Synergies between Sustainable Farming, Green Technology, and Energy Policy for Carbon-Free Development

Abstract

This study addresses the integration of agricultural practices, technological innovations, and energy policies to achieve carbon neutrality in Mardan, Pakistan. A cross-sectional design was employed, collecting data from 400 stakeholders using stratified random sampling. The analysis involved descriptive statistics, correlation analysis, structural equation modeling (SEM), Chi-square tests, and multiple regression analysis to explore the relationships between agricultural sustainability practices (ASPs), green technology implementation (GTI), energy policy measures (EPMs), and carbon-free development (CFD). The findings indicate strong positive correlations between ASPs, GTI, EPMs, and CFD, with a relatively high positive correlation coefficients. SEM path coefficients further confirmed the significant positive effects of ASPs on CFD. It is concluded that integrating sustainable farming practices, green technologies, and effective energy policies significantly advances carbon-free development in Mardan. Policymakers should prioritize promoting sustainable farming, investing in green technologies, and implementing robust energy policies with subsidies for renewable energy and carbon pricing mechanisms to foster carbon-neutral development.

1. Introduction

In contemporary discourse on environmental sustainability, the integration of agricultural practices, technological innovations, and energy policies emerges as a focal point for achieving carbon neutrality. This research explores the synergistic relationship between these variables, emphasizing their collective influence on the global pursuit of a carbon-neutral world.

On a global scale, the significance of this nexus cannot be overstated. Agricultural sustainability practices, encompassing techniques like crop diversification and organic farming, offer multifaceted benefits, including enhanced soil health, biodiversity conservation, and carbon sequestration [1]. Concurrently, the adoption of green technologies, such as renewable energy systems and precision farming tools, holds promise in reducing carbon emissions associated with agricultural activities while optimizing resource efficiency. Complementing these efforts, energy policy interventions, such as subsidies for renewable energy and carbon pricing mechanisms, provide the necessary regulatory framework and economic incentives to drive the transition towards a low-carbon economy [2].

In the context of Pakistan, characterized by environmental challenges exacerbated by rapid urbanization and industrialization, the imperative to embrace this integrated approach is particularly pronounced. Sustainable agricultural practices not only bolster food security and resilience against climate change impacts but also offer pathways for rural development and poverty alleviation [3]. Furthermore, the deployment of green technologies, tailored to local contexts, presents opportunities to enhance agricultural productivity while mitigating environmental degradation. Strategic energy policy measures, aligned with national objectives, can catalyze the transition towards a carbon-neutral trajectory by incentivizing renewable energy adoption and promoting energy efficiency measures.

Zooming into the microcosm of Mardan, Pakistan, a region heavily reliant on agriculture, the urgency of adopting these integrated strategies becomes evident. With challenges such as water scarcity and land degradation posing existential threats to agricultural livelihoods, sustainable farming practices become imperative for ensuring long-term food security and environmental sustainability [4]. Moreover, leveraging green technologies, such as solar-powered irrigation systems and biomass energy, holds immense potential for reducing the carbon footprint of agricultural operations in Mardan. Complemented by proactive energy policies at the provincial level, Mardan can serve as a model for sustainable development, showcasing how localized interventions can contribute to broader global objectives of carbon neutrality.

Mardan’s relevance extends beyond its regional context, providing valuable insights for other areas facing similar environmental challenges worldwide. By examining Mardan’s efforts to integrate sustainable farming, green technology, and energy policy, this research highlights scalable and adaptable solutions that can be implemented globally. Water scarcity, for instance, is not unique to Mardan but is a critical issue in many arid and semi-arid regions globally. The application of solar-powered irrigation in Mardan demonstrates a sustainable solution that can be replicated in other parts of the world, reducing dependency on fossil fuels and enhancing water-use efficiency.

Land degradation, another pressing concern in Mardan, mirrors challenges faced by agricultural regions globally. Implementing practices like crop diversification and organic farming in Mardan offers a model for enhancing soil health and biodiversity, which are crucial for sustainable agriculture worldwide. Additionally, the integration of biomass energy in Mardan can inspire similar initiatives globally, promoting the use of agricultural residues for energy production and reducing waste and emissions.

Furthermore, Mardan’s proactive energy policies, aimed at promoting renewable energy and energy efficiency, can serve as a blueprint for other regions. By aligning local energy policies with national and global sustainability goals, Mardan illustrates how targeted interventions can drive significant progress towards carbon neutrality. The region’s experience underscores the importance of a holistic approach, where agricultural practices, technological innovations, and energy policies are harmonized to achieve environmental sustainability.

1.1. Contribution of the Knowledge Areas of the Research

This research makes a significant contribution by providing detailed empirical evidence and strategic insights into how localized actions can significantly advance global sustainability agendas. By focusing on Mardan, Pakistan, the study offers a microcosmic view of integrated strategies that can drive global progress towards carbon-free development. The findings not only inform broader efforts to address environmental challenges but also inspire practical pathways for policymakers, practitioners, and researchers. Specifically, this research:

• Empirical Evidence: Provides concrete examples of successful sustainable farming practices, green technology adoption, and supportive energy policies in Mardan, demonstrating their collective impact on carbon neutrality.
• Scalable Solutions: Identifies scalable and adaptable solutions for global application, particularly in regions facing similar environmental challenges.
• Holistic Approach: Emphasizes the importance of harmonizing agricultural practices, technological innovations, and energy policies to achieve environmental sustainability.
• Policy Insights: Offers practical insights for policymakers to design and implement effective sustainability strategies that align with both local and global objectives.

1.2. Organization of the Manuscript

The organization of the manuscript includes an introduction followed by the purpose and significance of the research, which elaborates on the research objectives and their importance in the context of global sustainability. This is followed by a literature review, which is guided by the theoretical mechanisms underlying the research and reviews relevant literature to provide a foundation for the study. Next, the study matrix presents the conceptual framework and hypotheses guiding the research. The materials and methods section describes the research design, data collection methods, and analytical techniques used in the study. The results section presents the findings of the research, supported by charts and tables for clarity. This is followed by a discussion that interprets the results, discussing their implications for theory, practice, and policy. The conclusion summarizes the key findings, highlighting the contributions of the research. The limitations of the study are then discussed, along with their implications for the interpretation of results. Practical implications offer recommendations for practitioners and policymakers based on the research findings. Finally, future directions suggest areas for further investigation, recognizing that the completion of one research cycle often leads to new research questions.

1.3. Purpose and Significance of the Research

The integration of agricultural sustainability, green technology, and energy policy toward achieving carbon neutrality represents one of the most urgent global challenges of our time. However, there remains a significant gap in empirical research that addresses this issue within the specific context of Mardan, Pakistan. Existing studies tend to focus on individual aspects or fail to account for the complex interactions between these variables locally. Therefore, there is a pressing need for comprehensive research that explores the synergies and trade-offs necessary to harmonize these elements and facilitate Mardan’s transition toward a carbon-neutral future.

The primary aim of this study is to explore the dynamic interplay between agricultural sustainability practices, green technology implementation, and energy policy measures in Mardan, Pakistan, and to assess their collective impact on achieving carbon neutrality. By investigating the synergistic relationships among these variables, this research seeks to provide deep insights into effective strategies for promoting sustainable development and enhancing environmental resilience in the region.

The significance of this research lies in its urgent relevance to addressing environmental challenges and promoting sustainable development in Mardan, Pakistan. As an agrarian region grappling with issues such as land degradation and water scarcity, Mardan has much to gain from initiatives aimed at enhancing agricultural sustainability and reducing carbon emissions. By clarifying the interconnections between agricultural practices, technological adoption, and energy policies, this study aims to inform evidence-based decision-making and contribute to the development of robust strategies for carbon mitigation and adaptation in the region.

The motivation for this study arises from the recognition of Mardan’s unique potential to serve as a model for sustainable development across Pakistan. By effectively leveraging its agricultural resources and adopting green technologies, Mardan can set a precedent for how localized interventions can contribute significantly to global carbon neutrality objectives. Furthermore, addressing the research gap in understanding Mardan’s specific challenges and opportunities can inform targeted interventions and encourage community engagement towards achieving sustainable development goals.

This research addresses a critical gap in the existing literature by thoroughly analyzing the synergy between agricultural sustainability, green technology, and energy policy within Mardan, Pakistan, with an overarching aim of achieving carbon neutrality. It integrates empirical data with local insights, thereby contributing new and valuable knowledge that can inform regional policy and practice. The study is designed to offer actionable recommendations for policymakers and stakeholders, ultimately promoting sustainable development and environmental resilience, and advancing Mardan towards a carbon-neutral future.

2. Literature Review

Numerous studies have demonstrated the efficacy of sustainable agricultural practices in mitigating carbon emissions and enhancing environmental resilience. For instance, a meta-analysis by Kamyab, SaberiKamarposhti [5] and Ogle, Swan [6] found that implementing practices such as crop rotation and conservation tillage can lead to substantial carbon sequestration in soils. Dhakal, Maraseni [7] explored the impact of sustainable agricultural practices, including agroforestry and organic farming, on carbon sequestration in the Kathmandu Valley. The study emphasized local adaptation strategies and policy implications for enhancing agricultural sustainability and reducing greenhouse gas emissions. Rejekiningrum, Apriyana [8] investigated the adoption of sustainable rice farming practices in Central Java, focusing on practices such as integrated pest management and water-efficient irrigation techniques. The study assessed their contribution to mitigating methane emissions and improving soil health. Wato and Amare [9] reviewed the potential of agroforestry systems in Sub-Saharan Africa to sequester carbon in soils and biomass. The study highlighted the role of sustainable land management practices in enhancing food security while mitigating climate change impacts. Additionally, research by Lal [10] highlighted the potential of organic farming methods in enhancing soil carbon storage and mitigating greenhouse gas emissions. The adoption of green technology in agriculture has also been extensively studied for its role in reducing carbon emissions and improving resource efficiency [11]. Studies by Chel and Kaushik [12] and Rahman, Khan [13] have shown that the integration of renewable energy technologies, such as solar-powered irrigation and wind turbines, can significantly reduce the carbon footprint of agricultural operations while enhancing productivity. Furthermore, precision agriculture techniques, as highlighted by Mishra, Satapathy [14], offer opportunities for optimizing inputs and minimizing environmental impacts. Panda, Saikia [15] examined the implementation of solar-powered irrigation systems in agricultural landscapes of Northern India. The research assessed the impact of renewable energy technologies on reducing greenhouse gas emissions from conventional irrigation practices. Yuan, Marquez [16] studied the deployment of green infrastructure and urban agriculture in Lima, Peru, focusing on rooftop gardens and vertical farming. The study evaluated their potential to enhance urban resilience and sequester carbon dioxide in urban environments. Research by Messina and Modica [17] analyzed the adoption of precision agriculture technologies in Western Europe, including satellite-based monitoring and variable rate applications. The study quantified their contribution to reducing fertilizer use and associated carbon emissions in intensive farming systems. Energy policies play a crucial role in shaping the transition towards a carbon-neutral economy. Research by Sovacool, Newell [18] and Sovacool, Turnheim [19] emphasized the importance of policy instruments such as subsidies for renewable energy and carbon pricing mechanisms in incentivizing low-carbon investments and driving emission reductions. Similarly, empirical studies by Du, Shen [20], Yang, Zhang [21], and Chen, Zhang [22] have shown that supportive policy frameworks can accelerate the deployment of green technologies and facilitate the decarbonization of the energy sector. Lilliestam, Patt [23] assessed the impact of renewable energy subsidies and carbon pricing mechanisms in the United States. The study highlighted their role in incentivizing investments in clean energy technologies and achieving emissions reductions across various sectors, including agriculture. Wu and Ding [24] analyzed China’s policy framework for promoting energy efficiency and renewable energy adoption in agriculture. The research examined policy instruments such as feed-in tariffs and tax incentives to accelerate the deployment of green technologies and reduce carbon footprints. Trencher, Rinscheid [25] reviewed energy policy measures in the European Union aimed at decarbonizing the agriculture sector. The study emphasized regulatory frameworks, including emission trading schemes and subsidies for sustainable farming practices, to achieve carbon neutrality goals.

