Major Economies Research Articles

Connectedness Among Geopolitical Risk, Inflation, Currency Values, and Exports by TVP-VAR Analysis: A Worldwide Perspective

Abstract The article discusses the spillover impacts between geopolitical uncertainty, inflation, exchange rates, and exports worldwide by employing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology. The net directional pairwise and net total directional causality among these variables is examined in major advanced economies (US, UK, Germany, France, Israel, South Korea and Japan) and rising economies (Russia, Türkiye, China, India, Brazil, and South Africa). Normal market conditions (since early 2000) and stressed ones since the latest crises (the Covid-19 disease and the Russia–Ukraine conflict) are covered. It is revealed that the national currency value constitutes the most influential determinant of exports and inflation but also in the system examined. Currency values exert direct impacts on inflation but also indirect, affecting exports and enhancing geopolitical risk as this is found to increase inflationary pressures. Intriguingly, this gives credence to the emergence of a new channel of inflation-creation that works through geopolitical risk. Such linkages are more pronounced in the US, South Korea, and Brazil while Germany and France present the weakest relations. Inflation, the currency value, and exports turned out to be very decisive regarding the geopolitical risk in Russia during the Russia–Ukraine war.

India’s Rising Economy Amidst Global Financial Crisis

This paper explores the impressive economic growth of India amid global monetary crisis. Despite the challenging global economic conditions, India has managed to maintain positive growth trajectory. It examines the key factors driving India’s economic rise, including domestic consumption, government policies, industrial diversification and foreign direct investment. The COVID-19 pandemic has had a profound impact on the global economy, causing a monetary crisis and significant slowdown in major economies worldwide. This research paper aims to explore the various causes behind these effects, analyzing the direct and indirect consequences of the pandemic and other factors on economic sectors, fiscal policies, supply chains, and consumer behavior

Team Collaboration Vs Team Competition: Pandavas Vs Kauravas - A Literature Evidence from Kurukshetra (Mahabharata) War

Purpose: Mahabharata is an epic war described in poetic form, an integral part of Indian mythology. While a full-blown war of such a colossal scale in an ancient time of around 3000 B.C. is intriguing for the civilizations today, the first war involving almost every contemporary major economy of the world whose horrors survived for generations, giving ramifications to date is in itself a “lesson learnt hard way”. The main purpose of this paper is to reflect on the management lessons based on Indian traditions. In this paper, the researchers discuss team collaboration vs. team competition between two cousins of Mahabharta the Pandavas and the Kauravas and to bring literature evidence of why the Kauravas lost the Mahabharata war despite having the best-in-class warriors and a larger army. Objective: The researchers have set the following main objectives and the present study investigates 3 wide-ranging research questions: (1) To recognize team collaboration and team competition characters from the Mahabharata War. (2) To assess the reasons why Kauravas, lose the Mahabharata War. (3) To evaluate which team effectiveness model is best for collaboration & and competition. Design/Methodology/Approach: The study is based on secondary data and available shreds of evidence. The hypotheses have been stated for the selected variables and were investigated to prove or disprove the same without any errors. Findings/Results: The researchers draw lessons from the epic story and highlight the great war of Mahabharata is no doubt an epic war, described perfectly which gives numerous lessons on every nook and corner. In the complex scenario, when the Kauravas were in crisis, they often tended to rush into decisions without proper reflection and demonstrated an inability to decide based on subtle, intangible, and often very vital elements like emotion, intuition, and spiritual discernment. At the same time, the Pandavas team's effectiveness and setting the stage for great performance are very much needed in teams to work together successfully. In the end, researchers consider these factors, to have an edge in Mahabharata War. Research Limitations/Implications: Further the fact that the battle between the Pandavas and the Kauravas was already battled, the decision had been made and concluded before it even began, as revealed by Shri Krishna in his Visvarupa to Arjuna, the reason that Kauravas lost was basically due to an act of deceptive stupidity which can be traced back to one crucial event. Originality/New Knowledge/Interpretation/Value: Thus, it can be aforesaid that if we apply the finest practices of the Mahabharata War to the finest practices of Management, we might have renewed associate and can function better in the coming years and make the organization & team glance and perform better in the years to come. Paper Type: The study is based on secondary data and available shreds of evidence.

