Dynamic Lag Research Articles

How sustainable environment is influenced by the foreign direct investment, financial development, economic growth, globalization, innovation, and urbanization in China.

This study aims to investigate the impact of foreign direct investment (FDI) inflow, financial development, economic growth, globalization, innovation, and urbanization on the carbon dioxide emissions in China by using long-ran time series dataset from 1970 to 2021 which for the first time used the newly developed dynamic autoregressive distributed lags (ARDL) simulation model for results analysis. Dynamic ARDL simulation model removes the shortcomings of the traditional ARDL models by predicting the actual change (positive and negative shocks) in the independent variables and its impact on the main dependent variables by 5000 simulations through graphical representation. The findings of the long-run dynamic ARDL simulations indicate that FDI inflow, globalization, and innovation negatively and significantly impact the environmental degradation while financial development, economic growth, and urbanization cause to increase the environmental degradation in China. Recommendations are suggested based on the findings of this study.

An analysis of the effects of oil and non-oil export shocks on the Saudi economy

As the world’s largest oil exporter, Saudi Arabia faces the same pressures as any other government to expand its economy. Saudi Vision 2030 is to reduce the country’s reliance on oil exports and revenues. One of the main goals of Saudi Vision 2030 is to increase the share of GDP that does not come from oil. Dynamic autoregressive distributed lag (ARDL) cointegration is used to look at how oil exports and exports of goods other than oil affect GDP growth. The results of the dynamic ARDL simulation show that there is both long-term and short-term cointegration between the variables. The dynamic ARDL simulation tests rely on the presence of cointegration to show that a 1% increase in oil exports will boost Saudi Arabia’s economic growth by about 0.48% in the long run and 0.18% in the short run, depending on the type of time frame. In the same way, the results about non-oil exports showed that an increase in non-oil exports would boost Saudi Arabia’s economic growth by 0.26 percentage points in the long run and by 0.16 percentage points in the short run. This is a good sign of Saudi Arabia’s efforts to diversify its economy away from oil exports and make room for international investors to help the country reach its Vision 2030 goals. AcknowledgmentThis study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).

Role of financial development, foreign direct investment inflow, innovation in environmental degradation in Pakistan with dynamic ARDL simulation model.

The purpose of this research is to determine the impact of innovation, economic growth, financial development, trade, foreign direct investment (FDI), electricity consumption, and urbanization on the environmental degradations in Pakistan. This study has employed the dynamic autoregressive distributed lag model (ARDL), to investigate the actual change in the independent variables and its impact on the dependent variable through graphs. The findings demonstrate that energy consumption, GDP growth, urbanization, and trade negatively influence the carbon emissions in the short term. On the other hand, the findings indicate that in the long term, only GDP growth and trade had a significantly negative impact on emissions. Urbanization has a positive and considerable impact on the emissions of carbon dioxide in the long run. On the other hand, financial development and foreign direct investment (FDI) help reduce the environmental degradation in the short term and long term. Moreover, innovation positively affects the carbon emissions in both the long and short run. Policy recommendations are given based on the findings of this study.

Revisiting the nexus between fiscal decentralization and CO2 emissions in South Africa: fresh policy insights

The argument over fiscal decentralization and carbon dioxide emission (CO2) reduction has received much attention. However, evidence to back this claim is limited. Economic theory predicts that fiscal decentralization affects environmental quality, but the specifics of this relationship are still up for debate. Some scholars noted that fiscal decentralization might lead to a race to the top, whereas others contended that it would result in a race to the bottom. In light of the current debates in environmental and development economics, this study aims to provide insight into how this relationship may function in South Africa from 1960 to 2020. In contrast to the existing research, the present study uses a novel dynamic autoregressive distributed lag simulation approach to assess the positive and negative changes in fiscal decentralization, scale effect, technique effect, technological innovation, foreign direct investment, energy consumption, industrial growth, and trade openness on CO2 emissions. The following are the main findings: (i) Fiscal decentralization had a CO2 emission reduction impact in the short and long run, highlighting the presence of the race to the top approach. (ii) Economic growth (as represented by the scale effect) eroded ecological integrity. However, its square (as expressed by technique effect) aided in strengthening ecological protection, validating the environmental Kuznets curve hypothesis. (iii) CO2 emissions were driven by energy utilization, trade openness, industrial value-added, and foreign direct investment, whereas technological innovation boosted ecological integrity. Findings suggest that further fiscal decentralization should be undertaken through further devolution of power to local entities, particularly regarding environmental policy issues, to maintain South Africa’s ecological sustainability. South Africa should also establish policies to improve environmental sustainability by strengthening a lower layer of government and clarifying responsibilities at the national and local levels to fulfill the energy-saving functions of fiscal expenditures.

