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Environmental protection and sustainable development are now profoundly ingrained in the public consciousness in the modern era of the global green revolution. First, this paper uses an evolutionary game model to theoretically analyse the impact of environmental regulation and green finance on enterprises by dissecting the game behaviour between local governments, banks and enterprises. Then, we incorporate entrepreneurial spirit into the model to determine whether entrepreneurial spirit accelerates the achievement of green sustainability objectives. Environmental regulation and green finance are found to contribute substantially to the innovation of green technologies by local enterprises, while entrepreneurial spirit accelerates this process. Finally, we empirically test the findings based on a prefecture-level panel dataset from 2007 to 2020. We find that green finance has a positive impact in Central and Western cities of China, while Eastern cities are in greater need for government environmental regulation controls. For entrepreneurial spirit, the incentive effect of entrepreneurial spirit on the level of green technology innovation is more significant in Central and Western regions of China. This paper enriches the field of research on entrepreneurial spirit and regional green sustainable development by providing empirical evidence for the evaluation of environmental regulatory and green financing policy impacts. [ABSTRACT FROM AUTHOR]

We tested six kinds of commercially available baby products containing nano-silver and divided them into two categories: baby food containers and baby oral care products, according to usage. We measured the total mass of silver, particle size, particle number concentration and the mobility under different simulated conditions. The total mass of silver in baby products and migration solutions was quantified by ICP-MS analysis, and the size of the migrated particles was investigated by single particle ICP-MS and transmission electron microscopy-energy dispersive spectrometer. Nano-silver has different migration behaviour in different materials, and the total mass of silver released in 3% acetic acid was generally highest for plastic material, while highest in 10% ethanol for silicone material. For baby food containers and baby oral care products, the total amount of silver in the migration solutions varied from 2.98 to 126.46 ng/dm2 and from 2.01 to 2.83 ng/g, respectively. The research shows that the use of baby products containing silver may expose babies to silver nanoparticles. [ABSTRACT FROM AUTHOR]

As the climate is warming up and climate disasters occur more frequently, China's carbon peak and carbon neutrality targets are gaining attention, with provinces starting to set carbon peak targets and constantly exploring low carbon economic development opportunities. However, the existing evaluation index system mainly focuses on low carbon cities and green cities, and there is no evaluation index system for carbon peak and carbon neutrality. This study adopts a combination of theoretical and empirical research and draws on domestic and international research results to construct the first index system for evaluating China's provincial carbon peak and carbon neutrality. The evaluation index system includes 18 indicators from three dimensions: climate ambition, low carbon status, and emissions trends. Using this index system, we calculated the carbon peak and carbon neutrality indices of 30 Chinese provinces. Further, the 30 provinces were classified into six categories, and corresponding policy recommendations for achieving the "Dual carbon" targets were put forward. The evaluation index system constructed in this paper can provide a reference for future carbon peak and carbon neutrality assessments. By disclosing the integrated index of each province, we hope to effectively curb the increasing local carbon emissions; avert inaction in areas with good natural endowments; and motivate the advancement of the latter, to boost the implementation of the "Dual carbon" targets coherently for the whole country. [Display omitted] • An integrated evaluation system on China's Provincial "Dual carbon" was proposed. • 30 provinces in mainland China were ranked based on the integrated index. • 6 groups were classified to show provincial differences on dual carbon performance. • Policy recommendations to fulfill China's climate commitment were put forward. [ABSTRACT FROM AUTHOR]