An intelligently adjusted carbon price forecasting approach based on breakpoints segmentation, feature selection and adaptive machine learning

Accurate carbon price prediction is conducive to the stable operation and development of carbon financial markets. Affected by major policies and economies, the laws of carbon price may show huge changes, generating various breakpoints that hinder the prediction work. Therefore, a hybrid model based on adaptive segmentation and feature clustering is developed to forecast carbon price, which responds to the growing requirement of the high precision prediction. It solves the issue that the accuracy and stability of model are limited by the breakpoint via the adaptive processing and the fully learning for data. Meanwhile, the proposed adaptive structure improves the robustness and adaptability of prediction model, achieving accurate prediction for carbon emission trading markets with different features. The eight carbon emission trading markets in China are used to evaluate prediction performance. The obtained results indicated that the proposed model was effective and robust, with the average mean absolute error and root mean square error of only 0.2272 and 0.3321, respectively. According to the comparative analysis, the segmentation based on breakpoint and adaptive prediction based on feature clustering improve the model forecasting accuracy by 69.87% and 45.51%, respectively. Hence, the proposed model can meet the requirements of carbon financial markets and provide a benchmark for the carbon emission reduction work.

The Effect of the G20 Summit in Bali on Creative Economy and Tourism: Economic Impact Analysis of High-Level Events

This research examines the impact of the G20 Summit in Indonesia in 2022 on the country's economy, especially in the creative economy and tourism sectors. The G20 summit in Bali became a platform for multilateral collaboration between 19 major economies and the European Union. The main focus of the conference is on issues of global economy and sustainable development with the theme "Recover Together, Recover Stronger." This research method involves the analysis of economic and statistical data, including official statements from Finance Minister Sri Mulyani Indrawati and the Central Statistics Agency (BPS). This data includes the impact on domestic consumption, Gross Domestic Product (GDP) growth, and Micro, Small and Medium Enterprises (MSMEs) sector growth. In addition, the research involved an analysis of women's empowerment programs in MSMEs, fund allocation, and the level of realization of the Kredit Usaha Rakyat (KUR) program. The research includes an analysis of the positive effects on infrastructure in Bali and the improvement of tourism image. Data on the influence on the tourism sector includes statistics on foreign tourist visits, hotel occupancy, and an increase in tourist visits during the conference period. The study also includes analysis from the MSMEs, restaurants, arts and crafts, and transportation sectors to detail the economic impact in various sectors.

Effect of Yttrium on Ce/Ni-Metakaolin Catalysts for CO2 Methanation.

In recent years, major economies have implemented carbon reduction and carbon neutrality policies. Furthermore, with advancements in science and technology, carbon dioxide (CO2) is now considered a valuable raw material for producing carbon-based fuels through hydrogenation. Various concentrations of yttrium (referred to as Y hereafter) were introduced to assess their influence on the catalytic performance of CO2 methanation. At a temperature of 300 °C, the catalyst exhibited an impressive CO2 conversion rate of 78.4% and maintained remarkable stability throughout a rigorous 100 h stability assessment. The findings suggest that the inclusion of yttrium (Y) promotes the formation of oxygen vacancies and alkaline sites on the catalyst. This, in turn, enhances the reducibility of nickel species, improves the dispersion of nickel particles, and plays a pivotal role in enhancing thermal stability. Furthermore, it offers an innovative design approach for creating highly efficient composite CO2 methanation catalysts by controlling particle size and harnessing synergistic catalytic effects at the metal/support interface.

