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Full Research Article

The Impact of Active Rigs on Agricultural Green Total Factor Productivity in the Organization of the Petroleum Exporting Countries

D
Dhornor Tarir Duok GAI1
0009-0006-7915-1873
Z
Zhaohui Qin1,*
M
Mihasina Harinaivo Andrianarimanana2
M
Manana Gaddis Elia1
W
Winnie Kudzai Mazheti1
T
Tiavina Andriamahenina Nasolomampionona1
1College of Economics and Management, China Three Gorges University, Yichang 443002, China.
2Department of Agri-Food Economics and Consumer Sciences, Laval University, Paul Comtois Bldg, Quebec City, Canada.

ABSTRACT

Background: This paper investigates the impact of active oil rigs (ARIGS) on agricultural green total factor productivity (AGTFP) in 12 organization of the petroleum exporting countries (OPEC) during the period from 1998 to 2019. Although ARIGS play a prominent role in OPEC economies, their ecological implications for agricultural productivity have received less attention.

Methods: We estimate a fixed-effects panel data model to explore the association between ARIGS, Oil Production (OPROD), GDP per capita, Population (POP) and AGTFP. The analysis includes control variables like methane (CH4), nitrous oxide (N2O) and the balance of payments in current accounts (CAB). OPROD is tested in a mediation analysis, with GDP and POP as moderators, including interaction terms.

Result: The findings show that AGTFP is negatively affected by ARIGS, a relationship mediated by OPROD. GDP alleviates the adverse effects of ARIGS and POP amplifies them. Such results highlight the need for policies to balance oil extraction and agricultural sustainability. Cited as an expected side effect of ARIGS, this underscores the need for OPEC nations to invest sufficiently in cleaner technologies and implement environmental regulations. Future studies should consider disaggregated data, as well as governance and institutional factors, to better understand the ARIGS-AGTFP association.

INTRODUCTION

This research covers the organization of the petroleum exporting countries (OPEC), as oil production (OPROD) is a crucial element in productive national economic structures, measured by active rigs (ARIGS). These economies are highly dependent on petroleum extraction for revenue, export receipts and economic growth. Yet the environmental impacts of this reliance are growing more profound, especially as they relate to sustainability in agriculture, according to (Alsalman et al., 2023). The extraction of oil adds to GDP but also generates negative externalities that affect Agricultural green total factor productivity (AGTFP), such as soil degradation, water pollution and greenhouse gas emissions (Razek et al., 2025). These environmental impacts are compounded by the broader effects of climate change, such as erratic rainfall, droughts and rising temperatures, which further threaten agricultural output. Dependence on oil can divert focus and energy away from developing agriculture-based practices (Chukwuneke et al., 2025). As such, the economic incentive of crude oil extraction needs to be weighed against its environmental impacts, especially in areas where agriculture is central to the rural economy and food security (Montant, 2025).
   
However, some studies articulate the general environmental effects of oil production in the literature; empirical research focusing on the impact of ARIGS on AGTFP through OPEC is scarce. There is little studies for the mediation and moderation roles of GDP per capita and population density (POP) in this relationship (Iqbal et al., 2025). This study addresses these gaps by providing an empirical analysis of the direct and indirect impacts of ARIGS on AGTFP, with a focus on their role in improving agricultural sustainability towards OPEC.
 
Literature review and hypotheses
 
The relationship between oil industry activity and agricultural sustainability in OPEC is limiting and represents a substantial research problem. In this study, ARIGS represents the extent of oil exploration and AGTFP considers not only output growth but also environment restrictions. AGTFP is an important statistical indicator for evaluating sustainable agricultural development (Shanmugan and Prakash, 2018). The association between activity in the oil industry as indicated by ARIGS and AGTFP for OPEC is not straightforward. Although these advanced reservoir intervention gas wells ARIGS enhance OPROD potential (Ansari, 2017). Industrial and price dynamics that block investment spillovers and therefore price stabilization, indirectly affect AGTFP (Zhou and Zhang, 2024). These dynamics are a function of the OPEC institutional role.
       
