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. Based on the pressure-state-response model, this paper uses a combination of multi-case comparative analysis and content analysis to conduct a comparative analysis of five typical regional cases of climate change adaptation around the world. It found that: (1) The pressure to regional climate change adaptation refers to the movement of elements such as finance, population and species across regions. (2) The status is based on regional climate change predictions. (3) The response is two-fold, one being develop a hot map of climate change to assess regional climate vulnerability; the other is to take advantage of decision making and implementation by actively promoting horizontal and vertical cooperation in multi-level governance. In the future, regional climate adaptation will focus on inter-regional climate justice, regional climate change adaptation pathways optimization, and how to effectively learn from typical regional climate adaptations.

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.

Background The associations between short- and long-term exposure to ambient fine particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5) and allergic symptoms in middle-aged and elderly populations remain unclear, particularly in China, where most cities have severe air pollution. Methods Participants (n = 10,142; age = 40–75 years) were recruited from ten regions in China from 2018 to 2021 for the Predictive Value of Inflammatory Biomarkers and Forced Expiratory Volume in 1 s (FEV1) for Chronic Obstructive Pulmonary Disease (PIFCOPD) study. Short-term (lag0 and lag0–7 day) and long-term (1-, 3- and 5-year) PM2.5 concentrations at residences were extracted from the air pollutant database known as Tracking Air Pollution (TAP) in China. Multivariate logistic regression models were used to estimate associations for short- and long-term PM2.5 exposure concentrations and long-term exposure models were additionally adjusted for short-term deviations. Results A 10 µg/m3 increase in PM2.5 on the day the allergic symptoms questionnaire was administered (lag0 day) was associated with higher odds of allergic nasal (1.09, 95% CI 1.05, 1.12) and eye symptoms (1.08, 95% CI 1.05, 1.11), worsening dyspnea caused by allergens (1.06, 95% CI 1.02, 1.10), and ≥ 2 allergic symptoms (1.07, 95% CI 1.03, 1.11), which was similar in the lag0–7 day concentrations. A 10 µg/m3 increase in the 1-year average PM2.5 concentration was associated with an increase of 23% for allergic nasal symptoms, 22% for eye symptoms, 20% for worsening dyspnea caused by allergens, and 21% for ≥ 2 allergic symptoms, similar to the 3- and 5-year average PM2.5 concentrations. These associations between long-term PM2.5 concentration and allergic symptoms were generally unchanged after adjustment for short-term deviations. Conclusions Short- and long-term exposure to ambient PM2.5 was associated with an increased risk of allergic nasal and eye symptoms, worsening dyspnea caused by allergens, and ≥ 2 allergic symptoms. Trial registration Clinical trial ID: NCT03532893 (29 Mar 2018).