Forest Products Trade and Sustainable Development in China and the USA: Do Bioenergy and Economic Policy Uncertainty Matter?

Abstract

The United Nations Agenda 2030 for Sustainable Development has induced the empirics to find the factors that can contribute to sustainable development. However, limited empirical evidence has estimated the impact of forest trade, bioenergy, and economic policy uncertainty on sustainable development. This study fills the gap by analyzing the impact of forest trade, bioenergy, and economic policy uncertainty on sustainable development in China and the USA using the ARDL and QARDL approaches. The findings of the ARDL model suggest that forest trade helps boost both short- and long-run sustainable development in China and the USA, while bioenergy fosters sustainable development in the short and long run only in China and in the USA, bioenergy improves sustainable development only in the long run. In contrast, economic policy uncertainty hurts sustainable development in the short and long run in China, while in the USA, only the long-run negative association between the two variables is observed. Thus, policymakers in China and the USA need to focus on enhancing trade in forest products, fostering bioenergy generation, and reducing uncertainties in economic policy to promote sustainable development.

1. Introduction

In order to achieve an equilibrium between economic performance, ecological preservation, and social well-being, the topic of sustainable development has become the central focus for policymakers worldwide [1]. In 2015, the United Nations (UN) took a significant step by introducing 17 sustainable development goals (SGDs), which were expanded on the previous Millennium Development Goals of 2000. The SDGs include but are not limited to efforts to combat climate change and the preservation of biodiversity. The UN aims to attain these targets by 2030 [2].

Climate change and global warming are believed to be the biggest hurdles to achieving sustainable development. Consequently, policymakers and professionals in advanced and emerging countries are thoroughly seeking solutions to address the challenges of climate change and global warming. To address these challenges, the Paris Agreement 2015 emphasizes limiting global warming to its peak and ensuring that the temperature increase stays below 2 °C [3]. Hence, it is crucial to comprehend the factors that can successfully tackle the problem of climate change and achieve SDGs. In this regard, research has revealed several factors contributing to environmental sustainability. Some of these factors included but not limited to globalization [4], ICT [5], renewable energy [6], environmental policy stringency [7], economic policy uncertainty, financial development [8], and trade development [9]. Nevertheless, the influence of forest trade, bioenergy, and economic policy uncertainty (EPU) on sustainable development remains uncertain, leaving a notable gap in the existing body of research. The main focus of this analysis is to fill this gap in the literature by examining the influence of the forestry trade, rural bioenergy, and EPU on environmental sustainability.

In order to maintain the balance of the ecosystem, the role of forest cannot be underestimated. Forests are crucial in the absorption of carbon emissions and, therefore, instrumental in uplifting the environmental standards [10]. In addition, forests help protect biodiversity by providing shelter to many plant and animal species. Over the past few decades, from 1990 to 2020, global forest cover has been reduced by approximately 178 million hectares [11]. This reduction has worsened environmental quality through soil erosion, loss of biodiversity, and increased carbon emissions [12]. Several factors have contributed to reducing forest cover area, including agriculture, urbanization, and socio-economic problems. Trading forest products is another crucial aspect that has contributed to forest depletion but has not received much attention. In November 2001, Doha served as the venue for the “Fourth Ministerial Conference of the World Trade Organisation (WTO)”, where it was decided to start a new series of talks given the name of “New Round”. The “New Round” of talks was initiated to finish all duties on all forest products by implementing a “zero-for-zero” tariff policy [13]. Japan rejected the same idea at the previous Uruguay Round, impeding the formation of a consensus.

In order to liberalize forest trade, several theoretical debates have taken place since the start of the 21st century. It is widely agreed upon that forest trade should be liberalized; however, the role of forest trade in achieving sustainable development has been disregarded in prior talks. Forest trade products greatly influence environmental sustainability as they may disrupt the natural balance and availability of resources [14]. Dependence on unsustainable methods during logging and extracting forest products leads to a substantial rise in deforestation and the loss of habitats and biodiversity [15]. As a result, the ecosystem suffers further degradation, increasing soil erosion, carbon footprints, and climate change. In contrast, a well-regulated forest trade may significantly boost the sustainability of the ecosystem and contribute to the local and national economies [16]. For sustainable and eco-friendly management, the Forest Stewardship Council (FSC) has issued comprehensive standards. These principles stress the efficient way of extracting the wood and other forest resources that do not harm the overall health of the forest. Conserving and protecting the forest is necessary for the promotion of biodiversity and the regrowth of trees. Thus, the influence of forest trade on sustainable development might vary depending on how the forest trade is managed.

With the growing significance of renewable energy, the demand for bioenergy is also growing because it is low-carbon energy [17]. Bioenergy has a crucial role in driving sustainable development because it can replace carbon-intensive energy sources with more eco-friendly ones [18]. Increasing the share of bioenergy in the total energy mix can reduce the carbon emissions attached to consumption and production activities. Moreover, increased reliance on bioenergy resources may contribute to the development of rural areas by generating employment opportunities in agriculture and bioenergy processing, thus fostering economic objectives while ensuring environmental sustainability. In addition, bioenergy enables the shift towards a low-carbon economy by absorbing waste materials and encouraging more effective land use, which is crucial in achieving sustainable development [19,20].

Uncertainty has also come under the limelight in recent times. The debate on uncertainty has gathered pace with the introduction of various uncertainty indices, such as EPU [21] and climate policy uncertainty [22]. Several empirics have analyzed the role of these uncertainties in assessing economic performance. For instance, Baker et al. [21] observed that the EPU index helps measure the number of changes in economic uncertainty connected to policies and forecasts declines in the overall economic performance of large economies. On the other hand, Hunjra et al. [23] stated that EPU is strongly connected to the energy market, de-carbonization, and carbon markets, which has increased its significance manifold in achieving sustainable development goals. EPU lowers the production of green technologies and energy as an uncertain economic policy creates an unpredictable atmosphere for investments in green projects and ventures, impeding progress toward sustainability [24]. Moreover, in the wake of uncertain economic policy, firms and entrepreneurs are not ready to invest money in long-term and riskier ventures that seek to reduce carbon emissions and promote sustainability.

Despite the significance of forest products trade, bioenergy, and climate policy uncertainty in achieving sustainable development, the literature in this regard is still in its infancy. Thus, a significant gap exists in the literature with regard to the nexus between forest products trade, bioenergy, climate policy uncertainty, and sustainable development. This study fills the gap by making the following contributions. First, no past study has investigated the impact of forest products trade, bioenergy, and EPU on sustainable development, while this is the first study that investigates the above-stated nexus empirically. Second, the analysis is conducted in the context of China and the USA, which are the top carbon emitters in the world and the two largest economies in the world. Thus, investigating the factors that impact the sustainable efforts in both countries has global consequences. Third, the study employs ARDL and QARDL methods, which can offer robust estimates.

Lastly, the findings of the analysis are helpful in devising the policies that can help achieve sustainable development in China and the USA. Sustainable development is the primary concern for policymakers all around the globe. The USA and China are the two economic superpowers and the largest contributors to global carbon emissions. Thus, the factors contributing to sustainable development in these economies are crucial for global sustainable development objectives. Forests are crucial for maintaining the balance of the ecosystem. The significance of forest trade has increased over the past few decades; however, forest trade can lead to deforestation if not managed properly, which has implications for sustainable development objectives. In addition, bioenergy is a clean and green form of energy that can fulfill the energy demand linked to economic activities and contribute to environmental sustainability. Further, economic policies are also crucial in driving sustainable development. Given the significance of sustainable development objectives, this study aims to examine the influence of forest trade, bioenergy, and economic policy uncertainty on sustainable development in China and the USA.