The Concepts of the resource curse and environmental sustainability among others are part of the theoretical framework (Omokpariola et al., 2025). However, from an environmental sustainability point of view oil recovery brings land degradation retrieving massive costs with ecosystem stress (Li et al., 2023). High rig activity levels may negatively affect AGTFP, whereas oil revenues impact GDP positively. Recent studies examine the correlation between climate policy and the oil-agriculture nexus and Find a 6.5% reduction in oil and gas investments while land tenure security has a positive effect AGTFP (Séogo and Zahonogo, 2023). The use of entropy in the green productivity assessment for China suggests a negative relationship between industrial efficiency and pollution through oil regions, which affected investor behavior to tilt their future investments towards AGTFP and sustainability (Wang et al., 2024). There is an empirical gap between developed, developing and other countries. In advanced economies, regrowth of environmental standards, cleaner technologies and higher adaptive capacity attenuate degradation effects on any agriculture from rig expansion and climate induced shocks. Limited investment in sustainable agricultural practices, governance issues surrounding land and climate proclivity to increased damage results in extractive action having a net negative influence on agricultural productivity and environmental degradation (Yu and Deng, 2021) within developing nations where there are less fortified institutions than their developed counterparts. Globally, however, the relationships between ARIGS and AGTFP are determined by the exposure to climate factors, economic structure, GDP and institutional quality (Kumar and Upadhyay, 2019).  Information in OPEC about the dual threats of oil dependence and climate risk about how ARIGS affect AGTFP are sorely lacking. This study addresses this gap.
 
Conceptual framework
 
The conceptual framework (Fig 1) illustrates the multifaceted impact of ARIGS on AGTFP, emphasizing both direct and indirect effects.

Fig 1: Conceptual framework.


 
The direct impact of the ARIGS on AGTFP
 
The AGTFP in OPEC and ARIGS. An Increase in the significant level of rig activity, along with a price rise, gives a green light for more contribution to CH4 emission and leads to ground process deterioration. This decreases the AGTFP, especially in regions where these trade-offs are not neutralized by digital innovation (Xiong et al., 2023). Hypothesis 1: The direct effect of ARIGS on AGTFP is negative and significant in OPEC.
 
The mediating effect of OPROD
 
The effect of ARIGS on AGTFP significantly depends on OPEC OPROD, significantly evident imposes a great influence on regional agricultural production. OPROD decisions are determined by global demand and non-OPEC supply. OPEC’s production policy varies with differences in technological advancement and market stability, which affect the economy’s performance (Cui et al., 2025). This role connects the energy industry to AGTFP and ecosystem management, in line with Hypothesis 2, in which OPROD suggests mediating effects.
 
The moderating role of GDP and POP
 
The roles of GDP growth and population are explored among OPEC that depend on fossil fuels. This potentially poses environmental and economic limitations (Montant, 2025). while GDP revenue also impacts the direction of development. As a result, it exacerbates ecological problems without making serious investments in greener capital. This is consistent with Hypothesis 3, as both POP and GDP moderate the effect of ARIGS on AGTFP.

MATERIAL AND METHODS

Data source and study area
 
This study utilizes data from two primary sources. The data were mainly collected in 2025 from the World Bank and OPEC databases (Table 1) at the Center of Research in the College of Management Science and Engineering, China Three Gorges University. All data were processed and analyzed using STATA version 17.0 and R version 4.5.2 to examine the impact of ARIGS on AGTFP in 12 existing OPEC countries: Algeria, Congo (Brazzaville), Equatorial Guinea, Gabon, Iran, Iraq, Kuwait, Libya, Nigeria, Saudi Arabia, UAE and Venezuela, based on panel studies over the period 1998-2019. The selection of these countries was based on their favorable policies regarding oil and food. This study focuses on oil-dependent economies, investigating how their resource-extraction infrastructure affects the sustainable use of agricultural land, especially amid digitalization and the shift away from reliance on a single industry.

Table 1: Variables used in the study.


 
Econometric models
 
We apply a fixed-effects panel-data model with moderating mediation to account for unobserved country-specific heterogeneity and identify country-level changes over time. This helps clarify the effect of AGTFP change on ARIGS. This paper examines the association between ARIGS, OPROD and GDP by using Baron and Kenny’s (Apergis and Ozturk, 2015). Mediation analysis. The econometric models are as follows:
 
          AGTFPit = β0 + β1ARIGSit+βi Xit + λi + εit            ...(1)
 
Where:
AGTFPit = Agricultural total factor productivity for country i at time t. 
ARIGSit = ARIGS in country i at time t is a vector.
Xit = control variables.
εit = Random disturbance term.
β0 = Model intercept term.
β1= Coefficient of the ARIGS due to the basic explanatory variables.
       
Finally, the moderate mediation of the long-term and the mediation by ARIGS, OPROD and GDP. The models become as:

 AGTFPit = β0 + β1ARIGSit+β2Controlit + λi + εit            ...(2)

  OPRODit = ∝0 + ∝1ARIGSit+ ∝2 GDPit+∝3 ARIGSit × GDPit+ Controlit+λi+εit      ...(3)

 AGTFP = ϕ0 + ϕ1 ARIGSit + ϕ2GDPit + ϕ3 ARIGSit × GDPit+ ϕ4 OPRODit + ϕ5POPit + ϕ6OPRODit × POPit + ϕ7 Controlit + λi + εit    ...(4)
 
Where:
GDPit = At time t for country i.
ARIGSit × GDPit = Indicates the interaction relationship between the ARIGS and GDP: Estimate at time t for country i.
OPRODit = At time t for country i;
POPit = Population at time t for country i;
ϕ6OPRODit× POPit = Interaction relationship between the OPROD and POP at time t for country i.
∝0 and ϕ0 = denotes the model intercept term.
∝1 to ∝3 and ϕ1 to ϕ6  = Coefficient of variables.
       
