Next Article in Journal
Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches
Previous Article in Journal
Seismicity and Stress State in the Ryukyu Islands Subduction Zone
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Can Environmental Regulation Drive the Environmental Technology Diffusion and Enhance Firms’ Environmental Performance in Developing Countries? Case of Olive Oil Industry in Morocco

Département d’Économie et de Sociologie Appliquée à l’Agriculture, Institut Agronomique et Vétérinaire Hassan II, Madinat Al Irfane, B.P. 6202, Rabat 10101, Morocco
Département d’Economie Rurale, Ecole Nationale d’Agriculture de Meknès (Morocco), B.P. S/40, Meknès 50001, Morocco
Department of Management of Natural Resources, Economics and Sociology and Quality, Regional Agricultural Research Center, Meknes, B.P. 578 (VN), Meknès 50000, Morocco
Département de Statistique et Informatique Appliquées, Institut Agronomique et Vétérinaire Hassan II, Madinat Al Irfane, B.P. 6202, Rabat 10101, Morocco
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 15147;
Submission received: 3 October 2022 / Revised: 4 November 2022 / Accepted: 9 November 2022 / Published: 15 November 2022
(This article belongs to the Section Economic and Business Aspects of Sustainability)


Literature on the drivers of environmental technology has been increasing in recent years. However, few studies analyze the issue in developing countries. The main goals of understanding the drivers of, and obstacles to the ecological decisions made by firms, are to design efficient environmental regulatory instruments to achieve the environmental targets. This article analyzes the determinants, including the effects of environmental regulations, of the environmental technology adoption and improvement of environmental performance in the olive oil industry in managing the olive mills wastewater (OMWW). To meet our research objective, we applied a two-stage Heckman probit model to the data collected from 156 olive oil producers in three Moroccan provinces known by a high concentration of olive oil production activity. Our results showed that the environmental regulation is one main determinant of the adoption of environmental technology by olive oil producers. However, our findings indicate that the environmental regulation, as currently designed, is not strict enough to encourage producers to improve their environmental performance in the developmental context of this particular country. The conclusions of this research also suggest that the environmental performance of oil mills, while not explained by the severity of the environmental regulation, are influenced by other external and internal factors. On the one hand, the influence of the external environment of firms via the effect of external collaboration and international market orientation, and, on the other hand by internal factors related to environmental awareness of the producer and the firm’s technical competency. In the light of our findings, we recommend to the policy makers to carry out a reform of the regulatory measures through: (i) the implementation of the current legislation, (ii) designing an effective incentive system, combining better targeted green subsidy with an optimal tax on industrial wastewater discharge (iii) setting up programs to raise awareness and reinforce technical capacities.

1. Introduction

Environmental technology (ET) plays a major role in reconciling environmental goals with the imperatives of growth and competitiveness. Defined as any technique, product or process that conserves or restores the quality of the environment [1], (ET) appears to be a sustainable response by companies to environmental issues. However the adoption of this kind of technology is influenced by internal factors as well as by pressures from many institutional and social actors [2].
An abundant empirical literature has aimed to identify the different factors that influence the diffusion of environmental technologies. This literature is constructed around two main theoretical contributions: “the double externality problem” [3] and the Porter Hypothesis [4] which underline the central role played by environmental regulation and the development and diffusion of environmental technology on the one hand, and, on the other, by improving firms’ environmental performance. However, in the particular context of developing countries, where environmental regulation is less established, results can differ depending on the regulatory approach applied and on the perception of the stringency of environmental regulations [5,6,7]. As reported by Dhrifi and Sahli [8,9,10], who explored the role of institutions in environmental protection, poor institutional quality in developing countries can compromise the effective implementation of environmental policies, thus preventing the achievement of environmental targets. The uncertain effect of environmental regulation on the environment requires researchers and policy makers to understand why some companies fail to meet their environmental objectives while others do.
At the firm level, (ET) is considered as indispensable to improve environmental performance. A microeconomic understanding of the technological change process can therefore help policy makers design and implement effective and efficient regulatory instruments to stimulate the adoption of (ET) and to lead firms towards better environmental performance.
In Morocco, despite abundant environmental legislation, environmental problems are increasing at different territorial scales. In the current context marked by scarcity, the Moroccan government identified the problem of water sustainability as being one of its main priorities. Given the current expansion of the agri-food sector, the management of industrial organic pollution is a major environmental challenge in Morocco. One of the most urging discussions thus concerns the question of how to achieve sustainable production.
Considering these issues, this study sheds light on a firms environmental behavior in the specific case of the olive oil industry. To this end, we focus on the determinants of the adoption of (ET) and on the environmental performance of Moroccan oil mills.
In Morocco, the olive mill wastewater (OMWW) is one of the most complex agro-industrial effluents that have to be managed, due to their very high load of organic pollutants, that represent a major environmental constraint. The failure to guarantee strict control of (OMWW) discharges and their mismanagement results in significant pollution that particularly affects the quality of water resources. The situation risks will probably worsen with the expected expansion of olive oil production given the increase in the international demand and particularly the political determination to expand the olive oil sector in the country.
Since the 2000s, Morocco has increased its efforts to strengthen its national environmental legislation. Thus, the existing corpus of environmental laws is abundant, and many laws have been adopted concerning the protection of the environment and sustainable development including water management. However, since then, the national environmental legal framework has been facing difficulties in applying the laws, due to the absence of the secondary legislation [11]. Furthermore, the lack of implementing legislation limits the dissuasive effect of environmental regulation and reduces its effectiveness in promoting environmental technological change and reducing emissions of pollutants.
Against this background, we formulate the central hypothesis that existing environmental regulation leads producers to adopt environmental technology, but is not strict enough to improve their environmental performance.
The contribution of this article to the existing literature on the determinants of the diffusion of ET is relevant for the two following reasons: firstly, the study was conducted in the specific context of an agri-food industry; to date, the large number of empirical studies that have analyzed the determinants of the adoption of (ET) usually focused on ‘high-tech’ technologies [12]., while the agri-food sector itself has been the subject of limited empirical research. As mentioned in [13,14], the agri-food sector is generally considered as a traditional, labor intensive sector in which the use of advanced technologies is limited. Likewise, very few studies have focused on environmental technological change in the olive oil industry. The few contributions that have been conducted in developed countries were in Spain [15,16]. Moreover, the studies by Rabadán et al. [15] and Carillo-Labella et al. [16] were limited to analyzing the voluntary adoption of (ET) not in response to regulatory constraints. Secondly, considering the adoption of an environmental technology by a company is a necessary but not sufficient condition to improve its environmental performance. The present study extends the analysis of the determinants of environmental technology to an explanation of the environmental attitude of companies by simultaneously modeling the two parts of the decision-making process: the adoption of ET and the improvement of environmental performance.
The structure of the remainder of this article is as follows: Part 2 presents a review of previous studies about the determinants of environmental technology and environmental performance followed by a development of our research hypotheses. Afterwards, a conceptual framework is proposed. Part 3 describes the methodology adopted and outlines the data collection process, the econometric model and the key variables adopted. Finally, Part 4 reports and discusses the empirical findings and concludes with major implications for environmental measures and public policies.

