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Under the dispute between China and the United States, the international field of public opinion is dominated by increasing tensions, and the network rumors triggered by the COVID-19 epidemic are intensifying. In view of the above-mentioned context, this paper focuses on the development and the evolution process of public opinions. Since the evolution of public opinion is often accompanied by the spread and diffusion of information, this paper combines the process of information diffusion with the development process of polarization behavior, and brings in the dynamic network and the timeliness factor of public opinion dissemination, so as to better explore the polarization process of public opinion under the dynamic network. Then, this paper focuses on the analysis of the parameters of the model and through the dynamic adjustment of parameters, finding out the main factors that affect the trend and development of network public opinion. In addition, this paper introduces an actual case, and takes the actual case data as the support to demonstrate the reliability and practical application value of the model. Finally, based on the simulation results and analysis of actual cases, this paper puts forward the corresponding preventive measures to alleviate the polarization behavior of the group. [ABSTRACT FROM AUTHOR]

The rapid growth of China's national resources in the space domain may change the gap in power between China and the United States in relevant international rulemaking. This article proposes an analytical framework for identifying, comparing, and analyzing states' power resources critical to international rulemaking and uses the framework to compare the relevant resources between China and the United States in the space domain. First, this paper develops a method for identifying resources crucial to international rulemaking in outer space. The relevant resources of the two countries are then compared in this article. Finally, the article explores comparative advantages, consensus, differences, and cooperation possibilities of the two countries in specific international space rules. The framework could be adapted to compare diverse nations' resources critical to international rulemaking in other fields; it contributes to research on space law, international legislation, and national power. [ABSTRACT FROM AUTHOR]

Drawing on Hyland's model of interactional metadiscourse (2005; 2005a), this paper investigates the distribution of interactional metadiscourse in news commentaries of China Daily (CD) and The New York Times (NYT), two leading newspapers in China and the United States. The corpus retrieved includes 60 news commentaries on the topic of COVID-19 covering six months from January to June, 2022 when the resurgence of COVID-19 in China has aroused public debates and attracted world-wide media attention. This study reveals that both stance and engagement, are frequently employed in CD and NYT. Nevertheless, the occurrences of interactional features in NYT are far more frequent than those in CD, which reflects a higher level of explicit interaction of the former. Results of chi-square test of the sub-categorical interactional metadiscourse mark significant differences in the distribution of hedges, self-mention and engagement markers between the two corpora. NYT employs greater number of all the three subcategories, indicating NYT writers' attempts to both express a clear personal stance to their views and to closely align with readers, whereas CD shows a strong tendency of conveying their evaluations and commitment towards propositions by employing stance nearly three times as many as engagement. • Interactional metadiscourse is frequently used in news commentaries. • The New York Times employs both stance and engagement more frequently. • China Daily inclines to explicitly manifest its stance towards propositions. • The New York Times conveys a clear stance as well as closely aligns with readers. • Significant differences are marked in the use of hedges, self-mention and engagement. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of real exchange rate volatility and misalignment on exports and imports between China and the United States. Our empirical analysis used the quarterly time series data over the 1994Q:1–2019Q:4 period. We used the ARDL-bound testing approach for the short- and long-run relationship. The empirical results reveal that China's real exchange rate volatility and misalignment significantly impact China's real exports and imports over the sample period. In the short-run, the relationship between real exchange rate misalignments and China's exports is positive, whereas negative with imports from the United States. In the long run, the real exchange rate misalignment positively affects exports and negatively affects imports. The evidence suggests that both higher real exchange rate volatility and misalignment directly affect the relative price of goods and encourage China exports to United States. [ABSTRACT FROM AUTHOR]

