The Role of Organic Farming in Reducing Greenhouse Gas Emissions from Agriculture in the European Union

Abstract

Agriculture remains a key source of greenhouse gas (GHG) emissions within the European Union, posing substantial obstacles to achieving climate objectives and fostering sustainable development. On this background, organic farming stands out as a viable alternative, offering significant potential for reducing emissions. This study explores the impact of expanding organic farming on GHG emissions in the EU agricultural sector. The empirical research examines the connection between organic farming practices and GHG emission levels using structural equation modeling, complemented by Holt and ARIMA forecasting models, to project future trends based on expected growth in organic farmland. The findings highlight a robust negative influence (p < 0.001), demonstrating that organic farming practices are associated with tangible reductions in emissions. Forecasting analyses further reinforce this, predicting considerable declines in GHG emissions (by almost 14 percent below the level of 2008) as organic farming continues to expand for over 23% of agricultural land by 2035, according to the projections in this research. These insights underscore the critical role of organic farming in advancing the EU’s climate ambitions. The study concludes that broader adoption of organic practices offers a practical and impactful pathway for building a more sustainable agricultural system while mitigating environmental harm across member states.

1. Introduction

Climate change profoundly affects plant growth cycles, reducing crop yields and quality due to increasingly frequent and intense extreme weather events such as prolonged droughts, frost periods without snow cover, and heat waves. These extreme weather conditions also correlate with food scarcity, as they compromise the quantity and quality of agricultural production [1,2,3]. Climate change facilitates the spread of pests and invasive species that damage crops, increasing costs for farmers and reducing harvest quality. The impact extends beyond crop cultivation, significantly affecting the livestock sector. Heat stress during extreme heatwaves compromises animals’ adaptability, increasing their vulnerability to diseases and lowering their natural immunity. Additionally, droughts and environmental changes reduce the availability of feed crops, further straining livestock production [4].

Agriculture stands at a crossroads. On one side, it must significantly lower its carbon footprint. On the other, it needs to adapt rapidly to changing climatic conditions to sustain food production at levels sufficient to feed a growing global population. Implementing agricultural practices that reduce emissions while managing resources is vital for environmental protection and ensuring future food security [4]. To address these challenges, the agricultural sector is adopting strategies such as efficient irrigation systems, drought-resistant crop varieties, and improved fodder storage infrastructure to ensure stability in production [1,2,5].

Agriculture must drastically reduce energy and natural resource consumption to align with sustainable development goals, as these are critical factors influencing GHG emissions [6,7,8]. EU policies emphasize cutting fertilizer use by at least 20% and reducing chemical pesticides by 50%, which are significant contributors to agricultural emissions [9]. The EU’s Action Plan for Organic Agriculture aims for at least 25% of the Union’s agricultural land to transition to organic farming (OF) by 2030. This transition is expected to help lower GHG emissions by adopting practices that limit synthetic inputs and enhance carbon sequestration [10].

These measures highlight the EU’s commitment to combating climate change, preserving biodiversity, and fostering a more responsible and efficient organic farming system. OF promotes production methods that minimize reliance on chemicals, encourage biodiversity, and aid soil regeneration. Moreover, these practices significantly reduce GHG emissions by decreasing synthetic fertilizers and pesticides, which are significant sources of agricultural emissions [9,10].

European farmers are encouraged to adopt crop rotation, composting, and natural pest control practices. These methods reduce agriculture’s ecological footprint and actively contribute to reducing GHG emissions by improving soil carbon sequestration and lowering energy use. While these transitions present economic and technical challenges, EU financial support through various programs and subsidies facilitates their implementation. This assistance helps farmers overcome obstacles and aligns their practices with long-term sustainability and climate goals [7,8].

Organic farming has become an increasingly important alternative in addressing climate change and environmental challenges. Beyond fostering eco-friendly agricultural practices and contributing to sustainable rural development, OF directly impacts soil physicochemical properties, which are key to mitigate GHG emissions. For example, OF practices such as composting, crop rotation, and reduced reliance on synthetic fertilizers improve soil structure, enhance organic matter content, and reduce the denitrification process responsible for nitrous oxide (N₂O) emissions. By leveraging local resources and promoting biodiversity, OF minimizes environmental impacts while building resilience to climate change. This approach aligns with SDGs, particularly those focused on reducing GHG emissions and fostering long-term agricultural sustainability [11].

Organic farming has gained traction as a promising solution for lowering emissions. However, the existing literature remains fragmented regarding the precise quantification of its impact on GHG emissions and the long-term predictions of this relationship. This study aims to fill these significant gaps by providing an integrative approach that quantitatively correlates OF practices with GHG emission dynamics and projects these relationships into the future. Moreover, the absence of robust, model-based analyses further limits the ability of these studies to predict or forecast potential future scenarios. It is not easy to assess the long-term effects of scaling up organic farming practices without incorporating advanced analytical tools, such as statistical models or simulation techniques. This type of analysis is central for identifying trends, testing different policy scenarios, and providing policymakers with actionable insights for informed decision-making. This research seeks to bridge this gap by introducing a predictive framework beyond simple descriptive assessments.

The originality of this research lies in its application of structural equation modeling to analyze causal relationships between OF practices and emission levels, as well as in using Holt and ARIMA forecasting models to predict emission trajectories based on the expansion of OF. Furthermore, the study makes a methodological contribution by integrating these techniques into a coherent framework customized to the particularities of EU agriculture.

This research revolves around two central questions: RQ1. How does OF influence agricultural GHG emissions, both in absolute terms and as a proportion of total emissions? RQ2. What are the predicted future trends in agricultural GHG emissions based on the expansion of OF? Two hypotheses are formulated and explored in the study to address these questions: H1, which addresses the relationship between OF and GHG emissions, and H2, which focuses on the future impact of OF on emission intensities.

The structure of the paper is as follows. The first section introduces the research context and objectives, followed by a literature review and hypothesis formulation. The third section details the materials and methods used. Subsequently, the analysis of the results is presented, accompanied by discussions on policy implications and paths for future research. The paper concludes with a synthesis of key findings.

2. Materials and Methods

2.1. Literature Review and Hypotheses Development

OF has emerged as a key solution for reducing agriculture’s GHG emissions. By avoiding synthetic fertilizers and pesticides, OF minimizes environmental impacts and enhances biodiversity. Crop rotation, composting, and natural pest management improve soil health, increase water retention, and reduce GHG emissions by lowering the need for energy-intensive inputs [12,13]. Compared to conventional farming, which often degrades ecosystems and contributes to climate change through high chemical use and monocultures, OF supports long-term sustainability by fostering climate resilience and reducing carbon footprints [14,15]. Moreover, OF practices such as improving soil structure and fostering biodiversity help mitigate climate change and promote carbon sequestration, making it a viable method for large-scale and smallholder farms to adapt to and mitigate climate change’s effects [11,16,17,18,19].

