Energy Consumption, Economic Growth, and Climate Change Nexus in Jordan: Insights from the Toda Yamamoto Causality Test

Abstract

Jordan faces significant challenges related to energy security due to its limited natural resources and reliance on energy imports, which meet over 90% of its energy needs. The adoption of renewable energy (RE) technologies is viewed as a crucial step in reducing dependence on fossil fuels, enhancing energy security, and addressing environmental concerns such as CO₂ emissions. Jordan has set ambitious goals to reduce greenhouse gas emissions by 30% by 2030 through RE and energy efficiency initiatives, in line with the global push to combat climate change. Using the Toda-Yamamoto causality test, this study examines the relationship between economic growth, energy consumption, and greenhouse gas emissions in Jordan from 1980 to 2021. The results indicate a bidirectional causality between energy consumption and economic growth, highlighting the energy-dependent nature of Jordan’s economy. Additionally, greenhouse gas emissions were found to cause economic growth, reflecting the role of emissions-intensive industries in the country’s development. The study also shows that economic growth and greenhouse gas emissions jointly influence energy consumption, while energy consumption does not directly drive emissions. These findings emphasize the need for Jordan to adapt and accelerate its energy transition. By investing in renewable energy and improving energy efficiency, Jordan can reduce its contributions to climate change while enhancing energy security and fostering sustainable economic growth.

1. Introduction

Climate change is a significant challenge and a pressing crisis that human communities face. Swift and decisive action is required to avoid its negative effects, mitigate its economic impact, avert catastrophe, minimize financial losses, and limit unfavorable effects that jeopardize human life and future prosperity [1].

Energy security is currently Jordan’s most discussed policy issue since it’s the key driver of economic [2,3] and social development [4] in the country. Rising living standards and faster economic growth have elevated energy security to the top of policymakers’ global agendas. Jordan faces unique challenges regarding energy security because of its limited natural resources, unstable regional environment, and ongoing wars.

Jordan is an energy-poor country [5,6], whose conventional resources do not satisfy its demands; it only provides up to 10% of the energy needed. Most energy needs are met through imports [7], so energy security is dependent on its neighbors, namely the Arab Gulf nations, Iraq, and Egypt [8,9,10]. The primary source of energy in Jordan comes from fossil fuels [11], which includes coal, oil, and natural gas. Natural gas is Jordan’s most widely used fossil fuel to generate power [12]. In addition, mining, processing, and combustion of fossil fuels negatively impact the environment, ecosystems, and human health, further exacerbated by oil spills and air pollution. The adoption of cost-effective and environmentally friendly renewable energy technologies is crucially important [13]. RE may contribute to enhancing energy security, cutting expenses, safeguarding the environment, and creating new job opportunities [14].

Jordan is vulnerable due to its lack of natural resources and reliance on energy imports. According to the official reports of MEMR 2018, Jordanian imports account for more than 94% of the country’s energy consumption [15]. As a result of the unstable regional environment, fluctuating prices and the unpredictable nature of supplies are causing problems [8,16]. Recently, the government has reevaluated its energy management plan, prioritized the search for clean energy sources, and analyzed the potential of new international accords. Therefore, one viable solution to fulfill this increasing energy need is switching to energy systems based on renewable energy. The action would also help reduce greenhouse gas (GHG) emissions in line with the Paris Agreement; in Jordan, 23 million tons of CO₂ were produced in 2018 [15].

Moreover, applying RE may lessen financial limitations and boost local employment and economic growth [14,17]. There is a global trend toward the utilization of renewable energy sources to mitigate climate change-related environmental issues as their ability to reduce greenhouse gas emissions [18], improve air quality, and have a less negative impact on the environment [19,20].

Jordan understands how urgently the issue of climate change needs to be resolved. As such, it establishes aggressive targets to reduce CO₂ emissions by advancing renewable energy projects. By 2030, Jordan wants to reduce its greenhouse gas emissions by around 30% using RE and energy efficiency activities [14,15]. Several renewable energy projects, including wind, solar, hydroelectric, and biogas projects [8] are distributed throughout the country. Between 2015 and 2030, Jordan developed several energy strategies focused on enhancing energy security, increasing domestic production, exploring alternative sources, and investing in renewable energy and efficiency. These efforts aim to reduce reliance on imported fossil fuels and diversify energy sources to mitigate price volatility and external risks [3]. Transitioning to a 100% renewable energy system has been deemed technically feasible and economically viable [21]. Additionally, energy consumption in Jordan is significantly linked to human-wellbeing and household social status [22]. Given the increasing global prices and regional instability, a new vision for Jordan’s energy sector is critical.

