The Impact of Energy Efficiency Technologies, Political Stability and Environmental Taxes on Biocapacity in the USA

Abstract

The increasing human demand for natural resources is leading to critical resource depletion. This depletion is exacerbated by exceeding the Earth’s biological regeneration rate, threatening ecosystems’ ability to renew biomass. This ecological challenge hinders the potential for simultaneous economic, social, and environmental progress. This study investigates the complex relationships between the USA’s per capita income, energy efficiency innovations, environmental taxation, political stability, and its biocapacity. Using annual data from 1990 to 2024, the paper employs a comprehensive causality testing framework that accounts for the nonlinear nature of the data, as asymmetric effects are observed. This framework includes the Quantile Autoregressive Distributed Lags model (Q-ARDL), the Wald test for parameter consistency, and the Granger-causality in Quantiles test (GC-Q), enabling the estimation of unique parameter vectors for each quantile. A key finding reveals that the impact of per capita GDP on biocapacity is significantly larger than that of other regulatory mechanisms. This suggests that carbon pricing and energy efficiency technologies require widespread implementation to offset the environmental impact of economic growth. The quantile regression reveals complex short-run impacts on biocapacity with persistent positive effects from its lag, contrasting with the diminishing negative influence of GDP and positive influence of energy efficiency at higher quantiles, while long-run analysis shows a consistent negative impact of GDP and varying positive or nonlinear effects of other factors. Granger-causality tests indicate significant unidirectional positive effects from energy efficiency and political stability to biocapacity, a bidirectional relationship for environmental taxes in upper quantiles and GDP across all quantiles. The associated methodological and policy implications aim to assist policymakers in achieving a better balance between the benefits and costs of natural resource use in the USA, promoting sustainable development.

1. Introduction

The security of agricultural supply and coastal populations is significantly threatened by climate change. Rahmstorf (2024) warns of potential global average temperature increases exceeding 1.5 degrees Celsius if current trends continue [1]. This is largely driven by the extensive extraction, burning, and use of resources. These human activities not only pollute the atmosphere but also severely impact biodiversity, creating substantial ecological imbalances that demand urgent attention from policymakers. This situation has led to the development of the concepts of biocapacity surplus and deficit.

Biocapacity represents the environment’s ability to regenerate, reflecting the capacity of various land types (built-up, cropland, fishing grounds, forest, and grazing) to meet human needs [2]. It quantifies the ecosystem’s ability to both regenerate resources and absorb waste generated by human activity [3]. This waste can take many forms, including soil and water contamination, municipal solid waste, harmful gas emissions, and untreated wastewater discharge [4]. Biocapacity is a key component of the ecological footprint analysis (EFA), a framework used to assess the ecological and biodiversity assets required by a population to support its consumption of natural resource-based products and services [5]. The EFA methodology, initially developed by Rees [6] and Rees and Wackernagel [7], calculates the minimum bio-productive land area necessary to offset average human consumption and waste generation, based on realistic technological assumptions. While the ecological footprint measures the use of productive land, biocapacity measures the land’s ability to regenerate. A biocapacity deficit occurs when a population’s cumulative ecological footprint surpasses its available biocapacity, potentially leading to irreversible environmental damage as the environment’s regenerative capacity is overwhelmed [8].

This study examines the factors influencing global biocapacity, focusing on its responsiveness to several key variables: gross domestic product (GDP), innovations in energy efficiency, environmental taxes, and the estimate of political stability and absence of violence/terrorism. Recognizing the complex relationship between economic growth and environmental degradation [9], international agreements like the Kyoto Protocol and the Paris Agreement promote policy interventions (e.g., carbon pricing, taxes, and subsidies for low-carbon energy) and investments in energy efficiency R&D to foster sustainability [10]. Several leading OECD nations, including Belgium, Denmark, Finland, and Sweden, pioneered environmental tax reforms, a trend that has since extended to emerging and Asian economies [11,12]. For example, China’s 2018 Environmental Protection Tax Law is projected to generate substantial annual revenue [13,14], while Singapore, Indonesia, South Korea, and Vietnam have implemented carbon taxes or emissions trading systems. Similarly, energy efficiency initiatives are central to climate action strategies. With Asian economies projected to account for a significant share of global primary energy consumption by 2035 [15], various programs promoting energy savings, such as South Korea’s Energy Efficiency Management System (EEMS), the Philippines’ Technology Transfer for Energy Management (TTEM) plan, and Japan’s Energy Conservation Centre (ECC), are gaining prominence [16].

Existing research has primarily concentrated on the drivers of carbon dioxide (CO₂) emissions, frequently employing Environmental Kuznets Curve (EKC) frameworks. These studies have extensively explored how technological advancements [17,18,19], improvements in energy efficiency [20,21,22], and environmental taxation [23,24,25] can influence the relationship between GDP and CO₂ emissions, aiming to decouple economic growth from environmental degradation. However, the findings of these studies are often inconsistent and contradictory, despite variations in sample selection, data aggregation, and methodological approaches. Furthermore, the impact of environmental taxes and energy efficiency technologies on biocapacity remains under-explored. Recent studies utilizing large, diverse country panels (e.g., Zhang et al. (2017) for 139 countries [26]; Chen et al. (2019) for 16 CEE countries [27]; Pathak (2020) for 187 countries [28]; Sarkodie (2021) for 188 countries) [3] have yet to reach a consensus on the causal relationships between economic and ecological indicators. Critically, these analyses have not incorporated innovation and regulatory factors, highlighting a significant gap in the literature. Therefore, an investigation into the dynamic interplay between biocapacity, income growth, energy efficiency innovations, and environmental tax policies is warranted.

This paper focuses on the USA, which exhibits a substantial environmental impact, with a per capita ecological footprint of 8.04 gha, ranking among the world’s highest. However, its per capita biological capacity is limited to 3.45 hectares. Consequently, the nation faces a total ecological deficit of −1.49 billion hectares and a per capita biocapacity reserve of −4.59 gha [29].

This study contributes to the existing literature in three main ways. First, it is the first to examine the dynamic relationships between per capita income, energy efficiency technology innovations, environmental taxes, and political stability. Second, it uniquely focuses on USA, a nation characterized by a critical biocapacity deficit despite its status as a leader in energy efficiency. Third, it employs a causality analysis that accounts for nonlinear patterns and asymmetries, offering more nuanced insights than traditional linear estimation methods.

