Load Capacity Factor as Metrics for Land and Forests Sustainability Assessment in G20 Economies: Fresh Insight from Policy, Technology, and Economy Perspectives

Abstract

Traditional environmental research remains affixed in fragmented metrics (e.g., CO₂ emissions or ecological footprints) that undermine the systemic equilibrium between economic demand and ecological regeneration. Biocapacity, representing the capacity of lands (crop and grazing), forests, and other natural systems, is the backbone of economic livelihoods and environmental resilience. Recent literature frequently calls for operationalizing models with robust environmental sustainability indicators, such as the load capacity factor (LF), a comprehensive compass that measures biocapacity (e.g., forests, croplands) relative to ecological footprint. For this purpose, the integrated model combined environment-related policies (regulations, ENRs), technologies (ERTs), sectoral structures, and LF, with the latest available data (2000–2022) of G20 economies. Results of the multiple tests, including feasible generalized least squares, sensitivity tests (alternate proxies), and panel-corrected standard errors, highlighted a paradox: even though ENRs and ERTs tend to improve environmental sustainability through forestation, land use, and green initiatives, the results showed adverse effects of both indicators on environmental sustainability (LF), reflecting a misalignment between policies and environmental outcomes. While industrialization, renewable energy use, and rising per capita income had enhanced environmental sustainability (LF) gains, structural frictions in the services, manufacturing, and trade sectors undermined these advantages, revealing diffusion lags and transitional lock-ins across sampled countries. With LF embedded as a new tool for sustainable governance of forests and land management, the paper advances three critical contributions: (i) uncovering paradoxical deteriorations in sustainability under misaligned policy and technology interventions, (ii) showing an imperative need for performance-based, adaptive, and innovation-financed policies, and (iii) demonstrating LF as a standard for positioning technology, economic transitions, and policy with ecological and cropland-forests resilience.

1. Introduction

Many global economies, especially those in the G20, have accelerated the development of various environment-related technologies (ERTs) and environmental policies (ENRs) to attain environmental sustainability targets related to the Sustainable Development Goals (SDGs) and the efficient utilization of forests and cropland. Even though this group of nations contributes to 85% of global GDP and 2/3 of the world’s foreign direct investment flows [1], they are responsible for 74% of the global energy-based emissions. These emissions are linked to the excessive exploitation of forests, land, and marine systems by sectors such as rapid industrialization (80% fuel dependency), transport, and trade, which use vast amounts of energy [2], thereby contributing to land degradation. As part of the 2015 Paris Agreement, these countries actively engage in climate transparency programs to promote reporting and accountability for environmental sustainability initiatives [3]. Several studies have empirically supported that ERTs and robust policies (e.g., taxation, regulations, policies) related to green trade, trade diversification, and corporate governance [4,5] have improved environmental sustainability within the G20 states, even though such findings are traditional proxies like carbon dioxide emissions (CO₂e) or ecological footprint (EF). They further add that high energy demand, dependence on carbon-intensive sources (e.g., forests, oil), and industrialization have disrupted such gains [6,7]. Recent econometric analyses, specifically ref. [8], reveal that the adoption of ENRs by the EU, the UK, China, and Brazil has significantly improved environmental sustainability through CO₂e reduction. Still, such findings offer limited insight into how these ENRs translate into EF vs. biocapacity gains, particularly in forests, land, and marine systems.

Recent data, however, contradicts the pre-stated findings by demonstrating weak decoupling between economic growth and environmental sustainability gains in 2021. The energy-led CO₂e rebound effect signals that stress on biocapacity (i.e., land, forests, marine systems) persists, possibly due to insufficient or ineffective ENRs (Available online: https://www.climate-transparency.org/wp-content/uploads/2022/10/CT2022-SR-key-graphs.png (accessed on 15 September 2025)). This systematic misalignment has become more alarming, as policy projections and the Nationally Determined Contributions of major high-emission G20 countries are not fully aligned with the 1.5 °C targets [9]. In this context, experts have called for strengthening policy action, evaluating existing assessment tools, and increasing monitoring of environmental sustainability initiatives to bridge the determination gap and achieve Paris targets [10]. Researchers (e.g., [11]) explain that such gaps may stem from reliance on fragmented indicators, e.g., CO₂e or EF. These traditional metrics provide only a partial view of environmental sustainability, which can hinder the efficient adaptation and alignment of ENRs and ERTs across different contexts. Instead, the load capacity factor (LF) provides a robust solution as an integrative environmental sustainability indicator. It offers more profound insight into the equilibrium ratio between biocapacity and EF. Biocapacity denotes the assimilative capacity of an ecosystem against the waste absorption capacity of humans. It signifies the supply side of nature, including the following: (i) productive cropland, forests, grazing land, timber, and fiber; (ii) the capacity of land and forests to absorb atmospheric emissions; (iii) the ecosystem’s ability to process waste. Figure 1 presents an overview of the nexus between LF, ENRs, and ERTs across the G20 blocs for conceptual clarity. Argentina, Australia, Brazil, Canada, and Russia may appear to be progressing on the environmental sustainability pathway (LF > 1), yet are still not fully aligned. Major economies, such as South Africa, Mexico, Indonesia, China, India, and the United States, are experiencing economic slowdowns. ENRs must be fully aligned with the Nationally Determined Contributions (NDCs) to ensure that environmental sustainability delivers targeted gains in land and forest capacity. In short, all members must demonstrate tangible steps to bridge the gaps between their ambitions, NDCs, and actual environmental sustainability performance. As extensively discussed in Section 2, although scholars have frequently asserted the need for more robust environmental sustainability frameworks, no published study offers insight into the interdependencies among ENRs, ERTs, the economic structure (e.g., services, trade), and LF, specifically for G20 economies. This study represents an initial step in the same direction.