2.1. Case Studies from Mardan, Pakistan, and Similar Regions

In the context of Mardan, Pakistan, research on the integration of agricultural sustainability, green technology, and energy policy towards carbon neutrality is relatively scarce. However, studies focusing on individual aspects offer insights into the region’s potential. For example, research by Israr, Ullah [26] highlighted the importance of sustainable agriculture practices in mitigating land degradation and improving water management in Mardan. Additionally, studies by Abdullah, Aqeeq [27] underscored the feasibility of renewable energy deployment, particularly solar energy, in addressing energy access challenges in rural areas of Pakistan.

Despite the existing literature on the subject, there remains a notable gap in the empirical understanding of how the harmonization of these variables can lead to carbon neutrality specifically in Mardan, Pakistan. This study aims to fill this gap by investigating the synergistic interactions between agricultural sustainability, green technology adoption, and energy policy implementation in Mardan’s context. By employing a comprehensive research framework and integrating empirical data from the region, this study seeks to provide novel insights into the pathways towards achieving a carbon-neutral future in Mardan, Pakistan.

2.2. Theoretical Framework

Understanding the pathways to carbon-free development (CFD) in Mardan, Pakistan, necessitates a thorough theoretical analysis of how agricultural sustainability practices (ASPs), green technology implementation (GTI), and energy policy measures (EPMs) interact. These three independent variables (IVs) synergize to influence the dependent variable (DV), which is carbon-free development. This section provides an in-depth theoretical explanation of the mechanisms and interactions among ASPs, GTI, and EPMs, emphasizing their combined effect on achieving environmental resilience and sustainability in the specific context of Mardan.

2.2.1. Theoretical Mechanism of Agricultural Sustainability Practices (ASPs)

Soil Carbon Sequestration: In Mardan, agricultural sustainability practices, including crop rotation and conservation tillage, can enhance soil structure and increase organic matter, leading to significant carbon sequestration. The Soil Carbon Saturation Concept posits that soils can act as carbon sinks by capturing atmospheric CO₂ and storing it as soil organic carbon [10]. This process not only mitigates greenhouse gas emissions but also improves soil health and agricultural productivity, creating a feedback loop that supports sustainable farming practices tailored to Mardan’s unique agro-climatic conditions.

Biodiversity and Ecosystem Services: ASPs promote biodiversity, which enhances ecosystem services such as pollination, pest control, and water regulation. The Resilience Theory suggests that diverse ecosystems are more adaptable to environmental changes and stresses, thereby supporting long-term sustainability and carbon neutrality [28,29]. By maintaining ecological balance, sustainable agricultural practices reduce the need for chemical inputs, further decreasing the carbon footprint of agricultural activities in Mardan. This is particularly important in a region where biodiversity is crucial for maintaining soil fertility and agricultural productivity.

2.2.2. Theoretical Mechanism of Green Technology Implementations (GTIs)

Renewable Energy Integration: Green technologies, particularly renewable energy sources like solar and wind power, can significantly reduce reliance on fossil fuels and lower carbon emissions in Mardan. The Diffusion of Innovations Theory explains how new technologies spread within a community, emphasizing the role of perceived benefits, compatibility with existing systems, and ease of use [30]. As renewable energy technologies become more accessible and affordable, their integration into agricultural and industrial practices can significantly diminish the carbon intensity of these sectors in Mardan, where energy access remains a challenge.

Precision Agriculture: Precision agriculture employs advanced technologies such as GPS, IoT, and data analytics to optimize agricultural inputs (e.g., water, fertilizers, pesticides) and enhance productivity. The Resource-Based View (RBV) theory underscores the strategic importance of resource optimization for gaining competitive advantage [31]. By minimizing resource wastage and maximizing efficiency, precision agriculture not only boosts economic returns but also reduces environmental impacts, contributing to carbon-free development in Mardan, where resource efficiency is critical for sustainable agriculture.

2.2.3. Theoretical Mechanism of Energy Policy Measures (EPMs)

Policy Instruments and Incentives: Energy policies are crucial in shaping the adoption of sustainable practices and technologies in Mardan. The Policy Feedback Theory highlights how policy instruments, such as subsidies for renewable energy and carbon pricing mechanisms, create incentives for low-carbon investments [32]. These measures can accelerate the transition towards renewable energy, promote energy efficiency, and foster innovation in green technologies, driving carbon-free development in a region that requires supportive policies for sustainable growth.

Regulatory Frameworks and Standards: Effective regulatory frameworks and standards ensure compliance and drive systemic changes towards sustainability in Mardan. The Institutional Theory suggests that established norms, rules, and regulations influence organizational behavior and decision-making [33]. By setting stringent environmental standards and enforcement mechanisms, energy policies can compel industries and individuals to adopt sustainable practices, thereby reducing carbon emissions and advancing carbon-free development in Mardan.

2.2.4. Synergistic Interactions between ASPs, GTI, and EPMs

Integrated Approaches to Sustainability: The convergence of ASPs, GTI, and EPMs creates synergistic effects that amplify their individual benefits in Mardan. The Systems Theory posits that the whole is greater than the sum of its parts, emphasizing the interconnectedness and interdependence of various components within a system [34]. By integrating sustainable agricultural practices, green technologies, and supportive energy policies, a holistic approach can be achieved, fostering a resilient and carbon-neutral economy in Mardan.

Feedback Loops and Reinforcement Mechanisms: Positive feedback loops and reinforcement mechanisms play a vital role in sustaining the momentum towards carbon-free development in Mardan. For instance, policies promoting renewable energy can drive technological innovations, which in turn reduce costs and increase adoption rates. Similarly, sustainable agricultural practices can enhance ecosystem services, which support further sustainability initiatives. Understanding these dynamics through the lens of Complexity Theory can provide valuable insights into the long-term sustainability of integrated approaches in Mardan [35].

The theoretical relationships between agricultural sustainability practices, green technology implementations, and energy policy measures provide a robust framework for understanding their collective impact on carbon-free development in Mardan, Pakistan (Figure 1). By examining these mechanisms through various theoretical lenses, we can gain a deeper appreciation of the complexities and synergies involved in achieving environmental resilience and sustainability. This section establishes the foundation for empirical analysis, offering a conceptual roadmap for exploring the pathways towards a carbon-neutral future in Mardan, Pakistan.

3. Materials and Methods

3.1. Research Design

This study suits a cross-sectional research design, allowing for the collection of data at a single point in time to assess the relationships between agricultural sustainability practices, green technology implementation, energy policy measures, and the achievement of carbon neutrality in Mardan, Pakistan.

3.2. Study Setting

The study was conducted in Mardan, Pakistan, a region heavily reliant on agriculture and facing environmental challenges such as water scarcity and land degradation. Mardan’s significance as an agrarian region and its potential as a model for sustainable development make it an ideal study setting. This ensures relevance to the local context and facilitates the identification of actionable insights for addressing specific environmental issues in the area.

3.3. Population and Target Population

The suitable population for this study includes all individuals and organizations in Mardan, Pakistan, who are involved in or affected by agricultural practices, green technology adoption, and energy policy measures. The target population is a more specific subset of the broader population and includes those who are directly engaged in or have significant influence over agricultural practices, technology adoption, and energy policies.

This includes:

• Farmers and Agricultural Workers: Particularly those who have adopted or are knowledgeable about sustainable agricultural practices.
• Agricultural Extension Officers and Agronomists: Professionals who provide advice and support to farmers on best practices.
• Technology Providers: Companies and individuals who supply and support the implementation of green technologies such as renewable energy systems and precision agriculture tools.
• Policymakers and Government Officials: Those involved in creating and implementing policies related to agriculture, energy, and environmental sustainability.
• Environmental NGOs and Advocacy Groups: Organizations actively promoting sustainable practices and influencing policy changes.
• Academics and Researchers: Experts conducting research on sustainable agriculture, green technology, and energy policy in the context of Mardan.

Focusing on this target population allows the study to gather detailed and specific information from key stakeholders who have direct knowledge and experience with the variables of interest. These individuals are likely to provide valuable insights into the practical challenges and opportunities associated with harmonizing agricultural sustainability, green technology, and energy policy. Additionally, targeting these groups ensures that the data collected are relevant and actionable, facilitating the development of effective strategies and interventions tailored to the local context of Mardan.

3.4. Demographic Characteristics of Participants

Understanding the demographic characteristics of participants is crucial in ensuring that the data are representative and identifying potential factors influencing the relationship between agricultural sustainability practices (ASPs), green technology implementation (GTI), energy policy measures (EPMs), and achieving carbon neutrality in Mardan, Pakistan. Demographic features include age, gender, education level, occupation, years of experience in agriculture, farm size, income level, access to resources, and residential status in Mardan.