The Status, Trend, and Global Position of China’s Forestry Industry: An Anatomy Based on the Global Value Chain Paradigm

This study aims to systematically examine the developmental attributes and trends within China’s forestry sector through the lens of a global value chain (GVC) framework. To this end, this research analyzes the Organization for Economic Co-operation and Development Inter-Country Input–Output (OECD-ICIO) database, using a cohesive set of GVC methods, including the forward decomposition of industry value-added, an assessment of industry upstream and downstream positions, the decomposition of export values, and analysis of trade competitiveness indicators. The trajectory of China’s forestry industry hinges upon the interplay between foreign demand and domestic demand. The results reveal a transition in China’s forestry sector development model from export-focused to a domestically driven approach. The proportion of value-added that is devoted to meeting domestic demands within the wood processing and papermaking industries has surged to 76% and 82%, respectively. Among the major economies, China has the highest output upstream index and input downstream index in the forestry industry, playing a vital role in propelling and pulling other industries into the global value chain system. The proportion of domestic added-value in China’s forestry industry exports ranks among the top, reflecting strong self-sufficiency in export production. Although China’s forestry industry possesses a high world market share, its overall international competitiveness is weak, especially with clear signs of weakening comparative advantages in the wood processing industry.

Driving towards a sustainable future: Transport sector innovation, climate change and social welfare

The growing population and rising need for transportation amplify emissions, elevating the corresponding welfare costs. This research examines the effect of transportation sector Innovation in mitigating emissions and decreasing welfare costs. The study selected the 15 most polluted European economies from 1994 to 2020 and employed second-generation econometric approaches. The study used the Westerlund panel co-integration technique and pooled mean group estimate strategy to examine the long and short-term relationships. Additionally, a fixed and random-effects model is employed to assess the consistency of the results. The research utilized the Granger causality methodology to investigate the outcomes specific to each country. The findings indicate that the implementation of new technologies for transport has a restraining effect on demand, transportation, and overall emissions in both the long and short term. Moreover, the interaction of renewable technologies with green energy profoundly impacts the European economies. The results show that environmental degradation increases temperature and rainfall uplift welfare costs. The country-specific results demonstrate a significant causal association among variables in the major economies, endorsing the effect of innovation in mitigation efforts and climate change on surging the welfare cost. The results suggest that green transport using renewable energies is the way to achieve carbon neutrality, decrease welfare costs, and 2050 sustainable transport targets.

Does reducing income inequality promote the decoupling of economic growth from carbon footprint?

Mitigating climate change, improving economic growth, and reducing income inequality are three of Sustainable Development Goals (17 SDGs). It has been well understood that economic growth has a significant impact on carbon footprint, but it remains unclear whether this impact will be mitigated or intensified by reducing income inequality. By applying the Multi-Regional Input-Output model, this study assesses the consumption-based carbon footprints of 43 major economies over the period 1995–2019 and conducts the corresponding empirical analysis. Through examining the moderating effect of income inequality (Gini coefficient) on the relationship between economic growth (GDP per capita) and consumption-based carbon footprint (CO2 per capita), we demonstrate that a lower Gini coefficient – a more equitable distribution of income − could significantly promote the decoupling of economic growth from carbon footprint. The results also show a heterogeneous moderating effect of income inequality – reducing income inequality in rich countries helps decouple economic growth from carbon footprint more than in poor countries. This study contributes to a better understanding of the synergy among economic growth (SDG 8), income inequality reduction (SDG 10), and carbon mitigation (SDG 13). Given that reducing income inequality in the context of economic growth can have significant benefits for carbon mitigation, we would suggest that income inequality reduction should be incorporated into climate action, particularly in rich countries.