Can fiscal decentralization be the route to the race to zero emissions in South Africa? Fresh policy insights from novel dynamic autoregressive distributed lag simulations approach.

There has been a plethora of debate on the link between fiscal decentralization and a drop in carbon dioxide (CO2) emissions, even though the evidence supporting this assertion is relatively sparse. Although the precise nature of this relationship is still up for discussion, economic hypothesis postulates that fiscal decentralization has an impact on environmental sustainability. Some researchers claim that fiscal decentralization could potentially result in a race to the top, while some believe it would lead to a race to the bottom. This analysis intends to shed light on the precise processes by which this connection may work in South Africa between 1960 and 2020 in light of current discussions in environmental and development economics. In contrary to previous studies, this paper employs a cutting-edge dynamic autoregressive distributed lag simulations methodology to evaluate the positive and negative variations in fiscal decentralization on CO2 emissions. Our findings demonstrate the prevalence of the race to the top strategy by illustrating how fiscal decentralization has a bearing on CO2 emissions reduction in the short and long terms. In accordance with the findings, greater fiscal decentralization should be implemented through the transfer of more powers to regional authorities, especially in the realm of environmental legislation considerations, in a bid to preserve South Africa's environmental integrity. By establishing a lower tier of government and defining roles at the federal and provincial divisions, South Africa could adopt strategies to improve green environment in an effort to fulfill the energy-saving tasks of fiscal expenditures.

A comparative study of the policy response to COVID-19 in the ASEAN region: A dynamic simulated ARDL approach.

The COVID-19 epidemic is the most significant global health disaster of this century and the greatest challenge to humanity since World War II. One of the most important research issues is to determine the effectiveness of measures implemented worldwide to control the spread of the corona virus. A dynamic simulated Autoregressive-Distributed Lag (ARDL) approach was adopted to analyze the policy response to COVID-19 in the ASEAN region using data from February 1, 2020, to November 8, 2021. The results of unit root concluded that the dependent variable is integrated of order one while the independent variables are stationarized at the level or first difference, and the use of a dynamic simulated ARDL technique is appropriate for this paper. The outcomes of the dynamic simulated ARDL model explored that government economic support and debt/contract relief for poor families is substantially important in the fight against COVID-19. The study also explored that closing schools and workplaces, restrictions on gatherings, cancellation of public events, stay at home, closing public transport, restrictions on domestic and international travel are necessary to reduce the spread of COVID-19. Finally, this study explored that public awareness campaigns, testing policy and social distancing significantly decrease the spread of COVID-19. Policy implications such as economic support from the government to help poor families, closing schools and public gatherings during the pandemic, public awareness among the masses, and testing policies must be adopted to reduce the spread of COVID-19. Moreover, the reduction in mortality shows that immunization could be a possible new strategy to combat COVID-19, but the factors responsible for the acceptability of the vaccine must be addressed immediately through public health policies.

Nexus between CO2 emissions, renewable energy consumption, militarisation, and economic growth in South Africa: Evidence from using novel dynamic ARDL simulations

The South African government battles with global warming issues while attempting to achieve the United Nations Sustainable Development Goals −7&13 (that is, affordable clean energy and climate change mitigation). To the best of our knowledge, no studies have focused exclusively on this research topic, and it is on this basis that in this article, the novel dynamic autoregressive distributed lag (ARDL) simulations approach was employed for the purpose of estimating, simulating and plotting to investigate the CO2 emissions-renewable energy consumption-militarisation-economic growth nexus in South Africa (SA). The study spans between 1960 and 2019 and applies frequency domain causality. The main findings suggest that: (i) there is a long-run equilibrium relationship between the variables; (ii) the causality results differ; (iii) the treadmill theory of destruction and the Environmental Kuznets Curve (EKC) hypothesis hold in SA. Overall, this study recommends a synergy between defence, renewable energy, growth and environmental policies in both the short- and long-run for the purpose of promoting and maintaining environmental quality in SA.

