Understanding the systemic sources of uncertainty for the management of greenhouse gas emissions in supply chains

Global supply chains (SCs) must rapidly decarbonize to mitigate climate change and its severe impacts on ecological and socioeconomic systems (Intergovernmental Panel on Climate Change, 2023). The steel industry is particularly critical, being responsible for approximately 8% of global CO₂ emissions and subject to intensifying pressures for comprehensive emissions mitigation practices across its SCs (International Energy Agency, 2023; Cornwall, 2024). Yet, recent research highlights that the adoption of emissions mitigation strategies in this sector remains low, partly due to persistent challenges in emissions measurement and reporting (Fagundes Alves et al., 2024).

Accurate measurement and disclosure of indirect greenhouse gas (GHG) emissions (i.e. Scope 3 emissions) are fundamental to the decarbonization of SCs (Vieira et al., 2024). Addressing Scope 3 emissions effectively requires organizations to integrate emissions-related considerations into strategic SC planning stages, including procurement, production, distribution, and sales (Fahimnia et al., 2015; Allaoui et al., 2019). However, organizations often face several uncertainties that can affect planning (Oliva and Watson, 2011; Srinivasan and Swink, 2015; Kristensen and Jonsson, 2018). Challenges that add to these uncertainties include unclear SC boundaries, data quality issues, the dynamic nature of SC relationships (Vieira et al., 2024), unclear reporting standards (Patchell, 2018), excessive transaction costs, and the general lack of know-how related to carbon accounting within organizations (Hettler and Graf-Vlachy, 2024a, b). Such challenges increase information processing demands for SC planning, as organizations must collect and validate emissions-related data from heterogeneous sources (Dahlmann and Roehrich, 2019). Consequently, many organizations continue to rely on rough emissions estimation techniques, which is a major constraint on managing the decarbonization of SCs (Vieira et al., 2024).

While prior research acknowledges these challenges, a critical gap remains in understanding how uncertainty in measuring and reporting Scope 3 emissions emerges and how they are addressed in SC planning (Jonsson and Holmström, 2016; Ellram and Tate, 2025). Studies have explored emissions-related information processing in SCs (Dahlmann and Roehrich, 2019), the effects of SC complexity on the capacity to reduce emissions (De Stefano and Montes-Sancho, 2024), and how SC visibility, integration, and analytics capabilities may contribute to decarbonization efforts (Balci and Ali, 2024). Importantly, research has further implied that uncertainty, arising from incomplete information and unreliable disclosures by SC partners, is a major barrier hindering the capacity to understand and reduce emissions in SCs (Talbot and Boiral, 2018; Dahlmann and Roehrich, 2019; Boiral et al., 2020; De Stefano and Montes-Sancho, 2024). Uncertainties can directly undermine effective carbon management across SCs, making it difficult for organizations to reliably disclose emissions (Akhavan and Zvezdov, 2021). The need to address uncertainties in SC planning is also prevalent due to broader systemic risks that may arise from insufficient information processing on SC emissions (Ghadge et al., 2020). For example, organizations relying on global outsourcing may risk the so-called carbon leakages (Huang et al., 2021), indicating that the actual progress of environmental performance falls short of the desired levels stated in the Paris Agreement (United Nations, 2015). Research also highlights the significant risk of non-disclosure or misrepresentation of GHG emissions in SCs, which can lead to companies being unaware of their contribution to climate change, exposing them to financial and reputational risks (Talbot and Boiral, 2018; Boiral et al., 2020; Blanco, 2021). These concerns highlight the urgent need to target information processing on Scope 3 emissions in SC planning to improve measurement and disclosure reliability in emission-intensive industries like steel (Vieira et al., 2024; Ellram and Tate, 2025).

To investigate how uncertainties affect efforts to measure and reduce emissions in SC planning, this study adopts the organizational information processing theory (OIPT) as its analytical lens (Galbraith, 1974; Tushman and Nadler, 1978). We draw from prior literature suggesting that information processing in SCs may be hindered by uncertainties related to environmental factors, organizational tasks, partner locations, the complexity of an organization’s supply network, technology integration, and partnership dynamics (Bensaou and Venkatraman, 1995; Premkumar et al., 2005; Stock and Tatikonda, 2008; Busse et al., 2017; Foerstl et al., 2018). The empirical study is set in the steel industry, a sector under increasing regulatory and market pressure to significantly reduce its carbon emissions due to its high environmental impact (Joint Research Centre, 2022; Cornwall, 2024). Given the growing regulatory and market pressure to reduce carbon emissions in this sector, there is an urgent need to consider emissions-related aspects in SC planning. Such planning should account for suppliers’ production processes and carbon footprints to support strategic emissions reduction (Sengupta et al., 2025). Environmental sustainability should also serve as a guiding principle throughout planning decisions, although further research is needed to understand how this integration can be effectively operationalized (Xu et al., 2021). This study addresses the research questions:

We employed a qualitative research design, using semi-structured interviews with 16 senior managers and experts from 12 European companies representing different positions in the steel SC, including raw material production, manufacturing, logistics, wholesale, and service providers. This study responds to ongoing calls in the SC literature for further research into the management of Scope 3 emissions (Vieira et al., 2024; Ellram and Tate, 2025) and its connection to SC planning (Sengupta et al., 2025). We contribute by explaining how 18 subdimensions across six sources of uncertainty hinder the measurement and reporting of GHG emissions in the steel SC. In response, we empirically identify information processing mechanisms that organizations can use to mitigate these uncertainties. By linking these mechanisms to key SC planning stages, we illustrate how emissions-related uncertainties can be proactively addressed through strategic planning.

The remainder of the study is organized as follows. Section 2 presents the theoretical background, Section 3 explains the methodology, Section 4 presents the empirical findings, Section 5 discusses these findings, and Section 6 highlights contributions and implications for research and practice.

OIPT posits that organizations operate most effectively when there is a balance between their information processing needs and capacities (Galbraith, 1974). Uncertainty—defined as the lack of information relevant to achieving specific organizational aims (Galbraith, 1974)—increases information processing needs because it necessitates the collection, processing, and sharing of more information for effective decision-making (Daft and Lengel, 1986). Uncertainty related to Scope 3 emissions may stem from factors such as inconsistent data formats from suppliers, limited transparency in emission sources, and the dynamic nature of SC relationships (Dahlmann and Roehrich, 2019; Vieira et al., 2024). In line with prior research on SC planning and information processing (Oliva and Watson, 2011; Srinivasan and Swink, 2015), these factors are expected to require organizations to adopt targeted information processing mechanisms that facilitate the collection, processing, and disclosure of emissions data, which ensure alignment with SC planning efforts to achieve emission reduction goals.