2. Literature Review

Sustainable development involves economic growth, social equity, and environmental protection. Sustainable development is affected by various factors, such as forest product trade, bioenergy, and economic policy uncertainty. Forest trade significantly enhances economic growth in those economies that are rich in forest resources by generating export revenues and income [25]. The empirical literature on the linkage between forest product trade and sustainable development is relatively scarce. However, various studies documented the impact of timber exports on economic growth. For instance, Kastner et al. [26] described the significant role of wood product trade on ongoing forest transitions. The study stated that wealthy nations accelerate returns and economic development from the wood product trade. Njimanted and Aquilas [27] investigated the influence of timber exports on economic growth in Cameroon. The study employed Johanson’s cointegration approach to data spanning from 1980 to 2014. The results report a positive influence of timber export on economic growth in the long run and an insignificant impact in the short run. Parallel to this, using the Co-integration approach, Idumah and Awe [28] explored the influence of timber exports on Nigeria’s economic growth. The study reported a positive contribution of timber export to Nigeria’s GDP. In contrast, Baumgartner [29] revealed the dual role of forest trade on sustainable development. The study concluded that forest trade has both positive and negative sustainability effects.

Like forest product trade, the relevant literature discussing the role of bioenergy in sustainable development is relatively scarce. However, many studies documented the importance of bioenergy and biomass for environmental sustainability. Biomass energy sources include wood and wood wastes, crops, agricultural waste byproducts, animal wastes, food processing waste, municipal solid waste, algae, and aquatic plants. Ciubota-Rosie et al. [30] considered the case of the Romanian economy for empirical investigation and elaborated that biomass is a kind of clean and renewable energy source that improves economic and environmental sectors in Romania. Mangoyana [31] assessed the effect of forestry and agricultural waste and cultivated bioenergy sources on the sustainable development of Sub-Saharan Africa. The study found that biomass energy production contributes significantly to sustainable development. Contrary to this, Finco and Doppler [32] reported a positive effect of oil seed production on deforestation and a negative impact of oil seed activity on local food production for the Brazilian economy. Bilgili et al. [33] explored the effect of biomass energy consumption on environmental sustainability and economic growth in the USA. The study found a negative nexus between biomass energy consumption and CO₂ emissions per capita and a positive nexus between biomass energy consumption and GDP per capita.

Robledo-Abad et al. [34] used different sources of bioenergy, such as agricultural residues, combined agricultural resources, combined forest and agricultural resources, combined forest sources, forest residues, and organic waste. They reported a positive effect of bioenergy production on technological aspects (e.g., technology development and transfer, infrastructure coverage, and access to infrastructure) and economic aspects (e.g., economic activity, economic diversification, market opportunities, and employment). However, they reported negative impacts on environmental aspects (e.g., deforestation, soil and water quality, biodiversity, and GHG emissions) and social aspects (e.g., food security, conflicts or social tensions, and health impacts). Pathak and Das [35] highlighted the role of bioenergy (e.g., bioethanol, biodiesel, biogas, and solid biomass) in fostering social, economic, and environmental sustainability, such as promoting sustainable growth in rural areas, enhancing food production, improving water quality, protecting ecosystems, and enhancing human health. Guney and Kantar [36] reported a positive influence of biomass energy consumption on the sustainable development of 36 OECD economies. Destek et al. [20] highlighted the importance of efficiently using biomass energy for sustainable development. Biomass energy is extracted from crops, grazed biomass and fodder crops, wood, and wild catch and harvest. The study considered a sample of the top five biomass energy-consuming economies: the USA, China, Brazil, Germany, and India. The study assessed sustainable development through CO₂ emissions and ecological footprint. The analysis indicated that biomass energy escalates ecological footprint but reduces CO₂ emissions.

EPU can deter economic growth and market stability, which are crucial for sustainable development. Various studies have documented the nexus between EPU and environmental sustainability, while the nexus between EPU and sustainable development has scarcely been explored. The studies conducted by Athari [37] explained that EPU negatively affects environmental sustainability through policy adjustment and demand effects. In contrast, Adedoyin and Zakari [38] reported the positive effect of EPU on environmental sustainability in the short run and, ultimately, ecological degradation in the long run. Conversely, Li et al. [39] reported the detrimental impact of EPU on ecological footprint, which is used as a proxy variable for environmental sustainability. Using a panel of 47 developing countries, Hunjra et al. [23] assessed the nexus between macroeconomic policy uncertainty and sustainable development. The analysis confirmed that macroeconomic policy uncertainty negatively influences sustainable development in the short- and long-run. Udeagha and Muchapondwa [40] also reported the negative effect of EPU on ecological sustainability in South Africa. Anser et al. [41] described that EPU is continuously increasing around the globe. The study reported the negative effect of EPU on environmental sustainability in the case of the top ten carbon-emitter countries. Zahra and Badeeb [42] found that EPU significantly negatively impacts the ecological footprint in Germany, the USA, and the UK, while insignificant in Canada and Australia.

Given the above discussion, it is apparent that the relationship between forest product trade, bioenergy, EPU, and sustainable development has been limited. The existing literature has assessed the individual impacts of forest product trade, bioenergy, and EPU on environmental sustainability and economic growth. However, a significant research gap persists in understanding their combined effects on sustainable development, particularly in the context of the USA and China. Therefore, the present study fills this gap by investigating the impact of forest product trade, bioenergy, and EPU on sustainable development in the USA and China.

3. Theoretical Framework and Model

The role of international trade is widely recognized as an instrument in promoting economic growth. There are several advantages for countries that take part in international trade. As a result of international trade, consumers can enjoy a wide variety of goods and services, firms can utilize the technological inflows from different parts of the world, entrepreneurs can sell their products worldwide, and resource efficiency can be improved [43]. According to the theory of comparative advantage, the benefits of international trade can be derived from the comparative advantage principle [44]. Nevertheless, it is also evident that trade openness has serious consequences for sustainable development, particularly in developing economies where environmental regulations are not that stringent. This can be detrimental to the ecosystem. The negative impact of trade liberalization on sustainability is in line with the pollution haven hypothesis, which states that less stringent environmental regulations can attract companies that generate higher pollution levels [45]. This may result in a clustering of pollution-intensive activities in certain places. Consequently, there is a contention that trade liberalization, especially without robust environmental protections, may increase environmental damage. However, trade liberalization can also allow the transfer of green technologies between developed and developing economies, reducing carbon emissions and contributing to sustainability [46]. This phenomenon can be justified on the basis of the pollution halo hypothesis.

Energy is generally recognized as a vital component of the production function in every business, as it significantly contributes to enhancing economic output [47]. Increased reliance on conventional energy resources is the primary source of carbon footprints, resulting in environmental degradation. Thus, energy consumption is considered the primary driver of deteriorating environmental quality. Therefore, policymakers are striving to reduce energy demand, control emissions, and decrease dependence on fossil-fuel-based energy sources by promoting their efficient usage [48]. Moreover, boosting renewable energy production in the total energy mix is a viable option for accomplishing the goals mentioned above. Biomass is an abundant and renewable energy source that has the potential to replace fossil fuels in the energy mix and promote economic and environmental sustainability [49].

EPU erupts as a result of the ambiguity attached to fiscal and monetary policies that impact the environment [50]. Global uncertainties impact the economic and political stability of every country in the globe, and vice versa. For example, the second Gulf War in 2003 significantly affected worldwide markets [51]. In addition, the COVID-19 pandemic has generated economic instability on a global scale [52]. On the theoretical front, EPU, in addition to economic implications, also has economic implications. EPU can facilitate the use of conventional and outdated techniques that lead to environmental deterioration [53]. In addition, the investment in renewable energy and green production techniques can be significantly hampered due to increased EPU, ultimately resulting in negative impacts on sustainable development.

The Environmental Kuznets Curve (EKC) highlights the nexus between economic growth and environmental degradation in an inverted U-shaped relationship. The basic idea behind this theory is that environmental quality worsens during the initial stage of economic growth, which improves in the later stages when a certain level of economic growth is achieved [54]. Thus, EKC is used to provide a theoretical base for the sustainable development model. In the context of forest trade and sustainable development, forest trade can help foster economic growth and protect the ecosystem if the trade is conducted following the principles of sustainable forest management and certifications. With the rising economic growth, the demand for sustainable forest products increases, which confirms the EKC’s idea of improved environmental quality at higher economic growth [55]. Bioenergy is a carbon-free source of energy, which can replace fossil fuels in the energy mix at the later stages of economic development along with improved technology and increased ecological awareness, thereby contributing to the development of the EKC nexus between economic growth and environmental degradation [56]. Likewise, EPU can also impact the path of the EKC by hurting investments in green technologies and clean energy ventures [57]. Uncertain economic policies can prioritize short-term economic goals at the cost of environmental quality, delaying the turning point in EKC and thus negatively impacting sustainable development.

