Bootstrap Autoregressive Distributed Lag Research Articles

The Impact of Climate Change on Life Expectancy in the UK: Empirical Evidence from Fourier Bootstrap ARDL Procedure

This study aims to determine the impact of surface temperature change, considered a proxy for climate change in the United Kingdom, on life expectancy using annual data from 1990–2021. Government expenditure is used as a control variable. The Fourier function-based bootstrap autoregressive distributed lag model is utilized to examine the cointegration among variables. The analysis outcomes reveal a long-term relationship between life expectancy, surface temperature change, and government expenditure. Furthermore, the findings reveal that surface temperature change significantly reduces life expectancy in the long run, while there is no significant relationship between the two variables in the short term. The Toda-Yamamoto causality analysis results show a unidirectional causality relationship between surface temperature change and life expectancy. Additionally, a significant bidirectional causality relationship is found between life expectancy and government expenditure.

Oil rent, corruption and economic growth relationship in Nigeria: evidence from various estimation techniques

PurposeDespite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.Design/methodology/approachVarious estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.FindingsThe bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.Originality/valueExisting studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.

Navigating the green future: Unraveling the role of fintech, decentralization, natural resources, and monetary policy uncertainty in China

Improving environmental integrity is a crucial policy agenda in achieving sustainable development goals, and reliable carbon emission measure is imperative in devising an effective climate strategy. Increasing climate susceptibility instigates us to explore the factors of ecological sustainability. Therefore, we scrutinize the dynamic influence of monetary policy uncertainty, mineral resources dependency, Fintech, fiscal decentralization, and trade on consumption-based carbon emissions (CCE) in China using the quarterly data from Q1-2005 to Q4-2021. For estimation, the study employed the bootstrap autoregressive distributed lag (ARDL) method, and the overall findings exhibit that fiscal decentralization significantly decreases the CCE, while monetary policy uncertainty, Fintech, and natural resources dependency increase CCE. Imports are responsible for higher CCE, while exports report the opposite. Consistent findings are endorsed by the short-run estimates with lower magnitude, and the error correction term significantly adjusts towards the steady state equilibrium with 61.0% quarterly adjustment in case of any deviation from stready state equilibrium. The results also confirm a bi-directional causality among all variables except Fintech, policy uncertainty, and CCE. This study offers relevant policy recommendations.

Circular Economy, Solid Waste Recovery, and Growth: An Empirical Analysis for Sustainable Development in the 100th Anniversary of the Republic

This article aims to examine the relationship between solid waste recycling and economic growth within the framework of the circular economy perspective in Turkiye. As Turkiye approaches its 100th anniversary of the Republic, achieving a sustainable and inclusive economic growth goal is of paramount importance. Solid waste recycling is now recognized as a process that not only reduces waste but also contributes positively to both economic growth and environmental preservation. In this context, the study utilizes quarterly data from 1994Q1 to 2020Q4 to comprehensively analyze the impact of solid waste recycling on the Turkish economy. The analyses were conducted using the Bootstrap Autoregressive Distributed Lag (BARDL) methodology. The results confirm that solid waste recycling has a positive influence on economic growth in both the short and long terms. This underscores the essential role of solid waste recycling as a fundamental component of the circular economy, emphasizing its potential to contribute to Turkiye's sustainable growth goals. Additionally, the study offers policy recommendations based on these findings. These recommendations include accelerating the transition to a circular economy, investing in technological innovations and infrastructure enhancements, organizing awareness and education programs, fostering collaboration between the public and private sectors, adopting innovative financing approaches, and establishing comprehensive legal regulations. In conclusion, the findings and recommendations of this study can contribute to Turkiye's achievement of sustainable economic growth and development goals, particularly as it approaches the 100th anniversary of the Republic. Prioritizing solid waste recycling as part of the circular economy vision can help create a better living environment and a sustainable future for the upcoming generations.

