Adoption of Innovative Energy Facilities in the Tertiary Sector Buildings: Exploring Interdependencies and Key Drivers

Abstract

Innovative energy facilities, such as solar panels, heat pumps, and smart control ventilation, offer substantial opportunities to improve energy efficiency and environmental performance in the tertiary sector, aligning with green building objectives. This study aims to identify the key factors influencing the adoption of these facilities by small and medium-sized enterprise owners in the tertiary sector and to explore the interdependencies among them. To achieve this, we employed a stated choice experiment to assess preferences and decision-making by presenting respondents with multiple hypothetical scenarios, each containing alternatives described by varying attributes. A simultaneous equation model was used to analyze the key drivers of adoption and the interrelationships among these facilities. The results reveal that cost-related attributes and government incentives significantly impact the acceptance of energy facilities. Notably, while environmental responsibility is slightly associated with solar panel adoption, it shows no significant link with heat pumps or ventilation systems. Furthermore, we identified a bi-directional relationship between the adoption of solar panels and heat pumps, suggesting that acceptance of one positively influences the other. Conversely, a unidirectional relationship exists between ventilation and solar panels, where the adoption of ventilation positively influences solar panel adoption, but not the other way around. These findings contribute to a deeper understanding of decision-making processes in green building projects and provide valuable insights for policymakers and industry stakeholders aiming to promote sustainable energy solutions in the tertiary sector.

1. Introduction

Accelerating electrification and promoting renewable energy are crucial strategies for mitigating global warming, aligning with the European Green Deal’s target to cut emissions by 55% from 1990 levels by 2030. The building sector represents a significant component of energy consumption, accounting for nearly 40% of total energy use in the EU [1]. Commercial buildings, in particular, contribute substantially, making up 12% of end-use energy consumption in the US [2]. Yet, fewer than 30% of companies in Europe have implemented energy conservation measures, indicating a significant potential for energy savings through effective strategies.

Innovative energy facilities not only offer higher energy efficiency but also provide flexibility, automation, and various economic, social, and environmental benefits in the long run. The term “energy facilities” in this study means equipment or facilities that can be used to generate, storage, transfer, or save energy sources. The “innovative” refers to recently developed renewable energy facilities and energy-efficient systems that have not yet been widely adopted. We specifically focus on technologies such as solar panels, air source heat pumps, and smart control ventilation systems, which represent advancements designed to enhance energy efficiency and sustainability. These innovations have significant potential for improving energy performance and reducing carbon emissions, particularly for small and medium-sized enterprises (SMEs) in the tertiary sector. Solar photovoltaic panels and heat pumps are among the key renewable energy technologies expected to play pivotal roles in achieving the EU 2030 and 2050 energy and climate goals. Traditional ventilation systems also contribute significantly to energy consumption, with heating, ventilation, and air conditioning systems accounting for about 40% of energy use in office buildings [3]. The integration of smart control systems, such as advanced mechanical ventilation with heat recovery, can lead to energy consumption reductions of approximately 30% while maintaining thermal comfort levels [4].

Despite the advantages, the market penetration of renewable energy technologies remains slow in commercial buildings [5,6]. Factors hindering adoption include relatively low gas prices compared with electricity, the need for customer awareness regarding the benefits and features of innovative facilities, and the alignment of facility characteristics with specific business needs. For instance, restaurants prioritize temperature and air quality, while butcher shops focus more on cooling requirements. In addition, the functional integration of multiple energy facilities often brings more possibility, e.g., heat pump plus ventilation may bring not only good air quality but also comfortable temperature; however, such a combination often comes with a price, especially at the initial stage, which might hinder the adoption process.

Thus, it is also critical whether companies would want to accept the new energy facilities because the market potential of energy facilities depends on the adoption behavior of customers. It is necessary and timely to understand the decision-making mechanism of companies in the adoption of different innovative energy facilities. This research specifically focuses on the willingness of small and medium-sized enterprises (SMEs) owners in the service industry to adopt these innovative energy facilities. The tertiary sector includes SMEs such as butchers, barbers, small food and non-food shops, restaurants, and pubs.

This study aims to investigate the adoption of energy facilities, paying particular attention to the mutual dependencies between different technologies. To achieve this, we designed a dedicated stated choice experiment to collect data from targeted companies, enabling us to capture their adoption behaviors regarding a set of innovative energy facilities characterized by specific attributes and choice contexts. This methodological approach is especially useful in situations where historical data and variation are insufficient.

Furthermore, we estimated a simultaneous equation model, which is particularly suited for examining potential interdependencies and interactions among multiple alternatives. By enhancing our understanding of the decision-making mechanisms influencing companies’ adoption of energy facilities, this research aims to inform policymakers and technology developers about effectively promoting the use of green energy solutions. Former studies largely emphasize the residential areas [7,8,9,10,11], while the decision-making mechanism of companies could be unique or dramatically different. To the best of our knowledge, this is perhaps the first attempt to examine companies’ adoption of innovative energy facilities systematically.

The remainder of this paper is organized as follows: a literature review will precede a discussion of data collection and the design of the stated choice experiment, followed by a detailed analysis. We will then present the results from the simultaneous equation model estimation, concluding with future considerations.

2. Literature Review

The adoption of renewable energy facilities was influenced by a variety of factors, including financial, technological, policy-related, and environmental considerations, as shown in

Table 1. Variables influencing preferences for energy facilities.

	Variables	References
Finance-related variables	purchase price and maintenance costs	[14,15,16,17,18]
	payback period	[19,20]
Technical performance-related variables	off-grid capability	[21]
	cooling capability	[22]
	energy recovery performance	[23]
	air filtration	[24]
Policy-related variables	policy incentives and tax refunds	[25]
environmental considerations-related variables	environmental concerns	[15,21]

. While these factors have been widely recognized in the decision-making process of households [12,13], their relative importance and interplay remain underexplored in the context of SME adoption.

2.1. Financial Barriers and Constraints

A consistent theme in the literature is the financial barriers that customers face when considering the adoption of energy-efficient technologies. The high initial investment costs and ongoing maintenance expenses are often cited as major deterrents [14]. For instance, access to capital is frequently identified as a significant constraint for companies, with studies indicating that many businesses lack the necessary financial resources to invest in energy-efficient solutions. This issue is particularly pronounced in SMEs, which generally face more stringent budgetary limitations compared with larger enterprises [15]. While financial barriers such as these are well-documented in residential energy adoption research, SMEs face additional complexities, such as concerns about return on investment and the long payback periods associated with renewable energy systems [16]. Research has highlighted that these concerns often lead to delayed or avoided investment despite the long-term benefits of renewable technologies [17].

2.2. Technological Considerations and Performance

The technical performance of energy facilities plays a critical role in adoption decisions. Businesses often prioritize technologies that offer tangible benefits, such as cost savings or increased energy efficiency. For example, the self-sufficiency and off-grid capabilities of solar panels have made them a popular choice, particularly in regions where energy security is a concern [18]. Similarly, heat pumps are attractive because of their energy-saving potential, especially for businesses seeking to reduce heating and cooling costs [19]. The energy recovery performance of ventilation systems is a significant concern for small businesses, with advanced features [20] such as demand control and air filtration being critical for energy conservation [21].