Age can influence the adoption of new technologies and practices. Younger individuals may be more open to adopting innovative green technologies and sustainable practices, while older individuals may rely more on traditional methods. Age groups are categorized as follows: 18–30, 31–45, 46–60, and 61 and above.

Gender can impact access to resources, decision-making power, and participation in agricultural and technological activities. Understanding gender dynamics is important for ensuring inclusive policy recommendations. Gender is recorded as male and female.

Education influences awareness and understanding of sustainability practices, green technologies, and policy implications. Higher education levels are often associated with greater knowledge and implementation of sustainable practices. Education levels are categorized as follows: no formal education, primary education, secondary education, and tertiary education.

Different occupations (e.g., farmer, extension officer, and policymaker) have varying levels of engagement and influence over agricultural practices, technology adoption, and policy implementation. Occupations are recorded to categorize participants into relevant occupational groups.

Experience in agriculture can affect familiarity with traditional versus modern practices and openness to adopting new technologies. More experienced individuals might have deeper insights into practical challenges and opportunities. Participants are asked to provide their years of experience in agriculture, categorized as follows: 0–5 years, 6–10 years, 11–20 years, and 21+ years.

Farm size can influence the feasibility and impact of adopting certain sustainability practices and technologies. Larger farms may have more resources to invest in green technologies. For the purpose of this study, farm size is recorded in hectares and categorized as follows: small farms are less than 5 hectares, medium farms range from 5 to 20 hectares, and large farms are more than 20 hectares.

Income level affects the ability to invest in new technologies and practices. Higher income levels can facilitate the adoption of green technologies and sustainable practices. For the purpose of this study, income levels are categorized as follows: low income is less than PKR 40,000 per month, middle income ranges from PKR 40,000 to PKR 80,000 per month, and high income is more than PKR 80,000 per month.

Access to financial resources, technical support, and information is critical for the implementation of sustainable practices and technologies. Participants are asked about their access to various resources, including credit, technical assistance, and training programs. Residential status within Mardan is classified as urban, suburban, or rural (see

Table 1. Demographic Characteristics of Participants.

Demographic Features	Categories	Frequency
Age	18–30	98
	31–45	122
	46–60	105
	61 and Above	75
Gender	Male	276
	Female	124
Educational Level	No Formal Education	44
	Primary Education	96
	Secondary Education	137
	Tertiary Education	123
Occupation	Farmer	174
	Extension Officer	66
	Policymaker	32
	Researcher	48
	Technology Provider	45
	NGO Representative	18
	Other	17
Years of Experience in Agriculture	0–5 Years	97
	6–10 Years	123
	11–20 Years	104
	21 Plus Years	76
Farm Size (For Farmers)	Small <5 Hectares	145
	Medium 5–20 Hectares	118
	Larger >20 Hectares	137
Income Level	Low	148
	Medium	154
	High	98
Access to Resources	No Access	72
	Limited Access	168
	Adequate Access	160
Geographical Location Within Mardan	Urban	96
	Suburban	142
	Rural	162

Source: Compiled by authors in relation to Studies and ICF [36], Pakistan Demographic and Health Survey, 2017/18. (The sample size for each demographic variable in Table 1 is 400).

and Figure 2).

3.5. Sampling Procedures and Sample Size

To collect data from the various stakeholders involved in agricultural practices, green technology adoption, and energy policy measures in Mardan, Pakistan, we employed the sampling methods described by Sekaran and Bougie [37]. This approach ensures a representative sample that is statistically valid and provides comprehensive insights into the study’s variables. The total target population includes farmers, agricultural workers, agricultural extension officers, agronomists, technology providers, policymakers, government officials, environmental NGOs, advocacy groups, academics, and researchers in Mardan. Based on preliminary surveys and secondary data, the estimated numbers of individuals in each stakeholder group are as follows (

Table 2. Estimated Target Population.

Stakeholder Group	Estimated Population
Farmers and Agricultural Workers	15,000
Agricultural Extension Officers	500
Technology Providers	300
Policymakers and Government Officials	200
Environmental NGOs	100
Academics and Researchers	150
Total	16,250

Source: Compiled by authors in relation to Pakistan Demographic and Health Survey, 2017/18.

and Figure 3).

3.5.1. Step 1

Define the Population: The population includes all individuals and organizations in Mardan involved in or affected by agricultural practices, green technology adoption, and energy policy measures. To ensure that all relevant stakeholders are included, the population for this study is defined broadly and then narrowed down to a target population:

• Broader Population: This includes all individuals and organizations in Mardan, Pakistan, who are involved in or affected by agricultural practices, green technology adoption, and energy policy measures. This broader population captures a wide range of perspectives and experiences related to the study’s variables.
• Target Population: A more specific subset of the broader population, the target population includes those directly engaged in or significantly influencing agricultural practices, technology adoption, and energy policies. This ensures that the study focuses on individuals and organizations with direct knowledge and experience. The target population is categorized into six key groups:
  • Farmers and Agricultural Workers
  • Agricultural Extension Officers and Agronomists
  • Technology Providers
  • Policymakers and Government Officials
  • Environmental NGOs and Advocacy Groups
  • Academics and Researchers

3.5.2. Step 2

Determine the Sample Size: Accurate sample size determination is crucial for balancing representation and feasibility. This study uses established statistical methods to achieve this:

• Target Population Size: The estimated total target population size is 16,250 individuals.
• Sample Size Calculation: Using the sample size determination table provided by Sekaran and Bougie, for a target population of 16,250, a sample size of approximately 400 is sufficient to achieve a confidence level of 95% and a margin of error of ±5%. This ensures that the sample is large enough to provide statistically significant results while remaining manageable for data collection and analysis.

3.5.3. Step 3

Stratified Random Sampling: Given the diverse nature of the target population, stratified random sampling ensures comprehensive representation of all stakeholder groups:

• Stratification: The target population is divided into distinct strata based on stakeholder groups. This stratification ensures that each group is adequately represented in the sample, addressing potential biases that could arise from over- or under-representation of certain groups.
• Random Sampling within Strata: Within each stratum, a random sample is selected. This approach minimizes sampling bias and enhances the representativeness of the sample.

3.5.4. Step 4

Allocate Sample Size Proportionally: To ensure that each stakeholder group is represented proportionally to its size within the target population, the proportional allocation method is employed:

• Population Proportions: The proportion of each stakeholder group within the total target population is calculated.
• Sample Allocation: The sample size for each group is determined based on its population proportion. This ensures that larger groups (e.g., farmers and agricultural workers) have a sample size reflective of their population size, while smaller groups (e.g., policymakers and researchers) are also represented proportionally. This method avoids over-representation of smaller groups and ensures that the perspectives of all groups are included.

Allocating the sample size proportionally to the population size of each group ensures that larger groups, like farmers and agricultural workers, which constitute the majority of the target population, are adequately represented. Smaller groups, such as policymakers and researchers, also have representation proportional to their size, ensuring that their perspectives are included without over-representation. With a sample size of 400, the study achieves a confidence level of 95% and a margin of error of ±5%, which is considered statistically robust for social science research (

Table 3. Sample Frame Table.

Stakeholder Group	Estimated Population	Sample Proportion (%)	Sample Size
Farmers and Agricultural Workers	15,000	92.31%	370
Agricultural Extension Officers	500	3.08%	12
Technology Providers	300	1.85%	8
Policymakers and Government Officials	200	1.23%	5
Environmental NGOs	100	0.62%	3
Academics and Researchers	150	0.92%	4
Total	16,250	100%	400

Source: Compiled by authors in relation to Sekaran and Bougie (2016) [37].

and Figure 4). This sample size allows for meaningful analysis and reliable conclusions (Figure 5).

The detailed sampling procedures described above ensure the reproducibility of this study by providing a clear framework for data collection. The steps include:

• Population Definition: Clearly defining the broader and target populations to capture all relevant stakeholders.
• Sample Size Determination: Using statistical methods to determine a sample size that balances representation and feasibility.
• Stratified Random Sampling: Dividing the target population into strata and sampling proportionally to ensure comprehensive representation.
• Proportional Allocation: Allocating sample sizes based on the proportion of each stakeholder group within the total population.

By following these detailed procedures, the study ensures that the data collected are representative and reliable, facilitating meaningful analysis and robust conclusions. This approach not only enhances the quality of the research but also allows for the study to be replicated by other researchers in similar contexts, contributing to the broader field of environmental sustainability research.

3.6. Tool of Data Collection

To collect data from various stakeholders involved in agricultural practices, green technology adoption, and energy policy measures in Mardan, Pakistan, a structured questionnaire was used. The questionnaire was designed to capture detailed information regarding demographic variables, agricultural sustainability practices, green technology implementation, energy policy measures, and the dependent variable, Carbon-Free Development. The distribution process was carefully planned and executed to ensure comprehensive coverage and high response rates. Initially, a list of potential respondents, including farmers, agricultural workers, agricultural extension officers, agronomists, technology providers, policymakers, government officials, environmental NGOs, advocacy groups, academics, and researchers, was compiled through local agricultural offices, community leaders, and industry directories. The questionnaire was then distributed through a combination of methods to maximize reach and convenience for respondents.

Online distribution involved making the questionnaire available on survey platforms, with links shared via email and social media channels, effectively reaching educated and tech-savvy respondents. For stakeholders with limited internet access, printed copies were distributed in person, including visits to farms, agricultural offices, local markets, and community centers. Field staff assisted respondents in filling out the questionnaires where necessary. Additionally, questionnaires were distributed during workshops, training sessions, and community meetings focused on sustainable agriculture and green technologies, allowing for direct engagement and explanation of the study’s purpose. The data collection process spanned three months, from January 2024 to March 2024, coinciding with key agricultural activities to ensure relevance and availability of stakeholders. Follow-up reminders were sent via phone calls, SMS, and emails, with the assistance of community leaders and local agricultural officers to encourage participation and address any questions. Through these methods, the study aimed to achieve a diverse and representative sample, ensuring the robustness and reliability of the collected data.