A sound and well-established financial structure is a crucial driver of economic growth [58]. However, in the early stages of economic growth, increased access to financial resources can significantly boost economic activities, increasing the extraction and use of natural resources and ultimately contributing to environmental degradation. However, due to the development of the financial sector, financial institutions and markets are in a position to provide financial capital for investments in clean and green projects, which are crucial for fostering sustainable development [59]. Likewise, the theoretical link between ICT and sustainable development is complicated. Although ICT contributes to economic growth during the early stages of ICT development, it also fosters energy demand and carbon emissions [60]. The increased use of ICT has revolutionized every sector of the economy by replacing physical resources with information, leading to dematerialization and decarbonization, crucial aspects of sustainable development [61]. ICT diffusion has a crucial role in spreading knowledge regarding the benefits of a clean and green environment, encouraging businesses and consumers to adopt more sustainable behaviors [62]. Financial development and ICT are crucial in helping nations reach the turning point at EKC more quickly, where the nation’s environmental, social, and economic goals coincide. This study primarily examines the impact of forest products trade, bioenergy, and EPU on sustainable development. Following the above theoretical and empirical literature, the basic Equation (1) of the model can be written as:

$${\mathrm{S}\mathrm{D}}_{\mathrm{t}}={\mathsf{\delta }}_0+{\mathsf{\delta }}_1{\mathrm{F}\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E}}_{\mathrm{t}}+{\mathsf{\delta }}_2{\mathrm{B}\mathrm{E}}_{\mathrm{t}}+{\mathsf{\delta }}_3{\mathrm{E}\mathrm{P}\mathrm{U}}_{\mathrm{t}}+{\mathsf{\delta }}_4{\mathrm{F}\mathrm{D}}_{\mathrm{t}}+{\mathsf{\delta }}_5{\mathrm{I}\mathrm{C}\mathrm{T}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(1)

where, ${\mathrm{S}\mathrm{D}}_{\mathrm{t}}$ is the sustainable development, ${\mathsf{\delta }}_0$ is the constant term, and εit is the error term. Sustainable development depends on forest products trade (FTRADE), bioenergy (BE), economic policy uncertainty (EPU), financial development (FD), and information and communication technology (ICT). Forest trade is expected to impact sustainable development either positively or negatively. The impact of forest trade on sustainable development will be positive if the forest products are produced by utilizing green production methods and adopting sustainable forest management practices. On the other hand, the excessive cutting of forests for the production of forest trade without adopting sustainable forest management practices can increase carbon emissions during the production and trading of forest goods. The expected outcome of the bioenergy on sustainable development could also be positive or negative. The positive outcome is possible because bioenergy is used as an alternative to fossil fuels and thus produces far less carbon than conventional energy sources during economic activities, contributing to sustainable development. On the other hand, the negative impact of bioenergy is possible as bioenergy cultivation may cause deforestation, biodiversity loss, and reduced food output, which may worsen food security. Due to bioenergy production, land-use changes may occur that can lead to solid erosion, water scarcity, and enhanced greenhouse gas emissions, negatively impacting sustainable development. EPU is expected to negatively influence sustainable development because uncertain economic policies hinder investment in green ventures and delay the implementation of climate policies, leading to enhanced carbon footprints and hurting sustainable development. Financial development is expected to enhance sustainable development as well-established and developed financial systems can facilitate investment in renewable energy technologies and green projects by increasing access to financial products and services. Enhancing investment in renewable energy projects and green manufacturing techniques can decouple economic growth and carbon emissions, thus contributing to sustainable development. Lastly, information and communication technology is important in transitioning an economy towards a weightless and less capital-intensive one due to excessive reliance on information resources, negatively impacting sustainable development.

4. Econometric Methods

4.1. Unit Root Tests

Checking whether a series contains a unit root or not is the initial step in a time series analysis. Two well-known unit root tests used for this purpose are the Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests. The ADF is an extension of the Dickey-Fuller (DF) test. The ADF test is considered superior as it includes the lagged differences and has the power to account for serial correlation. In contrast, the PP also extends the DF test by transforming the DF into a non-parametric test to address issues of serial correlation and heteroskedasticity in the error terms. The rejection of the null hypothesis in both of these tests supports the conclusion that the variable is non-stationary, while the alternative hypothesis provides evidence that the variable is stationary.

4.2. ARDL

While using time series data, several cointegration techniques (e.g., Engle and Granger [63], Johansen [64], and Johansen and Juselius [65] can be used for analyzing the long-term relationship between the outcome variable and the regressors [66]. Nonetheless, our choice for this study is ARDL, which is considered better in many aspects. Since estimating the short and long-run effects is one of our objectives, we chose the ARDL due to its capability to fulfill this objective. The aforementioned specification (1) provides only long-term estimates ranging from ${\mathsf{\delta }}_1-{\mathsf{\delta }}_5$. In order to evaluate the short-run effects of our selected regressors on sustainable development (outcome variable), we restate Equation (1) in an error-correction form as below:

$$\begin{gathered} {\mathsf{\Delta }\mathrm{S}\mathrm{D}}_{\mathrm{t}}={\mathsf{\delta }}_0+\sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{{\rm Y}}}_{1\mathrm{k}}{\mathsf{\Delta }\mathrm{S}\mathrm{D}}_{\mathrm{t}-\mathrm{k}}+\sum _{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{{\rm Y}}}_{2\mathrm{k}}{\mathsf{\Delta }\mathrm{F}\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E}}_{\mathrm{t}-\mathrm{k}}+\sum _{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{{\rm Y}}}_{3\mathrm{k}}{\mathsf{\Delta }\mathrm{B}\mathrm{E}}_{\mathrm{t}-\mathrm{k}}+\sum _{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{{\rm Y}}}_{4\mathrm{k}}{\mathsf{\Delta }\mathrm{E}\mathrm{P}\mathrm{U}}_{\mathrm{t}-\mathrm{k}}+\sum _{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{{\rm Y}}}_{5\mathrm{k}}{\mathsf{\Delta }\mathrm{F}\mathrm{D}}_{\mathrm{t}-\mathrm{k}} \\ +\sum _{\mathrm{k}=0}^{\mathrm{n}}{\mathsf{{\rm Y}}}_{6\mathrm{k}}{\mathsf{\Delta }\mathrm{I}\mathrm{C}\mathrm{T}}_{\mathrm{t}-\mathrm{k}}+{\mathsf{\delta }}_1{\mathrm{S}\mathrm{D}}_{\mathrm{t}-1}+{\mathsf{\delta }}_2{\mathrm{F}\mathrm{T}\mathrm{R}\mathrm{A}\mathrm{D}\mathrm{E}}_{\mathrm{t}-1}+{\mathsf{\delta }}_3{\mathrm{B}\mathrm{E}}_{\mathrm{t}-1}+{\mathsf{\delta }}_4{\mathrm{E}\mathrm{P}\mathrm{U}}_{\mathrm{t}-1}+{\mathsf{\delta }}_5{\mathrm{F}\mathrm{D}}_{\mathrm{t}-1}+{\mathsf{\delta }}_6{\mathrm{I}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{\mathrm{t}} \end{gathered}$$

(2)

Pesaran et al. [67] provided us the procedure to present Equation (1) into error-correction framework (2), which is formerly known as ARDL. The most unique feature of specification (2) is the provision of short and long-run estimates in a single step. The short-term (${{\rm Y}}_{2k}$ − ${{\rm Y}}_{6k}$) and the long-term (${\delta }_2-{\delta }_6)$ effects can be easily identified from specification (2). Two cointegration tests are recommended to make sure long-term estimates are accurate. The first of these tests is the F-test employed to estimate the “combined significance of the lagged-level variables” and the second test is the t-test or ECM (−1). By relying on the Monte Carlo experiment, Pesaran et al. [67] create new critical values, which take into consideration the nonstandard distributions of both tests in this specific scenario. While designing the critical values, Pesaran et al. [67] also account for the integration characteristics of the variables, and they demonstrate that their approach allows the variables to be a combination of I(0) and I(1), removing the need for pre-unit-root testing. Nonetheless, it is preferable to use the unit root to verify that no variable is I(2) since this method has a restriction in handling I(2) variables. Further, producing accurate estimates in the presence “small sample, endogeneity, and serial correlation”, are some of the notable features of the ARDL.

