Pragmatism in Eco-Economy and Social Influence in Environmental Policy Management

Abstract

This research examines the integration of eco-economics principles into environmental policy management, with a focus on resource limitations and pollution resulting from economic activities. This study aims to identify rational behaviors in energy sector companies that promote sustainable production and consumption practices. A survey of 232 respondents, all employees from companies within the energy sector, was conducted to examine the inclination towards eco-efficient economic behavior and the impact of environmental policies on production processes. The research methodology combines quantitative and qualitative approaches, using econometric models and statistical analysis to interpret responses to a structured questionnaire. The findings contribute to a broader understanding of how companies can align their production methods with sustainable growth policies, mainly through creative and non-polluting technologies. This research highlights the importance of integrating environmental policy into business strategies to promote long-term sustainability and green economic practices.

1. Introduction

In the contemporary economic landscape, business organization models specific to a regenerative economy reflect flows of response to the behavior of economic agents [1]. Thus, companies need to address the problem of waste generated by economic activities in a much more responsible way, with effects on society [2,3]. These must be reintegrated into the economic flow and finally become a growth factor. The emergence of the eco-economy has characterized this shift [4]. This concept integrates environmental stewardship with economic practices to address the challenges of unsustainable resource use and ecological degradation. The eco-economy emphasizes the circular use of resources, minimizing waste and preserving natural capital for future generations [5].

The pragmatism refers to the strategic, outcome-driven behavior of firms operating at the intersection of ecological responsibility and economic necessity. In eco-economics, pragmatism refers to fostering ethical behavior in economic processes. This involves using a variety of techniques and tools to measure the impact of actions on the financial resources utilized, aiming to minimize waste and its repercussions on the natural environment [6]. Rather than viewing sustainability as an idealistic endeavor, the pragmatic eco-economy approach emphasizes practical, scalable solutions that businesses can adopt under current market and regulatory conditions. This includes cost-effective environmental standards, resource-efficient practices, and measurable sustainability outcomes aligned with long-term profitability. Founded on the literature of eco-pragmatism [7,8], this approach models pragmatic behavior via eleven model configurations, each simulating a different balance of ecological regulation strength, economic pressure, and social feedback. The pragmatic frontier is revealed in those configurations where models converge to stable policies that score high on both sustainability and profitability, demonstrating real-world applicability under diverse policy regimes.

As the global economy grapples with the environmental consequences of industrial growth, policymakers and businesses recognize the need to reorient economic models toward sustainability. Thus, they shape a new vision of financial advantage, oriented both toward waste reduction and streamlining access for those who own it. Moreover, an ecologically responsible economy—an eco-economy—entails changes in the occupational structure of human resources and needs new environmental policies, both at the community and global levels [9].

A significant body of literature highlights the need for innovative approaches to resource management, production, and consumption that align with eco-economy principles [10,11]. These approaches require businesses to adopt new environmental policies and technologies that reduce pollution, optimize resource use, and integrate environmental sustainability into their core operations. However, despite the theoretical support for eco-economy practices, a noticeable gap exists in empirical research on how these principles are being implemented at the organizational level, particularly in sectors with a high environmental impact, such as the energy industry.

This study addresses the knowledge gap by investigating how companies adopt eco-economy principles in their environmental policy management, with a specific focus on the energy sector. Through the analysis of data collected from a survey of 232 energy sector employees, this research explores how businesses incorporate sustainable production methods, non-polluting technologies, and environmental standards into their operations. The research question driving this study is as follows: To what extent do ecological policies influence rational corporate behavior and the adoption of sustainable practices within companies?

The methodological approach combines quantitative and qualitative methods, using econometric models to analyze the survey responses. The study examines five key objectives: (1) the sustainable use of natural resources, (2) the application of environmental standards in production, (3) the effects of competition on eco-economy practices, (4) the pre-testing of eco-economy principles, and (5) the role of carbon market instruments in encouraging non-polluting behavior. By addressing these objectives, the research provides a comprehensive understanding of how environmental policies can be leveraged to foster sustainable development within corporate structures.

This research contributes to the ongoing academic discourse on sustainability by offering empirical evidence on applying eco-economy principles in a high-impact industry. It also provides insights for policymakers and business leaders on integrating environmental policies into corporate strategies to achieve long-term sustainability goals. However, this study is not without limitations. The use of convenience sampling, while practical, may introduce selection bias and limit the generalizability of findings beyond the energy sector. Additionally, the exclusive reliance on self-reported perceptions could be complemented in future research by triangulating managerial interviews or field observations.

The paper is structured as follows: Section 2 reviews the relevant literature, formulates research hypotheses, and describes the research methodology. In Section 3, the study’s results are outlined. The Section 4 discusses the findings and highlights the main contributions of the research. Finally, Section 5 address the study’s conclusions, limitations and propose directions for future research.

2. Materials and Methods

The growing environmental challenges in modern economies, such as climate change, resource depletion, and pollution, have sparked increased academic and policy interest in sustainable economic models [12]. One of the most prominent frameworks emerging from this discourse is the eco-economy, which seeks to integrate environmental sustainability into financial processes, emphasizing the circular use of resources and minimizing waste [4].

The structural shifts in the labor market, coupled with changes in education, scientific advancements, and the evolution of the global market, necessitate new economic approaches that align with producer demands, consumer expectations, and market dynamics. The principles of rational resource use and consumer utility should guide economic activities in production and consumption [13]. Individuals should strive to make eco-efficient use of natural resources when making financial decisions [14].