Academically, environmentalists have extensively analyzed the connection between ERTs, ENRs, and environmental sustainability using indicators for single-country, group, and regional studies. Previous research has examined the effects of various indicators of ERTs [12,13] and ENRs [14,15,16] on different proxies of environmental sustainability [17,18], and many have focused on CO₂e or EF. However, there are notable inconsistencies in the results, with some studies showing positive, negative, or no significant effects of these indicators on environmental sustainability. Experts attribute these discrepancies to an over-reliance on less comprehensive environmental sustainability indicators, such as CO₂e and EF, which undermine our understanding of soil degradation in croplands, deforestation dynamics, and forest ecosystem services. Technically, CO₂e data do not fully capture environmental sustainability complexities and broader implications, as they overlook critical issues such as water pollution and biodiversity losses, as well as a notable deterioration in forest biodiversity, croplands, and other ecosystem services [8,11,19]. Although EF measures human impacts on the ecosystem, its ability to reflect overall human influence on footprints of forests, croplands, and other critical resources remains limited [8,20]. The load capacity factor (LF) overcomes these methodological limitations by offering a holistic view and rich insight into the systematic balance between EF from and the biocapacity of forests, cropland, grazing land, fishing grounds, and built-up land required to absorb anthropogenic emissions that are not sequestered by the global oceans. Despite the above multidimensional benefits, a comprehensive analysis of LF with ENRs (specifically regulations), ERTs, and related sectoral indicators within a single econometric framework is scarce, especially in the context of G20 countries.

This study introduces several innovations by directly addressing the above gaps through an integrated benchmark analysis. The model evaluates the impact of under-researched ecological indicators (i.e., environmentally-related tax revenue and the environment-related technologies index) on LF, using the latest data from 2000 to 2022 for G20 countries. Additionally, the proposed model assesses the economic value of natural resources (i.e., forests, croplands) and ERTs and ENRs to help policymakers understand their comprehensive impact on environmental sustainability within candidate countries. Furthermore, the paper differs conceptually and methodologically from earlier studies, particularly those by ref. [8] for the G20. Unlike studies [8,11,12,21] employing datasets up to 2020 and using proxy indicators heterogeneities, this paper employs multiple new step-wise techniques, including benchmark (FGLS), sensitivity, and robustness analyses to evaluate the latest data (2000–2022) and more comprehensive indicators for both ENRs and ERTs to deliver a fresh perspective. Lastly, the study goes beyond traditional LF frameworks by quantifying the roles of key sectoral indicators (industry, manufacturing, and services), energy (renewables), and economic growth (GDP) within the main model, offering a holistic, evidence-based, and policy-relevant perspective. By doing so, the analysis overcomes the limitations of traditional models to account for land productivity, forest conservation, and resource governance.

The rest of the study is organized into the following parts: (i) the theoretical framework and literature review provide critical insights into previous research; (ii) data, model, and methods offer details about data sources, the proposed model, and estimation techniques used for analysis; (iii) results and discussions present the main findings, summaries of key insights, and comparisons based on the analysis; (iv) conclusions and recommendations summarize the main findings while offering policy suggestions drawn from the analyses.

2. Theoretical Framework and Literature

2.1. Previous Research on Environmental Sustainability Drivers: The Role of Environment-Related Policies and Technologies

Environment-related policies (ENRs), often proxied by taxes, regulations, and environmental stringencies, have remained a topic of significant academic interest over the past decades. Policymakers and governments frequently use it to achieve broader environmental sustainability goals, e.g., improving biocapacity and reducing ecological footprint and emissions. As noted earlier, environmental experts have mainly relied on fragmented indicators like CO₂e or EF to present empirical or conceptual case studies of environmental sustainability drivers and outcomes. Experts have reported different effects of ENRs on environmental sustainability across various methods (linear and non-linear), institutional settings (micro and macro), and contexts. For example, ref. [22] monitored direct and indirect (through energy use structure) benefits of ENR adoption across South Asian economies. ENRs improved environmental sustainability by reducing the harmful effects of human activities on land, forests, and marine systems. Ref. [23] analyzed data for a panel of G7 through the FMOLS-based analysis to confirm similar results, even though the authors relied on CO₂e as an indicator of environmental sustainability. Ref. [24] computed data from 1995 to 2017 using the ARDL and Driscoll and Kraay methods, demonstrating that ENRs enabled seventeen European countries to control CO₂e effectively, thereby enabling environmental sustainability gains over the studied period. Recently, ref. [25] examined 891 country-year observations across 81 countries from 2010 to 2020 using multiple estimators. They concluded that weak adoption of ENRs attracts more FDI, undermines sustainable energy efforts, and degrades the environment. In another study, ref. [26] found that ENRs significantly reduced EF levels across a panel of G7 countries between 2000 and 2020. Another estimation of the G7 time-lagged dataset by ref. [27] confirmed this argument through a different method, Method of Moments Quantile Regression (MMQR), over the period (2000–2022). Ref. [28] observed significant improvement in environmental sustainability, as estimates supported a negative association between ENRs and CO₂e across a panel of provinces in China.

Alternatively, the CS-ARDL-based estimates for OECD economies by ref. [18] showed that although ENRs lessened the EF levels, these effects were more profound in the long term, when ENRs were supported by renewables and technological innovations. Another study by ref. [29], based on quantile regression, found that lower quantiles support improvements in environmental sustainability (EF) with increasing ENR intensity, whereas higher quantiles exhibit adverse environmental impacts due to rising externalities. Another study for the MENA region by ref. [14] added complexity to the above case, as the analysis showed insignificant contributions of ENRs to improving environmental sustainability (EF), specifically in mitigating the degradation of ecosystem services. Ref. [30] contradicted the above evidence using a panel of 29 OECD countries. For China, a firm-level investigation by ref. [31] attempted to provide a feasible explanation for such heterogeneities using firm-level data. The authors empirically established that ENRs generated tangible environmental sustainability benefits among firms that followed strict compliance with environmental sustainability and governance (ESG) practices, compared to those with less efficient ESG compliance. This finding highlights the need for more non-linear and context-sensitive modeling.

