Unpacking the Renewable Pull Effect: Conditions for Green Industrial Relocation
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Colen, Sven; Mohnen, Alwine Article — Published Version Unpacking the Renewable Pull Effect: Conditions for Green Industrial Relocation Business Strategy and the Environment Provided in Cooperation with: John Wiley & Sons Suggested Citation: Colen, Sven; Mohnen, Alwine (2025) : Unpacking the Renewable Pull Effect: Conditions for Green Industrial Relocation, Business Strategy and the Environment, ISSN 1099-0836, Wiley, Hoboken, NJ, Vol. 34, Iss. 6, pp. 7767-7790, https://doi.org/10.1002/bse.4301 This Version is available at: https://hdl.handle.net/10419/330189 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
Business Strategy and the Environment, 2025; 34:7767–7790 https://doi.org/10.1002/bse.4301 7767 Business Strategy and the Environment RESEARCH ARTICLE OPEN ACCESS Unpacking the Renewable Pull Effect: Conditions for Green Industrial Relocation SvenColen | AlwineMohnen School of Management, Technical University Munich, Munich,Germany Correspondence: Sven Colen ([email protected]) Received: 13 November 2024 | Revised: 19 March 2025 | Accepted: 7 April 2025 Keywords: analytical hierarchy process| directreduced iron| energyintensive industries| green ammonia| green relocation| industrial relocation| renewable pull effect| sustainable transition ABSTRACT The renewable pull effect theoretically leads to the relocation of green production facilities within energyintensive industries to regions rich in renewable energy resources. This study employs a qualitative research design, integrating the analytic hierarchy process method, by interviewing top managers and experts from globally leading steel and chemical corporations, predominantly based in Europe and Germany. The findings reveal that whereas factors associated with the renewable pull effect are most influential in green production location decisions, three critical conditions must be met for this effect to translate into actual green relocations: weaker agglomeration and demand pull effect, limited counteracting subsidies from regulatory bodies, and fulfillment of essential sociopolitical and environmental prerequisites. This study demonstrates that the renewable pull effect only conditionally drives green relocations, providing the first comprehensive evaluation of the key factors shaping green relocations within the ongoing academic debate. Moreover, by providing empirical insights from leading industry practitioners, the study advances the existing literature, which has been predominantly based on theoretical models and technoeconomic analyses. Additionally, the study suggests several foremost avenues for future research. Finally, it delivers practical implications for energyintensive industries' top management and policymakers in both renewableenergyrich and scarce regions, informing strategic decisionmaking for sustainable transition. 1 | Introduction The transition to a lowcarbon economy has become a global imperative, with the industrial sector—responsible for approximately 34%1 of global greenhouse gas (GHG) emissions (IPCC 2023)—facing mounting pressure to decarbonize. International policy frameworks such as the Paris Agreement, along with regional initiatives like the Inflation Reduction Act in the United States (The White House 2023) and the Green Deal Industrial Plan in the European Union (European Commission2023), are forcing a sustainable transition by indorsing renewable energy adoption (Kabongo2019). For energyintensive industries (EIIs) such as steel and chemicals, this sustainable transition presents a complex challenge (Egerer etal.2024). It requires the adoption of electrified production processes and the substitution of fossil fuels with lowto zeroGHG energy carriers, including green hydrogen (GH2; Eicke and De Blasio2022; Hermundsdottir etal.2024; Rissman et al. 2020). However, the increasing demand for renewable electricity and GH2—particularly in highly industrialized countries—combined with limited local renewable energy resources (RER) and high transportation costs of renewable energy and GH2, substantially raises production costs (Day2022). These dynamics may create cost disadvantages for regions like Europe, NorthEast Asia, and parts of the United States (Neuwirth etal.2022). Consequently, Samadi etal.(2023) introduce the “renewable pull effect” concept, proposing that countries with abundant RER could become attractive for the relocation of green production2 facilities of EIIs from RERpoor countries. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd.
7768 Business Strategy and the Environment, 2025 Recently, the academic literature debates whether the largely undisputed costsaving potential of the renewable pull effect actually translates into the relocation of green production facilities—termed “green relocation” by Verpoort et al. (2024). Scholars argue that other location factors, such as labor market dynamics, supply chain infrastructure and regulatory environments, could outweigh the energy cost advantages and deter EIIs from relocating (Egerer etal.2024; Samadi etal.2023; Verpoort etal.2024). Accordingly, almost all studies related to the renewable pull effect call for further research, as “a better understanding of the future relevance of the renewables pull effect requires first and foremost an analysis of the importance of energy costs in comparison to other location factors” (Samadi etal.2023, 10). This study, therefore, aims to identify and empirically evaluate these other location factors by addressing the following research question: Does the renewable pull effect lead to green relocation of energyintensive industries? Given the prospective and dynamic nature of green production location decisions, this study employs an explorative research approach by conducting 41 semistructured interviews with top managers and experts with direct influence on green production location decisions of global leading steel and chemical corporations, headquartered primarily in Europe and mostly in Germany. The study concentrates on directreduced iron (DRI) and green ammonia, as both products are highly likely to be affected by the renewable pull effect (Samadi etal.2023). The analytical hierarchy process (AHP), a wellestablished methodology for multiplecriteria decision analysis (Saaty1987), is applied to assess the relative importance of location factors, derived from literature. Additionally, thematic analysis based on Braun and Clarke(2006) and Mayring's(2016) qualitative content analysis is applied to interpret the interview statements under which circumstances the renewable pull effect could lead to green relocation. This study offers three central contributions to the ongoing academic discourse. Firstly, it identifies and categorizes additional location factors that influence green relocation decisions beyond renewable energy access and cost savings. Although previous research primarily present selfconceptualized factors (Verpoort etal.2024), this study bridges the gap by deriving relevant factors through existing location decision literature, offering a broader and more grounded perspective. Secondly, by leveraging the AHP, the study not only identifies but also evaluates the importance of all factors, providing a structured assessment of the relative importance of the renewable pull effect compared to other location