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The development of the digital economy holds great significance for alleviating haze pollution. To estimate the impact of the digital economy on haze pollution, this paper explores the spatiotemporal evolutionary characteristics of the digital economy and PM2.5 concentration in the middle and lower reaches of the Yellow River Basin from 2011 to 2019 and conducts regression analysis by combining a fixed effect (FE) model and the spatial Durbin model (SDM). Moreover, this study divides the mitigation effect of haze pollution into a direct effect and a spatial spillover effect, and it further analyzes the mechanism from the perspectives of technological innovation and the industrial structure. The empirical results show that the development level of the digital economy increases year by year and that the concentration of PM2.5 decreases year by year. The digital economy level and PM2.5 concentration in the downstream region are higher than those in the middle region, and the digital economy is negatively correlated with haze pollution. Similarly, the spatial spillover effect of the digital economy is conducive to curbing haze pollution. The robustness test also supports this conclusion. In addition, there is regional heterogeneity in the impact of the digital economy on haze pollution. The direct effect and spatial spillover effect of the digital economy on haze pollution in the downstream region are greater than those in the middle region. This study suggests that to realize air pollution prevention and control, it is necessary to strengthen the construction of digital infrastructure and create a good digital economy development environment based on local conditions. Encouraging the development of digital technological innovation and promoting industrial digital transformation hold great significance for alleviating haze pollution.

In China, exploring the relationship between human capital and green economy in the context of economic transformation can promote the construction of an ecological civilization and high-quality economic development. This paper is dedicated to assessing the impact of human capital stock, human capital structure, and the difference between the two on China’s green economy. Using the super-efficiency slack-based measure (SBM) model, this paper evaluated the green total factor productivity (GTFP) of 30 Chinese provinces to assess the greenness of its economy and analyzed its spatiotemporal evolution from 2000 to 2017. A spatial error model (SEM) was established to explore the impact of human capital on green economy. The results show that (1) the GTFP of China increased continuously from 0.219 to 0.457, showing a regional spatial differentiation of “eastern region > central region > western region.” The spatial gaps of the green economy in the east-west and north-south directions are narrowing. (2) Moran’s I index and Moran’s I scatter plot indicate a significant spatial correlation between human capital and China’s green economy. The local spatial correlation between human capital and green economy is mainly characterized by “high-high” and “low-low” types of agglomeration. (3) The effects of human capital stock and human capital structure on green economy were both positive, with coefficients of 0.0005 and 0.1601, respectively, but the effects of human capital structure were not significant. (4) The results of regional regression show that the difference between the human capital impact coefficients on green economy in the eastern and midwestern regions is small. The impact of human capital stock and human capital structure on green economy is consistent with the national level results. It can be preliminarily concluded that the development of China’s green economy relied more on improving the human capital stock than on the improvement of the human capital structure. This study further enriches the literature on the green economy and provides information that can support government policy-making.

Comparing the carbon emission efficiency (CEE) of resource and non-resource-based cities in the Yellow River Basin (YRB) can guide their synergistic development and low-carbon transition. This study used the super-efficiency slacks-based measure (super-SBM) model to measure the CEE of cities in the YRB. Kernel density estimation and Theil index decomposition methods were used to explore the spatiotemporal evolutionary patterns, and a panel regression model was established to analyze the influencing factors of CEE. The research results showed that the CEE of the two types of cities have an overall upward trend in time, with a widening regional gap. Resource-based cities mainly displayed the characteristics of decentralized regional agglomeration, while non-resource-based cities mainly showed the characteristics of convergent regional agglomeration. Panel regression results showed that the levels of economic development, indus-trial structure, and population density are significantly positively correlated with CEE in the YRB, while foreign direct investment and resource endowment are significantly negatively correlated with CEE. Except for economic development and industrial structure, there is some variability in the contribution of the remaining influencing factors to the CEE of the resource and non-resource-based cities. The research results suggest developing classification measures for low-carbon transition in the YRB.

