Quantile Autoregressive Distributed Lag Research Articles

Study on agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution

Agricultural water resource utilization efficiency in China is facing significant challenges due to the dual constraints of carbon emissions and water pollution. The inefficiency in water usage in agriculture not only impacts the sustainability of water resources but also contributes to environmental degradation through increased carbon emissions and water pollution. Agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution has been a critical issue in China from 2005 to 2022. This study employs the Quantile Autoregressive Distributed Lag (QARDL) method to comprehensively assess and analyze the complex relationship that exists between agricultural water usage, carbon emissions, and water pollution. By analyzing distinct quantiles of the data distribution, the research investigates how different levels of water resource utilization efficiency relate to carbon emissions and water pollution under various conditions. The findings reveal nuanced insights into the dynamic interactions among these components within the agricultural sector. This research project focuses on the efficiency of water resource utilization in agriculture while considering the constraints of carbon emission and water pollution. Given the dynamic and time-dependent character of these components, the QARDL methodology makes it possible to get a detailed knowledge of how they interact within the framework of agriculture. The study aims to give significant insights and policy suggestions to improve agricultural practices while minimizing environmental concerns linked to carbon emissions and water pollution.

Transition towards natural resource rents and green technology to achieve China's COP26 success: A novel insights in the case of trade openness and environmental pollution

This study aimed to investigate the impact of various factors such as natural resource rents, trade openness, green technology innovation, oil prices, and economic growth on environmental pollution in China by covering the period from 1970 to 2021, aligning with the COP26 agenda. These factors included. To accomplish this, we utilized an advanced Quantile Autoregressive Distributed Lag model to scrutinize the short- and long-term quantile patterns for both the dependent and independent variables. The findings reveal that green technologies and natural resource rents play a negative role in reducing emissions, whereas economic growth and trade openness have positive effects on emissions levels in China in the short and long run during different quantile patterns. Additionally, we employed the Wald test to confirm the presence of asymmetric patterns in environmental pollution concerning economic growth, natural resource rents, green technological innovation, and trade openness in both the short and long terms. These results emphasize the significance of prioritizing key policy measures to align with the COP26 agenda, including enhancing green technology via natural resource rents and addressing immediate climate change challenges in China.

Environmental sustainability, terrorism, and military expenditure in Sub-Saharan Africa: Evidence from panel quantile ARDL regression and panel causality

The prevalence of terrorism and military expenditure in Sub-Saharan Africa (SSA) is experiencing rapid and significant growth over time. This study investigates the impact of terrorism and military expenditure on environmental sustainability in 35 economies in SSA from 1990 to 2021, utilizing panel quantile autoregressive distributed lag regression and causality analysis techniques to bridge the gap in existing studies on the military expenditure, environmental sustainability, and terrorism nexus. The study's novelty is evident because it assesses the short-term and long-term impacts of military expenditure and other covariates across various quantiles of CO2 emissions. The study achieves this by categorizing SSA nations based on their varying levels of CO2 emissions, specifically low, moderate, and high, an approach that is extremely rare among SSA countries. The study's findings revealed that terrorism and military expenditure have a positive impact on CO2 emissions across the quantiles, implying that these variables contribute to environmental degradation in the region. The study also reveals a bidirectional relationship between CO2 emissions and the selected predictors (terrorism, military expenditure, trade, and economic growth). Thus, the study proposes military strategies and macroeconomic policies that ensure environmental sustainability.