determinants. This addresses a crucial missing element in the ongoing debate, enhancing the understanding of the dynamics that drive or hinder green relocation (Samadi etal.2023). Thirdly, the empirical approach taken by this study adds considerable value. Unlike prior research that predominantly relies on theoretical models and technoeconomic analyses (Egerer etal.2024; Samadi etal.2023; Verpoort etal.2024), this study is the first to empirically investigate the renewable pull effect. Furthermore, the rare opportunity to incorporate the perspectives of decisionmakers at the highest corporate levels, adds a crucial layer of expertise to the debate. The empirical insights contribute to refining and substantiating existing studies with a more tangible basis for decisionmaking. Beyond its theoretical contributions, the study delivers practical insights for both corporate leaders and policymakers. The findings equip EII decisionmakers with a framework to evaluate production locations by balancing energy costs with strategic concerns. For policymakers in RERpoor regions, the study provides insights to assess their attractiveness for EIIs and suggests strategies to mitigate the risk of emigration. Conversely, regulators in RERrich regions can use the outcomes to attract green investments from EIIs and capitalize on their competitive advantage. The remainder of the paper is structured as follows: Section2 reviews the location decision theory including the renewable pull effect and identifies location decision factors for green production of EIIs. Section3 describes the applied qualitative methodology. Section4 presents the study's results. Section5 answers the research question and discusses theoretical contributions, practical implications, limitations, and future research demands. Section6 concludes. 2 | Literature Review: Location Decision Factors for Green Production Facilities of EIIs The decision of production facility location has long been a focal topic in academic research (Tsai and Urmetzer2024), evolving substantially alongside shifts in economic paradigms and, more recently, the growing emphasis on green production practices (Day2022). This section synthesizes the current state of academic literature on location decision factors, tracing their evolution and highlighting how traditional factors are increasingly integrated with environmental considerations. Finally, we establish a comprehensive set of location factors pertinent to the green production decisions of EIIs. 2.1 | The Evolution of Location Decision Factors Towards Sustainable DecisionMaking Theoretical foundations for industrial location decisions date back to early models by von Thünen(1826) and Alonso(1964), which prioritize land rent and transportation costs in agricultural and industrial contexts. Later, Weber(1909) introduces a model balancing transportation, labor, and agglomeration effects (Murray etal.2020). These traditional factors are further developed in Krugman's(1991) New Economic Geography, which posits that firms select locations based on labor costs, market size, transportation expenses, and agglomeration economies. Historically, high transportation costs led industries to cluster around natural locational advantages, such as waterways, ports, and resourcerich areas (Day2022). However, advancements in logistics and technology have diminished transportation costs, contributing to the “death of distance” perception by Glaeser and Kohlhase (2004). This shift has enabled production to relocate from resourcedependent regions to areas offering benefits like low labor costs or strong agglomeration advantages, ultimately facilitating the creation of global value chains (Mudambi2018). The “pollution haven hypothesis” (Levinson and Taylor2008) highlights how firms in global value chains often prioritize
7769 costeffective locations, however, frequently at the expense of environmental considerations. Later research by PerezBenitez etal.(2021) indicate that although traditional factors, including agglomeration and labor costs, remain influential, emerging criteria such as technology access, political stability, gender equality, climate conditions, and renewable energy supply are increasingly relevant. Nevertheless, environmental considerations are still viewed as secondary, signaling a need for deeper integration of sustainability in location decision models. However, growing regulatory pressures drive industries towards sustainable practices, necessitating a broader interpretation of industrial location theory to incorporate environmental imperatives (GonzálezBenito and GonzálezBenito 2010). As traditional models become less adequate for explaining green production location decisions, the stakeholder approach is gaining traction, advocating for more inclusive decisionmaking processes. Renn et al. (1997) emphasize the importance of discursive processes among diverse stakeholders, whereas Bjartmarz and Bocken (2024) and Marcon Nora et al. (2023) argue for stakeholder and actornetwork theories to better capture the dynamics of sustainability transitions in the energy sector. Furthermore, the concept of industrial symbiosis also gains prominence as an extension of agglomeration benefits for green production. Studies by Parto(2000), Doménech and Davies(2011), and Desrochers(2001) highlight the advantages of collaborative frameworks where companies exchange materials, energy, and knowledge. Vahidzadeh etal.(2021) further underscore the need to scale up such practices to maximize regional environmental benefits. Future location decisions should place greater emphasis on sustainable decisionmaking perspectives (Zagonari 2024). Therefore, in addition to traditional location factors, a broader range of considerations should be incorporated into the decisionmaking process of green production location decisions. 2.2 | Renewable Pull Effect Evolving as Location Factor for Green Production Facilities of EIIs For green production location decisions, transportation costs of renewable energy are reemerging as a critical factor (Day 2022). Historically, declining transportation costs of fossil fuels reduced industries' dependence on proximity to natural resources. However, this trend may not hold for renewable energy sources. Saadi etal.(2018) demonstrate that electricity transmission costs can be up to 19 times higher and hydrogen transportation costs up to 17 times higher than those of fossil fuel pipelines. Day(2022) posits that, given the significantly higher transportation costs associated with renewable energy—particularly electricity and hydrogen—compared to fossil fuels, energy costs will gain substantial importance in future industrial location decisions. This discrepancy creates a competitive advantage for regions rich in RER. A renewable pull effect represented by highly relevant renewable energy access and cost factors may occur. Countries abundant in RER may attract EIIs to establish new or relocate existing green production facilities. Unlike carbon leakage, which is often driven by the desire to evade stringent climate regulations, such green relocations are keeping environmental production standards by seeking to enhance costefficiency (Samadi etal.2023). However, the likelihood of the renewable pull effect leading to green relocations varies across industries. Samadi etal.