2. Literature Review and Research Hypotheses

2.1. Determinants of Environmental Technology (ET)

According to Rennings [3] the main characteristic of (ET) is the production of a double externality in both the innovation and diffusion phases. In fact, in addition to the spillover effects typical of the R&D efforts associated with the innovation process, environmental technology also produces positive externalities by improving the environmental quality of processes (or products). This characteristic is called the “Double externality problem", in a sense that it reduces the incentives for firms to adopt environmental innovations and justifies public intervention through instruments of environmental regulation [3]. In accordance with Rennings [3], Porter [4] suggest that well-designed environmental regulation, besides stimulating the adoption of environmental technologies, leads the firm to a “win-win” situation resulting in an improvement of its economic performance and simultaneously in a reduction in its negative environmental impact [4,17].
In line with Porters’ Hypothesis [4], and the Double externality problem stated by Rennings [3], numerous research publications highlight the key role of environmental regulation in driving the adoption of (ET). This issue has been the subject of various empirical verifications [18,19,20,21,22,23,24]. In general, the results of these studies carried out in different countries, see [18] in Irish context, [19,20] in Germany, [21] in Italy, [22] in Spain, [23,24] in the UK; and through an empirical approach, confirm the positive and the significant effects of environmental regulation on the development and diffusion of environmental technologies. This effect is known as the “regulatory push/pull effect” [3].
Admittedly, empirical evidence of the regulatory push/pull effect is still scarce in the context of developing countries. However, the few results produced seem to be in line with those published in the context of developed countries. Indeed, through an econometric analysis, the positive influence of environmental regulation on environmental technology adoption has been empirically shown by [25] in Nigeria for the manufacturing sector [26], for Chilean companies in all sectors and [27] for Chinese industrial companies.
Although environmental regulation has been identified by a wide literature as an important driver of environmental innovations, it is not the only one to be considered. The determinants of (ET) have been extensively studied in the literature. A review of this literature identifies a series of factors that del Rio Gonzàlez [2] proposes to classify into internal factors and others external to the firm.
The internals factors refer to the firm’s characteristics that can constitute resources and pre-conditions for its engagement in a process of environmental technological change. Among these internal factors, the literature on the determinants of (ET) pays particular attention to firm-specific characteristics, especially the age, size and awareness of the producer. Regarding the size factor, Rehfeld et al. [28] and Triguero et al. [29] have shown that small firms have to deal with the complexity and high investment required for the green transition. However, Wagner [30] finds that firm size has no effect on probability of adoption (ET). Concerning the age of the firm, it is assumed to have a positive effect on the adoption of (ET), considering that older plants are more easily able to develop and implement (ET), due to the process of accumulation of knowledge about innovation in general [28,29].
Another internal factor of a sociocultural nature is identified as an important determinant of (ET) adoption, that is, environmental awareness [31]. The environmental awareness of a producer (owner/manager) is described as a full knowledge of the environmental impact of his activity and his willingness to reduce his negative effects [32]. In a study on small and medium-sized companies, Gadenne [33] finds that the level of environmental awareness has a significant positive impact on the implementation of environmental innovations. This finding was also confirmed by Sun [34] who studied the case of the industrial sector in China. The adoption of (ET) is also driven by external factors, which relate more specifically to incentives and stimuli from the firm’s external environment. This influence takes the form of pressure to comply with environmental policy requirements, and external collaboration as a source of information/knowledge. Several empirical studies on the determinants of (ET) diffusion highlight the key role of external collaboration [35,36,37,38,39]. Rabadàn [39] has empirically shown that the collaboration with external actors is the key to successful environmental technological change in the context of the agri-food sector in Spain. Another study that concerned the French industrial sector was conducted by Horbach et al. [36] which showed that access to knowledge, whether through professional organizations or through universities and research institutes, has a positive effect on the diffusion of (ET).

2.2. Determinants of Environmental Performance

Environmental technology is seen as an imperative for environmental performance. Buhl et al. [40] states that improved environmental performance is possible through the successful implementation of environmental technology. It is commonly admitted that the adoption of environmental technologies contributes to the reduction in negative environmental impacts of firms [36,41,42,43,44]. However, the intensity of this effect varies according to the technology adopted and the nature of the pollutant examined [45].
In line with Porter’s hypothesis [4], several works highlight the influence of environmental regulation instruments on reducing the polluting activities of firms [18,23,24,41]. With limited knowledge of the benefits and opportunities offered by (ET), companies subject to stricter and more effective environmental regulation can be induced to this awareness, by developing or adopting ET, in order to reduce the costs of compliance with existing standards and regulations [23]. Aung [46] proves empirically, in the Chinese context, that the stringency of environmental regulation has a statistically significant positive effect on the environmental performance of firms in China. This causal relationship has been earlier proven in previous studies [23,41,47].
In addition to the regulation effect, the structural characteristics of the firm namely age and size can also explain the company’s environmental performance level. Based on the hypothesis that companies have specific characteristics that constitute preconditions for their environmental commitment, several authors have investigated the links between the size and age of the company and the improvement of its environmental performance [48,49,50,51,52]. As stated by Wang et al. [53] and Kasim [50], large firms are more able to make technological changes in order to improve their environmental performance through optimal resource use, efficient waste management and/or production of green products. The age of the company can also influence its performance. Aung et al. [54], Winter and May [46] consider that older companies have accumulated more experience in environmental technologies allowing them to make a successful green transition.
Another internal factor that is considered in explaining the environmental performance of firms is the environmental awareness of producers. While many contributions agree on the positive effect of environmental awareness on ET adoption, empirical studies that have analyzed the link between attitudes and environmental performance have yielded mitigated results. According to a study by Chen et al. [55] in which they investigated the impact of environmental performance, it was found that a high level of environmental awareness encourages the implementation of environmental performance. Schaper [56] in his study rejects the hypothesis that a high level of environmental awareness leads to higher environmental performance. Other factors of the “technology push” can condition the achievement of the expected environmental objectives. These are mainly the effect of the company’s technical skills and the effect of collaboration with external agents. According to the literature on the firm’s technological capacity, the lack of experience and the absence of internal technological skills may appear as barriers to the successful implementation of ET, compromising the firm’s compliance with environmental requirements [7]. It has been shown that some producers may be reluctant to adopt environmental technology due to a lack of internal technical skills and capacity (unskilled labor for the operation of environmental technology) [57].
In addition to the internal factors of the firms, several works and empirical studies highlight the effect of factors related to the external environment of the firm: exportation, green subsidy and external collaboration. In a literature review, Ozusaglam [58,59] highlights barriers related to access to information as the main obstacle to the diffusion of ET and the achievement of environmental objectives. Considering that successful implementation of ET, which can lead to improved environmental performance, may require specific knowledge and also skills that do not necessarily belong to the company’s area of competence [58]. The fundamental role played by external sources of information also called external collaboration was also highlighted by Horbach et al. [23] who found in the context of German industry, the positive effect of knowledge flow on the diffusion of ET and improvement of environmental performance. Wong [27] found that the sharing of information and knowledge by actors outside the company has a positive impact on its compliance with environmental requirements. The empirical literature on the influence of a company’s international openness on ET adoption and environmental performance is not very convergent and the available results are still very mixed. For Conceição [60] in the Portuguese context, Horbach [61,62] in the German context, and Galliano and Nadel [63] in the French context, firms with a high share of production destined for export, due to their high exposure to international competition, are the most compliant with environmental requirements. This positive link is also confirmed by Triguero et al. [29] for the case of Spanish industrial and service firms. However, this positive link is challenged by Carrión-Flores and Innes [41] who found a negative link between exportation intensity and firms environmental performance.
Among the external factors, we also consider the effect of pollution control subsidies. The green subsidy is a form of environmental regulation instrument favored by public decision-makers to encourage companies to adopt ecological behavior and lead them on high levels of environmental performance [64]. The empirical literature that has examined the link between the green subsidy, and the level of a firm’s environmental performance shows heterogeneous results. On the one hand, empirical work in line with studies supporting Porter’s hypothesis [4], shows that the application of a subsidy for pollution control encourages firms to adopt ET and allows them to improve their environmental performance [61,64,65,66,67]. On the other hand, some authors such as Görg and Strobl [68] have not found any significant relationship between these variables.

