,23]. These studies emphasize that industrial design and new technologies are intertwined, necessitating knowledge and skills from psychology, sociology, economics, and other disciplines for effective integration with new technologies.…”
The fusion of emerging technologies with industrial design has catalyzed a fundamental shift in the aesthetics, user experiences, and service frameworks of products in the Industry 4.0 era. Simultaneously, this convergence has heightened the demands placed on the technological integration competencies of designers. Consequently, there exists a necessity to articulate a precise developmental trajectory for proficiency in industrial design that incorporates these novel technologies. This study initiates with a bibliometric analysis to quantify the scholarly literature relevant to this research domain. Subsequently, leveraging the insights from this analysis, semi-structured interviews were conducted with 15 experts spanning the United States, Europe, South Korea, and China. Our conclusions show the following: (1) Co-word analysis and cluster analysis techniques are applied to identify 80 technologies and four technological clusters that demonstrate strong associations with industrial design in the Industry 4.0 era. (2) Employing coding techniques and thematic analysis, four distinct skill domains emerge for technology-integrated industrial design: Industrial Design Skills, Industrial Design Knowledge, Ethical Considerations in Industrial Design, and Industrial Design Industry Insight. Furthermore, a limitation that affects these competencies is identified. (3) A recommended methodology for assessing these competencies is proposed. This study represented an expansion upon existing industrial design competencies. The empirical data generated herein serves as a valuable resource for practitioners and educators within the field of industrial design. Furthermore, it provides a theoretical groundwork for future models addressing technology-infused industrial design capabilities.
“…Researchers considered the ecological and environmental capacities for an interregional transfer of polluting industries [43]. Similarly, from an empirical data standpoint, one can establish that an environmental supervision path under collaboration by governments at different levels offers implications for achieving green innovation and optimizing pollution emission mechanisms [44]. Researchers adopted the MCDM approach in the decision making of a green and water-saving development in agriculture [45].…”
Worldwide manufacturing and service sectors are choosing to transform the existing manufacturing sector, particularly reconfigurable manufacturing systems using the technologies of the next generation Industry 4.0. In order to satisfy the demands of the fourth industrial revolution, model evaluation and assessing various candidate configurations in reconfigurable manufacturing systems was developed. The proposed model considers evolving consumer demands and evaluates manufacturing configurations using a gray relational approach. For the case at hand, it is evident that considering all possible dynamic market scenarios 1 to 6, the current manufacturing configuration, i.e., alternative 1, has 89% utilization, total 475 h of earliness and 185 h of lateness in the order demand delivery to the market, and a total of 248 throughput hours and around 1143 bottleneck hours. The main challenge is to make a perfect match between the market demands, variations in product geometry, manufacturing processes and several reconfiguration strategies/alternatives. Furthermore, it is evident that alternative 1 should be reconfigured and that alternative 3 is the best choice. Alternative 3 exhibits 86% system utilization, a total of 926 h of earliness and 521 h of lateness in the order demand delivery to the market, and a total of 127 throughput hours and around 853 bottleneck hours. A simulation framework is used to demonstrate the efficacy of each possible reconfigurable production setup. The sensitivity analysis is also carried out by adjusting the weights through principal component analysis and validating the acquired ranking order. Thus, if the decision makers want to provide a preference to all criteria, the order of the choices of configurations is found to be alternative 3, alternative 1, alternative 4, alternative 2 and alternative 5.
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