In the second equation, we regressed AGTFP on ARIGS along with control variables and the direct effect of ARIGS on AGTFP. The third equation includes OPROD and its interaction (ARIGS. GDP) and taking OPROD as a mediator variable between ARIGS and AGTFP, over the score of independent-valued GDP variables for the moderate relationship between effect estimates from the two models on the eastern and western sides. The fourth equation builds on this by adding a term for POP and its constructive interaction with the production coefficient, OPROD * POP.
 
Construction of the AGTFP index
 
The entropy method provides a systematic process for weighing; it can be used as an index of the complexity and diversity among different countries in an inclusion-level comprehensive index for some years, directly measured from the AGTFP.
Step 1: Normalization of the decision matrix

     
Step 2: Computation of the entropy measure of project outcomes using this equation:

 
In which:

            K = 1/ln(m)               ...(7)
 
Step 3: Defining the objective weight based on the entropy concept.

           Dj = 1 - Ej              ...(8)

      
Where,
m = Number of alternatives for the years of evaluation.
Pij = Standardization value.
xij = Actual value of evaluation index.
K = Entropy constant.
Ej  = Entropy.
Dj = Diversity.
Wj = Signifies the objective weight.
 
Step 4: AGTFP Formula, Output/Input Efficiency Ratio

  
Variance Inflation Factor (VIF) Formula.
 
To compute the VIF of a given independent variable is calculated using the following formula:

 
Where:
VIFj = Variance inflation factor for the j-th variable.
Rj2 = The coefficient of determination (R-squared) obtained when the j-th variable is regressed on all other variables.
       
Table 2 presents the AGTFP indicators, including inputs and outputs. It classifies AGTFP indicators by their description and measurement, characterizing both agricultural performance and environmental achievement. The inputs are: Agricultural area, freshwater withdrawal, labour and use of fertilizer; and the outputs are the food production index and CO2 emission, according to (Lu et al., 2024). These indicators are not only often used in TFP measurement, but also measure efficiency with non-zero slack.

Table 2: The indicators of AGTFP.

RESULTS AND DISCUSSIONS

Descriptive statistics in Table 3
 
The average value of AGTFP is 0.0835, with a range from 6.93e-06 to 0.229, indicating a moderate spread in efficiency. The mean ARIGS is 39.87, with extraction intensities ranging from 0 to 221. The average population is 32.67 million (range: 0.57-222.2 million), showing substantial demographic heterogeneity. GDP ranges from negative to positive values (-50.34 to 110.5). Financial volatility ranges from 13,983 to 173,664. OPROD (2,310-10,591) and Reserves (average = 87,616; maximum = 303,000) also vary, as CH4 and N2O signal environmental stress of sustainable agriculture.

Table 3: Descriptive statistics.


 
Benchmark result
 
The positive effect of ARIGS on AGTFP is particularly notable when it interacts with GDP (2.816) in Model 3, as illustrated in (Table 4). That means that all industrial activities that were encouraged by rigs in rich countries can improve agriculture productivity, which echoes the results of previous literature showing that technology transfer and better economic situations boost green agricultural outcomes, as stated by  (Ansari, 2017). But the negative impact of greenhouse gases also suggests that these benefits are not environmentally neutral. Policy-wise, this implies that high-income OPEC should encourage more drilling, manage methane emissions and reinvest oil revenues in climate-smart agriculture, land restoration and sustainable irrigation (Séogo and Zahonogo, 2023). The negative impacts of OPROD and AGTFP here indicate that energy production will provide environmental benefits, but its costs must also be identified in line with the claims made by (Qu et al., 2022). The moderating effects of GDP and population density also indicate that development and governance are important in dynamic oil-agriculture.

Table 4: Benchmark result.



Heterogeneity analysis
 
As illustrated in Table 5, the impact of ARIGS on AGTFP is greater in high-income countries than in low-middle-income countries. ARIGS generate positive effects in rich nations and negative effects in poor nations. The divergence indicates that the same extractive activity yields different outcomes, depending on the structure of the economy and the strength of institutions. Advanced economies have stronger regulations, superior technology and higher adaptive capacity to avoid the negative spillovers of drilling, while low-income countries face governance issues, low green investment and climate vulnerability that magnify the adverse effects of extraction on agriculture aligned with (Shanmugan and Prakash, 2018). The oil industries in low- and middle-income OPEC should include mandatory environmental impact assessments, stronger protection for agricultural land and rehabilitation funds paid by oil companies.