As the human demand for natural resources accelerates, resource depletion is reaching critical levels. Ecosystems’ ability to regenerate biomass is under threat as Earth’s biological regeneration rate is consistently exceeded. This ecological challenge undermines the potential for sustainable economic, social, and environmental progress. USA’s case provides valuable insights into this issue and requires in-depth analysis. This paper investigates the dynamic relationships between per capita income, energy efficiency innovations, environmental taxes, political stability, and biocapacity in the USA. Using data from 1990 to 2024, we employ a causality approach that accounts for the nonlinear nature of the data. Following Sharif et al. (2020) [30], our methodology includes the Brock, Dechert, and Scheinkman (BDS) test for non-linearity (Brooks et al., 1997) [31], the Zivot and Andrews unit root test with structural breaks (Zivot and Andrews, 2002) [32], the Quantile Autoregressive Distributed Lags (Q-ARDL) model (Cho et al., 2015) [33], the Wald test for parameter consistency, and the Granger-causality in quantiles (GC-Q) test (Troster, 2018) [34]. The Q-ARDL model’s strength lies in its ability to estimate distinct coefficients across quantiles, providing valuable insights when nonlinearities and asymmetric effects are present [33]. The GC-Q test offers the additional advantage of evaluating nonlinear causalities and potential causal links at all conditional quantiles, with desirable statistical properties including correct asymptotic size, consistency against fixed alternatives, and power against Pitman deviations from the null.

The results are intended to inform policymakers in the USA as they strive to balance the benefits and costs of natural resource use. While Romania’s GDP per capita is significantly lower than the USA’s (the fact that it used up its annual resources on 12 July in 2019, earlier than the global average, suggests potential issues with resource management efficiency or a relatively high ecological footprint compared to its biocapacity, despite the lower GDP.

The remainder of this paper is structured as follows: Section 2 presents the key stylized facts regarding biocapacity levels and related issues. Section 3 provides a comprehensive literature review and identifies critical research gaps. Section 4 describes the data, preliminary statistics, and the econometric methodology. Section 5 presents the empirical results, while Section 6 discusses these findings, provides a methodological note, and offers policy recommendations. The paper concludes with a summary of key findings and policy implications.

2. Literature Review

This literature review is organized into three parts. The first part examines key studies that have explored the relationship between economic and environmental indicators using environmental Kuznets curve (EKC) frameworks. The second part reviews research investigating the links between technological innovation and environmental degradation. Finally, the third part highlights foundational studies that have initiated research on biocapacity and its influencing factors.

The environmental Kuznets curve (EKC) hypothesis, stemming from Grossman and Krueger’s (1991) work on NAFTA’s impact on toxic concentrations, posits an inverted U-shaped relationship between economic development and environmental pollution [35]. It suggests that pollution initially rises with economic growth, reaches a turning point, and then declines as further economic development occurs. This implies a potential decoupling of economic and environmental goals. While numerous studies have explored the EKC across various countries and contexts, a clear consensus has yet to emerge. This section reviews significant and recent contributions to the EKC literature.

Several large-scale panel studies have provided support for the EKC hypothesis. Notable examples include Shafik and Bandhopadhyay (1992) for 149 countries [36]; Panayotou (1993) for 68 countries [37]; Selden and Song (1994) for 30 countries [38]; Dijkgraaf and Vollebergh (2005) for 24 countries [39]; Sarkodie (2018) [40] for 17 African countries; Ben Jebli et al. (2013) for 25 OECD countries [41]; Bilgili et al. (2016) for 17 OECD countries [42]; Iwata et al. (2012) for 11 OECD countries [43]; Le (2019) for 10 ASEAN countries [44]; Adeel-Farooq et al. (2020) for 6 ASEAN countries [45]; and Leal and Marques (2020) for 20 OECD countries [46]. The EKC hypothesis posits an inverted U-shaped relationship between economic development and environmental degradation, suggesting that environmental quality deteriorates in the early stages of growth but improves after a certain income threshold is reached. This pattern is theorized to occur due to a combination of scale, technique, and composition effects [35], alongside stronger environmental regulations and changing societal preferences in wealthier nations. Several large-scale panel studies have provided empirical support for the EKC across various country samples [36,37,38]. However, the EKC hypothesis is not universally accepted, with numerous studies finding mixed or contradictory evidence (Stern, 2004) [24]. Concerns such as the pollution haven hypothesis, time lags in policy effectiveness, the type of pollutant under consideration, and methodological limitations contribute to these inconsistencies. Furthermore, the EKC primarily focuses on relative decoupling and may not guarantee absolute reductions in environmental impact [47]. Despite these debates, the EKC framework offers a valuable lens for this study, which investigates the relationship between economic growth (proxied by GDP per capita) and biocapacity in the USA. Understanding whether the USA has exhibited or is approaching an EKC pattern for its natural resource availability is crucial for assessing the long-term sustainability of its economic trajectory. Moreover, this study aims to explore how factors such as energy efficiency technologies, environmental taxes, and political stability might influence the shape and potential turning point of this EKC relationship for biocapacity in a high-income, developed context like the USA, where shifts towards a service-based economy and advanced technologies are prominent. By examining the interplay between these factors and the income–biocapacity nexus within the EKC framework, this research seeks to provide nuanced insights into the drivers of biocapacity deficits and inform more effective sustainability policies.

Conversely, numerous multi-country studies have failed to validate the EKC hypothesis. These include research by Moutinho et al. (2020) for 12 OPEC countries [48]; Moomaw and Unruh (1997) for 16 emerging economies [49]; Agras and Chapman (1999) for 34 countries [50]; Heidari et al. (2015) for 5 ASEAN countries [51]; Pata and Aydin (2020) for 6 hydropower-consuming countries [52]; and Mehmood (2021) for 6 SAARC nations [53]. Furthermore, some studies have yielded mixed results. For instance, Lee et al. (2010) found evidence of an inverted U-shaped EKC in the Americas and Europe, but not in Africa, Asia, and Oceania [54].

At the country level, support for the EKC hypothesis has been found in studies such as Jalil and Mahmud (2009) for China [55]; Shahbaz et al. (2014) [56] and Fodha and Zaghdoud (2010) for Tunisia [57]; Baek and Kim (2013) for Korea [58]; Pata (2018) for Turkey [59]; Rana and Sharma (2019) for India [60]; Sarkodie and Ozturk (2020) for Kenya [61]; and Sarkodie (2021) for China [3]. However, the EKC hypothesis has not been supported by research such as Soytas et al. (2007) for the US [62]; Wang et al. (2016) [63] and Fei et al. (2014) for China [64]; and Minlah and Zhang (2021) for Ghana [65].

More recently, researchers have explored the EKC framework using alternative environmental indicators. For example, Ozturk et al. (2016) [66], Al-Mulali et al. (2015) [67], and Ulucak and Bilgili (2018) [68] used an ecological footprint; Koc (2020) examined the water use [69]; Wang et al. (2020) focused on urbanization and air pollutants [70]; and Magazzino et al. (2022) investigated municipal solid waste generation [71].

