IntroductionPollution reduction, carbon reduction, green expansion and economic growth—the synergistic effects of the four—have become essential in maintaining urban ecological security and promoting a green and low-carbon transition. And it is inherently consistent with the globally accepted concept of sustainable development. MethodsBased on the evaluation index system and the coupling mechanism of the four, we adopt the entropy method and the coupling coordination model to measure the synergistic level of “pollution reduction, carbon reduction, green expansion and economic growth” in 243 cities above prefecture level in China from 2005 to 2020. Furthermore, the study examined the temporal and spatial evolution and regional differences by utilizing the center of gravity-standard deviation ellipse, Dagum Gini coefficient method, Kernel density estimation, and Markov chain. In addition, the spatial econometric model was used to analyze the driving factors affecting the synergistic development.ResultsThe results show that the overall synergistic level is rising, the spatial distribution characteristics of “high in the east and low in the west.” The standard deviation ellipse shows a “northeast–southwest” pattern, and the center of gravity moves in a “southeast–northwest–southwest” migration trend. Regional differences are mainly rooted in inter-regional differences. The intra-regional differences are East > West > Central, with the most prominent East–West inter-regional differences. Without considering the spatial factor, the synergistic level shows a steady increase and has continuity. Under the spatial condition, the synergistic level has a positive spatial correlation. However, the positive spatial correlation decreases significantly as the years go by. Also, the probability of “rank locking” of synergistic development has been reduced, and there is a leapfrog shift. In terms of driving factors, the innovation level, level of external openness, population size, and industrial structure positively drive synergistic development. While government intervention negatively affects synergistic development. DiscussionsBased on the above findings,policy recommendations are proposed to strengthen the top-level design and build a policy system, play the radiation linkage, apply precise policies according to local conditions, and optimize the industrial structure fully. Which is of great significance for improving the urban ecological resilience and helping to achieve the “double carbon” target.

Exploring the effect of new-type urbanization (NTU) on urban carbon abatement is of great practical significance for promoting urban green construction and coping with the challenge of global climate change. This study used data from 250 cities in China from 2008 to 2020 and constructed the NTU evaluation indicator system from five dimensions. We used classical panel regression models to examine the effects of NTU on urban CO2 emissions, and further used spatial econometric models of SEM, SAR, and SDM to identify the spatial spillover effects of NTU on urban CO2 emissions. The main results are that China’s NTU and CO2 emissions are generally rising, and NTU has a significantly negative effect on urban CO2 emissions, with an impact coefficient of −0.9339; the conclusions still hold after subsequent robustness tests. Heterogeneity analysis reveals that NTU’s carbon abatement effect is more pronounced in resource-based cities, old industrial areas, and cities with lower urbanization levels and higher innovation levels. Mechanism analysis shows that improving urban technological innovation and optimizing resource allocation are important paths for realizing urban CO2 emission reduction. NTU’s effect on urban CO2 emissions has a noticeable spatial spillover. Our findings provide policy makers with solid support for driving high-quality urban development and dual-carbon targets.

Climate change is an epochal problem that all countries in the world need to face and solve together. Actively exploring the path of carbon emission reduction is an inevitable choice to deal with climate change. Based on measuring the carbon emissions of China's rural residents' living consumption from 2000 to 2019, this study further adopts the Dagum Gini coefficient, Kernel density estimation, Markov chain, σ Convergence, and β Convergence Conduct empirical analysis on the measurement results. It is found that the differences in carbon emissions of rural residents' living consumption in the whole country, low, middle-low and middle-high level regions are all significantly decreasing, and the regional differences are the main source of the overall differences. There are no very high or very low carbon emissions of rural residents' living consumption in the middle-low level areas, while there is obvious two-stage differentiation in the middle-high level areas. There is instability in the carbon emissions of rural residents' living consumption, which can be transferred downward toward the ideal state, and there is also the risk of increasing carbon emissions and transferring upward; The whole country and the four regions showed typical σ Convergence and β Convergence characteristics. On this basis, the paper puts forward policy recommendations to reduce the spatial imbalance of carbon emissions from rural residents' living consumption. It provides a factual basis for reducing the carbon emissions of rural residents' living consumption at the current and future stages and provides a new scheme for sustainable development based on the concept of a community of shared future for mankind.

Actively exploring a reduction in carbon emissions from rural residents’ living consumption (RRLC) is necessary to address climate change and achieve high-quality development of the rural economy. Based on the measurement of the carbon emissions from RRLC in China between the years 2000 and 2021, and it uncovers regional differences, dynamic evolution and convergence. The main findings are as follows: (1) Using the Dagum Gini coefficient, it was found that the differences in carbon emissions from RRLC in the nationwide and low-income level group (LLLG), low-middle-income level group (LMLG), upper-middle-income level group (UMLG), and high-income level group (HHLG) are all significantly decreasing, and the intensity of transvariation is the primary source of the overall difference. (2) Using the kernel density estimation, it was found that the level of carbon emissions from RRLC in the nationwide and the four major regions have generally gone upward,,as well as a polarisation phenomenon. (3) Using the Markov chain, it was shown that there is an instability in the carbon emissions from RRLC, which can be transferred downward to the ideal state, but there is also a risk of increasing the upward shift of carbon emissions. (4) The nationwide level and the four regions showed typical σ convergence characteristics and absolute β convergence. After considering the influence of socio-economic and natural climatic factors, conditions β convergence trend is shown. There are significant regional differences in spatial β convergence, with spatial absolute β convergence in the UMLG region, and spatial conditional β convergence in the nationwide and UMLG regions. The limitation of this study is that the data on carbon emissions from RRLC are only obtained at the macro level, which cannot accurately reflect the micro and individual impact on RRLC. On this basis, the paper puts forward policy recommendations to reduce the spatial imbalance of carbon emissions from RRLC.