OIPT provides two primary strategies for optimizing planning and decision-making: reducing the need for information processing and increasing the capacity to process information (Galbraith, 1974). Reducing the need involves, for example, simplifying tasks and workflows, improving communication, and preplanning to limit uncertainty (Tushman and Nadler, 1978). In the context of GHG emissions, this could involve standardizing data collection methods or establishing clear reporting protocols with suppliers (Akhavan and Zvezdov, 2021; Lintukangas et al., 2023). Increasing capacity focuses on enhancing organizational mechanisms for collecting, analyzing, and distributing emissions-related information, such as implementing organizational structures, technological systems, and interorganizational coordination tools (Tushman and Nadler, 1978; Premkumar et al., 2005; Foerstl et al., 2018; Dahlmann and Roehrich, 2019).

Although much of the research and conceptualization on OIPT has focused on intraorganizational information processing, studies have increasingly extended the framework to interorganizational contexts (Premkumar et al., 2005; Fan et al., 2017). Recent research on applying OIPT to environmental sustainability in SCs explores the specific information processing needs for managing sustainability initiatives (Busse et al., 2017; Foerstl et al., 2018). However, studies on information processing related to SC emissions remain limited (Dahlmann and Roehrich, 2019; Akhavan and Zvezdov, 2021; De Stefano and Montes-Sancho, 2024).

Prior literature has offered various conceptualizations for the sources of uncertainty, including information processing needs arising from the organizational task structure, external market factors, and interactions with business partners (Daft and Lengel, 1986; Bensaou and Venkatraman, 1995; Premkumar et al., 2005). Drawing on prior research, we define six distinct sources of uncertainty for the theoretical framing of this study: (1) environmental uncertainty, (2) task uncertainty, (3) source uncertainty, (4) SC uncertainty, (5) technology uncertainty and (6) partnership uncertainty.

Bensaou and Venkatraman (1995) emphasize the capacity, complexity, and dynamism of interorganizational relationships as sources of environmental uncertainty. This uncertainty may be amplified by external disruptions, decision-making interdependencies, and unpredictable changes in the business environment (Premkumar et al., 2005). For example, unpredictability in customer or competitor behavior can increase environmental uncertainty (Wong et al., 2011). To allow a clear distinction between external factors and complexity-related uncertainties, such as product and supply-related issues (Duncan, 1972; Premkumar et al., 2005), we specifically define environmental uncertainty as the information processing needs arising from external conditions and macroeconomic forces. Organizations pursuing decarbonization efforts in their SCs are likely to experience heightened uncertainty related to external factors, such as unpredictable market demands or institutional pressures (Lo, 2014). These types of factors, in turn, may increase the information processing demands on organizations as they attempt to measure and disclose GHG emissions across their SCs. Similarly, prior research has highlighted how external business environments can shape SC sustainability practices (Busse et al., 2017), suggesting a parallel need to investigate how such external market factors may influence information processing on Scope 3 emissions.

Task uncertainty is the imbalance of required information for organizational tasks, influenced by their analyzability, variety, and interdependence (Galbraith, 1974; Bensaou and Venkatraman, 1995). Busse et al. (2017) highlight that the variety and complexity of products increase task uncertainty, which often drives information processing needs for environmental sustainability. Prior research indicates that low information processing capacity for carbon accounting and environmental disclosure hinders an organization’s ability to manage GHG emissions in the SC, which may be challenged by uncertainty emerging from task-specific information needs (Schaltegger and Csutora, 2012).

Source uncertainty refers to information processing needs arising from the number and diversity of suppliers within a SC network (Busse et al., 2017). It is influenced by the global nature of SC operations, where factors such as cultural differences, socioeconomic conditions, and geographic diversity complicate the sourcing of goods and services. Source uncertainty has been linked to information processing performance in sustainable SC management (Busse et al., 2017; Foerstl et al., 2018), and we expect that information processing needs related to GHG emissions may be connected to source uncertainty as well.

The structural characteristics of SCs, such as horizontal, vertical, and spatial complexity, contribute to what is referred to as SC uncertainty (Busse et al., 2017). Research has identified a connection between the complexity of SCs’ structural characteristics and transparency in environmental disclosure, as well as the capacity to manage GHG emissions (Gualandris et al., 2021; De Stefano and Montes-Sancho, 2024). Building on prior studies, we aim to investigate how such structural characteristics may increase information processing needs and complicate GHG emissions measurement and reporting.

Technology uncertainty refers to the increased information needs associated with the acquisition, implementation, and integration of technologies (Stock and Tatikonda, 2008). In this study, technological uncertainty represents the information needs that are expected to be related to internal or partner-specific technology investments, which are essential for managing indirect GHG emissions. This also extends to the management of technological tools and infrastructure aimed at enhancing information processing, particularly in relation to carbon accounting and environmental disclosure within SCs (Melville and Whisnant, 2014).

The information processing needs arising from exchanges related to GHG emissions in business relationships are affected by partnership uncertainty. Partnership uncertainty stems from the relational dynamics among business partners (Bensaou and Venkatraman, 1995) and is influenced by investments in firm- and partner-specific assets and the degree of trust between SC partners. Contractual risks and opportunistic behavior among partners contribute to partnership uncertainty (Premkumar et al., 2005), limiting information sharing and hindering efficient information processing. This issue is essential because organizations often struggle to manage sustainability expectations, especially beyond the first-tier suppliers (Wilhelm et al., 2016). Partnership uncertainty in GHG emission disclosure is also an important consideration due to its cost and legitimacy implications for the reporting organization (Chen et al., 2014; Peters and Romi, 2014).

Sustainable SC planning is defined as the integration of sustainability concerns into SC decision-making, aiming to improve social, economic, and environmental performance, including GHG emissions (Boukherroub et al., 2015). As illustrated in Figure 1, strategic SC planning tasks related to procurement, production, distribution, and sales influence the prerequisites for future SC development, covering a long-term time horizon (Stadtler, 2005; Fleischmann et al., 2015).

