Renewable and Non-Renewable Energy Consumption and Trade Policy: Do They Matter for Environmental Sustainability?

Abstract

In the extant literature, there are numerous discussions on China’s environmental sustainability. However, few scholars have considered renewable energy consumption and trade policy simultaneously to debate environmental sustainability. Therefore, this paper attempts to examine how renewable and non-renewable energy consumption, bio-capacity, economic growth, and trade policy dynamically affect the ecological footprint (a proxy for environmental sustainability). Using the data from 1971 to 2017 and employing the auto-regressive distributed lag model to perform an empirical analysis, the results demonstrate that renewable energy consumption and trade policy are conducive to environmental sustainability because of their negative impacts on the ecological footprint. However, the results also indicate that bio-capacity, non-renewable energy consumption, and economic growth are putting increasing pressure on environmental sustainability due to their positive impacts on the ecological footprint. Moreover, to determine the direction of causality between the highlighted variables, the Yoda-Yamamoto causality test was conducted. The results suggest a two-way causal relationship between renewable energy consumption and ecological footprint, non-renewable energy consumption and ecological footprint, and economic growth and ecological footprint. Conversely, the results also suggest a one-way causal relationship running from bio-capacity and trade policy to the ecological footprint.

1. Introduction

According to the definition of the Global Footprint Network, an ecological footprint refers to an operable qualitative approach in which a specific number of people consume, according to a certain lifestyle, various commodities and service functions provided by the natural ecosystem, and the waste generated in this process that needs to be absorbed by the environment, expressed in terms of bio-productive land area. By comparing the demand of the ecological footprint with the carrying capacity of the natural ecosystem (also known as ecological footprint supply), we can quantitatively judge the state of sustainable development of a country or region so as to make scientific planning and suggestions for human survival and socioeconomic development in the future. As a result, a large number of scholars [1,2,3] use the ecological footprint as a proxy for environmental sustainability to study the effects of other factors on environmental sustainability.

China relies significantly on its natural and intellectual resources as it pursues fast economic growth [4]. Natural resources are mostly used and consumed in the early stages of China’s economic growth because they are easy to utilize and consume. Although natural resource consumption contributes to China’s economic growth, this kind of consumption pattern degrades China’s environmental sustainability [5,6]. As a result, China has begun to employ intellectual resources to substitute for natural resources in order to reduce the continuous consumption of natural resources and progressively improve the degree of environmental deterioration in China. However, because of the high cost of implementation, China may not have been able to deploy alternative resources, as the cost of implementation may have an influence on economic development [7]. Consequently, the consumption of fossil fuels is primarily to seek energy generation to advance China’s industrialization. Because China’s absorption capacity of water, land, and air may not be adequate to satisfy the wastewater generated during an economic expansion, China’s bio-capacity is limited by the degradation of the environment induced by the use of fossil energy alternatives [8]. In reality, the phrase “ecological footprint” is a term used to describe this carrying capacity. Generally speaking, the ecological footprint is described as the entire amount of water and territory claimed by economic participants to generate all the resources they use and continually absorb all the trash they generate using standard measures. As China enters the system of sustainable development goals, the necessity of achieving sustainable development goals by 2030 becomes clearer. As a result, China is attempting to restructure its energy and environmental policies to establish the groundwork for achieving sustainable development goals by reducing environmental degradation caused by its ecological footprint.

At the moment, several areas of China are experiencing ecological deficits, despite the fact that the ecosystem is meant to rebound spontaneously and adapt to environmental changes. Nonetheless, according to China’s ecological accounting, this is not the case, resulting in major environmental sustainability issues. Previous studies [9,10,11,12], particularly for China, have identified rapid economic growth, a large population, and massive energy consumption (particularly non-renewable energy consumption) as factors creating environmental degradation. The implementation of China’s new trade policies, such as the outward transfer of China’s domestic high-polluting enterprises or the restriction on the import of foreign garbage and other high-polluting goods, has impacted the dynamics of environmental quality in recent years. China, for example, limited its overseas foreign garbage trading activities. This suggests that prior to the establishment of this trade policy, China imported more foreign garbage from other countries. Despite rising economic growth, environmental pollution worsened. Aside from the dynamics of China’s trade policies, the rise in China’s renewable energy consumption is another factor that is exacerbating the need for its ecological footprint. It is worth mentioning that these factors are linked to China’s bio-capacity deterioration. Based on the aforementioned purpose, this paper investigates the effects of renewable and non-renewable energy consumption, trade policy, bio-capacity, and economic growth on China’s ecological footprint. This work utilizes yearly data from 1971 to 2017, providing a fresh perspective to the existing literature.

This work extends the previous research by looking at the influence of trade policy, and renewable and non-renewable energy consumption on China’s ecological footprint (which is used as a proxy for environmental sustainability in this paper), respectively. Despite the fact that renewable energy sources are prospective in China, little is documented about their significance as a constraining element in non-renewable energy-related environmental sustainability—especially in light of current trade policy fluctuations. Consequently, it is critical to comprehend the critical influence of trade policy and renewable energy consumption in achieving environmental sustainability. The outcomes provided by this work indicate what kind of variables, including renewable and non-renewable energy consumption, trade policy, bio-capacity, and economic growth, place downward or upward stress on China’s environmental sustainability.

The remainder of this work is structured as follows: Section 2 is a review of literature; Section 3 is a variable description and model formulation; Section 4 is a report of results and discussions; and Section 5 is a conclusion with policy implications.

