Air pollution has seriously hindered China’s sustainable development. The impact mechanism of industrial upgrading on air pollution is still unclear, given the rapid digital economy. It is necessary to analyze the impact of industrial structure upgrading on air pollution through the digital economy. To investigate the impact of industrial upgrading and the digital economy on air pollution, this paper selected the industrial advanced index and the digital economy index to construct a panel regression model to explore the improvement effect of industrial upgrading on air pollution and selected China’s three typical areas to construct a zonal regression model. The concentrations of air pollutants showed a downward trend during 2013–2020. Among them, the SO₂ concentration decreased by 63%, which is lower than the PM_2.5 and NO₂ concentrations. The spatial pattern of air pollutants is heavier in the north than in the south and heavier in the east than in the west, with the North China Plain being the center of gravity. These air pollutants have significant spatial spillover effects, while local spatial correlation is dominated by high-high and low-low clustering. Industrial upgrading has a stronger suppressive effect on the PM_2.5 concentration than the suppressive effect on the SO₂ and NO₂ concentrations, while the digital economy has a stronger improvement effect on the SO₂ concentration than its improvement effect on the PM_2.5 and NO₂ concentrations. Industrial upgrading has a stronger improvement effect on air pollution in the Yangtze River Delta urban agglomeration than in Beijing–Tianjin–Hebei and its surrounding areas, while the improvement in air pollution attributable to the digital economy in Beijing–Tianjin–Hebei and its surrounding areas is stronger than in the Yangtze River Delta urban agglomeration. There are significant differences in the effects of industrial upgrading and the digital economy on the various types of air pollutants.

Industrial production is currently the main source of global carbon emissions. There are obvious differences in regional carbon emission efficiencies (CEE) at different industrial stages. We investigate CEE and explore its factors in mainland China at different industrialization stages from 2008-2020 using the super-SBM model with an undesirable output and the STIRPAT model. There is significant spatial heterogeneity in regional CEE, with gaps gradually widening. CEE’s spatial heterogeneity in mid-industrialized provinces is narrowing, while in late-industrialized and post-industrialized provinces, it is widening. CEE’s factors also differ in provinces at different industrialization stages. At the mid-industrialization stage, the industrial structure (IS) is the dominant factor, while population urbanization (PU) is dominant at the late-industrialization stage, and both PU and IS are dominant at the post-industrialization stage. Based on CEE’s characteristics at different industrialization stages, we propose suggestions for green development.

Urban activities are a significant source of global carbon emissions. Balancing economic development and environmental protection during urban construction and development has become a common concern worldwide. The urban form structure, as the spatial foundation of urban activities, is critical for both current and future urban development. In this study, an urban economic activities perspective is taken to examine the relationship between the urban form structure and carbon emission efficiency in 63 prefecture-level cities within China’s three major urban agglomerations from 2013 to 2022. Two dimensions are considered: land resource development and the urban spatial layout. The research findings indicate that (1) the built-up areas of the three major urban agglomerations in China generally exhibit a pattern of “core cities expanding outwards and peripheral cities emerging sporadically”. (2) Various urban form structure indicators have different effects on carbon emission efficiency, with interaction detection via geographic detectors showing a dual-factor enhancement effect. (3) Urban form structure influences carbon emission efficiency through the mediating pathway of economic agglomeration. This study enriches empirical research on the impact of the urban form structure on carbon emission efficiency from an economic activities perspective and provides empirical evidence for urban spatial planning and achieving sustainable development.

China’s rapid urbanization has spurred economic growth and posed environmental challenges. We investigate the relationship between multidimensional urbanization and environmental pollution by a fixed effect model based on the panel data of 70 cities in three major urban agglomerations in the Yangtze River Economic Belt during 2005–2020. Overall, environmental pollution aggravates and then decreases in three major urban agglomerations, which is closely related to China’s environmental pollution control policies. Environmental pollution shows obvious spatial heterogeneities by five levels in three major urban agglomerations, which have been gradually changed from high-value levels to low-value levels. In the Yangtze River Economic Belt, environmental pollution is dominated by “High–High” and “Low–Low” clusters. Among them, “High–High” clusters move eastwards, while “Low–Low” clusters gradually concentrate southeastwards. Multidimensional urbanization helps to alleviate regional environmental pollution. Economic, social, and land urbanization reduces environmental pollution in three urban agglomerations to a certain extent. Population urbanization has a non-significant effect on environmental pollution. Social urbanization has improved environmental pollution in the Chengdu–Chongqing urban agglomeration (CC) and the Yangtze River Delta (YRD), which is increased by population urbanization in the middle reaches of the Yangtze River (MYR) and is improved by comprehensive urbanization in CC.

Accurate assessment of ecosystem service value (ESV) is crucial for sustainable environmental management, especially in regions with high ecological sensitivity and significant socioeconomic importance. This study focuses on the Yellow River Basin and integrates the land-use transition matrix, equivalent factor method, ecosystem service trade-off and synergy analysis, and the optimized parameters geographical detector to analyze the spatiotemporal evolution and driving mechanisms of ESV from 2000 to 2023. The results show that (1) cropland and grassland are the main land-use types in the Yellow River Basin, and during rapid urbanization, the expansion of construction land mainly comes at the expense of cropland and grassland. (2) the total ESV in the basin has steadily increased, with grassland as the primary contributor among land types; regulating services, particularly hydrological regulation, are the core ecosystem services in terms of supply, regulation, support, and cultural functions. (3) High-ESV areas in the eastern and central parts of the basin have expanded over time, exhibiting a spatial pattern of higher values in the west and lower in the east, distributed mainly along the river, with clustering effects gradually weakening. (4) Ecosystem services demonstrated predominantly synergistic relationships, suggesting potential for integrated ecosystem management. (5) Population density, DEM, mean annual temperature, and slope are the dominant factors influencing spatial variation in ESV, with the combined effects of topography and climate significantly enhancing the explanation of ESV heterogeneity. This study deepens the understanding of the evolutionary mechanisms of ecosystem services in the Yellow River Basin and provides scientific support and decision-making references for regional ecological compensation mechanisms, optimized land resource allocation, and watershed ecosystem management.