2.3. Policy and Incentive Structures

Government policies, particularly financial incentives such as subsidies, tax breaks, and rebates, have been widely recognized as important drivers of adoption. In many countries, these policies have successfully boosted the adoption of renewable energy technologies. For example, tax incentives for solar panels have been shown to increase adoption rates in the residential sector significantly [22]; however, the impact of policy incentives on SMEs has not been analyzed extensively. While some studies indicate a positive relationship between government incentives and adoption rates, it is important to note that the effectiveness of such policies may vary across different types of businesses, depending on their specific needs, financial situations, and energy consumption patterns. Furthermore, the influence of policies on SMEs may be overshadowed by other constraints, such as a lack of concern about the long-term viability of the technologies.

2.4. Environmental Considerations and Corporate Social Responsibility

Environmental concerns have also been cited as motivators for renewable energy adoption, though their influence on SMEs is less clear. On the one hand, SMEs in certain industries are increasingly recognizing the importance of corporate social responsibility (CSR) and the role they can play in reducing carbon emissions; however, there is evidence that environmental motivations do not always translate into a willingness to invest in renewable energy technologies. While some studies have found that environmental concerns significantly influence the adoption of energy-efficient solutions [23], others suggest that these motivations are less important for SMEs [15], which often prioritize financial and operational considerations over environmental benefits. This is particularly true for businesses in the tertiary sector, where the adoption of green technologies may be seen as a way to enhance their social image and meet consumer demand for sustainability rather than being driven by intrinsic environmental values.

2.5. Research Gaps

In summary, while the existing literature has provided valuable insights into the factors influencing renewable energy adoption [24], there remain significant gaps, particularly in understanding the complex decision-making processes of SMEs. Companies may have a more significant role in energy-saving because of their responsibility for carbon reduction [25]; however, balancing this responsibility with innovation efforts can be challenging because of various constraints.

Additionally, the integration of multiple energy technologies and the synergies between them have been largely overlooked. Existing studies have primarily focused on the adoption of individual renewable energy technologies [26], with limited attention given to the potential benefits of integrating multiple systems. For example, the combination of solar panels and heat pumps has been shown to reduce energy consumption. The literature largely overlooks the synergies that may exist between different energy technologies, which could influence adoption decisions [27]. This lack of focus on the integration of multiple technologies in SME settings represents a critical gap in the literature. This study aims to address these gaps by examining the adoption behavior of SMEs in the tertiary sector, with a focus on the interdependencies between solar panels, heat pumps, and ventilation systems.

3. Materials and Methods

In order to collect data for this analysis, a dedicated experiment needs to be designed. For this purpose, we designed a stated choice experiment and implemented a survey to obtain data from SMEs in the tertiary sector. The stated choice experiment methodology for this study is due to its effectiveness in capturing the nuanced decision-making processes. Given limited historical data on these specific technologies in the tertiary sector, stated choice experiments offer a robust framework for evaluating preferences based on hypothetical scenarios. By the stated choice experiment methodology, we aim to uncover the key factors influencing adoption intentions, which is essential for informing policy and practice in promoting renewable energy solutions. This section will describe in detail the experimental design and the survey management.

3.1. Experiment Design

The purpose of designing a choice experiment is to capture the adoption behavior of companies given a set of innovative energy facilities represented by the characteristics of the alternatives and the choice contexts. The stated choice experiment is a research method used to understand preferences and decision-making processes by presenting respondents with hypothetical scenarios. This approach is especially useful when no sufficient historical data and variation exist. Through the definition of attributes and the associated levels, one can create a hypothetical situation in which respondents can make a choice on the provided alternatives. Normally, respondents are presented with a series of randomized hypothetical choice sets in which the attribute values of the alternatives are varied across choice situations. Thus, defining the attributes and attribute levels is critical to warrant reliable observations. Since companies’ adoption of innovative energy facilities has not been addressed systematically in the literature, we borrow the knowledge of relevant existing works to define the attributes and attribute levels for each energy facility.

3.1.1. Attributes of Solar Panel

As presented in the literature review, cost, policy, and technology performance are generally found to affect the adoption of solar panels. The cost in practice involves many types, including installation, insurance, number of solar panels, brand, etc. The policy could refer to the incentive programs operated at the municipality or national level, e.g., tax reduction for purchasing. The technology performance normally means the efficiency of the solar panel, which influences the total payback years compared with the initial investment for installation. These different factors play a different role in affecting the adoption of solar panels. Furthermore, solar panel as a renewable energy source also helps companies to reduce CO₂ emissions; therefore, in the choice experiment, we consider CO₂ emission as a factor that might indirectly influence the choice decisions bounded by social responsibility. In the end, the alternative of solar panels involves five attributes: total installation price, municipal incentive, annual reduction in energy charges, payback years, and the percentage of CO₂ reduction.

To define proper levels for each attribute, company profiles need to be considered because of the large variation in types and scales. For instance, the installation price may be associated with the company size because larger companies may demand more solar panels due to bigger energy demand. Respondents were informed that the prices included acquisition, installation, and maintenance costs before conducting the experiment. Considering the market situation in the Netherlands, the range of price is set from €2500 to €10,000 if the size of the company (building square meters) is smaller than 70 m², from €9000 to €18,000 if the company size is between 70 m² and 140 m², and from €12,000 to €24,000 if company size is larger than 140 m².

The range of payback years is another important factor. The payback period is calculated based on the rate of return, which depends on the price of solar panels, the price of electricity, and the annual power yield. In Belgium and the Netherlands, the maximum annual power generation is approximately 850–900 h. Considering the local solar power efficiency and dynamic electricity rates, €10,000 can purchase 4700 Watt Peak solar panels, with an annual power generation value ranging from €750 to €1800. To cover the actual situation as much as possible, the payback year of solar panels in this study is varied from 5 years to 20 years.

Apart from that, we define policy incentives as the incentives from municipalities. This is also the common practice in the Netherlands where a certain amount of tax refund is offered for purchasing solar panels. In our experiment, the amount of the incentives is a percentage that is relative to the purchase price. We consider the values from the extreme case of no incentives (i.e., when solar panels are popular) to 30% of the purchase price as the maximum tax refund.

In spite of the effects of saved energy charge can be partly reflected by the payback year, this is an indicator related to the energy use behavior, e.g., energy-efficient behavior will save more energy cost; however, the magnitude of the cost savings is also closely related to the number of solar panels. In general, the more solar panels installed, the larger energy savings can be achieved. A small number of solar panels can support basic energy use partly or fully, while a large number of solar panels will generate more profit. Thus, we set the range of potential cost saving into four levels: 20%, 40%, 60%, and 80% of the current energy cost.

Apart from these cost-related factors, we also include social responsibility in reducing carbon emissions. By informing users of a certain amount of carbon reduction attached to solar panels, we can observe their responses to solar panels respective to the carbon reduction. Here, we set the percentage of emission reduction as 10%, 20%, 30%, and 40%, respectively.

3.1.2. Attributes of Heat Pump

Similar to solar panels, cost-related factors are the primary concerns in the acceptance of heat pumps, such as installation price, policy incentives, and performance in carbon emission reduction. In addition, differing from solar panels, which generate electricity, heat pumps consume electricity and are mainly featured with functions of cooling and/or warming; therefore, a binary attribute related to the function, varied with cooling and heating, was specified for heat pumps.