The questionnaire design process for this study began by clearly defining research objectives focused on measuring Agricultural Sustainability Practices (ASPs), Green Technology Implementations (GTIs), Energy Policy Measures (EPMs), and Carbon-Free Development as the dependent variable. A thorough literature review informed the development of a comprehensive question pool, ensuring that questions were clear, specific, and grounded in established knowledge. The questionnaire was structured into sections for each variable, including demographic data and Likert scale questions to assess effectiveness, impact, and policy awareness. Open-ended and multiple-choice questions provided detailed insights into practices, technologies, progress towards carbon neutrality, and challenges. Pilot testing with stakeholders identified ambiguities and allowed for refining question clarity and relevance. Final adjustments ensured that the questionnaire was concise, unbiased, and covered all relevant aspects effectively. This systematic approach aimed to create a reliable instrument for capturing nuanced data on ASPs, GTI, EPMs, and Carbon-Free Development in Mardan, Pakistan. It facilitates a detailed exploration of pathways towards achieving carbon neutrality, contributing valuable insights to sustainable development efforts in the region.

3.7. Measurement of Variables

3.7.1. Independent Variables

Agricultural Sustainability Practices (IV): Measured through the adoption of specific practices (crop rotation, conservation tillage, etc.) and their perceived effectiveness and impact on soil health and crop yield. Likert scale questions (1–5) for effectiveness and impact ratings.

3.7.2. Green Technology Implementation (IV)

Measured through the implementation of green technologies (solar-powered irrigation, wind turbines, etc.) and their perceived effectiveness and cost-effectiveness. Likert scale questions (1–5) for effectiveness and cost-effectiveness ratings.

3.7.3. Energy Policy Measures (IV)

Measured through awareness and utilization of government policies promoting renewable energy and sustainable practices. Likert scale questions (1–5) for the effectiveness of policies.

3.7.4. Dependent Variable: Carbon-Free Development

Measured through self-reported progress towards carbon neutrality and identification of challenges. Likert scale questions (1–5) for progress ratings and multiple-choice for challenges.

3.8. Reliability and Validity of the Tool

The reliability and validity of the tool were checked using SPSS. Reliability was assessed using Cronbach’s alpha, while validity was evaluated through a comprehensive validity assessment, including content validity, construct validity using factor analysis, and criterion validity [38].

Ensuring the reliability and validity of the questionnaire involved several critical steps. Reliability was assessed using Cronbach’s alpha for each variable (ASPs, GTI, EPMs, Carbon-Free Development) to measure internal consistency. Values above 0.70 were targeted, indicating good reliability. Test–retest reliability was conducted to ensure that responses remained consistent over time, especially for questions related to ongoing practices or perceptions. Validity was ensured through various measures. Content validity was confirmed by ensuring that questions covered all dimensions of each variable, reviewed by experts, and aligned with theoretical constructs and research objectives. Construct validity was assessed using factor analysis to verify that questions designed for ASPs, GTI, and EPMs loaded onto their respective factors, as detailed in the study’s tables. Criterion validity involved comparing questionnaire results with external criteria or existing measures to validate the accuracy of intended constructs. Data analysis included statistical techniques such as factor analysis for construct validity and Cronbach’s alpha for reliability, as depicted in

Table 4. Reliability Analysis.

Scale	Cronbach’s Alpha	Number of Items
Agricultural Sustainability Practices	0.85	6
Green Technology Implementation	0.82	5
Energy Policy Measures	0.78	4
Carbon-Free Development	0.88	3

Source: Authors’ calculations.

and

Table 5. Validity Analysis.

Factors	Items	Factor Loadings	Eigenvalue	% of Variance
Factor 1: Agricultural Practices	Crop Rotation, Conservation Tillage, Organic Farming	0.72–0.84	3.2	32.5%
Factor 2: Green Technologies	Solar Irrigation, Wind Turbines, Precision Agriculture	0.68–0.80	2.5	25.0%
Factor 3: Energy Policies	Subsidies, Tax Incentives, Carbon Pricing	0.70–0.78	1.8	18.0%

Source: Authors’ calculations.

and Figure 6 and Figure 7. Findings were reported comprehensively to illustrate reliability and validity metrics, ensuring that the questionnaire effectively captured nuances in ASPs, GTI, EPMs, and Carbon-Free Development in Mardan, Pakistan. This rigorous approach aimed to provide robust insights into pathways for achieving carbon neutrality, contributing to sustainable development efforts in the region.

3.9. Ethical Considerations

Ethical considerations, including obtaining informed consent, ensuring participant confidentiality, and adhering to ethical guidelines, are crucial for protecting the rights and well-being of study participants. Obtaining ethical approval from the Ethics Committee of the Department of Sociology demonstrates a commitment to ethical research conduct and compliance with ethical standards.

3.10. Data Analysis

The data analysis for this study involved a comprehensive approach to understand the relationships and impacts of various variables on the achievement of carbon neutrality and carbon-free development. Initially, descriptive statistics were utilized to summarize the demographic characteristics of the participants and the frequency distribution of their responses, providing a foundational overview of the data. To explore the relationships between the independent variables and the dependent variable, correlation analysis was conducted to identify the strength and direction of the associations between variables. Structural equation modeling (SEM) was used to assess both the direct and indirect relationships between the variables, offering a comprehensive understanding of the overall model fit and the intricate dynamics within the data. To examine associations between categorical variables, a Chi-square test was employed. Following this, multiple regression analysis was performed to determine the influence of multiple independent variables on the dependent variable, specifically focusing on carbon-free development.

To ensure the robustness of our findings, robustness tests such as bootstrapping and Monte Carlo simulations were conducted alongside traditional statistical analyses. These methods complement Structural Equation Modeling (SEM), Chi-square tests, and multiple regression analyses by assessing the stability and reliability of our estimated coefficients and statistical significance under varying conditions.

Bootstrapping assesses the stability of estimated coefficients by generating multiple datasets from the original sample, confirming consistent estimates across variations. This method is particularly valuable in mitigating biases from specific assumptions inherent in SEM and regression models. By demonstrating consistent estimates across bootstrapped samples, we confirm the reliability of relationships between Agricultural Sustainability Practices (ASPs), Green Technology Implementation (GTI), Energy Policy Measures (EPMs), and Carbon-Free Development (CFD) in Mardan, Pakistan.

Monte Carlo simulations, on the other hand, evaluate the reliability of statistical significance by simulating data under different scenarios. This approach ensures that the observed relationships hold under varying conditions, validating our findings beyond theoretical assumptions. By simulating data across a range of scenarios, we strengthen the generalizability of our conclusions, affirming that the identified relationships are robust and applicable across different contexts in Mardan, Pakistan.

In conjunction with correlation analysis, SEM, Chi-square tests, and multiple regression analyses, these robustness tests provide empirical validation of our research outcomes. They confirm that ASPs, GTI, and EPMs significantly contribute to CFD in Mardan, Pakistan, under various statistical and simulated conditions. This comprehensive approach not only enhances the rigor of our study but also underscores the reliability and applicability of our findings in informing sustainable development policies and practices.

3.11. Models of the Study

3.11.1. Simple Correlation Models

The diagram in Figure 8 illustrates the correlation links between the analyzed variables in the study. Specifically, it shows the simple correlations between three independent variables (IVs)—Agricultural Sustainability Practices (ASPs), Green Technology Implementation (GTI), and Energy Policy Measures (EPMs)—and the dependent variable (DV), Carbon-Free Development (CFD). Each independent variable is correlated with the dependent variable through a straightforward correlation process, indicating the direct relationship between each IV and CFD.

3.11.2. Structural Equation Modeling (SEM) Path Model

The diagram in Figure 9 depicts a Structural Equation Model (SEM) that illustrates the linkages between the variables in the study. This model shows how three independent variables (IVs)—Agricultural Sustainability Practices (ASPs), Green Technology Implementation (GTI), and Energy Policy Measures (EPMs)—are related to the dependent variable (DV), Carbon-Free Development (CFD). The SEM framework allows for the simultaneous analysis of multiple relationships, demonstrating how each independent variable contributes to the overall goal of achieving carbon-free development.

3.11.3. Chi-Square Model

The diagram in Figure 10 illustrates the use of the Chi-square test to analyze the relationships between variables in the study. It shows how three independent variables (IVs)—Agricultural Sustainability Practices (ASPs), Green Technology Implementation (GTI), and Energy Policy Measures (EPMs)—are linked to the dependent variable (DV), Carbon-Free Development (CFD), through bivariate analysis. Each IV is tested for its association with the DV using the Chi-square test, highlighting the bivariate relationships between these variables.

3.11.4. Multiple Regression Model

The diagram in Figure 11 represents a Multiple Regression Model used to analyze the relationships between variables in the study. It shows how three independent variables (IVs)—Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), and Energy Policy Measures (EPM)—influence the dependent variable (DV), Carbon-Free Development (CFD). The model employs multivariate analysis to assess the combined effects of the independent variables on the dependent variable, allowing for a more comprehensive understanding of how these factors collectively contribute to achieving carbon-free development.

4. Results

4.1. Correlation Analysis

Table 6. Simple Correlation between ASP and CFD.

ASP (IV) and CFD (DV)	Measures	ASPs (IV)	CFD (DV)
Agricultural Sustainability Practices (ASP) (IV)	Pearson Correlation	1	0.87 **
	Sig. (2-tailed)		0.000
	N	400	400
Carbon-Free Development (CFD) (DV)	Pearson Correlation	0.87 **	1
	Sig. (2-tailed)	0.000

** Correlation is highly significant at the 0.05 level (2-tailed), r (400) =0.87 **; p < 0.05. r² = 0.76; (Since 76% of the variance is shared, the association is obviously a strong one). Source: Authors’ calculations.

and Figure 12 examine the relationship between Agricultural Sustainability Practices (ASPs) and Carbon-Free Development (CFD) in Mardan, Pakistan. It explores how sustainable farming practices can contribute to achieving carbon-free development. The correlation coefficient of 0.87 indicates a very strong positive linear relationship between ASPs and CFD. This suggests that as ASPs increase, CFD also tends to increase significantly. The p-value (<0.001) is well below the standard significance threshold of 0.05, indicating that the observed correlation is statistically significant and not due to random chance, reinforcing the reliability of the results. This implies that enhancing ASPs can significantly contribute to achieving higher levels of CFD in Mardan. The r² value indicates that approximately 76% of the variance in Carbon-Free Development can be explained by variations in Agricultural Sustainability Practices. This suggests that ASPs are a major contributing factor to CFD.

The scatter plot on the left shows the relationship between Agricultural Sustainability Practices (ASP) and Carbon-Free Development (CFD). Each yellow plus sign represents a data point from the study, indicating a specific observation’s ASP and CFD values. The red line is the regression line, which shows the best fit line through the data points, indicating the trend or relationship between ASP and CFD. The heatmap on the right displays the correlation matrix between ASP and CFD, with the color intensity representing the strength of the correlation. The values (1 and 0.99) indicate a very high positive correlation between the variables, suggesting that increases in ASP are strongly associated with increases in CFD.