Leapfrog logistics: digital trucking platforms, infrastructure, and labor in Brazil and China

Critical political economy analyses have principally conceptualised platforms as unproductive economic forms (rentier capital) skimming value off each intermediated transaction and/or illegitimately extracting and capitalizing user data. This scholarship has also focused heavily on extractive dimensions of global North platforms’ operations within the global South. However, a small but growing literature is examining the productive aspects of digital platforms, while important digital platforms are emerging in Southern economies. These trends draw attention to the prospect of platforms playing a role in leapfrog development. This article examines digital trucking platforms in two major economies of the global South: Brazil and China. We argue that trucking platforms are engendering dramatic transformations of these countries’ disorganised road transport logistics systems. Platforms are centralising investment and co-ordinating control. Utilising technologies which surpass those deployed in the global North, they rationalise disorganised and inefficient transport systems by subjecting them to algorithmic rule and minimising bureaucratic inefficiencies. Through greater centralisation, co-ordination and rationalisation, trucking platforms further serve to proletarianize their owner-operator workforces, driving down costs and eliminating barriers to the geographical circulation of capital. While this offers some benefits to workers, it also exposes them to the vagaries of market discipline in new ways.

Development Status of Digital Economy in Northeast Asian Countries and China’s Opportunities

With the vigorous development of digital technology, the development of the digital economy has become an important component of the global economy. Against the background of negative growth of major global economies hit hard by the COVID-19 epidemic, the steadily rising digital economy has become a key force to boost the global economy and an important engine to promote global economic development. Northeast Asian countries should use the dividends released by the digital economy to promote the coordinated development of digital technology innovation within the region, respond to technological revolution and industrial transformation, and build new international competitive advantages. This article provides a detailed overview of the current development status of digital economy in various countries in Northeast Asia, analyzes the challenges faced by countries in the region in developing cross-border digital economic and trade cooperation from three aspects: political mutual trust, digital divide, and network security. It further proposes to build a cross-border digital service trade platform in Northeast Asia, establish a China North Russia Far East digital free trade zone, so as to promote the deepening of cooperation and common development in digital trade within the region.

Smart city pilots, marketization processes, and substantive green innovation: A quasi-natural experiment from China.

The world's major economies are striving to control carbon emissions and avoid irreversible impacts on the natural environment. Therefore, innovative green technologies are crucial for both government departments and the private sector as an important way to address carbon emissions. This study aims to investigate the link between the government's smart city construction and corporate green innovation and optimize the policy guidelines that drive green innovation in enterprises. This study analyzes 6,104 panels of Chinese listed companies from 2007-2019. An approach called the Differences-in-Differences model was applied to evaluate hypotheses. The empirical results suggest that smart city pilots drove substantial green innovation in businesses. The marketization process has a moderating effect on the impact of smart city pilots on substantive green innovation in enterprises. Moreover, marketization process has a threshold effect in smart city pilots influencing the substantive green innovation of enterprises, and the effect of smart city drivers influencing the substantive green innovation of enterprises increases significantly when regional marketization process reaches a certain level. The findings of this study provide valuable guidance for policy designers to promote corporate green innovation at both the hardware facility level and the market system level of cities when developing policies related to green innovation.

Will falling domestic labor compensation share really be improved when global trade slowdown?

The participation of Global Value Chains (GVCs) in the world’s major economies has changed from rising to falling since 2010. Based on GVCs income, SDA method and multiple panel regression, this paper explores the cross-cycle effect, mechanism and persistence paths of GVCs participation on domestic labor compensation share in 51 economies from 2000 to 2018. The paper concludes that the overall effect of participating in GVCs on domestic labor compensation share is negative. However, forward participation in GVCs perform better than backward GVCs participation in stabilizing domestic labor compensation share. Capital-biased technological progress and availability of overseas labor are conditions under which the GVCs participation undermines domestic labor compensation share. This paper propose to seize the opportunity to increase the labor compensation share through measures and policies, such as promoting fair competition in domestic market, servitization of industrial structure and the establishment of relational value chains.

