Tourism Sector: The Supply Chain Social Footprint of an Italian Accommodation Facility

Abstract

In Italy, as in the rest of the world, the COVID-19 pandemic had an important impact on tourism. In particular, in the Abruzzo region, there was a decrease of circa 60% in tourist arrivals from 2019 to 2021. A driving force for the recovery of the tourism sector may be investment in sustainability activities, especially those connected to social aspects, which appear to be less considered. Social sustainability can be investigated through the Social Life Cycle Assessment methodology, thus allowing the assessment of the social risks that are connected to the life cycle of a delivered service. This study aims at providing a social footprint of the supply chain of one overnight stay at an accommodation facility using the Social Hotspot Database. The main results show that electricity consumption has the greatest impact within the life cycle, and it is the “Health and Safety” category that is the most influenced, followed by “Labour Rights and Decent Work”, “Governance” and “Human Rights”. Finally, at a subcategory level, “Occupational Toxics and Hazards” is the most influenced one, followed by “Occupational Injuries and Deaths”, “High Conflict”, “Corruption” and “Legal System”. These findings allow us to understand the overall main social risks related to the sector in Italy, since the assessment of the foreground system was conducted at a country and a sector level.

1. Introduction

Tourism is amongst the fastest-growing economic sectors, with the number of international arrivals reaching 1.5 billion in 2019, an increase of almost 53% within the decade 2010–2019 [1]. Recently, this trend was abruptly interrupted by the COVID-19 pandemic, which affected the entire globe. Indeed, UNWTO [2] showed that international tourist arrivals decreased by 73% for the year 2020 and by 72% for 2021 (when compared to 2019). When it comes to Italy, the pandemic’s impact was also important [3,4]; in particular, the region of Abruzzo showed a decrease of circa 60% in tourist arrivals from 2019 to 2021 [5]. Furthermore, the impact was found to be significant for art/cultural heritage tourism, whilst it was somewhat weaker for business tourism [6]. It is to be noted, however, that the year 2022 witnessed the recovery of international tourist arrivals to 63% of pre-pandemic levels [2]; in Abruzzo, they reached circa 50% [5].

In particular, over time, the tourism sector has been the subject of increasing attention towards sustainability among various stakeholders [7]. This may refer to consumers or political decision makers, as demonstrated by the numerous financial incentives implemented in order to support sustainable initiatives (e.g., [8]). Sustainability can therefore represent a driving force for the recovery and restructuring of the sector, assuming a role of the utmost importance [9,10]. The quality of the tourist experience plays a fundamental role in the future of tourism itself [11] and thus the tourist can become aware of the attention paid to him. Indeed, tourism can be regarded as sustainable if it respects not only the environment but also the traveller, the local population and the cultural heritage [10,12]. Despite this, to date, the social dimension of sustainability appears to be the least investigated, also in terms of its footprint [7]. Nonetheless, in order for the effective diffusion of this concept to be achieved, it is essential to assess the effective sustainability of tourist services, especially the social aspect, using suitable methods and indicators.

To this end, the Social Life Cycle Assessment (S-LCA) methodology allows the assessment of the social profile of a product and/or service from a life cycle perspective [13]. The combination of quantitative and qualitative data [13] allows a systematic evaluation of both positive and negative social and socio-economic aspects in the life cycle of a product and/or service through the integration of different methods, models and data.

In support of such a methodology, specific software and databases are currently available, such as the Social Hotspot Database (SHDB) [14] and the Product Social Impact Life Cycle Assessment (PSILCA) [15]. These allow access to generic data for background processes and their integration with primary data related to foreground processes. In a recent literature review on S-LCA studies [16], the SHDB was found to be the most used database, having been applied across different sectors and countries, but not within the tourism sector. Such a database provides information on supply chain social risks, thus offering a holistic overview of the points at which the most pertinent social impacts may be located. In this way, organisations can become aware of their potential human rights risks and identify their strengths and weaknesses in terms of prioritising and supporting decision making in the framework of the Sustainable Development Goals [17].

Despite the fact that SHDB is one of the most widely used databases, to the best of the authors’ knowledge, it has not been adequately applied to the tourism sector yet. According to a recent literature review [18] (updated by the authors), although there has been rapid growth in the number of S-LCA studies during the last decade (e.g., agriculture, bioenergy, transport, water management, chemical products, electronics, etc.), such a methodology has not yet been extensively explored in this area. Indeed, to date, there are only two case studies related to the hospitality sector: Arcese et al. [19] aimed at analysing the social impact of an accommodation facility through the use of data from social accounting and business management tools, and Arzoumanidis et al. [20] aimed at providing the preliminary results of a social risk assessment for an accommodation facility in Romania.

This study has the objective of answering the following research questions: What is the social footprint related to an accommodation facility supply chain located in Abruzzo, Italy? Which are the most affected social impact categories? Which are the most affected social subcategories in terms of the foreground and background systems? Such a methodological approach can help in understanding the social risk conditions related to the accommodation sector within the Italian context. It is to be noted that the data collection was performed for the year 2019 (before the pandemic occurred) in order to study a representative year and thus avoid any bias.

The article is structured as followed: first, the methodology used as well as the hypotheses devised are detailed in Section 2; then, the results of the analysis are presented (Section 3) and discussed (Section 4); finally, some conclusions are drawn, and future developments are proposed in Section 5.

2. Materials and Methods

S-LCA is based on the ISO 14040:2006 [21] and ISO 14044:2006 [22] standards and hence it follows the four-phase framework: the goal and scope definition phase, life cycle inventory, life cycle impact assessment and life cycle interpretation. The main reference of S-LCA is reflected by the Guidelines for Social Life Cycle Assessment for products and organisations (i.e., [13]), which is published by the United Nations Environment Programme (UNEP) in collaboration with the Social Life Cycle Alliance and the Life Cycle Initiative. Indeed, the latest guidelines [13] replace the previous ones [23], with the aim to provide an update of the methodological developments of S-LCA for practitioners, organisations and policy makers. Moreover, further social subcategories (i.e., sexual harassment, employment relationships and smallholders, including farmers, for the “worker” stakeholder; wealth distribution for the “value chain actors” stakeholder; and poverty alleviation and the ethical treatment of animals for the “society” stakeholder), as well as a stakeholder category (i.e., children) and its subcategories (e.g., education provided in the local community, health issues for children as consumers, children’s concerns regarding marketing practices), were added.

Furthermore, S-LCA is a methodology under development and a full consensus has not been reached yet [24]. A recent systematic literature review (i.e., [24]) on S-LCA showed that the main methodological challenges are related to defining a set of social indicators for a specific sector; to identifying and selecting the relevant stakeholders, impact categories and subcategories; and to applying an objective method to assess the social aspects. Indeed, the two types of impact assessment methods (i.e., Reference Scale Approach—type 1 and Impact Pathway approach—type 2) suggested by the guidelines [13] still require more practical implementations to be made. In particular, the Reference Scale Approach is based on the use of a Likert Scale, based on a set of levels and/or a set of scores (e.g., Subcategory Assessment Method [25]; Handbook for Product Social Impact Assessment [26]). The Type 1 method provides a social performance or social risk evaluation by taking into account the current social situation of the products/services under study. On the other hand, Type 2 is a cause–effect evaluation, as life cycle assessment, performed by predicting the potential consequences of the considered product/service system [13]. On the other hand, the social assessment is performed differently when social databases, such as SHDB and PSILCA, are used. Indeed, the social assessment obtained by using the databases provides a social performance assessment at a risk level [13]. The assessment delivered by the databases can provide insights at the sector and country level of a supply chain. This allows us to acquire an overview of the areas that are mostly at social risk and to which more attention must be paid when choosing a product or a supplier over another.

The SHDB is based on a multiregional input/output (I/O) model launched in 2009, which includes 160 indicators, classified into five impact categories: “Labour Rights and Decent Work”, “Health and Safety”, “Human Rights”, “Governance” and “Community” [27]. Each category includes different subcategories (i.e., 11 subcategories for “Labour Rights and Decent Work”, 2 subcategories for “Health and Safety”, 5 subcategories for “Human Rights”, 2 subcategories for “Governance” and 5 subcategories for “Community”).

Table 1. Impact categories and subcategories’ correspondence in SHDB. Elaborated by the authors.

Impact Categories	Impact Subcategories
1 Labour Rights and Decent Work	1A Wage Assessment
	1B Poverty
	1D Child Labour
	1E Forced Labour
	1F Excessive Overtime
	1G Freedom of Association, Collective Bargaining and Right to Strike
	1H Migrant Workers
	1I Social Benefits
	1J Labour Laws and Conventions
	1K Discrimination and Equal Opportunities
	1L Unemployment
2 Health and Safety	2A Occupational Toxics and Hazards
	2B Occupational Injuries and Deaths
3 Human Rights	3A Indigenous Rights
	3B Gender Equity
	3C High Conflict
	3D Human Health, Non-Communicable and Health Issues
	3E Human Health, Communicable Diseases
4 Governance	4A Legal System
	4B Corruption
5 Community	5A Access to Improved Drinking Water Sources
	5B Access to Improved Sanitation
	5C Children Out of School
	5D Hospital Bed Access
	5E Smallholder vs. Commercial Farms

shows the correspondence between the impact categories and subcategories in SHDB, whilst Figure 1 illustrates the methodological design of this study.

2.1. Goal and Scope Definition

The objective was set to identify the main social hotspots (i.e., locations and/or activities regarding the life cycle of the product/service under study where social issues are likely to occur [13]) originating from an overnight stay service at a small accommodation facility (bed and breakfast—B&B) of five rooms, located in the Abruzzo region (Italy). In particular, the B&B was situated in a location of high cultural heritage value [28] that was greatly affected by the COVID-19 pandemic, particularly from a social point of view. This study was conducted using the SimaPro software v.9 [29] and the SHDB database v. 2019, which provided a holistic overview of where the social risks may lie within the supply chain [17]. The data collection was performed for the year 2019 in order to study a representative year and thus avoid any bias that the pandemic might have brought about.

The functional unit (FU) was defined as the overnight stay (1 night) of a guest, including all the activities inherent to the overnight stay itself (i.e., reception/check-in, accommodation in the room, overnight stay, check-out, maintenance/cleaning), excluding the breakfast service. Figure 2 shows the processes involved in the delivery of an overnight stay service and the boundary of the case study system, which was identified as “gate-to-gate”. Furthermore, all inputs included in the modelled service system, as well as the considered social impact categories, are detailed in Figure 2.

The social risk assessment was performed directly by SimaPro v.9 using the SHDB impact assessment method (i.e., Social Hotspot 2019 Subcategory and Category Method with Damages), which expresses the risks in terms of average risk equivalent hours by sector. In particular, all impact categories, subcategories and relative social and socio-economic indicators included in the SHDB were taken into account.

2.2. Life Cycle Inventory

The inventory analysis was carried out by collecting the primary data via a specific data collection sheet (i.e., questionnaire) that was submitted to the B&B administration.

In order for the system to be modelled (i.e., one overnight stay service), it was necessary to identify each input and, for each one of them, the category of the relative Global Trade Analysis Project (GTAP) sector (I/O model on the basis of the SHDB). In more detail, the data that were collected for each input included the type and its quantity; worker hours; unit cost (in local currency, EUR) and country of origin. Subsequently, the collected data were processed and scaled to the FU. Moreover, the unit cost in EUR was converted into USD (2011), given that the SHDB provides information on the labour intensity of the economic sector relating to each country per 2011 USD of production. In addition, for each indicator, the social risk level was assigned by using the Risk Mapping Tool for the “recreational and other services” sector. This online tool provides access to social risks for 244 countries and territories and 57 sectors [17].

Moreover,

Table 2. Types of inputs and their corresponding GTAP sectors and countries selected in SHDB. Elaborated by the authors.

Input	GTAP Sector and Country in SHDB	Functional Unit
Bath caps	Chemical, rubber, plastic products/ITALY	One overnight stay
Bleach	Chemical, rubber, plastic products/ITALY
Brooms	Wood products/ITALY
Cleaning detergents	Chemical, rubber, plastic products/ITALY
Electricity	Electricity/ITALY
Gas	Gas/ITALY
Gloves	Chemical, rubber, plastic products/ITALY
Lamps	Electronic equipment/ITALY
Laundry detergents	Chemical, rubber, plastic products/ITALY
Plastic cups	Chemical, rubber, plastic products/ITALY
Rags	Textiles/CHINA
Shampoo	Chemical, rubber, plastic products/ITALY
Toilet paper	Paper products, publishing/ITALY
Water	Water/ITALY

demonstrates the collected inputs and their corresponding sectors (GTAP) selected in the SHDB to model the analysed process.

3. Social Footprint Results

The social footprint results are expressed as the total medium risk hours equivalent for each input of the evaluated system and per impact category [27]. In particular, in this case, the software provides characterisation results, which allow the identification of the main social hotspots of the considered service system at a subcategory level for both background (i.e., the supply-chain-related processes of inputs considered in the evaluated system at a sector level) and foreground processes (i.e., all SHDB indicators and related risk levels, considered as the output of one overnight stay for Italy and for the “recreational and other services” sector). Then, the normalised as well as the single-score results are presented and discussed subsequently.

As far as the characterisation results are concerned, it can be seen that there are some processes that most influence some subcategories. Figure 3 demonstrates that the foreground system (in yellow) appears to have the greatest influence for most of the subcategories (e.g., 1J “Labour Law and Conventions” for 93.5% of the total; 3C “High Conflict” for 80.4%; 1H “Migrant Workers” for 78.2%; 2A “Occupational Toxics and Hazards” for 78.1%; 2B “Occupational Injuries and Deaths” for 77.3%).

Nevertheless, the background system affects mostly subcategories, such as 1B “Poverty” for 100% of the total; 3A “Indigenous Rights” for 100%; 5E “Smallholder vs. Commercial Farms” for 89.1%; 1G “Freedom of Association, Collective Bargaining and Right to Strike” for 88.4%; and 1F “Excessive Overtime” for 86.7%. In more detail, when it comes to the background, the most impacting processes/sectors seem to be “Electricity/ITALY”, “Gas/ITALY”, “Water/ITALY”, “Chemical, rubber, plastic products/ITALY” (i.e., shampoo) and “Textiles/CHINA” (i.e., rags).

In particular, electricity consumption appears to have the greatest impact for almost all subcategories apart from 1I “Social Benefits”, 2B “Occupational Injuries and Deaths” and 3C “High Conflict”, for which gas consumption has the greatest impact.

Such results depend on the SHDB information on GTAP sectors per country combined with the unit costs in 2011 USD for each sector/input (for the background), as well as the worker hours for a specific economic sector and the associated risk levels (for the foreground system).

At an impact category level, the foreground system has the greatest impact for 2 “Health and Safety”, for 77.7% of the total, followed by 3 “Human Rights” for 72%, 1 “Labour Rights and Decent Work” for 69.6% and 4 “Governance” for 59.7%, whilst has the least impact for 5 “Community” (45.2%).

The normalised results showed that it is the 2 “Health and Safety” category that is the most influenced, followed by 1 “Labour Rights and Decent Work”, 4 “Governance” and 3 “Human Rights” (please refer to Figure 4). These results appear to be in accordance with those found by Arzoumanidis et al. [20] for the Romanian facility.

With regard to the subcategories, however, it is 2A “Occupational Toxics and Hazards” that is the most influenced one, followed by 2B “Occupational Injuries and Deaths”, 3C “High Conflict”, 4B “Corruption” and 4A “Legal System” (please refer to Figure 5). Such results are in line with those identified by Arzoumanidis et al. [20], except for 5C “Children Out of School”, which was highlighted for the Romanian accommodation facility as a critical indicator for that country [30].

4. Discussion

A detailed discussion of the most influenced subcategories will follow, for which the foreground system is always the most affecting.

As regards 2A “Occupational Toxics and Hazards”, the level of risk depends on the results of the Risk Mapping Tool applied in Italy to those indicators that refer to this subcategory. Indeed, a high risk level was attributed to indicators relating to accidents at work, the exposure of people to harmful noise, carcinogens and particulates [17] (e.g., “deaths due to occupational-related lung cancer”, “deaths due to occupational-related leukaemia”, “disability-adjusted life years due to occupational-related lung cancer”, “disability-adjusted life years due to occupational-related leukaemia”, “overall occupational cancer risk—loss of life (DALYs)”, “overall occupational cancer risk—deaths” and “heart disease due to particulate matter (DALYs)”). In Italy, in the last few decades, overall cancer mortality has decreased, whilst cancer-related deaths have increased. This may be due to the incremental ageing of the population, which requires more efforts in terms of prevention, early detection and treatment [31].

As regards 2B “Occupational Injuries and Deaths”, this is related to safe conditions during employment in terms of personal injury and the risk of death or disease [17]. Such indicators include a high risk level attributed to “non-fatal work-related injuries by sector” and “non-fatal injuries by country” and a medium risk level assigned to “fatal injuries by sector” and “fatality rate of injuries by country”. As a matter of fact, in Italy, such risks are considered to be important in general and the related rates have slightly increased recently (e.g., Giraudo et al. [32]). However, as aforementioned, the Risk Mapping Tool provides the risk level of a more generic/overarching sector (i.e., in this case, “recreational and other services”). When it comes to the specific sector under study (tourism), it seems that a reduction has been registered in such accidents/incidents during the last few years in the country [33], thus not reflecting the attributed generic risk level.

The subcategory 3C “High Conflict” is related to humanitarian crises due to civil, ethnic and interstate welfare, the fragility of a country in managing conflict, the delivery of essential services, the preservation of system coherence, cohesion and quality of life [17], which can be of great importance for this sector. For Italy, indicators such as “high conflict UNDP” and “overall high conflict” were given a high risk score, whilst no evidence of risk was assigned for “high conflict Heidelberg Institute—overall”. This was due to the fact that the Heidelberg Institute indicators considers the number of conflicts, their intensity and the overall changes in conflicts with respect to the previous years [17] and there were no data about Italy.

Regarding 4B “Corruption”, the indicators take into consideration the presence of corruption (in terms of bias, bribery, extortion, cronyism, patronage and embezzlement) both for the public and private sectors [17]. In this case, most of the indicators present a medium risk level (i.e., “Worldwide Governance Indicators Corruption Index World Bank”, “World Economic Forum Competitiveness Report”, “Transparency International Corruption Perception Index (2017)”, “Overall Corruption”), whilst only one (i.e., “Transparency International 3-year trend (2014–2017)”) has a low risk score. Such a result is in agreement with the Corruption Perceptions Index of Italy, which, in 2019, achieved 51st place out of 180 [34].

Amongst the five most affected subcategories, 4A “Legal System” is related to the presence of civil law (i.e., legal system inspired by Roman law), with the aim of highlighting the fragility of the judiciary and legal system [17]. A high level of risk was detected for the indicator “CIRI Human Rights Data Project Independent Judiciary”, whilst a medium risk level was found for indicators such as “World Bank Worldwide Governance Rule of Law Indicator”, “Global Integrity Index”, “World Justice Project Rule of Law Index” and “overall fragility in legal system”. According to the WJP Rule of Law Index 2021, Italy has a score of 0.66 out of 1, thus representing strong adherence to the rule of law: it is 34th out of 139 [35]. The index of “rule of law”, which considers the quality of contract enforcement, property rights, the police and the courts, as well as the likelihood of crime and violence, was 0.30 for Italy in 2019 (74th out of 192 in the global ranking) [36].

5. Conclusions

This study aimed at identifying the social footprint related to an accommodation facility supply chain located in Italy in the region of Abruzzo. In particular, the activities related to an overnight stay itself were analysed, with the exception of the breakfast service. The evaluated system was modelled with SHDB v.4 2019 for SimaPro v.9, by collecting primary data via a questionnaire on each input of the modelled system, including the worker hours, unit cost and country of origin. Indeed, the social footprint was calculated as the medium risk hours equivalent and highlighted that electricity consumption had the greatest impact for almost all subcategories, with the exception of three, for which gas consumption was the most impactful.

The normalisation results showed that it is “Health and Safety” that is the most influenced impact category, followed by “Labour Rights and Decent Work”, “Governance” and “Human Rights”, whilst, the at the subcategory level, “Occupational Toxics and Hazards” held first place, followed by “Occupational Injuries and Deaths”, “High Conflict”, “Corruption” and “Legal System”. Such results appeared to be in accordance with the scientific literature and international indices for the country under study, as well as with a case study on another accommodation facility situated in another country (for almost all impact (sub)categories).

One of the database-related limitations of these results is that the potential positive impacts are not considered (i.e., benefits occurring during the life cycle that contribute positively to the improvement of human well-being perceived by affected stakeholders [13]) related to the life cycle of one overnight stay, especially for the “local community” stakeholder. Indeed, this tourism service supports the valorisation and social conditions of local communities and promotes their cultural heritage [37,38], which is strong in the area under study. Moreover, tourism activities can help to avoid the delocalisation and migration of the local villagers, by supporting local employment and acting as a means of economic growth and social cohesion, thus promoting sustainable tourism [38].

Furthermore, another limitation is the absence of the assessment of the “consumer” stakeholder, which, in this case, would be the tourists. For example, it is important to consider customer satisfaction in terms of health and safety issues, feedback mechanisms, the protection of consumer privacy and transparency and the commitment to promoting sustainability issues [38].

Moreover, this study provided a social footprint of an accommodation facility at a country and sector level. Nevertheless, it is necessary to deepen the context under analysis via a social performance assessment, which would allow us to identify the particular characteristics of the service as well as its potential positive impacts (e.g., for the local community). S-LCA can be integrated within management tools, such as those related to corporate social responsibility (i.e., Global Reporting Initiative [39], ISO 26000 [40], SA8000 [41], AA1000 [42]), in analysing, assessing and monitoring social issues over time. Indeed, S-LCA provides a methodology framework that can be supported by data collection conducted for sustainability reporting. Furthermore, S-LCA can enhance the awareness of facilities’ owners regarding their social conditions, which affect and involve their customers, value chain actors, workers and local communities. In this way, tourism facilities can define a sustainable strategy that ranges from the choice of their supplier to the promotion of the cultural heritage (related to local community stakeholder), to the protection of the working conditions and to the health and safety conditions for the facilities’ guests.

Future developments of the project may include the integration of the results of this social footprint assessment with a Reference Scale Approach (Type I), which would allow us to assess, in a more representative way, the system under study (e.g., cultural heritage and other potential positive impacts). Other future developments include a study on post-pandemic data for the same facility, in order to highlight the influence of COVID-19 in terms of social impacts within this context.