The Impact of Forest Rents on Ecological Footprints in China: The Moderating Role of Government Effectiveness

Abstract

Forests serve as the lungs of our planet, yet their mismanagement causes environmental problems and threatens global sustainability. Global forest footprints continue to increase, requiring studies to investigate and provide solutions. This study aims to establish how forest rents and government effectiveness shape forest footprints in China. Specifically, it assesses the impact of forest rents (FRs), fossil fuel consumption (FFC), foreign direct investment (FDI), economic growth (GDP), population (POP), and ecological footprints (EFFs) while considering the moderating role of government effectiveness (GEFF). This study used quantile regression, ordinary least squares, and Granger causality tests for a comparative analysis. This study found that forest rents significantly increase ecological footprints, but the impact diminishes at higher quantities, an indication that environmental policies can mitigate their adverse effects. Moreover, GEFF plays a crucial role in reducing EFFs across all quantiles, signifying the relevance of effective governance in achieving sustainability. Again, while FFC and FDI contribute to environmental sustainability, economic growth exacerbates ecological degradation, particularly at higher quantiles. The Granger causality test further indicates that forest rents and government effectiveness drive ecological changes, while population growth exerts a bidirectional influence on sustainability. These findings provide critical insights for policymakers and emphasize the need for robust governance, sustainable forest management, and eco-friendly economic strategies.

1. Introduction

Increasing deforestation and unsustainable energy consumption patterns have placed the global environment under immense pressure. The threats of climate change are more serious now than ever [1]. This arises from high greenhouse gas emissions in the atmosphere, primarily carbon emissions. This has a detrimental effect on human survival [2]. Studies have argued that developing sustainable practices can be a way to mitigate these emissions. Since all aspects of human activities contribute to emissions, the best way to offset atmospheric emissions is to provide enough carbon neutralization avenues. Engaging in a higher percentage of sustainable activities emerges from using the forest reserves as a source of emissions abatement. However, the forests that act as carbon sinks and biodiversity reservoirs are being depleted at an alarming rate to meet rising demands for agriculture, infrastructure, and fuelwood [3]. Moreover, there is heavy reliance on fossil fuels that has exacerbated greenhouse gas emissions [4]. This has, in turn, accelerated climate change and environmental degradation. The loss of forest cover threatens the ecological balance and reduces natural ecosystem resilience, causing severe consequences like biodiversity loss, soil erosion, and other issues. However, increasing population and industrial development for human survival have squeezed nations into giving up their forest reserves for agriculture, settlements, and industrial development [5]. In some cases, compensation is provided to offset this damage or plant new forests. During these developments, CO₂ emissions continue to increase, with China leading the global race [6]. Given the intensifying environmental crisis, understanding the economic drivers behind forest exploitation and their implications for sustainability is crucial.

China prioritizes carbon neutrality goals as the nation embarks on meeting its ambitious dual carbon goals by 2060 [7]. This quest demands that government and citizens in all areas contribute their quotas. The major aspects of this are massive afforestation, low-carbon technologies, and sustainable behaviors. The country has embarked on planting trees and increasing its forest areas, as others are being converted for agriculture, industrial, and settlement purposes. According to [8], as of 2022, China’s forest area was estimated at approximately 248.46 million hectares, accounting for about 23.83% of the country’s land area. This reflects a significant increase from 154.57 million hectares in 2000, indicating a recovery rate of approximately 4 million hectares per year. In 2020, the country had 142 Mha of natural forest, extending over 15% of its land area. In 2023, it lost 382 kha of natural forest, equivalent to 355 Mt of CO₂ emissions. In terms of carbon dynamics, between 2001 and 2023, China’s forests emitted an average of 242 million tonnes of CO₂ equivalent per year while removing approximately 728 million tonnes annually. This results in a net carbon sink of about 486 million tonnes of CO₂ equivalent per year. These numbers signify the crucial role the country’s forests are playing in mitigating climate change by acting as a substantial carbon sink, offsetting a portion of the country’s greenhouse gas emissions.

In this domain, forest rents (FRs), which are the economic returns derived from forest resources, play a significant role in shaping environmental outcomes [9]. Forest rents can incentivize sustainable forest use, promote conservation efforts, and support economic diversification away from environmentally harmful practices [10]. However, many countries continue to experience environmental issues and forest depletion with no FRs to account for. This stems from ineffective governance and mismanagement, leading the country toward doom. When countries have weak governance mechanisms, forest rents may instead fuel unsustainable exploitation, leading to long-term ecological degradation. Government effectiveness, therefore, emerges as a crucial tool that can moderate forest rents to contribute to environmental sustainability or exacerbate deforestation. Effective governance ensures the enforcement of environmental policies, enhances institutional frameworks for forest management, and promotes responsible resource utilization [11]. Therefore, the main objective of this study was to determine how forest rents and effective governance shape the ecological footprints of forest products in China. By examining the interplay between forest rents and government effectiveness, this study provides valuable insights into policy interventions that can mitigate environmental harm.

Studies have attempted to establish a link between forest activities and environmental sustainability. However, these are gaps that need to be filled to ensure the achievement of sustainable development goals is on the right track. This study introduces a novel approach by focusing on forest footprints rather than general ecological footprints or carbon emissions, which have been the primary metrics in previous research. By doing so, it provides a more targeted assessment of the environmental impact of forest resource utilization. It provides specific knowledge and insight into how forests and their rents are performing in the overall ecological landscape. Additionally, the moderating role of government effectiveness in the forest rents–sustainability nexus has been largely overlooked in the literature. Effective governance plays a pivotal role in changing the dynamics of forest management [12]. By addressing these gaps, this study offers fresh insights into how governance structures can shape sustainable forest management practices. Secondly, the findings contribute to the broader discourse on environmental policy and provide a basis for future studies. This study provides a framework (Figure 1) that highlights the role of effective governance in moderating the performance of forest rents, economic growth, investment, and ecological footprints. Moreover, the results provide empirical evidence to guide policymakers in balancing economic benefits from forest resources with long-term ecological sustainability.

In the study mechanism, the crucial role effective governance plays in maximizing forest rents while ensuring sustainable resource management is highlighted. When strong policies are implemented, environmental regulations are enforced, and responsible land use is promoted, governments can prevent illegal logging and overexploitation. The existence of transparent governance attracts sustainable investments in forestry, boosting economic returns while preserving biodiversity. The institutionalization of forest rents can be effective when government policies are adhered to [13]. Developing countries are usually at the mercy of these issues where institutions do not work in their favor, exacerbating environmental and forest problems. Additionally, initiatives like reforestation programs, carbon credit markets, and eco-tourism enhance forest value and longevity. When technological monitoring is introduced and community engagement is enhanced, governments can curb deforestation, improve conservation efforts, and reduce overall ecological footprints. In [14], it was highlighted that the relevance of technologies cannot be overlooked. The study emphasized that there is a need to increase investments in green technologies and promote the consumption of clean energies that can transform the environmental situation we face. Therefore, effective governance transforms forest rents into a tool for both economic growth and environmental sustainability.

