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Engineered nanomaterials have emerged as a promising technology for water treatment, particularly for removing heavy metals. Their unique physicochemical properties enable them to adsorb large quantities of metals even at low concentrations. This review explores the efficacy of various nanomaterials, including zeolites, polymers, chitosan, metal oxides, and metals, in removing heavy metals from water under different conditions. Functionalization of nanomaterials is a strategy to enhance their separation, stability, and adsorption capacity. Experimental parameters such as pH, adsorbent dosage, temperature, contact time, and ionic strength significantly influence the adsorption process. In comparison, engineered nanomaterials show promise for heavy metal remediation, but several challenges exist, including aggregation, stability, mechanical strength, long-term performance, and scalability. Furthermore, the potential environmental and health impacts of nanomaterials require careful consideration. Future research should focus on addressing these challenges and developing sustainable nanomaterial-based remediation strategies. This will involve interdisciplinary collaboration, adherence to green chemistry principles, and comprehensive risk assessments to ensure the safe and effective deployment of nanomaterials in heavy metal remediation at both lab and large-scale levels.

Climate change has broadly impacted on the China areas. There will be severe challenges due to the variations of precipitation and temperature in the future. Therefore, a comprehensive understanding of the future climate change over China areas is desired. In this study, future annual precipitation and annual mean temperature under two SSPs over China areas were projected through multiple global climate models. Meanwhile, to explore the sources of uncertainty in projecting future climate change, the multi-factorial analysis was conducted through GCMs (five levels) and SSPs (two levels). This study can help us understand the possible changes in precipitation, temperature, and the potential extreme climate events over the China area. The results indicate that China would have more annual precipitation and higher annual mean temperature in the future. Compared with the historical period, the annual mean temperature would face a continuously increasing trend under SSPs. Regardless of SSP245 or SSP585, the growth rate of annual precipitation and annual mean temperature increase in the northern region (e.g., Northeast China, North China, and Northwest China) are higher than those in the southern parts (e.g., East China, South China, and Central China). The future temperature rise may increase the frequency of heat-related extreme climate events, which needs to be focused on in future research. Moreover, GCM was the main contributing factor to the sources of uncertainty in projecting future precipitation and SSP was the main factor for future temperature. Overall, climate change is an indisputable fact in China. The annual precipitation and annual mean temperature would increase to varying degrees in the future. Reducing the systemic bias of the climate model itself will undoubtedly be the top priority, and it would help to improve the projection and evaluation effects of relevant climate variables.

The textile industry is responsible for a significant amount of global CO2 emissions, exceeding those from several other sectors such as international aviation and shipping. This article outlines the reasons for the textile industry’s contribution to climate change along with an overview of current trends. Finally, it outlines several measures to reduce its carbon footprint.

Based on the panel data of 30 provinces in China from 2010 to 2019, this study empirically analyzes the relationship between fiscal decentralization and carbon productivity using a spatial econometric model and calculates the direct effect, spatial spillover effect, and total effect of fiscal revenue decentralization and fiscal expenditure decentralization on carbon productivity through effect decomposition. The empirical results show that 1) the spatial agglomeration effect of China’s provincial carbon productivity is obvious, which shows an upward trend. The heterogeneity of carbon productivity among different provinces is obvious. The overall performance is as follows: Eastern provinces > Central provinces > Western provinces. 2) Fiscal revenue decentralization and fiscal expenditure decentralization can significantly promote the improvement of carbon productivity. Fiscal expenditure decentralization plays a greater role in promoting carbon productivity than fiscal revenue decentralization. 3) Fiscal revenue decentralization and fiscal expenditure decentralization have significant positive direct effects and negative spatial spillover effects on the improvement of carbon productivity. Increasing fiscal decentralization is conducive to improving the carbon productivity of the province, but it will inhibit the carbon productivity of neighboring provinces. Finally, it puts forward policy suggestions to promote the improvement of carbon productivity from the perspective of fiscal decentralization.