Next, a part of the literature reviews the relationship between technological innovations and environmental degradation. The role of technological innovation in economic development has been a central focus for economists for decades. Schumpeter (1942) [72] introduced the concept of “creative destruction”, describing the process of replacing inefficient industries with more efficient, technologically advanced ones. However, technologies can sometimes have negative environmental consequences, diminishing their overall benefits. Grossman and Krueger (1991) [35] argued that technological progress is a key channel through which economic growth influences environmental degradation, a point later echoed by Balsalobre-Lorente et al. (2018, p. 358), who stated that the “technical effect” is the primary driver of pollution reduction when the relationship between growth and pollution is analyzed [73]. This perspective has spurred a considerable amount of empirical research.

Early investigations into the role of technological innovation in moderating the relationship between economic growth and environmental degradation include the work of Torras and Boyce (1998) [74] and Arrow et al. (1995) [75]. These studies paved the way for numerous subsequent analyses. Notable recent examples include Fei et al. (2014), who demonstrated the importance of technological innovation in mitigating environmental degradation in Norway and New Zealand [64], a finding consistent with the long-run estimates of Ahmed et al. (2016) for 24 EU countries [76]; Wang et al. (2020) for N-11 economies [70]; and Qayyum et al. (2021) for India [77]. Alvarez-Herranz et al. (2017) [78] and Lorente and Álvarez-Herranz (2016) [79] made a methodological contribution by introducing a “dampening technological variable” to moderate the energy consumption–GDP nexus and, consequently, aggregate emissions. Using data from 17 OECD economies, they observed an N-shaped EKC pattern and found that, while technology initially reduces emissions, it can later have a direct negative impact on the environment. Chen and Lee (2020), analyzing 96 countries, partially corroborated these findings, suggesting that technological innovation mitigates pollution only in high-income countries [80]. This highlights the dependence of the impact of technology on a country’s stage of economic development. Similarly, Yu and Du (2019) showed that innovation leads to significant carbon emission reductions in rapidly growing countries but only marginal benefits in slower-growing economies [81]. Churchill et al. (2019), using data from the G-7 countries spanning 1870–2014, found an indirect relationship between R&D and CO₂ emissions for most of the period studied [82]. Examining the feedback loop, Lin and Zhu (2019) demonstrated that technological innovation responds significantly to changes in carbon intensity in China [83]. Shahbaz et al. (2020), also focusing on China and employing a bootstrapping autoregressive distributed lag (BARDL) model, confirmed that technological advancements reduce the impact of economic activity on pollution [84]. More recent studies have provided further evidence of the mitigating effect of technological innovation on pollution, including Wang et al. (2020) for China’s construction industry [70]; Cheng et al. (2021) for 35 OECD countries [17]; and Zhao et al. (2021) for 63 countries [19]. A broader discussion of the role of technology in the economy can be found in Çalışkan (2015) [85]. Stable political systems are more likely to develop and implement long-term environmental policies and regulations, fostering a consistent approach to resource management and environmental protection [85]. Frequent political changes can lead to policy uncertainty and reversals, hindering effective environmental action (Ahmed et al., 2021; Ahmed et al., 2016) [12,76]. The environmental taxes can simultaneously improve environmental quality and stimulate economic activity (e.g., through reduced labor taxes) (Rafique et al., 2022) [86].

The last part reveals the use of biocapacity in empirical assessments. In recent years, a growing body of research has focused on identifying the determinants of biocapacity and how it responds to various socio-economic factors. Zhang et al. (2017) pioneered the use of biocapacity as a proxy for natural capital, examining its relationship with subjective well-being across 139 countries [26]. Their OLS results indicated a positive association between biocapacity and well-being, but only in high-income nations. Chen et al. (2019) expanded the traditional economic growth–energy–environment framework by incorporating human resources and biocapacity [27]. Using a dynamic seemingly unrelated cointegration regression (DSUR) approach on data from 16 Central and Eastern European (CEE) countries, they found no significant long-term interaction between income and biocapacity, while financial development did have an impact. Hassan et al. (2019) investigated the relationships between economic growth, ecological footprint, biocapacity, and human resources in Pakistan [87]. Employing an ARDL model with structural breaks on data from 1971 to 2014, they found no significant link between economic and biocapacity indicators. Ali et al. (2021) assessed the influence of economic growth, agricultural innovation, energy use, and biocapacity on CO₂ emissions in Nigeria, finding that both economic growth and biocapacity had long-term negative impacts on emissions [88]. Gabbi et al. (2021) introduced the “biocapacity-adjusted economic growth” indicator, arguing that it provides a more comprehensive measure of sustainable growth than traditional metrics [2]. Sarkodie (2021) explored carbon footprint, ecological footprint, and biocapacity across 188 countries, confirming the existence of convergence across nations [3]. His analysis of 56-year biocapacity trends revealed that Germany, India, and the US exceeded the global average, while Brazil, Canada, China, Russia, and Japan fell below it. Notably, India showed the largest increase in biocapacity over this period, while Japan experienced the largest decline. This paper contributes by focusing specifically on the USA case, which offers valuable insights due to its unique circumstances.

3. Data and Econometric Approach

This part presents the data series, preliminary statistics, and the econometric approach. A region or country possesses a biocapacity reserve when its available biocapacity exceeds its ecological footprint. Conversely, a biocapacity deficit arises when a population’s ecological footprint surpasses its biocapacity. This deficit can lead to irreversible environmental damage because the environment’s ability to regenerate is unable to keep pace with the rate of resource depletion and waste generation. This relationship can be expressed as follows:

$$B_{i,t}-{EF}_{i,t}=\left\{\begin{array}{c}{BR}_{i,t}\left(>0\right)\\ {BD}_{i,t}\left(<0\right)\end{array} \right.$$

where i represents the population and t represents the time period. BC, EF, BR, and BD refer to biocapacity, ecological footprint, biocapacity reserve, and biocapacity deficit, respectively. These indicators can be measured in global hectares (gha), gha per person, or the number of Earths required to support the population’s resource consumption.

This study analyzes data from USA spanning the period 1990–2024, focusing on the following variables: biocapacity, GDP, energy efficiency technologies, environmental taxes, and political stability. Biocapacity is measured in global hectares (GHa), as this unit effectively translates human resource consumption into a standardized measure, making it a suitable proxy for the Earth’s average carrying capacity. Biocapacity data are sourced from the Global Footprint Network. GDP data, expressed as constant 2010 USD per capita, are obtained from the World Development Indicators database. Research and Development (R&D) expenditures in energy efficiency technologies and environmental tax revenues, both in constant 2010 USD, are provided by OECD Statistics. Political stability and absence of violence/terrorism assesses the perceived risk of political instability and politically motivated violence, including acts of terrorism. A country’s score on this composite indicator is standardized, ranging from approximately −2.5 to 2.5. All variables are transformed using the natural logarithm, excepting political stability for which semi-log transformation is applied. The description of the variables is also made in the Appendix A.