Table 5: Heterogeneity result.


 
Robustness results
 
The robustness of these associations across different model specifications is presented in (Table 6). Both AGTFP are consistently explained by the main and multiple term effects of ARIGS, GDP and their interaction, noting that GDP does not merely serve as a control variable but is critical to determining whether rig exposure translates into agricultural opportunity or risk. Findings are consistent with prior research that indicates productive spillovers from oil revenue emerge in instances where fiscal, production and institutional properties are aligned with (Wang et al., 2022). This fact implies that the policies of OPEC’ energy development should be linked to agricultural upgrading, rather than treated separately. These significant effects emerge in low-income countries, where countervailing policies can mitigate the adverse impacts of ARIGS.

Table 6: Robustness result.


 
Margins plot
 
The fluctuations of AGTFP in accordance with GDP and population are shown in (Fig 2). The positive effect of ARIGS on AGTFP becomes more pronounced as GDP increases, especially in high-income countries. Moreover, population density also significantly moderates the effect of OPROD on AGTFP. This verifies that macroscale capacity and demographic stress determine the scale and intensity of the AGTFP*ARIGS link. Far better GDP countries can convert oil revenue into productive agricultural inputs the similar finding was reported (Amand, 2020). The policy response must consider economic and demographic dynamics, in which oil revenues support sustainable agricultural practices to alleviate stress on the environment.

Fig 2: Margins plot of GDP and POP in AGTFP.


 
Correlation matrix
 
Strong multicollinearity between OPROD variables is observed in the pairwise correlation matrix illustrated in (Fig 3). The positive correlations (r = 0.88) between CO2 and OPROD, (OR r = 0.84), or (ARIGS, r = 0.77) corroborate previous studies that associate higher emissions with increased extraction activities 24-27. Relatedness among these variables is illustrated by the moderate correlations between CH4 and CO2 (r = 0.60) and between CH4 and OPROD emissions (r = 0.52), implying similar control challenges for these variables. Similar results were reported by (Otse et al., 2025). Hydrocarbon-related variables exhibit negative or weak correlations with both GDP and POP, with the notable exception of a weak negative correlation between GDP and CO2, OPROD and OR. POP is modestly positively correlated with ARIGS (r = 0.21), indicating weak collinearity with OPROD activities that align with (Kumar et al., 2024).

Fig 3: Multicollinearity correlation matrix of predictor variables.


       
The study suggests differentiated policy interventions appropriate to the economic and environmental circumstances across OPEC. High-income nations should focus on reforms to ARIGS efficiency and on the adoption of sustainable energy technologies, such as a renewable-based energy acquisition policy, which would have positive effects on AGTFP and reduce environmental degradation. By contrast, low and middle-income OPEC must implement policies that facilitate sustainable energy generation and integrate environmental sustainability in their industrial strategies. These capacity-building endeavors will safeguard agricultural prosperity in these regions (Semrau, 2026). The findings reveal that diversified enterprises could foster GDP growth and increase agricultural resilience, underscoring the need for policies to harmonize economic growth, the electricity sector and ecological sustainability in OPEC by embracing sustainable agriculture.

CONCLUSION

This study advances understanding of the complex relationship between ARIGS and AGTFP in OPEC. The findings reveal a context-dependent effect; while the Poorest countries experience more substantial adverse effects from environmental degradation, land competition and institutional weakness. Combining economic, demographic and ecological considerations, the present paper offers a more inclusive view of sustainable development in an oil-based economy. The results support the view that a country's wealth in oil revenue provides no guarantee of sustainable agriculture and that the sustainability outcome is influenced by governance systems and dynamics of technological change. To prevent the resource curse, OPEC needs to pursue sustainable oil investments that include greener technologies and policies. Caveats included pooled data and excluded recent issues. Future research should use disaggregated data and mixed methods to incorporate governance and institutional elements into the analysis. Policy implications include the need for stronger environmental regulation and investment in sustainable agriculture.

ACKNOWLEDGEMENT

The National Social Sciences Foundation of China (21BMZ138) and the Three Gorges Cultural and Economic Social Development Research Center (SXKF202204) supported this study.
 
Disclaimers
 
The views and conclusions presented in this article are those of the authors alone and do not necessarily reflect the perspectives of their affiliated institutions. The authors assume full responsibility for the accuracy of the information provided but disclaim any liability for direct or indirect consequences arising from the use of this content.
 
Informed consent
 
The relevant committees at the University of Animal Care approved all animal procedures and handling techniques.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest in relation to the publication of this article. The study design, data collection, analysis, decision to publish and manuscript preparation were not influenced by any funding or sponsorship.