Impact of banking development and renewable energy consumption on environmental sustainability in Germany: Novel findings using the bootstrap ARDL approach

This study examines the effects of banking development, economic growth and consumption of renewable energy on carbon dioxide (CO2) emissions and load capacity factor (LCF). Previous empirical studies have assessed the interrelationship between banking development and CO2 emissions; however, these studies have ignored supply-side ecological issues. To overcome this issue, this study evaluates the effect of banking development on LCF, which is considered to be one of the most comprehensive ecological proxies to date, including both biocapacity and ecological footprint (EF). Using the bootstrap autoregressive distributed lag model, the study reveals that renewable energy improves ecological quality in Germany. The results of the investigation demonstrate that the environmental Kuznets curve hypothesis is valid in Germany using CO2 emissions and LCF indicators. Furthermore, this study demonstrates that banking growth and renewable energy in Germany correlate with improved environmental quality. These findings provide policymakers with important insights. In this context, the study advises the banking industry and government authorities to leverage banking expansion to support green energy to achieve the national goal of zero CO2 emissions by 2045.

Renewable energy, natural resources, technological innovation, and consumption‐based carbon emissions in China: Tracking environmental neutrality

AbstractAlthough numerous empirical studies have scrutinized the impact of technological innovation, renewable energy, and natural resources on carbon emissions (CCO2), there is a lack of empirical knowledge of consumption‐based CCO2 emissions. This study examines the impacts of technological innovation, renewable energy, and natural resources factors on CCO2 emissions in China. In addition, the present work considers the role of the banking sector in environmental neutrality in China from 1990 to 2019. For this purpose, the sophisticated approach of “bootstrap autoregressive distributed lag (BARDL)” is applied to explore the association of the selected indicators on CCO2. The outcomes indicate that (i) economic growth and natural resources negatively impact environmental neutrality; (ii) environment‐related technologies and renewable energy are crucial to promoting environmental neutrality; and (iii) banking sector in China has a positive association with CCO2 emissions. The findings of this work recommend using effective measures to mitigate ecological pollution by using green energy sources and strengthening the banking sector by offering environment‐friendly investment loans. Likewise, strategies should be designed that reinforce sustainable development.

The Interplay of Green Technology and Energy Consumption: A Study of China’s Carbon Neutrality and Sustainable Digital Economy

In light of the carbon neutrality goals set post-Paris Climate Conference (COP21), this study delves into the relationship between green technology innovations, energy consumption, and CO2 emissions in China, spanning the period of 1990 to 2021. The objective of this paper is to creatively present the idea of a low-carbon digital economy from the viewpoint of digital technology. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we scrutinize this relationship, employing unit-root testing to verify the integrative attributes of the variables, inclusive of structural break data. Further analysis using the bootstrap Autoregressive Distributed Lag (ARDL) bound testing method corroborates the relationship between these key variables. The study reveals unidirectional co-integration over time among green technology innovations, renewable and non-renewable energy, per capita income, population, and CO2 emissions as per the Granger causality test. Interestingly, our findings suggest that while green technology innovation, per capita income, and renewable energy contribute to the reduction of CO2 emissions, non-renewable energy consumption and population growth exacerbate them. These insights offer valuable guidance for policymakers in formulating comprehensive strategies to enhance environmental quality through the promotion of renewable energy and green technology innovations, with a specific emphasis on the Chinese context.

Testing the effects of banking development, economic growth and foreign direct investment on renewable energy in South Africa

AbstractThe world's climate‐related policies need to be revised to combat environmental issues. Therefore, adopting renewable energy technologies in conjunction with energy efficiency measures are needed to make safe pathways to reduce over 90% of energy related CO2. In this context, this study intends to assess the influence of foreign direct investment (FDI), economic growth, and banking and development on renewable energy consumption in South Africa from 1970 to 2020. The study uses a novel ‘bootstrap Autoregressive Distributed Lag (ARDL)’ testing to empirically analyse the short and long links among the tested variables. The ARDL estimations demonstrate a FDI and economic growth have positive effect on renewable energy consumption. Furthermore, the outcomes reveal that banking sector development affects positively renewable energy use in long run. Therefore, it is suggested that government and playmakers of South Africa must make more efforts to link the economic and baking development with renewable energy by investing capital and funds in renewable energy infrastructure. It will not only boost the economy of the country but also provide an opportunity to clean and green the environment with decreasing rate of greenhouse gases emission like carbon dioxide.

Testing the impact of renewable energy and oil price on carbon emission intensity in China's transportation sector.

