The impacts of air pollution on human and natural capital in China: A look from a provincial perspective

Abstract Air quality has a significant impact on human health and natural systems worldwide. China, as one of the largest developing countries, faces very much serious air pollution and requires much attention. While the influences of air pollution on human or nature have been extensively investigated, few scholars considered the two effects of air pollution on human health and nature simultaneously based on the same framework. Indeed, human and nature coexist in the same biosphere on which they depend for their development and the impacts of air pollution on human health and nature occur at the same time with different and synergic effects. Only by considering both impacts we can develop a more comprehensive understanding of air pollution impacts, in particular including SO2, NO2, CO, PM10 and PM2.5. Impacts can be looked at from the point of view of damage provided and damage repair (health recovery, replacement cost). Therefore, considering the different pollutants and sectors, the influences of air pollution on human health and nature are accounted for in this study by applying the Emergy Accounting and Life Cycle Assessment Eco-indicator 99 methods under a unified framework in 31 provinces of China taken as case study. While LCA provides an accurate assessment of the direct consequences of pollution on human and natural capital (human health and biodiversity losses), the Emergy Accounting approach quantifies the biosphere work associated to repair or replace such losses over time. Furthermore, the spatial agglomeration characteristics of emissions, human and natural capital losses analyzed by means of Moran's I index. Results show that: (1) Concerning human capital losses, the amount of emissions of PM10 and PM2.5 only account for 10% of total impacts, compared to SO2, NO2, and CO emissions, but in some provinces cause more than 70% of human capital losses. And more than 80% of PM2.5 and PM10 that cause human capital losses come from the industrial and civil sectors. (2) As far as natural capital losses are concerned, compared with SO2, the losses caused by NO2 account for 80% in most provinces. And the power, industrial and transportation sectors are the three major sources of NO2 causing natural capital losses. (3) The spatial agglomeration characteristics, such as high-high cluster, high-low cluster, low-low cluster and low–high cluster, are different for air pollution emissions, human and natural capital losses. A comprehensive and detailed understanding of the impacts of air pollution is crucial for policy makers to take informed decisions

The Multiscale Conformation Evolution of the Financial Time Series

Fluctuations of the nonlinear time series are driven by the traverses of multiscale conformations from one state to another. Aiming to characterize the evolution of multiscale conformations with changes in time and frequency domains, we present an algorithm that combines the wavelet transform and the complex network. Based on defining the multiscale conformation using a set of fluctuation states from different frequency components at each time point rather than the single observable value, we construct the conformational evolution complex network. To illustrate, using data of Shanghai’s composition index with daily frequency from 1991 to 2014 as an example, we find that a few major conformations are the main contributors of evolution progress, the whole conformational evolution network has a clustering effect, and there is a turning point when the size of the chain of multiscale conformations is 14. This work presents a refined perspective into underlying fluctuation features of financial markets

Extracellular vesicle-mediated communication between CD8+ cytotoxic T cells and tumor cells

Tumors pose a significant global public health challenge, resulting in numerous fatalities annually. CD8+ T cells play a crucial role in combating tumors; however, their effectiveness is compromised by the tumor itself and the tumor microenvironment (TME), resulting in reduced efficacy of immunotherapy. In this dynamic interplay, extracellular vesicles (EVs) have emerged as pivotal mediators, facilitating direct and indirect communication between tumors and CD8+ T cells. In this article, we provide an overview of how tumor-derived EVs directly regulate CD8+ T cell function by carrying bioactive molecules they carry internally and on their surface. Simultaneously, these EVs modulate the TME, indirectly influencing the efficiency of CD8+ T cell responses. Furthermore, EVs derived from CD8+ T cells exhibit a dual role: they promote tumor immune evasion while also enhancing antitumor activity. Finally, we briefly discuss current prevailing approaches that utilize functionalized EVs based on tumor-targeted therapy and tumor immunotherapy. These approaches aim to present novel perspectives for EV-based tumor treatment strategies, demonstrating potential for advancements in the field

The Multiscale Fluctuations of the Correlation between Oil Price and Wind Energy Stock