Abstract: A Bayesian approach was applied to river water quality modeling (WQM) for load and parameter estimation. A distributed-source model (DSM) was used as the basic model to support load reduction and effective water quality management in the Hun-Taizi River system, northeastern China. Water quality was surveyed at 18 sites weekly from 1995 to 2004; biological oxygen demand (BOD) and ammonia (NH4 +) were selected as WQM variables. The first-order decay rate (ki ) and load (Li ) of the 16 river segments were estimated using the Bayesian approach. The maximum pollutant loading (L m) of NH4 + and BOD for each river segment was determined based on DSM and the estimated parameters of ki . The results showed that for most river segments, the historical loading was beyond the L m threshold; thus, reduction for organic matter and nitrogen is necessary to meet water quality goals. Then the effects of inflow pollutant concentration (Ci− 1) and water velocity (vi ) on water quality standard compliance were used to demonstrate how the proposed model can be applied to water quality management. The results enable decision makers to decide load reductions and allocations among river segments under different Ci− 1 and vi scenarios. [Copyright &y& Elsevier]

Abstract Using a probabilistic material flow analysis model, we investigate the mass balance and occurrence of nano-silver in the environment of Hunan, renowned as "the home to non-ferrous metals" in China. The model builds on China-specific production and environmental information and incorporates sulfidation of nano-silver in urban wastewater treatment systems. We predict that the bulk of nano-silver (>90%), primarily originating from production and consumption of nano-silver products, ends up in sewage treatment plants (STPs, 5.3 t/a), followed by the landfill (4.7 t/a). More than 99% of nano-silver in STPs reacts with sulfide and thus does not appear in effluents. Direct release of nano-silver from production and consumption is identified as the dominant source of nano-silver in the environment, most of which enters surface water (0.71 t/a). As such, regulation of direct emissions from production and consumption of nano-silver products can be of priority for local environmental management. The modeled regional concentrations (modes) of nano-silver are 0.9 ng/L in surface water, 20 ng/kg in soil, 6.9 μg/kg in sediment, and negligible in the air, which agree well with measurements in the modeled region. Based on the modeled concentrations, we calculate that the risk characterization ratio is <1 in the air, surface water and soil, which means that nano-silver currently poses no risk to organisms living in these environmental compartments. Graphical abstract Unlabelled Image Highlights • Considering the vulcanization of nano-silver in urban wastewater treatment system • China's special conditions and data are applied to PMFA model. • We predicted nano-silver exposure and environmental concentration in Hunan, China. • We discuss the main sources of nano-silver in environmental media. [ABSTRACT FROM AUTHOR]

As a developing country, South Africa faces the risk of having to undertake disruptive actions to meet its internationally pledged emission reduction targets. It has become necessary to find alternative measures for South Africa to meet its climate targets without such a disruptive and aggressive transformation of its energy sector. To truly reach net-zero CO 2 emissions, South Africa must ensure that residual emissions from its recalcitrant sectors are equally compensated by carbon dioxide removal (CDR). Given the potential for CDR technologies to delay the need for rapid emissions cuts, we leverage this characteristic to explore their role in helping South Africa achieve its climate targets on time, but through a less disruptive and overly aggressive pace of energy system decarbonization. Using an integrated assessment model, we show that while reaching its emission reduction targets, the presence of novel CDR (nCDR) approaches could significantly reduce South Africa's mitigation costs to $155-240/tCO 2 instead of $190–590/tCO 2 by 2050 in the absence of nCDR. Furthermore, the availability of nCDR may allow for a more gradual transition towards renewable and nuclear power generation compared to scenarios without nCDR. Importantly, we reveal that nCDR could help avoid asset stranding of up to 4–6 GW and $15–25 billion in associated costs between 2016 and 2050. However, this comes at the cost of higher residual greenhouse gas emissions due to the continued reliance on fossil fuels under nCDR availability. Overall, South Africa's priority must remain on decarbonization, especially in sectors where fuel switching and energy efficiency are feasible. Nonetheless, the complementary role of nCDR must not be underestimated, and South Africa should begin investing in these emerging technologies to support its ambitious climate goals. • Study employs GCAM to model South Africa's net-zero ambitions. • Role of carbon removal technologies explored. • Coal power generation quickly phases down in the absence of CO 2 removal technologies. • The presence of CO 2 removal technologies reduces stranded investments and capacities. • Carbon removal reduces disruptive and over-aggressive energy transition. [ABSTRACT FROM AUTHOR]

