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.

Scientifically measuring the level of environmental governance (EGL) and understanding its spatial convergence has important reference value for ecological governance. In this paper, the global entropy method is applied to measure the EGL of 284 prefecture-level cities in China from 2007 to 2019, which are divided into three major river basins, including the Yellow River, Yangtze River, and Pearl River, to observe the spatial–temporal evolutionary characterization through a standard deviation ellipse model. The coefficient of variation and the spatial econometric model are the tools used to conduct the spatial convergence test. The results are as follows: (1) China’s EGL is low overall, though it is fluctuating upward at low magnitude, and the three major river basins follow the ranking: The Pearl River Basin > The Yangtze River Basin > The Yellow River Basin. (2) Spatially, the distribution pattern of China’s EGL changes from “scattered and sporadic” to “multipolar core”. (3) The center of China’s environmental governance was concentrated in the east from 2007 to 2019, and the EGL in the midstream and downstream regions of the three major river basins increased rapidly. (4) Environmental governance in China has significant absolute and conditional β-convergence characteristics, as do the three major basins, while the ranking of convergence speed remains “Yangtze River Basin > Yellow River Basin > Pearl River Basin”. Of these, economic development accelerated the convergence rate of environmental governance in China and its three major river basins; financial pressure significantly inhibited the convergence of the EGL of the Yellow River Basin. The improvement of the EGL in the Pearl River Basin was also negatively influenced by the industrial structure.

Protected areas play a significant role in curbing the loss of biodiversity and promoting the recovery of ecosystems. Treating the gradual upgrading of protected areas in China since 2007 as a quasi-natural experiment, this study employs balanced panel data of China's protected areas from 2000 to 2022 and utilizes a multi-period difference-in-differences (DID) model to evaluate the policy effects of protected areas upgrading in China. The research results show that: (1) The upgrading of protected areas in China has significantly promoted the improvement of habitat quality, with the policy effect being ''delayed but ultimately effective'' and exhibiting a fluctuating upward trend. (2) Protected areas located in Eastern and Central China, included in priority areas for biodiversity conservation, and managed by the National Forestry and Grassland Administration (NFGA) are more sensitive to the policy response of upgrading and are more conducive to the improvement of habitat quality in upgraded protected areas. (3) The study recommends encouraging protected areas upgrading to enhance conservation effectiveness, with approval decisions taking into account factors such as geographic location, priority conservation status, and administrative authority. Additionally, dynamic monitoring, adaptive management, and full life-cycle assessments should be implemented to maximize the policy effects of protected areas upgrading.