Sage Journals: Discover world-class research

Abstract

The increasing concern regarding the degradation of the environment caused by unsustainable agricultural practices necessitates a more comprehensive examination of farmers’ nature-friendly behavior and the influence of environmental legislation on their actions. This study employs face-to-face surveys conducted with a sample of 476 Chinese farmers to investigate the complex relationship between ecological commitment (ECO), environmental knowledge (EKN), the new ecological paradigm (NEP), and ecological behavior (EBH). Partial least squares structural equation modeling (PLS-SEM) was used as the analytical approach. The findings indicate that ECO (β = .421, p < .01), EKN (β = .301, p < .01), and NEP (β = .271, p < .01) have statistically significant positive effects on EBH. This research also establishes the moderating role of legal cognition, highlighting the influence of farmers’ understanding of environmental legislation on the connection between their commitment, knowledge, and ecological behavior. The significance of environmental legislation is underscored as a crucial mechanism for regulating and encouraging nature-friendly behavior at farms. This study recommends the implementation of increased awareness strategies and emphasizes the need for local administrations to promote and motivate environmentally sustainable behaviors by improving the legal cognition of farmers as a means to address the issue of environmental degradation due to unsustainable farming behavior.

Keywords

environmental legislation environment protection ecological behavior environmental policy pro-environmental behavior

Introduction

The interdependent system that sustains life on Earth is most dramatically shaped by the mutually beneficial interactions of ecosystems, agriculture, and personal human actions (Chapin et al., 2011). The need to find a balance between development and environmental protection is growing as we move through an era where environmental concerns and economic growth coexist (Awan, 2013). Therefore, sustainable development is affected by the complex nexus that develops in the complex relationship of economic growth, environment, and agriculture. Responsible farming is essential since economic growth and environmental sustainability are interdependent. Unfortunately, economic growth is accompanied by wasteful practices like the overexploitation of natural resources in today’s world. This has resulted in a number of environmental issues (Guo et al., 2020). These dual challenges not only slow down economic progress but also pose a significant hazard to human existence (Alharbi et al., 2018), but also underscores the importance of achieving the healthy balance between the agricultural advancements and environmental preservation. Attaining this state of equilibrium is of utmost importance in order to guarantee sustained food security, ecological resilience, and the holistic well-being of both the economy and the planet. Within the broader framework of economic and environmental dynamics, agriculture assumes a pivotal function, serving as a linchpin that plays a key part in crafting a sustainable future.

The significance of agriculture and the behavior of farmers in influencing a sustainable environment is of utmost importance (Blackstock et al., 2010). Farmers, in their capacity as custodians of the land, assume a crucial role in the adoption and execution of strategies that facilitate the integration of food production and ecological preservation (Ul Haq et al., 2023). The actions taken by individuals, ranging from the selection of crops to the management of resources, have a significant and far-reaching impact on the overall well-being of ecosystems. The adoption of sustainable agricultural techniques entails the reduction of environmental effects, the optimization of resource utilization, and the promotion of biodiversity. Farmers, as significant stakeholders, possess the capacity to serve as influential catalysts in promoting an enduring and ecologically sound ecosystem. Farmers play a crucial role in promoting the integration of sustainable practices into agriculture and environmental conservation. The adoption of eco-friendly practices, such as organic farming techniques, agroecological approaches, and resource efficiency, is what makes this possible (Ul Haq & Boz, 2020). The commitment to sustainability not only serves to protect natural resources but also guarantees the enduring sustainability of agricultural systems, thereby providing advantages to both present and future generations. It also shed light on the importance of nature friendly behavior of farmers for the prosperity of ecosystem and agricultural communities.

Agriculture’s impact on ecological health is a growing source of worry. Even though large-scale agricultural enterprises are often at fault, individual farmers are frequently accused of being guilty. This is particularly true if farmers have knowledge of issues and the ability to act but do not (Karlsson, 2007). Furthermore, farmers who are responsible for harming the environment may face legal sanctions. According to Scanlon (2002), an organic farmer whose crops have been infected with GM pollen from a neighbor’s fields may, in some cases, sue the neighbor under a claim of nuisance or trespass, even though the law should balance environmental harm with the social benefits of advances in agriculture. This emphasis on agriculturalists, in addition to large-scale agricultural enterprises or the farm and food industry as a whole is based on the idea that sound conservational practices and good management of the environment are essential to the development of a sustainable agricultural sector and that farmers are better positioned to implement these than businesses in general.

All countries are paying close attention to environmental concerns, and finding solutions is now an essential part of their environmental policies. Changing individuals’ behavior to lower its influence on the ecology is vital since many environmental issues are caused by human activity (Khan et al., 2020). The positive effects of contemporary science and technology on the environment are not yet large enough to compensate for the negative effects of human activity. Therefore, actively advocating for people to adopt ecologically friendly materials, environmentally conscious consumption, green energy usage, low carbon transport, and other nature-friendly behavior in regards to fulfilling daily necessities (clothing, food, housing, etc.) is one of the most efficient and rational methods to lessen ecological degradation and attain sustainable development (Ritter et al., 2015). Therefore, agricultural practices have a significant impact on the environment, but farmers can also help to reduce environmental problems. Farmers are increasingly receiving training and incentives from governments and environmental groups to adopt sustainable and eco-friendly techniques. It assists in addressing the local environmental issues and advance global ecological goals by encouraging farmers to be environmentally responsible. This coordinated effort makes environmentally conscious agriculture practices part of the global agenda for a sustainable and resilient future.

It is well established that environmental regulations significantly influence the promotion of sustainable development on a global scale (Cui et al., 2018). Costantini and Mazzanti (2012) demonstrated that environmental legislation fosters eco-friendly commerce. Environmental taxes may promote green innovation, according to an analysis by Shen et al. (2020), who also found that trading CO₂ emissions had no effect on green innovation at the national level. The impact of environmental legislation on low-carbon patents was investigated by Calel and Antoine (2012), who concluded that there was no relationship between environmental legislation and low-carbon patents. Cui et al. (2018) investigated greenhouse gas emission trading in China and found that the regulations had a big and positive effect on low-carbon technologies.

Furthermore, environmental contamination, such as polluted water and waste products, has caused significant harm to individuals (Q. Wang & Yang, 2016; Xie et al., 2016). As a consequence, fundamental changes in China’s environmental governance institutions are necessary. Notably, in China, environmental management is essentially dependent planning and legislation. However, ecological legislation is insufficient to meet the needs of social development due to steadily rising economic growth and environmental degradation worldwide (Hystad et al., 2019). Public consciousness of the ecological legislation is critical for its execution as well as for creating environmental responsibility among the general public. Can legislation be used as an auxiliary instrument to command and regulate agricultural entrepreneurs’ ecological behavior (J. Wang, 2009)? Is present legislation capable of encouraging agricultural entrepreneurs to participate in ecological governance? As a result, it is critical to determine if ecological regulations can really influence what individuals do to safeguard the nature and ecological systems. Furthermore, it is critical to establish and strengthen public policies aimed at ecological protection over time. Ecological regulations have a substantial influence on the individuals’ environmental behavior (Zhang et al., 2018).

Most previous research on ecological legislation focused on the evaluation of—and challenges with—regulation implementation in sectors other than agriculture (Fredriksson et al., 2010; Li et al., 2019; Lu et al., 2012; Ran, 2013; Yu et al., 2013; Zhan et al., 2013). Furthermore, since the bulk of the available studies has concentrated on jurisprudential research as well as economic views, mentally oriented behaviors have largely gone unnoticed (Nielsen, 2017; Yang & Weber, 2019).

It’s significant because there is mounting evidence that farmers are evolving towards a “nature-friendly” mind set (Burton & Wilson, 2006). Additionally, it is becoming more widely acknowledged that changes in farmer attitudes towards “nature friendly thinking” are a sign of the effectiveness of educational and policy initiatives (Wilson & Hart, 2001). To this purpose, various studies (Baumgart-Getz et al., 2012) have looked at the significance of farmers’ views towards optimal management practices for environmental protection. What motivates farmers to adopt more “nature-friendly” behavior s and actions, however, is not entirely understood. The urgent need for a thorough understanding of the intricate processes that affect farmers’ decisions and actions with regard to environmentally sustainable practices is what drives the research. Agriculture, as a crucial element of worldwide economies, assumes a critical role in either worsening or reducing environmental challenges. Understanding the underlying reasons that drive farmers’ adoption of nature-friendly behavior is essential in order to devise specific interventions and policies that promote the adoption of sustainable farming practices. Since sound nature-friendly behavior is essential for sustainable agricultural development and economic prosperity, our paper’s motivation comes from the realization that understanding the relationship(s) between ecological commitment, environmental awareness, and nature-friendly behavior of farmers can help identify alternative strategies for promoting sound environmental practices among farmers. In addition, it is crucial to examine the potential moderating effect of environmental legislation as a regulatory framework in order to develop a deep understanding of the complex relationship between individual factors and external influences.

