Integrating LCA and Multi-Criteria Tools for Eco-Design Approaches: A Case Study of Mountain Farming Systems

Abstract

Designing sustainable farming systems in mountainous regions is particularly challenging because of complex economic, social, and environmental factors. Production models prioritizing sustainability and environmental protection require integrated assessment methodologies that can address multiple criteria and incorporate diverse stakeholders’ perspectives while ensuring accuracy and applicability. Life cycle assessment (LCA) and multi-actor multi-criteria analysis (MAMCA) are two complementary approaches that support “eco-design” strategies aimed at identifying the most sustainable options, including on-farm transformation processes. This study presents an integrated application of LCA and MAMCA to four supply chains: rye bread, barley beer, cow cheese, and goat cheese. The results show that cereal-based systems have lower environmental impacts than livestock systems do, although beer’s required packaging significantly increases its footprint. The rye bread chain emerged as the most sustainable and widely preferred option, except under high-climatic risk scenarios. In contrast, livestock-based systems were generally less favorable because of greater impacts and risks but gained preference when production security became a priority. Both approaches underline the need for a deep understanding of production performance. Future assessments in mountain contexts should integrate logistical aspects and cooperative models to enhance the resilience and sustainability of short food supply chains.

1. Introduction

In agricultural research, a substantial and growing body of literature addresses issues related to agricultural sustainability, recent structural transformations, and associated environmental concerns. Additionally, some of these studies focus on the social and cultural aspects of agriculture and farming communities, linking these elements to the broader concept of resilience [1]. The debate on the sustainability of agricultural production systems, particularly in relation to the territories in which they operate, extends beyond the scientific community and influences economic and political domains, as evidenced by various measures and regulations that support the economic, social, and environmental sustainability of the agricultural sector.

Digitization has enabled access to large amounts of data related to farm production processes. In addition, several powerful and fast methodological tools are now available to process such data into information, enhancing the efficiency of decision-making processes. These decisions can be related directly to business operations (e.g., quickly reacting to weather trends, diseases, or stress conditions in plants and animals) or to the strategies of individual companies, associations, or regions (e.g., adapting to climatic trends such as water scarcity, product price fluctuations, and social and environmental standard demand). To react appropriately, however, realistic and scientifically consolidated data and information are necessary. In the absence of such data, there is a risk of remaining in a limbo of opinions, resulting in significant uncertainty and instability in the decisions and actions taken.

Historically, mountain agriculture has been characterized by extensive and low-intensity practices. Nevertheless, since the Second World War, European mountain ecosystems have undergone two significant transformations driven by agricultural mechanization: the development of the European Common Agricultural Policy and the effects of market globalization. First, there has been a progressive abandonment of marginal agricultural land. Second, there has been a growing tendency to cultivate the most suitable soils intensively, significantly increasing productivity, crop specialization, and expansion of plot sizes [2]. Hence, resource optimization and efficiency have become increasingly important in promoting sustainable agriculture with a high degree of self-sufficiency and independence from external resources [3]. In recent decades, at the European level, accompanying measures in agricultural policy have been developed to support agricultural functions beyond food production, such as landscape maintenance, soil health, and biodiversity preservation. Among the various policies aimed at integrating agricultural production with environmental and biodiversity goals, outcome-oriented measures are increasingly becoming a priority [4]. Consequently, there is a growing need to assess the performance of production systems and align them effectively with established objectives, such as ensuring adequate income and meeting requirements for subsidies or product certification. These strategies are fundamental in supporting farm resilience [5]. This depends on a balanced mix of capacities to (i) assimilate changes without altering structure and functions (buffer), (ii) develop by adapting to the current situation (adaptive), and (iii) implement radical changes by implementing new production systems (transformative). Especially in alpine (mountain) regions, social and ecological resilience factors are equally important and result in complex social–ecological systems. Several studies have investigated the relationship between traditional agriculture (crop and livestock farming) and other forms of income, such as forestry, tourism, and subsidies. These external sources can either support or hinder mountain farming, depending on their design and societal perception [1,6,7,8]. Mountain agriculture faces constraints in adapting to natural variations due to terrain limitations, which restrict major changes in cultivation techniques and intensification or scaling-up of production [1]. Consequently, traditional practices are often preserved, and efforts are made to maintain biodiversity and landscape characteristics that support the tourism sector that has increasingly supplanted agriculture as a primary income source in these areas [9].

To resolve this conflict between tradition and innovation, it is necessary to reconsider existing business models and introduce sustainable innovations that are compatible with traditional practices. The collaborative networks of companies and institutions play a crucial role in supporting regional sustainable development initiatives [7]. Generally, small-scale farms display considerable morphological diversity and biodiversity and are influenced by strong regional socioeconomic interactions and the multi-functionality of landscape elements [10]. In recent decades, there has been a notable shift in the agricultural sector’s structure, as small-scale farms have increasingly shifted toward highly specialized and economically profitable activities [10].

Extensive mountain agriculture and animal husbandry have a public utility value that is economically challenging to quantify and can be expressed in terms of biodiversity improvement, water and soil quality maintenance, erosion control, climate regulation, and landscape protection with the promotion of recreation and tourism and preservation of aesthetics and related emotional value. For these reasons, farmers often struggle because agricultural policies frequently overlook or simply ignore these values. In such cases, it is important for super enterprise entities to intervene, where farms and institutions align with objectives to find the best way to support these types of common good services. Defining appropriate objectives and strategies that require a clear assessment of both the positive and negative impacts of various practices as well as the relative importance of each impact from the perspective of different stakeholders is crucial. This involves a multi-dimensional (e.g., multi-criteria) and multi-actor decision-making process where diverse needs must find a solution that is as satisfactory as possible. In other words, it is essentially about designing a production system or a set of production systems located in a portion of territory in the most satisfactory way possible. The methodologies related to life cycle assessment (LCA) and multi-criteria analysis (MCA), once integrated, are suitable for this purpose [11,12]. These methodologies help systematically evaluate and compare different options based on their environmental, social, and economic impacts, thereby supporting informed decision making toward sustainable agricultural practices.

Nevertheless, studies on integrated applications of the two methodologies in mountainous agricultural contexts are lacking. This paper contributes to filling this gap by presenting an analysis conducted on a case study of a mountain farming system in an alpine environment. In response to the research question of how to support multi-factor decision making based on strong scientific data and evidence in such environments, this study leverages the strengths of two well-established methodologies, namely LCA (life cycle assessment) and MCA (multi-criteria analysis), and proposes an integrated approach to address the need for sustainable agriculture in different aspects. This approach considers the following:

(a)

Addressing simultaneously the impacts on different environmental, social, and economic compartments to move beyond the mono-criteria approach, which can lead to misleading results and decisions. For example, focusing on climate change can only overlook other critical impacts [13,14];

(b)

Employing multi-factor assessments, where the impacts of the system are analyzed from diverse, often opposing and conflicting viewpoints, typical of complex socioeconomic systems.

LCAs, indeed, consider the most relevant impacts throughout the entire life cycle in both spatial and temporal terms, relating primarily to environmental performance. However, information obtained from technical analyses such as LCAs is not always easy to interpret and use in decision-making contexts. MCA, on the other hand, allows for the simultaneous consideration of aspects from different domains and perspectives in an analysis based on a multi-actor approach. Both methodologies aim to support decision-making processes by providing decisionmakers with comprehensive information about the system under study and by seeking the best compromise solution.

When making decisions on complex systems, especially those with conflicting alternatives, it is necessary to have analysis support tools that are able to keep the “subjective” and “objective” problem components separate to guarantee better transparency in the decision-making process. The “objective” component concerns the consequences (technical, operational, economic, and environmental) that can be expected with the implementation of each decision-making alternative. These consequences, i.e., impacts, are described through a set of criteria. For each alternative solution, there is a set of values for each criterion, and the resulting matrix “Criteria X Alternatives” is the so-called “Impact Matrix”. The definition of the Impact Matrix is crucial: it is derived from the description of the system and the identification of the main impacts and critical points by a group of experts who must unanimously converge in its construction in an objective manner, without any reason for conflict. For this reason, the LCA can be very useful at this stage of the MCA.

The “subjective” component concerns the points of view—often opposing and conflicting—of the various actors taking part in the decision-making process. Each actor expresses its point of view by assigning a value of importance to each criterion. The resulting matrix “Criteria X Actors” is called the “Priority Matrix”. The MCA allows both components to be managed transparently and unambiguously through a well-established methodology. More details about the methodology are given in the Materials and Methods (Section 2.2).

The aim of this work concerns the application of this integrated LCA/MCA methodology in the choice of the most satisfactory farming system for a mountainous context. The production chain also extends to the processing of primary products directly at the farm to guarantee more robust and efficient production conditions. Four typical mountain farming value chains are considered here: (a) two are based on the cultivation of cereals, i.e., rye for bread production and barley for beer production, and (b) the other two value chains are based on mountain forage crops to supply alternative dairy farming systems (cows or goats) to produce mature cheese.

Although it is one of the most recognized methodologies for eco-design and the assessment of impacts and hotspots along a product life cycle, LCA also shows a certain variability in results depending on the choices/settlements taken in the different steps of the analysis process (system boundaries, functional unit, allocation, etc.). In the agri-food sector, this variability is even greater since cultivation and breeding methods, plant and animal species, environmental and climatic conditions, and many other variables make production processes extremely heterogeneous. The comparison of analyses conducted in different studies and different production systems always proves difficult, as described by some works conducted on bread, beer, and dairy products [15,16,17,18,19,20,21]. Furthermore, ref. [22] highlights both the limitations of traditional LCA applications in the agri-food sector and the need to complement this type of analysis with other more holistic methodologies capable of understanding the different aspects of the sector that are also linked to the cultural and social spheres, for example. For the present study, therefore, production systems that realistically adhere to mountain farm conditions are considered based on real data and expert estimates of the different domains rather than product-related guidelines per se (i.e., PCR). Since mountain farming is characterized by different production systems, which translate into a high variety of cultivated species and livestock breeds [23], the data used do not refer to a single real farm but rather to a “farm type”. This avoids the risk of being too specific and provides more usefulness to achieve the purpose of the research.

2. Materials and Methods

2.1. Goal and Scope Definition

The objective of this study was to support strategic decision making regarding the selection of four different agri-food value chains suitable for marginal mountain areas: (a) rye–bread, (b) barley–beer, (c) hay–cow cheese, and (d) hay–goat cheese. To develop strategies that ensure the sustainability of mountain farming at all levels, both traditional and innovative production processes were considered. For example, in cereal-based supply chains, prototype machines are used to accelerate cultivation activities that are traditionally performed manually. The case study was designed ad hoc to highlight the different aspects of alternative production systems, which present both strengths and weaknesses with respect to each other, considering quantitative and qualitative criteria to evaluate performance in the different macrodomains.

To assess environmental performance and identify critical processes, each alternative supply chain underwent LCA analysis via a cradle-to-gate approach, with the assumption that the entire supply chain operates on the farm. Products are subsequently either consumed onsite (farmhouse) or sold directly at the farm.

Estimating economic and operational performance derived from analyzing individual processes within production systems relies on models and data obtained either directly through measurements or through collaborative input from domain experts.

2.2. LCA and MCA Tools and General Approaches

Exploratory LCAs were performed via OpenLCA v2.4 software (GreenDelta, Berlin, Germany) with the ReCiPe midpoint (H) and endpoint (H) life cycle impact assessment (LCIA) methods to capture insights into both the impacts generated and the effects on target groups. The analysis of the target group highlights the contributions of the different impact categories. Consequently, the most representative midpoint impact categories for the study systems were included as criteria in the following MCA application.

For the MCA, free web-based multi-criteria software (https://my.scientificnet.org/inest-amc accessed on 29 April 2025, previously known as MEACROS [24]; see Appendix A for instructions,) was used, whose algorithms were derived from the ELECTRE method (ELimination Et Choix Traduisant la REalitè, named concordance/discordance analysis [25,26]), which was originally developed in France in the 1960s to address multi-dimensional decision-making processes at territorial and regional scales. In ELECTRE, several discrete alternatives inducing different impacts (Impact Matrix = Alternatives X Criteria) are compared pairwise, considering the priorities expressed by a set of different decisionmakers, featuring different standpoints (Priority Matrix = Criteria X Decisionmaker, where each priority is held by a single actor taking part in the decision-making process). The objective and subjective components of the analysis are then represented by the Impact and Priority Matrices, respectively.

The impacts provide a “measure” of the effects of each criterion (identified by a specific indicator) in relation to each of the proposed alternatives. Notably, the MCA methodology allows the use of both cardinal (objectively measurable effect) and ordinal (effect requiring the attribution to a class, within a series of predefined ordered classes) indicators. In the latter case, the assignment of a class to an effect—usually proposed by experts—must be previously agreed upon and accepted by all decisionmakers involved in the analysis since the Impact Matrix cannot contain subjective elements of conflict.

The concordance/discordance analysis used in this study provides a series of mathematical procedures that, starting from the two previous input matrices, construct a final Appraisal Matrix (=Alternative X Decisionmakers) that contains indices with which it is possible to reconstruct a preference ranking of each alternative with respect to each decisionmaker. There are different preference indices, depending on whether the analysis intends to highlight the strengths (concordance) or weaknesses (discordance) of the different alternatives. Thus, a variety of Appraisal Matrixes can be obtained according to the type of preference index used in the MCA exercise.

The preference index used here integrates both the concordance and discordance indices; it is algebraically calculated from the weighted aggregate concordance index with the weighted aggregate discordance index [24,27]. This approach yields a new index, named the global synthetic index (GSI), that integrates information on both the positive and negative aspects of each project: the higher the GSI value is, the higher the preference rank that must be attributed to the alternative at hand. The sum of the GSI values of all alternatives considered is always zero. This means that for alternatives with GSI > 0, positive aspects prevail, whereas the opposite is true when GSI < 0. Accordingly, the “best” and “worst” solutions are highlighted by the conditions: GSI_best = max(GSI_i) and GSI_worst = min(GSI_i), respectively.

In any case, since the purpose of MCA is not to find an optimal solution but rather to identify a reasonable compromise solution able to mediate the satisfaction rate among different decision profiles, it is also appropriate to perform sensitivity analyses to assess the stability of the identified solutions. This is possible through the following steps:

(1)

Grouping the individual criteria within homogeneous groups, thus creating a macro-criterion (MC_j) of a higher order and taking into account the relative weight of each single criterion within its own macro-criterion; the weight of each MC_j is determined by the sum of the weights of the individual criteria that constitute it;

(2)

The overall weight of each j-th macro-criterion is then varied between 0% and 100% (with a 1% step) such that each combination always results in $\sum _{\mathrm{j}=1}^{\mathrm{N}}{\mathrm{M}\mathrm{C}}_{\mathrm{j}}=100\mathrm{\%}$; each combination, therefore, corresponds to a new Priority Matrix with respect to which a new respective Appraisal Matrix will then be generated;

(3)

The results of the various Appraisal Matrixes can then be mapped graphically, highlighting the best- or worst-ranked solutions with respect to the priority combination set by the macro-criteria; in the case of N = 3, the ranking mapping can be represented on triangular diagrams.

In the case at hand, a grouping based on N = 3 macro-criteria were chosen: (1) economic (ENV), (2) environmental (ENV), and (3) operational (OPE). The results of the related analysis are then provided on triangular map diagrams proposing the best and worst solutions for each combination of weights. Since the analysis is also influenced by the relative weight of each individual criterion within its MC_j, it was decided to take this into account by starting from the internal criterion breakdowns proposed by the profiles of three decisionmakers chosen from the original pool.

MCA and LCA refer to the same systems and functional units. Therefore, the definitions of systems and inventories were designed to be functional for both analyses. Below, a detailed description of the main steps that these two approaches have in common is provided.

2.3. System Boundary and Functional Units

The reference system is a typical alpine (mountain) farming system. The primary data on cereal cultivation used for the analysis consider innovative mechanization methods and are largely derived from a research project conducted in South Tyrol (Brotweg) [28], aiming at defining new proper machines (prototypes) suitable for use even on extremely steep lands to enable the mechanization of traditionally manually operated fields. The four value chains are completely on-farm as far as land and machinery are concerned. Cultivated lands are characterized by steep slopes and are close to the farm center, where postharvest food transformation processes take place. The building structures (barn, stable, processing and storage rooms, etc.) are already in place, and no changes are planned for them except for minor adaptations and adjustments (mainly consisting of structural woodwork by local craftsmen).

The main features of the different solutions are outlined below, considering their potential characteristics in the mountainous area of South Tyrol and based on project findings and direct input from experts in the agriculture, livestock, and food processing sectors.

The analysis was conducted using a functional unit (FU) defined as 1 ha of cultivated land to facilitate accurate comparisons of various production systems. The choice of a cultivated land portion as a functional unit is in line with the goal of the analysis: to identify the most sustainable use of a mountain farming area, considering all the different aspects and actors involved. In fact, LCA was used to analyze, on a strong scientific basis, some of the environmental impacts of different alternative farming systems, and these results were used as inputs for the following decision-making support methodology. In other words, the focus was on the impacts generated on a portion of territory and not on a unit (kg) of product per se. Specifically, the area is part of a mountainous terrain located at an altitude of 1000–1300 m a.s.l., with maximum slopes ranging from 70–80%. This aligns with the project’s objective of exploring solutions for marginal soils that are more challenging to manage and are at risk of abandonment. Regarding the end products of the four supply chains, the functional unit of 1 ha provides the following annual outputs:

• 2095 kg of whole meal rye bread;
• 9600 L of barley craft beer;
• 458 kg of ripened cow cheese;
• 484 kg of ripened goat cheese.

Nevertheless, in addition to the main functional unit of 1 ha of cultivated area, the impacts estimated through LCA were also evaluated with respect to 1 kg of final product, both for the sake of completeness and for performing more reliable comparisons with the achievements already available in the literature. In fact, the latter usually provides results referring to the unit of mass of the final product, especially when dealing with agricultural and zootechnical applications.

The system boundaries for each supply chain encompass the cultivation of cereals for bread and beer production, hay production for livestock feed, cheese production, and the final saleable products. Detailed descriptions of the complete systems are provided below: they represent the four alternative production systems analyzed through MCA.

Production systems differ widely in terms of the production and utilization of byproducts. In these cases, the LCA proposes different approaches for strategies for both allocation (distribution of impacts between the various products and byproducts) and expansion (enlargement of the system boundary), with often divergent consequences, as this is a function of both the type of byproduct and the relative masses involved. In general, reuse within the production system—possibly expanded in such a way as to create short-range local transport conditions—is often advantageous, that is, as long as it does not require profound transformations and demanding additional processes for the implementation of reuse. Moreover, the combination of allocation and expansion strategies, especially when very different production chains are compared, would lead to such a proliferation of assumptions that the real usefulness of the comparison would be undermined. Since the purpose of this paper is to provide information on strategic choices for better land utilization, the various scenarios are united by the presence of small farms with limited cultivated areas. Therefore, it is realistic to assume that there are situations of mixed farming systems or farms close enough to be able to reuse various types of waste symbiotically. It follows that the following analyses consider solutions for the reuse of byproducts on a short scale, with application details that are described in relation to each supply chain considered.

Similar considerations were also confirmed for transport. The following analyses do not consider transport-related processes for the supply of production inputs, as logistics in mountainous areas are complicated and difficult to generalize, making it difficult to formulate a representative context of an average situation. As this paper focuses on a comparative evaluation of different farming systems alternatively run on the same portion of land, it is assumed that for the same farm, the supply of raw materials entails the same transport effort as those defined under optimal conditions. The stages following the preparation of products for consumption at the farm or direct sale were not considered. For each supply chain, the target is “zero-kilometer” production, which is also a common strength of all the supply chains being considered. Obviously, in mountain areas, transport can be very challenging and cause significant impacts, particularly if fields are dislocated in different places or if the transformation activity is performed far away from the farm. The optimization of farming systems considering the logistics of transport could be an interesting topic for further investigation.

Finally, impacts associated with the construction and maintenance of production facilities are excluded from the system boundaries.

The phases of the four farming systems are shown in Figure 1, Figure 2 and Figure 3 for the cereal–bread, cereal–beer and hay–cheese supply chains, respectively. The phases of each value chain, along with their respective strengths and weaknesses, are summarized in

Table 1. Phases, strengths/opportunities, and weaknesses/challenges of the value chains considered in the case study.

Value Chain	Phases	Strengths/Opportunities	Weaknesses/Challenges
A Rye wholemeal bread	Rye cultivation Postharvesting (corn cleaning and drying) Milling Dough preparation and baking	Favor land preservation through its direct management High-income local product compared to the same industrial product	Field fragmentation Strong competition with existing flora Soils with slopes impossible for conventional machines Risks of primary product losses due to severe winter weather conditions Corn losses at harvesting Mechanization costs
B Barley craft beer	Barley and hop cultivation Postharvesting (corn cleaning and drying) Malting Brewing and bottling	Favor land preservation through its direct management High-income local product in a niche market	Same as previous (A) Malting problems with small masses Agricultural input materials are generally more expensive than industrial ones
C Cow mature cheese	Hay cultivation and haymaking processes Herd management and milk production Cheese production	Favor land preservation through its direct management Good breeding opportunities in marginal contexts Low production risks Good yields of processed products	Labor shortages in the face of constant year-round work commitments Strict health and hygiene protocols for the herd Need for highly specialized infrastructures Relevant byproduct management problems (animal wastes and whey)
D Goat mature cheese	Same as previous (C)	Same as previous (C) Better hay–milk conversion factors More digestible processed product, ideal for intolerances	Same as previous (C)

.

2.4. Activity Data: Life Cycle Inventory and Impact Matrix

The creation of the impact matrix and the life cycle inventory are the core components of the MCA and LCA, respectively, requiring the involvement of sector experts. In the case study, various sources were reviewed, interviews were conducted with stakeholders from the supply chains under study, and consultations were held with experts in agriculture, animal husbandry, and food processing. For the MCA, criteria were selected based on the findings of this preliminary investigation to consider the variety of aspects considered by farm stakeholders in their strategic decisions. At the same time, an attempt was made to ensure a comprehensive analysis without excessive complexity or interconnected criteria. Thus, 14 criteria were chosen to evaluate the performance of the four alternatives (for more detail see Section 2.4.5—Table 7).

The data used in the LCI were derived from the analysis of the four supply chains under consideration. In the case of incomplete or missing primary data, secondary data from other databases were used and selected to be sufficiently approximate for the purposes of the analysis. Whenever possible, primary data related to field crops and food processing processes were directly measured or estimated on the basis of typical situations of mountain farms through direct interviews. In the case of missing data, however, bibliographic and secondary data were also used since the main objective of the analysis was to identify the most satisfactory use of mountainous areas, particularly from the point of view of agricultural production.

The transformation processes were evaluated to realistically reflect typical mountain farm settings. Machinery and plant specifications included a farm area of 16 ha and herd sizes of 80–100 and 16–20 lactating heads for goats and cows, respectively. All the performance metrics were finally normalized to a reference area of 1 ha, as were any other economic and operational output (e.g., yields, revenues, working hours, etc.). Details of the operations and machinery considered for these calculations are provided in

Table 2. Operations and machines (fw = front width; Pn = nominal power).

Phase	Machine	Value Chain
Cultivation	Mountain tractor (Pn 63 kW)	Bread, Beer
	Rotary harrow (fw 1.6 m; depth 10–12 cm)	Bread, Beer
	Drum mulcher (fw 1.8 m)	Bread, Beer
	Combi seeder (fw 1.6 m)	Bread, Beer
	Stripper head (fw 1.6 m), rotor speed 450 rpm	Bread, Beer
	Trailer	Bread, Beer, Cheese
	Mountain tracker (Pn 25 kW)	Cheese
	Rake (fw 3.8 m)	Cheese
	Hay turner (fw 4.0 m)	Cheese
	Mowing bar (fw 2.2 m)	Cheese
Postharvest and storage	Stand thresher	Bread, Beer
	Cleaner	Bread, Beer
	Dryer	Bread, Beer
	Vertical silos	Bread
Milling and baking	Mill	Bread
	Kneaders	Bread
	Oven	Bread
	Bakery room, flour storage, shop	Bread
Brewing	Malting equipment	Beer
	Fermenters	Beer
	Bottling machine	Beer
	Packaging machines	Beer
	Cooking room, cellar	Beer
Stable management	Milking parlor plant	Cheese
	Barn infrastructure	Cheese
	Manure treatment equipment	Cheese
	Hay and feed equipment	Cheese
Cheese production	Maturation cell	Cheese
	Curd room with equipment	Cheese
	Storage cell	Cheese

.

2.4.1. Inventory: Whole Meal Rye Bread (A)

Owing to the characteristics of the cultivated area, the considered cultivation techniques involve minimum tillage with a subsequent combined operation of sowing and fertilization with organic nitrogen fertilizer. No weeding or irrigation is planned. Harvesting is carried out with a stripper header, which removes the plant apical part only and allows for better operational efficiency on sloping terrain, especially in the presence of bedded cereals. The residual part is then shredded and left in the field to favor the soil organic matter balance. The rye yield is 2.5 t d.m. ha⁻¹ y⁻¹, which refers to clean rye grain (approx. 20% w.b. at harvest). The transformation coefficient for bread is 83.4%, i.e., 2095 kg ha⁻¹ y⁻¹ of whole meal bread, which is packed in 1 kg loaves, each with paper wrapping.

The bread is made according to a basic recipe with whole meal rye flour, water, brewer’s yeast and salt. The selling price is EUR 8 kg⁻¹, corresponding to a total revenue of EUR 16,760 ha⁻¹ y⁻¹.

The global investment is achieved based on cost estimates of the facilities and machines needed to start the whole supply chain and is equivalent to EUR 17,688 ha⁻¹ (based on the aforementioned 16 ha farm reference).

The labor units required for the various phases were estimated based on the operations involved, considering their duration and the personnel needed. The total is 179.4 man*h ha⁻¹ y⁻¹, to which the steps related to cultivation (including postharvest), milling, and bread-making contribute 90.9 (51%), 8.6 (5%), and 79.8 (44%) man*h ha⁻¹ y⁻¹, respectively.

Energy consumption was calculated considering both the primary data from machines and systems monitored during the project Brotweg [28] and the performances of the machines and systems typically used for similar operations and under similar conditions.

The risks associated with the preservation of the primary product (in this case, rye grain) and the final processed product (bread) were assessed on an ordinal scale of 1–10. In the cereal–bread supply chain, the risk of deterioration of the grain is typically considered low, whereas the risk associated with bread is greater.

This supply chain does not present any critical issues regarding the management of wastewater or production waste, as these quantities are easily managed and valorized at the farm. Field losses during harvesting are left on site, residues resulting from cleaning are composted, and milling residues are composted or used for animal feed. Since they are relatively small quantities and are recovered in the farm’s internal processes, they are not considered in the analyses.

One of the main problems linked to the cultivation of cereals in mountains is the risk of crop loss, which is due mainly to adverse weather conditions. This problem is more common for autumn sowing. This risk was estimated, on an ordinal scale from 1 to 10, as very high.

With respect to the impact on the landscape, the introduction of small plot cereal cultivation in disadvantaged areas leads to improved visual perception and greater vegetative diversity. Land preservation is also guaranteed as minimum tillage and low-input agricultural strategies are applied. Thus, medium–high performances for the landscape criterion are ensured here.

For more detail, in Section 2.4.5—Table 8 (Column A), the estimated impacts for the cereal–bread supply chain used for the AMC are schematized, while

Table 3. Bread value chain (LCI); values refer to 1 ha of cultivated land (per year).

Phase	Input	Output	Notes
Cultivation	Rye seeds: 180 kg Organic fertilizer: 220 kg Fuel (diesel): 156 kg	Rye corn (20% w.b.): 2500 kg	Operations: mulching, harrowing, sowing, fertilization (organic), harvesting, transport to the warehouse (distance 0.2 km); irrigation is required only seldom in spring in extreme dry seasons and for this reason is omitted in the study; pesticides are not employed; impact of infrastructures is not considered. Harvest losses remain on the field.
Postharvest	Rye corn (20% w.b.): 2500 kg Fuel (Diesel): 28 kg Electricity: 78 kWh	Rye corn (12% w.b.): 2159 kg Residues: 114 kg Water (evaporated): 227 kg	Operations: threshing, cleaning, drying. The residues (5%) result from the cleaning and screening process and are composted on the farm.
Milling	Rye corn (12% w.b.): 2159 kg Electricity: 238 kWh	Flour: 2095 kg Residues: 64 kg	The residues (3%) are used as fertilizers and animal feed.
Bread making and baking	Flour: 2095 kg Salt: 31 kg Yeast: 52 kg Water: 1571 L Electricity: 519 kWh Paper: 21 kg	Packed bread: 2095 kg Water (evaporated): 1655 L	1 kg bread in a paper bag

summarizes the LCI used.

2.4.2. Inventory: Craft Barley Beer (B)

The barley cultivation techniques in mountainous areas are assumed to be the same as those in the previous supply chain (A).

The barley yield is 2.3 t d.m. ha⁻¹ y⁻¹, which refers to clean barley grain (approx. 20% w.b. at harvest). Additionally, the hop is directly cultivated at the farm entirely via manual operations. The malt/barley transformation yield is 80% (grain 12% w.b.), whereas the beer/malt yield is 60%, with a craft beer production of 9600 l ha⁻¹ y⁻¹. For beer production, barley malt, hops, yeast, and water are needed.

The brewing phase is represented mainly by secondary data estimated from the scientific literature and best-practice guidelines [29,30,31,32] that consider a craft barley beer as the final product. In terms of energy consumption, generally, the greatest impacts are associated with the production of glass bottles, independent of beer type [33]. For the other processes, some differences can be found between ale beer and lager beer regarding the boiling and hopping phases (slightly higher for ale beer) and the fermentation phase (significantly higher for lager beer). Here, the brewing phase refers to an average process.

Bottling takes place in glass bottles with a capacity of 0.75 L, thus implying that 12,800 bottles ha⁻¹ y⁻¹ are sold at EUR 16 L⁻¹ for a total revenue of EUR 153,600 ha⁻¹ y⁻¹.

The investment (always referring to 16 ha farms) is achieved based on cost estimates of the facilities and machines needed to start the entire supply chain and corresponds to EUR 9750 ha⁻¹.

The overall charge of work is 424.1 man*h ha⁻¹ y⁻¹, to which the steps related to cultivation (including postharvest), malting, and brewing (including bottling) contribute 148.4 (35%), 82.8 (20%), and 193 (45%) man*h ha⁻¹ y⁻¹, respectively.

Energy consumption is calculated considering both the machines and systems monitored during the project and the performance of the machines and systems typically used for similar operations and under similar conditions.

The risks associated with the preservation of the primary product (in this case, barley grain) and the final processed product (beer) were assessed on an ordinal scale of 1–10. In the cereal–beer supply chain, both are considered low. With respect to crop loss risk and landscape impacts, the same considerations made in the previous supply chain (A) apply.

The cereal–beer chain does not present any particular critical issues regarding the management of production waste in the field phase. The manufacturing processes, however, lead to a greater mass of spent grain than the quantity of raw material processed, as it is a residue rich in water. Spent grains can be considered for animal nutrition [29]. Since additional feed to hay represents an important cause of the impact of livestock farming, its use in this sense through agreements between neighboring agricultural companies is considered reasonable. In fact, given the logistics and location of farms in mountainous areas, it is likely that the valorization of byproducts takes place either directly at the farm (in the case of mixed production systems) or as close as possible so as not to have the burden of transporting high-moisture materials. As already highlighted, for the purpose of this research, multiple possible scenarios were not explored since each situation would still be independent, and there would be a risk of introducing greater uncertainty and losing sight of the objective of the analysis performed here. Since the analysis does not end with the LCA but is then completed with the MCA, considerations of risks and opportunities related to the reuse of spent grain are considered in the subsequent multi-criteria analysis.

For more detail, in Section 2.4.5—Table 8 (Column B) outlines the estimated impacts for the cereal–beer supply chain used for the AMC, while the LCI used is shown in

Table 4. Beer value chain (LCI); values refer to 1 ha of cultivated land (pro year).

Phase	Input	Output	Notes
Cultivation	Barley seeds: 180 kg Organic fertilizer: 220 kg Fuel (diesel): 156 kg	Barley corn (20% w.b.): 2316 kg	Operations: mulching, harrowing, sowing, fertilization (organic), harvesting, transport to the warehouse; irrigation is required only seldom in spring in extreme dry seasons and for this reason is omitted in the study; pesticides are not employed; impact of infrastructures is not considered. Harvest losses remain on the field.
Postharvest	Barley corn (20% w.b.): 2316 kg Fuel (diesel): 26 kg Electricity: 72 kWh	Barley corn (12% w.b.): 2000 kg Residues: 106 kg Water (evaporated): 211 kg	Operations: threshing, cleaning, drying. The residues result from the cleaning and screening process and are composted on the farm.
Malting	Barley corn (12% w.b.): 2000 kg Natural gas: 92 Nm3 Electricity: 143 kWh Water: 3344 L	Malt: 1600 kg Barley sharps: 20 kg Malt sprouts: 72 kg	Operations: steeping, germination, kilning, cleaning. The malt/barley grain yield is 0.8 in line with [29,32]. For the inputs and outputs of the malting phase, reference is made to [29,32]. The residues are used as fertilizers and animal feed. Water is found in the waste and evaporates in the processes.
Brewing and bottling	Malt: 1600 kg Hop: 48 kg Yeast: 202 kg Water: 57,600 L Natural gas: 457 Nm3 Electricity: 739 kWh Glass: 7040 kg	Bottled beer: 9600 L Brewers’ grain: 1280 kg Wastewater: 39,031 L	Operations: cooking the wort, fermentation, filtration, bottling. 0.75 L glass bottle 1 kg beer = 1 L beer Water/beer ratio = 6 in accordance with [30,31]. Beer/malt ratio = 6 in accordance with [29,31,32]. Input yeast reference to [30]. Brewers’ grain approximately 0.8 following [29]. Wastewater 0.7 of input water following [34]

.

2.4.3. Inventory: Ripened Cow Cheese (C)

Mountain animal farming systems typically involve forage production through haymaking. Here, a harvest of loose forage (at 18% w.b.m.) after a total drying natural process in the field is considered, with a total hay yield of 3.5 t ha⁻¹y⁻¹, distributed across four cuts during the productive season. No weeding or irrigation is foreseen; fertilization occurs through the spread of self-produced manure.

Since a herd diet consists mainly of hay and concentrates, the amount of hay produced can reach 0.51 heads ha⁻¹y⁻¹, with each lactating cow capable of a milk productivity of 5000 kg head⁻¹y⁻¹. Thus, an overall yield of 458 kg ha⁻¹y⁻¹ of ripened cheese is expected on average. To produce cheese, only rennet is added; however, rennet is added in negligible quantities compared with the results of the evaluations. The cheese is ripened in “wheel” forms that are not packaged. Buildings and machinery, medicines, washing detergents, and various inputs, such as salt and disinfectants, are excluded from the analysis.

The selling price is EUR 22 kg⁻¹, with a revenue of EUR 10,086 ha⁻¹y⁻¹.

The investment is achieved on the basis of cost estimates of the facilities and machines needed to start the entire supply chain. Additionally, in this case, investments refer to a chain capable of managing 16 ha of meadow land and a herd of 8–10 animals, with a corresponding specific investment of EUR 22,375 ha⁻¹.

Manpower requirements were estimated from the operations planned in the different phases, considering their duration and the number of workers needed. The overall charge of work was 334.5 man*h ha⁻¹y⁻¹, to which the steps related to cultivation (including postharvest), stable and herd management, and cheese processing at the on-site dairy contribute with 79.1 (24%), 139.1 (41%), and 116.3 (35%) man*h ha⁻¹y⁻¹, respectively.

Energy consumption is calculated considering the performance of machines and systems typically used for similar operations and under similar conditions as well as information received from domain process experts.

The risk associated with the conservation of the primary product (in this case, milk) and the finished product (ripened cheese) was assessed on an ordinal scale from 1 to 10. In the case in question, the risk of deterioration of cheese is considered low, whereas that associated with milk is very high.

The risk of loss of production is not particularly high, both for hay and milk. This risk was estimated, on an ordinal scale of 1–10, as very low.

With respect to the effects on the landscape, impacts are estimated at a medium level (on an ordinal scale of 1–10), given the need for adequate and cumbersome structures (e.g., fences and shelters) and the unevenness of the meadows due to the areas where cows are stable. Finally, the land preservation function associated with dairy farming is high, as relatively large areas are managed through traditional haymaking, minimizing natural grassland maintenance. The overall impact is therefore positive and medium–high.

A critical point of this supply chain is the management of animal waste (slurry, in particular). If slurry is used internally at the farm, allocation is usually not necessary [35]. The scenarios of the use of slurry internally rather than in the lands of neighboring farms can be multiple and introduce positive or negative performance depending on the type of expansion of the basic production system in terms of both generated and avoided impacts. Since the aim of this study is to investigate the combined application of LCA and MCA to support strategic choices, the plausible hypothesis of using slurry as fertilizer in surrounding areas was considered regardless of the ownership of the land, considering that the impacts generated by slurry management are compensated by the avoided impacts of obtaining and using other types of fertilizers.

As far as possible, when whey is used as waste in dairy laboratories, the impact on the main product does not induce relevant changes [19].

For more detail, in Section 2.4.5—Table 8 (Column C), the estimated impacts for the cereal–beer chain used for the AMC are schematized, whereas in

Table 5. Cow cheese value chain (LCI); values refer to 1 ha of cultivated land (pro year).

Phase	Input	Output	Notes
Cultivation	Fuel (diesel): 66 kg Manure: 9490 kg	Hay (18% w.b.): 3500 kg	Operations: mowing, hay turning, raking, harvesting (loose forage), transport, fertilization (manure). Harvest losses remain on the field.
Milk production	Hay (18% w.b.): 3500 kg Fuel (diesel): 68 kg Electricity: 356 kWh Water: 1921 L Poudre milk: 72 kg Feed: 826 kg	Milk: 2547 kg Wastewater: 1343 kg Manure: 667 kg	Operations (daily): hay pick-up/distribution, other feed distribution, calf feeding, milking, milking plant cleaning, barn cleaning and manure management. Wastewater 50% of milk production.
Cheese production	Milk: 2547 kg Electricity: 1159 kWh	Cheese: 458 kg Whey: 2089 kg	Operations (daily): ripening/cooking cell, curd room management, refrigeration/storage cell.

, the LCI used for the corresponding LCA is shown.

2.4.4. Inventory: Ripened Goat Cheese (D)

The part related to the cultivation of the land for the livestock supply chain is the same as that in the previous supply chain (C).

Considering a herd diet consisting of hay and concentrates, the quantity of hay produced can reach 5.37 heads ha⁻¹ y⁻¹, with each lactating goat capable of milk production of 500 kg ha⁻¹ y⁻¹. Thus, an overall yield of 484 kg ha⁻¹ y⁻¹ of ripened cheese is expected on average. Here, the same assumption applies as for the previous chain (C) regarding rennet, buildings and machinery, inputs and packaging, as well as the reference farm size (16 ha). The selling price is considerably higher and equal to EUR 32 kg⁻¹, with a revenue of EUR 15,485 ha⁻¹y⁻¹.

Additionally, in this case, investments refer to a system managing 16 ha of meadow land and a herd of 80–90 animals, with a corresponding specific investment of EUR 21,125 ha⁻¹.

Manpower requirements were estimated from the operations planned in the different phases, considering their duration and the number of workers needed. The overall charge of work was 392.1 man*h ha⁻¹y⁻¹, to which the steps related to cultivation (including postharvest), stable and herd management, and cheese processing at the onsite dairy lab contribute 79.1 (20%), 165.2 (42%), and 147.8 (38%) man*h ha⁻¹y⁻¹, respectively.

With respect to the considerations of (i) energy consumption, (ii) risk associated with the conservation of primary and transformed products, (iii) risk for losing the primary product for weather conditions, and (iv) slurry and whey management, the same concepts apply as described for the proceeding chain (C). Landscape impacts here are just slightly worse with respect to value chain C because of the tendency of goats to graze more intensively and aggressively.

For more detail, in Section 2.4.5—Table 8 (Column D), the estimated impacts for the cereal–beer chain used for the AMC are schematized, whereas in

Table 6. Goat cheese value chain (LCI); values refer to 1 ha of cultivated land (pro year).

Phase	Input	Output	Notes
Cultivation	Fuel (diesel): 66 kg Manure: 667 kg	Hay (18% w.b.): 3500 kg	Operations: mowing, hay turning, raking, harvesting (loose forage), on-farm transport, fertilization (manure). Harvest losses remain on the field.
Milk production	Hay (18% w.b.): 3500 kg Fuel (diesel): 68 kg Electricity: 505 kWh Water: 2504 L Poudre milk: 129 kg Feed: 1184 kg	Milk: 2687 kg Wastewater: 1343 kg Manure: 667 kg	Operations (daily): hay pick-up/distribution, other feed distribution, goat feeding, milking, milking plant cleaning, barn cleaning and manure management. Wastewater 50% of milk production.
Cheese production	Milk: 2687 kg Electricity: 1222 kWh	Cheese: 484 kg Whey: 2203 kg	Operations (daily): ripening/cooking cell, curd room management, refrigeration/storage cell.

, the LCI used for the corresponding LCA is shown.

2.4.5. Impact Matrix

The Impact Matrix is the “objective” component of the MCA methodology, i.e., the part that encompasses the expertise of domain experts in describing the alternative systems analyzed. This description occurs through both the choice of the criteria themselves and the identification of the value of each criterion. Fourteen criteria were identified to describe and compare the four production systems, which were divided into three categories (e.g., a group of homogeneous criteria) depending on their environmental (ENV), economic (ECO), and operational (OPE) nature (

Table 7. The criteria used for the MCA. Measure type: cardinal (CAR); ordinal (ORD). Nature: cost (C); benefit (B). Categories: economic (ECO), operational (OPE), and environmental (ENV).

Criterion	Measure Type	C/B	Description	Category
Revenue (EUR)	CAR	B	Gross value of production	ECO
Investment (EUR)	CAR	C	Initial investment in machinery and plant	ECO
Difficulty in preserving the primary product (points 1–10)	ORD	C	Difficulty in preserving the primary product	OPE
Difficulty in preserving the processed product (points 1–10)	ORD	C	Difficulty in preserving the processed product	OPE
Production loss risk (points 1–10)	ORD	C	Risk of production loss due to adverse weather conditions	ECO
Labor for primary production (h UL)	CAR	C	Operator labor for plant production	OPE
Labor for first intermediate product (h UL)	CAR	C	Operator labor for first processing	OPE
Labor for final product (h UL)	CAR	C	Operator labor for second processing	OPE
Landscape and territorial stewardship (points 1–10)	ORD	B	Impact on the landscape of all stages of the supply chain and territorial protection function in conjunction with supply chain activities	ENV
Byproducts valorization potential (points 1–10)	ORD	B	Availability (quantity and quality) of byproduct to be valorized	ECO
Land occupation (m2a) *	CAR	C	Impact on the land due to agriculture, anthropogenic settlement and resource extractions	ENV
Climate change (kg CO2 eq) *	CAR	C	Alteration of global temperature caused by greenhouse gases	ENV
Fossil depletion (kg oil eq) *	CAR	C	Decrease in the availability of non-biological resources (non- and renewable) because of their unsustainable use	ENV
Particulate matter formation (kg PM10 eq) *	CAR	C	Suspended extremely small particles originated from anthropogenic processes such as combustion, resource extraction, etc.	ENV

* Derived from LCA.

). The criteria and related values were assigned both by averaging the results derived from the processes carried out with the resources listed in the inventory analysis (see Section 2.4.1, Section 2.4.2, Section 2.4.3 and Section 2.4.4) and, in the case of ordinal criteria, by involving experts from different processes through dedicated focus groups and ad hoc interviews (12 experts were involved in total).

To compare different production systems on each criterion, the impact values must then be transformed into normalized scores between 0 and 1 (i.e., normalization). The method used here is the one that assigns the highest score (1) to the highest impact and the lowest score (0) to the impact with the lowest value. Normalization also considers the nature of each criterion, distinguishing between benefit criteria (B) and cost criteria (C). In fact, for cost criteria, the greater the impact in absolute value, the worse the performance of that criterion. For the benefit criteria, the opposite rule applies. The Impact Matrix with the values already normalized is presented in

Table 8. Impact matrix. Nature: cost (C); benefit (B). Categories: economic (ECO), operational (OPE), and environmental (ENV).

Criterion	C/B	Rye Bread (A)	Barley Beer (B)	Cow Cheese (C)	Goat Cheese (D)	Category
Revenue (EUR)	B	16,760.00	153,600.00	10,086.47	15,474.64	ECO
Investment (EUR)	C	17,687.50	19,750.00	22,375.00	21,125.00	ECO
Difficulty in preserving the primary product (points 1–10)	C	3.0	3.0	10.0	10.0	OPE
Difficulty in preserving the processed product (points 1–10)	C	8.0	1.0	2.0	2.0	OPE
Production loss risk (points 1–10)	C	9.0	8.0	3.0	3.0	ECO
Labor for primary production (h UL)	C	90.9	148.4	79.1	79.1	OPE
Labor for first intermediate product (h UL)	C	8.6	82.8	139.1	165.3	OPE
Labor for final product (h UL)	C	79.8	193.0	116.3	147.8	OPE
Landscape and territorial stewardship (points 1–10)	B	7.5	7.5	8.0	7.0	ENV
Byproducts valorization potential (points 1–10)	B	3.0	7.0	9.0	9.0	ECO
Land occupation (m2a)	C	685.7	3271.9	195.0	204.7	ENV
Climate change (kg CO2 eq)	C	1313.0	9735.0	3687.6	3890.0	ENV
Fossil depletion (kg oil eq)	C	215.6	2967.1	334.5	352.8	ENV
Particulate matter formation (kg PM10 eq)	C	3.3	22.3	22.9	24.2	ENV

and

Table 9. Normalized impact matrix. Colors indicate relative impact: red = worst; green = best; yellow and light green: intermediate. Nature: cost (C); benefit (B). Categories: economic (ECO), operational (OPE), and environmental (ENV).

Criteria	C/B	Rye Bread	Barley Beer	Cow Cheese	Goat Cheese	Category
Revenue	B	0.05	1.00	0.00	0.04	ECO
Investment	C	1.00	0.56	0.00	0.27	ECO
Difficulty in preserving the primary product	C	1.00	1.00	0.00	0.00	OPE
Difficulty in preserving the final product	C	0.00	1.00	0.86	0.86	OPE
Production loss risk	C	0.00	0.17	1.00	1.00	ECO
Labor for primary production	C	0.83	0.00	1.00	1.00	OPE
Labor for intermediate product	C	1.00	0.53	0.17	0.00	OPE
Labor for final product	C	1.00	0.00	0.68	0.40	OPE
Landscape and territorial stewardship	B	0.50	0.50	1.00	0.00	ENV
Byproducts valorization potential	B	0.00	0.67	1.00	1.00	ECO
Land occupation	C	0.84	0.00	1.00	1.00	ENV
Climate change	C	1.00	0.00	0.72	0.69	ENV
Fossil depletion	C	1.00	0.00	0.96	0.95	ENV
Particulate matter formation	C	1.00	0.09	0.06	0.00	ENV

. It can be observed how each alternative has positive and negative impacts compared to the others, and none is completely dominated or dominant.

The Impact Matrix partly anticipates the results, as it collects some criteria that are derived both from the modeling and inventory of production systems and from the results of the impact assessment through LCA. In the context of this work, at a methodological level, the impact matrix is part of the inputs on which the MCA is based and is therefore presented in the Section 2.

2.5. Multi-Actor Approach: Decisionmakers’ Priorities

The criteria serve as a means of comparing scenarios. However, in decision-making processes, it is common that different decisionmakers attribute different levels of importance to criteria. Each decisionmaker, therefore, is called upon to assign their own priorities to the criteria by attributing a relative weight. The MCA methodology allows the viewpoints of different subjects involved in the decisions to be transparently expressed. This is done by assigning weights from 0 to 100 to the criteria. In the case study, profiles of typical decisionmakers were used, as summarized in

Table 10. Priority matrix. Weights assigned above the average of the weights are highlighted.

		Farmer S_ECO	Farmer S_ENV	Farmer S_OPE	Community	Policymaker Traditional	Policymaker Innovative	Farmer No Risk	Balanced
ECO	Byproducts val.	2.1%	21.2%	1.4%	13.3%	3.5%	10.9%	0.3%	7.1%
OPE	Preserving FP	14.5%	2.7%	8.4%	2.4%	3.7%	9.2%	12.8%	7.1%
OPE	Preserving PP	14.5%	2.9%	12.3%	1.9%	3.9%	8.6%	10.4%	7.1%
ECO	Product loss	14.5%	3.4%	5.0%	3.8%	12.0%	5.0%	47.8%	7.1%
ECO	Revenue	18.1%	15.8%	6.5%	2.8%	17.7%	5.2%	14.6%	7.1%
ENV	Climate change	0.4%	11.9%	1.4%	12.9%	2.7%	9.2%	0.5%	7.1%
ENV	Fossil depletion	0.4%	3.3%	1.4%	11.7%	3.4%	9.3%	0.8%	7.1%
ECO	Investment	32.1%	20.8%	4.4%	4.1%	8.4%	9.7%	2.6%	7.1%
OPE	Labor FP	0.7%	2.4%	15.4%	4.0%	3.1%	4.6%	2.0%	7.1%
OPE	Labor PP	0.7%	2.3%	18.7%	3.4%	3.5%	4.7%	1.7%	7.1%
OPE	Labor IP	0.7%	2.4%	17.7%	3.6%	3.8%	4.7%	1.3%	7.1%
ENV	Land occup.	0.4%	1.3%	0.2%	12.0%	14.6%	6.3%	0.6%	7.1%
ENV	Landscape	0.7%	6.3%	3.4%	18.3%	15.9%	5.9%	1.5%	7.1%
ENV	Particulate	0.4%	3.2%	4.0%	5.9%	3.8%	6.7%	3.1%	7.1%

. The decisionmakers’ profiles used here are hypothetical scenarios. The profiles were constructed based on interviews with different stakeholders (e.g., farmers, farmer associations, etc.) and experiences gathered by experts of the production systems considered (both considering primary production and food transformation). This approach aimed to represent scenarios of realistic thinking and beliefs while also incorporating decision-making profiles with extreme positions that could lead to conflict situations. In addition to a “Balanced” profile, which assigns equal importance to each criterion, four profiles of farmers, two politicians, and one expression of the local community were considered.

With respect to the profiles of the farmers:

(1)

S_ECO: This profile places greater emphasis on the economic sustainability of their activities, assigning higher weights to criteria related to the economic and preservation aspects of their products;

(2)

S_ENV: This profile prioritizes criteria linked to the economic sustainability of the farm but also values alternative income sources from the potential valorization of byproducts. This farmer profile considers the environmental performance of its activities to be important;

(3)

S_OPE: This profile primarily considers the relevance of the work carried out on the farm in all the production steps, paying particular attention to product preservation and recovery;

(4)

Farmer No Risk: This profile focuses primarily on minimizing the risks of product and harvest loss, assigning almost 50% of the weight to this criterion. It also considers health and economic aspects crucial for the sustainability of business.

There are two types of political representatives:

(1)

Traditionalist Politics: This approach focuses on managing the territory in a way that makes it attractive and usable for both tourism and agriculture. It also pays attention to economic aspects, intervening with potential support measures;

(2)

Innovative Politics: This approach emphasizes new criteria such as the valorization of byproducts, encouraging symbiosis and supply chains among multiple enterprises in the area, and promoting activities aimed at preserving environmental aspects and minimizing the environmental impacts of activities. This includes innovative forms of business management and operations.

Finally, the Community profile places current environmental issues as priorities, favoring choices that enhance byproduct recovery chains and minimize impacts on the landscape and other environmental aspects.

In addition to considering these decision profiles in the analyses, the approach proposed in this work also aims to group the criteria into homogeneous macro-criteria groups (ECO, ENV, and OPE) to perform a sensitivity analysis. This helps identify the most relevant criteria to focus on for managing conflicts in cases of highly divergent viewpoints and interests [24]. As mentioned above, during the i-th analysis, the sum of the weights in each cluster is varied between 0% and 100% so that it always results in ECO_i + ENV_i + OPE_i = 100%. The results of the analysis, in terms of best or worst ranking, are finally mapped within triangular diagrams whose axes represent the relative importance of each macro-criterion. To consider the variability of the relative weights of the criteria inside the same macro-criterion (cluster), the sensitivity analysis should be repeated a significant number of times to explore sufficiently representative yet highly discordant decision profiles. Here, the following three different intra-cluster priority distributions were proposed, starting from the profiles of some decisional actors previously described:

(a)

In the original decision pool, a balanced relationship is maintained among the three macro-criteria as well as the individual criteria of the same cluster;

(b)

In the Farmer No Risk profile, which always attributes a very large importance to the criteria that consider aspects related to risks for the loss of primary production, from the analysis of Table 8 and the related Priority Matrix (Table 10), the criterion product loss is included in the macro-criterion ECO, together with the other criteria of investment, revenue, and byproducts; in the pool of this original decisionmaker, the ECO cluster covered 65.3% of the total priority, with an internal distribution of its aforementioned individual criteria of 47.8%, 2.6%, 14.6%, and 0.3% (corresponding to an internal relative incidence of 73.2%, 4.0%, 22.4%, and 0.5%, respectively); similar considerations can be taken into account as far as the criteria related to the difficulties of preserving primary and transformed products, both clustered into the OPE macro-criterion (together with the other three criteria, for a total priority of 28.2%) and covering more than 82% of internal relative incidence;

(c)

The Community profile, which tends to assign high priority to criteria that invoke aspects of environmental protection, is often reiterated in many social media networks; these are the criteria related to climate change, fossil depletion, land occupation, landscape preservation, and particulate matter, which are all grouped in the ENV cluster (which has a total priority for this decision profile of 60.8%), with internal relative incidences of 21.2%, 19.2%, 19.7%, 30.1%, and 9.7%, respectively, thus delineating a much smaller variability among the relevance of the criteria inside the same macro-criterion than that observed for the ECO cluster of the Farmer No Risk.

Regardless of the profile considered, the internal distribution of the priorities of the criteria of the same macro-criterion was kept constant throughout all iterations of the sensitivity analysis, regardless of the weight given to the individual macro-criteria in each iteration.

3. Results and Discussion

3.1. LCA Results

The results of the LCA for the four supply chains were compared to highlight the impacts generated by the production systems on a cultivated area of 1 ha. First, the results from the LCIA endpoint methodology are considered, as shown in Figure 4, where the impacts across all three target groups are presented (EQ, environmental quality; HH, human health; R, resources), and in Figure 5.

The barley–beer supply chain has the greatest impact, a significant portion of which, in all categories, is attributable to the production of glass bottles used for primary packaging. Given that these are zero-kilometer supply chains, with consumption or sale directly at the farm, the products that allow for this—such as bread and mature cheeses—are preserved and loose-sold or sold with packaging as minimal as possible. Different solutions for the primary packaging of beer could be studied and implemented; however, this study used the typical packaging solution for craft beers, and exploring alternative packaging scenarios is beyond the scope of this work. The hay–cheese value chain has a low impact on the quality of the ecosystem because the hay cultivation process does not involve the use of pesticides or fertilizers; only self-produced manure is reused. The main impacts in this category are due to the feed. Wastewater management impacts human health through particulate matter. Compared with other production systems, the rye–bread supply chain has lower impacts. This is due to the extensive low-impact cultivation method of rye and the fact that transport for distribution to sales points other than the cultivation and baking site itself is not needed.

This approach in LCA provides a robust basis for evaluating environmental impacts across diverse production systems, supporting informed decision making and strategies aimed at improving overall sustainability.

On the basis of the analysis results for the target groups, for all production systems, the most significant impacts are due to climate change (for both EQs and HHs), land occupation (agricultural and urban), particulate matter formation, and fossil depletion (Figure 6). Livestock supply chains also have significant impacts on the terrestrial acidification and human toxicity categories, but they still contribute to a lower extent than other impact categories do (<4%). Figure 7 displays the outcomes for these four indicators, assessed via the ReCiPe midpoint H method, which were subsequently employed in the MCA. Specifically, these indicators derived from LCA were then used as criteria based on their absolute values (each with its own cardinal measurement unit, as proposed by the conventional LCA approach). The detailed values are also presented in Table 11, referring to both 1 ha of cultivated area and 1 kg of product.

With respect to the impacts on the 1 ha functional unit, the following conclusions can be drawn:

(a)

For the impacts related to climate change, land occupation, and fossil depletion, the barley–beer chain has values approximately 2–6 times greater than those found for the other production systems, confirming the previously mentioned effects of packaging; considering a breakdown of impacts based on the different phases, the cultivation of barley and rye are very similar, and impacts rise during beer production and packaging;

(b)

The rye–bread chain tends to have very low impacts, being the least impactful for three out of the four indicators considered;

(c)

The main critical impacts of cheese supply chains are related to particulate matter formation and derive mainly from processes related to the management of farm byproducts (from animal wastes); those impacts are comparable with those of the barley–beer supply chain.

The results presented here derive from the modeling assumptions used in the present work; thus, their generalizability should be handled with the right care. Moreover, as mentioned in the introduction, several researchers and practitioners have already focused on the typical high variability found in conducting LCAs in the agrifood sector. Nevertheless, whenever possible, it is obviously useful to provide comparisons with analogous studies already available in the literature despite the often enormous differences occurring in the systems analyzed.

A comparison with results already available in the literature is possible considering impacts on climate change only. This is the almost exclusive indicator considered, usually computed in terms of impact on 1 kg of product. With respect to this indicator, the barley–beer production system had the greatest impact when the functional units of 1 ha and 1 kg of product were considered. This value is in line with values reported in other studies, indicating a range between 0.5 and 1.2 kg CO₂ eq kg⁻¹ [18,30]. The value can triple depending on the type of packaging used, from the 30 L keg to the 6 X 0.33 L bottles pack assembled in cartons [36].

The impact of rye bread is 0.63 kg CO₂ eq kg⁻¹, which is similar to that reported by [37], although the same authors as well as other studies reported large variability due to the type of baking supply chain, with values between 0.3 and 6.6 kg CO₂ eq kg⁻¹ [15,37]. The range of this indicator reported in the literature (see citations in Table 11) is very wide, reflecting the known variability of agricultural production systems and the difficulty in being able to compare them even through LCA. The values we found are in line with the lower end of the ranges, probably because these are extensive mountain cultivations, with low use of inputs such as inorganic fertilizers and herbicides, non-irrigated, and with low mechanization. In addition, the processing plants are also small-scale and have low energy consumption.

The climate change impact of the hay–cheese supply chain is approximately 8 kg CO₂ eq kg⁻¹. Additionally, in this case, such a value is consistent with other results in the literature, confirming that most of the impacts are attributable to the milk production phase. For most environmental impact categories, over 90% of cheese impact comes from animal husbandry and processes related to milk production [38]. However, the impact of mature cheese is highly variable [20,39], adding to the differences linked to dairy processes, including conservation.

Table 11. Impacts per unit area (1 ha) and unit product (1 kg).

Impact	Rye Bread		Barley Beer		Cow Cheese		Goat Cheese
	1 ha	1 kg	1 ha	1 kg	1 ha	1 kg	1 ha	1 kg
Land occupation (m2a)	685.66	0.33	3271.89	1.56	194.96	0.09	204.70	0.10
Climate change (kg CO2 eq)	1312.98	0.63 1	9734.99	1.01 2	3687.59	8.05 3	3890.00	8.04
Fossil depletion (kg oil eq)	215.63	0.10	2967.13	0.31	334.45	0.73	352.77	0.73
Particulate matter formation (kg PM10 eq)	3.34	0.002	22.29	0.002	22.89	0.05	24.15	0.05

¹ [15]: range 0.5–6.6; [37]: 0.7 and range 0.3–2.3; ² [30]: range 0.5–0.8; [18]: range 0.9–1.2; ³ [21,40]: range 0.9–1.3 for milk; [39]: range 6.2–17.5.

Table 11. Impacts per unit area (1 ha) and unit product (1 kg).

Impact	Rye Bread		Barley Beer		Cow Cheese		Goat Cheese
	1 ha	1 kg	1 ha	1 kg	1 ha	1 kg	1 ha	1 kg
Land occupation (m2a)	685.66	0.33	3271.89	1.56	194.96	0.09	204.70	0.10
Climate change (kg CO2 eq)	1312.98	0.63 1	9734.99	1.01 2	3687.59	8.05 3	3890.00	8.04
Fossil depletion (kg oil eq)	215.63	0.10	2967.13	0.31	334.45	0.73	352.77	0.73
Particulate matter formation (kg PM10 eq)	3.34	0.002	22.29	0.002	22.89	0.05	24.15	0.05

¹ [15]: range 0.5–6.6; [37]: 0.7 and range 0.3–2.3; ² [30]: range 0.5–0.8; [18]: range 0.9–1.2; ³ [21,40]: range 0.9–1.3 for milk; [39]: range 6.2–17.5.

3.2. MCA Results: Comparative Analysis of Scenarios vs. Original Priority Profiles

The results of the MCA highlight which option is preferable when considering the standpoints of the numerous actors in the decision-making processes, going beyond a simple comparison of the environmental implications of the four production systems considered. Based on the standpoints expressed through the virtual decisionmakers described previously, the Preference Matrix in Figure 8 was obtained.

Decisions involving multiple stakeholders and alternatives, each with their own set of strengths and weaknesses compared with others, are often not straightforward. The Rye–Bread solution is generally the best alternative (for four decisional profiles out of eight) with sub-optimal preferences (three out of eight profiles). Interestingly, it also takes a positive index in the rankings of decision-making profiles with quite extreme positions, with one exception. Indeed, it is classified as the worst option only when trying to minimize or completely avoid the risks related to the loss of production, which are linked mainly to weather risks (profile: Farmer No Risk).

The Cow Cheese chain meets various best and sub-optimal preferences, even if it is not economically preferable (worst solution for the Farmer S_ECO profile). Interestingly, it meets with high favor in the Community profile, as do broadly positive ratings for the profiles associated with policymakers and farmers with contrasting attitudes toward balancing priorities between criteria (Farmer No Risk and Balanced profiles). This means that it tends to be a generally reliable solution.

Under the conditions investigated, the Goat Cheese solution is almost always among the least favored solutions, with the sole exception of the Farmer No Risk profile, for which it is a sub-optimal solution. The impacts related to operational performance and some environmental risks affect this overall negative judgment because the management of pasture/meadows is considered somewhat more problematic for these ruminants.

With great contrast, finally, are the choices for the Barley–Beer solution, which ranks as the best and worst solution in two and three decisional profiles, respectively. It is therefore not very stable, as the subjective positions change.

Therefore, for the pool of decisionmakers considered, assuming no hierarchic difference among the actors involved, the best production system should be addressed to the Rye–Bread chain, with possible spaces for further discussions about the opportunity to consider Cow Cheese as a motivated alternative. In any case, the first step for a closer examination of the results obtained concerns the evaluation of the stability of the classifications obtained via sensitivity analyses on the priority matrix considered.

3.3. MCA Results: Sensitivity Analysis

The sensitivity analysis was performed by grouping the criteria into three macro-criteria namely, ECO (economic), ENV (environmental), and OPE (operational), according to the indications shown in Table 8. The level of preference (i.e., best or worst) for each alternative while varying the priorities among any ECO-ENV-OPE combination can then be shown in a triangular map diagram. Nevertheless, the final preferences are also affected by the relative priorities among the criteria inside each macro-criteria group. Thus, it is important also investigate this aspect when performing a sensitivity analysis. The variability of the weight of each criterion inside the same macro-criterion group was fixed according to the three mentioned profiles: Balanced, Farmer No Risk, and Community.

In summary, the first weight distribution proposes an equilibrated profile, in which all criteria of various natures assume a homogeneous incidence within the decision-making profile. The other two distributions, on the other hand, reflect the positions of the two original decisionmakers, who approach the choices from “extreme” positions. In the Farmer No Risk profile, economic criteria are in fact absorbed by the need to have no risk in the achievement of the final product (with a “whatever it takes” logic). Aspects related to income or investment levels are also prioritized at approximately 3 and 18 times lower, respectively, than those attributable to production risk. In the case of the Community profile, on the other hand, we have a less extreme situation, with the criteria of each cluster varying just 2–3 times among themselves. This situation is therefore also reflected in the relative differences in weights within each homogeneous cluster

To read and interpret the results, it is first useful to observe the trends of the rankings in the vertices of the triangles (Figure 9). Starting from the top and proceeding clockwise, the ECO, OPE, and ENV rankings tend to prevail here. The following can be observed:

• In the case of the sensitivity analysis with the Balanced profile, a large part of the triangular diagram gives the best rank to the solution Rye Bread; this happens both in a large part of the central area of the triangle and near the vertices where the OPE and ENV aspects prevail. Only in the case of a clear prevalence of ENV aspects (upper vertex) does the best solution fall on the Barley–Beer chain. Moreover, Rye Bread never appears as the worst solution, whereas Barley Beer is the least favored when ENV criteria prevail;
• Rye Bread is also the favored solution in the sensitivity analysis of the Farmer No Risk and Community profiles; this is observed in situations in which the priorities for the ENV and OPE criteria prevail. In both cases, however, it is now the least favored situation when the ECO criteria prevail; all these cases demonstrate a high instability of preferences for this solution when the criteria vary;
• The extreme situation of the Farmer No Risk profile is also evidenced by a very heterogeneous situation in the barycentric area of the triangular diagram; indeed, here, the best solution is disputed by three alternatives (Cow Cheese, Rye Bread, and Barley Beer). The ranking of the worst solutions, instead, is disputed between the alternatives Rye Bread and Goat Cheese, showing a condition of high instability with respect to the nature of the criteria, since they appear at the same time in the maps of the best and worst solutions;
• For the Community profile, a much more stable situation emerges, with the Cow Cheese solution being the best over a large barycentric portion of the triangular diagram, including the extreme corner where the ENV criteria prevail. Only to a limited extent are the Rye Bread and Goat Cheese solutions the preferred solutions (when the OPE and ECO criteria prevail, respectively). Their stability, however, is rather critical, as they become the worst solutions in the extreme conditions in which the ECO and OPE criteria prevail. The Barley Beer solution, on the other hand, is the least preferred solution in a large portion of the triangular diagram (barycentric area and vertex in which the ENV criteria prevail).

To summarize, we have the following:

(a)

The Rye Bread solution appears among the best in all three profiles considered; it appears to be the worst solution only in the Farmer No Risk and Community profiles, when the ECO criteria clearly prevail;

(b)

The Cow Cheese solution is clearly the best only in the case of the Community profile, provided that the OPE criteria remain confined to an incidence of approximately 30% of the total; it is never fully appreciated in the case of the Balanced profile;

(c)

The barley Beer solution is significantly appreciated in the Farmer No Risk profile (with an incidence of OPE criteria of more than approximately 30%) and only marginally appreciated in the Balanced profile, when ECO criteria largely prevail;

(d)

The Goat Cheese solution shows only very marginal interest in the Farmer No Risk and Community profiles, whereas it clearly shows criticalities in all three profiles.

Therefore, a reasonable compromise solution could fall on the two solutions Rye Bread and Cow Cheese, with a clear preponderance for the former in the case of decisionmakers with a balanced criteria profile. All other solutions are only acceptable in situations of tendentially extreme preferences, thus posing greater risks of conflict in the final choice. Analyses of complex systems always require a balance between the simplification and robustness of the results. The production systems analyzed here include the processes we considered relevant to building the foundation (LCI and Impact Matrix) for analyzing supply chains consistent with the goal of the decision-making process at hand. Despite efforts to acquire most of the relevant data directly as primary data and the involvement of several experts and different stakeholders in defining processes and flows as well as in estimating missing data, ordinal impacts, and criteria weights, the analysis inevitably has margins for subjectivity. However, the accurate and punctual documentation of the methodologies and their integration makes the entire decision-making process and the context of the results obtained transparent. In this sense, the collection of information and priorities from more stakeholders could be structured in a more systematic way to analyze in even deeper detail the stability of the preferred alternatives. A further aspect open for more investigation is related to logistics. Our model excludes transport to the farm and from the farm, as described in Section 2.3, but a specific analysis could be performed considering the impact of different logistic solutions to solve the complex transport issue of mountain regions.

4. Conclusions

Agricultural activities that integrate transformation processes within the same entrepreneurial structure require complex evaluations of a multi-dimensional nature that can consider the multiple interrelated factors involved. This is especially true in mountainous contexts, where intrinsic difficulties caused by specific economic, social, and environmental factors (e.g., contexts that are difficult to mechanize, with relevant safety problems for operators; the need for appropriate technologies, which are usually very expensive; a scarce and often professionally unskilled workforce; logistical and communication difficulties; etc.) compound decision-making processes.

In these environments, production objectives based on approaches of sustainability and environmental protection often assume high priority. Therefore, it is necessary to set up integrated assessment methodologies to meet the need to consider multiple criteria simultaneously while ensuring sufficiently accurate environmental analyses that are both reliable and comprehensively extended to all production systems. These methodologies must also accommodate the inclusion of stakeholders with often divergent and conflicting interests and perspectives.

Within this framework, the integration of the MCA and LCA methodologies can amply satisfy many of the above requirements, provided that the data collection phases (i.e., the construction of the Impact Matrix of the MCA) are carried out with adequate professionalism to guarantee an objective starting point for the entire evaluation process. The latter, through the MCA, can be set up through procedures that guarantee adequate understanding and transparency regardless of the decision-making profiles involved, whereas LCA provides validated methodologies to characterize the environmental impacts of production processes according to different approaches. This allows the selection of the most appropriate indicators based on the specific context under analysis. The present work proposes an integrated application of the two methodologies to evaluate the choice of a complete production value chain, i.e., including transformation processes, to be implemented directly on small mountain farms. The following four alternative supply chains were analyzed: (1) rye–flour–bread, (2) barley–malt–beer, (3) hay–cow milk–cheese, and (4) hay–goat milk–cheese.

Given the intrinsic variability of the abovementioned production systems, it was essential to utilize actual data, models, and estimates that can effectively highlight the unique characteristics and performance of each activity involved. The involvement of experts in system modeling, inventory analysis, and the construction of impact matrices was therefore paramount.

LCA proved essential in identifying environmental impacts across each value chain, ensuring that the consequences of activities were not overlooked by focusing solely on specific segments. For example, in the barley–beer value chain, the environmental impact of glass bottle packaging would have been missed if it had not been considered within the system boundaries.

Once major environmental impact sources are identified through LCA, this knowledge can address strategic or operational changes and guide necessary investments. However, the location of impacts within the supply chain can influence decisionmakers differently depending on the location of the impacts themselves. For example, impacts related to livestock waste or spent malt are managed directly by those internally responsible for production activities. In contrast, impacts associated with glass bottles used for beer packaging could be upstream or downstream of primary production activities, with consequently different perceptions of the decisionmakers involved. Multi-actor MCA proves valuable in addressing these complexities, as it allows for the consideration of impacts with varying priority perceptions (weights) across different decisionmakers.

Whatever the case, the LCA offers the possibility of precisely quantifying (according to cardinal scales) a series of environmental impacts through detailed and codified procedures, which are also supported by data drawn from external databases of widely recognized and shared reliability. This, of course, also applies to other criteria (e.g., investments, working hours, etc.) whose determination can follow independent and parallel paths with respect to the LCA. The MCA, on its side, also makes it possible to manage criteria of a qualitative nature, expressible by means of ordinal values, which are in any case useful for guiding the decision-making process, provided that their qualitative determination is unanimously approved by all the decisionmakers involved. The result of the MCA consists of a ranking of preferences among all the alternative solutions considered. Since the rankings of the “best” and “worst” solutions are largely influenced by the decision profiles of the individual decisionmakers, further comparative evaluations are then necessary to test the stability of the proposed solutions as well as the “consensus dimensions” that each solution can offer. These evaluations involve both tabular comparisons between the various preference indices adopted and the performance of sensitivity analyses on the weights of groups of homogeneous criteria, the results of which can finally be formulated in graphical form (triangular map diagrams) to facilitate the reading of the consensus levels obtained by each solution. This process should also be carried out interactively to stimulate participation and the search for understanding on the part of the individual decisionmakers involved through transparent computational procedures.

The analysis proposed here illustrates how to articulate the choice process through sensitivity analyses set up by varying the relative ratios of priorities (weights) within homogeneous groups of criteria. It is evident that the results do not propose clear and unambiguous choices but rather contribute to the identification of reasonable compromise solutions while simultaneously interactively stimulating critical reasoning concerning the allocation of weights by each decisionmaker. In the case study presented, the analysis revealed that the most interesting cases fall on the production value chain for rye–flour–bread (Rye Bread) and hay–cow milk–cheese (Cow Cheese). The former clearly tends to prevail in the case of decisionmakers with a fairly balanced criteria profile. All other solutions were acceptable, mainly in situations of tendentially extreme preferences, thus posing greater risks of conflict in the final choices.

This study, however, acknowledges certain limitations. First, the multi-criteria analysis incorporated several ordinal criteria on the basis of expert judgment, which may introduce a degree of subjectivity and affect the robustness of the outcomes. Second, although the analysis focused on a short, zero-kilometer supply chain, the environmental impacts associated with transportation and broader logistics were excluded from the system boundaries of the LCA. While this choice aligns with the local nature of the supply chains under study, it may limit comparability with other contexts where logistics play a more significant role. Future research should consider the integration of transport-related emissions and investigate alternative packaging options—particularly in the barley–beer supply chain, where packaging contributes substantially to overall impacts. Furthermore, incorporating dynamic or probabilistic modeling approaches could improve the capacity to account for variability and uncertainty in input data, thereby enhancing the reliability and generalizability of the results. These developments could contribute to refining integrated assessment frameworks and supporting more informed decision making in the design of sustainable food systems in mountain areas.

In conclusion, the proposed study adequately represents an integrated “Participatory Eco-Design” approach, combining (a) LCA procedures that support the identification of low environmental impact farm systems in the design, guaranteeing objective parameters of sustainability, and (b) a shared multi-actor MCA platform that can incorporate economic and social aspects in the choice processes, which is equally important for finding a solution that can be deemed acceptable with an adequate level of satisfaction from a wide range of actors involved. The application of such an integrated approach in participatory decision making is fundamental in mountain regions, where all the processes lead to a complex set of impacts affecting different domains of interest, and the perception of the impact (i.e., the priority assigned to each criterion) is a fundamental part of the consensus process. Furthermore, the flexibility of MCA can support strategic policy decisions, allowing the exploration of different territorial strategies in advance, such as the creation of common transformation centers or the incentivization of short value chains.