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With the promotion of marine power strategies and marine ecological civilization construction, marine green development has become an important issue. This study utilizes the mixed distance function Epsilon-based measure and the global Malmquist–Luenberger index model, which accommodates both radial and non-radial models, to evaluate the marine green total factor productivity (GTFP) across 11 coastal regions of China. The findings of this study are as follows. First, the marine GTFP showed a gradual increase in China’s coastal regions during the study period, with notable differences observed among the three primary marine economic circles. Second, technological advancement was the primary driver of the transformation of marine GTFP. Third, the distribution across the space pattern of marine GTFP tended to be concentrated continuously, which shows that the overall spatial distribution pattern of marine GTFP remained relatively stable. Fourth, there is no obvious σ convergence in a northern, eastern marine economic circle. Both absolute and conditional β convergence exists, indicating that the marine GTFP converged to their respective steady-state levels during the study period. This study can provide decision-making reference for accelerating the green growth and sustainable development of China’s marine economy.

The massive consumption of fossil energy has resulted in high CO2 emissions, posing a formidable challenge to global sustainable economic development (SED). As countries endeavor to shift from fossil to clean energy sources to achieve SED, research on the impact of clean energy is scarce, and quantitative analysis is lacking. This study measured China's SED and used a spatial econometric model to examine the impact of clean energy consumption and production on SED across 30 provinces in China from 2008 to 2020. Results show that (1) China's SED exhibits significant positive spatial autocorrelation characteristics, forming a “point-to-area” development pattern. (2) Clean energy consumption, production, and consumption structure all contribute to the promotion of SED in the region and have positive spatial spillover effects. (3) A considerable regional disparity exists in the spatial impact of clean energy on SED. The eastern and central regions have significant positive spatial spillover effects, whereas the western region is opposite. Notably, the estimated coefficient of the spatial Durbin model is relatively small, reflecting China's ongoing transition to clean energy and its limited role in promoting economic sustainability. Joint efforts and differentiated policies are essential to develop clean energy and sustainable economic.

We conducted an experiment of planting a dead cow and a metal-framed cage with cameras on the 1629 m deep sea floor off the southeast coast of Hainan Island in the northwestern South China Sea, using ROV diving and setting up a video camera on the cage to observe animals who came to eat the bait. The deep-sea cameras captured footage of eight Pacific sleeper sharks (Somniosus pacificus) swimming and feeding around the dead cow. To our knowledge, this is the first time the occurrence of such a shark species has been reported in the South China Sea. Eight individuals were differentiated based on the characteristic differences displayed in the images, with lengths of 1.9 to 5.1 m estimated. The video camera also recorded the predators’ behavior of tearing at the dead cow on the seabed. It was discovered that Pacific sleeper sharks are not strictly solitary and exhibit queue-feeding behavior. This study is significant as it documents a record of a data-scarce shark species, for which little information is available in the literature. It also documents an expansion of the species’ known habitat from the north Pacific Ocean into the South China Sea. Such sharks diving into the deep sea to predate on dead animals also suggests that occurrences of large chunks of dead organic bodies falling onto the deep sea might have been more frequent than we previously thought in the South China Sea. The findings have implications for understanding the geographic connectivity of large swimming animals between the South China Sea and the Pacific Ocean and provide scientific evidence for formulating conservation and management strategies for sharks and other large animals in the oceans.