The lengthening of the global value chain (GVC) and the deepening of the transnational labor division system have led to frequent cross-border flows of metal values worldwide, and the pattern of the international metal trade is facing reconstruction. This paper combines a new trade accounting framework and a complex network model to construct global metal domestic value added (DVA) and foreign value added (FVA) flow networks based on global multi-regional input‒output (MRIO) tables for 2000 and 2007–2022 and then analyzes their structural topological features and spatio-temporal evolution trends at the macro, medium, and micro levels. Then, the main driving factors are investigated using the exponential random graph model (ERGM). The results show that the DVA and FVA networks are expanding and have obvious aggregation effects and small-world characteristics, that the community divisions show a trend toward regional integration and worldwide multipolarity, and that Germany is the most important transportation hub and trade medium in the FVA network, while China and the United States are, respectively, the largest creator and consumer of the world's metal value flow. In addition, the DVA network is more susceptible to node attribute effects such as GDP, trade openness, population size and foreign direct investment, while the FVA network is more susceptible to exogenous network effects such as geographic distance and colonial ties. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 in 2020 has a severe impact on all countries. This paper first studies the impact of the epidemic on the macro-economy in China, which is one of the countries with early epidemic situation, in the rapid development stage of the epidemic situation, and the specific performance in macro-economy aspects. On this basis, study the development trend and macroeconomic performance of the United States as one of the most serious countries, and the relationship between the epidemic development and macro-economy in the United States. As for the differences in epidemic control measures between China and the US, China's indicators are significant in terms of sentiment factors, but the impact of COVID-19 on the sentiment factors in the US is different. The Chinese government has not adopted unlimited easing and maintained consistent policies. At present, the economic impact of COVID-19 on the US is far greater than that of China. [ABSTRACT FROM AUTHOR]

Using machine learning coupled with stock price data to predict stock price trends has attracted increasing attention from data mining and machine learning communities. An accurate prediction results can help investors reduce investment risks and improve investment returns. The research on correlation stocks is one of the most important directions among many studies. Due to the high volatility and randomness of stock data, the correlation between stocks changes over time, which makes the stock correlation in static correlation stock sets often inconsistent with reality. Furthermore, various raw data related to stocks contain sufficient stock history information to analyze the future trend of stocks, but traditional prediction models cannot make good use of this information, which restricts the learning ability of the model and reduces the prediction accuracy. In this paper, we propose a stock prediction model combining multi-view stock data features with dynamic market correlation information (MDF-DMC). The model extracts stock trend features by combining multi-view raw data of a single stock with a Multi-layer Perceptron Mixer (MLP-Mixer); The improved Transformer encoder learns the correlation between the stock to be predicted and all the selected stocks in the stock market dynamically and extracts the features of the market correlation. We have conducted a large number of experiments on a total of 578 stocks in the stock markets of China and the United States, and the results show that our model has achieved excellent accuracy and returns across all data sets. • Stock movements are influenced by both itself and the market. • Stock multidimensional data can be used to improve the forecasting performance. • The attention mechanism can effectively extract the correlation of market dynamics. [ABSTRACT FROM AUTHOR]

Probabilistic load forecasting (PLF) aims to predict the future uncertainties of loads to reduce the potential risks in power system planning and operation. In the increasingly complex power market environment, exploring advanced approaches to obtain more accurate PLF is still a significant topic. Optimizing individual forecasting method is no longer the only direction to improve the accuracy of load forecasting in recent years. Researchers started to focus on combination methods because of their better accuracy in most cases than a single model. There are existing combination methods designed for parametric environment, where some results are based on the certain assumption (e.g., Gaussian distribution assumption of single prediction). Combining probabilistic forecasts in nonparametric environment is rarely investigated, because modeling the combination problem without assuming distributions of parameters is hard. This paper proposes a novel combined model for probabilistic forecasting tailored to nonparametric environments, which combines multiple quantile-based models by minimizing the overall loss function composed of continuous ranked probability score (CRPS) under kernel density estimation (KDE). We define a multilayer Gaussian mixture distribution, which is an extended form of Gaussian mixture distribution that can simulate any distribution type in nonparametric environment. Based on the multilayer Gaussian mixture distribution, the combined model is further formulated into a quadratic programming problem with linear restrictions that can be solved efficiently. Case studies are performed using benchmark and competition datasets from the United States and China. The results show that our proposed method outperforms the best individual model and other existing combination methods. In summary, this paper constructs a complete theoretical framework of nonparametric probabilistic combination forecasting and proves its effectiveness in practical application. • Exploit an ensemble framework to combine multiple quantile forecasts. • Design a novel combined model to determine the optimal weights of base models. • Cast the combined model to a quadratic programming problem with the help of KDE. • Overcome the limitation of distribution assumption on predicted results. • Conduct case studies on ten datasets to verify the performance of the combined model. [ABSTRACT FROM AUTHOR]

This paper aims to build theoretical foundations for a new paradigm of MNC-driven interactive growth. Catch-up growth (during which eMNCs emerge on the global stage) is derived from close interactions between aspiring emerging economies and advanced economies (including aMNCs) under the liberal global order. Since sovereignty-conscious emerging economies are naturally determined to foster the growth of national enterprises (including latent eMNCs), the state predictably plays a significant role in their catch-up efforts. Schumpeterian structural economics, flying-geese frames of reference, and endogenous growth perspective are employed as building blocks for the terms of the evolving relations between the leader and the led, a relationship that is destined dialectically to engender both political and economic rivalries due to the very success encouraged by liberalism. East Asian experiences (with full-fledged catch-ups and the rise of dominant eMNCs) are cited in conceptualising the paradigm. [ABSTRACT FROM AUTHOR]

