The Role of Agriculture in Shaping CO₂ in Saudi Arabia: A Comprehensive Analysis of Economic and Environmental Factors

Abstract

This research examines the critical issue of greenhouse gas emissions, focusing on carbon dioxide (CO₂) as a significant contributor to climate change and its threats to environmental sustainability. The primary objective of this paper is to highlight the environmental impacts resulting from economic growth, energy consumption, and agricultural development in Saudi Arabia. The purpose of the empirical research is to investigate the dynamic causal relationships between CO₂ emissions, agricultural development, economic growth, energy consumption, and additional control variables in Saudi Arabia from 1990 to 2022. It is hypothesised that increases in agricultural land, economic activity, and energy use contribute to rising CO₂ emissions. This study examines these relationships using the Autoregressive Distributed Lag (ARDL) and Fully Modified Ordinary Least Squares (FMOLS) methodologies, along with unit root tests, the ARDL bounds test, and Vector Error Correction Model (VECM) causality analysis, to assess both short-term and long-term interactions among the variables. The findings reveal that agricultural land expansion, economic growth, and energy consumption significantly contribute to increased CO₂ emissions. Specifically, a 1% increase in agricultural land correlated with a 0.16% rise in CO₂ emissions, while a 1% increase in economic growth and energy use led to 0.28% and 0.85% rises, respectively. These results underscore the environmental challenges posed by economic expansion and energy dependence. This paper emphasises the need for policies that balance economic growth with emissions reduction, in line with Saudi Vision 2030. Transitioning to a low-carbon, circular economy supported by renewable energy and innovation is essential for sustainable development and climate change mitigation.

1. Introduction

The atmospheric concentrations of greenhouse gases, primarily carbon dioxide (CO₂), that are mainly released from human activities, such as the consumption of fossil fuels and agricultural production, make global climate change a pressing concern [1,2,3]. The continuous rise in CO₂ emissions is expected to have disastrous effects on all aspects of society and significant repercussions for the global climate system, posing challenges to Sustainable Development Goals (SDGs) [4,5]. Thus, minimising CO₂ emissions and improving environmental quality have emerged as global priorities to promote sustainable development and alleviate the adverse impacts of climate change. The traditional energy and agriculture sectors remain crucial as they underpin the development of industries and the economy as a whole [6]. Moreover, the agricultural sector possesses the capacity to contribute to environmental protection by adopting technological advancements and renewable energy sources [7]. Consequently, agricultural growth may foster a sustainable environment, ultimately aiding in pollution mitigation. Despite structural modifications in various countries, the agricultural sector remains a significant concern for many economies due to its potential to exert both positive and negative environmental impacts.

Saudi Arabia’s economy is predominantly dependent on oil, with petroleum exports constituting a significant fraction of its GDP. The nation had substantial economic expansion in recent decades, primarily propelled by the oil industry. Saudi Arabia is actively pursuing economic diversification through its Vision 2030 project, which seeks to diminish reliance on oil and foster areas such as tourism, entertainment, and technology. In 2023, Saudi Arabia’s GDP was roughly USD 1.07 trillion, exhibiting consistent growth in recent years despite variations in world oil prices [8]. Saudi Arabia ranks second in global oil production, holding over 30% of the world’s oil output [9]. In 2022, 80.8% of Saudi Arabia’s land area was classified as arable and suitable for agricultural [10]. The nation has been investing in technology such as hydroponics and desalination to address its issues of water scarcity and soil quality. In 2022, the agricultural sector contributed approximately USD 19.37 billion to the GDP [11]. Owing to its severe desert climate and the substantial water requirements for agriculture, Saudi Arabia has been enhancing food security by investing in international agriculture and forming partnerships with other nations.

Saudi Arabia is a developing country that has experienced substantial economic growth over the past decades. To achieve economic growth aligned with long-term sustainability, the country has formulated strategic initiatives. Saudi Arabia has ratified international agreements, such as the Paris Agreement and the Kyoto Protocol, to combat climate change and reduce carbon emissions [12]. The Kingdom has committed to implementing activities, initiatives, and plans outlined in its submission to cut CO₂ emissions by 278 million tons annually by 2030, using 2019 as the baseline year for this Nationally Determined Contribution (NDC) [13,14]. This target represents a more than twofold increase compared to the prior goal outlined in the Kingdom’s intended NDC of 130 million tons of CO₂ equivalent. This submission signifies both progress and ambition [15]. Saudi Arabia has stated in its current NDC that, compared to a business-as-usual scenario, it aims for a 55% reduction in CO₂ emissions by 2023 [16]. The country faces various environmental challenges and is striving to balance policies that promote sustainable development while mitigating climate change. Implementing policies that achieve both challenges simultaneously presents a significant challenge due to the trade-off between economic development and pollution control. Addressing the question of how Saudi Arabia can reduce CO₂ emissions is crucial, and this can be explored by examining the country’s environmental factors.

Saudi Arabia’s Vision 2030 seeks to diminish the nation’s reliance on oil earnings by diversifying into sectors including tourism, entertainment, renewable energy, and technology. The objective is to establish a more sustainable and resilient economy. Saudi Arabia is a prominent leader in oil production; Russia and the United States are also significant oil producers. In the Middle East, nations like as Iraq, Iran, and the UAE are notable producers; however, none rival the output magnitude of Saudi Arabia. Saudi Arabia’s strategy is more extensive and aspirational, focusing on diversifying its economic foundation beyond oil, developing infrastructure and generating employment in emerging areas. Saudi Arabia is making substantial investments in renewable energy, targeting the generation of 50% of its energy from renewable sources by 2030 [16]. This transition aligns with global environmental trends and will reduce the nation’s long-term reliance on oil. Within the GCC framework, all member states aspire to augment their renewable energy capacity by harnessing solar and wind resources, aligning with their energy policies to achieve the renewable energy objectives set for 2030 [17]. The Saudi Green Initiative, launched in 2021, supports the Kingdom of Saudi Arabia in achieving its climate goals. As a national programme, its objectives are to safeguard future generations and enhance their quality of life [18]. With its ambitious aims, the Saudi Green Initiative represents a significant step towards the Kingdom’s aspiration to take a global leadership role in creating a greener future. Thus, the Kingdom’s agricultural plan should be implemented to promote efficient water use and ensure a secure and sustainable environment [19,20]. Agriculture is a key contributor to environmental degradation, as it is associated with CO₂ emissions and is highly vulnerable to climate change [21,22]. Saudi Arabia’s agricultural sector has progressed significantly, with agricultural land accounting for 80.77% of the country’s total land area in 2022 according to the World Bank’s collection of development indicators [10]. Figure 1, shown below, illustrates that Saudi Arabia has witnessed a notable and rapid increase in CO₂ emissions from agriculture in recent years. This indicates that land use and agriculture are significant contributors to rising global temperatures and climate change. Moreover, this demonstrates that climate change could have substantial economic impacts on developing nations, with estimates suggesting a potential reduction of approximately 0.3% in the gross domestic product (GDP) of Saudi Arabia based on the CO₂ emission data from agriculture [10].