The installation price was defined according to the size of the company, ranging from €5000 to €8000 if the company size is smaller than 70 m², from €8000 to €10,000 if the company size is between 70 and 140 m², and from €10,000 to €16,000 if company size is larger than 140 m². Note that the price here is approximated to the average of a standard heat pump without consideration of luxury additions.

The payback period of a heat pump is influenced by several key factors, including the initial costs of the heat pump itself, the energy consumption it incurs, the prevailing energy prices, and any available tax refunds or grants. In the Netherlands, the price of natural gas for small-sized enterprises ranges from €1.0 to €1.5 per cubic meter. For instance, a heat pump priced at €5000 can lead to substantial savings by reducing natural gas consumption by approximately 500 m³ annually; however, it is important to note that this system also requires electricity, consuming between 600 and 1200 kWh each year. Given the average electricity price of €0.40 per kWh, the annual savings generated from the heat pump can be estimated to fall between €200 and €500. Furthermore, considering the availability of a 30% grant, the overall financial landscape becomes more favorable. With this grant taken into account, we project that the payback period for the investment in a heat pump could extend from 5 to 20 years, depending on the specific operational parameters and energy usage patterns of the enterprise. Similar to solar panels, the tax refund is set to range from no incentives to a maximum of 30%, considering the realistic policy situation in the Netherlands.

The carbon emissions of heat pumps are correspondingly high when the supplied electricity is produced by high-emission energy resources and relatively low in the case of renewable energy-generated electricity. Overall, the performance of carbon emission of heat pumps is lower than that of solar panels; therefore, the proportion of carbon emission for heat pumps is set from 10% to 40%.

3.1.3. Attributes of Ventilation

The ventilation was characterized by installation price, incentive, carbon emission reduction, and the function of demand control, pollutant filter, and heat recovery. According to the market situation of ventilation in the Netherlands, prices were set from €1000 to €10,000. The possible value of the tax incentive in the stated choice experiment was set as 0, 10%, 20%, and 30%, considering the realistic policy situation. In addition to the price, incentive, environmental benefits, and the function of demand control were considered since it can provide an optimized ventilation scheme and has higher energy efficiency. According to the literature review section, the function of recovering heat can reduce energy consumption for heating. According to the previous study [28], ventilation with demand response strategies or heat recovery can reduce around 20% of emissions. The filter-out pollutants function can provide the promotion of comfort and a healthy environment. The ranges of attributes for this experiment are shown in

Table 2. Attributes of renewable energy facilities.

Attribute	Solar Panel	Heat Pump	Ventilation
Price (€) (<70 m2)	2500; 5000; 7500; 10,000	5000; 6000; 7000; 8000	1000; 3000; 5000; 10,000
Price (€) (70–140 m2)	9000; 12,000; 15,000; 18,000	8000; 10,000; 2000; 14,000	1000; 3000; 5000; 10,000
Price (€) (>140 m2)	12,000; 16,000; 20,000; 24,000	10,000; 12,000; 4000; 16,000	1000; 3000; 5000; 10,000
Incentive from municipality	0; 10%; 20%; 30%	0; 10%; 20%; 30%	0; 10%; 20%; 30%
Annual reduction in energy charges	20%; 40%; 60%; 80%	20%; 40%; 60%; 80%	-
CO2 reduction	10%; 20%; 30%; 40%	10%; 20%; 30%; 40%	-
Payback years	5; 10; 15; 20	5; 10; 15; 20	-
Function	-	Cooling + Heating; Heating only	-
Have demand control?	-	-	Yes/No
Can recover heat?	-	-	Yes/No
Can filter out pollutants?	-	-	Yes/No

.

3.1.4. Choice Set Generation and Implementation

A detailed list of attributes and attribute levels is presented in Table 2, where 2-levels and 4-levels are adopted according to each alternative. To generate the choice sets, the feasible number of combinations between attributes and levels needs to be identified. Because, in total, we have twenty 4-level attributes and three 2-level attributes, a full factorial design will result in 4²⁰ × 2³ combinations, which makes the actual implementation unrealistic. Therefore, we use an orthogonal fractional factorial design which still warrants the orthogonality and the observation of the main effects.

The fractional factorial design includes 64 combined profiles varied by the levels of attributes. The choice sets were then randomly assigned to the respondents. Each respondent was given eight different choice situations, which means every participant answered eight times the choice questions, and each question included different combinations of attribute levels. Figure 1 shows an example of the choice task, which includes three alternatives: solar panel, heat pump, and ventilation.

In a standard stated choice experiment, each respondent is often asked to choose the option they prefer the most, leaving other alternatives not chosen. This assumes that the choice alternatives are independent. As noted in the first section, the interrelationship between different energy facilities should be captured. This means the synergy effect between alternatives will not be observed if only one alternative can be selected. For example, if one respondent chooses the solar panel as the preferred option, then how the respondent values the integration between solar panels and other options becomes unknown, losing the information on the correlation between alternatives.

Therefore, we propose a different approach. Instead of choosing only one of the options, respondents were asked to report the intention to adopt each of the provided options at a level of scale. More specifically, respondents were asked to indicate their intention to install each type of energy facility using the options including “definitely not”, “unlikely”, “neutral”, “likely”, and “definitely yes”. The intention to install each innovative energy facility is then recorded. In this way, the potential mutual dependency underlying individuals’ decision-making behavior is captured.

Table 3. Example of the choice scenario.

	Solar Panel	Heat Pump	Ventilation
Price (€)	4500	8000	1000
Incentive from municipality	No incentive	10%	20%
Annual reduction in energy charges	80%	20%
CO2 reduction	20%	10%
Payback years	20	5
Function		Cooling + Heating;
Demand control			Yes
Can recover heat?			No
Can filter out pollutants?			No
Your choice	☐Definitely NOT ☐Unlikely ☐Neutral ☐Likely ☐Definitely YES	☐Definitely NOT ☐Unlikely ☐Neutral ☐Likely ☐Definitely YES	☐Definitely NOT ☐Unlikely ☐Neutral ☐Likely ☐Definitely YES

presents an example of the choice scenario provided to respondents.

3.2. Survey Management and Descriptive Statistics

The data collection for this study was conducted in the Netherlands in October 2020. The questionnaire started with a general introduction to the research objective. Then, respondents were asked to fill in the general profiles of the company, e.g., location, business scope, size, investment plan, and energy consumption details of their companies, and then the choices of the experiment. Because managers may not be familiar with the innovative energy facilities, i.e., features and price, some screening information about these facilities was explained before the choice experiment. Completing the survey took about 15–20 min.

A marketing company was engaged to recruit respondents from various cities, ensuring diverse representation from the tertiary sector. Given the focus on the purchasing intentions of small and medium-sized enterprises (SMEs) regarding energy facilities, the respondents were typically SME owners or individuals in managerial roles. Figure 2 shows the spatial distribution of the companies (several are located within Belgium). Additionally, we required respondents to have a recent investment intention, which led to the relatively low response rate of 15.6%, with 126 out of 810 contacted individuals providing valid data. While this response rate may seem modest, it reflects the challenges inherent in engaging this specific demographic. To enhance the sample size, we employed the stated choice method, where each respondent answered eight choice scenarios, each varying in context, resulting in a total of 1008 choice observations for analysis. This approach not only increases the robustness of the dataset but also strengthens the validity and reliability of the findings presented in this study.