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    Full Research Article

    The Impact of Active Rigs on Agricultural Green Total Factor Productivity in the Organization of the Petroleum Exporting Countries

    D
    Dhornor Tarir Duok GAI1
    0009-0006-7915-1873
    Z
    Zhaohui Qin1,*
    M
    Mihasina Harinaivo Andrianarimanana2
    M
    Manana Gaddis Elia1
    W
    Winnie Kudzai Mazheti1
    T
    Tiavina Andriamahenina Nasolomampionona1
    1College of Economics and Management, China Three Gorges University, Yichang 443002, China.
    2Department of Agri-Food Economics and Consumer Sciences, Laval University, Paul Comtois Bldg, Quebec City, Canada.

    ABSTRACT

    Background: This paper investigates the impact of active oil rigs (ARIGS) on agricultural green total factor productivity (AGTFP) in 12 organization of the petroleum exporting countries (OPEC) during the period from 1998 to 2019. Although ARIGS play a prominent role in OPEC economies, their ecological implications for agricultural productivity have received less attention.

    Methods: We estimate a fixed-effects panel data model to explore the association between ARIGS, Oil Production (OPROD), GDP per capita, Population (POP) and AGTFP. The analysis includes control variables like methane (CH4), nitrous oxide (N2O) and the balance of payments in current accounts (CAB). OPROD is tested in a mediation analysis, with GDP and POP as moderators, including interaction terms.

    Result: The findings show that AGTFP is negatively affected by ARIGS, a relationship mediated by OPROD. GDP alleviates the adverse effects of ARIGS and POP amplifies them. Such results highlight the need for policies to balance oil extraction and agricultural sustainability. Cited as an expected side effect of ARIGS, this underscores the need for OPEC nations to invest sufficiently in cleaner technologies and implement environmental regulations. Future studies should consider disaggregated data, as well as governance and institutional factors, to better understand the ARIGS-AGTFP association.

    INTRODUCTION

    This research covers the organization of the petroleum exporting countries (OPEC), as oil production (OPROD) is a crucial element in productive national economic structures, measured by active rigs (ARIGS). These economies are highly dependent on petroleum extraction for revenue, export receipts and economic growth. Yet the environmental impacts of this reliance are growing more profound, especially as they relate to sustainability in agriculture, according to (Alsalman et al., 2023). The extraction of oil adds to GDP but also generates negative externalities that affect Agricultural green total factor productivity (AGTFP), such as soil degradation, water pollution and greenhouse gas emissions (Razek et al., 2025). These environmental impacts are compounded by the broader effects of climate change, such as erratic rainfall, droughts and rising temperatures, which further threaten agricultural output. Dependence on oil can divert focus and energy away from developing agriculture-based practices (Chukwuneke et al., 2025). As such, the economic incentive of crude oil extraction needs to be weighed against its environmental impacts, especially in areas where agriculture is central to the rural economy and food security (Montant, 2025).
       
    However, some studies articulate the general environmental effects of oil production in the literature; empirical research focusing on the impact of ARIGS on AGTFP through OPEC is scarce. There is little studies for the mediation and moderation roles of GDP per capita and population density (POP) in this relationship (Iqbal et al., 2025). This study addresses these gaps by providing an empirical analysis of the direct and indirect impacts of ARIGS on AGTFP, with a focus on their role in improving agricultural sustainability towards OPEC.
     
    Literature review and hypotheses
     
    The relationship between oil industry activity and agricultural sustainability in OPEC is limiting and represents a substantial research problem. In this study, ARIGS represents the extent of oil exploration and AGTFP considers not only output growth but also environment restrictions. AGTFP is an important statistical indicator for evaluating sustainable agricultural development (Shanmugan and Prakash, 2018). The association between activity in the oil industry as indicated by ARIGS and AGTFP for OPEC is not straightforward. Although these advanced reservoir intervention gas wells ARIGS enhance OPROD potential (Ansari, 2017). Industrial and price dynamics that block investment spillovers and therefore price stabilization, indirectly affect AGTFP (Zhou and Zhang, 2024). These dynamics are a function of the OPEC institutional role.
           
    The Concepts of the resource curse and environmental sustainability among others are part of the theoretical framework (Omokpariola et al., 2025). However, from an environmental sustainability point of view oil recovery brings land degradation retrieving massive costs with ecosystem stress (Li et al., 2023). High rig activity levels may negatively affect AGTFP, whereas oil revenues impact GDP positively. Recent studies examine the correlation between climate policy and the oil-agriculture nexus and Find a 6.5% reduction in oil and gas investments while land tenure security has a positive effect AGTFP (Séogo and Zahonogo, 2023). The use of entropy in the green productivity assessment for China suggests a negative relationship between industrial efficiency and pollution through oil regions, which affected investor behavior to tilt their future investments towards AGTFP and sustainability (Wang et al., 2024). There is an empirical gap between developed, developing and other countries. In advanced economies, regrowth of environmental standards, cleaner technologies and higher adaptive capacity attenuate degradation effects on any agriculture from rig expansion and climate induced shocks. Limited investment in sustainable agricultural practices, governance issues surrounding land and climate proclivity to increased damage results in extractive action having a net negative influence on agricultural productivity and environmental degradation (Yu and Deng, 2021) within developing nations where there are less fortified institutions than their developed counterparts. Globally, however, the relationships between ARIGS and AGTFP are determined by the exposure to climate factors, economic structure, GDP and institutional quality (Kumar and Upadhyay, 2019).  Information in OPEC about the dual threats of oil dependence and climate risk about how ARIGS affect AGTFP are sorely lacking. This study addresses this gap.
     