2. Literature Review and Hypotheses

2.1. Forest Rents and Ecological Sustainability

Forest rents, defined as the economic returns derived from the utilization of forest resources, play a crucial role in shaping both the economy and the environment [6]. Forests, as vital carbon sinks and biodiversity reservoirs, contribute significantly to mitigating climate change and maintaining ecological balance. However, when forests are exploited for economic gain, the long-term environmental consequences can be severe, leading to deforestation, habitat loss, and ecological degradation. The concept of forest rents is often tied to the economic incentives generated by logging, land conversion for agriculture, and other resource-extraction activities. While these activities provide immediate financial returns, they can contribute to the depletion of forest resources, thereby exacerbating environmental problems, such as carbon emissions and biodiversity loss. Several studies have indicated that the economic returns from forest resources or forest rents frequently incentivize unsustainable practices, particularly in regions where governance is weak or the enforcement of environmental regulations is limited. The exploitation of forest resources for short-term economic gains often leads to unsustainable logging practices and land conversion, which contribute to the depletion of forests and increase the ecological footprint of these activities. This creates a paradox in which forest rents, which are meant to foster the development of the forest industry and improve economic outcomes, contribute to ecological damage instead. Despite the significance of forest rents in shaping environmental outcomes, the literature on their impact remains limited. This gap in research may be a contributing factor to the overexploitation seen in the forest industry. Among the few studies available, the evidence suggests that forest rents are often detrimental to environmental sustainability. For instance, Ref. [9] found that forest rents in China led to increased carbon emissions due to the overexploitation of forest resources, which undermined efforts to reduce environmental degradation. Similarly, Ref. [1] highlighted that forest rents in Russia contributed to increased deforestation, further exacerbating ecological damage. A study covering BRICS economies from 1995 to 2017 [15] reinforced these findings, showing that forest rents significantly increased ecological footprints, further detracting from environmental sustainability. These studies underscore that while forest rents provide short-term economic benefits, they often come at the cost of long-term ecological stability. Therefore, while forest rents can potentially benefit the economy, they can also drive ecological harm if not managed carefully and sustainably.

Hypothesis 1: 

Forest rents increase ecological footprints.

2.2. Government Effectiveness and Ecological Sustainability

Government effectiveness is defined by the quality of public services, policy implementation, and regulatory enforcement and plays a pivotal role in mitigating environmental degradation. Studies consistently show that strong governance frameworks are essential in achieving long-term ecological sustainability. For instance, Ref. [16] emphasizes that effective government institutions are key to controlling the negative environmental impacts that often arise from rapid industrialization and population growth. In countries with well-established governance systems, the enforcement of environmental regulations is more robust, and there is a greater emphasis on promoting sustainable development, which directly contributes to reducing ecological footprints. A study [17], which examined 152 countries from 2002 to 2018, found that government effectiveness significantly improves the ecological environment. Countries with more effective governance structures not only enforce environmental laws more rigorously but also implement policies that encourage sustainable practices, such as renewable energy adoption and efficient resource management. The research demonstrates that government effectiveness is positively correlated with improvements in environmental quality, as it helps to implement and monitor policies that curb environmental harm. Similarly, Ref. [18] explored the relationship between government effectiveness and environmental outcomes within the BRICS economies from 2000 to 2022. The study concluded that effective governance in these countries negatively affected emissions, suggesting that stronger government measures lead to lower pollution levels and reduced environmental degradation. These findings underscore the importance of enhancing governance in emerging economies to achieve overall ecological sustainability. Further supporting this view, Ref. [16] found that countries with higher levels of government effectiveness experienced lower ecological footprints. This outcome was attributed to better enforcement of environmental regulations, particularly in areas related to air quality, water conservation, and waste management. Effective governance enables the establishment of monitoring systems, ensuring compliance with environmental standards and holding industries accountable for their impact on the environment. Additionally, Ref. [17] demonstrated that effective governance can moderate the negative environmental impacts of resource exploitation, particularly in developing countries. In regions where resource extraction is a primary economic activity, weak governance often leads to unsustainable practices, such as illegal mining, overfishing, and deforestation. However, the study showed that when governments are effective, they can regulate and manage these activities more efficiently, reducing the likelihood of environmental degradation. These studies collectively underscore the critical importance of strong and effective governance in promoting ecological sustainability. The literature highlights the need for governments, particularly in developing and emerging economies, to strengthen their governance structures to address the growing environmental challenges and ensure that economic growth does not come at the expense of ecological health. Therefore, this study hypothesizes the following:

Hypothesis 2: 

Government effectiveness has a moderating effect on ecological footprints, reducing environmental degradation.

2.3. Fossil Fuel Consumption and Ecological Sustainability

Fossil fuel consumption is a major driver of ecological footprints, largely due to its substantial contribution to greenhouse gas emissions and environmental degradation. Numerous studies have consistently demonstrated a positive relationship between fossil fuel use and ecological footprints, emphasizing the negative environmental consequences associated with non-renewable energy consumption [1,19,20,21]. The combustion of fossil fuels, such as coal, oil, and natural gas, releases large amounts of carbon dioxide (CO₂) and other harmful greenhouse gases into the atmosphere, exacerbating global warming and accelerating ecological deterioration [3,22]. This direct link between fossil fuel consumption and environmental harm underscores the urgency of transitioning to cleaner, more sustainable energy sources. Several studies have illustrated the significant role of fossil fuel consumption in driving ecological degradation. For example, a study [3] found that in Vietnam, fossil fuel consumption was a primary contributor to carbon emissions, leading to significant ecological degradation. The country’s heavy reliance on coal and oil for energy production was found to be closely linked with environmental damage, such as air and water pollution, deforestation, and the depletion of natural resources. Similarly, Ref. [22] demonstrated that in South Asia, the consumption of non-renewable energy sources has led to a marked increase in ecological footprints, highlighting the environmental costs of continued dependence on fossil fuels. The research revealed that the region’s high reliance on coal for energy exacerbates environmental challenges, including poor air quality and ecosystem degradation. Furthermore, a study [15] using data from the BRICS countries confirmed that non-renewable energy consumption has a detrimental impact on environmental sustainability. The study found that as these countries continued to rely on fossil fuels for economic development, their CO₂ emissions increased significantly, contributing to a rise in their ecological footprints. The research also emphasized the need to enhance carbon sinks through forestation efforts, such as large-scale tree planting, to offset the environmental damage caused by fossil fuel use. These findings collectively suggest that reducing fossil fuel consumption is essential for achieving ecological sustainability. Transitioning to renewable energy sources, coupled with efforts to enhance carbon sequestration, is crucial for mitigating the adverse environmental impacts associated with fossil fuel dependency. Without such measures, achieving a sustainable future will remain a significant challenge.

