With the rise of labor costs in China, constraints on resources and environment, and ongoing geopolitical conflicts, the global manufacturing hub is facing relocation. India has emerged as the most likely candidate to undertake industrial relocation. This shift could reshape the global carbon emission landscape. However, ex ante measurement of the environmental effects of such industrial relocation is poorly understood. As an illustrative case, we first measure the impact on carbon emissions from shifting iPhone production to India and find that such a move would double the carbon footprint of production. We then extend our analysis to the general industrial chain and examine the implications of such shifts on a broader scale. We find that India's process of becoming the global manufacturing hub will lead to increased carbon emissions and reduced global economic growth. The carbon burden surpasses the emission reductions achieved by the EU since the Copenhagen Climate Conference. At the sector level, the computer, basic metals, electronic equipment, and automotive sectors are the largest sources of incremental carbon emissions. To offset the extra emissions, it is essential to ensure that the industrial structure of these sectors temporarily remains unchanged while promoting technological progress in developing countries are essential to offset the extra emissions.

The worldwide trend of decreasing corporate tax in recent years has contributed to an increase in global carbon emissions, but implementing a global minimum tax rate of 15% could partially mitigate this impact. Policymakers should coordinate corporate tax policies with climate regulations.

Financial market liquidity is a popular research topic. Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate, the lower the returns. However, the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models. To explore high machine-driven liquidity and the impact of high turnover rates on returns, this study establishes a dual-market quantitative trading system, introduces a variational modal decomposition (VMD)-bidirectional gated recurrent unit (BiGRU) model for data prediction, and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S. and Chinese stock markets. The experimental results show that given a principal amount of 210,000.00 CNY, the final predicted net return is 226,538.30 CNY, a net return of 107.86%, which is 40.6% higher than the net return of a single Chinese market. We conclude that, under machine-driven trading, increasing liquidity and turnover increase returns. This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.

Many countries have cut their corporate tax rates in the past decades to attract foreign investment. To prevent this, a global minimum tax policy was approved by OECD countries in 2021. Global changes in corporate tax rates could reshape production and investment networks while impacting welfare and global emission patterns. Here we develop a theoretical multi-country multi-industry general equilibrium model and show that global corporate tax competition during 2005-2016 would increase global carbon emissions and shift more emissions to developing economies. Implementing a global minimum tax rate of 15% would reduce global carbon emissions and effectively decrease the developing economies' emissions. The results highlight that corporate tax policies should be coordinated with climate regulations. Countries use corporate tax cuts to attract foreign investment, which reshapes patterns of global production. This research shows that such competition will lead to higher carbon emissions and shift them to developing countries, while a global minimum tax could help alleviate these problems.

Salience theory has been proposed as a new stock trading strategy. To assess the validity of this proposal, a complex decision trading system was constructed based on salience theory, a variational mode decomposition (VMD) model, a bidirectional gated recurrent unit (BiGRU) model, and high-frequency trading. The system selected 30 Chinese new energy concept stocks, ranked the stocks using salience theory, and selected the top and bottom three stocks for two portfolios. Twelve stages were established, following which the VMD and BiGRU models were applied to the predictions. The final predicted annualized returns for the high ST (salience theory value) group A (GA) and low ST group B (GB) were 194.06% and 165.88%, respectively. This finding validates the powerful utility of salience theory and deep learning to analyze the Chinese new energy market. Moreover, it explains the theoretical practicality issues that the short selling restriction is the essential reason, or even perhaps the only reason, that leads to the strength of salience theory.

Statistical analysis of COVID-19 mortality is challenging due to its non-stationarity and cross-sectional instability. In this paper, the authors introduce a unified method to evaluate the fatality rate of COVID-19 across countries, whose method provides more reliable information for cross-country comparison than the traditional case-fatality rate（CFR）. It emerges that the new method, the blockwise case-fatality rate（BCFR）, varies for different countries and in different periods. The authors also decompose the COVID-19 fatality data by three factors: 1) The virus infection dynamics over population in different countries, 2) pure distribution and evolution of instantaneous death rate attributed to different individual’s physical characteristics such as age and health, and 3) individual countries’ variations affecting interactions between the virus infection and the instantaneous mortality due to individual’s physical characteristics. Based on the new three-factor model, the authors obtain six key findings of the COVID-19 fatality rate. Our study suggests that, on average, the estimated instantaneous fatality rate contributes about 57.0% to the global BCFR while the time-varying weight contributes about 41.5% in December 2020. The country-specific contribution of instantaneous fatality rate is significantly higher than that of the time-varying weight. Besides, the country-specific characteristics in demographical, social, and economic aspects would affect the relative severity of the disease.

In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.