OF contributes to combating climate change by reducing GHG emissions linked to conventional agricultural practices. As conventional farming significantly contributes to global GHG emissions, shifting to eco-friendly practices can substantially lower the agricultural sector’s carbon footprint [14].

2.1.1. Main Sources of GHG Emissions from Agriculture

In the context of the European Union’s agricultural practices, OF has the potential to significantly reduce GHG emissions from agriculture, particularly those arising from crop cultivation and livestock farming [20]. The primary sources of GHG emissions in agriculture are fertilization, plant protection activities, fossil fuel combustion for agricultural operations, soil microbial activities, and animal emissions on livestock farms.

Traditional chemical fertilization is one of the most critical contributors to GHG emissions in agriculture. Excessive nitrogen accumulation in the soil, resulting from the use of nitrogen-based fertilizers, is a major driver of N₂O emissions, a GHG that is far more potent than carbon dioxide (CO₂) and methane (CH₄) [21]. Studies have consistently shown the role of fertilization in increasing the carbon footprint of crop production, mainly using nitrogen-based fertilizers [22,23]. The production and application of fertilizers also require substantial energy, which indirectly contributes to CO₂ emissions [24,25,26]. These emissions exacerbate climate change and disrupt soil health and biodiversity by altering the natural nutrient balance in the soil [27]. Continuous chemical inputs, such as fertilizers and pesticides, deplete soil resources, harm microbial biodiversity, and undermine the soil’s natural regenerative capacity [28]. In contrast, organic fertilization promotes natural nutrient cycles, fosters healthier soils, and mitigates pollution risks, helping crops withstand extreme climatic conditions [29,30]. Fertilizer applications should follow a tailored, scientific approach to meet the specific nutrient needs of crops, with precision techniques that prevent nitrogen overdoses and reduce GHG emissions [31].

Another significant issue from current fertilization practices is water pollution, particularly from groundwater and irrigation sources, exacerbated by chemical fertilizers [32]. Over time, soil microbial diversity and enzymatic activity degradation occur due to the continuous use of fertilizers and pesticides, compromising soil health [33,34]. Additionally, excessive use of these chemicals can lead to soil acidification, salinization, degradation, and the accumulation of harmful heavy metals like cadmium, lead, and arsenic, posing risks to plants, animals, and human health [35]. In contrast, OF practices contribute to soil fertility by enhancing ecological balance, protecting the environment, and reducing dependency on chemical inputs. These practices improve soil resilience and ensure sustainable agricultural productivity [36,37,38].

Furthermore, while plant protection measures also contribute to the carbon footprint of agricultural activities, their impact on GHG emissions is generally smaller than that of fertilization. Fertilization involves higher energy and chemical resource consumption, resulting in a significantly larger carbon footprint [3]. The use of fossil fuels in agricultural operations is another major source of GHG emissions, with CO₂ released into the atmosphere due to intensive energy consumption during activities such as plowing, irrigation, and harvesting [39]. The intensity of GHG emissions varies depending on the agricultural system employed, with crop rotation practices playing a significant role in reducing emissions by improving soil health and determining input requirements [39].

GHG emissions from agricultural systems also stem from soil microbial activities and the metabolic processes of cultivated plants [40]. During plant growth, crops absorb carbon from the atmosphere and incorporate it into their biomass [41]. However, when plant matter decomposes, this carbon is released back into the atmosphere as CO₂ and other gases, demonstrating the cyclical nature of GHG emissions in agricultural ecosystems [42]. This cyclical process highlights the need for practices that balance carbon fluxes to mitigate GHG emissions.

Livestock farming, especially in intensive systems, is another significant source of GHG emissions. Livestock farms produce substantial GHGs, with pasture-based systems contributing to carbon sequestration and biodiversity promotion [43,44,45,46,47,48,49,50,51]. In contrast, organic livestock farming minimizes the carbon footprint by avoiding synthetic chemicals and external resources [52,53]. Adopting extensive livestock farming systems can reduce environmental pressure by sequestering more CO₂ than the emissions generated during animal husbandry, contributing to the overall reduction of GHG emissions [54]. These practices align with sustainability goals by promoting ecological balance and regenerative approaches to resource management [55].

2.1.2. The Past and the Future Effects of OF on GHG Emissions

OF has gained significant attention for its ability to address pressing environmental challenges, including reducing GHG emissions. Through crop rotation, organic fertilization, and integrated pest management, OF enhances soil quality, biodiversity, and carbon sequestration in the soil, ultimately reducing atmospheric CO₂ concentrations [56,57]. As part of the broader concept of “carbon farming”, OF contributes to climate change mitigation while maintaining agricultural productivity and ecosystem health [58,59,60]. It stabilizes soil structure, increases carbon storage, and improves the resilience of agricultural systems, offering a regenerative approach to agriculture [56,61]. Moreover, the sale of carbon credits further incentivizes OF adoption, aligning with the European Green Deal’s vision of environmentally friendly agriculture [6].

Despite these advancements, key gaps remain. The effects of OF on food security and its potential unintended consequences, such as shifts in agricultural productivity, have not been fully assessed [62,63]. While OF can reduce GHG emissions locally, decreased yields in certain areas may increase the global demand for agricultural land elsewhere, potentially offsetting environmental benefits [64,65,66]. High-performance agriculture, which achieves high yields with reduced environmental impact, may offer complementary solutions. However, integrating OF into a broader agricultural strategy is essential for balancing biodiversity conservation, soil health, and global food security [67].

Given these complexities, this research addresses two hypotheses to explore the role of OF in mitigating GHG emissions and shaping sustainable agricultural practices:

Hypothesis H1:

Organic farming significantly reduces GHG emissions in agriculture in absolute terms and as a percentage of total agricultural emissions.

Existing studies support this hypothesis, indicating that transitioning to OF could achieve an 8% reduction in GHG emissions compared to conventional farming methods [68,69,70]. This reduction is attributed to practices such as crop rotation, sustainable nutrient management, and the avoidance of synthetic fertilizers, which enhance carbon sequestration and reduce emissions from agricultural inputs [71]. Moreover, international reports estimate that increasing OF’s share in the European Union to 25% by 2030 could lower agricultural GHG emissions by 12–14%, underscoring its critical role in climate change mitigation [72].