Long-run impact of coal usage decline on CO2 emissions and economic growth: Evidence from disaggregated energy consumption perspective for China and India by dynamic ARDL simulations

This study examines the long-run impact of coal usage decline from a disaggregated energy consumption perspective by considering the role of coal usage on carbon dioxide (CO2) emissions and economic growth. For this purpose, the study focuses on China and India as the leading coal-user countries; considers coal, oil, natural gas, and renewable energy consumption as explanatory variables; uses yearly data from 1990 to 2021, and applies a novel dynamic autoregressive distributed lag (DYNARDL) simulations. Also, the Kernel-based regularized least squares (KRLS) approach is performed for robustness checks. The outcomes show that (i) there is cointegration between coal usage and CO2 emissions, and between coal usage and economic growth in the long run; (ii) coal usage is the most important indicator among explanatory variables; (iii) coal usage has a statistically significant impact on CO2 emissions in both China and India; (iv) however, coal usage is not significantly effective on economic growth for both China and India based on DYNARDL simulations; (v) negative counterfactual shocks in coal usage have a decreasing impact on CO2 emissions; (vi) KRLS results show that coal usage has a causal impact on CO2 emissions and the results of the DYNARDL simulations are robust. Hence, counterfactual shocks present important insights about the future impacts of coal usage decline on both CO2 emissions and economic growth. Based on the empirical results, policy implications, such as decreasing the amount of coal usage on and increasing the amount of alternative energy sources in the total energy mix, are also proposed.

Exploring the moderating role of financial development in environmental Kuznets curve for South Africa: fresh evidence from the novel dynamic ARDL simulations approach

The extant literature has produced mixed evidence on the relationship between financial development and ecological sustainability. This work addresses this conundrum by investigating financial development’s direct and indirect consequences on ecological quality utilizing the environmental Kuznets curve (EKC) methodological approach. Our empirical analysis is based on the novel dynamic autoregressive distributed lag simulations approach for South Africa between 1960 and 2020. The results, which used five distinct financial development measures, demonstrate that financial development boosts ecological integrity and environmental sustainability over the long and short terms. In the instance of South Africa, we additionally confirm the validity of the EKC theory. More importantly, the outcomes of the indirect channels demonstrate that financial development increases energy usage’s role in causing pollution while attenuating the detrimental impacts of economic growth, trade openness, and foreign direct investment on ecological quality. Moreover, the presence of an inadequate financial system is a requirement for the basis of the pollution haven hypothesis (PHH), which we examine using trade openness and foreign direct investment variables. PHH for both of these variables disappears when financial development crosses specified thresholds. Finally, industrial value addition destroys ecological quality while technological innovation enhances it. This research provides some crucial policy recommendations and fresh perspectives for South Africa as it develops national initiatives to support ecological sustainability and reach its net zero emissions goal.

Explanatory factors for the balance of current transactions in the CEMAC zone

Most of the countries in Central Africa have recorded current account deficits since the coronavirus pandemic in 2020. The aim of this article is to identify the main factors explaining the behavior of the current account balances of CEMAC member countries. Using a dynamic panel autoregressive distributed lag (ARDL) model, we use annual data for the period 1970-2018. The results indicate that there is a long-run relationship between the current account balance of the CEMAC countries and the explanatory variables (real exchange rate, terms of trade, domestic savings, GDP growth rate, domestic investment, inflation rate, oil price). We also show that domestic investment boosts the competitiveness of the countries concerned, that domestic savings are directed towards the consumption of foreign goods, that oil revenues do not fuel productive investment, and that there is a structural difference between the CEMAC countries; thus, the terms of trade have an asymmetrical effect on their current accounts. The results of this study lead to recommendations to the effect that diversifying the productive base of CEMAC countries is the most effective way of enabling them to rebalance their current account balances in the long term and reduce their vulnerability to external shocks.

