A Study of the Impact of the Digital Economy on the Efficiency of Urban Green Economy--Based on the Yangtze River Delta City Cluster

This paper adopts the non-expected output-super-efficiency SBM(Slacks-Based Model)model and principal component analysis to calculate the green economy efficiency and the digital economy level of 27 prefecture-level cities in China’s Yangtze River Delta urban agglomeration between 2011 and 2019,respectively,and examines the impact of the digital economy on the green economy efficiency by using benchmark regression and mechanism analysis.The findings show that,first,the digital economy has a significant contribution to the green economic efficiency of cities,and this conclusion still holds after robustness tests such as replacing explanatory and interpreted variables and introducing province-fixed effects.Second,through the mechanism test,it is found that the digital economy can indirectly promote urban green economic efficiency through the positive mechanism effect of promoting industrial structure upgrading.

Regional Planning and Development Under the Maximization of Urban Agglomerative Economies

Starting from the meaning and types of urban agglomerative economies, with the analysis of the characteristicsand causes of urban agglomerative economies, this paper puts forward that the regional planning and development shouldattach importance to urban agglomerative economies, follow the law of the maximization of regional urban agglomerativeeconomies. It also points out the countermeasures and advices to facilitate the regional planning and development based on theprinciple.

MEASUREMENT OF AGGLOMERATION ECONOMIES AT COUNTY LEVEL IN JIANGSU PROVINCE

Agglomeration economies are the important factors for the regional development. However, the common indicators to measure them, such as Gini Coefficients neglect the spatial ingredient of data, leading to a-spatial estimates. In order to assess spatial neighbor effects of agglomeration economies, this study makes the new attempts by applying a series of techniques of spatial autocorrelation analysis, specifically, measuring the economies of urbanization and localization at the county level in the secondary and tertiary industries of Jiangsu Province in 1999 and 2002. The conclusions in this study reveal that on the whole, the localization effects on the economies of the secondary industry might be stronger than urbanization effects for that period, and highly agglomerative economies were limited within the southern Jiangsu and parts of middle along the Changjiang (Yangtze) River. Moreover, the tertiary industry has been strong urbanization rather than localization economies in the whole Jiangsu. Unlike the secondary industry, the tertiary industry held the high levels of agglomeration economies can be also found in the poor northern Jiangsu, and then the spatial clusters of trade and services might be basically seen in each of urban districts in 13 cities. All in all, spatial autocorrelation analysis is a better method to test agglomeration economies.

An Empirical Study on Agglomeration Economies and Optimal Agglomeration for China＇s Manufacturing Industries

Employing the statistics of 750, 000 firms obtained from China ＇s Third National Industrial Census, this paper estimates the production functions of 112 3-digit industries in four categories to examine the patterns of agglomeration economies, optimal scale of agglomeration, and the endogeneity of agglomeration level in manufacturing industries. Results indicate the existence of localization economies at city level and urbanization economies at province level, while the latter has very small economic significance. Decreasing returns to scale at firm level suggest that the source of agglomeration economies is technological externality emphasized by urban economic theory. With a rising scale, agglomeration economies exhibit an inverted U-shaped curve. Each industry has an optimal scale of agglomeration where the agglomeration economy is maximized. However, the actual scale of agglomeration is generally far lower than the optimal level. Corresponding to the optimal scale of agglomeration, the level of industrial agglomeration measured by the index is endogenous.

Evaluation of Urban Resource and Environmental Efficiency in China Based on the DEA Model

This paper illustrates the spatial variations in urban resource and environmental efficiency （REE） amongst 285 cities in China using a Data Envelopment Analysis （DEA） model, and examines the factors that have had the greatest effect on this spatial pattern by regression models. The results gave an average urban REE of 0.6381, and an average pure technical efifciency （PTE） and scale efifciency （SE） of 0.6964 and 0.9225, respectively. The results support the existence of a U-shaped relationship between REE and income level, which means that an increase in urban GDP does not result in an equivalent increase in environmental efficiency. Economic growth affects REE in three ways： scale effects （population scale and urbanization rate）; composition effects; and spatial effects. Improvements in urban resource use and environmental efifciency depend upon both technological innovation and effective governance. Policies designed to achieve these improvements should therefore be implemented at al levels of government and local enterprise.

Embodied energy consumption and carbon emissions evaluation for urban industrial structure optimization

Cities are the main material processors asso- ciated with industrialization. The development of urban production based on fossil fuels is the major contributor to the rise of greenhouse gas density, and to global warming. The concept of urban industrial structure optimization is considered to be a solution to urban sustainable develop- ment and global climate issues. Enforcing energy con- servation and reducing carbon emissions are playing key roles in addressing these issues. As such, quantitative accounting and the evaluation of energy consumption and corresponding carbon emissions, which are by-products of urban production, are critical, in order to discover potential opportunities to save energy and to reduce emissions. Conventional evaluation indicators, such as ＂energy consumption per unit output value＂ and ＂emissions per unit output value＂, are concerned with immediate consumptions and emissions; while the indirect consump- tions and emissions that occur throughout the supply chain are ignored. This does not support the optimization of the overall urban industrial system. To present a systematic evaluation framework for cities, this study constructs new evaluation indicators, based on the concepts of ＂embodied energy＂ and ＂embodied carbon emissions＂, which take both the immediate and indirect effects of energy consumption and emissions into account. Taking Beijing as a case, conventional evaluation indicators are compared with the newly constructed ones. Results show that the energy consumption and emissions of urban industries are represented better by the new indicators than by conventional indicators, and provide useful information for urban industrial structure optimization.