    Conceptual framework
     
    The conceptual framework (Fig 1) illustrates the multifaceted impact of ARIGS on AGTFP, emphasizing both direct and indirect effects.

    Fig 1: Conceptual framework.


     
    The direct impact of the ARIGS on AGTFP
     
    The AGTFP in OPEC and ARIGS. An Increase in the significant level of rig activity, along with a price rise, gives a green light for more contribution to CH4 emission and leads to ground process deterioration. This decreases the AGTFP, especially in regions where these trade-offs are not neutralized by digital innovation (Xiong et al., 2023). Hypothesis 1: The direct effect of ARIGS on AGTFP is negative and significant in OPEC.
     
    The mediating effect of OPROD
     
    The effect of ARIGS on AGTFP significantly depends on OPEC OPROD, significantly evident imposes a great influence on regional agricultural production. OPROD decisions are determined by global demand and non-OPEC supply. OPEC’s production policy varies with differences in technological advancement and market stability, which affect the economy’s performance (Cui et al., 2025). This role connects the energy industry to AGTFP and ecosystem management, in line with Hypothesis 2, in which OPROD suggests mediating effects.
     
    The moderating role of GDP and POP
     
    The roles of GDP growth and population are explored among OPEC that depend on fossil fuels. This potentially poses environmental and economic limitations (Montant, 2025). while GDP revenue also impacts the direction of development. As a result, it exacerbates ecological problems without making serious investments in greener capital. This is consistent with Hypothesis 3, as both POP and GDP moderate the effect of ARIGS on AGTFP.

    MATERIAL AND METHODS

    Data source and study area
     
    This study utilizes data from two primary sources. The data were mainly collected in 2025 from the World Bank and OPEC databases (Table 1) at the Center of Research in the College of Management Science and Engineering, China Three Gorges University. All data were processed and analyzed using STATA version 17.0 and R version 4.5.2 to examine the impact of ARIGS on AGTFP in 12 existing OPEC countries: Algeria, Congo (Brazzaville), Equatorial Guinea, Gabon, Iran, Iraq, Kuwait, Libya, Nigeria, Saudi Arabia, UAE and Venezuela, based on panel studies over the period 1998-2019. The selection of these countries was based on their favorable policies regarding oil and food. This study focuses on oil-dependent economies, investigating how their resource-extraction infrastructure affects the sustainable use of agricultural land, especially amid digitalization and the shift away from reliance on a single industry.

    Table 1: Variables used in the study.


     
    Econometric models
     
    We apply a fixed-effects panel-data model with moderating mediation to account for unobserved country-specific heterogeneity and identify country-level changes over time. This helps clarify the effect of AGTFP change on ARIGS. This paper examines the association between ARIGS, OPROD and GDP by using Baron and Kenny’s (Apergis and Ozturk, 2015). Mediation analysis. The econometric models are as follows:
     
              AGTFPit = β0 + β1ARIGSit+βi Xit + λi + εit            ...(1)
     
    Where:
    AGTFPit = Agricultural total factor productivity for country i at time t. 
    ARIGSit = ARIGS in country i at time t is a vector.
    Xit = control variables.
    εit = Random disturbance term.
    β0 = Model intercept term.
    β1= Coefficient of the ARIGS due to the basic explanatory variables.
           
    Finally, the moderate mediation of the long-term and the mediation by ARIGS, OPROD and GDP. The models become as:

     AGTFPit = β0 + β1ARIGSit+β2Controlit + λi + εit            ...(2)

      OPRODit = ∝0 + ∝1ARIGSit+ ∝2 GDPit+∝3 ARIGSit × GDPit+ Controlit+λi+εit      ...(3)

     AGTFP = ϕ0 + ϕ1 ARIGSit + ϕ2GDPit + ϕ3 ARIGSit × GDPit+ ϕ4 OPRODit + ϕ5POPit + ϕ6OPRODit × POPit + ϕ7 Controlit + λi + εit    ...(4)
     
    Where:
    GDPit = At time t for country i.
    ARIGSit × GDPit = Indicates the interaction relationship between the ARIGS and GDP: Estimate at time t for country i.
    OPRODit = At time t for country i;
    POPit = Population at time t for country i;
    ϕ6OPRODit× POPit = Interaction relationship between the OPROD and POP at time t for country i.
    ∝0 and ϕ0 = denotes the model intercept term.
    ∝1 to ∝3 and ϕ1 to ϕ6  = Coefficient of variables.
           