This paper investigates how biocapacity in USA responds to changes in several endogenous variables: economic growth, energy efficiency technologies, environmental taxes, and political stability. The standard log-linear specification for the baseline model is expressed as follows:

$${Biocapacity}_t=\alpha +\beta {GDP}_t+\psi {efficiency}_t+\gamma {taxes}_t+\delta {stability}_t+\epsilon$$

(1)

In Equation (1), the elasticities of income, energy efficiency technologies, environmental tax, and political stability are represented by $\alpha , \beta ,\gamma , \mathrm{a}\mathrm{n}\mathrm{d} \delta$, respectively. ε represents the error term.

Investments in energy efficiency and green energy technologies promote the adoption of cleaner energy sources and tools for industrial production and transportation. This increased use of energy-efficient technologies reduces the consumption of fossil fuels and non-renewable resources, which in turn can lead to lower emissions and smaller ecological deficits. Concurrently, environmental and carbon taxes incentivize industries to switch to greener energy sources and technologies to reduce greenhouse gas emissions and ecological footprints.

We begin by testing for nonlinear patterns in the time series data using the Brock, Dechert, and Scheinkman (BDS) test [31]. The effectiveness of causality tests is contingent upon the assumption of linearity within the data. If nonlinear patterns are present, the results of these tests can be misleading and unreliable. The BDS test examines whether the residuals of a vector autoregression (VAR) model are independently and identically distributed (i.i.d.). Following the approach outlined in [89], we apply the BDS test. This method utilizes the correlation integral, developed by Grassberger and Procaccia (1983) [90], which is based on estimating the spatial probabilities of the data series.

By first testing for nonlinearity using the BDS test, the researchers are rigorously checking the validity of applying standard linear causality tests to their data. If nonlinearity is detected, it necessitates the use of methods that can accommodate these nonlinear patterns, such as the quantile autoregressive distributed lags (Q-ARDL) model and the Granger-causality in quantiles (GC-Q) test, which the study subsequently employs. The BDS test is specifically designed to detect various forms of nonlinear dependence in the residuals of a time series. It goes beyond simply identifying linear correlations and can uncover more complex patterns such as chaotic behavior, conditional heteroscedasticity, and other nonlinear structures that might be present in the data. By examining the i.i.d. nature of the VAR residuals, the BDS test provides a comprehensive assessment of whether the linear model adequately captures the underlying dynamics of the data. If the residuals are not i.i.d., it strongly suggests the presence of nonlinearities that need to be accounted for in subsequent analysis. The correlation integral, developed by Grassberger and Procaccia (1983) [90], provides a way to estimate the probability that two randomly chosen points in the reconstructed phase space of the time series are within a certain distance of each other. Deviations from the expected behavior under the null hypothesis of i.i.d. data indicate the presence of dependence, including nonlinear dependence. This method is particularly useful for detecting complex nonlinear dynamics that might be missed by simpler tests. Its focus on the spatial distribution of the data allows it to capture intricate patterns and dependencies.

Let us consider an $m$-dimensional time series (m = 1, m = 2, …), denoted by $X_t$ and the corresponding values $(X_t, X_{t+1}, \dots , X_{t+m-1})$, so the correlation integral measures the fraction of ${ (X}_t^m,X_s^m)$ data pairs having the highest-norm distance of $e$:

$$C_m\left(T,e\right)=\sum _{t=1}^{T_m-1}\sum _{s=t+1}^{T_m}\begin{array}{c}I\left(X_t^m,X_s^m, e\right)\times {\displaystyle \frac{2}{T_m\left(T_m-1\right)}} \end{array}$$

(2)

where $I\left(X_t^m,X_s^m, e\right)$ is an indicator function such that:

$$I\left(X_t^m,X_s^m, e\right)=\left\{\begin{array}{l}1, { \left|\right|X}_t^m,X_s^m\left|\right|<e\\ 0, otherwise\end{array}\right.$$

(3)

where ${ \left|\right|X}_t^m,X_s^m\left|\right|$ represents the Euclidian distance between ${ X}_t^m$ and $X_s^m$, $T_m$ counts for the sample volume, and $T$ can be grouped in $T_m$ sub-samples of vectors with $m$ elements. The statistic of BDS test is computed as:

$$W_m\left(T,e\right)={\displaystyle \frac{\sqrt{T}\left[C_m\left(T,e\right)-C_1{\left(T,e\right)}^m\right]}{{\sigma }_m\left(e\right)}}$$

(4)

where $T$ is the sample volume, and ${\sigma }_m\left(e\right)$ is the standard deviation knowing the $m$ dimensions, while the BDS test is based on standard normal distribution. If the BDS test rejects the null hypothesis, it indicates the presence of nonlinear structures in the analyzed time series. In such cases, standard autoregressive distributed lag (ARDL) and Granger-causality methods are no longer appropriate, and models that account for non-linearity are required. Following Sharif et al. (2020) [30], we employ the quantile autoregressive distributed lag (Q-ARDL) model developed by Cho et al. (2015) [33] and the Granger-causality in Quantiles (GC-Q) test proposed by Troster (2018) [34]. These methods are described below.

Pesaran et al. (2001) [91] introduced a bounds testing approach to determine the existence of significant long-run relationships between variables. A key advantage of this approach is its ability to handle a mix of I(0) (stationary at level) and I(1) (stationary at first difference) variables. Xiao (2009) [92] later extended this model to incorporate quantiles, allowing for greater volatility in the endogenous variables under the assumption of conditional heteroskedasticity. Cho et al. (2015) [33] made further progress by developing the Q-ARDL procedure. This paper uses the Q-ARDL model to assess the long-run quantile equilibrium impact of economic growth, energy efficiency technologies, environmental taxes, and political stability on the USA’s biocapacity (denoted by bio). The standard linear ARDL model is specified as follows:

$${Bio}_t=\alpha +\sum _i^p\lambda B_{t-i}+\sum _i^q\beta {GDP}_{t-i}+\sum _i^m\psi {efficiency}_{t-i}+\sum _i^n\gamma {taxes}_{t-i}+\sum _i^r\delta {stability}_{t-i}+{\epsilon }_t$$

(5)

${\epsilon }_t$ represents a white noise process, and p and q are the lag lengths, determined using the Schwarz Information Criterion (SIC). All variables are used in their natural logarithmic form, except for political stability which is in semi-log form because some initial values are negative. Following Cho et al. (2015) [33], the quantile-augmented version of the ARDL model, which accounts for sequential correlation in the error term, is expressed as follows:

$$\begin{array}{c}{Q}_{\Delta {Bio}_t}=\alpha \left(\tau \right)+\rho {Bioy}_{t-i}+{\phi }_1{GDP}_{t-i}+{\phi }_2{efficiency}_{t-i}+{\phi }_3{taxes}_{t-i}+{\phi }_4{stability}_{t-i}\\ +{\displaystyle \sum _i^p}\lambda {\left(\tau \right)Bio}_{t-i}+{\displaystyle \sum _i^q}\beta {\left(\tau \right)GDP}_{t-i}+{\displaystyle \sum _i^m}\psi {\left(\tau \right)efficiency}_{t-i}+{\displaystyle \sum _i^n}\gamma {\left(\tau \right)taxes}_{t-i}\\ +{\displaystyle \sum _i^r}\delta {\left(\tau \right)stability}_t+{\epsilon }_t\left(\tau \right) \end{array}$$