2.3. Conceptual Framework and Research Hypotheses

In this research, our initial theoretical position is based on Porter’s hypothesis [17] and the “double externality problem” [3]. These two theoretical contributions make central the role of environmental regulation in the diffusion of (ET) and the improvement of environmental performance.
However, taking into account the literature review, the contextual elements and the specific characteristics of the firms analyzed, we hypothesize that:
Environmental regulation is a determinant of environmental technology adoption.
Environmental regulation is not a determinant of the environmental performance.
We are assuming, therefore, that a firm’s decision to adopt ET and improve environmental performance is influenced by factors other than environmental regulation.
Based on the review of a vast empirical literature, we propose to include in our conceptual framework (Figure 1), other factors in addition to environmental regulation, that may influence the decision to adopt (ET) and explain the environmental compliance of the firm.
The originality of our research is to integrate both internal and external factors in a global analysis. The external factors such as green subsidies, the international market and external sources of information, and also the effect of the internal characteristics of the firm, notably its resources, skills and knowledge.

3. Materials and Methods

3.1. Study Area and Data Collection

In order to achieve the objectives of this work, we have chosen the Sebou hydraulic basin in Morocco, which is considered as the basin most impacted by the OMWW problem. We concentrated the data collection in three provinces: Meknes, El Hajeb and Ouazzane. This choice is mainly driven by the high concentration of the olive oil production activity in this area. Indeed, the geographical concentration of oil mills, operating on top in a short time period (October to January), leads to intense water pollution. This is reflected by high organic pollutant loads in surface and ground water.
To collect the required data for our study, we conducted an exhaustive survey among olive oil producers in the three provinces using a questionnaire designed in accordance with the objectives of the study and our research hypotheses. In preparation for this survey, a number of individual interviews and two focus group discussions; (two focus group discussions: the first in Meknes (12 producers) and the second in Ouazzane (17 producers)) were conducted with a number of olive oil producers. The aim of this “exploratory” phase was to identify the possible answers to each question and also to choose the variables that would be considered in our model. Before its effective use for data collection, the questionnaire was first validated through a test survey with 20% of the total number of respondents. The results of the pre-test were used to make some necessary changes. For a widespread diffusion of the questionnaire, we adopted three modes of administration: face-to-face, telephone and internet. Complementary data was provided by the Sebou Water Basin Agency. This secondary data concerns the status and compliance of olive oil mills with existing environmental standards and requirements.
With a response rate of 83%, we collected data from 156 oil mills. Table 1 below shows the share of the surveyed oil mills by province and by mill type. The semi-modern oil mills adopting the three-phase process or the traditional press process account for 68% of the sample, whereas the modern units using two-phase processes represent 32% of the sample. Most of the mills (76%) are located in the province of Ouazzane, followed by Meknes and El Hajeb, respectively, with 46% and 34%.

3.2. Econometric Model

To meet our research objective, we have structured our analysis as follows:
Analyze the determinants of (ET) adoption.
Analyze the determinants of environmental performance only for firms that have adopted (ET).
For this purpose, we chose an econometric model that considers this selection in our sample. More specifically, we use Heckman probit (Heckprobit) based on two-stage models with a control for a possible selection bias due to the exclusion of firms that have not adopted (ET).
The Heckman Probit model is derived from the analytical framework proposed by Heckman [69] and fine-tuned by Van de Ven and Van Praag [70] to deal with a binary dependent variable in both stages. In the first stage of our empirical model, the dependent variable Y A , corresponding to the adoption of ET, takes the value of 1 if the firm adopts an environmental technology, and 0 otherwise. In the second stage, the dependent variable Y C , reflecting environmental performance is observable only if Y A = 1 . It takes the value of 1 if the firm is environmentally performing, and 0 otherwise.
From an analytical point of view, the Heckman probit model assumes two latent responses Y A * (the selection propensity variable) and Y C *   (the outcome variable). The two latent Equations can be stated, respectively, (1) and (2) as follows:
Y A * = Z α + v
Y A = 1   i f   Y A * > 0 0   i f   Y A * 0
The outcome variable is observed only if latent selection propensity exceeds zero, thus:
Y C *   = X β + u
Y C = 1   i f   Y C * > 0 0   i f   Y C * 0
where α and β the parameters to estimate, X is the vector of internal and external factors that can influence the decision to adopt the ET, Z is the vector of internal and external factors to the firm that can determine the environmental performance; finally, u and v are the error terms verifying the following assumption:
u ~ N 0 , 1 v ~ N 0 , 1 C o r r u , v = ρ
Since only the binary result is observed, our econometric model is given as a system of the two equations:
Selection equation:
P r o b i t Y A = 1 | Z = Z α
Outcome equation:
P r o b i t Y C = 1 | X = X β
Only if Y A > 1
The model is estimated using the maximum likelihood estimator.

3.3. Variables Description

The empirical analysis is based on a two-stage model (Heckman’s Probit) analyzing in a first step the determinants of environmental technology adoption by oil mills, and in a second step the determinants of environmental performance. In this section, we present the dependent and independent variables considered in the two sub-models: the selection model and the outcome model.

3.3.1. Dependent Variables

In the selection model, the dependent variable is the probability of adopting the environmental technology. In the context of this study, the environmental technology refers to the management practices of the Olive Mill Waste Water (OMWW) industry. Despite the diversity of solutions developed for the management of OMWW, the common practice in the study area remains natural evaporation in ponds. Producers consider it as the cheapest and easiest solution to implement. Indeed, in a study [71] the use of lagoon evaporation process is particularly recommended in the context of North African countries, because of the favorable climatic conditions and the financially and technically accessible installation for olive oil producers. After drying, the residual sludge is either incinerated or used as organic fertilizer.
For our selection model, we estimate the probability for the olive mills to adopt or not the environmental technology by the dichotomous variable “environmental technology”. This variable will take the value of 1 if the oil mill has installed an evaporation basin for the OMWW, and 0 otherwise. Our survey data show that 83% of the oil mills have an evaporation basin for the treatment of the OMWW, but the oil mills that do not have an evaporation basin (17% of the observation) discharge their effluents into pits and clandestinely into the watercourses.
In the second step, we try to explain the environmental performance of oil mills by a number of internal and external factors according to our conceptual scheme. In view of the specificities of the studied sector and the available data, we adopt in this study the compliance status of the oil mill with respect to environmental requirements as an indicator of its environmental performance.
In order to define the compliance status of oil mills, we referred to an inventory of all oil mills located in the study area, carried by the commissioned water quality control officers. An assessment of environmental conformity was made based on indicators identified from the regulatory framework. These indicators are related, on the one hand, to the presence of evaporation basins built far from the hydrographic network, and in conformity with the required technical standards, namely: (i) the waterproofing and sealing lining to avoid any risk of infiltration; (ii) a basin depth of 60–70 cm to guarantee the efficiency of the process. On the other hand, the respect of the commitments in terms of the used production process and the authorized quantities of triturated olives. An oil mill is considered to be environmentally performing when it satisfies all these environmental conformity requirements.
In our econometric model, we adopt the environmental compliance status as a proxy for the environmental performance of the olive mills, it will take the value of 1 if the oil mill is in conformity with the environmental requirement, and 0 otherwise. On the basis of the assessment made by the environmental control authority, it appears that among the oil mills with an evaporation pond for OMWW, only 29% of the surveyed plants meet the requirements of environmental conformity.

