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Item Public and scholarly interest in social robots: An investigation through Google Trends, bibliometric analysis, and systematic literature review(Technological Forecasting and Social Change, 2024-09) Mishra, Nidhi; Bharti, Teena; Tiwari, Aviral Kumar; Pfajfar, GregorThe COVID-19 pandemic has expedited the integration of social robots within the healthcare sector. This research employs a tripartite methodology, combining Google Trends analysis, bibliometric analysis, and a systematic literature review, to gauge both public and research interest in social robots within the healthcare domain. In the Google Trends analysis, search query data for “Social Robots” was retrieved, encompassing both “all categories” and the specific “health” category. Seasonal effects on relative search volumes (RSV) were assessed through the cosinor model. The analysis confirmed statistically significant seasonal patterns in RSV for “social robots” within the “health” category. Conversely, for the broader “all categories,” only the intercept showed significance, while sinw and cosw were deemed insignificant. For bibliometric analysis, the global literature on “robotics” and “healthcare” was examined in the SCOPUS database. From the extensive pool of publications, 144 relevant studies were identified out of 4037 publications. These studies were further analyzed using VOSviewer, providing insights into recent trends and hot topics concerning social robots in healthcare. The systematic literature review focused on 46 articles published from 2019 to the end of 2023. The findings revealed a lack of consensus on the drivers, barriers, and outcomes associated with social robot acceptance and human-robot interaction (HRI). The study systematically maps the existing research on these aspects, introducing a novel categorization and presenting the concept of a “robot user's ecosystem.” This concept emphasizes the imperative involvement of all stakeholders in the development and understanding of social robots. Ultimately, this methodological approach not only identifies nine research gaps in the current literature but also formulates numerous research questions to guide future researchers in this domain.Item Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning(Management Research Review, 2024-07) Jaiswal, Rachana; Gupta, Shashank; Tiwari, Aviral KumarPurpose Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022. Design/methodology/approach Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment. Findings Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years. Research limitations/implications This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook. Practical implications Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing. Social implications By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies. Originality/value This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.Item Asymmetric spillover effects in energy markets(International Review of Economics & Finance, 2024-04) Tiwari, Aviral Kumar; Abakah, Emmanuel Joel Aikins; Doğan, Buhari; Adekoya, Oluwasegun B.; Wohar, MarkThis paper explores the asymmetric relationship between clean and dirty energy markets. The study uses the time-varying and frequency-domain spillover approaches, while accounting for asymmetries. We use natural gas, gasoline, gas oil, heating oil, crude oil, coal, petroleum, kerosene, propane, and diesel to denote dirty energy markets and wind, solar and clean energy markets to denote clean energy markets. We use daily data running from May 18, 2011, to August 12, 2020. According to the results obtained, good news in fluctuations in global energy market indices increases the integration of international energy markets in the long run compared to bad news. Our result show that transmission of good and bad volatilities in global energy market indices are dispersed with different time-varying intensities. Empirical evidence further reveals that good news increases integration of international energy markets in the long run compared to bad news. Additionally, markets transmit more bad volatility on average than good volatility during global events. According to the results of the research, we foresee that portfolio managers and investors may experience difficulties in diversifying opportunities in financial volatility periods in the short term. Overall, our findings reveal asymmetric risk effects in investment opportunities between clean and dirty energy. As a result of this information, investors can diversify their investments in the clean energy sector in the long term by using the asymmetry in good and bad fluctuations.Item The Dynamic Relationship Between Gas and Crude Oil Markets and the Causal Impact of US Shale Gas(Computational Economics, 2024-06) Ghosh, Sudeshna; Tiwari, Aviral Kumar; Doğan, Buhari; Abakah, Emmanuel Joel AikinsAlthough the recent debate in energy economics on the importance of oil price indexation versus shale gases suggest that big data can be used in predictive analysis in energy economics, little is known particularly in the context of shale gas and oil price interlinkages. Grounding our investigations in such directions we