Effects of ICT and Energy Transition on Sustainable Fiscal Growth: Empirical Evidence from China

It is crucial for sustainable growth policies and strategies, which are currently being used internationally, to balance the various aspects of development, including the economic, environmental, and social ones. There is currently broad agreement that energy transition and Information and communication technologies (ICT) offer fresh momentum for long-term sustainable fiscal growth. The main goal of the current research is to study the effects of energy transition and ICT on sustainable fiscal growth in China over 1995-2020 period. To empirically estimate the above mentioned nexus, the recently introduced Quantile Autoregressive Distributed Lag Model (QARDL) is applied. The study findings for long run indicate that energy transition impacts sustainable fiscal growth negatively over lower qauntiles and at extreme higher quantiles. However, the ICT is found to promote sustainable fiscal growth at all quantiles. The short run dynamics also indicate that current and previous values of ICT and energy transition have significant impact on sustainable fiscal growth. The Wald test of parameter constancy rejects the null hypothesis and shows that non linear and asymmetric impact is present between sustainable fiscal growth and ICT and energy transition in the short run and the long run. Test of Quantile Granger Causality reveals bidirectional causal association between study variables. The study recommends the Chinese government and policy makers to adopt efficient policies to promote ICT use among all the sectors of the economy that can help in achieving sustainable fiscal growth.

Towards Achieving Sustainable Development in China: What Role Does Digital Technologies and Green Technologies Play?

There is always discussion about how to use digitalization to promote sustainable development. While one side vehemently defends using ICT to direct and carry out sustainable development, another strongly struggles to highlight its many drawbacks. Similarly, green technologies are also under the limelight for promoting sustainable development since these technologies combine economic growth and environmental conservation. The purpose of the study is to empirically estimate the effect of digital technologies and green technologies in sustainable development in China. Time series data spanning over 1995-2021 period is analyzed using recently introduced Quantile Autoregressive Distributed Lag Model (QARDL). The findings of the study suggest that there exists positive association between digital technologies and sustainable development over (0.05-0.80) quantiles. The significant and positive association exists between green technologies and sustainable development at all quantiles (0.05-0.95). The study also finds positive and negative association of economic growth and urbanization respectively with sustainable development. The study results emphasize the limitations of conclusion of past studies and provide significant contributions by confirming that the associations among variables is quantile-dependent. The Wald test results also disprove the parameter constancy for the Chinese economy and the findings of Quantile Granger Causality prove the presence of bidirectional causal association between digital technologies, green technologies and sustainable development. The study recommends Chinese policy makers and government to adopt relevant policies to promote sustainable development by effectively utilizing digital and green technologies.

Does crude oil price volatility respond asymmetrically to financial shocks?

Recent literature has explored the effect of monetary and financial shocks, along with oil supply and global demand shocks, in determining oil price volatility. This paper elucidates whether the change in oil price volatility in response to financial shocks differs over quantiles over the period 1993–2021. First, we disintegrate the structural innovations to oil price volatility, originating from crude oil production, global demand, oil demand, oil inventories, interest rates, and financial market volatility, using the SVAR approach during pre-crisis and post-crisis phases. Furthermore, using quantile autoregressive distributed lag models (QARDL) and nonlinear autoregressive distributed lag (NARDL) models, we analyze the plausible asymmetric effects of these shocks. The findings indicate the significance of supply-side and global demand shocks on oil price volatility in the pre-crisis periods, followed by the increase in the explanatory power of financial shocks on oil price volatility after the crisis. It also suggests that oil price volatility responds asymmetrically to oil supply, inventories, and financial shocks.

Managing inflation expectations and the efficiency of monetary policy responses to energy crises

While ascertaining low and stable inflation expectations is a significant challenge across developed and emerging economies, energy shocks are central to this endeavor. Therefore, this study's prime objective is to investigate the role of energy shocks, monetary policy, and fiscal policy in managing inflation expectations in the context of the Federal Reserve's inflation targeting regime, adopted in 2012. We analyze monthly data on the United States from 1994 to 2022. Using the linear Autoregressive Distributed Lag (ARDL) and the Quantile ARDL (QARDL) estimators, we analyze and compare three different samples: full period, pre-inflation targeting regime (1994 to 2012), and post-inflation targeting regime (2012 to 2022). The conclusions suggest that inflation, energy shocks, and money supply have significant implications for inflation expectations in most quantiles during pre- and post-inflation targeting regimes. Policy implications for research and practice are also discussed.