Hypothesis H2:

Expanding organic farming practices in the EU will significantly influence the trajectory of future agricultural air emission intensities, promoting long-term environmental sustainability.

Building on findings from prior studies and European policy reports, this hypothesis explores the potential of OF to drive systemic changes in emission intensities. By increasing soil organic matter, OF reduces GHG emissions and enhances soil fertility and crop resilience to climate variability, supporting a transition to sustainable agricultural practices [54].

These hypotheses aim to bridge gaps in the existing literature by investigating the causal mechanisms linking OF practices to emission reductions and forecasting future impacts using advanced modeling techniques, such as SEM, Holt, and ARIMA. This dual focus addresses both immediate and long-term sustainability challenges in European agriculture.

2.2. Research Design

This study follows a quantitative research design with multiple stages to assess the impact of organic farming practices on GHG emissions within the European Union. The methodology is structured as follows.

The first stage involves a literature review and obtaining data from established European sources like Eurostat. The study scrutinizes over one hundred publications relevant to the research objectives, particularly in organic farming, greenhouse gas emissions, and agricultural practices. Eurostat as a source ensures that the data are accurate and reliable for the study. The objective of this stage is to collect variables such as agricultural land area, regions practicing OF, crop and livestock production values, and GHG emissions from agriculture. The data span several years, allowing for a longitudinal trend analysis.

The second stage focuses on identifying and transforming these variables into operational measures. The selected variables are chosen based on their relevance to the research questions and availability in European datasets. When analyzing OF practices and GHG emissions, operationalizing the variables ensures consistency and comparability.

In the final stage, structural equation modeling (SEM) is employed to explore the complex relationships between the variables, particularly the impact of OF on GHG emissions. Additionally, forecasting models like the Holt and ARIMA models are used to project future GHG emission trends under scenarios of expanding OF. This combination of methodologies offers a comprehensive approach to the current analysis and future projections.

2.3. Selected Variables

The study examines several key variables to understand the relationship between OF and GHG emissions (

Table 1. Research variables.

Variable	Dataset	Measures	Sources
UTAA	Utilized agricultural area	Main area (1000 ha)	[73]
AUOF	Area under OF	Percentage of total utilized agricultural area	[74]
CRO	Crop output	Million euro—production value at basic price	[75]
ANO	Animal output	Million euro—production value at basic price	[76]
GHGAGR	GHG emissions from agriculture	Million tonnes	[77]
GHGAGRP	GHG from agriculture—percentage of the total GHG	Percentage	[78]
AEIA	Air emissions intensities in agriculture	Index, 2008 = 100	[79]

Source: developed by the author based on [73,74,75,76,77,78,79].

). These variables provide both economic and environmental insights into the agricultural sector.

The variables included in this study were selected to comprehensively understand the complex relationships between OF practices and GHG emissions generated within the agricultural sector. The utilized agricultural area (UTAA) is an important indicator of land use in agriculture, measuring the total area dedicated to agricultural activities and expressed in thousands of hectares. It offers an overview of a region’s agricultural capacity. In the context of OF, the area under OF (AUOF) is expressed as a percentage of the total utilized agricultural area, capturing the scale and extent of OF practices. AUOF, thus, reflects the degree to which sustainable practices are implemented within the agricultural sector.

Regarding agricultural production, variables that describe the economic values of crop and livestock production, namely crop production (CRO) and livestock production (ANO), are expressed in millions of euros. These represent an economic dimension of the agricultural sector, providing information on the value added through the production of plant-based and animal-based food. Their values contribute to analyzing the agricultural sector’s economic performance and allow for exploring a potential correlation between economic intensity and ecological impact.

GHG emissions from agriculture (GHGAGR) measure these emissions in absolute terms, expressed in millions of tons, offering a quantitative view of the agricultural sector’s contribution to climate change. The percentage of GHG emissions from agriculture (GHGAGRP) indicates the agricultural sector’s share in total GHG emissions, placing agriculture’s role in generating emissions into context. In addition, air emission intensities in agriculture (AEIA) indicate the agricultural sector’s efficiency in managing emissions. This index is based on 2008 values, highlighting how agriculture adjusts its ecological performance concerning economic activity and technological development, facilitating an analysis of the impact of emission intensities on the surrounding environment.

Together, these variables enable an integrated approach to examining the interactions between the development of OF and the level of GHG emissions, providing a solid foundation for assessing the potential impact of sustainable agricultural practices on the climate and formulating policies supporting the transition to more environmentally friendly agriculture.

2.4. Research Methods

The paper adopts a mixed-methods approach that integrates statistical and forecasting techniques to examine the impact of OF on GHG emissions within the EU, enabling the investigation of the two research hypotheses.

To test Hypothesis H1, the study used structural equation modeling (SEM), a method that allows for investigating complex relationships among multiple variables, providing a detailed and refined approach to model the interactions among them [80]. The formula used in SEM is as follows (1):

$${\eta }_i={\alpha }_{\eta }+B{\eta }_i+\mathit{\Gamma }{\xi }_i+{\zeta }_i$$

(1)

• η, ξ—endogenous and exogenous variable vectors;
• B—effects of the latent endogenous variables on each other;
• Γ—effects of the latent exogenous variables on the latent endogenous variables;
• ζ—disturbances;
• i—cases.

SEM enables an in-depth analysis of OF’s effects on GHG emissions by capturing direct and indirect influences. This method allows for developing a comprehensive model in which the relationships between different variables, such as the agricultural area under OF and GHG emissions, can be assessed simultaneously. Furthermore, SEM facilitates exploring how GHG emissions might act as a mediating variable, offering valuable insights into the mechanisms through which OF practices influence emissions. By providing a detailed analysis of how OF impacts various stages in the agricultural system, SEM helps uncover the underlying processes that drive emission trends, such as changes in agricultural practices, land use, and crop productivity. The technique also helps capture the interdependencies between agricultural areas utilized for organic crops and GHG emissions, allowing the study to observe not only the direct effects of OF, but also how the broader dynamics of the agricultural economy might influence these emissions over time [81]. Figure 1 presents the conceptual model, which integrates the research variables.