(2023) identify specific characteristics that render certain industries, notably steel with DRI and chemistry with green ammonia, more susceptible to this effect (Armijo and Philibert 2020; Fasihi et al. 2021; Gielen et al. 2020). Traditional primary crude steel production involves processing iron ore in blast furnaces using coal coke, followed by refinement in an oxygen converter. Transitioning to green steel production requires extensive investments in new facilities, replacing blast furnaces with direct reduction processes powered by GH2 (Samadi etal. 2023).Similarly, whereas the Haber–Bosch process for ammonia synthesis remains constant, green ammonia production diverges primarily in the hydrogen production method (Egerer et al. 2024). Existing ammonia plants, optimized for steam methane reforming, must be reconfigured to utilize GH2 as a feedstock, necessitating a redesign to integrate external steam and nitrogen supplies. Samadi etal.(2023) suggest that for both DRI and green ammonia, existing conventional facilities offer limited utility for green production transitions. By 2035, energy costs—including transportation—could constitute up to 50% of total production costs for DRI and as much as 90% for green ammonia. Therefore, mentioned characteristics for DRI and green ammonia production contribute to an increased likelihood of the renewable pull effect. Moreover, Egerer etal.(2024) calculate potential cost savings of a relocation for green steel and chemicals. The production of three energyintensive products located in a relatively RERpoor country, in this case in Germany, are individually juxtaposed in three scenarios: The relocation to a RERexcellent site of the entire production, only primary production including GH2 production or only the GH2 production. The maximal cost savings are in steel 19%, in urea 30%, and in ethylene 25% by relocating the entire production to a RERexcellent site. Verpoort etal.(2024) pursue a similar approach and calculate potential cost savings of 18% in steel, 32% in urea, and 38% in ethylene. Thus, the renewable pull effect potentially substantially influences the location decisions of EIIs, particularly within green steel and chemicals production sectors. However, the academic discourse continues to debate whether the costsaving potential of the renewable pull effect opposing other factors will manifest in actual relocations of green production facilities (Egerer etal.2024; Samadi etal.2023). The renewable pull effect is a relatively novel concept with limited exploration and existing research predominantly focuses on such outlaid technoeconomic analyses and investigations into this debate remain sparse. Only Verpoort etal.(2024) propose a broader conceptual framework for green relocations by introducing soft factors, categorized by perspectives from private investors, policymakers, and society. However, as these factors are conceptual and not derived empirically or from the literature, it remains uncertain whether they encompass all relevant aspects. Nevertheless, they form a valid and highly
7770 Business Strategy and the Environment, 2025 valuable input for the objective of this study to derive a comprehensive list of pertinent factors in the subsequent section. Moreover, a deeper investigation on how these soft factors impact a green relocation decision is not pursued. Therefore, only very limited existing research discusses such soft or other factors, despite nearly all researchers in the field acknowledging their importance. 2.3 | Development of Comprehensive List of Factors for Green Production Location Decision of EIIs The literature declares a wide array of valid factors potentially influencing green production location decisions of EIIs. Table1 encapsulates the factors including the underlying primary references.3 From a financial perspective, several production costs play a critical role, including capital expenditures (CAPEX) and operational expenditures (OPEX), which encompass labor, raw materials, energy, and outbound transportation costs. Notably, inbound transportation costs for raw materials and energy— particularly the rising costs of transporting renewable energy— are integrated within the broader categories of raw material and energy costs. Given that the renewable pull effect influences only green production facilities, this analysis considers solely renewable energy sources, such as green electricity and GH2. Additional key location factors identified in the literature include carbon costs, representing the external costs of GHG emissions, available subsidies, access to capital markets for financing investments, and the implications of local customs and general taxation policies. The availability and accessibility of assets, as well as supply and demand dynamics, are also pertinent to location decisions. Assets in this context refer to foundational prerequisites, including the facility under construction, the labor force, and critical infrastructure and logistics. New green facilities can benefit from agglomeration economies and integrated production systems, leveraging synergies in production or energy networks, the specialized technical expertise of the workforce, and shared support and maintenance functions. From a supply perspective, factors such as the geographical proximity to raw material and energy suppliers, as well as the reliable and qualified provision of these essential inputs, are substantial. Considering the current scarcity of high volumes of green energy, access to transitional bridging energy sources, such as natural gas, becomes an important consideration. From a demand perspective, the literature underscores the importance of flexible delivery capabilities, proximity to customers, and the size of the local market for final products. However, if the green production facility generates only primary products that require further processing into final products, the location decision may depend more on proximity to subsequent or potential secondary4 production stages within the value chain. Beyond financial and value chain considerations, political factors emerge as critical in the scholarly discourse. A stable legal and political environment, coupled with a generally industryfriendly political stance, plays a pivotal role in the decisionmaking process. This includes the assurance of reliable property rights, transparent general regulations, and the ability of a country to support products with globally recognized quality certifications. As regional perceptions and definitions of green production vary—illustrated by the ongoing debate surrounding nuclear energy—the strictness and implementation of climate action regulations and provisions also influence location choices. Rounding out the compilation of location factors from the literature, social and environmental considerations weigh into the decision process. Certain locations may present pollution risks, environmental constraints, or a heightened potential for impacting local communities. Additionally, the quality of life, along with health and safety standards for employees, can vary extensively between potential locations. 