Assessing the Impact of Green Energy Transition, Technological Innovation, and Natural Resources on Load Capacity Factor in Algeria: Evidence from Dynamic Autoregressive Distributed Lag Simulations and Machine Learning Validation

Abstract

Algeria’s resource-dependent economy faces significant challenges in balancing hydrocarbon reliance with environmental sustainability, yet existing research largely overlooks the comprehensive load capacity factor (LCF) metric in favor of traditional emissions analyses. This study examines the relationships between the LCF and key economic–environmental factors in Algeria from 1980 to 2023, including total natural resource rents, energy transition, technological innovation, GDP, primary energy consumption, and urbanization. Using ARDL and DARDL econometric approaches complemented by a kernel-based regularized least squares analysis, the research captures both linear and nonlinear relationships while accounting for asymmetric dynamics in short- and long-term perspectives. The findings reveal that natural resource rents, technological innovation, and urbanization significantly impair Algeria’s LCF, while primary energy consumption shows a minimal positive impact. The energy transition initiatives demonstrate mixed effects, highlighting the complexities of green energy implementation in resource-dependent economies. These results suggest that Algeria’s sustainable development requires targeted policies focusing on resource management efficiency, environmentally conscious urban planning, and green technology adoption, providing valuable insights for other resource-rich nations pursuing similar sustainability transitions.

1. Introduction

Economic theory has increasingly come to recognize the inseparability of economic growth and environmental sustainability, viewing them not as competing priorities but as interconnected dimensions of a shared reality. The environment provides the essential context within which human life unfolds, while development represents humanity’s efforts to enhance the quality of life within this context. In recent years, this interdependence has been underscored by global challenges such as climate change, resource depletion, and biodiversity loss, which have necessitated a reevaluation of traditional growth models. Against this backdrop, Algeria—the largest country in both the Maghreb region and the African continent—offers a compelling case study, uniquely positioned at the intersection of resource wealth, ecological vulnerability, and aspirational sustainability policies [1,2]. Its vast and diverse landscape, spanning the Sahara Desert to Mediterranean coastlines, is marked by significant variations in economic productivity, demographic density, and climatic conditions, presenting both opportunities and challenges for sustainable development [3]. Algeria’s dual identity as a major hydrocarbon exporter and a nation striving toward renewable energy leadership makes it a critical exemplar of the tensions facing resource-dependent economies globally [4,5].

Algeria’s particularity lies in its paradoxical role as a regional economic powerhouse with one of Africa’s highest GDPs per capita, yet one burdened by a severe ecological deficit [6]. The country’s economy remains heavily reliant on hydrocarbon revenues, which account for 93% of export earnings and 20% of GDP, epitomizing the “resource curse” dynamic widely documented in the development literature [7]. However, Algeria’s recent pivot toward sustainability distinguishes it as a proactive actor in the Global South. Beginning in the 2000s, the government launched the National Renewable Energy and Energy Efficiency Program (2015), targeting 22 GW of renewable capacity by 2030, alongside a USD 3 billion green infrastructure initiative during the COVID-19 pandemic to bolster solar energy and smart-grid systems. These efforts align with its Nationally Determined Contribution (NDC), which commits to reducing greenhouse gas (GHG) emissions by 7–22% by 2030 [8,9], and the ambitious 2035 Hydrogen Strategy, aiming for 15,000 MW of green hydrogen production [10]. Such policies reflect Algeria’s recognition of renewable energy as both an economic diversification tool and a climate resilience imperative, as emphasized in the African Union’s Agenda 2063 [11].

The necessity of studying Algeria is further underscored by its regional leadership in sustainability metrics. According to the Sustainable Development Goals (SDGs) Index, Algeria ranks first in the Arab world and Africa, and 64th globally, highlighting its alignment with global sustainability frameworks [12]. Yet, this progress contrasts starkly with persistent ecological deficits. The Global Footprint Network [6] reports an alarming 240% ecological deficit, driven by a biocapacity of 0.66 global hectares per capita against an ecological footprint of 2.22 (see Figure 1). This imbalance mirrors challenges faced by other resource-rich nations, such as Nigeria and Venezuela, where fossil fuel dependency undermines environmental resilience [13]. Algeria’s struggle to reconcile industrialization with ecological limits—evidenced by soil degradation in the Tell Atlas region and water scarcity in the Sahara—exemplifies the broader dilemma of balancing economic growth with planetary boundaries [14].

Technological innovation (TI) has emerged as a dual-edged sword in this context. While Algeria’s patent filings surged from 32 in 2000 to 268 in 2021 [15], driven by state-backed start-up incubators and R&D tax incentives, the environmental impact of such innovation remains underexplored. The World Intellectual Property Organization’s IPC Green Inventory [16] classifies Algeria’s advancements in solar desalination and carbon capture as pivotal to reducing its carbon footprint. However, as observed in Japan and South Korea, TI’s short-term costs—such as workforce retraining and infrastructure retrofitting—can initially exacerbate ecological strain [17]. This duality underscores the need for nuanced policy frameworks that prioritize “green” over “dirty” innovation, a distinction central to Acemoglu et al.’s [18] seminal work on direct technological change.

Despite these efforts, Algeria’s environmental trajectory remains precarious. Traditional metrics like CO₂ emissions fail to capture the full scope of its ecological challenges, including water pollution from hydrocarbon extraction and biodiversity loss in the Ahaggar national park. The load capacity factor (LCF), introduced by Siche et al. [19], offers a more holistic measure by integrating biocapacity and ecological footprint [20]. Algeria’s LCF of 0.3—far below the sustainability threshold of 1—signals an urgent need for policies that address both supply-side biocapacity (e.g., reforestation) and demand-side resource efficiency [21].

Algeria’s sustainability challenges are compounded by its socio-economic landscape. Rapid urbanization—with 73% of the population concentrated in northern coastal cities—has intensified pressure on limited arable land and water resources [15]. The government’s National Urban Development Plan 2035 seeks to decentralize growth through “eco-cities” in the Sahara, yet implementation lags due to bureaucratic inertia and funding gaps [22]. Meanwhile, youth unemployment persists at 26% [15], fueling social unrest and complicating the transition to green industries. Geopolitically, Algeria’s role as Europe’s third-largest gas supplier [23] ties its energy policies to EU decarbonization mandates, creating a paradox: hydrocarbon revenues fund renewable investments, yet fossil fuel exports lock the nation into carbon-intensive partnerships [10]. While prior studies have examined Algeria’s CO₂ emissions [24,25], few adopt the LCF to assess multidimensional environmental degradation. The existing literature also neglects the interplay between natural resource rents (NRRs), energy transition (ET), and technological innovation (TI) in resource-dependent contexts. For instance, [26] analyzes Algeria’s renewable potential but overlooks how TI mediates the ET–LCF nexus. Similarly, global studies on the resource curse [5] rarely incorporate biocapacity metrics, limiting their applicability to Algeria’s ecological realities. This study bridges these gaps by employing a dynamic autoregressive distributed lag (DARDL) framework to quantify how TNRR, ETI, and TI asymmetrically impact the LCF, offering policymakers a roadmap to balance economic and ecological imperatives.