This paper examines the relationship between economic growth, energy consumption, and climate change in Jordan using the Toda Yamamoto technique from 1980 to 2021. This study is important since it tries to fill the gap and revisit the nexus of the mentioned factors to offer possible recommendations for future policies in the country. This paper is critical and addresses the gap in understanding how energy consumption is linked to economic growth and how these relationships are affecting the contribution of the country to climate change. New policies that rely on such new results will contribute to accelerating economic growth while maintaining Jordan’s commitments toward GHG reductions. The paper is divided as follows: the second section lists the relevant literature on economic growth, energy, and climate change. The third section details the methodology used in this study and the data. The fourth section discusses the study’s results, and lastly, the study ends with conclusions and policy recommendations.

2. Literature Review

2.1. Four Possible Directions of Granger Causality

The literature recognizes four main hypotheses (namely the conservation, growth, bidirectional, and neutrality hypothesis) concerning the relationship between energy consumption and economic growth.

According to the conservation hypothesis, the gross domestic product (GDP) growth unidirectionally causes energy consumption, and conserving energy does not necessarily decrease GDP. However, different energy conservation strategies may have different economic consequences, just as energy can be used to boost GDP growth or otherwise. It is reasonable to anticipate that some energy-saving measures will, at the very least, not impede economic growth [23,24]. On the one hand, this hypothesis means that households can spend their extra income on energy-intensive activities, such as buying computers, heating more, etc. On the other hand, economic growth would expand activities requiring more energy [25].

The growth hypothesis results when a unidirectional causality runs from energy consumption to economic growth. Because energy consumption is crucial for economic growth to occur, either directly or indirectly, as a complement to labor and capital in this environment, conservation measures will impede it [26,27].

The existence of bidirectional causality between energy use and economic growth lends credibility to the feedback hypothesis. This interdependence means using energy conservation measures that lower energy use may also affect economic growth. Similar variations in economic growth may also be reflected in energy usage [25,27].

Finally, the neutrality hypothesis indicates that causality between energy consumption and economic growth is absent. When energy consumption does not play a role in economic growth or vice versa, it means that other factors lead to growth [28,29].

The literature on the energy-growth relationship is extensive; researchers studying the issue related to energy consumption utilize different methods and techniques. One of the predominant methods used in this field is the causality test, which can be applied using various steps and detailed techniques. There has been a growing interest in the relationship between energy consumption and economic growth in recent years. This is because energy is essential in driving economic development, and understanding the link between these two factors can help policymakers and researchers develop effective strategies for promoting economic growth and reducing poverty.

The relationship between energy consumption, economic growth, and poverty is complex and interconnected. Energy consumption is vital for economic growth, as energy demand increases as economies grow [30]. However, if energy is constrained, GDP growth may suffer. It is important to note that the relationship between energy consumption and economic growth is not straightforward. While short-term production increases may result in a statistically significant increase in energy consumption, short-term increases do not cause economic growth rate changes [30].

2.2. Energy Consumption and Economic Growth in Jordan

Shahateet examined the relationship between energy consumption and economic growth in 17 Arab countries [31]. The auto-regressive distributed lag (ARDL) model results revealed that 16 of 17 countries, including Jordan, support the neutrality hypothesis, where energy conservation will not hinder economic growth. Shahateet et al. studied the causal relationship between economic growth and energy consumption in Jordan, and the study modeled the relationship using data on energy consumption, labor, capital, and economic growth between 1970–2011 [32]. In contrast to Shahateet’s main findings [32], the results indicated a long-term causal relationship running from GDP to energy consumption, concluding that introducing a new energy constraint may not limit Jordanian economic growth. In the same context, Al-Bajjali and Shamayleh studied electricity consumption in Jordan between 1985–2015 [33]. They built a multivariate time series model including six variables: GDP, electricity prices, population, urbanization, the structure of the economy, and aggregate water consumption. Their results showed that factors such as GDP, urbanization, the economy’s structure, and aggregate water consumption have a positive relationship with electricity consumption, while consumption is negatively linked to electricity prices. Khawaldeh and Al-Quadah studied the impacts of electric and oil energy consumption on economic growth in Jordan from 1992–2016 [34]. The variables were tested using the multiple linear regression model. The results showed that both energies have a significant and positive relationship with economic growth.

Dar-Mousa and Makhamreh studied energy consumption patterns in Amman using Global Moran’s I analysis [35]. They indicated an inverse relationship with population distribution, family size, and building characteristics. The study concluded that small families in some parts of the city could consume more energy related to their income and household size. Alshawawreh and Almuhtady [16] showed that Jordan is sensitive to regional conflicts due to its high dependency on imported energy and the lack of diversity in energy resources. They concluded that policies should focus on increasing the local share of energy resources and enhancing strategic energy storage to increase Jordan’s resilience. Azzuni et al. studied the impact of energy transition on achieving energy security in Jordan [21]. The fact that Jordan has the potential to achieve 100% energy transition by 2050 [36] can contribute to more security and reduce GHG emissions. They concluded that utilizing renewable energy can contribute to solving water scarcity. Al-Qudah studied the impacts of renewable energy consumption, labor, and capital on Jordan’s economic growth from 1996–2018 [37]. The methodology used was the Johansen co-integration test, the Cobb-Douglas production function, and the dynamic ordinary least squares (DOLS). The results revealed that renewable energy consumption, labor, and capital have a significant positive relationship with economic growth. The study recommended developing policies to increase investment in renewable energy in Jordan. Publications related to energy and economic growth nexus in Jordan are summarized in Table 1.

2.3. The Role of Energy Consumption and Economic Growth in Environmental Change

Acaravci and Ozturk explored the long-term causal relationships among CO₂ emissions, energy consumption, and economic growth using an autoregressive distributed lag model and the Granger causality test across 19 European countries [38]. Their findings indicated a significant long-run causal link between energy consumption and CO₂ emissions and a long-run elasticity between carbon emissions and real GDP. Similarly, [39] applied the cointegration technique and causality test to analyze the dynamic relationships between pollutant emissions, energy consumption, and real output in Russia. The study found significant bidirectional causality among emissions, energy use, and output, with real output negatively impacting emissions and contradicting the environmental Kuznets curve hypothesis. Using a combination of path analysis and the STIRPAT model, [40] identified GDP per capita, industrial structure, population, urbanization, and technology as the primary factors influencing CO₂ emissions. While the effects of these factors varied, technological advancements had the most substantial impact on reducing emissions. In the MENA region, Arouri et al. tested the environmental Kuznets curve (EKC) hypothesis by employing bootstrap unit root tests and panel cointegration methods to assess the relationship between real GDP, energy consumption, and CO₂ emissions [41]. Their results validated the EKC for Jordan and emphasized the significant long-term impact of energy consumption on emissions in the region.

Hamit-Haggar investigated the Canadian industrial sector using cointegration analysis to study the relationship between GHG emissions, energy consumption, and economic growth [42]. The results highlighted a significant long-term influence of energy consumption on emissions and a nonlinear EKC-consistent relationship between emissions and economic growth. Similarly, Saboori et al. employed the fully modified ordinary least squares cointegration method to analyze the bidirectional relationship between road sector energy consumption, CO₂ emissions, and economic growth in OECD countries [43]. Their findings revealed a significant positive long-term bidirectional relationship and showed that CO₂ emissions responded more quickly to GDP shocks than energy consumption.

Mugableh examined the equilibrium and dynamic causality among economic development, CO₂ emissions, energy consumption, financial development, foreign direct investment, and gross fixed capital formation in Jordan from 1976 to 2010 [44]. The results demonstrated the existence of the EKC hypothesis and unidirectional causality from financial development to energy consumption and economic growth. Saidi and Hammami utilized the generalized method of moments to assess the impact of economic growth and CO₂ emissions on energy consumption in 58 countries, finding a positive correlation between emissions and energy consumption across various regions [45]. However, the relationship between economic growth and energy consumption varied by region, with stronger links observed in the MENA and Sub-Saharan African regions.

Jamel and Derbali analyzed panel data from eight Asian countries using cointegration tests, fully modified OLS, and panel causality methods, uncovering a long-term relationship among environmental degradation, energy consumption, economic growth, and additional factors such as trade openness and urbanization [46]. Spetan explored causal relationships in Jordan between renewable energy consumption, CO₂ emissions, labor, capital, and economic growth from 1986 to 2012 [47]. The study revealed that renewable energy consumption influenced GDP and CO₂ emissions and suggested that increased renewable energy use could mitigate emissions.

In China, Gui et al. employed partial least squares path analysis and regression models to investigate the drivers of CO₂ intensity, emphasizing the pivotal role of technology and research and development in emissions control [48]. Chen and Lei analyzed the transportation sector in Beijing using a path analysis model, finding that reducing energy and transportation intensity could mitigate emissions, with population growth exerting the most significant pressure on the transportation sector [49].

These studies collectively highlight that economic growth in Jordan remains heavily reliant on energy consumption, with the nation yet to reach the EKC point where economic growth decouples from carbon emissions. While previous analyses examined energy use, GDP, and emissions relationships separately, this paper integrates these variables into a single model using the Toda Yamamoto causality test to provide holistic insights and formulate actionable policy recommendations.

3. Materials and Methods

The causality test, a well-established method in literature, is applied to analyze the causal relationship between multiple variables over a long period. The Toda-Yamamoto non-Granger causality test is chosen to determine the causal relationship between energy consumption (EC), GDP in constant prices US$ of the year 2015, and GHG emissions, including land use changes in carbon dioxide equivalent. The data is collected for the period between 1980 – 2021. The primary energy consumption is chosen instead of the final energy consumption to provide a more comprehensive view of the energy system and its impacts. Understanding the relationship between energy consumption and economic growth is essential, and the literature review section provides an overview of the various methods and approaches used to study this relationship. This understanding can help Jordan formulate policies that achieve sustainability in the energy sector while continuing economic growth. Climate change is also an urgent topic, and studying the relationship between energy consumption, economic growth, and GHG emissions can provide insight into how growth actions can affect those emissions and help formulate policies to reduce them. Table 2 defines the variables included in the analysis in this study.

The descriptive analysis results in Table 3 present the mean, maximum, minimum, standard deviation, skewness, kurtosis, and the Jarque-Bera probability for the time series included in the Toda-Yamamoto causality test. The standard deviation (Std. Dev.) results indicate that GDP deviates significantly above its average mean, while GHG and EC show relatively lower deviations than their respective means. The skewness coefficients reveal that all three variables—GHG, GDP, and EC—are positively skewed, indicating slightly right-tailed distributions. The kurtosis values for all variables are below 3, suggesting that the data distributions are platykurtic (flatter than a normal distribution). The Jarque-Bera p-values for all variables indicate no statistically significant departure from normality during the study period, as the p-values are greater than 0.05. These descriptive statistics confirm that the variables are well-suited for the causality analysis.

Economic time series often exhibit trends in their mean, variance, or both dimensions [50]. A time series is classified as stationary if its mean and variance remain constant over time [51]. Consequently, assessing the stationarity of a time series is a critical step in analysis. Addressing non-stationarity is essential before conducting stationarity tests; this can typically be achieved through methods such as first differencing and de-trending [50]. Several approaches are available to evaluate the stationarity of time series data by testing for unit roots. Among these, the augmented Dickey-Fuller (ADF) test, developed by [52], is the most widely employed. The ADF test is designed to reject the null hypothesis of non-stationarity, but its results may require further validation. In such cases, the Phillips-Perron (PP) test, introduced in 1988 [53], serves as a complementary tool to verify the presence of unit roots and corroborate the ADF test findings.

When conducting tests for non-stationarity, researchers can incorporate one of three configurations in the model equation: (1) no intercept or trend, (2) an intercept without a trend, or (3) both an intercept and a trend [54].

Toda-Yamamoto Technique

The long-term relationship between energy consumption and economic growth for the study period is investigated using the non-Granger causality test by Toda and Yamamoto [55]. The Toda and Yamamoto causality method is effective and applicable regardless of the order of integration of the series or whether they are cointegrated. This method applies to stationary time series at level, first, or second difference. The technique was structured based on the augmented Vector Autoregressive (VAR) modeling method and the Wald test statistic. The test is based on asymptotic chi-square (χ²) values that are distributed regardless of the cointegration properties and the stationarity of the data series. In this technique, the (k + d_max)^th-order VAR is estimated, where k is the determined lag length, and d_max is the maximal order of integration. The analysis performed the Toda-Yamamoto long-run non-Granger causality test using VAR with 4 lags (k = 3 and d_max = 1). As a result, the Toda Yamamoto approach provides more robust causality inferences in the presence of non-stationary data and avoids the risk of pre-test biases. This makes it particularly suitable for time-series data with mixed integration properties [55].

The equation is written in the following equation:

(1)$\left[\begin{array}{c}{lnGDP}_t\\ {lnEC}_t\\ {lnGHG}_t\end{array}\right]={\eta }_0+{\eta }_1\left[\begin{array}{c}{lnGDP}_{t-1}\\ {lnEC}_{t-1}\\ {lnGHG}_{t-1}\end{array}\right]+{\eta }_2\left[\begin{array}{c}{lnGDP}_{t-2}\\ {lnEC}_{t-2}\\ {lnGHG}_{t-2}\end{array}\right]+{\eta }_3\left[\begin{array}{c}{lnGDP}_{t-3}\\ {lnEC}_{t-3}\\ {lnGHG}_{t-3}\end{array}\right]+{\eta }_4\left[\begin{array}{c}{lnGDP}_{t-4}\\ {lnEC}_{t-4}\\ {lnGHG}_{t-4}\end{array}\right]+\left[\begin{array}{c}{\mu }_{lnGDP}\\ {\mu }_{lnEC}\\ {\mu }_{lnGHG}\end{array}\right]$

Here, ln is the natural logarithm sign, lnGDP_t represents the natural log of GDP, lnEC_t represents the natural log of EC, and lnGHG represents the natural log of GHG. ${\eta }_1\dots \dots {\eta }_4$ represent the 4 × 4 matrices of quantities with ${\eta }_0$ identity matrix. The disturbance terms that have zero mean and constant variance are represented by ${\mu }_s$. The logical steps for performing the Toda-Yamamoto non-Granger causality test are shown in Figure 1.

4. Results and Discussion

This study uses the Toda-Yamamoto technique to show the relationship between EC, GDP, and GHG. A simple VAR equation was estimated. Using the optimal lag length criteria, the VAR model was re-estimated to include the order of integration of the variables and the number of lags. Then, the non-Granger Causality, or the Toda-Yamamoto causality test, was performed.

4.1. Results of the Toda-Yamamoto Causality Test

Understanding the relationship between energy consumption and economic growth is essential. The outcomes regarding the direction of the causality relationships have significant policy associations. As mentioned earlier in the paper, energy is one of the main factors incentivizing economic growth. Thus, determining which one of the four hypotheses the relationship follows would result in better policy suggestions.

4.2. Unit Root Test

The unit root of energy consumption and economic growth was tested using the Augmented Dicky-Fuller (ADF) and Phillips–Perron (PP) tests. The unit root test for both level and first difference forms was performed, including constant only and constant and linear trends. The results of the unit root tests in Table 4 and Table 5 show that the time series are integrated at first difference. The order of integration value will be used later when testing the modified VAR model and the causality test.

After determining the variables’ integration level, the next step is determining the model’s optimal lag length to ensure it is free from serial correlation and other problems. The model indicates that the optimal lag order is three, as indicated by three criteria. This number is used in the modified VAR model alongside the order of integration, as noted earlier in the methodology. Table 6 shows that the optimal number of lags is two according to three criteria.

The causal relationships between energy consumption, economic growth, and greenhouse gases are listed in Table 7. The results of the Toda-Yamamoto causality tests reveal significant causal relationships between energy consumption (EC), economic growth (GDP), and greenhouse gas emissions (GHG) in Jordan over the period 1980–2021. These findings highlight the intricate linkages between energy, the economy, and the environment in Jordan and provide valuable insights for policy-making.

The results confirm a significant causal relationship from energy consumption to greenhouse gas emissions, indicating that increased energy consumption leads to higher emissions. This is expected given Jordan’s heavy reliance on fossil fuels, such as oil and natural gas, for its energy needs. As energy consumption increases to support industrialization, urbanization, and economic activities, greenhouse gas emissions rise, reflecting the carbon-intensive nature of the energy system. This finding aligns with the work of [42], who found that energy consumption has a significant long-run impact on greenhouse gas emissions in the Canadian industrial sector, and [38], who observed a similar relationship in 19 European countries. The strong link between energy consumption and emissions underscores the importance of transitioning to renewable energy sources, such as solar and wind, to mitigate emissions while meeting growing energy demand in Jordan. Studies such as [21] suggest that Jordan has the potential to achieve a full energy transition by 2050, which could significantly enhance energy security and reduce greenhouse gas emissions.

Economic growth is also found to Granger-cause greenhouse gas emissions, indicating that expanding economic activity in Jordan contributes significantly to environmental degradation. This relationship is consistent with the Environmental Kuznets Curve (EKC) hypothesis, which posits that emissions rise in the early stages of economic growth before declining as economies transition to cleaner technologies. However, Jordan’s economic structure, which relies on emissions-intensive sectors such as industry, energy production, and transportation, exacerbates the environmental impact of GDP growth. This result is supported by studies such as [41], who found that energy consumption significantly impacts CO₂ emissions in the MENA region, including Jordan. Addressing this dynamic requires policies aimed at decoupling economic growth from environmental degradation through investments in green technologies, low-carbon energy, and sustainable urban planning.

Interestingly, the results reveal that greenhouse gas emissions Granger-cause economic growth at the 10% significance level. This highlights the feedback role of emissions-intensive industries in supporting Jordan’s economy. Activities such as energy production and manufacturing, which are key sources of emissions, also contribute significantly to GDP. Similar findings have been reported in developing economies, where emissions-intensive sectors drive economic activity [40]. However, Jordan’s reliance on these sectors for economic growth may pose risks to long-term sustainability, particularly as global and domestic efforts to curb emissions intensify. This highlights the need for Jordan to diversify its economy and promote cleaner industries to ensure resilience in the face of environmental constraints.

A bidirectional causal relationship is observed between energy consumption and GDP, confirming the energy-dependent nature of Jordan’s economy. Increased energy consumption stimulates economic growth by supporting industrial production, infrastructure development, and household consumption. Conversely, economic growth drives higher energy consumption as expanding industrial, residential, and transportation sectors require more energy inputs. These results align with the feedback hypothesis discussed in the literature [27], which posits that energy use and economic growth are interdependent. Jordan’s heavy reliance on imported fossil fuels, however, poses challenges for energy security and exposes the country to price volatility and supply disruptions [35]. Transitioning to renewable energy, improving energy efficiency, and diversifying the energy mix are essential strategies to reduce dependency on external energy sources while maintaining sustainable economic growth [21].

Finally, the results indicate no significant causal relationship from greenhouse gas emissions to energy consumption. This suggests that energy consumption patterns in Jordan are primarily driven by economic and industrial needs, rather than emissions levels. This finding is consistent with [38], who observed that emissions often result from energy consumption rather than being a driver of it. However, the absence of this direct relationship does not diminish the importance of addressing emissions through energy efficiency improvements and the adoption of cleaner energy sources. Efforts to decouple emissions from energy consumption remain critical for reducing Jordan’s environmental footprint and achieving its climate goals.

5. Conclusions

Jordan has made notable progress in developing energy strategies that prioritize domestic production, renewable energy, and reduced reliance on fossil fuel imports. The Green Growth National Action Plan (GG-NAP 2021–2025) highlights the country’s commitment to expanding its green economy across multiple sectors. Achieving Jordan’s renewable energy goals, including the shift to a 100% renewable system, remains both feasible and essential in the face of rising global energy prices and regional challenges.

This study uses the Toda-Yamamoto causality test on annual data from 1980–2021. The findings highlight the complex and interdependent relationships between energy consumption, economic growth, and greenhouse gas emissions in Jordan. The results emphasize the need for a balanced approach to policy-making that promotes economic development while addressing energy security and environmental sustainability. Expanding the use of renewable energy, investing in energy-efficient technologies, and adopting low-carbon production processes are critical steps that can help Jordan reduce its contributions to climate change while ensuring long-term economic growth. By integrating energy, environmental, and economic policies, Jordan can build a sustainable development pathway that aligns with its national goals and global climate commitments.

Our findings align with previous research, reinforcing the urgent need to accelerate Jordan’s transition to sustainable energy. While progress has been made, it’s clear that current efforts are not enough. Economic growth in Jordan is still deeply tied to fossil fuels, and without bold action, the transition to renewables will remain too slow. To change this, we need policies that push for a cleaner energy mix, ensure economic stability, and make sustainable choices easier for businesses and households.

• -. Policy Implications:

To facilitate this transition, policymakers in Jordan should build upon existing initiatives and consider the following strategies:

1. Expanding financial incentives such as subsidies, tax exemptions, and low-interest loans for households and businesses investing in renewable energy. The Baynouna Solar Power Plant, Jordan’s largest photovoltaic project, demonstrates the success of large-scale solar investments, but further incentives are needed to encourage small and medium-sized enterprises (SMEs) and residential consumers to follow suit.

2. Jordan must modernize its electricity grid to accommodate higher shares of renewable energy while enhancing energy storage capacity. The recently announced Mujib Dam Electricity Storage Project represents a critical step toward integrating storage solutions, ensuring grid stability, and addressing intermittency challenges in renewable power generation. Scaling up similar projects will be essential for energy resilience.

3. Promoting decentralized energy solutions, including community-based renewable energy initiatives. The Sahara Forest Project in Aqaba serves as an example of how integrating solar energy with sustainable agriculture can create local employment opportunities while supporting the broader energy transition. Expanding such decentralized energy models would enhance energy security and community participation.

4. Implementing stricter efficiency standards for buildings, appliances, and industrial processes, alongside public awareness campaigns on energy-saving behaviors. The recent Energy Efficiency in Public Buildings initiative, which improved energy use in over 200 government buildings, is a successful model that could be expanded to private-sector buildings, schools, and hospitals to achieve broader efficiency gains.

5. Streamlining permitting processes and ensuring transparent, long-term policies to attract private investments. The Renewable Energy Integration System introduced in 2024 is a positive step in improving grid connectivity for renewable energy projects. Policymakers should continue refining this system to facilitate the continuous adoption of renewables and ensure fair access to energy markets.

• -. Future Research Directions:

While this study provides valuable insights, further research is needed to explore:

1. The socio-economic impacts of transitioning to renewable energy, particularly on vulnerable populations.

2. The effectiveness of existing energy policies in incentivizing behavioral changes toward sustainable energy consumption.

3. Comparative analyses with other countries in the region to identify best practices for energy transition.

4. The role of digitalization and smart technologies in optimizing energy use and efficiency.

6. Study Limitations

During the preparation and implementation of this study, we encountered two notable limitations. First, the availability and timeliness of data in Jordan posed a significant challenge. Existing datasets were limited in scope and infrequently updated, which restricted our ability to conduct a more comprehensive, in-depth analysis. Second, the rapidly evolving nature of Jordan’s energy sector was complicated by a lack of clearly defined, consistent policies, which made it difficult to establish long-term trends or predict future developments with accuracy.

Author Contributions

Conceptualization, M.M.J. and T.S.; methodology, M.M.J.; software, M.M.J.; validation, M.M.J. and T.S.; formal analysis, M.M.J.; investigation, M.M.J., T.S., A.R.E.-N. and S.A.A.; resources, M.M.J., T.S., A.R.E.-N. and S.A.A.; data curation, M.M.J.; writing—original draft preparation, M.M.J. and T.S.; writing—review and editing, M.M.J., T.S., A.R.E.-N. and S.A.A.; visualization, M.M.J.; supervision, T.S.; project administration, T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data used in this research are available from the official sources referred to in the article.

Acknowledgments

The authors would like to acknowledge that the paper is based on a Ph.D. dissertation prepared by the lead author—Jaber, M., Towards Better Understanding of Energy Poverty in Jordan: A Multidimensional Phenomenon, 2024, University of Miskolc, Ph.D. degree.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figure and Tables

Figure 1. The Toda Yamamoto non-Granger causality test using EViews 10 (IHS Global Inc., Irvine, CA, USA). Source: Own compilation.

Table 1

Main results of the studies focusing on energy and economic growth in Jordan.

Authors	Year	Method	Country (Study Area)	Main Findings
[31]	2014	ARDL model	17 Arab countries	Neutrality hypothesis supported in 16 out of 17 countries, including Jordan, suggesting that energy conservation does not hinder economic growth.
[32]	2014	Causal relationship analysis	Jordan	Long-term causal relationship observed from GDP to energy consumption.
[33]	2018	Multivariate time series model	Jordan	GDP, urbanization, economy’s structure, and aggregate water consumption positively related to electricity consumption, while electricity prices negatively linked to consumption.
[35]	2019	Global Moran’s I analysis	Amman, Jordan	Inverse relationship observed between energy consumption patterns and factors such as population distribution, family size, and building characteristics. Small families in certain parts of the city consume more energy relative to their income and household size.
[37]	2022	Johansen co-integration test, Cobb-Douglas production function, dynamic ordinary least squares	Jordan	Renewable energy consumption, labor, and capital have a significant positive relationship with economic growth in Jordan. Recommended policies include increasing investment in renewable energy.