Hypothesis 3: 

Fossil fuel consumption increases ecological footprints.

2.4. Foreign Direct Investment and Ecological Sustainability

Foreign direct investment (FDI) is often regarded as a double-edged sword when it comes to its impact on environmental sustainability, particularly in relation to ecological footprints. On the one hand, FDI can bring advanced technologies, managerial expertise, and access to international markets, which can promote environmentally sustainable practices. On the other hand, it may also result in resource exploitation and environmental degradation, especially in countries with inadequate environmental governance or weak regulatory frameworks. Many studies have established the significant impact of FDI on ecological sustainability, indicating mixed results [23,24,25,26]. Specifically, Ref. [27] highlights that in countries like China, FDI has had a significant impact on carbon emissions due to the rapid industrialization fueled by foreign capital. In the study, it was found that FDI led to the expansion of energy-intensive industries, such as manufacturing and construction, which, in turn, increased carbon footprints. The study attributes this rise in emissions to both the high energy demand of foreign-invested industries and the insufficient environmental regulations in the host country. This underscores a critical issue: when FDI is channeled into sectors that rely heavily on fossil fuels or unsustainable practices, it may exacerbate environmental degradation rather than alleviate it. Conversely, Ref. [28] provides a contrasting view, focusing on the impact of FDI in South Asia. Its findings suggest that foreign investment can play a crucial role in advancing environmental sustainability by promoting the adoption of green technologies, clean energy solutions, and renewable energy projects. It is argued that FDI in South Asia has enabled local firms to access cleaner technologies and international expertise, particularly in the fields of solar energy and wind power. This aligns with the growing body of literature that recognizes the potential of FDI to facilitate the transition toward greener economies in developing regions. These contrasting findings illustrate the importance of the host country’s regulatory framework and the quality of governance in determining the environmental outcomes of FDI. In countries with strong environmental policies, FDI can contribute to the development of sustainable industries and the diffusion of green technologies. Ultimately, these mixed outcomes highlight the necessity for effective policy frameworks and the enforcement of environmental regulations to harness the positive potential of FDI while mitigating its negative impacts. Governments that aim to attract FDI should ensure that investment projects are aligned with national sustainable development goals and incorporate environmental performance criteria. Such governance mechanisms can help maximize the benefits of FDI in fostering green innovation and environmental sustainability while minimizing its potential harms. Thus, the relationship between FDI and ecological footprints is contingent upon several factors, including the specific sector receiving the investment, the regulatory environment, and the host country’s overall governance capacity. Without careful management, FDI may worsen environmental outcomes, but with appropriate safeguards, it can serve as a catalyst for sustainable development.

Hypothesis 4: 

Foreign direct investment increases ecological footprints.

2.5. Economic Growth, Population, and Ecological Sustainability

Economic growth and population growth are often cited as central drivers of ecological footprints, reflecting the demand placed on natural resources and the environment due to human activity. The relationship between economic development and environmental degradation is complex and has been widely discussed in the literature [29], with varying perspectives depending on the stage of economic development and regional context. The Environmental Kuznets Curve (EKC) hypothesis offers a foundational explanation for this relationship. It posits that economic growth initially leads to increased environmental degradation as industries expand, resource consumption rises, and pollution intensifies.

Despite the theoretical promise of the EKC, numerous empirical studies challenge the notion that economic growth necessarily leads to environmental improvements, particularly in developing economies. Many of these studies argue that rather than reducing ecological footprints, economic growth often exacerbates environmental degradation in these regions [3,30]. This divergence can be attributed to several factors, including the reliance of many developing countries on resource-intensive industries, weak environmental governance, and the limited availability of green technologies that could mitigate the ecological impact of growth. For instance [30] highlights that in many developing nations, the expansion of industries such as mining, agriculture, and manufacturing has directly contributed to the depletion of natural resources and increased carbon emissions without sufficient counterbalancing measures to reduce environmental harm.

Similarly, population growth has long been recognized as a key driver of ecological pressure. As populations increase, the demand for resources such as food, water, energy, and land also rises, leading to greater environmental stress. Population growth contributes to greater land use changes, including deforestation and urban sprawl, as well as an increase in waste generation, all of which result in higher ecological footprints [5,6]. For example, Ref. [5] observes that population growth in the Pacific region has been a significant driver of deforestation, leading to extensive ecological degradation. Similarly, Ref. [6] notes that population growth, particularly in rapidly urbanizing regions, exacerbates the consumption of resources and contributes to unsustainable waste production. The expansion of cities and the spread of informal settlements are linked to rising energy demand, increased transportation needs, and the degradation of urban and peri-urban environments. In these areas, environmental degradation tends to occur at a faster rate, as urban sprawl often encroaches on valuable natural habitats, while the infrastructure needed to support growing populations remains inadequate. These studies underscore the dual role of economic and population growth in driving environmental pressure. Economic growth can either exacerbate or alleviate environmental harm, depending on factors such as the nature of the growth, the level of technological innovation, and the effectiveness of policy frameworks. Likewise, population growth, while essential for economic development in some contexts, remains a significant challenge to sustainability, especially in areas where it outpaces the development of green technologies and resource-efficient practices. Based on the dominance of the literature, the study hypothesizes the following:

Hypothesis 5: 

Economic and population growth increase ecological footprints.

3. Materials and Methods

3.1. Data and Sources

Data used in the study were sourced from reliable databases for China from 1990 to 2022. The variables used are ecological footprints on forest products, forest rents, government effectiveness, fossil fuel consumption, foreign direct investment, economic growth, and population. The data and their characteristics are discussed below and highlighted in

Table 1. Data characteristics.