Regional trade agreements (RTAs) have been widely adopted to facilitate international trade and cross-border investment and promote economic development. However, ex ante measurements of the environmental effects of RTAs to date have not been well conducted. Here, we estimate the CO2 emissions burdens of the Regional Comprehensive Economic Partnership (RCEP) after evaluating its economic effects. We find that trade among RCEP member countries will increase significantly and economic output will expand with the reduction of regional tariffs. However, the results show that complete tariff elimination among RCEP members would increase the yearly global CO2 emissions from fuel combustion by about 3.1%, doubling the annual average growth rate of global CO2 emissions in the last decade. The emissions in some developing members will surge. In the longer run, the burdens can be lessened to some extent by the technological spillover effects of deeper trade liberalization. We stress that technological advancement and more effective climate policies are urgently required to avoid undermining international efforts to reduce global emissions.

The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.

This paper proposes an integrated TVP-VAR model to investigate the volatility spillover mechanisms among different financial markets as well as their respective roles in the global volatility transmission system, including Bitcoin, crude oil, gold, stocks, foreign exchange and natural gas market. Utilizing the time-varying volatility spillover indices, we find that the Bitcoin, gold, foreign exchange and natural gas serve as volatility communicators. On the contrary, the crude oil and stock market act as volatility receivers. In addition, roles of each major commodity in the volatility spillover system are found to be significantly correlated with their respective financial properties. Specifically, Bitcoin is found to be a hedge rather than a safe haven in the financial system. Moreover, we discover that the overall volatility spillovers across the financial market system are more likely to be caused by shifts in external market attention among the different markets.

A complex system is composed of many interrelated elements,and the interaction between these elements makes the overall performance of the system greater than the sum of member performance[1-5].In the context of management,various forms of organizations,from micro enterprises to macroeconomic systems,can be seen as systems formed by a large number of interactive individuals acting on their own lim-ited information.In these systems,individuals exchange resources with each other in the absence of a unified central command,even with-out knowing how other individual behaviors affect them.Any change in the characteristics of members or the relationship between members may"emerge"complex management phenomena at the system level,which is one of the hot spots and difficulties in management research today.

Since 2017, the Bitcoin blockchain system has experienced 105 fork divergences. The rapidly increasing blockchain forks have resulted in fierce competition and created significant controversies in blockchain community. To analyze this competitive aspect, we consider blockchain as a two-sided platform that serves both customers and miners. We develop a game-theoretic model to investigate how a blockchain platform's decision on its settings, such as block size and transaction fee, affects the competition between blockchain platforms as well as the participation behavior of customers and miners. Our findings suggest that increasing the transaction fee alleviates congestion on the platform when customers have a relatively balanced need between efficiency and safety. In contrast, it induces congestion when efficiency is valued over safety. In addition, under hard fork competition, the difference in blockchain platforms' block sizes directs the attention of miners towards different types of mining rewards. Moreover, it also affects the optimal types of customers the blockchain platforms should target. Furthermore, we find that the degree of congestion and the risk attitudes of participants play vital roles in differentiating different block-sized platforms' optimal transaction fees. We provide empirical evidence on the theoretical findings and practical implications for blockchain platform competition with respect to the behaviors of platform participants. (c) 2021 Elsevier B.V. All rights reserved.

There is a research gap in accurately predicting an individual stock’s finances from industry environment factors. Therefore, to predict trading strategies for a target stock’s closing price, this study constructed a prediction module and an environment module for a hybrid variational mode decomposition and stacked gated recurrent unit (VMD-StackedGRU) model, with individual stock information input into the prediction module and industry information input into the environment module. The results from the U.S. banking industry generalization tests proved that the proposed model could significantly improve prediction performances and that the environment module did not play an important role and was not equal to the prediction module. The hybrid neural network framework was a new application for financial price predictions based on an industry environment. Profitable trading strategies and accurate predictions can be valuable in hedging against market volatility risk and in assuring significant returns for investors and investment institutions.

ABSTRACT Strict carbon emission regulations are set with respect to countries’ territorial seas or shipping activities in exclusive economic zones to meet their climate change commitment under the Paris Agreement. However, no shipping policies on carbon mitigation are proposed for the world’s high seas regions, which results in carbon intensive shipping activities. In this paper, we propose a Geographic-based Emission Estimation Model (GEEM) to estimate shipping GHG emission patterns on high seas regions. The results indicate that annual emissions of carbon dioxide equivalent (CO2-e) in shipping on the high seas reached 211.60 million metric tonnes in 2019, accounting for about one-third of all shipping emissions globally and exceeding annual GHG emissions of countries such as Spain. The average emission from shipping activities on the high seas is growing at approximately 7.26% per year, which far surpasses the growth rate of global shipping emission at 2.23%. We propose implementation of policies on each high seas region with respect to the main emission driver identified from our results. Our policy evaluation results show that carbon mitigation policies could reduce emissons by 25.46 and 54.36 million tonnes CO2-e in the primary intervention stage and overall intervention stage, respectively, with 12.09% and 25.81% reduction rates in comparison to the 2019 annual GHG emissions in high seas shipping.

