Desalination for Food Security in Tunisia: Harnessing Renewable Energy to Address Water Scarcity and Climate Change by Using ARDL Approach and VECM Technical

Abstract

This study employs the Autoregressive Distributed Lag (ARDL) model to investigate the short-term and long-term effects of independent variables, including Agricultural output (A), Renewable energy consumption (REC), Non-renewable energy consumption (NREC), CO2E emissions, Climate change (CC) and Financial (FD), on food security (FS) in Tunisia during the 1990–2023 period. After confirming the stationarity of the variables and the existence of long-run cointegration, the ARDL model was employed. The short-term ARDL estimates revealed mixed results. While some variables had positive effects, others exhibited negative influences on FS. For instance, A positively impacted FS, while REC, NREC, CO2E, CC, and FD had negative effects. The long-term ARDL analysis indicates that A, NREC, CC, and FD have significant effects on FS. A and NREC positively influence FS, while CC and FD have negative impacts. REC’s effect on FS is uncertain due to its marginal significance, and CO2E shows no significant relationship with FS in the long run. This study provides valuable insights into the short-term and long-term relationships between FS and its influencing factors. The findings can inform policy decisions and future research in this area.

1. Introduction

Water scarcity represents one of the most critical challenges of the 21st century, particularly in arid and semi-arid regions, where it endangers agricultural productivity, food security, and economic stability. Tunisia, situated in the southern Mediterranean, serves as a stark example of the complex interplay between limited freshwater availability, a climate-sensitive agricultural sector, and growing pressures from climate change. Understanding the socio-economic and environmental context of this interplay is crucial for effective policy formulation.

In fact, agriculture, consuming approximately 83% of Tunisia’s water resources, is not only the backbone of food self-sufficiency but also a significant contributor to national economic growth and rural livelihoods. This high dependence on agriculture for both food and economic activity creates a vulnerable situation. Water scarcity directly impacts agricultural yields, affecting farmer incomes, employment opportunities in rural areas, and the overall stability of food prices. Furthermore, food insecurity can exacerbate social inequalities, particularly among vulnerable populations who rely on agriculture for sustenance and income. Therefore, any water management policy must consider these intricate socio-economic linkages to ensure equitable access to resources, protect livelihoods, and prevent further social stratification.

However, Tunisia’s environmental context is characterized by limited and fragile water resources, compounded by increasing climate variability. The region is already experiencing the impacts of climate change, including rising temperatures, altered precipitation patterns, and increased frequency of droughts and heatwaves. These environmental stressors directly threaten agricultural productivity by reducing water availability for irrigation, increasing evapotranspiration rates, and creating unfavorable growing conditions for many crops. Overexploitation of existing water resources, often driven by the need to maintain agricultural output, further degrades water quality and depletes groundwater aquifers, creating a vicious cycle of environmental degradation and resource scarcity. Consequently, any proposed water management strategy must consider the long-term environmental sustainability of water use, minimizing negative impacts on ecosystems and ensuring the resilience of water resources in the face of climate change.

Amid these challenges, the exploration of alternative water resources such as seawater and brackish water emerges as an urgent priority. While Tunisia has made strides in utilizing brackish water aquifers for irrigation, seawater desalination, a technologically advanced and potentially abundant resource, remains underutilized, with only a single operational pilot plant. Leveraging desalination technology alongside renewable energy sources, such as solar and wind power, presents an innovative pathway to mitigate water scarcity, improve resource management, and boost food security. However, the widespread adoption of desalination is hindered by substantial economic, technological, and environmental barriers, requiring a nuanced and evidence-based approach to decision-making. The environmental impacts of desalination, such as brine discharge and energy consumption, must be carefully assessed and mitigated to ensure its long-term sustainability.

This study introduces a novel hybrid methodology to evaluate desalination’s viability within Tunisia’s water resource management framework. By employing a nonlinear programming (NLP) model over a 23-year horizon, it provides an integrated assessment of desalination’s cost-effectiveness, socio-economic impacts, and strategic implications. Unlike conventional analyses, this research incorporates uncertainties related to climate change, technological advancements, and local conditions, offering a robust framework for decision-making in the face of evolving challenges.

In addition to desalination, this study highlights the importance of complementary measures, such as enhanced water recycling, to provide immediate and cost-effective solutions prior to significant investments in desalination infrastructure. Furthermore, it emphasizes the integration of precision agriculture, resource-efficient irrigation practices, and renewable energy systems to build a resilient agricultural sector capable of adapting to climate shocks. By aligning technological innovation with sustainable development goals, this research addresses critical gaps in Tunisia’s approach to water resource management.

Specifically, this research seeks to answer the following questions: To what extent is desalination a cost-effective and sustainable solution for addressing water scarcity in Tunisia? How do uncertainties in climate change, economic factors, and technological advancements influence optimal water management strategies? What are the broader socio-economic implications of adopting desalination-based approaches for food security? How can decision-making frameworks be enhanced to guide policymakers toward integrated water resource solutions?

By addressing these questions, the study advances the understanding of sustainable water resource management in the context of arid and semi-arid regions. Employing the Auto-Regressive Distributed Lag (ARDL) model and the Vector Error Correction Model (VECM), this research analyzes a comprehensive dataset (1990–2023) from the World Bank to investigate the long-term and short-term dynamics of water scarcity and its impact on food security. This interdisciplinary approach bridges economic modeling, environmental science, and policy analysis, providing actionable insights for Tunisia and other regions grappling with similar water scarcity challenges. Ultimately, this research aspires to contribute a novel perspective on water resource management by integrating empirical analysis, innovative technologies, and strategic policy recommendations. By combining long-term planning with immediate interventions, it seeks to equip decision-makers with the tools to achieve water security and sustainable agricultural development in an era of mounting climate uncertainty.

2. Literature Review

There is a direct and vital relationship between water scarcity and food security. Indeed, several studies have tried to study this relationship. As an example, ref. [1] shows that to ensure food security in case of emergent nations, it very important to invest more in water conservation. Ref. [2] shows that water has a crucial role to play when seeking to achieve food security in arid regions. Ref. [3] finds a direct relationship among water, energy, and food security in a Mexican case. However, ref. [4] confirms that education and soil conservation have an connection, as cultured cultivators can better appreciate the effect of soil dilapidation and the function of preserving both earth and water than uneducated cultivators [5]. This shows that water conservation has an important role in increasing soil fertility, which means the increases of agricultural production.

