Interconnections Between Environmental Awareness and Green Technology Adoption: Empirical Evidence from Informal Business Enterprises

Abstract

Environmental awareness is widely recognized as a key factor of environmentally friendly behavior, especially as human activities persist in exacerbating global environmental issues. While previous research has largely focused on environmental regulations to promote green technology, such approaches often fall short in developing countries due to weak enforcement mechanisms and the prominence of informal economic activities. This study takes a different approach by exploring how environmental awareness can foster the adoption of green technology in informal manufacturing enterprises, thereby enhancing both environmental and social outcomes. Enterprise-level survey data, collected from a major city in a developing country, serves as the basis for this analysis. The survey captures information related to knowledge attitudes and the behavioral practices of owners or managers with respect to the environment, as well as pollution and its management. Utilizing the collected data, and guided by established theoretical frameworks, the study develops an environmental awareness (EA) index. This index is then applied in probit and logit models to estimate its effect on the likelihood of adopting pollution-reducing technologies. The marginal effect analysis reveals that informal SMEs with a higher environmental awareness are 28.5% more likely to adopt green technologies. This probability increases to 30.1% when demographic- and business-related variables are incorporated into the model. Based on empirical findings, this study recommends targeted investments in awareness building initiatives, alongside long-term educational and training programs for enterprise owners and managers to instill environmental values and practices across operations. Given the financial constraints faced by informal enterprises, this study also recommends both public and private sector support to make this transition feasible and sustainable.

1. Introduction

Environmental awareness (EA) is widely considered a prerequisite for environmentally friendly behavior [1,2,3]. It is defined as the conception of environmental sensitivity through an individual’s conscious discernment of environmental problems, and as behaving and taking environmental safeguards accordingly [4,5]. Consequently, an individual’s environmental awareness is crucial for preventing environmentally harmful economic activities, and increasing its levels can help mitigate the adverse effects of human activities on the environment [6]. The development of personal characteristics that promote environmental consciousness is decisive in addressing environmental challenges resulting from the production and consumption patterns established since the industrial revolution, which were further intensified by capitalism in the 20th century [7,8,9,10]. Beyond acquiring knowledge and adopting behaviors conducive to environmental protection, individuals must also cultivate a caring attitude toward the environment and actively seek to minimize harm [3,5,11]. The adoption of green technology exemplifies such an environmentally conscious orientation. However, decisions regarding the adoption of green technology are often influenced by conflicts between social and private incentives, since private agents typically lack sufficient motivation to develop, adopt, or innovate green technologies, even when these technologies generate substantial social benefits. As a result, the adoption and diffusion of green technologies are often constrained, despite their potential to reduce external costs, as these costs are not aligned with a reduction in the private costs borne by firms. Nevertheless, given the significant pollution associated with industrial production, the widespread dissemination of pro-environmental or green technologies is the ultimate solution to addressing environmental concerns [12,13,14].

In developing countries, governments are under pressure to improve their production procedures, and they typically respond to this by placing rigorous environmental regulations on formal sector production, even though both the formal and informal sector industries contribute significantly to pollution [15,16,17]. These countries are characterized by a substantial informal sector, largely composed of low-technology small- and medium-sized manufacturing enterprises (SMEs) that operate outside the purview of government regulations [18,19,20]. Many of these business enterprises directly expose themselves to harmful emissions by neglecting to adopt pollution control equipment or proper waste disposal practices. Managing environmental issues in this sector is particularly challenging [18,21,22,23]. Informal sector enterprises often evade environmental regulations [24,25], as traditional command and control regulations—especially those relying on peer monitoring—are largely ineffective in this context [21,26]. Thus, the dominance of the informal sector, combined with the weak and incomplete enforcement mechanisms in developing countries, hampers the complete and effective enforcement of regulations addressing pollution, occupational health, and safety [15,21]. Consequentially, the “Porter hypothesis,” which posits that stringent environmental legislation spurs innovations and advances in abatement technologies [27,28], appears inapplicable within the context of developing countries.

Against this backdrop, innovative approaches are needed in the environmental management systems of developing countries, particularly for those with a dominance of informal sector activities [19]. Cognitive factors—such as values, awareness, motivation, intention, and self-efficacy, which are linked to moral character and pro-environmental principles—can play a significant role in fostering sustainable value creation [29,30,31]. This study focuses on these factors for environmental management in informal businesses, with a precise emphasis on environmental awareness, by constructing an environmental awareness (EA) index. According to behavioral change models, when individuals or organizations possess higher levels of awareness, they are more likely to act in environmentally responsible ways [32]. However, the relationship between environmental awareness and pro-environmental behavior remains contested. [For example, refs. [33,34] found that environmental awareness promotes pro-environmental behavior, whereas ref. [1] reported no significant influence of awareness on such behavior.] To contribute to this ongoing debate, the present study investigates this nexus for small and medium business enterprises (SMEs) in the informal sector of Bangladesh through the construction of an environmental awareness (EA) index.