Demand planning is a fundamental task, particularly when carbon emission objectives are incorporated, because accurate demand forecasts enable firms to align production volumes, sourcing strategies, and logistics operations with emissions reduction targets, minimizing unnecessary transportation, inventory holding, and resource consumption (Fahimnia et al., 2015; Sengupta and Dreyer, 2023). Product program outlines the entire product range a firm intends to offer, illustrating dependencies between existing product lines and potential expansion into new sales regions (Fleischmann et al., 2015). Physical distribution structure is another key planning task with direct implications for emission reductions (Allaoui et al., 2019). Decisions regarding distribution structure are closely linked to plant location choices, as these influence logistical efficiency and carbon footprint (Stadtler, 2005; Xu et al., 2023). Materials program defines plans for raw materials and predefined components necessary for the offerings outlined in the product program. Material requirement planning calculates the production and order quantities for all items. Identifying materials with larger carbon footprints is a critical aspect of SC planning (Fahimnia et al., 2015), particularly in the steel industry, where different steel grades and production technologies may cause significant variation in a product’s carbon footprint (Ren et al., 2021). Supplier selection is also a central planning task, incorporating supplier evaluations based on quality, service, delivery, sustainability and procurement costs (Xu et al., 2023). The inclusion of GHG emissions criteria in supplier selection is increasingly recognized as an essential area of SC planning (Ellram and Tate, 2025). Finally, strategic cooperations with suppliers and their engagement in GHG emission reduction efforts are integral to SC planning (Ellram and Tate, 2025). Within procurement, additional planning tasks include personnel planning, detailed material requirements planning and planning of contracts (Fleischmann et al., 2015).

This study employs qualitative research methods to explore how uncertainties hinder the measurement of GHG emissions in the steel SC and to examine the organizational information processing mechanisms used to address these uncertainties in relation to SC planning. A qualitative approach was selected due to the limited research on uncertainty and OIPT in the context of GHG emissions. Our analysis was guided by uncertainty categories drawn from the literature, which structured the investigation of how these uncertainties influence information processing in emissions measurement and reporting. This design supports construct validity and offers deeper insight into a rapidly evolving domain (Grodal et al., 2021).

We conducted semi-structured interviews with 16 senior managers and experts from 12 European organizations across the steel SC. These organizations represented various roles, ranging from upstream to downstream positions (Table 1). Semi-structured interviews offer flexibility in exploring complex issues like GHG emissions measurement and reporting, while ensuring consistency across interviews (Creswell, 2013). This approach aimed to gather rich and context-specific insights into the challenges surrounding GHG emissions measurement and disclosure in the steel industry.

We used purposeful and criterion-based sampling (Patton, 2002) to select participants with relevant expertise, ensuring meaningful contributions to our research objectives and facilitating in-depth understanding of the phenomenon (Coyne, 1997). We began our investigation with Company D, a steel manufacturer actively involved in Scope 3 emissions measurement and reporting. Their prominent role in carbon management initiatives provided crucial insights into the steel industry’s challenges, forming the basis for expanding our sample.

To ensure comprehensive coverage, the sample included companies that either maintain a direct commercial relationship with Company D (companies A, G, H, I, B) or operate in comparable roles within the broader steel SC context. This sampling strategy allowed us to capture emissions-related uncertainties across a range of interconnected roles, including raw material production, downstream manufacturing, logistics, wholesale, and intermediary services such as sustainability consulting and analytics. Participants were selected based on their direct involvement in sourcing, processing, transporting, or using steel and steel-based products, as well as their active engagement in Scope 3 emissions reporting and management. This ensured that key actors with varied roles were purposefully sampled to generate rich insights (Guest et al., 2006).

Given the significant role of logistics in SC emissions (Ellram et al., 2022; Tate et al., 2023) we included logistics providers to capture the complexities of tracking and reporting distribution-related emissions. To validate GHG measurement and disclosure challenges, especially technological aspects, we included business analytics and environmental consulting firms for insights into sustainability data management across SC partners.

Interviews were conducted using video calls to facilitate participation from various locations and to maintain consistency in the interview process. All interviews were recorded and transcribed verbatim, ensuring the accuracy of the data and providing a robust foundation for in-depth qualitative coding and analysis. To enhance reliability and validity, at least two researchers attended each interview, enabling cross-validation of the data and minimizing interviewer bias. We further validated our findings through post-analysis correspondence with Company D, whose expert feedback corroborated key themes, refined interpretations, and confirmed practical relevance.

We adopted thematic content analysis as the qualitative coding method (Auerbach and Silverstein, 2003; Saldaña, 2013), which enabled us to systematically derive codes from the interview data. Our analysis process focused on understanding how various sources of uncertainty challenge the measurement and disclosure of Scope 3 emissions, as well as identifying the information processing mechanisms organizations can use to address these challenges.

The codebooks evolved iteratively through continuous discussion and refinement among the research team to ensure that the coding remained grounded in the data while remaining theoretically robust. To maintain internal consistency, all coding categories were repeatedly reviewed and refined to accurately represent the key ideas expressed by informants. Through a cross-case analysis approach, we identified constructs shared among the studied organizations, which allowed for a generalization of findings across the sample.

Following the methodology described by Gioia et al. (2013), our data structure consisted of first-order codes derived directly from primary data citations, second-order themes emerging through thematic categorization, and their subsequent linkage to the theoretical constructs underpinning our study (Grodal et al., 2021). The findings, illustrated in Table 2, present a comprehensive mapping of the sources of uncertainty and corresponding information processing mechanisms, and their connection to SC planning stages. The codebook, data structure, and selected primary data citations are illustrated in Appendices 1 and 2.