3.3.2. Explanatory Variables

To analyze the determinants of the adoption of environmental technology by oil mills and the determinants of their environmental performance, we employ two sets of explanatory variables in the two stages of our Heckman Probit model.
Internal factors
The first set of variables relates to the internal factors of oil mills. These include the structural characteristics of oil mills, i.e., its age, size, technical competency and the environmental awareness of the producer. The age variable, expressed in number of years, is a continuous variable calculated by the difference between the year of this study (2020) and the year of firm’s constitution. The size of the oil mills was apprehended by the turnover indicator given for the 2019–2020 campaign and expressed in Million MAD.
Another variable relating to internal factors of the olive mills was considered in both stages of our econometric estimation, which is the environmental awareness of the olive oil producer. This variable reflects the olive mills owner’s understanding and knowledge of the environmental impact of its activity. The idea is that the producer’s perception of the environmental impact as being close, which could compromise his activity in the near future, leads to a more serious assessment of the risks, which should normally result in more intentions to act in favor of environmental protection by adopting effective management solutions for OMWW.
To assess this level of environmental awareness, following [32,33], we considered two aspects underlying this ecological awareness: (i) the perception of environmental risk, specifically the risk of water pollution from the discharge of OMWW without prior treatment, and (ii) the perception of potential benefits from the adoption of environmental technology for the management of OMWW (cost reduction, increased revenues, access to new market, etc.). Each aspect was deconstructed into items introduced in our questionnaire and scored according to the 5-point Likert attitude scale ranging from “1” for strongly disagree to “5” for strongly agree. The internal consistency of the scale was proven with a value of the Alpha Cronbach (α = 0.9) higher than the recommended value (α = 0.7). On the basis of individual scores, we were able to categorize oil mills owners according to three attitudes: a positive attitude if the score is between (24 and 30 points), a neutral attitude with a score of (17–23 points) and a negative attitude if the score is (<17 points). For our econometric estimation, the environmental awareness variable takes the value of 1 if the producer has a positive attitude, which means that the producer is aware of the negative environmental impact of his industrial activity and of the potential benefits of the OMWW management technology.
In addition to the variables detailed above, another variable was introduced in the second stage of the Heckman Probit model, reflecting the difficulty related to the technical management of the environmental technology, and concerns: (a) maintenance of the impermeability of the ponds; (b) renewal of the geomembrane; (c) management of the residual sludge after evaporation of the OMWW. The main technical difficulties linked to the management of the ponds were identified by individual interviews with the producers and validated by focus group discussions conducted in the study area. In this research, we consider that an oil mill has a low technical competency if the producer declares to have encountered, in the management process of OMWW, at least two of the difficulties listed and which affect the efficacy of the process.
External factors
The second set of variables relates to the external factors of oil mills. This concerns external collaboration, export and the environmental regulation.
The firm’s collaboration with external actors plays a major role in the success of environmental technological change. The idea of integrating this variable is that oil mills need more knowledge and external sources of information in order to be able to adapt their production process to the requirement of the current environmental regulations. For this study, we have identified as external sources of information the different professional organizations operating in the olive industry, in particular: (a) producers’ associations; (b) Economic Interest Groupings (EIGs); (c) cooperatives.
Regarding the regulatory push/pull effect, we chose to separately analyze the effect of the two regulation mechanisms adopted in Morocco. The first is a command-and-control mechanism based on the enforcement of wastewater discharge standards. The second is a funding mechanism based on subsidies for pollution reduction which are financed through the National Environmental Protection and Sustainable Development Fund (FNEDD) and the Industrial Pollution Reduction Fund (FODEP). For the olive oil industry, these subsidies are granted exclusively to the creation of two-phase oil mills or to the conversion of oil mills from a three-phases to a two-phases process.
In our model, the command-and-control mechanism was proxied by “stringency of environmental regulation”. We approached this variable by the self-declared perception of the severity of environmental regulation instruments following Aung et al. [46] and Schmidt et al. [72]. Three dimensions were taken into account for this perception measure: (a) the degree of the incidence of environmental regulatory instruments on production activity; (b) the stringency of the sanction system; (c) the degree of effective implementation of environmental regulation. Each of these dimensions was deconstructed into a series of items that were introduced in our questionnaire and whose answers were formulated on a 5-point Likert scale: ranging from 1 for “strongly disagree” to 5 for “strongly agree”. The reliability of our multi-item construction was proven with a value of the Alpha Cronbach coefficient (α = 0.79). Based on the individual scores, we categorized the producers surveyed into three categories: producers with a perception of the severity of ER if the score is between (28 and 36 points). Producers with a rather mixed perception of the severity of ER with a score between (19 and 27 points) and producers who do not have a perception of the severity of ER with a score (<19 points).
The subsidy mechanism was directly represented in our model using the binary variable “subsidy”.
Table 2 presents the descriptive statistics and modalities of each explanatory variables.

4. Results

In order to verify that there is no interdependence between the explanatory variables included in our empirical model, we computed the contingency coefficient between these variables. These analyses were conclusive, as some variables seemed to be related to each other. As a fact, the analysis of contingency tests reveals the existence of a middle intensity relationship between “environmental awareness” and “external collaboration” with (C = 0.296, p = 0.000); between “external collaboration” and “exportation” with (C = 0.270, p = 0.001) and between “external collaboration” and “technical competency” with (C = 0.229, p = 0.007). This result supports evidence from previous observations [27,61] highlighting the key role played by the external collaboration, via professional organizations, as an important source of information and knowledge, which facilitates environmental awareness raising. In addition, these organizations act as technology transfer platforms.
To avoid any bias in the interpretation of our results due to the interdependence between some factors, we adopt a new estimate of the Heckman Probit model (Model (2)). In Model (2) we have excluded in the selection and outcome equations the variable “external collaboration”.
Table 3 presents the parameter estimations considered and their statistical significance and the robustness indicators of the two econometric models (1) and (2).
The results of the Wald   χ 2 test reject the null hypothesis that all the regression coefficients of the twostep models are simultaneously equal to zero, attesting to the model’s global significance.
The results of the LR test of independent equations show that for our two-step models (selection and outcome), there is no evidence of a selection bias in the two simulations, meaning that environmental performance (compliance) is a priori independent of the factors that drove the decision to adopt (ET). This finding is supported by the non-significance of Athrho, rejecting the existence of non-observable characteristics in the oil mills adopting (ET) which may promote their environmental performance.
The estimation results for econometric model show a highly significant effect of environmental regulation on environmental technological change, in line with theoretical assertions. Indeed, a producer’s perception of the severity of environmental regulation has a positive influence on his decision to adopt the (ET), thus confirming the results of previous research [24,25,73]. This finding supports our research hypothesis H1 which states that environmental regulation stimulates the diffusion of (ET).
However, the perceived severity of environmental regulation has no significant effect on the environmental performance an oil mill. This is a fundamental finding in our research leading to the conclusion that the environmental regulation is not a key determinant of environmental performance. This result confirms our research hypothesis H2 underlining that environmental regulation, as currently configured, does not lead polluting producers to comply with environmental requirements and to improve their environmental performance in general. Nonetheless, this conclusion is in contradiction with results of previous studies [20,21,42,43] that have empirically proven the significant effect of environmental regulation stringency on environmental performance.
The regulation push-effect on environmental performance was also examined through the green subsidy variable introduced in the second stage of our model. However, unlike the results obtained in the previous empirical literature [64,65,66,67], our findings show that this variable, reflecting the public support for pollution abatement, has no significant effect on the environmental performance of oil mills.
Regarding the influence of external collaboration on the adoption of (ET), the results of model (1) show that membership in a professional organization is not a determining factor in the adoption of (ET). This finding is contrary to previous studies [36,37,38] which showed that access to knowledge via the collaboration with external actors has a highly significant positive effect on the diffusion of (ET). Furthermore, our results emphasize the positive and highly significant effect of external collaboration through professional organizations, on environmental performance in line with the contribution of Wong [27], who noted the positive effect of knowledge and skill sharing by external actors on the successful implementation of (ET) and improvement of environmental performance.
Moreover, according to the outcomes of our econometric model (2), after removing the variable “external collaboration” we find that the environmental performance of the oil mills is also explained by the orientation towards the international market. The exportation variable has a significant positive effect on environmental performance. This result is in accordance with the empirical literature [29,60,63] which pointed out that exporting firms, facing international competition, are the most likely to comply with environmental regulations. It is noted that the influence of “exportation” on “environmental performance” is an effect hidden and captured by the “external collaboration” variable in the model (1), that appeared in model (2) after removing the correlated variable.
Regarding the internal factors, more specifically the influence of environmental awareness; the results of our model show, on the one hand, that environmental awareness is positively correlated with the adoption of (ET). On the other hand, the environmental awareness has a highly significant positive effect on the environmental performance of the olive oil mills. These results corroborate the findings of previous studies [32,34,55] emphasizing that a high level of environmental awareness promotes the diffusion of (ET) and drives the environmental performance of firms.
As regards the firms’ structural characteristic, the current study found that the age of oil mills has a highly significant negative effect on the adoption of (ET). These findings do not support the previous research [28,29] considering that older firms are more able to implement (ET) due to the accumulation of knowledge about innovation. However, In contrast to earlier studies [46,47], no evidence of the age effect on the environmental performance of oil mills was detected.
Concerning the size factor, the findings would tend to show that large oil mills have a higher probability to adopt (ET). These results are consistent with the previous studies [28,29] indicating that the larger the firm, the more likely it is to probably dispose of the necessary financial resources for the adoption of environmental technologies. Surprisingly, this study did not find a significant effect of size factor on environmental performance of olive oil mills. Although, these result differ from previous studies [50,74].
Last but not least, the results also point out that a low level of technical competency can hamper the environmental conformity of oil mills and thus reduce their environmental performance. The technical difficulties encountered by oil mills in the implementation of (ET) form a barrier to compliance and improvement of environmental performance. Therefore, technical competency is a key determinant of environmental performance according to the findings of previous empirical studies [7,74].
Following the results of our econometric model, we propose in (Figure 2) an adaptation of the conceptual framework to the context of the olive oil industry in Morocco.