2. Literature Review

The areas of environmental sustainability, economic growth, and energy consumption have been vastly investigated and discussed in the literature. Sharma et al. [13] used the eight developing countries of South and Southeast Asia as a sample to explore the effect of renewable energy consumption on the ecological footprint from 1990 to 2015. Employing a cross-sectional augmented autoregressive distributed lag model to undertake an empirical analysis, they found that renewable energy consumption significantly contributed to improving environmental sustainability in the long and short run. Specifically, a 1% increase in renewable energy consumption resulted in a 0.216% decrease in the ecological footprint in the long run and a 0.318% decrease in ecological footprint in the short run. Ulucak and Khan [14] studied the same topic in BRICS countries from 1992 to 2016. They conducted an empirical analysis using both the dynamic ordinary least squares method and the fully modified ordinary least squares method and discovered that renewable energy consumption reduced the ecological footprint, implying that it contributed to environmental sustainability. Subsequently, Ansari et al. [15] used the same methods to discuss this topic from 1991 to 2016. They also found that renewable energy consumption had a negative effect on the ecological footprint. Similarly, Caglar et al. [16] used the world’s top 10 pollutant footprint countries as an example of how renewable energy consumption affects the ecological footprint quality. Through the estimation of an auto-regressive distribution lag model, they found that renewable energy consumption abated environmental deterioration in these countries, implying that renewable energy consumption was conducive to environmental sustainability. Moreover, these findings were also supported by Nathaniel et al. [17] and Kongbuamai et al. [18]. However, using a sample from the Middle East and North Africa region from 1990 to 2016, Nathaniel et al. [19] employed the Augmented Mean Group algorithm to study the role of renewable energy consumption on the ecological footprint. They found that renewable energy did not contribute significantly to environmental sustainability. Based on the empirical findings of the above-mentioned relevant literature, the following hypothesis was proposed in this paper:

Hypothesis 1 (H1):

Renewable energy consumption is conducive to environmental sustainability.

Recently, the relationship between trade and environmental sustainability has become a hot topic. With the expansion of trade links between Turkey and the Caspian Sea area countries (including Turkmenistan, Iran, Russia, Kazakhstan, and Azerbaijan), an increasing number of people are demanding the environmental quality of trade. Onifade et al. [20] discovered that trade expansion greatly slowed environmental deterioration using dynamic ordinary least squares and fully modified ordinary least squares. Meanwhile, Khan et al. [21] studied the environmental impact of trade in 176 countries throughout the globe. They discovered that trade contributed greatly to environmental sustainability by using the ordinary least squares, generalized methods of moments, and fixed effects. Alola et al. [22] investigated the impact of trade policy on environmental sustainability using a balanced panel of 16 EU countries from 1997 to 2014 and the panel pool mean group autoregressive distributive lag model. They found that trade policy aided environmental sustainability. However, Chakraborty and Mukherjee [23] studied the impact of exports on environmental sustainability in 114 countries from 2000 to 2010. Using panel data for empirical studies, they discovered that exports had a favorable impact on environmental sustainability. Furthermore, Iheonu et al. [24] found that international trade had enhanced environmental sustainability in certain countries with the lowest and highest levels of current carbon dioxide emissions. These results were consistent with Saint Akadiri et al. [25], Nathaniel and Khan [26], and Nathaniel et al. [27]. As a result of the above literature review, the following hypothesis is advanced in this paper:

Hypothesis 2 (H2):

Environmental sustainability has improved as a result of trade policy.

Considering the effect of economic growth on environmental sustainability, Ahmed et al. [28] discussed the impact of economic growth on the ecological footprint. They used G7 countries as a case study from 1985 to 2017 to perform an empirical analysis. Their results, based on the CUP-FM method and Dumitrescu Hurlin test, reported that economic growth increased the ecological footprint. In Nigeria, Udemba [29] employed the auto-regressive distributed lag method and Granger causality approach to analyze the effect of economic growth on the ecological footprint from 1981 to 2018. Their results suggested that economic growth positively affected the ecological footprint via the estimation of the auto-regressive distributed lag method. Their results also revealed that a causal relationship runs from economic growth to the ecological footprint via the analysis of the Granger causality approach. Using similar approaches, Ahmad et al. [30] also examined this topic in 22 emerging economies from 1984 to 2016. They found that, in the long run, economic growth expanded the ecological footprint. Moreover, they also found that the quadratic term for economic growth has a negative effect on the ecological footprint. Meanwhile, by the estimation of the Dumitrescu-Hurlin Granger causality test, their results revealed that economic growth significantly altered the ecological footprint. In addition, Baz et al. [31] used a different method, an asymmetric and nonlinear approach, to investigate the effect of economic growth on the ecological footprint from 1971 to 2014 in Pakistan. Their results showed that a neutral effect was found between environmental quality and economic growth. Additionally, using a STIRPAT framework, Kihombo et al. [32] studied a similar proposition from 1990 to 2017 in West Asia and Middle East countries. They also obtained a consistent result. Furthermore, these above findings were supported by Ikram et al. [33], Acar and Aşıcı [34], and Hussain et al. [35]. Therefore, the following hypothesis is proposed in this study as a consequence of the preceding literature review:

Hypothesis 3 (H3):

Environmental sustainability is negatively affected by economic growth.

Concerning the effect of non-renewable energy consumption on environmental sustainability, Usman and Makhdum [36] used India, Russia, Brazil, China, South Africa, and Turkey as samples to study this topic from 1990 to 2018. Employing the second-generation co-integration and causality tests to perform an empirical analysis, they found that non-renewable energy consumption positively affected the ecological footprint. Concretely, a 1% increase in non-renewable energy consumption led to a 0.551% increase in the ecological footprint. Additionally, Usman et al. [37] investigated this proposition with the 15 highest emitting countries from 1990 to 2017. Their findings suggested that non-renewable energy consumption was one of the most significant factors that hindered environmental sustainability because of its positive effect on the ecological footprint. Meanwhile, Christoforidis and Katrakilidis [38] also considered this issue. Regarding the 29 Organization for Economic Co-operation and Development countries, employing the robust cross-sectional augmented distributed lag approach and using panel data from 1984 to 2016 for the empirical study, their results revealed that non-renewable energy consumption was detrimental to environmental sustainability. Furthermore, there was a two-way causal association between non-renewable energy consumption and environmental sustainability. With similar research objects from 1990 to 2015, and Wasteland’s panel co-integration test, Khan et al. [39] also found that non-renewable energy consumption deteriorated environmental sustainability because non-renewable energy consumption positively affected the ecological footprint. With a similar method, Sahoo and Sethi [40] studied this problem with a sample of developing countries from 1990 to 2016. They achieved the same result. Moreover, they also used the dynamic ordinary least squares and the fully modified ordinary least squares approaches to re-examine this result. The result of this problem was reliable and robust. In addition, these findings were also supported by Rout et al. [41], Khan and Hou [42], Wang et al. [43], and Naqvi et al. [44]. The following hypothesis is thus proposed in this study as a consequence of the aforesaid literature review:

Hypothesis 4 (H4):

Non-renewable energy consumption hinders environmental sustainability.