4.3. BDS Test

Since it is widely believed that variables in social sciences are dependent on human behavior, which is unpredictable, these variables follow the asymmetric path [68]. Therefore, we should check whether the variables included in our model exhibit asymmetric or non-linear behavior or not. This can be performed using the BDS test, which was first suggested by Broock et al. [69] as a test of non-linearity. This test is reliant on two hypotheses, null and alternative ones. The null represents the linear, while the alternative represents the non-linear series.

4.4. QARDL

The QARDL technique of Cho et al. [70] has been used to investigate the asymmetric connection between forest trade, bioenergy, economic policy uncertainty, and sustainable development based on the outcomes of the BDS test. Merging the advantages of quantile regression into the ARDL framework, the QARDL enables the estimation of both short- and long-term estimates at diverse quantile levels for sustainable development. The selection of QARDL was motivated by a number of considerations, such as its capacity to investigate associations by tying together quantiles and calculate the influence of bioenergy, forest trade, and economic policy uncertainty on sustainable development, so enabling a more comprehensive and exhaustive examination of linkages. This distinguishes and sets QARDL apart from other varieties of approaches [70]. The methodological flowchart is shown in Figure 1.

4.5. Strengths and Limitations of the Methods

One of the greatest strengths of the ARDL and QARDL is their ability to offer short and long-run estimates at once [71]. Moreover, accounting for the integrating properties of the variables makes these methods so special as they can estimate the model even with I(0) and I(1) variables. However, these methods do not allow the I(2) variable to be included in the model [72]. Further, the QARDL requires a long data series; for this, we need to convert yearly data into quarterly, which is a major limitation of this technique [73].

5. Data and Descriptive Analysis

This study aims to assess the impact of forest products trade, bioenergy, and economic policy uncertainty on sustainable development in China and the USA while controlling for financial development and ICT. The dependent variable in this research is sustainable development (SD). Sustainable development (SD) is proxied by adjusted net savings, excluding particulate emission damage as % of GNI. It is widely recognized as a comprehensive measure of SD [74,75]. It accounts for all three SD measures, including economic, social, and environmental. SD is estimated using three pillars: economic (net national savings), social (education expenditure), and environmental (energy depletion, forest depletion, mineral depletion, and carbon emissions). Education expenditure is an important determinant of social and economic inequality in the economy, while environmental indicators are crucial for measuring environmental performance. Forest products trade (FTRADE) encompasses various forest products’ export and import volumes, including timber and non-timber forest products. Existing research suggests that FTRADE can enhance economic development [27]. FTRADE is measured by the forest products traded in USD 1000. Bioenergy (BE) reduces carbon emissions and promotes renewable energy consumption, which is crucial for sustainable development [31]. The bioenergy (BE) variable is assessed through bioenergy production in petajoules. Bioenergy production includes biogasoline and biodiesel. Economic policy uncertainty (EPU) influences sustainable development by affecting consumer and producer confidence [23]. EPU is assessed through the economic policy uncertainty index. The economic policy uncertainty index is constructed by Baker et al. [21] using three components: news-based policy uncertainty, tax code expiration data, and disagreement among economic forecasters. Financial development (FD) encompasses financial institutions and market development that directly influence sustainable development [71]. Financial development (FD) is determined through the financial development index. ICT infrastructure promotes social, economic, and environmental development that significantly contributes to sustainable development [72]. The ICT index is composed of mobile cellular subscriptions (per 100 people), individuals using the internet (% of the population), and fixed telephone subscriptions (per 100 people). This index is made with the help of principal component analysis (PCA), a well-known approach for integrating different indicators into one weighted composite index [76]. First, the correlation between the variables is checked via a correlation matrix, and then the eigenvalues are calculated for each separate factor. The factors with an eigenvalue greater than 1 are selected for the construction of the index, while the factors with an eigenvalue less than 1 are rejected and not included in the construction of the weighted index.

Our study sourced data from various sources: SD data is assembled from the World Development Indicators (WDI), FTRADE data is taken from Food and Agriculture Organization statistics (FAOSTAT), BE data is collected from the British Petroleum (BP), EPU data is gathered from the Baker, Bloom, and Davis, FD data is retrieved from the International Monetary Fund (IMF), and the authors calculate ICT index data.

Table 1. Variables description and sources.

Variables	Symbol	Definitions	Sources
Sustainable development	SD	Adjusted net savings, excluding particulate emission damage (% of GNI)	WDI
Forest products trade	FTRADE	Forest products trade (1000 USD)	FAOSTAT
Bioenergy	BE	Bioenergy production (petajelous)	BP
Economic policy uncertainty	EPU	Economic policy uncertainty index	Baker, Bloom and Davis
Financial development	FD	Financial development index	IMF
Information and communication technology	ICT	ICT index (Individuals using the Internet (% of population), mobile cellular subscriptions (per 100 people), fixed telephone subscriptions (per 100 people))	Authors’ calculation

provides detailed information regarding the model variables and data sources.

Table 2. Descriptive analysis.

	Mean	Median	Max	Min	Std. Dev.	Skewness	Kurtosis	Jarque-Bera	Prob.
China
SD	3.052	3.063	3.320	2.755	0.161	−0.059	1.990	4.655	0.098
FTRADE	17.36	17.38	18.10	16.45	0.583	−0.258	1.491	11.44	0.003
BE	3.393	4.254	4.946	−1.354	1.607	−1.111	3.069	22.25	0.000
EPU	4.678	4.662	6.213	3.199	0.748	0.086	2.133	3.515	0.172
FD	0.512	0.517	0.717	0.352	0.105	0.090	1.605	8.906	0.012
ICT	2.823	3.534	3.998	−1.553	1.482	−1.560	4.416	52.80	0.000
USA
SD	1.731	1.880	2.292	−0.257	0.533	−2.097	7.588	173.8	0.000
FTRADE	17.64	17.66	18.05	17.38	0.156	0.552	3.236	5.740	0.057
BE	6.302	6.834	7.358	4.262	1.047	−0.564	1.682	13.55	0.001
EPU	4.795	4.820	5.996	3.953	0.399	0.282	2.878	1.500	0.472
FD	0.901	0.912	0.942	0.787	0.030	−1.947	6.178	113.7	0.000
ICT	4.084	4.230	4.282	3.110	0.294	−1.890	5.508	92.62	0.000

summarizes the descriptive statistics for China and the USA. Descriptive statistics provides estimates of central tendency through mean scores spread of data through standard deviation, and the normality of variables is confirmed through the Jarque-Bera test. The highest mean scores for FTRADE in China and the USA are 17.36 and 17.64. Meanwhile, the lowest mean scores for FD in China and the USA are 0.512 and 0.901. The mean scores for the rest of the variables in China (USA) are recorded as 3.052 (1.731) for SD, 3.393 (6.302) for BE, 4.678 (4.795) for EPU, and 2.823 (4.084) for ICT. The SD scores for China (USA) are reported as 0.161 (0.533) for SD, 0.583 (0.156) for FTRADE, 1.607 (1.047) for BE, 0.748 (0.399) for EPU, 0.105 (0.030) for FD, and 1.482 (0.294) for ICT. The skewness and kurtosis tests highlight all variables’ shape and tail behaviors. The skewness test signifies positively skewed distributions in EPU and FD for China and FTRADE and EP for the USA. Conversely, the rest of the variables exhibit negative skewness. In the end, the p-values under the Jarque-Bera test suggest that only the EPU variable follows a normal distribution, and the rest of the variables do not. China’s and the USA’s graphical trends of key variables are reported in Figure 2 and Figure 3. The data reveal that the highest levels of FTRADE were recorded in 2022, reaching 18.08 thousand USD in China and 18.04 thousand USD in the USA. The figures also indicate a steady increase in BE production in both countries since 2000, with peak values in 2022 at 4.608 petajoules for China and 7.353 petajoules for the USA. Lastly, the highest EPU score in China was observed in 2022, at 5.747, while in the USA, the highest score was recorded in 2000, at 5.777. In short, between 2000 and 2022, China and the USA maintained relatively stable FTRADE. China and the USA BE production has increased more rapidly. EPU has risen in both countries.