The Agenda 2030 poses critical elements regarding the transition towards a more sustainable development. This paper aims at exploring and comparing the path of sustainable development within the United States and China at the subnational level. An index of Integrated Sustainable Development is introduced to measure local sustainable development on an internationally-comparable scale. This Index is computed both at the national and subnational level and the resulting scores are compared within and across the two countries, also through the adoption of convergence and cluster analysis, allowing to answer four questions: Are the US and China moving towards a sustainable development agenda? Are the current achievements uniformly distributed within the two countries? Are states/provinces converging towards a more uniform level of sustainable development? Do the existing differences and analogies between states and provinces give rise to common clusters across the two countries? The results lead to one robust conclusion: while the US can claim a better result in the national and subnational rankings of sustainable development, China exhibits a more balanced achievement in terms of synergies across Goals and spatial distribution. Nonetheless, both countries are characterized by subnational disparities and scarce achievements of Planet-related Goals. Policy recommendations are discussed accordingly. [ABSTRACT FROM AUTHOR]

Trade needs to be evaluated by more comprehensive indicators that complement market-based economic value. The Emergy Accounting (EMA) method proved to be a valuable tool to help address trade complexity by means of environmental quality-oriented indicators. EMA is used in this paper to evaluate the environmental and resource flows involved in China-United States (USA) trade in the years 1993, 2000 and 2008. Results show that China emergy exports (i.e. exports of raw and less processed resources) exceed the imports from USA. Although the money received by China from exports is higher than the money paid for imports, the real imbalance relies in the huge amount of resources that outflow from China, hardly compensated by the value of imports in terms of support to Chinese economy. The conclusion is that trade accounting methods should include holistic valuations beyond the financial costs of traded goods. Policy implications of these results are discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Visualization and quantitative analysis of the global ecological carbon sinks and sources. • Enhance the prediction of carbon sequestration dynamics under climate change. • Climate change potentially affecting the carbon-climate feedback. • Research content is becoming clearer and more systematic. • Keyword clustering the classification of the research into six relatively independent subtopics. Climate change is extensively affecting the global ecosystem, especially the ecosystem carbon sequestration and sequestration potential, which are issues of global concern. The proposed concept of "carbon neutrality" in 2020 has brought ecological carbon sequestration to the forefront. Therefore, the research on climate change and ecosystem carbon sequestration need to be systematically reviewed, summarized, and examined. Based on the Web of Science database, this paper analyzed 4005 articles (1992–2022 year) after rigorous screening via a bibliometrics analysis and then presented the research trends and future research focus of ecological carbon sequestration. The following conclusions are drawn: (1) the research over the last 30 years has steadily improved, and the annual number of publications has increased in a cubic polynomial fashion (R 2 = 0.9937), with 87.57 % of the total publications appearing after 2009. Global Change Biology and Science of the Total Environment are the most influential journals in the field; (2) participation in such research is becoming increasingly common with expanding research areas covering Eurasia, America, Oceania, and Africa. The United States and China are the most productive and influential countries; (3) the diversity of this research is increasing, and the research content is becoming more explicit and systematic. Most research focuses on climate change, carbon sequestration, management, land-use changes, and nitrogen and soil organic carbon; (4) although traditional ecological evaluation techniques were essential early on, remote sensing and modeling have become the primary methods for assessment; (5) keyword clustering allows the classification of the research into six relatively independent subtopics: climate change and terrestrial ecosystems, sequestration and organic-carbon, ecosystem services, nitrogen and carbon cycling, biomass and forest, and blue carbon, which provides a reference for further research on ecological carbon sequestration and carbon neutrality. [ABSTRACT FROM AUTHOR]