As the largest carbon emitter in the world, with its transportation sector contributing the largest shares of its emission, the need for a low-carbon transition economy has become a policy agenda for China because in order to reach carbon neutrality by 2050, lowering the intensity of carbon emissions in the transportation sector will be crucial. In this regard, we used the "bootstrap autoregressive distributed lag model" to explore the impact of clean energy and oil prices on the intensity of carbon emissions in China's transportation sector. The study found that an increase in oil prices decreases the intensity of carbon emissions in the short and long run. Similarly, an increase in the level of renewable energy and economic complexity declines the intensity of carbon emissions in the transportation sector. On the contrary, the research demonstrates that non-renewable energy contributes positively to carbon emission intensity. Therefore, the authorities must promote green technology to neutralize the transportation system's detrimental effects on China's environmental quality. The implications for successfully promoting carbon emission intensity mitigation in the transportation sector are examined in the conclusion.

Nexus between renewable energy consumption, economic growth, and CO2 emissions in Algeria: New evidence from the Fourier‐Bootstrap ARDL approach

AbstractGovernments often impose new energy strategies to support new CO2 emission‐reducing technologies without affecting economic growth. Hence, this study aims to re‐investigate the relationship between economic growth, renewable energy use, and CO2 emissions in Algeria from 1990 to 2018. Motivated by the mixed findings of the existing literature, which ignore the Fourier function and bootstrap test and apply the newly developed Fourier bootstrap autoregressive distributed lag model (FARDL). Our findings indicate that renewable energy use and growth have a long‐run relationship with CO2 emissions and do not accept the existence of the Environmental Kuznets Curve (EKC) hypothesis for CO2 emissions in Algeria. In the long term, the results show that renewable energy use has a negative and significant impact, and growth has a positive and statistically significant effect on CO2 emissions. In the short run, the findings indicate that renewable energy use reduces CO2 emissions, while both the growth and squared growth had positive and statistically insignificant impacts on CO2 emissions, confirming the lack of evidence supporting the EKC hypothesis. Moreover, the causality test indicates a one‐way causation from growth to renewable energy use, confirming the conservation hypothesis for Algeria and from growth to CO2 emissions. Interestingly, we found one‐way causality from CO2 emissions to renewable energy use, attributing this to the fact that renewable energy usage has yet to reach a point that it can significantly cause a CO2 emissions reduction. Based on the results, we recommend that policymakers design appropriate policies to decarbonize energy consumption, e.g., increasing fossil fuel costs and implementing a carbon tax. In contrast, Algeria should promote new CO2 emission‐reducing technologies without affecting economic growth, e.g., tax exemptions and reductions for enterprise owners in the renewable energy industry.

Testing the Effect of Oil Prices, Ecological Footprint, Banking Sector Development and Economic Growth on Energy Consumptions: Evidence from Bootstrap ARDL Approach

Energy generation from carbon fuels produces a major portion of the greenhouse gases that envelop the planet and trap the sun’s heat. Fossil fuels, including coal, oil, and gas, account for approximately 75% of global greenhouse gas emissions and almost 90% of carbon dioxide emissions. Therefore, there is an urgent need to finance cleaner, efficient, low-cost, sustainable, and energy-reliable alternative fuels. Therefore, we investigated the effect of oil prices, ecological footprint, banking sector development, and economic growth on energy consumption in South Africa. We employed the newly developed bootstrap autoregressive distributed lag (ARDL) model to the link between explanatory antecedents and explained facets in the short and long term. The outcome of our study witnessed the positive and significant effect of economic growth and ecological footprint on energy consumption in the short and long run, in the case of South Africa. This suggests that a drastic boost in South African economic growth and environmental quality results in the increased use of energy. However, ARDL outcomes affirm that industrial structure has effects positively and significantly in the short run only. Moreover, oil price shocks have a negative and significant link with energy use in the short and long run, suggesting that in the case of South Africa, increased oil prices reduce the use of energy. Based on the evidence obtained from the results of our study, we proposed several policy suggestions and recommendations to the government authorities, policymakers, environmentalists, and other stakeholders in order to develop an energy strategy in line with sustainable economic growth and the environment.