Table 4. Characteristics of the companies.

Variable	Classification	# of Cases	Percentage
Location	Center	25	19.8%
	Suburb	54	42.9%
	Rural	47	37.3%
Number of employees	Less than 5	45	35.7%
	From 6 to 20	40	31.8%
	More than 20	41	32.5%
Business scope	No Food-related	51	40.5%
	Food-related	43	34.1%
	Others	32	25.4%
Plan to install innovative energy facilities	Yes	77	61.1%
	No	49	38.9%
Building ownership	Own	59	46.8%
	Rent	67	53.2%

presents the descriptive statistics of the company characteristics. The location represents the built environment around the company. It is related to the building density and available space for the installation of the energy facilities. Companies are mostly located in the suburbs (42.9%) and rural areas (37.3%) and less in the city centers (19.8%). Company size is another important indicator as it approximates the potential energy demand; the size of companies is almost equally distributed, and each takes about one-third.

Building ownership is linked to influencing the authority to install a facility and uncertainty about the future. Obviously, those who own the building are more flexible in the adoption of energy facilities. The results show a slightly larger number of companies rented the building (53.2%) instead of ownership. In addition, the business scope of companies can significantly impact the structure of energy demand and the facilities. Here, we defined detailed categories in the survey; however, considering the high level of variations, we recategorized the type into food-related (butcher shops, bakery, food shops, restaurants, bars, and hotels), non-food related (retailing, haircut, and offices), and others.

Moreover, whether a company owner has an investment plan could be related to the reserved budget to invest in energy facilities. Interestingly, these data show more than 60% of the companies have a plan in the future to install innovative energy facilities if possible. This means that keeping everything else constant, the intention to upgrade companies’ current facilities is relatively big.

3.3. Methodology

In this study, the adoption intentions of company owners toward innovative energy facilities are examined using a simultaneous equation model. This methodological approach is particularly suitable for capturing the potential interdependencies and interactions among multiple alternatives. A simultaneous equation model is a robust econometric tool that can simultaneously deal with two or more equations that interact with each other. It is specifically designed to address situations where the observational phenomena are assumed to be reciprocally causal, thereby enabling a more comprehensive understanding of the dynamics at play. By modeling such interdependencies, the simultaneous equation model can effectively manage issues of endogeneity, which often arise when there are mutual causal relationships between variables. This capacity to disentangle and analyze the relationships between multiple endogenous variables is a distinguishing feature of the simultaneous equation model, making it an invaluable tool for exploring complex decision-making processes, such as those involving the adoption of innovative technologies. Through this approach, this study aims to provide insights into how different factors jointly influence the adoption of energy innovations, offering a more nuanced perspective on corporate decision-making behavior in this domain. Here, we consider the attributes of energy facilities and company characteristics as the exogenous variables and the intention to purchase innovative energy facilities as endogenous variables in a simultaneous equation model. In specific, the model can be written as follows:

$$y_{sp}={\mathrm{c}}_{sp}+{\mathsf{\alpha }}_{sp}\mathit{z}+{\mathsf{\beta }}_{sp}{\mathit{x}}_{sp}+{\mathsf{\gamma }}_{sp}{\mathbf{y}}_{h,v}+{\mathsf{\epsilon }}_{sp}$$

(1)

$$y_{hp}={\mathrm{c}}_{hp}+{\mathsf{\alpha }}_{hp}\mathit{z}+{\mathsf{\beta }}_{hp}{\mathit{x}}_{hp}+{\mathsf{\gamma }}_{hp}{\mathbf{y}}_{s,v}+{\mathsf{\epsilon }}_{hp}$$

(2)

$$y_{vt}={\mathrm{c}}_{vt}+{\mathsf{\alpha }}_{vt}\mathit{z}+{\mathsf{\beta }}_{vt}{\mathit{x}}_{vt}+{\mathsf{\gamma }}_{vt}{\mathbf{y}}_{s,h}+{\mathsf{\epsilon }}_{vt}$$

(3)

where $y_{sp}$, $y_{hp}$, and $y_{vt}$ are dependent variables, representing the level of acceptance of solar panel (sp), heat pump (hp), and ventilation (vt), respectively.$c_{sp}$, $c_{hp}$, and $c_{vt}$ are the intercepts for the three alternatives. $\mathit{z}$ denotes the vector of company characteristics that are invariant across alternatives and refers to location, size, building ownership, business scope, and investment plan. ${\mathit{x}}_{\mathit{s}\mathit{p}}$, ${\mathit{x}}_{\mathit{h}\mathit{p}}$, and ${\mathit{x}}_{\mathit{v}\mathit{t}}$ are the vectors of the main attributes included in the stated choice experiment with respect to solar panels, heat pumps, and ventilation, respectively. ${\mathit{x}}_{\mathit{s}\mathit{p}}$ includes the price, the incentive from the municipality, the annual reduction in energy charges, CO₂ reduction, and payback years. ${\mathit{x}}_{\mathit{h}\mathit{p}}$ includes price, the incentive from the municipality, the annual reduction in energy charges, CO₂ reduction, payback years, and cooling function. ${\mathit{x}}_{\mathit{v}\mathit{t}}$ includes price, the incentive from the municipality, the annual reduction in energy charges, payback years and the function of demand control, heat recovery, and pollutants filtering. $\mathbf{y}$ is a vector to address the endogeneity, representing the acceptance of other energy facilities. In concrete, ${\mathbf{y}}_{h,v}$ represents the effects of the choices of heat pump and ventilation, ${\mathbf{y}}_{s,v}$ represents the effects of the choices of solar panels and ventilation, and ${\mathbf{y}}_{s,h}$ represents the effects of the choice of solar panel and heat pump. ${\mathit{\alpha }}_{sp},{\mathit{\alpha }}_{hp},{\mathit{\alpha }}_{vt}$, ${\mathit{\beta }}_{sp}$, ${\mathit{\beta }}_{hp}$, ${\mathit{\beta }}_{vt}$, ${\mathit{\gamma }}_{sp}$, ${\mathit{\gamma }}_{hp}$, and ${\mathit{\gamma }}_{vt}$ are the vectors of the parameters to be estimated. ${\epsilon }_{sp}$, ${\epsilon }_{hp}$ and ${\epsilon }_{vt}$ denote the error terms, which are normally distributed with 0 mean and a variance-covariance matrix V (${\mathsf{\epsilon }}_{sp}$,${\mathsf{\epsilon }}_{hp}$, ${\mathsf{\epsilon }}_{vt}$). The variance covariance matrix is given by

$$V=\left(\begin{array}{c}1{\rho }_{sp,hp}{\rho }_{sp,vt}\hfill \\ {\rho }_{hp,sp}1{\rho }_{hp,vt}\hfill \\ {\rho }_{vt,sp}{\rho }_{vt,hp}1\hfill \end{array}\right)$$

(4)

where the off-diagonal elements in the covariate matrix, $\rho$, represent the correlation between the endogenous components of three alternatives. To deal with the endogeneity in the simultaneous equation model, the error terms are correlated with each other. This means the traditional ordinary least square (LS) estimation, which deals with a single equation standalone, does not apply. In addition, because of the ignorance of the simultaneity in the equation system, LS normally results in simultaneity bias and inconsistent estimation [29].