3 | Methodology: Interview Study With Top Managers and Experts of EIIs To address the research question, this study employs an empirical qualitative research design to gain indepth insights into potential strategic management decisions of EIIs. A qualitative approach is particularly suitable due to the forwardlooking, novel, and complex nature of the topic, as well as the need for a nuanced understanding of top managers' and experts' perceptions, given that green relocation decisions are heavily influenced by strategic visions and anticipated opportunities and challenges. Moreover, at present, green relocations largely remain hypothetical future scenarios, and only very limited data on actual green relocations exist, reinforcing the appropriateness of a qualitative research design (Robinson2014). The study's research design, depicted in Figure 1, follows a threestep sequential approach. The first step involves the development of a semistructured interview guideline and sampling of interviewees as proposed by Robinson(2014), explained in Section3.1. The second step consists of conducting 41 indepth interviews with top managers and experts who could directly influence green production location decisions at leading global steel and chemical corporations, predominantly headquartered in Europe and Germany. Given that location decisions are inherently multicriteria decision problems, the study incorporates an AHP to assess the importance of renewable pull effect factors, thereby enhancing the study's analytical depth. Through AHP, the interviewees rank factors via pairwise comparisons (Kangas and Kangas2002), yielding a weighted score for each factor that reflects its relative importance (Yang and Lee1997). As AHP is a highly detailed and timeconsuming method— particularly when evaluating numerous factors—its application during interviews with highprofile managers, who have naturally limited availability, is constrained. Consequently, we gain the opportunity to conduct AHP assessments in 20 out of 41 interviews. This methodical enhancement of the qualitative study for almost half the interviews allows for an increased understanding of the relevance of the renewable pull effect factors, offering a profounder analysis of strategic
7771 TABLE 1 | Overview of the identified location decision factors for green production of EIIs. Strategic consideration Factor References Production costs CAPEX Alberto2000; Greenhut1956; Hoover1948; Verpoort etal.2024 Labor costs Gothwal and Saha2015; Schmenner1982; Singh etal.2018; Weber1909 Raw material costs Day2022; Gothwal and Saha2015; Schmenner1982; Singh etal.2018 Energy costs (incl. GH2)Day2022; Gielen etal.2020; Gothwal and Saha2015; Samadi etal.2023; Thekkethil etal.2024; Verpoort etal.2024; Zhu etal.2014 Outbound transportation costs Beckmann1986; Samadi etal.2023; Verpoort etal.2024; Weber1909 Regulation and financing costs Carbon costs Babiker2005; Samadi etal.2023; Timilsina2022; Wu etal.2017 Access to subsidies Atthirawong and MacCarthy2014; Verpoort etal.2024 Access to capital market (incl. WACC) Ambos etal.2021; Atthirawong and MacCarthy2014; Gothwal and Saha2015 Local taxation Alberto2000; Atthirawong and MacCarthy2014 Customs Alberto2000; Atthirawong and MacCarthy2014 Access to assets Access to green production facility Gold1991; Gothwal and Saha2015; Walters and Wheeler1984 Access to labor forces Alberto2000; Gothwal and Saha2015; Sharma2004; Zhu etal.2014 Access to infrastructure and logistics Beckmann1986; McMillan1965; Thekkethil etal.2024; Verpoort etal.2024 Existing assets at location Samadi etal.2023; Zhu etal.2014 Agglomeration and integration benefits Glaeser and Kohlhase2004; Schneider2022; Smith1966, 1981; Weber1909 Access to supply Proximity to raw material suppliers Day2022; Gothwal and Saha2015; Schmenner1982; Wheeler and Mody1992 Reliable raw material supply Gielen etal.2020; Greenhut1956; McMillan1965; Verpoort etal.2024 Proximity to energy supplier (incl. GH2) Alberto2000; Thekkethil etal.2024; Verpoort etal.2024; Zhu etal.2014 Reliable energy supply (incl. GH2) Gielen etal.2020; Samadi etal.2023; Thekkethil etal.2024; Verpoort etal.2024 Access to bridging energy Bessi etal.2021; Thekkethil etal.2024; Verdolini etal.2018 Access to demand Proximity to next production step Thekkethil etal.2024; Verpoort etal.2024 Proximity to secondary production steps Gothwal and Saha2015; Samadi etal.2023; Sharma2004 Proximity to customers of final products Gothwal and Saha2015; Moriarty1980; Schmenner1982; Verpoort etal.2024 Flexible delivery of final products Atthirawong and MacCarthy2014; Singh etal.2018 Size of local market of final products Gothwal and Saha2015; Moriarty1980; Schmenner1982 (Continues)
7772 Business Strategy and the Environment, 2025 priorities within EIIs' top management (ZambujalOliveira etal.2025). The third step involves a comprehensive analysis of all 41 interviews using qualitative content analysis techniques based on Braun and Clarke(2006) and Mayring (2016), depicted in Section3.2. This method facilitates a detailed examination of the data, systematically categorizing information into thematic groups. Additionally, we analyze the 20 AHP assessments following the classical methodology approach proposed by Saaty(1987) using these findings to enrich and validate the qualitative thematic analysis, described in Section3.3. 3.1 | Interview Sampling This step includes preparing a guideline for the semistructured interview, as outlined in Appendix A, and applying a fourstep sampling technique based on Robinson(2014). Firstly, we define a sample universe by establishing criteria for selecting interviewees, summarized in Table2. Given the highly strategic nature of the decisions under investigation, which are typically made at the highest management levels, we focus on top management profiles and advisors with direct influence on such decisions. All interviewees have over 10 years of experience in the steel or chemical industries and specialize in decarbonizing production processes. As DRI and ammonia are highvolume commodities and the renewable pull effect could drive further internationalization, we select individuals from large and global active corporations. Although these corporations operate worldwide, we prioritize those embedded in a regulatory environment that emphasizes decarbonization and green production transitions to ensure the relevance of the renewable pull effect. Europe, particularly Germany, serves as an ideal representative region due to its ambitious climate targets of achieving GHG neutrality by 2045 (German Government2021), high industrialization, and relatively low RER (Day2022). Consequently, our expert sampling concentrates on corporations headquartered in Europe, primarily in Germany. Secondly, recognizing the limited availability of highprofile interviewees, we set a target sample size of 30 interviews, split evenly between AHP and nonAHP, which we ultimately exceed. Thirdly, we adopt a purposive and convenience sampling Strategic consideration Factor References Political environment Stable legal and political system Alberto2000; Atthirawong and MacCarthy2014 Industrial friendly political attitude Atthirawong and MacCarthy2014; Day2022 Low climate action regulations Alberto2000; Babiker2005; Huang etal.2021 Reliable production certification Verpoort etal.2024 Environmental and social risks Pollution and environmental constraints Coughlin etal.1990; Moriarty1980; Singh etal.2018; Zhu etal.2014 Affected communities' constraints Gothwal and Saha2015; Moriarty1980 Quality of life for employees Alberto2000; Coughlin etal.1990; Gothwal and Saha2015; Hudson1983, 1988 Occupational health and safety standards Amrina and Vilsi2015; Singh etal.2018 TABLE 1 | (Continued) FIGURE 1 | Overview of the study's research design.
