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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. The growing energy consumption and carbon emissions of Bitcoin mining could potentially undermine global sustainability efforts. Here, the authors show 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 emissions.

Since 2013, China has implemented a large-scale initiative to systematically deploy solar photovoltaic (PV) projects to alleviate poverty in rural areas. To provide new understanding of China’s targeted poverty alleviation strategy, we use a panel dataset of 211 pilot counties that received targeted PV investments from 2013 to 2016, and find that the PV poverty alleviation pilot policy increases per-capita disposable income in a county by approximately 7%-8%. The effect of PV investment is positive and significant in the year of policy implementation and the effect is more than twice as high in the subsequent two to three years. The PV poverty alleviation effect is stronger in poorer regions, particularly in Eastern China. Our results are robust to alternative specifications and variable definitions. We propose several policy recommendations to sustain progress in China’s efforts to deploy PV for poverty alleviation. There lacks a comprehensive analysis on the large-scale deployment of solar photovoltaic projects and its impact on poverty alleviation. Here the authors show that solar photovoltaic poverty alleviation pilot policy increases per-capita disposable income in a county by approximately 7%-8%.

In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

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 CO 2 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 CO 2 emissions from fuel combustion by about 3.1%, doubling the annual average growth rate of global CO 2 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 Regional Comprehensive Economic Partnership (RCEP) will come into force in January 2022. Here the authors quantify ex ante economic and environmental effects following RCEP tariff reductions.

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries. Guan et al. analyse the impacts of COVID-19 restrictions on global supply chains. Earlier, stricter and shorter lockdowns can minimize overall losses. A ‘go-slow’ approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns.

As the second-largest oil producer and natural gas exporter, Russia’s war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by$37.14, a 52.33% surge, and the Brent crude oil price to rise by $ 41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks.

Information technology advancements integrated with the e-commerce supply chain allow participants in the business process to effectively work with large volumes of data and control transactions. To improve the profitability and competitiveness of e-commerce companies, a blockchain solution was incorporated into the global B2B (Business-to-Business) supply chain. This technology simplified the transaction process by providing all participants in the sustainable B2B buying process with the same data about the trade. Overall, the use of blockchain improved the efficiency of logistics and digital documentation which reached 74% and 75%, respectively. The main advantage of using blockchain is that it creates a decentralized database that is secure. In addition, it increases the speed of payment and the reliability and transparency of data transfer. Further research may focus on the use of blockchain in green logistics to improve environmental sustainability in the e-commerce supply chain.

Significant differences in key results across the various climate models and integrated assessment models (IAMs) represent a critical challenge to reliable scientific findings and the robust design of climate policies, which leads to an enormous amount of attention and the urgent call for a multi-model study. In this paper, we develop an integrated literature-survey framework by combining the typical content analysis with a simple statistical analysis to systematically examine the developing trends of IAM-based multi-model studies and explore the model-robust climate policy findings; we also conduct an extended analysis to identify the role of a multi-model approach in global warming and other global change research by employing co-citation network analysis. The results reveal that multi-model comparison and ensemble are effective methods to explore reliable scientific findings and yield robust policy conclusions. The current multi-model studies are sparse as a whole, especially for IAM-based climate economic and policy research; future multi-model works, at both the global and regional levels, are therefore promising. We observe that the developed countries (the EU and the US) dominate the current multi-model study, which could be proved by the number of primary IAMs developed, frequency of models adopted, and number of works published. Addressing the risks of global warming relies on reliable scientific research and robust climate policy design, particularly for the developing large emitters, which heavily depends on consistent efforts toward primary model development and comprehensive cooperation with state-of-the-art model teams all over the world.

Purpose A serious global public health emergency (GPHE) like the COVID-19 aggravates the inequilibrium of medical care and other critical resources between wealthy and poor nations, which, coupled with the collision of cultures, indicates the vital need for developing humanitarian knowledge transcending cultures. Given the scarcity of literature addressing such unprecedent issues, this paper thus proposes new, unconventional viewpoints and future themes at the intersection of knowledge management (KM) and humanitarian inquiry. Design/methodology/approach This paper is conceptual in nature. The data of the World Bank and the Office for the Coordination of Humanitarian Affairs are analysed to introduce some emerging real impact topics regarding cross-cultural conflicts and humanitarian knowledge in the post-COVID business world. The theoretical foundation was built upon a critical literature review. Findings This paper synthesizes the perspectives of culture, KM and the humanistic philosophy to distil the core component of cultural intelligence and comparatively and thereby illuminating why cross-cultural metacognition acts as a priori for achieving cosmopolitan humanitarian knowledge. Research limitations/implications This paper provides profound implications to academics by highlighting the importance to formulating new, inter-disciplinary themes or unorthodox, phenomenon-driven assumptions beyond the traditional KM domain. This paper also offers practitioners and policymakers valuable insights into coping with the growing disparity between high- and low-income countries by showing warning signs of a looming humanitarian crisis associated with a GPHE context. Originality/value This paper does not aim to claim the birth of a new domain but call for more research on developing a normative theory of humanitarian knowledge as transcendence of cultures. It implies uncharted territories of great interest and potential for the real impact KM community.

In recent years, the crude oil market has entered a new period of development and the core influence factors of crude oil have also been a change. Thus, we develop a new research framework for core influence factors selection and forecasting. Firstly, this paper assesses and selects core influence factors with the elastic-net regularized generalized linear Model (GLMNET), spike-slab lasso method, and Bayesian model average (BMA). Secondly, the new machine learning method long short-term Memory Network (LSTM) is developed for crude oil price forecasting. Then six different forecasting techniques, random walk (RW), autoregressive integrated moving average models (ARMA), elman neural Networks (ENN), ELM Neural Networks (EL), walvet neural networks (WNN) and generalized regression neural network Models (GRNN) were used to forecast the price. Finally, we compare and analyze the different results with root mean squared error (RMSE), mean absolute percentage error (MAPE), directional symmetry (DS). Our empirical results show that the variable selection-LSTM method outperforms the benchmark methods in both level and directional forecasting accuracy.