Table 7. Simple Correlation between GTI and CFD.

GTI (IV) and CFD (DV)	Measures	GTI (IV)	CFD (DV)
Green Technology Implementation (GTI) (IV)	Pearson Correlation	1	0.89 **
	Sig. (2-tailed)		0.000
	N	400	400
Carbon-Free Development (CFD) (DV)	Pearson Correlation	0.89 **	1
	Sig. (2-tailed)	0.000

** Correlation is highly significant at the 0.05 level (2-tailed), r (400) =0.89 **; p < 0.05. r² = 0.79; (Since 79% of the variance is shared, the association is obviously a strong one). Source: Authors’ calculations.

and Figure 13 explore the relationship between Green Technology Implementation (GTI) and Carbon-Free Development (CFD) in Mardan, Pakistan. It aims to understand how the adoption and implementation of green technologies can drive carbon-free development. The high correlation coefficient (0.89) indicates a strong positive association between Green Technology Implementation and Carbon-Free Development. This suggests that enhancing GTI can significantly contribute to achieving higher levels of CFD in Mardan. With 79% of the variance in CFD explained by GTI, it is evident that green technology plays a crucial role in promoting carbon-free development. This high shared variance underscores the importance of integrating green technology into broader development strategies aimed at reducing carbon emissions. The statistically significant p-value (<0.001) strengthens the validity of the findings, confirming that the strong correlation between GTI and CFD is not due to random chance, thus reinforcing the reliability of the results.

The scatter plot with the regression line and the correlation matrix heatmap together provide a clear visual representation of the relationship between Green Technology Implementation (GTI) and Carbon-Free Development (CFD). The yellow plus signs in the scatter plot represent individual data points, showing the observed values of GTI on the x-axis and the corresponding values of CFD on the y-axis. The red line is the best-fit regression line, modeling the linear relationship between GTI and CFD; the closer the data points are to this line, the stronger the linear relationship. The correlation matrix heatmap visually represents the correlation coefficients between GTI and CFD. The values range from −1 to 1, where 1 indicates a perfect positive linear relationship, −1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. The color gradient from blue to red indicates the strength of the correlation, with red representing a strong positive correlation. In this case, the correlation coefficient between GTI and CFD is 0.99, indicating a very strong positive linear relationship.

Table 8. Simple Correlation between EPM and CFD.

EPM (IV) and CFD (DV)	Measures	EPMs (IV)	CFD (DV)
Energy Policy Measures (EPM) (IV)	Pearson Correlation	1	0.94 **
	Sig. (2-tailed)		0.000
	N	400	400
Carbon-Free Development (CFD) (DV)	Pearson Correlation	0.94 **	1
	Sig. (2-tailed)	0.000

** Correlation is highly significant at the 0.05 level (2-tailed), r (400) =0.94 **; p < 0.05. r² = 0.88; Since 88% of the variance is shared, the association is obviously a strong one). Source: Authors’ calculations.

and Figure 14 display the Pearson correlation coefficient (r) between Energy Policy Measures (EPMs) and Carbon-Free Development (CFD) is 0.94, indicating an extremely strong positive correlation. This suggests that as the implementation of Energy Policy Measures increases, the level of Carbon-Free Development also significantly increases. The significance value (p-value) for this correlation is 0.000, which is less than the threshold of 0.05, indicating statistical significance. The high level of significance suggests a very low probability that the observed correlation is due to random chance. The coefficient of determination (r²) is 0.88, implying that 88% of the variance in Carbon-Free Development can be explained by Energy Policy Measures. This high percentage indicates that EPMs play a crucial role in influencing CFD.

The scatter plot with the regression line and the correlation matrix heatmap illustrate the relationship between Energy Policy Measures (EPM) and Carbon-Free Development (CFD). Yellow plus signs in the scatter plot show observed EPM and CFD values, with the red line representing the best-fit regression line. The correlation matrix heatmap, using a blue-to-red gradient, indicates correlation strength between variables. A correlation coefficient of 0.99 between EPM and CFD suggests a very strong positive linear relationship. This visual representation confirms the significant impact of EPM on CFD.

4.2. Structural Equation Model

Table 9. Structural Equation Modeling, Path Coefficients and their Values.

Path	Description	Coefficient (β)	Standard Error (SE)	t-Value	p-Value
ASP → CFD	Effect of Agricultural Sustainability Practices	0.35	0.05	7.00	<0.001
GTI → CFD	Effect of Green Technology Implementation	0.45	0.06	7.50	<0.001
EPM → CFD	Effect of Energy Policy Measures	0.50	0.07	7.14	<0.001

Source: Authors’ calculations.

and Figure 15 present the effects of Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), and Energy Policy Measures (EPM) on Carbon-Free Development (CFD) using Structural Equation Modeling (SEM) path coefficients. The analysis examines the relationships between ASPs, GTI, EPMs, and CFD in Mardan, Pakistan, focusing on understanding how these factors interact to promote carbon-free development.

4.2.1. Effect of Agricultural Sustainability Practices (ASP) on Carbon-Free Development (CFD)

The path coefficient (β) between ASPs and CFD is 0.35, indicating a positive relationship between agricultural sustainability practices and carbon-free development. This coefficient suggests that for every one-unit increase in ASP, there is a predicted increase of 0.35 units in CFD. The p-value of <0.001 indicates that this relationship is statistically significant, implying that the effect of ASPs on CFD is not due to random chance. This suggests that adopting sustainable farming practices can contribute positively to carbon-free development in Mardan.

4.2.2. Effect of Green Technology Implementation (GTI) on Carbon-Free Development (CFD)

The path coefficient (β) between GTI and CFD is 0.45, indicating a positive relationship between green technology implementation and carbon-free development. This coefficient suggests that for every one-unit increase in GTI, there is a predicted increase of 0.45 units in CFD. Similar to ASP, the p-value of <0.001 suggests that this relationship is statistically significant, underscoring the importance of implementing green technologies in driving carbon-free development in Mardan.

4.2.3. Effect of Energy Policy Measures (EPM) on Carbon-Free Development (CFD)

The path coefficient (β) between EPM and CFD is 0.50, indicating a positive relationship between energy policy measures and carbon-free development. This coefficient suggests that for every one-unit increase in EPM, there is a predicted increase of 0.50 units in CFD. Again, the p-value of <0.001 indicates that this relationship is statistically significant, highlighting the role of effective energy policy measures in promoting carbon-free development in the region.

Table 10. Measurement Model Loadings.

Construct	Indicator	Loading (λ)	Standard Error (SE)	t-Value	p-Value
ASP	Crop rotation	0.80	0.04	20.00	<0.001
ASP	Organic farming methods	0.75	0.05	15.00	<0.001
GTI	Renewable energy usage	0.85	0.03	28.33	<0.001
GTI	Precision agriculture techniques	0.78	0.04	19.50	<0.001
EPM	Subsidies for renewable energy	0.82	0.04	20.50	<0.001
EPM	Carbon pricing mechanisms	0.80	0.05	16.00	<0.001
CFD	Carbon emission levels	0.90	0.03	30.00	<0.001
CFD	Renewable energy percentage	0.88	0.03	29.33	<0.001

Source: Authors’ calculations.

and Figure 16 present Structural Equation Modeling (SEM) results, focusing on the Measurement Model Loadings to understand the relationships between latent constructs (ASP, GTI, EPMs, and CFD) and their corresponding indicators in promoting carbon-free development in Mardan, Pakistan. Loadings close to 1 indicate a strong relationship between the latent construct and the observed variables.

4.2.4. Indicator Loadings for ASPs (Agricultural Sustainability Practices)

The indicators “Crop rotation” and “Organic farming methods” have loadings of 0.80 and 0.75, respectively. These high loadings suggest that both indicators are strongly associated with the latent construct of agricultural sustainability practices. This indicates that crop rotation and organic farming methods are reliable measures of agricultural sustainability practices in the region.

4.2.5. Indicator Loadings for GTI (Green Technology Implementation)

The indicators “Renewable energy usage” and “Precision agriculture techniques” have loadings of 0.85 and 0.78, respectively. Similar to ASP, these high loadings indicate a strong relationship between the indicators and the latent construct of green technology implementation. It suggests that the adoption of renewable energy usage and precision agriculture techniques serves as effective measures of implementing green technology in Mardan.

4.2.6. Indicator Loadings for EPM (Energy Policy Measures)

The indicators “Subsidies for renewable energy” and “Carbon pricing mechanisms” have loadings of 0.82 and 0.80, respectively. Again, these high loadings suggest a strong association between the indicators and the latent construct of energy policy measures. It implies that providing subsidies for renewable energy and implementing carbon pricing mechanisms are reliable measures of energy policy measures in the region.

4.2.7. Indicator Loadings for CFD (Carbon-Free Development)

The indicators “Carbon emission levels” and “Renewable energy percentage” have loadings of 0.90 and 0.88, respectively. These exceptionally high loadings indicate a very strong relationship between the indicators and the latent construct of carbon-free development. It suggests that monitoring carbon emission levels and promoting renewable energy usage are reliable measures of carbon-free development efforts in Mardan.

4.3. Chi-Square Test

The results presented in

Table 11. Chi-square Test.

Independent Variable	Dependent Variable	Chi-Square Value	p-Value
Agricultural Sustainability Practices (ASP)	Carbon-Free Development (CFD)	χ2 = 45.675	<0.001
Green Technology Implementation (GTI)	Carbon-Free Development (CFD)	χ2 = 65.346	<0.001
Energy Policy Measures (EPM)	Carbon-Free Development (CFD)	χ2 = 56.923	<0.001

Source: Authors’ calculations.

and Figure 17 demonstrate the statistical analysis of the relationship between different independent variables (Agricultural Sustainability Practices—ASP, Green Technology Implementation—GTI, Energy Policy Measures—EPM) and the dependent variable, Carbon-Free Development (CFD), within the context of Mardan, Pakistan. The Chi-square test was utilized to assess the associations between these variables.

4.3.1. Agricultural Sustainability Practices (ASP) and Carbon-Free Development (CFD)

The Chi-square value for the relationship between ASPs and CFD is χ² = 45.675. The p-value associated with this Chi-square value is less than 0.001, indicating strong statistical significance. This suggests that there is a significant association between Agricultural Sustainability Practices and Carbon-Free Development in Mardan, Pakistan. It implies that certain agricultural practices contribute significantly to the carbon-free development agenda in the region.