Is reducing fossil fuel intensity important for environmental management and ensuring ecological efficiency in China?

Since China is a country with high environmental pollution, researchers have intensively studied China's environmental problems using various environmental indicators such as carbon emissions and ecological footprint. Unlike previous literature, this study analyzes the effects of economic growth, globalization, foreign direct investment, and fossil energy intensity on ecological efficiency in China. As an innovation to the literature, the study examines the Chinese ecosystem simultaneously with its economic and environmental aspects by focusing on ecological efficiency. To this end, the study applies dynamic autoregressive distributed lag (DARDL) simulations and kernel-based regularized least squares (KRLS) methods for the period from 1990 to 2018. The results of the DARDL simulations show that globalization, and economic growth enhance ecological efficiency in China. The findings also demonstrate that both foreign direct investment and fossil fuel intensity have a negative impact on environmental quality in China. Based on these results, the study suggests that the Chinese government should adopt policies to channel foreign direct investment into environmentally friendly production, reduce fossil fuel intensity, and improve ecological efficiency by making use of environmentally friendly technologies provided by globalization and economic development.

Public sector innovation outcome-driven sustainable development in Bangladesh: applying the dynamic autoregressive distributed lag simulations and Kernel-based regularised least square machine learning algorithm approaches

AbstractThis research investigates the role of public sector innovation outcomes, e.g. trademark innovation, information and communication technology (ICT), renewable energy, and governance, in the sustainable development of Bangladesh during 1980–2019. Utilising the dynamic autoregressive distributed lag (DARDL) simulation approach, this study divulges a favourable long-term influencing profile of public sector innovation outcomes, i.e. trademark innovation, ICT, and renewable energy on sustainable development, while governance has a heterogeneous impact. Besides, the findings from the DARDL simulations area plots display 10% counterfactual shocks to the public sector innovation outcomes on sustainable development. Furthermore, the Kernel-based regularised least square machine learning algorithm approach used in the study examines the marginal effects of the public sector innovation outcomes on sustainable development for robust findings. Therefore, the policy suggestions are solely concerned with the public sector’s adoption of more innovation dynamics through appropriate policy formulation.

Exploitation of Energy Optimal and Near-Optimal Control for Traction Drives with AC Motors

The main contribution of this paper is the verification of a new train control system applied to the drive with a.c. motors that is energy near optimal with respect to electrical and mechanical energy minimization. All simulations are related to the one traction motor yielding its best exploitation. Load torque treated as a state variable consists of constant, linear, and quadratic components as a function of rotor speed. The required performance of the energy-saving control is rendered straightforward using precomputed energy optimal state variables or a prediction of losses for a symmetrical trapezoidal speed profile as the second alternative. An energy-saving reference position generator provides the state variables, which are faithfully followed by a feedback control based on field orientation, which is completed with a matched zero dynamic lag pre-compensator yielding prescribed closed-loop dynamics. Simulation results mutually compared confirm the capability of an overall drive control system to follow generated state variables for two different service conditions and the possibility of energy savings.

Natural resource dependency and environmental sustainability under N-shaped EKC: The curious case of India

Even though the literature is becoming more swamped with research on the causes of environmental degradation, the uniqueness of some nations that strongly emphasize resource depletion has been largely ignored. Following the concept of the Environmental Kuznets Curve (EKC), this study considers the resource richness of India while examining the consequences of natural resource rent (NRR), GDP, and the interaction of NRR and GDP on environmental degradation controlling urbanization and energy use. To this end, we harnessed data from 1970 to 2018 and applied dynamic autoregressive distributed lag (DARDL) simulation and frequency domain causality (FDC) approach. The baseline model reveals that economic activities have heterogeneous yet significant impacts on environmental quality, and we validate the existence of an “N-shaped” EKC hypothesis in India in the presence of CO2 emission as the dependent variable. However, we failed to verify the N-shaped EKC with the ecological footprint as our dependent variable. The natural resource rent, energy consumption, and urbanization are also positively linked to long-term environmental degradation. The interaction term of NRR and GDP positively impacts CO2 emissions and ecological footprint. In addition, the value of the turnaround point was higher with the interaction term compared to that without the interaction term, further indicating the negative externalities of the interaction term. According to FDC's results, a feedback loop between economic development and CO2 emissions is highlighted. The robustness check further endorses the findings of our baseline models. The insights have far-reaching policy implications, discussed in the ultimate section.

