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Precise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand. [ABSTRACT FROM AUTHOR]

While terrorism brings fear and chaos, tourism is the epitome of peace and happiness. Sri Lanka has always attracted international tourists due to tropical climate and beautiful tourist destinations. However, terrorism and the recent debt crisis have led to chaos and economic failure; in this study, the impact of terrorism, external debt, and military expenditure on international tourism in Sri Lanka have been empirically studied to unravel the dynamics. Dynamic ARDL simulation and cumulative fourier frequency causality results highlight the presence of an inverted N‐shaped association between terrorism and tourism, as well as external debt and tourism. The phenomenal results from this analysis provide essential policy suggestions for Sri Lanka for economic recovery through the promotion of international tourism. [ABSTRACT FROM AUTHOR]

Tourism industry is the lifeblood of many developing economies across the globe, and India is a hotspot of diverse cultural heritage and a beautiful natural landscape that has emerged as an attractive tourism destination recently. However, the global challenges of greenhouse gas emission have been acting as a detriment in sustainable tourism-led economic growth for India. In this study, the relationship between economic growth, hydropower consumption, and CO2 emission has been revisited for India by including tourism polynomials and energy intensity in the Environmental Kuznets Curve specification. The unique outcome provides evidence of "Inverted N-shaped" relationship between tourism and CO2 emission. Further, the negative impact of hydropower consumption suggests that a higher proportion of hydropower in the Indian energy mix would help to achieve its sustainable development goals while accelerating its economic growth. The outcome suggests sustainable tourism practices aligned with capital investment and hydropower promotion to achieve environmental sustainability in India. [ABSTRACT FROM AUTHOR]

In 2018, the world witnessed a full‐fledged trade war between United States and China with both countries levying successive tariffs on each other. Though the impact of trade war could be visibly assessed from declining export volume and rising bankruptcy filing from farmers, what went unnoticed was a far‐reaching negative impact at a granular market microstructure level. In this study, the effect of trade war on the leading role of US soybean futures in price discovery has been evaluated. The results corroborate the negative impact of trade war on the information leadership share of US soybean futures market. Moreover, the results shows that the negative impact gets manifested through various sentiment indicators such as Google search trends, hedger's sentiment, and so on. Hence, trade war may have given some temporary economic and strategic benefit to the United States, but it is no less than the proverbial double‐edged sword as it nearly killed the soybean market of the United States. [ABSTRACT FROM AUTHOR]

India is a major developing world economy that has predominantly been highly energy-intensive and fossil fuel dependent. Consequently, this South Asian nation has not been able to safeguard its environment from persistent degradation through the discharge of greenhouse gases. Accordingly, this study tries to reveal the relationships between Carbon Dioxide (CO2) emissions, renewable energy consumption, international trade, and economic growth in India in order to recommend policies that can help the nation attain carbon neutrality. Both the conventional Autoregressive Distributed Lag (ARDL) and the newly developed Quantile ARDL (QARDL) models are used in this study. The ARDL results unveil that 1% increase in renewable energy consumption contributes to 0.8% reduction in CO2 emissions, while economic growth boosts CO2 emissions in the long run. Besides, international trade deteriorates the environment by amplifying emissions only in the short run. The QARDL results reveal that economic growth positively and renewable energy consumption negatively affect CO2 emissions across all quantiles of CO2 emissions. In contrast, international trade is not found to exert any statistically significant effect on CO2 emissions. Lastly, the quantile-based causality analysis somewhat supports the regression findings by verifying causal associations between the variables of concern in India's context. Thus, in light of these findings, several carbon neutrality-related policy insights are put forward. [ABSTRACT FROM AUTHOR]