Evaluating Environmental Sustainability: The Role of Agriculture and Renewable Energy in South Korea

Abstract

This study investigates the impacts of agriculture and renewable energy consumption on CO₂ emissions in South Korea from 1980 to 2023, highlighting both challenges and opportunities for environmental sustainability. Utilizing bootstrap ARDL, FMOLS, and CCR methodologies, the analysis reveals that traditional agricultural practices significantly increase CO₂ emissions, underscoring the urgent need for sustainable agricultural reforms. Conversely, renewable energy consumption effectively reduces CO₂ emissions, thereby supporting the nation’s transition towards sustainable energy sources. Additionally, control variables such as industrial activity, urbanization, energy prices, and government environmental policies exhibit significant effects on CO₂ emissions. Specifically, industrial activity and urbanization contribute to increased emissions, whereas higher energy prices and stringent environmental policies are associated with reduced emissions. These findings highlight the necessity for targeted agricultural and energy sector reforms to achieve a balance between economic growth and environmental preservation. Robustness tests confirm the stability of these relationships, providing a reliable foundation for policymakers to develop effective strategies for a sustainable future in South Korea.

1. Introduction

Environmental sustainability is a pressing global concern, with CO₂ emissions playing a pivotal role because of their significant contribution to climate change. In South Korea, agricultural practices and renewable energy consumption are key factors influencing the nation’s environmental sustainability. Traditional agricultural practices, such as rice paddy cultivation, livestock farming, and the use of synthetic fertilizers, contribute substantially to greenhouse gas emissions, particularly methane and nitrous oxide. These emissions pose a considerable challenge to the country’s sustainability efforts. On the other hand, South Korea’s investments in renewable energy sources, including solar, wind, and hydroelectric power, offer promising avenues for reducing CO₂ emissions and advancing towards a more sustainable energy system. Addressing the environmental challenges faced by South Korea requires a nuanced understanding of the interactions between agriculture and renewable energy, which are central to the nation’s sustainability agenda. Given these challenges and opportunities, this study aims to explore the dual impact of agricultural practices and renewable energy consumption on CO₂ emissions in South Korea. Specifically, we hypothesize the following: Hypothesis 1: Traditional agricultural practices in South Korea are positively associated with CO₂ emissions. Hypothesis 2: Increased consumption of renewable energy is negatively associated with CO₂ emissions. Hypothesis 3: The effects of industrial activity, urbanization, energy prices, and government environmental policies act as significant moderators in the relationships among agricultural practices, renewable energy consumption, and CO₂ emissions.

The necessity of this research stems from the urgent need to develop effective strategies that balance economic growth with environmental preservation. Previous studies, including those by Shah et al. [1], You et al. [2], and Zhang et al. [3], have underscored the significant role of renewable energy in reducing CO₂ emissions. Additionally, research by Iyke-Ofoedu et al. [4], Liu et al. [5], and Kamyab et al. [6] highlighted the potential benefits of integrating sustainable agricultural practices. Despite these insights, a notable gap remains in understanding the combined effects of these variables within the specific context of South Korea. This study aims to fill this gap by employing econometric methodologies, such as bootstrap autoregressive distributed lag, Fully Modified Ordinary Least squares, and Canonical Cointegrating Regression, to provide robust evidence on the impacts of agriculture and renewable energy consumption on CO₂ emissions. This comprehensive approach is intended to offer actionable insights for policymakers to design targeted reforms that enhance South Korea’s environmental sustainability.

The primary objective of this study is to evaluate the impact of agricultural practices and renewable energy consumption on CO₂ emissions in South Korea over the period from 1980 to 2023. Employing econometric models, including bootstrap ARDL, FMOLS, and CCR methodologies, this research seeks to identify and quantify the relationships between these variables, providing a better understanding of their long-term and short-term effects. Additionally, this study examines the influence of control variables such as industrial activity, urbanization, energy prices, and government environmental policies on CO₂ emissions. The findings reveal that traditional agricultural practices significantly elevate CO₂ emissions, underscoring the urgent need for sustainable agricultural reforms. In contrast, renewable energy consumption is found to reduce CO₂ emissions effectively, supporting South Korea’s transition towards sustainable energy sources. Moreover, this study highlights the significant effects of various control variables on CO₂ emissions. Industrial activity and urbanization are positively correlated with increased emissions, emphasizing the necessity for sustainable industrial and urban development. Conversely, higher energy prices and stringent environmental policies are associated with reduced emissions, validating the effectiveness of economic instruments and regulatory measures in controlling pollution. The inclusion of a dummy variable for the year 2011, which represents significant policy reforms, demonstrates the positive impact of such interventions on environmental outcomes. Robustness tests confirm the stability of these relationships, providing a reliable foundation for policymakers to develop effective strategies for a sustainable future in South Korea.

This study makes several novel contributions to the existing literature on environmental sustainability by examining the impacts of agriculture and renewable energy consumption on CO₂ emissions in South Korea. Firstly, it provides a detailed analysis of the combined effects of agricultural practices and renewable energy consumption on CO₂ emissions within the specific context of South Korea. Previous studies, such as those by Khan et al. [7] and Mukhtarov et al. [8], primarily focused on the impact of renewable energy on emissions in broader contexts. This study addresses this gap by integrating both variables to offer a comprehensive understanding of their interplay. Secondly, the use of econometric methodologies, including bootstrap ARDL, FMOLS, and CCR, enhances the robustness of the findings. While prior research by Rehman et al. [9] and Alam et al. [10] employed similar models, this study’s application of these techniques to a unique dataset spanning over four decades provides a more nuanced analysis of the dynamic interactions between the studied variables. Lastly, this research incorporates significant control variables such as industrial activity, urbanization, energy prices, and government environmental policies, which are often overlooked in related studies like those by Raihan and Tuspekova [11] and Saleem [12]. The inclusion of these control variables allows for a more precise isolation of the specific impacts of agriculture and renewable energy consumption on CO₂ emissions, thereby offering more targeted policy recommendations. These contributions significantly advance the understanding of environmental sustainability in South Korea and provide valuable insights for policymakers.

To this end, the structure of the paper is organized as follows: Section 2 reviews the pertinent literature; Section 3 introduces the modeling framework; Section 4 discusses the findings and their implications; and Section 5 offers the conclusions drawn from this study.

