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"As organizations grapple with the challenges of a dynamic market, the integration of Artificial Intelligence (AI) emerges not only as a technological progression but a strategic necessity. The transformative potential of AI, particularly through OpenAI, holds the promise of redefining operational paradigms, accelerating innovation, and unlocking unprecedented growth opportunities. However, lurking beneath this promise are challenges that demand urgent attention - from tailoring relevance for specific business units to ethical and safe integration practices. The specifics of how OpenAI can amplify labor productivity and enhance decision-making processes remain elusive. Generative AI and Multifactor Productivity in Business offers a guide surrounding the complexities of OpenAI's role in business operations. It contends that understanding OpenAI is not just beneficial; it is essential for organizations seeking to navigate economic uncertainties and unlock high levels of efficiency and growth.The book delves into the effects of OpenAI on business, with a primary objective of illuminating the scholarly and practitioner-based contributions that push the boundaries of OpenAI in business research. This exploration encompasses applications of advanced generative AI tools, language models, and innovative technologies specific to diverse businesses across sectors, scales, and regions. It emphasizes that as AI becomes more seamlessly integrated into business processes, the potential for multifactor productivity to fuel economic growth, new industries, and job opportunities is unparalleled."--

AbstractTo a large extent, the theories and concepts behind the effect of ecological footprint have been the paramount concern of the recent literature. Since the rising and falling of environmental degradation have been a continuous issue since the first phase of development, determinants such as economic complexity may play a critical role in achieving long-term sustainable development in the framework of environmental Kuznets curve (EKC) paradigm. Therefore, this research expands on the notion of an EKC paradigm for the world's top ten most complex economies by considering four variables, such as real GDP per capita, electricity consumption, trade openness, and a new putative factor of environmental obstacle, the economic complexity index (ECI). This is one of the first studies to look at the impact of ECI on the ecological footprint of a specific sample from 1998 to 2017. The findings demonstrate a continuous inverted U-shaped link between real GDP per capita, the square of real GDP per capita, and ecological footprint. The EKC hypothesis is found to be valid in the long term in the examined complex economies. The findings of the panel autoregressive distributed lag (ARDL) of the pooled mean group (PMG) and fully modified ordinary least squares (FMOLS) estimations demonstrate that in the long term, electric power usage contributed to the carbon footprints. Furthermore, the economic complexity index and trade openness increase environmental performance over time. To determine if there is causation between the variables, we employ the panel vector error correction model (VECM) framework. Particularly, the results show unidirectional causality running from electric power consumption to ecological footprint and bidirectional causal relationship between (1) economic growth and ecological footprint; (2) square of economic growth and ecological footprint; (3) economic complexity index and ecological footprint; and (4) trade openness and ecological footprint.

Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news' readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders.

Tourism contributes approximately one‐fifth to total global employment. However, growth in tourism can promote an increase in transportation, energy consumption, natural resource exploration, and consequential ecological distortions. This study applies a battery of second‐generation econometric techniques to investigate the influence of tourism development and natural resource on a comprehensive environmental indicator; the ecological footprint (EF), in the ten most visited destinations. The findings show that tourism receipts have an increasing effect on EF, while tourism arrivals have a reducing effect on EF. The country‐wise results reveal that tourism receipts increase the EF in China, Italy, Spain, and the UK, while the reverse holds true for France, Germany, Thailand, Turkey, Mexico, and the US. The influence of natural resource on the EF is mixed. Natural resource increases the EF in China, France, Germany, Spain, and the UK. A feedback causality exists among EF, natural resource, and tourism development. Policy directions are discussed.

AbstractIn less than two decades, the global tourism industry has overtaken the construction industry as one of the biggest polluters, accounting for up to 8% of global greenhouse gas emissions as reported by the United National World Trade Organization (UNWTO 2018). This position resonates the consensus of the United Nations Framework Convention on Climate Change (UNFCCC). Consequently, research into the causal link between emissions and the tourism industry has increased significantly focusing extensively on top earners from the industry. However, few studies have thoroughly assessed this relationship for small island economies that are highly dependent on tourism. Hence, this study assessed the causal relationship between CO2 emissions, real GDP per capita (RGDP) and the tourism industry. The analysis is conducted for seven tourism-dependent countries for the period 1995 to 2014 using panel VAR approach, with support from fully modified ordinary least square and pooled mean group–autoregressive distributed lag models. Unit root tests confirm that all variables are stationary at first difference. Our VAR Granger causality/block exogeneity Wald test results show a unidirectional causality flowing from tourism to CO2 emission, RGDP and energy consumption, but a bi-directional causality exists between tourism and urbanization. This implies that in countries that depend on tourism, the behaviour of CO2 emission, RGDP and energy consumption can be predicted by the volume of tourist arrivals, but not the other way around. The impulse response analysis also shows that the responses of tourism to shocks in CO2 appear negative within the 1st year, positive within the 2nd and 3rd years but revert to equilibrium in the fourth year. Finally, the reaction of tourism to shocks in energy consumption is similar to its reaction to shocks in RGDP. Tourism responds positively to shocks in urbanization throughout the periods. These outcomes were resonated by the Dumitrescu and Hurlin causality analysis where the growth-induced tourism hypothesis is validated as well as feedback causality observed between tourism and pollutant emission and urbanization and pollutant emission in the blocks over the sampled period. Consequently, this study draws pertinent energy and tourism policy implications for sustainable tourism on the panel over their growth trajectory without compromise for green environment.