Variable	Description	Mean	Med.	SD	Skew.	Kurt.	JB	Prob.	Source
EFFs	Ecological footprints on forests	19.288	19.186	0.200	0.594	1.898	3.608	0.165	[31]
FRs	Forest rents	−1.501	−1.668	0.797	0.561	2.054	2.962	0.227	[32]
GEFF	Government effectiveness	0.017	0.015	0.373	0.230	2.109	1.382	0.501	[33]
FFC	Fossil fuel consumption	4.441	4.464	0.076	−0.069	1.761	2.137	0.343	[32]
FDI	Foreign direct investment	0.930	1.248	0.754	−1.411	4.664	14.750	0.001	[32]
GDP	Gross domestic product	28.698	28.643	1.285	−0.120	1.593	2.801	0.247	[32]
POP	Population	20.986	20.994	0.067	−0.373	2.128	1.810	0.404	[32]
Correlation matrix
		EFFs	FRs	GEFF	FFC	FDI	GDP	POP
EFFs		1.000
FRs		−0.622	1.000
GEFF		0.570	−0.707	1.000
FFC		0.798	−0636	0.531	1.000
FDI		−0.688	0.450	−0.460	−0.535	1.000
GDP		0.529	−0.727	0.637	0.779	−0.495	1.000
POP		0.502	−0.766	0.539	0.579	−0.465	0.683	1.000

.

-

Ecological Footprints of Forest Products (EFFs): This was used as the main dependent variable for the study. It is measured by the total footprints of forest products in Gha. Data were sourced from the Global Footprints Network (GFN) database, which provides standardized ecological footprint metrics. The forest-specific footprint was extracted from the total ecological footprint to isolate the impact of forest-related activities.

-

Government Effectiveness (GEFF): In our study, GEFF is considered a major independent variable, and how it influences dependent variables was determined. It is measured in estimates by the World Bank, based on surveys and expert assessments that measure the quality of public services, civil service, policy formulation, and implementation. This variable was sourced from the Worldwide Governance Indicators (WGIs) database;

-

Forest Rents (FRs): In this study, forest rents are considered a main independent variable, and their impact on dependent variables was assessed. This is calculated by determining the difference between the value of timber production and the cost of production, expressed as a percentage of GDP. Data were obtained from the WDIs database;

-

Fossil Fuel Consumption (FFC): Fossil fuel consumption, which is the final energy consumption derived from fossil fuels, including coal, oil, and natural gas, is also considered a main independent variable in this study. Data were obtained from the World Development Indicators (WDIs) database, which provides standardized metrics on energy use across countries. The variable is calculated as the ratio of energy consumption from fossil fuels to the total final energy consumption, expressed as a percentage;

-

Foreign Direct Investment (FDI): FDI is considered a control variable. It is the net inflows of investment from foreign entities into a country, expressed as a percentage of GDP. It includes investments in physical assets, such as factories, infrastructure, and natural resources, as well as financial assets. FDI is calculated as the total net inflows of foreign investment divided by GDP, expressed as a percentage (Net Inflows (% of GDP)). Data on FDI were sourced from the WDI database;

-

Gross Domestic Product (GDP): GDP is included as a control variable to account for the impact of economic growth on ecological footprints. Gross domestic product represents the total monetary value of all goods and services produced within a country in a given year. It is a widely used indicator of economic growth and development. GDP is measured in current US dollars, adjusted for inflation, to reflect the real economic output of a country. GDP data were obtained from the WDI database;

-

Population (POP): This refers to the total number of people residing in a country. Population is included as a control variable because it directly affects ecological footprints. It is a fundamental demographic variable that influences resource consumption, land use, and environmental pressure. The variable is measured as the total population count in a given year. Data were sourced from the WDI database.

According to the data presented in Table 1, the mean values recorded for EFFs, FRs, GEFF, and FFC were 19.288, −1.501, 0.017, and 4.441, respectively. The median value for FRs is −1.668, and the median values of GEFF and FFC are 0.015 and 4.464. GDP recorded the highest standard deviation of 1.285, followed by FRs with 0.797, and POP recording the least (0.067). EFFs, FRs, and GEFF are all right-skewed, while FFC, FDI, GDP, and POP are left-skewed. The probability shows that only GDP is normally distributed. All other series are not normally distributed, providing a solid basis for the application of quantile regression. The correlation matrix shows that FRs and EFFs have a strong negative correlation (−0.622), GEFF and EFFs have a strong positive correlation (0.570), FFC and EFFs have a strong positive correlation (0.708), FDI and EFFs have a strong negative correlation (−0.688), GDP and EFFs have a strong positive correlation (0.529), and POP and EFFs have a strong positive correlation (0.502). The correlation between FFC and EFFs is the strongest, whereas the correlation between FDI and FRs was the weakest. Other descriptives and data characteristics can be found in Table 1.

The trends of the major variables are displayed in Figure 2A–C. In Figure 2A, it can be seen that the ecological footprints of forest products have been increasing over the years. From 1990 to 2006, they increased steadily, until they experienced a sharp increase from 2008 to 2012. They reduced in the subsequent year but increased again in the next year. For the last 5 years, according to the diagram, they have increased consistently. This corresponds to the tremendous increase in emissions in China, and forests form a part of this. As shown in Figure 2B, forest rents have been reducing at an alarming rate. In the earlier part of the 1990s, forest rents showed a steady trend. However, from 1995 to 2002, they saw a sharp decline. This corresponds to how ecological footprints rose in the regions. Weak institutions and governance caused the country to make these losses while increasing its footprints. In the last two decades, the country has managed to reduce the rate at which forest rents are falling. With the intense government sustainability structure, the number can be projected to rise, considering the performance in the last five years of the graph. Finally, Figure 2C depicts the trend in government effectiveness in China. It can be seen that the government has been very effective since the 2000s. The extensive development and growth in China can be attributed to this growth. When these numbers continue to rise, China can leverage its good governance to achieve its dual carbon goals.

3.2. Diagnostic Tests

To ensure the validity and reliability of the data in this study for further econometric modeling, several diagnostic tests were employed to assess the underlying assumptions and robustness of the regression models. These tests included the unit root test, serial correlation test, heteroskedasticity test, and the variance inflation factor (VIF). Each of these tests plays a crucial role in verifying the quality of the data and ensuring that the results are both credible and interpretable.

3.2.1. Unit Root Test

The unit root test is a diagnostic tool used to check for stationarity in time series data. Stationarity refers to a statistical property where the mean, variance, and autocovariance of a series are constant over time. If a time series is non-stationary (i.e., it exhibits a unit root), it can lead to spurious regression results, where the relationships between variables may appear significant when they are not. To test for unit roots, this study adopted the Augmented Dickey–Fuller (ADF) test, or the Phillips–Perron (PP) test was used. In this study, the unit root test is vital because it ensures that the data do not exhibit non-stationary behavior, which could distort the estimation of regression models and lead to misleading conclusions.