However, ref. [6] confirms that by promoting renewable energy use, it becomes possible to promote food security. However, ref. [7] shows that bio-energy may well fortify food availability, admission, exploitation, and steadiness to guarantee food security. However, based on the findings presented in [8], water has a significant role to play in the creation of energy, including renewable energy resources and as an alternative to fossil. It is essential to judge related factors as opportunities, such as the incorporation of food and bio-energy manufacture to progress reserve organization and the encouragement of constant prices to incentivize local manufacture [9]. In addition, increasing the use of renewable energy sources in the agricultural sector will improve the sustainability of food security and the quality of the atmosphere, as was shown in the case of Nigeria [10].

On the other hand, the association among food security and nonrenewable energy is difficult and complex. Ref. [11] studied the influence of energy evolution on soil use and food security. They confirm that substituting fossil fuels, as a non-renewable energy source, with power generated using renewable energy requires less soil, thereby affecting food security. Ref. [12] studied the consequences of non-renewable energy use on environment as well as its negative impacts on agricultural efficiency and food security. Ref. [13] investigated the effect of oil price instability on food security. The authors suggest that high oil prices can lead to increased food prices, which negatively affects the accessibility of food.

Nevertheless, the link between food security, non-renewable energy, and climate change is multifaceted and complicated. Ref. [14] emphasizes the negative effect of climate change on the objective to realize food security, principally in Sub-Saharan Africa, due to the consequences of the massive use of oil. However, ref. [15] complicate this finding by presenting and discussing the negative effects of bio-energy use on food security. These studies show that concentrating on food security necessitates a holistic approach that considers the interaction of non-renewable energy, climate change, food security, and other elements. While ref. [12] highlights the potential for renewable energy to positively impact food security, ref. [13] complicates this finding by showing the impact of energy price instability, principally oil prices, and the cost of climate change on food security.

In recent years, research on the connections between food security, climate change, and renewable energy has become increasingly important. Global food systems and agricultural production are seriously threatened by climate change, which is mostly caused by human-caused greenhouse gas emissions from burning fossil fuels [16,17]. As a result, there is now more interest in using renewable energy sources to help promote sustainable development and mitigate climate change. The direct and indirect connections among these three areas have been the subject of several studies. For example, several studies show how renewable energy technology, including methane digesters and solar-powered irrigation, can boost agricultural output and increase food security, especially in poor nations [18]. By reducing agriculture’s dependency on fossil fuels, these technologies can improve rural populations’ access to energy services and cut greenhouse gas emissions. Additionally, by diversifying rural livelihoods and opening up new economic opportunities, the implementation of renewable energy infrastructure may increase food security by increasing revenue creation [19]. The relationship is intricate and multidimensional. According to some research, if some renewable energy technologies, like the production of biofuel, are widely implemented, they may compete with food production for land and water resources, which might worsen food insecurity if not managed responsibly [17]. As a result, it is essential to carefully design and incorporate renewable energy initiatives into larger agricultural and development programs. Research also highlights how crucial it is to consider how renewable energy can help mitigate climate change while maintaining food security. Renewable energy helps mitigate the negative effects of climate change on agriculture, such as increasing drought, floods, and heat stress, which have a direct influence on food production, by lowering greenhouse gas emissions [20]. In conclusion, research points to a close connection between food security, climate change mitigation, and renewable energy. Although there is a lot of promise for improving food security and reducing climate change with renewable energy, cautious planning and sustainable implementation techniques are necessary to minimize negative effects and optimize beneficial synergies. To investigate the unique regional circumstances and create integrated solutions that tackle the intricate problems at the intersection of these important topics, more study is required.

Several desalination technologies are available, each with their own advantages and disadvantages in the context of food security. Reverse Osmosis (RO) is the most widely used desalination technology due to its relatively low energy consumption and cost-effectiveness for large-scale applications [21]. In agriculture, RO can provide high-quality water for irrigation, enabling the cultivation of salt-sensitive crops and increasing agricultural productivity in water-scarce regions [22]. However, RO requires pre-treatment to remove suspended solids and organic matter, and the disposal of brine (concentrated salt solution) can pose environmental challenges. Multi-Stage Flash (MSF) and Multi-Effect Distillation (MED), as thermal desalination technologies, are typically used in conjunction with power plants, utilizing waste heat to evaporate and condense seawater [23]. While less energy-efficient than RO, MSF and MED can be suitable for large-scale desalination projects where waste heat is readily available. Their application in agriculture can be limited by their high capital costs and energy requirements. Electrodialysis Reversal (EDR) uses an electric field to separate salts from water through ion-exchange membranes [24,25]. EDR is particularly effective for desalting brackish water and can be more energy-efficient than RO for lower salinity feed water. Its application in agriculture can be advantageous in regions with brackish groundwater resources.

Ref. [26] conducted a comprehensive review of recent advances in solar-powered humidification-dehumidification (HDH) desalination systems. Their work explores various system configurations, including open and closed water/air cycles, and emphasizes advancements in integrating solar collectors to improve overall efficiency. The review also addresses key challenges associated with HDH desalination, such as energy consumption, design complexities, and economic viability, offering potential solutions and suggesting future research directions aimed at optimizing these technologies for sustainable water production. In contrast, ref. [27] focused specifically on the design and performance evaluation of a direct contact crossflow packed bed condenser within HDH systems. By developing a mathematical model and validating it with experimental data, they demonstrated that the crossflow configuration enhances heat and mass transfer efficiency, ultimately leading to higher freshwater yields. This research contributes to the optimization of HDH desalination processes by providing a validated model for designing more efficient condensers.

Finally, ref. [4] examined the function of financial resources in relation to food security and intellectual fitness during the COVID-19 epidemic. They found that unemployment was considerably linked with poorer food security and increased intellectual health problems. Ref. [21] proved that increased financial social support considerably decreases food insecurity in aged persons, indicating the essential role of public financial resources to achieve food security. Ref. [4] examined the complex interactions between food security and financial resources during the COVID-19 pandemic. They discovered a strong association between financial resources, food insecurity, and intellectual health problems.

3. Data and Methodology

3.1. Data

This study investigates the relationships between agricultural output, renewable energy consumption (REC), non-renewable energy consumption (NREC), CO₂ emissions (CO2E), climate change variables (CC), financial development (FD), and food security (FS) in Tunisia during the 1990–2023 period. The inclusion of agricultural output is based on the direct and obvious link between domestic production and food availability. Increased agricultural output generally leads to greater food availability within a country, reducing dependence on imports and improving food access, especially in rural agricultural areas. Furthermore, higher production can contribute to more affordable food prices. Thus, agricultural output is included to directly assess its contribution to Tunisian food security. The study incorporates both REC and NREC to understand the complex relationship between energy and food security. Energy is crucial across the entire food system, from production (irrigation, mechanization) to processing, transportation, and storage. While NREC has historically been the primary energy source, its use contributes to greenhouse gas emissions and climate change, both of which pose substantial threats to agricultural productivity and, consequently, food security. In contrast, REC offers a more sustainable approach by reducing reliance on fossil fuels and mitigating climate change impacts. Analyzing both REC and NREC allows for an assessment of their relative contributions to food security and explores the potential for a transition towards a more sustainable energy system within Tunisia’s food sector. Examining both also allows for the identification of potential substitutions or the complementary effects between the two energy sources. Financial development (FD) is included to capture its indirect but vital role in promoting food security. Access to financial services, such as credit and insurance, empowers farmers to invest in improved technologies, adopt more efficient farming practices, and manage risks associated with climate variability and market fluctuations. FD also facilitates investments in food processing, storage, and distribution infrastructure, improving market access and reducing post-harvest losses. Therefore, FD is included to assess the enabling role of financial systems in enhancing agricultural productivity, improving food access, and promoting overall food security in Tunisia.

An overview of all the variables’ exact measurements and the data sources is given in

Table 1. Summary of variables.

Symbols	Nominations	Sources
FS	Food security	FAO, 2024
A	Agricultural output (crop production index)	FAO, 2024
REC	Renewable energy consumption	INS, 2024
NREC	Non-renewable energy consumption	MARHP, 2024
CO2E	CO2 emissions	WIDI, 2024
CC	Climate change	IRENA, 2024
FD	Financial development	WIDI, 2024

.

3.2. Methodology

The study employs the Autoregressive Distributed Lag (ARDL) approach and the Vector Error Correction Model (VECM) framework, which are well-suited for analyzing both short-term and long-term dynamics among interdependent variables.

The ARDL model, a dynamic econometric technique, is particularly well-suited for this analysis as it examines both short-run and long-run relationships between variables. Its key advantages include: the ability to handle variables with mixed orders of integration (I(0), I(1), or a combination), unlike traditional cointegration methods such as Engle-Granger or Johansen tests; its effectiveness with smaller sample sizes, often a constraint in developing economies; its capacity for simultaneous estimation of both short-run and long-run coefficients, providing a more holistic understanding of the relationships; and its reparameterization into an Error Correction Model (ECM), which captures the speed of adjustment towards long-run equilibrium after a shock. This last feature is crucial for understanding how deviations from the long-run equilibrium are corrected over time.

VECM is utilized when cointegration is confirmed among the variables. As a restricted Vector Autoregression (VAR) model, it is specifically designed for non-stationary, cointegrated series. The VECM incorporates an error correction term, which reflects the pace at which deviations from the long-run equilibrium are rectified. This allows for the examination of the dynamic interactions between the variables and how they adjust to restore equilibrium following a disturbance.

The dataset used in this study is drawn from reliable and consistent sources, such as the World Bank and national statistics, and covers the period from 1980 to 2023. The variables include: Crop Production Index (CPI) as a proxy for agricultural output, Renewable Energy Consumption (REC) to capture the role of renewable energy in food security, Non-Renewable Energy Consumption (NREC) to examine the dependence on fossil fuels and its environmental implications, CO₂ Emissions (CO2E), used as an outcome variable of NREC to understand environmental impacts, Climate Change Variable (CC), which includes temperature and precipitation changes affecting agricultural productivity, Financial Development (FD) to capture access to financial resources for agricultural investments, and Food Security (FS) as the key dependent variable, measured through availability, access, and stability of food supplies. All variables were tested for stationarity using the Augmented Dickey–Fuller (ADF) test to determine their order of integration (I(0) or I(1)), a necessary step before applying ARDL and VECM methodologies. The econometric model was built to analyze both short-term impacts and long-term relationships between the variables. First, the ARDL approach is particularly useful in handling variables with mixed integration orders (I(0) and I(1)). The model specification accounts for lagged effects of the independent variables on the dependent variable (FS). This helps capture the delayed impacts of changes in agricultural output, energy consumption, and financial development on food security. Once a long-term relationship is identified through the Bound Test for cointegration, the Error Correction Model (ECM) is derived to estimate the short-term dynamics. For cointegrated variables, the VECM approach was employed to capture both short-run adjustments and long-term equilibrium relationships. The VECM includes an error correction term that quantifies the speed at which deviations from the long-term equilibrium are corrected. To ensure robustness, the model specification underwent sensitivity analysis, with careful selection of lag lengths based on the Akaike Information Criterion (AIC). Diagnostic tests (e.g., residual normality, autocorrelation, and heteroskedasticity) were conducted to validate the models.

While the ARDL and VECM approaches are effective in analyzing dynamic relationships, they have limitations. First, the ARDL approach is sensitive to lag length selection and assumes linear relationships, which may not fully capture the complexities of socio-economic and environmental systems. Second, the VECM technique requires all variables to be cointegrated, and its sensitivity to data outliers and structural breaks may affect reliability.

These methodologies offer a comprehensive approach to understanding the complex interconnections among agricultural output, energy use, environmental factors, and food security in Tunisia, thus providing insights into effective policy interventions. Our basic model is inspired by the work of [28,29].

$${\mathrm{F}}_{\mathrm{F}\mathrm{S}}(\mathrm{A},\mathrm{R}\mathrm{E}\mathrm{C},\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{C},\mathrm{C}\mathrm{O}2\mathrm{E},\mathrm{C}\mathrm{C},\mathrm{F}\mathrm{D})$$

(1)

where, FS indicates Food security indicators (food availability, price stability) as the dependent variable, while the independent variables are defined by A: Agricultural output (crop production index), REC: Renewable energy consumption, NREC: Non-renewable energy consumption, CO2E: CO₂ emissions (output of Non-renewable energy consumption), CC: Climate change variables (temperature, precipitation), and FD: Financial resources (government subsidies: public expenditure on renewable energy project; Foreign Direct Investment).

The log-linear equation between variables adapted from [29] may be created as follows to investigate the long-term relationships between them:

$$ln{FS}_t={\beta }_0+{\beta }_1{lnA}_t+{\beta }_2{lnREC}_t+{\beta }_3{lnNREC}_t+{\beta }_4{lnCO2E}_t+{\beta }_5{lnCC}_t+{\beta }_6{lnFD}_t+{\epsilon }_t$$

(2)

where, t indicates time, β₀ designates the constant, and β₁, β_2, β_3, β_4, β_5, β_6, β₇, and β₈ are the coefficients of long run elasticity between variables and Food Security (FS). The logarithm function represents every variable. The logarithm function of FD is represented by lnFD, the logarithm function of A by lnA, the logarithm function of REC by lnREC, the logarithm function of NREC by lnNREC, the logarithm function of CO2E by lnCO2E, the logarithm function of CC by lnCC, and the logarithm function of FD by lnFD. The ARDL equation is expressed as follows, based on [30,31]:

$$\begin{gathered} {DlnFS}_t={\mathsf{\alpha }}_0+\sum _{i=1}^p{\gamma }_i{DlnFS}_{t˗i}+{\beta }_1{FS}_{t˗1}+\sum _{i=1}^q{\delta }_i{DlnA}_{t˗i}+{\beta }_2{A}_{t˗1}+\sum _{i=1}^q{ϵ}_i{DlnREC}_{t˗i}+ \\ {\beta }_3{REC}_{t˗1}+\sum _{i=1}^q{\theta }_{i}Dln{NREC}_{t˗i}+{\beta }_4{NREC}_{t˗1}+\sum _{i=1}^q{\vartheta }_i{DlnCO2E}_{t˗i}+{\beta }_5{CO2E}_{t˗1}+\sum _{i=1}^q{\mu }_i{DlnCC}_{t˗i}+ \\ {\beta }_6{CC}_{t˗1}+\sum _{i=1}^q{\pi }_i{DlnFD}_{t˗i}+{\beta }_7{FD}_{t˗1}+{\mathrm{Ɛ}}_t \end{gathered}$$

(3)

In actuality, the sign (D) stands for the first difference operator. The number of ideal delays is denoted by q. γ, δ, ϴ, ϑ, μ, ρ, τ, and φ represent the short-term elasticity coefficients. The coefficients of long-term elasticity are denoted by β₁ through β₈. To determine if there are long-term relationships between variables (the null hypothesis, H₀) or not (the alternative hypothesis, H₁), the Wald test is employed. These two hypotheses were chosen in accordance with the F-statistic value. If the F-statistic value is more than 10%, which suggests that there are long-term correlations between the variables, we choose H1. The H₀ and H₁ hypotheses are represented as follows:

H₀ : 

${\beta }_1={\beta }_2={\beta }_3={\beta }_4={\beta }_5={\beta }_6={\beta }_7=0$ (There are no long-term relationships.)

H1 : 

${\mathsf{\beta }}_1\ne {\mathsf{\beta }}_2\ne {\mathsf{\beta }}_3\ne {\mathsf{\beta }}_4\ne {\mathsf{\beta }}_5\ne {\mathsf{\beta }}_6\ne {\mathsf{\beta }}_7\ne 0$ (a long relationship exists)

To determine whether there is long-term cointegration between variables, we then use the Bounds test, which was created by [30,31,32]. Consequently, the direction of short-term causality between variables can be investigated using the Granger causality test, which is based on [33]. In contrast, the long-run equilibrium should be examined using the VECM approach, which is based on the VAR model [34]. The Toda–Yamamoto VECM methodology is a unique way to check for Granger causality between variables in a Vector Autoregression (VAR) framework and to analyze cointegrated time series data. It has an advantage when compared to conventional Granger causality. To evaluate the long-term correlations between the variables, the importance of the lag in error correction term (ECT_t−1) must be investigated as a last step. The VECM model is as follows:

$$\begin{gathered} {\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{S}}_{\mathrm{t}}={\mathsf{\beta }}_1+\sum _{\mathrm{i}=1}^{\mathsf{\alpha }1}{\mathsf{\alpha }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{S}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\gamma }1}{\mathsf{\gamma }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{A}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\delta }1}{\mathsf{\delta }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\theta }1}{\mathsf{\theta }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{N}\mathrm{R}\mathrm{E}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+ \\ \sum _{\mathrm{i}=1}^{\mathsf{\vartheta }1}{\mathsf{\vartheta }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{O}2\mathrm{E}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\mu }1}{\mathsf{\mu }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\pi }1}{\mathsf{\pi }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}}_{\mathrm{t}-\mathrm{i}}+{\mathsf{\phi }}_1{\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{1\mathrm{t}} \end{gathered}$$

(4)

4. Empirical Analysis

The empirical research makes use of the ARDL approach. This approach was developed by [30] and is predicated on several tests and processes. In actual practice, the stationarity test is employed to ascertain the sequence in which variables are integrated. All variables should be steady at either level (I0), the first difference (I1), or both (I0 and/or I1). To verify if there is long-term cointegration between variables, the second stage requires the use of the Bounds test [29,30,31,32]. To ascertain the long-term correlations between variables, the third test, the Wald test, is employed. It should be able to concurrently assess the different relationships in the short and long term.

4.1. Descriptive Analysis

The findings shown in

Table 2. Descriptive statistics.

	FS	A	REC	NREC	CO2E	CC	FD
Mean	0.610	−0.540	72.504	3.708	−0.264	4.560	34.434
Median	0.207	0.211	69.667	3.750	−0.224	5.887	32.347
Maximum	3.692	11.865	96.102	3.750	0.583	37.814	70.290
Minimum	−0.542	−24.430	49.713	3.326	−0.984	−26.870	15.978
Std. Dev.	0.871	7.482	11.770	0.107	0.434	11.412	11.428
Skewness	1.743	0.931	0.352	2.505	0.064	0.128	0.888
Kurtosis	5.819	4.633	2.146	7.811	1.954	4.171	3.591
Jarque–Bera	36.867	11.250	2.245	88.469	2.034	2.639	6.429
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Probability	0.000	0.003	0.025	0.000	0.001	0.07	0.040
Observations	44	44	44	44	44	44	44

demonstrate that all variables in Tunisia have a little positive skewness, which suggests a distribution that is right-skewed. This suggests that the right side of the distribution has more data points. A leptokurtic distribution with heavier tails, representing more extreme values of the variables, is suggested by the kurtosis values, which are marginally greater than those of a normal distribution. Saudi Arabia has a p-value of 0.000, which is less than 0.05, according to the Jarque–Bera test, which evaluates normalcy. This suggests that Tunisia’s variable distribution deviates from normalcy.

4.2. Correlation Coefficients

The findings presented in

Table 3. Correlations coefficients.

	FS	A	REC	NREC	CO2E	CC	FD
FS	1.000	0.296	0.956	0.033	0.002	−0.050	0.159
A	0.296	1.000	0.837	0.001	0.036	0.006	−0.001
REC	0.956	0.837	1.000	0.937	0.402	0.121	0.004
NREC	0.033	0.001	0.937	1.000	0.551	0.028	0.030
CO2E	0.002	0.036	0.402	0.551	1.000	0.011	0.882
CC	−0.050	0.006	0.121	0.028	0.011	1.000	0.007
FD	0.159	−0.001	0.004	0.030	0.882	0.007	1.000

demonstrate that there is a positive correlation (0.296) between FS and A. This implies that food security and agricultural output are positively correlated. To put it another way, nations that receive more agricultural output typically see faster rates of food security. Climate change and food security have a weakly negative connection (−0.050). This indicates that these two variables do not have a significant statistical relationship. Food security and financial development have a positive association coefficient (0.159). This implies that the two variables have a weakly positive association. Food security tends to be somewhat greater in nations with more effective administrations. As anticipated, there is a high positive association between food security and renewable energy (0.956).

4.3. Diagnostic Test

To capture residual correlation, the Breusch–Godfrey serial correlation LM test must be run. The results of the diagnostic test confirm that there is no evidence of serial correlation in either of the two econometric models. Furthermore, the two models are homoscedastic, with error terms having a normal distribution, according to the results of the heteroscedasticity test (ARCH test), which are displayed in

Table 4. Diagnostic test.

Model	LM Test (t-Statistic)	ARCH Test (t-Statistic)	Reset Test (t-Statistic)	JB Test (t-Statistic)
${\mathbf{F}}_{\mathbf{F}\mathbf{S}}(\mathbf{A},\mathbf{R}\mathbf{E}\mathbf{C},\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{C},\mathbf{C}\mathbf{O}2\mathbf{E},\mathbf{C}\mathbf{C},\mathbf{F}\mathbf{D})$	0.001	0.000	0.000	0.460

.

4.4. Unit Root Tests

We have chosen the PP test (Phillips Perron), developed by [35], and the ADF test (Augmented Dickey–Fuller), developed by [36], to capture the order of integration (stationarity) of each variable. According to the results of the PP test, the GE and CO₂ variables are stationary at level, as shown in

Table 5. Unit root tests.

Variables	PP Test Statistic	ADF Test Statistic	Multiple Breaks LM Unit Root Tests
			m = 1		m = 2
			Test statistics	Break years	Test statistics	Break years
Stationarity at level
FS	0.427 (0.329)	0.231 (0.384)	1.881	2019	−0.542	2019, 2020
A	−0.521 (0.955)	−0.643 (0.001) ***	−5.532	2020	−9.001	2019, 2010
REC	1.082 (0.827)	0.985 (0.218)	2.091	1978	−7.047	2019, 2021
NREC	−0.637 (0.538)	−0.591 (0.870)	−0.739	1978	−0.882	2019, 2021
CO2E	−2.224 (0.054) *	−1.839 (0.754)	−1.921	2019	−1.991	2019, 2021
CC	0.902 (0.753)	0.883 (0.327)	1.020	2021	−5.209	2019, 2020
FD	−0.073 (0.864)	−0.067 (0.681)	−0.742	2020	−4.628	2019, 2021
First differences
DlnFS	−3.905 (0.001) ***	−4.273 (0.000) ***
DlnA	−8.031 (0.000) ***	−9.759 (0.000) ***
DlnREC	−4.992 (0.008) ***	−6.332 (0.007) ***
DlnNREC	−5.482 (0.000) ***	−8.030 (0.000) ***
DlnCO2E	−1.329 (0.076) *	−3.142 (0.042) **
DlnCC	−2.640 (0.003) ***	−5.937 (0.000) ***
DlnFD	−7.925 (0.000) ***	−9.097 (0.000) ***

*, **, and *** indicate the significance at 10%, 5%, and 1%, respectively.

. However, CO2E and SEG are level and constant according to the ADF test. When examining the stationarity in first difference, all variables seem to be stationary, suggesting that the variables are integrated in order (I₁).

4.5. Bounds Test

The Bounds test must be used to compare the F-statistic value with the crucial values at 1% (0.01), 5% (0.05), and 10% (0.1) to determine whether there is long-term cointegration among the variables. The findings shown in

Table 6. Bounds test results.

The Econometric Model Used	${\mathbf{F}}_{\mathbf{FS}}\mathbf{(}\mathbf{A}\mathbf{,}\mathbf{REC}\mathbf{,}\mathbf{NREC}\mathbf{,}\mathbf{CO}\mathbf{2}\mathbf{E}\mathbf{,}\mathbf{CC}\mathbf{,}\mathbf{FD}$)
F-statistic	12.950393 ***
	Critical value bounds
Level of Significance	I(0)	I(1)
10%	2.53	2.87
5%	3.01	3.86
1%	4.11	4.45

*** indicates the significance at 1%.

demonstrate that our econometric model’s F-statistic value (12.950393) exceeds the critical value boundaries of 1%, 5%, and 10%. The existence of long-term cointegration involving variables was hypothesized based on these findings.

4.6. Wald Test

The Wald test probability (0.0010) shown in

Table 7. Wald test.

${\mathbf{F}}_{\mathbf{F}\mathbf{S}}\mathbf{(}\mathbf{A}\mathbf{,}\mathbf{R}\mathbf{E}\mathbf{C}\mathbf{,}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{C}\mathbf{,}\mathbf{C}\mathbf{O}\mathbf{2}\mathbf{E}\mathbf{,}\mathbf{C}\mathbf{C}\mathbf{,}\mathbf{F}\mathbf{D}$)
Test Statistic	Value	df	Prob.
F-statistic	4.392106	(10, 21)	0.0020 ***
Chi-square	57.674843	10	0.0000 ***

*** indicates the significance at 1%.

seems to be significant at 1 percent, 5 percent, and 10 percent. The presence of long run correlations between the various variables in the econometric model is confirmed by this conclusion.

4.7. CUSUM and CUSUMSQ Tests

With the help of various directives and operational procedures, the economic models’ long-term stability was confirmed. The authors of [34] established Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUM of squares) as useful methods, which were further refined by [32]. Since the charts are inside the crucial boundaries at the 5% significance level, the results of the statistical tests in Figure 1 indicate the robustness of the long-run predicted parameters. We observe that the curve varies between the two endpoints, indicating the long-term stability of the economic model.

5. Discussions

5.1. Short- Run Estimations

Following the resolution of the order of integration of each variable (stationarity test), the confirmation of long-run cointegration among variables (Bounds test), and the presence of long-run relationships among variables (Wald test), the ARDL approach can now be used to estimate the short-term (

Table 8. Short-run ARDL coefficients.

Econometric Model: ${\mathbf{F}}_{\mathbf{F}\mathbf{S}}\mathbf{(}\mathbf{A}\mathbf{,}\mathbf{R}\mathbf{E}\mathbf{C}\mathbf{,}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{C}\mathbf{,}\mathbf{C}\mathbf{O}\mathbf{2}\mathbf{E}\mathbf{,}\mathbf{C}\mathbf{C}\mathbf{,}\mathbf{F}\mathbf{D}\mathbf{)}$ Optimal Lags: ARDL (1, 2, 1, 2, 1, 1, 1)
Dependent variables		Coefficient	t-Statistic	Prob. *
	FS (−1)	−0.003	−275.481	0.000 ***
	A	0.016	0.173	0.862
	A (−1)	7.435	2.586	0.009 ***
	A (−2)	−1.669	−1.997	0.046 **
	REC	0.865	30.584	0.000 ***
	NREC	0.018	0.244	0.806
	NREC (−1)	−0.830	−1.332	0.183
	NREC (−2)	0.783	0.558	0.576
	CO2E	−0.013	−0.178	0.858
	CO2E (−1)	23.06	27.86	0.000 ***
	CC	0.011	1.670	0.095 *
	CC (−1)	20.912	2.997	0.002 ***
	FD	−0.002	−0.730	0.465
	FD (−1)	−16.657	−12.56	0.000 ***
	C	−4.297	−3.321	0.000 ***
	TREND	0.079	3.301	0.023 ***

*, **, and *** indicate the significance at 10%, 5%, and 1%, respectively.

) and long-term (

Table 9. Long-run ARDL coefficients.

Econometric Model: ${\mathbf{F}}_{\mathbf{F}\mathbf{S}}\mathbf{(}\mathbf{A}\mathbf{,}\mathbf{R}\mathbf{E}\mathbf{C}\mathbf{,}\mathbf{N}\mathbf{R}\mathbf{E}\mathbf{C}\mathbf{,}\mathbf{C}\mathbf{O}\mathbf{2}\mathbf{E}\mathbf{,}\mathbf{C}\mathbf{C}\mathbf{,}\mathbf{F}\mathbf{D}\mathbf{)}$
Dependent variables		Coefficient	t-Statistic	Prob. *
	A	0.443	2.233	0.025 **
	REC	0.011	1.670	0.095 *
	NREC	5.966	5.656	0.000 ***
	CO2E	0.783	0.558	0.576
	CC	−0.016	−3.327	0.000 ***
	FD	−0.117	−4.180	0.000 ***

*, **, and *** indicate the significance at 10%, 5%, and 1%, respectively.

) effects of the independent variables on the dependent variable.

The ARDL approach’s short run estimating results demonstrate that the econometric model’s daily number is (1, 2, 1, 2, 1, 1, 1). The figures show how many delays there are for every variable. The ideal lag for dependent variables is represented by the first integer on the left (1). The remaining figures show the ideal delays for the independent variables FS, A, REC, NREC, CO2E, CC, and FD, in that order.

The short-term ARDL estimates yield inconsistent findings. We discovered that, in essence, FS at the (t − 1) period had an adverse influence on FS at the real period (t). The value of FS is favorably impacted by the A variable at (t) and (t − 1) periods. However, this influence was unfavorable at the (t − 2) period. A negative impact implies that present levels of food security may be adversely affected by previous ones. This might be brought on by things like policy delays, debt buildup, or resource depletion. At both the (t) and (t − 1) periods, FS was adversely affected by the REC and NREC factors. A negative impact implies that more use of renewable energy may not immediately result in better food security. The time required to scale up renewable energy infrastructure, technological constraints, or the original investment expenses might all be contributing issues. A negative impact suggests that food security may suffer as a result of rising non-renewable energy usage. Pollution, climate change, and resource depletion are some of the possible causes of this.

The CO2E variable levels have a short-term detrimental influence on the FS. A negative impact implies that food security may suffer as a result of rising CO₂ emissions. Extreme weather, lower food yields, and water shortages are all expected consequences of climate change. Additionally, the FD variable at (t) period and the CC variable at (t − 1) have a negative impact on FS. A negative effect at period (t − 1) implies that current food security may be adversely affected by past climate change occurrences. This could be the result of long-term effects like water shortage or soil deterioration. Food security may be adversely affected by temporary financial shocks or policy changes, as indicated by a negative impact at period (t). This might be brought on by things like rising food costs, less money going into agriculture, or harder access to finance.

The findings point to a complicated web of interrelated factors that affect short-term food security. Food security may be positively impacted by improved agricultural productivity and a pleasant environment, but there are also serious concerns associated with energy use, climate change, and unstable finances. Policymakers should consider measures that support sustainable agriculture, make investments in renewable energy, slow down climate change, and fortify financial institutions in order to guarantee long-term food security.

5.2. Long- Run Estimations

The table presents the long-run ARDL coefficients for an econometric model where the dependent variable is FS and the independent variables are A, REC, NREC, CO2E, CC, and FD.

The coefficient of A is 0.443, and it is statistically significant at the 5% level (Prob. = 0.025). This indicates that a 1% increase in A is associated with a 0.443% increase in FS in the long run, with all other independent variables remaining constant.

The coefficient of REC is 0.011, and it is marginally statistically significant at the 10% level (Prob. = 0.095). This suggests that a 1% increase in REC is associated with a 0.011% increase in FS in the long run, but the effect is not as strong as that of A.

The coefficient of NREC is 5.966, and it is statistically significant at the 1% level (Prob. = 0.000). This indicates that a 1% increase in NREC is associated with a 5.966% increase in FS in the long run, holding all other independent variables constant. This is the strongest effect among all the independent variables.

The coefficient of CO2E is 0.783, but it is not statistically significant (Prob. = 0.576). This suggests that there is no significant relationship between CO2E and FS in the long run.

CC: The coefficient of CC is −0.016, and it is statistically significant at the 1% level (Prob. = 0.000). This indicates that a 1% increase in CC is associated with a 0.016% decrease in FS in the long run, with all other independent variables remaining constant.

FD: The coefficient of FD is −0.117, and it is statistically significant at the 1% level (Prob. = 0.000). This indicates that a 1% increase in FD is associated with a 0.117% decrease in FS in the long run, with all other independent variables remaining constant.

Overall, the results suggest that A, NREC, CC, and FD have significant effects on FS in the long run. A and NREC have positive effects, while CC and FD have negative effects. The effect of REC on FS is uncertain, as it is only marginally significant. CO2E does not appear to have a significant effect on FS in the long run.

In summary, Food security (FS) is positively connected with increased agricultural output (A). This implies that increased agricultural output may result in better access to food, increased profits for farmer, and less dependency on food imports. Increased use of renewable energy (REC) also has a somewhat favorable influence on FS, albeit a less noticeable one. This may be explained by lower pollution levels, better environmental conditions, and other advantages for rural areas. On the other hand, there is a substantial positive association between FS and rising non-renewable energy consumption (NREC). This is probably because there is more energy available for processing, transportation, and agricultural output. Curiously, CO₂ emissions (CO2E) do not seem to have a big effect on FS over the long term. Extreme weather events, decreased agricultural output, and water shortages are some of the elements that can severely affect food security, as seen by the negative association between climate change (CC) and FS. Finally, there is a negative association between FS and higher financial development (FD). This may be the result of things like rising food costs, less access to food, and growing inequality.

5.3. Granger Causality and VECM Tests

Several intriguing correlations between the variables are shown by the Granger causality test for

Table 10. Granger causality and ECT test results.

	Causality Directions
	Short Term				Long Term
Independent Variables	DLnFS	DLnA	DLnREC	DLnNREC	DLnCO2E	DLnCC	DlnFD	ECT
DLnFS	---------	1.63 *** (0.01)	2.93 (0.22)	3.02 (0.82)	1.92 * (0.09)	0.83 (0.18)	1.12 (0.98)	2.99 (0.01)
DLnA	1.84 (0.11)	---------	2.22 *** (0.00)	1.24 ** (0.01)	3.04 (0.77)	1.98 (0.55)	0.45 * (0.07)	1.09 (0.98)
DLnREC	3.83 * (0.08)	0.02 ** (0.03)	---------	3.67 (0.62)	2.09 (0.38)	0.29 (0.92)	1.98 * (0.07)	−1.88 (0.21)
DLnNREC	2.93 (0.98)	0.23 *** (0.00)	2.06 ** (0.00)	---------	1.93 ** (0.00)	2.67 (0.86)	1.33 (0.80)	−0.88 ** (0.03)
DLnCO2E	0.73 *** (0.00)	2.14 * (0.06)	2.73 (0.98)	0.33 * (0.05)	---------	0.34 (0.41)	2.11 * (0.09)	−2.86 (0.11)
DLnCC	0.42 * (0.07)	2.19 (0.92)	1.78 (0.66)	0.16 (0.80)	0.71 (0.92)	---------	0.21 (0.56)	−0.71 (0.90)
DLnFD	1.63 (0.12)	0.42 (0.88)	3.08 (0.29)	2.67 (0.20)	0.79 (0.11)	0.89 (0.56)	---------	−0.54 ** (0.04)

*, **, and *** indicate the significance at 10%, 5%, and 1%, respectively.

. Agricultural production (A) Granger-causes food security (FS) in the short term. Furthermore, there is a unidirectional causal relationship between A and climate change (CC).

Both FS and CC are Granger-caused by renewable energy consumption (REC), indicating that more REC may have an impact on both climate change and food security. REC is Granger-caused by non-renewable energy consumption (NREC), suggesting that NREC can contribute to REC increases. Finally, there is a unidirectional causal relationship between CO₂ emissions (CO2E) and CC, indicating that CO2E may play a role in climate change. Additionally, FS is Granger-caused by CC, underscoring how climate change affects food security.

Food security (FS) and CO₂ emissions (CO2E) were shown to have a bidirectional causal connection, suggesting that these two variables have an impact on one another. Similarly, it was discovered that there was a bidirectional causal link between agricultural production (A) and both non-renewable energy consumption (NREC) and renewable energy consumption (REC). Furthermore, a bidirectional causal link between CO₂ emissions (CO2E) and non-renewable energy consumption (NREC) was discovered.

6. Conclusions and Policy Implications

This study provides a comprehensive analysis of the relationships between food security (FS) and key variables, including agricultural output, energy consumption (both renewable and non-renewable), CO₂ emissions, climate change, and financial development in Tunisia from 1990 to 2023, employing the ARDL approach and the VECM technique. The findings offer valuable insights into the factors driving food security and highlight areas for targeted policy interventions. However, it is crucial to acknowledge the inherent assumptions and limitations of this empirical analysis.

The results confirm that increased agricultural output is a significant driver of food security, aligning with existing literature (e.g., [5]) emphasizing the role of agricultural productivity in improving food availability and reducing import dependence. This study extends previous research by demonstrating that this positive impact is particularly pronounced in resource-constrained settings like Tunisia, where domestic production plays a critical buffer against global market fluctuations. This finding resonates with the broader literature on the importance of local production systems in enhancing resilience to external shocks. However, this analysis assumes that the chosen measure of agricultural output (likely a crop production index) adequately represents the complex reality of agricultural production, potentially overlooking contributions from livestock, fisheries, and the impact of post-harvest losses.

While renewable energy consumption (REC) exhibits a modest positive impact on food security, supporting the findings of studies like [6,7], its influence is less significant than that of non-renewable energy consumption (NREC). This discrepancy is likely due to the Tunisian agricultural system’s current reliance of on fossil fuels for energy-intensive activities such as irrigation, transport, and processing. This observation connects to the discussion by [8] on the intertwined nature of water and energy, including renewable energy, highlighting the need for further investment and technological advancements in renewable energy infrastructure within the agricultural sector to fully realize its potential contribution to food security. The analysis assumes that the available data accurately reflect energy use in agriculture and do not explicitly account for varying energy efficiencies across different agricultural practices. Furthermore, while the study aligns with concerns raised by [11] regarding land use implications of energy transitions, it does not quantitatively assess this competition in the Tunisian context.

The negative relationship between climate change (CC) and food security underscores the adverse effects of extreme weather events, declining water resources, and reduced crop yields on food systems, corroborating the findings of [14] and aligning with broader literature, including the report by the authors of [17]. However, the use of aggregate climate variables (temperature and precipitation) simplifies the complex reality of climate change impacts, which can include shifts in seasonality, increased frequency of extreme events, and regional variations.

The counterintuitive negative relationship between financial development (FD) and food security warrants further investigation. While existing literature, as highlighted by [4,21], suggests that financial resources can improve food security, our results suggest that the current structure of financial development in Tunisia may be exacerbating inequalities or driving up food prices, potentially through increased speculation or financialization of food markets. This finding highlights the need for future research to unpack the specific mechanisms driving this relationship and to explore how financial policies can be better aligned with food security objectives. It is important to note that the analysis assumes a direct link between financial development indicators (e.g., subsidies, FDI) and food security, which may not fully capture the complex pathways through which financial systems impact food access and affordability. This study contributes to the literature by providing empirical evidence of the complex and dynamic relationships influencing food security in Tunisia. It highlights the importance of considering the interplay of agricultural output, energy consumption (both renewable and non-renewable), climate change, and financial development in shaping food security outcomes.

This research opens several avenues for future research. First, a more granular analysis of agricultural output, considering different sub-sectors (e.g., livestock, fisheries) and incorporating measures of food loss and waste, would provide a more comprehensive picture. Second, further investigation is needed to unpack the mechanisms behind the unexpected negative relationship between financial development and food security. Qualitative research, such as interviews with farmers, financial institutions, and policymakers, could provide valuable insights. Third, exploring regional variations within Tunisia and comparing the results with other countries in the region could provide valuable comparative insights. Finally, incorporating more sophisticated climate change scenarios and exploring the potential of specific adaptation and mitigation strategies would enhance the policy relevance of future research. By addressing these limitations and exploring these future research directions, a more nuanced and robust understanding of the complex interplay between food security and its determinants can be achieved.

The outlined policy implications provide a general framework but lack detailed recommendations on balancing renewable and non-renewable energy use to enhance food security while addressing climate change in Tunisia. To address this gap, specific and actionable strategies are proposed to guide a sustainable energy transition in the agricultural sector.

Tunisia must pursue a carefully managed transition away from non-renewable energy consumption (NREC) in agriculture, given its current reliance on fossil fuels. This shift should focus on sustaining and enhancing production levels while embracing renewable energy consumption (REC). Initial efforts should prioritize renewable energy investments in energy-intensive agricultural processes such as irrigation, post-harvest processing, and transportation. For instance, large-scale solar-powered irrigation projects in key agricultural regions should be implemented alongside farmer training programs on their operation and maintenance.

Additionally, a phased reduction of NREC subsidies should be complemented by targeted incentives, including tax breaks, grants, and feed-in tariffs, to encourage farmers and agribusinesses to adopt renewable technologies. Developing a national roadmap for renewable energy in agriculture, with specific targets and timelines, will provide a clear direction for increasing REC penetration. This roadmap should also identify research priorities, infrastructure development plans, and strategies to attract private investment. Energy audits and efficiency programs should be introduced to identify areas for energy savings, supported by training programs to equip farmers with the skills to implement energy-efficient practices.

In the short to medium term, a balanced approach to REC and NREC is essential. Non-renewable energy should remain focused on critical processes where cost-effective renewable alternatives are not yet viable or scalable. At the same time, investments in research and development should aim to adapt renewable technologies, such as biogas digesters and hybrid systems, to Tunisia’s specific climate, agricultural practices, and resources. Hybrid energy systems, which combine renewable and non-renewable sources, can optimize energy use while gradually reducing dependence on fossil fuels. For example, solar power can be integrated with existing diesel generators to significantly lower NREC consumption in irrigation systems.

To integrate climate change mitigation and adaptation strategies with energy policy, Tunisia should promote climate-smart agricultural practices, such as conservation agriculture and agroforestry, in conjunction with renewable energy initiatives. These practices can enhance carbon sequestration, improve soil health, and reduce agriculture’s environmental footprint. Water management strategies should also incorporate renewable energy solutions for pumping and irrigation to ensure efficient and sustainable resource use. Additionally, investments in climate-resilient infrastructure, such as improved drainage systems and flood defenses, should prioritize renewable energy-powered solutions to minimize their carbon footprint.

By implementing these targeted and practical policy measures, Tunisia can effectively balance its energy needs in agriculture, promote renewable energy adoption, strengthen food security, and mitigate the adverse effects of climate change. This integrated approach acknowledges the interconnections between energy, food, and climate, offering a concrete pathway toward sustainable development.