The informants highlighted market-driven sustainability trade-offs as a major source of environmental uncertainty, noting tensions between cost-efficiency and resource allocation for environmental disclosure and carbon management in the steel SC. For example, Company C noted that their goal to increase cost-efficiency by purchasing cheaper materials from developing countries contradicts their attempts to also reduce SC emissions. Such trade-offs may increase uncertainty due to organizations’ hesitation to make long-term commitments to decarbonization and invest in carbon accounting to better disclose emission-related information in the SC:

Companies expressed concerns about the complexity and technicalities of impending environmental disclosure regulations in the steel sector, perceiving them as vague and economically unfeasible. These concerns increased information processing needs due to ambiguity around compliance expectations. A recurring issue was that market developments are shaped by inadequate or unclear regulatory frameworks. Company D mentioned that European Union (EU) Corporate Sustainability Reporting Directive (CSRD) is challenging for them and for the steel industry. Some informants also criticized the leniency of upcoming regulations, such as the permission of average-based emission factors, which could undermine disclosure accuracy. New regulations on GHG emission reporting introduce challenges for companies in maintaining compliance across diverse markets, indicating difficulties in regulatory integration on a global scale. Together, these factors contribute to heightened environmental uncertainty:

To address environmental uncertainty, organizations may adopt targeted information processing mechanisms. First, analyzing market developments and preparing for a transition to more transparent and environmentally friendly products was identified as a critical approach. This includes systematically monitoring market trends, competitive dynamics, and changes in customer demand to support strategic decision-making and reduce uncertainty. For example, Company D observed that market demand for more sustainable steel proceeds slowly in their product lines. Second, many informants emphasized the importance of continuously monitoring and adapting to shifts in cross-regional regulation and policies on climate change. This allows organizations to adjust compliance strategies and reduce uncertainty regarding evolving expectations for carbon accounting:

The informants identified the lack of standardized methods for emission reporting between SC partners as a key source of task uncertainty. Although Company D found that the new CSRD regulations may support greater standardization in the future, the current absence of common carbon accounting protocols complicates the measurement and exchange of emissions data. Divergent methodologies hinder comparability and obstruct disclosure, as organizations carry out non-uniform tasks in estimating and reporting emissions across SCs. This increases information processing needs and amplifies uncertainty, especially given the interdependent nature of carbon accounting tasks:

All studied organizations reported having limited access to up-to-date, supplier-specific emission factors and often relied on average-based factors in their GHG estimations. Task uncertainty increases due to unavailable or low-validity data in the SC, undermining the effectiveness of carbon accounting and disclosure. Company D emphasized the importance of third-party validation, as well as the up-to-dateness of Environmental Product Declarations (EPDs), which are often lacking in the steel industry. Company D has found that many of its smaller suppliers lack the necessary data, creating challenges in reporting Scope 3 emissions across the SC. Lifecycle assessments of products were also problematic due to their reliance on specific production times and conditions. Outdated assessments may misrepresent current emissions and thus contribute to task uncertainty:

Task uncertainty was also increased by the lack of human resources and emissions reporting expertise. For many organizations, carbon accounting and GHG disclosure remain emerging issues, often limited by financial constraints and insufficient staffing. In some cases, responsibility was assigned to individuals lacking relevant expertise. Organizations also face challenges hiring qualified personnel for carbon accounting due to a scarcity of methodology experts, which may exacerbate task uncertainty:

Task uncertainty was increased by the variety, complexity, and modularity of products manufactured and sold by organizations and their extended SCs. Variations in raw materials, components, or other product characteristics increased information processing demands, by requiring continuous data updates to estimate emissions accurately. Diverse product portfolios also raised resource requirements for validating and disclosing emissions. Ongoing product development and redesign further complicated the availability of up-to-date lifecycle assessments, leaving companies with incomplete data on how different product specifications may affect the total carbon footprint:

The use of materiality thresholds, which are often applied in the estimation and disclosure of GHG emissions, may increase task uncertainty. Materiality thresholds are intended to limit the disclosure to the most significant sources of emissions, thereby reducing information processing demands when high-quality data are available. However, when there is greater uncertainty about the intensity of GHG emissions, for example, due to low-quality or outdated data, materiality ambiguity may emerge as an additional driver of uncertainty. In such cases, the absence of fully validated and regularly updated disclosures may result in estimation errors for both reporting organizations and their SC partners:

To mitigate task uncertainty, organizations may employ several information processing mechanisms. First, adopting industry-specific standards and joint methodologies for GHG emissions reporting was considered to reduce variability in reporting practices by aligning with widely accepted frameworks and collaborating with peers. Second, organizations often used internal and third-party assurance to validate data points used for GHG emissions reporting purposes to enhance data quality and compliance to stakeholder expectations. Third, simplifying product designs and reducing variety or using standardized components and production inputs was considered to streamline carbon accounting and decision-making processes, by reducing the complexity of product-level assessments and improving data consistency across product lines. Fourth, periodically assuring the materiality of production and use-phase related indirect emissions helped organizations focus on the most significant emission sources and allocate limited resources more efficiently. Finally, organizations may implement specified training programs and recruitment strategies to enhance personnel competency in GHG emissions reporting to address emerging skill gaps and building Scope 3 emissions-related expertise that is required by upcoming regulations:

Many informants identified knowledge deficits related to climate change issues in the SC as barriers to information processing. Some SC partners lacked a shared understanding of climate change and GHG emissions disclosure, especially regarding inclusion of Scopes 1, 2, and 3. Institutional and infrastructural differences led to uneven knowledge among SC partners, resulting in miscommunication, particularly with upstream suppliers, and complicating the implementation of carbon engagement practices. These issues indicate increasing source uncertainty:

A factor contributing to source uncertainty was the size and geographical spread of a company’s supply base. When the number of direct suppliers increases, emission disclosure becomes more complex. A larger number of suppliers requires more resources in terms of carbon engagement, and may introduce comparability issues between emissions data from culturally or institutionally distant suppliers, due to the lack of transparency and common measurement methodologies:

To mitigate source uncertainty, organizations considered two information processing mechanisms. First, consolidating the supply base or implementing GHG reporting criteria in the sourcing and procurement process helps address transparency and data consistency issues. By reducing the number of suppliers or incorporating specific reporting requirements into contracts and procurement policies, organizations could focus on engaging a manageable set of suppliers. For instance, Company C described initiatives to evaluate suppliers based on their environmental performance, gradually integrating Scope 3 emissions criteria into supplier negotiations. Second, educating SC partners about global climate change issues and environmental sustainability played a critical role in reducing disparities in knowledge among SC partners. Informants emphasized the importance of providing resources, training, and support to less experienced suppliers, enabling them to better understand climate-related responsibilities and reporting practices:

SC uncertainty emerged as a significant challenge, particularly due to many organizations’ strategic reliance on global sourcing, manufacturing, and logistics networks. In the steel industry, this is amplified by geographical distances, the multitude of actors involved, and the mutual reliance on shared emissions data, all of which created substantial information processing challenges. In large multinational organizations, the complexity of intraorganizational structures further complicated the process of measuring, reporting, and managing GHG emissions. For instance, collaboration between departments or across geographic regions can make it difficult to allocate emissions to specific business units, thereby increasing uncertainty:

SC uncertainty was further heightened by regional data asymmetries, arising from variations in the availability and reliability of information across geographic regions. These disparities in the measurement and reporting capabilities of global SC partners created significant information processing needs. In particular, these asymmetries were suggested to lead to inconsistencies in data reliability and comparability, complicating efforts to ensure accurate and transparent environmental disclosures across SCs:

To mitigate SC uncertainty, organizations considered two information processing mechanisms. First, reducing supply network complexity through sourcing decisions and simplifying SC operations was identified as a key approach. This involves strategically selecting suppliers and optimizing logistics routes to reduce operational complexity. For instance, some organizations had implemented real-time systems to optimize vehicle usage, minimize travel distances, and integrate renewable energy sources into logistics operations. Second, sourcing from regions that are institutionally committed to tangible emission reduction targets emerged as another important mechanism. By prioritizing suppliers from regions with strong environmental policies and commitments to climate goals, organizations sought to improve the reliability and consistency of emissions data. Several informants noted that stricter regulatory environments may enhance GHG reporting transparency and support compliance with sustainability goals. However, Company D, for example, has struggled to adopt this approach due to inconsistencies in reporting practices across the European steel industry:

Technology uncertainty was evident in the widespread use inefficient tools for carbon accounting in the SC. Many organizations lacked dedicated information technology (IT) systems for effective carbon data collection and processing, relying instead on simplistic, error-prone solutions. Informants highlighted issues such as technical limitations and poor scalability to business needs:

Another source of technology uncertainty was the lack of automation in data management processes. Companies reported increasing needs to integrate SC information flows across multiple systems and ensure data validity for emissions estimation. The absence of automation placed a heavy burden on already limited resources and increased the risk of errors due to a continued reliance on manual work:

Technology integration with existing tools and SC partners was a crucial driver of technology uncertainty, complicating the management of emissions data. Companies reported operating multiple systems that deliver varied data for carbon accounting, but integration challenges often arose due to customized and non-interoperable IT infrastructures. These issues restricted internal and interorganizational communication, increasing information processing needs. Informants linked integration problems to higher costs, reduced accuracy in emissions calculations, and limited information sharing across SC partners:

To address technology uncertainty, organizations may implement following information processing mechanisms. First, investing in collaborative IT systems or platforms to support GHG reporting in the SC was a frequently emphasized. Advanced digital platforms were considered essential for enabling shared access to emissions data, enhancing transparency, and streamlining reporting with SC partners. Some organizations also noted the value of systems that allow customers and partners to independently access and utilize emissions information. Second, increasing automation in manual data management tasks related to GHG reporting was identified as a key mechanism to enhance efficiency and reduce error risks related to emissions data. Automation was considered to replace manual processes, and enable faster data collection, calculation, and reporting. Third, organizations stressed the need to ensure compatibility and scalability of purchased carbon accounting tools. Selecting systems that could integrate seamlessly with existing IT infrastructure and support future scaling was seen as critical to overcoming integration challenges. Ensuring compatibility reduced costs and minimized problems associated with managing emissions data in non-interoperable systems:

Power asymmetry and lack of leverage in the SC contributed to increased partnership uncertainty, manifesting as environmentally proactive organizations’ inability to assert their interests through negotiations, governance, and collaboration with their SC partners. Such situations increase uncertainty, particularly when the SC partners have incongruent environmental goals. Additionally, power asymmetries were found to increase uncertainty due to the potential lack of commitment by the suppliers’ other key customers:

The fear of reputational damage and of being vilified for “greenwashing” by the organizations’ stakeholders was found to contribute to partnership uncertainty. This is because the disclosure of information about GHG emissions may be prevented by the organizations’ inclination to protect their image and reputation. This could lead to them providing more conservative estimations about GHG emissions. Moreover, regulatory pressures, such as the EU’s Green Claims Directive (European Commission, 2023), were cited as further disincentives for disclosing unverified or low-validity emissions data. One informant explained that certain suppliers declined to share emissions information due to fear of regulatory backlash:

There is a risk of opportunistic behavior of suppliers or third parties hindering the sharing of information about GHG emissions between SC partners, which increases partnership uncertainty. Such behavior may involve self-serving practices aimed at limiting transparency, reinforcing the need for external validation to support effective decarbonization decisions. In some cases, however, the provision of inaccurate emissions data may stem not from intent but from incompetence or a lack of awareness about the importance of reliable disclosure:

Companies independently developed carbon accounting methods based on financial data (e.g. purchased materials, expenditure and revenue) to meet regulatory and market expectations. This increased partnership uncertainty by discouraging SC partners from disclosing (1) complete GHG emissions data or (2) the methodologies used in their calculations. Sharing such information can expose sensitive business data and, in some cases, grant competitors a “free lunch” by revealing validated best practices in the estimation of GHG emissions. Thus, the disclosure of GHG emissions may be withheld if organizations believe that they may expose proprietary data or allow a financial advantage for competitors due to the sunk costs of developing GHG emissions disclosure methods:

To address partnership uncertainty, organizations utilized several information processing mechanisms. First, developing contractual and relational governance for monitoring purposes was highlighted as a critical mechanism. By defining governance through contracts or collaborative agreements, organizations can integrate emissions monitoring into regular supplier governance processes. Second, creating information sharing mechanisms helped improve emissions transparency across the SC by establishing formal communication channels and data-sharing procedures to overcome internal system limitations. These mechanisms enabled upstream suppliers and downstream customers to exchange product-specific emissions data that would otherwise remain inaccessible through internal systems alone. Finally, building and maintaining trust for long-term partnerships was seen as essential for promoting ongoing collaboration and shared commitment to sustainability goals. Transparent practices and active supplier engagement were noted to strengthen data reliability and mutual accountability. However, some informants—such as Company D—noted that in the steel sector, where price often dominates procurement decisions, the opportunity to pursue deeper relational governance may be limited:

This section reflects our findings in the light of manufacturers’ relationships to other actors in the SC: raw material producers, wholesalers, logistics and service providers.