4.3.2. Green Technology Implementation (GTI) and Carbon-Free Development (CFD)

The Chi-square value for the relationship between GTI and CFD is χ² = 65.346. The p-value associated with this Chi-square value is less than 0.001, indicating strong statistical significance. This indicates a significant association between Green Technology Implementation and Carbon-Free Development in Mardan, Pakistan. It suggests that the implementation of green technologies plays a crucial role in advancing the carbon-free development agenda within the region.

4.3.3. Energy Policy Measures (EPM) and Carbon-Free Development (CFD)

The Chi-square value for the relationship between EPMs and CFD is χ² = 56.923. The p-value associated with this Chi-square value is less than 0.001, indicating strong statistical significance. This highlights a significant association between Energy Policy Measures and Carbon-Free Development in Mardan, Pakistan. It suggests that effective energy policy measures are instrumental in driving forward the carbon-free development objectives within the region.

4.4. Multiple Regression

The results presented in

Table 12. Multiple Regression Analysis.

Variable	Coefficient (β)	Standard Error	t-Value	p-Value	Significance	Impact on CFD
Constant (β0)	1.234	0.123	10.03	<0.001	***	-
ASP (β1)	0.456	0.045	10.13	<0.001	***	High
GTI (β2)	0.389	0.050	7.78	<0.001	***	High
EPM (β3)	0.287	0.055	5.22	<0.001	***	High

*** Significance at 1% level. Source: Authors’ calculations.

and Figure 18 provide insights into the relationship between multiple independent variables (Agricultural Sustainability Practices—ASP, Green Technology Implementation—GTI, Energy Policy Measures—EPM) and the dependent variable, Carbon-Free Development (CFD), using a multiple regression analysis.

4.4.1. Constant (β0)

The constant term represents the intercept of the regression equation. Coefficient (β) = 1.234 indicates that when all independent variables are zero, the predicted value of CFD is 1.234. The standard error (0.123) is the standard deviation of the sampling distribution of the coefficient estimate. The t-value (10.03) is the coefficient divided by its standard error, indicating the significance of the coefficient. The p-value (<0.001) is highly significant (***), suggesting that the intercept is significantly different from zero.

4.4.2. Agricultural Sustainability Practices (ASP) (β1)

Coefficient (β1) = 0.456 indicates that for every one-unit increase in ASP, there is an expected increase of 0.456 units in CFD, holding other variables constant. The t-value (10.13) and p-value (<0.001) suggest a highly significant positive relationship between ASP and CFD. The impact on CFD is labeled as “High,” indicating that ASP have a substantial positive influence on Carbon-Free Development in Mardan.

4.4.3. Green Technology Implementation (GTI) (β2)

Coefficient (β2) = 0.389 indicates that for every one-unit increase in GTI, there is an expected increase of 0.389 units in CFD, holding other variables constant. The t-value (7.78) and p-value (<0.001) indicate a highly significant positive relationship between GTI and CFD. Similar to ASP, GTI also has a labeled impact on CFD as “High,” suggesting a substantial positive influence on Carbon-Free Development in Mardan.

4.4.4. Energy Policy Measures (EPM) (β3)

Coefficient (β3) = 0.287 indicates that for every one-unit increase in EPMs, there is an expected increase of 0.287 units in CFD, holding other variables constant. The t-value (5.22) and p-value (<0.001) indicate a highly significant positive relationship between EPM and CFD. EPMs are labeled as having a “High” impact on CFD, suggesting a significant positive influence on Carbon-Free Development in Mardan.

While the findings from this study underscore the significant positive relationships between Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), Energy Policy Measures (EPM), and Carbon-Free Development (CFD) in Mardan, Pakistan, their broader applicability warrants careful consideration across different regions. The high correlation coefficients and strong statistical significance observed suggest that enhancing sustainable agricultural practices, adopting green technologies, and implementing effective energy policies can indeed advance carbon-free development goals in similar agricultural contexts. However, the effectiveness of these strategies may vary due to diverse socio-economic, environmental, and policy landscapes in different regions. Factors such as local climate variability, agricultural traditions, policy frameworks, and community engagement levels can significantly influence the outcomes of sustainability initiatives and technology adoption. Therefore, while the findings provide valuable insights for policymakers and practitioners in Mardan and comparable regions, further cross-regional studies and contextual analyses are essential to validate and refine these relationships, ensuring their relevance and applicability in diverse agricultural settings globally.

5. Discussion

The findings from correlation analysis underscore the critical role that Agricultural Sustainability Practices (ASPs) play in promoting Carbon-Free Development (CFD) in Mardan, Pakistan. The strong positive relationship observed between ASP and CFD suggests that efforts to enhance sustainable farming methods are likely to yield significant environmental benefits. Several studies corroborate the strong linkage between sustainable agricultural practices and environmental outcomes. For instance, Farage, Ardö [39] demonstrated that sustainable farming practices, including crop rotation and organic farming, significantly reduce carbon emissions and enhance soil carbon sequestration. Similarly, Stout, Lal [40] found that conservation agriculture practices improve soil quality and increase carbon storage, thereby contributing to carbon-free development goals. However, some studies offer contrasting findings. For instance, Smith, Davis [41] argued that while sustainable farming practices have positive environmental impacts, their contribution to carbon sequestration can be limited by other factors such as soil type and regional climate conditions. These contrasting findings highlight the complexity and context-specific nature of the relationship between ASPs and environmental outcomes. The present study aligns with previous research in emphasizing the significant impact of sustainable agricultural practices on carbon-free development. The strong correlation observed in Mardan is consistent with global findings that sustainable farming practices are crucial for mitigating climate change and promoting sustainable development. However, a key difference in this study is its regional focus. Most previous research has been conducted in diverse geographical contexts, primarily in developed countries. This study’s emphasis on Mardan, Pakistan, fills a geographical research gap by providing insights specific to a developing country context. Additionally, the high r² value observed (76%) is notably higher than those reported in some global studies, suggesting that ASP might have a more pronounced effect in this specific regional context. This study addresses several gaps in the existing literature. Firstly, it provides empirical evidence from a developing country, which is often underrepresented in environmental and agricultural sustainability research. Secondly, it underscores the significant impact of ASP on CFD in a specific regional context, which can inform localized policy and practice. Previous studies often generalize findings across diverse regions without accounting for local variations in environmental, economic, and social factors. While the study focuses on Mardan, Pakistan, similar research in diverse regions has shown varying strengths of correlation between ASPs and environmental outcomes. For instance, studies in Southeast Asia by Nguyen, Chidthaisong [42] and in Latin America by Hoyos, Escobar [43] have reported positive correlations but with differing magnitudes. This suggests that while ASPs generally support carbon-free development globally, local factors such as soil types and climatic conditions can influence the extent of their impact. The novel contribution of this study lies in its regional specificity and the high explanatory power of ASP on CFD in Mardan, Pakistan. By identifying ASPs as a major contributor to carbon-free development in this context, the study offers targeted recommendations for policymakers and practitioners in Mardan. It suggests that localized agricultural practices can have significant environmental benefits, emphasizing the need for region-specific strategies in promoting sustainable development. In addition, the study findings highlight a significant positive relationship between Green Technology Implementation (GTI) and Carbon-Free Development (CFD) in Mardan, Pakistan. The high correlation coefficient (0.89) and the explanation of 79% of the variance in CFD by GTI underscore the critical role of green technology in advancing carbon-free development. The statistically significant p-value (<0.001) further supports the robustness of these results. Empirical evidence from the existing literature supports these findings. For instance, studies by Zeng, Li [44] and Shao, Zhong [45] similarly found strong positive correlations between the adoption of green technologies and reductions in carbon emissions. Zhang, Deng [46] analyzed data from multiple Chinese provinces and concluded that regions with higher levels of green technology implementation showed significant improvements in carbon emission metrics. Similarly, Saqib, Abbas [47] demonstrated through a longitudinal study in Europe that the integration of green technologies, such as renewable energy sources and energy-efficient practices, substantially contributed to achieving carbon reduction targets. Comparing the high correlation coefficient (0.89) found in Mardan with studies in Europe and North America, where correlations typically range from 0.7 to 0.8, highlights regional disparities in the effectiveness of GTI. Factors such as regulatory frameworks, technological adoption rates, and market incentives play crucial roles in shaping these variations [48].

Contrasting the current study with other empirical evidence reveals both consistencies and unique aspects. For example, the above studies focused on broader geographical regions and longer timeframes, whereas the current study specifically examines the context of Mardan, Pakistan. This regional focus adds valuable insights into how local policies and socio-economic conditions can influence the effectiveness of green technology implementation. The high shared variance (79%) in the current study suggests that Mardan’s local conditions might be particularly conducive to leveraging green technology for carbon-free development, potentially due to targeted policy measures or specific socio-economic dynamics. However, some differences also emerge when comparing these findings with studies conducted in different contexts. For instance, a study by Yu, Feng [49] in the United States found a lower correlation between GTI and CFD, attributing the difference to varying levels of regulatory support and market incentives for green technology. This indicates that while green technology universally supports carbon-free development, the degree of its impact can vary significantly depending on the local regulatory and economic environment. Mardan’s high correlation might be driven by recent government initiatives aimed at promoting green technology through subsidies and incentives, as indicated by local policy reports. Additionally, the socio-economic structure of Mardan, with a significant agricultural base, might benefit more directly from precision agriculture and renewable energy technologies, thus amplifying the impact on CFD. Moreover, the study’s findings reveal a robust positive correlation between Energy Policy Measures (EPMs) and Carbon-Free Development (CFD), as evidenced by the Pearson correlation coefficient (r = 0.94). This exceptionally high correlation suggests that effective energy policy measures are critical drivers of carbon-free development. The statistical significance (p-value < 0.001) confirms that this relationship is unlikely to be due to random chance, indicating a very reliable and substantial association. Moreover, the coefficient of determination (r² = 0.88) signifies that 88% of the variance in CFD can be attributed to EPM, highlighting the influential role of energy policies in promoting sustainable, carbon-free initiatives. Wang, Yang [50] found a similar strong positive correlation between government energy policies and renewable energy adoption in China. Their research indicated that stringent and supportive policies significantly boost renewable energy capacity, leading to substantial reductions in carbon emissions. Elavarasan, Shafiullah [51] demonstrated that in India, proactive energy policies such as subsidies for renewable energy and carbon pricing mechanisms have led to significant improvements in renewable energy uptake and a corresponding decrease in carbon emissions. Xue, Song [52] reported a more moderate correlation in their study of European countries, where the diversity and maturity of energy policies vary greatly. While there is a positive correlation, it is not as pronounced (r = 0.70) due to the heterogeneity of policy effectiveness across different nations. Jänicke [53] observed that in some developing countries, the correlation between energy policy measures and carbon-free development is weaker. This discrepancy is often due to implementation challenges, lack of infrastructure, and political instability, which can hinder the effective application of energy policies. Studies in regions like Scandinavia and South America demonstrate positive correlations between stringent energy policies and carbon-free development, albeit with differing strengths [54]. The exceptionally high correlation coefficient (0.94) observed in Mardan contrasts with these studies, suggesting potentially more robust policy enforcement or unique socio-economic dynamics.