Algeria’s experience holds lessons for resource-rich developing nations navigating the green transition. Its USD 3 billion COVID-era green stimulus, for example, mirrors Indonesia’s post-pandemic renewable push but contrasts with Nigeria’s stalled solar initiatives [27]. By dissecting Algeria’s successes (e.g., solar patent growth) and failures (e.g., persistent subsidies), this research informs strategies to mitigate transitional inefficiencies. For advanced economies, the findings underscore the need to align international climate finance with local governance structures, ensuring initiatives like the EU’s Global Gateway do not replicate centralized models that marginalize vulnerable regions [28].

This study is motivated by Algeria’s dual challenge of balancing its hydrocarbon-dependent economy with the urgent need for climate resilience. While the existing literature has extensively examined carbon emissions and ecological footprints, the multidimensional LCF—which integrates biocapacity and ecological footprint—remains underexplored, particularly in resource-rich economies like Algeria. By focusing on the LCF, this research addresses a critical gap in understanding how natural resource rents, green energy transitions, and technological innovation collectively shape environmental sustainability in fossil fuel-dependent nations.

The purpose of this study is to provide actionable insights for reconciling economic growth with ecological preservation in Algeria, offering a roadmap for sustainable transition strategies that can be adapted to similar resource-dependent economies. Using Algeria as a regional testbed, the research explores the asymmetric impacts of key drivers—such as TNRR, ETI, and TI—on environmental sustainability, measured through the LCF. This approach not only advances the analytical framework for assessing sustainability but also provides policymakers with evidence-based recommendations to navigate the complexities of green energy transitions in resource-rich contexts.

This study contributes to the empirical literature by exploring the interplay between total natural resource rents (TNRRs), green energy transition (ET), and technological innovation (TI) and their collective impact on Algeria’s environmental sustainability, as measured by the LCF. Unlike previous studies, this research examines the overlooked dimensions of ET and TI within the Algerian context from 1980 to 2024. Key contributions of the study include:

Advancing the analytical framework: The research offers a novel analytical framework by integrating TNRR, ET, and TI into the analysis of Algeria’s environmental sustainability, measured through the LCF. This integrated approach permits a comprehensive understanding of the multifaceted drivers of environmental health and degradation, surpassing traditional metrics such as carbon emissions or ecological footprint. By adopting the dynamic autoregressive distributed lag (DARDL) methodology, the study captures both short-term and long-term interactions between these variables, providing deeper insights into their temporal dynamics.

Quantitative assessment of influential factors: The study quantifies the immediate and extended impacts of TNRR, ET, and TI on environmental stability. It evaluates how variations in these factors influence Algeria’s LCF, offering a granular perspective on the degree to which these elements contribute to or mitigate environmental pressures. This contribution is particularly relevant in the context of Algeria’s persistent ecological deficit, as it provides actionable data for targeted policy interventions aimed at achieving sustainability.

Dynamic impact analysis: By assessing the impact of a 10% increase or decrease in ET, TI, and TNRR on the LCF, the study offers nuanced insights into the dynamic influence of these factors. This aspect of the research highlights the elasticity of environmental stability in response to policy changes and economic shifts, providing valuable guidance for adaptive policy formulation. The analysis also underscores the critical role of balancing economic growth with environmental considerations, demonstrating how strategic investments in ET and TI can mitigate the adverse effects of resource dependency.

Robustness and validation through machine learning: To ensure the robustness of its findings, the study incorporates a machine learning-based econometric approach—kernel-based regularized least squares (KRLS). This method is recognized for its superior predictive accuracy and efficiency in handling complex, nonlinear relationships between variables. The application of KRLS not only validates the main findings but also introduces a cutting-edge methodological contribution to the field of environmental economics, showcasing the potential of machine learning tools in empirical research.

Contributions to global sustainability discourse: The study contributes to the global discourse on sustainability by demonstrating how resource-rich developing countries like Algeria can reconcile economic growth with ecological preservation. It also underscores the role of technological innovation and green energy transition in achieving the SDGs, offering lessons that extend beyond Algeria to other nations grappling with similar challenges.

In conclusion, this study not only advances the understanding of Algeria’s environmental sustainability but also provides a roadmap for integrating economic growth with ecological preservation. By leveraging innovative methodologies and offering policy-relevant insights, the research contributes to both academic scholarship and practical policymaking, paving the way for a more sustainable future.

2. Literature Review

2.1. Technological Innovation–Load Capacity Factor Nexus

The relationship between TI and environmental quality has garnered substantial scholarly attention, though much of this work has centered on CO₂ emissions rather than the more holistic LCF. Recent studies highlight the dual role of TI, revealing both its potential to mitigate and exacerbate environmental impacts depending on contextual factors. For instance, Fatima et al. [29] employed dynamic panel estimators on G7 data (1990–2020) and found that TI significantly reduced CO₂ emissions, aligning with the Environmental Kuznets Curve (EKC) hypothesis. This finding is reinforced by Bergougui [9], who used a nonlinear ARDL framework (1980/Q1–2021/Q4) to demonstrate that positive shocks to TI reduced emissions, while negative shocks intensified them. However, regional disparities complicate this narrative. In African economies, Obobisa et al. [30] reported that TI increased CO₂ emissions during 2000–2018, contrasting sharply with Dunyo et al. [31], who observed emission reductions across 58 countries (2000–2020). Such contradictions suggest that TI’s environmental impact may depend on developmental stages, energy infrastructure, and policy frameworks.

Shifting our focus to LCF, a metric combining biocapacity and ecological footprint, studies reveal even greater complexity. Ragmoun and Ben-Salha [32] applied quantile regression to Saudi Arabian data (1988–2021) and found TI enhanced the LCF, validating the EKC. Similarly, Uche and Ngepah [12] demonstrated that green technology improved the LCF in South Africa (1970–2018), albeit only in upper quantiles, suggesting threshold effects. Conversely, Yingjun et al. [33] used Fourier ARDL/NARDL models in Egypt and Turkey (1990–2022) and found TI reduced the LCF, a result echoed by Islam et al. [25] in nuclear energy-consuming nations (1990–2020), where TI undermines ecological sustainability. These divergent outcomes underscore the need for context-specific analyses, particularly in understudied regions like Algeria, where TI’s role in balancing economic growth and ecological limits remains unexplored.