This literature review examines the theoretical underpinnings of eco-economy, the role of environmental policies in driving sustainable corporate practices, and the increasing importance of CSR in addressing ecological challenges [15,16]. Through actions focused on human development and entrepreneurship, society will ensure the potential for generating rational economic behaviors based on the efficient use of resources, awareness of the dangers of resource depletion, and implicitly on the possibility of market extension. The financial process must be based on the reproductive capacity of pre-existing economic factors with a limited useful life, which implies considering a limited stock of ecological capital [17].

The concept of eco-economy has its roots in the broader framework of sustainable development, as defined by the Brundtland Report (1987) [18], which emphasizes meeting the needs of the present without compromising the ability of future generations to meet their needs [19]. Brown (2001) argues that society must transition from an economy based on resource consumption to one within ecological limits [4]. In an eco-economy, natural resources are treated as finite, and the focus shifts towards their regeneration and efficient use, aligning economic growth with environmental preservation.

Other researchers further develop the eco-economy concept by discussing the circular economy, which promotes closed-loop systems where materials are reused, and waste is reintegrated into the production cycle [10,20]. This shift requires innovation in business models and production methods, as well as the adoption of policies that support sustainable practices at both microeconomic (organizational) and macroeconomic (national and global) levels.

The role of environmental policies in fostering eco-economic principles is essential, as regulatory frameworks provide the foundation for companies to align their operations with sustainability goals [21]. Policies such as the Kyoto Protocol (1997) [22] and the European Union’s ETS have been instrumental in establishing mechanisms to reduce greenhouse gas emissions and promote cleaner technologies [23]. These instruments, particularly the carbon market, offer economic incentives for companies to adopt non-polluting technologies and reduce their environmental footprint [24].

The issue of climate change provides a framework to address the challenges posed by rapid economic growth and align with the Kyoto Protocol’s (1997) policy [22]. Emissions, deforestation, and pollution can be approached as privately produced public goods. If knowledge is non-trivial and globally available through new information technologies, CO₂ emissions are global, reflecting the same infinite expandability. Private economic agents are responsible for knowledge and CO₂ emissions [21]. By comparison, both knowledge and CO₂ emissions are privately produced public goods. These “treatment” implications are immediate and institutional, bringing development and equity issues to the same level as reducing environmental damage [25]. The trade of public goods is a significant movement, particularly in the context of CO₂ emissions. When private entities produce different ideas and blueprints for public goods, a new institutional system or property rights regime becomes necessary. Experts advocate for a market for the commercialization of property rights related to pollution or atmospheric use, named global emission markets [26,27]. This enables the internalization of negative externalities through rights policy, thereby countering the market’s tendency to externalize costs. It also provides the potential for emissions to decrease in intensity by aligning costs with benefits more effectively. This system grants economies that pollute less or rely more on natural resources, but are not as polluting as industrialized economies, greater rights to pollute. Conversely, economies that pollute more have fewer rights to pollute and must pay more to acquire additional rights [28].

The transformation of economies implicitly leads to a shift in general consumption towards rational consumption based on the utility of the final product, benefiting both the environment and the consumer. A sensible economy, aligned with the principles of the eco-economy, must explore various waste management possibilities. This involves a creative industry that emphasizes the resource–product–consumption relationship through the intelligent and innovative recycling of all products [29]. This process requires that the resources used to make products are easily absorbed by the environment after consumer use, necessitating technologies for converting waste into new sources for production. The primary greenhouse gases in the Earth’s atmosphere include water vapor, carbon dioxide, methane, methane oxide, and ozone, all of which are byproducts of production processes and pose significant challenges to the restoration of the biosphere. The impact on human populations and habitats is severe, affecting psycho-emotional and socio-economic levels [30,31]. Some countries choose to purchase pollution quotas, specifically greenhouse gas emission certificates, as part of an ETS based on the pollution levels of economic operators [32]. The quantity of EUA certificates is adjusted according to the permitted pollution quota, which allows operators who pollute less to sell their surplus EUA certificates on the carbon market. However, the current market dynamics are primarily driven by profit rather than a genuine commitment to sustainable economic growth, despite institutional efforts aimed at limiting greenhouse gas emissions.

Research indicates that environmental policies not only encourage compliance but can also drive innovation. In one study, the author suggests that the European Green Deal, which aims for carbon neutrality by 2050, has spurred significant investment in eco-innovations and green technologies across industries [33]. These policies have become relevant in guiding businesses toward more sustainable production methods by mandating stricter environmental standards and offering financial incentives for compliance. The efforts of EU member states to reduce the level of greenhouse gas emissions are accelerated by the intensification of the implementation of non-polluting technologies at the level of economic activities, the progress of scientific research and biotechnologies in waste management, the rational use of natural resources, and the production of economic goods by considering the principles of sustainability and its sustainable approach to economic actions and processes. The EU is making progress in adopting a sustainable approach to economic activity, both in terms of strategies and regulations related to the economic system, and by intensifying its financing efforts through specific programs adopted by European Union institutions [34,35].

Adopting environmental standards at the organizational level has been shown to improve environmental outcomes, product quality, and competitiveness [11]. Studies reveal that companies that incorporate sustainability into their core strategy are better positioned to adapt to changing market conditions and consumer preferences, particularly in industries such as energy, where the environmental impact is significant [36,37].

The analysis of specialized literature highlights that, at the level of the economic operator, the effort to implement non-polluting technologies is both an investment and logistical. It depends on the financial capacity, the degree of product absorption on the market, to ensure a profitable rate, as well as the capacity of institutions to implement and appropriate the legislative elements that would place the company in the area of sustainable economic growth [38]. A group of specialists recommends building a sustainable and equitable partnership between people and nature that uses resources to benefit sustainable development by rethinking production and distribution systems [39]. A transition to production methods based on eco-economy tools can contribute to creating new policies that ensure the sustainability of growth and reduce the risk of increasingly unpredictable disturbances in the economic environment [40].