3.2.2. Serial Correlation Test

The study conducted a serial correlation test to determine the presence of autocorrelation in the residuals of the regression model. Autocorrelation occurs when the residuals (errors) from one period are correlated with those from another period, violating the assumption of the independence of errors. Serial correlation can lead to inefficient estimators and distorted standard errors, potentially inflating or deflating statistical significance. In this study, serial correlation testing was essential to ensure that the regression model’s residuals were independent, as this is a fundamental assumption for valid hypothesis testing and the reliability of regression coefficients. Identifying and correcting serial correlation improves the robustness of the results and prevents misleading conclusions about the relationships between variables. Therefore, the Breusch–Godfrey test was used in this study.

3.2.3. Heteroskedasticity Test

The heteroskedasticity test was used to assess whether the variance of the errors in the regression model was constant (homoscedasticity) or varied across observations (heteroskedasticity). Heteroskedasticity can occur when the variability of the dependent variable changes across the levels of the independent variables, which often happens in financial or economic data. This violates one of the core assumptions of OLS regression, leading to biased standard errors, which affect hypothesis tests and confidence intervals. In this study, testing for heteroskedasticity was crucial to ensure that the results were not distorted by non-constant error variance. This study adopted the Breusch–Pagan–Godfrey test to determine if there were heteroskedasticity issues among the series.

3.2.4. Variance Inflation Factor (VIF)

The variance inflation factor (VIF) was used in this study to detect multicollinearity among the independent variables in the regression model. Multicollinearity arises when two or more independent variables are highly correlated, making it difficult to assess the individual effect of each predictor. High multicollinearity inflates the variance of regression coefficients, leading to unstable estimates and making it challenging to determine the true relationship between variables. VIF quantifies how much the variance of a regression coefficient is inflated due to multicollinearity. A VIF value greater than 10 typically indicates significant multicollinearity, warranting further investigation or remedial action, such as removing one of the correlated variables. In this study, calculating VIF was essential to ensure the model estimates were reliable and the results could be interpreted with confidence.

3.3. Methodology and Estimation Strategy

This study employed both ordinary least squares (OLS) regression and quantile regression (QR) to provide a comprehensive understanding of the relationship between independent and dependent variables. OLS regression, the standard technique for estimating linear relationships, provides insights into the average effect of independent variables on the dependent variable. However, this approach assumes homoscedasticity and normality in the errors, which may not always hold in real-world data, particularly when the distribution of the dependent variable is skewed or contains outliers.

In contrast, quantile regression offers a more nuanced view by estimating the relationship between the variables at different quantiles of the dependent variable’s distribution. Unlike OLS, QR is robust to heteroscedasticity and outliers, making it an ideal method when the data deviate from the assumptions underlying OLS. By analyzing different quantiles, QR highlights the varying impact of predictors across different segments of the population, capturing the effect on both the lower and upper extremes, as well as the median. Together, OLS and QR complement each other by providing both a general, average estimate of relationships and more granular insights into how those relationships differ at various points in the distribution. While OLS offers a broad view, QR reveals potential inequalities or heterogeneous effects that OLS might obscure. This dual approach enhances the study’s robustness, allowing for comparative purposes, more detailed policy recommendations, and a deeper understanding of the data’s dynamics, particularly when the effects of independent variables are not uniform across all individuals or contexts. In this study, we believe the QR method was especially helpful for determining if the consequences of forest rents and the efficacy of governance differ in nations with various environmental circumstances. A general econometric model was developed for the study in Equation (1):

$$EFF={\beta }_0+{\beta }_{1}F{R}_{it}+{\beta }_{2}GEF{F}_{it}+{\beta }_{3}FF{C}_{it}+{\beta }_{4}FD{I}_{it}+{\beta }_{5}GD{P}_{it}+{\beta }_{6}PO{P}_{it}+{ϵ}_i$$

(1)

where EFF is the ecological footprint of forest products, FR is forest rent, GEFF is government effectiveness, FFC is fossil fuel consumption, FDI is foreign direct investment, GDP is economic growth, and POP is population. β₀ is the intercept, β₁–β₆ are the coefficients to be estimated, and ∈ is the error term.

Moreover, the baseline quantile regression model is specified in Equation (2):

$$Q_{\tau }(EFF)={\beta }_0+{\beta }_{1}F{R}_i+{\beta }_{2}GEF{F}_i+{\beta }_3(F{R}_i\times GEF{F}_i)+\sum {\beta }_K{X}_{ik}+{ϵ}_i$$

(2)

In this equation, Q_τ (EFF) represents the conditional quantile τ of ecological footprints, FR_i denotes forest rents, GEFF_i represents government effectiveness, FR_i × GEFF_i is the interaction term capturing the moderating effect of government effectiveness, X_ik is a vector of control variables, and ϵ_i is the error term.

4. Results

4.1. Diagnostics Test Results

First, the study conducted unit root tests using two approaches: ADF from [34] and PP from [35]. These were to ensure the data were stationary in at least the first difference. According to the results presented in

Table 2. Unit root test.

	ADF				PP
	Level		1D		Level		1D
	Stats.	Sig.	Stats.	Sig.	Stats.	Sig.	Stats.	Sig.
EFFs	0.941	0.995	−4.066	0.004 ***	1.260	0.998	−3.887	0.006 ***
FRs	−1.019	0.734	−4.758	0.001 ***	−1.018	0.735	−4.713	0.001 ***
FFC	0.126	0.963	−5.152	0.000 ***	0.098	0.961	−5.159	0.000 ***
GEFF	−1.586	0.478	−4.071	0.004 ***	−1.582	0.480	−3.033	0.043 **
FDI	0.708	0.990	−2.927	0.054 *	0.003	0.952	−2.897	0.057 *
GDP	−1.588	0.476	−2.845	0.064 *	−1.215	0.656	−2.813	0.068 *
POP	−0.452	0.888	−3.256	0.026 **	−5.318	0.000 ***	−3.256	0.026 **

Note: p-values < 1% ***, 5% **, and 10% *.

, the ADF approach shows that none of the variables were significant at level. In the PP approach, all the variables except POP were not significant. These could not reject the null hypothesis. However, when the series were translated to their first difference (1D), they all became significant in both the ADF and PP techniques. This gives the green light for further econometric models since the null hypothesis can be rejected.

Serial correlation issues can cause problems in a study’s results. To ensure there were no serial correlation and heteroskedasticity issues, this study applied the Breusch–Godfrey Lagrange Multiplier (LM) serial correlation test and the Breusch–Pagan–Godfrey heteroskedasticity tests. The null hypothesis of these tests states no serial correlation and homoscedasticity, respectively. The results for serial correlation show more than 10% significance (0.213), which means the null hypothesis cannot be rejected; hence, there are no serial correlation issues. The heteroskedasticity test also shows a significant value of 0.450, which is more than 10%. Therefore, it is also not rejected, indicating no heteroskedasticity issues in the series. These findings indicate validity in the series for further analysis (

Table 3. Serial correlation and heteroskedasticity.