(6)

The vector error correction model (VECM) representation of the Q-ARDL framework, as presented in Cho et al. (2015) [33], can be expressed as follows:

$$\begin{array}{c}{Q}_{\Delta {Bio}_t}=\alpha \left(\tau \right)+\rho \left(\tau \right)\left[{Bioy}_{t-i}-{\omega }_1{\left(\tau \right)GDP}_{t-i}-{\omega }_2\left(\tau \right){efficiency}_{t-i}-{\omega }_3{\left(\tau \right)taxes}_{t-i}-{\omega }_4{\left(\tau \right)stability}_{t-i}\right]\\ +{\displaystyle \sum _{i=1}^{p-1}}\lambda {\left(\tau \right)Bio}_{t-i}+{\displaystyle \sum _{i=0}^{q-1}}\beta {\left(\tau \right)GDP}_{t-i}+{\displaystyle \sum _{i=0}^{m-1}}\psi {\left(\tau \right)efficiency}_{t-i}+{\displaystyle \sum _{i=0}^{n-1}}\gamma {\left(\tau \right)taxes}_{t-i}\\ +{\displaystyle \sum _{i=0}^{r-1}}\delta {\left(\tau \right)stability}_t+{\epsilon }_t\left(\tau \right) \end{array}$$

(7)

where $\lambda \ast ={\sum }_{i=1}^{q-1}\lambda$ captures the aggregate short-run impact of the previous biocapacity on the actual one; $\beta \ast ={\sum }_{i=1}^{q-1}\beta$ captures the aggregate short-run impact of previous economic growth levels on the current changes in biocapacity; $\psi \ast ={\sum }_{i=1}^{q-1}\psi$ captures the aggregate short-term impact of R&D in energy efficiency technologies on the current level of biocapacity; $\gamma \ast ={\sum }_{i=1}^{q-1}\gamma$ captures the aggregate short-term impact of environmental taxes on the current level of biocapacity; $\delta \ast ={\sum }_{i=1}^{q-1}\delta$ captures the aggregate short-term impact of the political stability on the current level of biocapacity, while λ represents the coefficient associated with the error correction term (ECT). A negative and statistically significant ECT coefficient indicates long-run convergence among the variables. A key advantage of the Q-ARDL model is its ability to estimate distinct coefficients across different quantiles, which is particularly useful when nonlinearities and potential asymmetric effects are present in the data, as is the case in our study. For both short-run and long-run estimates, the stability of the coefficients across quantiles is assessed using the Wald test of parameter consistency [93].

In a last stage, we propose the Granger-causality in quantiles (GC-Q) test. The standard linear Granger-causality test [94] infers causality when the actual value of a regresand is generated by the lagged variable, and the lagged values associated to the regressor. According to the Granger representation theorem [94] , a variable $X_i$ does not represent a Granger-cause for a variable $Y_i$, if, given the previous values of $Y_i$, changes in $X_i$ determine changes in $Y_i$. Later, Troster (2018) [34] proposed a quantile-augmented version of this test. Suppose that there exists a vector ${{(N}_i=N_i^y, N_i^x)}^{\prime }$ $\in R^e, s=o+q,$ where $N_i^x$ refers to the past indication group of $X_i{N}_i^x≔{(X_{i-1},\dots ,X_{i-q})}^{\prime }\in$ $R^q$. Hence, for all $y\in R$, the null assumption for Granger causality going from $X_i$ to $Y_i$ can be defined as:

$$H_0^{X\nrightarrow Y}: F_Y\left(\mathrm{y},N_i^y,N_i^x\right)=F_Y\left(\mathrm{y},N_i^y\right)$$

(8)

$F_Y\left(.|N_i^y,N_i^x\right)$ corresponds to the repartition of $Y_i$ supplying ${(N}_i^y,N_i^x)$. Based on that, the $D_T$ check is then tested by characterizing the following QAR method m(•) for all π $\in \Gamma \subset \left[\mathrm{0,1}\right]$, which takes the below form on the null hypothesis:

$$QAR\left(1\right): m^1\left(N_i^y, \vartheta \left(\mathsf{\pi }\right)\right)={\lambda }_1\left(\mathsf{\pi }\right)+{\lambda }_2\left(\mathsf{\pi }\right)X_{i-1}+{\mu }_t{\Omega }_Y^{-1}\left(\mathsf{\pi }\right)$$

(9)

The parameters $\vartheta \left(\mathsf{\pi }\right)={\lambda }_1\left(\mathsf{\pi }\right)$, ${\lambda }_2\left(\mathsf{\pi }\right)$, and ${\mu }_t$ are computed using the highest likelihood at equal quantile points; and ${\Omega }_Y^{-1}(.)$ refers to the inverse of the standard distribution functional form. To infer and accept causality among variables, the QAR method in Equation (9) is estimated using both variables in lagged forms. The above can be rewritten as:

$$Q_{\mathsf{\pi }}^Y\left({Y_i, N}_i^Y,N_i^X\right)={\lambda }_1\left(\mathsf{\pi }\right)+{\lambda }_2\left(\mathsf{\pi }\right)Y_{i-1}+\eta \left(\mathsf{\pi }\right)X_{i-1}+{\mu }_t{\Omega }_Y^{-1}\left(\mathsf{\pi }\right)$$

(10)

As pointed out by Troster (2018) [34], while Granger causality is theoretically defined using conditional distributions, most empirical studies employ Granger-causality tests based on conditional mean regressions with linear relationships. The Granger-causality in quantiles (GC-Q) test offers the advantage of assessing nonlinear causal relationships and potential causal links within specific conditional quantiles. It also possesses desirable statistical properties, including correct asymptotic size, consistency against all fixed alternatives, and power against Pitman deviations from the null hypothesis.

Using data from 1990 to 2024, we employ a causality approach that accounts for nonlinear patterns in the time series. Our empirical methodology consists of six stages: (i) the BDS test for nonlinearity; (ii) the augmented Dickey–Fuller (ADF) unit root test; (iii) the Zivot and Andrews unit root test with multiple structural breaks; (iv) the quantile autoregressive distributed lags (Q-ARDL) model; (v) the Wald test of parameter consistency; and (vi) the Granger-causality in quantiles (GC-Q) test.