Regarding Hypothesis H2, the research applies forecasting models, namely the Holt model and the ARIMA model, to investigate the long-term impact of the expansion of OF on the intensities of air emissions from agriculture. The Holt model is well-suited for capturing long-term trends and identifying structural changes or steady developments in emission intensities that may occur over extended periods. This model is based on exponential smoothing, allowing it to account for the time series data’s level and trend. Holt [82] extended the basic exponential smoothing method to make it suitable for forecasting data that exhibit a trend, such as the changes in emission intensities over time. This method includes a forecasting Equation (2) and two smoothing equations, one for the level (3) and one for the trend (4) [83]. The forecasting equation uses estimates of the series level at time t and its slope, with smoothing parameters adjusting both the level and trend, enabling adaptive and accurate forecasting.

$${\widehat{y}}_{t+\left.h\right|t}=l_t+h{b}_t$$

(2)

$$l_t={\alpha y}_t+(1-\alpha )(l_{t-1}+b_{t-1})$$

(3)

$$b_t={\beta (l}_t-l_{t-1})+(1-\mathsf{\beta })b_{t-1}$$

(4)

• $l_t$—an estimate of the level of the series at time t;
• $b_t$—an estimate of the trend (slope) of the series at time t;
• α—the smoothing parameter for the level;
• β—the smoothing parameter for the trend.

The ARIMA model, known for its approach to time series analysis, enables the capture and modeling of patterns within historical data, which is crucial for understanding the cyclical and fluctuating trends in emission intensities associated with agriculture. The ARIMA (autoregressive integrated moving average) model is specifically designed to analyze the historical evolution of data and make forecasts based on past data points, independent variables, or time itself. By incorporating autoregressive and moving average components, the ARIMA model allows for identifying patterns and quantifying the influence of various factors on emission trends. This model is beneficial for capturing seasonal effects, fluctuations, and other dynamics that may influence agricultural GHG emissions over time [84,85]. The general formula of the ARIMA model (5), applied in this study to forecast the intensity of emissions from agriculture based on the expansion of OF, is as follows:

$$\left(1-\sum _{i=1}^p{\phi }_i{L}^i\right){(1-L)}^d{X}_t=\left(1+{\sum }_{i=1}^q{\theta }_i{L}^i\right){\epsilon }_t$$

(5)

• $X_t$—data series;
• $L$—lag operator;
• ${\phi }_i$—parameters of the autoregressive part of the model;
• ${\theta }_i$—parameters of the moving average part;
• ${\epsilon }_t$—error.

Combining SEM, Holt, and ARIMA models provides a comprehensive statistical framework for investigating OF practices’ current and future impacts on GHG emissions. These techniques complement each other by offering a detailed, system-wide understanding of the relationships among the variables (SEM) and robust forecasting capabilities (Holt and ARIMA) that allow for accurate predictions of future emission trends under different OF expansion scenarios.

3. Results

The reflexive SEM model used in this research was implemented through the SmartPLS software, version 3.0 [86], a platform recognized for analyzing partial least squares (PLS-SEM) models. The reflexive SEM model allows for investigating causal relationships between latent variables through observational indicators that reflect the theoretical characteristics of these latent variables. In this model, each latent variable is viewed as a theoretical construct influencing its indicators, varying depending on how these indicators reflect the construct’s traits [87]. The SEM model used in this study evaluates the relationships between key variables influencing GHG emissions in agriculture. The model is constructed to capture direct and indirect effects, providing insights into the dynamics of agricultural practices, land use, and environmental outcomes. Figure 2 illustrates the SEM-PLS model used to determine various agricultural measures’ influences on agriculture’s GHG emissions.

To assess the discriminant validity of the SEM model, we used the heterotrait–monotrait ratio (HTMT) matrix to analyze the degree of correlation between latent constructs (

Table 2. The heterotrait–monotrait ratio.

	Agricultural Output	Area Under OF	GHG Emissions from Agriculture	GHG from Agriculture—Percentage of the Total GHG	Utilized Agricultural Area
Agricultural output
Area under OF	0.045
GHG emissions from agriculture	0.829	0.118
GHG from agriculture—percentage of the total GHG	0.013	0.117	0.109
Utilized agricultural area	0.625	0.055	0.606	0.102

Source: author’s design using SmartPLS v3.0.

). In SEM, discriminant validity indicates the extent to which the latent constructs are distinct, and HTMT serves as a robust criterion for this evaluation [81].

Discriminant validity among the constructs was confirmed using the heterotrait–monotrait (HTMT) matrix. The low HTMT ratio values, such as 0.045 between agricultural output and the area under OF, indicate a clear differentiation between these variables. Meanwhile, moderate ratios like 0.606 for GHG emissions and utilized agricultural areas reflect interdependencies without compromising the distinctiveness of the constructs.

The HTMT matrix provides strong evidence of discriminant validity between the model constructs, as most values fall below the acceptable threshold (below 0.85), reinforcing that each construct represents a unique aspect of the phenomenon under study.

Model fit was also verified. The indicators illustrating model fit recorded significant values: SRMR (standardized root mean square residual) < 0.08 and NFI (normed fit index) > 0.9 [80].

Table 3. Model fit.

	Saturated Model
SRMR	0.034 < 0.08
d_ULS	0.024
d_G	0.054
Chi-Square	107.357
NFI	0.942 > 0.9

Source: author’s design using SmartPLS v3.0.

shows the model fit indicators.

The SEM model’s evaluation through fit indicators emphasizes its statistical robustness and reliability, supporting the conclusions’ validity. One of the most important indicators, SRMR, had a value of 0.034, below the acceptable threshold of 0.08. This result suggests an adequate fit between the observed and predicted covariance matrices, implying that the differences between observed and expected values are minimal. A low SRMR thus indicates a good representation of the data by the model [80].

The values for d_ULS and d_G, which measure the global deviation of the model, were low (0.024 and 0.054), signaling no significant deviations from the ideal model and strengthening the reliability of the results. The reduced global deviations confirm that the model successfully captured the structure of the relationships in the analyzed data. NFI, which measures the model’s efficiency compared to a reference model, had a value of 0.942, surpassing the accepted threshold of 0.9. This result suggests an excellent model fit and a higher capacity to explain the data variations accurately.

For structural model analysis, we employed the bootstrapping algorithm to determine the statistical significance of the path coefficients, thus validating the relationships between latent variables and interpreting the direct and indirect influences in the model. SmartPLS 3.0 allows the investigation of these complex relationships to understand how theoretical factors influence the observed outcomes within the study’s context [81].

Table 4. Path coefficients.

	Original Sample	Sample Mean	Standard Deviation	T Statistics	p Values
Agricultural output → GHG emissions from agriculture	0.626	0.638	0.106	5.913	0.001 < 0.05
Area under OF → GHG emissions from agriculture	−0.107	−0.105	0.020	5.203	0.000 < 0.05
GHG emissions from agriculture → GHG from agriculture—percentage of the total GHG	0.109	0.110	0.044	2.488	0.013 < 0.05
Utilized agricultural area → GHG emissions from agriculture	0.241	0.229	0.120	1.998	0.046 < 0.05

Source: author’s design using SmartPLS v3.0.

presents the model’s path coefficients.