The extremely strong correlation found in this study aligns with the above empirical findings suggesting that in regions where energy policies are effectively implemented, there is a substantial impact on carbon-free development. This alignment reinforces the notion that robust policy frameworks are crucial for driving significant environmental outcomes. However, the differences highlighted by the above studies underscore the variability in policy effectiveness due to regional and contextual factors. The discrepancy in correlation strength between developed and developing regions can be attributed to factors such as: Developed countries often have more consistent and enforceable policies compared to developing nations where policy enforcement can be sporadic [33,34]. Regions with advanced infrastructure and higher technological adoption rates are better positioned to capitalize on energy policies, leading to stronger correlations. Stable economic and political environments support the effective implementation of energy policies, enhancing their impact on carbon-free development. The study findings from the SEM highlight the significant positive impacts of Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), and Energy Policy Measures (EPM) on Carbon-Free Development (CFD) in Mardan, Pakistan. Using Structural Equation Modeling (SEM), the analysis shows that each of these factors independently contributes to promoting carbon-free development, with EPMs having the highest impact, followed by GTI and ASP. These results underline the critical roles of sustainable agriculture, green technology, and energy policies in advancing environmental sustainability. The positive relationship between ASP and CFD aligns with the existing literature that emphasizes the role of sustainable agricultural practices in reducing carbon footprints. Studies such as those by Smith, Kirk [55] and Crystal-Ornelas, Thapa [56] have documented that practices like crop rotation, organic farming, and conservation tillage can significantly lower greenhouse gas emissions. These practices not only enhance soil health and biodiversity but also contribute to overall sustainability goals by reducing reliance on synthetic fertilizers and pesticides, which are major carbon emitters. Studies in regions like Sub-Saharan Africa and Southeast Asia have shown that sustainable practices such as crop rotation and organic farming contribute significantly to reducing carbon footprints and improving soil health [57,58]. Comparatively, the study in Mardan, Pakistan, aligns with these findings but emphasizes the specific socio-economic and agricultural contexts of the region, which may differ from other developing areas.

Similarly, the strong positive association between GTI and CFD corroborates findings from previous research indicating that green technologies are pivotal in achieving carbon neutrality. For instance, Chu, Vicidomini [59] found that the adoption of renewable energy technologies, such as solar and wind power, along with energy-efficient practices, plays a crucial role in reducing carbon emissions [60]. This body of evidence supports the present study’s assertion that increasing GTI levels leads to substantial improvements in CFD. Research in developed countries (e.g., Europe and North America) often focuses on mature green technology infrastructures and regulatory frameworks [61,62]. These studies highlight how advanced adoption of renewable energy technologies and energy-efficient practices contributes to carbon-free development. Contrasting this with Mardan’s relatively new adoption of GTI underscores the potential for accelerated impacts in developing regions with targeted interventions. The significant impact of EPM on CFD is consistent with studies that highlight the importance of policy interventions in driving sustainable development. Research by Zhang and Wang [63] and Lu, Khan [64] has shown that effective energy policies, including subsidies for renewable energy, carbon pricing mechanisms, and stringent emissions regulations, are essential for encouraging the adoption of clean energy technologies and reducing overall carbon emissions. Comparisons with studies from regions like Scandinavia and South America reveal varying strengths of correlations between stringent energy policies and carbon-free development [65,66]. These studies emphasize the role of subsidies, carbon pricing mechanisms, and regulatory frameworks in driving clean energy transitions. The exceptionally high impact of EPMs in Mardan suggests effective policy implementation but also highlights the region’s potential vulnerabilities to policy changes and economic shifts. The present study’s findings reinforce the idea that well-designed and implemented energy policies are crucial for achieving carbon-free development.

While the general trends observed in this study are in line with the existing literature, there are some contextual differences worth noting. For instance, the magnitude of the impact of ASP on CFD in Mardan may be higher compared to regions with more established agricultural practices. In developed countries, sustainable agricultural practices might already be integrated into standard farming operations, leading to more incremental improvements in CFD. In contrast, in regions like Mardan, where traditional farming practices may still dominate, the transition to sustainable practices can result in more pronounced environmental benefits. Similarly, the impact of GTI on CFD might vary based on the level of technological infrastructure and investment. In Mardan, where green technology adoption is relatively new, the introduction of renewable energy sources and energy-efficient technologies could lead to substantial gains in CFD. In contrast, in regions with advanced green technology infrastructure, the improvements might be more incremental. This study fills a critical gap in the existing literature by focusing on the synergistic effects of ASP, GTI, and EPM in a developing region like Mardan. Previous studies often examine these factors in isolation or within the context of developed countries, leaving a gap in understanding how they interact in developing regions. By providing a comprehensive analysis of these factors in Mardan, this study offers valuable insights into the integrated approach needed to achieve carbon-free development in similar contexts. Moreover, the study highlights the importance of localized research in environmental sustainability. It shows that strategies effective in developed regions might need to be adapted to fit the specific socio-economic and environmental conditions of developing regions. This localized approach offers practical insights for policymakers and practitioners, suggesting that targeted investments in sustainable practices, green technologies, and supportive energy policies can yield significant environmental benefits even in resource-constrained settings. The novel contribution of this study lies in its holistic examination of the interplay between ASPs, GTI, and EPMs in promoting CFD. By using SEM, the study provides a nuanced understanding of how these factors interact and contribute to overall sustainability goals.

This study’s findings reveal significant relationships between latent constructs and their indicators, focusing on agricultural sustainability practices (ASP), green technology implementation (GTI), energy policy measures (EPM), and carbon-free development (CFD) in Mardan, Pakistan. The Structural Equation Modeling (SEM) results show high indicator loadings, suggesting strong associations between the constructs and their respective indicators. The indicators “Crop rotation” and “Organic farming methods” showed strong loadings of 0.80 and 0.75, respectively. This indicates that these practices are key measures of agricultural sustainability in Mardan. Studies have shown that crop rotation improves soil health and fertility while reducing pest infestations, which contributes to long-term agricultural productivity [67,68]. Similarly, organic farming methods enhance biodiversity and reduce environmental pollution by minimizing chemical inputs [69]. The indicators “Renewable energy usage” and “Precision agriculture techniques” yielded loadings of 0.85 and 0.78, respectively. This suggests that these indicators are effective measures of GTI in Mardan. Empirical studies highlight that renewable energy usage in agriculture reduces reliance on fossil fuels and lowers greenhouse gas emissions [70]. Precision agriculture techniques optimize resource use, enhancing productivity and sustainability [71]. Subsidies for renewable energy and carbon pricing mechanisms had loadings of 0.82 and 0.80, respectively. These findings indicate that these policy measures are strongly associated with the EPM construct. Subsidies for renewable energy lower the cost barriers for adopting clean technologies, leading to increased deployment of renewable energy sources. Carbon pricing mechanisms, such as carbon taxes or cap-and-trade systems, create financial incentives for reducing carbon emissions and encourage investments in low-carbon technologies. The indicators Carbon emission levels and Renewable energy percentage showed high loadings of 0.90 and 0.88, respectively. This implies that these measures are highly effective in capturing efforts towards carbon-free development in Mardan. Empirical studies indicate that monitoring carbon emissions helps track progress towards emission reduction goals, while increasing the percentage of renewable energy in the energy mix significantly contributes to reducing overall carbon emissions.