5. Discussion

The main contributions of our study are summarized in (Figure 2). This figure identifies the determinants of (ET) adoption and environmental performance that are empirically validated for the case of oil mills in Morocco, among the set of factors proposed by the empirical literature in other research contexts.
Regarding the regulatory push-pull effect, the results of this research reveal, on the one hand, that environmental regulation is a main determinant of the (ET) adoption by olive oil producers. On the other hand, the absence of a significant effect of environmental regulation on environmental performance. This result can be explained by the fact that the current Moroccan environmental legislation, due to the absence of implementing texts, do not provide the environmental authorities with explicit powers of inspection and enforcement, which limits the dissuasive effect of environmental regulation. Specifically on the issue of water quality management, due to the delay in the application of economic instruments (i.e., the industrial wastewater discharge fee) and other regulatory instruments (i.e., specific discharge standards for olive oil industry effluents), the control officers are unable to apply any penal measure against polluting producers who do not comply with all environmental compliance requirements.
All these failures in the transposition of environmental policy offer the possibility of creating a loophole to avoid environmental compliance. Given this situation, which is marked by considerable regulatory loopholes, some producers are adopting a defensive attitude by adopting a middle ground position. As a defense against possible sanctions if inspected, producers adopt (ET) without complying with the environmental requirements. As an indication, 71% of olive oil mills in our sample have installed evaporation ponds with dimensions and capacities that are not sufficient to contain the total volume of OMWW; the remaining volume is clandestinely and directly rejected into nature without any prior treatment.
In addition, concerning the regulatory push-pull effect, the results show that the granting of green subsidies does not influence the environmental performance of oil mills. This green subsidy consists of financial support for pollution abatement programs to reduce the cost of investing in environmental technology. However, it was pointed out by producers that access to this pollution control subsidy remains unequal to the disadvantage of small and old oil mills. Moreover, the subsidy rate is considered unattractive, according to producers’ statements, since it is accorded mainly for the creation of oil mills with a two-phase extraction process. This type of process produces wet olive pomace that has to be dried before being valorized as a by-product, thus imposing an additional cost on producers. The profitability of the two-phase process is therefore a major problem compared to other extraction processes (traditional and three-phase) in which direct valorization of the by-product (dry pomace) is possible without any additional cost. Confronted with this dilemma, many producers remain reluctant to use the two-phase process, despite the public support provided. In light of these findings, environmental regulation cannot be considered as a determinant to improve environmental performance in this particular developing country context and within this particular sector of the olive oil industry.
Furthermore, our empirical estimates show the importance of external collaboration within professional organizations (producers’ associations and cooperatives) as an external factor in the adoption of environmental technology. The results of this study also show that collaboration between producers in professional organizations facilitates the sharing of experiences and good practices in the management of OMWW.
Concerning internal factors, the results confirm that the oldest oil mills have difficulties to adopt evaporation ponds. This may be explained by the fact that since the promulgation in 2008 of law (12–03), every creation project of a new oil mill must be subject to an environmental impact assessment that includes measures to manage OMWW. However, even with the adoption of (ET), these new oil mills are not necessarily in compliance with environmental requirements due to the lack of an environmental auditing system. Regarding the size factor, our research findings reveal that while larger oil mills may have greater facilities for the adoption of (ET), their environmental compliance is subject to a number of constraints. Apart from the factors highlighted by our econometric results, some large oil mills installed in peri-urban areas may face another obstacle that can be qualified as a “limiting factor” of environmental compliance. This is the non-availability of large land areas for the construction of evaporation ponds. This leads oil mills to dispose their OMWW in under-sized “storage” ponds that do not fulfill the technical requirements.
In addition to that, the study results indicate that the lack of environmental risk awareness may hinder environmental compliance of oil mills, or even the adoption of environmental technology. Moreover, this research points out that the environmental performance of oil mills, while not pushed by the severity of the environmental regulation, is hampered by the low technical competency of many producers. As matter of fact, 64% of producers adopting (ET) claim to have a difficulty to manage the evaporation pond. More specifically, in maintaining the impermeability of the basins and managing the residual sludge.