Considering the eco-innovation role of ERTs in environmental sustainability, there is a growing consensus that ERTs, such as improvements in digital infrastructure, green finance tools, and/or renewable energy (RE) systems, have the potential to achieve environmental sustainability. Still, significant differences persist in past findings due to lower methodological sophistication, a biased geographical focus, and an overreliance on specific proxies. For instance, ref. [32] utilized multiple econometric approaches to support environmental sustainability gains from ERTs in a panel of 38 Asian economies from 1990 to 2019. In another study, ref. [33] employed a dynamic ARDL (DARDL) model to assess the impact of ERTs, financial innovations, and economic globalization on EF in China from 1991 to 2017. The results indicated that ERTs adversely affected EF, whereas financial innovations and economic globalization positively affected EF in the long run. Ref. [34] employed sophisticated panel estimators like CCEMG and AMG to verify that ERTs (including technological innovations and RE) had improved environmental sustainability (EF) in fourteen developing European countries from 1995 to 2020. FD and non-RE had disrupted progress towards environmental sustainability goals. Another panel study conducted by ref. [35] for selected South Asian countries from 1995 to 2020 predicted a significant positive implications of ERT systems (green technologies and RE) for environmental sustainability, specifically their role in controlling CO₂e. Other indicators, including population growth, economic growth, trade, and institutional quality, were ineffective drivers of environmental sustainability. Notably, ref. [36] highlighted that technological developments in South Asian economies have facilitated the expansion of the forest area, thereby enabling swift progress toward green transitions and environmental sustainability.

Beyond traditional metrics-based studies, Ref. [37] employed a panel MMQR estimator to validate the load capacity curve (LCC) for G20 economies from 1990 to 2021. The study identified both disruptive (i.e., natural resources and urbanization) and constructive (i.e., human capital and RE) drivers of LF. For Brazil, ref. [38] estimated the LCC of forests between 1970 and 2019 using various estimators, including FMOLS, DOLS, and the CCR framework. Besides validating forest-based LCC for the candidate country, the authors confirmed energy consumption, trade, and population as factors degrading forest resources. Ref. [39] found similar evidence for the USA after testing the LCC for deforestation using augmented ARDL between 1980 and 2022. For a panel of ten economies with the highest forest systems, the CS-ARDL model by ref. [40] demonstrated that forest intensity had promoted environmental sustainability by limiting CO₂e growth. Thus, ref. [41] emphasized efficient utilization of technologies (ERTs) for restoring the ecosystem and reducing biocapacity stress to attain ecological civilization and environmental sustainability goals. For sectoral indicators, ref. [42] studied the combined ecological effects (CO₂) of ERTs, agriculture, and transport for the most populous nation in the world using the CS-ARDL framework. The outputs confirmed the positive influence of ERTs in CO₂ mitigation in the selected countries from 1990 to 2021. Applying AMG and CCEMG for 17 Asia Pacific countries (APEC), ref. [43] confirmed the effectiveness of ERTs in enhancing environmental sustainability (LF) for 17 APEC countries between 1990 and 2019. Another second-generation (CS-ARDL) study by ref. [44] for ASEAN economies found that environmental innovations (ERTs), ENRs, RE, industrialization, and CO2e are significant. Based on data from 1990 to 2022, the model indicated that these factors improve environmental sustainability (CO₂e). The authors suggested that this paradox might stem from weak environmental governance structures that prioritize economic outcomes over the SDGs. Then again, ref. [45] highlighted that ERTs, specifically AI-driven forest management (climate-smart forestry), are effective for sustainable forestry and climate change mitigation, since both the latest technologies (ERTs and forests) play a significant role in mitigating climate issues. For example, ref. [46] estimated DOLS, FMOLS, and dynamic common correlated effect (CEE) for 15 countries between 1996 and 2022 and found that biotechnology and forests are productive in improving green economic activities. They emphasized the need for coordination across forests and ERTs (biotechnology) to enhance green growth in the studied countries.

2.2. Possible Contributions to Filling Research Gaps

In retrospect, the current literature review highlights several issues. For instance, acute emphasis on certain independent (e.g., ENR, ERT, or both) and dependent variables (e.g., CO₂ and EF) lends a partial view of environmental sustainability by understating the role of key ecosystem assets, with forests serving as carbon sinks and croplands supporting food–energy systems. Therefore, integrating new indicators and testing them across diverse contexts could enrich policymaking. The proposed model of ENR-ERTs- environmental sustainability (LF), tested through a multi-layer methodological approach and the latest dataset (2000–2022) of an under-researched context (G20), could help capture how policy, technology, and economic structure directly and indirectly impact land-use change, deforestation trends, fishing grounds, cropland, and soil conservation. Besides responding to previous calls, the model informs how technology and policy dynamics interact with cropland management and forest resource governance in major global economies.

3. Data, Models, and Methods

3.1. Data and Sources

The data for the main variables were collected from various global sources [47,48] between 2000 and 2022. Data for controls and other variables were obtained from the World Bank database [49]. The sources, variables, and measurement units are listed in

Table 1. Variables and their sources of data.

Indicator	Symbol	Source
Load capacity factor (Obtained by biocapacity divided by ecological footprints)	LF	GFN
Ecological balance (Obtained by subtracting ecological footprints from biocapacity)	ECB	GFN
Development of environment-related technologies (index)	ERTI	OECD
Environmentally related tax revenue (US dollars, Millions)	ENR	OECD
Development of environment-related technologies: % of technologies	ERT	OECD
Environmental Policy Stringency Index	ENR1	OECD
GDP per capita (constant 2015 US$)	PCG	WDI
Renewable energy consumption (% of total final energy consumption)	RE	WDI
Industry (including construction), value added (current LCU)	IND	WDI
Services value added (current LCU)	SR	WDI
Manufacturing, value added (current LCU)	MNF	WDI
Trade (% of GDP)	TR	WDI

. Recent methodologies [50,51] were used to calculate the dependent variable (LF), which was determined by dividing each region’s ecological footprint by its biocapacity, representing forests, cropland, grazing land, fishing grounds, and built-up land. According to ref. [52], LF results can be interpreted as follows: sustainable (above 1), unsustainable (below 1), and on the brink of sustainability (equal to 1). Low scores indicate a decline in degradation of land, forests, and marine/ocean systems, and carbon sinks. The study included two proxies as independent variables from previous models. ERTI, an index of environmentally related technologies, was adapted from ref. [53]. ENR, the environmental tax, was adapted from ref. [23], but we used values in millions of US$. The sources listed above were also consulted for key related controls, such as economic growth (measured by per capita GDP) [23] and RE [54]. Additionally, industrial value-added, manufacturing, services, and trade were included based on their theoretical importance and policy implications.