6. Empirical Results

Our study performs unit root tests employing the ADF and PP methods.

Table 3. Results of unit root tests.

	ADF		PP
	I (0)	I (1)	I (0)	I (1)
China
SD	−2.275	−2.838 *	−1.527	−4.239 ***
FTRADE	−1.602	−2.805 *	−0.996	−5.274 ***
BE	−2.437	−2.884 *	−5.830 ***
EPU	−2.373	−11.97 ***	−2.281	−11.97 ***
FD	0.599	−2.650 *	0.047	−4.551 ***
ICT	−5.926 ***		−11.43 ***
USA
SD	−2.716 *		−2.092	−4.656 ***
FTRADE	−0.484	−2.608 *	−1.182	−4.901 ***
BE	−1.678	−2.798 *	−2.785 *
EPU	−3.322 **		−3.322 **
FD	−2.903 **		−3.906 ***
ICT	−4.494 ***		−14.19 ***

Note: ** p < 0.05, * p < 0.1, *** p < 0.01.

summarizes the findings of both ADF and PP unit root tests. The upper panel in Table 3 reports the findings for China, while the lower panel shows the results for the USA. For the Chinese sample, the outcomes of the ADF test reveal that only ICT exhibits I(0), whereas SD, FTRADE, BE, EPU, and FD are integrated at the order I(1). The PP test shows that BE and ICT are integrated at I(0), while SD, FTRADE, EPU, and FD are integrated at order I(1). In the USA, the ADF test reports that SD, EPU, FD, and ICT are integrated at I(0), whereas FTRADE and BE signify stationarity at I(1). The PP test illustrates that BE, EPU, FD, and ICT are I(0) integrated, while SD and FTRADE are I(1) integrated. These findings confirm a mixed order of integration among variables in both China and the USA samples. Given the mixed order of integration among variables, performing ARDL models for regression tasks becomes feasible. After the confirmation of unit root properties, we investigate the presence of nonlinearity in data. The BDS test is applied to detect the nonlinearity of the data series. In

Table 4. BDS test results.

		SD		FTRADE		BE		EPU
	Dimension	BDS-Stat	Std. Error	BDS-Stat	Std. Error	BDS-Stat	Std. Error	BDS-Stat	Std. Error
China
	2	0.180 ***	0.004	0.202 ***	0.005	0.208 ***	0.007	0.138 ***	0.005
	3	0.300 ***	0.007	0.343 ***	0.007	0.354 ***	0.011	0.239 ***	0.008
	4	0.376 ***	0.008	0.439 ***	0.009	0.457 ***	0.013	0.301 ***	0.009
	5	0.421 ***	0.008	0.506 ***	0.009	0.529 ***	0.014	0.341 ***	0.010
	6	0.445 ***	0.008	0.553 ***	0.009	0.579 ***	0.014	0.361 ***	0.009
USA
	2	0.180 ***	0.011	0.158 ***	0.008	0.208 ***	0.006	0.088 ***	0.006
	3	0.300 ***	0.017	0.251 ***	0.012	0.352 ***	0.009	0.143 ***	0.009
	4	0.376 ***	0.021	0.301 ***	0.015	0.453 ***	0.011	0.179 ***	0.011
	5	0.421 ***	0.022	0.324 ***	0.015	0.523 ***	0.011	0.193 ***	0.011
	6	0.447 ***	0.021	0.328 ***	0.015	0.571 ***	0.011	0.193 ***	0.011

Note: *** p < 0.01.

, the finding depicts that the coefficient estimates of all variables are significant. It confirms the nonlinearity of both samples’ SD, FTRADE, BE, and EPU variables.

Table 5. Long and short-run estimates (ARDL).

	China				USA
Variable	Coefficient	Std. Error	t-Stat	Prob.	Coefficient	Std. Error	t-Stat	Prob.
Long-run
FTRADE	1.446 ***	0.290	4.990	0.000	4.159 *	2.419	1.719	0.089
BE	2.479 ***	0.814	3.044	0.003	1.873 **	0.840	2.229	0.028
EPU	−0.012 ***	0.004	−2.896	0.005	−1.572 **	0.707	−2.224	0.028
FD	0.873 **	0.427	2.045	0.044	4.674 ***	0.900	5.193	0.000
ICT	0.581 **	0.259	2.246	0.027	5.537	21.25	0.260	0.795
Short-run
FTRADE	0.120 **	0.055	2.176	0.032	1.397 ***	0.273	5.116	0.000
FTRADE (−1)	−0.099 *	0.055	−1.794	0.076	−1.673 ***	0.273	−6.119	0.000
BE	0.061 **	0.029	2.091	0.039	−0.452	0.396	−1.141	0.257
BE (−1)	−0.086 ***	0.028	−3.065	0.003	0.576	0.382	1.507	0.135
EPUU	0.001	0.005	0.140	0.889	−0.008	0.039	−0.214	0.831
EPU (−1)					0.404	1.509	0.268	0.790
FD	0.963 ***	0.216	4.463	0.000	−0.679	1.370	−0.496	0.621
FD (−1)	−0.880 ***	0.227	−3.881	0.000
ICT	0.303 ***	0.106	2.861	0.005	0.758 ***	0.134	5.644	0.000
ICT (−1)	−0.322 ***	0.101	−3.172	0.002
C	8.314 ***	3.169	2.624	0.010	11.41 ***	3.993	2.858	0.005
Diagnostic
F-test	4.665 ***				6.797 ***
ECM (−1) *	−0.509 ***	0.092	−5.524	0.000	−0.553 ***	0.080	−6.904	0.000
LM	1.654				1.325
RESET	1.916				0.658
CUSUM	S				S
CUSUM-sq	S				S

Note: ** p < 0.05, * p < 0.1, *** p < 0.01.

shows the long-run and short-run ARDL estimates for the Chinese and USA economies. Long-run findings in Table 5 show that FTRADE exerts a significant positive effect on SD in China and the USA, where a 1% increase in FTRADE enhances SD by 1.446% in China and 4.159% in the USA in the long run. Thus, the increasing impact of FTRADE implies that higher levels of FTRADE are associated with an enhancement in sustainable development in China and USA. Regarding BE in the model, the results report that the coefficients of BE are significant and positive in both models, implying that a 1% upsurge in BE positively enhances SD by 2.479% in China and 1.873% in the USA in the long run. Therefore, these findings affirm that enhanced bioenergy investment could be conducive to sustainable development in China and the USA. Regarding EPU in the model, we find a significant and negative impact of EPU on SD in both models, where a 1% rise in EPU dampens SD by 0.012% in China and 1.572% in the USA in the long run. Thus, the dampening effect of EPU implies that an upsurge in EPU exerts a detrimental impact on sustainable development in China and the USA. Regarding control variables in the model, the results report that the estimates of FD are significant and positive in both models. It depicts that a 1% rise in FD improves SD by 0.873% in China and 4.674% in the USA in the long run. These findings confirm financial development’s significant role in enhancing sustainable development in China and the USA. However, financial development’s impact on sustainable development is more prominent in the USA. Our study finds a significant and positive impact of ICT on SD in the Chinese economy model only, where a 1% improvement in ICT increases SD by 0.581% in China in the long run. Although ICT exerts a positive impact on SD in the case of the USA, the coefficient estimate is insignificant.