The literature has widely discussed the integration of CSR with eco-economy principles. Some researchers argue that CSR strategies focused on environmental sustainability can lead to long-term competitive advantages by fostering innovation, improving brand reputation, and ensuring regulatory compliance [41]. Companies prioritizing environmental responsibility are seen as leaders in the transition toward a circular economy, where resource efficiency, waste reduction, and social welfare are vital components.

CSR plays a pivotal role in the eco-economy framework, bridging the gap between regulatory compliance and voluntary corporate actions aimed at sustainability. Additionally, CSR entails businesses assuming responsibility for the social, environmental, and economic impacts of their operations, often exceeding the legal requirements established by environmental policies [42].

CSR initiatives also promote greater transparency and accountability in corporate governance. By publishing sustainability reports and adopting eco-friendly practices, companies can enhance their credibility with stakeholders, including consumers, investors, and policymakers. Arnould (2022) emphasizes the need for businesses to view environmental sustainability as a core aspect of their social contract, where economic activities are aligned with the broader goals of social equity and ecological stewardship [43].

The link between eco-economy, environmental policies, and CSR highlights the multidimensional approach required for achieving sustainable development. The eco-economy provides the theoretical foundation for integrating environmental concerns into economic activities, while ecological policies serve as regulatory mechanisms that enforce sustainability standards. CSR complements these efforts by encouraging voluntary corporate actions that promote social and environmental well-being [44].

A growing body of research suggests that these three elements have synergistic effects. For example, companies that adopt stringent environmental policies and proactive CSR strategies are more likely to achieve long-term sustainability and financial success [40]. This integrated approach enables businesses to reduce their ecological footprint. It promotes innovation in production processes, ultimately leading to more sustainable consumption patterns and a decreased reliance on non-renewable resources.

Furthermore, transitioning to an eco-economy depends on collaboration between governments, businesses, and civil society. Policymakers must continue designing frameworks that incentivize sustainable practices, while companies must embed eco-economy principles into their business models. CSR can act as a catalyst for creating a sustainable and resilient economy that benefits from this process, encouraging companies to go beyond compliance and contribute to the creation of a sustainable and resilient economy that benefits both society and the environment.

This study employed a mixed-methods approach to explore the integration of eco-economy principles within corporate environmental policies, with a specific focus on the energy sector. The research was conducted using both quantitative and qualitative methods to gather and analyze data from a sample of employees working in companies that are directly impacted by environmental regulations.

The research design is structured around a questionnaire survey designed to capture employees’ perceptions and attitudes regarding the adoption of eco-economy principles, environmental policy management, and sustainable practices. This design was selected to obtain a broad overview of how companies in the energy sector implement ecological standards and to assess their commitment to sustainability through quantitative analysis of the responses. Additionally, a descriptive statistical analysis was used to summarize the data. At the same time, correlational tests were conducted to identify relationships between key variables, such as environmental protection support and adoption of non-polluting technologies.

A structured questionnaire consisting of 18 questions was distributed to 232 employees from various companies within the energy sector. The survey targeted professionals from the energy sector, including environmental officers, technical managers, and production engineers, whose roles involve implementing or responding to environmental regulations. Aggregated data were used in the data analysis, and no personal data of the respondents were collected, complying with the conditions of inclusion in the GDPT or compliance with the Declaration of Helsinki. The participants were selected using convenience sampling, as they were available and relevant to the study’s objectives. The energy sector was chosen for its significant environmental impact and role in transitioning to sustainable economic models. All participants were informed of the study’s purpose and provided consent before participating in the survey. The anonymity of the respondents was maintained throughout the data collection and analysis process, ensuring compliance with the ethical standards of research.

The questionnaire was designed to measure five key objectives:

• Sustainable use of natural resources (Questions 1, 2 and 3).
• Application of environmental standards in production (Questions 4, 5 and 6).
• Effect of competition on eco-economy principles (Questions 7, 8 and 9).
• Testing the eco-economy principles (Questions 10 to 14).
• Use of carbon market instruments (Questions 15 to 18).

The questionnaire consisted of both closed-ended and Likert-scale questions designed to assess various factors, including perceptions of the necessity of environmental protection, the role of non-polluting materials in reducing emissions, compliance with environmental regulations, and support for market mechanisms such as emission certificates and the carbon market. To ensure its reliability and validity, the questionnaire was pre-tested on a smaller sample before full deployment, allowing for the refinement of the questions to improve accuracy and clarity.

Responses were collected using a Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), allowing for a detailed evaluation of participants’ agreement with each statement. The data collected was analyzed using descriptive statistics to generate means, frequencies, and percentages for each question. The statistical model summary identifies the model and the number of predicates. It quantifies the recorded data using (1) Model Fit Statistics (Stationary R²) and (2) the Ljung–Box Q-test for the validity of survey results. Additionally, two statistical tests were employed to explore correlations between variables: (3) Pearson’s correlation test was used to evaluate the linear relationship between support for environmental protection and the adoption of non-polluting materials (questions 1 and 2), and (4) Spearman’s rank correlation test was applied to assess the association between beliefs in environmental protection and support for carbon market mechanisms (questions 1 and 15).

To assess the internal consistency and empirical alignment of the conceptual model, Pearson correlation coefficients were calculated between the dependent variable Q5 (perceived impact of sustainable production standards on product quality) and each of the five associated predictor items: responsibility (Q1), necessity (Q2), relevance (Q4), compatibility (Q14), and quality orientation (Q13). All correlation values were positive and statistically significant, with coefficients ranging from r = 0.73 to r = 0.87. The strongest correlation was noted between Q1 and Q5 (r = 0.87), indicating that individuals who recognize the necessity of environmental protection are more likely to perceive sustainability as contributing to enhanced product quality. These findings support the coherence of Model 3 and suggest that the grouped items effectively measure interconnected aspects of sustainability perception. The strength of the correlations further validates the use of aggregated logical predicates within a linear weighted model structure.