3.2. Models

The standard IPAT model by ref. [55] determines the impact of human activities [Population (P), affluence (A), and technologies (T)] on the environment (I), which is statistically stated as $I=PAT$. The environmental aspect not only accounts for carbon emissions but also explains the change in land use, including cropland stress and the extent of deforestation damage. This identity undermines the testing of hypotheses and interpretations of causality. Ref. [56] modified this fundamental equation into STIRPAT to explain the Stochastic Impact of Regression on Population, Affluence, and Technology. The current model is built on the modified version [57].

$$I_{it}={ℂ}_{\mathit{it}}{P_{\mathit{it}}}^{\alpha }{A_{\mathit{it}}}^{\beta }{T_{\mathit{it}}}^{\gamma }{\epsilon }_{\mathit{it}}$$

(1)

In Equation (1), the impact (environmental), PAT is population, affluence, and technology, $ℂ$ is the constant term, and $\epsilon$ is the error term. The superscripts $\alpha , \beta , \mathrm{and} \gamma$ are the elasticities of the variables. As seen below, the augmented model is transformed into the logarithmic version, represented by Ln, to generate a traceable econometric equation [57]. After adding the required variables, the following model is developed:

$${\mathit{LnLF}}_{\mathit{it}}={\alpha }_0+{\beta }_m\left|\begin{array}{c}{\mathit{LnENR}}_{\mathit{it}}\\ {\mathit{LnERT}}_{\mathit{it}}\end{array}+{\beta }_c{\mathit{LnZ}}_{\mathit{it}}+{\mu }_{\mathit{it}}\right.$$

(2)

Ln is used to represent the logarithmic forms of LF, ENRs, and ERT, while Z denotes the set of controlled variables (PCG, IND, RE, SR, MNF, TR). LnLF equals biocapacity divided by EF. Equation (2) serves as the basic function that captures the effects of ERTs, ENRs, and Z on environmental sustainability (LF). Figure 2 illustrates the links between the primary and controlled drivers of environmental sustainability (LF) used in this study. While a positive and significant LnLF coefficient indicates that EF do not exceed biocapacity budgets, a negative coefficient indicates the opposite, and an insignificant LnLF coefficient denotes a low or no effect.

The model positions LF as a function of technology, policy, and structural dynamics, with biocapacity adopted from heterogeneous land systems. Maintenance of biodiversity relies on forests that contribute via carbon sequestration, while croplands ensure the sustainability of bioenergy supply and food, collectively creating the ecological foundation against which the environmental demand is assessed. While effective ENRs may help to curb prohibited logging or incentivize soil management in countries with high forest coverage (e.g., Brazil and India), ERT advances, including digital monitoring of forests or precision agriculture, can improve land productivity in Canada and China, respectively. Notably, the coefficients capture asymmetries across the diverse sample. In forest-rich countries like Brazil and Russia, outcomes depend on conserving massive woodland sinks, while environmental sustainability depends on cropland efficiency in densely populated countries like Indonesia and India. Hence, Equation (2) is a robust tool to diagnose environmental sustainability gaps and identify which levers—such as land management, forest conservation, aquaculture restoration, or sectoral restructuring—provide the highest potential benefits for sustaining ecological capacity against human demands (EF).

3.3. Methods

3.3.1. Cross-Sectional Dependence (CD) and Unit Roots

In panel datasets, CD has been identified as a primary concern if such issues are detected. Since spillover effects between testing units can disrupt and mislead the overall analytical process, leading to prediction errors and discrepancies, researchers rely on the most appropriate approach to achieve unbiased conclusions [58]. Following recent procedures [59], the study initially assessed the nature of the data for CD through a simple test following [58], to address estimation issues:

$$\mathit{CD}=\sqrt{{\displaystyle \frac{2T}{N(N-1)}}{\displaystyle \sum _{i=1}^{N-1}{\displaystyle \sum _{j=i+1}^N\frac{(T-k){\stackrel{⌢}{\rho }}_{ij}^2-E[(T-k){\stackrel{⌢}{\rho }}_{ij}^2}{\mathrm{var}[(T-k){\stackrel{⌢}{\rho }}_{ij}^2]}}}}$$

(3)

The null and alternative hypotheses in the study are

H₀: 

There is no CD.

H_a: 

CD exists in the panel data.

Following the CD test to control for cross-sectional correlation and to capture the stationarity and order of the variables, the study applied the ref. [60] Cross-sectional IPS (CIPS). The standard CIPS test can be computed by averaging the Cross-sectional Augmented Dickey–Fuller (CADF) statistics, as shown below.

$$\mathsf{\Delta }{Y}_{\mathit{it}}={\alpha }_0+{\beta }_i{Y}_{i,t-1}+{\gamma }_i{Y^{\prime }}_{i,t-1}+\varphi \mathsf{\Delta }{Y^{\prime }}_{\mathit{it}}+{\epsilon }_{\mathit{it}}$$

(4)

$\mathit{CIPS}={\displaystyle \frac{1}{N}}{\displaystyle \sum _{i=1}^N{\mathit{CADF}}_i}$. Here, CADF is the cross-sectional statistic of individuals produced by the t-ratio of the coefficient of Y.

3.3.2. Co-Integration Tests

After estimating the integration orders and the CD, the study identified the integration vectors in the model. This study employed the ref. [61] co-integration test to obtain accurate and reliable results. This method considers the CD test of ref. [62], correlated errors, slope variations, and the coefficient of determination [63]. The Westerlund test conducts two variance ratio (VR) tests, one for the entire panel and one for specific panels within the dataset. Rejecting the null hypothesis in these tests indicates that the models share a co-integrating relationship. Additionally, the study employed the residual-based co-integration test by ref. [64], which effectively produced cross-sectional intercepts of individuals at the first regression value with consistent weights [63].