4. Results

The results of the non-parametric BDS test, presented in

Table 1. The results of BDS test.

Dimension	m = 2	m = 3	m = 4	m = 5	m = 6
Biocapacity	0.030 *	0.056 **	0.073 **	0.074 **	0.059 *
Efficiency	0.065 ***	0.078 ***	0.065 ***	0.097 ***	0.087 ***
Taxes	0.043 *	0.079 ***	0.094 ***	0.090 ***	0.046 *
Stability	0.020 *	0.063 ***	0.075 ***	0.093 ***	0.092 ***
GDP	0.679 ***	0.960 ***	0.590 **	0.734 ***	0.744 ***

Source: own calculations. Note: BDS Z-score are reported, being based on the residuals of the VAR model. *, **, and *** indicate significant nonlinear connection at the 10%, 5%, and 1% level, respectively.

, indicate that all variables are nonlinearly dependent. The null hypothesis of independently and identically distributed (i.i.d.) series is rejected at the 1% significance level. This test effectively detects nonlinear patterns in the data without disregarding general time-based linear dependencies. The findings suggest that biocapacity, energy efficiency technology, environmental taxes, political stability, and GDP in the USA economy exhibit nonlinear behavior. Therefore, standard causality approaches that assume linearity or fail to account for structural breaks may produce inconsistent estimates.

Table 2. Results of unit root tests.

Variable	Biocapacity	Efficiency	Taxes	Stability	GDP
ADF (Level)	−3.537 *	−2.948 *	−1.949	−3.054 *	−1.698
ADF (Δ)	−5.663 ***	−4.367	−6.370 ***	−6.911 ***	−5.746 ***
ZA (Level)	−3.845 *	−4.087 **	−3.117	−4.911 ***	−1.482
Breaking point (year)	2001	2015	2005	2018	2021
ZA (Δ)	−12.398 ***	−5.146 ***	−7.890 ***	−7.648 ***	−7.389 ***
Breaking point (year)	2004	2003	2020	2020	2009

Notes: lag length is based on SIC for maximum 10 lags. ***, ** and * show statistical significance at the 1%, 5%, and 10% levels. Source: our elaborations.

presents the results of the univariate unit root tests, including both the augmented Dickey–Fuller (ADF) test and the Zivot and Andrews (ZA) test, which accounts for multiple structural breaks. These tests are used in conjunction to assess the integration properties of the time series and determine whether they exhibit unit root processes. The ADF test assumes linearity, while the ZA test allows for breaks and nonlinearities. Comparing the results of these two tests helps determine whether exogenous shocks to the data have temporary or permanent effects. The break period for the ZA test is selected based on the ADF test statistic with the most negative value.

For biocapacity, the ZA test identifies a break in 2001 at the level and in 2004 after first-differencing. The energy efficiency technology variable is stationary at the level, according to the ADF test, but the ZA test reveals structural breaks in 2015 at the level and in 2003 after first-differencing. The political stability is stationary at the level (ADF), while the ZA test identifies breaks in 2018 at the level and 2020 after first-differencing. GDP is stationary only after first-differencing (ADF), and the ZA test identifies a break in 2021, but does not indicate stationarity at the level. For environmental taxes, the break occurs in 2005 (ADF), and the endogenously determined structural break is in 2020 after first-differencing (ZA).

In summary, both the ADF (Dickey and Fuller, 1979 [95]) and ZA tests suggest that the time series are integrated of at least order 1 (I(1)). While the level series do not consistently reject the null hypothesis of non-stationarity, the first-differenced series do.

Based on the case study of the USA from 1990 to 2024, the structural breaks identified by the Zivot and Andrews (ZA) test in the time series of biocapacity, energy efficiency technology, political stability, GDP, and environmental taxes can likely be attributed to significant economic, political, technological, and environmental events or policy shifts that occurred during those specific break periods. For example, 2001 aligns with the aftermath of the dot-com bubble burst and the 11 September terrorist attacks. These events could have led to economic restructuring, shifts in consumption patterns, and potentially temporary changes in environmental pressures or resource management priorities, affecting the level of biocapacity. The continued economic recovery following the 2001 recession and the increasing focus on national security and potential shifts in energy policy could have influenced the rate of change in biocapacity, leading to a break in the first-differenced series. The COVID-19 pandemic and the subsequent social and political unrest in the USA likely caused significant disruptions and shifts in societal norms, policy focus, and potentially even data collection or reporting related to political stability and environmental issues, leading to a break in the rate of change.

Table 3. Long and short run results based on quantile ARDL model (Q-ARDL).

Quantiles	p-Value for Harvey–Collier Test	Intercept	Error Correction Term	Long-Run				Short-Run
		α	ρ	βefficiency	βtaxes	βstability	βGDP	φ1 (Biocapacity)	ω0 (Efficiency)	λ0 (Taxes)	θ0 (Stability)	δ0 (GDP)
0.05	0.134	1.334 *	−0.330 ***	0.077 **	0.039 **	0.047 *	−1.564 ***	0.728 ***	0.022 *	0.021	0.077	−0.613 ***
0.10	0.176	1.439 *	−0.323 ***	0.081 **	0.037 **	0.084 **	−1.322 ***	0.655 ***	0.040 *	0.024	0.184	−0.605 ***
0.20	0.113	0.984	−0.315 **	0.093 **	0.082 ***	0.091 **	−1.118 ***	0.486 ***	0.033 *	0.058 **	0.233 *	−0.579 **
0.30	0.204	0.943	−0.303 **	0.099 ***	0.090 ***	0.108 ***	−1.144 ***	0.704 ***	0.041 *	0.049 **	0.194 **	−0.533 **
0.40	0.174	0.691	−0.256 *	0.102 **	0.081 ***	0.121 ***	−0.899 ***	0.685 ***	0.034	0.066 **	0.336 **	−0.422 **
0.50	0.296	0.993 *	−0.182	0.053	0.083 **	0.011	−0.763 **	0.506 ***	0.014	0.051 *	0.395 **	−0.417 **
0.60	0.332	1.117 **	−0.165	0.059	−0.029 *	0.033	−0.722 ***	0.500 ***	0.021	0.038	0.322 **	−0.367 **
0.70	0.275	1.255 **	−0.140	0.041	−0.022 *	0.031	−0.617 **	0.598 ***	0.028	0.011	0.259 *	−0.258 *
0.80	0.406	1.367 **	−0.176 **	0.058	−0.015	0.056 *	−0.502 **	0.636 ***	0.022	0.013	0.268 *	−0.139
0.90	0.385	1.445 **	−0.199 **	−0.021	−0.011	0.029 *	−0.419 **	0.755 ***	0.031	0.012	0.119	−0.144
0.95	0.274	1.494 **	−0.221 **	−0.026	−0.010	0.014	−0.295 **	0.725 ***	0.016	0.019	0.075	−0.166

Source: own calculations. Notes: ***, ** and * show significance at various levels (1%, 5% and 10%).

presents the results of the quantile autoregressive distributed lags (Q-ARDL) model developed by Cho et al. [33]. This model is well suited for examining asymmetric relationships in the time series data, uncovering both long-term and short-term associations, and accounting for autocorrelations between variables. This aligns with the paper’s objective of investigating the asymmetric relationships between biocapacity and energy efficiency technology, environmental taxes, political stability, and GDP in both the short and long run.