Do recycling and regulations influence trade-adjusted resource consumption? Exploring the role of renewable energy

Sustainable resource consumption and environmental sustainability are core objectives of every economy. Particularly, the United States (US) has shown enormous growth in material and resource usage. However, few studies have investigated the influence of recycling on the trade-adjust material footprint. Therefore, this study employed the bootstrap autoregressive distributed lag model to evaluate the dynamic impact of recycling, renewable energy, and regulation on the consumption-based material footprint in the US from Q1-1994 to Q4-2019. The long-run findings exhibit that recycling significantly reduces resource consumption. Besides, ecological regulations and renewable energy significantly mitigate the per capita material footprint. The short-run estimates are also compatible with long-run findings but smaller in coefficient magnitude. The negative and significant coefficient of error correction term adjusts the model with 24.8% speed quarterly towards the steady state in case of any disturbance from the short-run. The granger causality test affirms the bi-directional causal relationship between the model's variables. The findings suggest that policymakers should design sustainable strategies that encourage recycling and renewable energy consumption with stringent control to stimulate resource efficiency.

The effect of mineral saving and energy on the ecological footprint in an emerging market: evidence from novel Fourier based approaches.

Environmental degradation has been one of the hot topics discussed since the 1980s. In the literature, CO2 emissions are generally used to represent environmental degradation. However, in this study, environmental degradation is examined in the context of ecological footprint. The study aims to investigate the effect of economic growth, energy consumption, and mineral saving on the ecological footprint in Turkey for the period of 1975-2017. For this purpose, the bootstrap autoregressive distributed lag model with a Fourier function (FARDL) method is utilized to test the long-term relationship between the variables. The findings indicate a long-term relationship between the variables. In addition, long-run estimation results based on the FARDL model show that economic growth and mineral saving increase the ecological footprint in Turkey. The conclusion discusses these findings and presents long-term policy recommendations for Turkey.

Drivers of renewable energy transition: The role of ICT, human development, financialization, and R&D investment in China

The study investigates key drivers of renewable energy transition in China using the quarterly data from Q1-2000 to Q4-2020. We employed the Bootstrap Autoregressive Distributed Lag method for long-run and short-run parameters. The results exhibit that financial development and research and development expenditures are the most prominent factors that encourage energy transition. In addition, human resources and information and communication technology (ICT) also contribute to stimulating the renewable energy transition in China. The overall long-run estimates have pronounced impact than the short-run results, except for the ICT, which has an insignificant effect in the short run. Moreover, the significant negative value of the error correction term converges the model by a 32.9% adjustment rate in case of any deviation. The empirical outcome recommends that the government of China should promote financial development resources and R&D spending to spur the energy transition.

Renewable Energy Consumption-Economic Growth Nexus in Saudi Arabia: Evidence from a Bootstrap ARDL Bounds Testing Approach

The paper investigates the relationship between Saudi economic growth, renewable energy consumption and trade openness during the 1980-2017 period. By using the Bootstrap Autoregressive Distributed Lag (BARDL) approach and the Granger causality analysis, the results prove the existence of a cointegration relationship between the considered variables. In order to test for Granger causality in the presence of cointegration among the variables, the results indicate that there is a short-run unidirectional causality running from GDP to trade openness. Thus, a bidirectional causality is detected both between (REC-TOP) and between (PIB-REC). In contrast, in the long run, there is a one-way causal relationship running from renewable energy consumption, trade openness to economic growth. These new findings will help policymakers and government officials better understand the role of renewable energy and economic growth in Saudi Arabia's development.

Testing The Effect of Islamic Financial Inclusion, Infrastructural Quality on Economic Growth in Nigeria: New Insight From Bootstrap and Non-Linear ARDL

<p>Inclusive growth can hardly be achieved without improvement in the inclusive finance that would enable the vulnerable households to become key economic players. While efforts are now shifted towards the Islamic financial inclusion based on the realization of weakness of traditional financial inclusivity, the impact of Islamic financial inclusion particularly, its pathways of bridging infrastructural gaps and growth drive have been partially explored. This study examines the effect of Islamic financial inclusion, infrastructural quality on economic growth in Nigeria. The study uses monthly data covering from 2017 until 2020 which was analyzed via a newly developed Bootstrap Autoregressive Distributive Lag. The results reveal a number of findings. First, the result of bootstrap autoregressive distributed lag demonstrates that there is strong co-movement between economic growth, Islamic financial inclusion, Sukuk and infrastructural quality. This finding points to the existence of a strong positive relationship between Islamic financial inclusion, infrastructural quality and economic growth in Nigeria. Second, results from nonlinear models have revealed that Islamic financial inclusion and Sukuk have asymmetric effects on economic growth. This asymmetric pattern reinforces the claim that Islamic financial inclusion can play a dedicated role in ameliorating economic downturn. Third, the results of nonlinear causality reveal strong bidirectional causality Islamic financial inclusion, economic growth and Sukuk. In this sense, inclusive Islamic finance could play a dedicated role to help countries in overcoming economic recession.</p>