7773 strategy due to the specific selection criteria and the scarcity of suitable experts (Robinson2014). Finally, sample sourcing is primarily conducted via professional networking platforms such as LinkedIn and the authors' professional contacts. 3.2 | Qualitative Thematical Analysis As listed in Table3, the study conducts 41 interviews, 20 in chemistry and 21 in steel, from August 2024 to January 2025, resulting in 1834 min and 542 pages of transcribed interview data. We employ a qualitative thematic analysis by Braun and Clarke(2006), based on Mayring's(2016) qualitative content analysis approach. We perform two iterative rounds of structuring reduction processes of the interview data to derive meaningful insights. Our main objective is to systematically identify and categorize the key considerations influencing green production location decisions. Initially, we inductively code all statements into 10 categories using paraphrasing and generalization techniques, ensuring each code is formulated in a nominal style for clarity and consistency. We then distill the 10 categories into three overarching themes. The themes represent the main conditions of green relocation under the renewable pull effect, as posed in Table5 in Section 4. 3.3 | AHP As explained above, we conduct a multiplecriteria decision analysis using Saaty's(1987) AHP to boost the assessment of the relevance of the renewable pull effect by counteracting on potential inaccuracies inherent in qualitative research. The AHP method is widely used for industrial location decisions in peerreviewed research and is recognized as an effective tool for multiplecriteria decision analysis (Boardman Liu et al. 2008; Gothwal and Saha 2015; Ho and Ma 2018; Krstić etal.2024; Russo and Camanho2015; Vogl etal.2018; ZambujalOliveira et al. 2025). It is particularly suitable for handling numerous factors, integrating both objective and subjective judgments, and systematically prioritizing complex decisionmaking scenarios (Partovi 1994; Saad 2001; Singh etal.2007; Vaidya and Kumar2006). Previous studies also validate the AHP for sustainabilityrelated decisionmaking (Brogi and Menichini2024; Dos Santos etal.2019; Ocampo2019), and Pereira and Bamel(2023) specifically recommend AHP for research on sustainable manufacturing. We apply the AHP5 in three steps: model development, data assessment, and data synthesis. Firstly, we develop an AHP model, setting the study's objective and establishing strategic considerations (SCs) with associated location factors, as shown in Figure2. We conceptualize the hierarchical decision model by reviewing relevant literature and logically grouping factors under SCs, adhering to Saaty's(2001) guidelines for AHP hierarchy construction. Through various prediscussions with industry experts, we refine the model to include 7 SCs and 33 location factors, as detailed in Section2.3 and Table1. Factors related to renewable energy access or costs, including GH2, represent the renewable pull effect. Secondly, we conduct AHP assessments with 20 interviewees6 (10 in chemistry and 10 in steel), as listed in Table3. During the interviews, we ask the interviewees on how they would evaluate the relative importance of location factors if they must recommend the location of a new green production facility,7 as outlined in the interview guide in Appendix A. Thereby, using Saaty's(1987) classical AHP scale (Table 4), participants perform pairwise comparisons of the SCs and factors. Their assessments generate pairwise comparison matrices, with inverse fractional values for corresponding cells. For n factor Fi, the matrix A with aij as relative importance of factor i over factor j, is as follows: Thirdly, the AHP data synthesis is conducted, using “AHPOS” software (Goepel 2018). We normalize the comparison matrices and calculate the relevance rates for each SC and factor in the AHP model. Normalizing each element aij defines a normalized matrix: An approximation of the eigenvector can then be used to calculate the weight of each SC and factor. We categorize the relevance rates into local weight (LW), which indicate priority within the preceding hierarchical level, and global weight (GW), (1) A = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ 1a12 ⋯a1n 1 a12 1…a2n ⋮ ⋮ ⋱⋮ 1 a1n 1 a2n ⋯1 ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ (2) a norm ij = a ij ∑ n i=1aij TABLE 2 | Overview of sampling selection criteria. Criteria Requirements Decision proximity With direct or indirect (involved in decision preparation) influence on green relocation decisions Industrial expertise Min. 10 years of working experience in steel or chemical industry Functional expertise Deep knowledge about green production processes of green steel or green chemicals Representing corporate Works for globally active corporate in steel or chemical industry Regulatory embedding Works for a corporate embedded in a regulatory environment, which prioritizes decarbonization efforts
7774 Business Strategy and the Environment, 2025 TABLE 3 | Overview of the conducted 41 semistructured interviews. #Industry AHP Corporation Interviewee profile Duration [in min] 1Chemistry Yes DAXlisted basic chemical producer President, Net Zero Accelerator 48 2Chemistry Yes DAXlisted basic chemical producer Head of Global Sustainability 49 3Chemistry Yes DAXlisted basic chemical producer Director, Operations and Site Development 44 4Chemistry Yes DAXlisted specialty chemical producer Senior Vice President, Energy & Utilities 60 5Chemistry Yes DAXlisted specialty chemical producer Head of Sustainability 54 6Chemistry Yes DAXlisted specialty chemical producer Head of Sustainability Technologies 39 7Chemistry Yes DAXlisted basic chemical producer Head of Advocacy & Regulatory Affairs 53 8Chemistry Yes Global leading strategy consultancy Partner, Top management advisor for basic chemicals 53 9Chemistry Yes Global leading strategy consultancy Partner, Top management advisor for basic chemicals 50 10 Chemistry Yes Global leading strategy consultancy Partner, Top management advisor for basic chemicals 54 11 Chemistry No DAXlisted basic chemical producer Director, Head of Decarbonization 48 12 Chemistry No DAXlisted basic chemical producer Head of Global Sustainability 36 13 Chemistry No DAXlisted specialty chemical producer Senior Vice President, Public Affairs & Sustainability 44 14 Chemistry No DAXlisted specialty chemical producer Global Category Lead for Energy & Utilities 54 15 Chemistry No DAXlisted specialty chemical producer Sustainability & Circular Economy Manager 35 16 Chemistry No Leading Industrial Association Senior Advisor for Chemicals 40 17 Chemistry No Leading Industrial Association Senior Researcher Green Chemicals 36 18 Chemistry No DAXlisted specialty chemical producer Vice President, Operations and Site Development 54 19 Chemistry No Global leading specialty chemical producer Supply Chain Manager 38 20 Chemistry No German leading chemical producer Senior Vice President, Chief Product Officer 51 21 Steel Yes Global leading steel producer Head of Research & Development 52 22 Steel Yes Global leading steel producer Program Manager Decarbonization 60 23 Steel Yes European leading steel producer Chief Technical Officer 37 24 Steel Yes European leading steel producer Chief Operations Officer 54 25 Steel Yes Global leading strategy consultancy Partner, Top management advisor for steel & iron 58 26 Steel Yes Global leading strategy consultancy Partner, Top management advisor for steel & iron 37 27 Steel Yes Global leading strategy consultancy Partner, Top management advisor for steel & iron 41 (Continues)