With regard to the effect of bio-capacity on environmental sustainability, Hassan et al. [45] used the auto-regressive distributed lag model to estimate the influence of bio-capacity on the ecological footprint from 1971 to 2014. They found that bio-capacity increased the ecological footprint. Meanwhile, Galli et al. [46] found that in China, bio-capacity positively affected the ecological footprint, while in India, this result was not consistent. Moreover, Chen et al. [11] employed 16 Central and Eastern European countries as an example to measure environmental sustainability from 1991 to 2014. Using the dynamic, seemingly unrelated co-integration regression, feasible generalized least squares, and generalized method of moment to undertake an empirical analysis, their results reported that a significant effect of bio-capacity on the ecological footprint was positive, which implied that bio-capacity hindered environmental sustainability. Nathaniel [47] used the G7 as a sample to reassess the effect of bio-capacity on the ecological footprint. He found that bio-capacity increased the ecological footprints in Japan, the UK, the USA, Germany, Italy, and France, but not in Canada. With MINT countries from 1971 to 2017, Agbede et al. [48] used the panel pooled mean group, auto-regressive distributed lag modeling technique, and the Granger causality test to reevaluate this topic. They found that bio-capacity positively and significantly affected environmental degradation in the long run. Meanwhile, they also found that there is a one-way causal relationship running from bio-capacity to the ecological footprint. Furthermore, these findings above were also supported by Pata and Isik [49], Banerjee and Mukhopadhayay [50], Chu [51], and Shittu et al. [52]. To conclude, the following hypothesis is proposed in this research as a consequence of the aforementioned literature review:

Hypothesis 5 (H5):

Bio-capacity has a detrimental effect on environmental sustainability.

3. Variable Description and Model Specification

3.1. Variable Description

Dependent variable: The purpose of this paper attempts to examine environmental sustainability. Following Rafique et al. [53] and Ahmed et al. [54], the ecological footprint is a proxy for environmental sustainability. Independent variable: There are two independent variables highlighted in this paper. They are renewable energy consumption and non-renewable energy consumption. According to Khan et al. [55], trade policy which is a proxy for China’s trade policy uncertainty is also introduced in this paper. Control variable: In addition to independent variables, there are other factors affecting environmental sustainability. Following Chen et al. [56], Zheng et al. [57], Ünal and Aktuğ [58], Hassan et al. [59], and Galli et al. [60], bio-capacity is introduced in this paper. Following Eregha et al. [61] and Liu et al. [62], economic growth is also introduced in this paper. The period covered by these variables ranges from 1971 to 2017. To comprehend these highlighted variables more intuitively, their definitions, forms, and sources are presented in

Table 1. Variables Description.

Variable	Form	Definition	Source
Ecological footprint	efp	Ecological footprint (gha\person) in log	Global Footprint Network
Renewable energy consumption	rec	Renewable energy consumption (million tons) in log	OECD Database
Non-renewable energy consumption	nec	Fossil fuel energy consumption (% of total final energy consumption)	World Bank Database
Bio-capacity	bio	Bio-capacity (gha\person) in log	Global Footprint Network
Economic growth	gdp	GDP per capita (current US$) in log	World Bank Database
Trade policy	tra	Trade policy index in log	FRED Economic Database

.

3.2. Model Specification

The baseline regression model, as shown by Usman et al. [63] and Iorember et al. [64], is as follows:

$${\mathrm{efp}}_{\mathrm{t}} = {\mathrm{a}}_0 + {\mathrm{a}}_1{\mathrm{rec}}_{\mathrm{t}} + {\mathrm{a}}_2{\mathrm{nec}}_{\mathrm{t}} + {\mathrm{a}}_3{\mathrm{bio}}_{\mathrm{t}} + {\mathrm{a}}_4{\mathrm{gdp}}_{\mathrm{t}} + {\mathrm{a}}_5{\mathrm{tra}}_{\mathrm{t}} + {\mathsf{\mu }}_{\mathrm{t}},$$

(1)

where a₀ denotes the constant; [a₁,a₅] denote the coefficients to be estimated; and μ_t denotes the white noise. Equation (1) only evaluates the impact of fundamental factors on environmental sustainability. Following Sharif et al. [65] and Udeagha and Ngepah [66], this paper employs the auto-regressive distribution lag model that was developed by Pesaran et al. [67] to re-evaluate this topic so as to better understand the influence of these basic factors on environmental sustainability. Using the unrestricted error correction model, Equation (1) can be rewritten as follows:

$$\begin{array}{ll}{\mathsf{\Delta }\mathrm{efp}}_{\mathrm{t}}& ={\mathrm{b}}_0 + {\mathrm{b}}_1{\mathrm{efp}}_{\mathrm{t} - 1} + {\mathrm{b}}_2{\mathrm{rec}}_{\mathrm{t} - 1} + {\mathrm{b}}_3{\mathrm{nec}}_{\mathrm{t} - 1} + {\mathrm{b}}_4{\mathrm{bio}}_{\mathrm{t} - 1} + {\mathrm{b}}_5{\mathrm{gdp}}_{\mathrm{t} - 1} + {\mathrm{b}}_6{\mathrm{tra}}_{\mathrm{t} - 1}\\ & +{\displaystyle {\displaystyle \sum }_{\mathrm{i} = 1}^{{\mathrm{n}}^{\mathrm{i}}}}{\mathrm{c}}_{\mathrm{i}}{\mathsf{\Delta }\mathrm{efp}}_{\mathrm{t} - \mathrm{i}}+{\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^1}}{\mathrm{d}}_{1,\mathrm{i}}{\mathsf{\Delta }\mathrm{rec}}_{\mathrm{t} - \mathrm{i}} + {\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^2}}{\mathrm{d}}_{2,\mathrm{i}}{\mathsf{\Delta }\mathrm{nec}}_{\mathrm{t} - \mathrm{i}} + {\displaystyle {\displaystyle \sum }_{\mathrm{i}=0}^{{\mathrm{m}}^3}}{\mathrm{d}}_{3,\mathrm{i}}{\mathsf{\Delta }\mathrm{bio}}_{\mathrm{t} - \mathrm{i}}\\ & +{\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^4}}{\mathrm{d}}_{4,\mathrm{i}}{\mathsf{\Delta }\mathrm{gdp}}_{\mathrm{t} - \mathrm{i}} + {\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^5}}{\mathrm{d}}_{5,\mathrm{i}}{\mathsf{\Delta }\mathrm{tra}}_{\mathrm{t} - \mathrm{i}} + {\mathsf{\mu }}_{\mathrm{t}}\end{array}$$