Bangladesh is widely recognized as a country where the informal sector plays a pivotal role in economic activity. Approximately 99% of its industrial units are micro-, small-, or medium-sized enterprises (SMEs), with a significant proportion operating informally [35]. These informally operated manufacturing enterprises are mostly located in the surrounding areas of Dhaka city, and other major cities, and are strongly associated with unplanned urbanization and environmental crises [36]. Informal SMEs are frequently responsible for the uncontrolled disposal of waste, contributing to environmental degradation and serious public health concerns in urban Bangladesh [37,38]. While the environmental impact of each individual enterprise may be relatively small, their cumulative effect is substantial to the environment due to their dominance in the business landscape of developing countries, including Bangladesh [39]. For this reason, SME leadership in sustainable development has been identified as crucial [40]. However, SMEs are often viewed as laggards in terms of their commitment to sustainability [40,41]. Therefore, the owners or managers of SMEs are increasingly under pressure to enhance their environmental management practices [39]. Yet, because these practices impose additional production costs, many SMEs that are already operating at marginal levels of profitability tend to avoid them, thereby externalizing costs onto the environment and surrounding community.

The present study contributes to addressing environmental concerns in informal enterprises by focusing on raising awareness among SME owners/managers. An increased level of awareness is expected to translate into self-motivation for pollution/emission control, which leads to the adoption of green technologies. This research makes several contributions to existing knowledge. First, it provides valuable insights into a new context—the informal sector—through the use of a pilot data set. Second, an index of environmental awareness (EA) is developed for SME stakeholders operating informally, based on indicators reflecting behavioral dimensions of environmental literacy, concerns, and practices. In addition, a complementary index is constructed to assess the general perception of environmental management among the same group of enterprises. These assessments provide rare empirical evidence on an underexplored and largely unaccounted-for sector of the economy, offering insights that can guide advanced research on environmental management and inform future environmental policies. Third, by employing qualitative response models, this study explicitly takes into account firms’ decision-making regarding technology adoption for environmental protection. It examines how such decisions are shaped by the awareness level of SME owners/managers, thereby revealing the responsiveness of informal enterprises to environmental awareness. Finally, the findings provide policymakers with baseline information on the effectiveness of indirect environmental interventions in the conservation strategy of developing countries.

2. Literature Review

A study [16] has argued that sustaining environmental concern, which is influenced by the level of education, knowledge about the environment, and the perceived environmental burden, is crucial for environmental protection. In this study, a multilevel analysis of the International Social Survey Program data for 1993 and 2000 highlighted that concern for the environment varied more within countries than between countries. This outcome suggested that national averages might hide significant internal differences and that contextual as well as individual-level factors might contribute to shaping concern for the environment. Another cross-national study [42] structured an environmental awareness index (EAI) and revealed a higher awareness in European countries and a weaker awareness in countries near the Equator. Their EAI approach, which combined motivation, knowledge, and skills, correlated more effectively with national-level indicators compared to EA that only considered environmental issues. Still, a geographic generalization might risk overlooking intra-regional disparities. Looking at the country level, ref. [43] assessed the state of environmental awareness of Omani citizens from three perspectives, environmental knowledge, environmental attitudes, and environmental behaviors, using a questionnaire-based survey aiming to investigate the willingness to contribute to environmental protection. The results revealed a lack of basic environmental knowledge among the public, in general, despite a greater knowledge of local and international environmental issues. This finding exposed a gap between a broader sense of awareness and a practical understanding of environmental practices. Similarly, ref. [44] examined the EA of citizens in China and Japan through a questionnaire survey in urban and rural areas and revealed that concern was context driven. Chinese people prioritized local problems, whereas Japanese people were satisfied with the existing environmental conditions. However, both populations felt the necessity of enhancing government-led environmental education and publication to improve their awareness and behavior towards environment. A study [1] added a behavioral dimension to the environmental awareness of Road Freight Transport (RFT) drivers in China and revealed that an awareness of environmental issues did not always guarantee pro-environmental behavior. Instead, knowledge, environmental concern, and attitude influenced pro-environmental behavior indirectly through behavioral intention and perceived policy efficacy. This facilitated the transformation of awareness into behavior by bridging the awareness–behavior gap. These empirical findings were echoed by [45] in a Western Australian business context.