7781 in strategic green relocation decisions, our study advances this understanding by empirically identifying and assessing these factors in direct comparison to those representing the renewable pull effect—an approach not previously addressed in academic literature, to the best of our knowledge. Our analysis reveals that for the renewable pull effect to translate into investmentintensive and thus longterm green relocation decisions, three critical conditions must be met: weaker agglomeration and demand pull, minimal counteractive subsidies from regulators, and the fulfillment of sociopolitical and environmental prerequisites, as summarized in Figure3. Firstly, this study identifies agglomeration and demand pull as particularly strong counterforces to the renewable pull effect. Customers of EIIs increasingly prefer close proximity to suppliers, which strengthens agglomeration benefits and minimizes transportation costs, tariff risks, and geopolitical uncertainties (Verpoort etal.2024). This outcome challenges foundational assumptions in prior renewable pull effect research, which often compares RERpoor regions with strong industry and customer demand to RERrich regions without considering additional criteria beyond RER availability (Egerer etal.2024; Verpoort etal.2024). Our analysis suggests that regions with abundant renewable resources might not be viable if they lack adequate agglomeration advantages and demand. Moreover, our findings reveal that the ongoing tugofwar between affordable access to renewable energy and integration with downstream production processes near customers (localtolocal production) varies substantially depending on the specific energyintensive product. As highlighted in Section4, even within the context of DRI and green ammonia production, notable differences emerge, although the renewable pull effect may prevail for these specific product steps. However, these products represent only a portion of the broader value chain involved in producing green steel or green chemicals, as shown by Egerer etal.(2024) and Verpoort etal.(2024). We argue that the precise configuration of a green value chain from a strategic perspective for energyintensive products remains uncertain, leaving ambiguity about which value creation steps or entire chains of green production in EIIs are likely to relocate. Therefore, future research modeling potential geographical separations within value chains for individual energyintensive products, driven by the renewable pull effect, could lead to novel insights. Secondly, the study highlights the strong influence of regulatory subsidies on location decisions, with the potential to hinder green relocations. Currently, CAPEX subsidies play a decisive role, such as in keeping DRI production within Germany. To promote green relocations, subsidies should align with rather than counteract the renewable pull effect, potentially being offered by regulators in RERrich countries. The current global subsidy race to attract green EII production facilities further emphasizes this need. This study demonstrates the high risk for regulators in RERpoor regions, particularly in countries with large industrial sectors like Europe and Northeast Asia, underscoring the critical importance of strategic subsidy policies. Additionally, our research identifies practical pathways for regulators to influence EII location decisions. OPEX incentives may hold the greatest way, with policies like “Carbon Contracts for Difference” (applied in the United Kingdom, Netherlands, and potentially in Germany) showing promise. Such policies equalize cost disparities between carbon costs and green production expenses, including OPEX (BWK2024). Future research should explore effective policy incentives from both RERrich and RERpoor regulators, helping policymakers design frameworks and strategies to retain or attract green EII facilities and assess the broader macroeconomic implications of these measures. Thirdly, our analysis indicates that decision prerequisites must be met for green relocations to occur. On the one hand, RERrich locations must satisfy minimum political, legal, environmental, and social standards. Many potential host countries could be excluded if they fail to meet these prerequisites. This is particularly concerning as regions with high RER potential often belong to the economically weaker countries in the Global South (Berger2023). The renewable pull effect not only offers a pathway to costeffective green production for EIIs but also presents socioeconomic growth opportunities for developing countries, particularly in Africa and South America (Day2022). However, researchers have already raised concerns over major investments in green energy projects in developing countries, where the primary goal is to export generated energy to developed nations (Achuo etal.2023; Agyekum2024). This approach risks repeating the resourcecurse pattern seen with oil, coal, and natural gas, where limited wealth generation remains in the local economy. UNIDO etal. (2024) advocate for developing countries to prioritize domestic green energy applications, thereby fostering local business development. Future research should explore policy models that enable developing countries to leverage green relocations for socioeconomic growth while avoiding the pitfalls of a resource curse, including examining how these nations can meet the decision prerequisites. On the other hand, our study underscores the need for transparency regarding how conflicting climate regulations and instruments from major industrialized nations will affect global trade dynamics. The current lack of climate policy clarity creates uncertainty that can delay green production facility location decisions, particularly for EIIs with large production volumes. The realworld impact of this perceived lack of climate policy clarity could be explored through quantitative studies, examining whether it contributes to the postponement of investments in green production facilities within EIIs. Thus, the study's findings emphasize the importance of fulfilling decision prerequisites for countries to capitalize on their RER abundance. They also echo the call from EII top management to major regulators to establish longterm FIGURE 3 | Theoretical model illustrating how the renewable pull effect leads to green relocation of energyintensive production.