The path coefficient between agricultural production and GHG emissions in agriculture was 0.626, with a p-value of 0.001, below the significance threshold of 0.05. These results indicate a positive and significant relationship, suggesting that an increase in agricultural production is associated with an intensification of emissions in this sector.

The relationship between the area dedicated to OF and GHG emissions in agriculture (path coefficient of −0.107 and p-value < 0.001) suggests that the expansion of OF contributes to reducing GHG emissions. The relationship between total agricultural land use and GHG emissions in agriculture indicates a significant positive association (path coefficient of 0.241 and p-value of 0.046). This result suggests that agricultural land use significantly influences emission levels, confirming the hypothesis that intensive land use is related to increased emissions.

Another meaningful relationship was found between GHG emissions from agriculture and their percentage of total GHG emissions (path coefficient of 0.109 and p-value of 0.013), suggesting a positive and significant relationship between these variables. This result indicates that agricultural emissions consistently contribute to the total GHG emissions, reinforcing the need to monitor and manage this sector. Overall, the analysis of the path coefficients highlights the role of OF in reducing these emissions, providing empirical support for validating Hypothesis H1, which posits that OF has a significant impact on agricultural GHG emissions. The analysis of indirect effects within the SEM model adds a dimension to understanding the complex interactions among the variables included in the study, demonstrating how some variables indirectly influence others through a mediated path (

Table 5. Specific indirect effects.

	Original Sample	Sample Mean	Standard Deviation	T Statistics	p Values
Agricultural output → GHG emissions from agriculture → GHG from agriculture—percentage of the total GHG	0.068	0.068	0.027	2.574	0.010 < 0.05
Utilized agricultural area → GHG emissions from agriculture → GHG from agriculture—percentage of the total GHG	0.026	0.027	0.020	1.294	0.196 > 0.05
Area under OF → GHG emissions from agriculture → GHG from agriculture—percentage of the total GHG	−0.012	−0.012	0.005	2.204	0.028 < 0.05

Source: author’s design using SmartPLS v3.0.

).

Following the analysis of specific indirect effects, the results suggest that, in addition to the direct impact of agricultural production on emissions, there is also an indirect effect, in that an increase in agricultural production indirectly leads to a rise in the percentage of emissions from agriculture within the total GHG emissions. While there was a relationship between agricultural land use and GHG emissions, the indirect effect of this variable on the percentage of emissions within total GHG emissions was not significant (p-value = 0.196 > 0.05). Thus, in this context, the indirect impact of land use does not substantially contribute to modifying the proportion of agricultural emissions within total GHG emissions. The expansion of OF had a weak but statistically significant indirect effect (p-value = 0.028 < 0.05) consisting of reducing the percentage of GHG emissions from agriculture, indicating that organic agricultural practices may contribute to a reduction in indirect emissions, thereby validating Hypothesis H1 regarding the influence of OF on GHG emissions in agriculture as a relative percentage of total GHG emissions.

A SEM model’s total effects reflect the direct and indirect influences of the variables on a specific outcome, offering a comprehensive view of the relationships between them [81].

Table 6. Total effects.

	Original Sample	Sample Mean	Standard Deviation	T Statistics	p Values
Agricultural output → GHG emissions from agriculture	0.626	0.638	0.106	5.913	0.000 < 0.05
Agricultural output → GHG from agriculture—percentage of the total GHG	0.068	0.068	0.027	2.574	0.010 < 0.05
Area under OF → GHG emissions from agriculture	−0.107	−0.105	0.020	5.203	0.000 < 0.05
Area under OF → GHG from agriculture—percentage of the total GHG	−0.012	−0.012	0.005	2.204	0.028 < 0.05
GHG emissions from agriculture → GHG from agriculture—percentage of the total GHG	0.109	0.110	0.044	2.488	0.013 < 0.05
Utilized agricultural area → GHG emissions from agriculture	0.241	0.229	0.120	1.998	0.046 < 0.05
Utilized agricultural area → GHG from agriculture—percentage of the total GHG	0.026	0.027	0.020	1.294	0.196 > 0.05

Source: author’s design using SmartPLS v3.0.

presents the total effects of the model.

The total effect of agricultural production on emissions was strongly positive, underscoring that an increase in production can significantly increase GHG emissions. Furthermore, the total effect of agricultural production on the percentage of GHG emissions in total emissions was positive, suggesting that the expansion of production may indirectly contribute to an increase in the proportion of agricultural emissions within total GHG emissions. On the other hand, the area dedicated to OF significantly impacted GHG emissions from agriculture, suggesting that the development of OF helps reduce emissions, thus mitigating the negative impact of agricultural production.

Hypothesis H2 aims to investigate the long-term impact of expanding OF on the intensity of air emissions in agriculture using Holt and ARIMA models [82,83,84,85]. First, the Holt model was applied to forecast the expansion of OF (area under OF—AUOF) using an exponential smoothing approach.

The Holt model was calibrated based on historical data (2008–2022), and the model parameters were estimated using SPSS v.27 (

Table 7. Exponential smoothing model parameters for the area under OF forecasting.

Model			Estimate	SE	t	Sig.
AUOF-Model_1	No transformation	Alpha (level and trend)	0.989	0.117	8.435	0.000

Source: author’s design using SPSS v.27.

).

The model parameters indicate a high value for the Alpha coefficient (0.989), suggesting a solid sensitivity to recent trends. The estimates were statistically significant, with a t-value of 8.435 and p < 0.001. Also, the Holt model showed a strong performance overall.

Figure 3 and

Table A1. Historical and future trends of the area under organic farming and air emissions intensities in agriculture.