JOINT VENTURE, TECHNOLOGY TRANSFER AND THE PERFORMANCE OF NIGERIAN OIL AND GAS INDUSTRY

Purpose: The objective of this study is to examine joint venture, technology transfer on the performance of Nigeria's oil and gas sector between 1981-2021. Theoretical Framework: It is indisputable that the Nigerian oil and gas sector is not at peak performance when compared to what is obtainable from its peers in the Organization of Petroleum Exporting Countries (OPEC) (Iheukwumere, 2021; OPEC, ASB 2020). One of the factors responsible for the abysmal performance is ineffective and incoherent technology transfer management through joint venture arrangements (Odusina, 2022). Therefore, there is a need to empirically investigate the impact of joint venture arrangements on Nigeria's oil and gas sector production which lacks sufficient research. Methodology: The ex-post facto design was used where data were collected through secondary sources on the aggregate output of the joint venture companies and the total yearly output of the upstream sector of Nigeria’s oil and gas industry represented the performance of the Nigerian oil and gas sector in the period 1980 to 2021. The collected data were analyzed using the Quantile Autoregressive Distributed Lag (QARDL) approach to test for short and long-run impacts. Findings: The study revealed that there is a significant impact of joint venture arrangements on oil and gas production in both the short run and long run. Research, Practical & Social Implication: The study therefore recommends that policymakers and industry stakeholders should carefully evaluate the terms and conditions of joint ventures to ensure their alignment with the goals of maximizing oil and gas production. Originality/Value: The use of joint venture as a proxy for technology transfer in the production of oil and gas in Nigeria and use of secondary data between 1980-2021 for joint ventures is an eye-opener for further exploration of the study areas in oil and gas production management, particularly in the area of technology transfer, which lacks sufficient research.

The asymmetric impact of input prices, the Russia-Ukraine war and domestic policy changes on wholesale electricity prices in India: A quantile autoregressive distributed lag analysis

This paper investigates the presence of asymetry in the long-run relationship between wholesale electricity prices (ep) in the day-ahead market and fossil fuel (coal, oil and gas) prices, the exchange rate (er) and economic policy uncertainty (epu) in India, and how the Real-Time Market introduced on 01/06/2020, and the Russia-Ukraine war have affected these relationships. Using daily data from 28/06/2008 to 30/04/2023, and both the quantile auto-regressive distributed lag and the quantile regression methods, we find asymmetric long run relationships, with pronounced and asymmetric impacts in the upper quantiles of ep. Results with the full sample suggest that increases in er and coal significantly raise ep in the upper quantiles; vol and epu have a negative long-term effect on ep. Furthermore, the Real-Time Market has reduced price distortions and brought efficiency to the day-head market. The war increases ep because of supply-chain shockwaves in the input markets. Our results suggest introducing policies aiming at reducing inefficiency to mitigate external shocks.

Fintech inclusion in natural resource utilization, trade openness, resource productivity, recycling and minimizing waste generation: Does technology really drive economies toward green growth?

Ever since the industrial revolution, financial sector is considered a significant claimant of driving growth in society. However, adverse eco-friendly projects by financial means raise various concerns that either financial market products benefit environment. In this lieu, the present study aims to analyze the role of fintech in natural resource utilization, trade openness, resource productivity and recycling and minimizing waste generation in China. For this purpose, data from the 1995 to 2021 period is analyzed using the “Quantile Autoregressive Distributed lag (QARDL) model. The Granger causality test is also used to analyze the causal association between variables and to know about their relative significance. According to the findings, the error correction term (ECM) has an expected negative sign with statistical significance across different quantiles showing that the relationship between these variables reverts to the long-run equilibrium. QARDL short run and long run estimation indicates that fintech promotes natural resource utilization, resource productivity, trade openness, and recycling of waste materials and management with the significance of these effects varying at different quantiles. The findings of Granger Causality indicate that a bidirectional association is present between fintech and recycling and waste management, and unidirectional causality is found to be present from fintech to resource productivity. The study recommends the government formulate and implement policies aimed at promoting fintech and integrating them with streamlined natural resources and industrial and trade sectors.