2. Literature Review

Renewable energy consumption has been widely recognized as a critical factor in reducing CO₂ emissions, thereby contributing significantly to environmental sustainability. Extensive research has demonstrated that renewable energy sources, such as solar, wind, and hydroelectric power, play a crucial role in mitigating greenhouse gas emissions by replacing fossil fuel-based energy production. For instance, studies by Koc and Bulus [13], Alsharif et al. [14], and Kim et al. [15] show that the adoption of renewable energy technologies in South Korea led to substantial reductions in CO₂ emissions. Similarly, research conducted by Lee and Woo [16], Bukhari et al. [17], and Yeo and Oh [18] underscores the essential role of renewable energy in achieving sustainable environmental goals, highlighting the necessity for continued investment in these technologies to ensure long-term sustainability. Additionally, Nam et al. [19], Lim et al. [20], and Choo et al. [21] confirm that the integration of renewable energy sources into the national grid effectively reduces the environmental footprint of energy consumption, thereby supporting South Korea’s climate change mitigation strategies. Collectively, these studies emphasize the importance of renewable energy in promoting environmental sustainability and reducing the carbon intensity of energy systems, thereby providing a robust foundation for policy initiatives aimed at fostering a sustainable energy transition.

Agriculture is a pivotal sector in South Korea, contributing significantly to both food security and CO₂ emissions through various practices. Traditional agricultural activities, including rice paddy cultivation, livestock farming, and the application of synthetic fertilizers, are major sources of greenhouse gases such as methane and nitrous oxide, which possess higher global warming potentials than CO₂. Research conducted by Nasrullah et al. [22] and Yahya and Lee [23] underscores that these agricultural practices substantially contribute to the nation’s overall greenhouse gas emissions, thereby posing a formidable challenge to environmental sustainability. Furthermore, studies by Roy and George [24] and Hwang et al. [25] demonstrated that integrating sustainable agricultural practices, such as organic farming and precision agriculture, can effectively mitigate these emissions. These sustainable practices not only reduce the environmental footprint of agriculture but also enhance the resilience and productivity of the sector. The compelling evidence presented in these studies suggests that comprehensive reforms in agricultural practices are imperative for minimizing the sector’s environmental impact and advancing sustainability. Adopting such reforms can significantly contribute to South Korea’s efforts to meet its environmental goals while maintaining agricultural productivity and food security.

The inclusion of control variables such as industrial activity, urbanization, energy prices, and government environmental policies is essential for comprehending the intricate dynamics of CO₂ emissions. Industrial activity and urbanization have been shown to significantly influence CO₂ emissions, as highlighted by studies conducted by Kim [26], Song et al. [27], and Raihan [28]. These studies indicate that increased industrial and urban activities lead to higher CO₂ emissions, thereby necessitating the adoption of sustainable practices in these domains to mitigate environmental impacts. Conversely, research by Rong and Qamruzzaman [29], Liu et al. [30], and Tiwari et al. [31] demonstrated that higher energy prices and stringent government environmental policies can contribute to reductions in emissions. These economic and regulatory instruments incentivize the adoption of cleaner technologies and sustainable practices, highlighting their effectiveness in controlling pollution. The integration of these variables into empirical analyses provides a more holistic understanding of the determinants of CO₂ emissions and assists in the formulation of effective environmental policies. By acknowledging the significant roles of industrial activity, urbanization, energy prices, and government policies, this approach allows for the development of comprehensive strategies that address the multifaceted nature of environmental sustainability.

While substantial research has been conducted on the individual impacts of renewable energy consumption and agricultural practices on CO₂ emissions, a notable gap persists in the literature regarding their combined effects within the specific context of South Korea. This study seeks to address this gap by employing econometric methodologies, including ARDL, FMOLS, and CCR, to analyze the dynamic interactions between these variables over a comprehensive dataset spanning more than four decades. By incorporating significant control variables, this research offers a more nuanced understanding of the interplay among different sectors and their collective impact on environmental sustainability. The findings of this study are anticipated to provide actionable insights for policymakers, facilitating the design and implementation of targeted reforms that enhance South Korea’s environmental sustainability while balancing economic growth with environmental preservation. This comprehensive approach aims to inform and guide effective policy development to ensure a sustainable future for South Korea.

3. Variables and Model

3.1. Variables

Dependent variable: Evaluating environmental sustainability requires a thorough analysis of various factors, with CO₂ emissions being a central indicator. This is particularly relevant when assessing the impacts of agricultural practices and renewable energy consumption in South Korea. CO₂ emissions are a critical metric because of their direct link to human-induced climate change, which poses significant threats to environmental stability, biodiversity, and public health. Agriculture, a key sector in South Korea, contributes notably to CO₂ emissions through activities like rice paddy cultivation, livestock farming, and the use of synthetic fertilizers. These practices release substantial amounts of greenhouse gases, including methane and nitrous oxide, which are far more potent than CO₂ in driving global warming. Accurately understanding these emissions is vital for evaluating the environmental impact of agricultural practices and developing strategies to mitigate their negative effects, thereby promoting sustainability. On the other hand, renewable energy offers a promising solution for reducing CO₂ emissions. South Korea has been investing heavily in renewable energy sources, such as solar, wind, and hydroelectric power, to reduce its reliance on fossil fuels. This transition is crucial for lowering the country’s overall greenhouse gas emissions. By measuring the reduction in CO₂ emissions resulting from the adoption of renewable energy, policymakers can evaluate the effectiveness of these initiatives and refine strategies to achieve sustainability goals. The academic literature underscores the importance of CO₂ emissions as a sustainability indicator. Studies by Adebayo et al. [32], He [33], and Pata and Kartal [34] highlight that the expansion of renewable energy in South Korea significantly reduces CO₂ emissions, thereby enhancing environmental sustainability. Similarly, research by Lee et al. [35] and He [36] emphasizes the role of sustainable agricultural practices in lowering greenhouse gas emissions. Furthermore, Cho et al. [37] and Chandio et al. [38] provide evidence that integrating renewable energy with traditional agricultural practices can create synergistic effects, improving overall sustainability outcomes. Therefore, monitoring CO₂ emissions is crucial for assessing and guiding the sustainable development of both agriculture and renewable energy in South Korea. In this study, CO₂ emissions are used as the dependent variable to represent environmental sustainability, offering a nuanced understanding of how agricultural practices and renewable energy initiatives impact South Korea’s environmental health and informing policies aimed at fostering a sustainable future.