The relationship between raw material producers and manufacturers is critical in shaping the uncertainties faced by manufacturers, such as Company D. Company A, a key steel supplier, provides verified Scope 3 emissions data, enabling Company D to rely on up-to-date, accurate reporting for its downstream customers. The ability to obtain verified data from suppliers represents a strategic advantage for Company D, as it enhances transparency and strengthens customer trust. However, due to Company A’s globally distributed sourcing, it cannot always provide comprehensive or up-to-date emissions data across all purchases. As a result, Company D still faces blind spots in its Scope 3 reporting, especially when relying on alternative suppliers with even lower data quality.

One of the key concerns raised by both Company A and Company D is the lack of standardized methods for emissions reporting between partners, making it difficult to ensure comparability and accuracy in emissions accounting across the SC. The steel industry’s complexity, characterized by specialized product groups and multiple variations in raw material composition, further compounds these challenges. The widespread use of average-based emission calculations fails to reflect product-specific characteristics, leading to further data inconsistencies. In response, Company D has invested in IT systems to enable more precise product-level emissions calculations.

Wholesale and trade companies (B, K) maintain relationships with manufacturers, including Company D, whose products are distributed by Company B. Company B noted that validated EPDs are often lacking in the steel industry, and customers are generally unwilling to pay extra for sustainable steel. As a wholesaler, Company B prioritizes diverse product offerings to meet demand and does not require emissions-related disclosures unless mandated by regulation. This reflects the sustainability-driven market trade-offs influencing what wholesalers are asking for manufacturers. Company D, by contrast, has EPDs for its products and has received increasing customer inquiries regarding product-specific emissions data. However, ensuring that EPDs remain updated for a wide range of products has proven challenging, as Company D depends on its suppliers to provide accurate and timely emissions data. Looking ahead, Company D expects a growing demand for EPDs but notes that the primary pressure comes from end customers rather than wholesalers. Meanwhile, wholesaler Company K, which has a vast supply base of over 20,000 suppliers, has attempted to encourage its suppliers to participate in voluntary reporting initiatives such as the Carbon Disclosure Project. Despite these efforts, Company K emphasized that the availability of validated emissions data from suppliers remains a persistent challenge from the wholesale sector’s perspective.

Logistics companies (G, H, I) face challenges in accurately allocating emissions due to diverse subcontractors, vehicle types, and multi-purpose deliveries. Shared transportation complicates calculations, requiring more data processing. EU regulations promote renewable fuels, but providers struggle to determine exact fuel types, leading to reliance on average values and inconsistent methods. Logistics provider G, which transports products for Company D, acknowledged that current emissions calculations are largely based on approximations rather than precise data. Company D recognized similar challenges, highlighting that the reliance on average emissions factors introduces uncertainty into their Scope 3 reporting. In response to these issues, Company G emphasized the need for digitalization and process optimization to improve logistics efficiency.

Company D identified Company J as a service provider that assists in calculating emissions for components and services not covered by supplier-issued material certificates. However, there is currently no formal contract between the two companies, instead Company D utilizes another service provider for that purpose. A broader challenge noted by Company J is that sustainability-related calculations—including Scope 1, 2, and 3 emissions—are often fragmented across multiple IT systems and spreadsheets, making it difficult for companies to obtain a comprehensive view of their emissions data. In line with this, Company D acknowledged that they currently rely on spreadsheet-based calculations but recognize the urgent need for automation and improved data management tools. Company J further emphasized that their ability to provide accurate emissions estimates is constrained by the unavailability or low quality of supplier data. According to Company J, the accuracy of emissions estimates is further constrained by the unavailability or low quality of supplier data—particularly when supplier-specific emission values or steel production data are inconsistent or not comparable across sources.

Organizations in the steel industry are under increasing pressure to improve the transparency and accuracy of GHG emissions reporting (Cornwall, 2024; Fagundes Alves et al., 2024; Hettler and Graf-Vlachy, 2024a, b). Our study shows that this process may be challenged by multiple sources of uncertainty. Drawing on OIPT, the findings demonstrate that information processing mechanisms can help organizations manage environmental, task, source, SC, technology, and partnership-related uncertainties. As illustrated in Table 2, we structure the discussion of the findings within the context of SC planning tasks covering the stages of procurement, production, distribution, and sales.

Procurement planning emerged as a critical stage for reconciling emissions-related uncertainties in the steel SC. While upstream stages of industrial SCs are known to carry the greatest emissions burden due to energy-intensive material production (Fagundes Alves et al., 2024; Vieira et al., 2024), our findings show that emissions-related uncertainties can materialize during procurement planning across multiple SC tiers. These data-related bottlenecks can propagate through the SC, undermining emissions reporting accuracy and the reliability of downstream planning activities. As shown in Table 2, procurement planning is exposed to multiple forms of uncertainty, which require the integration of specific information processing mechanisms.

Task uncertainty was found salient for procurement planning. Organizations highlighted challenges in supplier evaluation due to the lack of standardized emissions reporting frameworks and the prevalence of average-based emission factors, which undermine the comparability and credibility of carbon accounting and emissions disclosures. These deficiencies can create information processing bottlenecks that hinder firms’ ability to interpret and act on emissions data received from upstream partners. While prior research has highlighted similar task-related barriers to information processing in SCs (Busse et al., 2017; Foerstl et al., 2018), our findings extend this understanding by illustrating how these issues affect planning routines and increase information processing demands for tasks particularly in the procurement stage. Our findings further show that organizations could adopt several mechanisms to address these issues, such as using joint reporting standards to reduce methodological variation, applying internal and third-party assurance to validate supplier data, and investing in training and recruitment to strengthen emissions accounting capabilities. These mechanisms support procurement planning tasks such as materials program development, cooperations, and personnel planning, and can enhance organizations’ ability to manage emissions-related data more consistently from upstream SC partners (Ellram and Tate, 2025; Vieira et al., 2024).