(2)

where b₀ denotes the constant and Δ denotes the first difference. It is noteworthy that the coefficients [b₁,b₆] denote the long-run coefficients to be estimated, and the coefficients [c_i,d_5,i] denote the short-run coefficients to be estimated. Furthermore, Alola et al. [68] pointed out that if any of the highlighted variables are affected, the ecological footprint (a proxy for environmental sustainability) might not move to the long-run equilibrium path. Therefore, the error correction model will be used to capture the adjusting speed of the ecological footprint from short-run to long-run equilibrium. The estimated model is shown as follows:

$$\begin{array}{ll}{\mathsf{\Delta }\mathrm{efp}}_{\mathrm{t}}& ={\mathrm{e}}_0 + {\displaystyle {\displaystyle \sum }_{\mathrm{i} = 1}^{{\mathrm{n}}^{\mathrm{i}}}}{\mathrm{c}}_{\mathrm{i}}{\mathsf{\Delta }\mathrm{efp}}_{\mathrm{t} - \mathrm{i}} + {\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^1}}{\mathrm{d}}_{1,\mathrm{i}}{\mathsf{\Delta }\mathrm{rec}}_{\mathrm{t} - \mathrm{i}} + {\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^2}}{\mathrm{d}}_{2,\mathrm{i}}{\mathsf{\Delta }\mathrm{nec}}_{\mathrm{t} - \mathrm{i}} + {\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^3}}{\mathrm{d}}_{3,\mathrm{i}}{\mathsf{\Delta }\mathrm{bio}}_{\mathrm{t} - \mathrm{i}}\\ & \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^4}}{\mathrm{d}}_{4,\mathrm{i}}{\mathsf{\Delta }\mathrm{gdp}}_{\mathrm{t} - \mathrm{i}}+{\displaystyle {\displaystyle \sum }_{\mathrm{i} = 0}^{{\mathrm{m}}^5}}{\mathrm{d}}_{5,\mathrm{i}}{\mathsf{\Delta }\mathrm{tra}}_{\mathrm{t} - \mathrm{i}}+{\mathsf{\lambda }\mathrm{ect}}_{\mathrm{t} - 1}+{\mathsf{\mu }}_{\mathrm{t}}\end{array}$$

(3)

where e₀ denotes the constant; ect_t−1 denotes the error correction term and λ denotes the adjusting speed of the ecological footprint from short-run to long-run equilibrium.

Then, to confirm that the highlighted variables are I(0), I(1), and mixed, the auto-regressive distribution lag model bounds test is used. When the sample size is small, this test is used because estimators based on this test perform better than estimators based on other tests. Thus, the co-integration test developed by Pesaran et al. [67] is performed as a bound test. The null hypothesis is shown as: c_i = a₁ = a₂ = a₃ = a₄ = a₅ = 0 while the alternative hypothesis is shown as: c_i ≠ a₁ ≠ a₂ ≠ a₃ ≠ a₄ ≠ a₅ = 0. Additionally, the stationarity of these highlighted variables needs to be tested. In this paper, the Augmented Dicky-Fuller and Phillips-Perron tests are used. The null hypothesis is shown as: b₁ = 0 while the alternative hypothesis is shown as: b₁ < 0.

In addition, to explore the causal relationship between these highlighted variables, the Toda-Yamamoto conditional Granger causality test is used. The reason is that understanding the causality between them is necessary for developing strategies for energy consumption, trade policy, and environmental sustainability. Using a vector auto-regressive model with lag p that has a Wald test statistic that has been modified, this method effectively investigates the direction of causality between these highlighted variables. In fact, this test outperforms the Pairwise Granger causality technique which implies all investigated variables must be integrated I(0) or I(1). Fortunately, the Toda-Yamamot Graner causality technique may be easily implemented and yield reliable findings if the investigated variables are integrated I(0) or I(1). Following Toda and Yamamoto [69], this method is based on the vector auto-regressive distributed lag model. The model is shown as follows:

$$\begin{array}{c}\left[\begin{array}{c}{\mathrm{efp}}_{\mathrm{t}}\\ {\mathrm{rec}}_{\mathrm{t}}\\ {\mathrm{nec}}_{\mathrm{t}}\\ {\mathrm{bio}}_{\mathrm{t}}\\ {\mathrm{gdp}}_{\mathrm{t}}\\ {\mathrm{tra}}_{\mathrm{t}}\end{array}\right] = \left[\begin{array}{c}\mathrm{a}\\ \mathrm{b}\\ \mathrm{c}\\ \mathrm{d}\\ \mathrm{e}\\ \mathrm{f}\end{array}\right] + {\displaystyle \sum _{\mathrm{i} - 1}^{\mathrm{n}}}\left[\begin{array}{cccccc}{\lambda }_{11\mathrm{i}}& {\lambda }_{12\mathrm{i}}& {\lambda }_{13\mathrm{i}}& {\lambda }_{14\mathrm{i}}& {\lambda }_{15\mathrm{i}}& {\lambda }_{16\mathrm{i}}\\ {\lambda }_{21\mathrm{i}}& {\lambda }_{22\mathrm{i}}& {\lambda }_{23\mathrm{i}}& {\lambda }_{24\mathrm{i}}& {\lambda }_{25\mathrm{i}}& {\lambda }_{26\mathrm{i}}\\ {\lambda }_{31\mathrm{i}}& {\lambda }_{32\mathrm{i}}& {\lambda }_{33\mathrm{i}}& {\lambda }_{34\mathrm{i}}& {\lambda }_{35\mathrm{i}}& {\lambda }_{36\mathrm{i}}\\ {\lambda }_{41\mathrm{i}}& {\lambda }_{42\mathrm{i}}& {\lambda }_{43\mathrm{i}}& {\lambda }_{44\mathrm{i}}& {\lambda }_{45\mathrm{i}}& {\lambda }_{46\mathrm{i}}\\ {\lambda }_{51\mathrm{i}}& {\lambda }_{52\mathrm{i}}& {\lambda }_{53\mathrm{i}}& {\lambda }_{54\mathrm{i}}& {\lambda }_{55\mathrm{i}}& {\lambda }_{56\mathrm{i}}\\ {\lambda }_{61\mathrm{i}}& {\lambda }_{62\mathrm{i}}& {\lambda }_{63\mathrm{i}}& {\lambda }_{64\mathrm{i}}& {\lambda }_{65\mathrm{i}}& {\lambda }_{66\mathrm{i}}\end{array}\right] \times [\begin{array}{c}{\mathrm{efp}}_{\mathrm{t} - \mathrm{i}}\\ {\mathrm{rec}}_{\mathrm{t} - \mathrm{i}}\\ {\mathrm{nec}}_{\mathrm{t} - \mathrm{i}}\\ {\mathrm{bio}}_{\mathrm{t} - \mathrm{i}}\\ {\mathrm{gdp}}_{\mathrm{t} - \mathrm{i}}\\ {\mathrm{tra}}_{\mathrm{t} - \mathrm{i}}\end{array}]\\ +{\displaystyle \sum _{\mathrm{j} = \mathrm{p} + 1}^{{\mathrm{m}}_{\max }}}[\begin{array}{cccccc}{\lambda }_{11\mathrm{j}}& {\lambda }_{12\mathrm{j}}& {\lambda }_{13\mathrm{j}}& {\lambda }_{14\mathrm{j}}& {\lambda }_{15\mathrm{j}}& {\lambda }_{16\mathrm{j}}\\ {\lambda }_{21\mathrm{j}}& {\lambda }_{22\mathrm{j}}& {\lambda }_{23\mathrm{j}}& {\lambda }_{24\mathrm{j}}& {\lambda }_{25\mathrm{j}}& {\lambda }_{26\mathrm{j}}\\ {\lambda }_{31\mathrm{j}}& {\lambda }_{32\mathrm{j}}& {\lambda }_{33\mathrm{j}}& {\lambda }_{34\mathrm{j}}& {\lambda }_{35\mathrm{j}}& {\lambda }_{36\mathrm{j}}\\ {\lambda }_{41\mathrm{j}}& {\lambda }_{42\mathrm{j}}& {\lambda }_{43\mathrm{j}}& {\lambda }_{44\mathrm{j}}& {\lambda }_{45\mathrm{j}}& {\lambda }_{46\mathrm{j}}\\ {\lambda }_{51\mathrm{j}}& {\lambda }_{52\mathrm{j}}& {\lambda }_{53\mathrm{j}}& {\lambda }_{54\mathrm{j}}& {\lambda }_{55\mathrm{j}}& {\lambda }_{56\mathrm{j}}\\ {\lambda }_{61\mathrm{j}}& {\lambda }_{62\mathrm{j}}& {\lambda }_{63\mathrm{j}}& {\lambda }_{64\mathrm{j}}& {\lambda }_{65\mathrm{j}}& {\lambda }_{66\mathrm{j}}\end{array}] \times [\begin{array}{c}{\mathrm{efp}}_{\mathrm{t} - \mathrm{j}}\\ {\mathrm{rec}}_{\mathrm{t} - \mathrm{j}}\\ {\mathrm{nec}}_{\mathrm{t} - \mathrm{j}}\\ {\mathrm{bio}}_{\mathrm{t} - \mathrm{j}}\\ {\mathrm{gdp}}_{\mathrm{t} - \mathrm{j}}\\ {\mathrm{tra}}_{\mathrm{t} - \mathrm{j}}\end{array}] + [\begin{array}{c}{\mathsf{\mu }}_{1\mathrm{t}}\\ {\mathsf{\mu }}_{2\mathrm{t}}\\ {\mathsf{\mu }}_{3\mathrm{t}}\\ {\mathsf{\mu }}_{4\mathrm{t}}\\ {\mathsf{\mu }}_{5\mathrm{t}}\\ {\mathsf{\mu }}_{6\mathrm{t}}\end{array}]\end{array}$$

(4)

where $\mathrm{n}$ denotes the vector auto-regressive order with $\mathrm{m}$ extra lags (${\mathrm{m}}_{\max }$) and ${\mathrm{m}}_{\max }$ indicates the maximum order if integrated into the vector auto-regressive system. The asymptotic ${\mathsf{\chi }}^2$ distribution of Wald statistics is computed with a vector auto-regressive model ($\mathrm{n} + {\mathrm{m}}_{\max }$). As a result, the hypothesis of the Toda-Yamamot Graner causality technique can be confirmed. The null hypothesis is shown as: ${\mathsf{\lambda }}_{\mathrm{i}6\mathrm{i}}\ne 0$ for ${\vee }_{\mathrm{i}}$. This suggests that there is a causal relationship between the two estimated variables. On the contrary, the alternative hypothesis is shown as: ${\mathsf{\lambda }}_{\mathrm{i}6\mathrm{i}} = 0$ for ${\vee }_{\mathrm{i}}$. This suggests that there is no causal relationship between the two estimated variables.

4. Empirical Analysis

4.1. Fundamental Statistics

This subsection focuses on fundamental statistics, such as descriptive statistics, correlation tests, and unit root tests. The results of fundamental statistics are presented in

Table 2. Results of fundamental statistics.