Therefore, this study employs the Three-Stage Least Squares (3 SLS) estimation method, a widely recognized approach for addressing both contemporaneous residual correlation and predictor endogeneity. The 3 SLS method extends the capabilities of the Two-Stage Least Squares (2 SLS) approach by not only accounting for the potential endogeneity of explanatory variables but also allowing for correlations between the error terms of different equations within the system. This is particularly relevant when multiple equations are involved, as the unobserved disturbances across these equations may be correlated, which can bias the estimates if not properly accounted for. It is more efficient compared with 2 SLS and allows correlations between unobserved disturbances across various equations. In this study, the exogenous variables uncorrelated with the error terms were selected as the instrument variables which are used to regress the endogenous predictor. More specifically, the variables that are independent of all the other variables (including error terms) but not independent of the endogenous variables were selected.

4. Results

An essential test was conducted before estimating the simultaneous equation model with 3 SLS. The Hausman test was applied to test the endogeneity of the equations. The null hypothesis of the endogeneity is that the ordinary least squares (OLS) estimator would not be significantly impacted by the endogeneity problem. The OLS estimators provide consistent estimate results. A rejection of the null hypothesis shows instrument variables are required in the estimate process. The Haussmann statistics provide evidence of acceptance or rejection of the null hypothesis. The test statistic value is 21.429 (df = 3), which is insignificant (p > 0.99) such that the null hypothesis can be rejected. We conclude that OLS is inconsistent. The McElroy R-squared value of the simultaneous model with 3 SLS is 0.738, which is satisfactory. The detailed estimation results are summarized in Table 5, Table 6 and Table 7.

4.1. Results of Solar Panel

As reported in Table 5, most of the estimated parameters are statistically significant. The installation price of €2500 (0.181) has a significant and positive effect on the adoption of solar panels, while the adoption intention is negatively related to the installation price when the price is higher than €7500. This means the acceptance decreases with the increasing installation price. The difference between the effects of price at €2500 and €10,000 is 0.326. These results are broadly in line with the findings of existing studies [30].

Table 5. Results of solar panel.

		Estimate		t-Value	p-Value
	Intercept	1.495	***	6.687	0.000
Price	€2500	0.181	***	7.491	0.000
	€5000	0.030		1.263	0.207
	€7500	−0.066	***	−2.767	0.006
	€10,000	−0.145
Incentive	0%	−0.152	***	−6.438	0.000
	10%	−0.046	**	−1.934	0.053
	20%	0.073	***	3.110	0.002
	30%	0.125
Annual reduction in energy charges	20%	−0.143	***	−6.020	0.000
	40%	−0.013		−0.561	0.575
	60%	0.020		0.853	0.394
	80%	0.136
CO2 reduction	10%	−0.084	***	−3.558	0.000
	20%	0.007		0.276	0.783
	30%	0.030		1.287	0.198
	40%	0.047
Payback years	5	0.139	***	5.835	0.000
	10	0.079	***	3.322	0.001
	15	−0.066	***	−2.787	0.005
	20	−0.152
Location	City center	−0.116	***	−1.967	0.049
	Suburb	−0.044	***	−2.261	0.024
	Rural	0.160
Have an investment plan	Yes	0.299	***	10.761	0.000
	No	−0.299	***	−10.761	0.000
Building ownership	Own	−0.027		−1.074	0.283
	Rent	0.027		1.074	0.283
Number of employees	≤5	0.002		0.043	0.966
	5–20	−0.116	***	−5.011	0.000
	≥20	0.114
Business scope	Food	0.228	***	11.192	0.000
	Non-food related	−0.391	***	−19.847	0.000
	Other	0.163
Endogenous effects	Heat pump	0.450	***	5.652	0.000
	Ventilation	0.152	***	2.571	0.010
R-Squared		0.730

Notes: “**”, “***” indicate significance at the 90%, 95% and 99% confidence level, respectively.

Table 5. Results of solar panel.

		Estimate		t-Value	p-Value
	Intercept	1.495	***	6.687	0.000
Price	€2500	0.181	***	7.491	0.000
	€5000	0.030		1.263	0.207
	€7500	−0.066	***	−2.767	0.006
	€10,000	−0.145
Incentive	0%	−0.152	***	−6.438	0.000
	10%	−0.046	**	−1.934	0.053
	20%	0.073	***	3.110	0.002
	30%	0.125
Annual reduction in energy charges	20%	−0.143	***	−6.020	0.000
	40%	−0.013		−0.561	0.575
	60%	0.020		0.853	0.394
	80%	0.136
CO2 reduction	10%	−0.084	***	−3.558	0.000
	20%	0.007		0.276	0.783
	30%	0.030		1.287	0.198
	40%	0.047
Payback years	5	0.139	***	5.835	0.000
	10	0.079	***	3.322	0.001
	15	−0.066	***	−2.787	0.005
	20	−0.152
Location	City center	−0.116	***	−1.967	0.049
	Suburb	−0.044	***	−2.261	0.024
	Rural	0.160
Have an investment plan	Yes	0.299	***	10.761	0.000
	No	−0.299	***	−10.761	0.000
Building ownership	Own	−0.027		−1.074	0.283
	Rent	0.027		1.074	0.283
Number of employees	≤5	0.002		0.043	0.966
	5–20	−0.116	***	−5.011	0.000
	≥20	0.114
Business scope	Food	0.228	***	11.192	0.000
	Non-food related	−0.391	***	−19.847	0.000
	Other	0.163
Endogenous effects	Heat pump	0.450	***	5.652	0.000
	Ventilation	0.152	***	2.571	0.010
R-Squared		0.730

Notes: “**”, “***” indicate significance at the 90%, 95% and 99% confidence level, respectively.

The reduction in annual energy charges is an indicator of the economic benefit for companies. The parameter is found negative at the level of 20% reduction and positive at the level of 80% reduction, indicating that a larger reduction in energy charges brought by the innovative energy facilities does increase the intention to adopt innovative energy facilities.

The payback period has been considered a highly relevant indicator to invest in solar panels. Results show a five-year payback period has a positive impact on the adoption, while such an effect decreases sharply when the payback period is longer than 10 years. This is understandable in that a long payback period for solar panels, in general, hinders the adoption behavior because of the unpredictable future. These results are also consistent with the findings by [31]. Furthermore, in the case of household decisions, the initial cost was recognized to have a higher impact than the payback period on the choice of solar panels; however, the results in this study indicate that the payback period has a higher impact on the acceptance of solar panel [30]. This is perhaps triggered by the difference between companies and households, where companies could be more cautious when facing an uncertain future [32].