ABSTRACT The superoxide anion radical (O2•−), the primary reactive oxygen species (ROS), is detrimental to cellular functions. Marine group II (MGII) archaea predominantly inhabit surface ocean environments that are rich in ROS; however, their defense mechanisms against ROS damage remain unknown. In this study, we integrated the analyses of archaeal 16S rRNA gene amplicons and metagenome-assemblage genomes of MGII archaea from the northern South China Sea and 250 other previously published MGII genomes to investigate their antioxidant mechanisms. The results show that the proportion of oxygen-promoted MGII operational taxonomic units in the iron/manganese superoxide dismutase (Fe/MnSOD) gene-containing group was significantly higher than that in the nickel SOD (NiSOD) group, suggesting that MGII members with Fe/MnSOD may have superior antioxidant capacity relative to members possessing NiSOD. Molecular clock analysis of MGII genomes revealed frequent species divergence correlating to the differentiation of Fe/MnSOD or NiSOD genes. The acquisition/loss of these genes in the MGII genomes was synchronized with the historical changes in atmospheric oxygen and oceanic metal element concentrations over the last 2.4 billion years, suggesting that variations in the antioxidant capacity of MGII archaea in response to changing oxygen levels may have driven their divergences over time. IMPORTANCE Reactive oxygen species (ROS), including superoxide anion, is a series of substances that cause oxidative stress for all organisms. Marine group II (MGII) archaea are mainly live in the surface seawater and exposed to considerable ROS. Therefore, it is important to understand the antioxidant capacity of MGII. Our research found that Fe/Mn- superoxide dismutase (Fe/MnSOD) may be more suitable for MGII to resist oxidative damage, and the changes in oxygen concentrations and SOD metallic cofactors play an important role in the selection of SOD by the 17 clades of MGII, which in turn affects the species differentiation of MGII. Overall, this study provides insight into the co-evolutionary history of these uncultivated marine archaea with the earth system.

Corporate green innovation performance can serve as a critical tool for policymakers to identify the best practice and provide support to micro-entities in need. Accurate forecasting of corporate green innovation performance plays a vital role in innovation incentives by simulating the effects of regulations and strategies. Based on the data of China’s A-share listed companies during 2010–2020, this paper elaborates the gradient boosting algorithm to predict the corporate green innovation performance and compares the prediction results of the gradient boosting model with the linear model, the decision tree model, and the random forest model. Subsequently, it examines the effectiveness of the influencing factors related to the enterprise’s internal driving mechanism and external policy pressure in promoting corporate green innovation performance. It finds that: 1) The gradient boosting model outperforms other methods in its predictive effect. 2) An enterprise’s resource base is a critical factor influencing its green innovation activities, and in particular, the influence of financial indicators on corporate green innovation performance has a significant incentive effect, indicating that the impetus from enterprises’ internal driving mechanism is crucial for enterprises’ green transformation. 3) The effect of secondary indicators is heterogeneous. In the command-based environmental regulation tools, the administrative penalties can activate enterprises’ green innovation better than the approvals of Environmental Impact Assessment (EIA) documents for construction projects do; as for the incentive-based environmental regulation, investment in pollution control projects has an apparent inducing effect on the corporate green innovation performance, while the environmental tax presents an inverted U-shape, implying that overly stringent taxation crowds out the corporate green innovation performance. 4) Similarly, in the operating capacity indicators, the increasing operating income growth rate can trigger the improvement of green innovation performance; nevertheless, the total asset turnover ratio shows a suppressing effect. The key to promoting corporate green innovation performance lies in effectively regulating the enterprises’ internal driving mechanism and the rational choice of external policy tools. This study helps to prospectively identify how corporate green innovation performance changes and provides theoretical guidance and micro evidence for the policymakers on choosing environmental regulation tools and for enterprises on adjusting the resource bases.

Purpose – The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system. Design/methodology/approach – Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system. Findings – Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized. Originality/value – The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

