Does tourism sustain the economic growth? A wavelet based evidence from United States

This study explores the relationship between tourism development and economic growth in high tourist arrival country such as the United States of America (USA) by adopting the wavelet transform approach using monthly data over the period 1996M01-2015M08. Three innovative techniques that are continuous wavelet, wavelet coherence power spectrum and wavelet based Granger causality that consider the decomposition of time-series at different time frequencies, are utilized to conduct the study. The results of autoregressive distributed lag and combine cointegration tests show that there is a significant long-run relationship occurs between tourism development and economic growth in USA. Furthermore, the results indicate that there is a unidirectional causal influence of economic growth on tourism development in the short-run whereas, in the long-run the opposite causal relationship is evident in USA. Thus it can be recommended that government needs to increase and promote tourism demand and further providing and nurturing the expansion of tourism supply with the advancement of economic growth

Dynamic linkages between tourism, transportation, growth and carbon emission in the USA: evidence from partial and multiple wavelet coherence

The present paper endeavors to analyze and provide fresh insights from the dynamic association between tourism, transportation, economic growth and carbon emission in the United States. The analysis employs a novel Morlet’s Wavelet Approach. Precisely, the paper implements Partial and Multiple Wavelet Coherence techniques to the monthly data spanning from 2001-2017. From the frequency domain point of view, the study discovers remarkable wavelet coherence and robust lead and lag linkages. The analysis discovers significant progress in variables over frequency and time. The variables display strong but inconsistent associations between them. There exist a strong co-movement among the variables considered, which is not equal across the time scales. The study may help the policymakers and regulars to devise strategies and formulate policies pertaining to tourism development, which can contribute towards environmentally sustainable economic growth

Dynamic linkages between tourism, transportation, growth and carbon emission in the USA: evidence from partial and multiple wavelet coherence

The present paper endeavors to analyze and provide fresh insights from the dynamic association between tourism, transportation, economic growth and carbon emission in the United States. The analysis employs a novel Morlet’s Wavelet Approach. Precisely, the paper implements Partial and Multiple Wavelet Coherence techniques to the monthly data spanning from 2001-2017. From the frequency domain point of view, the study discovers remarkable wavelet coherence and robust lead and lag linkages. The analysis discovers significant progress in variables over frequency and time. The variables display strong but inconsistent associations between them. There exist a strong co-movement among the variables considered, which is not equal across the time scales. The study may help the policymakers and regulars to devise strategies and formulate policies pertaining to tourism development, which can contribute towards environmentally sustainable economic growth

Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from Quantile ARDL approach

The current study re-investigates the impact of renewable and non-renewable energy consumption on Turkey’s ecological footprint. This study applies Quantile Autoregressive Lagged (QARDL) approach for the period of 1965Q1-2017Q4. We further apply Granger-causality in Quantiles to check the causal relationship among the variables. The results of QARDL show that error correction parameter is statistically significant with the expected negative sign for all quantiles which confirm an existence of significant reversion to the long-term equilibrium connection between the related variables and ecological footprint in Turkey. In particular, the outcomes suggested that renewable energy decrease ecological footprint in long-run on each quantile. However, the results of economic growth and non-renewable energy impact positively to ecological footprint in long-short run period at all quantiles. Finally, we tested the Environmental Kuznets Curve (EKC) hypothesis and the results of QARDL confirmed the EKC in Turkey. Furthermore, the findings of causal investigation from Granger-causality in quantiles evident the presence of a bi-directional causal relationship between renewable energy consumption, energy consumption and economic growth with ecological footprint in the Turkish economy

How does economic complexity affect natural resource extraction in resource rich countries?

Several studies debate whether natural resources benefit or hurt an economy. In natural resource-rich economies researchers cannot conclude it. This study examines the relationship between natural resource rent, economic complexity, clean technology, and natural resource productivity capacity in 20 resource-rich economies from 2000 to 2021. Coal, oil, minerals, natural gases, and forest rents are disaggregated in the study. The economic complexity curvilinear function illustrates the inverse U-shaped relationship under the environmental Kuznets curve (EKC) or the U-shaped relationship under load capacity curve (LCC) between economic complexity and natural resources rent. This study hypothesizes that economic complexity increases resource extraction curvilinearly, changing resource rents which may have implications in the transition towards clean energy under COP27 to achieve SDGs. The study shows the marginal effects of economic complexity at different levels of complexity and resource extraction using quadratic and quantile functions. This study first examines resource extraction quantiles. Economic complexity raises forest, coal, and mineral rents at low resource extraction. Economic complexity lowers forest, gas, oil, coal, and mineral rents at high resource extraction. This study describes the curvilinear function. At the median resource extraction level, economic complexity has an inverted U-shaped effect on forest, mineral, and coal rents and a U-shaped effect on gas and oil rents. This implies that an increase in economic complexity can be targeted which may reduce reliance on forests, minerals, and coal while reducing reliance on gas and oil, government effort, green technology, and productive capacity needed to be pursued

Dynamic and causality interrelationships from municipal solid waste recycling to economic growth, carbon emissions and energy efficiency using a novel bootstrapping autoregressive distributed lag