Impact of Political Risk on Foreign Direct Investment with Fourier Approach: The Case of Turkiye

Foreign direct investment (FDI) is a desirable form of capital inflows to emerging and developing countries. Such investments are less susceptible to crises and sudden breakdowns. Therefore, foreign direct investment between the political risks of using annual data for the years 1984-2019 which might influence Turkey has been tested. For this purpose, Fourier-based unit root and cointegration analysis are used. As a result of the Fourier unit root analysis, it is concluded that the FDI variable is difference stationary and the political risk variable is level stationary. For this reason, Yılancı et al. (2020), a fractional-frequency Fourier bootstrap autoregressive distributed lag (ARDL) boundary test was applied to the literature. This test, which is in nonlinear form, captures smooth transitional changes in long-term relationships. As a result of the analysis, it is seen that there is a cointegration relationship between foreign direct investments and political risk and the variables come to equilibrium in the long run. In addition, it is seen that the calculated long-term coefficient is positive and significant and the error mechanism works correctly in the short term. Therefore, as political stability is achieved foreign direct investment inflows will increase and the economies of the country will gain an advantage in reaching high levels of financial development. As a result, it is concluded that the Weak Partisan model is valid in the Turkish economy.

Nexus between Industry 4.0 and environmental sustainability: A Fourier panel bootstrap cointegration and causality analysis

The paper aims at investigation of the nexus between Industry 4.0 (I4.0) and environmental sustainability in addition to exploring the long-run and short-run effects of Industry 4.0 on CO2 emissions. For this end, energy consumption, internet and communication technology (ICT) exports, research and development (R&D), artificial intelligence (AI), ICT technology patents and bitcoin are taken as control variables of Industry 4.0 for a panel of 9 countries that contribute to 62% of the total CO2 emissions in the world. For this purpose, the paper follows two approaches. First, the paper proposes utilization of AI and ICT technology patents, technology-related R&Ds and ICT exports as variables of I4.0 in addition to investigating the effects of economic growth, energy consumption and bitcoin. Second, to control structural changes and nonlinearity in the cointegrating relations and existence of degenerate cointegration, Fourier panel bootstrap autoregressive distributed lag model (FPBARDL) is utilized. Afterwards, FPBARDL based long, short and strong causality analyses are conducted. The empirical findings revealed positive impacts of all I4.0-related variables on emissions in the long-run. Though I4.0 related AI and ICT innovation has no direct effect in the short-run, its effects are determined through increased energy consumption towards emissions. The strong positive effects of energy consumption and positive effects of economic growth, ICT exports and R&D are observed both in the short and long-run. In addition to positive impacts of I4.0 on environment, the findings favor insufficiency of policies focusing on lowering emissions in I4.0 context.Policy recommendations include strong commitment to energy efficiency and renewable energies and trade policies with environmental concerns.

Oil prices and gold prices on housing market in China: novel findings from the bootstrap approach

PurposeThis study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.Design/methodology/approachThis study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.FindingsThe ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.Originality/valueTo the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

Do Renewable Energy and the Real Estate Market Promote Environmental Quality in South Africa: Evidence from the Bootstrap ARDL Approach

Recent empirical research indicates that South Africa’s present level of wealth and energy, given its fast-expanding population, is unsustainable. Studies in this domain focus on the impact of economic growth and energy use on environmental quality; the role of the real estate market on environmental quality in South Africa is ignored in the emerging literature. The current study aims to deliver a fresh empirical analysis in this context by analyzing the impact of South Africa’s real estate sector expansion and renewable energy sources on carbon emissions. Using the newly developed “bootstrap autoregressive distributed lag (ARDL) approach”, the results of the empirical investigation showed that renewable energy improves South Africa’s environmental quality. The current research also shows that the South African real estate industry has a negative impact on the environment. According to the current research, South African policymakers should create new regulations for the sustainable real estate sector to improve environmental quality by encouraging the usage of and investment in renewable energy.