Descriptive Statistics
Variable/Statistics	efp	rec	nec	bio	gdp	tra
Mean	0.267	2.311	0.771	−0.055	2.853	1.812
Maximum	0.569	2.446	0.906	0.551	3.948	2.374
Minimum	0.025	2.195	0.598	−0.108	2.074	0.664
Standard deviation	0.175	0.052	0.089	0.092	0.595	0.376
Correlation Test
Variable	efp	rec	nec	bio	gdp	tra
efp	1.000 (----)
rec	−0.751 *** (−7.622)	1.000 (----)
nec	0.965 *** (24.951)	0.821 *** (9.675)	1.000 (----)
bio	0.383 *** (2.782)	0.397 *** (2.909)	0.326 ** (2.314)	1.000 (----)
gdp	0.993 *** (57.416)	0.756 *** (7.756)	0.950 *** (20.489)	0.408 *** (3.004)	1.000 (----)
tra	−0.511 *** (−3.989)	0.673 *** (6.112)	0.605 *** (5.097)	0.168 (1.144)	0.480 *** (3.673)	1.000 (----)
Unit Root Test
Level	ADF test	PP test	First Difference		ADF test	PP test
efp	−1.496	−1.638	efp		−5.765 ***	−5.759 ***
rec	−2.282	−1.674	rec		−5.054 ***	−1.953 **
nec	−3.037	−2.377	nec		−5.318 ***	−5.346 ***
bio	−6.684 ***	−6.683 ***	bio		−13.141 ***	−13.240 ***
gdp	−1.480	−1.248	gdp		−5.739 ***	−5.722 ***
tra	−4.521 ***	−4.573 ***	tra		−10.144 ***	−11.526 ***

Note: ** 5% significance level; *** 1% significance level; T-statistics shown in parentheses.

.

Considering the results of descriptive statistics, the ecological footprint has a mean of 0.267 with a standard deviation of 0.175. This indicates that our ecological footprint fluctuates dramatically. The renewable energy consumption has a mean of 2.311 with a standard deviation of 0.052. This indicates that renewable energy consumption is on the rise and swings only slightly. The non-renewable energy consumption has a mean of 0.771 with a standard deviation of 0.089. This indicates that non-renewable energy consumption accounts for a large proportion of the total energy consumption and that it fluctuates slightly. The bio-capacity has a mean of −0.055 with a standard deviation of 0.092. The economic growth has a mean of 2.853 with a standard deviation of 0.595. The trade policy has a mean of 1.812 with a standard deviation of 0.376. This indicates that trade policy swings significantly, which corresponds to reality.

Concerning the results of the correlation test, we find that the correlation between the renewable energy consumption and ecological footprint is negative, as is the correlation between trade policy and ecological footprint. In other words, it can be primarily judged that trade policy and renewable energy consumption positively contribute to environmental sustainability. Equally, we find that the correlation between the ecological footprint and the highlighted variables (non-renewable energy consumption, bio-capacity, and economic growth) is positive. That is, it can also be primarily judged that the highlighted variables hinder environmental sustainability.

With regard to the unit root test, the results of the ADF test developed by Dickey and Fuller [70] and the results of the PP test developed by Phillips and Perron [71] suggest that the ecological footprint, renewable energy consumption, non-renewable energy consumption, and economic growth are integrated at order one [$\mathrm{I}\left(1\right)$] while bio-capacity and trade policy are integrated at order zero [$\mathrm{I}\left(0\right)$]. As a result, we can draw the conclusion that the highlighted variables’ integrating properties are a mixed-order process.

4.2. Bounds Test

Before the model estimation, the results of the unit root test used to detect the highlighted variables’ stationarity properties have built up a benchmark to choose a proper estimation model. In this subsection, a co-integration test will be conducted using the auto-regressive distributed lag bounds testing framework. An advantage of using the auto-regressive distributed lag bounds testing method is that the results of unit root suggest that the highlighted variables are a mixed order process [67]. Based on the unconstrained constant and no trend, the auto-regressive distributed lag bounds testing co-integration is used. The maximum lag order is set to two, and the Akaike Information Criterion determines the optimal lag order. The results of the auto-regressive distributed lag bounds testing co-integration are shown in

Table 3. Results of bounds test.

Panel A: efp = f(rec, nec, bio, gdp, tra)
Test Statistics			Value			k
F-statistics			7.221 ***			5
T-statistics			−6.417 ***
Critical Value Bounds
Significance			I(0)			I(1)
10%			2.08			3.0
5%			2.39			3.38
1%			3.06			4.15
Panel B: Kripfganz and Scheneider Critical Values and Approximate p-Values
k = 5	10% Significance Level		5% Significance Level		1% Significance Level		p-Value
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F-critical	2.276	3.297	2.694	3.829	3.674	5.019	0.000	0.000
T-critical	−2.306	−3.353	−2.734	−3.920	−3.657	−5.256	0.000	0.000
F-calculated	7.587 ***
T-calculated	−5.519 ***

Note: *** 1% significance level; k = 5 denotes five independent variables; maximum lag order sets to 4; Akaike Information Criterion is the standard to select the optimal lag order.

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As the results of Table 3 for Panel A suggest, based on T-statistic and F-statistic, the null hypothesis of no long-run relationship is rejected at a 1% significant level. Said differently, the long-run relationship between the investigated variables will be fully presented. Moreover, we employ the bounds testing co-integration developed by Pesaran et al. [67] using the Kripfganz and Scheneider [72] critical values and approximate p-values for the robustness test. As the results of Panel B suggest, according to the approximate p-values at the upper and lower bounds, the null hypothesis of no long-run relationship is also rejected. Consequently, in the subsection, Equations (2) and (3) will be estimated to explore the relationship between the highlighted variables.

4.3. Analyses of Long-Run and Short-Run Auto-Regressive Distribution Lag Coefficients

Based on the results of the 4.2. bounds test, the long-run and short-run effects of renewable energy consumption, non-renewable energy consumption, bio-capacity, economic growth, and trade policy on the ecological footprint will be examined. The results are presented in

Table 4. Results of the long-run and short-run auto-regressive distribution lag estimation.