It is of great constructive significance to predict total energy consumption accurately. At present, there are many prediction tools for processing and analyzing energy consumption, but most of them have the disadvantages of complex modeling or low accuracy, and less involve the modeling in the case of mixed-frequency data. For the sake of reflecting the timeliness of prediction, this paper analyzes the annual energy consumption influenced by high-frequency data. Based on mixed-frequency data, a novel grey model which comprehensively considers the time-delay cumulative effect of system behavior variable is designed. When there are high-frequency correlation factor sequences and low-frequency system behavior sequences, the model can automatically assign weights according to the characteristics of the high-frequency sequence. In addition, the inertia of the system behavior variable is considered more systematically, and the time-delay cumulative effect of the system behavior variable is incorporated into the original grey model. From the angle of verifying the feasibility of this new constructed model, an original multivariable grey prediction model and two non-grey methods are selected as the comparative models. Empirical findings indicate that the mean absolute percentage errors of the proposed model are 0.0387% and 0.0287% in the simulated stage and predicted stage, which is the lowest among the four models, and highlights the excellent performance. Eventually, the proposed model is applied to make a short-term forecast about total energy consumption in China from 2020 to 2023, and the predictions provide guidance for the formulation of energy consumption objectives.

In oligotrophic seas such as South China Sea, the subsurface or deep chlorophyll maximum (DCM) is always present. The surface planktonic community receives sufficient light, but is short of nutrients. The DCM layer is under light limitation, but frequent supply of nutrients. Therefore, vertical mixing becomes critical in determining their community composition and drives their changes by responding to light and nutrients. In this study, we conducted an onboard experiment by collecting seawater samples at surface and the DCM layer and adding nutrients, and incubated them under full sunlight and 10% light, and examined the diversity of bacterial and eukaryotic communities and their cell abundance using 16/18S high throughput sequencing and FCM approaches. Our study found large differences in bacterial and eukaryotic community structure and cell abundance between the surface and DCM. After 72 hours of culture, taxonomically the incubated surface water was dominated by pico-eukaryotic phytoplankton, while the incubated DCM layer water is dominated by diatoms, which suggests diatoms are the main functional group of phytoplankton bloom after a vertical mixing event. These findings indicate that phytoplankton at the DCM respond to enhanced light and frequent supplied nutrients due to vertical mixing and thus maintain primary productivity in the otherwise oligotrophic oceans.

Marine biology carbon sinks function is vital pathway to earned carbon neutrality object. Algae and shellfish can capture CO2 from atmosphere reducing CO2 concentration. Therefore, algae and shellfish carbon sink capability investigate and forecast are important problem. The study forecast algae and shellfish carbon sinks capability trend base on 9 China coastal provinces. Fractional order accumulation grey model (FGM) is employed to forecast algae and shellfish carbon sinks capability. The result showed algae and shellfish have huge carbon sinks capability. North coastal provinces algae and shellfish carbon sinks capability trend smoothness. South and east coastal provinces carbon sinks capability trend changed drastically. The research advised coastal provinces defend algae and shellfish population, expand carbon sink capability. Algae and shellfish carbon sink resource will promote environment sustainable develop.

Purpose – The purpose of this paper is to measure the high-quality development level of China's marine economy and analyze corresponding spatial and temporal distribution characteristic. Design/methodology/approach – Design and optimize the index system of high-quality development level of marine economy and use entropy and TOPSIS method for comprehensive evaluation. Findings – The research finds that from 2017 to 2019, the high-quality development tendency of China's marine economy is on the rise, but the overall level is still low. The level of each subsystem has different distribution characteristics in different provinces and cities. Guangdong, Shandong and Shanghai have a high comprehensive level. According to the comprehensive level of high-quality development of marine economy, 11 coastal provinces are divided into three types: leading, general and backward. Research limitations/implications – This paper clarifies the temporal and spatial distribution law of high-quality development level of China's marine economy, providing basis for promoting comprehensive and coordinated improvement of coastal provinces and cities. Originality/value – An indicator system for the high-quality development level of the marine economy has been established, including social development guarantee, marine economic foundation, marine science and technology drive and green marine sustainability.