Regional and persistent PM 2.5 pollution seriously undermines the development of urban ecological civilizations and the advancement of high-quality economies. The producer service sector, an example of a typical knowledge-intensive service industry, plays an important role in advancing the manufacturing industry and fostering economic growth while concurrently improving urban environmental conditions. Based on panel data of prefecture-level cities in the Yellow River Basin from 2006 to 2019, this study constructed a Spatial Durbin Model and a mediation effect model to comprehensively explore the impact of producer services agglomeration on PM 2.5 pollution. The main conclusions are as follows: (1) From 2006 to 2019, PM 2.5 pollution in the study area exhibited an initial rise followed by a subsequent decline, with notable spatial heterogeneity. PM 2.5 pollution in the lower reaches of the Yellow River was significantly higher than in the middle and upper reaches. In addition, the spatial pattern of producer services agglomeration showed distinct “core-edge” characteristics. (2) The agglomeration of producer services had a significant negative impact on local and adjacent PM 2.5 pollution, and there was a more pronounced haze reduction effect in the case of specialized agglomerations of producer services and low-end producer services. (3) The agglomeration of producer services indirectly improved PM 2.5 pollution by promoting technological innovation and optimizing industrial structure, with the latter playing a greater mediating effect. This study not only helps expand the theoretical and empirical research on producer services agglomeration but also offers valuable insights for pursuing a green transformation of the Yellow River Basin by optimizing industrial patterns through the producer services sector. This approach represents a reference for curbing PM 2.5 pollution and guiding the region toward a greener future.

With the acceleration of urbanization, domestic waste has become one of the most inevitable factors threatening the environment and human health. Waste classification is of great significance and value for improving urban environmental quality and promoting human well-being. Based on the theory of planned behavior, we added external and socio-economic factors to systematically examine how they affect residents’ waste classification behavior (WCB). We collected 661 valid data through a questionnaire survey conducted in Jinan, a pilot city for waste classification in China. Key driving factors were identified by combining binary logistic regression and the principal component analysis. The results showed that the elderly, women, and people with higher education are more likely to participate in waste classification. Attitude, collaborative governance, and institutional pressure positively affect WCB, while subjective norm and infrastructure have a negative effect. Knowledge mastery and degree of publicity are positively and significantly related to WCB, but other perceived behavioral control sub-variables negatively affect WCB. Based on the results and status of waste classification in Jinan, we propose the multi-agent linkage governance pattern from various dimensions to explore a powerful guiding incentive that can enhance WCB and provide a reference for waste management policymakers.

Urban agglomerations have become the core areas for carbon reduction in China since they account for around 75% of its total emissions. Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which are its most important poles of regional development and technological innovation, are key to achieving China’s carbon peak emissions target. Based on the panel data of these three major urban agglomerations from 2003 to 2017, this study estimated the carbon emission efficiency (CEE) by the super-efficiency slacks-based measure (super-SBM) model and analyzed its spatiotemporal distribution pattern. The Dagum Gini coefficient was used to evaluate the difference in CEE between the three major agglomerations, while panel data models were established to analyze the impact of technological innovation on the three agglomerations. The overall CEE showed an upward trend during the study period, with significant spatial and temporal variations. Additionally, the main source of urban agglomeration difference in CEE evolved from inter-regional net differences to intensity of transvariation. While technological innovations are expected to significantly improve CEE, their effect varies among urban agglomerations. These results provide policymakers with insights on the collaborative planning of urban agglomerations and the low-carbon economy.

The development of the digital economy holds great significance for alleviating haze pollution. To estimate the impact of the digital economy on haze pollution, this paper explores the spatiotemporal evolutionary characteristics of the digital economy and PM concentration in the middle and lower reaches of the Yellow River Basin from 2011 to 2019 and conducts regression analysis by combining a fixed effect (FE) model and the spatial Durbin model (SDM). Moreover, this study divides the mitigation effect of haze pollution into a direct effect and a spatial spillover effect, and it further analyzes the mechanism from the perspectives of technological innovation and the industrial structure. The empirical results show that the development level of the digital economy increases year by year and that the concentration of PM decreases year by year. The digital economy level and PM concentration in the downstream region are higher than those in the middle region, and the digital economy is negatively correlated with haze pollution. Similarly, the spatial spillover effect of the digital economy is conducive to curbing haze pollution. The robustness test also supports this conclusion. In addition, there is regional heterogeneity in the impact of the digital economy on haze pollution. The direct effect and spatial spillover effect of the digital economy on haze pollution in the downstream region are greater than those in the middle region. This study suggests that to realize air pollution prevention and control, it is necessary to strengthen the construction of digital infrastructure and create a good digital economy development environment based on local conditions. Encouraging the development of digital technological innovation and promoting industrial digital transformation hold great significance for alleviating haze pollution.

