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The construction of China’s Free Trade Zone (FTZ) is an important strategy for China’s thorough deepening of opening up and achievement of long-term high-quality development. Based on the panel data of 283 prefecture-level and above cities in China from 2008–2019, this paper adopts the methods of Spatial Differences-in-Differences (SDID), Spatial Differences-in-Differences-in-Differences (SDDD), and spatial intermediary effect test to empirically examine the impact and the spatial spillover of China’s free trade zone on the environmental pollution of the pilot areas and its influencing mechanism. According to the findings of the study, the establishment of pilot free trade zones may suppress urban PM2.5 emissions by around 2.9 percent, and FTZs can also greatly enhance the air quality of neighboring cities. Further examination of the influencing mechanism reveals that the establishment of a FTZ inhibits PM2.5 pollution and has a significant positive spillover on PM2.5 reduction in surrounding cities by the following means: attracting more foreign direct investment; improving the industrial structure through increasing the proportion of tertiary industry; prompting the local government to strengthen environmental regulation as part of the FTZ’s supporting policies; increasing the investment in science and technology innovation, developing scientific and technological level to achieve green production. The empirical results of this paper are still robust after a series of robustness tests when the explained variable is replaced by the traditional air pollution indicator industrial nitrogen oxide emissions, another sort of spatial matrix is introduced, the propensity score matching SDID (PSM-SDID) and placebo tests as well as winsorize method are carried out. Furthermore, the inhibitory effect of FTZs on air pollution is modified by changes in city size, geographic location and city type, according to heterogeneity analysis. Finally, this paper proposes feasible policy recommendations.

Based on the connotation and process of innovation-driven development, we have developed a comprehensive evaluation system containing 20 indicators in five aspects, including innovation factors, innovation subjects, innovation environments, innovation outputs, and development performance, to measure the levels of innovation-driven development in Sichuan province. Selecting 21 cities and prefectures in Sichuan province as research objects, we evaluated and measured the innovation-driven development levels of each city and prefecture using the entropy weight method (EWM). According to the evaluation results, the 21 cities and prefectures were divided into four categories depending on their levels of innovation-driven development: advanced-level, high-level, medium-level, and low-level. The results show that there are obvious spatial differences in terms of innovation-driven development levels among cities and prefectures in Sichuan province. Specifically, Chengdu, Mianyang, Panzhihua, and Deyang cities rank among the top four cities because of their advanced and high levels of innovation-driven development, while other cities and prefectures are at the medium and low levels. We also analyzed the innovation-driven development policies and practices of cities and prefectures in Sichuan province, to provide guidance for implementing innovation-driven development strategies in the cities and prefectures in the future.

The predation on insects on leaves was measured by direct observation using live larvae of Calliphora vicina (Diptera: Calliphoridae) as bait placed on 15 common species of woody plants in a floodplain forest in the temperate region. The predation rate was measured in terms of the proportion of the larvae that were missing or had been attacked after 30 min of exposure on leaves. Despite the fact that the important predators differed during the course of a season, the most frequently recorded predators were birds and ants and less frequently recorded wasps and spiders. Analysis of the pattern in the distribution of the attacks confirmed that it is best described by a negative binomial distribution, which corresponds to a clumped dispersal of predation. Based on the results of the best-fitted generalized additive model, we could not reject an equal probability of attacks on the different species of woody plants. Further, predation at the forest edge was notably higher than in the forest interior. The model also predicted marked variations in the incidence of attacks during the course of a day and a season, with the attacks occurring mainly in three periods during the year and two during the day. In general, the sampling method used could become the standard measure of the risk of insects living on trees being attacked by predators in future studies. [PUBLICATION ABSTRACT]

The digital economy is critical to national economic growth and high-quality economic development. It is theoretically and practically significant to measure the development level and spatial differences in the digital economy to promote the construction of a digital China. This study constructed a digital economy evaluation index and analyzed the dynamic evolution, spatial differences, and driving factors of China’s provincial digital economy from 2011 to 2020 using a spatial Markov chain, the Dagum Gini coefficient, and geographical detector methods. The results demonstrated that China’s provincial digital economy grew from 2011 to 2020. The spatial distribution of the digital economy was high in eastern provinces and municipalities such as Beijing, Shanghai, Guangdong, Jiangsu, and Zhejiang, and low in central and western provinces and autonomous regions. The probability of upward transfer in developing China’s provincial digital economy was greater than that of preserving the original state, and China’s provincial digital economy has great potential for development. A region with a medium-high level in the digital economy is more likely to achieve high-level development when neighboring regions are characterized by a medium-high or high level of digital economy development, as the spillover effects from the neighbors may be strongly favorable and the region takes advantage of its developed surroundings. There were significant spatial differences in the development of China’s provincial digital economy, caused primarily by inter-regional differences. The spatial differentiation of China’s provincial digital economy was caused by the interaction of multiple factors, led by economic conditions and R&D expenditure.