Incentive is an important policy measure to promote the adoption of innovative energy facilities. Results show a significant effect of government incentives on companies’ acceptance of solar panels. The coefficient of no government incentive is negative, indicating no interest, which is consistent with the recent studies about the effects of the incentive on attitudes toward solar panels [33]. When the incentive increases from 10% (−0.046) to 20% (0.073), the effect increases by 0.119, while when the incentive increases from 20% to 30%, the effect increases by only 0.052 (Figure 3). These results indicate that the marginal effect of a 10% incentive to a 20% incentive is relatively high. In other words, incentives between 10% and 20% are more cost effective to increase the acceptance of solar panels.

As illustrated before, the CO₂ reduction was included as an indicator of environmental benefits related to solar panels. The intention is to examine, by showing the percentage of CO₂ reduction, if this environmental feature has an impact on the adoption decision. The results show that CO₂ reduction has a positive impact on the acceptance of solar panels in terms of the signs of parameters, although the effects are not fully significant.

Regarding company characteristics, the intention to adopt solar panels for the companies located in city centers is lower than the companies in suburb and rural areas. This may be because of the limited space in central urban areas. The barrier caused by constraint has also been reported by [32].

Moreover, the company size at the level of “more than 20 employees” has a positive sign, which means the acceptance of solar panels increases with the number of employees. Companies with more employees may be more flexible in financial situations, while smaller companies may have to deal with the initial capital. In addition, the companies who have an investment plan are found to have a higher intention to adopt solar panels. This indicates promoting solar panels would be easier for those having investment plans.

The adoption behavior of companies in solar panels also differs according to their business. We found that food-related companies such as restaurants and hotels have a higher intention to install solar panels than other types of companies, which is probably because the electricity demand of food-related businesses is relatively high, and solar panel can significantly reduce their energy cost.

4.2. Results of Heat Pump

As presented in Table 6, most parameters are significant. Similar to the heat pump, an installation price of €5000 level has a positive and statistically significant effect on the acceptance of heat pumps. The effect decreases linearly when the price of the heat pump increases from €5000 to €7000; however, the effects do not change much when the price increases from €7000 to €8000.

Table 6. Results for heat pumps.

		Estimate		t-Value	p-Value
	Intercept	2.025	***	12.752	0.000
Price	€5000	0.069	***	3.008	0.003
	€6000	0.011		0.502	0.615
	€7000	−0.043	**	−1.898	0.058
	€8000	−0.037
Incentive	0%	−0.069	***	−2.979	0.003
	10%	−0.023		−1.023	0.307
	20%	0.032		1.391	0.164
	30%	0.060
Annual reduction in energy charges	20%	−0.118	***	−5.057	0.000
	40%	−0.081	***	−3.564	0.000
	60%	0.062	***	2.709	0.007
	80%	0.137
CO2 reduction	10%	−0.044	**	−1.934	0.053
	20%	−0.028		−1.238	0.216
	30%	0.035		1.544	0.123
	40%	0.043
Payback years	5	0.133	***	5.701	0.000
	10	0.012		0.535	0.593
	15	−0.053	***	−2.303	0.021
	20	−0.092
Function	Cooling and heating	0.007		0.556	0.579
	Heating	−0.007		−0.556	0.579
Location	City center	−0.430	***	−9.767	0.000
	Suburb	−0.028		−1.453	0.146
	Rural	0.458
Have investment plan	Yes	0.195	***	7.184	0.000
	No	−0.195	***	−7.184	0.000
Have building ownership	Own	0.066	***	2.729	0.006
	Rent	−0.066	***	−2.729	0.006
Number of employees	<5	−0.349	***	−7.314	0.000
	5–20	0.110	***	5.093	0.000
	≥20	0.239
Business scope	Food	−0.017		−0.730	0.466
	Non-food	0.034		1.199	0.231
	Other	−0.017
Endogenous effects	solar panel	0.181	***	3.380	0.001
	Ventilation	−0.105	*	−1.804	0.071
R-Squared		0.635

Notes: “*”, “**”, “***” indicate significance at the 90%, 95% and 99% confidence level, respectively.

Table 6. Results for heat pumps.

		Estimate		t-Value	p-Value
	Intercept	2.025	***	12.752	0.000
Price	€5000	0.069	***	3.008	0.003
	€6000	0.011		0.502	0.615
	€7000	−0.043	**	−1.898	0.058
	€8000	−0.037
Incentive	0%	−0.069	***	−2.979	0.003
	10%	−0.023		−1.023	0.307
	20%	0.032		1.391	0.164
	30%	0.060
Annual reduction in energy charges	20%	−0.118	***	−5.057	0.000
	40%	−0.081	***	−3.564	0.000
	60%	0.062	***	2.709	0.007
	80%	0.137
CO2 reduction	10%	−0.044	**	−1.934	0.053
	20%	−0.028		−1.238	0.216
	30%	0.035		1.544	0.123
	40%	0.043
Payback years	5	0.133	***	5.701	0.000
	10	0.012		0.535	0.593
	15	−0.053	***	−2.303	0.021
	20	−0.092
Function	Cooling and heating	0.007		0.556	0.579
	Heating	−0.007		−0.556	0.579
Location	City center	−0.430	***	−9.767	0.000
	Suburb	−0.028		−1.453	0.146
	Rural	0.458
Have investment plan	Yes	0.195	***	7.184	0.000
	No	−0.195	***	−7.184	0.000
Have building ownership	Own	0.066	***	2.729	0.006
	Rent	−0.066	***	−2.729	0.006
Number of employees	<5	−0.349	***	−7.314	0.000
	5–20	0.110	***	5.093	0.000
	≥20	0.239
Business scope	Food	−0.017		−0.730	0.466
	Non-food	0.034		1.199	0.231
	Other	−0.017
Endogenous effects	solar panel	0.181	***	3.380	0.001
	Ventilation	−0.105	*	−1.804	0.071
R-Squared		0.635

Notes: “*”, “**”, “***” indicate significance at the 90%, 95% and 99% confidence level, respectively.

The payback period at a five-year level has a positive sign, which suggests the shorter payback period has positive impacts on the acceptance of heat pumps. When the payback time is fifteen years or longer, the probability of adopting a heat pump decreases. In addition, results show that cost reduction increases the acceptance of heat pumps. The coefficient of energy reduction increases obviously from 40% reduction (−0.081) to 60% reduction (0.062), which means the heat pump will become more acceptable when it can save nearly half of the energy expense.

Regarding government incentives, the coefficient for no incentive (−0.067) is negative and statistically significant, which indicates incentive also has a significant impact on the acceptance of heat pumps. The result is in line with previous studies about heat pump diffusion [34]. The acceptance of heat pumps increases near-linearly if the incentive increases from 0 to 30%, which is different from the impact on solar panels. This is perhaps because heat pumps are relatively new, and many EU cities are implementing incentives to promote their installation. In contrast, the incentives for solar panels in some cities have already diminished or closed.

In addition, the coefficient of emission reduction related to heat pumps is insignificant, indicating that the environmental benefits are not the primary motivation for companies to install heat pumps. The cooling function has no significant impact on the acceptance of heat pumps. This is, however, dependent on the microclimate. In this study area, the need for cooling is not strong. The values and changes in heat pump attributes are presented in Figure 4.