Hypothesis H1. 

Technological innovation significantly influences the load capacity factor in Algeria.

2.2. Energy Transition–Load Capacity Factor Nexus

The global shift toward renewable energy has intensified scholarly interest in the energy transition (ET)–LCF nexus, though findings remain context-dependent. Amin et al. [34] applied ARDL models to Chinese data (1990–2019), showing that ET reduced CO₂ emissions, while Maalel [35] emphasized RET’s role in decarbonization. Recent work extends this to the LCF: Özkan et al. [36] analyzed Germany (1980–2021) and found ET improved the LCF across all quantiles, whereas Sarabdeen et al. [37] reported negative impacts, urging balanced policy design. Such contradictions highlight the mediating role of the energy mix and institutional quality. For example, Degirmenci et al. [38] linked Mexico’s renewable energy adoption (1971–2018) to LCF gains, while Pang et al. [29] demonstrated similar outcomes in the U.S. (1990–2021), though non-renewables and fiscal policies (e.g., [39] on BRICS) often counteract these benefits. Notably, Pata [40] revealed heterogeneous effects: renewable energy boosted the LCF in the U.S. (1982–2016) but had negligible impacts in Japan, underscoring the importance of complementary investments (e.g., healthcare, infrastructure).

Recent studies on energy transition in the Middle East and North Africa (MENA) region highlight the complex interplay between geopolitical dependencies, energy security, and sustainability goals. Schuetze et al. [28] provide a critical analysis of Jordan’s renewable energy transition, which initially positioned the country as a global leader in clean energy investment by 2018. However, technical constraints, entrenched fossil fuel dependencies, and centralized governance structures led to a stagnation of progress by 2019. Unlike sustainability-driven transitions in Europe or East Asia, Jordan’s efforts were primarily motivated by energy security concerns, revealing a tension between short-term fossil fuel reliance and long-term renewable potential. The authors argue that decentralized renewables, communal ownership models, and the empowerment of municipal authorities could mitigate concentrated power dynamics and deepen energy democracy—a lesson particularly relevant for resource-poor MENA nations.

This contrast is instructive for Algeria, a resource-rich yet fossil fuel-dependent economy. While Algeria’s state-led renewable investments (e.g., its USD 3 billion solar program and 2035 hydrogen strategy) aim to align energy security with sustainability [10], Schuetze et al.’s findings caution against over-reliance on centralized, large-scale projects that may replicate existing power structures. Unlike Jordan, Algeria’s hydrocarbon wealth provides fiscal leverage for transition financing, yet both nations face similar risks: prioritizing short-term energy security (e.g., Algeria’s delayed subsidy reforms) could undermine long-term sustainability, as seen in Jordan’s renewed fossil fuel turn. This regional comparison underscores the need for adaptive governance frameworks that balance scalability with inclusive participation—a gap in Algeria’s current policy design.

Hypothesis H2. 

The energy transition and load capacity factor in Algeria are significantly correlated.

2.3. Total Natural Resource Rent–Load Capacity Factor Nexus

Natural resource rents (TNRRs) have paradoxical environmental implications, acting as both economic catalysts and ecological stressors. Kibria [41], using NARDL in Bangladesh (1995–2018), showed that natural resources asymmetrically affected ecological footprints, with positive shocks degrading sustainability and negative shocks offering modest improvements. Similarly, Shah et al. [42] linked TNRR to CO₂ emissions in ASEAN nations (1990–2019), while Raihan [43] employed DOLS for India (1970–2022), revealing that the TNRR reduced the LCF by promoting extractive industries. Cross-country analyses further complicate this: Zhao et al. [44] used CS-ARDL in BRICS nations (1990–2018) and found the TNRR undermined the LCF in both short and long terms, a trend exacerbated by weak governance. However, Adekoya et al. [45] recently argued that resource-rich nations could achieve sustainability through TI-driven diversification, a perspective absent in Algeria-focused studies.

Algeria, deriving 20% of its GDP from hydrocarbons [15], epitomizes the “resource curse” dilemma. Yet, no study has empirically tested how the TNRR interacts with the LCF in this context, leaving policymakers without evidence to balance economic dependence on fossil fuels with ecological preservation.

Hypothesis H3. 

There is a significant relationship between total natural resource rents and the load capacity factor in Algeria.

2.4. Research Gaps

A synthesis of the literature reveals critical gaps this study addresses:

Geographical bias: while the ET, TI, and TNRR impacts are well-documented in G7, BRICS, and Asian economies, Algeria—a resource-rich, transitioning African nation—remains overlooked despite its unique energy profile and climate vulnerabilities [46].

Methodological limitations: prior work prioritizes CO₂ emissions over the LCF, neglecting the latter’s capacity to capture both supply (biocapacity) and demand (ecological footprint) dynamics [47].

Integrated analysis: the existing research examines TI, ET, and TNRR in isolation, ignoring their synergistic or antagonistic effects on the LCF—a gap this study bridges through a multivariate framework.

By addressing these gaps, this study contributes novel insights into sustainable development pathways for resource-dependent economies, offering actionable strategies for Algeria’s green transition.

3. Data and Methodology

3.1. Data

This research employed yearly time-series data from Algeria, spanning the years 1980 to 2023, comprising 43 observations, in light of their scope and data availability. The pertinent data for this analysis were sourced from two principal entities: the Global Footprint Network (GFN) and the World Development Indicators (WDI) database maintained by the World Bank.

Table 1. Description of data series.

Variables	Code	Measurement	Source
Load capacity factor	LCF	The ratio of biocapacity to ecological footprint.	GFN
Total natural resource rent	TNRR	% of GDP	WDI
Energy transition index	ETI	TWh	Our World in Data
Technological innovation	TI	Total number of patent applications by residents	WDI
Gross domestic product	GDP	GDP per capita (constant 2015 USD)	WDI
Primary energy consumption	PEC	TWh	Our World in Data
Urbanization	URB	Urban population (% of the total population)	WDI

Note. GFN: Global Footprint Network: WDI: World Development Indicators.

offers a comprehensive summary of the dataset, encompassing its source, sign, and measurement. To address size inconsistencies and avoid possible heteroscedasticity issues, some variables in the data series were transformed using natural logarithms [48].