Ref. [46] highlighted that, unlike the managers in large firms, the owners/managers in small firms often had greater freedom in decision-making, and their personal motivations and responsibilities were influential in shaping firm strategy, leading to a greater engagement with social and environmental issues. They found that managers of environmentally proactive SMEs were engaged in a number of pro-environmental activities and were aware of and involved in the personal and business challenges of climate change. The findings suggested that public policy and business advice in this area should be more focused on personal values and motivation to contribute to environmental protection in their engagement with small businesses. Complementing this, [41] explored SMEs’ use of sustainability management tools and confirmed that managers’ awareness of sustainability management tools was the major determinant for their adoption in SMEs. Firm size and perception of a relative advantage compared to older practices played important roles in their application. Contrary to this, [3] found that a knowledge of green technology was not influential, while green products and government policies directly shaped awareness. They emphasized targeted campaigns and top-down policy interventions and underscored the limits of individual knowledge alone in driving sustainable adoption. These studies offered practical insight into the drivers of sustainability practice but oversimplified causality, which indicated that, if structural barriers were present, then awareness may be necessary but not sufficient.

Legislation was found to be a critical driver of SMEs’ environmental responsiveness by [39], when firms were unaware of the cost-saving benefits of eco-friendly practices. This highlighted regulations’ effectiveness in promoting changes to business processes and environmental strategies. In a study [47], the effectiveness of environmental management training investments among two electricity companies was examined, and its limited impact on employees’ environmental awareness was found. However, this study emphasized the need for more effective environmental education and awareness training initiatives, along with research on training formats. This study also recognized the utilization of other tools for promoting environmental awareness and the adoption of an environmental culture within organizations. In an organization-level survey on manufacturing SMEs in Bangladesh, study [48] attempted to understand the impact of different kinds of environmental knowledge (system knowledge, action knowledge, and effectiveness knowledge) among SMEs on their environmental sustainability practices via the mediation of employee environmental behavior and the moderation of resource commitment. Analyzing survey data, the empirical outcome of this study presents evidence that the system, action, and effectiveness of knowledge dimensions boost employees’ environmental behavior and thus improve environmental sustainability practices in SMEs.

After reviewing the literature, it is evident that environmental awareness is multidimensional (knowledge, concern, motivation, attitudes, and skills), context-dependent (varying by region, country, and group), and not automatically predictive of behavior. This suggests that a reliance on individual motivations and awareness alone may not always translate into sustainable practices, since behavioral outcomes are heavily shaped by contextual factors. Moreover, the existing studies reveal inconclusive findings regarding the relationship between environmental awareness and behavioral actions, particularly within SME businesses. The evidence on the connection between environmental awareness (EA) and green technology adoption remains inadequate to provide a concrete solution. While much of the existing literature emphasizes the importance of environmental awareness among business stakeholders, relatively few studies empirically assess the link between EA and pro-environmental activity. Furthermore, EA remains a neglected dimension in the economic literature on businesses operating in unregulated sectors that primarily serve a socio-economically disadvantaged population. Against this backdrop, the present study takes the initiative to explore the nexus between environmental awareness and green technology adoption in the context of informally operated SMEs. To date, no study has investigated this relationship within economies characterized by informality. This study therefore seeks to address the probability of bridging the gap between awareness and pro-environmental behavioral action through the lens of the behavioral intention of owners/managers of informal SMEs. Concrete evidence on this topic is particularly relevant for developing countries, where traditional command and control policies are often ineffective in influencing the behavioral action of small and medium informal enterprises.

3. Theoretical Background and Hypothesis

Studies show that behavioral decisions result from a combination of individual perceptions, environmental factors, and expected outcomes [49,50]. The present study seeks to capture this dynamic by integrating two well-established behavioral theories: the Theory of Planned Behavior (TPB) [51] and the Technology Acceptance Model (TAM) [52], with additional insights from prior research [50,53]. The theory of reasoned action provides the foundational framework for this integration, as it underpins both TPB and TAM. TPB is a general behavioral theory designed to explain and predict planned behaviors. Its central focus is the individual’s intention to perform a given behavior, which is influenced by three key constructs: (i) attitude towards the target behavior, (ii) subjective norms about engaging in the behavior, and (iii) perceived behavioral control [51,54]. These factors shape decision-making by influencing intention, which in turn drives actual behavior. Notably, TPB does not prescribe which beliefs should be linked to a particular behavior, allowing researchers flexibility in their application. This theoretical flexibility establishes a robust basis for assessing whether the Theory of Planned Behavior (TPB) serves as a solid theoretical framework for examining the relationship between attitudes and behavioral intentions, as well as the predictive capacity of these intentions on actual behaviors [55,56]. In this context, awareness plays a critical role in activating TPB variables for work that influences behavior [50]. However, TPB alone is insufficient to fully explain users’ intentions to adopt and use technology in particular [54]. Conversely, the technology acceptance concept, TAM, focuses more narrowly on technology adoption. While explaining technology use, TAM does not fully account for the formal behavioral intention to use (see [54]). This gap provides the rationale for combining TPB and TAM to establish the theoretical basis of the present study. Adopted from the theory of reasoned action, TAM is one of the most widely applied models in technology acceptance research [52]. According to TAM, two core constructs—the perceived ease of use (PEOU), reflecting the effort required to use a particular technology, and perceived usefulness (PU), reflecting the extent to which technology enhances performance—are the most influential determinants of technology adoption [55]. TAM has demonstrated a high predictive power in explaining technology use, with behavioral intention serving as the primary predictor of actual usage behavior [56]. Building on these complementary perspectives, the present study hypothesizes that behavioral intention functions as a mediating construct linking TPB and TAM, thereby shaping actual technology-related behavior. Figure 1 captures the theoretical framework of the study, where TPB reflects a more general behavioral theory and TAM focuses on technology-specific beliefs in the behavior (taking insights from [54,56]).