Does carbon dioxide emission influence food security in the United States amidst economic expansion, population growth, and inflation? A QARDL and Wavelet Coherence analysis

AbstractFood security is an indispensable concept because human life depends on it. Although numerous studies have examined food security in developing economies, there has been a lack of attention given to this issue in developed countries. Thus, this study investigates the influence of carbon dioxide emissions (CO2) on food security in the United States from 1965 to 2022 using Quantile Autoregressive Distributed Lag (QARDL) and Wavelet Coherence methods. Food security is proxied by the food production index (FPI), while the control variables are gross domestic product (GDP), population growth (POP), and inflation. The Quantile Autoregressive Distributed Lag results show that: (i) In the short‐run, CO2 influences FPI positively at the 0.05 quantile, while it harms food security at the 0.50 and 0.95 quantiles. However, in the long‐run, the impact is positive across all quantiles; (ii) In the short‐run, GDP harms FPI at the 0.05 quantile while it benefits FPI at the 0.50 and 0.95 quantiles. In the long‐run, GDP's impact across all quantiles is beneficial; (iii) In the short‐run, POP negatively influences FPI at 0.05 and 0.50 quantiles, while it positively spurs FPI at 0.95 quantiles. In the long‐run, POP positively influences FPI at quantiles of 0.05, whereas it negatively influences FPI at 0.50 and 0.95 quantiles; (iv) inflation positively influences FPI in the short and long‐run across all quantiles; (v) The Wavelet Coherence analysis showed a robust correlation across all frequencies. Policy suggestions and implications are discussed further.

Türkiye Stock Market in the Shadow of COVID-19 Pandemic: A QARDL Approach

This study examines the effects of the COVID-19 pandemic on the Türkiye stock market between March 14, 2020, and April 29, 2022, using the Quantile Autoregressive Distributed Lag (QARDL) model. The research investigates the relationship between the BIST100 index and selected economic indicators across quantiles ranging from 0.05 to 0.95. These indicators include daily new COVID-19 cases, the dollar exchange rate (FX), Brent crude oil prices (OIL), and credit default swap (CDS) rates. Findings indicate that an increase in COVID-19 cases has a negative impact on the stock market across various quantiles. While the dollar exchange rate generally shows a positive relationship with the BIST100 index, Brent crude oil prices, and CDS rates exhibit a negative impact. These results highlight the complex effects of both internal and external factors on the Türkiye stock market, providing significant insights for policymakers and future research.

Quantifying digital economy and green initiatives for carbon neutrality targets: a Kilian bias-adjusted bootstrap model evaluation of China economy.

Digitalization has emerged as a new hope for low-carbon sustainable economic growth after its successful trial during the COVID-19 measures. Therefore, both developed and developing economies focus on digitalization to cope with carbon neutrality targets. Thus, this study attempted to generate a meaningful relationship between the digital economy and green energy, innovation, and environmental tax policy to capture the role of factors in acquiring carbon neutrality. For the abovementioned objectives, modern econometric methods, such as the Kilian bias-adjusted bootstrap, were adopted to evaluate the Chinese dataset between 1990 and 2021. The results indicate that the study factors play a significant role in acquiring carbon neutrality in the long-term Chinese economy. Furthermore, quantile autoregressive distributed lag model (QARDL) indicates that all the factors influence carbon neutrality in various quantiles. Consequently, the digital economy, green energy and innovation, and environmental taxes significantly assist in attaining carbon neutrality in the long term, and the ecological Kuznets curve prevails in the economy. Therefore, radical and wide-ranging policy implications are required in many areas including environmental restrictions, digital economy promotion, green and sustainable technologies, and clean energy sources.