Recent studies revealed the un-negligible impact of airborne organophosphate esters (OPEs) on phosphorus (P)-limited ecosystems. Subtropical forests, the global prevalence P-limited ecosystems, contain canopy structures that can effectively sequester OPEs from the atmosphere. However, little is known about the behavior and fate of OPEs in subtropical forest ecosystem, and the impact on the P cycling in this ecosystem. OPE concentrations in the understory air (at two heights), foliage, and litterfall were investigated in a subtropical forest in southern China. The median ∑OPE concentrations were 3149 and 2489 pg/m3 in the upper and bottom air, respectively. Foliage exhibited higher ∑OPE concentrations (median = 386 ng/g dry weight (dw)) compared to litter (median = 267 ng/g dw). The air OPE concentrations were ordered by broadleaved forest > mixed forest > coniferous forest, which corresponds to the results of canopy coverage or leaf area index. The spatial variation of OPEs in foliage and litter was likely caused by the leaf surface functional traits. Higher OPE concentrations were found in the wet season for understory air while in the dry season for foliage and litter, which were attributed to the changes in emission sources and meteorological conditions, respectively. The inverse temporal variation suggests the un-equilibrium partitioning of OPEs between leaf and air. The OPE concentrations during the litter-incubation presented similar temporal trends with those in foliage and litter, indicating the strong interaction of OPEs between the litter layer and the near-soil air, and the efficient buffer of litter layer played in the OPEs partitioning between soil and air. The median OPEs-associated P deposition fluxes through litterfall were 270, 186, and 249 μg P/m2·yr in the broadleaved, mixed, and coniferous forests, respectively. Although the fluxes accounted for approximately 0.2% of the total atmospheric P deposition, their significance to this P-limited ecosystem may not be negligible. [Display omitted] • Spatial patterns of ∑OPE levels in the foliage and litter are associated with the leaf surface functional traits. • Higher OPE air concentrations were observed in the wet season, while the inverse was documented in foliage and litter. • Forest litter is an efficient buffer for the OPE exchange between soil and air. • The importance of OPEs for the P cycling through litterfall in a subtropical forest ecosystem was suggested. [ABSTRACT FROM AUTHOR]

Crop straw, a renewable resource, holds a crucial position in transition towards alternative, clean bioenergy. However, there is a dearth of analysis on the land- and water-saving potential associated with producing bioenergy from crop straw instead of clean energy. To comprehensively address these deficiencies, we conducted a face-to-face field survey for deep analysis. Our results revealed that most crop straw in the five southern provinces of China is retained in the field or openly burned, less for energy utilization. However, we also found that promoting bioenergy production potential from crop straw is significant and could meet the current energy demand for transportation or households in the five southern provinces. The production of briquette fuel and bioethanol from crop straw can significantly reduce GHG emissions of 23.3 Mton and 23.2 Mton, respectively, compared with coal and gasoline consumption. Furthermore, our study illustrated that replacing energy crops like Jerusalem artichoke with crop straw to produce bioethanol could reduce 11% of the total sown area occupied and save 3% of the irrigation water used in the five provinces. In conclusion, this study offers a more sustainable and efficient approach to producing bioethanol and other bioenergy while minimizing environmental impact and reducing resource consumption. [ABSTRACT FROM AUTHOR]

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues >1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values, with R 2 =0.80. For SEM, Chl a and eight variables were used: NH4-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu. [Copyright &y& Elsevier]