Source uncertainty affects procurement planning in the steel SC, particularly due to large supply bases and uneven climate-related expertise across upstream actors. Many SC partners may lack sufficient understanding of environmental sustainability and climate change issues, which weakens their ability to generate or interpret emissions-related information. This misalignment creates information asymmetries between buyers and suppliers, complicating procurement planning tasks regarding supplier selection, contract development, and strategic cooperation. According to previous studies, source uncertainty increases the need for mechanisms that enhance the acquisition, transmission, and interpretation of supplier-related sustainability information (Busse et al., 2017; Foerstl et al., 2018). In our study, organizations responded to source-related uncertainty by consolidating their supply base, and embedding environmental sustainability criteria into supplier evaluations and contract terms, and by initiating educational practices directed at upstream partners to improve emissions literacy across the SC. These information processing mechanisms support procurement planning by strengthening supplier cooperation, improving emissions data reliability, reducing non-compliance and inaccuracies in Scope 3 emission disclosures, and ensuring that key contractual decisions are informed by credible sustainability performance indicators. Thus, our findings extend on earlier work considering information processing on source uncertainty and partner engagement for improving sustainability (Busse et al., 2017; Foerstl et al., 2018; Dahlmann and Roehrich, 2019).

Technology uncertainty presents a persistent constraint on procurement planning in the steel SC. Our findings reveal that organizations face underdeveloped digital infrastructures for carbon accounting, resulting in dispersed datasets, manual reporting, and delays in emissions verification. Compatibility issues between internal and supplier systems may further complicate the aggregation of emissions across organizations. Such technological challenges undermine the ability of procurement functions to access, validate, and compare emissions data when selecting or contracting suppliers. Under conditions of high technology uncertainty, organizations should increase their information processing capacity to ensure planning accuracy (Stock and Tatikonda, 2008). In line with this, we found that scalable, interoperable carbon accounting tools and collaborative IT platforms serve as critical information processing mechanisms. By supporting emissions data exchange and reducing manual input requirements, these technologies can strengthen compliance and improve procurement planning reliability, particularly related to supplier evaluation, contract development, and ongoing cooperation. These insights add to prior research on carbon accounting technologies (Melville, 2010; Schaltegger and Csutora, 2012), and further illustrate their purpose in a strategic planning context by highlighting the importance of integrating digital tools into procurement planning to enable better measurement and information sharing on emissions.

Partnership uncertainty introduces complexity to procurement planning in the steel SC, as relational dynamics shape the willingness of buyers and suppliers to engage in emissions-related collaboration. Our findings show how partnership uncertainty can inhibit information sharing and weaken supplier commitment to emissions disclosure. These issues were especially visible in interactions between manufacturers and wholesalers, where hesitancy to reveal proprietary emissions data or submit to external verification constrained cooperation efforts. Such uncertainty increases the need for governance structures that go beyond transactional mechanisms. As supported by prior research (Chen et al., 2014; Meena et al., 2023; Touboulic et al., 2014), we find that trust-building and formal contracting practices may play a central role in stabilizing information exchanges between SC partners. Early movers in the steel SC pursue long-term partnerships to enable more sustainable steel production and Scope 3 reporting (Hettler and Graf-Vlachy, 2024a, b). Specifically, organizations in the steel industry can mitigate partnership uncertainty through contractual and relational governance mechanisms that embed emissions reporting expectations into long-term agreements. These mechanisms can reinforce transparency norms, reduce the burden of repeated data verification, and facilitate the development of more stable and reciprocal information flows. These efforts support planning tasks related to supplier selection, cooperation, and contract development. In doing so, they contribute to more consistent and enforceable environmental sustainability communication across tiers in the steel SC (Akhavan and Zvezdov, 2021).

Production planning in the steel SC is exposed to task uncertainty, as organizations face difficulties aligning emissions reporting practices with the complex and heterogeneous nature of steel products. Our findings show that there is a commonly shared reliance on average-based emission factors, which may obscure important differences in the composition of raw materials and production processes. This can undermine the precision of Scope 3 disclosures and complicate emissions data comparability across SC partners. Task uncertainty is amplified in production planning when firms have diverse product lines or frequently adjust specifications in response to customer needs, which are prevalent conditions in steel manufacturing. These challenges affect planning tasks, including product program design, material requirements planning, and production system development. In response, organizations can improve information processing capacity by simplifying product architectures, standardizing input components, and harmonizing emissions measurement procedures across product categories. Our findings support prior research emphasizing the role of such design simplification strategies in mitigating data inconsistencies and improving cross-tier coordination in carbon reporting (Busse et al., 2017; Foerstl et al., 2018). We also highlight the critical role of materiality assessments in production planning, which help organizations place emissions disclosure efforts on high-impact sources and reduce informational overload. While often overlooked in SC contexts, our findings highlight the value of integrating materiality thresholds to increase the decision-usefulness of reported data (Dahlmann and Roehrich, 2019; Eggert and Hartmann, 2021). Embedding these mechanisms into production planning can enhance the reliability, relevance, and granularity of emissions reporting, thus enabling more accurate product-level disclosures.

Distribution planning in the steel SC is primarily shaped by SC uncertainty stemming from the global dispersion of production sites, complex logistics networks, and the diverse emissions data practices across regions. These structural conditions challenge SC participants’ ability to coordinate emissions measurement and reporting across upstream suppliers and third-party logistics providers. Also, the ability to allocate transportation emissions is constrained by regional variability in data availability and the limited standardization of logistics-related carbon metrics. As indicated in Table 2, these uncertainties impact several planning tasks, namely supplier selection, physical distribution planning, and plant location decisions. While distribution planning traditionally emphasizes cost-efficiency and lead-time optimization (Fleischmann et al., 2015), our findings show that emissions-related uncertainties could be accounted for as an additional distribution planning constraint. These insights extend on prior research, demonstrating how complex SC structures can limit visibility and increase variability in emissions reporting across organizations (De Stefano and Montes-Sancho, 2024). To mitigate these challenges, organizations can enhance information processing by reducing network complexity and aligning SC design with institutional environments that are proactive in carbon management. Our findings support the notion that designing SCs in regions with robust environmental policies and mature emissions disclosure frameworks can improve the comparability and credibility of carbon data (Dahlmann and Roehrich, 2019; Gualandris et al., 2021). This, in turn, can support more reliable emissions-based decisions on physical distribution structure, such as the location of distribution centers or allocation of transport modes, which is essential for planning low-carbon logistics strategies in the steel SC.