Also, all tests were conducted to determine whether more substantial support for environmental protection was correlated with greater acceptance of eco-economy principles and policy tools, such as carbon credits. Results from these analyses provided insight into the behavioral patterns of employees regarding sustainable practices.

Based on specialized literature, at the level of the study, two primary hypotheses were formulated to guide the statistical analysis:

H0. 

There is no significant correlation between corporate behavior towards environmental protection and the adoption of sustainable practices—Null Hypothesis

H1. 

There is a significant correlation between support for environmental protection and the adoption of eco-economy principles, particularly in using non-polluting materials and support for emission certificates—Alternative Hypothesis

These hypotheses were tested using the correlation coefficients obtained from the Pearson and Spearman tests.

3. Results

This research methodology provides a structured framework for investigating the integration of eco-economy principles within corporate environmental policies. By employing a quantitative survey and statistical analysis, the study offers valuable insights into the relationship between environmental attitudes and sustainable corporate behavior in the energy sector.

Table 1. Survey responses and descriptive statistics.

Question/Affirmations	Strong Disagree	Disagree	Undecided	Agree	Strong Agree	Mean	St. Dev.	Importance
1. Environmental protection is a necessity	6.9%	3.9%	12.5%	24.1%	52.6%	4.12	1.14	High
2. Non-polluting materials reduce greenhouse gas emissions	6.0%	1.7%	6.9%	36.2%	49.1%	4.20	1.07	High
3. Ecological education and continuing professional training increase proactive environmental attitudes	6.9%	0.9%	6.9%	42.2%	43.1%	4.14	1.05	High
4. Applying national and European environmental norms reduces pollution risk	6.5%	0.4%	5.2%	29.3%	58.6%	4.33	0.98	High
5. Compliance with sustainable production standards increases product quality	4.3%	5.2%	12.9%	30.6%	47.0%	4.11	1.08	High
6. Legal and administrative measures decrease polluting production practices	6.0%	2.2%	9.9%	33.6%	48.3%	4.16	1.07	High
7. Competition reduces production costs, which lowers raw material quality	2.6%	2.2%	7.3%	46.1%	41.8%	4.22	0.91	Moderate/High
8. The market discourages ethical business behavior	1.7%	11.2%	16.8%	42.0%	28.3%	3.84	1.08	Moderate
9. Consumerism encourages waste	12.5%	10.3%	17.7%	32.3%	27.2%	3.51	1.27	Moderate
10. Eco-economy protects future resources and humankind	3.0%	1.7%	5.2%	43.9%	46.1%	4.28	0.94	High
11. Work quality protects nature and society	4.3%	6.0%	21.1%	39.2%	29.3%	3.83	1.05	Moderate
12. Creative work is sustainable	3.0%	3.0%	9.5%	48.3%	36.2%	4.12	0.96	High
13. A sustainable business approach positively impacts the environment	3.0%	5.6%	7.3%	40.7%	43.5%	4.16	0.98	High
14. Eco-economy creates sustainable solutions for humanity	3.5%	3.5%	9.5%	43.5%	40.0%	4.13	1.00	High
15. Greenhouse gas emission certificates stimulate non-polluting behavior	3.0%	3.9%	11.6%	41.8%	39.7%	4.11	1.01	High
16. Buying emission certificates is an unsustainable transaction over time	2.1%	4.7%	5.6%	47.4%	40.2%	4.19	0.94	High
17. The carbon market should decline to protect the environment	2.1%	4.7%	5.6%	47.4%	40.2%	4.19	0.94	High
18. It is an option for a company to have emission certificates in its portfolio	37.5%	24.1%	17.7%	16.8%	3.9%	2.25	1.23	Low

Source: Results of authors’ analyses.

presents the responses to the 18 research questions. Each answer demonstrates how the respondents appreciate and value the message of the question, recognizing its added value to the company. The respondents’ assimilation of the eco-economy principles, in their capacity as representatives of the business environment and specific energy field policies, serves as a premise for validating the strategic-level objectives formulated through environmental policy.

Most of the questions, particularly those related to environmental protection and sustainable practices, showed high levels of agreement, with mean scores above 4.00. Most respondents strongly agreed or agreed that environmental protection is necessary (76.2% agreed or strongly agreed) and that using non-polluting materials contributes to reducing greenhouse gas emissions (85.3%). Similarly, many respondents (84.5%) affirmed that compliance with environmental standards improves product quality, indicating strong support for integrating sustainability into production processes. The standard deviations were generally low, indicating that respondents shared similar views. However, for question 18, the mean was notably lower, suggesting less support for this specific option and more variation in responses, as reflected by the higher standard deviation.

The 18 questions represent 11 models, each with several predicates to underline whether the analysis answers the research objectives. The determination of the 11 statistical models is based on the degree of significance assigned to $\mathrm{R}\left(\mathrm{x},\mathrm{y}\right)$.

$${\mathrm{R}\left(\mathrm{x},\mathrm{y}\right)}^{\mathrm{d}\mathrm{e}\mathrm{f}}=\mathrm{x}\stackrel{\scriptscriptstyle\mathrm{def}}{=}\mathrm{y} \mathrm{m}\mathrm{o}\mathrm{d} \mathrm{d}\mathrm{or} \mathrm{R}{\left(\mathrm{x},\mathrm{y}\right)}^{\mathrm{d}\mathrm{e}\mathrm{f}}=\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\left(\mathrm{x}\right)=\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}\left(\mathrm{y}\right)$$

(1)

Instead of logical propositions of the type stated, predicates were named in agreement with the model’s objectives: responsibility, need, relevance, compatibility, and quality. Each model is assigned a different number of degrees of freedom, indicating the number of independent values that can vary in an analysis without violating any constraints (

Table 2. Determination of the logical predicates.

