In the face of increasingly growing health demands and the impact of various public health emergencies, it is of great significance to study the regional differences in the allocation efficiency of the rural public health resources and its improvement mechanism. In this paper, the game competition relationship is included in the evaluation model, and the game cross-efficiency model is used to measure the allocation efficiency of the rural public health resources in 31 provinces of China from 2008 to 2017. Then, the Theil index model and the Gini index model are applied in exploring the regional differences in the allocation efficiency of rural public health resources and its sources. Finally, the bootstrap truncated regression model is used to analyze the influencing factors of the allocation efficiency of the rural public health resources in China. The results show that, first, the total allocation efficiency level of the rural public health resources in China from 2008 to 2017 is relatively low, and it presents a U-shaped trend, first falling and then rising. Second, the changing trend of the allocation efficiency of the rural public health resources in the eastern, central, and western regions of China is similar to that in the nationwide region, and it shows a gradient trend that “the allocation efficiency in the eastern region is high, the allocation efficiency in the western region is low, and the allocation efficiency in the Central region is at the medium level”. However, the gap among the three regions is continually narrowing. Third, the calculation results of the Theil index and the Gini index show that intra-regional differences are the major source of the regional differences in the allocation efficiency of the rural public health resources in China, and the inter-regional differences demonstrate an expansion trend. Finally, the improvement of the education level and the social support level will generally improve the allocation efficiency of the rural public health resources in China and its three regions. The increased governmental financial support and urbanization level will reduce the allocation efficiency of the rural public health resources in China and its three regions. The economic development level, the living conditions and the population density are the important influencing factors of the allocation efficiency differences of the rural public health resources in the three regions. Therefore, on the basis of ensuring the increase of the total supply of the rural public health resources, more attention should be paid to the improvement of the allocation efficiency. Moreover, on the basis of continually narrowing the inter-regional differences among the eastern, central, and western regions, more attention should be paid to the intra-regional differences of the allocation efficiency of the rural public health resources among the different provinces. The various economic and social policies should be constantly optimized to jointly improve the allocation efficiency of the rural public health resources.

Emergency response capability evaluation is an essential means to strengthen emergency response capacity-building and improve the level of government administration. Based on the whole life cycle of emergency management, the emergency capability evaluation index system is constructed from four aspects: prevention and emergency preparedness, monitoring and early warning, emergency response and rescue, and recovery and reconstruction. Firstly, the entropy method is applied to measure the emergency response capability level of 31 Chinese provinces from 2011 to 2020. Second, the Theil index and ESDA (Exploratory Spatial Data Analysis) are applied in exploring the regional differences and spatial-temporal distribution characteristics of China’s emergency response capacity. Finally, the obstacle degree model is used to explore the obstacle factors and obstacle degrees that affect the emergency response capability. The results show that: (1) The average value of China’s emergency response capacity is 0.277, with a steady growth trend and a gradient distribution of “high in the east, low in the west, and average in center and northeast” in the four major regions. (2) From the perspective of spatial distribution characteristics, the unbalanced regional development leads to the obvious aggregation effect of “high-efficiency aggregation and low-efficiency aggregation”, and the interaction of the “centripetal effect” and “centrifugal effect” finally forms the spatial clustering result of emergency response capability level in China. (3) Examining the source of regional differences, inter-regional differences are the decisive factor affecting the overall differences in emergency response capability, and the inter-regional differences show a reciprocating fluctuation of narrowing–widening–narrowing from 2011 to 2020. (4) Main obstacles restricting the improvement of China’s emergency response capabilities are “the business volume of postal and telecommunication services per capita”, “the daily disposal capacity of city sewage” and “the general public budget revenue by region”. The extent of the obstacles’ impacts in 2020 are 12.19%, 7.48%, and 7.08%, respectively. Based on the evaluation results, the following countermeasures are proposed: to realize the balance of each stage of emergency management during the holistic process; to strengthen emergency coordination and balanced regional development; and to implement precise measures to make up for the shortcomings of emergency response capabilities.

Building resilient cities is playing an increasingly important role in enhancing urban safety and promoting sustainable urban development. However, few scholars pay attention to urban resilience in inland provinces. Choosing Henan Province, as it is a typical representative of China’s major inland economic provinces, has practical guiding significance. This study aims to provide a systematic indicator system and evaluation tool to measure the cuity’s resilience level. Therefore, based on a multidimensional perspective, this paper dissects the urban resilience spatial and temporal evolution characteristics of 18 Henan Province cities with the entropy method, Thiel index, and ESDA (Exploratory Spatial Data Analysis) and explores influencing factors with a spatial econometric model. The main results are as follows: (1) the overall resilience in Henan Province continuously grows, and the resilience level of the Zhengzhou metropolitan area is the highest. In the urban resilience subsystem, economic and social resilience notably drive urban resilience improvement in Henan Province. (2) The spatial difference of urban resilience has been significantly reduced, but the inner metropolitan area presents the characteristics of “core–periphery”. Urban resilience presents a positive spatial correlation, and local spatial agglomeration is relatively stable. (3) Under the state of spatial interaction, urbanization rate, administrative, innovation, market, and industrial structure factors all have significant direct effects and spatial spillover effects on overall resilience, but openness exerts downward pressure on local resilience. (4) On this basis, strategies have been proposed to continuously promote the development of new urbanization, improve the regional coordinated development mechanism, increase market activity, optimize the environment for scientific and educational innovation, and promote the optimization and upgrading of industrial structure. The approach taken in this research may also be useful for developing urban resilience assessment tools in other central plains cities as well as in other cities in the interior of the world with similar conditions.

