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This paper proposes a novel grey dynamic double incentive decision-making model to evaluate the high-quality development of manufacturing industry. First, we define the concepts of the improved grey incidence analysis and power weight Heronian aggregation (PWHA) operator. Then, we present the double incentive factors and determine incentive static...
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... Considering that this study analyzed the activity characteristics of the crowd from the perspective of people's stay or movement in the space, correlation analysis was conducted with various variables of the formats. Analysis methods such as Generalized additive models [84] and gray dynamic double excitation analysis [85,86] can handle the correlation analysis of the impact of multiple independent variables on a single dependent variable in the regression model, but they cannot solve the limitation of the number of dependent variables. However, PLSR (Formula (3)) is a many-to-many multiple regression modeling analysis method suitable for measuring the degree of association between variables at different scales [87]. ...
... Sustainability 2023, 15, x FOR PEER REVIEW 13 of 24 gray dynamic double excitation analysis [85,86] can handle the correlation analysis of the impact of multiple independent variables on a single dependent variable in the regression model, but they cannot solve the limitation of the number of dependent variables. However, PLSR (Formula (3)) is a many-to-many multiple regression modeling analysis method suitable for measuring the degree of association between variables at different scales [87]. ...
Crowd activity is an important indicator of commercial streets’ attractiveness and developmental potential. The development of positioning technologies such as GPS and mobile signal tracking has provided a large amount of trajectory data for studying crowd activities on commercial streets. These data can not only be used for the statistics, extraction, and visualization of crowd information, but they also facilitate the exploration of deeper insights into dynamic behaviors, choices, trajectories, and other details of crowd activities. Based on this, this article proposes a new framework for analyzing crowd activities to explore the spatial activity patterns of crowds and understand the dynamic spatial needs of people by analyzing their correlations with local formats. Specifically, we analyze the spatial activity characteristics of a crowd in the Lao Men Dong Commercial Street area by identifying the stay points and trajectory clusters of the crowd, and we establish a regression analysis model by selecting commercial street format variables to evaluate their impact on crowd activities. Through case analysis of the Lao Men Dong Commercial Street, this study confirms that our method is feasible and suitable for spatial research at different scales, thereby providing relevant ideas for format location selection, spatial layout, and other planning types, and for promoting the sustainable development of urban spaces.
... Similarly, M. developed an evaluation system consisting of eight dimensions: innovation, speed and efficiency, structural optimization, integrated development, high-quality brand, green manufacturing, business environment, and social security. Yu et al. (2022) utilized a Novel Grey Dynamic Double Incentive Decision-Making model to evaluate high-quality development in the manufacturing industry based on four dimensions: R&D and innovation capability, processing and manufacturing capability, brand marketing capability, and environmental protection capability. J. Y. Liu and Xie (2018) evaluated and analyzed high-quality development in the manufacturing industry from specific aspects such as scientific and technological innovation and environmental regulation. ...
China’s manufacturing industry has emerged as a major contributor to both the nation’s economic growth and global trade. Achieving high-quality development in this sector is not only crucial for China’s sustainable progress but also holds significant implications for the global economy. Considering the importance of Chinese manufacturing industry, this paper establishes a framework for exploring the mechanism of manufacture industry’s high-quality development in China’s three economic regions based on the Driver-Pressure-State-Response (DPSR) model and Structural Equation Modeling (SEM). SEM is established for analyzing the multiple paths of manufacture industry’s high-quality development in the eastern, middle and western economic region of China. The findings reveal distinct patterns in each region. In the eastern region, a unidirectional path of “Driver-Pressure-State-Response” is confirmed, with a particular emphasis on quality improvement and efficiency increase. The middle region exhibits a leapfrog path characterized by “Driver-Pressure-State” and “Pressure-Response,” where quality improvement and dynamic conversion play critical roles. Conversely, the western region demonstrates an interrupt path of “Pressure-State-Response,” highlighting the significance of structural rationalization and dynamic conversion. Based on these results, sustainable solutions and suggestions for promoting high-quality development in the manufacturing industry are proposed, providing valuable targeted insights for the transformation and advancement of China's manufacturing sector. This study makes a novel contribution by developing a DPSR-SEM model and expands literature and knowledge in high-quality development of manufacturing industry. Practically, this study can guide decision-makers, managers, and stakeholders in understanding the interactions between driving forces, pressures, states, and responses in the context of manufacturing industry.
Different preferences of the indicators would be showed in some situations. However, the preferences are not considered into the traditional possibility functions, which are always assumed to be the linear functions. It might not be proper to analyze all kinds of indicators with the traditional possibility functions. Therefore, the universal possibility functions are provided. Due to the multiple uncertain features of the indicators, then the universal possibility functions are extended for the generalized grey numbers. According to the importance of indicators and the time, the weights of indicators and the time are given respectively. Next, generalized grey dynamic clustering models with preferences are proposed. At last, the effectiveness of the suggested methods is verified via the case illustration and comparative analysis.
By evaluating the eco-environmental resources of China's major grain-producing regions, the development status of the eco-environmental resources in major grain-producing regions of China was clarified. This is of great significance in maintaining green and sustainable agricultural development and ensuring food security. This paper takes 13 major grain-producing regions in China as the study area, constructs the eco-environment index system by using the Driver-Pressure-State-Impact-Response (DPSIR). The average annual growth rate represents the dynamic changes of each indicator, and the random forest model by using Jupyter Notebook is selected to evaluate the current state of development of the eco-environment resources from 2005 to 2020. The combination of dynamic and static evaluation of the eco-environmental resources is more conducive to decision makers to keep abreast of developments in the eco-environmental resources. The results show that: Firstly, there are differences between static and dynamic conditions that more number of important indicators in static conditions than in dynamic conditions, and the number of important indicators located at the driving force criterion level is higher. Secondly, under static conditions, the evaluation results of eco-environmental resources show an overall upward trend (4.10 in 2005 and 4.65 in 2020); under dynamic conditions, the evaluation results of eco-environmental resources show an overall downward trend (4.35 in 2005 and 4.13 in 2020). Thirdly, there are differences in spatial distribution. Under static conditions, the spatial distribution of regional eco-environmental resource levels is generally “high in the north and low in the south”, while under dynamic conditions, it is “high in the south and low in the north”. Finally, according to the different important indicators in the north and south, the northern region should develop plans to increase the penetration rate of agricultural mechanization and improve the efficiency of water resources utilization, while the southern region should pay more attention to the development of green agriculture, accelerate the construction of urbanization, and improve the efficiency of water resources utilization.
