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On the 23rd of June 2016, the United Kingdom voted to leave the EU, leading to months and years of economic policy uncertainties. Such uncertainties have not only characterized the UK but have become a center point for energy debate in recent times. Given the foregoing, this paper progresses to provide evidence on the role of Economic Policy Uncertainty in the Energy Consumption - Emission nexus in the UK. We use annual data spanning the period of 1985–2017 for the UK for CO2 emissions in tons per capita (CO2), real GDP (RGDP), energy use (EU), and economic policy uncertainty (EPU). The Autoregressive distributed lag model (ARDL) bound test is used to test the fitness of the model in the short and long term. Our model shows that EPU matters most in the short run, as it reduces the growth of CO2 emissions, while prolonged use of EPU in the UK, exhibit controversial influence, where CO2 emissions continue to rise. In addition, pairwise Granger causality shows a one-way causality running from energy use to CO2 emissions, CO2 emissions to economic policy uncertainty, and also from energy use to economic policy uncertainty. However, two-ways causality is found between real GDP and real GDP per capita. Overall, our results imply that EPU is likely to yield a positive effect on climate change for a short time, but continue dependent will, in the long run, create an unhealthy environment. We suggest that the UK government should consider implementing an additional long-run policy that will supplement the effort of EPU.

"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.

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 UK continues with the existing specifics of the EU scheme or develops a more inclusive UK-specific GSP framework. This study quantitatively examines the efficiency of the EU GSP as it relates to UK 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.

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.

Given that the European Union-28 countries proposed a target of 3% of the Gross Domestic Product on research and development (R&D) expenditure by 2020, the current study attempts to examine the role of R&D on environmental sustainability. In addition, the study further investigates the long-run and causal interaction between, renewable energy consumption, nonrenewable energy consumption, and economic growth in an ecological footprint function. Notably, the study incorporates research and development (R&D) expenditure to the model as an additional variable, and measures impact of each variable on ecological footprint. Empirical evidence is based on a balanced panel data between annual periods of 1997-2014 for selected EU-16 countries. The Pedroni, Johansen Multivariate and Kao tests all reveal a cointegration between ecological footprint, economic growth, research and development expenditure, renewable, and nonrenewable energy consumption. The Fully Modified and Dynamic Ordinary Least Squares models (FMOLS and DOLS) both suggest a negative significant relationship between the countries' research and development expenditure and ecological footprint in the long-run. This implies that spending on R&D significantly impacts on the environmental sustainability of the panel countries. Our study affirms that nonrenewable energy consumption and economic growth increase carbon emission flaring while renewable energy consumption declines ecological footprint. The panel causality analysis reveals a feedback mechanism between ecological footprint, R&D expenditure, renewable, and nonrenewable energy consumption. We further observed a one-way causality between ecological footprint and economic growth. Effective policy implications could be drawn toward modern and environmentally friendly energy sources, especially in attaining the Sustainable Development Goals via spending on R&D.