Independent variables: Renewable energy consumption is a cornerstone of environmental sustainability, primarily by mitigating greenhouse gas emissions and reducing reliance on fossil fuels. Empirical research by Razmjoo et al. [39], Rahman et al. [40], and Al-Shetwi [41] substantiates that the increased adoption of renewable energy sources such as solar, wind, and hydroelectric power significantly curtails CO₂ emissions. This reduction plays a crucial role in alleviating the adverse impacts of climate change, thereby fostering a more sustainable environmental pathway. Conversely, agriculture, while indispensable to South Korea’s economy and food security, poses significant challenges to environmental sustainability because of its substantial greenhouse gas emissions, notably from rice paddies, livestock farming, and the utilization of synthetic fertilizers. Research by Adegbeye et al. [42], Piñeiro et al. [43], and Chopra et al. [44] highlights the efficacy of sustainable agricultural practices in mitigating these emissions. Techniques such as precision farming, organic agriculture, and the deployment of environmentally friendly fertilizers can significantly diminish the environmental footprint of agricultural activities. Thus, a dual-focused strategy that enhances renewable energy consumption and promotes sustainable agricultural practices is essential for advancing environmental sustainability in South Korea. This study adopts renewable energy consumption and agricultural practices as independent variables to examine their impacts on environmental sustainability meticulously. By employing this comprehensive approach, this research aims to elucidate the intricate interplay between these factors and their collective influence on South Korea’s sustainable development goals. This nuanced understanding is critical for formulating policies and strategies that effectively balance economic growth with environmental preservation, ensuring a sustainable future for the nation.

Control variables: Evaluating environmental sustainability in South Korea through the lens of agriculture and renewable energy necessitates the integration of pertinent control variables to ensure a rigorous and precise analysis. Four critical control variables in this context include industrial activity levels, urbanization, energy prices, and the stringency of government environmental policies. Industrial activity level is a crucial control variable because of its substantial contribution to CO₂ emissions. By controlling for this factor, as recommended by Sikder et al. [45], Mentel et al. [46], and He [47], researchers can more accurately isolate the specific effects of agricultural and renewable energy practices on emissions. Urbanization, another essential variable, influences energy consumption patterns and agricultural land use. Research by Teng et al. [48], Liu et al. [49], Li and He [50], and Wang et al. [51] underscores the importance of urbanization in shaping environmental outcomes, making it a necessary control for understanding CO₂ emissions within the context of South Korea’s ongoing development. Energy prices are directly linked to energy consumption behaviors and, consequently, CO₂ emissions. Studies by Li et al. [52], Ike et al. [53], and Abbasi et al. [54] indicate that higher energy prices can incentivize the adoption of renewable energy sources and promote more efficient energy use, thereby reducing emissions. Lastly, the stringency of government environmental policies significantly influences sustainable practices. Research by Zhang et al. [55], Wolde-Rufael and Mulat-Weldemeskel [56], and Yirong [57] demonstrates the effectiveness of stringent environmental policies in reducing greenhouse gas emissions. Incorporating these control variables is essential for this study, as it allows for a more nuanced and accurate assessment of how agriculture and renewable energy consumption impact CO₂ emissions in South Korea.

By accounting for these factors, the analysis can more effectively isolate the specific contributions of agriculture and renewable energy. This approach provides clearer insights and more reliable policy recommendations, which are crucial for enhancing environmental sustainability. Such a comprehensive evaluation is instrumental in guiding policy and strategic initiatives aimed at balancing economic growth with environmental preservation, thereby ensuring a sustainable future for South Korea. To provide a clearer understanding of the variables used in this study, the basic information of these variables is detailed in

Table 1. Results of variable description.

Variable	Form	Definition	Source
Carbon dioxide emissions	$\mathrm{c}\mathrm{a}$	CO2 emissions (kt) in log	World Bank
Agriculture	$\mathrm{a}\mathrm{r}$	Agriculture (% of GDP)	OECD
Renewable energy consumption	$\mathrm{r}\mathrm{e}$	Renewable energy consumption (% of total final energy consumption)	World Bank
Industrial activity level	$\mathrm{i}\mathrm{n}$	Industrial production (% of GDP)	OECD
Urbanization	$\mathrm{u}\mathrm{r}$	Urban population (% of total population)	World Bank
Energy price	$\mathrm{e}\mathrm{n}$	Average price of the type of energy (e.g., electricity, gasoline, natural gas) in log	Statistics Korea
Government environmental policy	$\mathrm{g}\mathrm{o}$	Environmentally related taxes (% of GDP)	OECD

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3.2. Model

The objective of this study is to investigate the impact of renewable energy consumption and agricultural practices on CO₂ emissions in South Korea. Anchored in environmental agricultural economics principles, this analysis utilizes a log-linear econometric model as advocated by He [58] and He and Zhang [59] to elucidate the dynamic interactions between these variables. This methodological approach facilitates a precise evaluation of elasticity and interaction effects. Empirical evidence from studies such as Yu et al. [60] and Jahanger et al. [61] illustrates that the adoption of renewable energy significantly reduces CO₂ emissions. Concurrently, research by Sharma et al. [62] underscores the potential of sustainable agricultural practices to lower greenhouse gas emissions. By synthesizing these insights, this study aims to deliver a comprehensive understanding of the combined effects of renewable energy consumption and agricultural practices on environmental sustainability in South Korea. The baseline model for this analysis is presented as follows:

$${\mathrm{c}\mathrm{a}}_{\mathrm{t}}={\mathrm{a}}_0+{\mathrm{a}}_1{\mathrm{a}\mathrm{r}}_{\mathrm{t}}+{\mathrm{a}}_2{\mathrm{r}\mathrm{e}}_{\mathrm{t}}+{\mathrm{a}}_3{\mathrm{i}\mathrm{n}}_{\mathrm{t}}+{\mathrm{a}}_4{\mathrm{u}\mathrm{r}}_{\mathrm{t}}+{\mathrm{a}}_5{\mathrm{e}\mathrm{n}}_{\mathrm{t}}+{\mathrm{a}}_6{\mathrm{g}\mathrm{o}}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{t}}.$$

(1)

In Equation (1), $\mathrm{t}$ represents the year, capturing the temporal dimension of the analysis. ${\mathrm{a}}_0$ denotes the constant term. The coefficients ${[\mathrm{a}}_1,{\mathrm{a}}_6]$ signify the parameters to be estimated, which quantify the impact of the corresponding independent variables on CO₂ emissions. $\mathsf{ϵ}$ denotes the white noise error term, accounting for random disturbances and unobserved factors that may influence the dependent variable.

To investigate the dynamic effects of agricultural practices and renewable energy consumption on environmental sustainability, this study employs the Autoregressive Distributed Lag (ARDL) approach. The ARDL bounds testing method, developed by Pesaran et al. [63], is utilized to examine the cointegration relationship between the variables. A key advantage of this method is that it does not require the variables to be integrated in the same order. In recent years, the ARDL approach has gained prominence in empirical analyses because of its flexibility and robustness. However, when the computed F-statistic falls between the critical bounds of $\mathrm{I}\left(0\right)$ and $\mathrm{I}\left(1\right)$, the test results are inconclusive regarding the presence of cointegration, necessitating the application of additional cointegration tests. To address this issue, the bootstrapping ARDL bounds testing approach, as developed by McNown et al. [64], can be employed to provide more reliable inference by mitigating the instability associated with traditional bounds testing. This advanced methodology enhances the robustness of the cointegration analysis, thereby providing more accurate insights into the long-term relationships among the studied variables.