7782 Business Strategy and the Environment, 2025 climate policy clarity, addressing the regulatory ambiguity that currently hampers strategic decisionmaking. The findings of this study are subject to certain limitations. The research focuses on highly complex, strategic, sensitive, and novel topmanagement decisions within leading corporations. This inherently constrains data availability but simultaneously justifies the chosen exploratory methodology, involving indepth interviews with top managers and experts. Data collection through semistructured interviews is susceptible to sample bias and potential inaccuracies in interviewee responses. These limitations were mitigated through two key measures. Firstly, the study employed a wellestablished sampling technique by Robinson (2014), widely used in peerreviewed research. Secondly, the application of the AHP method provides precise numerical judgments, complementing qualitative insights and enhancing the robustness of the study. Despite these measures, a degree of subjectivity within the interview responses remains (Birkin etal.2009). Nevertheless, addressing the limitations of the exploratory research design opens further avenues for research. The proposed location decision model and the AHP results could serve as a foundation for developing hypotheses for quantitative research validation. Additionally, new opportunities for further exploratory qualitative research emerge, including, but not limited to, firstly, an attractiveness analysis of different countries or region types for potential green relocation strategies, possibly considering the specific value chain steps likely to be relocated, and secondly, an empirical investigation into whether green relocations are already occurring in practice, as suggested by Samadi etal.(2023). Although comprehensive quantitative data might still be scarce, realworld evidence—such as company announcements, reports, and news articles—could help illustrate ongoing green relocations and potentially shed light on the published rationales behind such decisions. Lastly, an evaluation of how internationalization strategies, particularly green relocations, could accelerate the achievement of decarbonization targets in EIIs. Table7 summarizes all identified future research directions. In general, this study aims to stimulate interest in green relocations and encourage further investigation into their potential impacts. 6 | Conclusion The study sheds light on the complex dynamics of green relocation decisions within EIIs amidst the global push for sustainability. The renewable pull effect, driven by low green energy costs in regions with high RER, emerges as a substantial factor influencing location decisions for green production facilities, particularly for DRI and green ammonia (Egerer etal. 2024; Samadi etal.2023; Verpoort etal.2024). However, this effect is not absolute and is moderated by three critical conditions. Firstly, the interplay between the renewable pull effect and counteracting agglomeration and demand forces varies depending on the specific energyintensive product and its position in the value chain. Secondly, regulatory subsidies, particularly CAPEX subsidies, play a pivotal role in shaping location decisions, with a growing emphasis on OPEX incentives as a potential decisive influence in the global subsidy race. Thirdly, fulfilling political, legal, environmental, and social prerequisites is essential for RERrich regions to attract green relocations, with the current lack of climate policy clarity posing a barrier to timely decisionmaking. The study contributes to an ongoing debate by moving beyond theoretical models and technoeconomic analyses to provide comprehensive insights into the factors that drive or hinder green relocations in EIIs. By identifying and empirically evaluating location factors beyond cheap renewable energy access, employing both thematic analysis (Braun and Clarke2006) and TABLE 7 | Summary of derived future research potential. Potential future research avenues Description Green value chain configurations Investigate how agglomeration benefits and demand forces influence the applicability of the renewable pull effect by model potential geographical separations within value chains for different energyintensive products Regulatory subsidy strategies Explore effective policy incentives from RERrich and RERpoor regulators to attract or retain green EII facilities Policy models for developing countries Develop frameworks to attract green relocations to leverage socioeconomic growth by avoiding the resourcecurse pattern Impact of lacking climate policy clarity Quantify how the potential absence of a climate policy clarity affects investment decisions for green production facilities Country attractiveness analysis Analyze the attractiveness of different countries or regions for green relocation strategies depending on relocated value chain steps Empirical evidence of green relocations Examine realworld evidence, such as company announcements and reports, to determine if green relocations are already occurring and understand the underlying reasons Decarbonization strategy evaluation Assess how green relocation strategies contribute to achieving decarbonization targets within EIIs
7783 the AHP method (Saaty1987), the study provides a structured assessment of these factors' relative importance in the academic literature for the first time. The empirical approach, leveraging insights from top management of leading steel and chemical corporations, offers a tangible perspective. The findings not only enhance the theoretical discourse but also provide practical guidance for corporate leaders evaluating green production sites and for policymakers aiming to retain or attract green EII facilities. Future research should continue exploring how agglomeration benefits and demand forces affect green relocations, analyze effective regulatory subsidy strategies, and develop policy models that enable developing countries to attract green relocations while promoting socioeconomic growth. Additionally, quantitative studies could assess how climate policy clarity impacts investment decisions and evaluate how green relocations contribute to achieving decarbonization targets in EIIs. Empirical research is also needed to identify whether green relocations are already taking place. Overall, this study advances both theoretical understanding and practical application if the renewable pull effect leads to green relocation, contributing to the broader objective of sustainable industrial transition. Nomenclature AHP analytical hierarchy process CAPEX capital expenditures CR consistency ratio DRI directreduced iron EIIs energyintensive industries GH2 green hydrogen GHG greenhouse gas GW global weight LW local weight OPEX operational expenditures RER renewable energy resources SCs strategic considerations Author Contributions Sven Colen: writing – original draft, validation, methodology, data curation, conceptualization. Alwine Mohnen: writing – review and editing, validation, supervision, conceptualization. Acknowledgements The authors gratefully acknowledge the contributions of several individuals to this study. We thank Lucas Böttcher for his support with the thematic content analysis and Dr. Ulf Narloch for his invaluable mentorship and guidance throughout the research process. We also appreciate the insightful feedback from participants at the Doctoral Consortium of the 2024 Autumn Conference of the Sustainability Management Section of the German Association for Business Research, as well as the 2024 International Conference on Sustainable Development. Finally, we acknowledge the support of research colleagues from the Technical University of Munich and the University of California, Berkeley, both for their valuable input and for assisting in refining the paper's language. Artificial intelligence tools were used exclusively to enhance readability. Open Access funding enabled and organized by Projekt DEAL. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement Data will be made available on request. Endnotes 1 Including indirect emissions from power and heat generation. 