Dynamic Autoregressive Distributed Lag Research Articles

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.

Commodity price shocks related to the war in Ukraine and exchange rates of commodity exporters and importers

The war in Ukraine and new sanctions imposed on Russia have affected commodity prices and induced historic moves in exchange rate markets. In this paper, we examine the impact of commodity price shocks related to the war in Ukraine on three currencies (Canadian dollar, euro, and Japanese yen). Using four-hour price data for three commodities (wheat, crude oil, and natural gas) and two exchange rates (EUR/CAD and CAD/JPY) from February 1 to April 30, 2022, the analysis based on the quantile autoregressive distributed lag (ARDL) model suggests a long-run association between higher commodity prices and appreciation of the Canadian dollar against the euro and the yen. Furthermore, the dynamic simulated ARDL model shows a positive impact of commodity price shocks on the value of the Canadian dollar against the euro and the yen, which demonstrates the robustness of our findings. Oil price shocks have almost the same impact on the depreciation of the euro and the yen. The driving forces in the depreciation of the euro and the yen, in addition to higher oil prices, are higher gas prices and higher wheat prices, respectively.

COVID-19, lockdown measures and their impact on food and healthcare prices: empirical evidence using a dynamic ARDL model

PurposeThis research examines the impact of lockdown stringency measures and COVID-19 cases on food and healthcare prices in six Brazil, Russia, India, China, South Africa and Turkey (BRICST) countries. This research is conducted in these countries since previous studies failed to examine the effect of COVID-19 reported cases on food and healthcare prices.Design/methodology/approachTo achieve the objectives of this study, food and healthcare services were regressed against CVC and lockdown stringency measures using the dynamic autoregressive distributed lag (DARDL) model. For this purpose, we used daily data for BRICST countries such as Brazil, Russia, India, China, South Africa and Turkey.FindingsThe empirical evidence indicates that, in the long run, COVID-19 cases significantly and positively affect both food and healthcare prices in India, South Africa and China. In contrast, in the short run, COVID-19 positively affects food and healthcare prices in all countries except Russia and Turkey. Similarly, in the long run, the government stringency index (GSI) and Containment and Health Index (CHI) significantly affect health prices in India and South Africa. In contrast, GSI and CHI significantly affect healthcare prices in South Africa only in the short run. Finally, GSI and CHI significantly affect the food prices in the long run in India, South Africa and China and in the short run in South Africa only.Originality/valueThe widespread impact of the new Coronavirus (COVID-19) has made the world panic. COVID-19 affected all spheres of life, including food supplies and healthcare services. However, most of the empirical research failed to examine the impact of COVID-19 cases on food and healthcare prices which is the main focus of this study. Moreover, in the given context, the authors use a recently developed model that the previous studies failed to use.

<p>Sectoral Performances and Economic Policy Uncertainty in an Emerging Economy</p>

This study investigates the dynamic interference of economic policy uncertainties on sectoral performances in an emerging economy - Nigeria. This step becomes expedient considering the vulnerability of developing countries to economic shocks and the lack of convincing explanations in previous studies. The dynamic autoregressive distributed lag (DARDL) and Kernel Regularized Least Squares (KRLS) techniques, as well as annual time series for the period 1981 to 2019, were relied upon for empirical expositions. Accordingly, a long-run relationship is established among the performance of each sector and the relevant explanatory variables. Overall, the empirical details reveal time-varying negative and significant effects of uncertainties on the performances of all the sectors, especially, the agricultural and the services sectors. Also, it is established that trade openness is a significant growth inhibitor, suggesting that the vulnerability of Nigeria economy to the waves of uncertainties is heightened by the levels of trade openness. Meanwhile, productivity levels in all the sectors are positive and significant functions of the exchange rate and investments. On this note, policies to shield the local economy from uncertainties must be sensitive to sectoral peculiarities and time variations. However, such efforts will be futile if trade openness is not taken into considerations. Expectedly, these discoveries are equally beneficial to the government, the local and international investors.

Production-based disaggregated analysis of energy consumption and CO2 emission nexus: evidence from the USA by novel dynamic ARDL simulation approach.