3.3.3. Long-Run Estimators

To empirically validate the connection among ERTI, ERN, and LF, this study used the feasible generalized least squares (FGLS) estimator proposed by ref. [65] and the panel-corrected standard error (PCSE) estimator developed by ref. [66]. According to ref. [67], these methods perform better in autocorrelation, heteroscedasticity, and cross-sectional dependencies, in both balanced and unbalanced datasets. Notably, the FGLS estimator has the advantage of addressing heteroscedasticity, serial correlation, and cross-sectional correlation, thereby improving accuracy when the time dimension exceeds the number of cross-sections in panel data [68]. PCSE assumes that panel errors are heteroscedastic and autocorrelated, which may not hold for all panel datasets [57]. If the time dimension is smaller than the number of groups, using PCSE may not fully capture the data dynamics of the estimated outcomes. Therefore, this study mainly used FGLS as the primary estimator, with PCSE as a supportive estimator for empirical validation. Figure 3 illustrates the methodological steps employed in this study for various estimations.

4. Results and Discussion

4.1. Preliminary Analysis

This study initiated the estimations with descriptive statistics of the level variables and then tested these variables for correlation. This step followed an examination of CD and unit roots. The data were transformed into logs to test for co-integration.

4.1.1. Descriptive Statistics

Table 2. Descriptive Statistics.

Variable	Mean	Std. Dev.	Min	Max
LF	0.783	0.824	0.004	3.760
ERTI	1.002	0.328	0.185	2.979
ENR	33,930.013	35,549.487	0.000	155,106.172
PCG	23,608.239	17,627.987	756.704	64,342.117
RE	14.021	12.603	0.000	50.000
IND	2.221 × 1014	9.272 × 1014	4.596 × 1010	8.115 × 1015
SR	2.562 × 1014	9.833 × 1014	9.048 × 1010	8.186 × 1015
MNF	1.242 × 1014	4.691 × 1014	3.208 × 1010	3.592 × 1015
TR	51.884	17.221	19.560	105.566

Note: TR = Trade; MNF = Manufacturing; LF = Loaf capacity factor; PCG = Economic growth; ENR = Environmental regulations; ERTI = Environment-related technology innovations; RE = Renewable energy consumption; IND = Industry energy.

shows significant differences across economic and environmental indicators among G20 countries, highlighting the need for econometric methods to address distribution skewness and scale differences. As explained below, LF showed high variability, indicating environmental overshoot in some economies, while ERTI values revealed asymmetries in the spread of technology within the group. Additionally, the strong right skewness of ENR suggested data censoring (at lower bounds) or potential signs of financial underperformance in certain countries. Regarding economic indicators, PCG and sectoral value-added figures (such as IND, MNF, SR) spanned more than five orders of magnitude, signaling potential non-stationarity and the need for logarithmic data transformation. While RE values confirmed the uneven pace of energy transition across the group, the TR ranges indicated structural differences in external economic integration. In Table 2, the high level of variation most indicators exhibit justifies the use of robust scaling techniques and adjusted standard errors in further analysis.

4.1.2. Correlation Plots

In Figure 4, the heat map provides insight into the correlation statistics. The transition from light to darker shades of blue reflects a shift from low to high negative correlations, whereas light to darker shades of red indicate a change from weak to significant positive correlations. Of significance, the moderate to strong correlation between RE and LF (0.500) demonstrated that RE expansion in some countries had reduced pressure on biomass from forests and diverted cropland for biofuels in the candidate panel, ultimately boosting biocapacity. The negative coefficient for ENR (−0.400) indicated that policies are ineffective at delivering gains from croplands and forests, possibly due to unintended side effects or weak enforcement. Based on the ERTI coefficient (−0.045), it was predicted that green innovations had not achieved the targeted environmental sustainability.

Consistent with the above, the negative coefficients of PCG, TR, IND, MNR, and SR indicate that rising income, global trade, exports, and sectoral expansion have significantly burdened the biocapacity of the G20 economies, thereby threatening ecosystem services.

4.1.3. CD and Unit Root Testing

The results of the CD and unit root tests for the level variables are given in

Table 3. CD and Unit Root Test.

Variable	CD Test	CIPSlevels	CIPS1st-diff.
LF	6.55 ***	−2.034	−4.788
ERTI	4.60 ***	−2.814	−4.272
ENR	-	−1.324	−3.105
PCG	41.47 ***	−1.519	−3.180
RE	7.42 ***	−1.504	−4.072
IND	45.54 ***	−1.494	−3.200
MNF	46.42 ***	−1.165	−3.370
SR	55.87 ***	−0.818	−2.876
TR	7.20 ***	−1.108	−3.497

Note: TR = Trade; MNF = Manufacturing; LF = Loaf capacity factor; PCG = Economic growth; ENR = Environmental regulations; ERTI = Environment-related technology innovations; SR = Services; RE = Renewable energy consumption; IND = Industry. Critical values of CIPS at 1% are −2.38, 5% are −2.2, and 10% are −2.11. *** refers to significance at 1% level.

. The outcomes of [59] CD are shown in the first panel of the pre-stated table. Given the significant values for each variable, which suggest rejecting the null hypothesis (i.e., no CD in the panel data), it was concluded that the candidate variables were cross-sectionally correlated, warranting an adequate test for unit roots. Thus, the current study employed the CIPS unit root test to examine the order of integration between the variables chosen for analysis. Since the tests confirmed that not all variables were integrated at the same level (i.e., a majority exhibited an integration order of one), the long-run co-integration analysis was adopted as the next step.

4.1.4. Co-Integration Testing

The results of the two co-integration procedures recommended by earlier econometricians [63,66] are presented in

Table 4. Results of co-integration analysis.