Short-run findings in Table 5 report that FTRADE brings a significant and positive increase in SD in China and the USA. The estimated coefficients reveal that a 1% upsurge in FTRADE increases SD by 1.120% in China and 1.397% in the USA in the short run. The coefficients of BE are significant and positive only in the Chinese economy model. It reveals that a 1% increase in BE improves SD by 0.061% in China in the short run. Conversely, our study finds a statistically insignificant impact of EPU on SD in both models in the short run. On the other hand, FD only reports a significant and positive impact on SD in the Chinese economy model. A 1% increase in FD generates a 0.963% increase in SD in China in the short run. ICT brings significant improvements to SD in China and the USA. The estimated coefficients report that a 1% increase in ICT brings a 0.303% and 0.758% increase in SD in China and the USA in the short run. The lower panel of Table 5 summarizes the results for the F-test, speed of adjustment (ECM) test, serial correlation test, model specification test, and stability test. The F-test and ECM tests confirm the evidence of long-run cointegration connection among variables in both models. The ECM terms in both models are significant and negative, with coefficient estimates of 0.509 in China and 0.553 in the USA. It shows that any disturbance in China and the USA will converge towards equilibrium at the speed of 51% (China) and 55% (USA) in the span of 1 year. The LM test signifies the absence of serial correlation in both models. Both the models are correctly specified, as depicted by the results of the RESET test. Moreover, both models are stable, as shown by the outcomes of CUSUM and CUSUM-sq tests.

Our study employs the QARDL method for robustness testing.

Table 6. China long and short-run estimates (QARDL).

	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
Long-run
FTRADE	0.529 ***	0.516 ***	0.399 ***	0.370 **	0.286	0.089	0.164 *	0.263 ***	0.339 ***	0.249 **	0.277 ***
	(0.116)	(0.129)	(0.144)	(0.169)	(0.176)	(0.073)	(0.095)	(0.086)	(0.078)	(0.097)	(0.080)
BE	0.507 ***	0.486 ***	0.438 ***	0.382 ***	0.335 ***	0.262 ***	0.308 ***	0.356 ***	0.362 ***	0.321 ***	0.315 ***
	(0.039)	(0.041)	(0.049)	(0.070)	(0.075)	(0.039)	(0.052)	(0.046)	(0.042)	(0.041)	(0.037)
EPU	−0.084 ***	−0.083 ***	−0.064 **	−0.080 ***	−0.080 ***	−0.070 ***	−0.090 ***	−0.123 ***	−0.144 ***	−0.134 ***	−0.150 ***
	(0.018)	(0.021)	(0.026)	(0.025)	(0.023)	(0.017)	(0.022)	(0.024)	(0.023)	(0.023)	(0.023)
FD	0.059	0.067	0.553	0.290	0.586	1.268 ***	1.079 **	0.653	0.111	0.177	0.324
	(0.470)	(0.507)	(0.559)	(0.602)	(0.691)	(0.326)	(0.434)	(0.481)	(0.560)	(0.582)	(0.421)
ICT	0.353 ***	0.334 ***	0.300 ***	0.262 ***	0.233 ***	0.187 ***	0.209 ***	0.235 ***	0.232 ***	0.206 ***	0.204 ***
	(0.028)	(0.031)	(0.034)	(0.042)	(0.050)	(0.030)	(0.037)	(0.035)	(0.034)	(0.033)	(0.028)
Short-run
FTRADE	−0.147	−0.127	−0.139	−0.120	−0.085	−0.018	0.008	0.046	0.088	0.026	0.069
	(0.114)	(0.095)	(0.091)	(0.082)	(0.090)	(0.070)	(0.067)	(0.073)	(0.126)	(0.072)	(0.058)
FTRADE (−1)	0.201 ***	0.183 **	0.232 ***	0.177 **	0.142 *	0.079	0.063	0.020	−0.011	0.062	0.014
	(0.070)	(0.089)	(0.064)	(0.076)	(0.082)	(0.063)	(0.060)	(0.067)	(0.125)	(0.072)	(0.057)
BE	0.003	0.010	0.021	0.024	0.025	0.030 *	0.037 **	0.041 **	0.046 ***	0.048 **	0.040 **
	(0.013)	(0.016)	(0.016)	(0.016)	(0.017)	(0.016)	(0.015)	(0.016)	(0.018)	(0.021)	(0.015)
EPU	−0.023 ***	−0.023 ***	−0.019 ***	−0.020 ***	−0.020 ***	−0.019 ***	−0.019 ***	−0.017 ***	−0.021 **	−0.023 ***	−0.022 ***
	(0.003)	(0.004)	(0.003)	(0.003)	(0.003)	(0.003)	(0.004)	(0.004)	(0.009)	(0.007)	(0.005)
FD	−0.178	−0.179	0.251	0.472	0.565	0.506	0.418	0.432	0.328	0.763 **	0.778 ***
	(0.508)	(0.587)	(0.370)	(0.403)	(0.407)	(0.380)	(0.362)	(0.344)	(0.879)	(0.317)	(0.288)
FD (−1)	0.143	0.183	0.728 **	0.994 ***	1.090 ***	1.065 ***	1.089 ***	1.049 ***	0.403	0.098	0.066
	(0.505)	(0.589)	(0.335)	(0.313)	(0.297)	(0.279)	(0.268)	(0.241)	(0.792)	(0.323)	(0.298)
ICT	0.010	0.015	0.020	0.022	0.022	0.026 *	0.033 **	0.036 ***	0.038 **	0.037 **	0.028 **
	(0.011)	(0.012)	(0.013)	(0.014)	(0.014)	(0.014)	(0.013)	(0.013)	(0.015)	(0.018)	(0.014)
C	11.80 ***	11.61 ***	9.840 ***	9.377 ***	8.177 ***	5.216 ***	6.433 ***	8.040 ***	9.191 ***	7.712 ***	8.064 ***
	(1.785)	(1.985)	(2.188)	(2.564)	(2.659)	(1.118)	(1.452)	(1.315)	(1.169)	(1.501)	(1.265)
ECM (−1)	−0.296 ***	−0.312 ***	−0.276 ***	−0.297 ***	−0.294 ***	−0.311 ***	−0.349 ***	−0.303 ***	−0.386 ***	−0.433 ***	−0.432 ***
	(0.081)	(0.098)	(0.073)	(0.076)	(0.081)	(0.074)	(0.068)	(0.066)	(0.101)	(0.057)	(0.047)

Note: ** p < 0.05, * p < 0.1, *** p < 0.01. Standard errors in parentheses.

summarizes the QARDL estimates of China, and

Table 7. USA long and short-run estimates (QARDL).

