Impact of trade liberalization and renewable energy on load capacity factor: Evidence from novel dual adjustment approach

Abstract

Several nations, including Mexico, are encountering problems with accomplishing the targets of sustainable development goals (SDGs). This study addresses how to develop SDG agenda for Mexico, which may also be applied to other emerging markets with comparable profiles. Therefore, this study utilizes a newly developed load capacity factor (LCF, a detailed environmental evaluation tool) that combines biocapacity (supply side) and ecological foot (demand side) to examine the effect of renewable energy consumption and trade openness on LCF while controlling for nonrenewable energy consumption and economic growth in the case of Mexico during 1970–2017. Using the dual adjustment approach, we find evidence of long-run cointegration nexus among the series. In addition, trade openness affects LCF positively, whereas renewable energy consumption, economic growth, and nonrenewable energy consumption impact LCF negatively. Furthermore, results of the frequency domain causality show that all the variables can predict LCF in the long-term. Based on these results, Mexico should focus on promoting public understanding of green energy and environmental preservation measures and participate in the production of nonenergy-consuming and ecologically friendly products while compelling polluting enterprises to migrate to countries with less stringent environmental restrictions.

Keywords

Load capacity factor renewable energy nonrenewable energy economic growth environmental sustainability Mexico

Introduction

Climate change has become a strategic global issue that countries face today. For some countries, climate change has even been considered a major threat to their national security system because climate change can invite various natural disasters and inhibit a country from growing. Globalization is regarded as a reason behind this environmental issue. With globalization, countries are forced to utilize additional resources from Earth to produce extra output and participate further in international trade. The more open a country for international trade is, the riskier its environment becomes to a natural disaster. The global trade openness indicator, which is measured by the total of export and import relative to gross domestic product (GDP), demonstrates how countries are becoming more globalized. The World Bank estimates that the trade openness indicator has increased from 37.99% in 1990 to 52.82% in 2020.¹ However, within the same period, carbon emission has also been recorded high, increasing from 3903 metric tons per capita in 1990 to 4484 in 2018, which is equivalent to a 14.88% increase (World Bank, 2021). For this reason, many countries are struggling to reduce environmental degradation by substituting fossil-based energy with renewable energy. However, the true effect of trade openness and renewable energy on environmental quality remains unclear.

Many scholars have been working on understanding the influence of trade openness and renewable energy consumption on environmental degradation. Across the studies, the literature can be classified into some results. In terms of the relationship between trade openness and environment, the findings remain mixed. Some studies demonstrate that trade openness is significant in reducing environmental degradation. For example, Alola et al.² argue that in the case of EU countries, the openness of trade improves environmental quality in the long-term, although its impact is not significant in the short-term. Antweiler et al.³ also argue similarly where trade openness is good for environmental quality across 108 cities representing 43 countries as their sample. Using 24 OECD countries as a sample, Destek and Sinha⁴ find that a high degree of trade openness is associated with low environmental degradation. Kirikkaleli et al.⁵ also reveal that trade openness positively affects environmental quality in the case of Turkey. However, some other studies contradict these findings by arguing that trade is detrimental to the environment, as demonstrated in Aydin and Turan⁶; Tayebi and Younespour⁷; and Zafar et al.,⁸ among others. However, Gulistan et al.⁹ and Le et al.¹⁰ further argue that the trade–environment nexus is conditional on a country's income level where openness contributes to reducing environmental degradation only in high-income countries, and the impact is the opposite in low-income countries. Lastly, Mehrara et al.¹¹ find that in the case of Iran, trade openness is not significant for environmental quality.

In contrast to the mixed results of trade openness’ impact on environmental degradation, studies on the impact of renewable energy on environmental quality mostly demonstrate that renewable energy can reduce environmental degradation, although some mixed result remains, as demonstrated by Beghin et al.¹²; Nathaniel et al.¹³; Pata¹⁴; Rahmane et al.¹⁵; Sharma et al.¹⁶; and Xue et al.,¹⁷ among others. On the contrary, Zafar et al.⁸ demonstrates that renewable energy consumption increases carbon emission. Furthermore, at the regional level, some studies demonstrate no significant link between renewable energy consumption and environmental quality, as demonstrated in Nathaniel et al.¹³ and Nathaniel and Khan.¹⁸

Overall, among the studies elaborating the linkage among trade openness, renewable energy consumption, and environment, we observe gaps in the literature. First, the impacts of trade openness and renewable energy in the existing literature are still mixed. Second, despite the intense progress of research in the area, we find that environmental quality or environmental degradation is commonly proxied by carbon emission or ecological footprint. Fareed et al.¹⁹ argue that carbon emission is a less accurate ecological indicator because it only captures the production of carbon emission without considering the capacity of the environment in perceiving the emission. Likewise, measuring environment quality solely on ecological footprint can be misleading because ecological footprint reflects only about the ratio between human consumption rate over environment absorption rate without considering the ability of the environment to produce resources. Hence, in this study, we aim to utilize load capacity factor (LCF) as the proxy of environmental quality, constructed as the ratio between biocapacity and ecological footprint and considered more comprehensive than carbon emission or ecological footprint alone. To the best of our knowledge, no study has investigated the impact of trade openness and renewable energy consumption on LCF. Nevertheless, some closely related studies put trade openness and renewable energy in a single model (e.g., Refs.^2,4–8), but their proxies of environmental quality remain conventional. Third, another research gap in this area is related to the methodology applied. Although fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are commonly applied in environmental studies, to the best of our knowledge, applying the dual adjustment approach (DAA) before FMOLS and DOLS implementation is rarely implemented in the existing literature.

This study aims to address the research gaps above by examining the impact of trade openness and renewable energy on environmental quality in Mexico. Specifically, the objectives of this study are as follows: (1) investigate whether the increase in the trade openness of a country tends to worsen or improve the environmental quality in Mexico, holding the energy consumption constant; (2) understand whether the increase in renewable energy consumption reduces or improves the environmental quality in Mexico given the extent of trade openness in the country. In this study, the impact of trade openness and renewable energy are estimated amid the presence of nonrenewable energy consumption and economic growth as control variables. Furthermore, in addition to measuring the impact, this study measures whether a causal relationship exists among trade openness, renewable energy, and LCF in the short and long term and whether trade openness and renewable energy own predictive power to estimate the future path of environmental quality in Mexico.

The innovation of this study is found in the use of LCF as the main proxy of environmental quality and the presence of renewable energy and trade openness in a simultaneous single model. To the best of our knowledge, no study has investigated the impact of trade openness and renewable energy consumption on LCF, especially in the case of Mexico. Moreover, no study has examined whether trade openness and renewable energy have predictive power over LCF, either in the short or long run. In addition to incorporating the capacity of environment in providing resources (biocapacity), LCF is believed to perform better than carbon emission or ecological footprint because the ratio (LCF) covers more pollutant substances than only relying on carbon. Another innovation of this study is also seen from its methodology applying the novel DAA from Ismihan,²⁰ which, to the best of our knowledge, has not been adopted in most literature in the same area. A similar study was applied by Kirikkaleli et al.,⁵ but no study has applied the technique in the case of Mexico.