Co-Integration Test	Statistics	p-Value
Westerlund [61] test
Some panels’ variance ratio	−2.2077	0.01
All panels’ variance ratio	−1.7543	0.03
Kao [64] test
DF test	−2.6793	0.00
ADF test	−3.6415	0.00
MDF test	−2.2996	0.01
UDF test	−4.7136	0.00
UMDF test	−7.0280	0.00

. As shown below, the probability values support the existence of co-integration between the variables at a significance level of 5% or less. The variance ratio for some panels (probability values = 0.013) and all panels (0.03) [61] indicated that co-integration existed at 5% level. Results from other tests, including the Dickey-Fuller (DF), Augmented Dickey-Fuller (ADF), Modified Dickey-Fuller (MDF), Unadjusted Dickey-Fuller (UDF), and Unadjusted Modified Dickey-Fuller (UMDF) by ref. [64], showed significant statistics at 5%, supporting the estimation of long-term relationships.

4.2. Benchmark Regression

The interpretations of the main FGLS estimators are reported in

Table 5. Results of the benchmark model: feasible generalized least squares (FGLS) outputs.

Variables	Coefficients	Standard Errors
LnERTI	−0.117 **	0.0536
LnENR	−0.284 ***	0.0236
LnPCG	0.518 ***	0.0248
LnRE	0.939 ***	0.0296
LnIND	1.120 ***	0.117
LnMNF	−0.954 ***	0.0941
LnSR	−0.279 ***	0.0704
LnTR	−0.582 ***	0.0384
Observations	330
Number of cc	16

Note: Ln = Log form; TR = Trade; MNF = Manufacturing; LF = Loaf capacity factor; PCG = Economic growth; ENR = Environmental regulations; ERTI = Environment-related technology innovations; SR = Services; RE = Renewable energy consumption; IND = Industry. *** p < 0.01, and ** p < 0.05.

. The empirical data showed that LF declined in response to upsurges of 0.117% and 0.284% in both ERTIs and ENR, respectively, indicating ongoing stress on biocapacity within the G20 bloc. In other words, the result illustrated that both indicators had remained ineffective in mitigating or improving the state of ecosystem services (available productive sea, land, forests, and grazing) resources against human ecological consumption. Possibly, such contractionary evidence reflects intermediate challenges, specifically context-specific lags in the efficient utilization of early-stage eco-centric technologies ERTs across the G20 group. At the prime facia, it appears that ENRs and ERTs introduced by some members have lagged in fully operationalizing, internalizing, and deploying efficient monitoring and control systems to simultaneously account for excessive ecological demand and monitoring biocapacity depletion, i.e., deforestation, cropland desertification, and marine culture decline. From 1990, Brazil lost substantial amounts of forest due to insufficient policy compliance, even though the ambitious Forest Code (Brazil) and palm oil policy were introduced to sustain and promote biocapacity regenerative capacity [69]. In contrast, other members (specifically those in the EU) can enhance the regenerative capacity of ecosystem services by ensuring compliance and control over redirecting revenues from carbon taxation toward peatland and forestation initiatives.

Additionally, it appears that downtime arising from strict ENRs, constrained technical capacity, or compliance-related stress, particularly in emerging economies, has delayed or disrupted the capacity of some G20 members to achieve environmental goals. The counterintuitive results of ENRs and ERTIs also signal underutilization of capacity, possibly due to controlled, inefficiently adjusted operational standards, and sectoral disparity in technological growth. As seen in the results, the negative response of ERTIs to LF signals that the fund allocation and growth among sectors (e.g., energy, transportation vs. forests, land, and marine systems) is uneven across the G20 bloc. The results suggested that technological capacity has increased productivity over the years, but it has failed to keep pace with the rate at which biocapacity has been exceeded in some member states. For instance, despite the substantial expansion in green patent outputs, environmental sustainability remains under stress, as the rise in technological innovations does not support CO₂e reductions to a specific income threshold [70]. Likewise, the burden on ecological resources persists in India, even though the country has fast-tracked agricultural mechanization and technological development [71,72]. That said, many other high-income countries in the bloc are prime examples of places where policy-technology alignment initiatives, such as circular manufacturing, forest-monitoring technologies, and advances in marine and aquaculture analytics, have helped control or delay such disruptive trends.

Since it is essential to maintain economic growth and productivity across the G20 states, most G20 countries meet their high energy demand by consuming conventional energy sources like oil, coal, and gas, which has risen to 45% between 2000 and 2021 [73]. Although ref. [27] found ENRs to be an effective mechanism for enhancing environmental sustainability in G7 nations, other researchers [12] observed their effectiveness in improving LF in the top ten SDG-performing countries. Regarding ERTs, ref. [43] recently emphasized that technology-driven sustainability is evident in 17 APEC countries. Nonetheless, these results reveal a gap between policy efforts and actual sustainability outcomes, possibly due to misaligned policy targeting, delayed implementation, structural inefficiencies, and less integrated, progressive, and cohesive policy frameworks that weaken long-term impacts and hinder reinvestment in systemic green transformation and natural asset development within the G20.

Conversely, PCG (β = 0.518, p < 0.01), RE (β = 0.939, p < 0.01), and IND (β = 1.120, p < 0.01) emerged as key drivers of LF across the sampled economies. The results indicated that increasing wealth-driven efficiency, improving green energy capacity utilization, and a steady rise in demand have significantly contributed to enhancing LF, thereby reducing the burden on ecosystem resources. For example, aligning industrial growth policies with reforestation initiatives in Pakistan has promoted land resilience and increased forest cover, thereby improving carbon sequestration [74]. In China, the Grain for Green initiative has transformed more than 2.83 million hectares of croplands into forests, often cited as a prime example of income-driven ecological restoration [75]. In Canada, the expansion of PCG and RE coincides with the adoption of enabling technologies, forest management systems, and compliance with bodies such as the Forest Stewardship Council (FSC) and Sustainable Forestry Initiative (SFI). Still, the wildfires have consumed more than two million hectares, underscoring the vulnerability of forests, land, and marine systems under ecological stress [76]. Data from the Global Footprint Network (GFN) identifies some members, such as the USA, China, India, Germany, and Indonesia, as high-biocapacity debtors, where ecological footprints exceed biocapacity per person [77]. Experts note that transitioning from traditional sources (e.g., forest wood, oil) to renewables is key in generating long-term EF improvement across the member states, particularly by lowering stress on land, forests, and marine systems [78]. Similarly, adopting green industrial transformation, adherence to sustainability standards, cleaner production technologies, and efficiency improvements collectively reduce the environmental burden across the G20 nations.