This study contributes to estimate the municipal solid waste (MSW) recycling effect on environmental quality and economic growth in the United States. Few studies have been given to macro-level aggregate analysis through national scale MSW recycling, environmental, and economic indicators. This study employs bootstrapping autoregressive distributed lag modeling for investigating the cointegration relationship among MSW recycling, economic growth, carbon emissions, and energy efficiency utilized quarterly data from 1990 to 2017. The result implies that a one percent increase in MSW recycling contributes to economic growth and reduce carbon emissions by 0.317% (0.157%) and 0.209% (0.087%) in the long-run (short-run). Similarly, a one percent improvement in energy efficiency stimulates economic growth by 0.489% (0.281%) and mitigates carbon emissions by 0.285% (0.197%) in the long-run (short-run). A higher per capita income and population growth caused higher emissions by 0.197% and 0.401% in the long-run. The overall results reveal stronger impacts in the long-run than the short-run with significant convergence towards long-run equilibrium, suggesting a prominent long-run transmission of economic and environmental fallouts. This study confirms a uni-directional causality from MSW recycling to economic growth, carbon emissions, and energy efficiency. These outcomes signify that any policy intervention related to MSW recycling produces significant changes in the level of economic growth and carbon emissions. The finding provides valuable insight for policymakers to counteract carbon emissions through recyclable waste management that simultaneously create significant economic value

The Renewable Energy Consumption-Environmental Degradation Nexus in Top-10 Polluted Countries: Fresh Insights from Quantile-on-Quantile Regression Approach

This empirical examination explored the link between renewable energy utilization and environmental degradation in top-10 polluted countries by using monthly data from 1990-2017. The Quantile-on-Quantile regression (QQ) proposed by Sim and Zhou (2015) and Granger causality in quantiles developed by Troster (2018) are applied. In particular, we examine in what manners, quantiles of renewable energy consumption affect the quantiles of environmental degradation. Our empirical findings unfold overall dependence between renewable energy consumption and ecological deterioration. The findings recommend the presence of a significant negative association between renewable energy consumption and environmental degradation in China, USA, Japan, Canada, Brazil, South Korea and Germany, predominantly in high and low tails but results are totally contrasting in the case of India, Russia and Indonesia. Furthermore, the outcomes of Granger-causality in quantiles conclude a bidirectional causal link between renewable energy consumption and environmental degradation. The empirical findings suggest that governments should need to subsidize green energy in declining ecological degradation

Does Green Financing help to improve the Environmental & Social Responsibility? Designing SDG framework through Advanced Quantile modelling

Striving to achieve the Sustainable Development Goals (SDGs), countries are increasingly embracing a sustainable financing mechanism via green bond financing. Green bonds have attracted the attention of the industrial sector and policymakers, however, the impact of green bond financing on environmental and social sustainability has not been yet been confirmed. There is no empirical evidence on how this financial product can contribute to achieving the goals set out in Agenda 2030. In this study, we empirically analyze the impact of green bond financing on environmental and social sustainability by considering the S&P 500 Global Green Bond Index and S&P 500 Environmental and Social Responsibility Index, from 1st October 2010 to 31st July 2020 using a combination of advanced quantile modelling approaches. Our results reveal that green financing mechanisms might have gradual negative transformational impacts on environmental and social responsibility. Furthermore, we attempt to design a policy framework to address the relevant SDG’s objectives

Investigating the effect of inbound tourism on FDI: The importance of quantile estimations

The current study investigates the asymmetric effect of inbound tourism on foreign direct investment (FDI) in the world’s top tourist destinations based on monthly data for the period between 1995 and 2017. The quantile-on-quantile (QQ) approach introduced by Sim and Zhou was adopted for this study, because it assesses how various quantiles of inbound tourism affect different quantiles of FDI. Thus, the QQ approach gives a more detailed explanation of the general dependence of inbound tourism and FDI than traditional approaches, such as ordinary least squares or quantile regression. Further, the test of Granger causality in quantiles proposed by Troster et al. was also applied in this study to check the causal relationship between inbound tourism and FDI. The empirical outcomes explain that the relationship between inbound tourism and FDI is mostly positive for all countries except Mexico and Russia on low and middle quantiles, although there are significant differences throughout the nations and across all quantiles of inbound tourism and FDI

How oil prices, gold prices, uncertainty and risk impact Islamic and conventional stocks? Empirical evidence from QARDL technique

There are shreds of evidence of Islamic securities to behave differently from conventional ones, especially under the influence of certain factors such as oil, gold, economic policy uncertainty, and geopolitical risk. This paper has empirically evaluated such pieces of evidence through Quantile Autoregressive Distributed Lags Error Correction Model. Analysis has been performed on monthly returns from Dow Jones Islamic Market and Dow Jones Conventional Market Indexes for the sample period from January 1997 to July 2019. Results suggest that the Islamic stocks do behave differently from conventional stocks only for the long term in case of oil price influence under bullish market conditions; whereas, under bearish market conditions, economic policy uncertainty causes Islamic securities to behave differently. Hence, investment in Islamic stocks can be used for diversification of conventional securities’ portfolio under specific conditions. For instance, under oil price changes Islamic and conventional securities can diversify risk in bullish market trends; such diversification can also be achieved in case of the bearish market trend under economic policy uncertainty shock. The results of this study are significant for policymakers and investors as this will provide a clear picture to the investors regarding their investment with respect to Islamic or conventional markets. A further new basis will be provided to both speculators and portfolio managers of Islamic and conventional markets