Bacterial communities and porewater dissolved organic matter (DOM) pool are intimately interactive in sedimentary environments. Estuarine coastal regions are an interactive area between terrestrial and marine influences in terms of DOM origins and freshness. Yet, we know little about the relationships between the bacterial communities and DOM in those regions. In this study, porewater DOM samples were collected from 42 sites in the coastal East China Sea. The porewater DOM optical properties were determined by fluorescence and absorption spectra, while the corresponding bacterial community compositions of those sediments were examined by 16S rRNA gene high-throughput sequencing. The results showed that bacterial species richness was positively correlated with multiple terrestrial indicators based on the optical properties of DOM, which implied that heterogeneous DOM from terrestrial origins might harbor a wider spectrum of bacterial taxa in marine sediments. The analysis of the co-occurrence network of the bacterial communities showed that the edges and density for samples with low DOM freshness were 3.4 times and 3 times those for the samples with high DOM freshness, respectively. This suggested that the connection among the bacterial taxa under the lower DOM freshness condition were enhanced and that reduced freshness of DOM may encourage more complimentary utilization of resources. The findings provide a new insight into such interactive processes of heterogeneous organic matter utilization meditated by microorganisms in coastal sediments.

The impact of global value chain (GVC) division of labor on carbon emissions embodied in exports has attracted extensive attention from scholars, but existing studies have not distinguished the heterogeneous impact of different GVC embedding patterns on the carbon emissions embodied in exports. This study examines the effects of two GVC embedding patterns (forward embedding and backward embedding) on carbon emissions embodied in exports using data from 46 sectors in China from 2000 to 2014. The results show that both the backward and forward embedding patterns are characterized by increasing forward participation which increases carbon emissions embodied in Chinese exports. By contrast, the forward embedding pattern is characterized by increasing the length of the forward production chain and increasing the global value chain location index to help reduce the carbon emissions embodied in exports. There is significant heterogeneity in the effect of forward and backward embedding patterns on the carbon emissions embodied in exports. Increasing the length of forward production chains can effectively reduce the carbon emissions embodied in exports in labor-intensive and technology-intensive sectors but has no significant effect on capital-intensive sectors. Therefore, the Chinese government should start to reduce the carbon emissions embodied in China’s exports by changing the GVC embedding pattern, improving the position of GVC embedding, and vigorously developing energy-saving and emission reduction technologies and clean industries, to help China achieve its CO2 reduction target in 2030.

Previous studies have estimated the influence of control measures on air quality in the ecological environment during the COVID-19 pandemic. However, few have attached importance to the comparative study of several different periods and evaluated the health benefits of PM2.5 decrease caused by COVID-19. Therefore, we aimed to estimate the control measures' impact on air pollutants in 16 urban areas in Beijing and conducted a comparative study across three different periods by establishing the least squares dummy variable model and difference-in-differences model. We discovered that restriction measures did have an apparent impact on most air pollutants, but there were discrepancies in the three periods. The Air Quality Index (AQI) decreased by 7.8%, and SO2, NO2, PM10, PM2.5, and CO concentrations were lowered by 37.32, 46.76, 53.22, 34.07, and 19.97%, respectively, in the first period, while O3 increased by 36.27%. In addition, the air pollutant concentrations in the ecological environment, including O3, reduced significantly, of which O3 decreased by 7.26% in the second period. Furthermore, AQI and O3 concentrations slightly increased compared to the same period in 2019, while other pollutants dropped, with NO2 being the most apparent decrease in the third period. Lastly, we employed health effects and environmental value assessment methods to evaluate the additional public health benefits of PM2.5 reduction owing to the restriction measures in three periods. This research not only provides a natural experimental basis for governance actions of air pollution in the ecological environment, but also points out a significant direction for future control strategies.