3.2. Method

This research examined the impact of the TNRR, ETI, and TI on the LCF of Algeria. The model also incorporated additional variables such as GDP, PEC, and URB. The following economic equation is presented:

$$\mathrm{L}\mathrm{C}\mathrm{F}=\mathrm{f}\left(\mathrm{T}\mathrm{N}\mathrm{R}\mathrm{R},\mathrm{E}\mathrm{T}\mathrm{I},\mathrm{T}\mathrm{I},\mathrm{G}\mathrm{D}\mathrm{P},\mathrm{P}\mathrm{E}\mathrm{C},\mathrm{U}\mathrm{R}\mathrm{B}\right)$$

(1)

The econometric model utilized in this study is shown in Equation (1):

$${\mathrm{L}\mathrm{C}\mathrm{F}}_{\mathrm{t}}=\mathsf{\alpha }+{\mathrm{B}}_1{\mathrm{T}\mathrm{N}\mathrm{R}\mathrm{R}}_{\mathrm{t}}+{\mathrm{B}}_2{\mathrm{E}\mathrm{T}\mathrm{I}}_{\mathrm{t}}+{\mathrm{B}}_3{\mathrm{T}\mathrm{I}}_{\mathrm{t}}+{\mathrm{B}}_4{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{t}}+{\mathrm{B}}_5{\mathrm{P}\mathrm{E}\mathrm{C}}_{\mathrm{t}}+{\mathrm{B}}_6{\mathrm{U}\mathrm{R}\mathrm{B}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(2)

where LCF, ETI, TI, GDP, PEC, and URB represent the load capacity factor, green energy transition index, technological innovation, gross domestic product, primary energy consumption, and urbanization, respectively. Additionally, “Log” denotes the natural logarithmic transformation; α, β₁, β₂, β₃, and β₄ refer to the coefficients of the explanatory variables. Lastly, ε_t is the error term. Regarding the expected signs of the explanatory variables, it was hypothesized that the TNRR, ETI, and TI would positively influence the ecological load capacity factor, whereas resource rents and GDP were expected to exert a negative impact, as summarized in

Table 2. Variables and expected sign.

Variables	Expected Sign
Total natural resources rent	$B_1={\displaystyle \frac{\partial LCF}{\partial TNRR}}<0$
Energy transition	$B_2={\displaystyle \frac{\partial LCF}{\partial ETI}}>0$
Technological innovation	$B_3={\displaystyle \frac{\partial LCF}{\partial TI}}>0$
Gross domestic product	$B_1={\displaystyle \frac{\partial LCF}{\partial GDP}}<0$
Primary energy consumption	$B_5={\displaystyle \frac{\partial LCF}{\partial PEC}}<0$
Urbanization	$B_3={\displaystyle \frac{\partial LCF}{\partial URB}}>0$

.

The flowchart in Figure 2 illustrates the methodological framework applied in this study. To assess the effects of the TNRR, ETI, and TI on environmental sustainability, particularly focusing on variations in the LCF in Algeria, the research followed a structured approach consisting of six sequential steps.

4. Empirical Results

4.1. Preliminary Analyss

• Descriptive statistics

The summary statistics presented in

Table 3. Descriptive statistics.

Variable	LCF	ETI	TNRR	LTI	LGDP	LPEC	UEB
Min	1.441	0.001	0.892	0.538	3.449	3.976	2.215
Max	4.110	0.007	1.534	0.560	3.628	4.211	5.290
Mean	2.595	0.003	1.296	0.550	3.547	4.092	3.358
Std. dev.	0.689	0.002	0.140	0.007	0.055	0.068	0.982
Kurtosis	2.156	3.980	3.348	1.657	1.648	1.940	2.540
Skewness	0.350	1.184	−0.518	−0.151	−0.136	0.279	0.991
Jarque–Bera	2.252	12.320	2.244	3.554	3.564	2.688	7.758
Probabilities	0.324	0.002	0.326	0.169	0.168	0.261	0.021

provide a comprehensive overview of the dataset utilized in this study. The mean values reveal that the logarithmic load capacity factor (LCF) averaged 2.594 over the period 1980 to 2023. Similarly, the annual averages for the other variables were as follows: green energy transition index (ETI) at 0.002, total natural resource rents (TNRR) at 1.295, technological innovation (TI) at 0.549, gross domestic product (GDP) at 3.546, primary energy consumption (PEC) at 4.090, and urbanization (URB) at 3.358.

The maximum and minimum values highlight substantial variations in the logarithmic values of these indicators during the sample period. Specifically, the LCF ranged from 1.441 to 4.109, ETI fluctuated between 0.0005 and 0.007, TNRR varied from 0.891 to 1.533, TI spanned from 0.537 to 0.559, GDP ranged from 3.448 to 3.628, PEC fluctuated between 3.975 and 4.211, and URB showed a range from 2.215 to 5.290. These figures underscore the significant temporal variability across the study variables.

Additionally, the standard deviation values indicated that the LCF and URB exhibited the highest levels of variability, with standard deviations of 0.688 and 0.982, respectively, while the ETI demonstrated the least variability among the variables analyzed. The skewness coefficients revealed that the distributions of TI, TNRR, and GDP were negatively skewed, suggesting a concentration of values at the lower end of their respective distributions. Conversely, the distributions of the LCF, ETI, PEC, and URB were positively skewed, indicating a concentration of values toward the higher end.

The kurtosis values indicated that all the variables exhibited platykurtic distributions, characterized by flatter tails compared to normal distribution. Furthermore, the results of the Jarque–Bera test provided robust evidence that the variables in this study adhered to a normal distribution. These statistical insights provided a detailed understanding of the dataset’s characteristics, highlighting the variability and distributional properties of the key indicators analyzed in this research.

• Stationarity test

For the dynamic ARDL simulation method to be applied, it is imperative that the dependent variable be strictly integrated of order one, I(1), to ensure the robustness and reliability of the model’s estimation process. This condition allows for the accurate estimation of both short-term and long-term dynamics within the framework. In contrast, the explanatory variables can be stationary at either level (I(0)) or first difference (I(1)), adhering to established research conventions in econometric analysis.

As a preliminary step, it was crucial to determine the stationarity properties of the variables using the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests. These tests provide robust diagnostic tools to identify the order of integration of the series under study. The results, presented in

Table 4. Unit-root outcomes.

Variables	Augmented Dickey–Fuller (ADF)				Phillips–Perron (PP)
	I(0)		I(1)		I(0)		I(1)
	t-Stat	p Value	t-Stat	p Value	t-Stat	p Value	t-Stat	p Value
LCF	2.947	0.5928	2.95	0.0000 a	3.621	0.5857	3.628	0.0000 a
TNRR	2.947	0.0755	3.628	0.0000 a	3.621	0.0831	3.628	0.0000 a
ETI	3.621	0.0008	3.628	0.0000 a	3.621	0.0007	3.628	0.0000 a
TI	3.621	0.8167	3.628	0.0006 a	3.621	0.9009	3.628	0.0009 a
GDP	3.62	0.8175	3.628	0.0006 a	3.621	0.9008	3.628	0.0009 a
PEC	3.6	0.5677	3.628	0.0000 a	3.621	0.6442	3.628	0.0000 a
URB	3.62	0.5152	3.628	0.0667 c	3.621	0.3504	3.628	0.0714 c

^a 1% significance level ^c 10% significance level.