    In the second equation, we regressed AGTFP on ARIGS along with control variables and the direct effect of ARIGS on AGTFP. The third equation includes OPROD and its interaction (ARIGS. GDP) and taking OPROD as a mediator variable between ARIGS and AGTFP, over the score of independent-valued GDP variables for the moderate relationship between effect estimates from the two models on the eastern and western sides. The fourth equation builds on this by adding a term for POP and its constructive interaction with the production coefficient, OPROD * POP.
     
    Construction of the AGTFP index
     
    The entropy method provides a systematic process for weighing; it can be used as an index of the complexity and diversity among different countries in an inclusion-level comprehensive index for some years, directly measured from the AGTFP.
    Step 1: Normalization of the decision matrix

         
    Step 2: Computation of the entropy measure of project outcomes using this equation:

     
    In which:

                K = 1/ln(m)               ...(7)
     
    Step 3: Defining the objective weight based on the entropy concept.

               Dj = 1 - Ej              ...(8)

          
    Where,
    m = Number of alternatives for the years of evaluation.
    Pij = Standardization value.
    xij = Actual value of evaluation index.
    K = Entropy constant.
    Ej  = Entropy.
    Dj = Diversity.
    Wj = Signifies the objective weight.
     
    Step 4: AGTFP Formula, Output/Input Efficiency Ratio

      
    Variance Inflation Factor (VIF) Formula.
     
    To compute the VIF of a given independent variable is calculated using the following formula:

     
    Where:
    VIFj = Variance inflation factor for the j-th variable.
    Rj2 = The coefficient of determination (R-squared) obtained when the j-th variable is regressed on all other variables.
           
    Table 2 presents the AGTFP indicators, including inputs and outputs. It classifies AGTFP indicators by their description and measurement, characterizing both agricultural performance and environmental achievement. The inputs are: Agricultural area, freshwater withdrawal, labour and use of fertilizer; and the outputs are the food production index and CO2 emission, according to (Lu et al., 2024). These indicators are not only often used in TFP measurement, but also measure efficiency with non-zero slack.

    Table 2: The indicators of AGTFP.

    RESULTS AND DISCUSSIONS

    Descriptive statistics in Table 3
     
    The average value of AGTFP is 0.0835, with a range from 6.93e-06 to 0.229, indicating a moderate spread in efficiency. The mean ARIGS is 39.87, with extraction intensities ranging from 0 to 221. The average population is 32.67 million (range: 0.57-222.2 million), showing substantial demographic heterogeneity. GDP ranges from negative to positive values (-50.34 to 110.5). Financial volatility ranges from 13,983 to 173,664. OPROD (2,310-10,591) and Reserves (average = 87,616; maximum = 303,000) also vary, as CH4 and N2O signal environmental stress of sustainable agriculture.

    Table 3: Descriptive statistics.


     
    Benchmark result
     
    The positive effect of ARIGS on AGTFP is particularly notable when it interacts with GDP (2.816) in Model 3, as illustrated in (Table 4). That means that all industrial activities that were encouraged by rigs in rich countries can improve agriculture productivity, which echoes the results of previous literature showing that technology transfer and better economic situations boost green agricultural outcomes, as stated by  (Ansari, 2017). But the negative impact of greenhouse gases also suggests that these benefits are not environmentally neutral. Policy-wise, this implies that high-income OPEC should encourage more drilling, manage methane emissions and reinvest oil revenues in climate-smart agriculture, land restoration and sustainable irrigation (Séogo and Zahonogo, 2023). The negative impacts of OPROD and AGTFP here indicate that energy production will provide environmental benefits, but its costs must also be identified in line with the claims made by (Qu et al., 2022). The moderating effects of GDP and population density also indicate that development and governance are important in dynamic oil-agriculture.

    Table 4: Benchmark result.



    Heterogeneity analysis
     
    As illustrated in Table 5, the impact of ARIGS on AGTFP is greater in high-income countries than in low-middle-income countries. ARIGS generate positive effects in rich nations and negative effects in poor nations. The divergence indicates that the same extractive activity yields different outcomes, depending on the structure of the economy and the strength of institutions. Advanced economies have stronger regulations, superior technology and higher adaptive capacity to avoid the negative spillovers of drilling, while low-income countries face governance issues, low green investment and climate vulnerability that magnify the adverse effects of extraction on agriculture aligned with (Shanmugan and Prakash, 2018). The oil industries in low- and middle-income OPEC should include mandatory environmental impact assessments, stronger protection for agricultural land and rehabilitation funds paid by oil companies.