Knowledge mapping of an artificial intelligence application scenario: A bibliometric analysis of the basic research of data-driven autonomous vehicles

With the rapid development and maturation of basic research in related fields such as deep learning and convolutional neural networks, artificial intelligence (AI) has become a policy hotspot of high interest in all major economies. Furthermore, the governance of application scenarios has become one of the important topics of AI governance policy research. The practice of AI governance policy depends on a change in the paradigm of relevant governance policy research theories, and there is an urgent need to empirically analyze the structural characteristics of the knowledge evolution of AI application scenarios. This study conducts a bibliometric analysis of the basic research trends in autonomous vehicles, which is one of the most important application scenarios of AI. The study empirically analyzes the relevant literature data from the Science Citation Index Expanded (SCI-Expanded) and Social Sciences Citation Index (SSCI) databases of Web of Science, based on both technical and social dimensions through the knowledge mapping analysis tools in Bibliometrix (in R environment). Based on the empirical analysis, the results show that the basic research on autonomous vehicles is characterized by strong data-driven innovation under the influence of AI. The fusion of AI and basic research on autonomous vehicles has become a major driver of knowledge innovation in this domain while there is a lack of the integration of social science research.

Decarbonisation: A Case Study of Malaysia

Limiting global warming to well below 2°C demands an urgent acceleration of decarbonization efforts, posing challenges in multiple sectors like transportation, energy efficiency, and steel production. The feasibility of achieving these targets, akin to the transformation seen during the collapse of the Soviet Union, is being questioned. Yet, hope lies in leveraging tipping points within complex systems for rapid change. Internal carbon pricing (ICP) emerges as a pivotal tool in this endeavor, enabling companies to assess investments, manage risks, and align strategies towards a low-carbon future (Riedel et al., 2021; Trinks et al., 2022). ICP drives decarbonization across industries and influences capital investment decisions by estimating carbon costs. Companies like LG Electronics, Microsoft, and Royal Dutch Shell have adopted ICP, further reinforcing its value in transitioning to sustainability. Major economies have implemented carbon pricing policies, yielding significant emissions reductions. In China, aggressive carbon pricing strategies could drive notable energy consumption reductions (Fan et al., 2014). Meanwhile, Malaysia faces a challenge in reducing carbon emissions, with strategies like renewable energy integration, energy efficiency enhancement, and sustainable transportation promotion (Farabi et al., 2019). This study focuses on Malaysia's decarbonization efforts, aiming to illuminate factors influencing corporate decarbonization in alignment with national climate targets. Addressing gaps in sector-specific decarbonization studies and exploring strategies that make decarbonization a competitive advantage will provide critical insights for effective policymaking and sustainable practices, contributing to global climate action. Keywords: Decarbonization, Malaysia, Net-zero emissions, corporate practices.

A Review of Climate Finance

Climate finance is a financial solution to cope with climate change and develop a low-carbon economy, which is a very important trend in the development of the modern financial industry. The author conducts an analysis of climate finance based on the relevant literature. Specifically, on the basis of summarizing the impact path of climate change risks on the financial system and sorting out the measures taken by major economies at home and abroad to cope with climate change risks, this paper analyzes and studies the shortcomings of Chinas financial system in coping with climate change and puts forward policy suggestions for strengthening top-level design and overall coordination, attaching importance to climate risk management, strengthening incentives and constraints, and further increasing international cooperation.

Transmission Mechanism of U.S. Monetary Policy to Economies

Due to the special status of the United States and the U.S. dollar in the world economy, the U.S. monetary policy has a significant spillover effect on the world. Especially since the subprime mortgage crisis in 2007, the U.S. monetary policy has attracted more attention from the world. Combined with the previous literature, this paper finds that the transmission channels of the US monetary policy uncertainty spillover effect include exchange rate expectation channel, financial asset allocation channel, contagion of monetary policy uncertainty, and real option effect. Among them, the channel of exchange rate expectations, the channel of financial asset allocation and the channel of contagion of monetary policy uncertainty are responsive and rapid, while the real option effect has a slow and long-term impact on the real economy. At the same time, there are differences in the spillover effects of U.S. monetary policy uncertainty on different countries. The results of this study have certain guiding significance for monetary policy formulation in other countries, such as strengthening the supervision of cross-border bonds and cross-border investment. Especially under the impact of the new crown pneumonia epidemic, major developed economies have implemented a new round of quantitative easing monetary policies, and policymakers in emerging economies need to pay great attention to this.