Numerous studies using time-series data have analysed the relationships among environmental pollutant indicators, including CO₂ emissions and economic growth [23,24,25,26,27]. However, despite its growing prominence in global research, there has been limited examinations of the relationship between CO₂ emissions and environmental variables in Saudi Arabia [28,29,30,31]. This paper aims to identify the mechanisms and the magnitude of the impact of agricultural activity on the problem of climate change in Saudi Arabia. It provides a valuable contribution to discussions on environmental sustainability and development. This study analyses the relationship between agricultural activity, energy consumption, economic growth, and CO₂ emissions using econometric techniques. This empirical analysis aligns with national efforts to achieve the Sustainable Development Goals (SDGs) and offers practical insights for both researchers and policymakers.

The central research question addressed is the following: to what extent do agricultural development, energy consumption, and economic growth influence CO₂ emissions in Saudi Arabia? This study proposes that agricultural land significantly affects emissions, energy consumption contributes to their increase, and economic growth is associated with higher CO₂ levels. To examine these relationships, this paper employs econometric models such as ARDL and FMOLS regression, which explain the long-run relationship between the independent variables and the CO₂ emissions. In addition, the Vector Error Correction Model (VECM) for Granger causality testing is applied to clarify the causal relationships in both the short and long run.

This paper provides a significant addition by concentrating on Saudi Arabia, a setting seldom examined in research connecting agriculture and CO₂ emissions. Saudi Arabia, a prominent oil-producing nation experiencing economic upheaval, exemplifies a unique example, particularly with Vision 2030 and projects such as the Saudi Green Initiative. Comprehending the impact of agriculture on climate change is essential as the nation develops this sector and advocates for environmental sustainability. This study, unlike many other studies, includes particular control factors such as agricultural employment and technical progress, yielding more profound sector-specific conclusions. The ARDL model is methodologically appropriate for time-series data, enabling a comprehensive examination of both short- and long-term impacts. The literature review presented in next section provides a critical foundation for this paper by highlighting key theoretical perspectives, empirical findings, and existing research gaps that this paper aims to address.

However, this paper has certain limitations. It focuses on an oil-exporting country with distinct environmental regulations, developmental stages, and other aspects that may impact the environmental consequences of natural resource extraction. Additionally, the analysis relies on available data, which has inherent limitations, such as constraints in renewable energy data. Nonetheless, despite these challenges, this paper asserts that this research significantly enhances the understanding of the relationship between agricultural development, energy consumption, economic growth, and CO₂ emissions in Saudi Arabia.

The remaining sections of this paper proceed as follows: Section 2 addresses the literature review. Section 3 delineates the data and research methodology for empirical analysis. Section 4 presents and elucidates the results and discussion, whilst Section 5 closes and delineates specific policy implications and prospective suggestions.

2. Literature Review

The substantial and growing body of literature has examined environmental suitability and climate change [1,2,3,5,32,33]. Examining the relationship between environmental concerns and the probability of economic development is supported by the theory of economic growth. This makes it possible to understand and create empirical tests that evaluate relative magnitudes [34]. The theoretical underpinnings of the discussion of economic growth and environmental degradation are the most common assumptions used to explain the connection between economic expansion, energy consumption, and CO₂ emissions. Reducing emissions might not be the best action in both developed and emerging countries due to the impact of environmental protection on the levels and growth rates of essential economic variables, despite empirical evidence correlating growth to environmental conditions [35].

Three viewpoints on the relationship between CO₂ emissions and economic development are presented in the literature. The first holds that economic expansion leads to CO₂ emissions, suggesting that greater economic growth is associated with higher energy demand; the second holds that CO₂ emissions and economic growth are causally related in both directions; and the third holds that there is no causal relationship, thereby endorsing the neutrality hypothesis [36,37]. In this regard, strategies aimed at conserving energy must successfully reduce CO₂ emissions while maintaining economic expansion. Economies would be better equipped to create sustainable energy policies and efficiently exploit energy resources if they understood the relationship between CO₂ emissions and economic development.

Various factors influence environmental quality by increasing global CO₂ emissions across multiple sectors. One of the key contributors to environmental degradation is the agriculture sector, and a large number of analytical studies have analysed its role in this process [22,28,38,39,40,41,42,43,44,45,46]. Additionally, several other factors affect the level of CO₂ emissions, including economic growth, energy consumption, urbanisation, trade openness, and population growth, among others [21,22,23,27,47,48,49,50,51].

2.1. Agriculture Development and CO₂ Emissions

Prior studies have explored the relationship between CO₂ emissions and agricultural development. One of the main contributors to CO₂ emissions is agriculture, with research consistently identifying a connection between agriculture and CO₂ emissions. A study by Raihan and Tuspekova [23] examined the influence of agriculture on CO₂ emissions in Peru. Using time-series data spanning from 1990 to 2018 and employing the ARDL model, their results revealed a positive association between agricultural land and CO₂ emissions. Other research conducted in Pakistan confirmed this relationship [52,53].

These results align with the findings from Haider, Bashir and ul Husnain [38], who demonstrated that agriculture has a significant impact on the CO₂ emissions in both developed and developing nations. Using panel data from 1980 to 2012 and employing the pooled mean group method, their results confirmed that the expansion of agriculture has a positive and substantial consequence on CO₂ emissions. Similarly, an important study published in 2023 analysed the impacts of agricultural development on CO₂ emissions, reaching identical conclusions despite variations in the study period and methodology [43]. Along the same lines, Aydoğan and Vardar [54] and Balogh [55] argued that estimates suggest a positive connection between CO₂ emissions and agriculture.