The study investigates the effects of energy consumption on carbon dioxide (CO2) emissions by focusing on production sources. In this context, the study focuses on the USA as the leading economy, includes monthly data between January 1973 and April 2022, and performs dynamic autoregressive distributed lag (ARDL) (DYNARDL) simulations. Besides, kernel-based regularized least squares (KRLS) and cointegrating regression approaches are applied for robustness checks. The results reveal that (i) there is cointegration between sub-components of the energy production and CO2 emissions in the long run; (ii) fossil energy and nuclear energy production have an increasing effect on the CO2 emissions in the both short and long run; (iii) renewable energy production has an increasing effect on the CO2 emissions in the short run, but has a decreasing effect in the long run; (iv) negative (positive) shocks in the fossil energy production have a decreasing (increasing) effect on the CO2 emissions, whereas negative (positive) shocks in the renewable energy production have an increasing (decreasing) effect on the CO2 emissions in case of counterfactual shocks; (v) there is a casual-effect nexus between energy production sources and CO2 emissions; and (vi) KRLS and cointegrating regression results validate the robustness of the DYNARDL simulation outcomes. Moreover, policy implications are discussed.

Linking shadow economy and CO2 emissions in Nigeria: Exploring the role of financial development and stock market performance. Fresh insight from the novel dynamic ARDL simulation and spectral causality approach

First and foremost, the present study seeks to traverse the informal sector characterized by a shadow economy in the presence of financial development, economic growth, and stock market performance on environmental pollution in Nigeria from 1981 to 2019. The dynamic autoregressive distributed lag (DARDL) approach was used to measure the short- and long-run elasticities, while spectral causality is applied to categorize the causal directions. Findings from the study revealed that the structural break unit root test revealed that all variables are stationary at first difference. The ARDL bound test confirmed the existence of long-run association among the used variables. The ARDL long-run results reveal that economic growth, financial development, and stock market performance are significantly responsible for carbon emission in Nigeria, while the shadow economy significantly improves environmental quality in Nigeria. Findings from the spectral causality results show a unidirectional causal relationship between financial development, economic growth, trade, stock market performance, and shadow economy to carbon emission in Nigeria. The empirical findings of this study provide some perceptive policy recommendations to overcome the adverse effect of carbon emissions in the environment.

Evaluating the impact of stakeholder engagement for renewable energy sources and economic growth for CO2 emission

Financial institutions are enforcing strict environmental rules to curb climate change, spurring the shift to renewable energy (RE) sources due to rising public awareness and engagement with stakeholders. As a result, this research examines the factors that have influenced China's economic development from 1990 to 2019. Using the dynamic autoregressive distributed lag (ARDL) technique, we examine how changes in energy consumption, industrial expansion, urbanization, and carbon emissions affect China's GDP growth. According to these studies, which are inverted U-shaped, there is a high correlation between carbon dioxide emissions and gross domestic product in the long and near term. China's increased usage of fossil fuels has resulted in a rise in carbon dioxide emissions. Carbon dioxide emissions per capita rise by 0.311% for every one percent increase in fossil fuel consumption per capita. Consumption of RE may have a progressive impact on lowering carbon dioxide emissions. In the long term, a 1% rise in REC per capita reduces greenhouse gas emissions by 0.324%. At the same time, our findings show that the continued advancement of the RE industry in China simultaneously raises the emission of GHGs, and a 1% rise in REC increases the release of carbon dioxide gas by 0.245% to 0.252% in the short term. China must keep up the use of renewable energy and make people aware of the carbon emissions from China's rapid growth of renewable energy.