Nitrogen (N) mineralization and immobilization are two important N transformation processes in estuarine sediments. However, the biotic and abiotic mechanisms regulating those two processes in sediments of a eutrophic estuary remains poorly understood. Here, we used 15N stable isotope dilution technique to investigate the spatial and temporal variations of gross N mineralization (GNM) and gross NH4+ immobilization (GAI) in surface sediment (0–5 cm) along the Pearl River Estuary (PRE) in 4 cruises. Results showed that GNM rates varied spatially and increased with salinity, ranging from 0.15 to 1.99 μg N g−1 d−1, while GAI varied more temporally and increased with temperature, ranging from 0 to 1.96 μg N g−1 d−1. The net N mineralization (NNM) rates ranged from −0.24 to 1.84 μg N g−1 d−1, NNM was higher in lower estuary mainly due to higher chlorophyll-induced higher GNM, while NNM was higher in low temperature season mainly due to the inhibition of GAI. As high turbidity in upper estuary reduced light penetration and hence primary production, the decrease of settling labile organic matter from the water column weaken benthic bacterial abundances, GNM and NNM rates, whereas inorganic N (especially NH4+) had greater effect on GAI than organic matter. As an NH4+ source, the contribution of sediment daily NNM to NH4+ and dissolved inorganic N (DIN) in the overlying water column increased from upstream to downstream (−5.81 to 110.74% and −0.09 to 1.27%, respectively), suggesting an increased importance of NNM for supporting primary production in the lower estuary. Our study reveals the dynamics of net NH4+ production by distinguishing N mineralization and immobilization processes, and enhances the understanding of benthic-pelagic coupling influence on N-cycling in a eutrophic estuary.

Enrichment of nutrients is believed to lead to coastal hypoxia which have become a seasonal phenomenon over large river estuarine areas such as the Mississippi River-Northern Gulf of Mexico and Changjiang-East China Sea. A similar nutrient enrichment process exists in the Pearl River. However, hypoxia occurs only as episodic events over a relatively small area. We hypothesize that frequent wind events play the interruptive mechanism in preventing the seasonal formation of bottom hypoxia. We used 29 years’ time series data of dissolved oxygen (DO) and winds in the Hong Kong coastal waters to test the hypothesis. Our results show that bottom DO at 3 stations in southern waters of Hong Kong occasionally drops below the hypoxic level (2 mg/L), lasting only for less than one month in summer. Episodic hypoxia events appear to occur more frequently in recent years, but bottom DO does not show a significantly decreasing trend. The wind speed of 6 m/s appears to be a threshold, above which a wind event could destroy water column stratification and interrupt the formation of low-oxygen (DO

In this paper, we apply an RV coefficient network to investigate the topological structure of China’s new energy stock market via daily prices of 60 component stocks of CSI (China Stock Index) New Energy Index spanning the period January 4, 2012 to March 29, 2019. Compared with the Pearson correlation coefficient, RV coefficient can better reflect the similarity between stocks from the perspective of multi-dimensional data. The empirical result indicates that (1) the scale-free characteristics of China’s new energy stock market are not significant; (2) the new energy storage is the leading sub-sector of the new energy sector and the new energy interactive equipment plays a connecting role between renewable energy production and new energy storage; (3) the most influential stock in the network is Group DMEGC Magnetics Co., Ltd., Xiamen Tungsten Co., Ltd. and GEM Co., Ltd. play an important role in the network connection. These findings are of great significance to understand the interaction between Chinese new energy stocks and the pricing mechanism of stocks. The authority should pay more attention to the new energy storage industry. Investor’s portfolios can be optimized according to the influence assessment of stocks and sub-sectors. First published online 26 May 2020

Abstract The impact of environmental variables and processes of nutrient enrichment on phytoplankton community at Epe lagoon was studied for 18 months (November 2012–April 2014). Two cyanobacterial bloom incidences were observed in the two dry seasons during this period. Rainfall pattern regulated nitrogen limitation which triggered cyanobacterial bloom development and influenced the bloom duration. Nutrient-laden influx from agricultural practices accounted for the increase in nitrate, phosphate and the relatively low silicate. Hypoxic conditions during the two bloom episodes reflected the vulnerability of this lagoon ecosystem. The cyanobacterial blooms were dominated by potentially toxic species; Anabaena circinalis, A. flos-aquae, A. limnetica and A. spiroides. A comparison of ambient nutrient ratios with the Redfield ratio (N/P/Si = 16:1:16) showed clear temporal variations that coincided with phytoplankton dynamics and the bloom regime. Low N/P ratios were recorded during the first 8 months, and there was a shift to higher ratios during the subsequent 8 months, followed by low N/P ratios in the last 2 months. Higher silicate values coincided with decreased cyanobacterial biomass, cyanobacteria bloom collapse and proliferation of diatoms during the first annual cycle. Cyanobacterial abundance exceeded the alert level 1 and almost reached alert level 2 which call for continuous environmental monitoring and management of coastal waters.