Investigating the impact of ICT on transport-based CO2 emissions: empirical evidence from a quantile cointegration regression analysis.

In the global initiative to leverage information and communication technologies (ICT) for reducing emissions, sub-Saharan Africa (SSA), a region of unique significance, has exhibited a delay in adopting ICT. This study aims to investigate the intricate relationship between ICT and carbon dioxide (CO2) emissions from transport in SSA. Employing the panel quantile autoregressive distributed lag (PQARDL) technique, the study analyzes panel data from 24 SSA nations spanning from 2000 to 2021. The results indicate that internet usage and fixed telephone subscriptions have a mitigating effect on CO2 emissions from transport across all quantiles in both the short and long run. However, mobile phone subscriptions contribute to CO2 emissions from transport across all quantiles. Additionally, the middle-income groups demonstrate negative relationships between ICT variables and emissions from transport, while the low-income group exhibits significant positive associations. These findings imply that ICT plays a pivotal role in mitigating transport-based emissions and reveal pronounced disparities in ICT adoption across various income groups within SSA, highlighting overarching underdevelopment in ICT infrastructure. Robustness checks employing a two-step system generalized method of moment (GMM) model reinforce our findings. The study provides policy recommendations, including the promotion of ICT infrastructure development, implementation of smart transportation solutions, and fostering public-private partnerships to address these challenges, shedding light on the path toward a greener and more sustainable transport ecosystem in SSA.

Moving toward environmental mitigation in Algeria: Asymmetric impact of fossil fuel energy, renewable energy and technological innovation on CO2 emissions

Algeria's recent economic shifts have caused its macroeconomic data to exhibit an abnormal distribution, requiring a nonlinear approach to examine the asymmetric impact of technological innovation (TI), fossil fuel energy (FFE), and renewable energy (RE) on CO2 emissions. This study employs the nonlinear autoregressive distributed lag (NARDL) model to analyze the asymmetric impact of these factors on CO2 emissions. Furthermore, Quantile Autoregressive Distributed Lag (QARDL) and Quantile Granger Causality (QGC) approaches are employed for robustness checks. The NARDL results indicate that positive shocks in TI decrease CO2 emissions, whereas negative shocks increase CO2 emissions. Positive shocks in RE also decrease CO2 emissions, while negative shocks have no effect. In contrast, positive shocks in FFE increase CO2 emissions, but negative shocks have an even stronger effect, resulting in almost double CO2 emissions over time. These findings confirm the presence of asymmetry, as positive and negative shocks in regressors clearly influence CO2 emissions in Algeria. Moreover, the results from the asymmetric causality analysis indicate that TI, RE, and FFE have a causal effect on CO2 emissions in Algeria. These results are consistent with the findings from the QARDL and QGC approaches. Therefore, it is crucial for Algeria to prioritize investment in sustainable technology and implement carbon-neutral energy policies to reduce its reliance on fossil fuel and encourage the use of cleaner energy sources. The shift towards a green energy sector requires policymakers to ensure that innovation aligns with development objectives.

Economic policy uncertainty and natural resources commodity prices: A comparative analysis of pre- and post-pandemic quantile trends in China

This study examines the relationship between natural resource commodity prices and economic policy uncertainty in China, both before and after the COVID-19 pandemic. This study uses an innovative Quantile Autoregressive Distributed Lag (ARDL) model to uncover both short- and long-term linkages among variables of interest. This study finds that there is a quantile relationship between these variables, with economic policy uncertainty having a negative impact on gold and natural gas prices during and after the pandemic. In contrast, during the pre-pandemic era, economic policy uncertainty had a negative impact on gold and natural gas prices but not on oil prices. These findings have significant policy implications, particularly in the context of the pandemic and its effects on the global economy.