6. Conclusions and Implications for Public Policy

This study set out with the aim of examining the effect of environmental regulatory instruments on the diffusion of environmental technologies and on the improvement of environmental performance. For that purpose, we adopted the Heckman probit model with sample selection. The data used come from a survey conducted in Morocco with 156 owners of olive oil mills in three Moroccan provinces with a high concentration of olive oil production activity.
Our empirical study sheds new light on the issue of environmental technological change in a particular context that has been rarely covered by the literature, that of the olive oil industry in a developing country. Returning to our research hypotheses posed at the beginning of this study, it is now possible to state that: (i) environmental regulation can promote the adoption of environmental technology by olive oil mills, but cannot have an influence on their environmental performance; (ii) the adoption of environmental technologies to manage the OMWW and to comply with environmental compliance requirements is influenced by external factors which are external collaboration and international market orientation, on one hand, and on the other internal factors related to the environmental awareness and technical competency of the producer.
Our research findings suggest to undertake the following policy adjustments: firstly, the regulatory measures (command-and-control instruments) should be strengthened by implementing the current legislation texts, and reinforcing the environmental monitoring and control system. Secondly, an environmental regulation mechanism based on economic instruments should be applied. More concretely, an incentive system combining an optimal tax on industrial wastewater discharge with a subsidy for depollution (polluter pays/depollute paid) should be developed. Moreover, the green subsidies targeting may be improved to make it more attractive and accessible to the small producers, who are lacking technical and financial means to convert their extraction process from the traditional to the two-phase system. Thirdly, informational tools should be adopted by implementing programs of raising awareness, communication and technical capacities reinforcement. Finally, it is relevant to consolidate the role of professional organizations as a clever way to aggregate the small producers and thus, channel the government support of collective effluent treatment projects.
Finally, the major limitation of this study is the limited access to data. To overcome this constraint, we have used a set of proxies in our econometric estimation. Consequently, future research should adapt the proxies developed in this work for possible application in other research contexts, and when possible (depending on data availability), adopt more direct measures of the variables considered.

Author Contributions

The authors K.A., A.F. and M.D. contributed to the conceptualization and validation. Research methodology, investigation, formal analysis and writing—original draft was completed by I.B. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

Data are available on request from the first author of the manuscript.