Test	F-Statistic	Sig.
Serial Correlation LM Test:
Null hypothesis: no serial correlation
Breusch–Godfrey	1.651	0.213
Heteroskedasticity Test:
Null hypothesis: homoskedasticity
Breusch–Pagan–Godfrey	0.994	0.450

).

Making sure there are no multicollinearity problems in the series was the study’s priority. An issue arises in the model when two variables are nearly perfect linear combinations of each other. Thus, multicollinearity was tested using the variance inflation factor (VIF) technique. There are no problems with multicollinearity among the variables, according to the test findings in

Table 4. Variance inflation factor.

Variable	Centered VIF
FRs	3.206
GEFF	3.780
FFC	4.046
FDI	3.979
GDP	3.268
POP	3.822

. As a general guideline, figures should not exceed 10, as this indicates significant multicollinearity issues. FRs, GEFF, FFC, FDI, GDP, and POP have VIF values of 3.206, 3.780, 4.046, 3.979, 3.268, and 3.822, respectively, based on the centered VIF. Since none of these numbers is greater than 10, there are no indications that the variables exhibit significant multicollinearity.

4.2. Regression Analysis

The results in

Table 5. Quantile and OLS results.

	Quantiles
	OLS	0.2	0.4	0.5	0.7	0.8
Variable	β	β	β	β	β	β
FRs	0.145 ***	0.017 **	0.120 *	0.077 **	0.048 *	0.045 *
	(0.045)	(0.069)	(0.065)	(0.073)	(0.078)	(0.075)
FFC	−1.396 **	−2.617 ***	−1.633	−1.735	−1.979	−2.171 **
	(0.645)	(0.758)	(1.132)	(1.320)	(1.334)	(1.272)
FDI	−0.096 ***	−0.032	−0.042	−0.021 **	−0.041	−0.039
	(0.014)	(0.030)	(0.032)	(0.034)	(0.027)	(0.026)
GDP	0.106 **	0.030	0.003	0.059	0.129 *	0.130*
	(0.045)	(0.068)	(0.073)	(0.071)	(0.070)	(0.072)
POP	3.163 **	7.433 **	7.415 **	6.244 *	4.495 **	4.929 ***
	(1.199)	(2.723)	(3.338)	(3.506)	(1.713)	(1.605)
GEFF	0.446	−1.051 **	−0.760 *	−0.819 *	−0.707 *	−0.802 **
	(0.066)	(0.377)	(0.424)	(0.445)	(0.376)	(0.353)
C	−43.640	−126.012 **	−129.004 *	−105.651	−69.895 **	−78.172 **
	23.358	(53.214)	(64.397)	(67.485)	(32.526)	(30.752)
R-squared	0.956	[0.807]	[0.823]	[0.847]	[0.863]	[0.867]
Prob.	0.000	0.000	0.000	0.000	0.000	0.000
Wald Tests
Slope	9.995 *
Symmetry	4.443 **

Note: p-values < 1% ***, 5% **, and 10% *. Standard error in parenthesis. Pseudo R-squared in [ ].

highlight an important relationship between forest rents (FRs) and ecological footprints, showing that FRs have a significant positive impact across different regression methods. The OLS results indicate that forest rents increase ecological footprints, with a coefficient of 0.145. This suggests that a 1% increase in forest rents increases ecological footprints by 0.145%. This finding validates the study’s hypothesis and is in line with [13], which established that resource rents increase environmental fragility, but contradicts [14], which found otherwise. In the quantile regression results, the effect of FRs varies across different levels of ecological footprints. At the 20th quantile, the impact of forest rents is 0.017, indicating that in countries with lower ecological footprints, forest rents play a relatively minor role in environmental degradation. However, as we move to the 40th quantile, the impact increases significantly to 0.120, suggesting that in moderately impacted environments, forest rents have a stronger effect. Interestingly, in the 50th quantile, the impact slightly declines to 0.077, and it continues decreasing in the 70th (0.048) and 80th (0.045) quantiles. This decline suggests that in countries or regions with the highest ecological footprints, the role of forest rents in driving environmental degradation weakens, possibly because other more significant factors (such as industrialization, land conversion, and urban expansion) dominate.

Regarding fossil fuel consumption (FFC), this study found it to be a critical driver of environmental degradation. The OLS results reveal that FFC has a significant negative impact on ecological footprints, with a coefficient of −1.396. This shows that a 1% increase in FFC has the potential to reduce ecological footprints by 1.396%. The quantile regression results show that FFC has a significant negative impact on EFFs in the 20th quantile (−2.617) and 80th quantile (−2.171) while being insignificant in other quantiles. This suggests that fossil fuel consumption is a major environmental concern in countries with both low and high ecological footprints. For countries with lower footprints, this could be due to their reliance on fossil fuels for economic development. In high-footprint regions, it highlights the persistent and overwhelming effects of fossil fuel dependency despite advancements in energy transitions. These findings invalidate the study’s hypothesis and contradict the studies [21,36] that found that non-renewable energy consumption is detrimental to environmental sustainability. This could be attributed to the choice of variables, as past studies primarily focus on using CO₂ emissions and overall ecological footprints in their assessments. However, it is in tandem with [22], which established that fossil fuel consumption has a favorable impact on environmental sustainability.

Foreign direct investment (FDI) shows a consistent negative effect in OLS regression, with a coefficient of −0.096. This implies that FDI, on average, contributes to reducing ecological footprints, likely due to investments in cleaner technologies, sustainable forestry practices, and environmental management initiatives. However, in the quantile regression, FDI’s impact is largely insignificant except in the 50th quantile (−0.21). This suggests that in moderately impacted countries, FDI plays a role in reducing ecological degradation, possibly by funding sustainability projects, afforestation, or eco-friendly business practices. The lack of significance in lower and higher quantiles indicates that in highly degraded environments, foreign investments may not be sufficient to counteract large-scale environmental damage, while in less impacted regions, the effect may be too small to detect. These findings validate this study’s hypothesis and are in tandem with [30], which found that FDI is detrimental to ecological growth, but contradicts [27], which established that checked foreign direct investment can boost carbon productivity and enhance ecological footprints.

Concerning the impact of GDP on EFFs, the OLS results show that GDP has a positive impact on EFFs, with a coefficient of 0.106. This suggests that economic growth contributes to ecological degradation, as a 1% increase in economic growth increases footprints by 0.106%. The quantile regression provides deeper insights. GDP has a significant positive impact in the 70th quantile (0.129) and 80th quantile (0.130), meaning that in regions with already high ecological footprints, economic expansion leads to greater environmental stress. This aligns with the Environmental Kuznets Curve (EKC) hypothesis, which suggests that at lower levels of development, economic growth worsens environmental conditions before improvements are seen at higher income levels. These results validate this study’s hypothesis and correspond to the studies [28,30], which established that economic growth increases environmental sustainability.