Green transformation has become a central goal of China’s development strategy in response to mounting environmental pressure and long-term growth needs. Improving green innovation efficiency (GIE) is essential to achieving this transformation while sustaining economic momentum. This study evaluates the impact of the Pilot Policy for Innovative Industrial Clusters on GIE across Chinese cities. Using panel data from 280 prefecture-level cities between 2007 and 2021, we apply difference-in-differences and spatial difference-in-differences (SDID) models to estimate policy effects, spatial spillovers, and transmission mechanisms. The results are as follows: (1) The pilot policy significantly improves GIE in the pilot cities, with robust results after various tests. (2) The policy enhances urban green innovation through four main channels: reducing energy consumption intensity, upgrading industrial structure, fostering green technological innovation, and accelerating digital infrastructure development. (3) In addition to its direct impact on pilot cities, the policy also boosts the GIE of neighboring cities via spatial spillover effects. (4) Heterogeneity analysis reveals that the policy’s effect is more pronounced in central cities, non-resource cities, and cities with a strong environmental protection focus. This study contributes to the understanding of innovative industrial cluster policies in enhancing GIE and offers valuable policy insights for promoting urban green development.

Cities are areas featuring a concentrated population and economy and are major sources of carbon emissions (CEs). The spatial differences and influential factors of urban carbon emissions (UCEs) need to be examined to reduce CEs and achieve the target of carbon neutrality. This paper selected 264 cities at the prefecture level in China from 2008 to 2018 as research objects. Their UCEs were calculated by the CE coefficient, and the spatial differences in them were analyzed using exploratory spatial data analysis (ESDA). The influential factors of UCEs were studied with Geodetector. The results are as follows: (1) The UCEs were increasing gradually. Cities with the highest CEs over the study period were located in the urban agglomerations of Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu–Chongqing. (2) The UCEs exhibited certain global and local spatial autocorrelations. (3) The industrial structure was the dominant factor influencing UCEs.

To scientifically evaluate the dynamic operational efficiency, spatial differences, as well as the formation mechanisms of the urban Innovation Ecosystem within the Yellow River Basin is highly important for the high-quality development of China. In the present research, both the economic circulation theory with the Innovation Ecosystem and the Data Envelopment Analysis – Malmquist Productivity Index (DEA-Malmquist) model were adopted to analysis the database from 59 cities along the Yellow River Basin. In parallel, the kernel density estimation, the Gini coefficient, and Panel Vector Autoregression (PVAR) model were applied for further comparison. The results revealed that the dynamic operational efficiency of the Innovation Ecosystem within the Yellow River Basin exhibited an obvious fluctuating downwards trend. The efficiency of spatial distribution in the upstream and midstream basins shows a left-skewed and polarized pattern, whereas the downstream basins exhibited a right-skewed distribution with less pronounced polarization. The results also revealed that the overall Gini coefficients for dynamic operational efficiency (TFP) and technical efficiency (EFF) in the Yellow River Basin tended to convergence, whereas those for technological change (TECH) are of an increasing trend. Moreover, the hypervariable density emerged as the primary factor driving disparities in TFP, TECH, and EFF within the basin. Furthermore, the relationships among TFP, TECH, and EFF were featured with the regional heterogeneity. In the midstream areas, there existed a self-improvement mechanism for the TFP, TECH, as well as the EFF. However, there was a stronger self-improvement mechanism for TECH but a self-weakening mechanism for TFP and EFF in the downstream regions.

The improvement of green innovation efficiency (GIE) in the Yangtze river economic belt (YREB) is beneficial to China’s green transformation and upgrading because of its economic and ecological position. Therefore, based on the slacks-based measure of super-efficiency (Super-SBM) model, the paper studies the GIE and its spatial-temporal variation characteristics in the YREB during the period 2003–2015, and analyzes the spatial correlation and spatial-temporal convergence of GIE with the exploratory spatial data analysis (ESDA) method and convergence analysis method. The results show that the GIE in the YREB shows an “U-shaped” change pattern in time and an extremely unbalanced development pattern in space. The areas with high GIE contribute to the improvement of overall GIE, whereas they do not exert a radiation and driving effect on areas with low GIE. Accordingly, because of the short board effect, the convergent speed of the GIE is decreasing. To be specific, the GIE keeps converging in the upper and lower reaches, except for the year 2010 when GIE in the middle reaches changed from being convergent to being non-convergent. Even though environmental policy exerts great impacts on the improvement of GIE, the lack of collaborative environmental governance leads to the non-convergent and unbalanced development of the GIE. Therefore, green coordinated development of the YREB is necessary.

Utilizing National High-tech Industrial Development Zones (NHIDZs) as a quasi-natural experiment, this study has adopted 285 prefecture and above cities from 2003 to 2018 in China as the research samples and constructed the difference-in-differences (DID) and spatial difference-in-differences (SDID) models to investigate the effects of NHIDZs on economic development and environmental pollution at national, regional, and administrative levels. The results show that NHIDZs have basically achieved a win-win situation in promoting economic development and reducing environmental pollution at the national level. However, spatial heterogeneity is supported at regional and administrative levels. Specifically, “the law of diminishing marginal effect” of NHIDZs is proved in the eastern cities and key cities, which reveals the uneven development pattern of government leading in China, and highlights the importance and necessary of making policy according to local conditions and governing environmental pollution by classification.

In an attempt to investigate the impact of major events on urban air quality in terms of the extent, duration and spatial scope, data on the daily air quality index and the concentrations of individual pollutants are collected in 140 cities of China from January 2, 2015, to November 28, 2017. Based on a spatial differences-in-differences, the impact of major events, such as political conferences, sporting events at the national level, on urban air quality in the dimensions of time and space are explored. It is concluded that major events not only affected the air quality of the host city, but also exercised influence on the air quality of the surrounding areas. Recommendations for mitigating the impact of major events on urban air quality have been proposed, such as establish regional atmospheric environment management system and formulate regional unified standards for pollutant discharge, industrial access and law enforcement.