The digital economy (DE) is emerging as a crucial driver of economic growth and an effective tool for alleviating resource and environmental pressures, thereby evolving into a significant force in facilitating green transformation. This study elaborates on the theoretical mechanism of the impact of DE on green total factor productivity (GTFP), and conducts multidimensional empirical tests using panel data from 284 cities in China. The main findings are as follows: (1) DE exerts significant positive direct, indirect, and spatial spillover effects on GTFP, signifying its growing role as a robust driver of GTFP. Notably, technological innovation emerges as a key mediator of DE’s impact on GTFP. (2) The impact of DE on GTFP exhibits a distinct pattern: initially pronounced, gradually diminishing, and then rebounding as DE progresses. (3) DE tends to exacerbate, rather than alleviate, the development divide and resource curse, especially in underdeveloped and resource-rich cities where its benefits are constrained. (4) Government behavior is pivotal in influencing DE’s impact on GTFP. Supportive policies and strict environmental regulations are critical in harnessing DE’s positive contributions to GTFP. This study lays a scientific foundation for leveraging the “green attributes” of DE and offers insights into bridging the developmental disparities among cities.

The heterogeneity of investor sentiment plays a key role in causing the asymmetry of information transmission patterns and transmission intensity between markets. This paper analyzes the asymmetric risk spillover between the international crude oil market and other markets, including commodity market and financial market, using monthly data from June 2006 to October 2020. The risk from the international crude oil market is separated into upside and downside risks. The empirical results suggest that, first, from the perspective of static spillover, the risk spillover between the international oil market and commodity market or financial market enhances significantly in response to rising return; second, from the perspective of dynamic spillover, the asymmetric risk spillover of international crude oil market manifests the key roles played by important events happening in the crude oil market and alternating attributes of crude oil. Some policy suggestions are proposed in light of these empirical results.

The construction industry is an important material production sector of the national economy, and trade in goods and services between different industrial sectors in different regions may result in the transfer of embodied carbon emissions from the construction industry. A systematic identification of the relationships and structural characteristics of the embodied carbon transfer in the construction industry is crucial for rationally defining the responsibility for emission reduction and scientifically formulating emission reduction policies to promote the effective promotion of China’s carbon emission reduction actions. Based on the calculation of input-output theory, this study constructs a multi-regional input-output (MRIO) model of 31 provinces in China containing 28 industries to estimate the carbon emissions of the construction industry in 2017, it also combines the complex network theory to construct the industrial and regional embodied carbon transfer network of China’s construction industry, and calculates the network structure indexes to deeply explore the spatial transfer network structure characteristics of the embodied carbon transfer between regions of China’s construction industry in 2017. The results show that the construction, energy and building materials manufacturing sectors are at the core of the sectoral carbon transfer network structure, with strong network control. The embodied carbon transfer network between regions in the construction industry has a small-world character, more than 40% of all relevant regions have carbon transfer relationships with other regions, significant carbon emissions are transferred from the resource-rich, industrially well-endowed central-western and north-eastern provinces to the economically developed south-eastern coastal provinces. According to the results of the study, differentiated carbon emission reduction plans are formulated, and policy suggestions for optimizing the carbon emission reduction plan of the construction industry are put forward.

Prediction of bond return is a classic problem in financial area, providing an important basis for portfolio construction and risk management. The sustainable investment attribute of green bonds has been favored by investors, so that green bonds have become an important component for major asset allocation. However, due to the specific investment focus of green bonds, investors’ return expectations are influenced not only by traditional corporate bond factors, but also by related factors such as climate change and energy transition. Against the backdrop of increasingly severe climate risks and the global energy crisis, this paper analyses the volatility characteristics of China’s green bonds at multiple time scales, and introduces exogenous variables such as returns of the alternative financial assets, climate risks and returns of energy markets for prediction. Based on the LSTM model, the volatility of green bond yield at different time scales is separately predicted using optimal exogenous variable before integration. It is found that the new integrated prediction model can significantly improve the forecasting performance compared to traditional single LSTM models and simple decomposition-integrated models. Further, both climate risks and energy markets variables have a significant improvement effect on predicting green bond in low-frequency item, while energy markets variables also have a better predictive effect on trend items. Building on the use of only LSTM model, it could be further enhanced by integrating more algorithms to select the best single model for each component, further improve the prediction accuracy and provide a more effective quantitative tool for investment decision-making and risk management in related fields.