With regard to population, the OLS results establish a positive relationship with EFFs, showing a coefficient of 3.163. This indicates that 1% population growth increases footprints by 3.163%. Moreover, the results also find that population growth has a positive impact on ecological footprints across all quantiles. Interestingly, its effect decreases as the quantiles increase. In the 20th quantile, the impact is 7.433, but it drops to 4.929 in the 80th quantile. This indicates that in lower-footprint regions, population growth exerts a substantial environmental strain, likely due to land use changes, deforestation, and increased resource demand. However, in high-footprint regions, other factors (such as industrial activities and urbanization) may be more dominant in driving ecological footprints. The results validate this study’s hypothesis and correspond to past studies that found population growth threatens ecological sustainability.

One of the most significant findings of this study is the moderating role of government effectiveness (GEFF) in the relationship between forest rents and ecological footprints. While the OLS results show an insignificant effect of GEFF on EFFs, the quantile regression tells a different story. GEFF has a significant moderating impact across all quantiles, with coefficients of −1.051 (20th quantile), −0.760 (40th quantile), −0.819 (50th quantile), −0.707 (70th quantile), and −0.802 (80th quantile). The negative coefficients suggest that effective governance helps mitigate the negative environmental impacts of forest rents. The strongest moderation effect appears in the 20th quantile (−1.051), indicating that in countries with lower ecological footprints, strong governance plays a crucial role in ensuring sustainable forest management and limiting degradation. As ecological footprints increase, the moderating effect remains strong but slightly weakens, likely due to the presence of other environmental stressors that governance alone cannot fully control. These findings validate this study’s hypothesis and correspond with past studies [16,37,38], which established the favorable impact of effective governance on environmental sustainability.

The findings from the equality and symmetry diagnostic tests indicate statistical significance, leading to the rejection of the null hypothesis. At a 5% significance level, the assumption that slope parameters (9.995) remain constant across all quantiles is invalid, implying that these parameters vary depending on the quantile. Furthermore, the symmetric Wald test (4.443) also refutes the null hypothesis, confirming that the coefficients across different quantiles are not uniform.

Figure 3 presents an overview of the quantile regression results, shedding light on how FRs (A), FFC (B), GEFF (C), FDI (D), GDP (E), and POP (F) influence EFFs across various quantiles. In the graph, the vertical axis represents the coefficients of the independent variables, while the horizontal axis corresponds to the quantiles of the dependent variables. The quantile regression estimates are depicted by the blue line, with the red lines indicating the 95% confidence intervals, which define the statistical reliability of the estimates.

4.3. Granger Causality Analysis

Table 6. Granger causality.

	EFFs	FRs	GEFF	FFC	FDI	GDP	POP
EFFs		1.801	1.093	0.695	7.987	1.591	8.598
		(0.185)	(0.350)	(0.508)	(0.002) ***	(0.223)	(0.001) ***
FRs	4.931		2.380	2.318	3.633	1.416	3.874
	(0.015) **		(0.112)	(0.119)	(0.041) *	(0.261)	(0.034) **
GEFF	3.566	1.190		5.265	9.973	0.352	0.487
	(0.043) **	(0.320)		(0.012)	(0.001) ***	(0.707)	(0.620)
FFC	2.088	0.871	3.161		5.365	2.603	1.325
	(0.144)	(0.431)	(0.059)		(0.010) **	(0.090) *	(0.280)
FDI	1.156	0.406	0.583)	0.654		1.066	2.021
	(0.330)	(0.670)	(0.566	0.529		0.359	(0.153)
GDP	2.440	1.673	5.233	0.689	4.280		2.580
	(0.107)	(0.207)	(0.012) **	0.511	(0.025) **		(0.095) *
POP	4.738	4.112	5.956	1.668	7.313	1.752
	(0.018) **	(0.028) **	(0.007) **	(0.208)	(0.003) ***	(0.193)

Note: p-values < 1% ***, 5% **, and 10% *. Significance in parenthesis. F-stats are without parenthesis.

presents the Granger causality test results, providing insights into the direction of causality among forest rents, government effectiveness, fossil fuel consumption, foreign direct investment, economic growth, population, and ecological footprints. The results show that FR Granger causes EFFs (4.931), but EFFs do not Granger cause FRs (1.801). This finding indicates that changes in forest rents significantly impact ecological footprints over time, but fluctuations in ecological footprints do not drive changes in forest rents. This outcome shows the potential role of forest resource management in influencing environmental degradation. It reinforces the need for effective policies that regulate forest rents to mitigate adverse ecological impacts. Similarly, GEFF Granger causes EFFs (3.566), but the reverse is not true (1.093). This confirms that enhancing government effectiveness plays a crucial role in shaping ecological footprints. The absence of causality in the opposite direction indicates that environmental degradation does not directly influence governance effectiveness, but rather, proactive government policies and institutional frameworks are needed to curb environmental consequences. This finding underlines the importance of governance sustainable forest management and ecological footprint reduction. The results on fossil fuel consumption show that FFC does not Granger cause EFFs (2.088), nor does EFF Granger cause FFC (0.695). This indicates that short-term changes in fossil fuel consumption do not necessarily trigger immediate variations in ecological footprints and vice versa. However, this does not diminish the long-term influence of fossil fuel dependence on environmental degradation. The lack of a significant causal relationship could mean that other mediating factors could be at play in the interaction between fossil fuel use and ecological outcomes. Regarding foreign direct investment, the results reveal that FDI does not Granger cause EFFs (1.156), but EFFs Granger cause FDI (7.987). This asymmetric relationship suggests that environmental sustainability concerns can influence investment decisions and potentially shape foreign direct investment inflows. In this case, investors may respond to worsening environmental conditions by adjusting their capital allocation strategies, especially in sectors with high environmental problems. This finding calls for sustainable investment policies that align FDI with environmental conservation plans.