This study examines environmental awareness (EA), captured through environmental knowledge, concerns, and environmental practices (constructs aligned with the components of TPB), to predict the behavioral intention or motivational readiness of informally operated firms. It further extends this enquiry by forecasting behavioral decisions related to technology adoption, drawing on the Technology Acceptance Model (TAM). Accordingly, the central hypothesis of this investigation states that environmental awareness significantly influences firms’ decisions regarding technology adoption. Specifically, higher levels of environmental awareness are expected to strengthen firms’ willingness to adopt environmentally friendly technologies.

H1: 

There is a positive relationship between the environmental awareness of enterprise owners/managers and their adoption of green technologies. As environmental awareness increases, so too does the likelihood of accepting technologies that support sustainability.

4. Research Methodology

4.1. Survey and Data Collection

The present study involved the participation of enterprise owners/managers (drawing insights from the studies of [40,46,57,58,59], where managerial and organizational characteristics and practices were highlighted for environmental sustainability practices in SMEs) of informally operated firms. In the context of informal SMEs, organizational structures are less formalized, and ownership, control, and operations are often in the hands of a single individual or a small group of individuals [40,60,61] who have the potential to significantly influence the strategies and culture of the enterprise [46]. Therefore, the owners, who are often the managers (sometimes workers as well) of the informal enterprises, were found to be suitable informants for this study and were interviewed using a structured questionnaire. The questionnaire-based interviews were conducted with them between August and December 2021.

The authors designed the data collection instrument based on insights from [1,42]. The questionnaire was designed with consideration for the low socio-economic, educational, and cognitive backgrounds of the respondents. Owners from three types of manufacturing enterprises were selected for interviews to capture the heterogeneous nature of informal manufacturing. The Hemayetpur, Lalbagh (including Islambagh), and Keraniganj areas in Dhaka city in Bangladesh were selected for the interviewer-administered survey based on the cluster setup of the specific type of enterprise. Since the formal list or total number of informally operated enterprises was unavailable in official statistics, this study employed the snowball sampling method as a strategy to identify potential participants. However, all initiatives were taken to minimize the potential homogeneity and selection bias that may arise from this sampling technique. Details were mentioned to the survey plan submitted to the Human Resource Ethics (HRE) office of the University of Southern Queensland, Australia (UniSQ HREC ID: H21REA014). Primary information was collected from the owners’ association offices of the particular products, as the study sought their recommendations for enterprise selection. Based on the provided information, the Hemayetpur area was selected for interviewing firms in the leather and leather products industries, the Lalbagh (including Islambagh) area was selected for plastic and small machinery, and the Keraniganj area was selected for the dyeing and clothing industries. Although the economic activities of the firms are different, they are homogeneous in terms of their scale of activity. The instructions of the International Labor Organization, see [62], were followed initially for identifying informal firms, which included unincorporated private firms with no permanent premises, registered at the local level (not at the national industrial authority), which did not maintain accounts (no formal book-keeping) and paid no social security contribution or tax on wages. The selection criteria were also guided by the number of full-time workers in the enterprises and their size of production (discharge of metallic pollutant).

A total of 90 questionnaires were distributed to the respondents, and out of them 14 were returned incomplete, as the respondents were unwilling to answer questions related to their waste disposal practices. The COVID-19 restrictions during the study period posed challenges to the smooth data collection process and prevented us from reaching out to further enterprises. Thus, the data set used in this study is cross-sectional, consisting of information from manufacturing enterprises operating under informal conditions. The observation method was also employed as a means of collecting information, guided by owners’/managers’ behavior, their interactions with polluting activities, and the work conditions in the enterprises. Prior to the interview, the enumerators provided adequate information to the respondents on the aims and objectives of the study to ensure reliable responses. Consent was obtained from the respondents before conducting the interview. During the survey, researchers and enumerators adhered to all the guidelines and ethical norms directed by the National Statement on Ethical Conduct in Human Research (Australia, 2007).