Do renewable energy consumption, technological innovation, and international integration enhance environmental sustainability in Brazil?

The present paper explored the effect of renewable energy, financial development, and technological innovation on the ecological footprint in Brazil using a yearly dataset stretching between 1990 and 2018. The research leverage dynamic autoregressive distributed lag, Bayern and Hank cointegration, and frequency domain causality to explore these interrelationships. The ARDL bounds test outcome confirms a long-run connection between ecological footprint and its drivers. Besides, we employed the Bayern and Hank cointegration, and the outcome resonated with the ARDL bounds test result. Moreover, the dynamic ARDL outcomes disclosed that economic progress worsens the ecosystem whilst renewable energy, trade globalization, and technological innovation lessen the ecological footprint. In addition, no significant interrelationship was found between financial development and ecological footprint. Moreover, with the exemption of financial development, all the exogenous variables can forecast the ecological footprint in Brazil. The study proposes that policymakers in Brazil should undertake investments in research and development in order to achieve technological innovation. Furthermore, since technological innovation has been shown to contribute significantly to lowering the ecological footprint, it is possible to assume that enhancing current technology is the ultimate remedy to Brazil's ecological problems.

A significant wave height forecast framework with end-to-end dynamic modeling and lag features length optimization

Ocean wave energy is attracting more and more attention from researchers on observation of its clean and sustainable properties. Significant wave height (SWH) is one of the key wave parameters, making accurately forecasting the SWH important for coastal/ocean engineers. In this paper, we propose a new framework for forecasting the SWH. The data were decomposed and reconstructed, and the lag features length of the input data were adaptively optimized using the Bayesian optimization (BO) algorithm. A new paradigm for end-to-end dynamic modeling (EEDM) forecast is then proposed, where data are modeled and forecasted separately for buoys at various geographical locations, with automated machine learning (AutoML) as the back-end modeling support for the paradigm. It was also trained and tested on nine buoys from NOAA National Data Buoy Center, which are located at sites with different water depths. The results show that the forecast framework has provided reliable forecasts. We also discussed the reasons for the buoy with the worst forecast in terms of model interpretability and data quality. Finally, we compared three deep learning models (simple recurrent network, long short-term memory and gate recurrent unit) and three machine learning models (principal component regression, support vector machine and K-nearest neighbor). The comparisons indicate that the AutoML turns out to be the best.

Strategic pathways to combating remittance-induced carbon emissions; the imperatives of renewable energy, structural transformations, urbanization and human development

ABSTRACT Remittances are notable environmental quality (EQ) determinant, but their precise effect remains contextual. Moreover, it is still unclear how factors such as renewable energy consumption, structural transformations, urbanization processes and human capital development moderate the remittance-EQ nexus, particularly in countries with the highest share of remittances to gross domestic product. Thus, with annual panel series over 1990–2019 from the top twenty-one countries in such category and estimates of robust panel estimators, including the pool-mean group, dynamic cross-section autoregressive distributed lag (PMG and CS ARDL) and panel nonlinear (PNARDL), the following fresh empirical narratives subsist: (i) The enlisted panel series coevolve with EQ in the long term. (ii) Remittance is an all-time pollution enabler when the other explanatory variables were held constant. (iii) When the moderating variables were incorporated, the influence of remittances on EQ became insignificant (PMG and CS ARDL) and less severe (PNARDL) (iii) Renewable energy consumption and human capital development possess significant EQ enhancing attributes mostly in the long run, while structural transformations and urbanization promote pollution significantly. Instructively, the optimal pathway is not discouraging remittance inflows; rather, greater proportions of remittances should be invested in renewable energy and human capital. Likewise, it is essential to reappraise the subsisting structural arrangements and urbanization processes to make them answerable to sustainable development goals.