In this paper, we extract the qualitative information from crude oil news headlines, and develop a novel VMD-BiLSTM model with investor sentiment indicator for crude oil forecasting. First, we construct a sentiment score considering cumulative effect from contextual data of oil news texts. Then, we adopt an event-based method and GARCH model to investigate the impact of news sentiment on returns and volatility. A non-recursive signal decomposition method, namely variational mode decomposition (VMD), is applied to decompose the historical crude oil return and volatility data into various intrinsic modes. After that, a bidirectional long short-term memory neural networks (BiLSTM) is introduced as the deep learning prediction model that integrates both the qualitative and quantitative model inputs. Our empirical results indicate that the shock of news sentiment significantly causes the fluctuation of oil futures prices, and news sentiment has an asymmetric impact on the volatility of oil futures. The incorporation of sentiment score is always helpful for improving the forecasting performances in all benchmark scenarios. Specifically, our proposed data-decomposition based deep learning model is more effective than several econometric and machine learning models.

The growing popularity of cryptocurrency payment among supply chain members have resulted in considerable waiting in blockchain payment services. In response, service providers offer “priority payment” which allows users to pay additional transaction fee in exchange for priority transaction processing. Despite its growing popularity, there is very limited formal research that studies this service and its impact. In this paper, we examine the impact of the priority option on SC members' blockchain payment service adoption behavior, payment service provider's strategies, as well as his revenue generation. We find that offering priority blockchain payment option increases heavy members' usage of the option and reduces their usage of the waiting blockchain payment option. On the other hand, it boosts certain light players' usage of both blockchain payment services when the transaction fee rate is not too high. The service provider should charge a lower (higher) transaction fee rate if the trust-enabling rate of the priority blockchain payment option is relatively high (low). Finally, we demonstrate that offering priority blockchain payment option actually reduces his revenue when the trust-enabling rate of the option is sufficiently low. Thus, blindly charging transaction fees actually does more harm than good to the service providers.

The gold market plays a vital role in the world economy. Due to its complex and nonstationary nature, predicting the price of gold is particularly challenging. In this study, a new hybrid forecasting approach named variational mode decomposition (VMD)-iterated cumulative sums of squares (ICSS)-bidirectional gated recurrent unit (BiGRU) is proposed by integrating BiGRU deep learning model, VMD, and iterated cumulative sum of squares algorithm. The forecasting framework is able to extract the inner factors and patterns within the gold futures market movements, decompose its correlation with external markets and detect shifts within market conditions in order to accurately predict price movements in the gold futures market. The experimental results show that the hybrid forecasting approach can improve the prediction performance significantly in comparison to the benchmarks. Furthermore, we extend the proposed hybrid forecasting approach to generate trading strategies and test trading performance of the gold futures market. The testing results over an out-of-sample period of 11 years (2008–2019) indicate that the strategy generated based on the prediction of the proposed approach displays high levels of consistency in generating positive returns and outperforms several other common trading strategies under various market conditions. The approach also shows consistent better results when generalized to the spot gold market, providing practical guidance for minimizing investment risk and hedging strategies in the gold commodity market.

The growing energy consumption and associated carbon emission of Bitcoin mining could potentially undermine global sustainable efforts. By investigating carbon emission flows of Bitcoin blockchain operation in China with a simulation-based Bitcoin blockchain carbon emission model, we find that without any policy interventions, the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emission correspondingly. Internationally, this emission output would exceed the total annualized greenhouse gas emission output of the Czech Republic and Qatar. Domestically, it ranks in the top 10 among 182 cities and 42 industrial sectors in China. In this work, we show that moving away from the current punitive carbon tax policy to a site regulation policy which induces changes in the energy consumption structure of the mining activities is more effective in limiting carbon emission of Bitcoin blockchain operation.

This paper aims to develop an interpretable machine learning model to predict plays (pass versus rush) in the National Football League that will be useful for players and coaches in real time. Using data from the 2013-2014 to 2016-2017 NFL regular seasons, which included 1034 games and 130,344 pass/rush plays, we first develop and compare several machine learning models to determine the maximum possible prediction accuracy. The best performing model, a neural network, achieves a prediction accuracy of 75.3%, which is competitive with the state-of-the-art methods applied to other datasets. Then, we search over a family of simple decision tree models to identify one that captures 86% of the prediction accuracy of the neural network yet can be easily memorized and implemented in an actual game. We extend the analysis to building decision tree models tailored for each of the 32 NFL teams, obtaining accuracies ranging from 64.7% to 82.5%. Overall, our decision tree models can be a useful tool for coaches and players to improve their chances of stopping an offensive play.

The sharing economy has experienced rapid development in the past few years; nonetheless, at present, it is still in its infancy. As the sharing economy harnesses an innovative, novel business model, traditional marketing countermeasures are insufficient. Therefore, the sharing economy market patterns need to be explored and appropriate marketing countermeasures developed. To this end, text mining was applied to reviews on a typical sharing economy platform, Airbnb, to accomplish market segmentation. The results suggested that the host is the most valued factor to Airbnb guests. Thus, different from traditional hotel industry, it is essential for emerging peer-to-peer accommodation platform to encourage host to establish good interaction between the guests and the hosts. Meanwhile, cleanliness and convenience are also two major concerns to Airbnb guests, which indicates that Airbnb platform should encourage hosts to promote hotel-like properties for Airbnb listings. In addition, there is no strong evidence of heterogeneity in guests seeking accommodation in different locations.