Modeling the direct economic losses of storm surge disasters can assess the disaster situation in a timely manner and improve the efficiency of post-disaster management in practice, which is acknowledged as one of the most significant issues in clean production. However, improving the forecasting accuracy of direct economic losses caused by storm surge disasters remains challenging, which is also a major concern in the field of disaster risk management. In particular, most of the previous studies have mainly focused on individual models, which ignored the significance of reduction and optimization. Therefore, a novel direct economic loss forecasting system for storm surge disasters is proposed in this study, which includes reduction, forecasting, and evaluation modules. In this system, a forecasting module based on an improved machine learning technique is proposed, which improves the generalization ability and robustness of the system. In addition, the key attributes and samples are selected by the proposed reduction module to further improve the forecasting performance from the two innovative perspectives. Moreover, an evaluation module is incorporated to comprehensively evaluate the superiority of the developed forecasting system. Data on the storm surge disasters from three typical provinces are utilized to conduct a case study, and the performance of the proposed forecasting system is analyzed and compared with eight comparison models. The experimental results show that the mean absolute percentage error (MAPE) predicted by the Extreme Learning Machine (ELM) model was 16.5293%, and the MAPE predicted by the proposed system was 1.0313%. Overall, the results show that the performance of the proposed forecasting system is superior compared to other models, and it is suitable for the forecasting of direct economic losses resulting from storm surge disasters.

Abstract The color‐tuning capability of solid‐state lighting (SSL) systems are highly demanded for smart lighting according to the environmental conditions, as well as wireless sensing of the environmental information. In the meanwhile, state‐of‐the‐art triboelectric nanogenerator (TENG)‐based sensing systems rely on bulky and expensive devices, which require cable connections and additional power consumptions. This work aims at solving these challenges, through developing a tribo‐induced color tuner that can be integrated into the vastly distributed commercial SSL system. This tribo‐induced color tuner includes a concentric color conversion plate consisting of (Sr,Ca)AlSiN3:Eu phosphor and TiO2, a tribo‐induced liquid lens, and a rotary freestanding sliding TENG. The color oscillation between purple and pink is achieved upon the tribo‐charging by the TENG, which reveals the input mechanical motion signals. The signal can be conveniently sent by everywhere‐existed lamps and processed by everyone‐owned smartphone cameras or closed‐circuit televisions. Through this approach, the function of wireless sensing is achieved without the need of preamplification, with no additional power supply required, as demonstrated for wireless sensing of the rotation speed. The smart lighting for underwater photographing is also demonstrated by the color‐tunable SSL system with the best imaging quality achieved.

Purpose – The traditional grey relational models directly describe the behavioural characteristics of the systems based on the sample point connections. Few grey relational models can measure the dynamic periodic fluctuation rules of the objects, and most of these models do not have affinities, which results in instabilities of the relational results because of sequence translation. The paper aims to discuss these issues. Design/methodology/approach – Fourier transform functions are used to fit the system behaviour curves, redefine the area difference between the curves and construct a grey relational model based on discrete Fourier transform (DFTGRA). Findings – To verify its validity, feasibility and superiority, DFTGRA is applied to research on the correlation between macroeconomic growth and marine economic growth in China coastal areas. It is proved that DFTGRA has the superior properties of affinity, symmetry, uniqueness, etc., and wide applicability. Originality/value – DFTGRA can not only be applied to equidistant and equal time sequences but also be adopted for non-equidistant and unequal time sequences. DFTGRA can measure both the global relational degree and the dynamic correlation of the variable cyclical fluctuation between sequences.