	Historical Trend		Forecast of the Area Under Organic Farming Using the Holt Model			Forecast of Air Emission Intensities in Agriculture Depending on the Area Under Organic Farming Using the ARIMA Model			Forecast of Air Emission Intensities in Agriculture Depending on the Previous Annual Evolution Using the ARIMA Model
Year	AEIA	AUOF	Predicted AUOF	LCL AUOF	UCL AUOF	Predicted AEIA	LCL AEIA	UCL AEIA	Predicted AEIA	LCL AEIA	UCL AEIA
2008	100	4.3	-	-	-	-	-	-	-	-	-
2009	98.07	4.7	-	-	-	-	-	-	-	-	-
2010	99.83	5.1	-	-	-	-	-	-	-	-	-
2011	97.22	5.4	-	-	-	-	-	-	-	-	-
2012	101.36	5.66	-	-	-	-	-	-	-	-	-
2013	98.41	5.7	-	-	-	-	-	-	-	-	-
2014	94.13	5.78	-	-	-	-	-	-	-	-	-
2015	95.25	6.2	-	-	-	-	-	-	-	-	-
2016	96.42	6.68	-	-	-	-	-	-	-	-	-
2017	94.75	7.02	-	-	-	-	-	-	-	-	-
2018	95.31	7.47	-	-	-	-	-	-	-	-	-
2019	93.61	7.9	-	-	-	-	-	-	-	-	-
2020	95.51	9.1	-	-	-	-	-	-	-	-	-
2021	94.49	10.27	-	-	-	-	-	-	-	-	-
2022	96.74	11.19	-	-	-	-	-	-	-	-	-
2023	-	-	12.12	11.58	12.65	93.46	88.99	97.92	93.78	90.01	97.55
2024	-	-	13.04	11.86	14.23	92.88	88.42	97.35	93.41	89.64	97.18
2025	-	-	13.97	11.99	15.94	92.31	87.84	96.77	93.04	89.27	96.81
2026	-	-	14.89	12.01	17.78	91.73	87.27	96.2	92.67	88.91	96.44
2027	-	-	15.82	11.92	19.72	91.16	86.69	95.63	92.3	88.54	96.07
2028	-	-	16.74	11.73	21.75	90.58	86.12	95.05	91.93	88.17	95.7
2029	-	-	17.67	11.46	23.88	90.01	85.54	94.48	91.56	87.8	95.33
2030	-	-	18.59	11.1	26.09	89.43	84.97	93.9	91.19	87.43	94.96
2031	-	-	19.52	10.67	28.37	88.86	84.39	93.33	90.82	87.06	94.59
2032	-	-	20.45	10.16	30.73	88.29	83.82	92.75	90.45	86.69	94.22
2033	-	-	21.37	9.58	33.16	87.71	83.24	92.18	90.09	86.32	93.85
2034	-	-	22.3	8.94	35.65	87.14	82.67	91.6	89.72	85.95	93.48
2035	-	-	23.22	8.23	38.21	86.56	82.09	91.03	89.35	85.58	93.11

provide a detailed description of the forecast for the expansion of OF. Historical observations showed a steady increase in the area cultivated organically, from 4.3% in 2008 to 11.19% in 2022. The forecast indicates that this upward trend will continue until 2035, when the Holt model predicts a percentage of 23.22% of the total area dedicated to OF. This percentage is close to the European Union’s target of 25% of total agricultural land for organic farming.

The second model applied an ARIMA model to investigate the impact of expanding OF on the intensities of agricultural air emissions. The analysis was based on historical data from 2008 to 2022 and allowed for forecasting the evolution of this impact for the period 2023–2035. The ARIMA model, applied to analyze the intensities of air emissions intensities from agriculture (AEIA) based on the area allocated to OF (AUOF), was calibrated using historical data, and the model parameters were estimated using SPSS v.27 (

Table 8. ARIMA model parameters for air emission intensities in agriculture depending on area under OF.

					Estimate	SE	t	Sig.
AEIA-Model_1	AEIA	No transformation	Constant		100.983	1.934	52.221	0.000
	AUOF	No transformation	Numerator	Lag 0	−0.621	0.272	−2.283	0.040

Source: author’s design using SPSS v.27.

).

The model constant, estimated at 100.983, had a standard error of 1.934 and a high statistical significance (t = 52.221, p < 0.001). This constant represents the baseline level of agricultural emission intensities, independent of variations in the areas dedicated to OF. The coefficient associated with the AUOF variable (Lag 0) was −0.621, with a standard error of 0.272. The statistical significance of this coefficient was confirmed (t = −2.283, p = 0.040), indicating an inverse relationship between the expansion of OF and the intensity of air emissions. Specifically, an increase in the area of organic farmland contributes to a reduction in AEIA.

The results demonstrate an inverse relationship between the area allocated to OF (AUOF) and the intensity of agricultural emissions (AEIA), confirming the positive contribution of OF to reducing emissions (Figure 4).

During the forecast period, as AUOF continues to grow, the model estimates a gradual decrease in emission intensities. While AEIA was estimated to be 93.46 in 2023, this value is projected to decrease to 86.56 by 2035, reflecting a consistent and progressive decline driven by the expansion of OF.

The downward trend observed in the forecast confirms the effectiveness of expanding OF in reducing emissions per unit of land. The increase in AUOF from 11.19% in 2022 to over 23% in 2035, as predicted by the Holt model, suggests that a broader adoption of this type of agriculture can contribute to achieving the climate targets set by the European Green Deal.

The results demonstrate the positive impact of OF on environmental sustainability. Using OF as an integral part of emission reduction strategies provides a clear path for the decarbonization of agriculture. Therefore, the findings from this model underline the importance of continuous monitoring of the effects and the adjustment of strategies to ensure the long-term efficiency and sustainability of the implemented measures.

The ARIMA model used to forecast the evolution of air emissions intensities in agriculture (AEIA) based on the previous year’s annual evolution provides a long-term perspective on trends, showing how emissions are influenced over time. The model’s analysis was based on historical data from 2008–2022 and explored how temporal variations affect AEIA, enabling forecasts for 2023–2035.

Table 9. ARIMA model parameters for air emission intensities in agriculture on the previous annual evolution.

					Estimate	SE	t	Sig.
AEIA-Model_1	AEIA	No transformation	Constant		841.714	210.031	4.008	0.001
	Year	No transformation	Numerator	Lag 0	−0.370	0.104	−3.547	0.004

Source: author’s design using SPSS v.27.

presents the parameters of the ARIMA model for air emission intensities in agriculture based on the previous annual evolution.

The estimated parameters of the model suggest a gradual reduction in air emissions intensities from agriculture over the years. The model constant had a value of 841.714 and was statistically significant (p = 0.001), indicating the baseline level of AEIA. The coefficient associated with the “year” variable was −0.370, also significant (p = 0.004), reflecting a decreasing relationship between the passage of time and AEIA. This negative coefficient shows that AEIA decreases constantly each year, highlighting a clear trend of improvement in AEIA (Figure 5).

The forecast generated using this model indicates a progressive reduction in AEIA for the analyzed period, reaching 89.35 by 2035 (Table A1). This constant decline reflects the influence of adopting more efficient and environmentally friendly agricultural practices and the potential implementation of public policies aimed at sustainability.