Conversely, Kara, et al. [56], Sui and Lv [57], Sagheer and Ashraf [58], Raihan and Tuspekova [59] discovered a significant inverse correlation between agricultural productivity and CO₂ emissions. Sui and Lv [57] and Sagheer and Ashraf [58] employed the same methodology, analysing comprehensive time-series data. Additionally, Elhaj [60] found, through an analysis of panel data, that certain agricultural practises contribute to a decrease in CO₂ emissions. The GCC nations have implemented several sustainable agricultural practises to tackle issues such as water scarcity, soil degradation, and climate change. The United Arab Emirates and Saudi Arabia are investing in agricultural technologies and climate-controlled greenhouses to improve output and efficiency. The GCC initiated various green efforts to enhance environmental sustainability and diminish their ecological print. These initiatives encompass various sectors, including renewable energy production, conservation efforts, and sustainable urban planning. Saudi Arabia’s Saudi Green Initiative emphasises enhancing vegetation cover, mitigating desertification, and attaining net-zero emissions [16].

This perspective is further supported by Mahmood, Alkhateeb, Al-Qahtani, Allam, Ahmad and Furqan [28], whose study focused on Saudi Arabia. Utilising time-series data beginning in 1971 to 2014, they examined the impact of agriculture on CO₂ emissions and its relationship with the GDP and energy consumption. Applying ARDL and asymmetric analysis models, their findings revealed a significant and adverse effect of the agricultural sector on CO₂ emissions through both symmetrical and asymmetrical analyses. Their study represented the agricultural sector through the percentage of agricultural value added to GDP. This indicator emphasised the economic impact of agriculture. Nevertheless, it may not accurately reflect the intensity of agricultural operations, including energy usage for irrigation or machinery, or the carbon emissions associated with agricultural production. The agricultural sector in Saudi Arabia has likely experienced structural transformations over the years, encompassing technological advancements and alterations in the varieties of crops cultivated. The mentioned study failed to consider these structural changes or their effects on emissions, such as transitions from traditional to high-tech agriculture, which may elucidate the observed disparity.

Two important themes emerge from the studies discussed thus far. One suggests a positive correlation between CO₂ emissions and agricultural development, where increased agricultural activity leads to heightened CO₂ emissions. The second is precisely the contrary, as subsequent investigations indicated an inverse association between CO₂ emissions and agriculture, indicating that agricultural expansion and improved practises contribute to the reduction in CO₂ emissions.

H1: 

Agricultural development is clearly correlated with CO₂ emissions.

2.2. Economic Growth and CO₂ Emissions

Several studies have investigated the association between economic growth and CO₂ emissions. In 2023, Raihan [49] published a paper describing the intricate relationship between economic growth and CO₂ emissions in the Philippines. Employing the ARDL bounds testing strategy and the DOLS methodology, their study analysed annual time-series data from 1990 to 2020. Their findings indicated that while economic growth correlates with increased CO₂ emissions, the percentage increase is lower than that associated with higher economic growth rates.

Moreover, some papers have recognised a significant correlation between economic growth and CO₂ emissions across multiple countries. For instance, studies have demonstrated a link between CO₂ emissions and economic growth in Kazakhstan using ARDL, DOLS, and a causality test on time-series data from 1990 to 2020 [61]. Similar conclusions were drawn by Karaaslan and Çamkaya [62], who examined data from 1980 to 2016, as well as by Raihan, et al. [63], who analysed the years 1990 to 2019. All of these studies employed time-series data, while Ghazouani and Maktouf [50] used panel data and ARDL models, yielding results that supported the notion of a positive association between economic growth and CO₂ emissions.

The results of Aldegheishem [31] were based on an examination of Saudi Arabian data from 1991 to 2023. Using the ARDL model and a time-series analysis, along with variables such as trade openness and air travel, the paper identified a positive correlation between the GDP and CO₂ emissions. Conversely, Gershon, Asafo, Nyarko-Asomani and Koranteng [26] conducted an econometric analysis on certain African countries, applying panel estimating methods. Their data analysis from 2000 to 2017 indicated that a rise in economic growth adversely impacts CO₂ emissions. However, findings from the GMM model used by Karimzadeh, et al. [64] indicated a negative association between CO₂ emissions and the GDP in Shanghai.

Similarly, Ozturk, Aslan and Altinoz [29] examined the relationship among CO₂ emissions, GDP, energy consumption, and pilgrimage travel in Saudi Arabia from 1968 to 2017. Utilising the DOLS and FMOLS methodologies, their findings indicated that economic growth adversely affects CO₂ emissions. Overall, these studies clearly indicate a relationship between CO₂ and economic growth. Several studies suggested a positive correlation, while others reported a negative correlation between economic growth and CO₂ emissions.

H2: 

Economic growth is strongly associated with CO₂ emissions.

2.3. Energy Use and CO₂ Emissions

The interconnections between energy use and CO₂ emissions have been extensively documented in practical studies. A multitude of studies covering various countries, issues, and methodologies have been examined. The study by Raihan and Tuspekova [65] demonstrated the negative impacts of energy consumption on CO₂ emissions by utilising ARDL and DOLS methodologies, analysing data from 1996 to 2020 in Kazakhstan. Similarly, Raihan [24] employed ARDL estimators based on annual data from 1984 to 2020, determining that energy consumption negatively affects CO₂ emissions in Vietnam.

Other papers have found a positive association between energy consumption and CO₂ emissions. Raihan and Tuspekova [66] examined this correlation in Brazil from 1990 to 2019, revealing a robust association between the two variables. Likewise, Raihan, et al. [67] determined that energy consumption increases CO₂ emissions by using the ARDL model on time-series data from 1990 to 2019. A separate study conducted across 23 developing nations employed an ARDL regression analysis on panel data spanning from 1995 to 2018, demonstrating that a 1% growth in energy consumption results in a 0.23% rise in CO₂ emissions [68].

Furthermore, Raihan, Begum, Nizam, Said and Pereira [21] examined the interrelationships between energy consumption, agricultural land, deforestation, and CO₂ emissions in Malaysia. Using time-series data from 1990 to 2019, their study employed the ARDL and DOLS models, confirming that a 1% rise in energy use results in a 0.91% rise in CO₂ emissions. Additionally, Boukhelkhal [47] investigated the factors influencing CO₂ emissions in 35 African countries, employing mean group and dynamic common correlated effect mean group models on panel data from 1980 to 2016. Their findings indicated a positive correlation between CO₂ emissions and energy use. The evidence presented in this section strongly suggests that energy consumption contributes to environmental degradation.

H3: 

Energy use is definitely connected with CO₂ emissions.