Estuarine areas are not only the main gathering point for human sewage but also the place where one-way and two-way fluids interact, thus forming a complex and changeable geochemical physical field. Here, heavy metals (HMs) are adsorbed and desorbed due to physical, chemical, and biochemical processes. However, the adsorption and desorption behavior of HMs in the aquatic environment is complex, and physicochemical processes occurring in the estuarine sediment-water interface control the direction and boundaries of the system. This study analyzed the migration and distribution of HMs in rivers and lakes, and established a Bayesian network model to quantitatively understand the impact of nutrients and key environmental factors on the adsorption-desorption behavior of HMs in lake and estuaries, as well as the competitive relationship between environmental factors. The influence of environmental factors and the occurrence of HMs are both important model inputs. Our findings indicated that the migration risk of Cd in Qinghai Lake was high. Environmental factors such as Cation exchange capacity (CEC), Organic matter (OM), Soluble fluoride (SFL), and pH play the most important role in the adsorption and desorption of HMs. Our findings also indicated that the exchange and activity of HMs in sediments were much higher than in the overlying water. The organic matter content was the most complex environmental factor affecting HMS adsorption and desorption at the water-sediment interface. Additionally, the mass concentration of dissolved oxygen (DO) has a linear relationship with bioavailable HMs in river and lake sediments, but has no linear relationship with the concentration of water-soluble HMs. Interestingly, there are synergistic effects between environmental factors, which directly or indirectly affect the release of bioavailable HMs. However, it is important to determine whether the effects of different environmental factors on the exchange of bioavailable HMs are negative or positive. Our findings suggested that Bayesian network (BN) signals (positive or negative) could provide insights into the transfer direction of metals in the water-sediment interface. [Display omitted] • High migration risk of Cd in Qinghai Lake. • The exchange frequency of HMs in sediment is higher than that in overlying water. • OM is the most complex environmental factor affecting HMs adsorption and desorption. • DO increases, Cu and Zn bioavailability increases. • CEC increases, Cr bioavailability increases. [ABSTRACT FROM AUTHOR]

Abstract: Previous optimization-based watershed decision making approaches suffer two major limitations. First of all, these approaches generally do not provide a systematic way to prioritize the implementation schemes with consideration of uncertainties in the watershed systems and the optimization models. Furthermore, with adaptive management, both the decision environment and the uncertainty space evolve (1) during the implementation processes and (2) as new data become available. No efficient method exists to guide optimal adaptive decision making, particularly at a watershed scale. This paper presents a guided adaptive optimal (GAO) decision making approach to overcome the limitations of the previous methods for more efficient and reliable decision making at the watershed scale. The GAO approach is built upon a modeling framework that explicitly addresses system optimality and uncertainty in a time variable manner, hence mimicking the real-world decision environment where information availability and uncertainty evolve with time. The GAO approach consists of multiple components, including the risk explicit interval linear programming (REILP) modeling framework, the systematic method for prioritizing implementation schemes, and an iterative process for adapting the core optimization model for updated optimal solutions. The proposed approach was illustrated through a case study dealing with the uncertainty based optimal adaptive environmental management of the Lake Qionghai Watershed in China. The results demonstrated that the proposed GAO approach is able to (1) efficiently incorporate uncertainty into the formulation and solution of the optimization model, and (2) prioritize implementation schemes based on the risk and return tradeoff. As a result the GAO produces more reliable and efficient management outcomes than traditional non-adaptive optimization approaches. [Copyright &y& Elsevier]

Urban air quality is subject to the increasing pressure of urbanization, and, consequently, the potential impact of air quality changes must be addressed. A Bayesian hierarchical model was developed in this paper for urban air quality predication. Literature data on three pollutants and four external driving factors in Xiamen City, China, were studied. The air quality model structure and prior distributions of model parameters were determined by multivariate statistical methods, including correlation analysis, classification and regression trees (CART), hierarchical cluster analysis (CA), and discriminant analysis (DA). A multiple linear regression (MLR) equation was proposed to measure the relationship between pollutant concentrations and driving variables; and Bayesian hierarchical model was introduced for parameters estimation and uncertainty analysis. Model fit between the observed data and the modeled values was demonstrated, with mean and median values and two credible levels (2.5% and 97.5%). The average relative errors between the observed data and the mean values of SO2, NO x , and dust fall were 6.81%, 6.79%, and 3.52%, respectively. [Copyright &y& Elsevier]