Assessing the nexus between fintech, natural resources, government effectiveness, and environmental pollution in China: A QARDL study

Despite its pledge and efforts towards sustainability, China has not been able to live up to its environmental expectations. The road towards carbon neutrality still seems to be faraway. There has been light at the end of the tunnel with the various efforts China has undertaken to reduce environmental pollution (ENP). However, such efforts are not enough and the country must adopt some strategies which can be used to curb environmental pollution and promote more sustainable growth for the country in the long run. Hence, this study assess the role of Fintech (FNT), Natural resources (NTR), and government effectiveness (GVT) on environmental pollution. For that purpose, the study uses the novel Quantile Autoregressive Distributed Lag (QARDL) method, which assist in examining the impact of modelled series in multiple quantiles. The findings of the study suggest that Fintech can help to promote the use of digitalization within the financial industry and can be an efficient way to reduce CO2 emissions. Green finance and green investments can also be promoted using FinTech. The use and extraction of Natural resources are detrimental to the environment. Lack of government effectiveness can lead to severe environmental problems in the form of lax laws and corruption. The study recommends China promote the use of green energy, nurture the growth of Fintech and ensure that it works to develop a green image of the government to facilitate the growth of a green economy.

Renewable energy transition to sustainable tourism: extrapolating from core density and non-parametric approaches.

The globe has faced severe challenges recently, and environmental deterioration has become more prominent. Therefore, the world has taken several initiatives to deal with environmental issues while the problem remains intact. Interestingly, the OECD economies are the leading example to understand the accurate picture of sustainability across the near regions. This study makes an effort to introduce the core factors such as economic development, renewable energy, tourism, natural resources, and innovations in OECD economies over the period of 2000-2021. Similarly, to investigate the study's objectives, this study employs the quantile autoregressive distributed lag model (Q-ARDL). The analyzed results show the significant contribution of renewable energy, tourism, and natural resources to environmental sustainability. In contrast, income and innovations contribute to ecological deterioration. Moreover, the quantile causality is being used by this empirical study to investigate the causal association among studied variables. However, using green energy in sustainable tourism is highly recommended for specified economies. In order to deal with environmental pressure, this research proposes green implications to attain the desired sustainability level.

The heterogeneous role of economic and financial uncertainty in green bond market efficiency

Purpose This paper aims to explore the quantile-specific short- and long-term effects of economic policy uncertainty (EPU) on the efficiency of the green bond market. Design/methodology/approach This study examines the long-term cointegration relationship and the short-term fluctuation relationship of EPU, WTI crude oil price (WTI) and European Union Allowances price (EUA) with the green bond market efficiency (GBE) using the quantile autoregressive distributed lag method. Additionally, the authors analyze the differences before and after the Covid-19 pandemic. Findings EPU has a significant positive impact on the GBE before the outbreak. However, during the crisis period, the impact of EPU and WTI was greatly weakened, whereas the impact of EUA was strengthened. Practical implications This paper demonstrates the dynamics of GBE and its influencing factors under different periods. The findings provide insights for market participants and policymakers to gain a clearer understanding of the green bond market. Originality/value This paper extends the study of green bonds by quantifying the GBE and elucidating the nonlinear relationship between efficiency and independent variables at different quantiles over different periods.

Unleashing the power of emergency response: controlling natural disasters by addressing environmental risk.

The primary focus of the analysis is to investigate the impact of emergency response management and environmental risk on natural disasters by controlling the variables of national income and financial development. To investigate the model empirically, we have employed the quantile autoregressive distributed lag model that estimates the short- and long-run estimates across various quantiles. The long-run estimates of emergency response management are negative and significant only at higher quantiles, i.e., from 60 to 95th quantiles. In the short run, emergency response management's estimated coefficients are negative and significant from 70 to 95th quantiles. Environmental risk shows a significant positive correlation with natural disasters across quantiles, while national income and financial development decrease natural disasters in the long run. Furthermore, we observed the asymmetric impact of emergency response management on natural disasters in both the short and long run.