Dynamic ARDL Simulations Effects of Fiscal Decentralization, Green Technological Innovation, Trade Openness, and Institutional Quality on Environmental Sustainability: Evidence from South Africa

Fiscal decentralization and green innovation are important to a country’s economic progress, but the externalities of increased pollution as a result of a rise in the energy used and economic growth must not be overlooked. The destruction of the environment presents a serious threat to human existence. South Africa, like several nations, has been working on reducing its dependence on fossil fuels such as coal by utilizing modern energy-efficient technologies that allow to establish a more carbon-neutral economy. Several attempts have been made to identify the major sources of environmental deterioration. Within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework from 1960 to 2020, this study aims to check empirically the effect of fiscal decentralization (FD), green technological innovation (GI), trade openness (OPEN), population size (POP), per capita GDP (GDP), per capita GDP squared (GDP2), institutional quality (INS), and energy consumption (EC) on carbon emissions (CO2) in South Africa, as given its fast economic progress the country is facing problems with CO2 emission. The recently developed novel dynamic autoregressive distributed lag (ARDL)-simulations framework has been used. The outcomes of the analysis indicate that (i) FD, GI, and INS improve environmental sustainability in both the short and long run; (ii) OPEN deteriorates environmental quality in the long run, although it is environmentally friendly in the short run; (iii) per capita GDP increases CO2 emissions, whereas its square contributes to lower it, thus validating the presence of an environmental Kuznets curve (EKC) hypothesis; (iii) POP and EC contribute to environmental deterioration in both the short and long run; and (iv) FD, GI, OPEN, POP, GDP, GDP2, INS, and EC Granger cause CO2 in the medium, long, and short run, suggesting that these variables are important to influence environmental sustainability. In light of our empirical evidence, this paper suggests that the international teamwork necessary to lessen carbon emissions is immensely critical to solve the growing trans-boundary environmental decay and other associated spillover consequences. Moreover, it is important to explain responsibilities at different tiers of government to effectively meet the objectives of low CO2 emissions and energy-saving fiscal expenditure functions.

On the sustainability of growth from energy consumption: Empirical evidence from a dynamic autoregressive distributed lag simulation

Energy is among the key drivers of economic growth. As the attainment of sustainable development becomes the major goal of modern economies, energy resources must, however, be appraised not only based on their capacity to fuel growth but also on their ability to sustain the same. This paper adds to the growing research on the growth-energy nexus by incorporating the dynamics of sustainability alongside the traditional growth factors of capital and labor using France’s yearly series from 1980 to 2019. The newly developed dynamic autoregressive distributed lag (ARDL) simulation was adopted to explore the nature of the long-run dynamics among the variables. The empirical findings support the notion that energy consumption significantly impacts gross domestic product (GDP) positively whereas, in the case of the adjusted net savings (ANS) which is an index of sustainable development, its long-run impact is positive but insignificant. The results also show that the counterfactual effects of shocks to energy usage are smaller on economic growth than sustainable development. France is recommended to promote green growth by boosting the contribution of renewables in the energy mix to fully optimize and sustain growth from energy resources.

Decomposing the effect of trade on environment: a case study of Pakistan.

Using time-series data from 1984 to 2019, the study examines the vigorous trade-environment relation in Pakistan. Pakistan is an interesting case study in which trade liberalization has expanded economic activity while also increasing environmental pollution during the last two decades. As a result, determining whether trade and industrial operations have contributed to environmental degradation is crucial. Our first goal is to look at how trade affects the environment in terms of scale, composition, and technique. The second step is to look into the pollution haven theory. The study uses a new approach to measuring trade openness called composite trade intensity, which differs from the traditional approach. The dynamic autoregressive distributed lag (ARDL) simulation framework, which was recently developed, was employed. The findings show that the scale impact raises CO2 emissions while the technique effect helps to lessen them, proving the existence of an environmental Kuznets curve (EKC) hypothesis. The composition impact contributes to increased pollution in the environment. Through the expansion of pollution-intensive export businesses, trade openness degrades environmental quality over the long as well as in the short term. The notion of a pollution hypothesis has also been proven. The quality of the environment deteriorates as a result of urbanization, whereas it improves as a result of good governance. Economic growth, trade openness, urbanization, and CO2 emissions have bidirectional causality, according to frequency domain causality findings. Based on our empirical findings, the study concludes that individual efforts, as well as collective efforts at the international level to reduce carbon emissions, are critical to solving the problem of environmental degradation and making the world a completely peaceful place.