Furthermore, the significant negative coefficients of MNF (β = −0.954, p < 0.01), TR (β = −0.582, p < 0.01), and SR (β = −0.279, p < 0.01) confirmed their adverse effects on environmental sustainability in G20 countries. These economic drivers can create significant burdens on the environment in the following ways: (i) MNF requires extensive resources and energy for operation; (ii) SR generates high demand for Information & Communication Technologies (ICT) energy, urban infrastructure, and the growth of consumption-driven logistics; (iii) TR exacerbates environmental externalities through resource displacement and the embedded emissions of goods. For example, the resource-intensive industries and wildfires in Australia offset restoration initiatives by increasing pressure on biocapacity, specifically in forest systems [79]. Across wider Europe, power needs and urban expansion have compounded the drought-driven decline in forests, particularly during the heat events from 2018 to 2022 [80]. In 2024, the Gran Chaco (Argentina) lost almost 149,649 hectares of cropland to damage caused by dry forest ecosystems [81]. Although ref. [82] argues that income growth fosters energy efficiency by driving the adoption of energy-efficient products and the development of more sophisticated ERTs, experts believe that the extensive deployment and use of machinery for economic activities harm environmental sustainability [83]. These patterns suggest that emerging and developed exporters face numerous challenges. ICT and IND progress indirectly increase land stress, while TR linked to commodities continues to drive cropland and forest conversion in contexts with weak regulatory and policy systems.

4.3. Sensitivity Analysis

The sensitivity analysis described in

Table 6. Sensitivity analysis results: IV proxies substituted.

Variables	Coefficients	Standard Errors
LnERT	−0.0618 *	0.0332
LnENRI	−0.178 ***	0.0295
LnPCG	0.128 ***	0.0417
LnRE	0.626 ***	0.0433
LnIND	1.088 ***	0.151
LnMNF	−1.342 ***	0.124
LnSR	0.166	0.102
LnTR	−0.0934	0.0592
Constant	−1.189 *	0.648
Observations	351
Number of cc	17

Note: Ln = Log form; IV = Independent variable; TR = Trade; MNF = Manufacturing; LF = Loaf capacity factor; PCG = Economic growth; ENR1 = environmentally related tax revenue; ERT = % of ERT technologies; SR = Services; RE = Renewable energy consumption; IND = Industry. *** p < 0.01 and * p < 0.1.

involved two steps: first, two alternative proxies were compiled from the OECD database and used as independent variables to rerun the benchmark model: (i) ERT instead of ERTI; (ii) ENRI instead of ENR (see Table 1 for description of alternate proxies). Second, the baseline model was recalculated using a different proxy for LF, i.e., the normalized ecological balance (NEB). Although the coefficients were slightly smaller than the benchmark regression, the substituted ERTI and ENR proxies maintained the same negative relationship with ecological sustainability. IND and PCG remained weak but stable determinants of environmental sustainability in G20 countries. Overall, the model retained its structural integrity but exhibited increased variability at the sectoral level when variable definitions changed.

In

Table 7. Sensitivity analysis results: proxy for the dependent variable substituted.

Variables	Coefficients	Standard Errors
LnERTI	−0.0639 ***	0.0225
LnENR	−0.0513 ***	0.0101
LnPCG	−0.0738 ***	0.0192
LnRE	0.242 ***	0.0159
LnIND	0.436 ***	0.115
LnMNF	−0.383 ***	0.0879
LnSR	−0.0651 **	0.0332
LnTR	−0.00567	0.0297
Constant	0.383	0.273
Observations	330
Number of cc	16

Note: Ln = Log form; TR = Trade; MNF = Manufacturing; PCG = Economic growth; ENR = Environmental regulations; ERTI = Environment-related technology innovations; SR = Services; RE = Renewable energy consumption; IND = Industry. *** p < 0.01 and ** p < 0.05.

, the results for the alternate dependent variable, NEB, confirmed that ERTI and ENR disrupted environmental sustainability in the selected countries. The change in the sign of PCG suggested a proxy-sensitive reversal in the nexus between income and the environment. The negative PCG coefficient indicated that economic prosperity enhanced LF, but it worsened NEB. Despite their lower intensity, IND and RE remained positive contributors to NEB. While TR exerted insignificant influence on NEB, MNF and SR retained their harmful environmental impact in the G20 nations. The sensitivity analysis confirmed that sectoral outputs and policy indicators exhibit consistent patterns, but their interpretive weights may have changed under stringent environmental accounting.

4.4. Robustness Check

The outcomes reported in

Table 8. Robustness check results: PCSE method.

Variables	Coefficients	Standard Errors
LnERTI	−0.0723 **	0.0310
LnENR	−0.164 ***	0.0264
LnPCG	0.361 ***	0.0405
LnRE	0.704 ***	0.0433
LnIND	0.424 **	0.190
LnMNF	−0.567 ***	0.150
LnSR	0.0241	0.111
LnTR	−0.324 ***	0.0704
Observations	330
Number of cc	16
R-squared	0.798

Note: TR = Trade; MNF = Manufacturing; LF = Loaf capacity factor; PCG = Economic growth; ENR = Environmental regulations; ERTI = Environment-related technology innovations; RE = Renewable energy consumption; IND = Industry. *** p < 0.01 and ** p < 0.05.

, with the PCSE check verified, supported the reliability of the main findings. The independent variables, including ENR and ERTI, were found to have significant adverse effects, supporting their limited role in environmental sustainability. Notably, some sectoral indicators, such as SR, lost significance or weakened (e.g., IND), suggesting that the PCSE results are sensitive to structural explanations. Overall, the PCSE estimator’s results aligned with the benchmark model.