Wind energy is considered a clear and sustainable substitution for fossil fuel, and the stock index of the wind energy industry is closely related to the oil price fluctuation. Their relationship is characterized by multiscale and time-varying features based on a variety of stakeholders who have different objectives within various time horizons, which makes it difficult to identify the factor in which time scale could be the most influential one in the market. Aiming to explore the correlation between oil price and the wind energy stock index from the time–frequency domain in a dynamic perspective, we propose an algorithm combining the wavelet transform, complex network, and gray correlation analyses and choose the Brent oil price and the international securities exchange (ISE) global wind energy index from January 2006 to October 2015 in daily frequency as data sample. First, we define the multiscale conformation by a set of fluctuation information with different time horizons to represent the fluctuation status of the correlation of the oil–wind nexus rather than by a single original correlation value. Then, we transform the multiscale conformation evolution into a network model, and only 270 multiscale conformations and 710 transmissions could characterize 2451 data points. We find that only 30% of conformations and transmissions work as a backbone of the entire correlation series; through these major conformations, we identify that the main factor that could influence the oil–wind nexus are long-term components, such as policies, the status of the global economy and demand–supply issues. In addition, there is a clustering effect and transmissions among conformations that mainly happen inside clusters and rarely among clusters, which means the interaction of the oil–wind nexus is stable over a short period of time

Beyond threats: Extreme heatwaves and economic resilience in China

This study demonstrates extreme heatwaves have a positive impact on economic resilience in China at the provincial level, utilizing high spatial resolution temperature data. This effect may be attributed to heightened climate policy uncertainty and shifts in public climate perception. Furthermore, our findings reveal that these effects are particularly pronounced in the eastern regions of China and in provinces with large economic scales but relatively small agriculture output values

Changes in Air Quality during the Period of COVID-19 in China

This paper revisits the heterogeneous impacts of COVID-19 on air quality. For different types of Chinese cities, we analyzed the different degrees of improvement in the concentrations of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) during COVID-19 by analyzing the predictivity of air quality. Specifically, we divided the sample into three groups: cities with severe outbreaks, cities with a few confirmed cases, and cities with secondary outbreaks. Ensemble empirical mode decomposition (EEMD), recursive plots (RPs), and recursive quantitative analysis (RQA) were used to analyze these heterogeneous impacts and the predictivity of air quality. The empirical results indicated the following: (1) COVID-19 did not necessarily improve air quality due to factors such as the rebound effect of consumption, and its impacts on air quality were short-lived. After the initial outbreak, NO2, CO, and PM2.5 emissions declined for the first 1–3 months. (2) For the cities with severe epidemics, air quality was improved, but for the cities with second outbreaks, air quality was first enhanced and then deteriorated. For the cities with few confirmed cases, air quality first deteriorated and then improved. (3) COVID-19 changed the stability of the air quality sequence. The predictability of the air quality index (AQI) declined in cities with serious epidemic situations and secondary outbreaks, but for the cities with a few confirmed cases, the AQI achieved a stable state sooner. The conclusions may facilitate the analysis of differences in air quality evolution characteristics and fluctuations before and after outbreaks from a quantitative perspective

The Multiscale Fluctuations of the Correlation between Oil Price and Wind Energy Stock

Wind energy is considered a clear and sustainable substitution for fossil fuel, and the stock index of the wind energy industry is closely related to the oil price fluctuation. Their relationship is characterized by multiscale and time-varying features based on a variety of stakeholders who have different objectives within various time horizons, which makes it difficult to identify the factor in which time scale could be the most influential one in the market. Aiming to explore the correlation between oil price and the wind energy stock index from the time–frequency domain in a dynamic perspective, we propose an algorithm combining the wavelet transform, complex network, and gray correlation analyses and choose the Brent oil price and the international securities exchange (ISE) global wind energy index from January 2006 to October 2015 in daily frequency as data sample. First, we define the multiscale conformation by a set of fluctuation information with different time horizons to represent the fluctuation status of the correlation of the oil–wind nexus rather than by a single original correlation value. Then, we transform the multiscale conformation evolution into a network model, and only 270 multiscale conformations and 710 transmissions could characterize 2451 data points. We find that only 30% of conformations and transmissions work as a backbone of the entire correlation series; through these major conformations, we identify that the main factor that could influence the oil–wind nexus are long-term components, such as policies, the status of the global economy and demand–supply issues. In addition, there is a clustering effect and transmissions among conformations that mainly happen inside clusters and rarely among clusters, which means the interaction of the oil–wind nexus is stable over a short period of time