The relationship between GDP and EFFs shows no evidence of causality in either direction, suggesting that economic growth does not immediately influence ecological footprints and vice versa. This outcome may be attributed to the complexity of various economic and environmental factors, including industrialization and technological advancement. It indicates the importance of inculcating sustainability measures into economic policies to ensure long-term environmental protection. Population in this analysis shows a more intricate relationship with other variables. The results indicate that population Granger causes FRs, GEFF, and FDI but not vice versa. These results indicate that demographic changes cause variations in forest rents, governance effectiveness, and foreign investment flows. It signifies that a growing population can exert pressure on forest resources, calling for improved governance measures to manage deforestation and maintain sustainability. Again, when the population increases, it can attract foreign investments, especially in urban development, infrastructure, and resource exploitation. Moreover, the results show that GEFF Granger causes FFC and vice versa. This is an indication of a bidirectional causal link. These results conclude that governance effectiveness plays a role in shaping fossil fuel consumption patterns, while fossil fuel dependence can also influence governance decisions. It signifies that effective governance can help promote energy transition policies, enforce regulations, and incentivize clean energy initiatives to limit the fossil fuel dependence on fossil fuels. Conversely, higher fossil fuel consumption may necessitate stronger governance interventions to mitigate environmental consequences. Finally, POP Granger causes EFFs and vice versa, indicating a bidirectional relationship between population growth and ecological footprints. This highlights the significant impact of a growing population on environmental health. In essence, an increasing rising population causes increased resource consumption, waste accumulation, and overall ecological footprints.

5. Discussion and Policy Implications

This study’s results provide meaningful implications that can guide policymakers in formulating strategic policies to enhance forest management and environmental sustainability. First, government effectiveness has proven to be a pivotal tool in strengthening forest regulation and eventually leading to reduced ecological footprints. In line with the study’s framework, this study encourages policymakers to strengthen governance in low- and high-risk areas. We believe that when the government segregates areas and pays attention to some parts and leaves others, perpetrators of environmental degradation will take advantage of areas classified as low-risk. This will eventually turn the place into high risk if proper regulations are not put in place. Therefore, since this study found that governance significantly reduces ecological footprints across all quantiles, policymakers should focus on enhancing institutional quality, transparency, and enforcement of environmental laws in all areas. They should prioritize anti-deforestation laws, ensure strict monitoring of forest rents (FRs), and incentivize sustainable practices, like sustainable logging. When these effective mechanisms are in place, environmental accountability and responsible resource management can be enhanced as stipulated in the framework.

Moreover, this study established that ecological footprints possess a unidirectional causal link to foreign direct investment. This causality is an indication that decisions regarding environmental degradation impact investment decisions rather than foreign investments directly impacting ecological footprints. Considering this revelation, it will be prudent to ensure that green growth strategies are aligned. Many countries only focus on the financial benefits of foreign investments without considering the environmental impacts. There, this study admonishes policymakers to implement policies that attract green investment by enforcing environmental sustainability standards for foreign investors. These can be achieved by ensuring tax incentives, green financing mechanisms, and sustainability-linked investment criteria are in place, as elaborated in the study’s framework. These can encourage eco-friendly projects and low-carbon industrialization within the country. Therefore, governments must ensure that foreign investments align with climate goals to help mitigate the adverse impact effects of economic expansion on the environment.

Additionally, this study found a negative relationship between fossil fuel consumption and ecological footprints on forest products. This finding indicates that an increase in fossil fuel consumption reduces ecological footprints in China. Though the Grange causality found no causal relationship, this sign shows that over the years, reliance on fossil fuel in China has been reduced, and the impact is reflected in forest footprints. The quantile regression results show that the impacts are extreme at the 20th and 80th quantiles. This indicates that with time, a reduction in fossil fuel use can reflect in other environmental indicators. This is a caution to policymakers to continue in the development of green energies that emit low carbon to reduce carbon footprints. When policymakers prioritize and invest in the transition from non-renewable energy to clean energy, the country’s green growth and dual carbon goals can be achieved. In this period, it is imperative to accelerate energy transition policies by promoting renewable energy adoption in all areas across the country. In conjunction with the FDI results, prioritizing investments in solar, wind, and hydropower is a great plan to reduce dependency on fossil fuels and mitigate the long-term ecological impact of fossil fuel consumption on sustainability. When this is implemented, it will boost sustainable investment and reduce ecological footprints, as shown in the framework.

Finally, the population played a significant role in this study. The quantile regression results indicate that population growth positively influences EFFs across all quantiles. This means that higher population levels contribute to environmental degradation. However, it was also established in the Granger causality test that population Granger causes forest rents, government effectiveness, and foreign direct investment, stressing its influence on forest rents, governance quality, and foreign investments. Having established its role in forest management, it is admonished that governments implement policies that promote sustainable urban planning and eco-friendly consumption practices and invest in resource-efficient infrastructure. This can boost eco-tourism and reduce ecological footprints, as highlighted in the study’s framework. Also, the growing population can be managed through strategic policies that support education, sustainable resource use, and environmental conservation programs. When population dynamics are introduced into environmental policy formulation, the excessive ecological footprints that arise from uninformed citizens can be mitigated to ensure long-term sustainability.

6. Conclusions

This study assessed the impact of forest rents, fossil fuel consumption, foreign direct investment, economic growth, and population on the ecological footprints of forest products. It also examined the moderation role of government effectiveness in shaping forest rents’ impact on ecological footprints. Data from 1990 to 2022 were sourced for China from reliable databases. For a more robust analysis, the study used quantile regression and ordinary least-squares models to make a comparative analysis. The results reveal that forest rents increase ecological footprints, but the impact decreases at higher quantiles, signifying that better environmental policies can mitigate their negative effects. Moreover, government effectiveness significantly reduces ecological footprints across all quantiles, indicating that a strong governance mechanism is essential for sustainability. The study also established that fossil fuel consumption reduces ecological footprints significantly in extreme quantiles. However, the no-direct causality with ecological footprints suggests a long-term effect rather than an immediate one. Again, foreign direct investment was found to have a negative effect on ecological footprints, a sign that higher investment reduces environmental degradation. However, in causal terms, EFFs influence FDI decisions but not vice versa. Economic growth increases ecological footprints, especially in higher quantiles, depicting that economic expansion often comes at an environmental cost. Finally, population growth strongly increases ecological footprints across all quantiles. Also, the bidirectional causality found indicates that population growth and environmental degradation reinforce each other. These findings show the importance of government effectiveness in mitigating the negative environmental effects of forest rents. While forest rents can drive ecological degradation, strong governance plays a crucial role in ensuring that these resources are managed sustainably. Non-renewable energy consumption and foreign investment are all tools that can be used to fight against climate change, should the government decide to be at the forefront of the fight. The results provide meaningful insights into forest management and overall environmental policy formulation. Our policy implications can guide policymakers in forest management policies. The study is not devoid of limitations. There are some acknowledgments future studies can consider for novel results. The study used limited data from over 3 decades, which may have affected the results. Therefore, future studies could expand the timeframe for more robust results. Moreover, the study was conducted in China and may have generalizability issues. Countries that do not share environmental and forest similarities must apply these implications with caution. Also, future studies can replicate this study in other areas for different results.