We applied Harvey–Collier test that is specifically designed to detect linearity by examining whether the residuals have a non-zero mean when ordered by the fitted values. The null hypothesis states linearity. We presented the p-values of the test for each quantile and concluded that linearity is met.

In general, the lagged short-run biocapacity coefficient is positive and significant across all quantiles, although its overall response function is nonlinear. This contrasts with the short-run GDP (negative sign) and energy efficiency technology (positive sign) coefficients, whose magnitudes decrease across quantiles. The statistical significance of both GDP and energy efficiency technology becomes negligible after the seventh and fourth quantiles, respectively. Notably, both the short-run environmental tax and political stability coefficients exhibit a similar inverted U-shaped pattern, with statistical significance primarily around the median quantiles.

Regarding long-run parameters, the GDP coefficient shows a decreasing negative trend but remains highly significant across all quantiles (Table 3). Other variables do not exhibit this consistent pattern. The energy efficiency technology and political stability coefficients show increasing trends, with highly significant parameters up to the 5th and 6th quantiles. An inverted U-shaped pattern emerges for the environmental tax coefficient, with the associated coefficients appearing to reach a turning point at the median (Q = 0.50). Most variables show statistical significance across a large number of quantiles.

Overall, the short-run and long-run dynamic interactions reveal insightful findings, clearly demonstrating interconnections between the variables, with varying magnitudes depending on the time horizon considered. These findings highlight the asymmetric and nonlinear ways in which past ecological states, economic activity, and technological advancements influence the current level of biocapacity in the short run. The impact of GDP is consistently negative but less severe when biocapacity is higher, while the benefits of energy efficiency are positive but less impactful when biocapacity is already abundant. The strong and persistent positive effect of lagged biocapacity underscores the importance of maintaining healthy ecological reserves.

The inverted U-shaped relationship is likely a result of the complex interplay between the incentives created by environmental taxes and the broader economic and political context. While the EKC offers a potential framework for understanding why environmental quality (or in this case, factors influencing it like environmental policy) might have a nonlinear relationship with development indicators, the specific shape and turning point observed in this study are empirical results derived from the data analysis.

The break date is selected where the t-statistic from the ADF test of unit root is at a minimum (most negative). Consequently, a break date will be chosen where the evidence is least favorable for the unit root null. The break date is selected where the t-statistic from the ADF test of unit root is at a minimum (most negative). Consequently, a break date will be chosen where the evidence is least favorable for the unit root null.

The Wald test of parametric consistency is then conducted to evaluate the individual significance and fitness of each variable within the model. This test examines whether each variable’s coefficient is significantly different from zero. The results are presented in

Table 4. Results based on Wald test.

Parameters	Wald Statistics
Long-term Parameters
ρ	8.115 ***
βefficiency	6.557 ***
βtaxes	5.156 ***
βstability	5.987 ***
βGDP	4.543 ***
Short-term Parameters
φ1	5.886 ***
ω0	3.237 ***
λ0	2.045 **
θ0	2.103 **
δ0	2.778 **

Notes: *** and ** show significance at various levels (1% and 5%). Source: our elaborations.

. The null hypothesis of a zero coefficient is rejected at the 5% significance level for all variables. Furthermore, all associated Wald statistics are positive, regardless of whether the short-run or long-run specifications are considered. This indicates that all variables contribute meaningfully to the model. Excluding any of these variables could lead to misleading estimates due to the omission of potentially important determinants of biocapacity in the USA. Therefore, the chosen set of variables is deemed relevant for the model’s overall fit.

As a final step, the paper employs the Granger-causality in quantiles (GC-Q) test, the results of which are presented in

Table 5. Results based on Granger-causality in quantiles (GC-Q) test.

Quantiles	[0.05–0.95]	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
ΔEfficiencyt to Biocapacityt	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
ΔBiocapacityt to ΔEfficiencyt	0.411	0.420	0.423	0.354	0.399	0.433	0.450	0.438	0.453	0.423	0.456	0.422
ΔTaxest to Biocapacityt	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.319	0.309	0.328	0.375
ΔBiocapacityt to ΔTaxest	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.565	0.566	0.528	0.598	0.565
ΔStabilityt to Biocapacityt	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
ΔBiocapacityt to ΔStabilityt	0.933	0.904	0.888	0.888	0.772	0.716	0.694	0.669	0.683	0.683	0.714	0.856
ΔGDPt to Biocapacityt	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
ΔBiocapacityt to ΔGDPt	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Source: own calculations.

and discussed further in the next section. A significant unidirectional causal relationship from energy efficiency technology to biocapacity is observed, consistent and significant across all quantiles. This aligns with the expectation that energy-efficient technologies reduce resource consumption and, consequently, environmental degradation, supporting recent research [96,97,98,99,100,101]. Furthermore, a bidirectional causal relationship is found between environmental taxes and biocapacity in the upper quantiles. This highlights the importance and effectiveness of policy instruments in regulating resource extraction, fossil fuel combustion, and more broadly, balancing the marginal costs and benefits of carbon (making the use of fossil and exhaustible resources progressively less profitable), ultimately improving ecological conditions and biocapacity. Third, a unidirectional causal link from socio-economic conditions to biocapacity is established, supporting the findings of Moran et al. (2008) that the improvements in political stability can mitigate environmental degradation [102]. Finally, the remaining pairs of variables show neutral relationships, indicating the absence of significant causal links.

5. Discussion and Policy Recommendations

As human demand for natural resources continues to grow rapidly, ecosystems’ capacity to regenerate biomass is under increasing pressure. Earth’s biological regeneration rate is being significantly exceeded, hindering the possibility of reconciling economic and environmental objectives. Herbert and Leeves (1999) studied the interaction between economy, population, and environment and the effect of taxation and environmental self-purification [96].

This empirical analysis examined the dynamic interactions between per capita income, energy efficiency technology innovations, environmental taxes, political stability, and biocapacity in the USA to better understand the relationships between these variables. Key findings include the following: GDP has a significant but indirect impact on biocapacity, with a declining marginal coefficient across quantiles in both the short and long term. Energy efficiency technology and political stability have a positive and significant impact on biocapacity, but only in the long run. These effects diminish in significance after the 6th quantile in the long run, and their short-run impacts are negligible. Environmental taxes show an interesting inverted U-shaped relationship with biocapacity across quantiles, regardless of whether the long-run (with error correction term) or short-run specifications are considered. The turning point occurs around the 4th quantile, after which the coefficient becomes negative in both time dimensions.