2.4. Research Gap

Despite numerous studies on the impact of agriculture, energy, and economic growth on CO₂ emissions across various regions, a more comprehensive analysis focusing on Saudi Arabia is essential due to the country’s significance as a leading oil producer with ambitious developmental goals. However, previous studies examining the relationship between economic growth, energy consumption, and agricultural land on CO₂ emissions predominantly focus on aggregate CO₂ emissions, often neglecting the application of robust empirical models that integrate multiple influencing factors. In terms of policy implications, it is crucial to evaluate and incorporate all relevant components that may influence the country’s environmental outcomes. This paper examines the causative relationship between CO₂ emissions and agricultural land, economic growth, energy consumption, employment in agriculture, and technological advancements that impact environmental sustainability, in contrast to prior research.

Within the framework of Saudi Arabia, only a few studies have integrated the broad set of parameters employed in our paper, highlighting the significance of empirical examination in understanding the various factors influencing CO₂ emissions. As a result, this study may provide valuable insights for both scholars and policymakers. The current literature on the relationship between CO₂ emissions and agricultural development, economic growth, and energy consumption varies, often yielding conflicting findings. This indicates that the extent of the impact may vary across countries and time within unlike datasets and depending on analytical methodologies. Investigating the influence of agriculture on CO₂ emissions is crucial, yet existing studies on this topic remain limited, particularly in the context of Saudi Arabia. This paper empirically analyses agricultural development in combination with energy consumption and economic growth concerning CO₂ emissions, significantly contributing to the knowledge base in this field.

3. Data and Methodology

3.1. Data

The purpose of this paper is to examine the elements that affect CO₂ emissions by analysing several key factors. The empirical research aims to assess how agricultural land, economic development, and energy use influence CO₂ levels in Saudi Arabia. The hypotheses suggest that CO₂ emissions are significantly affected by agricultural land, that energy consumption increases emissions, and that economic growth is associated with higher emission levels. The relationships to be examined are based on the previous theoretical and empirical literature.

This paper posits that carbon emissions (CO₂) are contingent upon agricultural land (AL), gross domestic product per capita (GDP), and energy consumption (EU). Additionally, this paper includes specific control variables in conjunction with the key explanatory variables to mitigate their influence on carbon emissions (CO₂). The control variables are employment in agricultural (EA) and medium- to high-tech manufacturing (TEC).

The econometric analysis encompasses the period spanning from 1990 to 2022, limited by data accessibility. This paper utilised the World Bank database to gather data according to availability. The questions of this paper were formulated based on prior empirical findings as follows: what impact does agricultural land, economic growth, and energy use exert on the environmental sustainability of Saudi Arabia? This paper employs data sourced from the World Bank’s World Development Indicators (WDI) database, recognised as a comprehensive and credible repository for global development statistics. The WDI aggregates internationally comparable data on many economic, social, and environmental variables, obtained from officially recognised international agencies, national statistical offices, and other reputable institutions [69]. Time-series analysis is employed to evaluate each of the possibilities stated. This paper conducted a regression analysis for Saudi Arabia utilising data from 1990 to 2022. The WDI supplied the data for both the main variable and the additional explanatory variables.

Table 1. Variable clarification.

Variable	Variable Characteristics	Description of the Variable	Data Period	Data Source
CO2 emissions (CO2)	Environmental sustaiability	Carbon dioxide (CO2) emissions per capita (t CO2/capita)	Annual 1990–2022	WDI
GDP per capita (GDP)	Economic growth	GDP per capita (constant Local Currency Unit)	Annual 1990–2022	WDI
Energy Use (EU)	Energy manegment	Energy use (kg of oil equivalent per capita)	Annual 1990–2022	WDI
Agricultural Land (AL)	Agricultural development	Agricultural land (sq. km)	Annual 1990–2022	WDI
Employment Agriculture (EA)	Employment rate	Employment in agriculture (% of total employment)	Annual 1990–2022	WDI
Technology (TEC)	Technology advancement	Medium and high tech (% manufacturing value added)	Annual 1990–2022	WDI

delineates the explanatory factors alongside the main variable utilised in the econometric analysis.

3.2. Methodology

This paper investigates the association between CO₂ emissions and several socio-economic indicators by using the ARDL approach. Five variables representative of economic and environmental factors—namely GDP per capita, energy use, agricultural land, employment in agriculture, and technology—were used to assess their impact on environmental sustainability. A natural logarithm transformation was employed to ensure value compatibility and to interpret the calculated coefficients of the variables as elasticities [70]. The logarithmic formulation of the subsequent econometric model facilitates the analysis of both the short-term and long-term impacts of CO₂ emissions:

$${CO2}_t=f\left({GDP}_t,{EU}_t,{AL}_t{{,EA}_t,TEC}_t\right)$$

(1)

where t = time (the period of paper 1990–2022). Prior to presenting the econometric analysis, the descriptive statistics for the variables are presented in

Table 2. Descriptive analysis.

Variable	Obs	Mean	Std. Dev.	Skewness	Kurtosis	Min	Max
Ln CO2	32	2.8791	0.0897	0.3947	1.5647	2.7650	3.0292
Ln GDP	32	11.5230	0.0717	0.3061	2.6237	11.3845	11.6737
Ln AL	32	14.3053	0.1160	−1.6051	3.9666	14.0264	14.3682
Ln EU	32	8.5879	0.1889	−0.0732	1.7582	8.1953	8.8680
Ln EA	32	1.7757	0.3256	−1.3891	3.4216	0.9803	2.0614
Ln TEC	32	3.4033	0.2692	−0.3215	1.3528	3.0648	3.7434

Source: Author calculations.

, which contains the statistical results from two normality tests: skewness and kurtosis. Each variable has 32 observations of a time series covering the period from 1990 to 2022 for Saudi Arabia. The skewness ratings near 0 suggest that all variables adhere to normality.

Table 3. Correlation matrix.

Variable	Ln CO2	Ln GDP	Ln AL	Ln EU	Ln EA	Ln TEC
Ln CO2	1.0000
Ln GDP	−0.0349	1.0000
Ln AL	0.4713	−0.7838	1.0000
Ln EU	−0.5541	−0.0640	−0.3891	1.0000
Ln EA	0.9009	−0.1739	0.5947	−0.7941	1.0000
Ln TEC	0.5507	−0.5276	0.6612	−0.4841	0.7305	1.0000

Source: Author calculations.

shows the correlation analysis applied to examine linear correlations among the variables. The findings indicate that most of the variables have values less than ±0.80, demonstrating the absence of multicollinearity among the data. The correlation analysis drives the performance of unit root tests to assess the stationarity of the indicators.