As to the company’s characteristics, the number of employees has a positive impact, which implies the acceptance of heat pumps increases with the company size. This is similar to the case of solar panels. In general, smaller companies are less likely to invest in expensive energy facilities. Moreover, companies in the city center are also less likely to install a heat pump than companies in suburb and rural areas. Furthermore, building ownership shows a positive impact on investing in heat pumps in the sense that companies who own the building are more likely to adopt heat pumps. Apart from that, no significant impact was found for business scope on the adoption of heat pumps. Furthermore, the reduction in CO₂ emissions is found insignificant, which is different from the effect on solar panels. This means the environmental benefits are not yet a motivation for companies to adopt heat pumps; however, this conclusion may deserve further analysis because, in the choice experiment, we did not technically discriminate heat pumps according to natural resources or not.

4.3. Results of Ventilation

Table 7 lists the parameter results related to ventilation. As shown in Table 7, the parameter for a price of €1000 is 0.294, while the parameter for €10,000 is −0.185. Similar to solar panels and heat pumps, the installation price of ventilation has a significantly negative impact on its acceptance. The probability decreases obviously when the price of ventilation increases from €1000 to €3000. The coefficient decreases by 0.318 when the price increases from €1000 (0.294) to €3000 (−0.024), while the coefficient only decreases 0.1 when the price increases from €3000 to €10,000. These results imply that companies are more sensitive to the price when it is between €1000 and €3000.

Table 7. Results of ventilation.

		Estimate		t-Value	p-Value
	Intercept	1.496	***	7.588	0.000
Price	€1000	0.294	***	11.766	0.000
	€3000	−0.024		−0.997	0.319
	€5000	−0.085	***	−3.470	0.001
	€10,000	−0.185
Incentive	€0%	−0.077	***	−3.145	0.002
	€10%	−0.019		−0.782	0.434
	€20%	0.002		0.082	0.935
	€30%	0.094
Demand control	Yes	−0.040	***	−2.877	0.004
	No	0.040	***	2.877	0.004
Can recover heat?	Yes	0.062	***	4.368	0.000
	No	−0.062	***	−4.368	0.000
Can filter out pollutants?	Yes	0.103	***	7.242	0.000
	No	−0.103	***	−7.242	0.000
Location	City center	−0.631	***	−15.405	0.000
	Suburb	−0.035	*	−1.704	0.089
	Rural	0.666
Have investment plan	Yes	0.124	***	4.166	0.000
	No	−0.124	***	−4.166	0.000
Building ownership	Own	−0.329	***	−17.410	0.000
	Rent	0.329	***	17.410	0.000
Number of employees	≤5	−0.764	***	−23.743	0.000
	5–20	0.150	***	6.467	0.000
	≥20	0.614
Business scope	Food	0.066	***	2.662	0.008
	Non-food	−0.005		−0.170	0.865
	Other	−0.061
Endogenous effects	solar panel	0.095		1.510	0.131
	Heat pump	−0.049		−0.537	0.591
R-Squared		0.751

Notes: “*”, “***” indicate significance at the 90%, 95% and 99% confidence level, respectively.

Table 7. Results of ventilation.

		Estimate		t-Value	p-Value
	Intercept	1.496	***	7.588	0.000
Price	€1000	0.294	***	11.766	0.000
	€3000	−0.024		−0.997	0.319
	€5000	−0.085	***	−3.470	0.001
	€10,000	−0.185
Incentive	€0%	−0.077	***	−3.145	0.002
	€10%	−0.019		−0.782	0.434
	€20%	0.002		0.082	0.935
	€30%	0.094
Demand control	Yes	−0.040	***	−2.877	0.004
	No	0.040	***	2.877	0.004
Can recover heat?	Yes	0.062	***	4.368	0.000
	No	−0.062	***	−4.368	0.000
Can filter out pollutants?	Yes	0.103	***	7.242	0.000
	No	−0.103	***	−7.242	0.000
Location	City center	−0.631	***	−15.405	0.000
	Suburb	−0.035	*	−1.704	0.089
	Rural	0.666
Have investment plan	Yes	0.124	***	4.166	0.000
	No	−0.124	***	−4.166	0.000
Building ownership	Own	−0.329	***	−17.410	0.000
	Rent	0.329	***	17.410	0.000
Number of employees	≤5	−0.764	***	−23.743	0.000
	5–20	0.150	***	6.467	0.000
	≥20	0.614
Business scope	Food	0.066	***	2.662	0.008
	Non-food	−0.005		−0.170	0.865
	Other	−0.061
Endogenous effects	solar panel	0.095		1.510	0.131
	Heat pump	−0.049		−0.537	0.591
R-Squared		0.751

Notes: “*”, “***” indicate significance at the 90%, 95% and 99% confidence level, respectively.

The estimated parameter for incentives set at 0 is −0.077, while the estimated parameter for 30% is 0.094. Government incentives are found to have a significant and positive effect on the intention to install ventilation. Similar to the effects on heat pumps, the probability of installing ventilation increases linearly when the incentive increases from 10% to 30%. The effect of the demand control function is negative, which means companies in general are reluctant to the advanced control features. In addition, positive impacts were found for the function of filtering out pollutants and heat recovery, which suggests the added functions can increase the probability of adopting ventilation. The filtering function has the largest effect among the three functions, which means people weigh the filtering function more than the other two. The values and changes in the estimated coefficients are presented in Figure 4. The two marginal effect lines in Figure 4 indicate that the marginal effects of price and tax incentives are not linear.

In addition, company location, number of employees, and building ownership are statistically significant. Similar to the cases of heat pumps and solar panels, companies in the city center are less likely to install a ventilation system. Regarding the company size, acceptance of ventilation increases with the number of employees. The coefficient of having a food-related business scope is statistically significant and positive, which means food-related companies are more likely to adopt a ventilation system than other companies. This is understandable because food-related businesses may have a relatively higher requirement for air quality.

4.4. Mutual Dependency of Innovative Energy Facilities

The potential mutual dependency between different facilities is captured through the endogenous variables in the simultaneous equation model. The results are shown in Table 5, Table 6 and Table 7. It is found that the adoption of heat pumps and the adoption of ventilation both have a positive and significant effect on the adoption of solar panels. Keeping everything else constant, one unit increase in the degree of accepting heat pump and ventilation increases the level of acceptance of solar panels by 0.450 and 0.152, respectively. The mutual dependency between the acceptance of heat pumps and solar panels is stronger than that between ventilation and solar panels.

In the case of the heat pump, the effect of the adoption of the heat pump is significant and positive. This means the adoption of solar panels increases the adoption of heat pumps. This is probably because the combination of solar panels and heat pumps can largely improve overall energy efficiency; however, the acceptance of ventilation is found to have a negative and significant impact on the acceptance of heat pumps. This is probably because some advanced ventilation already integrates part of the heating and cooling, which overlaps the functionality of the heat pump. Last, in the case of ventilation, no significant impacts of the adoption of solar panels and heat pumps on the acceptance of ventilation are found.

To summarize, the results reveal a bi-directional positive relationship between the acceptance of solar panels and heat pumps and a unidirectional relationship between ventilation and solar panels (Figure 5). More precisely, it implies that companies who prefer one of the two energy facilities (solar panels and heat pumps) are more likely to install the other one. Companies that choose ventilation are more likely to install solar panels but less likely to install heat pumps.