The joint development of continental and marine economies has become an important driving force for the upgrading of industrial structures. However, because of the differences in resource endowment and development potential, developing industrial structures and the quality of economic development are uneven among regions. In this study, the added values of three land-sea industries in the three marine economic circles of northern, eastern, and southern China, were employed to clarify the evolutionary behavior of the industrial structure of these three circles on the land and sea; the synchronization, lag, equilibrium, and dislocation of developing the industrial structure were also explored which a gray relational model based on convex judgment and gray time difference analyses were used to construct a relational model from the static and dynamic aspects of the system, and the internal and external linkages of the industrial structure of the three circles were analyzed from the perspective of industrial correlation. The results show that: (1) Correlations among the linkages of the three economic circles in the marine industrial structure, both including and without temporal and spatial differences, and the marine feedback driver, differ markedly. (2) The effects of feedback for marine industrial development from the Eastern Marine Economic Circle were stronger, whereas those of the Southern Marine Economic Circle were weak and those of the Northern Marine Economic Circle were ambiguous. (3) A significant difference was observed in the degree of coevolution among the land-sea industrial structures of these areas. The Northern Marine Economic Circle exhibited a slightly higher degree of coevolution than the other two economic circles, showing a stable trend of coevolution and wide spatial development. The eastern and southern circles displayed high degrees of coordination in developing their industrial structures. The research results provide a reference for regional adjustment and optimization of industrial structure.

How the financial structure promotes the development of real economy has always been a research topic in academia. By analyzing the characteristics of China’s financial system, this paper constructs the Finance Structure Index (FSI) from the perspectives of structural efficiency, financing structure and industry structure, and interprets the trend of the FSI. Based on the quarterly data of China from 2004 to 2020, this paper constructs a time-varying parameter-vector autoregression (TVP-VAR) model to study the dynamic impact of finance structure on the growth and optimization of the structure of the real economy. The empirical analysis results show that the response of the real economy has time-varying characteristics. Early on, financial structure has a promotion effect on the scale of the real economy, but the impact on the structure is not clear. In the middle, the effect of promoting the scale decreases slightly and then rebounds rapidly, while the optimization of the structure is inhibited. Later, it has a significant promoting effect and an obvious time-lag effect. Moreover, the impact of the financial structure is unstable. It is necessary to improve the efficiency and quality of the transmission of the optimization of the financial structure to the real economy.

This paper analyzes the price correlation effect between domestic and foreign copper futures contracts. The VAR-BEKK-GARCH (1,1) spillover effect model and the BN-S class non-parametric model based on the jumping perspective are used. The co-integration test shows a long-term equilibrium relationship between the three copper futures markets, and the Granger causality test shows that copper futures contracts have significant two-way spillover effects between different periods in Shanghai for New York copper and unidirectional mean spillover effects for London copper. The BEKK model shows significant bidirectional fluctuation spillover effects between the futures contracts of the Shanghai, London, and New York copper markets before the stock market crash. After the crash, Shanghai and New York copper have significant one-way fluctuation spillover effects on London copper futures contracts. There are jumps within a single market, and the number of joint jumps between markets increases with the significance level.