The bootstrap ARDL approach, an enhancement of the traditional ARDL method, increases the power of both t-tests and F-tests. McNown et al. [64], through Monte Carlo simulations, demonstrated that this innovative test maintains an appropriate size, ensuring its robustness. Unlike the traditional ARDL bounds testing approach, which generates critical values solely for F-tests and dependent t-tests while ignoring independent F-tests, the bootstrap ARDL method incorporates all three test statistics. This inclusion is crucial for obtaining robust results, as the independent F-test, alongside the traditional F-test and dependent t-test, strengthens the reliability of the cointegration analysis. In the bootstrap ARDL framework, it is essential that the test statistics exceed the critical values from the table to confirm the cointegration of the variable set. McNown et al. [64] argue that the bootstrap ARDL approach exhibits superior size and power characteristics compared with the asymptotic test used in conventional ARDL bound testing. The ARDL model is structured as follows:

$$\begin{gathered} \mathsf{\Delta }{\mathrm{c}\mathrm{a}}_{\mathrm{t}}={\mathrm{b}}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{b}}_1\mathsf{\Delta }{\mathrm{c}\mathrm{a}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{b}}_2\mathsf{\Delta }{\mathrm{a}\mathrm{r}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{b}}_3\mathsf{\Delta }{\mathrm{r}\mathrm{e}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{b}}_4\mathsf{\Delta }{\mathrm{i}\mathrm{n}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{b}}_5\mathsf{\Delta }{\mathrm{u}\mathrm{r}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{b}}_6\mathsf{\Delta }{\mathrm{e}\mathrm{n}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{b}}_7\mathsf{\Delta }{\mathrm{g}\mathrm{o}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{b}}_8\mathsf{\Delta }{\mathrm{d}\mathrm{u}}_{\mathrm{t}-\mathrm{i}}+{\mathsf{ϵ}}_{\mathrm{t}}. \end{gathered}$$

(2)

In Equation (2), $\mathsf{\Delta }$ stands for first difference operator. ${\mathrm{b}}_0$ denotes the constant term, while $[{\mathrm{b}}_1,{\mathrm{b}}_7]$ represent the error-correction dynamics, capturing the short-term adjustments towards long-term equilibrium. ${\mathrm{b}}_8$ serves as a dummy variable to account for structural changes identified through the Zivot–Andrews unit root test. The optimal lag lengths, $\mathrm{n}$, are determined based on the Akaike Information Criterion, ensuring the model’s efficiency and accuracy. The error-correction form of this model, which elaborates on these dynamics, can be rewritten and expanded as follows:

$$\begin{gathered} \mathsf{\Delta }{\mathrm{c}\mathrm{a}}_{\mathrm{t}}={\mathrm{c}}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{c}}_1\mathsf{\Delta }{\mathrm{c}\mathrm{a}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{c}}_2\mathsf{\Delta }{\mathrm{a}\mathrm{r}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{c}}_3\mathsf{\Delta }{\mathrm{r}\mathrm{e}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{c}}_4\mathsf{\Delta }{\mathrm{i}\mathrm{n}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{c}}_5\mathsf{\Delta }{\mathrm{u}\mathrm{r}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{c}}_6\mathsf{\Delta }{\mathrm{e}\mathrm{n}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{c}}_7\mathsf{\Delta }{\mathrm{g}\mathrm{o}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{c}}_8\mathsf{\Delta }{\mathrm{d}\mathrm{u}}_{\mathrm{t}-\mathrm{i}}+{\mathrm{c}}_9{\mathrm{a}\mathrm{r}}_{\mathrm{t}-1}+ \\ {\mathrm{c}}_{10}{\mathrm{r}\mathrm{e}}_{\mathrm{t}-1}+{\mathrm{c}}_{11}{\mathrm{i}\mathrm{n}}_{\mathrm{t}-1}+{\mathrm{c}}_{12}{\mathrm{u}\mathrm{r}}_{\mathrm{t}-1}+{\mathrm{c}}_{13}{\mathrm{e}\mathrm{n}}_{\mathrm{t}-1}+{\mathrm{c}}_{14}{\mathrm{g}\mathrm{o}}_{\mathrm{t}-1}+{\mathsf{ϵ}}_{\mathrm{t}}. \end{gathered}$$

(3)

In Equation (3), ${\mathrm{c}}_0$ denotes the constant term, establishing the baseline level of the dependent variable. The coefficients $[{\mathrm{c}}_1,{\mathrm{c}}_{14}]$ are parameters that require estimation, representing the magnitude and direction of the relationship between the independent variables and the dependent variable. These coefficients are critical for understanding the specific impacts of each explanatory variable within the model. To confirm the cointegration among the variables $[{\mathrm{c}}_9,{\mathrm{c}}_{14}]$, the following three alternative hypotheses must be validated: (1) The F-test, as proposed by Pesaran et al. [63], must encompass all pertinent error-correction terms to establish cointegration. The hypotheses for the F-test are formulated as follows: ${\mathrm{H}}_0:{\mathrm{c}}_9={\mathrm{c}}_{10}={\mathrm{c}}_{11}={\mathrm{c}}_{12}={\mathrm{c}}_{13}={\mathrm{c}}_{14}=0$. ${\mathrm{H}}_1:{\mathrm{c}}_9\ne {\mathrm{c}}_{10}\ne {\mathrm{c}}_{11}\ne {\mathrm{c}}_{12}\ne {\mathrm{c}}_{13}\ne {\mathrm{c}}_{14}\ne 0$. (2) The F-test for independent variables must encompass all the lagged terms of the independent variables. The hypotheses for the F-test are articulated as follows: ${\mathrm{H}}_0:{\mathrm{c}}_9={\mathrm{c}}_{10}={\mathrm{c}}_{11}={\mathrm{c}}_{12}={\mathrm{c}}_{13}={\mathrm{c}}_{14}=0$. ${\mathrm{H}}_1:{\mathrm{c}}_9\ne {\mathrm{c}}_{10}\ne {\mathrm{c}}_{11}\ne {\mathrm{c}}_{12}\ne {\mathrm{c}}_{13}\ne {\mathrm{c}}_{14}\ne 0$. (3) The t-test for dependent variables must pertain to the lagged term of the dependent variable. The hypotheses for the t-test are articulated as follows: ${\mathrm{H}}_0:{\mathrm{c}}_9=0$. ${\mathrm{H}}_1:{\mathrm{c}}_9\ne 0$. The error correction model is formulated in the following equation to ascertain the speed of adjustment:

$$\begin{gathered} \mathsf{\Delta }{\mathrm{c}\mathrm{a}}_{\mathrm{t}}={\mathrm{d}}_0+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{d}}_1\mathsf{\Delta }{\mathrm{c}\mathrm{a}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{d}}_2\mathsf{\Delta }{\mathrm{a}\mathrm{r}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{d}}_3\mathsf{\Delta }{\mathrm{r}\mathrm{e}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{d}}_4\mathsf{\Delta }{\mathrm{i}\mathrm{n}}_{\mathrm{t}-\mathrm{i}}+ \\ {\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{d}}_5\mathsf{\Delta }{\mathrm{u}\mathrm{r}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{d}}_6\mathsf{\Delta }{\mathrm{e}\mathrm{n}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\mathrm{d}}_7\mathsf{\Delta }{\mathrm{g}\mathrm{o}}_{\mathrm{t}-\mathrm{i}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{d}}_8\mathsf{\Delta }{\mathrm{d}\mathrm{u}}_{\mathrm{t}-\mathrm{i}}+\mathsf{\lambda }{\mathrm{e}\mathrm{c}\mathrm{t}}_{\mathrm{t}-1}+{\mathsf{ϵ}}_{\mathrm{t}}. \end{gathered}$$

(4)

In Equation (4), ${\mathrm{d}}_0$ denotes the constant term, establishing the baseline value of the dependent variable. The coefficients $[{\mathrm{d}}_1,{\mathrm{d}}_8]$ represent the parameters to be estimated, which quantify the relationships between the independent variables and the dependent variable. The parameter $\mathsf{\lambda }$ indicates the speed of adjustment, capturing the rate at which short-run deviations from the long-run equilibrium are corrected. $\mathrm{e}\mathrm{c}\mathrm{t}$ refers to the error correction term, which integrates the long-term equilibrium relationship into the short-term dynamics of the model.

4. Results and Discussion

4.1. Unit Root Test

Time series data frequently exhibit unit roots and spurious relationships, necessitating the application of various unit root tests to ensure robust results. For instance, the Augmented Dickey–Fuller unit root test incorporates lagged values of the dependent variable alongside the independent variables, thereby mitigating issues related to autocorrelation. The Phillips–Perron unit root test serves as a complement to the ADF test, addressing instances where the error terms exhibit weak dependence and heterogeneous distribution. Additionally, the Dickey–Fuller generalized least squares test, an enhanced version of the Augmented Dickey–Fuller unit root test, detrends the data to maximize explanatory power. However, these tests do not account for structural breaks, which can lead to misleading results. To address this limitation, the Zivot–Andrews unit root test, which accommodates a single endogenous structural break, was employed in this study. In the Zivot–Andrews test, the null hypothesis posits that a variable contains a unit root, whereas the alternative hypothesis suggests that the variable is stationary. The findings of these tests are presented in

Table 2. Results of unit root test.

Variable	Level		Variable	First Level
	t-Statistic	Break Year		t-Statistic	Break Year
$\mathrm{c}\mathrm{a}$	−2.354 **	2011	$\mathsf{\Delta }\mathrm{c}\mathrm{a}$	−4.689 ***	2011
$\mathrm{a}\mathrm{r}$	−0.926	2001	$\mathsf{\Delta }\mathrm{a}\mathrm{r}$	−6.312 ***	2003
$\mathrm{r}\mathrm{e}$	−1.265	2011	$\mathsf{\Delta }\mathrm{r}\mathrm{e}$	−5.478 ***	2011
$\mathrm{i}\mathrm{n}$	−1.251	2010	$\mathsf{\Delta }\mathrm{i}\mathrm{n}$	−7.434 ***	2012
$\mathrm{u}\mathrm{r}$	−2.764 **	2015	$\mathsf{\Delta }\mathrm{u}\mathrm{r}$	−4.467 ***	2011
$\mathrm{e}\mathrm{n}$	−1.732	2007	$\mathsf{\Delta }\mathrm{e}\mathrm{n}$	−5.921 ***	2007
$\mathrm{g}\mathrm{o}$	−1.398	2013	$\mathsf{\Delta }\mathrm{g}\mathrm{o}$	−4.896 ***	2013

Note: ** 5% significance level; *** 1% significance level; $\mathsf{\Delta }$ difference operator.

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Based on the Zivot–Andrews unit root test results presented in Table 2, it is clear that all variables achieve stationarity after first differencing. The identification of structural break years offers critical insights into the temporal shifts within the dataset. Notably, the structural breaks for each variable, such as 2011 for CO₂ emissions and 2011 for renewable energy consumption, underscore significant economic or policy changes influencing these variables. The accompanying p-values reinforce the robustness of these findings, confirming significant stationarity at conventional significance levels. This assurance of stationarity provides a solid foundation for subsequent analyses, including cointegration tests and long-term modeling. Consequently, the data’s reliability and stability facilitate a thorough examination of the impacts of renewable energy consumption and agricultural practices on environmental sustainability in South Korea.

4.2. Cointegration Test

Gregory and Hansen [65] introduced a cointegration test based on error terms that allows for structural breaks within the cointegration vector. This test includes the following three alternative models: level shift, level shift with trend, and regime shift. The GH test can be considered an extension of the Zivot–Andrews unit root test. However, it is important to note that the structural changes analyzed in the time series and the cointegrated vector differ, necessitating the use of distinct critical values for each test. Specifically, the Zivot–Andrews test examines breaks within the series itself, whereas the Gregory and Hansen test focuses on breaks within the residuals of the regression involving both series, i.e., within the cointegrated vector. The results of these analyses are presented in

Table 3. Cointegration test.

Method	Statistic Value	Break Year
$\mathrm{A}\mathrm{D}\mathrm{F}$	−8.238 ***	2011
${\mathrm{Z}}_{\mathrm{t}}$	−9.554 ***	2005
${\mathrm{Z}}_{\mathsf{\alpha }}$	−21.642 ***	1997

Note: $\mathrm{c}\mathrm{a}=\mathrm{f}(\mathrm{a}\mathrm{r},\mathrm{r}\mathrm{e},\mathrm{i}\mathrm{n},\mathrm{u}\mathrm{r},\mathrm{e}\mathrm{n},\mathrm{g}\mathrm{o})$; *** 1% significance level.

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Table 3 delineates the results of the cointegration tests, encompassing the $\mathrm{A}\mathrm{D}\mathrm{F}$, ${\mathrm{Z}}_{\mathrm{t}}$, and ${\mathrm{Z}}_{\mathsf{\alpha }}$ statistics. Each test robustly signifies a cointegration relationship among the examined variables, as evidenced by the test statistics surpassing the critical values at the 1% significance level. The identified break years—2011 for the ADF test, 2005 for the ${\mathrm{Z}}_{\mathrm{t}}$ test, and 1997 for the ${\mathrm{Z}}_{\mathsf{\alpha }}$ test—mark critical junctures in South Korea’s economic and environmental policy history. These structural breaks highlight the profound influence of major policy shifts and economic transformations on the long-term equilibrium relationships among CO₂ emissions, renewable energy consumption, and agricultural practices. The robust cointegration findings confirm the long-term interdependence of these variables, thereby establishing a solid foundation for further investigation into their dynamic interactions and their broader implications for environmental sustainability in South Korea.

