QES Model Aggregating Quality, Environmental Impact, and Social Responsibility: Designing Product Dedicated to Renewable Energy Source

Abstract

The complexity of assessment is a significant problem in designing renewable energy source (RES) products, especially when one wants to take into account their various aspects, e.g., technical, environmental, or social. Hence, the aim of the research is to develop a model supporting the decision-making process of RES product development based on meeting the criteria of quality, environmental impact, and social responsibility (QES). The model was developed in four main stages, implementing multi-criteria decision support methods such as DEMATEL (decision-making trial and evaluation laboratory) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), as well as criteria for social responsibility and environmental impact from the ISO 26000 standard. The model was tested and illustrated using the example of photovoltaic panels (PVs): (i) five prototypes were developed, (ii) 30 PV criteria were identified from the qualitative, environmental, and social groups, (iii) the criteria were reduced to 13 key (strongly intercorrelated) criteria according to DEMATEL, (iv) the PV prototypes were assessed taking into account the importance and fulfilment of their key criteria according to TOPSIS, and (v) a PV ranking was created, where the fifth prototype turned out to be the most advantageous (QES = 0.79). The main advantage of the model is its simple form and transparency of application through a systematic analysis and evaluation of many different criteria, after which a ranking of design solutions is obtained. QES ensures precise decision-making in terms of sustainability of new or already available products on the market, also those belonging to RES. Therefore, QES will find application in various companies, especially those looking for low-cost decision-making support techniques at early stages of product development (design and conceptualization).

1. Introduction

To achieve sustainable development and high quality of life, it is crucial to provide environmentally friendly, safe, and reliable energy supplies. This is still hampered by significant social, but also political and economic, challenges in its provision [1]. The transition from conventional to renewable energy is still inevitable, especially in countries of the European Union, to achieve the goal of a low-emission economy [2,3]. Renewable energy sources (RESs) [4] are sources that can be found in nature (in whole or in part), e.g., biomass, geothermal energy, hydropower, wind energy, or solar energy [5]. Compared to conventional energy sources, renewable energy sources are characterised by social benefits for the environment, especially in the case of electricity. Governments use various mechanisms to drive the implementation of RES, such as direct subsidies, loans, and indirectly through research and development, demonstrations, or the Renewable Portfolio Standard (RPS) [6]. During the implementation of RES, other actions are also taken, such as eco-design, including taking into account the extended social responsibility of the products offered by the manufacturer [7]. This has been shown on the basis of the conducted review of the literature on the subject.

For example, in [8], the social benefits of popularising renewable energy systems in given territories were analysed. Local depopulation, energy prices, and lack of workplaces were taken into account, while at the same time interpreting the reduction of CO₂ emissions and the improvement of air quality. Subsequently, in [9], analyses of the integration of various landscape elements, the so-called “energy shape”, with renewable energy products was carried out, comparing energy consumption before and after their use. Another example is [10], where for selected RES, the life cycle assessment for many stakeholders was optimised, taking into account the aspect of reducing greenhouse gases in the form of an ecological indicator. Production costs and investment outlays were also interpreted, which were business indicators. Furthermore, the possibility of employment was analysed, which was a key social criterion. Similarly, in [11], where renewable energy generation technologies were assessed, sustainability indicators such as the price of the generated energy, greenhouse gas emissions in the technology life cycle, RES availability, energy conversion efficiency, and social effects or requirements for land and water consumption were used. A review of the literature was also conducted, e.g., [12], where modern technologies and methods were considered to evaluate economic performance, including the safety and environmental impact of RES. A framework was also developed to assess these solutions in an early design stage. Additionally, technical, economic, social, and environmental aspects were analysed [13,14] in the supply chain of renewable energy products. Research has also been conducted on the sustainability-oriented design of new photovoltaic technologies. An example is the work [15], in which the authors reviewed the current state of knowledge in the field of LCA. LCA studies were integrated and evaluated to determine the current sustainability status of organic photovoltaic technologies. However, this analysis focused on materials, production processes, and key life cycle paths, omitting other aspects of sustainability, such as society and quality.

Confirming the conclusions of the authors of [16], one of the major problems in implementing RES is the complexity of assessing its sustainability, and design decisions require taking into account aspects such as technical, economic, environmental, and social ones. Following [14], it is becoming more and more effective to integrate these aspects within the framework of simultaneous analysis of products. In particular, the use of multi-criteria methods for optimisation and life cycle assessment is useful. As shown by the literature review, efforts are being made to improve this process. Although there are several studies, including those using multi-criteria decision analyses (MCDA), different approaches, criteria, and objectives are used. They are difficult to reproduce and compare, which is also confirmed by the authors of [17]. Therefore, this is considered a research gap. The lack of a single coherent methodology ensuring the aggregation of sustainability criteria at the stage of designing and improving RES is confirmed. This is considered a research gap. To fill the identified gap, it was decided to develop a model that supports the design and improvement of products based on the example of RES.

The purpose of the research was to develop a model that supports decision-making regarding product development according to the satisfaction of selected criteria of sustainable development. These criteria were quality, environmental impact, and social responsibility. Therefore, the model was called QES—an extension of the main criteria of the model: quality, environmental impact, and social responsibility. The main advantage of the model is its simple form and transparency of application through a systematic analysis and evaluation of many different criteria, after which a ranking of design solutions is obtained that can be interpreted in quantitative and verbal terms.

The QES model is a supplement to the previous research on predicting beneficial product solutions, e.g., [18,19,20,21,22,23]. To demonstrate the novelty of QES, it was compared with the previous works, as presented in

Table 1. A review of selected works by authors that fit into the presented research topics.

Study	Aim	Techniques	Originality
[18]	Development of a DT-QLCA model framework supporting eco-innovation in sustainable product design.	Design Thinking (DT), QLCA (Quality Life Cycle Assessment).	An interdisciplinary DT-QLCA framework that integrates quality and sustainability metrics at the early stages of product development to support resource optimisation, reduce environmental impact, and increase customer satisfaction.
[19]	Development of a CQ-LCA (Cost–Quality–Life Cycle Assessment) model supporting the creation of alternative product solutions and their assessment in terms of the following: (i) environmental impact in the life cycle (LCA), (ii) quality, and (iii) production and/or purchase costs.	EDAS method (Evaluation Based on Distance from Average Solution), Life Cycle Assessment, the parametric model based on the static method and the cost value function, including the nominal least squares method, morphology analysis.	The novelty is the developed methodology, which provides qualitative and quantitative interpretation of the results, including verbally on a relative state scale and indicatively (CQ-LCA); the originality is the provision of a multi-faceted morphological analysis, after which different scenarios of product solutions are determined depending on quality, LCA, and costs.
[20]	Development of an eco-innovative QLCA method that enables the creation of new product solutions that integrate quality (customer satisfaction) and environmental impact assessment throughout the product life cycle.	Entropy method, Life Cycle Assessment, Importance-Performance Analysis (IPA)	A coherent approach and method supporting the development decision-making process from a qualitative and environmental perspective in relation to the life cycle.
[21,22]		Weighted Sum Model (WMS), Life Cycle Assessment, Importance-Performance Analysis (IPA).
[23]	Development of a method for calculating the CQ-LCA (Cost–Quality–Life Cycle Assessment) indicator, which supports the sustainable development of products.	TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution), Life Cycle Assessment, cost analysis (modified quality-cost analysis), Importance-Performance Analysis (IPA).	The method supports more precise alignment of product development with market requirements by ensuring that the gap between customer preferences and product assessments is reduced, thereby increasing the accuracy of life cycle assessments in real product development scenarios.

Source: own elaboration.

.

The novelty of the offered QES model compared to the authors’ previous work is based primarily on the inclusion of criteria from the area of social responsibility in the model. Previous studies focused mainly on the analysis of quality criteria and environmental impact in the life cycle. QES integrates the quality, environmental, and social aspects at the same time. The originality of QES is also the methodology of the procedure. In previous studies, the methodology was based on combining the results of the product quality assessment, the results of the environmental impact assessment in the life cycle, and/or integration with the costs of manufacturing the product. The QES methodology is the initial selection of key quality, environmental, and social criteria, ensuring their later assessment in the form of various modified states of these criteria. This also takes place in an aggregated form, but is relatively more accessible, which does not significantly expand the already complex prototyping process. QES supports dynamic product development decision-making and will be particularly useful in small and medium enterprises (SMEs) that are looking for low-cost solutions to support decision-making processes in the area of continuous improvement of processes and the resulting products.

The article is organised as follows: Section 1—literature review, research gap, research objective and its originality; Section 2—presentation of the QES model including the concept, main idea, assumptions, and comprehensive procedure step; Section 3—test and illustration of the model on the example of photovoltaic (PV) panels; Section 4—discussion including the main benefits, limitations, and perspectives for future research; Section 5—main conclusions and summary.