, indicate that the TNRR and ETI were stationary at level, confirming their I(0) status. Conversely, the remaining variables, including the LCF, TI, GDP, PEC, and URB, were found to be integrated of order one, I(1), demonstrating their stationarity only after the first difference.

These findings confirmed the appropriateness of the dynamic ARDL simulation method for the dataset, as it accommodates the mixed stationarity properties of the variables, thereby enabling a robust and reliable examination of the relationships among them.

4.2. ARDL Model Estimation

The findings presented in

Table 5. ARDL regression results.

Variables	ARDL Model
	Coefficients	t-Statistic	p-Values
Long-run
$\mathbf{E}\mathbf{C}\mathbf{T}$	−0.5259	−4.1700	0.0000
${\mathbf{U}\mathbf{R}\mathbf{B}}_{\mathbf{t}-1}$	−0.2476	−3.1200	0.0040
${\mathbf{T}\mathbf{N}\mathbf{R}\mathbf{R}}_{\mathbf{t}-1}$	−1.1228	−2.3200	0.0260
${\mathbf{L}\mathbf{P}\mathbf{E}\mathbf{C}}_{\mathbf{t}-1}$	1.7065	1.1500	0.2580
${\mathbf{L}\mathbf{T}\mathbf{I}}_{\mathbf{t}-1}$	−2.8057	−7.7800	0.0000
${\mathbf{E}\mathbf{T}\mathbf{I}}_{\mathbf{t}-1}$	−1.8902	−2.5000	0.0170
${\mathbf{L}\mathbf{G}\mathbf{D}\mathbf{P}}_{\mathbf{t}-1}$	3.0441	7.8200	0.0000
Shirt-run
$∆\mathbf{U}\mathbf{R}\mathbf{B}$	−0.1302	−2.1500	0.0390
$∆\mathbf{T}\mathbf{N}\mathbf{R}\mathbf{R}$	−0.5904	−2.2700	0.0290
$∆\mathbf{L}\mathbf{P}\mathbf{E}\mathbf{C}$	0.8974	1.0100	0.3170
$∆\mathbf{L}\mathbf{T}\mathbf{I}$	−1.9985	−3.8900	0.0000
$∆\mathbf{E}\mathbf{T}\mathbf{I}$	−5.2083	−2.5800	0.0140

The ARDL estimations for the long and short run are shown in Figure 3.

provide comprehensive estimates of the LCF within both long-term and short-term frameworks, including the corresponding t-statistics and p-values. The analysis revealed that Algeria’s real GDP per capita had a positive and statistically significant influence on the LCF in both temporal contexts. Furthermore, the results underscored the significant roles of urbanization, total natural resource rents, technological innovation, green energy transition, and gross domestic product as key predictors of the LCF. These variables collectively exhibited adverse effects on environmental quality in both short- and long-term assessments.

In contrast, primary energy consumption did not demonstrate any statistical significance in influencing the LCF, as evidenced by p-values of 0.258 in the long term and 0.317 in the short term. The model’s explanatory power was captured by the R-squared value of 0.437, indicating that 43.7% of the variability in the LCF was explained by the independent variables included in the analysis. These results highlight the critical interplay between economic, technological, and environmental factors in shaping Algeria’s environmental outcomes over time.

After conducting the stationarity tests, it was necessary to perform the bounds test to examine the presence of long-run cointegration among the variables using the KS critical value framework. The results, presented in

Table 6. Bounds test.

	10%		5%		1%		p-Value
K	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F	1.946	3.181	2.348	3.733	3.303	5.023	0.003	0.036
t	−1.613	−3.691	−1.97	−4.107	−2.68	−4.938	0.000	0.045

Note: I(0) and I(1) indicate lower and upper bounds.

, indicated that the p-values derived from the bounds test were below the 0.05 threshold. Consequently, at a 1% significance level, the null hypothesis of no cointegration was rejected.

This outcome confirms the existence of long-run cointegration among the variables, as validated by both the bounds test and the critical values. The findings underscore the necessity of further in-depth investigation to understand the characteristics and dynamics of these long-term relationships comprehensively.

Table 7. Diagnostic tests for model residuals: autocorrelation, heteroscedasticity, and normality.

Panel 1: Breusch–Godfrey LM Test for Autocorrelation
Lags (p)	F	Prob > F
1	0.006	0.94
2	0.541	0.587
3	1.749	0.176
4	1.357	0.271
Panel 2: Cameron and Trivedi’s Heteroscedasticity Test
Source	Chi2	p-value
Heteroskedasticity	39.43	0.2042
Skewness	4.53	0.7172
Kurtosis	0.01	0.9113
Total	43.98	0.3467
Panel 3: Skewness/Kurtosis Normality Test
Residuals	Pr(Skewness) = 0.7243
	Pr(Kurtosis) = 0.5340
	Adj χ2(2) = 0.53
	Prob > χ2 = 0.7691

summarizes the diagnostic tests conducted to assess the validity of the ARDL (1,0,0,0,0,0,0) model. The Breusch–Godfrey Lagrange Multiplier (LM) test for autocorrelation, applied with four lags, failed to reject the null hypothesis of no serial correlation at the 5% significance level (p-values > 0.05), confirming the absence of autocorrelation in the residuals. Heteroscedasticity was evaluated using Cameron and Trivedi’s decomposition of the IM-test, following Sarkodie and Owusu’s [49] methodology. The results (χ² = 39.43, p = 0.2042) indicated homoscedastic residuals. Additionally, skewness/kurtosis tests for normality revealed no significant deviations from a normal distribution (Adj χ² = 0.53, p = 0.7691), further validating the model’s robustness.

To rigorously evaluate the normality assumption, two complementary graphical diagnostics were utilized: (1) a standardized normal probability (P-P) plot (Figure 4a) and (2) a quantile–quantile (Q-Q) plot comparing residual quantiles to theoretical normal quantiles (Figure 4b). Both graphical analyses (Figure 4a,b) validated that the residuals of the ARDL (1,0,0,0,0,0,0) model adhered to a normal distribution, with observed values closely aligning with the expected theoretical distribution. Furthermore, the stability of the model’s parameters and potential structural breaks were assessed using the cumulative sum (CUSUM) test. As illustrated in Figure 4c, the CUSUM statistic remained bound within the 95% confidence bands across the entire study period, confirming the stability and reliability of the estimated coefficients and ruling out significant parameter instability or structural shifts.