The logarithmic form of the model from Equation (1):

$${lnCO2}_t={\beta }_0+{\beta }_1{lnGDP}_t+{\beta }_2{lnEU}_t+{\beta }_{3}ln{AL}_t+{\beta }_{4}ln{EA}_t+{\beta }_5{lnTEC}_t+{\epsilon }_{it}$$

(2)

This paper conducted some diagnostic tests, such as a correlation analysis, ARDL bound test, and unit-root test, before implementing the ARDL model. Performing a unit root test is vital for avoiding erroneous regression. It confirmed the stationarity of the variables in the regression by differencing them and estimating the relevant equation [71]. The necessity to ascertain the order of integration prior to examining cointegration among the variables is acknowledged in the empirical papers.

Numerous studies have proposed that due to the power disparity of unit root tests in relation to sample size, it is essential to employ different unit root tests. Consequently, this paper employed the Augmented Dickey–Fuller (ADF) test, as established by Dickey and Fuller [72], along with the Phillips Perron (PP) test, as provided by Phillips [73], to identify the autoregressive unit root. This paper conducted a unit root test to ensure that no variable surpassed the order of integration.

The ARDL model was utilised to identify the long-term relationship between the dependent and independent variables established by Pesaran and Shin [74]. There are some advantages of the ARDL bound model compared to other conventional cointegration methods. First, it is applicable in cases of mixed integration order. Second, it simultaneously integrates both short- and long-run coefficients. Third, it is ideally suited for small sample sizes, accommodates varying lag lengths, and eliminates the issue of autocorrelation [75].

The ARDL bounds test-computed F-statistics are juxtaposed with the lower and upper bound values. If the calculated F-statistic is less than the crucial value, the alternative hypothesis is rejected. When the computed F-statistic exceeds both the lower and higher critical values, the null hypothesis is rejected, demonstrating evidence of a long-term interaction among the variables. This paper utilises the ARDL method of cointegration to predict the long-run equilibrium association between CO₂ and the variables. The cointegration regression form of the ARDL framework is reformulated as follows:

$$\begin{gathered} {\Delta LCO2}_t={\tau }_0+{\tau }_1{\Delta LCO2}_{t-1}+{\tau }_2{\Delta LGDP}_{t-1}+{\tau }_3{\Delta LEU}_{t-1}+{\tau }_4{\Delta LAL}_{t-1}+ \\ {\tau }_5{\Delta LEA}_{t-1}+{\tau }_6{\Delta LTEC}_{t-1}+{\sum }_{i=1}^p{\gamma }_1{\Delta LCO2}_{t-i}+{\sum }_{i=1}^p{\gamma }_2{\Delta LGDP}_{t-i}+ \\ {\sum }_{i=1}^p{\gamma }_3{\Delta LEU}_{t-i}+{\sum }_{i=1}^p{\gamma }_4{\Delta LAL}_{t-i}+{\sum }_{i=1}^p{\gamma }_5{\Delta LEA}_{t-i}+{\sum }_{i=1}^p{\gamma }_6{\Delta LTEC}_{t-i}+{\mu }_t \end{gathered}$$

(3)

where ${\tau }_0$ is the intercept, p is the lag order, ${\mu }_t$ is the error term, and Δ is the first difference. To test the long-run equilibrium relationship between CO₂, GDP, EU, AL, EA, and TEC, F tests are used in this paper. Following the establishment of the long-run interaction, the ARDL model derives the VECM. It is obtained by assessing the model’s short-run parameters by the use of VECM. Consequently, the ARDL framework is reformulated as follows:

$$\begin{gathered} {\Delta LCO2}_t={\tau }_0+{\sum }_{i=1}^p{\gamma }_1{\Delta LCO2}_{t-i}+{\sum }_{i=1}^p{\gamma }_2{\Delta LGDP}_{t-i}+{\sum }_{i=1}^p{\gamma }_3{\Delta LEU}_{t-i}+ \\ {\sum }_{i=1}^p{\gamma }_4{\Delta LAL}_{t-i}+{\sum }_{i=1}^p{\gamma }_5{\Delta LEA}_{t-i}+{\sum }_{i=1}^p{\gamma }_6{\Delta LTEC}_{t-i}{\tau }_1{\Delta LCO2}_{t-1}+ \\ {\tau }_2{\Delta LGDP}_{t-1}+{\tau }_3{\Delta LEU}_{t-1}+{\tau }_4{\Delta LAL}_{t-1}+{\tau }_5{\Delta LEA}_{t-1}+{\tau }_6{\Delta LTEC}_{t-1}+ \\ \rho {\mathrm{E}\mathrm{C}\mathrm{M}}_{t-i}+{\mu }_t \end{gathered}$$

(4)

where ${\gamma }_i$ = 6 means coefficients in the short run, ${\mu }_t$ signifies the error term, ${\tau }_i$ = 6 denotes coefficients in the long run, t represents the lag lengths, and ${\mathrm{E}\mathrm{C}\mathrm{M}}_{t-i}$ denotes the error correction term. $\rho$ represents the ECM coefficients, which will be negative and substantial. Figure 2 illustrates the flowchart of the analytical approaches utilised in this paper to examine the dynamic effects of economic growth, energy consumption, and agricultural production on CO₂ emissions in Saudi Arabia.

This research strategically selects both the ARDL and FMOLS models to leverage the distinct characteristics of each method, ensuring a comprehensive and reliable analysis. The ARDL model is utilised as the main estimating method because of its adaptability and strength, especially when dealing with variables of mixed integration orders—specifically, a combination of I(0) and I(1) series. This attribute renders ARDL the most appropriate method for handling such datasets. Furthermore, ARDL proficiently resolves prevalent econometric challenges, including endogeneity, omitted variable bias, and autocorrelation, particularly in small sample contexts. According to Panopoulou and Pittis [76], ARDL rectifies asymptotic biases; therefore, it yields dependable long-run coefficient estimates and valid statistical inferences.

To corroborate and affirm the robustness of the ARDL findings, this paper additionally utilises the FMOLS approach. FMOLS is adept at addressing residual econometric challenges that may endure, including serial correlation, simultaneity bias, and panel data heterogeneity. Liddle [77] highlights that FMOLS provides consistent and unbiased long-term estimates by rectifying asymptotic bias. This paper used FMOLS as a robustness check to confirm that the long-run correlations revealed via ARDL are both statistically valid and robust against other estimating methods. This dual-model approach improves the dependability and credibility of the empirical results.

4. Results and Discussion

4.1. Results

This section delineates the analytical results observed in this paper. A unit root test was performed to evaluate stationarity and determine the integration variable order. The findings of unit root testing with ADF and PP are presented in

Table 4. Unit root test results.