Researchers are grateful to the Sebou Hydraulic basin agency which provided technical support and contributed to data collection, and to SIRMA network for financial support and scientific writing in English.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Kemp, R.; Arundel, A. Survey Indicators for Environmental Innovation; STEP Group: Kent, England, 1998. [Google Scholar]
  2. Del Río González, P. The empirical analysis of the determinants for environmental technological change: A research agenda. Ecol. Econ. 2009, 68, 861–878. [Google Scholar] [CrossRef]
  3. Rennings, K. Redefining innovation—Eco-innovation research and the contribution from ecological economics. Ecol. Econ. 2000, 32, 319–332. [Google Scholar] [CrossRef]
  4. Porter, M.E.; van der Linde, C. Toward a New Conception of the Environment-Competitiveness Relationship. J. Econ. Perspect. 1995, 9, 97–118. [Google Scholar] [CrossRef] [Green Version]
  5. Aloise, P.G.; Macke, J. Eco-innovations in developing countries: The case of Manaus Free Trade Zone (Brazil). J. Clean. Prod. 2017, 168, 30–38. [Google Scholar] [CrossRef]
  6. Brunel, C.; Levinson, A. Measuring the Stringency of Environmental Regulations. Rev. Environ. Econ. Policy 2016, 10, 47–67. [Google Scholar] [CrossRef]
  7. Luken, R.; Van Rompaey, F. Drivers for and barriers to environmentally sound technology adoption by manufacturing plants in nine developing countries. J. Clean. Prod. 2008, 16 (Suppl. 1), S67–S77. [Google Scholar] [CrossRef]
  8. Dhrifi, A. Does Environmental Degradation, Institutional Quality, and Economic Development Matter for Health? Evidence from African Countries. J. Knowl. Econ. 2019, 10, 1098–1113. [Google Scholar] [CrossRef]
  9. Madni, G.R.; Anwar, M.A.; Ahmad, N. Socio-economic Determinants of Environmental Performance in Developing Countries. J. Knowl. Econ. 2021, 13, 1157–1168. [Google Scholar] [CrossRef]
  10. Sahli, I.; Ben Rejeb, J. The Environmental Kuznets Curve and Corruption in the Mena Region. Procedia Soc. Behav. Sci. 2015, 195, 1648–1657. [Google Scholar] [CrossRef] [Green Version]
  11. MCA. Evaluation de la Filière Oléicole; Rapport Annuel; The Moroccan Court of Accounts (MCA): Rabat, Morocco, 2018. [Google Scholar]
  12. García-Granero, E.M.; Piedra-Muñoz, L.; Galdeano-Gómez, E. Multidimensional Assessment of Eco-Innovation Implementation: Evidence from Spanish Agri-Food Sector. Int. J. Environ. Res. Public. Health 2020, 17, 1432. [Google Scholar] [CrossRef]
  13. Cuerva, M.C.; Triguero-Cano, Á.; Córcoles, D. Drivers of green and non-green innovation: Empirical evidence in Low-Tech SMEs. J. Clean. Prod. 2014, 68, 104–113. [Google Scholar] [CrossRef]
  14. Labella, R.C.; Fort, F.; Rosa, M.P.; Armenteros, E.M.M. Determining factors of voluntariness in sustainable environmental innovation (eco-processes) and their certification: Agri-food sector. In Proceedings of the 12th European Conference on Innovation and Entrepreneurship, Paris, France, 21–22 September 2017; pp. 125–132. [Google Scholar]
  15. Rabadán, A.; Álvarez-Ortí, M.; Tello, J.; Pardo, J. Tradition vs. Eco-Innovation: The Constraining Effect of Protected Designations of Origin (PDO) on the Implementation of Sustainability Measures in the Olive Oil Sector. Agronomy 2021, 11, 447. [Google Scholar] [CrossRef]
  16. Carrillo-Labella, R.; Fort, F.; Parras-Rosa, M. Motives, Barriers, and Expected Benefits of ISO 14001 in the Agri-Food Sector. Sustainability 2020, 12, 1724. [Google Scholar] [CrossRef] [Green Version]
  17. Porter, M.E. Towards a dynamic theory of strategy. Strateg. Manag. J. 1991, 12, 95–117. [Google Scholar] [CrossRef]
  18. Doran, J.; Ryan, G. Regulation and firm perception, eco-innovation and firm performance. Eur. J. Innov. Manag. 2012, 15, 421–441. [Google Scholar] [CrossRef] [Green Version]
  19. Horbach, J.; Rammer, C.; Rennings, K. Determinants of eco-innovations by type of environmental impact—The role of regulatory push/pull, technology push and market pull. Ecol. Econ. 2012, 78, 112–122. [Google Scholar] [CrossRef] [Green Version]
  20. Rave, T.; Goetzke, F.; Larch, M. The Determinants of Environmental Innovations and Patenting: Germany Reconsidered; ifo Working Paper, ifo Working Paper No. 97; ifo Institute—Leibniz Institute for Economic Research at the University of Munich: Munich, Germany, 2011. [Google Scholar]
  21. Mazzanti, M.; Zoboli, R. Embedding environmental innovation in local production systems: SME strategies, networking and industrial relations: Evidence on innovation drivers in industrial districts. Int. Rev. Appl. Econ. 2009, 23, 169–195. [Google Scholar] [CrossRef]
  22. Del Río, P.; Peñasco, C.; Romero-Jordán, D. Distinctive Features of Environmental Innovators: An Econometric Analysis: Features of environmental innovators. Bus. Strategy Environ. 2015, 24, 361–385. [Google Scholar] [CrossRef]
  23. Kesidou, E.; Demirel, P. On the drivers of eco-innovations: Empirical evidence from the UK. Res. Policy 2012, 41, 862–870. [Google Scholar] [CrossRef]
  24. Kesidou, E.; Wu, L. Stringency of environmental regulation and eco-innovation: Evidence from the eleventh Five-Year Plan and green patents. Econ. Lett. 2020, 190, 109090. [Google Scholar] [CrossRef]
  25. Sanni, M. Drivers of eco-innovation in the manufacturing sector of Nigeria. Technol. Forecast. Soc. Change 2018, 131, 303–314. [Google Scholar] [CrossRef]
  26. Fernández, S.; Torrecillas, C.; Labra, R.E. Drivers of eco-innovation in developing countries: The case of Chilean firms. Technol. Forecast. Soc. Change 2021, 170, 120902. [Google Scholar] [CrossRef]
  27. Wong, S.K.S. Environmental Requirements, Knowledge Sharing and Green Innovation: Empirical Evidence from the Electronics Industry in China: Environmental Requirements, Knowledge Sharing and Green Innovation. Bus. Strategy Environ. 2013, 22, 321–338. [Google Scholar] [CrossRef]
  28. Rehfeld, K.-M.; Rennings, K.; Ziegler, A. Integrated product policy and environmental product innovations: An empirical analysis. Ecol. Econ. 2007, 61, 91–100. [Google Scholar] [CrossRef] [Green Version]
  29. Triguero, Á.; Cuerva, M.C.; Álvarez-Aledo, C. Environmental Innovation and Employment: Drivers and Synergies. Sustainability 2017, 9, 2057. [Google Scholar] [CrossRef] [Green Version]
  30. Wagner, M. Empirical influence of environmental management on innovation: Evidence from Europe. Ecol. Econ. 2008, 66, 392–402. [Google Scholar] [CrossRef] [Green Version]
  31. Reid, A.; Miedzinski, M. Eco-Innovation. Final Report for Sectoral Innovation Watch; Technopolis Group: Brussels, Belgium, 2008. [Google Scholar] [CrossRef]
  32. Peng, X.; Liu, Y. Behind eco-innovation: Managerial environmental awareness and external resource acquisition. J. Clean. Prod. 2016, 139, 347–360. [Google Scholar] [CrossRef]
  33. Gadenne, D.L.; Kennedy, J.; McKeiver, C. An Empirical Study of Environmental Awareness and Practices in SMEs. J. Bus. Ethics 2009, 84, 45–63. [Google Scholar] [CrossRef]
  34. Sun, Y.; Sun, H. Executives’ Environmental Awareness and Eco-Innovation: An Attention-Based View. Sustainability 2021, 13, 4421. [Google Scholar] [CrossRef]
  35. Galliano, D.; Nadel, S. Firms’ Eco-innovation Intensity and Sectoral System of Innovation: The Case of French Industry. Ind. Innov. 2015, 22, 467–495. [Google Scholar] [CrossRef]
  36. Horbach, J.; Oltra, V.; Belin, J. Determinants and Specificities of Eco-Innovations Compared to Other Innovations—An Econometric Analysis for the French and German Industry Based on the Community Innovation Survey. Ind. Innov. 2013, 20, 523–543. [Google Scholar] [CrossRef]
  37. Rennings, K.; Rammer, C. Increasing Energy and Resource Efficiency Through Innovation—An Explorative Analysis Using Innovation Survey Data. SSRN Electron. J. 2009, 59, 442–459. [Google Scholar] [CrossRef] [Green Version]
  38. Triguero, A.; Moreno-Mondéjar, L.; Davia, M.A. Drivers of different types of eco-innovation in European SMEs. Ecol. Econ. 2013, 92, 25–33. [Google Scholar] [CrossRef]
  39. Rabadán, A.; Triguero, Á.; Gonzalez-Moreno, Á. Cooperation as the Secret Ingredient in the Recipe to Foster Internal Technological Eco-Innovation in the Agri-Food Industry. Int. J. Environ. Res. Public. Health 2020, 17, 2588. [Google Scholar] [CrossRef]
  40. Buhl, A.; Blazejewski, S.; Dittmer, F. The More, the Merrier: Why and How Employee-Driven Eco-Innovation Enhances Environmental and Competitive Advantage. Sustainability 2016, 8, 946. [Google Scholar] [CrossRef] [Green Version]
  41. Carrión-Flores, C.E.; Innes, R. Environmental innovation and environmental performance. J. Environ. Econ. Manag. 2010, 59, 27–42. [Google Scholar] [CrossRef]
  42. Li, Y. Environmental innovation practices and performance: Moderating effect of resource commitment. J. Clean. Prod. 2014, 66, 450–458. [Google Scholar] [CrossRef]
  43. Liao, Z. Environmental policy instruments, environmental innovation and the reputation of enterprises. J. Clean. Prod. 2018, 171, 1111–1117. [Google Scholar] [CrossRef]
  44. Zhou, Y.; Shu, C.; Jiang, W.; Gao, S. Green management, firm innovations, and environmental turbulence. Bus. Strategy Environ. 2019, 28, 567–581. [Google Scholar] [CrossRef]
  45. Costantini, V.; Crespi, F.; Marin, G.; Paglialunga, E. Eco-innovation, sustainable supply chains and environmental performance in European industries. J. Clean. Prod. 2017, 155, 141–154. [Google Scholar] [CrossRef]