4.3. Dynamic ARDL Simulations Outcomes

Several studies have applied the modified ARDL simulation methodology to investigate the potential effects of future shocks on various socioeconomic and environmental sustainability factors [50]. In this study, the DARDL simulation methodology assumed a 10% contribution of shocks over a 20-year period, from 2024 to 2044, across key variables such as the LFC, TNRR, GDP, ETI, TI, LPEC, and URB. The empirical results from this simulation, presented in

Table 8. Dynamically simulated ARDL.

Variables	Dynamic ARDL Model
	Coefficient	Std. Error	t-Stat	p-Value
${\mathrm{L}\mathrm{C}\mathrm{F}}_{\mathrm{T}-1}$	−0.420	0.167	−2.52	0.017 **
$∆\mathrm{T}\mathrm{N}\mathrm{R}\mathrm{R}$	−0.660	0.390	−1.69	0.1 *
${\mathrm{T}\mathrm{N}\mathrm{R}\mathrm{R}}_{\mathrm{T}-1}$	−0.680	0.375	−1.81	0.079 *
$∆\mathrm{E}\mathrm{T}\mathrm{I}$	−13.357	19.245	−0.69	0.493
$∆\mathrm{L}\mathrm{G}\mathrm{D}\mathrm{P}$	83.723	184.370	0.45	0.653
$∆\mathrm{L}\mathrm{T}\mathrm{I}$	−645.506	1496.868	−0.43	0.669
$∆\mathrm{L}\mathrm{P}\mathrm{E}\mathrm{C}$	0.678	1.951	0.35	0.731
${\mathrm{U}\mathrm{R}\mathrm{B}}_{\mathrm{T}-1}$	−0.161	0.074	−2.16	0.038 **
${\mathrm{E}\mathrm{T}\mathrm{I}}_{\mathrm{T}-2}$	1.293	28.725	0.05	0.964
${\mathrm{L}\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{T}-1}$	13.181	5.976	2.21	0.035 **
${\mathrm{L}\mathrm{T}\mathrm{I}}_{\mathrm{T}-1}$	−83.321	42.112	−1.98	0.057 *
${\mathrm{L}\mathrm{P}\mathrm{E}\mathrm{C}}_{\mathrm{T}-1}$	0.390	1.156	0.34	0.738
R2	0.358
Observations	43
Prob > F	0.1 *
Simulations	5000

**, * denote statistical significance at the 5% and 10%level.

, offer a comprehensive analysis of the anticipated impacts of these shocks on sustainability dynamics within the examined framework.

The results from the DARDL simulations, presented in Table 8, demonstrate that the TNRR negatively affected the LCF, thereby hindering environmental sustainability in Algeria over both the short and long term. The p-value for the TNRR was below 0.1, with a long-term TNRR value of −0.680 and a short-term value of −0.660. These findings suggest that Algeria’s dependence on natural resource rents (TNRRs) diminishes the LCF and impedes environmental sustainability by stalling economic diversification, delaying progress in energy efficiency, and placing pressure on environmental systems. This reliance undermines sustainable development, reduces resilience to external shocks, and limits the country’s capacity to improve economic and energy efficiency for long-term growth, highlighting the potential adverse effects on Algeria’s environmental health.

The model further revealed that a 1% increase in TI was associated with a decrease in Algeria’s LCF by 83.321 units in the short run and 645.506 units in the long run. This finding suggests that during the transitional phase, TI exerts a net negative effect on environmental sustainability in Algeria. It is important to note that our measure of TI encompassed both “dirty” and “green” technologies. As Acemoglu et al. [18] propose, dirty technologies exacerbate environmental burdens and pollution, whereas green technologies are designed to mitigate these issues. The aggregated nature of our TI indicator, which reflects insights from the World Intellectual Property Organization’s “IPC Green Inventory” [16], implies that both types of technologies are present in Algeria’s evolving energy system.

In the short run, the negative impact of TI on the LCF can be partly attributed to the challenges associated with transitioning from conventional fossil fuel-based systems to renewable energy sources. Renewable technologies—such as solar and wind—often face intermittency issues that result in reduced system utilization and lower efficiency compared to fossil fuel systems. Furthermore, the implementation of advanced technologies introduces transitional inefficiencies, including adaptation costs, necessary infrastructure changes, and skill shortages. These factors, coupled with the “green skill gap” observed in emerging economies [51], can temporarily hinder the optimal performance of the system and lead to a decline in environmental quality.

In the long run, while the potential benefits of green technologies (e.g., enhanced energy efficiency, a shift toward cleaner energy consumption, and industrial upgrading) are well recognized, the aggregated effect of TI still reflects significant transitional challenges. The substantial long-run decrease in the LCF (645.506 units) suggests that the initial costs and inefficiencies involved in the transition process may overshadow the long-term environmental benefits until supportive measures—such as phased implementation strategies, public–private partnerships, and targeted policy interventions—are put in place.

Overall, these results underscore that although TI holds considerable promise for advancing sustainability through energy efficiency improvements and cleaner energy systems, the coexistence of “dirty” and “green” technologies along with the short-term transitional inefficiencies can lead to a pronounced negative impact on the LCF in Algeria. Addressing these challenges through more deliberate transition management and supportive policies could help mitigate the short-run drawbacks and ultimately unlock the long-term environmental benefits of technological innovation.

Our study also highlights a divergence between theoretical expectations and empirical findings regarding the impact of the GDP on environmental quality, specifically in reducing the LCF over both time periods. Contrary to some existing research, the results indicated that economic growth (GDP) had a positive effect on the LCF in both the short and long terms, with values of 83.721 and 645.506, respectively. In contrast, the long-term impact of ETI on the LCF was positive but negligible, at 1.293, while the short-term effect was negative and insignificant at −13.357. It is essential to note that ETI positively influences the LCF in the long run by improving system efficiency through the use of primary energy consumption (PEC), advanced technologies, and sustainable practices. These improvements stabilize energy production, optimize resource utilization, and reduce reliance on fossil fuels, thereby enhancing long-term resilience and sustainability, despite the negative short-term effect.

The analysis also revealed that PEC positively influenced the LCF, with a 1% increase in PEC resulting in a 0.303% increase in the LCF in the long run. This relationship suggests that higher energy consumption contributes to an increased environmental load capacity, emphasizing the significant role of energy utilization in shaping environmental sustainability and performance, especially in energy-intensive contexts.