Assessing the financial rеsоurсе curse hypothesis in Iran: Thе nоvеl dynаmiс АRDL approach

This study sсrutinizеs thе financial rеsоurсе сursе hypоthеsis in Irаn frоm 1979–2018, inсоrpоrаting rеаl GDP (RGDP), real grоss fixеd саpitаl fоrmаtiоn (RGFCF), аnd humаn саpitаl (HC) into the finance dеmаnd funсtiоn. The natural resource index (NRI) is constructed using principal component analysis through oil, coal, forest, natural gas, and mineral rents.Multiple econometrics methods were used to provide a more accurate and consistent analysis, including the dynamic autoregressive distributed lag, error correction model, three cointegration regressions: fully-modified ordinary least square, dynamic OLS, and canonical cointegrating regression. Thе еmpiriсаl findings validated thе lоng-run соintеgrаtiоn bеtwееn vаriаblеs. In thе lоng run, thе financial rеsоurсе сursе hypоthеsis is rеjесtеd. It implies the blessing role of nаturаl rеsоurсеs in developing Iran's financial system. Furthеrmоrе, HC and RGFCF are pоsitivеly аnd signifiсаntly linked to finаnсiаl dеvеlоpmеnt (FD). In соntrаst, RGDP is linkеd invеrsеly tо FD, whiсh соuld bе аttributеd tо thе gоvеrnmеnt's lеаding rоlе in thе finаnсiаl systеm аnd сlаssifying Irаn аs а lоwеr-middlе-inсоmе есоnоmy. Furthеrmоrе, this study provides pоliсymаkеrs with new frеsh insights into how tо use nаturаl rеsоurсеs аs an есоnоmiс policy instrument to improve the financial sector's performance. Finаlly, аpprоpriаtе pоliсy impliсаtiоns аrе prоvidеd fоr thе gоvеrnmеnt аnd pоliсymаkеrs.

Change in cereal production caused by climate change in Malaysia

This study attempts to estimate the effects of climate change variables, such as average temperature, CO2 emissions and average rainfall, on cereal production in Malaysia from 1969 to 2018. After preliminary tests on time series data, we employed a novel autoregressive distributed lag (ARDL) method known as the dynamic ARDL simulations technique. The results showed that a long-run co-integration relationship exists between cereal production and climatic and non-climatic factors. All climate variables have a negative impact on cereal yield, while energy consumption and cultivated land have a positive effect on cereal yield in the short- and long-run. Granger causality analyses also showed that a unidirectional causality link exists between rainfall and temperature with cereal production and between CO2 emissions and cereal production. Energy consumption, as a proxy for technology, has a one-way Granger cause with cereal production. The results of the dynamic ARDL simulations suggest that cereal yield was most sensitive to CO2 emissions, rainfall and temperature. In the long run, a 1% increase in temperature is associated with a 2.87% and 3.52% decrease in general and predicted estimates of cereal production, respectively. The dynamic ARDL simulations methodology provides a better understanding of the variability of cereal production in Malaysia as a result of climate change.

Shrimp export competitiveness and its determinants: a novel dynamic ARDL simulations approach

This study aims to explore the shrimp export competitiveness of Bangladesh, China, India, Indonesia, Thailand, and Vietnam time spanning from 1990 to 2019. However, emphasizing Bangladesh as a case country, this research looks into the factors influencing shrimp export competitiveness taking into account macroeconomic and policy variables. To do so, we used the recently developed Revealed Symmetric Comparative Advantage (RSCA) index to quantify export competitiveness, while a novel dynamic autoregressive distributed lag (ARDL) simulation approach to determine the factors driving shrimp export competitiveness. The findings show that all of the aforementioned countries seem to have some degree of shrimp export competitiveness during the study period, whereas Chain has entirely lost its export competitiveness after 2004. However, Bangladesh's shrimp export competitiveness has dipped marginally in recent years, despite continuous growth in competitor countries such as India, Indonesia, and Vietnam. Further, economic globalization, institutional quality, trade openness, number of trade agreements, and trade freedom all have a favorable impact on Bangladesh’s shrimp export competitiveness in the long-run, whereas the international or exporting price of shrimp has a detrimental influence in short-and long-run. Finally, these findings may have implications for a future shrimp export from Bangladesh and some policy recommendation are presented.

Non-Linear Effect of Government Debt on Public Expenditure in Nigeria: Insight from Bootstrap ARDL Procedure

This study employs the bootstrap autoregressive distributed lag (ARDL) approach alongside the dynamic ARDL simulations technique to investigate the non-linear effect of public debt on public expenditure in Nigeria during the 1981–2020 period. The result of the bootstrap bounds test illustrates the presence of a long-term relationship between public expenditure and public debt (along with oil rents, output growth and urbanisation). Further, the estimation results indicate that the effect of public debt on public expenditure is non-linear. In particular, public expenditure increases at early stages of rising public debt but declines at latter phases when public debt grows beyond specific threshold. This empirical outcome is further validated by the dynamic ARDL simulations approach which shows a significant decline in predicted public expenditure after short-term expansion due to counterfactual shock in public debt. Thus, policies which diversify public revenue from oil production and a reversal of the rising trend in public debt are recommended to avert the adverse welfare implications of declining public expenditure.

Do natural resources, economic growth, human capital, and urbanization affect the ecological footprint? A modified dynamic ARDL and KRLS approach

The interaction between the abundance of natural resources and environmental depletion has significant ecological consequences. Nonetheless, this area is not adequately studied, and numerous results are apparent throughout the literature. For massive economic development, it is vital to recognize the role of human capital, urbanization, and natural resources. Hence it is important to consider various factors that can play a constructive role in environmental sustainability. Therefore, this study investigates the relationship between total natural resources (TNR), gross domestic product (GDP), human capital index (HCI) and urbanization (URB) with ecological footprint (EFP) in Pakistan from 1980 to 2018. The research uses the latest versions of dynamic Autoregressive Distributed Lag (ARDL) simulations model. The key benefit of dynamic ARDL is to estimate positive and negative shifts between the selected variables with an immediate visual illustration over the short and long period. In addition, the Kernel-based Regularized Least Squares (KRLS) machine learning method is used to test robustness. The results show that the rise in TNR has a long-term detrimental influence on EFP. However, upsurge in GDP and HCI increases EFP in the long-term. Lastly, URB observed an important and detrimental long-term impact on the EFP. The KRLS also support the hypothesis. This study suggest a policies to the planners and government officials for managing rapid urbanization and minimizing its urban, environmental and economic challenges.

Revolution of nuclear energy efficiency, economic complexity, air transportation and industrial improvement on environmental footprint cost: A novel dynamic simulation approach

The expansion of a country's ecological footprint generates resources for economic development. China's import bill and carbon footprint can be reduced by investing in green transportation and energy technologies. A sustainable environment depends on the cessation of climate change; the current study investigates nuclear energy efficiency, economic complexity, air transportation, and industrial improvement for reducing environmental footprint. Using data spanning the years 1983–2016, the dynamic autoregressive distributed lag simulation method has demonstrated the short- and long-term variability in the impact of regressors on the ecological footprint. The study findings revealed that economic complexity in China had been found to have a statistically significant impact on the country's ecological footprint. Moreover, the industrial improvement process is helpful for the ecological footprint in China. In the short term, air travel has a negative impact on the ecological footprint, but this effect diminishes over time. Additionally, energy innovation is negative and substantial both in the short and long run, thus demonstrating its positive role in reducing the ecological footprint. Policy implications can be extracted from a wide range of issues, including economic complexity, industrial improvement, air transportation, energy innovation, and ecological impact to achieve sustainable goals.

Keeping up with the Joneses: macro-evidence on the relevance of Duesenberry’s relative income hypothesis in Ethiopia*

Despite being mysteriously ignored and displaced by mainstream consumption theories, Duesenberry’s relative income hypothesis appears to be highly relevant to modern societies where individuals are becoming increasingly obsessed with their social status. Accordingly, this study aims to provide some evidence on relative income hypothesis by investigating the relevance of Duesenberry’s demonstration and ratchet effects in Ethiopia using quarterly data from 1999/2000Q1 to 2018/19Q4. We estimate two specifications of the relative income hypothesis using the traditional Autoregressive Distributed Lag (ARDL) model and the dynamic ARDL simulations approach. The findings confirm a Backward-J-shaped demonstration effect, implying that an increase in relative income induces a steeper reduction in Average Propensity to Consume (APC) at lower-income groups (the demonstration effect is stronger for lower-income groups). The results also support the ratchet effect, indicating the importance of past consumption habits for current consumption decisions. In resolving the consumption puzzle, the presence of demonstration and ratchet effects reflects a stable APC in the long run. Therefore, consumption-related policies should be carefully designed, as policies aimed at boosting aggregate demand can motivate low-income households to gallop into a wasteful competition to ‘keep up with the Joneses’—the relative riches.

