Urban heat islands (UHIs) have a significant and negative impact on the urban ecological environment and on human health, and it is imperative to examine factors that lead to UHIs. Although numerous studies have been conducted in this field, little research has considered seasonal variations in UHIs in coastal cities. Moreover, parametric statistical analyses, such as regression and correlation analyses, have been typically applied to examine the influential factors. Such analyses are flawed because they cannot uncover the complicated relationships between UHIs and their factors. Taking Dalian, a coastal city in China, as an example, this paper reveals the dynamic mechanism of the UHI effect for different seasons using the cubist regression tree algorithm. Analyses suggest that the UHI effect only exists in spring and summer, and no obvious UHIs can be found in autumn and winter. The adjacency to the sea leads to moderate UHI effects in spring and summer and no UHI or urban cooling island (UCI) effects in autumn and winter. The distance to the coastline, however, does not play a role in the UHI effect. Furthermore, as one of the most important factors, the vegetation coverage plays a significant role in the UHI effect in spring and summer and significantly mediates the UHI in autumn and winter. Comparatively, the elevation (e.g., digital elevation models (DEMs)) is consistently negatively associated with the land surface temperature in all seasons, although a stronger relationship was found in spring and summer. In addition, the surface slope is also a significant factor in spring and winter, and the population density impacts the UHI distribution in summer as well.

The main purpose of the paper is to investigate the relationship between technological innovation and income inequality for China based on the financial Kuznets curve (FKC) hypothesis. The study uses time-series data from 1985 to 2019. We employ the Johansen cointegration, ARDL model and VECM Granger causality techniques to analyze the links between the variables. We also use the DOLS, FMOLS and CCR mechanisms to estimate the long-run parameters. The paper finds that the FKC is valid for China’s economy in the long run. Technological innovation positively affects the urban-rural income gap, while there is an inverted-U shaped between financial development and the urban-rural income gap. The relationship between financial development and the urban-rural income gap is bi-directional causality. Technological innovation and the urban-rural income gap cause each other. Empirical results suggest a twofold policy meaning: i) to further the financial system and ii) to eliminate the adverse impacts of technological innovations on income distribution.

Urban economic resilience provides a novel perspective on the sustainable development of urban and regional economy. Selecting 37 prefecture‐level cities in the Northeast China that also known as the rust belt in China as a regional study sample that has experienced significant economic decline and out‐migration in the last 20 years compared to many other regions in China, this study aims to construct an index system using the data collected in 2005, 2010, and 2016. This study evaluates urban economic resilience including five socioeconomic aspects: diversity, capabilities related to revenue and expenditure, innovation environment, trend of development, and openness. We analyze the spatial and temporal evolution characteristics of urban economic resilience, explore the key factors contributing to urban resilience and then provide decision‐making suggestions to enhance it. We find the following: (1) urban economic resilience in the Northeast China has gradually increased over time, but spatial heterogeneity of resilience was prominent. Specifically, coastal cities were more economically resilient than inland cities. (2) Urban economic resilience in the Northeast China is significantly contributed by the diversity of an economic system and the trend of development, which contribute to resilience with weights of 0.214 and 0.216, respectively. The dominant factors contributing to urban economic resilience are different among diverse urban economic types and size. (3) To enhance urban economic resilience, comprehensive economic cities need to focus on increasing the diversity of economic structures. Resource‐based and old industrial cities should focus on raising the innovation environment. Coastal cities should focus on increasing the diversity of their economic structures and creating positive trends of economic and social development. Agricultural cities should focus on creating positive trends of economic and social development.

Based on the panel data of 30 provinces in China from 2011 to 2019, this paper uses a two-stage DEA model to measure regional innovation efficiency, then non-parametric test is used to examine the impact of innovation network structure and government R&D investment on regional innovation efficiency. The results show that, at the provincial level, innovation efficiency of regional R&D is not necessarily in direct proportion to the innovation efficiency in the commercialization stage. Commercialization efficiency is not necessarily high in provinces with high technical R&D efficiency. At the national level, the innovation efficiency gap between our country’s R&D and commercialization stage is small, indicating that the development of the national innovation efficiency is more and more balanced. Innovation network structure can promote the R&D efficiency, but has no significant effect on the commercialization efficiency. Government R&D investment helps to improve the R&D efficiency, but it is not conducive to the improvement of commercialization efficiency. The interaction between innovation network structure and government R&D investment will have compound effects on regional innovation efficiency; the region with underdeveloped innovation network structure can increase the government R&D investment to make it have a higher level of R&D. This paper provides insights into how to improve innovation efficiency in different social networks and policy environments.