4.2. Methods

The concept of environmental awareness (EA) is strongly shaped by individual ideologies, which makes it challenging to reach a consensus on its precise definition. According to [1], EA is a multidimensional construct that encompasses concerns (affective), knowledge (cognitive), and behavioral activity factors (conative). Drawing on this perspective, the present study develops an intuitive approach to construct an environmental awareness (EA) index by integrating components of the Theory of Planned Behavior (TPB), namely social norms (SN), attitudes (A), and perceived behavioral control (PBC). Within this framework, knowledge is understood as emerging from social awareness and shared understanding, environmental concern serves as the value base for attitude formation, and environmental conservation practices reflect the perceived ease or difficulty of engaging in pro-environmental behaviors when resources and skills are constrained. The details of the items used to construct the EA index are presented in

Table 1. Description of the questionnaire survey items on environmental awareness and general perception of environmental management.

Question Grouping	Concept	Category	Covered Area
Knowledge about environmental pollution [1,2,3,4,5]	The respondent’s familiarity with various types of pollution and their causes.	SN	Knowledge about types of pollution Knowledge about polluted items Knowledge about sources of pollution Knowledge about human action generating pollution
Attitude to environmental concerns [1,2,3,4,5]	How do respondents rank different types of pollution in terms of their negative impact?	A	Level of concern with the environmental problems mentioned below: Overall pollution Air pollution Water pollution Soil quality degradation Noise pollution
Practice on environmental conservation [1,2,3,4,5]	How do the firms control environmental activities?	PBC	To what extent does the firm abide by environmental regulations Practice of solid waste disposal Practices of wastewater disposal Information access on using pollution-minimizing technology Information access on recycling practice
General perception of environmental management [1,2,3,4,5]	To what extent the respondents are familiar with environmental management and its importance.	Cognitive	Perception about environmental regulation Importance of clean environment Perception about solid waste disposal Perception about wastewater disposal Perception about pollution hazards

. Information on these three categories was collected through an interviewer-administered survey targeting owners/managers of informally operated SMEs. The initial guidelines for designing the questionnaire were drawn from the studies of [1,42]. The questionnaire began with questions on the demographic characteristics of respondents, followed by an intuitive approach aimed at interpreting the meaning of their responses. Given that the education and cognitive levels of the respondents may be limited, it was considered appropriate to elicit their opinions, ideas, and actions on these concrete elements. The responses to questions in the aforementioned sections were used to construct the environmental awareness (EA) index. The subsequent section of the questionnaire explored respondents’ general perception of environmental management. The sample questions covered under the aforementioned categories are reported in Table 1. The final section addressed the intention/decision of technology adoption for pollution control at the firm level. The respondents were asked to select their desired responses on a 5-point Likert-type scale for questions on categories A and PBC. Knowledge about environmental pollution and perception of environmental management were assessed in an innovative way to minimize exaggerated responses and were later combined empirically to construct the EA index.

An additive index was constructed for the binary variables, knowledge, and general perception of environmental conservation. Each question was assigned a value of 1 for a desired perception and 0 for a wrong perception, and then the values were added together to find the mean score. The mean score on the scale was also calculated for the other two sections of the questionnaire (A and PBC) and then standardized to apply the Principal Component Analysis (PCA). PCA was applied to combine and modify data from independent categories and to create a new set of mutually independent categories in the data set, which were suitable for further use (see [63]). PCA was applied in this study to construct the environmental awareness (EA) index comprising SN, A, and PBC aspects of the respondents’ responses regarding environmental pollution and conservation practices. Its construction through PCA ensured that the different aspects of the respondents were equally weighted and non-redundant.

Due to the dichotomous nature of the enterprises’ decisions on technology adoption for pollution prevention, a qualitative response model was deemed appropriate to apply. Qualitative response models relate the probability of an occurrence to various independent variables and are often useful when assessing the characteristics of firms that are associated with technology adoption decisions [64,65]. To provide a detailed analysis of the firms’ decisions and actions in technology adoption, the study applied discrete choice probit and logit models for binary choice (yes or no) responses to the questions about the use of emission reduction technology. The probit model is a statistical probability model with binary categories in the dependent variable [65,66], which is based on the cumulative normal probability distribution. In this study, the binary dependent variable, technology use, takes the value 1 if the response is “yes” and 0 if the response is “no” [65].

This study uses the following regression model to test the effect of firm-level environmental awareness on firm-level technology adoption.

$${TA}_i=f\left({EA}_i,{PEP}_i\right)$$

(1)

Here, $TA$ represents a firm’s decision on technology adoption, which is the dependent variable. $EA$ and $PEP$ are the independent variables in the model that represent environmental awareness and general perception of environmental management practice, respectively. In the next stage, the demographic variable, age $\left(AGE\right)$ of the respondents, and business-related variable, engagement of firms in subcontracting ($SC$, as a binary variable), have been incorporated into the model as moderating variables. The moderating variables can be treated as confounding factors that may limit the environmental behavior of firm owners. The subcontracting variable is incorporated to capture the impact of globalization on the model, as the informal economy is expected to grow in the context of a globalized world. It is expected that the globalization of production and trade often leads to subcontracting and outsourcing activity, as per the neo-Marxist viewpoint [67].

$${TA}_i=f\left({EA}_i,{PEP}_i,{AGE}_i,{SC}_i\right)$$

(2)

The probability $p_i$ of choosing technology against not choosing it can be expressed as in (3), following [68]:

$$\begin{array}{c}{p}_i=prob\left[Y_i=1|X\right]={\int }_{-\delta }^{X_{\iota }^{\prime }\beta }{\left(2\pi \right)}^{-{\displaystyle \frac{1}{2}}}\mathrm{e}\mathrm{x}\mathrm{p}(-{\displaystyle \frac{z^2}{2}})dz\\ =\varnothing (x^{\prime }\beta )\end{array}$$

$\varnothing$ represents the probability density function. The association between a specific variable and the outcome of the probability can be understood by means of the marginal effect that accounts for the partial change in the probability. The marginal effect that is associated with explanatory variables, Xs, for the probability P(Yi = 1|X), while holding the other variables constant, provides insights into how the explanatory variables shift the probability of the frequency of the explained variable [68].

In the next stage, logit analysis is applied to predict the probability of an event occurring. This type of analysis is particularly useful for models involving decision-making [69]. The logit transformation is based on the “ratio of chances” within the logistic regression and is important for establishing the dependence of magnitude y on the variable x. This transformation allows for the ultimate relationship between the dependent variable y and a vector of independent variables x, with a calculated probability ranging from 0 or 1 [70]. Since the dependent variable in this study is categorical and there is no requirement for the independent variables to be normally distributed or linearly related, or have equal variance in each group, logistic regression (LR) can be applied. Application of LR gives each predictor a coefficient that measures its independent contribution to the variation in the dependent variable.

$$\begin{array}{cc}p\left(y\right)& ={\displaystyle \frac{e^{{\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\cdots +{\beta }_k{x}_k}}{1+e^{{\beta }_0+{\beta }_1{x}_1+{\beta }_2{X}_2}+\cdots +{\beta }_k{x}_k}}\hfill \\ & ={\displaystyle \frac{1}{1+e^{{-(\beta }_0+{\beta }_1{x}_1+{\beta }_2{X}_2}+\cdots +{\beta }_k{x}_k)}}\hfill \end{array}$$

Here, $p\left(y\right)$ is the probability of technology adoption, $x_k$ is the kth explanatory variable that is estimated by using the method of maximal likelihood, and ${\beta }_k$ are the coefficients of individual indicators. By applying logistic analysis in regression models (1) and (2), this study attempts to identify the independent variables that affect the probability of adopting emission reduction technology in firms. This study has been formulated to test the following hypothesis: the use of clean or pro-environmental technology for emission control is positively related to environmental awareness among firm owners. Therefore, the expected sign of the EA coefficient is positive.

5. Results and Discussion

5.1. Results

Table 2. Estimates of the binary probit model.

Variables	Model-1				Model-2
	Coefficient	Std. Error	Z-Statistic	Marginal Effects	Coefficient	Std. Error	Z-Statistic	Marginal Effects
Environmental awareness (EA)	0.723 *** (0.004)	0.252	2.86	0.285	0.767 *** (0.005)	0.270	2.84	0.301
Perception of environmental management	2.163 (0.130)	1.429	1.51	0.852	2.145 (0.148)	1.481	1.45	0.842
Age					0.067 * (0.067)	0.016	1.83	0.012
Subcontract arrangements					0.225 (0.481)	0.319	0.70	0.087
Constant	−1.817 (0.104)	1.118	−1.63		−3.293 ** (0.017)	1.374	−2.40
Log-Likelihood LR Chi2(2) Prob>chi2 Pseudo R2 Predicted percentage correctly	−45.270 14.61 0.001 0.139 61.84				−43.207 18.73 0.001 0.178 60.53

p-values are presented in parentheses. ***: statistically significant variable at 1% significance level. **: statistically significant variable at 5% significance level and *: statistically significant variable at 10% significance level.

presents the results estimated from the binary probit model projected in Equations (1) and (2). The models have been estimated by the maximum likelihood method. The models are significant at a 1% level of significance.

The estimated coefficients and standard errors suggest that environmental awareness significantly affects the probability of adopting green technology in informal manufacturing firms. In the estimated models, the EA variable exhibits a positive sign and a strong statistical significance (at the 1% level). This implies that, as business owners/managers become more aware of environmental issues, they are more likely to implement pollution-prevention or pollution-reduction technology. The marginal effect analysis indicates that, for a firm with a higher environmental awareness, the probability of using pollution-reducing technology is 28.5% in model 1 and 30.1% in model 2. This implies that the inclusion of demographic (age) and global factors (subcontract) in conjunction with environmental awareness and the general perception of environmental management enhances the likelihood of technology use. Additionally, the study reveals that enterprise owners/managers of a mature age are more likely to adopt emission-reducing technology, and the marginal propensity of adopting pollution control technology increases by 1.2% with age. Interestingly, firm owners’/managers’ general perceptions about environmental management practices do not significantly influence their decisions on technology use for pollution prevention. The correct prediction rates obtained from probit models are 62% and 60%, respectively. This implies that the probit models predicted 62% of the cases correctly for model 1 and 60% for model 2.

In the next step, a logit regression model has been fitted to the data set to test the research hypothesis, and the results are presented in

Table 3. Estimates of dichotomous logit model.

Variables	Model-1				Model-2
	Coefficient	Std. Error	Z-Statistic	Marginal Effect	Coefficient	Std. Error	Z-Statistic	Marginal Effect
Environmental awareness	1.241 ** (0.012)	0.492	2.52	0.304	1.278 ** (0.011)	0.500	2.55	0.312
General perception of environmental management	3.348 (0.142)	2.282	1.47	0.819	3.381 (0.161)	2.410	1.40	0.827
Age					0.049 * (0.074)	0.027	1.79	0.012
Subcontract arrangements					0.357 (0.498)	0.526	0.68	0.087
Constant	−2.855 (0.111)	1.791	−1.59		−5.263 ** (0.021)	2.284	−2.30
Log-Likelihood LR Chi2(2) Prob>chi2 Pseudo R2 Predicted percentage correctly	−45.318 14.51 0.001 0.138 61.84				−40.215 24.07 0.000 0.229

p-values are presented in parentheses. **: statistically significant variable at 5% significance level and *: statistically significant variable at 10% significance level.

. It presents a significant alignment with the probit outcome and confirms that environmental awareness increases the propensity of informally operated SMEs to adopt emission-prevention technology. The result also appears similar for the demographic variable, age.

The results of logit regression indicate that environmental awareness is statistically significant in predicting behavioral decisions, such as technology adoption for emission control among informal SMEs. The robust results from empirical investigations suggest that, the more environmentally aware a firm is, the greater its likelihood of adopting pollution prevention technologies. In addition, the maturity of the business owners/managers in terms of age positively impacts the probability of technology adoption. These findings are in line with those of [6,39,47,71,72], but contradict the result of [45]. Global business-related information, such as contractual obligations from their engagement to subcontract arrangements, seems to be insignificant in predicting behavioral decisions of technology adoption by firms. Furthermore, the general perception of enterprise owners/managers on environmental management appears to have little predictive power in explaining firms’ decisions on green technology adoption. This indicates that the current perception of owners/managers on environmental behavior lacks the strategic weight needed to drive an actual investment in green technology. The pseudo-R² values of the models fall within an acceptable range, consistent with typical values reported in social science and environmental research, indicating that the models possess a relatively meaningful explanatory power.

5.2. Discussions

The empirical findings of this study emphasize the importance of checking on the background organizational factors for policy suggestions, especially for sustainability practices. Conducted from an environmental perspective, the empirical findings of this study expose that a greater awareness serves as a critical driver in shaping individuals’ pro-environmental decision-making processes, which are reflected by technology adoption behaviors (behavioral decisions). This behavioral outcome is consistent with the theoretical underpinnings of this study, which integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) (see [73]) and implies that an awareness of the environment ultimately influences the acceptance and utilization of technology. By integrating insights from both TPB and TAM, the study underscores the pivotal role of awareness as a catalyst that can bridge the gap between environmental knowledge, societal norms, attitudinal change, and the practical adoption of environmentally friendly technologies. Awareness programs like training, workshops, and campaigns that are low-cost compliant may help owners/managers to adopt simple low-cost technologies that prevent pollution, along with other affordable practices like safe disposal, waste segregation, and recycling. If owners/managers understand the economic and health benefits and long-term sustainability of conservation, they become more likely to self-regulate by adopting environmentally friendly technologies. Where regulatory or punishment-based measures are incapable of accomplishing this, awareness can be a way to build skills and knowledge that can empower enterprises to innovate their own solutions. Awareness-based approaches can encourage cooperation rather than avoidance. The statistical significance of the age variable and its probabilistic link to technology adoption underscores the importance of maturity accumulated over time in shaping enterprise owners’/managers’ capacity to practice green production. Age-related maturity, which generally reflects accumulated skills, managerial experience, and practical knowledge, not only strengthens their attitudinal disposition toward environmental responsibility (as explained by TPB) but also enhances their understanding of the perceived usefulness and perceived ease of adopting pollution control technologies (as explained by TAM). These findings are convincing in the context of informally operated SMEs, where institutional support and formalized structures are often absent. Despite the relatively small sample size, this study can be considered valuable preliminary evidence on enterprise responses to green or sustainable production practices and offers a solid foundation for future larger-scale investigations.