The transboundary health impacts of air pollution associated with the international trade of goods and services are greater than those associated with long-distance atmospheric pollutant transport. The international air pollution trade Air quality and mortality are affected by local air pollution, but not all local air pollution comes from local emissions. It is also fed by atmospheric transport of pollutants from distant sources, and some of the pollution in one region is due to the production of goods for consumption in another. This study investigates the effect of these two remote pollution sources on premature mortality linked to fine particulate matter pollution. Qiang Zhang et al . find that, in 2007, about 12 per cent of premature deaths related to fine particulate matter were attributed to air pollutants from distant sources and about 22 per cent were associated with goods and services produced in one region for consumption in another. The findings suggest that the health impacts of pollution associated with international trade are greater than those associated with long-distance atmospheric pollutant transport. Millions of people die every year from diseases caused by exposure to outdoor air pollution 1 , 2 , 3 , 4 , 5 . Some studies have estimated premature mortality related to local sources of air pollution 6 , 7 , but local air quality can also be affected by atmospheric transport of pollution from distant sources 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 . International trade is contributing to the globalization of emission and pollution as a result of the production of goods (and their associated emissions) in one region for consumption in another region 14 , 19 , 20 , 21 , 22 . The effects of international trade on air pollutant emissions 23 , air quality 14 and health 24 have been investigated regionally, but a combined, global assessment of the health impacts related to international trade and the transport of atmospheric air pollution is lacking. Here we combine four global models to estimate premature mortality caused by fine particulate matter (PM 2.5 ) pollution as a result of atmospheric transport and the production and consumption of goods and services in different world regions. We find that, of the 3.45 million premature deaths related to PM 2.5 pollution in 2007 worldwide, about 12 per cent (411,100 deaths) were related to air pollutants emitted in a region of the world other than that in which the death occurred, and about 22 per cent (762,400 deaths) were associated with goods and services produced in one region for consumption in another. For example, PM 2.5 pollution produced in China in 2007 is linked to more than 64,800 premature deaths in regions other than China, including more than 3,100 premature deaths in western Europe and the USA; on the other hand, consumption in western Europe and the USA is linked to more than 108,600 premature deaths in China. Our results reveal that the transboundary health impacts of PM 2.5 pollution associated with international trade are greater than those associated with long-distance atmospheric pollutant transport.

Minimizing exposure of the fetus to medication and reducing adverse off-target effects in the mother are the primary challenges in developing novel drugs to treat pregnancy complications. Nanomedicine has introduced opportunities for the development of novel platforms enabling targeted delivery of drugs in pregnancy. This review sets out to discuss the advances and potential of surface-functionalized nanoparticles in the targeted therapy of pregnancy complications. We first describe the human placental anatomy, which is fundamental for developing placenta-targeted therapy, and then we review current knowledge of nanoparticle transplacental transport mechanisms. Meanwhile, recent surface-functionalized nanoparticles for targeting the uterus and placenta are examined. Indeed, surface-functionalized nanoparticles could help prevent transplacental passage and promote placental-specific drug delivery, thereby enhancing efficacy and improving safety. We have achieved promising results in targeting the placenta via placental chondroitin sulfate A (plCSA), which is exclusively expressed in the placenta, using plCSA binding peptide (plCSA-BP)-decorated nanoparticles. Others have also focused on using placenta- and uterus-enriched molecules as targets to deliver therapeutics via surface-functionalized nanoparticles. Additionally, we propose that placenta-specific exosomes and surface-modified exosomes might be potential tools in the targeted therapy of pregnancy complications. Altogether, surface-functionalized nanoparticles have great potential value as clinical tools in the targeted therapy of pregnancy complications.

The proper regulation of transcription is essential for maintaining genome integrity and executing other downstream cellular functions 1 , 2 . Here we identify a stable association between the genome-stability regulator sensor of single-stranded DNA (SOSS) 3 and the transcription regulator Integrator-PP2A (INTAC) 4 , 5 – 6 . Through SSB1-mediated recognition of single-stranded DNA, SOSS–INTAC stimulates promoter-proximal termination of transcription and attenuates R-loops associated with paused RNA polymerase II to prevent R-loop-induced genome instability. SOSS–INTAC-dependent attenuation of R-loops is enhanced by the ability of SSB1 to form liquid-like condensates. Deletion of NABP2 (encoding SSB1) or introduction of cancer-associated mutations into its intrinsically disordered region leads to a pervasive accumulation of R-loops, highlighting a genome surveillance function of SOSS–INTAC that enables timely termination of transcription at promoters to constrain R-loop accumulation and ensure genome stability. SOSS–INTAC stimulates promoter-proximal termination of transcription and attenuates R-loops associated with paused RNA polymerase II to prevent R-loop-induced genome instability.