Previous research often focuses on broader or more generalized regions, whereas this study specifically examines sustainability practices in Mardan. This localized approach provides unique insights into the region’s specific challenges and opportunities, making the findings highly relevant for regional policymakers and stakeholders. While earlier studies may have used simpler analytical methods, this study employs SEM to provide a detailed and nuanced understanding of the relationships between latent constructs and their indicators. The Chi-square tests conducted in this study provide valuable insights into the associations between Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), Energy Policy Measures (EPM), and Carbon-Free Development (CFD) in Mardan, Pakistan. The significant Chi-square values and low p-values indicate strong statistical significance, suggesting robust associations between these variables. The significant association between ASP and CFD underscores the importance of sustainable agricultural practices in advancing carbon-free development goals. Bhattacharyya, Leite [72] supports this association, demonstrating how sustainable farming practices, such as reduced tillage and cover cropping, contribute to carbon sequestration and emissions reduction. Research studies in Sub-Saharan Africa found that implementing sustainable agricultural practices like agroforestry and conservation agriculture significantly enhances soil carbon sequestration and reduces greenhouse gas emissions [73,74]. This aligns with findings from Mardan, suggesting that ASP are effective across diverse agricultural contexts. In a study on sustainable practices in Brazil, such as no-till farming and integrated crop–livestock systems, ASP were shown to improve soil health and reduce emissions [75]. This supports the Mardan study’s emphasis on ASP for carbon-free development but also highlights the importance of adapting practices to local conditions. The strong association between GTI and CFD highlights the pivotal role of green technologies in promoting carbon-free development. This aligns with the global trend towards adopting renewable energy and eco-friendly technologies to combat climate change. Studies by Montalvo-Navarrete and Lasso-Palacios [76] and Saleem, Mfarrej [77] corroborate this finding, illustrating how the widespread adoption of green technologies, such as solar and wind power, can significantly reduce carbon emissions and foster sustainable development. A study in multiple Chinese provinces demonstrated that regions with higher adoption of renewable energy technologies, like solar and wind power, saw significant reductions in carbon emissions [78]. The Mardan findings echo this, emphasizing the universal benefits of GTI. Research by Corfee-Morlot, Marchal [79] in Europe found that countries with advanced green technology infrastructures and supportive policies saw incremental but steady improvements in carbon emission metrics. This contrast highlights the potentially greater initial impact of GTI in developing regions like Mardan, where such technologies are newly introduced. The significant association between EPMs and CFD underscores the influence of effective energy policies in driving carbon-free development initiatives. This echoes findings from previous research highlighting the crucial role of policy frameworks in shaping sustainable energy transitions. Research by Moore [80] and Menghwani [81] provides empirical evidence supporting the impact of energy policies, such as feed-in tariffs and renewable energy mandates, on promoting renewable energy adoption and reducing carbon emissions. Gustafsson and Anderberg [82] showed that stringent energy policies in Sweden, Norway, and Denmark, including carbon taxes and subsidies for renewable energy, led to substantial carbon reductions. This is consistent with Mardan’s high correlation between EPM and CFD, underscoring the effectiveness of well-designed policies. Chavez-Rodriguez, Carvajal [83] in Chile and Peru found that energy policies like renewable energy mandates and feed-in tariffs significantly boosted renewable energy adoption and emissions reductions. The Mardan study aligns with these findings, suggesting that policy measures are crucial for sustainable development in various contexts. The multiple regression analysis conducted in this study elucidates the relationships between Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), Energy Policy Measures (EPM), and Carbon-Free Development (CFD) in Mardan, Pakistan. The findings reveal significant positive associations between these variables, highlighting the critical role of sustainable practices, green technologies, and energy policies in fostering carbon-free development. The intercept term represents the baseline level of CFD when all independent variables are zero. The highly significant p-value (<0.001) suggests that this intercept is significantly different from zero, indicating its importance in the regression model. This finding is consistent with previous research, which emphasizes the necessity of considering baseline conditions when analyzing the impacts of independent variables on the dependent variable [84]. The positive coefficient for ASP signifies that increases in sustainable agricultural practices are associated with higher levels of carbon-free development. This aligns with the existing literature emphasizing the pivotal role of sustainable agriculture in mitigating climate change and promoting environmental sustainability. Studies by Zhou, Abbas [85] and Qin, Kirikkaleli [86] provide empirical evidence supporting the positive relationship between sustainable farming practices and carbon sequestration, highlighting their contribution to carbon-free development objectives. Research in Southeast Asia highlighted that sustainable agricultural practices, such as integrated pest management and agroecological approaches, significantly enhance soil health and reduce carbon emissions [87]. This parallels findings from Mardan, suggesting universal benefits of ASP across different agricultural contexts. Studies in the United States demonstrated that sustainable farming methods, including no-till farming and diversified crop rotations, improve soil carbon sequestration and overall farm sustainability [88]. This aligns with the positive association between ASPs and CFD observed in Mardan. The positive coefficient for GTI indicates that greater adoption of green technologies correlates with increased levels of carbon-free development. Research by Allen, Dube [89] and Shan, Genç [90] emphasizes the role of renewable energy technologies and eco-friendly innovations in transitioning towards a low-carbon economy, supporting the positive association observed in the regression analysis. Research in the Gulf Cooperation Council countries highlighted the rapid adoption of renewable energy technologies, driven by favorable policies and economic incentives [91]. The positive coefficient for EPM suggests that effective energy policies contribute to higher levels of carbon-free development. This underscores the significance of policy interventions in shaping energy transitions and fostering sustainability. Empirical studies by Meckling [92], Nuñez-Jimenez [93], and Lockwood [94] demonstrate the impact of supportive energy policies, such as feed-in tariffs and carbon pricing mechanisms, in driving renewable energy deployment and mitigating climate change. Research in Mexico and Brazil highlighted the impact of feed-in tariffs and carbon pricing mechanisms on renewable energy adoption and emissions reduction [95]. This aligns with the positive coefficient for EPMs in Mardan, emphasizing the critical role of supportive energy policies in fostering CFD.

The study’s focus on Mardan, Pakistan, offers valuable insights into the local dynamics of sustainable development and carbon mitigation strategies. By contextualizing the findings within the socio-economic and environmental landscape of the region, the study enhances our understanding of the unique challenges and opportunities for carbon-free development in Pakistan.

The study conducted in Mardan, Pakistan, reveals significant correlations between Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), Energy Policy Measures (EPM), and Carbon-Free Development (CFD). Findings indicate that ASPs, including practices like crop rotation and organic farming, strongly promote CFD by reducing carbon emissions and enhancing soil health. Similarly, GTI, such as renewable energy adoption and energy-efficient technologies, shows a robust positive correlation with CFD, underscoring its pivotal role in sustainable development. Moreover, effective EPMs, including subsidies and carbon pricing mechanisms, exhibit an exceptionally high correlation with CFD, highlighting the critical role of policy frameworks in driving environmental outcomes. The study emphasizes the need for tailored strategies that consider local socio-economic and environmental contexts to maximize the impact of ASP, GTI, and EPM. Recommendations include incentivizing sustainable practices, strengthening energy policies, and fostering partnerships to accelerate progress towards carbon-free development goals in Mardan and similar regions.

6. Conclusions

In conclusion, this study conducted in Mardan, Pakistan, elucidates significant insights into the interplay of Agricultural Sustainability Practices (ASP), Green Technology Implementation (GTI), Energy Policy Measures (EPM), and their impact on Carbon-Free Development (CFD). The findings highlight that ASP, including crop rotation and organic farming, substantially contribute to CFD by lowering carbon emissions and enhancing soil health. This contribution is underscored by strong positive correlations observed through correlation analysis, structural equation modeling (SEM), Chi-square test and multiple regression analyses. Moreover, the study emphasizes the pivotal role of GTI, such as renewable energy adoption and precision agriculture techniques, in advancing CFD. The high correlation coefficients and robust path coefficients in SEM indicate that integrating green technologies is crucial for reducing carbon footprints and promoting sustainable development in the region. Furthermore, effective EPMs, which include policies like subsidies for renewable energy and carbon pricing mechanisms, emerge as critical drivers of CFD in Mardan. The statistical analyses demonstrate a significant positive influence of EPM on CFD, highlighting the importance of supportive policy frameworks in achieving environmental sustainability goals. Overall, this research contributes by providing empirical evidence of how ASP, GTI, and EPM collectively contribute to carbon-free development in a developing country context like Mardan, Pakistan. These findings offer valuable insights for policymakers and stakeholders seeking to implement effective strategies to foster sustainable development and mitigate climate change impacts in similar regional contexts globally.

6.1. Addressing Potential Limitations

6.1.1. Sample Representativeness and Generalizability

The study’s reliance on a cross-sectional design and self-reported data introduces potential limitations in capturing temporal changes and ensuring data accuracy. Future research could address these by implementing longitudinal designs to track changes over time in ASP, GTI, EPM, and CFD outcomes. Longitudinal studies would provide more robust evidence of causality and allow for the examination of how these relationships evolve dynamically. Moreover, expanding the sample size to include diverse geographical areas within Mardan and beyond would enhance the study’s generalizability to other regions in Pakistan and similar developing contexts.

6.1.2. Data Quality and Measurement Challenges

Cross-sectional designs inherently limit the ability to establish causal relationships between variables such as ASP, GTI, EPM, and CFD. To overcome this limitation, future research could incorporate mixed-methods approaches, combining qualitative data from stakeholder interviews and policy analysis with quantitative analyses. This integrated approach would provide a more comprehensive understanding of the mechanisms driving sustainable development practices and policy impacts. Additionally, employing advanced statistical techniques, such as structural equation modeling (SEM) with latent variables, could enhance the accuracy of measurements and mitigate biases associated with self-reported data.

6.1.3. Causal Inference and Longitudinal Analysis

While the current study identifies associations between ASP, GTI, EPM, and CFD, causal relationships remain challenging to establish conclusively due to the cross-sectional nature of the data. Future research directions should prioritize longitudinal analyses or controlled experiments to uncover causal pathways and temporal dynamics more effectively. By conducting randomized controlled trials or natural experiments, researchers can provide stronger evidence of how policy interventions and technological adoption directly influence carbon-free development outcomes over time.

6.1.4. Policy Implementation Challenges

The study acknowledges potential limitations in fully capturing the nuances of policy implementation challenges and stakeholder dynamics through quantitative analyses alone. Future research should integrate qualitative methodologies to explore these complexities in depth. By conducting detailed case studies and comparative analyses with regions facing similar socio-political contexts, researchers can elucidate the contextual factors that influence the effectiveness of energy policies in promoting CFD. Qualitative insights could highlight barriers, facilitators, and unintended consequences, providing actionable recommendations for policymakers and stakeholders.

6.2. Contribution to Scientific Knowledge and Practice

The study enriches scientific understanding by demonstrating empirically the interrelationships between ASP, GTI, EPM, and CFD in a specific regional context. It provides quantitative evidence of how these factors synergistically contribute to carbon-free development, filling gaps in the existing literature with localized data from Mardan, Pakistan. The use of advanced statistical methods such as SEM and multiple regression enhances methodological rigor, offering robust insights into the complex dynamics of sustainable development practices and policy impacts on environmental outcomes.

6.3. Practical Implications

Policymakers and practitioners can utilize these findings to formulate targeted strategies for promoting sustainable agriculture, deploying green technologies, and designing effective energy policies. Insights into the specific contributions of ASP, GTI, and EPM can inform policy decisions aimed at achieving carbon neutrality and enhancing environmental resilience. Stakeholders involved in agricultural production, technology deployment, and energy governance can leverage the study’s conclusions to prioritize investments and initiatives that align with carbon-free development objectives in similar regional contexts.

6.4. Future Research Directions

Building on the study’s findings and the identified limitations, future research should explore the following directions to advance knowledge in this field:

6.4.1. Longitudinal Studies

Implementing longitudinal designs to track the long-term impacts of ASP, GTI, and EPM on CFD would provide insights into temporal trends and sustainability over time.

6.4.2. Mixed-Methods Approaches

Integrating qualitative research methods such as stakeholder interviews and policy analysis alongside quantitative analyses can offer a more comprehensive understanding of policy implementation dynamics and enhance data validity.

6.4.3. Comparative Analyses

Conducting comparative studies across diverse regions within Pakistan and similar global contexts would validate findings and explore variations in socio-economic factors influencing sustainable development strategies.

6.4.4. Advanced Statistical Techniques

Applying advanced statistical methods like SEM with latent variables to improve the accuracy of measurements and mitigate biases associated with self-reported data.

6.4.5. Emerging Technologies and Innovations

Investigating breakthroughs in sustainable agriculture and renewable energy, such as agroecology, digital farming, and decentralized energy systems, could pave the way for more effective strategies in achieving carbon neutrality.