Comparing the forecasts for air emissions intensities in agriculture (AEIA) produced by the two ARIMA models, one based on the area allocated to OF (AUOF) and the other on the previous year’s evolution, reveals significant differences in the downward trajectory of emissions. The data suggest that expanding OF substantially impacts reducing agricultural emissions more than continuing the historical trend (Figure 6).

The ARIMA model using AUOF as a predictive variable showed a more pronounced decrease in AEIA over time. In 2035, AEIA is projected to be 86.56 in the AUOF-dependent model, while, in the model based on the previous year’s evolution, the estimated value is 89.35. These results demonstrate a more significant cumulative reduction in the first case, emphasizing the role of OF in mitigating emissions.

This discrepancy between the two models suggests that factors related to expanding OF areas contribute to accelerating agricultural emission reduction. Organic farming, through its environmentally friendly practices, not only limits the use of chemicals and the intensity of mechanized processes, but also contributes to improving soil quality and reducing GHG emissions, confirming the validity of Hypothesis H2. In contrast, the model based on the previous year’s evolution reflects more of an inertia of the historical trend.

From a practical standpoint, these findings support the active promotion of OF as a strategic solution for reducing emissions from agriculture. Public policies should focus on expanding these areas through financial incentives and awareness campaigns, considering the significant impact on environmental sustainability.

4. Discussion

Organic farming contributes significantly to the health of agricultural ecosystems by promoting sustainable practices that enhance soil conditions and biodiversity. Unlike monocultures, which deplete the soil and increase vulnerability to pests and diseases, OF uses crop rotation and mixed systems to preserve soil fertility and reduce the need for chemical interventions [88,89,90,91]. By diversifying crops, OF fosters complex soil ecosystems that enhance resilience to external factors.

OF prioritizes environmental sustainability by minimizing synthetic chemicals and promoting ecological balance, focusing on resource conservation such as fertile soils, clean water, and biodiversity [92,93]. It also upholds stringent animal welfare standards, ensuring healthy and ethical livestock conditions, improving food quality, and aligning with consumer and societal values [94]. By eliminating agrochemicals and using natural methods, OF prevents soil degradation and water contamination, supporting the surrounding ecosystems [95,96]. Furthermore, OF responds to consumer demand for environmentally conscious food choices, offering a sustainable alternative that contributes to biodiversity conservation and soil health [3].

Given the increasing urgency of addressing climate change, research into the role of organic farming (OF) in the evolution of GHG emissions is essential to understand its potential as a sustainable solution for mitigating agricultural contributions to global warming. Two hypotheses were investigated to achieve the research goal. The first hypothesis examined the relationship between organic farming practices and the reduction of GHG within the agricultural sector, while the second hypothesis focused on the potential long-term impacts of expanding organic farming on air emission intensities.

The results confirmed Hypothesis H1, highlighting the significant influence of OF on agricultural GHG emissions, both in absolute and percentage terms. The relationship between agricultural production and GHG emissions was demonstrated through a path coefficient of 0.626 (p < 0.001), indicating a positive and significant link. This result strengthens the observations of Greiner and Gregg [97], who emphasize that the intensification of agricultural production, mainly through chemical fertilizers and conventional animal husbandry practices, contributes significantly to GHG emissions. This result highlights the need for policies aimed at transitioning to more sustainable production methods. Furthermore, our analysis adds a quantitative dimension, validating that this relationship remains robust and significant in SEM models.

Agricultural land use represents another significant factor, with a path coefficient of 0.241 (p = 0.046), suggesting that intensifying agricultural land use may amplify GHG emissions. This finding aligns with the observations of Horrillo et al. [54], who show that intensive agricultural systems are often more vulnerable to environmental degradation and generate a higher ecological impact.

On the other hand, OF demonstrates a significant negative effect on agricultural GHG emissions (path coefficient −0.107, p < 0.001). This relationship underscores the potential of organic practices to reduce emissions through mechanisms such as the use of natural fertilizers and soil biodiversity promotion [57]. This result corroborates the observations of Schmatz et al. [98] and Naorem et al. [99] regarding the beneficial effects of environmentally friendly practices on the soil carbon cycle.

The contribution of OF to the transition toward sustainability is well-documented in the literature. Gaspar et al. [100] highlight the need for a multidimensional approach combining ecological and economic requirements. Our study results support that OF reduces GHG emissions, contributes to biodiversity conservation, and improves ecosystem health [101].

Regarding the role of soil microorganisms, recent literature [102,103] suggests that no-till farming can support a diversified and healthy microbial population, aiding in carbon stabilization and GHG emission reduction. Although our analysis did not directly assess this relationship, the results indicate that OF holds the potential to create favorable conditions for such processes, indirectly supporting emission reduction.

Public policies remain a critical element for the widespread adoption of these practices. Our results confirm the observations of the European Commission [6], which states that financial support and incentives facilitate the transition to sustainable agricultural methods. Furthermore, Riccaboni et al. [104] stress the importance of promoting education and awareness among farmers, a direction that could amplify the adoption of organic practices and other environmentally friendly techniques.

The analysis demonstrates that OF has a significant impact on reducing GHG emissions from agriculture, both directly and through indirect mechanisms. These results emphasize the need for broader support for sustainable agricultural practices, highlighting their potential to contribute to global climate objectives. Moreover, the contributions of this study reinforce existing literature, offering new empirical perspectives on the complex relationships among agricultural production, land use, and GHG emissions [105].

The results obtained in this study underline the relevance of OF in reducing air emissions from agriculture, thereby contributing to the environmental goals set in the European Green Deal. Hypothesis H2, which posits that the evolution of OF influences air emission intensities, is supported by the analysis using the Holt model and the forecasts provided by ARIMA models.

OF is recognized in the literature as an environmentally friendly system that supports sustainable development and limits the use of chemicals. Biernat-Jarka and Trebska [106] and Navarro-Pedreño et al. [107] highlight the harmony between the principles of OF and sustainable agricultural development. This view is evident in our analysis, which shows that expanding organic areas gradually decreases agricultural air emission intensities. The negative coefficient (−0.621) associated with the AUOF variable in the ARIMA model confirms this inverse relationship, supporting the role of OF in reducing emissions.

Seufert and Ramankutty [108] and Das et al. [109] highlight the benefits of organic practices on soil organic matter content and reducing total GHG emissions. Our results corroborate this perspective, showing a progressive decrease in emission intensities from 93.46 in 2023 to 86.56 in 2035, projected based on the expansion of organic land. Thus, adopting these practices appears to support soil health and climate goals.

A comparison between the models used reveals significant differences in emission forecasts. The ARIMA model based on OF land area (AUOF) suggests a more pronounced reduction in AEIA than the model based solely on historical trends. In 2035, the forecasted values are 86.56 (AUOF-based model) and 89.35 (historical trend-based model). This discrepancy underscores the importance of OF as a transformative factor capable of accelerating the achievement of emission reduction targets.