Thus, the primary objective of the current study was to determine the impact of ecological commitment (ECO), environmental knowledge (EKN) and new ecological paradigm (NEP) on farmers’ nature-friendly behavior. Another purpose of the study was to analyze the moderating role of environmental legislations between the farmers’ individual factors and their nature friendly behavior. The conceptual approach used in the current study looks at the complicated relationship between ECO, EKN, NEP, and EBH among farmers, focusing on how ELG might affect this relationship. According to Sendawula et al. (2021), ecological commitment refers to an individual’s inherent motivation and unwavering dedication to the preservation of the environment, promotion of sustainability, and adoption of responsible behaviors. The concept incorporates the emotive, cognitive, and behavioral components of an individual’s relationship with the natural environment. Environmental knowledge refers to the cognitive grasp and recognition of environmental concerns, ecological principles, sustainable practices, and the outcomes associated with environmentally conscious conduct (Frick et al., 2004). It demonstrates an individual’s capacity to understand and utilize ecological knowledge. The New Ecological Paradigm (NEP) is a valuable instrument for assessing the ecological views and beliefs of farmers. It presents the high order beliefs and thoughts regarding the human environmental relationship (Derdowski et al., 2020). These beliefs possess the capacity to exert a substantial influence on the adoption of environmentally friendly behavior by farmers, hence playing a pivotal role in promoting sustainable and ecologically responsible behavior among farmers. Organic farming, sparingly using pesticides, conserving soil, and preserving biodiversity are just a few examples of the nature-friendly behaviors that farmers display. These practices collectively contribute to the promotion of sustainable and ecologically responsible agricultural methods (Pretty, 2008). Environmental legislation is a comprehensive framework of laws, regulations, and policies put in place by governmental bodies with the intention of addressing environmental issues, ensuring the implementation of conservation measures, and encouraging the adoption of sustainable agriculture practices (Gerba & Brusseau, 2019).

This research makes a multifaceted contribution. First, by examining the significance of ecological commitment in order to gain a deeper understanding of the underlying values and beliefs that motivate farmers to adopt ecologically friendly practices. Gaining insight into the underlying motivations driving these commitments is essential in order to develop specific initiatives that align with the values held by farmers. Furthermore, the analysis of environmental knowledge facilitates the evaluation of farmers’ level of awareness regarding ecological matters and the potential influence of this knowledge on their behaviors. Finally, the study examines the moderating effect of environmental legislation, acknowledging the external factors that might either support or impede farmers’ environmentally conscious behaviors. The comprehensive investigation presented herein enhances the scientific understanding of the complex spectrum of elements that impact farmers’ adoption of environmentally sustainable practices.

Hypothesis Development

Regardless of the extent to which individuals experience a sense of proximity or affiliation with nature, there exists a mutual dependence between humans and the natural environment, such that the welfare of one can impact the welfare of the other in a bidirectional manner. Studies investigating the relationship between commitment and ecological behavior have predominantly focused on commitment in a singular dimension, specifically commitment to behavior (Hines et al., 1987). The commitment to a partner is a crucial element in any relationship, as per Rusbult’s (1980) conceptualization of commitment as a distinct construct. Similarly, individuals may perceive their environment as an extension of a partner, with varying degrees of commitment. The theory of interdependence, as proposed by Kelley and Thibaut (1978) and later refined by Rusbult and Arriaga (2000), is widely regarded as the preeminent framework for understanding the dynamics of interpersonal relationships. This theoretical perspective emphasizes the impact of relationship structure on the motives and actions of individuals over the course of the relationship. Interdependence theory offers a comprehensive framework for describing the relationship between humans and the environment. The interdependence between human beings and the environment as a whole is such that the well-being of one entity can have an impact on the other. Dependence refers to the extent to which individuals rely solely on a specific relationship partner, such as the environment, to fulfil significant needs.

What are the factors that contribute to increased dependence? The theory of interdependence suggests that there are two primary possibilities through which individuals may become more reliant on their relationship partner. Firstly, individuals are likely to experience greater dependence on their partner if the partner is able to satisfy their significant needs, thereby leading to satisfaction with the partner. Secondly, if these needs cannot be fulfilled without the partner, the individual is likely to become dependent on the partner. The rationale behind the aforementioned argument is well supported by the existing literature on the subject of recreational place dependence, as evidenced by Stokols’ (1981) research. Scholars in the field of recreation have conducted studies to investigate the impact of reliance on specific environments, such as trout streams or local ecologically valuable areas, on individuals’ perspectives and behavior s towards these settings. These investigations have been documented in research works by Hammitt et al. (2006), as well as Vaske and Kobrin (2001).

The theory of relationship commitment developed by Rusbult (1980) represents a significant extension of interdependence theory and is widely regarded as a prominent framework in the field of interpersonal relationship psychology. One of the fundamental principles of her theoretical framework is that an increased level of dependency on a partner will result in a heightened sense of commitment towards them. According to Le and Agnew (2003), the experience of being dependent on a partner for the fulfilment of needs can be distinguished from commitment, which is a subjective perception of that dependence. The concept of commitment is characterized by three interconnected elements, namely cognitive attachment, a commitment to the future, and the intention to persist, as outlined by Rusbult et al. (2001). As per our argument, it is posited that individuals develop a subjective sense of commitment towards the environment. This sense of commitment is characterized by a psychological connection and a long-term inclination towards the natural world. The relevance of intent to persist in the relationship between humans and the natural environment appears to be limited. The level of an individual’s perceived dependence on the natural environment for their personal well-being, such as deriving pleasure from engaging in activities, is directly proportional to their level of commitment towards preserving the environment. This implies that individuals who enjoy spending time in ecological settings are more likely to exhibit a keen interest in ensuring the long-term well-being of natural resources.

Individuals who possess a perception of interdependence with the natural world may exhibit a corresponding transformation of motivation, leading to behavior s that align with the preservation of the environment. In the field of close relationship research, it has been observed that a significant level of perceived commitment towards one’s partner is positively associated with pro-relationship behavior, encompassing both cognitive and behavioral aspects. Agnew et al. (1998) discovered that in the cognitive domain, couples demonstrated greater cognitive interrelationships, which refers to a shared mental representation of oneself and one’s partner, when their level of commitment was higher. According to Van Lange et al. (1997), there is a positive correlation between high commitment and the willingness to make sacrifices for the benefit of one’s partner. In summary, it is probable that individuals who exhibit commitment towards their intimate relationships will undergo a shift in their motivational orientation, leading them to engage in behavior s that prioritize the welfare of their partner (i.e., the environment) or the relationship as a whole. Thus, it is postulated that:

H1: There is a positive interrelationship between ecological commitment (ECO) and the farmers’ ecological behavior.

Environmental knowledge pertains to the comprehension and recognition of environmental issues, along with prospective resolutions to these challenges. The widely accepted interpretation of environmental knowledge pertains to empirical data concerning environmental structures, functions, and processes (Schahn & Holzer, 1990). Sia et al. (1986) argue that the manifestation of responsible environmental behavior is a result of a learned process that is not developed in isolation but rather in response to various interacting components. The intellectual strand is a crucial component that encompasses environmental knowledge. This knowledge includes a comprehensive understanding of the ecological principles and processes that are fundamental to comprehending the impact of human activities on ecosystems. Additionally, it involves recognizing the interrelationship between human social structures and the environment and the ecological problems that arise from these complicated relationships. Furthermore, the intellectual strand encompasses strategies of environmental action, which include the ability to recognize and critically assess possibilities for rehabilitation (Morrone et al., 2001). The intricate and non-linear nature of the interplay between intellectual elements (knowledge) and behavior has been extensively studied by researchers. It has been demonstrated that augmenting someone’s environmental knowledge leads to a greater likelihood of exhibiting positive and responsible ecological behavior (Hsu, 2004). According to research, possessing knowledge about the environment is a significant indicator of engaging in environmentally conscious behaviors (Liu et al., 2010). Individuals who lack awareness of environmental issues are less likely to exhibit conscious concern or engage in environmentally sustainable behavior (Ma et al., 2023). The ability to make informed environmental decisions is hindered by the absence or inaccuracy of information (Fielding & Head, 2012). Thus, it is postulated that:

H2: There is a positive relationship between environmental knowledge (EKN) and ecological behavior (EBH).

In light of the heightened consciousness surrounding environmental concerns in recent decades, certain scholars have posited the emergence of a novel perspective that prioritizes ecological issues. Notably, Dunlap and Liere (1978) and other researchers have proposed that the New Environmental Paradigm (NEP) serves as a manifestation of these evolving beliefs. In summary, NEP denotes elevated cognitive constructs pertaining to the interconnection between humans and their surrounding environment. The proposition posits that the human species relies on a harmoniously balanced natural environment and that there exists a finite threshold to the expansion of economic activity. Both the NEP and the widely utilized attitude concept are founded on beliefs as a fundamental element, yet they diverge considerably in terms of the specific types of beliefs that are emphasized. The NEP is informed by fundamental principles regarding the nature of our existence (Xiao & Buhrmann, 2017). Whether denoted as higher-order beliefs or more foundational ecological beliefs, these beliefs continue to embody our understanding of the appropriate manner in which humans ought to interact with the environment. This suggests that whereas beliefs are invariably assessed in connection with a particular entity (such as a commodity, an individual, a behavior, or a concept), the object of measurement for NEP remains constant. This statement implies that the use of NEP and its corresponding scale elevates the explanatory factor to a more advanced level and guarantees a uniform assessment of these convictions when evaluated across various behavior al circumstances. Therefore, there is evidence to support the notion that the absence of precise measurement, which has been proposed to contribute to the inconsistencies between beliefs and behavior s, is unlikely to result in comparable issues for the associations between NEP and behavior (Derdowski et al., 2020).

