RESUMO
In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist-Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.
RESUMO
The rational allocation of industrial factors in resource-based cities is a necessary condition for promoting high-quality economic development. To systematically measure the mismatch of industrial factors, this study integrates the production function model and accounting framework of Aoki (2012). The capital, labor, and energy factors are integrated into the overall measurement framework, and the factor mismatch calculation model is innovatively constructed at the city level. To effectively reflect the factor mismatch of different cities, the relative distortion coefficient of factor price is introduced to establish the relationship between the distortion of factor-use cost and effectiveness of resource allocation. The degree and direction of factor mismatch are effectively measured. This study takes China's resource-based cities as the research object. To avoid the impact of the COVID-19 pandemic, data on 114 resource-based cities in China from 2003 to 2018 are selected, and 164 non-resource-based cities are used as the control group to evaluate the factors affecting the transformation of such cities. Regarding the diversity of resource-based cities in China, this study compares the characteristics of factor misallocation in these cities for different growth stages, geographical regions, and dominant resources. Finally, this study finds that on average, the cities' degree of energy misallocation is the most serious, and the degree of labor misallocation ranks second. The degree of capital misallocation ranks the lowest, but the misallocation of various factors is very different in different situations. Therefore, different types of cities should make detailed policy adjustments in various transformation directions according to the calculation results.
RESUMO
In-depth analysis of the factors affecting the transformation of resource-based cities can provide effective support for the transformation and development of resource-dependent regions. How to comprehensively identify the factors affecting the transformation of resource-based cities is a complex problem. This study starts from the total factor productivity model and focuses on the two core basic factors that affect the transformation process of cities reliant on resources. Economic benefits and energy efficiency, respectively, from the economic benefit analysis framework and energy efficiency analysis framework for reconstruction, the two frameworks are combined with the use of distorted prices of resource elements to solve the problem that the synergistic effect of economic benefits and energy efficiency can not be measured. In order to quantitatively analyze the factors that affect the development efficiency of cities reliant on resources under the single or synergistic effect of the comprehensive framework, this study optimizes the directional distance function from three perspectives: exogenous weight, direction vector endogeneity, and absolute distance transformation relative distance, thus achieving an accurate assessment transformation efficiency of cities reliant on resources. Considering the impact of the new coronavirus epidemic, this study only selected the data of resource-based cities from 2003 to 2018, and found through model calculation that the impact on the transformation of cities reliant on resources: (1) Labor mismatch is mainly achieved by affecting the structure about the production of resource-based enterprises and industrial human resources; (2) Capital mismatch is mainly realized by affecting the production of resource-based enterprises; (3) Energy mismatch is mainly achieved by affecting high energy consumption enterprises and low production technology level enterprises. Further research shows that the main objects of these factors are the four parts of production technology level, energy consumption, total factor productivity and industrial structure. Through these contents, they affect environmental efficiency and deeply affect the transformation process of resource-based cities.