Fytili and Zabaniotou [110] argue that an effective transition to low-carbon agriculture requires changes at the farming practice level and the active involvement of all actors in the value chain. Our results support this vision, suggesting that the expansion of OF should be accompanied by public policies and educational measures to assist farmers in the transition process. Furthermore, our analysis aligns with other papers’ findings [111,112,113], emphasizing that adopting agricultural methods adapted to climate change can reduce the environmental impact and increase agricultural resilience. This study’s projected expansion of organic land, from 11.19% in 2022 to 23.22% in 2035, marks a significant step in this direction, supporting the transition to a more sustainable agricultural sector.

The findings of this study validate Hypothesis H2, underscoring the role of OF as a pivotal solution for reducing agricultural emissions. As climate change intensifies the need for more sustainable farming methods, OF emerges as an alternative and essential component of the transition to environmentally conscious agriculture [114].

4.1. Theoretical Implications

Achieving sustainable agricultural development requires rigorous scientific inquiry and continuous technological advancement. This study makes a significant theoretical contribution by enhancing our understanding of how expanding OF can reduce agricultural air emissions. It establishes a robust framework for assessing the environmental impact of sustainable farming practices, integrating predictive models to forecast long-term trends. Holt and ARIMA forecasting models introduce a novel perspective, emphasizing the value of predictive approaches in analyzing OF’s potential to reduce GHG emissions.

The Holt model proved to be valuable in estimating the growth of land dedicated to OF, revealing a positive and steady trend consistent with the European Green Deal’s objectives. This innovative approach contributes to the academic discourse by offering insights into the long-term impacts of OF on emissions and highlights the need for supportive policies to help farmers transition to these practices. The ARIMA model, on the other hand, indicates that increasing OF areas could significantly impact air emission intensities, reinforcing the role of OF in achieving global climate goals.

However, the results also show that maintaining current practices without significant policy interventions will not lead to transformative changes in emissions. This finding underscores the importance of adopting targeted policies integrating OF into broader climate action strategies. Moreover, the study compares different forecasting models, providing a broader understanding of OF’s role in reducing emissions, highlighting its potential to mitigate climate change.

The paper promotes a systemic vision of OF, asserting that it is not only a sustainable option for food production, but also a central strategy for agricultural resilience and combating climate change. Adequate management strategies (soil management practices, more efficient fertilization techniques, and selecting crops suited to climatic and soil conditions) allow for more accurate nutrient application, reduce GHG emissions such as nitrous oxide, and prevent nutrient losses.

4.2. Practical Implications

This study provides valuable insights into the relationship between OF and GHG emissions, offering important implications for farmers and policymakers. The results demonstrate that expanding OF areas significantly reduces GHG emissions, reinforcing OF as a key solution for transitioning to a more sustainable agro-food system. By adopting OF, agricultural practices can be transformed to achieve environmental sustainability while maintaining productivity.

A key practical implication is the importance of targeted policies that incentivize the adoption of OF. These policies should focus on financial support, tax incentives, and educational programs for farmers. Such measures can accelerate the transition towards organic practices, helping farmers reduce emissions and contribute to climate goals. Furthermore, complementary policies focused on sustainable soil management and efficient fertilization techniques can enhance the environmental benefits of OF, promoting a healthier and more resilient agricultural system.

For policymakers, the study provides a strong empirical basis for developing strategies that reduce emissions in agriculture. It shows that OF practices are more effective than conventional agriculture in reducing GHG emissions. Consequently, integrating OF into national and regional strategies will be crucial for achieving climate targets and ensuring the long-term sustainability of agricultural practices. This approach will also support farmers’ economic well-being by improving soil health, increasing long-term productivity, and offering new market opportunities, such as carbon credits.

4.3. Limitations and Further Research

Although this study offers significant contributions to understanding the relationship between OF and the reduction of agricultural air emissions, it has limitations that should be acknowledged and explored. First, using historical data from the 2008–2022 period, while necessary for calibrating the forecasting models, may be influenced by contextual peculiarities during the analyzed years, such as political, economic, or climatic variations. These factors could introduce some uncertainty in the projections for the 2023–2035 period, especially given the complexity of the factors influencing agriculture.

Another consideration is the limited scope of the data used, which focused on the area allocated to OF and reported air emissions per unit area. While these variables are relevant to the study’s objectives, they do not fully reflect the diversity of OF practices and their potential effects on other sustainability dimensions, such as water use, biodiversity, or soil health.

Additionally, while the applied statistical models are robust and validated, they rely on linear assumptions. They may only partially capture the complex interactions among socio-economic, political, and environmental factors that influence agriculture. Future research should adopt an integrative approach, using multidimensional models or artificial intelligence techniques to capture the broader dynamics of these relationships.

Future studies could incorporate more complex econometric models to explore interactions among policies, technology, and agricultural practices. Expanding analyses to include qualitative data, such as farmers’ attitudes or consumer preferences, could provide an integrated perspective. This approach would strengthen the understanding of OF’s impacts and guide the development of more effective public policies.

Detailed studies on the sources and mechanisms of GHG emissions could guide authorities in developing more precise regulations tailored to the agricultural sector’s needs. In this way, future research could support emission reductions and the implementation of sustainable agricultural practices that aid the transition to a more ecological and resource-efficient food system.

5. Conclusions

This paper highlights the significant contribution of OF to reducing GHG emissions in agriculture and promoting a sustainable agricultural sector. Through the Holt and ARIMA forecasting models, the study demonstrates that expanding areas dedicated to OF is closely linked to decreased GHG emission intensities in agriculture. The investigation results support the hypothesis that ecological practices provide a viable solution for transitioning to a greener future.

The results support the idea that OF not only offers direct benefits by limiting synthetic inputs and intensive practices, but also contributes to achieving European climate objectives, such as dedicating 25% of the total agricultural land to OF by 2030. These findings are relevant for Romania and provide valuable lessons for other European countries in adapting to the European Green Deal’s requirements.

The conclusions suggest that the benefits of OF can be maximized only by implementing coherent public policies and ensuring farmers’ financial and educational support. Furthermore, adopting this form of agriculture requires a systemic change involving producers, consumers, policymakers, and the scientific community. The paper emphasizes that OF is not only a sustainable choice, but also a strategic necessity in the fight against climate change. Agriculture can become an important ally in the ecological transition by promoting and expanding this practice, contributing to environmental protection, and ensuring long-term food security.