investigate in this paper the relationship between gas and crude oil markets and the impact US shale gas by employing time-varying causality method by Shi et al. (J Time Ser Anal 39(6):966–987, 2018; J Financ Econom 18(1):158–180, 2020) and cross-quantilogram correlation approach by Han et al. (J Econom 193(1):251–270, 2016). In particular, as a representative of the crude oil market, we use OPEC oil; WTI; Crude oil Oman; Crude oil Dubai while for the gas market, we use natural gas prices of UK NBP (National Balancing Point), NYMEX HH (Henry Hub) and US shale gas prices. Data period is from 11th January 2013 to 8th September 2020. We find significant negative spillovers from crude oil markets to natural gas markets particularly during moderate market conditions. The results suggest crucial implications in energy economics literature, to diversify assets to hedge against risks. We further find strong causality association between oil markets, natural gas markets and further oil markets and shale gas markets. Our findings describe that aftermath of the shale-gas boom the predictability nexus between oil and natural gas increased. Once we condition for shale gas the significant negative spill overs from oil markets to natural gas markets increases in the long-run. We suggest important policy prescriptions which have interconnected market repercussions.Item A cross-quantile correlation and causality-in-quantile analysis on the relationship between green investments and energy commodities during the COVID-19 pandemic period(Studies in Economics and Finance, 2024) Sharma, Aarzoo; Tiwari, Aviral Kumar; Abakah, Emmanuel Joel Aikins; Owusu, Freeman BrobbeyPurpose This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis. Design/methodology/approach The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot. Findings From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets. Practical implications The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions. Social implications The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds. Originality/value This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.Item The conditional impact of market conditions, volatility and liquidity shocks on the arbitrage opportunities during pre-COVID and COVID periods(International Journal of Finance & Economics, 2024-07) Lakshmi, VDMV; Sisodia, Garima; Joseph, Anto; Tiwari, Aviral KumarThe study examines the effects of market conditions, volatility and liquidity shocks on the arbitrage profits during pre-COVID and COVID periods. The study uses a conditional quantile regression and finds no significant difference in the impact of market conditions on the arbitrage profits during pre-COVID and COVID crisis periods. The increase in volatility combined with low liquidity during the COVID period makes arbitrage non-viable. However, the decline in volatility during the COVID period encourages investors to initiate arbitrage. The results are useful to fund managers and market analysts to develop suitable trading strategies and stock market regulators to take necessary steps to improve price discovery mechanisms and market efficiency.Item Analyzing time-varying tail dependence between leveraged loan and debt markets in the U.S. economy(International Review of Finance, 2024-06) Tiwari, Aviral Kumar; Trabelsi, Nader; Abakah, Emmanuel Joel Aikins; Lee, Chi-ChuanThis study analyzes the time-varying dependence between U.S. leveraged loan and debt markets within a highly linked financial system using a quantile-based time-varying connectedness framework to determine the hedging benefits of leveraged loans for financial investors at various quantiles. Based on daily closing price data from November 28, 2008 to October 3, 2023, the evidence demonstrates considerable (moderate) spillovers across the leveraged loan and debt markets for severe (normal) occurrences, with additional results indicating symmetric interaction. In terms of risk spillover, we also affirm the dominance of short-term fixed-income instruments over leveraged loans and long-term bonds. These findings indicate that no hedging or diversification occurred among the investigated markets.Item Resource savings, recycling and utilization, and energy transition: introduction(Geoscience Frontiers, 2024-05) Zhao, Xin; Shahzad, Umer; Tiwari, Aviral KumarIn the context of increasingly tight constraints on global resources and environment, accelerating the conservation and recycling of fossil fuels and other mineral resources have become significant challenge. While promoting the transformation of energy development mode, and making efficient use of coal, oil, natural gas, iron ore, potassium salts, phosphate, sulfur and other mineral resources have become important issues in energy planning and energy transformation. Under the framework of Sustainable Development Goals, it is urgent and critical to investigate the relationship between geological resource saving, recycling and modern energy systems, which is an important step to establish a complete system of efficient use of natural resources. This special issue of Geoscience Frontiers focuses on mineral resource savings, recycling utilization and modern energy system construction in