Does the potency of economic globalization and political instability reshape renewable energy usage in the face of environmental degradation?

Since renewable energy is essentially non-carbohydrate in nature, it can generate little or no pollutants and can therefore help in achieving both sustainable development and environmental quality. In this regard, the question that continues to persist is whether economic growth, economic globalization, and political risk can potentially affect renewable energy in the presence of environmental deterioration. In this context, the current research provides evidence to support this theoretical context by investigating the impact of economic globalization, economic growth environmental degradation, and political risk, on the usage of renewable energy in Vietnam using a dataset spanning the period between 1984 and 2019. For empirical analysis, the dynamic autoregressive distributed lag approach is utilized. Based on our analysis, economic growth positively impacts renewable energy in the long and short term. Economic globalization also positively affects renewable energy in the long term, but a neutral impact is uncovered in the short term. Political risk and environmental degradation are adversely related to renewable energy in the short and long run. The findings from the frequency domain approach reveal a causal interaction from political risk to renewable energy, and from renewable energy to economic globalization, whereas a feedback causal interaction is discovered between renewable energy and environment degradation, as well as between economic growth and renewable energy. From a policy standpoint, we propose that the Vietnamese policymakers need to consider economic globalization as a renewable energy promotion tool via capital inflow, foreign direct investment, and technological transfer.

Dynamic influence of natural resources, financial integration and eco-innovation on ecological sustainability in EKC framework: Fresh insights from China

The relation between inclusive natural resources and ecological pollution has significant environmental implications. Though, the literature on this question is scarce and has inconclusive results from the perspective of financial integration. The substantial economic growth, energy sources, and technological advancement have increased China's extraction and consumption of natural resources. The study aims to probe the effects of natural resources, financial integration, eco-innovation, and clean energy on the ecological footprint in the Environment Kuznets Curve (EKC) concept. Notably, the authors further bifurcate the financial integration into Model 1 (the ratio of FDI to GDP) and Model 2 (capital account openness). This paper applied a novel dynamic autoregressive distributive lag (D-ARDL) technique that is efficient enough to automatically draw positive and negative simulations of the explanatory variables for the period 1980–2020. Outcomes of this study discovered that natural resources decrease ecological degradation in the short- and long-run. More so, the dynamic results explain that financial integration degrades the ecological quality whilst renewable energy and eco-innovation preserve the environmental performance. Interestingly, this study endorses the presence of the EKC hypothesis in developing countries like China. In the end, comprehensive policy recommendations and limitations are proposed to recover the ecological quality.

CO2 emissions-energy consumption-militarisation-growth nexus in South Africa: evidence from novel dynamic ARDL simulations.

This study draws ardent attention to the Sustainable Development Goal 13 (climate change mitigation) of the United Nations by investigating the CO2 emissions-energy consumption-militarisation-economic growth nexus for South Africa (SA) from 1960 to 2019. The researcher applied frequency domain causality and the novel dynamic autoregressive distributed lag (ARDL) simulation approaches to achieve the research objective. The main findings reflected that (i) there is a long-run equilibrium relationship between the variables; (ii) there is no causality between militarisation and energy consumption; (iii) unidirectional causality runs from militarisation to economic growth; (iv) there is no causality between militarisation and CO2 emissions; and (v) unidirectional causality runs from energy consumption to economic growth. The dynamic ARDL simulations’ main results suggest that (i) in the short-run, a positive and insignificant relationship exist between militarisation and CO2 emissions. Conversely, a negative and significant relationship was recorded in the long-run. Thus, the treadmill theory of destruction is not valid for SA. (ii) In the short-run, economic growth has a positive and significant impact on CO2 emissions, while in the long-run, economic growth has a negative and significant impact on CO2 emissions. This implies the environmental Kuznets curve (EKC) hypothesis holds for SA. Overall, this research suggests a synergy between defence, energy, growth, and environmental policies in the short- and long-run to promote and maintain environmental quality in SA.