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.

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.

As a composite indicator that incorporates economic efficiency and environmental protection, ecological efficiency is a valuable tool for measuring regional green development and accelerating regional green transformation. As the economy transitions, Chinese economic growth targets affect local governments’ behaviors, thereby impacting ecological efficiency. In this study, the ecological efficiency level of 284 cities in China was measured using the EBM-DEA method from 2007 to 2019, and the spatial exploration analysis method and the dynamic double fixed effect spatial Durbin model were applied to analyze urban ecological efficiency’s spatial correlations, impacts, and mechanisms. The conclusions are as follows: China’s urban ecological efficiency has increased over time. At the spatial level, it shows the distribution characteristics of east > northeast > middle > west. In terms of spatial agglomeration, there are typically spatial agglomerations, high–high agglomerations, and low–low agglomerations in Chinese cities’ ecological efficiency. There is an inverted U-shaped relationship between economic growth target and ecological efficiency. According to regional differences, the economic growth target in the eastern region has a U-shaped impact on ecological efficiency, while in the central, northeast, and western cities they have an inverted U-shaped effect on ecological efficiency. In terms of the impact mechanism, through the intermediary effect test, it is found that appropriate economic growth target setting can promote the proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure. Excessive economic growth target setting can inhibit the proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure. The proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure can promote ecological efficiency. The enlightenment is as follows: China should weaken the economic growth target in official promotion assessment, set differentiated economic growth targets for different regions, and increase the proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure to promote ecological efficiency.

People’s health is a necessary condition for the country’s prosperity. Under the background of the COVID-19 pandemic and frequent natural disasters, exploring the spatial and temporal distribution, regional differences and convergence of China’s provincial public health level is of great significance to promoting the coordinated development of China’s regional public health and achieving the strategic goal of a “healthy China”. Based on China’s provincial panel data from 2009 to 2020, this paper constructs an evaluation index system for China’s public health level from five dimensions: the popularization of a healthy life, optimization of health services, improvement of health insurance, construction of a healthy environment, and development of a health industry. In this paper, the entropy method, Dagum Gini coefficient, Kernel density function and spatial econometric model are used to analyze the spatiotemporal distribution, regional differences, dynamic evolution and convergence of China’s public health level since the new medical reform. The study found that, first, China’s public health level is generally low, structural contradictions are prominent and the construction of a healthy environment has become a shortcoming hindering the improvement of China’s public health level since the new medical reform. The public health level of the four major regions showed a spatial distribution pattern of “high in the eastern, low in the northeastern, central and western” areas. Second, the overall Gini coefficient of China’s public health level showed a “V-shaped” trend of first decreasing and then rising, but the overall decrease was greater than the increase, among which the regional difference was the main source of regional differences in China’s public health level, but its contribution rate showed a downward trend. Third, except for the basic maintenance of a healthy environment, the Kernel density curves of China’s public health level and its sub-dimensions have shifted to the right to a certain extent, and there is no polarization phenomenon. Finally, the level of public health in China has a significant spatial correlation. Except for the northeast region, the growth rate of low-level public health provinces in China and the other three major regions is higher than that of high-level public health provinces, showing a certain convergence trend. In addition, the impact of economic development, financial pressure, and urbanization on the convergence of public health levels in the four major regions is significantly heterogeneous.

Regional climate change is affected by global warming, large-scale inter-regional circulation, and land use/cover. As a result of different ecological, economic, and social conditions, climate adaptation actions vary from region to region, including community-based adaptation in small island developing states, enhancing flood resilience in Europe, weather index insurance promotion in Africa, climate change adaptation based on traditional knowledge in the Polar Regions, and global joint decision-making in terms of regional issues of the Ocean. This paper takes the above five typical cases as the research objects, and the multi-case comparative research method is adopted to discuss regional climate change adaptation based on the pressure–state–response framework. It found that: (1) regional climate change adaptation faces significant pressure from cross-regional flows of finance, population, and species under climate change; (2) climate change hotspot maps based on climate change projections show regional climate vulnerability; (3) responses for regional climate change adaptation require active promotion of multi-level governance with horizontal and vertical cooperation. In the future, regional climate change adaptation should focus on inter-regional climate justice and equality, regional climate change adaptation pathways optimization, and how to effectively learn from typical regional climate adaptation cases.

Climate change has emerged as one of the most significant threats to global biodiversity, and climate adaptation has become a critical component of biodiversity conservation. This paper reviews adaptive management strategies for enhancing biodiversity resilience under climate change, based on a cross-scale framework. The findings reveal that: (1) Biodiversity conservation adaptation to climate change requires a cross-spatial scale framework, which highlights the vertical interaction and interdependencies between regional, landscape, and site-level strategies. (2) Adaptive management strategies vary across spatial scales. At the regional scale, dynamic planning based on assessment and monitoring is prioritized. Landscape-scale initiatives emphasize protected areas as the core, expanding their scope while restructuring networks through corridors, stepping stone, habitat matrix permeability, and climate refugia. At the site scale, efforts focus on in situ and ex situ conservation of keystone species, along with real-time monitoring of invasive species. (3) Future challenges in biodiversity conservation under climate change may include social inequity in adaptation efforts, delayed responses in dynamic landscape conservation planning, disruptions to species’s ecological networks, barriers to interdisciplinary collaboration, and insufficient attention to human-climate interactions. By highlighting the differential application of adaptation strategies across spatial scales and underscoring the critical importance of cross-scale collaboration, our findings provide important insights for advancing research and practice in biodiversity adaptation to climate change, offering a theoretical foundation and practical guidance for developing multi-level, operable climate-adaptive conservation policies.