Dependent Variable: efp = f(rec, nec, bio, gdp, tra)
Type\Variable	Long-Run Estimation	Short-Run Estimation
rec	−0.297 *** (−3.544)	−0.092 *** (−3.790)
nec	0.551 *** (5.291)	0.259 *** (4.448)
bio	0.738 ** (2.218)	0.176 *** (2.571)
gdp	0.229 *** (16.609)	0.350 *** (6.342)
tra	−0.014 *** (−4.094)	−0.011 *** (−4.676)
c	−0.146 *** (−8.805)	−0.375 ** (−2.146)
ecm−1		−0.014 *** (3.633)
Diagnostic test
Statistical method	Statistical value	p-value
${\mathsf{\chi }}_{\mathrm{ARCH}}^2$	1.532	0.215
${\mathsf{\chi }}_{\mathrm{SERIAL}}^2$	1.388	0.499
${\mathsf{\chi }}_{\mathrm{RESET}}^2$	0.505	0.484
${\mathsf{\chi }}_{\mathrm{NORMAL}}^2$	0.953	0.621
CUSUM	Stable
CUSUM of squares	Stable
short-run auto-regressive distributed lag (1,2,2,2,2,2) regression

Note: ** 5% significance level; *** 1% significance level; T-statistics shown in parentheses; maximum lag order sets to 2; Akaike Information Criterion is the standard to select the optimal lag order; ${\mathsf{\chi }}_{\mathrm{ARCH}}^2$ denotes heteroscedasticity; ${\mathsf{\chi }}_{\mathrm{SERIAL}}^2$ denotes series correlation; ${\mathsf{\chi }}_{\mathrm{RESET}}^2$ denotes Ramsey’s RESET test; ${\mathsf{\chi }}_{\mathrm{NORMAL}}^2$ denotes normality test; CUSUM denotes cumulative sum; CUSUM of squares denotes the cumulative sum of squares; ecm denotes error correction term; and c denotes constant.

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The results in Table 4 suggest the short-term and long-term impacts of the investigated variables on environmental sustainability. With regard to the results of long-term effects, a 1% rise in renewable energy consumption results in a 0.297% fall in the ecological footprint, and a 1% rise in trade policy leads to the ecological footprint falling by 0.014%. Nevertheless, a 1% rise in non-renewable energy consumption, bio-capacity, and economic growth results in the ecological footprint increasing by 0.551%, 0.738%, and 0.229%. Equally, in regard to the results of short-run effects, renewable energy consumption and trade policy have a negative impact on the ecological footprint, but nonrenewable energy consumption, bio-capacity, and economic development have a favorable impact. Stated differently, a 1% rise in renewable energy consumption and trade policy reduces the ecological footprint by 0.092% and 0.011%, respectively, whereas a 1% rise in non-renewable energy consumption, bio-capacity, and economic growth increase the ecological footprint by 0.259%, 0.176%, and 0.350%. Furthermore, we also find that at a 1% level, the sign of the error correction term is significant and negative. This finding implies that the ecological footprint moves to the long-term equilibrium relationship at a rate of around 1.4% every year due to the shocks in the highlighted variables (renewable and non-renewable energy consumption, bio-capacity, economic growth, and trade policy).

As for the findings of the effects of renewable energy consumption and trade policy on the ecological footprint, there are two probable explanations. Firstly, the usage of renewable energy cuts pollution emissions. Secondly, China’s trade policy is being transformed, with increased taxes on highly polluting items and a restriction on the import of foreign waste. Meanwhile, these outcomes are consistent with Naqvi et al. [73], Abid et al. [74], Lu [75], and Nathaniel and Khan [26]. Moreover, Hypothesis (H1) and Hypothesis (H2) were verified. Similarly, considering the effects of non-renewable energy consumption, bio-capacity, and economic growth on the ecological footprint, these outcomes might be explained by the fact that non-renewable energy usage results in the development of a huge number of pollutants, and the ecological footprint exceeds the bio-capacity. Simultaneously, China’s long-term economic development strategy is geared toward energy consumption (especially petrochemical energy). At the same time, these findings were consistent with Christoforidis and Katrakilidis [38], Mujtaba et al. [76], Ünal and Aktuğ [58], and Kihombo et al. [32]. Furthermore, Hypothesis (H2), Hypothesis (H4), and Hypothesis (H5) were confirmed.

In addition, to make our results reliable and accurate, a series of diagnostic tests were conducted. The heteroscedasticity, serial correlation, normal distribution, and functional misspecification tests were used on the estimated models’ residuals to assess the residual and model’s stability diagnostics. The findings suggest that no heteroscedasticity or serial correlation exists. While the functional form of the model is correctly identified and explained, no evidence for the residual normal distribution is found. Furthermore, the cumulative sum and cumulative sum squared tests are employed to guarantee the stability of the model. As the findings of Figure 1 suggest, at a 5% significant level, the results clearly demonstrate the stable model.

4.4. Toda-Yamaimo Causality Test

Following Toda and Yamamoto [69], the modified Wald test instead of the conventional Granger causality test is used to explore the causal relationship between the ecological footprint and highlighted variables. The reason is that the conventional Granger causality test will produce an inconsistent result due to the highlighted variables’ integrating properties being a mixed-order process. The necessity for causality analysis is relevant to the predictability of each variable on another in order to provide adequate policy prescription. The results of the Toda-Yamamoto causality test are presented in

Table 5. Results of Toda-Yamamoto causality test (environmental sustainability).

Variable	efp	rec	nec	bio	gdp	tra	Overall χ2-Statistics
efp	---- (----)	7.372 *** (0.007)	16.004 *** (0.000)	9.999 *** (0.002)	7.209 *** (0.007)	4.249 ** (0.039)	6.168 *** (0.000)
rec	5.675 ** (0.017)	---- (----)	2.946 * (0.086)	5.574 ** (0.018)	17.197 *** (0.000)	7.964 *** (0.005)	20.225 *** (0.001)
nec	27.396 *** (0.000)	5.071 ** (0.024)	---- (----)	7.506 *** (0.006)	8.377 *** (0.004)	6.662 *** (0.009)	28.538 *** (0.000)
bio	0.006 (0.941)	1.532 (0.216)	0.038 (0.844)	---- (----)	4.364 ** (0.037)	0.865 (0.352)	16.045 *** (0.007)
gdp	14.873 *** (0.001)	15.140 *** (0.000)	7.179 *** (0.007)	1.649 (0.199)	---- (----)	21.780 *** (0.000)	24.965 *** (0.000)
tra	0.122 (0.792)	0.991 (3.319)	2.265 (0.132)	0.029 (0.863)	10.445 *** (0.001)	---- (----)	31.785 *** (0.000)

Note: * 10% significance level; ** 5% significance level; *** 1% significance level; p-value shown in parentheses; the maximum lad order is two; the Akaike Information Criterion determines the optimal lag order.