	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
Long-run
FTRADE	2.928 ***	2.758 ***	1.788 *	1.424	0.856	0.725	0.970	1.520	1.748 *	2.077 **	2.420 ***
	(0.362)	(0.406)	(0.982)	(1.111)	(0.986)	(1.048)	(1.011)	(1.187)	(1.004)	(0.983)	(0.747)
BE	−0.768 ***	−0.757 ***	−0.206	0.085	0.356 ***	0.359 ***	0.333 **	0.326 **	0.259 *	0.265 **	0.299 ***
	(0.161)	(0.178)	(0.332)	(0.368)	(0.119)	(0.127)	(0.140)	(0.153)	(0.150)	(0.134)	(0.112)
EPU	−0.002	0.079	−0.044	−0.141	−0.145	−0.211	−0.183	−0.187	−0.155	−0.196 *	−0.226 **
	(0.153)	(0.184)	(0.204)	(0.190)	(0.140)	(0.154)	(0.150)	(0.166)	(0.164)	(0.117)	(0.108)
FD	6.951 ***	4.280 ***	5.341 *	6.830 **	6.323 ***	5.705 **	4.647 **	4.319 **	4.051 **	3.666 **	3.052 *
	(1.257)	(1.211)	(2.822)	(3.054)	(2.204)	(2.275)	(2.100)	(2.015)	(1.944)	(1.719)	(1.698)
ICT	1.129 **	0.792	1.922 **	2.461 **	3.136 ***	2.922 ***	2.644 ***	2.647 ***	1.966 *	2.144 **	2.123 ***
	(0.521)	(0.535)	(0.844)	(1.017)	(0.433)	(0.569)	(0.763)	(0.927)	(1.052)	(0.925)	(0.733)
Short-run
FTRADE	0.455 ***	0.459 ***	0.439 ***	0.532 **	0.314	0.228	0.103	0.361	0.288	0.527 *	0.582 **
	(0.096)	(0.116)	(0.112)	(0.211)	(0.279)	(0.233)	(0.279)	(0.387)	(0.423)	(0.292)	(0.288)
FTRADE (−1)	0.281 **	0.302 **	0.285 **	0.361 *	0.125	0.015	0.129	0.140	0.066	0.268	0.357
	(0.109)	(0.134)	(0.112)	(0.217)	(0.298)	(0.226)	(0.270)	(0.376)	(0.412)	(0.379)	(0.277)
BE	0.217 ***	0.205 ***	0.184 ***	0.218 ***	0.238 ***	0.296 ***	0.290 ***	0.286 ***	0.296 ***	0.296 ***	0.301 ***
	(0.018)	(0.027)	(0.016)	(0.047)	(0.056)	(0.022)	(0.022)	(0.047)	(0.029)	(0.031)	(0.028)
EPU	−0.152 ***	−0.132 ***	−0.101 ***	−0.140 ***	−0.141 ***	−0.166 ***	−0.158 ***	−0.167 ***	−0.166 ***	−0.170 ***	−0.184 ***
	(0.017)	(0.025)	(0.018)	(0.030)	(0.031)	(0.019)	(0.022)	(0.032)	(0.027)	(0.024)	(0.019)
EPU (−1)	0.020	0.009	0.004	0.029	0.030 *	0.033 **	0.031 **	0.021	0.019	0.009	0.008
	(0.014)	(0.019)	(0.016)	(0.018)	(0.017)	(0.015)	(0.015)	(0.022)	(0.019)	(0.019)	(0.016)
FD	0.207	0.113	0.078	0.695	1.635	2.078	1.815	1.729	2.212	2.625 *	2.859 *
	(1.157)	(1.595)	(1.086)	(3.128)	(3.561)	(1.348)	(1.409)	(2.049)	(1.793)	(1.533)	(1.622)
ICT	2.065 ***	1.910 ***	1.834 ***	2.160 ***	2.299 ***	2.805 ***	2.785 ***	2.776 ***	2.877 ***	2.908 ***	2.959 ***
	(0.168)	(0.230)	(0.159)	(0.475)	(0.538)	(0.202)	(0.211)	(0.452)	(0.270)	(0.288)	(0.259)
C	−5.044	−4.378	−3.037	−1.180	4.554	8.478	9.202	8.785	3.350	8.284 *	10.18 **
	(5.623)	(6.328)	(7.661)	(7.929)	(9.504)	(9.778)	(7.712)	(7.623)	(6.988)	(4.555)	(4.259)
ECM (−1)	−0.581 ***	−0.500 ***	−0.488 ***	−0.574 ***	−0.594 ***	−0.702 ***	−0.697 ***	−0.712 ***	−0.741 ***	−0.761 ***	−0.779 ***
	(0.089)	(0.105)	(0.108)	(0.127)	(0.132)	(0.070)	(0.073)	(0.126)	(0.084)	(0.078)	(0.064)

Note: ** p < 0.05, * p < 0.1, *** p < 0.01. Standard errors in parentheses.

discusses the QARDL estimates of the USA. As shown in Table 6, FTRADE’s impact on SD positively influences the lower and higher quantiles in the long run. Specifically, a 1% of FTRADE results in an improvement in SD of 0.529%, 0.516%, 0.399%, and 0.370% in quantiles 5th, 10th, 20th, and 30th and 0.164%, 0.263%, 0.339%, 0.249%, and 0.227% in the 60th, 70th, 80th, 90th, and 95th quantiles. This finding corroborates earlier studies [1,2]. The BE variable also follows a similar positive as FTRADE. However, the effect remains significant throughout all quantiles. The effect of 1% of BE results in an improvement in SD by 0.507% at 5th, 0.486% at 10th, 0.438% at 20th, 0.382% at 30th, 0.335% at 40th, 0.262% at 50th, 0.308% at 60th, 0.356% at 70th, 0.362% at 80th, 0.321% at 90th, and 0.315 at 95th quantiles in the long-run in China. This indicates that bioenergy improves sustainable development in China. The long-run findings also show that the EPU variable has a negative effect on sustainable development across all quantiles (5th to 95th), confirming that EPU is detrimental to sustainable development. The impact of 1% of EPU tends to decline SD by 0.084% at 5th, 0.083% at 10th, 0.064% at 20th, 0.080% at 30th, 0.080% at 40th, 0.070% at 50th, 0.090% at 60th, 0.123% at 70th, 0.144% at 80th, 0.134% at 90th, and 0.150% at 95th quantiles in the long-run in China. FD reports a significant positive effect on SD in two quantiles only in the long run, where the impact of 1% of FD results in an upsurge in SD by 1.268% at 50th and 1.079% at 60th quantiles in China. On the other hand, ICT significantly impacts SD across all quantiles. A 1% upsurge in ICT enhances SD by 0.353% in the 5th, 0.334% in the 10th, 0.300% in 20th, 0.262% at 30th, 0.233% at 40th, 0.187% at 50th, 0.209% at 60th, 0.235% at 70, 0.232% at 80th, 0.206% at 90th, and 0.204 at 95th quantiles in the long-run in China.

In the short run, FTRADE’s impact on SD is insignificant across all the quantiles. However, BE’s influence on SD remains positive from the 50th quantile onward. Conversely, EPU negatively impacts SD across all quantiles in the short run. FD and ICT have a positive influence on SD in the short run. FD estimates are significant only in the 90th and 95th quantiles, while ICT estimates are significant in the 60th, 70th, 80th, 90th, and 95th quantiles.

In Table 7, the impact of FTRADE on SD shows a significant positive influence for both lower and higher quantiles in the long run in the USA. A 1% increase in FTRADE augments SD by 2.928%, 2.758%, and 1.788% in the 5th, 10th, and 20th quantiles and by 1.748%, 2.077%, and 2.420% in the 80th, 90th, and 95th quantiles, respectively. The BE variable exhibits a significant negative impact on SD in lower quantiles and a positive impact on SD in the 40th to 95th quantiles. However, the effect remains insignificant in the 20th and 30th quantiles. A 1% increase in BE brings decline in SD by 0.768% in the 5th quantile and 0.757% in the 10th quantile, whereas increases SD by 0.356% in the 40th quantile, 0.359% in the 50th quantile, 0.333% in the 60th quantile, 0.326% in the 70th quantile, 0.259% in the 80th quantile, 0.265% in the 90th quantile, and 0.299% in the 95th quantile in USA. The EPU variable has a significant negative effect on SD at the highest quantiles (90th and 95th), where a 1% increase in EPU reduces SD by 0.196% at the 90th quantile and 0.226% at the 95th quantile in the long run in the USA. Conversely, the FD variable exhibits a significant positive effect on SD across all quantiles in the USA. A similar positive nexus is reported between ICT and SD across all quantiles except the 10th quantile. A 1% increase in FD (ICT) enhances SD by 6.951% (1.129%) at the 5th quantile, 4.280% at the 10th quantile, 5.341% (1.922%) at the 20th quantile, 6.830% (2.461%) at the 30th quantile, 6.323% (3.136%) at the 40th quantile, 5.705% (2.922%) at the 50th quantile, 4.647% (2.644%) at the 60th quantile, 4.319% (2.647%) at the 70th quantile, 4.051% (1.966%) at the 80th quantile, 3.666% (2.144%) at the 90th quantile, and 3.052% (2.123%) at the 95th quantile in the long run in USA.

In the short run, FTRADE positively impacts SD in the USA’s lowest and highest quantiles (5th to 30th quantiles and 90th to 95th quantiles). However, BE and ICT report significant positive influence on SD across all quantiles. Conversely, EPU significantly negatively impacts SD across all quantiles in the short run. Meanwhile, FD reports a positive influence on SD only at the 90th and 95th quantiles in the short run.