Regarding the NEP, it can be inferred that individuals are inclined to conform their behavior to align with their beliefs, and any actions that contradict this relationship may lead to a sense of dissonance (Festinger, 1957) or imbalance (Heider, 1958). Given that an unwavering dedication to the fundamental principles of NEP transcends a strong mindset towards a particular object, such as a specific behavior, it is probable that it will exert a more extensive impact on a wider spectrum of behavior s than attitudes that are more specific in nature. Alternatively, as posited by previous research (Xiao et al., 2019), while specific views may result in corresponding behavior s, our underlying and fundamental perspectives on the world can serve to reinforce a broader spectrum of behaviors. The Vested Interest Theory (VIT) posits that a consumer’s level of interest in an object is contingent upon its alignment with their value orientation, something that is unique. According to scholarly literature, values are considered essential principles that direct behavior in various circumstances (Osburg et al., 2019). Furthermore, involvement with values plays a significant role in motivating individuals to act in accordance with their overarching convictions (Johnson & Eagly, 1989). Given the close association between NEP and values (Stern, 2000), it is our contention that deeply held convictions regarding NEP-related matters constitute a fundamental guiding principle that informs how we behave across different environments. According to Pahl et al. (2005), there is a positive correlation between the endorsement of the New Ecological Paradigm (NEP) and the self-reported ecological behavior s of the participants. Xiao and Buhrmann (2017) assert that NEP is the most commonly employed metric of environmental concern in scholarly works. Furthermore, the authors contend that a significant number of studies that use NEP as a catalyst for ecological behaviors consistently demonstrate its potency as a predictor of such behaviors. Therefore, the following hypothesis has been built:

H3: There is a positive correlation between the endorsement of the new ecological paradigm (NEP) by farmers and their ecological behavior (EBH).

The population as a whole may not possess in-depth knowledge of the detrimental impact of their consumption patterns. Even the most basic waste of everyday items, such as fresh produce, can potentially have significant adverse consequences for the environment (Hammett, 2010). According to the expectancy theory, legislative measures offer anticipated incentives or outcomes that have the potential to elicit favorable behavior. The principle of the rule of law holds immense significance in the routine affairs of individuals. The concept of the rule of law exerts a preventive and corrective effect on individuals’ behavior, establishing a legal incentive to adhere to ethical standards (Raymore, 2002). The aforementioned legislation holds significant sway in guiding endeavors within the context of ecological consciousness. Sutherland’s (2010) study highlights that environmental regulations and legislation incentivize farmers to receive financial aid for growth in agriculture. The present investigation examined the environmental-oriented behavior of farmers by considering both internal and external factors, including laws and policies. This approach was taken because individuals’ behavior is shaped by a variety of external factors, such as societal norms and legal regulations, along with their own life experiences (Liu et al., 2010). In the present study, an assessment was made regarding the moderating impact of laws, with legal cognition being taken into account as an external variable. Our argument posits that the implementation of environmental laws has a positive effect on curbing revolutionary behavior in farmers, as they are motivated to safeguard the environment.

H4a,b,c: The relationship between ecological commitment and farmers ecological behavior (H4a), Environmental knowledge and farmers ecological behavior (H4b), and farmers ecological behavior and farmers ecological behavior (H4c) is positively moderated by environmental legislation (ELG).

The summary of study hypothesis is presented in Figure 1.

Figure 1.

Study hypothesis.

Materials and Methods

Study Area and Sampling Procedure

With a population of 96.4 million, Henan Province stands as the most extensive agricultural province in central China. According to Zi et al. (2021), the urbanization rate of the province in the year 2020 was recorded at 54%. Additionally, a considerable proportion of the population resided in rural localities. Zi et al. (2021) have noted that the province experiences a range of climatic variations, which have an impact on the energy consumption patterns of rural inhabitants. In addition, the agricultural practitioners are implementing eco-friendly practices, such as the utilization of biogas technology (Gao et al., 2019). Henan Province is a significant contributor to China’s grain production and is also recognized as a region with pronounced agricultural pollution. To examine the farmer’s ecological behavior under the influence of their ECO, EKN, and NEP, the research focused on surveying farmers of Henan Province. The local government is highly concerned about the ecological environment in this region.

To gather data from the farmers, the research employed a multistage sampling approach that combined purposive and random sampling methods. The initial phase involved the deliberate selection of Henan Province as a representative study area for China, given its significant role in Chinese agriculture. During the second stage of sampling, a deliberate selection process was employed with the aim of enhancing the statistical representativeness of the sample. The aforementioned cities, Anyang, Dengfeng, and Kaifeng, of the province were selected. In the subsequent stage, a random selection of five counties was made from each of the chosen cities. During the fourth stage, a random selection process was employed to choose five villages from the previously designated counties. The villages were selected on the basis of their agricultural potential, location, and distance to the city center. In the final stage of the sampling process, a random selection of six-seven farmers was made from each village. The face-to-face survey yielded a total of 476 valid responses obtained through a well-designed structured questionnaire. All constructs in this study were measured on a five-point Likert scale questions. The survey was conducted by a proficient team of 10 researchers who possessed substantial expertise and received adequate training. The team comprised individuals of both genders.

Empirical Model Specification

In order to test and analyze multivariate causal linkages, scientists are increasingly turning to structural equation modeling (SEM), a sophisticated multivariate technique. In contrast to other modeling techniques, SEMs look for both direct and indirect effects on hypothesized causal linkages. For decades, ecologists have relied on SEM to examine numerous hypotheses at once. SEM is a sophisticated multivariate analysis tool that has significant potential in ecological research as data accessibility continues to expand. Large volumes of publicly available data are a characteristic of many ecological investigations, necessitating the use of multivariate data analysis. Users of SEM have the option of searching for relevant public data and discovering study patterns (Fan et al., 2016). The complex networks of interdependence in ecosystems can be comprehended SEM (Grace, 2006; Shipley, 2016). As a result of its ability to integrate confirmatory factor analysis with path analysis, SEM quickly gained traction in numerous academic disciplines. As a scientific technique, confirmatory factor analysis attempts to quantify intangible characteristics like attitude and behavior (Byrne, 2013; Kline, 2023). The most variance is shared by the latent construct, which is extracted from other variables. Path analysis, on the other hand, developed from informatics and sought to establish a chain of causation between a set of variables. Before latent variables were introduced, this method was known as SEM, and it proved to be a robust tool for examining and creating structural hypotheses involving both indirect and direct causal effects. Recent usage, however, has seen the two outcomes become synonymous. Cause-and-effect relationships between variables can be mapped out with path analysis. Assuming that one variable can affect the outcome both directly and indirectly via another variable is a common application of path analysis’s mediation function.

Therefore, the use of the structural equation model (SEM) amalgamates the advantageous features of factor and path analysis, thereby resulting in a potent multivariate statistical instrument. The application of Structural Equation Modelling (SEM) is an approach to statistics that facilitates the analysis of the interrelationships among various effects, various influences, and latent variables. It integrates various analytical methods such as analysis of variance, factor analysis, regression analysis, and path analysis (Byrne & Stewart, 2006; Hair et al., 2012). All variables examined in this study exhibited interrelatedness, either as latent variables or through their interaction. The PLS-SEM methodology is a type of multivariate structural equation modelling that is classified as a second-generation approach. According to research, the use of non-parametric methods in studies with limited sample sizes can eliminate distribution assumptions and yield greater statistical power compared to other methods. The process of reducing and validating constructs prior to constructing the ultimate structural equation for each obvious variable enables the simple verification of item validity through the use of PLS. Previous literature has established that a minimum of 100 respondents is required to achieve impartial results when utilizing this particular model (Reinartz et al., 2009). Moreover, the adequacy of the respondents for this model was established through Hair et al.’s (2017) 10 times rule and G*power. The present study heavily relied on the analytical approach put forth by Hair et al. (2017). As Chin (2009), indicates, the PLS-SEM methodology consists of a measurement model and a structural model.

Results

Descriptive Analysis of Individual Items and Constructs

Table 1 presents the descriptive analysis of individual items and constructs. The average response regarding environmental legislation (ELG) (=3.72) describes that they have a good level of familiarity with environmental legislation. They knew that environmental protection was a prime aspect of government policy. They are familiar with the fact that noise is a type of pollution that is also prohibited by law. Similarly, they knew the most important aspects of the legislation undergone in their country regarding the environment. In respect to their commitment to the environment, they displayed a strong commitment level with an average score of 4.07. They have good knowledge regarding the environment. They knew the causes of climate change. They agreed to the side effect of increasing CO₂ emissions, and they also strongly agreed with the burning of fossil fuels and its impacts on the atmosphere. Regarding the new ecological paradigm (NEP), which describes the good level of respondents’ beliefs regarding the human-environment relationship; they have a strong belief that the interference of humans in nature causes dangerous consequences. They believe that continued human changes in nature may have irreversible impacts on the environment. The respondents had good ecological behavior. They tried to use recycled and environmentally friendly items in their daily lives. Most of them prefer to use paper bags to shop, renewable energy sources, and solar panels, and they also try to use natural resources efficiently, such as water, etc. Complete statements of all constructs have been presented in supplementary Table 1.