Spillover effects of liquidity management in major economies and China's digital economy-driven countermeasures

PurposeThis paper aims to enhance the Global Projection Model (GPM) developed by the International Monetary Fund by constructing a GPM4 model that includes the United States of America, the Eurozone, Japan and China.Design/methodology/approachThis article introduces the United States of America, the Eurozone, Japan and China into a comprehensive global forecasting model, analyzing the impact of liquidity management in G3 economies on nine key macroeconomic variables in China.FindingsThe findings reveal that the liquidity management strategies employed by major economies do exert a certain influence on China's major macroeconomic variables. Different types of liquidity shocks elicit varying effects. Monetary shocks exhibit the strongest instantaneous impact, while credit conditions and policy rate shocks contribute more significantly to China's long-term macroeconomic fluctuations. However, no single shock stands out as the dominant factor.Originality/valueThis paper attempts to expand the GPM model developed by the International Monetary Fund and build a GPM4 model including China, the United States of America, the Eurozone and Japan. For the first time, the GPM model was used to analyze the spillover effects of liquidity management in major economies on China's macroeconomy and revealed the impact of non-price factors such as credit conditions on China's macroeconomic variables.

Global Health Expertise in the Shadow of Hegemony

What enables actors to shape norms in global health governance? Scholarship on global health has highlighted the role of experts and expertise in operationalizing norms across a variety of issues. The degree of expert consensus or dissensus and the negotiation processes between expert communities—for example, in international organizations, NGOs or academia—are commonly identified as centrally important for explaining these processes. In this article, we posit that norm-making in global health governance occurs in the shadow of hegemony; a system of status and stratification that is centered on economic and security concerns and maintained by countries at the core of the world system. These countries—notably the USA and other major economies in the Global North—project their hegemonic position in the world system across areas of global organizing, including in global health. We explore the relationship between epistemic consensus and hegemonic interests as parameters that shape the outcome of norm-making processes. To pursue this argument, we examine this relationship in the context of the development of policy norms to counter non-communicable diseases in developing countries and to pursue the securitization of global health.

Quantifying the impact of energy consumption sources on GHG emissions in major economies: A machine learning approach

This article aims to quantify the impact of different energy consumption sources on greenhouse gas (GHG) emissions for three major economies: the United States of America (USA), China, and the European Union (EU). To achieve this, energy consumption and GHG emissions data were obtained from “Our World in Data” for the period 1965–2021. Then, two machine learning techniques were utilized. Gradient Boosting (GB) was used to identify the major energy consumption sources contributing to GHG. While Artificial Neural Network (ANN) was used to quantify the effects of these major energy consumption sources on GHG emissions. The findings have significant implications for policymakers, as they suggest that effective strategies to reduce GHG emissions must be tailored based on the energy utilization sources of each country. Specifically, for the USA it was found that reducing coal consumption could be the most effective strategy to reduce GHG emissions, as increasing coal consumption by 25% would result in a 13% increase in GHG emissions. In contrast, increasing nuclear consumption by 25% in China would result in an 11% decrease in GHG emissions due to the displacement of fossil fuel-based energy sources. Increasing wind energy consumption by 25% in China would result in a 3% decrease in GHG emissions. In the EU, the study found that increasing oil consumption has a minor effect on GHG emissions while increasing coal consumption by 25% would result in an 11% increase in GHG emissions, highlighting the importance of reducing coal consumption. This study's originality lies in the use of machine learning techniques to identify the key energy consumption sources driving GHG emissions in the three major economies, as well as its specific recommendations for reducing emissions.