Source: own compilation.

Table 2

Definition and sources of the data used in the causality and path analysis.

Abbreviation	Indicator	Source
EC	Primary energy consumption (TWh)	Our World in Data Database
GDP	Gross domestic product (GDP) in constant prices US$ for the year 2015	World Bank
GHG	Annual greenhouse gas emissions, including land-use change and forestry, are measured in carbon dioxide equivalents (CO2e).	Our World in Data Database

Source: own compilation.

Table 3

Descriptive analysis of the variables used in the Granger causality test and the path analysis.

	GHG	GDP	EC
Mean	20,717,088	2.33 × 1010	69.59
Median	19,153,315	1.89 × 1010	65.03
Maximum	33,701,188	4.29 × 1010	120.45
Minimum	6,312,421	9.56 × 109	22.81
Std. Dev.	8,451,672	1.13 × 1010	29.32
Skewness	0.05	0.46	0.18
Kurtosis	1.73	1.62	1.79
Jarque-Bera	2.82	4.79	2.81
Probability	0.24	0.09	0.25
Sum	8.70 × 108	9.79 × 1011	2922.62
Sum Sq. Dev.	2.93 × 1015	5.25 × 1021	35,242.96
Observations	42	42	42

Source: own calculations.

Table 4

ADF unit root test results.

	Maximum Lag(AIC)	ADF Unit Root TestIntercept		ADF Unit Root TestIntercept and Trend
		I (0)	I (1)	I (0)	I (1)
lnEC	3	−2.985(0.045) *	−6.084(0.000) **	−2.197(0.479)	−6.672(0.000) **
lnGDP	3	0.191(0.969)	−5.351(0.000) **	−2.900(0.206)	−5.271(0.001) **
lnGHG	3	−3.018(0.041) *	−2.364(0.392)	−6.489(0.000) **	−7.049(0.000) **

Note: Values in ( ) are p-values, whereas * and ** are the 5% and 1% significance levels, respectively. Source: own calculations.

Table 5

PP unit root test results.

	PP Unit Root TestIntercept		PP Unit Root TestIntercept and Trend
	I (0)	I (1)	I (0)	I (1)
lnEC	−3.027(0.041) *	−2.261(0.445)	−6.094(0.000) **	−6.672(0.000) **
lnGDP	−0.561(0.868)	−5.359(0.000) **	−1.510(0.810)	−5.276(0.001) **
lnGHG	−3.248(0.024) *	−2.387(0.381)	−6.516(0.000) **	−7.932(0.000) **

Note: Values in ( ) are p-values, whereas * and ** are the 5% and 1% significance levels, respectively. Source: own calculations.

Table 6

VAR Lag Order Selection Criteria.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	156.0723	NA	7.82 × 10−8	−7.84986	−7.7219	−7.80395
1	300.449	259.1377	7.57 × 10−11	−14.7923	−14.28039 *	−14.60861 *
2	305.5309	8.339446	9.35 × 10−11	−14.5913	−13.6956	−14.2699
3	322.2799	24.90883 *	6.43 × 10−11 *	−14.98871 *	−13.7091	−14.5296

* Indicates lag order selected by the criterion. LR: sequential modified LR test statistic (each test at 5% level). FPE: Final prediction error. AIC: Akaike information criterion. SC: Schwarz information criterion. HQ: Hannan-Quinn information criterion. Source: own calculations.

Table 7

Toda-Yamamoto causality test results.

Causal Direction	Wald Statistic	p-Value	Causality?
EC → GHG	7.24	0.0071	Yes (at 5% level)
GDP → GHG	5.83	0.0158	Yes (at 5% level)
GHG → EC	2.14	0.1432	No
GHG → GDP	3.11	0.0785	Yes (at 10% level)
EC → GDP	6.52	0.0111	Yes (at 5% level)
GDP → EC	4.98	0.0265	Yes (at 5% level)

Source: own calculations.

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