Variable	ADF		PP
	I(0)	I(1)	I(0)	I(1)
Ln CO2	−1.149	−7.261 ***	−1.114	−7.017 ***
Ln GDP	−2.004	−5.521 ***	−1.937	−5.534 ***
Ln AL	−4.160 ***	−1.694	−3.118 ***	−1.846
Ln EA	0.990	−4.862 ***	1.063	−4.915 ***
Ln EU	−1.547	−9.954 ***	−1.599	−9.593 ***
Ln TEC	−1.608	−6.508 ***	−1.511	−6.590 ***

Source: Author calculations. Note: *** represents the 1% significance levels.

. The interpretation of the test indicates that if the p-value is less than 0.05, the null hypothesis that a unit root is present is rejected, indicating that the series is stationary. According to the results of the ADF test, CO₂, GDP, EU, EA, and TEC were non-stationary at the level but become stationary at the first difference, while AL was stationary at the level. Therefore, the existence of mixed-order integration assessed by the ADF and PP substantiates the utilisation of the ARDL bound test for cointegration.

Upon confirming the stationarity properties of the series, this paper proceeds to conduct the ARDL bounds test for cointegration. This methodology selected an appropriate lag period to evaluate the F-statistic based on the minimal values of the Akaike Information Criterion introduced by Akaike [78].

Table 5. ARDL bounds test results.

F-Bounds Test		Null Hypothesis: No Relationship
Test statistic	Value	Significance	I0	I1
Value of F-statistic	29.677	At 10%	2.26	3.35
K	5	At 5%	2.62	3.79
		At 2.5%	2.96	4.18
		At 1%	3.41	4.68

Source: Author calculations.

illustrates the ARDL bounds test outcomes to investigate the cointegration relationship between the variables. The results specify that a long-run relationship among the variables is confirmed if the predicted F-test value exceeds the lower and upper bounds. The interpretation of the test is that if the calculated F-statistic exceeds the upper critical bound value, then there is a long-run cointegration among the variables. In contrast, if the F-statistic is below the lower critical bound value, it implies that there is no cointegration. The findings show that the estimated F-statistic value of 29.677 exceeds the upper limits at the 10%, 5%, 2.5%, and 1% significance levels for both orders, thus rejecting the null hypothesis and suggesting the presence of a long-run cointegration among the examined indicators [79]. This indicates that the explanatory variables influence the dependent variable.

Following the confirmation of cointegration by the ARDL bound test, this paper advances to estimate the ARDL model. The outcomes of the ARDL model are presented in

Table 6. ARDL model result.

Variables	Coefficient	Prob
Short-run results
Δ Ln GDP	0.3616163	0.005
Δ Ln AL	0.1134504	0.672
Δ Ln EU	−0.5222751	0.003
Δ Ln EA	−0.2358567	0.000
Δ Ln TEC	0.125396	0.000
ECM(-)	−1.476528	0.000
Long-run results
Ln GDP	0.2837159	0.000
Ln AL	0.1591148	0.006
Ln EU	0.8533706	0.000
Ln EA	0.214465	0.000
Ln TEC	−0.1070373	0.000
$R^2$	0.9645
$\mathrm{Adjusted}{R}^2$	0.9234

Source: Author calculations.

. The R² and adjusted R² values are 0.96 and 0.92, respectively. This result demonstrates that the regressors account for 96% of the variation in CO₂, with the remaining percent assigned to the error term. The adjustment speed is observed to promote long-term convergence among the variables, characterised by a significant and negative error correction coefficient.

This result indicates the cointegration among the variables, which signifies the model’s ability to achieve a speed of adjustment towards the long-term equilibrium in CO₂ as influenced by the regressors (GDP, EU, EA, AL, and TEC). This paper reveals that energy use has a positive and substantial effect on CO₂. Consequently, energy consumption stimulates CO₂ emissions, as a 1% increase in energy consumption results in a 0.85% increase in CO₂ emissions. The computed value of the GDP is positive and significant at the 1% level, indicating that a 1% rise in economic growth results in a 0.28% increase in CO₂ emissions. These results indicate that energy consumption and economic growth precipitate environmental damage over time.

Moreover, the calculated coefficient of agriculture is positive and significant at the 1% level, indicating that an increase in agricultural area of 1% correlates with a 0.16% increase in CO₂ emissions. This indicates that the growth of agricultural land adversely affects environmental quality. The estimated agriculture employment coefficient is positive and significant. At a 1% significance, a 1% rise in agriculture employment consequences in a 0.21% increase in CO₂ emissions in Saudi Arabia.

Furthermore, a negative and significant association between technology advancement and CO₂ emissions indicates that, ceteris paribus, a 0.11% decrease in CO₂ emissions corresponds to a 1% increase in technology development. Overall, the empirical conclusions indicate that economic growth, energy consumption, agriculture, and employment induce environmental deterioration, while escalating technology advancement enhances environmental regeneration.

The FMOLS estimator is applied to confirm the reliability of the ARDL estimation. The results of the FMOLS estimation are displayed in

Table 7. FMOLS robustness result.

Variables	Coefficient	Standard Error	t-Statistic
Ln GDP	0.4813709	0.0568368	0.000
Ln AL	0.255836	0.0392615	0.000
Ln EA	0.1924257	0.0095218	0.000
Ln EU	0.7484868	0.0230216	0.000
Ln TEC	−0.0602217	0.011601	0.000
R2	0.9633331
Adjusted R2	0.9556942
Standard error	0.0186722
Long run variance	0.0087466

Source: Author calculations.

. The results of FMOLS demonstrate the reliability of the ARDL estimation. The FMOLS model confirmed that the coefficient of energy usage, economic growth, agriculture, and employment are positive and significant at the 1% level.

The findings further confirmed the adverse correlation between CO₂ emissions and technology advancement at a 1% significance level. Therefore, it can be asserted that energy consumption, economic growth, agricultural land, and employment elevate CO₂ emissions in Saudi Arabia, whilst a development in technology contributes to the reduction in CO₂ emissions. The conclusions of the FMOLS analysis are consistent with the results from the ARDL model. The R2 and adjusted R2 values from the FMOLS model replicate the model’s goodness of fit, suggesting that the explanatory variables can explain 96% of the variation in the main variable. The empirical analysis’s graphical findings are shown in Figure 3.

This paper conducted analyses for normality, heteroscedasticity, stability, regression specification error, and serial correlation to evaluate the robustness of the results. The findings of the diagnostic inspection are displayed in

Table 8. Diagnostic inspection.