Energy efficiency technologies, environmental taxes, and political stability have a significant positive effect on biocapacity per capita. GDP per capita, on the other hand, reduces biocapacity and increases the deficit. This contradicts the findings of Zhang et al. (2017) [26], who found a positive association between biocapacity and subjective well-being in high-income countries. Our results are in line with Chen et al. (2019), who found no causal links between these variables in 16 CEE countries [27], although they did find a significant influence of financial development on the biocapacity deficit. Hassan et al. (2019) also found no significant link between economic and biocapacity indicators in Pakistan [87]. In addition, Sarkodie (2021) demonstrated converging forces across 188 countries for ecological footprint, carbon footprint, and biocapacity [3]. However, the analysis reveals that the marginal contribution of GDP per capita to the biocapacity deficit is substantially larger (−1.62) than that of other regulatory variables (−0.08 on average). This suggests that, without comprehensive measures to reduce the economy’s resource dependence, alternative regulatory frameworks, such as environmental taxes and subsidies, as well as low-carbon technologies, may struggle to fully address the USA’s biocapacity deficit.

A methodological discussion is also warranted. Xiao (2009) [92] extended the bounds testing approach of Pesaran et al. (2001) [91] to incorporate quantiles, accommodating higher volatility in endogenous variables under conditional heteroskedasticity. Q-ARDL procedure allows for a dynamic framework that addresses the issues of serial correlation and endogeneity while estimating unique parameter vectors for each quantile. This approach captures nonlinear patterns without requiring pre-defined assumptions about the integration order of the time series, unlike the traditional ARDL model. The existing biocapacity literature, while growing, relies heavily on ARDL and Granger causality frameworks. With a few exceptions, including this paper, most studies employ methodologies that assume linearity, which is often inadequate when dealing with time series exhibiting structural breaks. Therefore, exploring time-varying parameter vector autoregressive (TVP-VAR) models could be a fruitful avenue for future research.

While conventional vector autoregressive (VAR) and Bayesian VAR models have demonstrated strong potential in time-series forecasting, they rely on restrictive linearity assumptions that preclude time variation in parameters. Consequently, when the integration properties of the series do not satisfy stationarity conditions, the resulting estimates can be misleading and spurious. Furthermore, VAR models have been criticized for failing to capture underlying nonlinearities, particularly during periods of crisis and recession (Lucas, 1976) [97]. TVP-VAR models offer a robust and flexible alternative, relaxing stationarity assumptions and capturing the potential time-varying behavior of economic structures. As illustrated by the empirical analysis above, TVP-VAR models can improve predictability when time series experience significant structural breaks and nonlinearities, which are often present during crises and recessions [98].

6. Conclusions

The accelerating demand for natural resources is leading to critical resource depletion. Ecosystems’ ability to regenerate biomass is threatened as the Earth’s biological regeneration rate is consistently exceeded, jeopardizing the potential for balanced economic, social, and environmental progress. The USA’s case is particularly illustrative and requires thorough investigation. This paper examines the dynamic interactions between per capita income, energy efficiency technology innovations, environmental taxes, political stability, and biocapacity in the USA. Using time series data from 1990 to 2024, this paper employs a nonlinear causality testing procedure. The results are intended to assist policymakers in the USA in better balancing the marginal benefits and costs of natural resource use.

The study’s quantile regression analysis reveals nuanced short-run dynamics where lagged biocapacity positively influences current biocapacity nonlinearly across all quantiles, while short-run GDP negatively impacts and energy efficiency technology positively impact biocapacity with decreasing magnitudes and diminishing significance at higher quantiles; notably, environmental tax and political stability exhibit an inverted U-shaped short-run relationship, significant mainly around the median. In the long run, GDP consistently shows a significant negative impact across all quantiles, whereas energy efficiency and political stability display increasing positive trends with significance up to the mid-quantiles, and environmental tax follows an inverted U-shaped pattern potentially peaking at the median. Causality analysis indicates a unidirectional positive influence of energy efficiency technology on biocapacity across all quantiles, a bidirectional relationship between environmental taxes and biocapacity in upper quantiles, a unidirectional positive impact of political stability on biocapacity, with other variable pairs showing no significant causal relationships.

The study focuses solely on the national level of the USA. This aggregation might mask significant variations and heterogeneous relationships that exist at regional and local levels. The impact of economic regulation on biocapacity could differ substantially across different states or even smaller geographical units within the USA due to varying economic structures, resource endowments, policy implementations, and environmental conditions. While the study examines the drivers of biocapacity, it acknowledges that the USA’s persistent biocapacity deficit is an understudied area. The current research might not delve deeply into the specific characteristics, consequences, and potential solutions related to this deficit. A more focused analysis on this aspect is needed. The paper focuses on the relationship between economic factors and biocapacity. It might not fully incorporate the intricate connections with other critical aspects of environmental sustainability, such as ecosystem services, biodiversity, and their ultimate impact on human well-being. A more holistic approach considering these interconnected dimensions is needed for a comprehensive understanding.

Future research could explore the economic regulation-biocapacity nexus at regional and local levels, contingent on data availability. The USA’s biocapacity deficit remains an understudied topic in the literature, requiring further analysis to build upon these findings. Furthermore, the incorporation of machine learning (ML) algorithms derived from artificial intelligence (AI) aligns with current research trends. Employing these techniques could address the limitations of traditional econometric approaches and generate new empirical insights [71]. Overall, a deeper understanding of the complex interrelationships between economic growth, ecosystem services, biodiversity, and well-being is needed [99].

In summary, the conclusions of this study offer valuable information for policymaking. Recognizing that a biocapacity deficit occurs when a country’s ecological footprint exceeds its biocapacity, comprehensive measures should prioritize reducing the ecological footprint. For the USA, this involves enacting and enforcing environmental regulations, as well as managing industrial externalities through policy instruments such as taxes on the most depleted and polluting resources, or subsidies for sustainable industries and practices. Given the reciprocal relationship between ecological footprint and biocapacity deficit, it is also crucial to establish and maintain protected areas that restrict resource extraction in certain locations, allowing ecosystems to regenerate biomass at the Earth’s natural rate. Finally, future policy planning must address the competition for land, particularly the conversion of natural or arable forestland to housing developments (with little reverse conversion), as this directly threatens food security and increases the ecological footprint [72,103,104,105]. These pressures are likely to be exacerbated by global warming, as rising temperatures are projected to decrease land productivity across various crops. This, in turn, will likely lead to farmers adopting adaptive strategies, including both intensification (increased use of energy, fertilizers, and water) and extensification (expanding cultivated area into natural lands) to offset climate-related losses.