The error correction term (ECT) quantifies the speed at which the system returns to equilibrium after external shocks. In this dynamic model, an ECT value between zero and one was expected. The analysis showed an ECT value of −0.420, indicating that approximately 42% of the long-term equilibrium deviations were corrected in each period following an external shock. This suggests that changes in the specified parameters have a considerable and effective impact on the LCF in Algeria, driving the system back toward its equilibrium state over time. The DARDL results are further illustrated in Figure 5, which visually represents the impact of these dynamics.

4.4. Simulated Shocks and Environmental Outcomes

Oil-dependent economies, such as Algeria, are highly vulnerable to external shocks due to their reliance on natural resource rents (TNRRs) as a primary source of revenue. These economies experience both positive shocks, such as windfalls from rising oil prices, and negative shocks, including declines in oil revenue due to price crashes, geopolitical instability, or global energy transitions. The ability of such economies to sustain growth, environmental quality, and economic diversification depends on their response to these shocks. The dynamic ARDL simulations in this study examined how Algeria’s environmental sustainability, measured by the load capacity factor (LCF), reacted to both positive and negative shocks across key variables such as total natural resource rent (TNRR), technological innovation (TI), and the green energy transition index (ETI). The findings provided insights into Algeria’s structural vulnerabilities and highlighted critical challenges associated with its dependency on fossil fuel revenues.

A negative shock on the TNRR—such as a sharp decline in global oil prices, a reduction in hydrocarbon exports, or increased international pressures for decarbonization—has significant implications for Algeria. As illustrated in Figure 6a, a 10% reduction in TNRR led to an improvement in environmental sustainability, as indicated by a higher LCF. This outcome suggests that lower resource rents reduce incentives for excessive fossil fuel extraction, leading to lower carbon emissions and environmental degradation. However, negative TNRR shocks also bring challenges, such as reduced government revenues and fiscal constraints. A decline in oil revenues limits public spending on infrastructure, subsidies, and social programs, which is particularly concerning for Algeria, where hydrocarbons account for over 90% of export revenues. This reliance makes the country susceptible to fiscal deficits when oil prices drop. Additionally, slower industrial expansion and reduced fossil fuel consumption may indirectly benefit environmental sustainability, but they also create pressure for economic diversification and a green transition. Policymakers may accelerate reforms toward non-oil sectors, including renewable energy, technology, and manufacturing, but these structural adjustments require proactive policy measures. Furthermore, negative economic shocks often lead to unemployment, wage cuts, and increased public dissatisfaction, posing risks of social and political instability. Countries like Algeria, which rely heavily on oil revenues to fund social spending programs, face heightened risks of unrest when subsidies and public services are reduced. Thus, while negative TNRR shocks may improve the LCF by reducing environmental pressures, they simultaneously pose serious economic and social risks that require strategic planning to mitigate adverse effects.

Conversely, positive shocks on the TNRR—such as a surge in global oil prices, increased hydrocarbon exports, or new resource discoveries—tend to have adverse effects on environmental sustainability. As shown in Figure 6b, a 10% increase in the TNRR led to a decline in the LCF, suggesting that increased resource rents exacerbate environmental degradation. Higher fossil fuel consumption and emissions growth are direct consequences of increased revenues, as governments and industries exploit natural resources more intensively. This often leads to expanded fossil fuel infrastructure instead of investments in renewable alternatives. Positive TNRR shocks can also trigger the Dutch Disease, where currency appreciation makes non-oil exports less competitive, further distorting the economy. The non-resource sector, including manufacturing and agriculture, may suffer from reduced investments due to the dominance of the oil industry. Additionally, higher oil revenues often result in relaxed environmental regulations and increased pollution levels, as expanding fossil fuel projects contribute to deforestation, soil degradation, and air pollution. While positive shocks boost GDP growth in the short term, they reinforce dependence on hydrocarbons, making the economy more fragile in the face of future downturns. Algeria’s fiscal policies, which often increase public spending during oil booms, become unsustainable during subsequent downturns, highlighting the resource curse dilemma—where temporary economic gains from resource wealth come at the expense of long-term sustainability and economic diversification.

The study also explored how TI and ETI influence environmental sustainability in the face of economic shocks (see Figure 7). The empirical findings indicated that TI had a negative impact on the LCF in the short run (−83.321), suggesting transitional inefficiencies. However, in the long run, TI’s impact became less negative (−645.506), indicating that technological advancements gradually contribute to sustainability. This transition period reflects the adaptation costs associated with shifting from traditional energy systems to renewable alternatives. The need for skilled labor, infrastructure adjustments, and policy frameworks can initially slow down the benefits of innovation. On the other hand, the simulations in Figure 8 reveal that a 10% increase in ETI leads to a positive improvement in LCF, confirming the role of an energy transition in promoting sustainability. Investments in renewable energy, efficiency improvements, and sustainable technologies can help oil-dependent economies mitigate environmental damage while fostering resilience against external shocks. However, the positive effects of ETI depend on policy support, investment incentives, and technological readiness.

Given the findings from Algeria’s case, similar resource-rich economies should adopt comprehensive strategies to navigate the challenges of positive and negative shocks. Diversification beyond oil and gas is critical, with a focus on strengthening non-resource sectors such as manufacturing, tourism, and services. Developing industrial policies that promote green growth and sustainable production can reduce reliance on hydrocarbons. Stabilization mechanisms for oil revenue management, such as establishing sovereign wealth funds (SWFs) and implementing counter-cyclical fiscal policies, can help smooth income fluctuations and avoid excessive spending during booms. Accelerating renewable energy deployment through investments in solar, wind, and green hydrogen projects is essential to reduce reliance on fossil fuels and improve energy efficiency. Strengthening environmental governance and regulation, including enforcing strict environmental impact assessments (EIAs) for fossil fuel projects and implementing carbon pricing mechanisms, can discourage excessive emissions. Finally, enhancing human capital and technological capabilities through investments in education and research and development (R&D) is crucial for building expertise in sustainable industries. Encouraging FDI in clean technologies can further support industrial modernization and long-term sustainability. By addressing these challenges, oil-dependent economies like Algeria can enhance their environmental sustainability, reduce vulnerability to external shocks, and achieve long-term economic and social development.

4.5. Kernel-Based Regularized Least-Squares Outcomes

This section presents the estimation of pointwise derivatives using the KRLS machine learning technique, as proposed by Bekun et al. [52], to analyze the causal relationships among the study variables.

Table 9. KRLS results.