Diagnostic Tests	Coefficient	p-Value	Decision
Jarque–Bera test	3.033	0.2195	Normally distributed
Breusch–Godfrey LM test	2.863	0.0906	No serial correlation
Breusch–Pagan–Godfrey test	29.00	0.4125	No heteroscedasticity
Ramsey RESET test	0.33	0.8050	No omitted variables
Cumulative sum test	0.8214	-	Stable

Source: Author calculations.

. The model demonstrates normality and the absence of autocorrelation and heteroscedasticity. Furthermore, this paper utilised the Ramsey test to assess the model’s regression specification error test. The test result proposes that the ARDL model does not suffer from misspecification related to omitted variables from the model. The cumulative sum of squares of recursive residuals test (CUSUM) was used to check the stability. The CUSUM from the model’s recursive estimation demonstrates that the model is stable, as the residuals remain within the critical confines of 5% significance, indicating no structural break.

This paper employed the VECM test to ascertain the causative relationships among CO₂, AL, EU, GDP, EA, and TEC. The results of the VECM causality test are presented in

Table 9. The Vector Error Correction Granger Causality (VECM) test.

Short-Run Causality							Long Run
Variables	∆ Ln CO2	∆ Ln GDP	∆ Ln AL	∆ Ln EA	∆ Ln TEC	∆ Ln EU	∆CME-1
∆ Ln CO2	-	−0.44866 ** (0.052)	−0.36657 (0.261)	−0.22378 ** (0.060)	0.00967 (0.869)	−0.63435 *** (0.014)	−0.98567 * (0.074)
∆ Ln GDP	0.73502 * (0.070)	-	−0.44492 (0.257)	−0.40203 *** (0.005)	0.06149 (0.385)	−0.61328 ** (0.048)	−0.88130 (0.185)
∆ Ln AL	−0.10499 (0.494)	−0.01050 (0.920)	-	0.04547 (0.401)	−0.02543 (0.342)	0.17065 (0.146)	0.28013 (0.265)
∆ Ln EA	0.30634 (0.738)	0.79865 (0.202)	1.44859 (0.101)	-	0.10426 (0.513)	0.00745 (0.991)	−0.80581 (0.590)
∆ Ln TEC	0.70140 (0.688)	−0.42636 (0.721)	−0.76265 (0.652)	−0.12572 (0.838)	-	−1.04660 (0.433)	−2.13273 (0.456)
∆ Ln EU	−0.05146 (0.865)	−0.51453 *** (0.013)	−1.15456 *** (0.000)	0.13870 (0.193)	−0.09479 * (0.072)	-	0.92286 ** (0.062)

Source: Author calculations. Note: ***, **, and * represent the 1%, 5%, and 10% significance levels, respectively.

. The findings indicate a bidirectional causality between CO₂ and the GDP in the short run. They assert that GDP is the primary factor influencing CO₂ emissions, whereas CO₂ emissions can influence economic growth. These findings align with the research conducted by Raihan [24] in Vietnam. A unidirectional causal link from energy use to co2 is confirmed. Therefore, renewable energy is essential for mitigating CO₂ emissions compared to natural energy sources. These findings are identical to those of [80,81].

Consequently, transitioning from fossil fuels to renewable energy decreases CO₂ emissions. This transition can improve environmental quality, and this aligns with the conclusions provided by AlNemer, et al. [82]. A unidirectional causal relationship from employment in the agriculture sector to CO₂ emissions was documented. However, this analysis revealed no causal relationships among agricultural productivity and CO₂ emissions. The coefficient of the lagged error correction component was shown to be negative and statistically significant over time. The outcomes reveal that economic growth and energy use are critical predictors of the reduction in CO₂ emissions.

4.2. Discussion

This paper examines the association between agricultural development, economic growth, energy consumption, and environmental degradation in Saudi Arabia. The analysed results indicate that agricultural development has a positive and substantial impact on CO₂ emissions in the long run. The results suggest that Saudi Arabia’s economic expansion coincides with a decline in environmental sustainability. This conclusion contrasts with [28], which identified a negative correlation between the agricultural development and CO₂ emissions in Saudi Arabia. Notably, studies examining the relationship between CO₂ emissions and agricultural development across various nations corroborate the findings of this paper. For instance, [38,43,52,54,56,57,83,84].

Increased economic growth correlates with heightened environmental pollution. In line with the present results, previous papers have demonstrated that economic growth leads to a rise in CO₂ emissions [23,26,28,31,49,63]. This is due to the higher consumption and development activities associated with economic expansion, leading to greater pollution, waste, and environmental degradation. Transitioning to a green economy is therefore crucial for Saudi Arabia, as its economy remains heavily dependent on natural resources. The Saudi government perceives the green and circular economy as a strategy for guiding the nation towards sustainable development by harmonising economic growth with enhanced social equity and income distribution, while simultaneously improving natural resource efficiency [19,85].

The most significant finding to emerge from this analysis is that energy consumption contributes to environmental damage over time. This finding broadly supports other studies that link energy consumption with CO₂ emissions [21,22,24,27,66]. Consequently, developing an advanced renewable energy to replace fossil fuels is a crucial strategy for reducing environmental degradation. Although fossil fuel-based energy output has not significantly changed, Saudi Arabia has prioritised renewable energy as one of the main aims of Vision 2030. A fundamental component of Saudi Arabia’s modern, diversified economy is the expansion of its renewable energy sector. Given the increasing urgency of climate change, the participation of renewable sources in the national energy combination is imperative [86].

The present paper raises the possibility that mitigating climate change and ensuring sustainable growth through renewable energy would reduce carbon emissions and offer significant economic benefits [22,63,67]. Due to increasing global environmental consciousness, it is imperative for Saudi Arabia to shift its energy balance towards renewables. This transition would enhance the accessibility of sustainable energy and contribute to the construction of an environmentally sustainable ecosystem. In this paper, agricultural employment was found to contribute to an increase in CO₂ emissions. This result differs from the estimates of Rehman, et al. [87], who examined agricultural employment in Bhutan, but it is broadly consistent with earlier studies [48,88].

Another key finding from the results is that technological advancement improves environmental quality. With the help of technology, Saudi Arabia has made excessive strides towards a low-carbon energy framework. By 2030, almost 50% of the Kingdom’s electricity is expected to come from renewable sources [89,90,91]. Several bold initiatives have been introduced to lower emissions while also transforming the domestic energy mix. These initiatives include developing critical energy transition metrics that indicate advancements in reducing CO₂ and energy intensity, alongside a decline in overall energy consumption, particularly within the agricultural sector. Additionally, these measures aim to increase energy efficiency and promote investments in new energy sources [92].