geosciences. It compiles contributions on the latest development, evaluation and application of natural resources including mineral resources. In the opening paper by Xu et al. (2024a) employs the principles of trade preference and import similarity to construct dependency and competition networks. They find that the global rare earth trade follows the Pareto principle, and the trade network shows a scale-free distribution. The paper by Singh et al. (2024) investigates the impact of the natural resources rent on the economic growth in some major wealthy economies of the world. The results show a negative relationship between natural resources rent and economic growth for the panel but a different impact on quantiles in each country. Tiwari et al. (2024a) investigate the effect of the circular economy on CO2 emissions growth by considering the role of energy transition, climate policy stringency, industrialization, and supply chain pressure. The results provide insights for policymakers of advanced economies and emerging markets to maintain the balance among circular economy, energy transition, environmental policy stringency, and supply chain pressure for reducing CO2 emissions without halting economic growth and sustainable development. Ma et al. (2024) construct a green energy consumption evaluation index system and measured the green energy consumption levels in 30 provinces of China. This paper further captures the spillover effects running from covariates to green energy consumption using the spatial Durbin model as the main identification approach. The contribution by Zhao et al. (2024) examines the relationships between five renewable energy sub-sectors markets and the geopolitical risk (GPR) and economic uncertainty indices (EUI). The renewable energy indices show differences in response directions, speed and trends for a standard information difference impulse from the GPR and the EUI. Farooq et al. (2024a) explore the effects of various organic amendments on the growth, morpho-physiological and biochemical attributes of three leguminous tree species: Dalbergia sissoo, Vachellia nilotica, and Acacia ampliceps, concerning sustainable productivity. Sarwar et al. (2024) evaluate the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price accurately. Zhou et al. (2024) construct a comprehensive evaluation index system for the provincial-level sustainable development of fossil energy in China covering three major dimensions (socio-economic, resource, and environmental). Moreover, a set of criteria for measuring the SDGs of fossil energy at the national level in China was developed. Liang et al. (2024) account for the embodied carbon emissions in buildings in 2020 for the Guangdong-Hong Kong Macau Greater Bay Area in China. The paper by Mahendru et al. (2024) explores the connections between renewable energy consumption, non-renewable energy consumption, gross fixed capital formation, the labor force, and economic growth in Renewable Energy Country Attractiveness Index countries. Farooq et al. (2024b) conduct a phytosociological survey to identify plant species with the highest importance value index in the vicinity of wastewater-irrigated areas. Teng et al. (2024) investigate the influence of disaggregated energy measures, e.g., renewable, and nuclear energy, income growth and urbanization on the load capacity factor (biocapacity divided by the ecological footprint) of major nuclear power countries, such as France, the USA, Canada, China, and Russia. Abbas et al. (2024) explore the export flow of Chilean copper in response to increasing demand side conditions in major 24 trading partners. The findings urge Chile to enhance production capacity of copper and other critical mineral and improve participation in global value chain to meet sharply increasing copper demand from environmental innovation and renewable energy transition. Li et al. (2024a) employ econometric panel techniques to explore the potential effects of education and green innovation in mitigating/exacerbating the role of natural resources in the Chinese provincial economy. Sun et al. (2024) apply the spatial Dubin model and threshold regression model to explore the impact of digital finance on carbon productivity, yielding the following key conclusions. Liu et al. (2024) take the construction of new energy demonstration cities as a quasi-natural experiment, study their impact on green technological innovation using difference-in-difference (DID), and conduct a robustness test using DID after propensity score matching. Wang et al. (2024) use two key factors (natural resource rent and anticorruption regulation) as threshold variables to reveal the effect of natural resources on the association between DE and carbon dioxide emissions. Xu et al. (2024b) build the ensemble learning model Random Forest and Gradient Boosting Regression to empirically analyse the relationship between industrial wastewater, industrial sulfur dioxide, PM2.5 and mangrove forests. Tiwari et al. (2024b) aim to demystify the role of green energy and green technology in establishing the nexus between behavioural intentions of tourists, technologies, and digital payments by using Perceived value, Compatibility, Perceived Enjoyment, and