5. Discussion

This study makes significant contributions to understanding the adoption of innovative energy facilities by small and medium-sized enterprises (SMEs) in the tertiary sector. It fills a critical gap in the literature, which has largely focused on residential energy adoption, by examining the specific decision-making processes of SME owners in service industries, such as butchers and restaurants. This contribution is crucial, as SMEs constitute a substantial portion of economic activity and energy consumption.

In comparing our findings with previous studies, we observe several similarities and differences. Similar to research on larger enterprises [35] and households [36], this study reveals that financial factors, such as price and installation costs, significantly influence the installation intentions of SMEs. Furthermore, government tax reductions and subsidy policies have a positive impact on adoption willingness, supporting the findings of earlier studies [37,38,39].

However, a notable difference emerges in the realm of environmental awareness. While some studies [38,40,41] suggest that the application of eco-technologies does not significantly affect investment intentions in renewable energy, our findings indicate that the technological performance of renewable energy facilities in reducing carbon emissions enhances the investment willingness of SMEs significantly. This divergence may be attributable to the differing priorities between the tertiary sector enterprises and other sectors, with the latter placing greater emphasis on how environmental performance can positively influence their social image. Additionally, SMEs in the Netherlands show a stronger focus on eco-labels compared with their counterparts in developing Asian countries [41,42], reflecting regional differences in the perception of sustainability.

Second, this study emphasizes the interdependencies between different energy facilities, a topic that has been insufficiently explored in the existing literature. Our findings indicate that the decision to adopt one facility can positively influence the acceptance of others. For instance, the adoption of solar panels may reduce electricity costs for heat pumps, creating a synergistic effect that encourages more comprehensive energy solutions. This interdependency aspect is particularly important for policymakers and technology developers, as it suggests that promoting one type of facility could enhance the overall adoption of multiple technologies, leading to greater energy efficiency and environmental benefits.

Notably, our results corroborate the widely recognized benefits of synergies among energy facilities in terms of energy savings [43,44] and economic viability [45,46], indicating that such collaborative effects are acceptable to SME managers; however, a key distinction from existing research on synergies lies in the directionality of these influences. Existing research typically asserts that combinations of these facilities enhance acceptance of them [47,48], but it often fails to analyze the internal interactions between individual devices separately. Specifically, we identified a bi-directional relationship between the adoption of solar panels and heat pumps, indicating that the acceptance of one positively influences the other. In contrast, we found a unidirectional relationship between ventilation and solar panels, where the adoption of ventilation positively influences solar panel adoption but not vice versa. This variation in directionality highlights the complexities of interdependencies among energy technologies, suggesting that the impacts of different facility combinations can differ significantly and require tailored strategies for promotion.

This study has several limitations that should be acknowledged. First, our research is focused on the Netherlands, which may limit the generalizability of the findings to other regions with different socio-economic and regulatory contexts. Factors influencing the adoption of innovative energy facilities can vary significantly across countries due to differing energy policies and cultural attitudes. While our insights are valuable for understanding adoption behaviors within the Netherlands context, they may not be directly applicable elsewhere. Additionally, the sample comprises small and medium-sized enterprises in the tertiary sector, which may not represent all industries.

6. Conclusions and Policy Implications

6.1. Conclusions

Companies in commercial buildings are a crucial sector of energy consumption and have great potential to reduce carbon emissions through the installation of innovative or renewable energy facilities. In the context of accelerated electrification and renewable energy development, a faster popularization of new energy facilities is needed and timely in the sense that companies may be expected to take a larger role in reducing carbon emissions through upgrading the current facilities; however, companies have a specific decision-making process that is different from households and has different obstacles and limitation. Identifying the decision-making mechanism of companies to adopt innovative energy facilities not only contributes to the rare literature in this perspective but also has important practical relevance in terms of policy decision-making.

In this study, we tackle three popular innovative energy facilities, including solar panels, heat pumps, and ventilation. By designing a dedicated stated choice experiment, we systematically examined companies’ adoption decisions and the potential mutual dependency between different energy facilities. We found price and payback period are the two most important factors influencing the acceptance of solar panels and heat pumps, whereas payback period has an even greater effect than the price. The environmental benefits, such as the reduction in carbon emissions, are not yet a motivation for companies to adopt solar panels, heat pumps, and ventilation. In addition, companies’ adoption behavior differs according to their profiles, e.g., companies located in the city center or a rented building are less likely to install energy facilities, but building ownership offers a mixed message. Furthermore, government incentive is found to play a role in promoting the adoption of innovative energy facilities, especially solar panels. In the case of the potential mutual effects, we observe a bi-directional relationship between the acceptance of solar panels and heat pumps and a unidirectional relationship between ventilation and solar panels, e.g., the intention to install ventilation increases the acceptance of solar panels.

6.2. Policy Implications

Practically, our findings have implications for policymakers and industry stakeholders. By identifying key drivers of adoption, this study can inform targeted policies that enhance the uptake of renewable energy technologies among SMEs. Moreover, our insights emphasize the need for increased awareness of the economic and environmental benefits of innovative energy facilities, guiding effective outreach and promotion efforts.

According to our results, policy recommendations were provided for stakeholders. The results indicate that cost-related attributes significantly influence the decision-making processes of company owners. By increasing grants, tax credits, and subsidies, government entities can effectively lower financial barriers for businesses, thereby promoting the uptake of sustainable energy solutions and advancing national environmental objectives. Local governments should establish initiatives that promote community awareness of innovative energy solutions, complemented by pilot programs demonstrating their effectiveness. By engaging communities and showcasing successful examples of energy facility adoption, local governments can create supportive environments for SMEs.

As for the industry associations, they should focus on developing and disseminating educational programs and resources that underscore the benefits and interdependencies of various energy facilities, supplemented by case studies and best practices. These organizations play a vital role in facilitating knowledge transfer and fostering collaboration among SMEs. By enhancing an understanding of the relationships between energy facilities, businesses can make informed decisions regarding their energy investments. This approach is expected to lead to increased adoption rates and improved environmental performance across the sector.

Manufacturers of energy facilities should consider collaborating with stakeholders to create bundled offerings that integrate solar panels, heat pumps, and ventilation systems, along with attractive financing options. This study highlights a bi-directional relationship between the adoption of solar panels and heat pumps. By promoting integrated solutions that emphasize these synergies, manufacturers can enhance market appeal and drive sales. This strategy not only contributes to higher sales figures but also fosters a more sustainable energy landscape.

6.3. Future Research

Although the present study provides a modeling approach and a perspective to investigate companies’ intention to adopt innovative energy facilities, several directions deserve further elaboration. First, the present study observed the adoption intention towards each energy facility separately, and the mutual dependency between alternatives is captured by the endogenous effects. As an alternative, a direct choice of the combination of two or more energy facilities will compensate and may provide more information on the synergy of different facilities. Second, the potential constraints of companies in adopting innovative energy facilities are worthy of further investigation. In the current study, the building ownership was found with mixed effects across different facilities, which could be attributable to the difficulty in imaging the actual situation out of the choice experiment. In this regard, a larger revealed dataset would be more suitable to identify the constraints. Third, this study models the acceptance of energy facilities at linear scales. Assuming linearity may have a limitation as the ordered responses were collected. Future studies can develop a generalized simultaneous model incorporating non-linearity in the responses.