Finally, sales planning in the steel SC is influenced by environmental uncertainty, particularly in relation to varying climate policies and evolving customer expectations. Customers that have upstream Scope 3 targets are driving steelmakers to understand their own upstream emissions (Hettler and Graf-Vlachy, 2024a, b), and local regulation has impact on customers’ interest in Scope 3 emissions. Nevertheless, there are tensions between cost-efficiency and resource allocation for environmental disclosure and carbon management in the steel SC. Organizations must navigate differing regional regulations, voluntary reporting regimes, and sector-specific disclosure standards—all of which introduce ambiguity in emissions-related decision-making. These uncertainties directly affect demand planning and product program design, especially as customers increasingly seek low-emission steel products (Fagundes Alves et al., 2024). Our findings align with prior research indicating that inconsistent or evolving regulations can undermine emissions comparability and create planning blind spots (Hoang, 2023; Hettler and Graf-Vlachy, 2024a, b). This is particularly salient in international markets, where steel manufacturers and wholesalers must meet diverse information demands to avoid reputational and regulatory risks. In this context, sales planning can become critical for aligning product strategies with decarbonization efforts, subsequently affecting other planning stages (Fleischmann et al., 2015). To manage these challenges, organizations can implement information processing mechanisms that enhance their ability to monitor policy developments and anticipate market shifts. These mechanisms help align sales and product strategies with external sustainability pressures and enable more proactive adjustments in pricing, product configuration, and customer engagement in the steel SC (Dahlmann and Roehrich, 2019; Kolk and Pinkse, 2005; Allaoui et al., 2019).

This study offers contributions to the literature on carbon management in SCs and SC planning. First, it uncovers how different sources of uncertainty hinder the measurement and reporting of GHG emissions in the steel SC, thereby extending prior work on the management of SC emissions (Dahlmann and Roehrich, 2019; Dahlmann et al., 2023; Lintukangas et al., 2023; De Stefano and Montes-Sancho, 2024), a topic that has warranted increasing calls for further research (Hettler and Graf-Vlachy, 2024a, b; Vieira et al., 2024; Ellram and Tate, 2025). Our findings demonstrate that these uncertainties may be pervasive across the SC, not confined to specific firm types or tiers, which advances earlier research focusing on information processing, the role of uncertainties, and SC emissions (Akhavan and Zvezdov, 2021; Dahlmann et al., 2023; De Stefano and Montes-Sancho, 2024). By situating these findings within the steel industry, we also introduce novel, sector-specific concerns, such as regulatory fragmentation and supplier data quality gaps, that complicate efforts to measure and manage Scope 3 emissions (Ren et al., 2021; Cornwall, 2024; Hettler and Graf-Vlachy, 2024a, b; Zhao et al., 2024). In doing so, we also offer insights into how sectoral dynamics may influence the scalability and comparability of emissions accounting practices, providing a foundation for future studies aiming at contextualizing sustainability transitions in such emission-intensive industries. Second, we identify information processing mechanisms that organizations may employ to mitigate these uncertainties, thus operationalizing and extending the core ideas of OIPT in the emerging field of managing Scope 3 emissions (Galbraith, 1974; Busse et al., 2017; Foerstl et al., 2018). Third, our findings contribute to the SC planning literature by linking these mechanisms to strategic SC planning stages. This enriches existing research by showing how emissions-related uncertainties emerge within these planning stages, and how they could be proactively addressed through the integration of specific information processing mechanisms (Stadtler, 2005; Fleischmann et al., 2015; Allaoui et al., 2019).

This study highlights the importance of uncertainties and the value of embedding information processing mechanisms into SC planning stages to address the unique emissions-related challenges of the steel industry. Managers should move beyond viewing emissions disclosure as a reporting task and begin treating it as a planning issue that requires alignment across procurement, production, distribution, and sales. In procurement, decision-makers can prioritize the development of supplier selection and contracting processes that explicitly account for data availability, verification, and emissions transparency. Building collaborative relationships and shared digital platforms with upstream suppliers can help mitigate data gaps and improve consistency. Additionally, investing in internal capabilities, such as staff training, IT integration, and emissions analytics, can be crucial to process and interpret increasingly complex Scope 3 data flows. In production, managers could use materiality assessments and design simplification strategies to reduce data complexity and improve the traceability of emissions across products. In distribution, sourcing from regions with strong environmental policies can help ensure consistent emissions accounting and support more reliable SC design decisions. Sales and commercial teams should proactively monitor market signals to anticipate sustainability-driven shifts in customer demand, ensuring that product programs and pricing strategies are aligned with future expectations for decarbonization. For policymakers, our findings highlight the need to design decarbonization policies that acknowledge the fragmented and multi-tiered nature of emissions data in the steel sector. Regulatory efforts must incentivize data harmonization, reporting consistency, and supplier engagement across organizations. A stronger policy emphasis on digital reporting infrastructure, third-party validation, and emissions transparency could also help reduce greenwashing risks and carbon leakage.

This study’s data collection is limited to European companies operating within the steel SC. As the steel industry is characterized by high carbon intensity and a particular set of regulatory and market-driven pressures, some findings may reflect this specific context. Therefore, the generalizability of our results to other industries, regions, or SCs may be limited. Future research should extend these insights by investigating the challenges of GHG measurement and disclosure across different industry contexts and geographical regions, especially given the potential for regional differences in regulatory environments, market dynamics, and SC structures.

Our research design and methods may also limit the generalizability of the findings to other organizations, industries, and SCs. More qualitative and quantitative studies are required in this rapidly evolving research area. Researchers could explore strategies to ensure access to accurate and comprehensive data on GHG emissions at all stages of a SC, a task that becomes particularly challenging when engaging with multiple suppliers and business partners. Some of the difficulties identified in this study are subject to change if significant regulatory developments occur. Therefore, it is crucial to conduct further research to understand the potentially shifting demands on SCs, particularly in light of the enactment and refinement of cross-regional legislation.

Lastly, while our findings offer insights across the steel SC, further data collection could enhance understanding of challenges specific to SC tiers. Future research could aim for a more balanced representation of SC participants to uncover additional nuances that may not have been fully captured in this study.

We would like to thank the anonymous reviewers and the Guest Editors for their constructive feedback and guidance throughout the review process. Their insights greatly contributed to improving the quality and clarity of this research.

The supplementary material for this article can be found online.