5. Conclusions and Recommendations

5.1. Conclusions

This investigation directly addressed recent calls for new perspectives on how ENRs and ERTI affect different aspects of environmental sustainability, more specifically, biocapacity (measured by LF). The study used a novel approach to estimate the impacts of ENRs, ERTI, and other related controls (TR, SR, MNF, IND) on LF for G20 countries from 2000 to 2022, employing FGLS and PCSE estimators. The FGLS results showed that misalignments between strict environmental regulations and technological upscaling have impaired infrastructure efficiency across the sample, leading to adverse sustainability outcomes. These unintended outcomes emphasize prioritizing croplands and forests not just as static reserves but as dynamic pillars/elements of the national biocapacity, highly responsive to policy, regulatory, and technological intensity and deployment. The coefficients of key indicators in the sensitivity analysis and the subsequent PCSE estimator confirmed the robustness of these findings. Collectively, these results underscore that the sampled economies face dual tests: (i) integrating economic factors with the regenerative boundaries of land systems; (ii) guaranteeing that policy–technology exchanges reinforce rather than weaken the ecological basis of sustainable development.

5.2. Policy Implications

The empirical analysis provides a data-driven basis for interpreting how technological, institutional, and structural determinants jointly regulate the LF within the G20 bloc. The findings affirm that advances in LF depend on ENRs that simultaneously compress ecological footprint (through material efficiency, energy transition, and urban containment) and improve biocapacity (via restoration of croplands, carbon-sink enhancement, forests, and marine ecosystems). The present findings have multidimensional implications for global environmental sustainability governance.

Firstly, the negative interaction between ENRs and LF demonstrates that ENRs have not translated into measurable improvements and restorations of soil fertility, forest biomass, and marine ecosystems, indicating compliance-led control without biocapacity regeneration. Thus, fiscal ecological reforms are needed to facilitate the effective allocation of carbon-tax returns towards afforestation and peatland programs, particularly in countries facing high biocapacity gaps. Specifically, ENRs should evolve into regenerative ecological fiscalism, in which a significant portion of environmental levies is directed toward soil-carbon enrichment, reforestation, marine ecosystem restoration, and the rehabilitation of coastal mangroves. These activities can directly increase the numerators (biocapacity) in the LF equation. The G20 countries can jointly initiate a policy-restoration index to enable efficient monitoring and improvements in the share of regulatory collections reinvested in reducing ecological deficits and enabling biocapacity gains.

Secondly, the negative influence of ERTIs on LF indicates a paradoxical rebound effect. Technical efficiency has contributed to gains in the production process, but it has increased total output/productivity faster than the regenerative biocapacity of the ecosystem. The finding emphasizes coherence in ERTs: coupling research and development with circular manufacturing, forest-monitoring technologies, green agriculture mechanization, and marine and aquaculture analytics. National and group-level collaborations are required to develop technologies (e.g., microbial soil inoculants, digital fishery quotas, and reforestation drones) that increase the productive and regenerative capacity of the ecosystem per unit of invested capital. The G20 states can establish a biocapacity-specific return-on-innovative-technology index to track hectares of restored land and forests and the capacity of surplus carbon sinks (in tons) generated per research and development dollar.

Thirdly, the significant positive RE-LF nexus highlights the dual energy transition benefits. The findings support the idea that RE not only minimizes the LF denominator (ecological footprint), but it also revitalizes the capacity of the biosphere through several ways, e.g., limiting peat oxidation, firewood consumption, and the extraction of fossil fuels. Thus, energy planners in the G20 bloc should adopt spatially adjusted ERTs, establishing wind and solar projects on saline or degraded land, and integrating seaward wind with marine and ocean carbon restoration projects. Thus, policymakers should initiate a shared ERTs-to-regeneration agreement among G20 members to facilitate the conversion of technology development into biocapacity expansion. For this purpose, every gigawatt of generated energy is used to restore at least fifty hectares of land, forests, and marine resources. Fourthly, the data validate that materials consumption in manufacturing and their associated emissions are the main drivers of declining biocapacity. Policymakers are encouraged to introduce LF-based quotas for manufacturing, allowing capacity growth only in areas with lower LF (≥1) within the G20 group. Integrating carbon sink acquisition commitments into the credit frameworks for exports is also critical. These actions would formalize the environmental costs of manufacturing activities in the green accounting system.

Finally, the negative value of TR substantiates the cross-border transfer of ecological stress through value chains worldwide. Exports and trade-linked deforestation in high forest-density areas (e.g., Brazil) to meet consumption in developed economies (e.g., the USA and China) pose an imminent threat to biocapacity. The ongoing losses in biocapacity can be mitigated through trade policies that internalize biocapacity-aligned accounting. To enable this mechanism, exporters should be encouraged to certify and support commodities free from deforestation, while importers should jointly finance regeneration relative to their embodied EF. Implementing a mechanism for biocapacity rewards, congruent with the UNFCCC Article 6, could operationalize fairness in the consumption of ecosystems worldwide.

5.3. Limitations and Future Directions

The study presents a new methodological approach and offers valuable insights; however, it has several limitations that future research needs to address. One major limitation is relying only on data from G20 countries, which may not fully reflect the diverse economic and environmental conditions of other regions. To improve the generalizability of the results, future models should include countries or groups beyond the G20, such as BRICS Plus and the Belt and Road Initiative (BRI). This broader scope would help provide a more complete understanding of global sustainability trends. Additionally, using more detailed forest metrics, such as forest rents or forest footprints, to more closely reflect the effects of ENRs and ERTs on the forest-related indicators, along with long-term data and linear and non-linear models, would allow for a deeper exploration of the complex relationships between regulations, technology, and forest sustainability outcomes.

This study has applied LF as a metric for the environmental and ecological sustainability of land and forest resources. A further limitation of this study is that using stringent environmental regulations and application of advanced technologies may restrict sustainable forest resource utilization by increasing operational costs. Furthermore, technological overuse can overexploit or create habitat disturbance, resulting in long-term issues for forest sustainability. Therefore, future researchers may address this issue by using forest resources as a significant parameter to develop an integrated framework for global policy analysis. Furthermore, future studies should adopt advanced econometric methodologies, particularly second- and third-generation techniques and machine learning approaches, to enhance the precision of sustainability forecasts. These modern methods would enable more accurate and dynamic monitoring of environmental trends, ultimately providing policymakers with more reliable data to design better-targeted strategies for sustainable development. By addressing these limitations, future research can significantly contribute to advancing our understanding of environmental sustainability on a global scale.