Given the innovations and research gap explained above, this study contributes positively to the existing literature either in terms of the model structure and the methodology applied. In terms of the model structure, the incorporation of trade openness, renewable energy consumption, and LCF under a single model answers a research gap in the literature regarding the inexistence of this model. That is, the model regarding how the two measures (trade openness and renewable energy consumption) affects LCF, not only relying on carbon emission or ecological footprint. Furthermore, this study contributes to future studies interested in understanding the determinants of LCF. In terms of the methodology applied, this study contributes to the existing literature regarding the applicability of the DAA before FMOLS and DOLS in environmental studies, given that not many past literature applies this method. As explained previously, applying the DAA before FMOLS and DOLS implementation is rarely implemented in the existing literature. The closest study applying FMOLS and DOLS in the similar area is the one by Fareed et al.¹⁹ and Pata,²¹ among others, but they applied the method without the DAA, and they used a different model structure.

This paper is presented in the following order: Section “Literature review” presents past related literature and methodological review. Section “Data and methodology” provides the models estimated. Section “Results and discussion of findings” describes regression parameters and empirical findings. Section “Discussion of results” analyzes the result from each model, compares the three models, and presents empirical findings. Section “Conclusion and policy recommendations” provides conclusion and recommendation.

Literature review

LCF is a proxy of environmental quality indicating whether a society's ecological system and current lifestyle are sustainable.¹⁹ This indicator is considered a more comprehensive ecological indicator than carbon dioxide emission in assessing environmental sustainability.¹⁹ LCF is commonly calculated as the ratio between biocapacity and ecological footprint. The environment is considered sustained if the LCF value is greater than 1 and unsustained if it is less than 1. An LCF greater than 1 indicates that the available resources are sufficient to fulfill human needs.

Environmental sustainability in this study is also proxied by LCF, which is calculated as biocapacity divided by ecological footprint. Studies investigating the effect of trade openness (TO), nonrenewable energy and renewable energy (RE) on the LCF are not widespread. This scarcity is due to the less popularity of LCF as a proxy of environmental quality. However, considering that ecological footprint is a component of LCF, this section incorporates the literature utilizing ecological footprint as the proxy of environmental quality.

Studies understanding the role of trade openness and renewable energy on environmental sustainability have been conducted through various samples and methodologies. The current literature offers mixed findings regarding such relationship. Some studies demonstrate a positive association, some demonstrate a negative relationship, and the rest finds insignificant relationship.

Despite the mixed findings, studies regarding the impact of RE on environmental quality mostly demonstrate similar findings in the sense that RE is evident in reducing environmental destruction (e.g., Refs.^22–30), although some mixed results remain. One recent study utilizing LCF as the proxy of environment sustainability is that of Fareed et al.,¹⁹ who studied how export diversification and LCF interact in Indonesia during 1965–2014. The authors applied the Fourier quantile causality test to investigate the nexus and found that renewable energy tends to increase LCF. Specifically, the study found that unidirectional causality runs from renewable energy to LCF. The work of Fareed et al.¹⁹ is the closest to our study. However, the main difference is that Fareed et al.¹⁹ focused on export diversification, whereas our study heavily focused on trade openness. Moreover, instead of implementing Fourier quantile causality test, we applied regression analysis using the DOLS and FMOLS techniques, which should provide more comprehensive results because the impact of trade openness and renewable energy is constrained by the presence of other variables within the same equation.

Zafar et al.⁸ is also close to our study. The presence of renewable energy consumption and trade openness in Zafar et al.⁸ is similar to our study, except that we implemented LCF, which is considered a more reliable environmental quality indicator than carbon emission. Zafar et al.⁸ shared a case of 18 emerging countries over the period of 1990–2015 regarding the impact of renewable energy consumption and trade openness on carbon emission. The study applied vector error correction model, in addition to the Pedroni and Westerlund panel cointegration test and Granger causality test. The impact of RE consumption and trade openness on carbon emission is estimated with the control of economic growth. The study showed that RE consumption reduces carbon emission, whereas nonrenewable energy drives more carbon emission. Furthermore, the study demonstrated that trade openness shows a bidirectional causal relationship with carbon emission in the long term. That is, trade openness significantly causes carbon emission, and vice versa.

The study of Alola et al.² is also close to our study in terms of the presence of trade openness and renewable and nonrenewable energy consumption as the explanatory variables. Alola et al.² investigated the key drivers of sustainable development in reducing environmental pollution in 16 EU member nations. The study applied the panel pool mean group autoregressive to the sample during 1997–2014. The dependent variable was ecological footprint, whereas the independent variables included renewable and nonrenewable energy consumption, TO, fertility rate, and real GDP. The study showed that in the short run, TO and RE consumption have no significant impact on ecological footprint. However, in the long run, TO is statistically significant and negatively correlated with ecological footprint, whereas RE consumption is positive and significant in affecting ecological footprint. Holding the biocapacity constant, this finding suggests that a more open trade policy is associated with greater LCF, whereas more RE consumption is associated with the lower ratio. However, in contrast to our study, Alola et al.² utilized ecological footprint instead of LCF as the dependent variable, whereas we considered LCF, which is expected to deliver more accurate results.

Similar to Alola et al.,² Destek and Sinha⁴ is also close to our study as it investigated the impact of RE, non-RE consumption, economic growth, and trade openness as the independent variables. The study investigated the impact of renewable energy and trade openness on ecological footprint across 24 countries in OECD during 1980–2014. The main gap between Alola et al.^2,27,28 and our study is the dependent variable; the former used ecological footprint instead of LCF. The study applied cross-sectional dependence test and heterogeneity test and FMOLS to estimate the impact of independent variables on ecological footprint. The estimation showed that RE consumption can reduce ecological footprint, hence reducing environmental degradation. In the case of the US during 1980–2016, Pata¹⁴ demonstrated that, in the long run, higher renewable energy consumption significantly improves environment, whereas nonrenewable energy consumption reduces it. The study was estimated using vector error correction model in the log linear form. The dependent variable is ecological footprint, whereas the independent variables include the first- and second-order of economic complexity index, renewable and nonrenewable energy consumption, and globalization index.

Xue et al.¹⁷ also utilized ecological footprint as the dependent variable, instead of LCF, which we are interested to improve. Xue et al.¹⁷ implemented GMM, random effects and fixed effects panel regression analysis to understand how RE consumption influences the environment in the case of South Asian fossil fuel-dependent countries (Bangladesh, India, Pakistan, and Sri Lanka) over the period of 1990–2016. At the individual country level, the study demonstrated that RE consumption per capita negatively affects the four countries, implying that higher consumption on RE improves the environment. Moreover, RE consumption can increase the LCF, holding biocapacity constant. Accordingly, at the panel country level, RE consumption is statistically significant and reduces ecological footprint, suggesting that it reduces environmental degradation.³¹ However, Xue et al.¹⁷ emphasized the role of foreign direct investment instead of trade openness, creating a gap of research interest.

In Algeria, the nexus between RE and ecological footprint during 1990–2017 was demonstrated by Rahmane et al.¹⁵ The study applied DOLS as the primary estimation method and Granger causality test and demonstrated that RE consumption reduces ecological footprint. Thus, holding biocapacity constant, renewable energy can increase the LCF, with everything else equal. Similarly, Sharma et al.¹⁶ investigated how RE imposes ecological footprint in the case of Asian emerging market countries in the short and long run over eight developing countries in South Asia over the period of 1990–2015. The study estimated the linkage using the cross-sectional ARDL and demonstrated that the increasing RE consumption significantly reduces ecological footprints, with everything else equal. This finding implies that RE increases the LCF and thus provides an improved environment. The study of Rahmane et al.¹⁵ is also close to our work in terms of its interest in understanding the impact of renewable energy. The study also utilized a similar estimation technique, that is, DOLS. However, despite the similarities, Rahmane et al.¹⁵ did not incorporate the role of trade openness in the analysis.

Although most studies demonstrate a positive impact between renewable energy, some others report nonpositive impact. For example, Nathaniel and Khan¹⁸ demonstrated that in the case of ASEAN countries over the period of 1960–2016, at the individual country level, trade is only statistically significant in a positive direction in the Philippines, but it is insignificant in other countries. By contrast, renewable energy does not seem to be statistically significant in any ASEAN countries. Similarly, at the panel level, the study found that trade significantly increases ecological footprint, suggesting higher environmental degradation. However, at the panel level, renewable energy consumption seems to have no impact on ecological footprint. The study of Nathaniel and Khan¹⁸ is similar to our study in terms of the presence of renewable and nonrenewable energy consumption in the model. However, the gap between this study and ours is that the former did not use LCF as the dependent variable as we did. In addition, we incorporated trade openness in the equation, which does not exist in Nathanel and Khan.¹⁸

Another example is the study of Nathaniel et al.,¹³ which focused on Middle Eastern and North African countries over the period of 1990–2016. Their results demonstrated that at the full sample level, RE consumption has no significant impact on ecological footprint. On the contrary, at the individual country level, RE consumption is statistically significant only in Egypt, Israel, and Jordan, where the correlation is negative. In contrast to our method, the study applied Pedroni panel cointegration test and augmented group panel regression method. Nathaniel et al.¹³ also emphasized more on financial development than trade openness by controlling urbanization level and GDP, which is different from our study incorporating trade openness without the presence of urbanization level and GDP.

Pata²¹ is one of a few studies utilizing LCF as the dependent variable and incorporating GDP as a control variable. This study also focused on the impact of renewable energy on the LCF but did not incorporate trade openness in the equation. The study examined whether RE and health expenditures improved LCF in the US and Japan during 1982–2016. The author approximated LCF as biocapacity over ecological footprint. The dependent variable was LCF, whereas the independent variables included renewable energy consumption, controlled by health expenditure and economic growth. Using the augmented autoregressive distributed lag method, the study demonstrated that renewable energy consumption positively and significantly affects LCF in the US and Japan, holding other variables constant. However, the finding is not robust across different estimation methods. Based on the FMOLS, DOLS, and CCR, the study showed that the positive impact of RE consumption on ecological footprint is only valid in the US but not in Japan. Nevertheless, the study found that RE consumption, health expenditure, LCF, and GDP are cointegrated.

Having reviewed the above literature, we observed the following: (i) the impact of trade openness and renewable energy in the existing literature remains mixed; (ii) despite the intense progress of research in the area, we found that environmental quality or environmental degradation are commonly proxied by carbon emission or ecological footprint. Moreover, measuring the environment quality solely on ecological footprint is misleading because ecological footprint reflects only the ratio between human consumption rate and environment absorption rate without considering the ability of the environment to produce resources. The present study aims to fill this gap by using LCF as the proxy of environmental quality, constructed as the ratio between biocapacity and ecological footprint and considered more comprehensive than carbon emission or ecological footprint alone, via the DAA.

Data and methodology

Data

The impact of trade openness and renewable energy on environmental quality in Mexico over the period of 1970–2017 is assessed in this study by using several novel and advanced techniques. Control variables are the GDP and nonrenewable energy consumption. By adopting the study of Kirikkaleli et al.,³² this study's model specification is presented as

L F_{t} = α_{0} + α_{1} T O_{t} + α_{2} GD P_{t} + α_{3} RE C_{t} + α_{4} NRE C_{t} + ε_{t},

(1)

where

L F_{i, t}

is the measure of environmental quality proxied by the LCF,

T O_{t}

refers to trade openness (computed as % of GDP),

GD P_{t}

is the gross domestic product,

RE C_{t}

is renewable energy consumption, and

NRE C_{i, t}

is nonrenewable energy consumption. Furthermore,

α_{1} \dots α_{4}

depicts the coefficients of the independent variables.

Following Li et al.,^22–33 Wang et al.,³⁴ Wang et al.,³⁵ Wang and Zhang,³⁶ Khan et al.,³⁷ and Kirikkaleli et al.³², we expect $α_{1} > 0, α_{2} < 0, α_{3} > 0, and α_{4} < 0$ . The description of the variables is presented in Table 1. Figure 1 presents the flow of analysis.

Table 1.

Data description.

Variable	Description	Measurement	Sources
$L F_{i, t}$	Load capacity factor	Biocapacity divided by ecological footprint	Global Carbon Atlas³⁸
$T O_{i, t}$	Trade openness	Trade % of GDP	Word Development Indicators³⁹
$GD P_{i, t}$	Gross domestic product	Constant US Dollars, 2010	Word Development Indicators³⁹
$RE C_{i, t}$	Renewable energy consumption	Addition of hydro, nuclear, wind, biomass, and solar	British Petroleum⁴⁰
$NRE C_{i, t}$	Nonrenewable energy consumption	Addition of oil, gas, and coal	British Petroleum⁴⁰

Methodology

Stationarity test

The DF-GLS unit root test was used to conduct stationarity tests on the variables. This test is imperative due to the unit root-sensitive nature of some of the tests conducted. The presence of structural breaks, however, impairs the reliability of traditional tests, such as the DF-GLS; thus, the Zivot and Andrews⁴¹ (ZA) structural stationarity test was also used. The ZA technique accommodates one structural break per variable. The null hypothesis is that a unit root is present, and the alternative hypothesis is the series stationary.

Dual adjustment approach

The DAA of Ismihan²⁰ was adopted alongside several innovative techniques to evaluate the impact of trade openness and renewable energy on environmental quality in Mexico. This approach is based on the Engle–Granger technique; however, it improves on other cointegration methods by relaxing the implied singular adjustment assumption in the cointegration analysis. The Engle–Granger technique and DAA were conducted. Similar to Ismihan,²⁰ the present study used the indirect stepwise approach and utilized the Hodrick–Prescott (HP) filter to measure the permanent and trend components of the variables. Although the DAA allows for the use of other filters for decomposition, this study used the HP filter.

The first stage of the DAA involves disintegrating the series into two separate parts using the HP filter. Consequently, the choice of smoothing parameter becomes important because the HP approach is used. While a smoothing parameter of 100 is specified for yearly data in the HP approach, Ravn and Uhlig⁴² proposed 6.25 for annual data; thus, this study utilized both values. In relation to the choice of critical values, the values from the Engle–Granger approach were used in the Co–Hodrick–Prescott trend analysis. The Co–Hodrick–Prescott trend analysis is divided into two phases, and Equation (2) was estimated using ordinary least squares (OLS) in the first step.