LCF	Avg.	SE	t	p > t	P25	P50	P75
URB	−0.045	0.022	−2.015	0.051 b	−0.104	−0.062	−0.005
ETI	−41.951	15.756	−2.662	0.011 a	−84.697	−37.250	−4.969
TNRR	−0.468	0.165	−2.834	0.007 a	−1.154	−0.623	0.364
LGDP	1.585	0.263	6.026	0.000 a	0.839	1.751	2.302
LTI	12.744	2.131	5.979	0.000 a	6.846	14.349	18.381
LPEC	1.497	0.342	4.371	0.000 a	0.298	1.718	2.682
Diagnostics
Lambda	0.165	sigma	4	R2	0.977	Obs	42
Tolerance	0.045	Eff. Df	18.970	Looloss	2.653

displays the pointwise form of the estimated KRLS model, which exhibited strong predictive power, with an R² value of 0.977, indicating that the regressors explained 97.7% of the variation in the LCF. The average pointwise marginal effects revealed that the LCF, URB, ETI, TNRR, LGDP, LTI, and LPEC had mean pairwise marginal effects of −41.95%, −0.46%, 1.58%, 12.74%, and 1.49%, respectively. The statistical significance levels indicated that ETI, TNRR, LGDP, and LPEC significantly influenced the LCF at the 1% level, while URB exerted a significant impact at the 5% level. These results highlight the critical role of URB, ETI, TNRR, LGDP, LTI, and LPEC in shaping Algeria’s environmental sustainability.

Additionally, the study explored the long-term effects of fluctuations in the TNRR, ETI, and TI on the LCF by plotting the pointwise derivatives of the TNRR, ETI, and TI against the LCF, as shown in Figure 9a–c. Figure 9a illustrates the marginal effect of the TNRR on the LCF, showing an initial modest increase in the LCF, followed by a stronger effect leading to a significant rise. Figure 9b depicts the fluctuating marginal impact of ETI on the LCF, demonstrating that higher ETI levels corresponded to greater improvements in the LCF. Initially, ETI and the LCF increased proportionally, but beyond a threshold, a lower level of ETI resulted in a steeper rise in the LCF. Figure 9c presents the marginal returns of LTI, showing an initial increase in the LCF, followed by a decline, before eventually leading to another upward trend.

5. Conclusions

This study aimed to examine the impact of total natural resource rents (TNRRs), the green energy transition indicator (ETI), technological innovation (TI), gross domestic product (GDP), primary energy consumption (PEC), and urbanization (URB) on Algeria’s load capacity factor (LCF) over the period 1980–2024. Employing a robust econometric framework, the analysis utilized the ARDL model to investigate both short- and long-term associations among the variables. The study also implemented the DARDL simulation technique to capture potential asymmetric responses to positive and negative shocks across these factors. Additionally, KRLS, a machine-learning methodology leveraging pointwise derivatives, was applied to provide supplementary insights into potential future shocks and nonlinear dynamics. The empirical findings derived from the ARDL and DARDL analyses revealed several critical insights. The TNRR exerted a statistically significant and negative impact on the LCF in both the short and long run. Similarly, TI negatively influenced the LCF in the long term, highlighting its potential adverse implications for environmental sustainability. ETI demonstrated a notable negative impact on the LCF in both time horizons; however, its effect was more pronounced in the long term. Interestingly, the DARDL results suggested that ETI’s short-term impact on the LCF, though negative, was relatively minor, while its long-term effect was positive yet statistically insignificant. Urbanization (URB) negatively and significantly affected the LCF in both the short and long run, underscoring the environmental costs associated with urban expansion. In contrast, PEC showed a negligible but favorable effect on the LCF in both timeframes. The KRLS estimates aligned with the findings from the DARDL analysis, affirming the significant role of the TNRR, ETI, TI, GDP, PEC, and URB in shaping Algeria’s LCF. Collectively, these results emphasize the criticality of these factors in achieving long-term environmental sustainability. The diagnostic tests conducted further corroborate the reliability and robustness of the study’s findings, offering a solid basis for policy implications.

This study offers critical insights into Algeria’s environmental sustainability challenges but is subject to several limitations. First, its focus on Algeria, while contextualized regionally through comparisons to Jordan’s energy transition paradoxes [28], restricts direct applicability to economies with distinct geopolitical or institutional frameworks. For instance, Algeria’s centralized, hydrocarbon-subsidized transition model contrasts sharply with Jordan’s decentralized, donor-dependent approach, limiting generalizability to resource-rich versus resource-poor MENA nations. Second, while variables like the TNRR and ETI capture core drivers of Algeria’s load capacity factor (LCF), the analysis omits critical factors such as decentralized governance models, community participation dynamics, and geopolitical alliances (e.g., regional energy partnerships or EU decarbonization pressures) [53], which may further mediate transition outcomes. Finally, the national-level scope overlooks subnational disparities in resource distribution, renewable energy potential, and urbanization patterns. For example, hydrocarbon-intensive Saharan regions may face distinct sustainability challenges compared to urban-industrial northern zones, yet these spatial heterogeneities remain unexamined. Additionally, while the study spans a robust historical period (1980–2024), it does not project long-term sustainability trends beyond the current timeframe. This limits its capacity to inform forward-looking policies aimed at mitigating future ecological risks or optimizing energy transition pathways.

Addressing these limitations opens avenues for impactful future work. Comparative regional studies could contrast centralized (Algeria, Saudi Arabia) and decentralized (Jordan, Morocco) governance models to identify institutional enablers or barriers to renewable integration, building on Schuetze et al.’s [28] analysis of Jordan’s stalled transition. Another critical area involves investigating how Algeria’s geopolitical strategies—such as leveraging hydrocarbon exports to Europe or navigating OPEC+ dynamics—shape its energy transition priorities, particularly in balancing short-term fiscal stability with long-term sustainability. Additionally, scholars could explore participatory energy transition frameworks, such as community-led solar cooperatives in southern Algeria, to assess their potential in mitigating centralized power concentration risks, a lesson underscored by Jordan’s reliance on top-down implementation. Subnational spatial analyses, leveraging geospatial econometrics, could map regional disparities in the LCF, renewable infrastructure, and resource extraction, offering granular insights into localized sustainability challenges. To enhance policy relevance, extending the research period and integrating predictive modeling techniques—such as scenario-based simulations or machine learning forecasts—could project long-term trends under varying energy transition pathways (e.g., accelerated renewables adoption vs. prolonged fossil fuel dependence). For instance, coupling DARDL simulations with climate–economy models like GCAM or TIAM-ECN would enable policymakers to quantify trade-offs between economic growth, resource rents, and ecological thresholds over a 2050–2100 horizon. Finally, integrating climate adaptation metrics—such as desertification rates or water scarcity—into energy transition models could clarify how environmental stressors interact with policy choices to shape Algeria’s ecological resilience. Collectively, these directions would deepen understanding of how resource-dependent economies navigate the tension between energy security, geopolitical constraints, and environmental sustainability, a challenge starkly illustrated by Jordan’s paradoxical trajectory and Algeria’s ongoing balancing act.