The drivers and spatial distribution of economic efficiency in China’s cities

Abstract

This study analyzes cities in China at the prefecture level and above to calculate indices for "urban economic efficiency" (the relationship between input factors and output) and "urbanization economic efficiency" (the relationship between newly increased output and increased economic input), based on the Stochastic Frontier Analysis (SFA) method. We compare and analyze the factors influencing change and their spatial distributions. The results show that capital and labor rather than urban land could effectively improve urban and urbanization economic efficiency. And, although the proportion of wages to GDP has a significant negative impact on urban economic efficiency, for social equity and stability, the proportion should be increased; if appropriate, it would not significantly reduce urbanization economic efficiency. Additionally, population density, population urbanization rate, and government fiscal expenditure significantly positively impact urban and urbanization economic efficiency. However, we also found that increases in the degree of industrial structure deviation and urban landscape fragmentation are harmful to urbanization economic efficiency. In terms of spatial distribution, the urbanization economic efficiency of most of China's northeastern and eastern coastal areas is significantly lower than that of other regions; at the same time, the urban economic efficiency of most of these cities has been decreasing, especially in the northeast, which warrants greater policy attention.

Environmental regulation, land use efficiency and industrial structure upgrading: Test analysis based on spatial durbin model and threshold effect

“…For the sake of investigate the robustness of the estimated results and make full use of the panel data, this paper adopts the adjustment model method to test, specifically: AIS and RIS are not only by the opening to the outside world, government intervention, but also by the labor cost and product consumption trend factors [ 110 , 111 ], therefore, this paper adds the control variables in the model, to test the stability of the model. High labor costs (LC) will promote enterprises to adopt more technology upgrades and automation equipment to replace artificial labor, so as to achieve the purpose of technological innovation [ 112 ], which will improve production efficiency, reduce production costs, and promote the industrial structure towards a more advanced direction.…”

Research on green development measurement and regional differences in the China's Yellow River Basin based on the ecological function orientation

“…(5) Desirable output: From the perspective of development, the output value created by the region every year is the most intuitive economic output (Cao et al 2022). This paper takes GDP as the index of desirable output and converts the variable price data of each year into the constant price in 2003 according to the GDP index of different regions.…”

Analysis of the Relationships between Tourism Efficiency and Transport Accessibility—A Case Study in Hubei Province, China

“…In addition, there is a lag between input and output (The input and the output don't match in the current year). Therefore, when calculating tourism efficiency, the input elements of the current year should correspond to the output level of the following year [2,13,14] Using ArcGIS10.5, the Hubei Province Traffic Map (2014 Edition) issued by the China Map Publishing House was georeferenced and vectorized. Then, the transport networks of Hubei Province in 2011 and 2017 were extracted; these consisted of highways, national roads, provincial roads, and railways, and the spatial reference was set to WGS_1984_UTM.…”

“…It is an important indicator to measure the overall allocation status and utilization efficiency of regional tourism resources [6,7]. Previous studies mainly examined the operating efficiency of tourist hotels [8,9], travel agencies and listed tourism companies [10][11][12], and the efficiency of tourist attractions [7,13,14], tourism transport [15], urban tourism [16], urban agglomeration or regional tourism input and output [17,18], and the relationship between driving mechanism of tourism efficiency and decomposition efficiency [13,14]. Some mathematical analysis models have been widely used in empirical research, such as data envelopment analysis (DEA) [13,14,[16][17][18], stochastic frontier analysis (SFA) [2,19] and constant elasticity of substitution production function (CES) [2,20].…”