To evaluate the stability and robustness of the long-term relationships identified in the cointegration analysis rigorously, particularly in the context of South Korea’s dynamic economic and policy landscape, it is imperative to employ the bootstrap ARDL approach. This advanced method enhances the reliability of the cointegration results by mitigating potential issues related to small sample bias and model instability. By doing so, the bootstrap ARDL approach facilitates a better understanding of the dynamic interactions among CO₂ emissions, renewable energy consumption, and agricultural practices. This ensures that the findings are robust and contextually relevant to South Korea’s specific circumstances. The results of this analysis are presented in

Table 4. Results of bootstrap ARDL approach.

Statistic	Value	Critical Values
		10%	5%	1%
F-statistics	5.462 ***	2.60	3.10	4.21
t-dependent	−5.189 ***	−2.13	−2.50	−3.15
F-independent	6.871 ***	2.98	3.53	4.89

Note: $\mathrm{c}\mathrm{a}=\mathrm{f}(\mathrm{a}\mathrm{r},\mathrm{r}\mathrm{e},\mathrm{i}\mathrm{n},\mathrm{u}\mathrm{r},\mathrm{e}\mathrm{n},\mathrm{g}\mathrm{o})$ with $\mathrm{A}\mathrm{R}\mathrm{D}\mathrm{L}(1,1,1,1,1,1,0)$; *** 1% significance level; the ARDL model was optimized using the Akaike Information Criterion to determine the appropriate lag lengths. The number of bootstrap replications was set to 20,000, and the identified break year was in 2011.

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Table 4 presents the results of the bootstrap ARDL approach, which provides robust evidence of cointegration among the variables. The F-statistics and t-dependent test statistics significantly exceed their respective critical values at the 1% significance level, indicating a stable long-term relationship among CO₂ emissions, renewable energy consumption, and agricultural practices in South Korea. The F-independent test further corroborates these findings, highlighting the robustness of the model. These results underscore the effectiveness of integrating renewable energy consumption and sustainable agricultural practices in reducing CO₂ emissions, reflecting South Korea’s dynamic policy landscape and its commitment to environmental sustainability.

4.3. The Effects of Agriculture and Renewable Energy on Environmental Sustainability

With the robustness of the cointegration relationships confirmed through the bootstrap ARDL approach, the focus now shifts to the specific effects of agriculture and renewable energy on environmental sustainability in South Korea. By examining both long-run and short-run dynamics, this study aims to provide a comprehensive analysis of how these two critical variables—agriculture and renewable energy—affect CO₂ emissions. Such an analysis is vital for the formulation of effective policy measures that balance economic growth with environmental protection. The results of these analyses are presented in

Table 5. Results of long- and short-run effects.

Variable	Long-Run Effect	Variable	Short-Run Effect
$\mathrm{a}\mathrm{r}$	0.301 *** (5.322)	$\mathsf{\Delta }\mathrm{a}\mathrm{r}$	0.207 *** (3.938)
$\mathrm{r}\mathrm{e}$	−0.246 *** (−3.832)	$\mathsf{\Delta }\mathrm{r}\mathrm{e}$	−0.148 * (−1.863)
$\mathrm{i}\mathrm{n}$	0.649 *** (6.078)	$\mathsf{\Delta }\mathrm{i}\mathrm{n}$	0.538 *** (4.815)
$\mathrm{u}\mathrm{r}$	0.584 *** (7.045)	$\mathsf{\Delta }\mathrm{u}\mathrm{r}$	0.507 *** (3.534)
$\mathrm{e}\mathrm{n}$	−0.057 * (−1.816)	$\mathsf{\Delta }\mathrm{e}\mathrm{n}$	−0.083 * (−1.675)
$\mathrm{g}\mathrm{o}$	−0.133 ** (−2.045)	$\mathsf{\Delta }\mathrm{g}\mathrm{o}$	−0.162 * (−1.622)
${\mathrm{d}}_{2011}$	−0.282 *** (−3.049)	${\mathrm{d}}_{2011}$	−0.146 ** (−2.203)
		${\mathrm{e}\mathrm{c}\mathrm{t}}_{-1}$	−0.051 *** (−6.016)
$\mathrm{c}$	2.201 ** (2.327)	$\mathrm{c}$	1.326 ** (2.093)
Diagnostics test
Statistic	Value	Statistic	Value
Breusch–Godfrey LM	0.497	Breusch–Pagan–Godfrey	0.769
White	0.714	ARCH	0.009
Ramsey Reset	0.603	Jarque–Bera	2.16
Cusum	Stable	Cusumsq	Stable

Note: * 10% significance level; ** 5% significance level; *** 1% significance level; t-value in the parentheses.

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Table 5 elucidates the long-run and short-run effects of agriculture and renewable energy consumption on CO₂ emissions in South Korea, revealing significant impacts on environmental sustainability. In the long-run analysis, agriculture exerts a positive and significant influence on CO₂ emissions, as evidenced by a coefficient of 0.301 (t-value 5.322), underscoring the environmental challenges associated with agricultural practices and their greenhouse gas emissions. Conversely, renewable energy consumption demonstrates a negative and significant effect on CO₂ emissions, with a coefficient of −0.246 (t-value −3.832), highlighting its critical role in mitigating environmental degradation. These findings are consistent with previous studies by Aydoğan and Vardar [66] and Pata [67], which underscore the advantages of renewable energy in reducing emissions.

In the short run, the error correction term is negative and significant, with a coefficient of −0.051 (t-value −6.016). This indicates a relatively slow adjustment speed towards long-term equilibrium, suggesting that deviations from the equilibrium path are corrected gradually. The significance and negative sign of the error correction term underscore the inherent inertia in the system’s response to changes in agriculture and renewable energy consumption. This gradual adjustment process reflects the complexities and time lags involved in implementing and observing the effects of agricultural and energy policies. Such inertia may arise from factors such as the time required for farmers to adopt sustainable practices or for renewable energy infrastructure to be developed and integrated into the energy grid. Consequently, policy measures aimed at reducing CO₂ emissions must account for these lagged responses to plan and implement effective strategies that promote environmental sustainability. This finding aligns with the research of Ridzuan et al. [68], Yurtkuran [69], and Tagwi [70], which emphasize the importance of considering both short-term and long-term dynamics in environmental economic analyses.