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As the results of Table 5 suggest, there is a two-way causal relationship between renewable energy consumption and ecological footprint, non-renewable energy consumption and ecological footprint, and economic growth and ecological footprint. These results are consistent with previous research [26,68,77,78]. Moreover, the results also suggest that there is a one-way causal relationship running from bio-capacity to ecological footprint and from trade policy to ecological footprint. These results are consistent with previous research [79,80,81]. In addition, the causal relationship that runs from economic growth to ecological footprint is consistent with the current literature’s hypothesis of growth-led environmental degradation [82,83,84].

5. Discussions

Human activities in China have severely harmed the ecological environment in order to expand the economy during the last several decades, attracting the attention of many specialists. The proof presented by a vast number of documents, as well as the current state of China’s ecological environment, has also attracted the Chinese government’s interest. As a result, the Chinese government has implemented a variety of environmental policies and energy policies, such as the ban on importing foreign garbage and renewable energy consumption, in order to minimize the reliance on fossil fuel energy which is the source of high-level environmental pollution that is damaging the health of Chinese citizens. Based on this background, this paper examines the effects of renewable and non-renewable energy consumption, bio-capacity, trade policy, and economic growth on the ecological footprint in China.

Using the auto-regressive distributed lag model to perform an empirical analysis, the results suggest that the ecological footprint moves to the long-run relationship with an annual adjustment speed of 1.4% when the highlighted variables are shocked. Trade policy and renewable energy consumption have been demonstrated to lower the ecological footprint, that is, enhance environmental sustainability. Bio-capacity, non-renewable energy consumption, and economic growth, however, impede environmental sustainability since they increase the ecological footprint. Specifically, a 1% increase in renewable energy consumption results in a 0.297% decrease in the long run and a 0.092% decrease in the short run. A 1% rise in trade policy reduces the ecological footprint by 0.014% in the long run and 0.011% in the short run. However, a 1% increase in nonrenewable energy consumption increases the ecological footprint by 0.551% in the long run and 0.259% in the short run. Every 1% increase in bio-capacity results in a 0.738% increase in the long-term ecological footprint and a 0.176% increase in the short-term ecological footprint. Economic growth of 1% has a long-run ecological footprint of 0.229% and a short-run ecological footprint of 0.350%. These results can be supported by Nathaniel [85], Nathaniel and Adedoyin [86], Ünal and Aktuğ [58], Usman et al. [87], and Elshimy and El-Aasar [88]. Meanwhile, these results also enrich China’s current literature on this topic. Equally, employing the Toda-Yamamoto causality test to investigate the causal relationship between the highlighted variables, the results reveal that a two-way causal relationship is found between renewable and non-renewable energy consumption and ecological footprint, as well as economic growth and ecological footprint. On the contrary, a one-way causal relationship running from bio-capacity and trade policy to ecological footprint is shown. These causal relationships are verified by Adekoya et al. [89], Ahmed et al. [90], Fonchamnyo et al. [91], and Wu [92]. The findings of this paper may then provide some evidence for Chinese policymakers to introduce environmentally sustainable development policies.

6. Conclusions

Based on the preceding sections’ analysis, this study comes to the following conclusions: (1) Because of the negative influence on the ecological footprint, renewable energy consumption is beneficial to environmental sustainability. (2) Nonrenewable energy consumption is detrimental to environmental sustainability since it has a positive influence on the ecological imprint. (3) Bio-capacity blocks environmental sustainability due to its positive effect on the ecological footprint. (4) Economic growth is a strain on environmental sustainability as a result of its positive effect on the ecological footprint. (5) Trade policy is a driving force behind environmental sustainability because it has a negative effect on the ecological footprint.

Moreover, in view of the findings and conclusions of this paper, some policy implications are proposed. Firstly, the combination of a decarbonized economy and clean, contemporary energy is critical for environmental sustainability and quality improvement. Surprisingly, environmental policy has always been at the heart of China’s aims for energy security, energy efficiency, and environmental sustainability. Secondly, more attention should be paid to developing renewable energy sources such as wind energy, solar energy, bio-fuels, etc. In fact, these kinds of renewable energy are conducive to environmental sustainability. Thirdly, to lower human-made greenhouse gas emissions, strict environmental rules and regulations should be put in place. For example, pollution and emission levies have the potential to significantly enhance environmental sustainability. Fourthly, trade policy may indeed be used to improve the quality of the environment. Specifically, it will promote environmental sustainability in the long or short run.

Finally, there are several limitations in this paper that may lead to future research possibilities for relevant experts. First, this paper only uses the time-series data to discuss China’s environmental sustainability. Future studies might employ provincial panel data to undertake an empirical analysis on this subject because of the vast disparities between different areas in China; this might lead to more interesting results. Second, some related variables, such as population and foreign direct investment, are not taken into account in this paper, which may result in the omission of some variables, resulting in the research results reported in this paper being skewed. Therefore, future studies can include all those variables to re-estimate the effects of the highlighted variables on environmental sustainability. Third, aside from China, future research may use other countries as research objects to revisit this topic and may achieve new findings. Fourth, foreign direct investment is an external force that can not be overlooked when considering the elements that drive environmental degradation in China. As a result, future studies may incorporate foreign direct investment to study this issue further and generate interesting findings. In addition, because China is experiencing a tremendous economic expansion, the conflict between rapid economic growth and environmental sustainability is becoming more apparent. The discoveries in this study give some evidence for synchronizing the sustainable development goals of both. The conclusion of this study may also provide some recommendations for developing countries similar to China. For instance, developing countries such as India and Thailand, similar to China, are now seeing fast economic expansion. Perhaps these developing countries have the same issues as China in terms of fast economic expansion and environmental sustainability. As a result, the conclusion of this study may provide some possible solutions to this issue for these similar developing countries. However, for developed countries and under-developed countries, the problems of economic growth and environmental sustainability may be different from those faced by China. Developed countries, under-developed countries, and the rest of the developing countries may become study objects for future relevant scholars who are interested in this topic.