Social Influence as a predictor variables, Trust and Satisfaction as a mediating variables and Behavioural Intentions as an outcome Variable. Li et al. (2024b) propose a novel hybrid model for accurately forecasting energy prices by reducing noise levels. The findings are useful for policy makers, investors and portfolio managers to forecast the energy trends, and hedge the portfolio risk accordingly. Bashir et al. (2024) establish a novel theoretical framework to analyze the role of energy prices, energy consumption, gold prices and economic growth on environmental degradation in newly industrialized economies. Mohammed and Pata (2024) investigate the interdependence between raw minerals material and sea level rise, considering the role of economic performance and material footprint employing wavelet locale multiple correlations. The last paper by Cai et al. (2024) aims to suggest a novel hybrid model that can efficiently decrease the noise level in PM10 data to forecast it accurately. The results show that the CSD-based ANN model has a higher predictability for PM10 levels in Saudi Arabia due to low error values and higher Dstat values. We extend our sincere gratitude to all contributors of this Special Issue, as well as the referees who offered meticulous and outstanding comments. These insightful inputs greatly aided authors in refining their ideas and interpretations. A special acknowledgment goes to Dr. Lily Wang, Editorial Assistant at Geoscience Frontiers, for her invaluable support and assistance over the past two years. We also express our appreciation to Prof. Santosh, Editorial Advisor, for providing both the opportunity and editorial guidance throughout the entire production process of this Special Issue—from acceptance to the final preface. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Item Real-world asset tokens and commodities: static and dynamic linkages(China Accounting and Finance Review, 2025-08) Tiwari, Aviral Kumar; Abdullah, Mohammad; Sarker, Provash Kumer; Abakah, Emmanuel Joel AikinsPurpose – This study explores the static and dynamic interconnectedness between real world asset (RWA) tokens and traditional commodities. Additionally, the study examines the role of uncertainty factors in explaining the interconnectedness. Finally, the study examines portfolio diversification opportunities. Design/methodology/approach – A novel R-squared based time-frequency connectedness approach is used to examine interconnectedness using data from March 14, 2018, to June 9, 2023. To compute optimal portfolio weights and hedging ratios for each pair, the DCC-GARCH model is utilized and the best weights and hedge ratios are estimated. Findings – The static connectedness result shows that RWA tokens and commodities demonstrate a relatively lower level of interconnectedness. The dynamic connectedness measures unveil time-varying interconnectedness, particularly heightened during economic events. Moreover, global uncertainty factors are positively associated with connectedness, emphasizing the multifaceted channels through which shock is transmitted. Portfolio analysis underscores potential diversification opportunities between RWAs and commodities, offering insights for informed decision-making in navigating the evolving landscape of blockchain-based assets and traditional commodities. Originality/value – The main novelty of this manuscript is the exploration of RWA tokens, an emerging asset class that has received limited academic attention compared to cryptocurrencies, NFTs and DeFi. Unlike prior studies, this research employs a novel R-Squared-based time-frequency connectedness approach to analyze the static and dynamic linkages betweenRWA and traditional commodities.It also examines global uncertainty factors and incorporates portfolio backtesting, providing insightsfor investorsseeking diversification in tokenized assets.Item Tracing the ties that bind: navigating the static and dynamic connectedness between NFTs and equity markets in ASEAN based on QVAR-approach(Financial Innovation, 2025-01-10) Naveed, Muhammad; Ali, Shoaib; Tiwari, Aviral KumarBased on market integration theory, we investigate the static and dynamic connectedness between nonfungible tokens (NFTs) and the Association of Southeast Asian Nations (ASEAN) equity markets using the Quantile Vector Auto Regressive model. We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential. Our analysis infers a moderate level of return transmission at the median quantile, where equity markets evolved as the net recipients of return spillover from the system, while NFTs emerge as key transmitters. In extreme market conditions, transmission between variables is amplified, but the increase is symmetrical across extreme quantiles, suggesting a similar impact. However, the interlinkage among assets is symmetric across conditional quantiles. The dynamic analysis demonstrates that the system integration amplifies during uncertain times (e.g., COVID-19 and the Russia–Ukraine conflict). Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions. However, the period of turmoil dampened the diversification potential, and hedging became expensive. Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.