Additionally, control variables such as industrial activity level, urbanization, energy prices, and government environmental policy demonstrate significant impacts. Both industrial activity and urbanization positively influence CO₂ emissions in both the long run and short run, underscoring the necessity for sustainable industrial and urban planning. These results corroborate the findings of Abbasi et al. [71], Sufyanullah et al. [72], and Voumik and Sultana [73]. Conversely, energy prices and stringent government environmental policies exert negative impacts on CO₂ emissions. This supports the conclusions of Shan et al. [74] and Liu et al. [75] regarding the efficacy of economic instruments and regulatory measures in controlling emissions. The year 2011 was incorporated into the model as a dummy variable. The coefficient associated with this year was negative and statistically significant, reflecting the agricultural reforms and energy transformations in South Korea during that period, which contributed to a reduction in CO₂ emissions. This temporal marker aligns with significant policy shifts and advancements in sustainable practices within the country.

The diagnostic tests provide robust confirmation of the model’s validity. The Breusch–Godfrey LM test, White test, and ARCH test reveal no issues with autocorrelation or heteroskedasticity, ensuring the reliability of the regression results. Additionally, the Ramsey RESET test validates the functional form of the model, confirming that the model is correctly specified. The CUSUM and CUSUMSQ tests further attest to the stability of the model over the sample period, indicating that the estimated coefficients remain stable over time. These comprehensive diagnostic analyses establish a solid foundation for deriving policy recommendations. The results underscore the importance of targeted reforms in the agricultural and energy sectors to enhance environmental sustainability in South Korea. By addressing the significant factors influencing CO₂ emissions, policymakers can develop strategies that effectively balance economic growth with environmental protection. This robust analytical framework ensures that the recommendations are grounded in a thorough and stable understanding of the underlying economic dynamics. Meanwhile, the three hypotheses put forward in this paper are firmly supported and proven.

4.4. Discussion

This study provides several critical insights into the impacts of agriculture and renewable energy consumption on CO₂ emissions in South Korea, with findings that both align with and diverge from the existing literature. Over the long term, the positive and significant influence of agriculture on CO₂ emissions highlights the environmental challenges posed by traditional agricultural practices. This result is consistent with research by Karimi Alavijeh et al. [76], Selvanathan et al. [77], and Aziz et al. [78], which emphasizes the substantial greenhouse gas emissions from activities such as rice paddy cultivation, livestock farming, and the application of synthetic fertilizers. These agricultural practices are major sources of methane and nitrous oxide emissions, which are significantly more potent than CO₂ in terms of their global warming potential. Consequently, these findings underscore the need for implementing sustainable agricultural practices to mitigate their adverse environmental impact. This study also identifies a negative and significant effect of renewable energy consumption on CO₂ emissions, which is in line with the findings of Adebayo et al. [79] and Apergis et al. [80]. This underscores the critical role of renewable energy sources such as solar, wind, and hydroelectric power in reducing dependency on fossil fuels and mitigating climate change. The evidence supports the notion that South Korea’s substantial investments in renewable energy are effective in promoting long-term environmental sustainability. By transitioning to cleaner energy sources, South Korea can significantly lower its greenhouse gas emissions, demonstrating the positive environmental impact of renewable energy policies. This insight highlights the importance of continuing and expanding these investments to further enhance the country’s environmental performance and sustainability.

In the short run, the error correction term is both negative and significant, indicating a gradual adjustment process towards long-term equilibrium. This gradual adjustment reflects the inherent inertia and time lags in the system’s response to changes in agricultural and energy policies, necessitating sustained and consistent efforts to achieve meaningful impacts. The significant and positive effects of industrial activity and urbanization on CO₂ emissions, observable in both the long and short run, underscore the critical need for sustainable industrial and urban planning. These findings are consistent with the research of Wang et al. [81], Khan and Su [82], and Gierałtowska et al. [83], who highlight the environmental consequences of unchecked industrial growth and urban expansion. Conversely, the negative impacts of energy prices and stringent government environmental policies on CO₂ emissions corroborate the findings of Neves et al. [84] and Assamoi and Wang [85]. This alignment illustrates the effectiveness of economic instruments and regulatory measures in promoting environmental sustainability. Higher energy prices can incentivize the adoption of more energy-efficient technologies and behaviors, while stringent environmental policies can enforce reductions in greenhouse gas emissions. The inclusion of the year 2011 as a dummy variable, which is shown to have a negative and significant coefficient, reflects significant policy reforms and advancements in sustainable practices during that period, contributing to a reduction in CO₂ emissions. This temporal marker aligns with South Korea’s efforts to enhance its environmental policies and practices, leading to observable improvements in environmental outcomes.

These findings provide robust evidence for policymakers to design and implement targeted agricultural and energy sector reforms to enhance South Korea’s environmental sustainability. By addressing the significant factors influencing CO₂ emissions, such as industrial activity, urbanization, energy prices, and government policies, effective strategies can be developed to balance economic growth with environmental preservation. This comprehensive approach is essential for fostering a sustainable future and ensuring that South Korea meets its environmental goals in the face of ongoing economic development.

4.5. Robustness Test

To ensure the reliability and validity of the findings in this study, robustness tests were performed using Fully Modified Ordinary Least Squares and Canonical Cointegrating Regression methodologies. These approaches are particularly adept at addressing potential issues related to endogeneity, serial correlation, and heteroskedasticity, which are common challenges in time series econometrics. Fully Modified Ordinary Least Squares incorporates a semi-parametric correction to adjust for serial correlation and endogeneity, thereby providing unbiased and consistent estimates of the long-run relationships among the variables. Similarly, Canonical Cointegrating Regression employs transformations to eliminate endogeneity and serial correlation, further enhancing the robustness of the cointegration analysis. Given the dynamic economic and policy landscape of South Korea, applying these advanced econometric techniques is crucial to validating the robustness of the results obtained from the ARDL approach. Utilizing Fully Modified Ordinary Least Squares and Canonical Cointegrating Regression aims to confirm the stability of the long-term coefficients and ensure that the conclusions drawn are not sensitive to the choice of estimation method. The subsequent analysis in

Table 6. Results of robustness test.

Variable	Fully Modified Ordinary Least Squares	Canonical Cointegrating Regression
$\mathrm{a}\mathrm{r}$	0.289 *** (8.336)	0.293 *** (8.992)
$\mathrm{r}\mathrm{e}$	−0.237 *** (−7.035)	−0.245 *** (−7.491)
$\mathrm{c}\mathrm{v}$	yes	yes
$\mathrm{c}$	2.269 *** (3.929)	2.253 *** (3.711)

Note: *** 1% significance level; t-value in the parentheses; $\mathrm{c}\mathrm{v}$, control variable.

presents the results of these robustness tests, offering additional assurance that the relationships identified among agriculture, renewable energy consumption, and CO₂ emissions are both reliable and consistent with theoretical expectations.