6. Limitations and Future Research

This study acknowledges several limitations that should be considered when interpreting the empirical outcome. First, the data used to estimate the models of this study were collected through a survey conducted within a specific period. As such, the results may reflect the situation at that time but not necessarily the future dynamics, as this study does not capture longitudinal processes. Second, the research faced limitations related to the lack of in-depth information, potential non-response bias, and a relatively small sample size, partly due to the constraints of the COVID-19 context. Third, the reliance on self-reported measures may overstate concerns and understate actual behavioral patterns (social desirability bias). Despite these challenges, the study sought to minimize their impact by critically examining the practical context and behavioral aspects of respondents and by applying robust empirical techniques. Future research could address these limitations by conducting longitudinal studies to analyze stakeholders’ intentions and behavior over time. Additionally, future research could contribute by constructing environmental awareness indices across different countries, while also incorporating cultural and institutional factors.

7. Conclusions and Policy Suggestions

This study conducts an empirical investigation into the informal sector, which is often praised for its capacity to generate employment and income but also criticized for its adverse environmental impacts. Specifically, it examines the role of environmental awareness in fostering pro-environmental technology adoption within the manufacturing and business units (SMEs) of the informal sector in a developing country. The ultimate aim is to advance sustainable practices in informal sector SMEs by upgrading awareness. To construct an environmental awareness (EA) index, the study integrates dimensions of enterprise owners’/managers’ behavioral attitude, social norms, and perceived behavioral control regarding environmental issues, firm-level pollution, and pollution management. Using this EA index in probit and logit regression models, the study empirically investigates whether environmental awareness can stimulate green technology adoption in informal enterprises, thereby augmenting environmental and social benefits. The findings reveal that, as firms become more environmentally aware, they are more likely to use pollution-reducing technologies. Moreover, the likelihood of technology adoption is positively influenced by age, reflecting the maturity or experience of the owners/managers in the business. Therefore, this study concludes that the personal characteristics of decision-makers, such as age and related experience, and accumulated awareness of the environment and its management play a decisive role in driving sustainable behavior in informal SMEs. Accordingly, policies and interventions aimed at promoting green practices in informal SMEs should focus on awareness-raising, capacity-building, and targeted training initiatives for enterprise owners/managers, thereby facilitating the adoption of technology and environmentally aligned behavioral decisions. The study offers practical implications for policymakers and development stakeholders in designing effective strategies for promoting environmental literacy and consciousness among informal sector entrepreneurs and managers. In particular, policies/programs that emphasize the cost of pollution/emission and highlight the benefits of adopting environmentally friendly technologies can encourage positive behavioral changes in the stakeholders. Taking this into account, the government, relevant agencies, and business institutions should play an active role in promoting environmental conservation initiatives and subsidizing green technologies to ensure that these technologies are affordable and accessible to SMEs in the informal sector. These initiatives are crucial in the face of increasingly dire reports, such as those of IPCC [74], on the consequences of human actions on the environment.

As policy measures, training programs with regular evaluations can be highly beneficial for implementing environmental management approaches and initiatives, while also fostering environmental values and culture within firms. Organizations should recognize training investments as equally important as other financial investments. Given the weak financial structure of informal enterprises, it is crucial for government and non-government organizations to step forward by providing accessible tools that promote environmental awareness, education, and the adoption of an environmental culture in these enterprises. A wide range of tools can be utilized for this purpose, including mass media, newspapers, outdoor educational programs for adults, government and non-government incentive-based training programs, leaflets, wall writings, voluntary campaigns, and direct engagement with relevant institutions. When made readily available to stakeholders of informal enterprises, these tools can play a vital role in promoting environmentally responsible behavior and improving environmental performance at the firm level.

Financial institutions can play a pivotal role in encouraging enterprises to adopt green technologies. One effective approach is to provide incentive packages that reward businesses for embracing sustainable practices. Additionally, technical, financial, and infrastructure-based support should be made available to assist enterprises—particularly within the production units in the informal sector—in introducing innovative technologies that can mitigate both local and global pollutants. By understanding the relationship outlined in this study, regulators, environmental organizations, and firm-level stakeholders can collaborate to promote environmental performance. Effective communication and information exchange among these groups are essential to ensure long-term technical and structural support. Such support is critical for translating pro-environmental intentions into sustained organizational practices, as structural and policy frameworks are necessary to translate awareness into meaningful and lasting action—something individual awareness alone cannot achieve.

Although this study acknowledges several challenges—such as the absence of a universal definition of informal enterprises, the difficulty of identifying informally operated firms, and the challenge of engaging the owners and/or managers in research—the findings indicate a behavioral practice among informal SMEs that helps to foster sustainability within the informal sector. These results are expected to advance the understanding of how indirect policy measures can effectively promote pollution prevention in ways that are both cost-effective and socially viable for informally operated manufacturing enterprises in the industrial hub of developing countries.