Table 1.

Descriptive of Responses to Individual Items and Construct.

	Mode	Mean	SD
Environmental legislation (ELG)		3.72	1.26
ELG1	5	4.89	0.98
ELG2	3	2.89	1.23
ELG3	5	4.88	1.03
ELG4	4	3.69	1.54
ELG5	4	3.77	1.49
ELG6	3	2.69	1.24
ELG7	4	3.72	1.47
ELG8	5	4.73	1.06
ELG9	3	3.01	1.53
ELG10	3	2.89	1.02
Ecological commitment (ECO)		4.07	1.20
ECO1	4	3.04	1.07
ECO2	5	4.88	1.03
ECO3	5	4.93	1.34
ECO4	4	3.99	1.03
ECO5	5	4.89	1.21
ECO6	4	3.82	1.44
ECO7	3	2.94	1.29
Environment knowledge (EKN)		4.04	1.25
EKN1	4	3.67	1.29
EKN2	4	3.84	1.32
EKN3	5	4.97	1.01
EKN4	4	3.79	1.33
EKN5	3	3.01	1.54
EKN6	5	4.95	1.02
New ecological paradigm (NEP)		4.04	1.33
NEP1	5	4.84	1.32
NEP2	4	3.76	1.58
NEP3	5	4.95	1.33
NEP4	4	3.87	1.39
NEP5	3	2.99	1.05
NEP6	5	4.99	1.48
NEP7	4	3.02	1.26
NEP8	4	3.91	1.23
Ecological behavior (EBH)		4.08	1.30
EBH1	5	4.86	1.43
EBH2	5	4.92	1.39
EBH3	5	4.79	1.22
EBH4	3	3.01	1.33
EBH5	3	2.77	1.10
EBH6	5	4.93	1.31
EBH7	4	3.89	1.46
EBH8	5	4.91	1.32
EBH9	4	3.79	1.04
EBH10	3	2.88	1.43

Testing Validity of Measurement Model

The assessment of the model’s fitness was conducted through the administration of discriminant validity and convergent tests (Table 2). For, convergent validity, the average variance extracted (AVE) and composite reliability (CR) was used. The application of convergent validity (CV) testing is utilized in the analysis of determinative measurement models within the context of PLS-SEM. CV signifies to the degree of concurrence between a particular measurement and other measurements that pertain to a comparable condition (Chen et al., 2020). CV was evaluated by scrutinizing the factor loading (FL) of each item, and comparing it to the cut-off value. As per the findings of published research, a construct can only be deemed convergent if its factor loadings exceed .70 (Cheah et al., 2018). Consequently, the findings ensured convergent validity based on the absence of an individual item with a factor loading less than .70. The average variance explained refers to the extent to which the construct captures variance relative to the variance attributable to measurement error. Achieving a score of .80 or greater is generally considered evidence of meeting the necessary requirements of formative measures, provided that there is convergent validity, which refers to strong loadings (Han et al., 2010). FL as displayed in Table 3 indicate that the items ought to be regarded as a component of the intended construct. The study’s convergent validity was established by the lack of any individual items exhibiting factor loadings lower than .80.

Table 2.

Validity Measures of Measurement Model.

	Factorloading	Cronbachalpha	CR	AVE
Environmental legislation (ELG)
ELG1	.984	.89	.96	.72
ELG2	.943
ELG3	.895
ELG4	.892
ELG5	.854
ELG6	.844
ELG7	.843
ELG8	.839
ELG9	.826
ELG10	.812
Ecological commitment (ECO)
ECO1	.943	.86	.95	.75
ECO2	.926
ECO3	.899
ECO4	.879
ECO5	.865
ECO6	.854
ECO7	.832
Environment knowledge (EKN)
EKN1	.923	.90	.96	.79
EKN2	.905
EKN3	.893
EKN4	.875
EKN5	.844
EKN6	.832
New ecological paradigm (NEP)
NEP1	.945	.82	.97	.79
NEP2	.921
NEP3	.894
NEP4	.854
NEP5	.868
NEP6	.843
NEP7	.859
NEP8	.836
Ecological behavior (EBH)
EBH1	.921	.86	.96	.71
EBH2	.912
EBH3	.897
EBH4	.881
EBH5	.877
EBH6	.864
EBH7	.859
EBH8	.844
EBH9	.837
EBH10	.824

Table 3.

Measures of Discriminant Validity.

	ELG	ECO	EKN	NEP	EBH
Fornell-Larcker criterion
ELG	.850
ECO	.538	.869
EKN	.492	.471	.889
NEP	.375	.382	.402	.892
EBH	.264	.493	.398	.538	.843
Heterotrait-Monotrait ratio
ELG
ECO	.463
EKN	.521	.442
NEP	.274	.294	.384
EBH	.21	.362	.274	.472

Note. ECO = ecological commitment; EKN = environmental knowledge; NEP = new ecological paradigm; ELG = environmental legislation; EBH = ecological behavior.

The evaluation of the construct’s validity was conducted through the computation of the CR coefficient. CR is a metric used to evaluate the internal consistency and reliability of measures by assessing the reliability of the latent variables that form the basis of a scale (Cheah et al., 2018). According to research findings, In terms of reliability, CR is a more precise measurement than Cronbach’s alpha, as it is based on factor loadings (Singh & Prasad, 2018). According to sources (Chin, 1998), the CR coefficient must reach .60 (minimum value) for establishing the construct validity of the model. Furthermore, it can be inferred that if the CR coefficient surpasses .70 (Hock et al., 2010), the model can be deemed satisfactory. Furthermore, it has been established that a confirmation threshold of .80 or higher for the CR value is required (Han et al., 2010). The requirement that all latent variables attain a CR value of at least .95 emphasizes the importance of conducting additional research.

Furthermore, it has been demonstrated that the AVE possesses convergent validity. AVE scores for each construct that were higher than the predetermined threshold of .50. When the variance of the explanation exceeds the variance of the error, the AVE exceeds .50 (Henseler et al., 2015). The results indicate that the measurement model exhibits substantial levels of construct and convergent validity. In order to establish the reliability of a latent variable, it is necessary for its Cronbach’s alpha coefficient to exceed the threshold of .70 (Ringle, 2014). The statistical measure known as Cronbach’s alpha (Cronbach, 1951) is frequently employed in scholarly discourse pertaining to the assessment of instrument reliability. This label has been attributed to a statistical measure that holds significant usage in the development of scales for the assessment of affective constructs (Taber, 2018). The validity of the questionnaire is established through the calculation of the mean correlations among its variables. The alpha coefficient of a given measure can range from 0 to 1. A higher value of alpha is indicative of a scale that is more reliable. The latent variable (Rahman et al., 2021) necessitates a Cronbach’s alpha value of .70 or higher. The Cronbach’s alpha coefficient demonstrated a range of .82 to .90 for all structures. The study’s structures exhibited Cronbach’s alpha scores exceeding .80, which suggests satisfactory internal reliability. This presents compelling evidence that the scale possesses significance and is appropriate for subsequent investigation.

Distinctiveness among latent variables is a prerequisite (Chin, 1998). For this reason, to ensure the distinction of each latent variable from the others, we have ensured that the model has discriminant validity. The expression of discriminant validity for a latent variable can be represented by the AVE’s square root. The aforementioned task is succeeded by juxtaposing the correlation coefficient of an individual latent variable with that of each and every other latent variable. According to Rahman et al. (2021), it is recommended that the square root of AVE scores of a latent variable should not exceed the total correlation scores between the particular individual variable and all other latent variables. According to the Fornell-Larcker criterion, the diagonal results of Table 3 show the presence of discriminant validity. The diagonal values of the latent variable exhibit greater variability in comparison to the correlation coefficients of other measures. The study also conducted an examination of the discriminant validity pertaining to the Heterotrait-Monotrait ratio (HMR) values. The HMR value being less than .90 (Hock et al., 2010; Rahman et al., 2021) provides evidence for the discriminant’s validity.

Measures of Model’s Goodness of Fit

To establish a baseline for the research hypothesis, the parameters of the structural model were initially assessed to determine the overall fitness of the models. A GFI that is deemed adequate suggests that the proposed covariance structure of the model is highly consistent with that of the data sample. In the context of assessing model fit for sizable samples, the χ² statistic is endowed with statistical power, but its practical utility is negligible. Several goodness-of-fit indices have been suggested as substitutes for the χ². The RMSEA, CFI, the NFI, and are widely recognized as some of the most commonly used measures. The results presented in Table 3 demonstrate that the model achieved a satisfactory level of fit to the data, as evidenced by the values of all indices in Table 4. The results of all the metrics yielded favorable outcomes for pursuing additional research. To evaluate the structure of the model, this study consulted prior works such as Su et al. (2023).

Table 4.

Model’s Goodness of Fit.

Types of indices	SEM results
χ²/df	2.780
GFI	0.919
CFI	0.923
AGFI	0.944
NFI	0.925
RMSEA	0.061

Structural Model’s Outcomes

The metric used to assess the accuracy of the structural model’s future predictions was the “explained variance” (R²). All of the hypotheses exhibit R² values exceeding .50, as presented in Table 5. The study conducted by Wetzels et al. (2009) employed the nonparametric bootstrapping technique to examine the validity of the proposed relationships among the latent variables. All of the hypotheses were confirmed. The results indicate that ECO has a statistically significant positive effect (β = .421, p < .01) on the ecological behavior (EBH) of the participants. The statistical significance of their positive influence is supported by the observation that their t-values exceed the threshold value of 2.32. Furthermore, the results indicated that EKN had a noteworthy impact on EBH (β = .301, p < .01). The statistical analysis revealed that the New Ecological Paradigm (NEP) had a significant impact on EBH (β = .271, p < .01), as evidenced by a t-value that exceeded the critical value of 2.32. This suggests that the effect of NEP on EBH was statistically significant.

Table 5.

Structural Model’s Outcomes.

	β-scores	SD	f ²	Q ²	R ²	Decision
ECO → BH	.421*	0.037	.736	.221	.716	Accepted
EKN → EBH	.301*	0.056	.544	.354	.662	Accepted
NEP → EBH	.271*	0.062	.478	.219	.722	Accepted

Note. ECO = ecological commitment; EKN = environmental knowledge; NEP = new ecological paradigm; ELG = environmental legislation; EBH = ecological behavior.

*p < .01 if t-value is 2.32.

The utilization of the f² value enabled the quantification and evaluation of the effect size of the variables on the ecological behavior of the respondents. Cohen (2013) has established that an effect size can be classified as small if its f² value is in range of .02 to .015, medium if its score is greater than .15 and less than .35, and large if its score is bigger than .35. Based on the results, it can be inferred that ECO, EKN, and NEP exhibit substantial effect magnitudes, as evidenced by their respective f² values of .736, .544, and .478. Furthermore, the computation of Q² was conducted to assess the predictive validity of each hypothesis. Fornell and Cha (1994) exposed that a Q² value greater than zero is imperative to ensure the predictive significance of all structures.

Effect of ELG on the Relationship Between ECO and EBH

Prior to examining the moderating influence of environmental legislation (ELG) on the relationship between ecological commitment (ECO) and EBH, all variables underwent normalization procedures to ensure comparability. The methodology employed in this study was based on the approach suggested by Preacher and Hayes (2004). Table 6 demonstrates the significant and immediate impact of the ECO on the EBH. Furthermore, the impact of households’ familiarity with environmental legislation (β = .21, p < .01) and ECO (β = .32, p < .01) on their EBH was found to be statistically significant. The study found that the interaction impact of the respondents’ environmental legislation and ECO on EBH was significant and positive (β = .254, p < .01). This outcome is significant as it depicts a higher probability of predicting EBH. The present research deals empirical support for the impression that respondents’ familiarity with environmental legislation serves as a moderator for the effect of ECO on EBH.

Table 6.

Effect of ELG on the Relationship Between ECO and EBH.

Variables	β scores	SE	R	R ²	F-value
Environmental legislation (ELG)	.21*	0.0312	.71	.504	51.273*
ECO	.32*	0.0342
ELG × ECO	.25*	0.054

Note. Dependent variable is EBH.

*Describes the significance level at 1%.

Effect of ELG on the Relationship Between EKN and EBH

Table 7 displays the positive impact of Environmental legislation and respondents’ EKN on their EBH. The interaction effect between respondents’ familiarity with environmental legislation and their EKN has a significant impact (β = .221, p < .01) on their behavior, thus moderating the relationship between the two variables.

Table 7.

Effect of ELG on the Relationship Between EKN and EBH.

Variables	β scores	SE	R	R ²	F-value
ELG	.336*	0.0472	.732	.535	49.321
EKN	.173*	0.0309
ELG × EKN	.221*	0.0453

Note. EKN = environmental knowledge; ELG = environmental legislation; EBH = ecological behavior. Dependent variable is EBH.

*Describes the significance level at 1%.

Effect of ELG on the Relationship Between NEP and EBH

The impact of households’ familiarity with environmental legislation (β = .266, p < .01) and NEP (β = .339, p < .01) on their EBH was found to be statistically significant. The study found that the interaction impact of the respondents’ environmental legislation and NEP on EBH was significant and positive (β = .451, p < .01). The present research describes that respondents’ familiarity with environmental legislation serves as a moderator for the effect of NEP on EBH (Table 8).

Table 8.

Effect of ELG on the Relationship Between NEP and EBH.

Variables	β scores	SE	R	R ²	F-value
ELG	.266*	0.038	.783	.613	49.349
NEP	.339*	0.041
ELG × NEP	.451*	0.033

Note. NEP = new ecological paradigm; ELG = environmental legislation; EBH = ecological behavior. Dependent variable is EBH.

*Describes the significance level at 1%.

Discussion

Behavior, especially in the environmental context, is a dependent variable that is influenced by numerous factors. It would be illogical to assume that a single factor would have a direct and straightforward impact on behavior, but this is not necessarily the case. Hence, it is imperative to analyze the impact of ecological commitment (ECO), environmental knowledge (EKN), and the new ecological paradigm (NEP) on ecological behavior while taking into account all plausible confounding variables and direct causal pathways simultaneously, within a single theoretical structure. This study introduces a theoretical framework that analyses the influence of ecological commitment (ECO), environmental knowledge (EKN), and the new ecological paradigm (NEP) on the ecological behavior (EBH) of individuals. Furthermore, the present study investigates the possible moderating impact of environmental legislation (ELG) on the relationship between ecological commitment (ECO), environmental knowledge (EKN), the new ecological paradigm (NEP), and ecological behavior (EBH).

Environmental legislation (ELG) around the world is one of the most important steps for the preservation of the environment. Most important is the familiarity of the people with the environmental legislation towards the development of ecologically oriented behavior. The descriptive results indicated that the respondents are familiar with the environmental legislation of their country. The current study is considered a very important aspect of the ecological behavior of people. First is ecological commitment, because the concept of commitment is a crucial element in any form of relationship, as per Rusbult’s (1980) distinct construct. In this regard, the natural environment can be perceived as a partner in a relationship wherein individuals exhibit varying degrees of commitment. The respondents have strong commitment level with the environment. Furthermore, second, environmental knowledge (ENK), according to Carlson et al. (2009), can be described as precise and retained information. The level of knowledge possessed by an individual is crucial in determining their ability to undertake environmentally conscious actions. The respondents have good knowledge of environmental-related aspects. Third the new ecological paradigm (NEP) statements which described strong beliefs of the respondents regarding the human-environmental relationship. At last, the ecological behavior (EBH) of the respondents was positive, which described that they have environmental-oriented behavior.

The study’s results suggest that the proposed model produced noteworthy path coefficients in every case when evaluated for structural outcomes. The current research emphasizes the noteworthy and affirmative impact of ECO on ecological behavior, suggesting that individuals who exhibit a deep commitment to the environment are more inclined towards ecological behavior. The available empirical data provides support for the hypothesis positing a beneficial relationship between the ECO and EBH. Individuals who demonstrate a greater level of commitment are inclined to exhibit behavior that is more focused on ecological concerns. The findings of our study are consistent with those of Davis et al. (2011), who similarly reported that the ECO variable serves as a noteworthy indicator of the EBH outcome. According to the statement, people undergo a personal level of commitment towards their natural surroundings, which is characterized as a psychological bond and a sustained inclination towards the natural realm. The level of an individual’s commitment to the environment is directly proportional to their understanding of their dependence on the natural environment for their own well-being. In the context of a relationship, individuals who are committed may experience the desire to prioritize their immediate self-interest when presented with alternative behaviors (Davis et al., 2009).

According to Carmi et al. (2015), ecological behavior (EBH) is impacted in a positive manner by environmental knowledge (EKN). Therefore, it is imperative to possess a deeper knowledge of the environment and its associated concerns to effectively facilitate behavior al change. Thus, EBH pertains to purposeful conduct aimed at promoting or averting the degradation of the natural world (Kollmuss & Agyeman, 2002), thereby highlighting the significance of knowledge to facilitate this kind of behavior (Frick et al., 2004; Levine & Strube, 2012). According to Duerden and Witt (2010), the dissemination of knowledge has emerged as a prevalent objective in various interventions that seek to induce modifications in behavior. The study conducted by Fraj-Andrés and Martínez-Salinas (2007) revealed a significant relationship between the extent of environmental knowledge (EKN) and the level of consumers’ ecological behavior (EBH).

The new ecological paradigm (NEP) affected ecological behavior (EBH) positively. It demonstrates that the human-environment relationship is not independent of their beliefs towards nature. Personal beliefs are an important predictor of personal behavior towards the environment. Human beings rely on a natural environment that maintains ecological equilibrium, and it is widely accepted that there is a finite threshold to economic expansion. While attitude-behavior models primarily center on beliefs associated with a specific object or behavior, as noted in previous studies (Lutz, 1991), the NEP framework encompasses broader beliefs concerning the global environment (Xiao & Buhrmann, 2017). Whether denoted as higher-order beliefs or more fundamental ecological beliefs, these beliefs reflect our perception of the appropriate human-environment relationship. There are multiple justifications for proposing that the implementation of NEP may have a significant impact on a consumer’s pro-environmental behavior across various domains. Initially, and in accordance with the analogous reasoning that is applicable to attitude-behavior models (Lutz, 1991), it can be stated that individuals tend to behave in a manner that aligns with their beliefs. Regarding the NEP, it suggests that individuals are inclined to show behavior that upholds a harmony between their beliefs and behavior s, and actions that conflict with this harmony may lead to mental dissonance or disparities (Derdowski et al., 2020). Numerous studies have demonstrated an important relationship between NEP and behavior al intentions, as well as a range of self-identified and noticed pro-environmental behaviors (Polonsky et al., 2014; Stern et al., 1995). The study conducted by Pahl et al. (2005) revealed that the New Ecological Paradigm (NEP) had a statistically significant and positive impact on 21 self-reported environmental behaviors. The findings suggest a positive correlation between the endorsement of NEP and the reported frequency of pro-environmental behavior s among participants. Xiao et al. (2019) assert that NEP is the most commonly employed metric of environmental concern in scholarly research. The authors further contend that numerous studies that have utilized NEP as a determinant of environmental behavior have consistently demonstrated its effectiveness as a robust predictor of behavior. The New Environmental Paradigm (NEP) has been utilized in research pertaining to tourism and recreation. Wurzinger and Johansson’s (2006) study revealed a significant correlation between the adoption of NEP and engagement in ecotourism activities that satisfy all the relevant criteria. The study conducted by Hedlund (2011) revealed that NEP exhibited a noteworthy predictive capability concerning the inclination to make economic concessions to safeguard the environment as well as the intention to purchase ecological tourism options. According to Mair’s (2011) research, individuals who exhibited high levels of environmental concern, as measured by the New Environmental Paradigm (NEP), were more inclined to engage in environmentally responsible behavior, such as voluntarily offsetting the carbon emissions resulting from their travel activities.

The present investigation aimed to explore the moderating effect of acquaintance with environmental legislation on the association between ecological commitment (ECO), environmental knowledge (EKN), the new ecological paradigm (NEP), and ecological behavior (EBH). Based on the results, it can be inferred that the implementation of environmental legislation holds promise for enhancing the connection between ECO, EKN, and NEP and ecological behavior among the general public. The adoption of environmentally protective behavior by residents on a daily basis is primarily motivated by their own values and objectives rather than legal restrictions (Preacher & Hayes, 2004). The regulations and laws are noteworthy, as is the approach taken to enforce them. Akella and Cannon (2017) and Dongol and Heinen (2012) suggest that the efficacy of law is directly correlated with the degree to which they are rigorously implemented. The efficacy of regulations is positively correlated with the level of trust and compliance they elicit from individuals. Through collaborative efforts between the government and society, it is possible to streamline the process of adhering to ethical standards. The government assumes a vital responsibility in guaranteeing the enforcement of the aforementioned laws (Akella & Cannon, 2017; Dongol & Heinen, 2012). Hence, a comprehensive understanding of environmental law plays a significant role in regulating the relationship between environmental commitment, ecological knowledge, the new ecological paradigm, and corresponding behavior.

Conclusions and Implications

Modern ecological behavioral research consistently underscores the incongruity between individuals’ knowledge, beliefs, and environmental commitments and their corresponding behavior. The aforementioned incongruity is commonly denoted as the divergence between individuals’ behavior and their commitment, beliefs, and ecological knowledge. The closing of gaps in order to encourage greater participation in voluntary pro-environmental actions presents a significant theoretical and practical challenge for those seeking to mobilize individuals. Furthermore, the significance of the legislation is paramount in daily lifestyles. Therefore, it serves as a means of regulating and inhibiting individuals’ actions, providing a lawful motivation for ethical behavior. The legislative structure exerts a significant influence on the operational practices within the realm of environmental management. The crux of this manuscript lies in the unique significance that a select few personality traits hold in comprehending the demonstrated interpersonal interactions. The moderating effect of the individual’s understanding of environmental laws on the relationship between their ecological commitment (ECO), ecological knowledge (EKN), New Ecological Paradigm (NEP), and adopted ecological behavior has been a topic of debate and experimentation.

The participants exhibited a deep commitment to their environment. They possess an in-depth knowledge of the natural world. Likewise, the ecological paradigm is characterized by distinct beliefs concerning the relationship between humans and the environment. The participants also reported a moderate-to-high degree of familiarity with the country’s environmental legislation. Their daily actions indicated good ecological behavior. The results of the study demonstrate that individuals’ ecological behavior is significantly impacted by their level of environmental commitment. Likewise, the extent of an individual’s environmental knowledge has a more pronounced impact on their ecological conduct, and the adoption of the new ecological paradigm also exerts a greater sway on individuals’ ecological behavior. Moreover, the confirmation of the moderating role of environmental legislation indicates that as an individual’s familiarity with environmental legislation increases, there is a gradual increase in the direct impact of their commitment, knowledge, and beliefs on ecological behavior s. Consequently, the findings of the present investigation indicate that the less stringent compulsory legislation may also exert a significant influence on people’s behavior s with regard to preserving a sustainable ecosystem. Drawing from the results, it is possible to suggest the subsequent policy implications:

The paramount concern of society ought to center on devising strategies to enhance individuals’ awareness regarding the importance of the environment. In addition, it is imperative for local governmental bodies and organizations to actively engage in promoting awareness among the populace. The implementation of regular awareness programs at the local level by the government and organizations can enhance individuals’ knowledge, beliefs, and commitment, which significantly impacts their ecological behavior.

In the realm of environmental legislation, the degree of familiarity one has with the subject at hand is a crucial factor in moderating the relationship between an individual’s level of commitment, beliefs, and knowledge and their subsequent behavior towards the environment. The municipal administration has the potential to offer monetary incentives and brief educational sessions at educational institutions, such as schools and colleges, as well as high-traffic areas within urban centers, to encourage individuals to embrace eco-friendly behaviors aimed at mitigating environmental degradation.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440241271073 – Supplemental material for Examining the Relationship Between Ecological Commitment, Environmental Knowledge, and Farmers’ Nature-Friendly Behavior: Investigating the Moderating Influence of Environmental Legislation

Supplemental material, sj-docx-1-sgo-10.1177_21582440241271073 for Examining the Relationship Between Ecological Commitment, Environmental Knowledge, and Farmers’ Nature-Friendly Behavior: Investigating the Moderating Influence of Environmental Legislation by Wei Wang, Pomi Shahbaz, Babar Aziz and Muhammad Nadeem in SAGE Open

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

Ethical Approval

The study was approved by Nanjing University of Aeronautics and Astronautics,Nanjing,China (2023-787)

ORCID iD

Pomi Shahbaz

Data Availability Statement

The data can be obtained from the corresponding author on reasonable request.

Supplemental Material

Supplemental material for this article is available online.

References

Agnew

C. R.

Van Lange

P. A. M.

Rusbult

C. E.

Langston

C. A.

(1998). Cognitive interdependence: Commitment and the mental representation of close relationships. Journal of Personality and Social Psychology, 74, 939–954.

Akella

A. S.

Cannon

J. B.

(2017). Strengthening the weakest links: Strategies for improving the enforcement of environmental laws globally. In White

(Ed.), Transnational environmental crime (pp. 459–492). Routledge.

Alharbi

O. M. L.

Basheer

A. A.

Khattab

R. A.

Ali

(2018). Health and environmental effects of persistent organic pollutants. Journal of Molecular Liquids, 263, 442–453.

Awan

A. G.

(2013). Relationship between environment and sustainable economic development: A theoretical approach to environmental problems. International Journal of Asian Social Science, 3(3), 741–761.

Baumgart-Getz

Prokopy

L. S.

Floress

(2012). Why farmers adopt best management practice in the United States: A meta-analysis of the adoption literature. Journal of Environmental Management, 96(1), 17–25.

Blackstock

K. L.

Ingram

Burton

Brown

K. M.

Slee

(2010). Understanding and influencing behaviour change by farmers to improve water quality. Science of the Total Environment, 408(23), 5631–5638.

Burton

R. J. F.

Wilson

G. A.

(2006). Injecting social psychology theory into conceptualizations of agricultural agency: Towards a “post-productivist” farmer self-identity. Journal of Rural Studies, 22(1), 95–115.

Byrne

B. M.

(2013). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge.

Byrne

B. M.

Stewart

S. M.

(2006). Teacher’s corner: The MACS approach to testing for multigroup invariance of a second-order structure: A walk through the process. Structural Equation Modeling, 13, 287–321.

10.

Calel

Antoine

(2012). Environmental policy and directed technological change: Evidence from the European carbon market. Review of Economics & Statistics, 98(1), 551–574.

11.

Carlson

J. P.

Vincent

L. H.

Hardesty

D. M.

Bearden

W. O.

(2009). Objective and subjective knowledge relationships: A quantitative analysis of consumer research findings. Journal of Consumer Research, 35, 864–876.

12.

Carmi

Arnon

Orion

(2015). Transforming environmental knowledge into behavior: The mediating role of environmental emotions. The Journal of Environmental Education, 46(3), 183–201.

13.

Chapin

F. S.

III Power

M. E.

Pickett

S. T.

Freitag

Reynolds

J. A.

Jackson

R. B.

Lodge

D. M.

Duke

Collins

S. L.

Power

A. G.

Bartuska

(2011). Earth stewardship: Science for action to sustain the human-earth system. Ecosphere, 2(8), 1–20.

14.

Cheah

J. H.

Sarstedt

Ringle

C. M.

Ramayah

Ting

(2018). Convergent validity assessment of formatively measured constructs in PLS-SEM: On using single-item versus multi-item measures in redundancy analyses. International Journal of Contemporary Hospitality Management, 30, 3192–3210.

15.

Chen

Huang

Sun

Hao

(2020). How does new environmental law affect public environmental protection activities in China? Evidence from structural equation model analysis on legal cognition. Science of the Total Environment, 714, 136558.

16.

Chin

W. W.

(1998). The partial least squares approach for structural equation modeling. In Marcoulides

G. A.

(Ed.), Modern methods for business research (pp. 295–336). Lawrence.

17.

Chin

W. W.

(2009). How to write up and report PLS analyses. In Esposito

Chin

Henseler

Wang

(Eds.), Handbook of partial least squares (pp. 655–690). Springer.

18.

Cohen

(2013). Statistical power analysis for the behavioral sciences. Academic Press.

19.

Costantini

Mazzanti

(2012). On the green and innovative side of trade competitiveness? The impact of environmental policies and innovation on EU exports. Research Policy, 41(1), 132–153.

20.

Cronbach

L. J.

(1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334

21.

Cui

Zhang

Zheng

(2018). Carbon pricing induces innovation: Evidence from China’s regional carbon market pilots. AEA Papers and Proceedings, 108, 1–5.

22.

Davis

J. L.

Green

J. D.

Reed

(2009). Interdependence with the environment: Commitment, interconnectedness, and environmental behavior. Journal of Environmental Psychology, 29(2), 173–180.

23.

Davis

J. L.

Coy

A. E.

(2011). Building a model of commitment to the natural environment to predict ecological behavior and willingness to sacrifice. Journal of Environmental Psychology, 31(3), 257–265.

24.

Derdowski

L. A.

Grahn

Å. H.

Hansen

Skeiseid

(2020). The new ecological paradigm, pro-environmental behaviour, and the moderating effects of locus of control and self-construal. Sustainability, 12(18), 7728.

25.

Dongol

Heinen

J. T.

(2012). Pitfalls of CITES implementation in Nepal: A policy gap analysis. Environmental Management, 50, 181–190.

26.

Duerden

M. D.

Witt

P. A.

(2010). The impact of direct and indirect experiences on the development of environmental knowledge, attitudes, and behavior. Journal of Environmental Psychology, 30(4), 379–392.

27.

Dunlap

R. E.

Liere

K. D. V.

(1978). The “new environmental paradigm”. The Journal of Environmental Education, 9, 10–19.

28.

Fan

Chen

Shirkey

John

S. R.

Park

Shao

(2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5, 1–12.

29.

Festinger

(1957). A theory of cognitive dissonance. Stanford University Press.

30.

Fielding

K. S.

Head

B. W.

(2012). Determinants of young Australians’ environmental actions: The role of responsibility attributions, locus of control, knowledge and attitudes. Environmental Education Research, 18, 171–186.

31.

Fornell

Cha

(1994). Partial least squares. In Bagozzi

R. P.

(Ed.), Advanced methods of marketing research (pp. 52–78). Blackwell.

32.

Fraj-Andrés

Martínez-Salinas

(2007). Impact of environmental knowledge on ecological consumer behaviour: An empirical analysis. Journal of International Consumer Marketing, 19(3), 73–102.

33.

Fredriksson

P. G.

Matschke

Minier

(2010). Environmental policy in majoritarian systems. Journal of Environmental Economics and Management, 59(2), 177–191. https://doi.org/10.1016/j.jeem.2009.10.001

34.

Frick

Kaiser

F. G.

Wilson

(2004). Environmental knowledge and conservation behavior: Exploring prevalence and structure in a representative sample. Personality and Individual Differences, 37(8), 1597–1613.

35.

Gao

Wang

Feng

(2019). Biogas potential, utilization and countermeasures in agricultural provinces: A case study of biogas development in Henan Province, China. Renewable and Sustainable Energy Reviews, 99, 191–200.

36.

Gerba

C. P.

Brusseau

M. L.

(2019). Environmental laws and regulations. In Brusseau

M. L.

Pepper

Gerba

(Eds.), Environmental and pollution science (pp. 565–572). Academic Press.

37.

Grace

J. B.

(2006). Structural equation modeling and natural systems. Cambridge University Press.

38.

Guo

Wang

Zhang

A. T.

(2020). Pollution exposure and willingness to pay for clean air in urban China. Journal of Environmental Management, 261, 110174.

39.

Hair

J. F.

Sarstedt

Ringle

C. M.

Mena

J. A.

(2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433.

40.

Hair

J. F.

Thomas

Hult

Ringle

C. M.

Sarstedt

(2017). A primer on partial least squares structural equation modeling (2nd ed.). Sage.

41.

Hammett

(2010). Reworking and resisting globalising influences: Cape Town Hip-Hop. GeoJournal, 10, 1–12.

42.

Hammitt

W. E.

Backlund

E. A.

Bixler

R. D.

(2006). Place bonding for recreational places: Conceptual and empirical development. Leisure Studies, 25(1), 17–41.

43.

Han

Hsu

L. T.

Sheu

(2010). Application of the theory of planned behavior to green hotel choice: Testing the effect of environmental friendly activities. Tourism Management, 31, 325–334.

44.

Hedlund

(2011). The impact of values, environmental concern, and willingness to accept economic sacrifices to protect the environment on tourists’ intentions to buy ecologically sustainable tourism alternatives. Tourism and Hospitality Research, 11, 278–288.

45.

Heider

(1958). The psychology of interpersonal relations. Wiley.

46.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity invariance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135.

47.

Hines

J. M.

Hungerford

H. R.

Tomera

A. M.

(1987). Analysis and synthesis of research on responsible pro-environmental behavior: A meta-analysis. The Journal of Environmental Education, 18(2), 1–8.

48.

Hock

Ringle

C. M.

Sarstedt

(2010). Management of multi-purpose stadiums: Importance and performance measurement of service interfaces. International Journal of Services Technology and Management, 14, 188–207.

49.

Hsu

S. J.

(2004). The effects of an environmental education program on responsible environmental behavior and associated environmental literacy variables in Taiwanese college students. The Journal of Environmental Education, 35(2), 37–48.

50.

Hystad

Duong

Brauer

Larkin

Arku

Kurmi

O. P.

Fan

W. Q.

Avezum

Azam

Chifamba

Dans

(2019). Health effects of household solid fuel use: Findings from 11 countries within the prospective urban and rural epidemiology study. Environmental Health Perspectives, 127(5), 057003. https://doi.org/10.1289/EHP3915

51.

Johnson

Eagly

(1989). Effects of involvement on persuasion: A meta-analysis. Psychological Bulletin, 106, 290.

52.

Karlsson

(2007). Allocating responsibilities in multi-level governance for sustainable development. International Journal of Social Economics, 34(1/2), 103–126.

53.

Kelley

H. H.

Thibaut

(1978). Interpersonal relations: A theory of interdependence. Wiley.

54.

Khan

M. K.

Khan

M. I.

Rehan

(2020). The relationship between energy consumption, economic growth and carbon dioxide emissions in Pakistan. Financial Innovation, 6(1), 1–13.

55.

Kline

R. B.

(2023). Principles and practice of structural equation modeling. Guilford Publications.

56.

Kollmuss

Agyeman

(2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environmental Education Research, 8(3), 239–260.

57.

Agnew

C. R.

(2003). Commitment and its theorized determinants: A meta-analysis of the investment model. Personal Relationships, 10, 37–57.

58.

Levine

D. S.

Strube

(2012). Environmental attitudes, knowledge, intentions and behaviors among college students. Journal of Social Psychology, 152(3), 308–326.

59.

Yang

Wei

Zhang

(2019). Authoritarian environmentalism and environmental policy implementation in China. Resources, Conservation and Recycling, 145, 86–93. https://doi.org/10.1016/j.resconrec.2019.02.011

60.

Liu

Ouyang

Miao

(2010). Environmental attitudes of stakeholders and their perceptions regarding protected area-community conflicts: A case study in China. Journal of Environmental Management, 91, 2254–2262

61.

(2012). Is there a pollution haven effect? Evidence from a natural experiment in China. Elsevier. http://dx.doi.org/10.2139/ssrn.2057475

62.

Lutz

R. J.

(1991). The role of attitude theory in marketing. In Kassarjian

H. H.

Robertson

T. S.

(Eds.), Perspectives in consumer behaviour (pp. 233–250). Prentice-Hall.

63.

Shahbaz

Haq

S. U.

Boz

(2023). Exploring the moderating role of environmental education in promoting a clean environment. Sustainability, 15, 8127. https://doi.org/10.3390/su15108127

64.

Mair

(2011). Exploring air travellers’ voluntary carbon-offsetting behaviour. Journal of Sustainable Tourism, 19, 215–230.

65.

Morrone

Mancl

Carr

(2001). Development of a metric to test group differences in ecological knowledge as one component of environmental literacy. The Journal of Environmental Education, 32(4), 33–42.

66.

Nielsen

K. S.

(2017). From prediction to process: A self-regulation account of environmental behavior change. Journal of Environmental Psychology, 51, 189–198. https://doi.org/10.1016/j.jenvp.2017.04.002.

67.

Osburg

V.-S.

Yoganathan

Brueckner

Toporowski

(2019). How detailed product information strengthens eco-friendly consumption. Management Decision, 58, 1084–1099.