L F_{t} = α_{0} + α_{1} {LTO}_{t}^{l} + α_{2} {LGDP}_{t}^{l} + α_{1} {LREC}_{t}^{l} + α_{1} {LNREC}_{t}^{l} + ε_{t}

(2)

Given that

{LTO}_{t}^{l}

{LREC}_{t}^{l}

{LGDP}_{t}^{l}

, and

{LNREC}_{t}^{l}

represent the HP trends of trade openness, renewable energy consumption, GDP, and nonrenewable energy consumption, respectively, and u represents the error term, Equation (3) is estimated in the next phase.

Δ μ = τ μ_{t - 1} + e_{t}

(3)

H_{0}

is that no common trend exists, and it is rejected when

τ

is equal to zero, suggesting that the residuals have unit roots. The short-run relationship is evaluated using the OLS technique as follows:

{LF}_{t}^{s} = α_{0} + α_{1} {LTO}_{t}^{s} + α_{2} {LGDP}_{t}^{s} + α_{1} {LREC}_{t}^{s} + α_{1} {LNREC}_{t}^{s} + ε_{t},

(4)

where

{LTO}_{t}^{s}

{LGDP}_{t}^{s}

{LREC}_{t}^{s}

, and

{LNREC}_{t}^{s}

represent the filtered gap components. When the existence of common Hodrick–Prescott trends and cointegration are established, the DOLS and FMOLS techniques are used to estimate = the long-run coefficients of the variables.

Bayer and Hanck combined cointegration

This study also uses the advanced Bayer and Hanck⁴³ technique to ascertain the existence of long-run relationship among the variables. The novelty of this technique stems from its ability to evade the limitations of most single and multiple cointegration tests by combining the individual statistics of the Johansen, Engle, and Granger tests and the Boswijk and Banerjee tests to create the Bayer and Hanck joint test statistic.⁴⁴ Thus, the test is more robust and powerful and provides more reliable estimates. The test is specified as follows:

EG - JOH = - 2 [\log (P_{roEG}) + (P_{roJOH})],

(5)

EG - JOH - BO - BDM = - 2 [\log (P_{roEG}) + (P_{roJOH}) + (P_{roBO}) (P_{roBDM})],

(6)

where

P_{roEG}

P_{roJOH}

P_{roBO}

, and

P_{roBDM}

represent the distinct test statistics as stated above.

H_{0}

is that no cointegration exists; therefore, the rejection of

H_{0}

implies the existence of equilibrium relationship between the variables of interest.

Frequency domain causality

Following Gokmenoglu et al.,⁴⁵ this study conducts the novel SCT, which is based on Geweke⁴⁶ and Hosoya⁴⁷ and developed by Breitung and Candelon,⁴⁸ as a frequency domain causality test Granger, Toda-Yamamoto, and Fourier Toda-Yamamoto are classified as time-domain causality tests, whereas the spectral causality test is a frequency domain test. This test differs from the former as it measures the extent of a specific change in a variable, whereas the time domain tests illustrate when a particular change occurs in a variable. An added advantage of frequency domain is observed in short series when seasonal patterns may be pertinent as it allows for the elimination of such variations. This advantage is in addition to its ability to capture nonlinearities and causality in low and high frequencies. To determine frequency domain causality between environmental quality and trade openness and renewable energy in Mexico, the Breitung and Candelon⁴⁸ spectral causality test is applied.

Results and discussion of findings

Preliminary results

Descriptive statistics, which is the pre-estimation analysis of the study, was undertaken to understand the univariate nature of the considered variables. The results of the descriptive statistics are unveiled in Table 2, and we detect that the highest value of mean is economic growth, followed by nonrenewable energy usage, renewable energy usage, trade openness, and LCF. The range of economic growth, LCF, trade openness, and nonrenewable and renewable energy usage are 8.574–9.194, −0.977 to 0.305, 2.795–4.345, 5.885–7.618, and 3.701–5.079, respectively. Given that the standard deviation shows whether the predominant series is around its mean, the series is more predominant when the standard deviation is small. In this context, economic growth is highly predominant around its means, following LCF, renewable energy usage, trade openness, and nonrenewable energy. All series understudy is normality distributed, as indicated by the Jarque–Bera test and its p-value.

Table 2.

Descriptive statistics.

	GDP	LF	TO	NREC	REC
Mean	8.960	−0.449	3.604	7.049	4.488
Median	8.967	−0.451	3.655	7.141	4.633
Maximum	9.194	0.305	4.345	7.618	5.079
Minimum	8.574	−0.977	2.795	5.885	3.701
Std. Dev.	0.162	0.354	0.480	0.489	0.435
Skewness	−0.717	0.420	−0.247	−0.834	−0.424
Kurtosis	2.774	2.087	1.699	2.766	1.790
Jarque–Bera	4.211	3.079	3.871	5.6736	4.363
Probability	0.122	0.214	0.144	0.058	0.112

Stationarity results

We begin this study estimation analysis by undertaking the stationary nature of the understudy series. We summarize the result of the DF-GLS and ZA unit root test at intercept and trend in Table 3. The null hypothesis for these tests is the existence of a unit root, whereas their alternative hypothesis is no unit root issue. From Table 3, the DF-GLS unit roots showed that three out of the five understudied series have a unit root at the level; however, at first difference, all the five series have no unit root. We find that trade openness and renewable energy usage have no unit root at level and first difference. However, this test lacks the capacity to account for a break during regression; therefore, for this purpose, we deploy the ZA unit root, which can detect a break during the regression process. Similar to the outcome of the DF-GLS unit root test, the outcome of the ZA unit root concludes that only trade openness and renewable energy usage have no unit root at level, with the break of 1986 and 2002. However, at first difference, we detect that all series have no unit root. With the break of economic growth, LCF, trade openness, nonrenewable and renewable energy usage are 1982, 2000, 1989, 2001, and 1982. Having established that no series is integrated at more than first difference, the FMOLS and DOLS approach can be utilized. However, whether a cointegrating association exists must be investigated.

Table 3.

Unit root tests.

	DF-GLS unit root test
	LF	GDP	TO	NR	REC
t-Statistic	−2.778	−1.858	−4.049*	−1.147	−3.766**
	ΔLF	ΔGDP	ΔTO	ΔNR	ΔREC
t-Statistic	−10.344*	−5.601*	−6.125*	−5.994*	−8.022*
	ZA unit root test
	LF	GDP	TO	NR	REC
t-Statistic	−3.816	−4.154	−5.261***	−4.368	−5.157***
SB	1996	1983	1986	1999	2002
	ΔLF	ΔGDP	ΔTO	ΔNR	ΔREC
t-Statistic	−10.624*	−7.050*	−6.278*	−8.386*	−7.223*
SB	2000	1982	1989	1982	2001

Note: Significance level of 1%, 5%, and 10% are depicted by ***, **, and *, respectively. First difference denote Δ. SB signifies structural break date. The trend and intercept are used.

DAA results

Table 4 provides the summary of the outcome of the DAA (also see Figure 2). Given that the null hypothesis of the dual approach is no common HP, the tests of Co-HP trend and Engle–Granger CI in all scenarios reveal the cointegration interaction among the series used, suggesting the presence of Co-HP trend. In addition, for the joint outcome of Co-HP trend, as well as the Engle–Granger CI, that is, (EG/Co-HP^w), at different scenarios (EG, CF, RU, HP, and BK) along with the different levels of significance, the null hypothesis is rejected. Hence, we can confirm the presence of Co-HP trend, indicating that the LCF is stationary around the component of renewable energy, trade openness, economic growth, and nonrenewable energy (Co-HP trend). Furthermore, this research tested the soundness of the dual approach's outcome using the Bayern and Hanck cointegration technique. Table 5 shows where the outcome of the Bayern and Hanck cointegration test is disclosed, revealing that we reject the null hypothesis of no cointegration at 5% significance level because the Fisher statistics (33.943 and 51.835) is greater than 5% critical value (10.576 and 20.143). Hence, cointegrating interaction is present among the variables under study. Therefore, the finding of the Bayern and Hanck cointegration test corroborates the result of the DAA.

Table 4.

EG and Co-HP trend tests (dual adjustment approach).

	Engle–Granger CI analysis	Co-HP Trend Analysis
		λ _HP	λ _RU	λ _BK	λ _CF
C	2.754 (5.6419)*	19.445 (4.972)*	10.185 (4.435)*	13.127 (6.211)*	2.363 (2.254)**
GDP	−1.737 (−6.669)*	−2.499 (−5.684)*	−1.341 (−3.505)*	−1.525 9 (−4.734)*	0.309 (2.017)**
TO	0.1733 (−1.973)***	0.734 (3.687)*	0.043 (−0.239)	0.173 (−1.197)	0.171 (6.432)**
REC	−0.665 (−5.059)*	−1.431 (−6.065)*	−0.498 (−2.333)**	−0.615 (−3.955)*	0.358 (4.250)**
NREC	−0.599 (−4.062)*	−0.627 (−3.615)*	−0.204 (−1.138)	−0.496 (−2.681)**	−0.001 (−1.738)*
EG/Co-HP^w	−4.755 [0.046]**	−5.899 [0.004]*	−5.114 [0.021]**	−6.096 [0.002]*	8.353 [0.000]*
Lambda	—	100	6.25	—	—

CF and BK signify Christiano–Fitzgerald and Baxter–King; RU = Ravn–Uhlig (Lambda = 6.25); and HP = Hodrick–Prescott (Lambda = 100). Significance level of 1%, 5%, and 10% are depicted by ***, **, and *, respectively. p-Value signifies (), while the t-statistics is signifies by [].

Table 5.

Bayer and Hanck test.

Model	Fisher statistics	Fisher statistics	Cointegration decision
	EG-JOH	EG-JOH-BAN-BOS
LF = f (GDP, TO, NREN, REC, NR)	33.943**	51.835**	Yes
	CV	CV
Significance level at 5%	10.576	20.143

Note: CV and ** represents critical value and 5% significance level, respectively.

Bayer and Hanck cointegration results

Long-run estimator outcomes

Given that we have established a cointegrating interaction among LCF, renewable energy, trade openness, economic growth, and nonrenewable energy in Mexico, this research uses the FMOLS and DOLS estimators. The outcomes of the estimators establish that all series (renewable energy, trade openness, economic growth, and nonrenewable energy) are significant determinants of LCF in Mexico over the given period. To elaborate further, the findings of the estimators indicate that a 1% surge in the economic growth of Mexico causes the load capacity to decrease by 0.926% (FMOLS) and 1.092% (DOLS). In addition, the impact of nonrenewable energy use on LCF is negative; hence, according to the outcomes of the FMOLS and DOLS estimators, the surge of 1% in nonrenewable energy usage decreases the LCF by 1.710% and 1.673%, respectively. Similarly, the effect of renewable energy usage on LCF is negative. As shown in Table 6, the rise in renewable energy usage by 1% results to a decrease in load capacity by 0.261% (FMOLS) and 0.215 (DOLS).

Table 6.

FMOLS and DOLS outcomes.

Variable	FMOLS			DOLS
	Coefficient	t-Statistic	Prob.	Coefficient	t-Statistic	Prob.
GDP	−0.9259**	−2.1202	0.0467	−1.0922***	−1.9857	0.0603
TO	0.3142*	3.3371	0.0033	0.3188**	2.6834	0.0139
REC	−0.2614*	−3.0800	0.0059	−0.2153***	−1.9469	0.0650
NREC	−1.7101*	−3.3176	0.0034	−1.6734**	−2.5636	0.0181
R ²	0.95			0.96
Adjusted R²	0.94			0.95
S.E. of reg	0.0781			0.0563

Note: 1%, 5%, and 10% significance level are illustrated by *, **, and ***, respectively.

In contrast to the aforementioned determinants, the coefficient of trade openness on LCF is positive. As reported by the FMOLS and DOLS estimators, if the level of trade openness increases by 1%, LCF increases by 0.314% and 0.319%, respectively. Finally, these regressors can explain the variation in LCF by 95%, as explained by the R² of 0.95, whereas the remaining 5% can be accounted for by the error term.

Frequency domain causality results

The study continues by investigating the causal interconnectedness of the determinant on LCF. This current research utilized the frequency domain causality approach, which is displayed in Figure 3(a)–(d). The purple line indicates the 5% significance level, and the blue line is the 10% significant level. The t-statistics of the approach is represented by the green dotted line. Figure 3(a) presents the causal interaction from economic growth to LCF, which identified only the causal interaction in the long run. Thus, economic growth is a predictive determinant of load capacity in the long term. Figure 3(b) indicates that only in the medium term that trade openness Granger causes LCF. Hence, LCF can be predicted by trade openness in the medium term. Furthermore, Figure 3(c) depicts that the causal association from renewable energy use to LCF is only in the short term; therefore, the renewable energy use can envisage the level of LCF in the long term. Finally, the causality effect of nonrenewable energy on LCF is seen in the long and medium term, as reported in Figure 3(d). This outcome could be summarized that in the long term, nonrenewable energy, economic growth, and renewable energy can predict LCF. Moreover, trade openness and nonrenewable energy use can predict LCF in the medium term. Figure 4 shows the summary of the findings.

Discussion of results

The detailed and extensive discussion on the aforementioned results and the adverse association between economic growth and LCF uncovered in this current research tend to conform with the research of Pata and Isik⁴⁹ in China, Xu et al.⁵⁰ in Brazil, and Awosusi et al.⁵¹ in South Africa, which used load capacity as a proxy for measuring the quality of the environment. However, the study of He et al.^52,53 also established that economic expansion increases environmental degradation in Mexico. Studies by Miao et al.⁵⁴ in NIC nations, Beton et al.⁵⁵ in Malaysia, Akadiri et al.⁵⁶ in China, Awosusi et al.^51–57 in BRICS nations, Akinsola et al.⁵⁸ in Brazil, which used ecological footprint, also corroborate our findings, indicating that the economic expansion experienced by the Mexicans spurs environmental degradation. Thus, the continuous expansion in growth over the years, most especially in 1975, has resulted in ecological deficit and environmental deterioration in Mexico. The current environmental issues or imbalances, such as mudslides, frequent floods, more intense rains, more drought, and longer hot periods, are the possible side effects of economic expansion. As a developing economy, Mexico invests majorly in the exploitation of its major natural resources most especially, oil, copper, silver, lead, and many more, leading to the increase in Mexico's ecological footprint over time. Mexico has evolved into a manufacturing powerhouse, providing a highly industrial layout and contemporary infrastructure throughout the nation due to significant infrastructural advancements over the previous year, thereby making the economy a carbon-intensive economy. The exploitation and consumption of natural resources aid the expansion of the economy. For instance, energy production is among Mexico's major prominent economic activity sectors, accounting for 3% of GDP. Oil commercialization accounts for approximately 8% of overall exports, whereas oil-related taxes contribute to 37% of the government budget, with energy projects accounting for 56.5% of the overall public investment. Around 250,000 people are employed by public firms in the oil and energy industries.⁵⁹ However, this consumption and exploitation pattern deteriorates the environmental quality of a nation.

In addition, the impact of nonrenewable energy use on LCF is negative. This outcome justifies the studies of Pata and Isik⁴⁹ in China, Oladipupo et al.⁶⁰ in Portugal, Yuping et al.⁶¹ in Argentina, Adekoya et al.⁶² in exporting and importing nations, and He et al.^52,53 in 10 selected nations. Similarly, the effect of renewable energy usage on LCF is negative. The study of Awosusi et al.⁵⁷ in Brazil reported similar results by disclosing an adverse interconnection between renewable energy use and LCF. However, this outcome contradicts the findings of Pata^14–21 and Akadiri et al.⁶³ in India. Fareed et al.¹⁹ confirmed an adverse interconnection between renewable energy use and LCF. Using ecological footprint, Kirikkaleli et al.⁶⁴ in BRICS and Fareed et al.⁶⁵ in Eurozone also oppose our findings. Thus, the utilization of fossil fuel and renewable energy increases Mexico's ecological deficit. Given that energy production places a crucial role in the economy, renewable energy is not enough to meet the demand of the nation. However, fossil fuel is one of the major drivers of environmental deterioration. Mexico has experienced tremendous economic expansion, which necessitates an increase in energy utilization. Currently, to meet the increased energy demand, Mexico depends mostly on fossil fuel-based energy options (Mexico's energy basket consists of 92.80% of fossil fuel in 2017) because present alternative energy sources are still mature enough to meet the current amount of energy demand, resulting in the negative effect of renewable energy on load capacity. Nevertheless, Mexico has a significant capacity for renewable energy growth, and only a small portion of this energy has been utilized (i.e., 7.20%). As a result, several investment possibilities can boost the utilization of these alternative options. Although efforts have been made to reduce the usage of fossil fuel in Mexico, such efforts have not been productive due to barriers, such as energy strategy, incentives, and technology; therefore, policymakers make measures to remove these barriers.

In contrast to the aforementioned determinants, the coefficient of trade openness on LCF is positive. Thus, trade openness contributes to the quality of the environment, thereby increasing the LCF in Mexico. Given that Mexico is an export-oriented economy, international trade opens an avenue for the transfer of technology across the border, in which the untapped potential of the country's renewable energy potential is accessed through imported technologies, thereby reducing the ecological deficit of the nation and promoting the quality of the environment. In addition, the trade policy of Mexico can help achieve sustainable growth; hence, the trade policy is green. The study of Liu et al.⁶⁶ in Pakistan, Gulistan et al.⁹ in 112 countries, and Le et al.¹⁰ in 98 nations on the nexus between trade and environment reported similar findings. However, the research of He et al.^52,53 confirmed an insignificant interaction between trade openness and environmental deterioration.

Conclusion and policy recommendations

Conclusion

The present research assesses the effect of energy (nonrenewable and renewable), trade openness, and economic growth on LCF in Mexico by utilizing a dataset stretching between 1970 and 2017. The ecological deterioration (such as EF, GHG emissions, and CO₂) of different nations (developed and developing ones) has been used by several researchers. The need arises where the goals of addressing the issue of environment degradation and achieving sustainable development require a comprehensive ecological metric. This concern was addressed by the present study by utilizing another distinct proxy for ecological deterioration, that is, LCF. This proxy considers biocapacity and EF simultaneously. Additionally, the LCF provides quality of the environment on the supply and demand sides.

The present research assesses several econometric approaches, such as the novel dual adjustment testing procedure, ARDL, and spectral causality. The outcomes of the DAA unveiled evidence of long-run association between LCF and its drivers (trade openness, nonrenewable energy, renewable energy, and economic growth). As a robustness check, we utilized the Bayer and Hank⁴³ cointegration test and the result affirmed the results of the DAA. Furthermore, we utilized the FMOLS and DOLS approaches to identify the effect of trade openness, nonrenewable energy, renewable energy, and economic growth on LCF. The outcomes from the FMOLS and DOLS long-run estimators unveiled that trade openness enhance quality of the environment, whereas renewable energy, nonrenewable energy, and economic growth deteriorate the quality of the environment in Mexico. Moreover, the spectral causality test disclosed that in the long-term, trade openness, economic growth, and renewable energy can predict LCF, whereas nonrenewable energy use can predict LCF in the short term.

Policy suggestions

Authorities in Mexico should focus on promoting public understanding of green energy and environmental preservation on the basis of the evidence above. Furthermore, the Mexican government should make an effort to participate in the production of nonenergy-consuming and ecologically friendly products, compelling polluting enterprises to migrate to countries with less stringent environmental restrictions. Mexico will profit greatly from greater trade with other nations in this direction as a result of this endeavor. Moreover, given that trade openness impacts LCF positively, trade openness must be used to enhance nonpolluting industries by imposing taxes on industries that are polluting and incentivizing industries that are nonpolluting to enable producers to shift to healthier and more ecologically friendly sustainable industries. To boost the trade balance and prioritize production and exports, the government must pursue a sustainable method of production by using energy-efficient technology in the export industry and transitioning from fossil fuels toward energy-efficient and less polluting practices. Policymakers should emphasize the technique effect of trade rather than the scale effect because it is advantageous for the nation's economy and ecosystem as it increases productivity and energy efficiency.

According to BP (2021), Mexico's energy mix comprises 92% fossil fuels. Given that energy usage degrades the quality of the environment, the Mexican government should promote energy efficiency and diminish fossil fuel usage in the country's energy mix. To achieve this goal, the levies by government on carbon taxes must be placed on businesses utilizing dirty energies, and tax breaks and incentives must be offered to businesses that use solar and wind energy. Furthermore, and perhaps more crucially, the Mexican government can lessen the country's reliance on dirty energy sources by promoting the usage of energy-saving technologies. Mexican policymakers can assist in reducing emissions from coal-fired power plants by boosting the combined-cycle technology and use of integrated gasification.

Caveat of the study

This research is restricted because it only focuses on Mexico. Future studies could utilize the panel dataset by using set of nations, such as MINT, BRICS, and ASEAN. Furthermore, the research does not consider all the factors that influence LCF. These research restrictions can be solved by future studies by assessing the influence of R&D expenditure, technological innovation, economic complexity, income inequality, human capital, financial development, and many more on LCF for dissimilar nations and groups of nations. Furthermore, this research has limitations due to the unavailability of data beyond 2017. Future studies could extend the timeframe of the dataset beyond 2017.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Seyi Saint Akadiri

Abraham Ayobamiji Awosusi

Wu Huilan Finished Bachelor degree from Shandong University of Finance and Economics and Master's degree from Shandong University. Wu Huilan currently work at School of Economics, Shandong Women's University Wu Huilan interests include Environmental Economics, International Economics, Macroeconomics, and Energy Economics.

Seyi Saint Akadiri is a Research Assistant at the Research Department, Central Bank of Nigeria. He holds BSc from the prestigious University of Jos, Plateau State, Nigeria, and had MA and PhD Economics at the Department of Economics, Eastern Mediterranean University, Turkey. His enlisted in the updated 2022 edition of the for global scientists top 2% for scientists list by Elsevier BV Sandford University. His research interests include economic growth, energy economics, environmental economics, tourism economics, food policy and forecasting. He has passion for research and has published extensively in reputable international journals.

Ilham Haouas is an Associate Professor of Economics and Finance, Abu Dhabi University. She has published in renowned journals such as Emerging Markets Review, Resources Policy, Energy & Environment, Economic Research-Ekonomska Istraživanja, and Journal of Sustainable Finance & Investment. Her research interests include Labor Market International, Economics Development, Economics, Energy Economics and Natural Resources

Abraham Ayobamiji Awosusi is a PhD student at Near East University. He has published in renowned journals such as Renewable Energy, Energy and Environment, Gondwana Journal, International Journal of Renewable Energy Development, Energy Reports, and International Journal of Hydrogen Energy. His research interests include Environmental Economics, International Economics, Macroeconomics, and Energy Economics.

Ada Tony Odu is a Senior Economist at the Central Bank of Nigeria. His areas of interest include, Applied Econometrics, Energy Economics, Financial and Monetary and Economics.

References

World Bank. Trade (% of GDP) Data. https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS. (2021, accessed 12th April 2022).

Alola

Bekun

Sarkodie

. Dynamic impact of trade policy, economic growth, fertility rate, renewable and nonrenewable energy consumption on ecological footprint in Europe. Sci Total Environ 2019; 685: 702–709.

Antweiler

Copeland

Taylor

. Is free trade good for the environment? Am Econ Rev 2001; 91: 877–908.

Destek

Sinha

. Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: evidence from organisation for economic co-operation and development countries. J Clean Prod 2020; 242: 1.

Kirikkaleli

Adebayo

Khan

, et al. Does globalization matter for ecological footprint in Turkey? Evidence from dual adjustment approach. Environ Sci Pollut Res Int 2021; 28: 14009–14017.

Aydin

Turan

. The influence of financial openness, trade openness, and energy intensity on ecological footprint: revisiting the environmental Kuznets curve hypothesis for BRICS countries. Environ Sci Pollut Res Int 2020; 27: 43233–43245.

Tayebi

Younespour

. The effect of trade openness on environmental quality: evidence from Iran’s trade relations with the selected countries of the different blocks. Iran Econ Rev 2012; 16: 19–40.

Zafar

Mirza

Zaidi

SAH

, et al. The nexus of renewable and nonrenewable energy consumption, trade openness, and CO2 emissions in the framework of EKC: evidence from emerging economies. Environ Sci Pollut Res Int 2019; 26: 15162–15173.

Gulistan

Tariq

Bashir

. Dynamic relationship among economic growth, energy, trade openness, tourism, and environmental degradation: fresh global evidence. Environ Sci Pollut Res Int 2020; 27: 13477–13487.

10.

T-H

Chang

Park

. Trade openness and environmental quality: international evidence. Energy Policy 2016; 92: 45–55.

11.

Mehrara

Karsalari

Musai

. The relationship among income, openness and environmental quality: an ARDL approach for the case of Iran. Int J Acad Res Bus Soc Sci 2014; 4: 32–40.

12.

Beghin

Roland-Holst

Mensbrugghe

. A survey of the trade and environment nexus: global dimensions. OECD J Econ Stud 1994; 23: 167–192.

13.

Nathaniel

Anyanwu

Shah

. Renewable energy, urbanization, and ecological footprint in the Middle East and North Africa region. Environ Sci Pollut Res Int 2020; 27: 1–13.

14.

Pata

. Renewable and nonrenewable energy consumption, economic complexity, CO₂ emissions, and ecological footprint in the USA: testing the EKC hypothesis with a structural break. Environ Sci Pollut Res Int 2021; 28: 846–861.

15.

Rahmane

Benelbar

Traich

. The Nexus between sustainable energy and ecological footprint: evidence from Algeria. Sustain Sci Pract Policy 2021; 17: 323–333.

16.

Sharma

Sinha

Kautish

. Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia. J Clean Prod 2020; 285: 124867.

17.

Xue

Haseeb

Mahmood

, et al. Renewable energy use and ecological footprints mitigation: evidence from selected South Asian economies. Sustainability 2021; 13: 1613.

18.

Nathaniel

Khan

SAR

. The nexus between urbanization, renewable energy, trade, and ecological footprint in ASEAN countries. J Clean Prod 2020; 272: 122709.

19.

Fareed

Salem

Adebayo

, et al. Role of export diversification and renewable energy on the load capacity factor in Indonesia: a Fourier quantile causality approach. Front Environ Sci 2021; 9: 770152.

20.

Ismihan

. The dual adjustment approach with an application to the consumption function. Central Bank Rev 2019; 19: 1–8.

21.

Pata

. Do renewable energy and health expenditures improve load capacity factor in the USA and Japan? A new approach to environmental issues. Eur J Health Econ 2021; 22: 1427–1439.

22.

Wang

. Does renewable energy reduce ecological footprint at the expense of economic growth? An empirical analysis of 120 countries. J Clean Prod 2022; 346: 131207.

23.

Usman

Alola

Saint Akadiri

. Effects of domestic material consumption, renewable energy, and financial development on environmental sustainability in the EU-28: evidence from a GMM panel-VAR. Renew Energy 2022; 184: 239–251.

24.

Usman

Akadiri

Adeshola

. Role of renewable energy and globalization on ecological footprint in the USA: implications for environmental sustainability. Environ Sci Pollut Res Int 2020; 27: 30681–30693.

25.

Alola

Adebayo

Onifade

Examining the dynamics of ecological footprint in China with spectral Granger causality and quantile-on-quantile approaches. Int J Sustain Dev World Ecol 2022; 29: 263–276.

26.

Akadiri

Adebayo

. Asymmetric nexus among financial globalization, non-renewable energy, renewable energy use, economic growth, and carbon emissions: impact on environmental sustainability targets in India. Environ Sci Pollut Res Int 2022; 29: 16311–16323.

27.

Alola

Yalçiner

Alola

, et al. The role of renewable energy, immigration and real income in environmental sustainability target. Evidence from Europe largest states. Sci Total Environ 2019; 674: 307–315.

28.

Alola

Akadiri

. Renewable energy consumption in coastline Mediterranean countries: impact of environmental degradation and housing policy. Environ Sci Pollut Res Int 2019; 26: 25789–25801.

29.