These findings highlight the need to replace conventional agricultural practises with modern technologies, thereby enhancing agricultural productivity. Increased productivity and lower emissions can be achieved by reducing the need for agricultural development to meet the demands of a rising population. Bathaei and Štreimikienė [32] indicated that varied renewable energy sources may be integrated into agricultural practises to diminish emissions originating from the agricultural sector. For instance, in Saudi Arabia, solar energy can be utilised for lighting and product aeration, as this is considered a primary source of renewable energy. Similarly, wind energy can be harnessed for power generators, irrigate agricultural lands, and mill specific produce. Furthermore, Saudi Arabia has set a goal to generate approximately 58.7 gigawatts of electricity from various renewable energy sources, including solar and wind, by 2030 [16,93].

In this context, the implementation of carbon pricing and taxation policies is essential to drive efforts and encourage sustainable practises [94]. This can be achieved by imposing financial costs on CO₂ emissions, levying taxes on companies and businesses that rely on carbon, and incentivising businesses to adopt clean energy sources and technology. These measures will motivate enterprises to undergo digital transformation, utilise sustainable and environmentally friendly resources, and enhance energy efficiency. Consequently, they will contribute to long-term sustainable development while maintaining economic competitiveness. Awareness plays a significant part in achieving sustainability objectives. Educational campaigns can be instrumental in fostering healthy habits among individuals and businesses. These initiatives should clarify the impact of carbon emissions and illustrate how Saudi Arabia can adopt environmentally friendly practises [95].

Promoting awareness of the advantages of renewable energy, resource efficiency, and carbon footprint reduction can empower society to successfully tackle environmental challenges [96]. Advancing environmental sustainability and attaining climate objectives is essential. Furthermore, fostering a culture of sustainability through corporate social responsibility initiatives and educational programmes will encourage individuals to embrace environmentally responsible practises.

The significant power generation from renewable energy sources could assist the nation in attaining SDGs. Both the Saudi government and its citizens have recognised climate change as a priority concern. Therefore, it is essential to examine the historical, current, and prospective development of renewable energy production in Saudi Arabia to mitigate CO₂ emissions. CO₂ emissions may be retained through appropriate management and technologies within agricultural enterprises, leading to a reduction in their carbon footprint [45]. In recent years, numerous international organisations have adopted sustainable and climate-resilient practises to achieve globally recognised objectives, such as the SDGs and the Paris Agreement.

Saudi Arabia should prioritise technologies that improve agricultural productivity while ensuring sustainability and minimising CO₂ emissions, including precision agriculture utilising drones, advanced artificial intelligence (AI)-driven drip irrigation, and the integration of renewable energy sources such as solar-powered greenhouses and desalination. Controlled environment agriculture techniques, like hydroponics and climate-regulated vertical farms, can optimise yield while minimising water consumption, making them suitable for arid regions. Furthermore, the use of AI-driven data platforms for crop planning and the promotion of regenerative methods such as biochar application and agroforestry would enhance long-term carbon sequestration and soil health [97].

5. Conclusions

This paper examines the dynamic relationships between CO₂ emissions, agricultural development, economic growth, energy consumption, and additional control variables in both the short and long run. The main objective of this study is to explore how these key socio-economic factors influence environmental sustainability in Saudi Arabia, providing empirical insights that may support future policy directions. Through its empirical approach, this paper aims to assess the quantitative effects of agricultural expansion, energy use, and economic performance on CO₂ emissions, offering a better understanding of their interconnected roles. The central concern driving this analysis is the extent to which these factors contribute to environmental degradation or sustainability. Based on theoretical expectations and the previous literature, this study assumes that increases in agricultural activities, higher energy consumption, and economic growth are likely associated with increased CO₂ emissions in Saudi Arabia.

This paper employs advanced econometric methods, including the ARDL and FMOLS approaches, to analyse the selected variables in a time series for Saudi Arabia from 1990 to 2022. Additionally, this paper applies statistical tests such as the unit root test, the ARDL bounds test, and VECM causality to determine the integration order and the relationships among these variables. The findings of this paper indicate that an increase in agricultural land contributes to higher CO₂ emissions, as a 1% increase in agricultural land leads to a 0.16% rise in emissions. Similarly, this paper reveals that the coefficient for economic growth utilisation is positive, suggesting that in the long run, a 1% increase in economic growth utilisation will result in a 0.28% increase in CO₂ emissions.

Based on these results, Saudi Arabian officials need to formulate an environmental strategy that diminishes CO₂ emissions without hindering economic development. A low-carbon and circular economy is the optimal path for Saudi Arabia [85]. Significant adjustments are required in the operation of a centralised state, and, particularly, citizens’ behaviours and technological advancement will facilitate decoupling at the regional level. The economy of Saudi Arabia is largely composed of industrial sectors, agriculture, and fossil fuel extraction. It does not significantly involve the production of high-tech, knowledge-intensive goods. The country’s GDP growth is primarily driven by industrial activities and the extraction of natural resources [98,99].

The most crucial factor is for Saudi Arabia to invest in research and development that promotes modernisation through innovative concepts. This approach would help meet growing demands while preserving natural resources and enhancing industry efficiency in terms of resource and energy usage. Additionally, to reduce its reliance on fossil fuels, Saudi Arabia could prioritise expanding its investments in renewable energy infrastructure, including solar, wind, and hydrogen technologies [100]. This would also help lessen its reliance on fossil fuels. For instance, the country could advocate for and advance renewable energy enterprises and technology. These initiatives would support economic growth by growing the share of renewable energy in the country’s total energy consumption, thereby replacing conventional, high-CO₂-emitting energy sources.

This paper has several limitations. For instance, it focuses solely on an oil-exporting nation, which has different environmental regulations, developmental phases, and other features that impact the environmental impression of natural resource exploitation. Additionally, this paper depends on readily available data, thus presenting inherent limitations, particularly in relation to renewable energy data. Despite these limitations, the authors assert that this research substantially improves the understanding of the interconnections among the agricultural expansion, energy consumption, economic growth, and CO₂ emissions in Saudi Arabia. Future researchers can expand on these environmental studies by exploring additional factors, such as water and soil contamination, biodiversity decline, and the effects of other greenhouse gases such as methane and nitrous oxide. They may also assess the effectiveness of renewable energy implementation, carbon pricing strategies, and green infrastructure projects in mitigating CO₂ emissions and promoting sustainability.