Modelling the Dynamic Interaction between Economic Policy Uncertainty and Commodity Prices in India: The Dynamic Autoregressive Distributed Lag Approach

This study examines the dynamic interaction between oil, natural gas, and prices with Indian economic policy uncertainty (EPU). The study finds that gold prices and industrial production are fundamental drivers of Indian economic policy uncertainty in both the short and long runs, using a dynamic autoregressive distributed lag (ARDL) model with monthly data ranging from January 2003 to July 2020. Gold prices are positively related to the Indian EPU, while industrial production is negatively related to it. Thus, investors in the Indian economy should use gold as a hedge for portfolio diversification and as a safe haven during an economic crisis. We also find a significant positive interconnection between gold prices and crude oil prices in both the short run and the long run, while the significant positive impact of natural gas prices on crude oil prices manifests only in the long run. The evidence also indicates that the EPUs of the US and Europe positively affect the Indian EPU, while the EPU of China does not have a significant effect. Higher crude oil prices are associated with higher gas prices, whereas higher gold prices are negatively associated with the natural gas price and vice versa. Furthermore, the evidence shows that the Indian EPU does not have a significant effect on the changes in the prices of goods.

An empirical investigation between renewable energy consumption, globalization and human capital: A dynamic auto-regressive distributive lag simulation

This study analyzes the dynamic effects of overall globalization and its three sub-dimensions (economic globalization, political globalization, and social globalization) on renewable energy consumption in Turkey using annual data from 1980 to 2017. As an estimation technique, the dynamic autoregressive distributed lag simulation (dynamic ARDL) developed by Jordan and Philips (2018) is employed. The results indicate that the overall globalization process, both in the short-run and in the long run, and its dimensions of economic and political globalization, only in the long-run, have positive and significant effects on Turkey's renewable energy consumption. However, social globalization does not have any significant effect on renewable energy consumption either in the short-run and in the long run. Additionally, increases in Turkey's human capital stock and economic growth rate appear to cause more renewable energy consumption. Based on those results, some important policy implications encouraging the globalization and economic growth processes and enriching the human capita stock are suggested.

Mexico at the crossroads of natural resource dependence and COP26 pledge: Does technological innovation help?

The nexus between natural resource depletion and environmental quality has been a heated topic of debate among policy experts in recent years. This nexus is crucial to achieve Sustainable Development Goal (SDG) 13 which deals with climate change mitigation. However, the extant literature has not paid due attention to this nexus, particularly in the case of Mexico. Against this backdrop, the present study examines the relationship between ecological footprint and natural resource depletion, economic growth, energy consumption, technological innovation, and foreign direct investment in the Mexican economy. The present research leverages the novel dynamic autoregressive distributed lag (ARDL) simulations approach and uses annual frequency data from 1980 to 2018. Empirical findings from the ARDL bounds test substantiate the presence of long-run association among studied variables. Furthermore, the result of the novel dynamic ARDL simulation does not validate the Environmental Kuznets curve (EKC) hypothesis. Thus, implying that Mexico's economy remains at the scale stage, with a scope of economic expansion relative to environmental quality. However, the Pollution Haven Hypothesis (PHH) is validated. Interestingly, natural resource rent accompanied with energy use and foreign direct investment deteriorate the environmental quality in the short-and long-run. However, technological innovation seems to emanate as a panacea for environmental degradation in the long-run. Result analysis also demonstrates that 10% positive and negative simulation graphs for the studied variables. The Frequency Domain Causality analysis also corroborates the above findings. These findings have far-reaching policy implications for Mexico's sustainable environment amid prudent use of natural resources, which are prescribed in the final section.