AbstractConsidering the need for environmental sustainability while ensuring economic growth and development by 2030, this study uses data on 123 developed and developing countries to examine factors that influence green growth. The empirical results show that economic development positively influences green growth. However, trade openness is detrimental to green growth. Regarding energy-related factors, we find energy consumption negatively affecting green growth, but renewable energy consumption significantly improves green growth. In further analysis, we find that the influence of these factors differs between developed and developing countries. The result implies that countries at a different development level will require different strategies in achieving the Sustainable Development Goals in 2030. The results are robust to alternative identification strategies such as the System Generalised Method of Movement, which accounts for potential endogeneity.

The United Nations Climate Conference 25, held in December 2019, reached a significant agreement against implementing the Paris agreement come 2020. Bound by the contract, 189 countries who are party to the deal agreed to constrain worldwide temperature to ascend to 1.5 degrees Celsius. To this end, the present study attempts to investigate the readiness of selected countries in the European Union to implement the agreement, which will better the quality of the global environment. In line with this, this study appraises the connection between economic growth, renewable and non-renewable energy consumption, on emissions in 11 countries in the European Union from 1990 to 2016. The study utilises the Pooled Mean Group-Auto Regressive Distributed Lag (PMG-ARDL) model estimator and Dumitrescu and Hurlin Panel Causality analysis to analyse the long-run and short-run impact and direction of causality among these factors, respectively. The long-run study's empirical results show a U-shaped Environmental Kuznets Curve (EKC) and a negative connection between renewable energy use and emissions in the EU-11 countries. In the short-run, non-renewable energy use worsens CO2 emissions while renewable energy use leads to a fall in emissions. Similarly, causality tests show a feedback mechanism between emissions and renewable energy use and between non-renewable energy and renewable use. Also, there is unidirectional causality from income to CO2 emissions, non-renewable energy use to CO2 emissions. The investigation recommends an expanded proportion of renewable energy sources in the EU countries' energy mix to cut down on emissions.

AbstractGlobal warming has been a pressing issue for the past decade as various economic activities have been flagged and are expected to reduce emissions. While previous studies have examined the energy consumption-emissions-economic growth nexus in significant detail, attention is yet to be given to the role of economic policy uncertainties and human activities such as tourism in a carbon function. Thus, this study aims to investigate the long-run relationship between energy consumption, tourists' arrivals, economic policy uncertainty, and ecological footprint in the top ten earners from international tourism over the period 1995 to 2015. The fully modified ordinary least square and dynamic ordinary least square estimation techniques and the Dumitrescu and Hurlin causality tests were used in the study. Empirical results suggest that economic policy uncertainties in addition to tourism and energy consumption are drivers of environmental degradation. However, the contribution of energy consumption to ecological footprint is significantly moderated by economic policy uncertainties such that a 1% increase in the latter reduces environmental damage by 0.71%. This study suggests that policy uncertainties matter a great deal for energy and environmental policies. Also, green economic growth is possible if the proper implementation of environmental protection policies can restrict the harmful impact of economic activities on the quality of the environment. Based on the empirical findings, vital energy policy recommendations are suggested.

The European Union (EU) generalised system of preferences (GSP Scheme) grants preferential treatment to 88 eligible countries. There are, however, concerns that the restrictive features (such as rules of origin, low preference margin and low coverage) of the existing scheme indicate gravitation towards commercial trade agenda to which efficiency imperatives appear subordinated. Whether these concerns are genuine is an empirical question whose answer largely determines whether, after Brexit, the United Kingdom continues with the existing specifics of the EU scheme or develops a more inclusive United Kingdom‐specific GSP framework. This study quantitatively examines the efficiency of the EU GSP as it relates to United Kingdom beneficiaries from 2014 to 2017. We draw on the descriptive efficiency estimation (the utilisation rate, potential coverage rate and the utility rate) using import data across 88 beneficiary countries and agricultural products of the Harmonised System Code Chapter 1 to 24. Asides the Rules of Origin that, generally, harm the uptake of GSP, low preference margin is found to cause low utilisation rates in a non‐linear manner. Essentially, a more robust option (such that allows "global Cumulation" or broader product coverage) could, substantially, lower the existing barriers to trade and upsurge the efficiency of the GSP scheme.

AbstractIn environmental management, many studies have examined the energy consumption-emission nexus in detail. However, for the first time in the literature, this study considers how the Economic Complexity Index (ECI) and economic policy uncertainty (EPU) moderate the contribution of energy consumption to emissions for the four World Bank Income clusters. The system generalised methods of moments are applied to data for 109 countries from 1996 to 2016. Based on the main model (grouped clusters) estimations, the result revealed the existence of the environmental Kuznets curve (EKC) hypothesis. Also, an increase in air transport and consumption of energy releases more carbon emissions to the climate. Interestingly, ECI decreases carbon emission significantly while EPU does not have a significant impact. Moreover, the study revealed that ECI moderated the impact of other variables on emission, but EPU is not a significant moderator. Furthermore, a comparative analysis among the four incomes suggests that the EKC hypothesis holds only in the high-income clusters; ECI is a significant predictor of carbon emission in the four clusters, but it only decreases the emission in high-income clusters. This corroborates the debate on climate change and the productive capacity of high-income countries. Given the foregoing, several policy measures were recommended.