Saint Akadiri

Alola

Akadiri

, et al. Renewable energy consumption in EU-28 countries: policy toward pollution mitigation and economic sustainability. Energy Policy 2019; 132: 803–810.

30.

Saint Akadiri

Alkawfi

Uğural

, et al. Towards achieving environmental sustainability target in Italy. The role of energy, real income and globalization. Sci Total Environ 2019; 671: 1293–1301.

31.

Amal

Ullah

Luo

, et al. Dynamics between environmental taxes and ecological sustainability: Evidence from top-seven green economies by novel quantile approaches. Sustainable Development 2021; 4: 59–72.

32.

Kirikkaleli

Shan

Ahmad

, et al. The role of energy prices and non-linear fiscal decentralization in limiting carbon emissions: Tracking environmental sustainability. Energy 2020; 234: 121243.

33.

Wang

, et al. Germany’s contribution to global carbon reduction might be underestimated—a new assessment based on scenario analysis with and without trade. Technol Forecast Soc Change 2022; 176: 121465.

34.

Wang

. Does urbanization redefine the environmental Kuznets curve? An empirical analysis of 134 countries. Sustain Cities Soc 2022; 76: 103382.

35.

Wang

, et al. The effects of energy prices, urbanization and economic growth on energy consumption per capita in 186 countries. J Clean Prod 2019; 225: 1017–1032.

36.

Wang

Zhang

. The effects of trade openness on decoupling carbon emissions from economic growth–evidence from 182 countries. J Clean Prod 2021; 279: 123838.

37.

Khan

Ali

Umar

, et al. Consumption-based carbon emissions and international trade in G7 countries: the role of environmental innovation and renewable energy. Sci Total Environ 2020; 730: 138945.

38.

GSA. Fossil fuel emission [Data file]. http://www.globalcarbonatlas.org/en/CO2-emissions. (2019, accessed 12 June 2022).

39.

WDI. World Bank Indicators. Mexico | Data. https://data.worldbank.org/country/mexico. (2021, accessed 4th June 2022).

40.

BP. Statistical Review of World Energy | Energy economics | Home. https://www.bp.com/en/global/corporate/energyeconomics/ statistical-review-of-world-energy.html. (2021, accessed 2 April 2022).

41.

Zivot

Andrews

DWK

. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. J Bus Econ Stat 2002; 20: 25–44.

42.

Ravn

Uhlig

. On adjusting the Hodrick-Prescott filter for the frequency of observations. Review of Economics and Statistics 2002; 84: 371–376.

43.

Bayer

Hanck

. Combining non-cointegration tests. J Time Ser Anal 2013; 34: 83–95.

44.

Udi

Bekun

Adedoyin

. Modeling the nexus between coal consumption, FDI inflow and economic expansion: does industrialization matter in South Africa? Environ Sci Pollut Res Int 2020; 27: 1–12.

45.

Gokmenoglu

Kirikkaleli

Eren

. Time and frequency domain causality testing: the causal linkage between FDI and economic risk for the case of Turkey. J Int Trade Econ Dev 2019; 28: 649–667.

46.

Geweke

. Measurement of linear dependence and feedback between multiple time series. J Am Stat Assoc 1982; 77: 304–313.

47.

Hosoya

. The decomposition and measurement of the interdependency between second-order stationary processes. Probab Theory Relat Fields 1991; 88: 429–444.

48.

Breitung

Candelon

. Testing for short-and long-run causality: a frequency-domain approach. J Econom 2006; 132: 363–378.

49.

Pata

Isik

. Determinants of the load capacity factor in China: a novel dynamic ARDL approach for ecological footprint accounting. Resour Policy 2021; 74: 102313.

50.

Salem

Awosusi

, et al. Load capacity factor and financial globalization in Brazil: the role of renewable energy and urbanization. Front Environ Sci 2022; 9: 689.

51.

Awosusi

Adebayo

Altuntaş

, et al. The dynamic impact of biomass and natural resources on ecological footprint in BRICS economies: a quantile regression evidence. Energy Rep 2022; 8: 1979–1994.

52.

Adebayo

Kirikkaleli

, et al. Consumption-based carbon emissions in Mexico: an analysis using the dual adjustment approach. Sustain Prod Consumpt 2021; 27: 947–957.

53.

Ramzan

Awosusi

, et al. Does globalization moderate the effect of economic complexity on CO2 emissions? Evidence from the top 10 energy transition economies. Front Environ Sci 2021; 9: 778088.

54.

Miao

Razzaq

Adebayo

, et al.

Do renewable energy consumption and financial globalisation contribute to ecological sustainability in newly industrialized countries?

Renew Energy 2022; 187: 688–697.

55.

Beton Kalmaz

Awosusi

. Investigation of the driving factors of ecological footprint in Malaysia. Environ Sci Pollut Res Int 2022; 29: 56814–56827.

56.

Akadiri

Adebayo

Asuzu

, et al. Testing the role of economic complexity on the ecological footprint in China: a nonparametric causality-in-quantiles approach. Energy Environ 2022: 12: 109–125.

57.

Awosusi

Kutlay

Altuntaş

, et al. A roadmap toward achieving sustainable environment: evaluating the impact of technological innovation and globalization on load capacity factor. Int J Environ Res Public Health 2022; 19: 3288.

58.

Akinsola

Awosusi

Kirikkaleli

, et al. Ecological footprint, public–private partnership investment in energy, and financial development in Brazil: a gradual shift causality approach. Environ Sci Pollut Res Int 2022; 29: 10077–10090.

59.

Alemán-Nava

Casiano-Flores

Cárdenas-Chávez

, et al. Renewable energy research progress in Mexico: a review. Renew Sustain Energy Rev 2014; 32: 140–153.

60.

Oladipupo

Adebayo

Rjoub

, et al. The asymmetric effects of renewable energy consumption and trade openness on carbon emissions in Sweden: new evidence from quantile-on-quantile regression approach. Environ Sci Pollut Res Int 2022; 29: 1875–1886.

61.

Yuping

Ramzan

Xincheng

, et al. Determinants of carbon emissions in Argentina: the roles of renewable energy consumption and globalization. Energy Rep 2021; 7: 4747–4760.

62.

Adekoya

Oliyide

Fasanya

. Renewable and non-renewable energy consumption–ecological footprint nexus in net-oil exporting and net-oil importing countries: policy implications for a sustainable environment. Renew Energy 2022; 189: 524–534.

63.

Akadiri

Adebayo

Riti

, et al. The effect of financial globalization and natural resource rent on load capacity factor in India: an analysis using the dual adjustment approach. Environ Sci Pollut Res Int 2022; 3: 3(5): 1–18.

64.

Kirikkaleli

Awosusi

Adebayo

, et al. Role of technological innovation and globalization in BRICS economies: policy towards environmental sustainability. Int J Sustain Dev World Ecol 2022; 8: 1–18.

65.

Fareed

Rehman

Adebayo

, et al. Financial inclusion and the environmental deterioration in eurozone: the moderating role of innovation activity. Technol Soc 2022; 69: 101961.

66.

Liu

Sadiq

Ali

, et al. Does tourism development, energy consumption, trade openness and economic growth matters for ecological footprint: testing the environmental Kuznets curve and pollution haven hypothesis for Pakistan. Energy 2022; 245: 123208.