    Table 5: Heterogeneity result.


     
    Robustness results
     
    The robustness of these associations across different model specifications is presented in (Table 6). Both AGTFP are consistently explained by the main and multiple term effects of ARIGS, GDP and their interaction, noting that GDP does not merely serve as a control variable but is critical to determining whether rig exposure translates into agricultural opportunity or risk. Findings are consistent with prior research that indicates productive spillovers from oil revenue emerge in instances where fiscal, production and institutional properties are aligned with (Wang et al., 2022). This fact implies that the policies of OPEC’ energy development should be linked to agricultural upgrading, rather than treated separately. These significant effects emerge in low-income countries, where countervailing policies can mitigate the adverse impacts of ARIGS.

    Table 6: Robustness result.


     
    Margins plot
     
    The fluctuations of AGTFP in accordance with GDP and population are shown in (Fig 2). The positive effect of ARIGS on AGTFP becomes more pronounced as GDP increases, especially in high-income countries. Moreover, population density also significantly moderates the effect of OPROD on AGTFP. This verifies that macroscale capacity and demographic stress determine the scale and intensity of the AGTFP*ARIGS link. Far better GDP countries can convert oil revenue into productive agricultural inputs the similar finding was reported (Amand, 2020). The policy response must consider economic and demographic dynamics, in which oil revenues support sustainable agricultural practices to alleviate stress on the environment.

    Fig 2: Margins plot of GDP and POP in AGTFP.


     
    Correlation matrix
     
    Strong multicollinearity between OPROD variables is observed in the pairwise correlation matrix illustrated in (Fig 3). The positive correlations (r = 0.88) between CO2 and OPROD, (OR r = 0.84), or (ARIGS, r = 0.77) corroborate previous studies that associate higher emissions with increased extraction activities 24-27. Relatedness among these variables is illustrated by the moderate correlations between CH4 and CO2 (r = 0.60) and between CH4 and OPROD emissions (r = 0.52), implying similar control challenges for these variables. Similar results were reported by (Otse et al., 2025). Hydrocarbon-related variables exhibit negative or weak correlations with both GDP and POP, with the notable exception of a weak negative correlation between GDP and CO2, OPROD and OR. POP is modestly positively correlated with ARIGS (r = 0.21), indicating weak collinearity with OPROD activities that align with (Kumar et al., 2024).

    Fig 3: Multicollinearity correlation matrix of predictor variables.


           
    The study suggests differentiated policy interventions appropriate to the economic and environmental circumstances across OPEC. High-income nations should focus on reforms to ARIGS efficiency and on the adoption of sustainable energy technologies, such as a renewable-based energy acquisition policy, which would have positive effects on AGTFP and reduce environmental degradation. By contrast, low and middle-income OPEC must implement policies that facilitate sustainable energy generation and integrate environmental sustainability in their industrial strategies. These capacity-building endeavors will safeguard agricultural prosperity in these regions (Semrau, 2026). The findings reveal that diversified enterprises could foster GDP growth and increase agricultural resilience, underscoring the need for policies to harmonize economic growth, the electricity sector and ecological sustainability in OPEC by embracing sustainable agriculture.

    CONCLUSION

    This study advances understanding of the complex relationship between ARIGS and AGTFP in OPEC. The findings reveal a context-dependent effect; while the Poorest countries experience more substantial adverse effects from environmental degradation, land competition and institutional weakness. Combining economic, demographic and ecological considerations, the present paper offers a more inclusive view of sustainable development in an oil-based economy. The results support the view that a country's wealth in oil revenue provides no guarantee of sustainable agriculture and that the sustainability outcome is influenced by governance systems and dynamics of technological change. To prevent the resource curse, OPEC needs to pursue sustainable oil investments that include greener technologies and policies. Caveats included pooled data and excluded recent issues. Future research should use disaggregated data and mixed methods to incorporate governance and institutional elements into the analysis. Policy implications include the need for stronger environmental regulation and investment in sustainable agriculture.

    ACKNOWLEDGEMENT

    The National Social Sciences Foundation of China (21BMZ138) and the Three Gorges Cultural and Economic Social Development Research Center (SXKF202204) supported this study.
     
    Disclaimers
     
    The views and conclusions presented in this article are those of the authors alone and do not necessarily reflect the perspectives of their affiliated institutions. The authors assume full responsibility for the accuracy of the information provided but disclaim any liability for direct or indirect consequences arising from the use of this content.
     
    Informed consent
     
    The relevant committees at the University of Animal Care approved all animal procedures and handling techniques.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest in relation to the publication of this article. The study design, data collection, analysis, decision to publish and manuscript preparation were not influenced by any funding or sponsorship.

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