2. Model: Quality–Environmental Impact–Social Responsibility (QES)

2.1. Concept and Main Idea

The research concept is based on the development of the QES model, which is to support the determination of the direction of development of the current product. This direction is determined within the following framework: (i) meeting customer satisfaction with the quality of products, (ii) limiting the negative impact on the environment in the context of the life cycle, and (iii) fulfilling social responsibility of the offered product. The idea of the QES model assumes that it is possible to make product development decisions based on a systematic selection and assessment of sustainable development criteria. The selection and assessment process are carried out on the basis of various product prototypes, which are created as alternative solutions of the current product. The offered approach is in line with the requirements of sustainable product development, which in this case is achieved in the form of a holistic analysis of product prototypes considering the aspects of quality, environmental, and social responsibility. The general concept of QES is presented in Figure 1.

QES is supported by multi-criteria decision-making methods, DEMATEL (decision-making trial and evaluation laboratory) [24], which was implemented due to the need to search for mutual influences of criteria, which are numerous and belong to different groups (quality, environment, and society). Then TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), which was implemented to evaluate prototypes according to the states of criteria belonging to the mentioned groups (quality, environment, society), where this method is used when the evaluated parameters can be measurable and non-measurable [25]. Another key element of the model is the list of social responsibility criteria (taking into account the environmental area), which is presented in the ISO 26000 standard [26]. It provides a standardised set of environmental and social criteria, which, in the offered model, are subject to initial selection and subsequent evaluation as the selection of the most advantageous product prototype.

2.2. Assumptions of the Model

The model assumptions adopted that are useful for their correct use in practice. These assumptions were based on a review of the literature [22,27,28,29,30,31], including previous studies in this area, for example:

• The product for testing is arbitrary, but it is recommended that it be a commonly used final product.
• Qualitative criteria are criteria directly related to the usability of the product, often interpreted as criteria influencing customer satisfaction.
• Environmental criteria are criteria concerning the impact of the product on the natural environment.
• Social criteria are criteria of the so-called corporate social responsibility (CSR), which concern the fulfilment of social responsibility of the product.
• It is proposed to strive for such a proportion of criteria that will reflect the importance of a particular aspect in the conducted analysis.
• The total number of all criteria from the qualitative, environmental, and social groups is arbitrary, but should be reduced to about 10–15 key criteria, but not less than 7 ± 2 criteria.

The model assumptions are developed in the description of the model stages. This is presented in the next section of the article.

2.3. Model Description

The model was developed in four main stages. These stages are the following:

• Stage 1. Selecting a product for testing.
• Stage 2. Defining product criteria.
• Stage 3. Establishing a shortlist of important product criteria.
• Stage 4. Prototyping and quality–environment–social assessment.

Each stage has inputs and outputs, which briefly described the main needs and results. The flow chart of the procedure during its use is presented in Figure 2.

A comprehensive description of the model stages along with an elaboration of the model assumptions is presented later in the article.

• Stage 1. Selecting a product for testing

The product for testing is optional; it is recommended to choose a commonly used one, which will facilitate the further process of adapting it to customer requirements. This product is selected, for example, by a designer or production manager. He or she bases it on market observations, his or her experience, and the individual needs of the company and customers. The selected product may be a newly created product or an existing product on the market that requires modification.

• Stage 2. Defining product criteria

The nature of the model is based on an in-depth assessment of the product in terms of the following criteria: quality, environmental impact, and social responsibility [32]. At later stages, the analysis is also supplemented with the cost aspect but is expressed by a measurable value in the form of estimated product costs [33]. Hence, at this stage of the model, it is necessary to define the product criteria. Firstly, product quality criteria, that is, customer satisfaction with the use of the product (measurable and non-measurable criteria) [27]. Secondly, the impact of the product on the environment [34]. Thirdly, the impact of the product (and company activities) on society, demonstrated through clear and ethical behaviour [35]. No total, maximum number of criteria is assumed in these three groups; however, following the authors of [31], it is possible to assume that it would be effective for the number of criteria in each group to be comparable and amount to up to 10 criteria.

The criteria can be selected by an independently selected team of experts. Selection can be made according to methods such as [36,37]. According to [38], the fewer experts in the team, the better. This results from the fact that the increase in the number of experts contributes to the increase in their moral room for manoeuvre in giving opinions, which may ultimately be less precise. According to research by other authors, a team of experts can be formed in the number of, e.g., 6–8 [39], 4–15 [40], or 10 [41]. Due to the interdisciplinary nature of research, it is worth including experts from various fields in the team, for example, designers, managers, or people with knowledge in the field of corporate social responsibility (CSR) [42]. When selecting criteria, the team of experts can be supported by brainstorming (BM) [43], including other decision support techniques, for example [44]. A product catalogue (specification) that includes product quality criteria may be useful in the selection of criteria. In turn, it was assumed that social and environmental criteria should be defined based on ISO 26000 (which also includes the environmental area) [26]. The environmental aspect should be omitted from the social criteria listed in the 26000 standard and used when defining the environmental criteria. The ISO 26000 standard distinguishes the following criteria:

• Organisational governance;
• Human rights:

  ○

  Due diligence;

  ○

  Human rights situations;

  ○

  Avoidance of complicity;

  ○

  Grievance resolution;

  ○

  Discrimination and vulnerable groups;

  ○

  Civil and political rights;

  ○

  Economic, social, and cultural rights;

  ○

  Fundamental principles and rights at work.

• Labour practices:

  ○

  Employment and labour relations;

  ○

  Working conditions and social protection;

  ○

  Social dialogue;

  ○

  Occupational health and safety;

  ○

  Workplace development and training.

• Environment:

  ○

  Pollution prevention;

  ○

  Sustainable use of resources;

  ○

  Climate change, mitigation, and adaptation;

  ○

  Environmental protection, biodiversity, and restoration of natural habitats.

• Fair operating practices:

  ○

  Anti-corruption;

  ○

  Responsible political engagement;

  ○

  Fair competition;

  ○

  Promoting social responsibility in the value chain;

  ○

  Respect for property rights;

• Consumer Affairs:

  ○

  Fair marketing, factual and unbiased information, and fair contractual practices;

  ○

  Protection of consumer health and safety;

  ○

  Sustainable consumption;

  ○

  Customer service, support, and resolution of complaints and disputes;

  ○

  Protection of consumer data and privacy;

  ○

  Access to essential services;

  ○

  Education and awareness.

• Community engagement and development:

  ○

  Community engagement;

  ○

  Education and culture;

  ○

  Job creation and skills development;

  ○

  Technology development and access;

  ○

  Wealth and income creation;

  ○

  Health;

  ○

  Social investment.

These criteria will be reduced to a list of the most important criteria, interdependent in the form of improving the product within the framework of its sustainable development.

• Stage 3. Establishing a shortlist of criteria that significantly affect the quality–environmental–social level of the product

Due to the fact that the number of criteria from the qualitative, environmental, and social groups may ultimately turn out to be large, it was assumed that they would be limited to the most important ones. In this case, this means limiting the criteria to those that significantly affect the quality–environmental–social level of the product. This results from the fact that the product improvement process should be based primarily on the most important product criteria. Then it is possible to observe the expected changes in the expected form of fulfilling customer satisfaction and social responsibility, and limiting the negative impact on the environment. It was assumed that the process of identifying key product criteria would be carried out using the DEMATEL method (decision-making trial and evaluation laboratory) [24]. This method is useful because it allows for bidirectional analysis of relationships, where in the case of unidirectional perspectives of relationships, it allows for effective modelling, mapping, and verification of various dependencies [45]. The use of DEMATEL allows for increased efficiency and understanding of the interactions between factors and groups, including criteria and sub criteria. As a result, hierarchical networks determine the order in which design solutions should be undertaken [46].

Initially, the direct impact between criteria is assessed. A scale from 0 to 4 is used, where 0—no impact, 1—slight impact, 2—medium impact, 3—strong impact, and 4—very strong impact. When assessing criteria using the DEMATEL method, the team of experts relies primarily on the practical significance of the criteria. Differences between the criteria that emerge are the result of the assessment. Therefore, the resulting assessment is comprehensive. Based on their knowledge and experience, the experts base their assessment on the actual conditions of product production and the company’s operation in the industry. According to the awarded scores, a direct impact matrix is created, as shown in Formula (1) [24]:

$${\mathrm{z}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{1}{\mathrm{l}}}\sum _{\mathrm{k}=1}^{\mathrm{l}}{\mathrm{z}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}},\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} \mathrm{i},\mathrm{j}=\mathrm{1,2},\dots .,\mathrm{n}$$

(1)

where ${\mathrm{z}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}$—expert assessment and l—expert opinion.

Then, the indirect impact of the criteria is determined. For this purpose, the direct impact matrix is used, which is normalised according to Formula (2) [46]:

$$\mathrm{X}={\displaystyle \frac{\mathrm{Z}}{\mathrm{s}}},\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}:\mathrm{s}=\mathrm{m}\mathrm{a}\mathrm{x}\left(\underset{1\le \mathrm{i}\le \mathrm{n}}{\max }\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{z}}_{\mathrm{i}\mathrm{j}},\underset{1\le \mathrm{i}\le \mathrm{n}}{\max }\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{z}}_{\mathrm{i}\mathrm{j}}\right)$$

(2)

where matrix elements are considered in the range 0$\le {\mathrm{x}}_{\mathrm{i}\mathrm{j}}\le$1, 0$\le {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}\le$1, of which the last matrix element is ${\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{z}}_{\mathrm{i}\mathrm{j}}\le \mathrm{s}$.

Accordingly, a total impact structure is developed that takes into account both direct and indirect impacts. To determine this structure, the direct and indirect effects are summed, as shown in Formula (3) [45]:

$$\mathrm{T}=\mathrm{X}+{\mathrm{X}}^2+{\mathrm{X}}^3+\dots +{\mathrm{X}}^{\mathrm{h}}=\mathrm{X}{\left(\mathrm{I}-\mathrm{X}\right)}^{-1},\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} \mathrm{h}\to \mathrm{\infty }$$

(3)

where X—normalised direct influence matrix and I—identity matrix.

According to the dependencies of all identified qualitative, environmental, and social criteria, key criteria are determined from among those analysed. Key criteria are those that are strongly correlated. According to the DEMATEL methodology, average values are calculated from all total impact matrices (4) [24]:

$$\mathsf{\alpha }={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}\left[{\mathrm{t}}_{\mathrm{i}\mathrm{j}}\right]}{\mathrm{N}}}$$

(4)

Then, the values of the T matrix are analysed. If there are values above the average value ($\alpha$), these criteria are considered key. They present a significant, mutual influence between the quality, environmental impact, and social responsibility.

Next, the relationship between the quality, environmental impact, and social responsibility criteria is determined. Then, the structure of the total impact is used. On its basis, the relationships (correlations and influences) between the criteria are determined, where the map of influence relations is used according to the Formula (5) [46]:

$$\begin{array}{cc}\mathrm{R}={\left[{\mathrm{r}}_{\mathrm{i}}\right]}_{\mathrm{n}\times 1}& \mathrm{C}={\left[{\mathrm{C}}_{\mathrm{j}}\right]}_{1\times \mathrm{n}}={\left[\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{t}}_{\mathrm{i}\mathrm{j}}\right]}_{1\times \mathrm{n}}^{\mathrm{T}}\end{array}$$

(5)

where R—sum of values in the rows of the total impact matrix, C—sum of values in the columns of the total impact matrix, r—sum of the i-th row in matrix T, specifying the sum of direct and indirect effects not included in the verified criteria, and c—sum of the j-th column in matrix T, specifying the sum of direct and indirect effects not included in the verified criteria.

In addition, it is possible to estimate the sum and difference between these dependencies. In the case of obtaining two positive values for a given criterion, it is considered the cause of the significant impact. In the case of at least one negative value for a given criterion, it is considered the effect of the significant impact. Only the criteria that are the “cause” are subjected to further analysis. They are considered significant criteria generating a significant impact on the effect. Then, only these criteria create the so-called shortlist of criteria that have a significant impact on the quality–environmental–social level of the product. Only these criteria are subjected to further analysis, as presented in the next stage of the model.

• Stage 4. Creating prototypes and qualitative–environmental–social assessment

This stage consists of searching for various product solutions (product alternatives). Therefore, at this stage, their development was adopted. For this purpose, it is proposed to describe each of the criteria (from a shortlist of criteria having a significant impact on the quality–environmental–social level) according to the parameters characterising them. A parameter is understood as a value, a range of values, for measurable criteria, e.g., weight, size, strength, or in the form of a description for non-measurable criteria, such as colour. In the case of non-measurable criteria, such as social and environmental, it is proposed to estimate their fulfilment according to the Likert scale, as 1—negligible, 2—small, 3—medium, 4—large, and 5—very large [47]. This is performed by a previously selected team of experts during brainstorming (BM), including using product catalogues. It was assumed that the minimum number of criteria states presented by the parameters is at least three states, i.e., current (current in the product) and two modified (new and alternative solutions) [48]. However, it is recommended to specify no more than 7 ± 2 criteria states, where, as stated by [31], such a number is efficient for comparing criteria states with each other. In addition, the expert team determines the criteria of the weights of the analysed product criteria. The weights of the criteria are determined by the expert team on a five-point Likert scale, where 1—criterion practically unimportant and 5—criterion definitely the most important [49].

Based on the specified states of the product criteria, including the weights of these criteria, the product prototypes are evaluated. Due to the fact that product prototypes can be described by criteria in measurable and unmeasurable states, it was assumed that they would be processed using the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution). This is a method for multi-criteria decision support, which allows for the evaluation of any product criteria. As a result, a ranking of prototypes (alternative product solutions) is obtained, according to the distance from the ideal positive and negative solution.

The TOPSIS method starts with the creation of a decision matrix that contains the data for analysis. In this case, the data are the quality, environmental impact, and social responsibility criteria of the product described by the states of these criteria. Alternative product solutions are prototypes for evaluation. The states of the criteria are expressed in different values, e.g., the Likert scale for the states of unmeasurable criteria, but also real values (parameters) for the states of measurable criteria. Therefore, they are normalised, as a result of which a normalised decision matrix is created, as follows (6) [50]:

$${\mathrm{z}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^2}}}$$

(6)

where x—criterion state value, i, j = 1, 2,…, n.

Then, a weighted normalised decision matrix is determined, which takes into account the weights of the product criteria transformed from 0 to 1 (7) [51]:

$${\mathrm{v}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{w}}_{\mathrm{j}}}{10}}\times {\mathrm{z}}_{\mathrm{i}\mathrm{j}}$$

(7)

where w—criterion weight expressed as a number from 0 to 1 and z—normalised value of the criterion state, i, j = 1, 2, …, n.

Afterward, the vector for the ideal (a⁺) and anti-ideal (a⁻) solution is calculated. The ideal positive alternative is the extremum of each criterion analysed. The negative ideal alternative is the inverted extremum for each criterion analysed. A positive solution is one that maximises the benefit criteria and minimises the cost criteria. A negative solution is one that minimises the benefit criteria and maximises the cost criteria. Benefit criteria are understood as those where the higher the value, the better. Cost criteria are understood as those where the higher the value, the worse. This is presented by Formula (8) [52]:

$$\left\{\begin{array}{c}{\mathrm{a}}^{+}=\left({\mathrm{a}}_1^{+},{\mathrm{a}}_2^{+},\dots ,{\mathrm{a}}_{\mathrm{n}}^{+}\right)≔\left\{\left(\underset{\mathrm{i}=1,\dots ,\mathrm{m}}{\max }{\mathrm{v}}_{\mathrm{i}\mathrm{j}}|\mathrm{j}\mathsf{ϵ}{\mathrm{J}}_{\mathrm{Q}}\right),\left(\underset{\mathrm{i}=1,\dots ,\mathrm{m}}{\min }{\mathrm{v}}_{\mathrm{i}\mathrm{j}}|\mathrm{j}\mathsf{ϵ}{\mathrm{J}}_{\mathrm{C}}\right)\right\}\\ {\mathrm{a}}^{-}=\left({\mathrm{a}}_1^{-},{\mathrm{a}}_2^{-},\dots ,{\mathrm{a}}_{\mathrm{n}}^{-}\right)≔\left\{\left(\underset{\mathrm{i}=1,\dots ,\mathrm{m}}{\min }{\mathrm{v}}_{\mathrm{i}\mathrm{j}}|\mathrm{j}\mathsf{ϵ}{\mathrm{J}}_{\mathrm{Q}}\right),\left(\underset{\mathrm{i}=1,\dots ,\mathrm{m}}{\max }{\mathrm{v}}_{\mathrm{i}\mathrm{j}}|\mathrm{j}\mathsf{ϵ}{\mathrm{J}}_{\mathrm{C}}\right)\right\}\end{array}\right.$$

(8)

where J_Q—collection of stimulants and J_C—collection of destimulants, i, j = 1, 2,…, n.

Later, the Euclidean distances from ideal and anti-ideal solutions are calculated, as shown in Formula (9) [52]:

$$\left\{\begin{array}{c}{\mathrm{d}}_{\mathrm{i}}^{+}=\sqrt{{\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}}{\left({\mathrm{v}}_{\mathrm{i}\mathrm{j}}-{\mathrm{a}}_{\mathrm{j}}^{+}\right)}^2}\\ {\mathrm{d}}_{\mathrm{i}}^{-}=\sqrt{{\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}}{\left({\mathrm{v}}_{\mathrm{i}\mathrm{j}}-{\mathrm{a}}_{\mathrm{j}}^{-}\right)}^2}\end{array}\right.$$

(9)

where v—values of the normalised decision matrix, a⁺—positive ideal solution, and a⁻—negative ideal solution, i, j = 1, 2,…, n.

Finally, the qualitative–environmental–social coefficient (QES) is determined, which according to the TOPSIS method will be presented as (10):

$${\mathrm{Q}\mathrm{E}\mathrm{S}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{d}}_{\mathrm{i}}^{-}}{{\mathrm{d}}_{\mathrm{i}}^{+}+{\mathrm{d}}_{\mathrm{i}}^{-}}}$$

(10)

where d⁺—Euclidean distance from the positive ideal solution and d⁻—Euclidean distance from the negative ideal solution, i, j = 1, 2,…, n.

Based on the QES indicator, product development decisions are made. A prototype classification is created according to QES. The indicator can be interpreted according to the scale of relative states presented, for example, in [22,23]. The higher the value of this indicator, the more beneficial the prototype will be. The highest QES value means a prototype that is characterised by the expected quality (meeting customer expectations), meeting social responsibility, and limited negative impact on the natural environment. With a ranking list, decisions regarding product production can be made. It is important to remember that once a decision is made, it is advisable to conduct a risk analysis of the project. The FMEA method (Failure Mode and Effects Analysis) [53,54,55,56] can be used for hazard analysis.

3. Results: Case Study Based on Photovoltaic Panels

Illustrations and model test were carried out for photovoltaic panels (PVs), which are considered to be one of the key products to limit negative climate change [57]. Photovoltaic panels are currently considered the cheapest source of electricity, which is used on a different scale. Photovoltaic energy has become an indispensable means to realise global ambitions in climate actions [58]. Over the next decades, research on photovoltaic systems will focus on the growth of technological progress, driven by demand and cost-effectiveness of the application [59]. In addition, alternative typologies of photovoltaic panel implementations are becoming increasingly popular. Among others, integrated photovoltaics, agrovoltaics, and transport infrastructure is being expanded [60]. Therefore, it seems reasonable to analyse this type of product. The functional unit to which future (potential) products are compared is a single unit of a currently manufactured product. System boundaries refer to the production and use phases and are dependent on the current level of technology. System boundaries should always be updated when conducting an analysis.

Due to the testing nature of the calculations verifying the model’s accuracy, the experts were the authors of the article, who selected other individuals associated with PV production and use. As planned, these were six experts with the required knowledge and experience. Based on the second stage of the model, the PV criteria were selected from the group of qualitative, environmental, and social criteria. Qualitative criteria were identified based on catalogues of these products, and on the basis of the literature on the subject, for example, [61,62,63,64,65,66]. For the purpose of model testing, the criteria were selected by a team of experts consisting of the authors of the article and an expert with knowledge of PV design. Among these criteria were technical and aesthetic criteria, i.e.,

• Nominal (installed) power (Wp)—means the maximum possible electrical power that a photovoltaic module can generate in ideal conditions.
• Short circuit current (A)—the current that flows through the PV cell in a short circuit situation, i.e., with no load and maximum solar radiation.
• Maximum output current (A)—the current that the photovoltaic panel actually supplies to the connected receiver.
• Open circuit voltage (V)—the voltage generated by the PV module when it is not connected to any load (the circuit is open).
• Maximum voltage (V)—the voltage value achieved by the panel at the point of maximum power, usually measured under standard test conditions (STC).
• Efficiency (%)—an indicator of how effectively the panel converts solar radiation energy into electrical energy—the higher the value, the better the performance.
• Maximum system voltage (VDC)—the highest voltage allowed for a PV system, limiting the number of modules that can be connected in series in a single installation.
• Maximum power (MPP)—the highest power value that a PV cell can achieve under optimal conditions—a key parameter when selecting a panel.
• Panel efficiency (%)—the degree to which the entire PV module converts sunlight into electrical energy; typically lower than the efficiency of a single cell due to losses when connecting them.
• Weight (kg)—the total weight of the photovoltaic panel.
• Warranty—the period during which the manufacturer provides free repair or replacement of the PV module in the event of a failure.
• Kinematics—the ability to adjust the angle of the photovoltaic panel to optimise its position in relation to the sun.
• Dimensions (mm)—the physical dimensions of the panel: length, width, and thickness.
• Single cell efficiency (%)—the efficiency of converting solar energy into electrical energy by a single cell in the PV module.
• Visibility—refers to the degree to which a photovoltaic (PV) installation is visible to customers and other interested parties. The more visible the installation, the greater its negative impact on the perceived quality of the surroundings may be. Visibility can be assessed using GIS tools or as the ratio of the area occupied by photovoltaics to the total area of the landscape. The analysis should take into account places that are important from the point of view of the recipient, e.g., within 5–10 km of natural, historical, monumental, or recreational areas that the user wants to protect from visual interference. It may be difficult to take into account the subjective perspective of the recipient.
• Degree of integration—determines how well the PV installation fits into the surrounding landscape, which is directly related to its visibility. It is important to distinguish here between BIPV (building-integrated) and BAPV (building-mounted) technologies. Experts recommend the highest possible degree of integration because it limits negative visual impressions. One can distinguish non-integrated, partially integrated, and fully integrated systems.
• Colour—refers to the colour, saturation, and brightness of the PV system elements, such as panel frames, and how well they match the surrounding landscape of the colour scheme.
• Light reflection—refers to the amount of light (natural or artificial) reflected by the PVs. Unwanted reflections can reduce the visual comfort of users—causing glare, the need to look away, eye fatigue, and even headaches.
• Pattern (texture)—describes the appearance of the PV surface in terms of its complexity and degree of similarity to the surrounding elements. This includes, among others, transparency, porosity, and surface density.

Then, social and environmental criteria were selected and determined based on ISO 26000 [26]. Finally, the following criteria were selected:

• Environmental criteria:

  ○

  Pollution prevention;

  ○

  Sustainable use of resources;

  ○

  Climate change, mitigation, and adaptation;

  ○

  Environmental protection, biodiversity, and restoration of natural habitats.

• Social criteria:

  ○

  For fair operating practices:

  ▪

  Fair competition;

  ▪

  Promoting social responsibility in the value chain.

  ○

  Consumer affairs:

  ▪

  Fair marketing, factual and impartial information, and fair contractual practices;

  ▪

  Protection of consumer health and safety;

  ▪

  Access to basic services.

  ○

  Community engagement and development:

  ▪

  Community engagement;

  ▪

  Technology development and access;

  ▪

  Social investment.

In total, 30 PV criteria were identified, belonging to the group of quality criteria (18), environmental criteria (4), and social responsibility criteria (8). Based on the literature [32], it was found that these criteria can adequately determine the quality–environmental–social level of the product.

Next, we started to limit ourselves to the most important criteria. In this case, this is understood as criteria that have common, strong correlations in the group of qualitative, environmental, and social responsibility criteria. The DEMATEL method is used for this purpose. Initially, the direct impact of the criteria was assessed using a scale of 0 to 4. According to Formula (1), a direct impact matrix was developed, as shown in

Table A1. Direct impact matrix of PV criteria in the group of qualitative, environmental, and social criteria.

	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12	C13	C14	C15	C16	C17	C18	C19	C20	C21	C22	C23	C24	C25	C26	C27	C28	C29	C30
C1	0	3	4	2	2	4	4	4	4	0	0	0	4	0	0	0	0	0	0	3	3	0	4	4	4	2	2	2	4	4
C2	2	0	2	2	2	3	2	2	3	0	0	0	3	0	0	0	0	0	0	1	1	0	1	1	1	1	1	1	1	1
C3	2	2	0	2	2	3	3	3	3	0	0	0	2	0	0	0	0	0	0	2	1	0	2	1	2	2	2	1	1	1
C4	2	0	2	0	2	3	2	2	3	0	0	0	3	0	0	0	0	0	0	1	1	0	1	1	1	1	1	1	1	1
C5	3	3	3	3	0	3	3	3	3	0	0	0	2	0	0	0	0	0	0	2	1	0	2	1	2	2	2	1	1	2
C6	1	1	2	1	1	0	1	1	3	0	0	0	3	0	0	0	0	0	1	4	4	4	4	4	4	4	4	4	4	4
C7	2	2	0	2	2	3	0	3	3	0	0	0	2	0	0	0	0	0	0	2	1	0	2	1	2	2	2	1	1	1
C8	2	1	2	1	1	2	2	0	2	0	0	0	2	0	0	0	0	0	1	1	1	1	2	1	1	1	1	1	1	1
C9	1	1	2	1	1	1	1	1	0	0	0	0	3	0	0	0	0	0	1	4	4	4	4	4	4	4	4	4	4	4
C10	0	0	0	0	0	0	0	0	0	0	0	4	0	2	1	0	0	1	2	2	2	2	1	1	1	1	1	1	1	1
C11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4	4	4	4	4	2	2	4	2	2	4	4
C12	0	0	0	0	0	1	0	0	1	1	0	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
C13	1	1	2	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	1	2	2	2	2	2	2	2	2	2	2	2
C14	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4	4	3	2	1	0	0	0	1	1	1	0	0	1	1	1
C15	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	1	1	2	0	1	0	0	1	1	1	0	0	1	1	1
C16	0	0	0	0	0	0	0	0	0	0	0	0	0	4	4	0	4	1	4	0	0	0	4	0	0	0	0	2	0	3
C17	0	0	0	0	0	0	0	0	0	0	0	0	0	3	3	1	0	1	1	1	0	0	4	0	0	0	0	2	0	3
C18	0	0	0	0	0	0	0	0	0	0	0	0	0	4	4	1	4	0	3	0	0	0	4	0	0	0	0	2	0	3
C19	0	0	0	0	0	4	0	0	4	0	0	0	4	1	1	1	0	0	0	1	2	2	1	2	2	2	2	2	2	2
C20	1	1	1	1	1	3	1	1	3	1	1	1	3	2	2	1	0	0	1	0	1	1	1	1	1	1	1	1	1	1
C21	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	1	1	0	2	2	2	2	1	1	2	2	2
C22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	1	1	1	1	1	1	1	1
C23	1	1	1	1	1	2	2	2	2	1	1	1	2	3	3	3	3	3	4	4	4	4	0	4	4	4	4	4	4	4
C24	1	1	1	1	1	1	1	1	1	1	3	1	1	1	1	1	1	1	3	3	3	3	3	0	3	3	3	3	3	3
C25	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	3	2	2	2	2	2	2	0	2	2	2	2	2
C26	0	0	0	0	0	4	0	0	4	0	4	0	4	0	0	0	0	0	2	2	2	2	2	2	2	0	2	2	2	2
C27	1	1	1	1	1	1	1	1	1	0	0	1	1	0	0	0	0	0	1	1	1	1	1	1	1	1	0	1	1	1
C28	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	2	2	2	2	2	2	2	0	2	2
C29	3	3	3	3	3	3	3	3	4	3	3	3	4	4	4	1	3	3	4	4	4	4	4	4	4	4	4	4	0	4
C30	2	2	2	2	2	4	2	2	4	2	4	2	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	0

where C1—rated (installed) power (Wp), C2—short circuit current (A), C3—maximum output current (A), C4—open circuit voltage (V), C5—maximum voltage (V), C6—efficiency (%), C7—maximum system voltage (VDC), C8—maximum power (W), C9—panel efficiency (%), C10—weight (kg), C11—warranty, C12—kinematics, C13—single cell efficiency (%), C14—visibility, C15—degree of integration, C16—colour, C17—light reflection, C18—pattern (texture), C19—pollution prevention, C20—sustainable use of resources, C21—climate change, mitigation, and adaptation, C22—environmental protection, biodiversity, and restoration of natural habitats, C23—fair competition, C24—promoting social responsibility in the value chain, C25—fair marketing, factual and impartial information, and fair contractual practices, C26—consumer health and safety protection, C27—access to essential services, C28—community engagement, C29—development of and access to technology, and C30—social investment.

. Next, the indirect impact of the criteria was determined. For this purpose, a direct impact matrix was used, which is normalised according to Formula (2). The result is shown in

Table A2. Indirect impact matrix of PV criteria in the group of qualitative, environmental, and social criteria.

	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12	C13	C14	C15	C16	C17	C18	C19	C20	C21	C22	C23	C24	C25	C26	C27	C28	C29	C30
C1	0.00	0.03	0.04	0.02	0.02	0.04	0.04	0.04	0.04	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.00	0.00	0.00	0.03	0.03	0.00	0.04	0.04	0.04	0.02	0.02	0.02	0.04	0.04
C2	0.02	0.00	0.02	0.02	0.02	0.03	0.02	0.02	0.03	0.00	0.00	0.00	0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.00	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
C3	0.02	0.02	0.00	0.02	0.02	0.03	0.03	0.03	0.03	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.01	0.00	0.02	0.01	0.02	0.02	0.02	0.01	0.01	0.01
C4	0.02	0.00	0.02	0.00	0.02	0.03	0.02	0.02	0.03	0.00	0.00	0.00	0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.00	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
C5	0.03	0.03	0.03	0.03	0.00	0.03	0.03	0.03	0.03	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.01	0.00	0.02	0.01	0.02	0.02	0.02	0.01	0.01	0.02
C6	0.01	0.01	0.02	0.01	0.01	0.00	0.01	0.01	0.03	0.00	0.00	0.00	0.03	0.00	0.00	0.00	0.00	0.00	0.01	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04
C7	0.02	0.02	0.00	0.02	0.02	0.03	0.00	0.03	0.03	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.01	0.00	0.02	0.01	0.02	0.02	0.02	0.01	0.01	0.01
C8	0.02	0.01	0.02	0.01	0.01	0.02	0.02	0.00	0.02	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.01	0.01	0.02	0.01	0.01	0.01	0.01	0.01	0.01	0.01
C9	0.01	0.01	0.02	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.00	0.03	0.00	0.00	0.00	0.00	0.00	0.01	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04
C10	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	0.00	0.02	0.01	0.00	0.00	0.01	0.02	0.02	0.02	0.02	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
C11	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	0.04	0.04	0.04	0.04	0.02	0.02	0.04	0.02	0.02	0.04	0.04
C12	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.01	0.00	0.00	0.01	0.01	0.01	0.00	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
C13	0.01	0.01	0.02	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02
C14	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	0.04	0.03	0.02	0.01	0.00	0.00	0.00	0.01	0.01	0.01	0.00	0.00	0.01	0.01	0.01
C15	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.00	0.01	0.01	0.02	0.00	0.01	0.00	0.00	0.01	0.01	0.01	0.00	0.00	0.01	0.01	0.01
C16	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	0.04	0.00	0.04	0.01	0.04	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.00	0.02	0.00	0.03
C17	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.03	0.03	0.01	0.00	0.01	0.01	0.01	0.00	0.00	0.04	0.00	0.00	0.00	0.00	0.02	0.00	0.03
C18	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	0.04	0.01	0.04	0.00	0.03	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.00	0.02	0.00	0.03
C19	0.00	0.00	0.00	0.00	0.00	0.04	0.00	0.00	0.04	0.00	0.00	0.00	0.04	0.01	0.01	0.01	0.00	0.00	0.00	0.01	0.02	0.02	0.01	0.02	0.02	0.02	0.02	0.02	0.02	0.02
C20	0.01	0.01	0.01	0.01	0.01	0.03	0.01	0.01	0.03	0.01	0.01	0.01	0.03	0.02	0.02	0.01	0.00	0.00	0.01	0.00	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
C21	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.00	0.02	0.02	0.02	0.02	0.01	0.01	0.02	0.02	0.02
C22	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.01	0.00	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
C23	0.01	0.01	0.01	0.01	0.01	0.02	0.02	0.02	0.02	0.01	0.01	0.01	0.02	0.03	0.03	0.03	0.03	0.03	0.04	0.04	0.04	0.04	0.00	0.04	0.04	0.04	0.04	0.04	0.04	0.04
C24	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.03	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.03	0.03	0.03	0.03	0.03	0.00	0.03	0.03	0.03	0.03	0.03	0.03
C25	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.02	0.02	0.02	0.02	0.02	0.02	0.00	0.02	0.02	0.02	0.02	0.02
C26	0.00	0.00	0.00	0.00	0.00	0.04	0.00	0.00	0.04	0.00	0.04	0.00	0.04	0.00	0.00	0.00	0.00	0.00	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.00	0.02	0.02	0.02	0.02
C27	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.01	0.01	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.00	0.01	0.01	0.01
C28	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.00	0.02	0.02
C29	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.04	0.03	0.03	0.03	0.04	0.04	0.04	0.01	0.03	0.03	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.00	0.04
C30	0.02	0.02	0.02	0.02	0.02	0.04	0.02	0.02	0.04	0.02	0.04	0.02	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.04	0.00

where C1–C30 as in Table A1.

. Later, a structure of the total impact was developed that takes into account direct and indirect impact. Formula (3) was used for this purpose. The values from the structure of the total impact, which were above the alpha value, determined strong correlations. The result is shown in

Table A3. Total impact matrix of PV criteria with significant correlations of quality, environmental impact, and social responsibility criteria.

	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12	C13	C14	C15	C16	C17	C18	C19	C20	C21	C22	C23	C24	C25	C26	C27	C28	C29	C30
C1	37	19	−16	−29	−19	−15	21	32	−5	14	39	−50	−20	−3	−14	−1	−12	−1	2	−5	36	−15	9	0	−7	−15	−108	−51	20	4
C2	−9	45	6	7	5	0	−22	−33	−4	7	12	3	10	8	12	−8	−7	7	−6	9	10	7	−12	−1	−6	1	−23	1	6	1
C3	−14	−7	46	−6	−4	6	−11	−16	10	7	26	3	10	19	35	10	−25	−8	−17	49	−10	4	−11	−13	−3	14	−23	−28	4	−15
C4	0	−50	0	50	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
C5	−5	−2	17	4	36	2	−12	−18	14	29	84	−23	14	36	58	9	−59	−9	−45	97	13	−2	−21	−27	−21	15	−138	−95	21	−14
C6	0	0	1	−1	−1	25	1	2	0	2	11	−17	−22	6	11	6	−10	−5	−26	28	−1	−2	2	−3	−4	−1	−24	−14	3	−4
C7	0	0	−33	0	0	0	33	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
C8	−10	−5	−32	−18	−12	−4	−23	16	−22	−57	−180	96	−42	−71	−113	−28	123	27	111	−206	−37	47	28	49	41	−18	324	206	−53	35
C9	0	0	0	0	0	−25	0	1	33	1	4	−6	−7	2	4	2	−3	−2	−9	9	0	−1	1	−1	−1	0	−8	−5	1	−1
C10	−9	−5	−14	3	2	4	−16	−24	−9	−32	−111	−23	−17	−43	−69	39	77	−24	88	−138	−30	−3	19	24	22	−8	205	137	−37	32
C11	21	10	16	−12	−8	−3	44	66	17	65	162	−58	22	63	94	1	−110	−4	−97	172	38	−36	−11	−44	−41	2	−319	−200	48	−39
C12	1	0	3	−4	−3	3	7	10	2	−15	25	1	10	4	15	2	−5	−4	−22	30	−8	−23	7	−11	−13	6	−38	−24	5	−2
C13	−12	−6	12	4	2	11	−21	−31	−36	−12	−40	35	54	−10	−14	−6	25	7	30	−41	−15	15	−5	9	10	1	83	50	−13	6
C14	2	1	4	1	0	−1	2	3	2	7	18	−2	2	23	−8	11	−14	−17	−5	1	11	6	−9	−2	1	4	−28	−25	8	−7
C15	19	9	22	−7	−4	−7	34	51	17	61	196	−128	28	64	147	8	−147	−7	−95	196	49	−12	−30	−42	−37	11	−371	−227	67	−44
C16	−21	−10	−12	9	6	10	−40	−59	−15	−56	−181	123	−15	−71	−113	23	116	−13	95	−184	−46	16	16	41	38	−7	349	207	−60	35
C17	3	2	4	−3	−2	0	8	12	3	12	43	−32	8	14	22	9	−7	−28	−33	68	11	3	−1	−13	−14	2	−80	−48	12	−8
C18	−1	0	−12	−1	0	−1	0	0	−4	−13	−43	34	−16	−27	−47	−60	28	69	7	−41	−1	14	15	7	4	0	84	47	−16	11
C19	−20	−10	1	10	6	12	−39	−59	−11	−44	−139	89	2	−44	−66	−17	88	18	88	−143	−45	3	1	34	34	−7	264	160	−44	25
C20	−6	−3	55	3	2	15	−12	−18	15	41	150	−111	79	80	140	81	−138	−70	−63	223	14	0	−55	−33	−28	11	−277	−172	50	−45
C21	4	2	−51	1	1	−17	4	5	−15	−46	−145	42	−79	−75	−128	−27	116	24	78	−194	42	14	41	40	37	−31	227	170	−42	43
C22	−2	−1	−17	4	3	−4	−8	−13	−7	−26	−78	37	−30	−29	−45	−7	51	8	43	−82	−23	80	9	20	19	−9	140	84	−24	16
C23	−6	−3	−24	−1	−1	−2	−7	−10	−10	−32	−97	52	−25	−42	−69	−15	65	13	48	−103	−20	13	34	23	19	−7	181	104	−31	16
C24	12	6	−2	−5	−3	−5	23	35	6	24	51	−16	−9	18	24	−1	−32	0	−29	47	9	−38	0	15	−18	−5	−112	−72	12	−13
C25	0	0	0	−1	−1	2	2	3	0	2	19	−26	0	−8	−17	16	6	−12	11	−17	4	8	3	−18	23	2	−34	−19	−5	2
C26	16	8	40	−15	−10	12	41	62	24	86	194	−55	64	94	144	−2	−145	−1	−161	254	22	−68	−21	−59	−53	46	−397	−252	53	−60
C27	−15	−7	−14	10	7	−1	−34	−51	−14	−51	−98	80	−21	−39	−52	−22	78	20	61	−112	−36	10	6	23	15	−5	285	125	−31	27
C28	−2	−1	−17	4	3	−4	−8	−13	−7	−26	−78	37	−30	−29	−45	−7	51	8	43	−82	−23	−20	9	20	19	−9	140	134	−24	16
C29	12	6	7	−4	−2	−12	20	31	8	30	105	−81	12	36	58	−4	−64	0	−41	84	25	−12	−13	−28	−30	4	−202	−122	56	−20
C30	4	2	6	−3	−2	2	9	14	5	17	39	−9	7	10	12	−3	−25	1	−34	44	9	−9	−5	−17	−18	1	−76	−57	7	11

where C1–C30 as in Table A1.

. Next, the relationship between qualitative, environmental, and social criteria was determined, as in Formula (4). A ranking of criteria was developed, and they were defined as “effects” or “causes” (

Table 2. Weights of PV criteria in the group of qualitative, environmental criteria.

Criteria	R	C	C − R	C + R	Ranking	Decision
C1	−0.25	−151.41	−151.16	−151.66	27	effect
C2	0.00	25.48	25.48	25.47	13	cause
C3	−5.39	33.40	38.79	28.01	12	cause
C4	0.86	0.29	−0.57	1.15	14	effect
C5	0.65	−43.20	−43.85	−42.55	20	effect
C6	0.21	−35.43	−35.64	−35.22	19	effect
C7	−1.17	0.34	1.51	−0.83	15	effect
C8	−1.91	170.22	172.13	168.31	2	cause
C9	−1.75	−11.44	−9.69	−13.18	17	effect
C10	−7.20	38.93	46.13	31.73	10	cause
C11	−13.64	−140.81	−127.17	−154.44	29	effect
C12	−5.19	−39.86	−34.67	−45.05	21	effect
C13	−9.19	91.77	100.96	82.58	6	cause
C14	−14.77	−11.94	2.82	−26.71	18	effect
C15	−26.20	−178.19	−151.99	−204.40	30	effect
C16	8.51	180.15	171.64	188.67	1	cause
C17	21.17	−34.17	−55.35	−13.00	16	effect
C18	−5.47	35.26	40.74	29.79	11	cause
C19	21.75	145.23	123.49	166.98	4	cause
C20	−37.95	−72.82	−34.86	−110.77	25	effect
C21	−4.25	40.93	45.18	36.68	9	cause
C22	−1.04	107.81	108.85	106.77	5	cause
C23	5.50	62.70	57.20	68.20	8	cause
C24	−5.08	−78.11	−73.03	−83.18	24	effect
C25	−11.23	−56.44	−45.21	−67.67	23	effect
C26	−1.34	−137.49	−136.15	−138.83	26	effect
C27	24.44	143.76	119.31	168.20	3	cause
C28	16.22	57.92	41.70	74.14	7	cause
C29	−10.58	−141.45	−130.87	−152.03	28	effect
C30	7.25	−58.47	−65.72	−51.22	22	effect

where C1—rated (installed) power (Wp), C2—short circuit current (A), C3—maximum output current (A), C4—open circuit voltage (V), C5—maximum voltage (V), C6—efficiency (%), C7—maximum system voltage (VDC), C8—maximum power (W), C9—panel efficiency (%), C10—weight (kg), C11—warranty, C12—kinematics, C13—single cell efficiency (%), C14—visibility, C15—degree of integration, C16—colour, C17—light reflection, C18—pattern (texture), C19—pollution prevention, C20—sustainable use of resources, C21—climate change, mitigation, and adaptation, C22—environmental protection, biodiversity, and restoration of natural habitats, C23—fair competition, C24—promoting social responsibility in the value chain, C25—fair marketing, factual and unbiased information, and fair contractual practices, C26—protection of consumer health and safety, C27—access to basic services, C28—community involvement, C29—development of and access to technology, and C30—social investment. Color shows criteria which should be analyses in next part of the research.

).

Only the criteria that are the “cause” are subject to further analysis. They are considered significant criteria generating a significant impact on the effect. In this case, the criteria that have the most significant impact on the qualitative–environmental–social level are the following: C2—short circuit current (A), C3—maximum output current (A), C8—maximum power (W), C10—weight (kg), C13—single cell efficiency (%), C16—colour, C18—pattern (texture), C19—pollution prevention, C21—climate change, mitigation, and adaptation, C22—environmental protection, biodiversity, and restoration of natural habitats, C23—fair competition, C27—access to basic services, and C28—community involvement. Thirteen criteria were selected from the thirty that were verified. Only these 13 criteria are subject to further analysis.

Following the adopted model, for the 13 criteria, parameters characterising them are determined. For each criterion, five different states were determined, which met the principles given in [31]. For this purpose, PV catalogues were used. In the case of non-measurable criteria, such as social and environmental criteria, their fulfilment was estimated using the Likert scale, as 1—negligible, 2—small, 3—medium, 4—large, and 5—very large [47]. Then, the weights (importance) of these criteria were determined on a five-point Likert scale, where 1—practically unimportant criterion and 5—definitely the most important criterion [49]. The results are presented in

Table 3. Statuses and weights for PV criteria having a significant impact in the group of qualitative, environmental, and social criteria.

Marked	Criterion 1	Weight	State 1	State 2	State 3	State 4	State 5
Quality criteria (technical and aesthetic criteria)
C2	short circuit current (A)	4	5.44	3.11	7.70	5.55	11.95
C3	maximum output current (A)	4	5.13	7.07	6.04	2.85	11.05
C8	maximum power (W)	5	100	140	60	100	200
C10	weight (kg)	3	6	3.3	8.5	23.5	11
C13	single cell efficiency (%)	5	18	23	15	20	25
C16	colour	3	black	grey	graphite	white	black
C18	pattern (texture)	2	matte	shiny, mirror-like	ribbed	smooth	semi-matt
	Environmental criteria
C19	pollution prevention	4	small	small	medium	large	medium
C21	climate change, mitigation, and adaptation	4	medium	medium	large	medium	large
C22	environmental protection, biodiversity, and habitat restoration	3	small	large	small	średnie	large
	Social criteria
C23	fair competition	4	large	very large	medium	large	very large
C27	access to basic services	3	very large	large	very large	large	medium
C28	community involvement	5	medium	large	very large	large	very large

.

Then, the level of evaluation of the quality–environment–social compliance of the prototypes was started. The TOPSIS method was used for this purpose. Based on Table 2, the states of the product criteria were normalised, where a normalised decision matrix was created according to Formula (6). Next, using Formula (7), the PV criteria weights were taken into account, creating a weighted normalised decision matrix. The result is presented in

Table 4. Decision matrices for PV prototypes taking into account the status of quality, environmental impact, and social responsibility criteria.

Prototype	C2	C3	C8	C10	C13	C16	C18	C19	C21	C22	C23	C27
Normalised decision matrix
P1	0.33	0.33	0.35	0.21	0.39	0.54	0.45	0.31	0.39	0.29	0.42	0.52
P2	0.19	0.45	0.49	0.12	0.50	0.33	0.56	0.31	0.39	0.57	0.52	0.42
P3	0.47	0.39	0.21	0.30	0.33	0.54	0.23	0.46	0.52	0.29	0.31	0.52
P4	0.34	0.18	0.35	0.83	0.44	0.11	0.56	0.62	0.39	0.43	0.42	0.42
P5	0.72	0.71	0.69	0.39	0.55	0.54	0.34	0.46	0.52	0.57	0.52	0.31
Weighted normalised decision matrix
Weight	0.40	0.40	0.50	0.30	0.50	0.30	0.20	0.40	0.40	0.30	0.40	0.30
P1	0.13	0.13	0.17	0.06	0.20	0.16	0.09	0.12	0.16	0.09	0.17	0.16
P2	0.08	0.18	0.24	0.04	0.25	0.10	0.11	0.12	0.16	0.17	0.21	0.13
P3	0.19	0.15	0.10	0.09	0.16	0.16	0.05	0.19	0.21	0.09	0.13	0.16
P4	0.13	0.07	0.17	0.25	0.22	0.03	0.11	0.25	0.16	0.13	0.17	0.13
P5	0.29	0.28	0.35	0.12	0.27	0.16	0.07	0.19	0.21	0.17	0.21	0.09

where C2—short circuit current (A), C3—maximum output current (A), C8—maximum power (W), C10—weight (kg), C13—single cell efficiency (%), C16—colour, C18—pattern (texture), C19—pollution prevention, C21—climate change, mitigation, and adaptation, C22—environmental protection, biodiversity, and restoration of natural habitats, C23—fair competition, C27—access to basic services, and C28—community involvement.

.

Later, using Formula (8), a vector was determined for the ideal (a⁺) and the anti-ideal (a⁻) solution, where the mass of PV was considered as cost criteria (the higher the mass, the worse in terms of installation, roof load, possible tilt settings, including material consumption, their costs, and environmental degradation). On the other hand, all other criteria were considered benefit criteria (which also resulted in part from the assessment method, where the higher the assessment, the higher the fulfilment of the criterion). According to the adopted assumptions, Formula (9) was used to determine the Euclidian distance to the ideal and anti-ideal solutions. Based on them, it was possible to calculate the qualitative–environmental–social coefficient (QES), as shown in Formula (10). The results were arranged in one ranking and their verbal interpretation was carried out according to the scale of relative states. The results are presented in

Table 5. Evaluation of ideal positive and negative solutions for PV prototypes taking into account quality, environmental impact, and social responsibility criteria.

Criteria	C2	C3	C8	C10	C13	C16	C18	C19	C21	C22	C23	C27
a+	0.29	0.28	0.35	0.04	0.27	0.16	0.11	0.25	0.21	0.17	0.21	0.16
a−	0.08	0.07	0.10	0.25	0.16	0.03	0.05	0.12	0.16	0.09	0.13	0.09
Perfect positive solutions
P1	0.02	0.02	0.03	0.00	0.01	0.00	0.00	0.02	0.00	0.01	0.00	0.00
P2	0.05	0.01	0.01	0.00	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00
P3	0.01	0.02	0.06	0.00	0.01	0.00	0.00	0.00	0.00	0.01	0.01	0.00
P4	0.02	0.04	0.03	0.05	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00
P5	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Perfect negative solutions
P1	0.00	0.00	0.00	0.03	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00
P2	0.00	0.01	0.02	0.05	0.01	0.00	0.00	0.00	0.00	0.01	0.01	0.00
P3	0.01	0.01	0.00	0.03	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00
P4	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00
P5	0.05	0.04	0.06	0.02	0.01	0.02	0.00	0.00	0.00	0.01	0.01	0.00

where C2—short circuit current (A), C3—maximum output current (A), C8—maximum power (W), C10—weight (kg), C13—single cell efficiency (%), C16—colour, C18—pattern (texture), C19—pollution prevention, C21—climate change, mitigation, and adaptation, C22—environmental protection, biodiversity, and restoration of natural habitats, C23—fair competition, C27—access to basic services, and C28—community involvement.

and

Table 6. Euclidean distances from ideal positive and negative solutions and PV ranking according to the QES index.

Indicator	d+	d−	QES	Ranking	Decision
P1	0.35	0.27	0.43	4	sufficient
P2	0.31	0.33	0.52	2	moderate
P3	0.35	0.29	0.45	3	sufficient
P4	0.42	0.20	0.32	5	unsatisfactory
P5	0.13	0.48	0.79	1	beneficial

.

The interpretation of the results according to the relative state scale is presented in Figure 3.

This visualisation aids in the interpretation of the results obtained from the method. It shows the values and the scatter of the results. Sensitivity studies are being conducted for the developed model. After their completion, publication of the results is planned. At this stage, it can be assumed that considering the social aspect influences (not necessarily critically) the obtained results, which are intended to support the decision-making process.

According to the results of the model, in this case, the most advantageous prototype is P5 (QES = 0.79). It is characterised by a favourable level of quality–environmental–social fulfilment. This means that P5 will be satisfactory to customers (quality), environmentally friendly, and cost-effective to a good extent. The next in the ranking is prototype P2, which achieved the QES = 0.52 index (34% lower than the index for prototype P5). It is characterised by a moderate level of quality–environmental–social fulfilment. Subsequently, prototypes P1 and P3 would be sufficient (QES = 0.43), but their index is up to 46% lower than the index of prototype P5 and 17% lower than the index of prototype P2. Prototype P4, which took the last position in the ranking and its QES index was 0.32, would be unsatisfactory. Based on the results of the model, it was shown that the P5 prototype is the analysis most advantageous in the considered and it should be chosen to determine the direction of the PV development. When selecting a P5 product, it is advisable to perform a risk analysis, e.g., using FMEA. However, the choice of the P2 prototype can also be considered if it would be more advantageous in terms of other conditions, such as the company’s resources, individual design predispositions, and individual expectations of the company regarding the development of the offered products.

4. Discussion

The development of products from the group of renewable energy sources (RESs) is a current activity that aims to support the sustainable development of products and manufacturing companies [67]. It has been observed that this is a difficult task and often requires a multi-criteria but also an integrative approach to various aspects [13,14]. Problems arise, among others, during the evaluation of various design solutions, where these assessments should concern their quality, impact on the natural environment, or social responsibility. These problems should be solved already at the early stages of creating new products, as well as during their improvement in order to meet the challenges of the dynamically changing market [68].

Therefore, the aim of the research was to develop a QES model that supports product development decisions based on the fulfilment of quality criteria, environmental impact, and social responsibility. The model operates based on a systematic selection, validity, and assessment of the fulfilment of product criteria. Various product solutions (prototypes) are developed using the model. The interpretation of the results is based on the QES index, which aggregates the assessment of quality (customer satisfaction), environmental impact, and the fulfilment of social responsibility. The model was illustrated and tested using the example of photovoltaic (PV) panels, after which its usefulness was demonstrated.

The main benefits of the QES model include the following:

• Ensuring simultaneous analysis of customer expectations regarding product quality, its impact on the environment, and ensuring social responsibility;
• Striving to increase customer satisfaction by analysing criteria regarding product usability;
• Limiting the negative impact of the product on the natural environment by analysing criteria regarding selected environmental burdens;
• Meeting expectations regarding social responsibility by analysing criteria regarding corporate social responsibility;
• Ensuring qualitative and quantitative analysis of the product and its prototypes, supporting the creation of hypothetical product solutions and making decisions about the direction of improving the current product;
• Creating a ranking of product prototypes based on which it is possible to focus on key solutions from the quality–environmental–social point of view, including the rejection of solutions with poor prospects;
• A low-budget model, the use of which does not require extensive databases, including access to software for assessing quality, environmental impact, or social responsibility aspects;
• Reducing the waste of resources by focusing on key solutions.

There are also some limitations of the proposed model. They emerged during the planning and testing of the model, e.g.,:

• The need for a qualified team of interdisciplinary experts;
• The possibility of inconsistency in clearly defining a shortlist of product criteria, resulting from the inability to completely limit subjectivity in expert assessments;
• The lack of complete certainty from the analysis conducted, resulting from a prospective assessment of hypothetical product modifications without physically testing them in practice.

In the case of enterprises with limited resources (SMEs), the barriers that can be distinguished are primarily: the number of experts, knowledge and awareness of experts, and financial resources. In the case of large enterprises, it is important to consider the fact that they typically produce large product series, have numerous customers, have specific distribution systems, significant financial capabilities, and pursue goals (including pro-social ones). Therefore, larger groups of experts with knowledge about the specific perception of the products manufactured within the enterprise should be included. In the case of micro-enterprises, the proposed model would need to be limited to a small number of experts, perhaps even a single expert. This fact may influence the subjective nature of the obtained results. The model was developed for typical consumer products from the electromechanical industry, found in households. Changing the scope of this model’s application requires adopting different assumptions, particularly regarding the number of criteria.

Due to the identified limitations, including observations after the model test, it was considered justified to undertake further research to strengthen the offered QES model. Therefore, future research will be based on expanding the model with other criteria, such as costs or analysis of the opportunities and threats of the developed product solutions. It is also planned to conduct in-depth research on the verification of the impact of the importance of criteria groups on the final product ranking. Future research plans to develop a broader set of environmental criteria, for example, based on a literature review, so as not to rely solely on the ISO 26000 standard. These criteria could address recyclability or end-of-life impacts, but also other issues directly related to the product life cycle. This seems appropriate given the current trends focusing on a “cradle-to-grave” approach to product assessment.

As a supplement, modelling was performed, taking into account various combinations of aspects. Initially, the analysis considered only the qualitative criteria. Then, following the DEMATEL method, it was determined that the key quality criteria were the following: short-circuit current, maximum output current, maximum voltage, efficiency (effectiveness), warranty, kinematics, visibility, degree of integration, and light reflection. These criteria were assessed in terms of their importance, including an assessment of their fulfilment under given conditions. The TOPSIS method was again applied, performing calculations according to the adopted model. This resulted in the following prototype ranking: P1—position 4 (index 0.43), P2—position 5 (index 0.31), P3—position 2 (index 0.51), P4—position 3 (index 0.46), and P5—position 1 (index 0.83). Comparing it to the previous ranking (according to the QES index), it was observed that the first position is still held by prototype P1. However, there are changes in the ranking for other prototypes, e.g., P3 (positions 2 and 3), P4 (positions 3 and 5), and P2 (positions 5 and 2). Invariably, prototype P1 is in position 4 in both cases. This demonstrates the significant impact of quality criteria on the model’s performance. The situation is different when the model omitted only the social criterion (while simultaneously considering quality and the environment). In this case, according to the DEMATEL method, the key criteria were the following: short-circuit current, maximum power, weight, visibility, colour, and pollution prevention. Next, calculations were repeated using the TOPSIS method, where appropriately selected states for the key criteria (qualitative and environmental) were considered. Despite changes in the indicator values, the final ranking of prototypes remained unchanged. In this case, this is due to the demonstrated superiority of the qualitative criteria over the others. Nevertheless, this is only a specific case where, under different assumptions, it may turn out that the environmental or social criterion is significantly more important and will result in significant changes to the ranking. At the same time, it is worth mentioning that the ranking in each of the cases considered was based on a different set of mutually significant criteria, which introduced different states for consideration in the analysed prototypes. Therefore, the test of changes in model results depending on different groups of criteria obtained for the initial verification is not fully reliable. Future research would consider conducting empirical studies (e.g., across companies) that would focus on a single specific product, such as PV. This research would aim to develop a single, coherent list (set) of criteria that have a strong, interconnected relationship with the product under consideration. In this case, the DEMATEL method would be replaced with these survey results.

Direct comparison of models supporting decision-making in the production of new products is unjustified, as each model uses different assumptions, and standardising these assumptions for comparison purposes introduces distortions into the calculations. A direct comparison could demonstrate a significant influence of society on production decisions, but this would likely be one of the rare cases. The introduction of society into the proposed QLCA model changes the ranking list of products proposed for production. However, the influence of social aspects is usually not dominant. The influence of these aspects will vary depending on the adopted values. According to the authors, this is important because, when creating a ranking list, depending on the adopted values (production specificity, company specificity, etc.), social influence will become apparent, but it will not be a critical criterion determining the selection of a new product for production.

5. Conclusions

The purpose of the research was to develop a QES model that supports product development decisions based on the fulfilment of quality criteria, environmental impact, and social responsibility. The model was built in four main stages, i.e.,: product selection, defining product criteria, establishing a shortlist of important product criteria, creating prototypes, and quality–environment–social assessment. The functioning of the model was based on the integration of decision-making methods, i.e., DEMATEL and TOPSIS, as well as based on the scope of environmental and social responsibility criteria from the ISO 26000 standard.

The model was tested and illustrated using photovoltaic panels as an example. Five different prototypes were developed and evaluated according to the thirteen most strongly correlated criteria (having mutual influence). The correlations were determined using the DEMATEL method, where the criteria were short circuit current; maximum output current; maximum power; mass; single cell efficiency; colour; pattern (texture); pollution prevention; climate change, mitigation, and adaptation; environmental protection, biodiversity, and habitat restoration; fair competition; and access to basic services and community involvement. The photovoltaic prototypes were evaluated using the TOPSIS method. Finally, a prototype ranking was developed, where the most advantageous prototype was coded for competitive reasons under number five (QES = 0.79). This prototype simultaneously meets the quality requirements (satisfaction with use), requirements for social responsibility to the highest possible extent, and is environmentally friendly.

The offered QES model has been shown to support dynamic decision-making regarding product development and can be used in any company, especially those looking for simple and low-cost techniques. The model can support the sustainable development of energy products, especially those that are popular and commonly used. Therefore, QES can be useful, for example, in small- and medium-sized enterprises (SMEs), which are the most numerous, and its application will be useful in the field of continuous improvement of processes and products created from them, taking into account quality, social responsibility, and environmental impact.