The viruses play an important role in limiting bacterial abundance in oceans and, hence, in regulating bacterial biogeochemical functions. A cruise was conducted in September 2005 along a transect in the deep South China Sea (SCS). The results showed the double maxima in the ratio of viral to bacterial abundance (VBR) in the water column: a deep maximum at 800–1000 m coinciding with the oxygen minimum zone (OMZ) and a subsurface maximum at 50–100 m near the subsurface chlorophyll maximum (SCM) layer. At the deep maximum of VBR, both viral and bacterial abundances were lower than those in the upper layer, but the former was reduced less than the latter. In contrast, at the subsurface maximum of VBR, both viral and bacterial abundances increased to the maximum, with viral abundance increasing more than bacterial abundance. The results suggest that two VBR maxima were formed due to different mechanisms. In the SCM, the VBR maximum is due to an abundant supply of organic matter, which increases bacterial growth, and stimulates viral abundance faster. In contrast, in the OMZ, organic matter is consumed and limits bacterial growth, but viruses are less limited by organic matter and continue to infect bacteria, leading to the maximum VBR. The OMZ in the deep-water column of oceans is over hundreds of years old and receives a constant supply of organic matter from the water above. However, the oxygen level cannot be depleted to anoxia. Bacterial respiration is largely responsible for oxygen consumption in the OMZ; and hence, any process that limits bacterial abundance and respiration contributes to the variation in the OMZ. Viral control of bacterial abundance can be a potential mechanism responsible for slowing down oxygen consumption to anoxia in the OMZ. Our finding provides preliminary evidence that viruses are an important player in controlling bacterial abundance when bacterial growth is limited by organic matter, and thus, regulates the decomposition of organic matter, oxygen consumption and nutrient re-mineralization in deep oceans.

Considering the characteristics such as fuzziness and greyness in real decision-making, the interval grey triangular fuzzy number is easy to express fuzzy and grey information simultaneously. And the partition Bonferroni mean (PBM) operator has the ability to calculate the interrelationship among the attributes. In this study, we combine the PBM operator into the interval grey triangular fuzzy numbers to increase the applicable scope of PBM operators. First of all, we introduced the definition, properties, expectation, and distance of the interval grey triangular fuzzy numbers, and then we proposed the interval grey triangular fuzzy numbers partitioned Bonferroni mean (IGTFPBM) and the interval grey triangular fuzzy numbers weighted partitioned Bonferroni mean (IGTFWPBM), the adjusting of parameters in the operator can bring symmetry effect to the evaluation results. After that, a novel method based on IGTFWPBM is developed for solving the grey fuzzy multiple attribute group decision-making (GFMAGDM) problems. Finally, we give an example to expound the practicability and superiority of this method.

The paper analyses the trend of global stock market linkages via daily data of 51 stock indices spanning the period 22 July 2005 to 30 June 2016 which covers four regions: America, Europe, Asia Pacific and Africa. A dynamic conditional multivariate generalized autoregressive conditional heteroskedasticity (DCC-MVGARCH) approach was used to calculate dynamic correlation coefficient in order to construct the volatility networks. The methods of minimum spanning tree (MST) and low pass filter were for the first time applied to analyze the variable periodicity of the comovement. The original contribution of this paper is that contrary to previous works, financial events such as Quantitative Easing (QE) and Bailouts are accounted for rather than only crisis factors such as the 2008 financial crisis and the European Debt crisis. The main findings of the paper are as follows: (1) Financial crisis promotes and strengthens global stock markets linkage in the short run; (2) Linkage cycles post crisis are significantly short, due to the effect of monetary policy spillover effects caused by QE from developed to developing countries; and (3) European stock markets are the information transmission hub for global stock market. The research conclusions would be significant for both government to regulate markets as well as for investors to diversify risks.

With economic and social development rapidly, carbon dioxide emission soared in the northwestern region. The importance of adopting emission reduction strategies cannot be overemphasized. Therefore, it is essential to accurately forecast carbon dioxide emission in northwestern China. The study used Lasso parameter estimation to select influential carbon dioxide emission features. FGM(1,1) model was used to forecastfeatures trend. The adjacent accumulation grey multivariate convolution model (AGMC) model forecasts carbon dioxide emission trend. The future two years forecast result shows that Shaanxi province's carbon dioxide emission will show a fluctuating trend. Qinghai autonomous regions will show a decreasing trend. Other regions will be in upward trend. The study suggests the central government should pay attention to the carbon emission problem in the northwestern region. Government increases science and technology investment and pays attention to urbanization spatial pattern rational layout. [ABSTRACT FROM AUTHOR]