  46. Aung, T.S.; Overland, I.; Vakulchuk, R. Environmental performance of foreign firms: Chinese and Japanese firms in Myanmar. J. Clean. Prod. 2021, 312, 127701. [Google Scholar] [CrossRef]
  47. Winter, S.C.; May, P.J. Motivation for Compliance with Environmental Regulations. J. Policy Anal. Manage. 2001, 20, 675–698. [Google Scholar] [CrossRef]
  48. Anton, W.R.Q.; Deltas, G.; Khanna, M. Incentives for environmental self-regulation and implications for environmental performance. J. Environ. Econ. Manag. 2004, 48, 632–654. [Google Scholar] [CrossRef]
  49. Hojat, A.H.M.; Rahi, K.A.; Chin, L. Firm’s Environmental Performance: A Review of Their Determinants. Am. J. Econ. Bus. Adm. 2010, 2, 330–338. [Google Scholar] [CrossRef] [Green Version]
  50. Kasim, A. Managerial attitudes towards environmental management among small and medium hotels in Kuala Lumpur. J. Sustain. Tour. 2009, 17, 709–725. [Google Scholar] [CrossRef]
  51. Nakamura, E. Does Environmental Investment Really Contribute to Firm Performance? An Empirical Analysis Using Japanese Firms. Eurasian Bus. Rev. 2011, 1, 91–111. [Google Scholar] [CrossRef]
  52. Zhang, B.; Bi, J.; Yuan, Z.; Ge, J.; Liu, B.; Bu, M. Why do firms engage in environmental management? An empirical study in China. J. Clean. Prod. 2008, 16, 1036–1045. [Google Scholar] [CrossRef]
  53. Cole, M.A.; Elliott, R.J.; Zhang, L. Foreign Direct Investment and the Environment. Annu. Rev. Environ. Resour. 2017, 42, 465–487. [Google Scholar] [CrossRef] [Green Version]
  54. Ben Dhaou, S.I.; Renard, L. Definition and Categorization of E-Government Capabilities: Lessons Learned from a Canadian Public Organization. J. E-Gov. Stud. Best Pract. 2017, 2017, 1–14. [Google Scholar] [CrossRef]
  55. Chen, X.; Huang, B.; Lin, C.-T. Environmental awareness and environmental Kuznets curve. Econ. Model. 2019, 77, 2–11. [Google Scholar] [CrossRef]
  56. Schaper, M. The Future Prospects for Entrepreneurship in Papua New Guinea. J. Small Bus. Manag. 2002, 40, 78–83. [Google Scholar] [CrossRef]
  57. Ashford, N.A.; van Geenhuizen, M.; Gibson, D.V. 1 Pathways to Sustainability: Evolution or Revolution? In Regional Development and Conditions for Innovation in the Network Society; Purdue University Press: West Lafayette, IN, USA, 2002. [Google Scholar]
  58. Ozusaglam, S. Environmental innovation: A concise review of the literature. Vie Sci. Entrep. 2012, 191–192, 15. [Google Scholar] [CrossRef]
  59. Conceição, P.; Heitor, M.V.; Vieira, P.S. Are environmental concerns drivers of innovation? Interpreting Portuguese innovation data to foster environmental foresight. Technol. Forecast. Soc. Change 2006, 73, 266–276. [Google Scholar] [CrossRef]
  60. Horbach, J. Determinants of environmental innovation—New evidence from German panel data sources. Res. Policy 2008, 37, 163–173. [Google Scholar] [CrossRef] [Green Version]
  61. Horbach, J.; Jacob, J. The relevance of personal characteristics and gender diversity for (eco-)innovation activities at the firm-level: Results from a linked employer–employee database in Germany. Bus. Strategy Environ. 2018, 27, 924–934. [Google Scholar] [CrossRef]
  62. Galliano, D.; Nadel, S. Les déterminants de l’adoption de l’éco-innovation selon le profil stratégique de la firme: Le cas des firmes industrielles françaises. Rev. Déconomie Ind. 2013, 142, 77–110. [Google Scholar] [CrossRef] [Green Version]
  63. Xie, X.; Zhu, Q.; Wang, R. Turning green subsidies into sustainability: How green process innovation improves firms’ green image. Bus. Strategy Environ. 2019, 28, 1416–1433. [Google Scholar] [CrossRef]
  64. Lin, H.; Zeng, S.X.; Ma, H.; Chen, H. How Political Connections Affect Corporate Environmental Performance: The Mediating Role of Green Subsidies. Hum. Ecol. Risk Assess. Int. J. 2015, 21, 2192–2212. [Google Scholar] [CrossRef]
  65. Da Motta, R.S. Analyzing the environmental performance of the Brazilian industrial sector. Ecol. Econ. 2006, 57, 269–281. [Google Scholar] [CrossRef]
  66. Van Leeuwen, G.; Mohnen, P. Revisiting the Porter hypothesis: An empirical analysis of Green innovation for the Netherlands. Econ. Innov. New Technol. 2017, 26, 63–77. [Google Scholar] [CrossRef] [Green Version]
  67. Görg, H.; Strobl, E. The Effect of R&D Subsidies on Private R&D. Economica 2007, 74, 215–234. [Google Scholar] [CrossRef] [Green Version]
  68. Heckman, J.J. Sample Selection Bias as a Specification Error. Econometrica 1979, 47, 153. [Google Scholar] [CrossRef]
  69. Van de Ven, W.P.; Van Praag, B.M. The demand for deductibles in private health insurance. J. Econom. 1981, 17, 229–252. [Google Scholar] [CrossRef]
  70. Khdair, A.; Abu-Rumman, G. Sustainable Environmental Management and Valorization Options for Olive Mill Byproducts in the Middle East and North Africa (MENA) Region. Processes 2020, 8, 671. [Google Scholar] [CrossRef]
  71. Schmidt, T.S.; Schneider, M.; Rogge, K.S.; Schuetz, M.J.; Hoffmann, V.H. The effects of climate policy on the rate and direction of innovation: A survey of the EU ETS and the electricity sector. Environ. Innov. Soc. Transit. 2012, 2, 23–48. [Google Scholar] [CrossRef]
  72. Fernando, Y.; Wah, W.X. The impact of eco-innovation drivers on environmental performance: Empirical results from the green technology sector in Malaysia. Sustain. Prod. Consum. 2017, 12, 27–43. [Google Scholar] [CrossRef]
  73. Wang, J.; Zhang, Y.; Goh, M. Moderating the Role of Firm Size in Sustainable Performance Improvement through Sustainable Supply Chain Management. Sustainability 2018, 10, 1654. [Google Scholar] [CrossRef]
  74. Hashim, R.; Bock, A.J.; Cooper, S. The Relationship between Absorptive Capacity and Green Innovation. Int. J. Ind. Manuf. Eng. 2015, 9, 1065–1072. [Google Scholar]
Figure 1. Determinants of environmental technology and environmental performance.
Figure 1. Determinants of environmental technology and environmental performance.
Sustainability 14 15147 g001
Figure 2. Determinants of (ET) adoption and environmental performance in the olive oil industry in Morocco.
Figure 2. Determinants of (ET) adoption and environmental performance in the olive oil industry in Morocco.
Sustainability 14 15147 g002
Table 1. Share of oil mills by province and by mill type.
Table 1. Share of oil mills by province and by mill type.
Mill TypeProvinceTotal Mills Type
Meknes El HajebOuazzane
Semi-modern 391750106 (68%)
Modern 7172650 (32%)
Total mills by province46 (29%)34 (22%)76 (49%)156
Source: own elaboration.
Table 2. Descriptive statistics of explanatory variables (n = 156).
Table 2. Descriptive statistics of explanatory variables (n = 156).
VariableDescription PercentageMean (S.D)
AgeSeniority of oil mills (number of years) 15.26
SizeOil mills turnover 2019–2020 (million dirhams) 17.26
Environmental Awareness1 = oil mills with an awareness of the environmental impact of OMWW;
0 otherwise
Technical competency1 = oil mills with technical difficulties (low technical competency);
0 otherwise
External collaboration1 = oil mills member of a professional organizations;
0 otherwise
Exportation1 = if the oil mills export its production to the international market;
0 otherwise
Environmental regulation1 = if the producer perceives the current ER as severe;
0 otherwise
Subsidy1 = if the producer has received a pollution abatement subsidy;
0 otherwise
Source: own elaboration.
Table 3. Determinants of (ET) adoption and environmental performance.
Table 3. Determinants of (ET) adoption and environmental performance.
Model (1)Model (2)
Outcome Equation: [Determinants of Environmental performance]
Age −0.023−0.023
Environmental regulation0.4900.430
Subsidy 0.3950.152
Exportation0.5161.166 **
Technical competency−1.598 ***−1.588 ***
Environmental awareness2.152 ***1.978 ***
External collaboration 1.603 ***
Constant−2.608 ***−1.303 ***
Selection Equation: [Determinants of Environmental Technology]
Age −0.052 ***−0.046 ***
Size 0.210 *0.182 *
Environmental regulation2.365 ***2.353 ***
Environmental awareness1.568 ***1.791 ***
External collaboration 0.829
Wald χ 2
[ p > χ 2 ]
LR test of indep. eqns (rho=0)
[ p > χ 2 ]
Athrho [ p > | z | ][0.982][0.982]
Estimated standard errors are expressed in brackets, significance level: *** p < 0.01, ** p < 0.05, * p < 0.1.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Bounadi, I.; Allali, K.; Fadlaoui, A.; Dehhaoui, M. Can Environmental Regulation Drive the Environmental Technology Diffusion and Enhance Firms’ Environmental Performance in Developing Countries? Case of Olive Oil Industry in Morocco. Sustainability 2022, 14, 15147.

AMA Style

Bounadi I, Allali K, Fadlaoui A, Dehhaoui M. Can Environmental Regulation Drive the Environmental Technology Diffusion and Enhance Firms’ Environmental Performance in Developing Countries? Case of Olive Oil Industry in Morocco. Sustainability. 2022; 14(22):15147.

Chicago/Turabian Style

Bounadi, Imane, Khalil Allali, Aziz Fadlaoui, and Mohammed Dehhaoui. 2022. "Can Environmental Regulation Drive the Environmental Technology Diffusion and Enhance Firms’ Environmental Performance in Developing Countries? Case of Olive Oil Industry in Morocco" Sustainability 14, no. 22: 15147.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop