Social Life Cycle Assessment of Organic Cotton Trousers in a Multinational Supply Chain

Abstract

The textile industry is highly globalized, with production processes spread across multiple countries. While environmental impacts have been widely examined, less attention has been paid to how social risks are distributed along these supply chains. This study applies a Social Life Cycle Assessment (S-LCA) framework, utilizing the Social Hotspots Database (SHDB), to assess social risks in the production of cotton trousers. The analysis focuses on a supply chain linking Turkey, Thailand, and Cambodia, and integrates factory-level data with sector-level risk indicators. The results show that social risks are unevenly distributed across the supply chain. The highest risks occur in fabric production in Thailand and garment manufacturing in Cambodia, particularly in relation to wages, labor conditions, and occupational health. These findings point to the importance of labor-intensive stages in shaping overall risk patterns. The results suggest that globalization does not eliminate social risks, but rather shifts them across countries with different institutional conditions. These findings highlight the need to better account for social factors in establishing sustainable supply chains.

1. Introduction

The textile industry is one of the oldest industries in the world and the global market is estimated to be valued at around 4016.5 billion US dollars by 2034 [1]. Due to the recent rapid development of fast fashion, the need for textile products has drastically increased [2]. Fashion brands are making twice the number of products than were produced before the year 2000 [3]. Between 1975 and 2018, global per capita textile production has increased from 6 kg to 13 kg annually [4]. These trends bring environmental problems. Referring to United Nation’s climate change report, the textile industry emits about 8% to 10% of global CO₂ annually [5]. In the relevant literature, the environmental impact of the textile industry has been analyzed, encompassing various aspects, including material level environmental impact comparisons [6,7], life cycle stages and tradeoffs [6,7], recycling technologies [8,9,10] and circular strategies [11].

The labor-intensive nature of the textile industry, along with its complex supply chain that spans multiple countries, involving various raw materials, has made it a focus of attention for various social issues, including excessive working hours, temporary employment contracts, and health hazards [12].The production and disposal processes of these products systematically transfer environmental health risks, labor exploitation and social costs to low-income and middle-income countries. At the same time, consumers in high-income countries benefit from low prices and convenient consumption systems [13]. This imbalance has drawn attention to sectoral issues at the social level.

Social life cycle assessment (S-LCA) is a tool that can assess the social impacts generated by products and services throughout their life cycle [14]. Social sustainability is an important aspect of sustainability, but its assessment is still relatively insufficient, especially when compared with the evaluation of environmental aspects [15]. Previous S-LCA review studies mainly focused on methodological issues such as the setting of system boundaries [14,15] rather than the complete coverage of the life cycle. Other S-LCA reviews concentrated on S-LCA approaches, for example, on the reference scale approach [16], and the impact pathway approach [17]. For specific sectoral research, some S-LCA studies focused on green hydrogen [18], bio-based plastics [19], five types of drinking water [20], and the sugar industry in Thailand [21].

Although social sustainability and assessment methods related to the social life cycle have been widely applied in numerous industrial and energy-related fields, their application in the textile and clothing industry is relatively limited. Existing S-LCA studies in the textile industry cover various aspects of sustainability. Some studies focused on a comprehensive social impact framework, combining existing guidelines with an expanded set of subcategories and new indicators to better capture the breadth of social impacts in textile production [22,23]. One study concentrated on a comprehensive assessment of social subcategories. The results showed that the current S-LCA studies in the textile industry exhibited a significant imbalance in terms of indicator coverage and the selection of stakeholders [23]. Studies tend to be overly focused on the worker dimension while neglecting key stakeholders such as local communities, social governance, consumers, and children [23]. Other studies applied S-LCA to specific regional contexts, demonstrating local social performance and challenges, such as the outcomes of integrating workers and community stakeholders in the Italian textile supply chain [24] or comparing inclusive production models in Bangladesh [25]. The S-LCA method based on input-output further indicates that some industries, such as the business and commercial service industries, can also have a significant impact on overall social risk exposure when combined with traditional textile production processes [26].

The S-LCA database based on secondary and national sector-level data (such as the Social Hotspot Database (SHDB) and the Product Social Impact Life Cycle Assessment (PSILCA) database) are often used for conducting S-LCA [27]. Subsequent studies expanded the analysis scope by applying different S-LCA databases and frameworks to assess issues related to labor rights, working conditions, health and safety, and governance within the textile supply chain [22,24,28]. However, most of the existing S-LCA studies rely on secondary data, limiting their ability to capture the social performance of specific product systems. In particular, the high degree of sector aggregation in PSILCA has been regarded as a key limiting factor, as it restricts the assessment of how enterprise-level management practices affect social outcomes [29]. Although PSILCA covers numerous economic sectors, complementary methods are often needed to reflect the heterogeneity within each sector [30]. Previous studies further emphasized that general social data cannot fully represent the actual social impacts, but they can serve as substitutes when specific company information is lacking [31]. Therefore, some authors have emphasized that the application of S-LCA should go beyond cost-based data collection and incorporate original social data, but in many empirical case studies, such data is still limited [27]. Beyond environmental and social sustainability concerns, previous supply chain study research has also emphasized the importance of systematic risk identification and management across global production networks, highlighting the need to evaluate operational, institutional, and stakeholder-related risks throughout supply chains [32].

Overall, these limitations highlight the need to adopt a life-cycle perspective, clearly identifying and comparing social risk hotspots in the production stages of the global clothing supply chain as well as in different geographical regions. In particular, existing research, when assessing social risks in international textile supply chains, rarely combines raw production data at the factory level with structured social risk databases. This gap limits current assessment approaches’ ability to capture the single-stage and location-specific social risk patterns generated by actual production practices. This supports the importance of explicitly mapping social risk hotspots across life cycle stages, as presented in this study, particularly given the labor-intensive nature and complexity of the textile production processes. In recent years, studies on the Regional Comprehensive Economic Partnership (RCEP) have shown that textile and apparel trade among member countries has increased significantly following tariff reductions and regional production integration. Based on UN Comtrade data, previous studies reported clear growth in textile exports among RCEP members, particularly in countries such as China, Cambodia, and Thailand, indicating an ongoing regional restructuring of textile and apparel supply chains [33,34]. Due to this trend, Cambodia has become an increasingly important garment manufacturing hub, while Thailand continues to serve as a regional base for yarn and fabric processing. Turkey was selected as the upstream raw material stage due to its established role in global cotton production and its advantages in fiber quality and supply stability [35]. Therefore, Turkey, Thailand, and Cambodia were selected in this study to represent three functionally distinct stages of a contemporary cross-border textile supply chain: raw material acquisition, fabric production, and garment manufacturing. In addition, stage-specific comparisons across raw material production, textile processing, and garment manufacturing remain insufficiently explored using primary industrial data. Therefore, this study contributes by integrating factory-based economic activity data with the SHDB to systematically identify and compare social risk hotspots across a cross-border textile supply chain involving Turkey, Thailand, and Cambodia.

To address these limitations, the present study aims to evaluate the social risks associated with cotton trouser production, cognizant of the multinational textile supply chain. The assessment combines primary data provided by an OEM factory with social risk information from the SHDB and examines three major life-cycle stages: raw material acquisition, fabric production, and garment manufacturing. The study seeks to identify the key social risk hotspots across 25 subcategories, comparing the differences between producing countries, clarifying how the combination of primary data and SHDB contributes to a more accurate and meaningful interpretation of S-LCA results. This study aims to answer the following research questions:

RQ1: How are social risks distributed across the different life cycle stages of a global apparel supply chain?

RQ2: How does the geographical relocation of production stages affect the spatial distribution of social risks along the textile supply chain?

The remainder of this paper is organized as follows: Section 2 outlines the methodology, including data sources, functional units, system boundaries, and S-LCA modeling approach employed. Section 3 presents the results. Section 4 provides a discussion of the key findings and implications. Finally, Section 5 outlines the conclusions and limitations of this research.

2. Materials and Methods

The study employs the methodological framework proposed in the Guidelines for S-LCA of products [14], whose organization parallels that of the standardized Life Cycle Assessment method. The objective of S-LCA is to evaluate the social impacts, socioeconomic effects, and associated risks generated by products and services throughout their life cycle [36]. S-LCA follows the general structure of the ISO 14040 framework used in environmental LCA [37]. The method comprises four core stages: defining the goal and scope, compiling the social life cycle inventory (S-LCI), conducting the social life cycle impact assessment (S-LCIA), and interpreting the results [14]. The approach is iterative, allowing the assessment to be refined as new information becomes available and enabling a gradual shift from broad, generic insights to findings that are tailored to specific sites or contexts. Figure 1 details a visual representation of the applied methodology, whose components will be elaborated in detail in subsequent sections.

2.1. Goal and Scope

The goal of this study is to evaluate the social sustainability performance of a specific garment, women’s trousers, by identifying and analyzing social impacts and areas for improvement throughout the product life cycle. An S-LCA framework is applied to identify social risk hotspots across different life cycle stages and geographic regions within apparel production.

By focusing on regions, production stages, and social subcategories with relatively higher risk levels, this study reveals the spatial distribution of social risks embedded in textile supply chains. The results are intended to support the development of targeted mitigation strategies and provide decision-making insights for risk management and policy formulation toward more socially sustainable apparel production.

The functional unit for this study was defined as one pair of women’s trousers made of 100% organic cotton, grey in color, medium size, of 560 g weight, representing typical daily wear from spring to autumn. The existing supply chain for these trousers spans Turkey, Thailand, and Cambodia. Figure 2 illustrates the simplified system boundaries and supply chain for the subject trousers. Raw materials are procured in Turkey, with cotton yarn processing and fabric production completed in Thailand. The fabric is then sold to garment factories in Cambodia, where the cutting, sewing, and final assembly of the trousers take place before the finished product is shipped to the retailer. The selected countries were not intended to represent a statistically global sample, but rather a function-based supply chain configuration reflecting recent regional production relocation in the textile industry.

According to the United Nations Environment Programme (UNEP), social impacts are typically evaluated from multiple stakeholder categories, which represent various participants with similar interests in aspects related to the product system [14]. Five stakeholder categories were considered in this study: Worker (in general), Worker (Health & Safety), Local Community, Society, and Consumers. As shown in

Table 1. Stakeholders’ categories and subcategories from the UNEP S-LCA guidelines.

Stakeholder Categories	Subcategories
Workers (General)	1A Wage
	1B Poverty
	1D Child Labor
	1E Forced Labor
	1F Excessive Work Time
	1G Freedom of Association
	1H Migrant Labor
	1I Social Benefits
	1J Labor Laws & Conventions
	1K Discrimination
	1L Unemployment
Worker (Health & Safety)	2A Occupational Toxicity & Hazards
	2B Injuries & Fatalities
Local Community	3A Indigenous Rights
	3B Gender Equity
	3C High Conflict Zones
	3D Non-communicable Diseases
	3E Communicable Diseases
Society	4A Legal System
	4B Corruption
Consumers	5A Access to Drinking Water
	5B Access to Sanitation
	5C Children out of School
	5D Access to Hospital Beds
	5E Smallholder vs. Commercial Farms

Source: Prepared by the authors based on UNEP S-LCA guideline.

, an analysis of 25 subcategories was conducted for the stakeholder categories.

The definitions of these subcategories were developed with reference to the guidelines for S-LCA [14]. These categories can comprehensively assess the social risks that may occur at different stages of the textile supply chain. The selected clothing factory is located in Phnom Penh, Cambodia, close to Phnom Penh International Airport. This area is home to numerous clothing manufacturing factories that are export-oriented. Due to Cambodia’s export-oriented production structure and relatively low labor costs, the country has become an important production center in the global textile value chain [38]. Therefore, the selected factory can be regarded as a typical representative of the labor-intensive clothing assembly stage within the global textile supply chain.

The social risk calculation was conducted using the openLCA version 2.5. The life cycle inventory of the product system was developed based on the ecoinvent v3.11 database, which provides process-based environmental inventory data. The social risk assessment used the SHDB, which contains social risk indicators for different countries and industries. According to the database-based S-LCA method, economic flows expressed in monetary units (US dollars) are linked to the national-industry activities in the SHDB. The total social risk is calculated as the product of the economic activity level, the industry-specific labor intensity (hours per US dollar), and the social risk characteristic factors.

2.2. Life Cycle Inventory Analysis

The SHDB was incorporated into the openLCA software and used to obtain and explore the life cycle social risks associated with the sectors directly involved in the supply chain. The inventory phase involved identifying all activities directly linked to the supply chain and collecting the information necessary to associate these activities with the appropriate SHDB sectors and corresponding country-level risk profiles. Primary data from the collaborating OEM factory were used to describe production-related activities, while secondary data from SHDB supplied the social background information for each relevant sector. This inventory formed the basis for connecting actual production processes with the social impact pathways used in the S-LCA model. All primary economic activity data used in this study were collected from the participating supply chain partners during the 2025 fiscal year. To improve temporal representativeness and reduce the influence of short-term fluctuations, expenditure data for each life cycle stage were calculated based on annual average production cost records rather than single-point observations. These data reflect actual production conditions within the selected supply chain configuration.

2.2.1. Definition of Activity Data

Activity data refers to the quantitative and qualitative information that describes the processes involved in the production of cotton trousers across the three life cycle stages examined in this study. These stages include raw material acquisition, fabric production, and garment manufacturing. Data was obtained from factory-level records provided by the OEM supplier and include production volumes, material flows, labor inputs, and cost data associated with each stage. These activity data were used to identify the corresponding economic sectors represented in the SHDB and to determine the country in which each activity takes place. Only processes directly contributing to the functional unit were included in the inventory. Primary cost data related to the production of the trousers were obtained directly from the manufacturing facility and used as inputs for the S-LCA model. By combining supplier-provided cost information with country and sector specific social risk indicators, this study identifies potential social risk hotspots across life cycle stages and geographic regions within the textile supply chain.

2.2.2. Mapping to SHDB Sectors and Countries

Each activity listed in the inventory has been associated with the most suitable SHDB sector and country combination to clearly define its social risk status. As the SHDB provides social indicators at the national-department level rather than at the facility level, the production activities reported by the factories have been matched with the corresponding industrial sectors defined in the SHDB that are closest in nature. Examples of these sectors include cotton cultivation, textile production, and clothing manufacturing. The country allocation follows the actual geographical locations of each stage of the supply chain, including the countries responsible for raw material production, textile processing, and clothing manufacturing. Through this mapping process, each activity in the product system is associated with the corresponding country-industry combination in the SHDB, enabling the retrieval of social risk indicators for specific industries and countries for subsequent social life cycle impact assessment.

Table 2. Life cycle inventory data.

Life Cycle Stage	Country	Cost Input (USD/Trouser)	SHDB Sector Mapping	Data Source
Organic cotton	Turkey	0.852	Crops nec-TR	OEM Factory
Textile yarn	Thailand	1.48	Textiles-TH	OEM Factory
Finished fabric	Cambodia	6.75	Textiles-KH	OEM Factory
Manufacturing	Cambodia	1.093	Wearing apparel-KH	OEM Factory

Source: prepared by the authors with OEM factory data.

summarizes the corresponding relationships between the main life cycle activities and their respective SHDB departments and countries, as well as the relevant cost data obtained from the original equipment manufacturer factories.

To further illustrate how the original data and secondary data were integrated in this study, the “wages” subcategory is used as an example. First, the production costs related to a specific life cycle stage were directly obtained from the accounting records at the factory level and expressed as economic activity in dollars per functional unit. Second, based on the actual geographical location and industrial activities of this process, this economic activity was matched with the corresponding country-industry combinations as determined in Table 2. Finally, the matched SHDB dataset provided the corresponding labor intensity and social risk characteristic factors, which were used to calculate the characterized social risk result represented by Medium-Risk-Hour Equivalents (mrheq). In this study, the same assessment procedure was applied to all life cycle stages and social subcategories.

2.3. Life Cycle Impact Assessment

In this study, the assessment of social risks followed the methodological framework proposed in the UNEP S-LCA Guidelines [14]. The impact assessment consisted of three main steps: classification of inventory data, description of social risks, and identification of potential social hotspots in the supply chain.

The social risk assessment was conducted using the SHDB implemented through openLCA. The economic activity associated with each life cycle stage was expressed as expenditure in USD per piece based on primary production cost data collected from each factory. To improve the transparency of the social risk calculation, the social risk for each life cycle stage and social subcategory was calculated based on the expenditure driven characterization approach implemented in the SHDB, as expressed in Equation (1).

$${Risk}_{i,j}=E_i\times {RF}_{i,j}$$

(1)

where $Ris{k}_{i,j}$ represents the social risk result of life cycle stage i for social subcategory j (mrheq); $E_i$ represents the expenditure associated with life cycle stage i (USD per piece); and $R{F}_{i,j}$ represents the corresponding country and sector-specific social risk factor obtained from SHDB (mrheq/USD). The results are expressed in medium risk hour equivalents (mrheq), which represent the estimated worker hours associated with medium-level social risk exposure. The social stakeholder groups and corresponding subcategories considered in this study are summarized in

Table 3. Categorization of social impacts and corresponding indicator definitions and risk reference scales.

Stakeholder	Impact Category	SHDB Indicator	Reference Scale
Worker (General)	1A Wage	Risk that sector average wage is below sweatfree wage	VH, HR
		Risk that average wage is below country minimum wage	VH, HR
		Risk that sector average wage is below living wage	VH, MR, LR, HR
	1B Poverty	Percent of population living under the relevant poverty line	HR, VH, LR
	1D Child Labor	Risk of child labor by sector	VH, HR, MR
	1E Forced Labor	Overall forced labor in country	VH, HR
	1F Excessive Work Time	Percent of population working > X h/week, >60 h/week	HR, MR, VH, LR
	1G Freedom of Association	Overall risk of freedom of association	VH, HR
	1H Migrant Labor	Evidence of risk to migrant workers	VH, HR
	1I Social Benefits	Overall risk of inadequate social benefits	HR, MR
	1J Labor Laws & Conventions	Number of labor laws by sector	HR, LR, MR
	1K Discrimination	Prevalence of discrimination in the workplace	VH, MR, HR
	1L Unemployment	Unemployment percentage at sector level	HR, VH, MR
Worker (Health & Safety)	2A Occupational Toxicity & Hazards	Overall occupational noise exposure risk	HR
		Overall occupational cancer risk-loss of life	VH, MR
		Disability-adjusted life years due to occupational-related lung cancer	VH, MR
	2B Injuries & Fatalities	Fatal injuries by sector	HR, VH
		Non-fatal work-related injuries by sector	MR, HR
Local Community	3A Indigenous Rights	Indigenous sector issues identified	VH, HR
		Overall risk of indigenous rights being infringed	HR
	3B Gender Equity	Overall gender inequity in country	MR, HR, VH
	3C High Conflict Zones	Overall high conflict	HR, MR, VH
	3D Non-communicable Diseases	Overall Non-communicable Diseases and other health risks	MR, HR
	3E Communicable Diseases	Cases of HlV (per 1000 adults 15–49 years)	VH, HR
		Cases of tuberculosis (per 100,000 population)	HR
		Notified cases of Malaria (per 100,000 population)	VH, HR
		Age-standardized MRs from communicable diseases (per 100,000 population)	MR
Society	4A Legal System	Overall fragility in legal system	VH, HR
	4B Corruption	Overall Corruption	VH, HR
Consumers	5A Access to Drinking Water	The percentage of total population with access to improved drinking water sources	HR, LR, MR, VH
	5B Access to Sanitation	The percentage of total population with access to improved sanitation	HR, VH, LR, MR
	5C Children out of School	Percent of children out of primary school, total	HR
	5D Access to Hospital Beds	Number of hospital beds per 1000 population	HR
	5E Smallholder vs. Commercial Farms	Overall risk of freedom of association	VH, HR
		Percentage of family-owned farms in country	VH

Note: Risk levels follow the SHDB classification, where VH = Very High risk; HR = High risk; MR = Medium risk; LR = Low risk. Source： prepared by the authors based on the Social Hotspot Database in OpenLCA.

.

The SHDB is built upon a global input–output model that links economic activities with country sector-specific social indicators to estimate potential social risks [39]. Subsequently, social risks were quantified using the characteristic factors provided by the SHDB. In this method, the economic activity level expressed in monetary units (dollars) was combined with the labor intensity of a specific industry (the number of working hours per dollar) and the social risk characteristic factors to calculate the overall social risk score.

Finally, social hotspots were identified by examining subcategories and life cycle stages associated with elevated risk levels or substantial contributions to overall risk scores. This procedure enabled the detection of specific countries, production stages, and social themes that warrant closer attention for risk mitigation and sustainability improvement.

3. Results

3.1. Overall Social Risks Along the Life Cycle Stages

Table 4. Hotspot Identification Across Life Cycle Stages for All Social Subcategories.

Subcategory	Raw Material Acquisition	Fabric Production	Garment Manufacturing	Hotspot
1A Wage	5.706	68.046	No data	Fabric production
1B Poverty	1.446	2.659	No data	Fabric production
1D Child Labor	No data	68.046	9.651	Fabric production
1E Forced Labor	No data	68.046	19.301	Fabric production; Garment manufacturing
1F Excessive Work Time	No data	34.023	1.93	Fabric production
1G Freedom of Association	No data	68.046	19.301	Fabric production; Garment manufacturing
1H Migrant Labor	No data	39.341	9.651	Fabric production
1I Social Benefits	1.446	6.805	1.93	Fabric production
1J Labor Laws & Conventions	No data	No data	No data	/
1K Discrimination	No data	68.046	19.301	Fabric production; Garment manufacturing
1L Unemployment	0.289	No data	No data	Raw material acquisition
2A Occupational Toxicity & Hazards	No data	56.705	16.084	Fabric production; Garment manufacturing
2B Injuries & Fatalities	0.289	1.595	No data	Fabric production
3A Indigenous Rights	No data	34.023	9.651	Fabric production; Garment manufacturing
3B Gender Equity	1.446	6.805	1.93	Raw material acquisition
3C High Conflict Zones	1.446	9.464	1.93	Fabric production
3D Non-communicable Diseases	No data	6.805	1.93	Fabric production
3E Communicable Diseases	No data	28.579	8.107	Fabric production
4A Legal System	No data	68.046	19.301	Fabric production; Garment manufacturing
4B Corruption	No data	68.046	19.301	Fabric production; Garment manufacturing
5A Access to Drinking Water	No data	34.023	9.651	Fabric production; Garment manufacturing
5B Access to Sanitation	No data	34.023	9.651	Fabric production; Garment manufacturing
5C Children out of School	No data	34.023	9.651	Fabric production; Garment manufacturing
5D Access to Hospital Beds	No data	34.023	9.651	Fabric production; Garment manufacturing
5E Smallholder vs. Commercial Farms	No data	30.93	8.773	Garment manufacturing

Note: Raw material acquisition includes cotton cost inputs; fabric production includes yarn and finished fabric processes; and garment manufacturing corresponds to the product assembling. Colors represent qualitative risk levels: grey█ = no data, green█ = low risk, yellow█ = medium risk, orange█ = high risk, and dark red█ = very high risk. Source: prepared by the authors.

presents the distribution of social risks across the three life cycle stages based on the 25 S-LCA subcategories.

Consistent with previous S-LCA studies in the textile sector, the results show a clear concentration of social risks in the fabric production stage, where the majority of subcategories fall within high or very high risk levels. This stage consistently records the highest values across most indicators, identifying it as the primary hotspot within the supply chain [23]. In contrast, garment manufacturing displays moderate to high risks across a broader set of subcategories. While the raw material acquisition stage shows relatively lower risk values within the available indicators, several labor related subcategories were reported as “no data”

Worker-related indicators including child labor (1D), forced labor (1E), excessive working time (1F), freedom of association (1G), and discrimination (1K) are particularly elevated in fabric production. This pattern may be attributed not only to the labor-intensive nature of textile processing, but also to the continuous multi-step operations involved in spinning, weaving, and finishing, which increase cumulative worker-hour exposure and occupational contact with machinery and industrial chemicals. Similar hotspot concentrations in textile processing stages have also been reported in a previous textile S-LCA study [28].

In contrast, the garment manufacturing stage exhibits a broader distribution of social risks across multiple labor-related indicators. Although the overall risk intensity is lower than that observed in fabric production, risks remain substantial across employment- and working-condition-related subcategories. This may be associated with the labor-intensive characteristics of apparel assembly, which still relies heavily on manual operations such as cutting, sewing, inspection, and packaging, resulting in widespread worker-hour exposure across multiple social dimensions.

By comparison, the raw material stage presents fewer high-risk categories, with relatively lower values for indicators such as wages, poverty, and unemployment. However, this observation should be interpreted with caution, as several labor-related indicators in the corresponding agricultural sector were reported as “No data” in the SHDB. These missing values may lead to an underestimation of social risk intensity in the raw material stage, particularly for labor-related indicators such as child labor, forced labor, and excessive working time, and may therefore influence the relative hotspot ranking of this stage. Previous textile S-LCA studies also found that upstream cotton production may involve labor and community related social risks, although generally at lower intensity than textile processing stages [14,38].

Overall, the results reveal a stage-specific distribution pattern, with risk intensity peaking in fabric production and risk breadth expanding in garment manufacturing. These findings suggest that social hotspots within the textile supply chain are shaped by differences in production complexity, labor intensity, and country-specific sectoral conditions, rather than being evenly distributed across life cycle stages.

3.2. Hotspot Identification by Stakeholder Category

3.2.1. Worker-Related Risks

Figure 3 and Figure 4 detail the specific social risk assessment results for the stakeholder group of workers, including subcategories such as general working conditions and occupational health and safety.

The highest risk values are found in free association (169.04 mrheq), forced labor (156.96 mrheq), child labor (144.89 mrheq), and risks related to wages (138.23 mrheq), indicating that labor rights and employment governance are the main social risk dimensions in the supply chain. The higher risks associated with free association may reflect the limited collective bargaining capacity and restricted worker representation, which remains a common challenge in global supply chains [40]. Similarly, the high values observed in forced labor, child labor, and migrant workers may be related to the labor-intensive production structure and the employment pressure in the globally dispersed textile and clothing production processes.

For occupational health and safety (Figure 4), occupational toxicity and hazards (137.06 mrheq) significantly exceed injuries and fatalities (4.36 mrheq). This suggests that chronic exposure to hazardous working environments is a more prominent risk than acute accidents within the assessed supply chain.

For occupational health and safety (Figure 4), occupational toxicity and hazards (137.06 mrheq) substantially exceeded injuries and fatalities (4.36 mrheq), indicating that chronic workplace exposure represents a more prominent worker-related risk within the supply chain. This pattern is consistent with previous studies showing that textile workers are frequently exposed to dyes, solvents, and other hazardous chemicals during daily production activities, which may result in cumulative occupational health risks over time [41]. By contrast, acute injuries and fatal accidents showed relatively limited contributions in the present assessment.

Overall, worker-related risks are both high in magnitude and concentrated in specific subcategories, highlighting labor conditions as the dominant contributor to total social risk.

3.2.2. Local Community Risks

The local community results (Figure 5) show that the highest risks were observed in indigenous rights (91.46 mrheq) and communicable diseases (68.29 mrheq), followed by high conflict zones (30.24 mrheq). This pattern suggests that community-related risks in the supply chain are primarily associated with institutional vulnerability and public health conditions rather than production-specific factors alone.

The elevated risk observed in indigenous rights may reflect land-use pressure and unequal access to local resources in upstream agricultural supply chains, where land tenure and community rights remain critical sustainability concerns [42]. Also, Herrera noted similar community-related hotspot patterns across global textile supply chains [28]. Existing social life cycle assessment studies indicate that local communities surrounding textile manufacturing clusters often face inadequate basic public services, scarce medical resources, and insufficient social security system coverage. Consequently, these communities exhibit high potential social vulnerability in terms of health risks, social inclusion, and community well-being [28].

Within global textile supply chains, production activities are typically concentrated in emerging economies with limited regulatory capacity. Local communities not only bear environmental pressures but also face multiple challenges including public health risks, inadequate infrastructure, and social inequality. Research utilizing the SHDB has revealed that indicators related to community health, access to drinking water, and public sanitation conditions consistently exhibit medium-to-high risk levels across textile-producing nations. This reflects the structural pressures imposed by globalized production systems on local communities.

In contrast, the risk contributions of gender equity and non-communicable diseases (NCDs) subcategories in this study are relatively low, though their societal impacts remain significant.

3.2.3. Societal Risks

Figure 6 shows the results for societal-level indicators.

Both corruption (162.76 mrheq) and legal system fragility (158.01 mrheq) exhibit high values, indicating that governance-related risks are a major component of the overall social risk profile. These risks are not confined to a single life cycle stage, but instead reflect cross-cutting governance conditions across multiple countries involved in the supply chain, including regulatory enforcement, institutional transparency, and compliance capacity.

The elevated risks observed in corruption and legal system indicators may reflect governance gaps between formal compliance systems and actual operational practices, which have been widely reported in global apparel supply chains involving emerging-market suppliers [43]. Such governance-related vulnerabilities may directly influence labor protection, contract enforcement, and supply chain transparency across multiple production stages.

3.2.4. Consumer-Related Risks

Figure 7 presents the results for consumer-related subcategories.

The highest values are observed for children out of school (85.45 mrheq), access to hospital beds (84.12 mrheq), and smallholder versus commercial farm structures (79.01 mrheq). Indicators related to sanitation (75.81 mrheq) and drinking water access (74.33 mrheq) also show elevated levels. Unlike worker-related risks, which are concentrated in specific labor-related indicators, consumer-related risks are more evenly distributed across multiple access-related subcategories. This pattern suggests that the consumer stakeholder in SHDB primarily reflects broader social infrastructure conditions, including access to education, healthcare, sanitation, and basic public services in production regions.

3.3. Comparison Across Countries and Life Cycle Stages

This section compares the distribution of social risks across the three countries represented in the supply chain.

Figure 8 presents the social risk characteristics in Turkey.

Turkey specifically represents the stage of raw material acquisition. The presented indicators show that the risks are relatively concentrated. Among them, the values of wages and social benefits are the highest. Other indicators, such as poverty, gender equality and conflict, are at relatively lower levels. This indicates that the identified risks are mainly related to labor remuneration and basic employment conditions. However, these results should be interpreted with caution as several subcategories related to labor in the agricultural sector were reported as “no data” in SHDB, which may lead to an underestimation of potential upstream social risks. Figure 9 details the social risk characteristics in Thailand.

In Thailand (Figure 9), representing yarn and fabric production, risk levels increased substantially compared with the raw material stage. Wage-related risks showed the highest value (68.046 mrheq), followed by migrant labor (5.318 mrheq), indicating that labor compensation and workforce mobility are the dominant social risk dimensions in this stage. This result may be related to the labor-intensive and export-oriented characteristics of Thailand’s textile and apparel sector, where firms have historically relied on migrant workers from neighboring countries to maintain competitiveness in labor-intensive production [41,42]. Therefore, the elevated wage and migrant labor indicators should be interpreted as reflecting both production-stage labor demand and the broader migrant labor structure of the Thai textile sector. Figure 10 details the social risk assessment results for Cambodia.

In Cambodia (Figure 10), representing fabric and garment manufacturing, social risks are both high in magnitude and widely distributed across multiple stakeholder categories. Elevated values were observed in labor-right-related indicators, including child labor, forced labor, excessive working time, and discrimination, as well as governance-related indicators such as legal system and corruption (114.334 mrheq). This pattern may reflect the labor-intensive and export-oriented characteristics of Cambodia’s garment sector, where high production pressure, temporary employment, and compliance gaps remain persistent challenges [43]. In addition, community-related indicators, including drinking water, sanitation, education, and healthcare, also showed consistently high values, suggesting that social risks in Cambodia extend beyond workplace conditions and are closely linked to broader institutional and infrastructure constraints. Across the three countries, a clear pattern emerges. Social risks are lower and more concentrated in upstream raw material production (Turkey), intensified and labor-focused in textile processing (Thailand), and the highest and most widely distributed in garment manufacturing (Cambodia).

This progression demonstrates a spatial and structural transfer of social risks along the supply chain. As production shifts toward more labor-intensive stages and lower-cost regions, both the intensity and diversity of risks increase. This finding directly addresses RQ2, confirming that geographical relocation plays a critical role in shaping the distribution of social risks.

4. Discussion

4.1. Distribution of Social Risks Across Life Cycle Stages

The results demonstrate that social risks are not uniformly distributed across the life cycle but instead exhibit stage-specific concentration patterns. Fabric production emerges as the primary hotspot in terms of risk intensity, while garment manufacturing encompasses the widest range of affected social subcategories. This distinction reflects the structural differences between upstream industrial processing and downstream labor-intensive assembly.

These findings are consistent with prior S-LCA research highlighting the sensitivity of hotspot identification to system boundaries and methodological choices. For example, input–output-based approaches have shown that social risks may extend beyond core production processes into supporting sectors and services [26]. This underscores the importance of adopting a life cycle perspective, where risks are interpreted as outcomes of interconnected production systems rather than isolated stages.

The concentration of risks in fabric production can be explained by the transition from agricultural to industrial processes. Textile manufacturing involves mechanized operations, chemical processing, and higher labor intensity, all of which increase exposure to occupational hazards and labor-related risks. This stage therefore represents a critical control point where social risks begin to escalate within the supply chain.

In contrast, garment manufacturing expands the scope rather than the intensity of risks. As a highly labor-dependent activity embedded in urban and peri-urban economies, it interacts with broader labor markets, subcontracting systems, and local infrastructure conditions. Consequently, risks extend beyond workplace conditions to include community-level and institutional dimensions.

The stage-specific hotspot patterns identified in this study further suggest that social risks are not evenly distributed across cross-border textile supply chains but are accumulated in specific production stages and geographical locations. This interpretation is consistent with recent performance attribution studies, which emphasize that decomposing overall system outcomes into stage-level contributions can provide more transparent explanations of performance differences and support targeted managerial interventions [44].

Together, these patterns indicate that different life cycle stages play distinct roles in shaping social risk profiles: upstream stages concentrate risk intensity, while downstream stages broaden the range of affected stakeholders. This reinforces the need for multi-stage intervention strategies that address both concentrated and systemic risks across the supply chain. Although the selected countries represent a specific cross-border supply chain configuration, the identified stage-specific social risk patterns may provide useful insights for other labor-intensive textile supply chains with similar production structures and institutional conditions.

4.2. Social Risk Redistribution Along the Global Textile Supply Chain

The cross-country comparison reveals a clear spatial redistribution of social risks along the supply chain. Risks increase in both magnitude and diversity as production shifts from raw material extraction in Turkey to textile processing in Thailand and garment manufacturing in Cambodia.

This pattern reflects the underlying structure of global value chains (GVCs), where production stages are geographically fragmented according to cost advantages and resource availability [45]. Labor-intensive activities are typically located in regions with lower labor costs and more flexible regulatory environments, which can increase exposure to social risks. The concentration of high-risk indicators in Cambodia and Thailand is consistent with this structural dynamic.

Importantly, the results suggest that globalization does not eliminate social risks but reallocates them across locations and stages. Countries hosting labor-intensive processes tend to bear a disproportionate share of risks related to wages, labor rights, and governance. This aligns with existing literature showing that outsourcing and global competition can exacerbate challenges related to labor protection, institutional capacity, and social inequality [39,46].

At the same time, the prominence of fabric production as a hotspot highlights that risks are not confined to final assembly stages. Upstream industrial processes also play a critical role in shaping overall risk profiles, particularly where regulatory oversight and occupational safeguards are limited.

These findings contribute to sustainability debates by demonstrating that social risks are systemically embedded within global production networks. Addressing them therefore requires coordinated action across multiple countries and supply chain stages, rather than isolated interventions at the point of final production [26,47].

While Cambodia exhibits the highest overall social risk intensity and the broadest distribution of hotspot indicators in the present assessment, these findings should not be interpreted as suggesting that relocating production away from Cambodia would automatically improve social sustainability. Previous development studies have shown that export-oriented manufacturing and foreign direct investment in emerging economies can simultaneously generate employment opportunities, improve wage formalization, and contribute to industrial upgrading and local economic development, despite persistent labor and governance challenges [32,48]. Therefore, the elevated social risks identified in Cambodia should be interpreted as highlighting areas requiring targeted governance, institutional strengthening, and supplier development, rather than as an argument for supply chain withdrawal or simple geographical substitution.

From a supply chain governance perspective, the selection of final production locations should not be driven solely by labor cost or short-term sourcing efficiency. Instead, brands and buyers should adopt a more integrated sourcing strategy that simultaneously considers economic competitiveness, environmental performance, labor conditions, and institutional risks throughout the supply chain [32]. Such an approach may support more responsible supplier selection and reduce the long-term transfer of environmental and social burdens to vulnerable production regions.

A social risk comparison among the three countries critical to the analyzed supply chain is detailed in Appendix A.

4.3. Stakeholder Implications and Pathways for Social Risk Mitigation

4.3.1. Implications for Workers and Supply-Chain Governance

S-LCA is increasingly being regarded as a tool for identifying social risks within global value chains and supporting the formulation of targeted mitigation measures [49]. Researchers also emphasize that enterprises should ensure fair wages, safe working conditions, and respect for human rights throughout the product life cycle. Relevant literature also emphasizes the need for more comprehensive case studies based on original data to enhance the reliability and practical applicability of social risk assessment [50].

By combining original production data with the SHDB, this study provides insights for multiple stakeholders, which can serve as a reference for risk mitigation strategies in the textile supply chain. The high scores observed in areas such as wages, forced labor, discrimination, trade union freedom, and occupational health risks indicate that there are persistent labor-related problems in global clothing production. This finding is consistent with previous research results, which show that in the global outsourcing production system, textile workers often face low wages, long working hours, and limited social security [51].

Global competition and outsourcing models place huge cost-cutting pressure on suppliers. This often leads to a decrease in wage levels and the weakening of labor protection measures. In many production regions, textile workers accept poor working conditions because they face the risks of unemployment and job instability. These structural factors help to explain the higher worker-related risks observed in the garment manufacturing sector in Cambodia.

Previous S-LCA studies showed that after identifying hotspots, targeted communication with suppliers should also be conducted, and relevant data for specific locations should be collected [28]. Therefore, strengthening supplier supervision, establishing transparent auditing mechanisms, and promoting closer cooperation between buyers and suppliers is crucial for improving labor conditions in the global textile supply chain.

However, supply chain governance is not only driven by internal management practices within firms, but also influenced by pressures from external stakeholders. External stakeholders, such as non-government organizations, consumers, and regulatory agencies, also influence the operation of the supply chain [52,53]. International sustainable development initiatives and certification systems, such as better cotton initiative (BCI), global organic textile standards (GOTS), and Bluesign, encourage enterprises to raise environmental and labor standards in their supply chains [54]. Additionally, concerns about brand reputation often prompt multinational companies to strengthen supplier supervision and sustainability plans [55,56].

4.3.2. Local Community Development

In this study, it was identified that some local community indicators remain at a high-risk level, particularly in terms of infectious diseases, indigenous rights, and access to water and sanitation facilities. These findings emphasize the importance of paying attention to broader community conditions while implementing workplace-focused intervention measures. An important issue is that economic upgrading within the textile industry does not naturally lead to inclusive local development. Evidence from textile production regions shows that economic growth may benefit certain participants while leaving others behind, thereby exacerbating local inequality. For example, previous studies of the textile production system have revealed that commercial success can coexist with an increasingly widening social and economic gap within the community, especially among different producer groups and market participants [57]. Shifting production to low-income regions often creates job opportunities, but the broader benefits to the local community may still be uneven. In many cases, the economic gains related to textile production are concentrated in middlemen, exporters, or resource-rich enterprises, while the improvement in the living conditions of smaller producers and vulnerable groups is limited. These insights help explain the relatively high risks at the community level found in this study. Even though the textile industry creates jobs, improvements in public health, infrastructure, and social services may lag behind. This gap is reflected in the higher risks observed at the clothing manufacturing stage related to diseases, hygiene conditions, and access to water.

To reduce these risks, all parties in the supply chain should go beyond factory-level compliance requirements and instead support community-level investments. Feasible measures include improving access to clean water and sanitation facilities, strengthening local medical systems, and supporting community education programs. These intervention measures can ensure that the benefits of global textile production extend beyond the factories and promote broader social development.

4.3.3. Implications for Consumers and Downstream Stakeholders

Risks related to consumers, such as issues in water usage, sanitation facilities, and healthcare, reflect the broader development challenges faced by the production area. These risks are not directly experienced by consumers but exist in the upstream supply chain and are identified through S-LCA hotspots. The SHDB framework emphasizes that social risks are closely related to the working hours and labor intensity of workers in various countries and industries. This means that consumers’ demand for low-cost and fast-fashion products will indirectly affect the labor demand and working conditions in the global supply chain [22]. This highlights the importance of transparent communication and responsible procurement strategies. Brands and retailers can play a key role in incorporating social risk factors into procurement decisions and sustainability reports.

Recent research emphasized that consumers’ behavior and awareness are important components of the circular textile economy, but their integration into the sustainability assessment framework is still insufficient [58]. Specifically, they mentioned that consumers influence product lifespan, reuse, and disposal decisions, making them a key component in the “product and material circular framework”. However, merely raising consumers’ awareness is often not sufficient to change their purchasing behavior.

This gap between awareness and behavior highlights the need for clearer supply chain transparency and more actionable governance practices. However, transparency should go beyond passive disclosure. Previous research shows that effective accountability requires supplier verification, objective audits, certification, internal accountability systems, and employee training [59]. Therefore, brands and retailers should strengthen payroll auditing, social insurance verification, third-party compliance assessments, and supplier-level sustainability disclosure. Digital traceability and clearer disclosure in certification schemes and sustainability reports may further reduce information asymmetry and improve consumer trust [60]. Such strategies can also enhance trust between brands and consumers and encourage responsible consumption patterns [61].

5. Conclusions and Limitations

This study employed an S-LCA method to evaluate the social sustainability performance of an organic cotton pair of trousers produced through a cross-border supply chain spanning Turkey, Thailand, and Cambodia. By combining original data from manufacturing factories with the social hotspots database, the study identified social risk hotspots at different stages of the life cycle, across geographical regions, and stakeholder categories.

The research results indicate that social risks are unevenly distributed throughout the supply chain. Specifically, the textile production stage has the highest concentration of risk hotspots, while garment manufacturing in Cambodia involves the widest range of affected social subcategories. This suggests that different production stages have different impacts upon social risk, with the upstream processing stage concentrating the risk intensity, while the downstream assembly stage affects a broader group of stakeholders. Among various stakeholder categories, the analysis highlights the prominent positions of labor-related risks, occupational health and safety issues, governance challenges, and development-related issues affecting local communities and consumers. Results confirm that social risks in the textile supply chain are multi-dimensional and are closely related to the geographical transfer of labor-intensive production processes.

By examining social risks from a life cycle perspective, this study helps to gain a deeper understanding of how social impacts are generated and accumulated in the global clothing supply chain. Findings provide practical insights for enterprises, policymakers, and supply chain participants seeking to prioritize risk reduction and improve the social sustainability of textile production.

When interpreting the results of this study, the following limitations should be recognized. Firstly, this analysis is predominantly based on secondary social risk data from the SHDB and combined with cost-based activity data. Although this method enables systematic identification of hotspots, it cannot fully cover the unique company-level social conditions of each production site. Furthermore, it should be noted that the social risk assessment in this study was conducted based on the annual average economic activity data for the year 2025. Although using the annual average values helps to reduce the impact of short-term operational fluctuations, the described risk values may change over time due to variations in wage levels, labor migration patterns, exchange rates, regulatory conditions, or changes in social policies in specific industries. Therefore, in other years, the absolute scale of social risks may differ, although the relative hotspots in each production stage are expected to remain roughly consistent under similar supply chain structures.

Future research should explore how enterprises can convert hotspot identification into specific social improvement actions and how these actions can promote the long-term sustainable transformation of the textile industry. Finally, expanding the research scope and comparing multiple supply chain models will help to better understand how institutional and socio-economic environments affect the distribution of social risks.