In recent years, emerging contaminants have been found in the wastewater, surface water, and even drinking water, which should be treated to ensure the safety of our living environment. In this study, we provide a comprehensive summary of wastewater treatment and emerging contaminants research from 1998 to 2021 by using the bibliometric analysis. This study is conducted based on the Web of Science Core Collection Database. The bibliometix R-package, VOSviewer and CiteSpace software are used for bibliometric analysis and science mapping. A dataset of 10, 605 publications has been retrieved. The analysis results show that China has produced the most publications. China and the United States have the closest cooperation. Analysis of the most cited papers reveals that the purification or removal techniques such as ozonation or membrane filtration can effectively remove pharmaceutical compounds from the water environment. We also found that the efficient detection of emerging contaminants and the optimization of removal methods are current challenges. Finally, future research directions are discussed. Co-occurrence network of keywords. [Display omitted] • 10,605 publications of wastewater treatment and emerging contaminants were analyzed. • China and the United States have the closest cooperation. • China, United States, and Spain have the most documents. • The publication published by Heberer (2002) receives the most citations. • Removal is a popular keyword in this research. [ABSTRACT FROM AUTHOR]

Abstract: The United States, China''s biggest export market, is planning to impose a carbon tariff against countries without strict GHG emissions reduction standards, which will necessarily have immense influence on China''s export trade. In this dissertation, its background and imposing methods are initially discussed. Impact on China''s export trade volume, commodity prices, various industrial sectors, foreign investment manner, corporate operation modes and so forth are then analyzed explicitly. Lastly, the paper presents several policy suggestions in allusion to these impacts. [Copyright &y& Elsevier]

The trade conflict between China and the United States will have a complex and far-reaching impact on international trade patterns. The significant change in regional trade will not only impact the global transportation industry, but also have an important impact on international shipping carbon emissions. However, there are few studies that explore this issue. Based on the global multi-regional computable general equilibrium model, this study sets up three scenarios according to the development trend of the Sino–US trade conflict to measure the changes in global shipping carbon emissions under different trade conflict scenarios. The results show that, compared with the no trade conflict scenario, the tariff increase will reduce international trade by 0.329%–0.345%, but global international shipping carbon emissions will rebound by 0.25%–0.33%. The main reason for this result is that the trade conflict caused notable trade diversion. The direct trade between China and the United States became indirect trade that must be undertaken by "third countries", which will increase the global freight mileage by 0.23%–0.34%. This paper finds that Sino–US trade conflict will not only hinder international trade volume, but also increase the transportation distance of international trade of goods, which will have a negative impact on the control of carbon emissions from international shipping. This study will help to clarify the impact of the Sino–US trade conflict on international maritime carbon emissions and provide a new perspective to comprehensively identify the economic and environmental impacts of trade conflict. [Display omitted] • The newest carbon emission factors of different ship types in IMO 2020 are used. • A global multi-regional CGE model is used to simulate Sino-US trade war impacts. • The impacts on global trade patterns and shipping carbon emissions are analyzed. • Tariff hikes will increase global shipping carbon emissions through trade diversion. [ABSTRACT FROM AUTHOR]

Ecosystem service (ES) supply and human needs are often spatially conflicting. Although the synergy between them is crucial for global sustainability, little is known about the real human needs for ES (RHN) and their integration with actual ES supply. This study established a novel hierarchy of human needs for ES. Taking China and the United States as case studies, multiple types of actual ES supplies and multi-dimensional human needs were integrated to calculate the value of effective ecosystem service supply (EFS). The results suggested that the overall EFS in the United States is higher than that in China. There was a significant gap between the United States and China in areas with the first grade of EFS (F-EFS); the United States accounted for 68% of the total in such areas in the two countries combined. Whereas China accounted for 32%, mainly concentrated in the Yangtze River Delta urban group. China exhibited a gradually decreasing trend from the east coast to the northwest. Moreover, the F-EFS in China displayed more spatial concentration than in the United States. It was also discovered that the reason for the gap was a sharp spatial mismatch between actual ES supply and RHN in China. Nevertheless, China was specifically found to have the potential to enhance its EFS. This paper proposes some solutions to mitigate the gap and also offers guidance on the realization of Sustainable Development Goals (SDGs). [Display omitted] • A novel hierarchy of human needs for ecosystem services was proposed. • A large-scale analysis integrated actual ES supply and real human needs. • Actual ES supply embodying goods-based and non-goods-based ES flows were assessed. • The overall effective ecosystem service supply in the US was higher than in China. • A sharp spatial mismatch between ES supply and human needs was discovered in China. [ABSTRACT FROM AUTHOR]

• A machine-learning-based method is proposed to predict the coastal algal blooms with environmental factors. • We perform the experiments on the real data of China and United States, respectively to identify key features. • The phytoplankton concentration can be predicted one or two weeks in advance by the GBDT model. Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms. [ABSTRACT FROM AUTHOR]