Model	Predicate	Questions
Model 1	3—responsibility, need, relevance	1
Model 2	3—responsibility, need, relevance	2, 3, 4
Model 3	5—responsibility, need, relevance, compatibility, quality	5
Model 4	5—responsibility, need, relevance, compatibility, quality	10, 14
Model 5	2—competition, efficiency	7, 8
Model 6	5—responsibility, need, relevance, compatibility, quality	11, 12
Model 7	1—waste	9
Model 8	2—relevance, opportunity	15, 16
Model 9	1—legality	6
Model 10	4—responsibility, need, relevance, opportunity	17, 18
Model 11	5—responsibility, need, relevance, compatibility, quality	13

Source: Authors’ processing using parametric and non-parametric statistical methods.

).

The number of predicates indicates the number of fields used for each objective.

Table 3. Determination of statistical models based on logical predicates.

Model	Number of Predicates	Model Fit Statistics	Ljung–Box Q(18)			Number of Non-Compliant Values
		Stationary R2	Statistics	DF	Sig.
Model 1	3	0.244	7.821	18	0.981	0
Model 2	3	0.110	8.894	18	0.962	0
Model 3	5	0.730	31.117	17	0.019	0
Model 4	5	0.359	10.598	18	0.911	0
Model 5	2	0.183	15.665	18	0.616	0
Model 6	5	0.359	24.015	18	0.155	0
Model 7	1	0.146	24.172	17	0.115	0
Model 8	2	0.067	7.483	18	0.985	0
Model 9	1	0.046	1.509	18	1.000	0
Model 10	4	0.448	45.448	17	0.000	0
Model 11	5	0.261	5.230	18	0.998	0

Source: Authors’ results using parametric and non-parametric statistical methods.

shows the results obtained by using these predicates.

The Stationary R² value estimates the proportion of the total variation in the series explained by the model using SPSS Statistics v30.0. The higher the value (up to a maximum of 1.0), the better the model fits. Statistics lines, degrees of freedom, and significance (Statistics, DF, and Sig) refer to the Ljung–Box statistics, a test of the randomness of the residual errors in the model; the more random the errors, the better the model is likely to be. Statistics are the Ljung–Box statistics themselves, while DF indicates the number of model parameters that can vary freely when estimating a given target. The significance line shows the significance value of the Ljung–Box statistics, providing another indication of whether the model is correctly specified. A significance value of less than 0.05 implies that the residual errors are not random and that there is structure in the observed series that the model cannot account for:

Model 1 contains 3 degrees and validates question 1 (protecting the environment is a necessity) by having 18 degrees of freedom (DF = 18), R² = 0.244, statistics = 7.821, and significance level = 0.981 (it describes how well the data distribution matches the predicted distribution under the null hypothesis H1).

Model 2 contains three degrees and validates question 2 (the use of non-polluting materials contributes to the reduction of greenhouse gases), question 3 (ecological education and continuous professional training increase the proactive attitude towards the natural environment), question 4 (the application of national and European norms in the field of environmental protection contributes to reducing the risk of pollution) by having 18 degrees of freedom (DF = 18), R² = 0.110, statistics = 8.894 and significance level = 0.962 (it describes how well the data distribution matches the distribution predicted under the null hypothesis of the statistical test used—it does not match the data distribution in the basic model and invalidates hypothesis H1).

Model 3 contains five degrees and validates question 5 (compliance with sustainable production standards increases product quality) with 18 degrees of freedom (DF = 18), R² = 0.730, statistics = 31.117 and significance level = 0.019 (it describes how well the data distribution matches the distribution predicted under the null hypothesis of the statistical test used). This model aligns most closely with the baseline model and supports hypothesis H1.

Model 4 contains five degrees and validates question 2 (the use of non-polluting materials contributes to the reduction of greenhouse gases), question 10 (eco-economy protects the future of resources and implicitly of humankind), question 14 (eco-economy creates sustainable solutions for the future of humanity) having 18 degrees of freedom (DF = 18), R² = 0.183, statistics = 15.665, and significance level = 0.616 (it describes how well the distribution of the data matches the distribution predicted under the null hypothesis of the statistical test used—it does not match the distribution of the data in the underlying model and invalidates the hypothesis H1).

Model 5 contains two degrees and validates question 2 (the use of non-polluting materials contributes to the reduction of greenhouse gases), question 7 (competition encourages the reduction of production costs, with an effect on the decrease in the quality of the raw materials used), question 8 (the market discourages ethical business behavior) having 18 degrees of freedom (DF = 18), R² = 0.183, statistics = 15.665, and significance level = 0.616 (it describes how well the data distribution matches the distribution predicted under the null hypothesis of the statistical test used—it does not match the data distribution in the basic model, and invalidates hypothesis H1).

Model 6 contains five degrees and validates question 2 (the use of non-polluting materials contributes to the reduction of greenhouse gases), question 11 (the quality of work protects nature and society), question 12 (creative work is sustainable) having 18 degrees of freedom (DF = 18), R² = 0.359, statistics = 15.665, and significance level = 0.616 (which describes how well the distribution of the data matches the distribution predicted under the null hypothesis of the statistical test used—it does not match the distribution of the data from the base model and invalidates hypothesis H1).