Reconstructing time series into a complex network to assess the evolution dynamics of the correlations among energy prices

Reconstructing a time series into a complex network can help uncover the dynamic information hidden in the time series. Previous studies mainly focused on the long-term relationship between two energy prices, and traditional econometric methods poorly reflect the evolution of correlations among variables from a short-term perspective. Thus, first, we divide natural gas, coal and crude oil price time series into a series of segments via a set of temporal sliding windows and then calculate the correlation coefficients for each pair of energy prices in each segment. Second, we define the correlation modes based on the correlation coefficients and a coarse graining process. Third, we reconstruct the time series into a complex network to assess the evolution dynamics of the correlations among energy prices. The results show that a few major correlation modes and transmission patterns play important roles in the evolution. The evolution of the correlation modes among energy prices exhibits a significant cluster effect. Approximately 30 days is a turning point at which one type of cluster transforms into another type. Then, we improve the betweenness centrality algorithm to measure the media capability of the correlation mode in the evolution process of different clusters. Based on the transmission probabilities between clusters, we can determine the evolution direction of the correlation modes based on energy prices. These results are useful for monitoring fluctuations in energy prices and making decisions for risk avoidance

Revisiting China-Africa trade from an environmental perspective

International trade patterns can be seen as ways to redistribute natural resources and manufactured products, by means of convergence and divergence pathways, in support of production and consumption processes worldwide. By making needed resources to potential users (individuals and economies) trade acts as a driver of resource extraction, processing, degradation, especially if this is facilitated by market dynamics in which prices are determined by contingent factors that have no links to the environmental dynamics of resource generation and do not match the real quality of natural capital and ecosystems services involved. A fair trade relationship should take these aspects into proper account, in so promoting additional criteria for resource value and, as a consequence, towards efficient resource use and cleaner production processes. A comprehensive cost and benefit evaluation to consider the economic and ecological impacts is therefore a much needed prerequisite for a balanced trade relationship. To conduct this evaluation, we firstly choose the trade data of China with South Africa, Sudan, Algeria, Nigeria, Egypt and Morocco in the years 2001, 2004, 2008 and 2012 as sample set. Then we apply the emergy accounting approach to the international trade dynamic between China and above selected African countries to quantify the exchange of natural capital and ecosystem services among partners (including resources that support know-how and technology exchange), as well as to identify benefits and compensation measures that may increase trade balance and equity via the prevention of uncompensated resource exploitation. By accounting for the environmental support embodied in traded resources and their capability to support an economic process, the emergy approach applied in this study provides a complementary tool to economic evaluation, which enables a more comprehensive understanding of trade, beyond the monetary terms of trade. In terms of the total emergy exchange, the investigated African countries (with the exception of South Africa and Sudan) receive more emergy from China over the investigated period, which appears to suggest a reversal of the typical trend in which industrialized economies exploit African countries and return small or no benefit to their economies. However, the composition of the emergy trade indicates that China's import from Africa is mainly composed of primary products, whereas manufactured products dominate its export. This composition is likely to promote the Chinese economy (supporting resource processing and jobs), as well as increase the lifestyle of the wealthy fractions of the African population as a result of increased access to consumer goods; however, in turn, it may contribute to heavier pollution in China and certainly does not favor the development of local industry in Africa. Thus, more balanced import and export relations and trade structure that simultaneously involves human and natural capital will be helpful to construct a cooperative relationship

Spatial and Seasonal Characteristics of Air Pollution Spillover in China

Air pollution spillover can cause air pollution to negatively affect neighboring regions. The structure of air pollution spillover varies with changes in season and space. Researching the spatial and seasonal characteristics of air pollution spillover is beneficial for determining air pollution prevention and control policies. First, this paper uses the GARCH-BEKK model to correlate the air pollution spillover among cities. Second, a complex network is constructed, and cities that have stronger spillover correlations are grouped into the same region. Finally, motifs are analyzed regarding the spillover relationships among regions. This paper also compares the structure of air pollution spillover during various seasons. This study determines that every season has a core region where the air pollution spillover exits the region. The core region in the spring is western East China, in the summer it is northern East China, in the autumn it is northern East China, and in the winter it is northern North China. These regions interact with most other regions. Furthermore, in spring and winter, the phenomena of air pollution spillover between regions are stronger than those in summer and autumn. We can weaken the air pollution spillover by controlling the air pollution in core regions