Results Discussion and Limitations

First, we observe that trade in forest products is positively connected to sustainable development. This result is in line with our prior expectations. The economic intuition of this result is quite obvious because forest products are obtained from forest wood and other resources. In addition to environmental advantages, the sustainable trading of forests also has several economic benefits as it helps boost employment opportunities, increases the living standards of the local communities, and promotes fair and balanced development of the society. These empirical inferences are supported by Kastner et al. [26]. Certification programs and fair-trade procedures guarantee the responsible sourcing of forest items, offering economic incentives for preserving and using natural resources. In order to ensure that the creation of forest products is based on sustainable practices, certification schemes and fair-trade practices play an important role by providing economic incentives for conservation and sustainable use. Forest products are considered green products, and trade of such commodities can significantly reduce the environmental impacts of trading activities. Promoting the trade of green goods by implementing appropriate policies and fostering international cooperation can help adopt green technologies and practices at a great pace, resulting in increased innovation, more jobs, and high economic growth. The forest trade result is also backed by Ha [77], who observed that trade in green products significantly reduces energy security and promotes environmental sustainability. On the other hand, the study by Dou et al. [78] observed the inconsistent impact of trade on sustainability, while the study by Golgeci et al. [79] believes that as the country is involved in international trade, it is more likely to face negative environmental impacts, which in turn hurt sustainable development objectives.

Second, the bioenergy helps foster sustainable development. Bioenergy is believed to be a low-carbon source of energy and an alternative to conventional energy sources. It fulfills the energy demand by providing a cleaner source of energy and mitigating climate change. In addition, it is an important energy source, particularly in rural areas, and helps promote the living standards of the rural population. However, it can hurt sustainable development objectives if not used appropriately and carefully because it can exacerbate carbon footprints if bioenergy production sources are outdated and conventional [19]. Our findings align with the findings of Robledo-Abad et al. [34], who observed that bioenergy can contribute to sustainable development. Nevertheless, the authors confirmed that it is crucial to implement suitable policies to produce biomass energy in a sustainable manner. These policies should provide long-term support to enhance crop yield and guarantee bioenergy cropping systems’ environmental, economic, and social advantages. Conversely, Qin et al. [80] revealed that bioenergy can significantly and negatively influence the ecosystem. However, they also confirm that negative environmental consequences can vary considerably due to differences in biomass types, land locations, and management practices.

Third, we find that EPU is detrimental to sustainable development. This result is also not surprising. On one side, EPU can significantly enhance the carbon footprint due to the delay in implementing the policies specifically designed for controlling climate change, resulting in more emissions for an extended period of time. Moreover, the progress towards renewable energy transition can be significantly impacted as a result of increased EPU due to inconsistent and uncertain economic policies. These empirical inferences are supported by Hunjra et al. [23]. On the other side, continuous postponement in implementing climate policies may prove detrimental to economic growth, hindering the nation’s journey toward a clean and green future [24]. The delay in the transition toward a green economy will largely depend on how long the country postpones the implementation of mitigating policies; the higher the delay, the greater the expense will be. The negative connection between EPU and sustainability is supported by Anser et al. [41] for the top ten carbon emitter countries and Zahra and Badeeb [42] for OECD countries. EPU has the ability to measure the level of uncertainty in an economy, which has a vital role in formulating policy decisions regarding sustainable development. In contrast, a major limitation of the EPU index is that data used to estimate this index is derived chiefly from advanced economies. Moreover, the data for many of these nations is only accessible from the early 1990s onwards, as Ahir et al. [81] stated. Additionally, there is a lack of data for several developing countries.

In addition to significant contributions to the literature, the study has the following limitations. First, the study is conducted in the context of China and the USA. Thus, the inferences drawn from the study can only be applied in the context of China and the USA. However, future studies should gather data for other countries and regions and analyze the nexus in the context of other countries and regions to enhance the scope of the study. Second, we have only used the adjusted net savings for sustainable development while ignoring other variables, such as the green growth indicator developed by the OECD. Future analyses should use different measures of sustainable development to enhance the study’s scope and increase our estimates’ robustness. Carbon emissions or environmental degradation are important factors that can impact sustainable development. However, carbon emissions have already been captured by the variable of adjusted net savings used as a proxy of sustainable development in this analysis. In the future, if the studies use different proxies of sustainable development, such as green growth, we suggest using carbon emissions as a regressor in the model. Our bioenergy variable does not capture non-forestry sources such as agricultural grains and residues. Future studies should include this dimension in their definitions and reexamine the empirical analysis. Lastly, we use linear modeling to estimate the nexus between forest trade, bioenergy, economic policy uncertainty, and sustainable development. However, asymmetric analysis in future analysis can add more value to the existing literature.

7. Conclusions and Implications

The UN Agenda 2030 for Sustainable Development has become the main point of discussion at international conferences and forums. There is consensus among world leaders, environmentalists, academics, and civil society that the world’s sustainable future is contingent on the sustainable development agenda proposed by the UN in 2015. In recent times, there has been a growing interest in findings on the factors that can contribute to sustainable development. To date, a growing body of empirical works has estimated the determinants of sustainable development; however, none of the empirical evidence has estimated the impact of forest trade, bioenergy, and economic policy uncertainty on sustainable development. This study fills the gap by analyzing the impact of forest trade, bioenergy, and economic policy uncertainty on sustainable development in China and the USA by employing the ARDL and QARDL approaches. The findings of the ARDL model suggest that forest trade helps boost both short- and long-run sustainable development in China and the USA, while bioenergy fosters sustainable development in the short and long run only in China and in the USA, bioenergy improves sustainable development only in the long run. In contrast, economic policy uncertainty hurts sustainable development in the short and long run in China, while in the USA, only the long-run negative association between the two variables is observed. In addition, the FD and ICT favorably influence China’s short- and long-run sustainable development. However, in the USA, FD boosts sustainable development in the long run, and ICT boosts sustainable development in the short run.

These results help provide some important policy directives for policymakers and forest managers. First, our findings suggest that forest trade fosters sustainable development. Thus, policymakers in both China and the USA should enhance the share of forest trade in total trade. However, they must adopt a cautious approach as forest trade depends on the forest woods; policymakers should promote the sustainable use of forests by implementing policies that support sustainable forest management practices. This will ensure that forest products should come from responsibly managed sources. The Chinese and American policymakers must induce the forest traders to invest in reforestation and afforestation projects in order to protect the forest cover and biodiversity. Moreover, the forest trade policies should be designed to encourage fair trade practices so that local people may get maximum material benefits from forest trade, improving their living standards and ensuring the protection of forest resources. Second, bioenergy fosters sustainable development in China and the USA. Policymakers in both countries should focus on increasing the share of bioenergy and reducing reliance on conventional energy sources. In this context, there is a need to increase awareness regarding bioenergy’s economic and environmental benefits. Moreover, policymakers should focus on expertise, training, financial assistance, and industrial investments to develop local bioenergy markets. Particularly, policymakers should invest in R&D activities related to the development of second-generation biofuels and biogas, which are better than first-generation bioenergy in terms of efficiency and environmental impacts. Further, promoting international collaboration and knowledge sharing in bioenergy can promote best practices in the bioenergy sectors that can foster the development of bioenergy technologies, which can significantly reduce the environmental consequences of bioenergy. Third, uncertainty in economic policy hinders sustainable development. Thus, policymakers need to reduce uncertainties in economic policies by creating unambiguous and robust policy frameworks that outline sustainable development objectives and devise strategies on how to achieve them. This will effectively lessen uncertainty for investors and stakeholders. In addition, developing strong institutions for efficient implementation and monitoring of policies can significantly reduce uncertainties.

Moreover, policymakers in the USA are closely following the rules and regulations of the Sustainable Forestry Initiative (SFI) and the Forest Stewardship Council (FSC), confirming that most forest products in the USA are obtained from sustainably managed forests. Due to these practices, the overall rate of deforestation in the USA has decreased manifold, improving biodiversity and enhancing the demand for USA forest products worldwide. Thus, policymakers need to strengthen the policies further in light of SFI and the FSC, which drive sustainable management practices and contribute to sustainable development. On the other hand, China has made significant progress in forest certification within the framework of SFI and FSC. Urbanization and industrialization are on the rise in China, which is exerting more pressure on the Chinese forest resources, demanding that Chinese policymakers develop guidelines for sustainable forest management, making sustainable management and certification critical. In this context, China has introduced its own certification system known as the China forest certification scheme, which has received a great global response and acceptance. Thus, China’s forest certification scheme should be part and parcel of any forest trade policy that aims to promote forest trade and sustainable development.