Model 7 contains one degree and validates question 9 (consumerism, as a societal effect, encourages waste) with 18 degrees of freedom (DF = 18), R² = 0.146, statistics = 24.172, and significance level = 0.115 (describes how closely the distribution of the data matches the distribution predicted under the null hypothesis of the statistical test used—does not match the distribution of the data in the underlying model and invalidates hypothesis H1).

Model 8 contains two degrees and validates question 15 (the use of non-polluting materials contributes to the reduction of greenhouse gases), question 16 (eco-economy protects the future of resources and implicitly of humankind), question 14 (eco-economy creates sustainable solutions for the future of humanity) having 18 degrees of freedom (DF = 18), R² = 0.067, statistics = 7.483, and significance level = 0.985 (it describes how well the distribution of the data matches the distribution predicted under the null hypothesis of the statistical test used—it does not match the data distribution in the basic model and invalidates hypothesis H1).

Model 9 contains one degree and validates question 6 (coercive measures of an administrative–legal nature have the role of reducing polluting production practices) by having 18 degrees of freedom (DF = 18), R² = 0.046, statistics = 1.509, and significance level = 1.000 (describes how well the distribution of the data matches the distribution predicted under the null hypothesis of the statistical test used—it does not match the distribution of the data in the basic model and invalidates hypothesis H1).

Model 10 contains four degrees and validates question 2 (the use of non-polluting materials contributes to the reduction of greenhouse gases), question 17 (the carbon market should be decreasing to protect the environment), question 18 (is it an option for the company to own greenhouse gas emissions certificates in its portfolio) having 18 degrees of freedom (DF = 18), R² = 0.448, statistics = 45.448, and significance level = 0.000 (this describes how much it matches the distribution of the data with the distribution predicted under the null hypothesis of the statistical test used—it does not match the distribution of the data in the underlying model and invalidates hypothesis H1).

Model 11 contains five degrees and validates question 13 (the sustainable business approach has a positive effect on the environment) having 18 degrees of freedom (DF = 18), R² = 0.261, statistics = 5.230, and significance level = 0.998 (describes how well the distribution of the data matches the distribution predicted under null hypothesis H1).

Among the 11 models evaluated against 18 questions, differentiated values were identified for the stationary model fit statistics. The lowest p-values (with significance at p < 0.05) suggest that the results are likely genuine rather than merely the result of random fluctuations. From the data correlation analysis, Model 3 emerged as the closest approximation to the base model. To improve methodological clarity, Model 3, which explores the relationship between sustainable production standards and perceived product quality, has been articulated through a calibrated linear regression framework. Initially developed using predicate-based grouping and descriptive statistics, the model is further formalized below to demonstrate its internal logic and predictive structure.

Model 3 incorporates five conceptual dimensions: responsibility, necessity, relevance, compatibility, and quality orientation. These dimensions were operationalized through corresponding questionnaire items evaluated on a 5-point Likert scale. Based on the observed mean response values, the model adopts the following regression equation:

Y = 0.17 × X₁ + 0.20 × X₂ + 0.18 × X₃ + 0.22 × X₄ + 0.21 × X₅,

(2)

where

• Y represents the average agreement level with the item “Compliance with sustainable production standards increases product quality” (Q5);
• X₁ through X₅ denote the mean Likert scores associated with the five predicate-based constructs: responsibility (4.12), necessity (4.20), relevance (4.33), compatibility (4.13), and quality orientation (4.16), respectively.

The model produces an output value of Y = 4.10, which is very close to the empirical mean recorded for the specific question in the dataset. This alignment indicates a consistent and internally valid weighting of the conceptual predictors.

The values recorded by the coefficient of determination (R²) indicate the proportion of variation in the dependent variable that is explained by the regression model, which ranges in value from 0 to 1. Lower values indicate that the model does not fit the data well (

Table 4. Statistical results of model fit.

Fit Statistic	Mean	Standard Error	Minimum	Maximum
R2 Stationary	0.268	0.200	0.046	0.730
R2 Regular	0.986	0.006	0.973	0.995
RMSE/Root means square deviation	0.111	0.018	0.071	0.127
MAPE/Absolute Mean Percent Error	0.869	0.161	0.667	1.186
MaxAPE/Absolute maximum percentage error	33.659	11.973	18.721	50.000
MAE/Mean absolute error	0.026	0.005	0.019	0.033
MaxAE/Maximum absolute error	0.929	0.159	0.562	1.000
Normalized BIC	−4.263	0.260	−4.864	−4.092

Source: Authors’ results using parametric and non-parametric statistical methods.

).

In this analysis, Model 3 meets the criteria of a statistically significant model, with stationary R² = 0.73, statistics = 31.117, DF = 17, and Sig = 0.019. Considering both stationary R² and significance values, model 3 is quite acceptable, with a significance level of 0.019 < 0.05, indicating that some experiments with better-fitting models for these variables may be required. The R² value estimates the total variation in the time series that the model can account for. Since the maximum value for this statistic is 1.0, the 11 models are good in this respect.

In

Table 5. Statistical prediction of model fit.

Fit Statistic	5%	10%	25%	50%	75%	90%	95%
R2 Stationary	0.046	0.050	0.110	244	0.359	0.673	0.730
R2 Regular	0.973	0.975	0.984	0.985	0.989	0.994	0.995
RMSE/Root means square deviation	0.071	0.074	0.107	0.115	0.124	0.127	0.127
MAPE/Absolute Mean Percent Error	0.667	0.681	0.757	0.803	0.971	1.164	1.186
MaxAPE/Absolute maximum percentage error	18.721	18.916	24.041	30.986	50.000	50.000	50.000
MAE/Mean absolute error	0.019	0.019	0.022	0.025	0.033	0.033	0.033
MaxAE/Maximum absolute error	0.562	0.582	1.000	1.000	1.000	1.000	1.000
Normalized BIC	−4.864	−4.826	−4.292	−4.151	−4.103	−4.094	−4.092

Source: Authors’ results using parametric and non-parametric statistical methods.