68.

Pahl

Harris

P. R.

Todd

H. A.

Rutter

D. R.

(2005). Comparative optimism for environmental risks. Journal of Environmental Psychology, 25, 1–11.

69.

Polonsky

M. J.

Vocino

Grimmer

Miles

M. P.

(2014). The interrelationship between temporal and environmental orientation and pro-environmental consumer behaviour. International Journal of Consumer Studies, 38, 612–619

70.

Preacher

K. J.

Hayes

A. F.

(2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. https://doi.org/10.3758/BF03206553

71.

Pretty

(2008). Agricultural sustainability: Concepts, principles and evidence. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1491), 447–465.

72.

Rahman

M. K.

Gazi

M. A. I.

Bhuiyan

M. A.

Rahaman

M. A.

(2021). Effect of Covid-19 pandemic on tourist travel risk and management perceptions. PLoS One, 16(9), e0256486.

73.

Ran

(2013). Perverse incentive structure and policy implementation gap in China’s local environmental politics. The Journal of Environmental Policy and Planning, 15(1), 17–39. https://doi.org/10.1080/1523908X.2012.752186

74.

Raymore

L. A.

(2002). Facilitators to leisure. Journal of Leisure Research, 34, 37.

75.

Reinartz

Haenlein

Henseler

(2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26, 332–344.

76.

Ringle

DaSilva

Bido

(2014). Structural equation modeling with the Smart PLS. Brazilian Journal of Marketing, 13, 56–73.

77.

Ritter

A. M.

Borchardt

Vaccaro

G. L. R.

Pereira

G. M.

Almeida

(2015). Motivations for promoting the consumption of green products in an emerging country: Exploring attitudes of Brazilian consumers. Journal of Cleaner Production, 106, 507–520.

78.

Rusbult

C. E.

(1980). Commitment and satisfaction in romantic associations: A test of the investment model. Journal of Experimental Social Psychology, 16, 172–186.

79.

Rusbult

C. E.

Arriaga

X. B.

(2000). Interdependence in personal relationships. In Ickes

Duck

(Eds.), The social psychology of personal relationships (pp. 79–108). Wiley.

80.

Rusbult

C. E.

Olson

Davis

J. L.

Hannon

M. A.

(2001). Commitment and relationship maintenance mechanisms. In Harvey

J. H.

Wenzel

(Eds.), Close relationships: Maintenance and enhancement (pp. 87–113). Erlbaum.

81.

Scanlon

S. M.

(2002). Should Missouri farmers of genetically modified crops be held liable for genetic drift and cross-pollination? Missouri Environmental Law & Policy Review, 10, 1–213.

82.

Schahn

Holzer

(1990). Studies of individual environmental concern: The role of knowledge, gender, and background variables. Environment and Behavior, 22, 767–786.

83.

Sendawula

Bagire

Mbidde

C. I.

Turyakira

(2021). Environmental commitment and environmental sustainability practices of manufacturing small and medium enterprises in Uganda. Journal of Enterprising Communities: People and Places in the Global Economy, 15(4), 588-607.

84.

Shen

Wang

Liao

(2020). The effect of environmental policy tools on regional green innovation: Evidence from China. Journal of Cleaner Production, 254, 120122.

85.

Shipley

(2016). Cause and correlation in biology: A user’s guide to path analysis, structural equations and causal inference with R. Cambridge University Press.

86.

Sia

A. P.

Hungerford

H. R.

Tomera

A. N.

(1986). Selected predictors of responsible environmental behavior: An analysis. The Journal of Environmental Education, 17(2), 31–40.

87.

Singh

Prasad

(2018). Application of PLS-SEM in modeling the significance of social valuation in the determination of entrepreneurial intention of business management students. The IUP Journal of Entrepreneurship Development, 15, 7–25.

88.

Stern

P. C.

(2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407–424.

89.

Stern

P. C.

Dietz

Guagnano

G. A.

(1995). The new ecological paradigm in social-psychological context. Environment and Behavior, 27, 723–743.

90.

Stokols

(1981). Group x place transactions: Some neglected issues in psychological research on settings. In Magnusson

(Ed.), Toward a psychology of situations: An interactional perspective (pp. 393–415). Lawrence Erlbaum.

91.

Imran

Nadeem

Haq

S. U.

(2023). The role of environmental law in farmers’ environment-protecting intentions and behavior based on their legal cognition: A case study of Jiangxi Province, China. Sustainability, 15(11), 8571.

92.

Sutherland

L. A.

(2010). Environmental grants and regulations in strategic farm business decision-making: A case study of attitudinal behaviour in Scotland. Land Use Policy, 27, 415–423.

93.

Taber

K. S.

(2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296.

94.

Ul Haq

Boz

(2020). Measuring environmental, economic, and social sustainability index of tea farms in Rize Province, Turkey. Environment, Development and Sustainability, 22, 2545–2567.

95.

Ul Haq

Shahbaz

Abbas

Alhafi Alotaibi

Nadeem

Nayak

R. K.

(2023). Looking up and going down: Does sustainable adaptation to climate change ensure dietary diversity and food security among rural communities or vice versa?. Frontiers in Sustainable Food Systems, 7, 1142826.

96.

Van Lange

P. A. M.

Rusbult

C. E.

Drigotas

S. M.

Arriaga

X. B.

Witcher

B. S.

Cox

C. L.

(1997). Willingness to sacrifice in close relationships. Journal of Personality and Social Psychology, 72, 1373–1395.

97.

Vaske

J. L.

Kobrin

K. C.

(2001). Place attachment and environmentally responsible behavior. The Journal of Environmental Education, 32(4), 16–21.

98.

Wang

(2009). Thirty years’ rule of environmental law in China: Retrospect and reassessment. Journal of China University of Geosciences (Social Sciences Edition), 9, 3–9. (In Chinese.)

99.

Wang

Yang

(2016). Industrial water pollution, water environment treatment, and health risks in China. Environmental Pollution, 218, 358–365. https://doi.org/10.1016/j.envpol.2016.07.011

100.

Wetzels

Odekerken-Schroder

Van Oppen

(2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.

101.

Wilson

G. A.

Hart

(2001). Farmer participation in agri-environmental schemes: Toward conservation-oriented thinking? Sociologia Ruralis, 41(2), 254–274.

102.

Wurzinger

Johansson

(2006). Environmental concern and knowledge of ecotourism among three groups of Swedish tourists. Journal of Travel Research, 45, 217–226.

103.

Xiao

Buhrmann

(2017). The structure and coherence of the new environmental paradigm: Reconceptualizing the dimensionality debate. Human Ecology Review, 23, 179–198.

104.

Xiao

Dunlap

R. E.

Hong

(2019). Ecological world view as the central component of environmental concern: Clarifying the role of the NEP. Society & Natural Resources, 32, 53–72.

105.

Xie

Dai

Dong

Hanaoka

Masui

(2016). Economic impacts from PM2. 5 pollution-related health effects in China: A provincial-level analysis. Environmental Science & Technology, 50(9), 4836–4843. https://doi.org/10.1021/acs.est.5b05576

106.

Yang

Weber

(2019). Who can improve the environment—Me or the powerful others? An integrative approach to locus of control and pro-environmental behavior in China. Resources, Conservation and Recycling, 146, 55–67. https://doi.org/10.1016/j.resconrec.2019.03.005

107.

Wang

(2013). How should the center lead China’s reforestation efforts?—Policy making games between central and local governments. Resources, Conservation and Recycling, 80, 64–84. https://doi.org/10.1016/j.resconrec.2013.09.001

108.

Zhan

C. W. H.

Tang

S. Y.

(2013). Contextual changes and environmental policy implementation: A longitudinal study of street-level bureaucrats in Guangzhou, China. Journal of Public Administration Research and Theory, 24(4), 1005–1035. https://doi.org/10.1093/jopart/mut004

109.

Zhang

Chen

Guo

(2018). Does central supervision enhance local environmental enforcement? Quasi-experimental evidence from China. Journal of Public Economics, 164, 70–90.

110.

Qian

Baozhong

(2021). The consumption patterns and determining factors of rural household energy: A case study of Henan Province in China. Renewable and Sustainable Energy Reviews, 146, 111142.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

Examining the Relationship Between Ecological Commitment,Environmental Knowledge,and Farmers’ Nature-Friendly Behavior: Investigating the Moderating Influence of Environmental Legislation

Abstract

Keywords

Introduction

Hypothesis Development

Materials and Methods

Study Area and Sampling Procedure

Empirical Model Specification

Results

Descriptive Analysis of Individual Items and Constructs

Testing Validity of Measurement Model

Measures of Model’s Goodness of Fit

Structural Model’s Outcomes

Effect of ELG on the Relationship Between ECO and EBH

Effect of ELG on the Relationship Between EKN and EBH

Effect of ELG on the Relationship Between NEP and EBH

Discussion

Conclusions and Implications

Supplemental Material

sj-docx-1-sgo-10.1177_21582440241271073 – Supplemental material for Examining the Relationship Between Ecological Commitment, Environmental Knowledge, and Farmers’ Nature-Friendly Behavior: Investigating the Moderating Influence of Environmental Legislation

Footnotes

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iD

Data Availability Statement

Supplemental Material

References

Supplementary Material