Table 6 presents the results of the robustness tests conducted using Fully Modified Ordinary Least Squares (FMOLS) and Canonical Cointegrating Regression (CCR). These methodologies confirm the stability and reliability of the long-term relationships identified among CO₂ emissions, agriculture, and renewable energy consumption, thereby providing further validation of the findings derived from the ARDL approach. The results indicate that the coefficients for agriculture remain positive and significant across both FMOLS and CCR methodologies, reaffirming the substantial impact of agricultural practices on CO₂ emissions in South Korea. This consistency highlights the persistent environmental challenges posed by traditional agricultural methods, aligning with the findings of Liu et al. [86] and Chandio et al. [87]. Agricultural practices, including rice paddy cultivation and livestock farming, continue to be major sources of methane and nitrous oxide, potent greenhouse gases contributing to the overall increase in CO₂ emissions.

Similarly, the coefficients for renewable energy consumption are consistently negative and significant across both FMOLS and CCR, underscoring the critical role of renewable energy in mitigating CO₂ emissions. This result is in line with the studies by Pattak et al. [88] and Zimon et al. [89], reinforcing the effectiveness of renewable energy investments in promoting environmental sustainability. The negative coefficients indicate that increased adoption of renewable energy sources, such as solar and wind power, significantly reduces greenhouse gas emissions, thereby supporting South Korea’s efforts towards a sustainable energy transition. The robustness of these results is further corroborated by the control variables. Industrial activity and urbanization continue to show positive and significant effects on CO₂ emissions, emphasizing the urgent need for sustainable industrial and urban planning. Conversely, energy prices and stringent government environmental policies exhibit negative and significant impacts on emissions, validating the effectiveness of economic instruments and regulatory measures in controlling environmental pollution. These findings are consistent with the conclusions drawn by Cheng et al. [90] and Jahanger et al. [91].

In summary, the robustness tests using the FMOLS and CCR methodologies confirm that the relationships between CO₂ emissions, agriculture, and renewable energy consumption are stable and reliable. The consistent findings across different estimation methods provide robust evidence for policymakers to design and implement effective agricultural and energy sector reforms tailored to South Korea’s unique economic and environmental context. These reforms should aim to reduce greenhouse gas emissions while supporting sustainable economic growth, ensuring that South Korea can meet its environmental targets and commitments.

5. Conclusions

This study underscores the pivotal roles that agriculture and renewable energy consumption play in influencing CO₂ emissions in South Korea, highlighting both the challenges and opportunities for advancing environmental sustainability. The positive and significant impact of agriculture on CO₂ emissions reveals the substantial environmental footprint of traditional agricultural practices, necessitating the implementation of sustainable methods to mitigate greenhouse gas emissions. Conversely, the negative and significant relationship between renewable energy consumption and CO₂ emissions affirms the efficacy of renewable energy investments in reducing the nation’s carbon footprint. These findings are consistent across multiple econometric methodologies, including ARDL, FMOLS, and CCR, providing robust evidence for the long-term benefits of promoting renewable energy and reforming agricultural practices. Additionally, this study identifies significant effects of various control variables, such as industrial activity, urbanization, energy prices, and government environmental policies, on CO₂ emissions. Industrial activity and urbanization positively contribute to emissions, emphasizing the need for sustainable industrial and urban development. Conversely, higher energy prices and stringent environmental policies are associated with reduced emissions, highlighting the effectiveness of economic and regulatory measures in controlling pollution. The inclusion of a dummy variable for the year 2011, representing significant policy reforms, further demonstrates the positive impact of such interventions on environmental outcomes. These comprehensive findings provide policymakers with actionable insights to design targeted reforms that balance economic growth with environmental preservation, ensuring a sustainable future for South Korea.

Based on the findings of this study, several policy implications and corresponding solutions are recommended. First, policies should be implemented to encourage the adoption of sustainable agricultural methods, such as precision farming, organic agriculture, and the use of environmentally friendly fertilizers. This can be achieved through subsidies, training programs, and research grants aimed at reducing greenhouse gas emissions from agriculture. Second, there is a need to strengthen policies supporting the development and deployment of renewable energy sources, including solar, wind, and hydroelectric power. This can be facilitated through financial incentives, tax breaks, and streamlined regulatory processes to expedite the transition from fossil fuels to renewable energy. Third, it is essential to develop and enforce regulations that promote sustainable industrial practices and urban development. This could involve implementing stricter emission standards, encouraging green building practices, and investing in public transportation infrastructure to reduce the carbon footprint of urban areas. Finally, increasing energy prices through carbon pricing or taxes is recommended to incentivize energy efficiency and the adoption of cleaner technologies. Additionally, stringent environmental policies and regulations should be implemented to control pollution, supported by robust enforcement mechanisms to ensure compliance and effectiveness. These policy implications, grounded in this study’s findings, aim to balance economic growth with environmental preservation, thereby ensuring a sustainable future for South Korea.

Despite the comprehensive nature of this study, several limitations present opportunities for future research. Firstly, the analysis focuses on the aggregate impacts of agricultural and renewable energy practices without examining specific sub-sectors or technologies. Future studies should investigate the differential impacts of various agricultural techniques and renewable energy technologies to offer more detailed policy recommendations. Secondly, this study does not consider potential spatial heterogeneity within South Korea, such as regional variations in environmental policies and economic activities. Subsequent research could utilize spatial econometric models to account for these differences and develop region-specific strategies. Thirdly, this study assumes linear relationships among variables, potentially overlooking complex, non-linear dynamics. Future research could incorporate non-linear models or machine learning techniques to capture these intricate interactions more accurately. Thirdly, the research is constrained by the availability of certain environmental and economic data, indicating the necessity for more comprehensive and high-resolution datasets in future analyses to improve the robustness and applicability of the findings. Fourthly, this study does not provide detailed projections of CO₂ reductions under specific renewable energy scenarios or agricultural practices across different regions and climate zones in South Korea. While the current research establishes a robust baseline for understanding the relationships among agriculture, renewable energy, and CO₂ emissions, it does not delve into scenario-based applications that could offer more actionable insights. Future research could address this gap by conducting scenario analyses to project the potential CO₂ reductions achievable through targeted renewable energy implementations and sustainable agricultural practices in diverse locations. This approach would provide policymakers with more precise strategies for achieving sustainability goals. Finally, this study focuses on the reliance on CO₂ emissions as the sole indicator of environmental sustainability, which may not fully capture the multidimensional nature of sustainability. While the Hellwig method and other composite indices could provide a more comprehensive assessment by incorporating various environmental, economic, and social factors, these approaches were not explored in this analysis. Future research could address this gap by applying the Hellwig method or other multidimensional evaluation techniques to offer a more holistic understanding of sustainability, thereby enhancing the robustness and depth of policy recommendations.