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This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006–2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations. [ABSTRACT FROM AUTHOR]

Five national urban agglomerations are selected according to the Fourteenth Five-Year Plan, namely the Yangtze River Delta, the Pearl River Delta, Beijing-Tianjin-Hebei, the Middle reaches of the Yangtze River, and Chengdu-Chongqing. The study on synergistic effects of such national strategic planning urban agglomerations, defined as coordinated degrees, and their time-series trends from 2016 to 2021 is significant for the practice of the national "double carbon" goal. Spatial differentiation of coordinated degree in five agglomerations is analyzed based on the Theil Index, along with regional linkage strength of coordinated degree under the gravity model. Conclusions include: (1) a downward trend is shown from 2016 to 2021 for the coordinated degree, along with the Pearl River Delta is the best among the five agglomerations; (2) the Middle reaches of the Yangtze River is the highest, followed by the Pearl River Delta and the Yangtze River Delta of coordinated degree in 2021; (3) the main cause of downwards in coordinated degree combined by a stable decline in carbon emissions and fluctuating increase in pollutants; (4) intra-regional differences in the Yangtze River Delta and the Middle reaches of the Yangtze River are relatively large, compared with the smallest in the Pearl River Delta measured by Theil Index; (5) coefficients of variation are relatively higher in the Middle reaches of the Yangtze River and the Yangtze River Delta, followed by the Beijing-Tianjin-Hebei, the Pearl River Delta and Sichuan-Chongqing urban. Consequently, countermeasures are proposed from the perspective of government, including technology and regional cooperation, policy innovation by overall coordination, synergy promotion by technological innovation, and regional synergy by win-win cooperation. [ABSTRACT FROM AUTHOR]

Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.5 measurements. As a measure of the directional dependence, we propose the so-called copula directional dependence (CDD) using beta regression models. An advantage of the CDD is that it does not rely on strict assumptions of specific probability distributions or linearity. We used hourly PM2.5 measurement data collected at four major cities in China: Beijing, Chengdu, Guangzhou, and Shanghai, from 2013 to 2017. After accounting for autocorrelation in the PM2.5 time series via nonlinear autoregressive models, CDDs between the four cities were estimated to produce directed network structures of statistical dependence. In addition, a statistical method was proposed to test the directionality of dependence between each pair of cities. From the PM2.5 data, we could discover that Chengdu and Guangzhou are the most closely related cities and that the directionality between them has changed once during 2013 to 2017, which implies a major economic or environmental change in these Chinese regions. [ABSTRACT FROM AUTHOR]