Green innovation is an important way to integrate China’s innovation-driven strategy with sustainable development strategy. Adopting the attention-based view in policy implementation analysis, this paper constructs an analytical framework of how the local government’s attention paid to green innovation (LGA-GI) affects green innovation efficiency (GIE). Using the panel data of 30 provincial administrative regions in China from 2009 to 2020, we describe the temporal and spatial characteristics of LGA-GI, empirically test the impact of LGA-GI on GIE through two-way fixed effects models, and then compare the effects in the three stages of green innovation. The major findings are as follows: (1) the LGA-GI in China from 2009 to 2020 shows an upward trend with mild fluctuations, and peaks three times in 2012, 2016, and 2018. The spatial distribution of LGA-GI has changed from a pattern of “low in the middle” (low LGA-GI in the central region) to “continuous highs with scattered lows”. (2) LGA-GI has a significant positive effect on the overall GIE, but the effect is concentrated in the stage of knowledge absorption and commercialization, rather than in the stage of knowledge innovation. The implication of these results is that local governments need to allocate more attention to green innovation and maintain its continuity, and governments at all levels should distribute policy implementation resources based on the characteristics of different green innovation stages.

The urban ecological civilization construction relates to welfare of the people and the national future. It is an important field of the high-quality economic development to improve the urban ecological efficiency level. The purpose of this research is to provide a new perspective and method for the quantitative study of the urban sustainable development, and also to provide some decision-making references for the improvement of the urban ecological efficiency in Henan province. This paper uses the slacks-based measure-data envelopment analysis (SBM-DEA) model containing the undesirable output and the Malmquist index model to fully evaluate the urban ecological efficiency level in Henan province during the period of 2005–2016, via both the static and dynamic analysis. Based on this, the bootstrap regression model is applied in analyzing the influencing factors of the urban ecological efficiency. The research shows three findings. First, according to the static efficiency analysis, the urban ecological efficiency in Henan province is low as a whole and has a big promotion space. Moreover, there is a significant difference in the urban ecological efficiency level among the five regions because of the different geographical locations and social and economic development situations of the cities. Second, according to the dynamic efficiency analysis, in the last 12 years, the urban ecological efficiency in Henan province has shown an overall growth trend, and the technological progress has played a major role in promoting the urban ecological efficiency in Henan province. Third, according to the influencing factor analysis, the governmental financial support hinders the improvement of the urban ecological efficiency in Henan province, while the level of opening to the outside world, the urban population density, and the urban greening level promote it.

In the “full world” where natural capital is scarce, within the limits of the ecological environment, the improvement of welfare is a fundamental requirement for sustainable development. The ecological wellbeing performance (EWP) of 284 cities in China from 2007 to 2020 was measured by the superefficient SBM-DEA model, considering undesirable output, and analyzing the evolutionary trends of overall comprehensive technical efficiency, pure technical efficiency, and scale efficiency. The Theil index was used to explore the source and distribution of the Chinese cities’ EWP differences. Exploratory spatial data analysis (ESDA) and the spatial Durbin model (SDM) were applied to analyze the spatial distribution characteristics and driving factors of cities’ EWP. The results showed the following: (1) Regarding spatial and temporal distribution, the EWP of Chinese cities showed a fluctuating upward trend, in which pure technical efficiency > scale efficiency. (2) Considering regional differences, the differences in cities’ EWP were mainly intraregional rather than interregional. The contribution rates of distinct regions to the differences in EWP varied, i.e., western region > eastern region > central region > northeastern region. (3) In terms of spatial correlation, China’s EWP showed positive spatial correlation, i.e., high–high agglomeration and low–low agglomeration. (4) Concerning influencing factors, the level of financial development, the structure of secondary industries, the level of opening-up, and the degree of urbanization significantly improved EWP. Decentralization of fiscal revenue significantly inhibited improvement of EWP. Decentralization of fiscal expenditure and technological progress had no significant impact on the EWP. In the future, to improve cities’ EWP, China should focus on reducing differences in intraregional EWP, overcoming administrative regional limitations, encouraging regions with similar locations to formulate coordinated development plans, promoting economic growth, reducing levels of environmental pollution, and paying attention to the improvement of social welfare.