Rapid urbanization has significant effects on China’s CO2 emissions and contributes to climate change. It is of great theoretical and practical significance to explore the influencing factors of carbon emissions. Both the demographic composition and residential consumption of urban-rural residents were included in the expanding vector form of Kaya identity. This study employed the Logarithmic Mean Divisia Index method (LMDI) to investigate the impact of urbanization on CO2 emissions from the perspective of residential consumption in China during 1980-2020. Six effects were included population size, urbanization, residential consumption, consumption restraint, energy intensity and emission coefficient. The results showed that residential consumption contributed far more to carbon emissions than the other five effects. The impact of urbanization on carbon emission had been greater than that of population size since the middle 1990s. The decline of residential consumption rate imposed stimulation rather than restraint on carbon emissions due to the synchronous growth of shares of higher carbon investment and export in the economy. The continuous decline of energy intensity has an obvious effect on carbon emission reduction. The research results are expected to give inspiration to guide the direction how to optimize resident consumption and to achieve green and low-carbon urbanization development.

Ground-level ozone (O₃) pollution has become a serious environmental issue in major urban agglomerations in China. To investigate the spatiotemporal patterns and regional transports of O₃ in Beijing–Tianjin–Hebei (BTH-UA), the Yangtze River Delta (YRD-UA), the Triangle of Central China (TC-UA), Chengdu–Chongqing (CY-UA), and the Pearl River Delta urban agglomeration (PRD-UA), multiple transdisciplinary methods were employed to analyze the O₃-concentration data that were collected from national air quality monitoring networks operated by the China National Environmental Monitoring Center (CNEMC). It was found that although ozone concentrations have decreased in recent years, ozone pollution is still a serious issue in China. O₃ exhibited different spatiotemporal patterns in the five urban agglomerations. In terms of monthly variations, O₃ had a unimodal structure in BTH-UA but a bimodal structure in the other urban agglomerations. The maximum O₃ concentration was in autumn in PRD-UA, but in summer in the other urban agglomerations. In spatial distribution, the main distribution of O₃ concentration was aligned in northeast–southwest direction for BTH-UA and CY-UA, but in northwest–southeast direction for YRD-UA, TC-UA, and PRD-UA. O₃ concentrations exhibited positive spatial autocorrelations in BTH-UA, YRD-UA, and TC-UA, but negative spatial autocorrelations in CY-UA and PRD-UA. Variations in O₃ concentration were more affected by weather fluctuations in coastal cities while the variations were more affected by seasonal changes in inland cities. O₃ transport in the center cities of the five urban agglomerations was examined by backward trajectory and potential source analyses. Local areas mainly contributed to the O₃ concentrations in the five cities, but regional transport also played a significant role. Our findings suggest joint efforts across cities and regions will be necessary to reduce O₃ pollution in major urban agglomerations in China.

Air pollution is influenced by a combination of pollutant emissions and meteorological conditions. Anthropogenic emissions and meteorological conditions are the two main causes of atmospheric pollution, and the contribution of meteorology and emissions to the reduction of PM_2.5 concentrations across the country has not yet been comprehensively examined. This study used the Kolmogorov–Zurbenko (KZ) filter and random forest (RF) model to decompose and reconstruct PM_2.5 time series in five major urban agglomerations in China, analyzing the impact of meteorological factors on PM_2.5 concentrations. From 2015 to 2021, PM_2.5 concentrations significantly decreased in all urban agglomerations, with annual averages dropping by approximately 50% in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Central Plain (CP), and Chengdu–Chongqing (CC). This reduction was due to both favorable meteorological conditions and emission reductions. The KZ filter effectively separated the PM_2.5 time series, and the RF model achieved high squared correlation coefficient (R²) values between predicted and observed values, ranging from 0.94 to 0.98. Initially, meteorological factors had a positive contribution to PM_2.5 reduction, indicating unfavorable conditions, but this gradually turned negative, indicating favorable conditions. By 2021, the rates of meteorological contribution to PM_2.5 reduction in BTH, YRD, PRD, CP, and CC changed from 14.3%, 16.9%, 7.2%, 12.2%, and 11.5% to −36.5%, −31.5%, −26.9%, −30.3%, and −23.5%, respectively. Temperature and atmospheric pressure had the most significant effects on PM_2.5 concentrations. The significant decline in PM_2.5 concentrations in BTH and CP after 2017 indicated that emission control measures were gradually taking effect. This study confirms that effective pollution control measures combined with favorable meteorological conditions jointly contributed to the improvement in air quality.