, the econometric analysis of the presented data shows that the series does not vary significantly from the model-predicted level, being expressed in the same units as the dependent series (the RMSE values obtained are a mean of 0.11 and SE of 0.018). At the same time, the econometric model is independent of the units considered and, therefore, can be compared in series with different units (the MAPE values obtained include a mean of 0.869).

The results of the econometric analysis effectively present model 3, which capitalizes on the assertion that “Compliance with sustainable production standards increases the quality of products,” and to which five predicates correspond: responsibility, need, relevance, compatibility, and quality. Thus, the hypothesis is validated because the principles of the eco-economy contribute significantly to the increase in production when sustainable production methods are considered, with a positive impact on both the environment and the individual. Also, Model 3 validates the assumed objectives. The application of this model presents opportunities for economic entities to incorporate the principles of the eco-economy as development priorities in line with the future economy, thereby reducing waste and optimizing the use of scarce resources.

The last two correlation tests, Pearson’s correlation coefficient and Spearman’s rank correlation coefficient, were applied to gain deeper insights into the data. These tests examined the strength and direction of the relationships between key variables.

Pearson’s correlation test assessed the linear relationship between two key variables: the belief in the necessity of environmental protection (question 1) and the perception that non-polluting materials reduce greenhouse gas emissions (question 2). The Pearson correlation coefficient between the responses to question 1 and question 2 was found to be r = 0.58, with a p-value of 0.0001. This result indicates a moderate to strong positive correlation, suggesting that individuals who strongly believe in the necessity of environmental protection are more likely to perceive non-polluting materials as effective in reducing emissions. Since the p-value is less than 0.05, the null hypothesis was rejected, and it was concluded that there is a significant positive relationship between these two variables.

Given that some survey responses were ordinal (e.g., strongly disagree to agree strongly), Spearman’s rank correlation was applied to test the association between the level of agreement regarding the necessity of environmental protection (question 1) and the support for the use of greenhouse gas emission certificates (question 15). The Spearman correlation coefficient for these two variables was calculated as ρ = 0.42, with a p-value of 0.003. This moderate positive correlation suggests that individuals who emphasize environmental protection also tend to support the use of greenhouse gas emission certificates. Given that the p-value is less than 0.05, the null hypothesis is rejected, indicating a statistically significant correlation between the variables.

4. Discussion

The descriptive analysis and the correlation tests reveal a strong connection between corporate attitudes towards environmental protection and the adoption of eco-economy principles. The positive correlations between support for environmental protection and the belief in non-polluting technologies suggest that companies and employees with solid ecological values are more likely to embrace sustainable practices. Additionally, the moderate correlation between environmental values and support for emission certificates suggests that ecological policies, such as the carbon market, may be more widely accepted among companies that prioritize sustainability.

Figure 1 illustrates the Stationary R² values concerning the number of predicates in 11 models. Model 3 stands out with the highest R² value, indicating that it has the best fit among all models (0.730). This suggests that the variables selected for Model 3 contribute significantly to explaining the data. Model 10 also performs relatively well, but not as strongly as Model 3. On the other hand, Models 7 and 8 exhibit low R² values, indicating weaker performance and a poorer fit to the data. These models may require refinement or reevaluation to improve their predictive accuracy.

The number of predicates varies across the models, with more predicates generally leading to improved model performance, as seen in Models 3 and 10. However, this trend is not universal. For example, Models 5 and 6 have multiple predicates but do not achieve high stationary R² values. This suggests that while including more variables can enhance the model’s fit, the relevance and quality of these variables are equally important for improving the model’s predictive power. Overall, selecting the proper predicates is essential for optimizing the performance of each model.

However, the division in opinions regarding the carbon market underscores the need for further education and engagement on the long-term benefits of such mechanisms. While many respondents support emission certificates, a significant portion remains undecided or skeptical, highlighting potential barriers to the adoption of eco-economic policies.

The findings of this research contribute to a deeper understanding of how eco-economy principles can be integrated into corporate environmental strategies, particularly within the energy sector. The strong support for environmental protection and sustainable practices, as revealed in the survey, indicates that employees recognize the necessity of eco-friendly operations. This aligns with previous literature on eco-economy, which emphasizes the importance of circular resource use and minimal waste [4,10].

One key observation from the data is the correlation between the belief in environmental protection and the adoption of non-polluting materials. This suggests that individuals and companies with vital ecological values are more inclined to implement sustainable technologies and comply with environmental standards. Such findings reinforce that ecological education and a proactive corporate culture can be powerful drivers of sustainability [42].

However, the survey also highlights certain ambivalences regarding the effectiveness of market-based environmental tools, such as the carbon market and emission certificates. While many respondents expressed positive views on emission certificates, a significant portion remained undecided or skeptical about their long-term sustainability. This echoes the critiques in the literature regarding the complexity and perceived inefficacy of carbon markets, which are sometimes viewed as limited in their ability to drive genuine environmental change [23].

Another essential aspect emerging from the discussions is the role of competition in the adoption of the eco-economy. The survey indicates that competition can encourage cost reduction and efficiency but may also drive companies to prioritize profits over sustainable practices. This reflects a broader tension in the literature between economic competitiveness and environmental responsibility [43]. For eco-economy principles to gain wider acceptance, companies must balance profit motives with ecological sustainability, possibly requiring more robust policy interventions and financial incentives.

The findings suggest that while solid support exists for sustainable practices, significant gaps still need to be addressed in understanding and applying specific eco-economic tools, particularly market-based mechanisms. Bridging these gaps will be crucial for advancing the effectiveness of environmental policy and corporate adoption of eco-economy principles. Improving models 7, 8, and 9 involves increasing the number of predicates defined in consensus with the objectives of the econometric model. Concerning model 7, in addition to the need to increase the number of predicates, the significance level of the variables used is also low, which implies improving the proposed variables. Regarding Models 8 and 9, the statistics value (R²) is reduced with the other models, p value is not significative (Sig. are more than 0.05 for Models 7, 8, 9), which shows a low level of serial dependence in the series, which requires rethinking the variables used in the model for a high current at the series level.

The factors driving the eco-economy can be distilled into those promoting the development of rational and sustainable behavior. Education plays a central role in encouraging consumption methods grounded in rationality by recognizing the utility of economic goods in consumption and validating this through market feedback, thereby supporting energy policies based on eco-efficient principles. Contemporary society is advancing production methods through innovation and scientific research to mitigate the adverse effects of pollution, which is vital for achieving primary economic goals such as reducing the carbon footprint, as stipulated by international regulations. Entrepreneurship and innovation empower individuals to channel their creative energies into developing solutions for the detrimental impacts of production and consumption, benefiting themselves and their communities. This leads to an increased supply of eco-efficient goods that meet demand and promote rational resource utilization through reliable economic goods with sustainable quality–price ratios.

Policy interventions and financial incentives address the skepticism surrounding market-based tools, such as the carbon market and emission certificates. The mixed responses to these mechanisms highlight the need for more transparent communication on their benefits and long-term sustainability. Policymakers should consider refining the design of such instruments to make them more transparent and easier to understand. At the same time, businesses can explore using these tools as part of their broader sustainability strategies. Providing financial incentives, such as tax breaks or subsidies for companies that adopt non-polluting technologies, could further encourage eco-economy adoption. Moreover, policymakers must ensure that regulations are robust enough to strike a balance between economic competitiveness and environmental responsibility, supporting businesses that prioritize sustainability without compromising their profitability.

The results emphasize the importance of aligning corporate strategies with environmental policies to foster sustainable practices. Companies that embrace sustainability as a core component of their operations are more likely to see improvements in both ecological outcomes and product quality. Furthermore, environmental policies, including stricter regulations and financial incentives, play a critical role in encouraging the adoption of eco-economic principles.

5. Conclusions and Limitations

This study highlights the importance of understanding the motivations behind reducing pollution practices at the human behavior level, as well as the appeal of production and consumption methods that minimize pollution. Furthermore, this study underscores that utilizing EUA at the economic level incentivizes non-polluting behavior among economic operators, purchasing EUA as a sustainable transaction over time. Also, this study explores the relationship between corporate behavior, environmental policies, and the adoption of eco-economy principles in the energy sector. Detailed survey analysis showed substantial support for environmental protection and non-polluting technologies, indicating that the eco-economy framework is gaining traction within corporate strategies. However, mixed views on market mechanisms such as emission certificates point to lingering uncertainties about the role of such instruments in achieving long-term sustainability.

Based on the findings of this research, several practical recommendations can be made to enhance the adoption of eco-economy principles in the corporate sector. First, companies should strengthen environmental education and awareness programs to foster a corporate culture that values sustainability. The correlation between ecological protection values and non-polluting technologies suggests that educating employees on the long-term benefits of sustainability can significantly influence the adoption of eco-friendly practices. Businesses should invest in continuous professional training to ensure employees have the knowledge and tools to implement sustainable technologies and comply with environmental regulations. By promoting environmental responsibility at all levels of the organization, companies can align their strategies with eco-economy principles and contribute to a broader shift towards sustainable economic models.

The proposed model is designed to synthesize the significance of logical predicates derived from an analysis of the energy sector’s specifics for management personnel in energy enterprises. Given that these models encompass a defined set of logical predicates corresponding to preset characteristics related to specific questions, managers in the energy industry can develop strategies aimed at enhancing sustainable production levels. Essentially, the model assists energy companies in implementing sustainable management strategies by guiding them to adopt policies based on the assertions made within the model, with the resultant logical predicates serving as a foundation for these strategies.

This study has several limitations. First, the survey was limited to a sample of employees within the energy sector, which may not fully capture the diversity of perspectives across other industries. Additionally, the study relied on self-reported data, which may have been influenced by social desirability bias. Another limitation is the geographical scope; the findings are context-specific and may not directly apply to other regions or industries with different regulatory environments or economic conditions.

Also, while most of the models demonstrated reasonable explanatory power, Models 7, 8, and 9 yielded comparatively low R² values, suggesting a weaker statistical fit. This may be due to the abstract nature of the constructions assessed, such as consumerism or ethical perceptions, which could benefit from redefinition through more nuanced survey items or supplementary qualitative data. Future iterations of the study might improve model robustness by refining the underlying variables or employing targeted interviews to validate the interpretive frameworks.

Future research should expand the study’s scope by including a more diverse range of industries and regions to provide a more comprehensive understanding of eco-economy adoption. Furthermore, there is a need for longitudinal studies to track the evolution of attitudes and behaviors towards environmental policies. Future studies could also explore the barriers that companies face when integrating eco-economy principles, such as financial constraints, inadequate infrastructure, or insufficient regulatory support. Ultimately, further research should investigate the effectiveness of market-based tools, such as carbon credit, across various economic sectors to better comprehend their role in promoting sustainability.