2 In this study, green production is defined as a production which minimalizes the release of GHG emissions. 3 Due to clarity and readability reasons, the references of individual factors are only gathered in Table1 and are not repeated in the text. 4 Secondary production refers to the further processing of any byproducts. 5 Beside the classical AHP, a fuzzy logic version has been applied frequently in literature. Van Laarhoven and Pedrycz (1983) and Buckley (1985) originally developed the modified version of AHP, which uses fuzzy triangular functions in the pairwise comparison process to reduce imprecisions. However, Chan etal.(2019) demonstrate that the application of fuzzy AHP could be redundant and even counterproductive if the examined problem becomes complex. In this case, classical AHPs, involving many factors, are thereby already fuzzy. Thus, since this study investigates a highly complex decision with 33 factors, the classical AHP is applied. 6 The study remains in optimal sample size to apply an AHP effectively, as suggested by Muralidharan et al. (2001) and Talib and Rahman(2015). 7 Green ammonia production facility for chemistry experts and DRI production facility for steel experts. 8 The Random Index is derived by approximating values for matrices of order 1 to 10, based on a sample size of 500. According to Saaty, the Random Index values for matrix sizes 2 through 10 are 0.00, 0.58, 0.90, 1.12, 1.24, 1.32, 1.41, 1.45, and 1.49, respectively (Fageha and Aibinu 2016). 9 This approach by the experts is also methodologically intended and essential. 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Introduction 0.0 Small talk • Establish rapport with the interviewee and briefly discuss current events or mutual professional interests. 0.1 Interviewer introduction • Selfintroduction, including research background. • Context: Analyzing green production in the steel and chemistry industry, with a focus on location selection. This interview specifically explores DRI or green ammonia production. 0.2 Declaration of consent • Assurance of anonymity: All collected data will be anonymized, and your name will not be disclosed. • Recording consent: To ensure accuracy, I would like to record this interview. Is this acceptable to you? • Time efficiency: Due to time constraints, brief and precise answers would be appreciated. 0.3 Agenda overview 1. Introductory questions 2. Analytical hierarchy process (AHP) exercise on location decision factors (if possible, otherwise skipped) 3. Deep dive questions on green relocation conditions 0.4 Interviewee introduction • What is your current position in the company? 1. Explorative questions (general questions) 1.0 Green production (warmup) • How advanced is your company in transitioning to green production? • In your opinion, what are the biggest hurdles in switching to green production? 1.1 Decision factors for site selection • What are the most important factors when selecting a location for a green production facility? • (Optional) Do you differentiate between location selection for green versus nongreen production facilities? 2. Analytical hierarchy process matrix (unbiased exercise) 2.1 Framing and exercise • In the next 30 min, we will conduct a structured exercise. ○ Imagine this scenario: You are advising on the location decision for a new green DRI or green ammonia production facility. ○ In the first step, you will assess the most relevant factors influencing location selection. ○ Your team has categorized relevant factors, and we will compare them in pairs to determine priority. ○ I will share my screen, and we will go through the factors systematically. • Guidance: Decisionmaking can be challenging, and intuitive responses are welcome. There are no absolute right or wrong answers. • This exercise abstracts realworld complexities and aims to identify fundamental decision drivers. 2.2 Followup questions (biased questions) • If strong ratings (7+) were given: Why is factor X so relevant for you? 3. Deep dive questions on green relocation conditions (biased questions) • Some researchers suggest a “renewable pull effect,” shifting production sites to regions with renewable energy surpluses. Do you see this trend impacting your company? ○ What could enable the renewable pull effect? ○ What are the biggest challenges? ○ What could hinder relocations based on the renewable pull effect? ○ What do you think about Europe's position in this shift? • Could your company expand its green production to new international locations? ○ Could developing countries become attractive locations? • How do you see internationalization affecting the green transformation of your industry? Will it strengthen or weaken the transition? 4. Interview closing • Is there anything else you would like to add regarding green production? • Thank you for your insights—this was extremely valuable. I will share the results in autumn 2024.
7788 Business Strategy and the Environment, 2025 Appendix Synthesized AHP Results for Each SC and Location Factor, Including All Experts' Judgments (n = 20) Strategic considerations Local weights SC rank Location factors Local weights Global weights Factor rank Production costs 0.246 2CAPEX 0.157 0.039 7 Labor costs 0.059 0.015 21 Raw material costs 0.293 0.072 5 Energy costs (incl. GH2)0.426 0.105 2 Outbound transportation costs 0.065 0.016 18 Regulation and financing costs 0.065 6Carbon costs 0.201 0.013 25 Access to subsidies 0.240 0.015 19 Access to capital market (incl. WACC) 0.259 0.017 17 Local taxation 0.132 0.009 30 Customs 0.167 0.011 29 Access to assets 0.127 4 Access to green production facility 0.092 0.012 28 Access to labor forces 0.120 0.015 20 Access to infrastructure and logistics 0.258 0.033 9 Existing assets at location 0.100 0.013 26 Agglomeration and integration benefits 0.431 0.055 6 Access to supply 0.294 1 Proximity to raw material suppliers 0.063 0.018 14 Reliable raw material supply 0.325 0.096 3 Proximity to energy supplier (incl. GH2) 0.097 0.028 11 Reliable energy supply (incl. GH2) 0.409 0.120 1 Access to bridging energy 0.106 0.031 10 Access to demand 0.164 3Proximity to next production step 0.491 0.081 4 Proximity to secondary production steps 0.073 0.012 27 Proximity to customers of final products 0.110 0.018 15 Flexible delivery of final products 0.105 0.017 16 Size of local market of final products 0.220 0.036 8 Political environment 0.067 5 Stable legal and political system 0.328 0.022 13 Industrial friendly political attitude 0.394 0.026 12 Low climate action regulations 0.074 0.005 33 Reliable production certification 0.204 0.014 23
7789 Appendix Synthesized AHP Results for SCs, Judgments Categorized by Chemistry (n = 10) and Steel (n = 10) Appendix Synthesized AHP Results for Factors, Judgment Categorized by Chemistry (n = 10) and Steel (n = 10) # Strategic considerations Chemistry local weights Steel local weights Delta local weights 01 Production costs 0.257 0.232 0.02 02 Regulation and financing costs 0.059 0.069 −0.01 03 Access to assets 0.118 0.135 −0.02 04 Access to supply 0.279 0.305 −0.03 05 Access to demand 0.201 0.132 0.07 06 Political environment 0.048 0.091 −0.04 07 Environmental and social risks 0.039 0.037 0.00 Note: A delta larger than 3% (0.03) is highlighted in gray. Strategic considerations Local weights SC rank Location factors Local weights Global weights Factor rank Environmental and social risks 0.038 7Pollution and environmental constraints 0.346 0.013 24 Affected communities' constraints 0.141 0.005 32 Quality of life for employees 0,148 0,006 31 Occupational health and safety standards 0,364 0,014 22 Note: A factor with a global weight larger than 4% (0.04) is highlighted in gray. SC Factors Chemistry global weights Steel global weights Delta global weights 01 CAPEX 0.061 0.021 0.04 Labor costs 0.013 0.014 0.00 Raw material costs 0.093 0.050 0.04 Energy costs (incl. GH2)0.076 0.129 - 0.05 Outbound transportation costs 0.013 0.017 0.00 02 Carbon costs 0.015 0.009 0.01 Access to subsidies 0.007 0.031 −0.02 Access to capital market (incl. WACC) 0.019 0.012 0.01 Local taxation 0.008 0.008 0.00 Customs 0.010 0.009 0.00 03 Access to green production facility 0.009 0.015 −0.01 Access to labor forces 0.011 0.020 −0.01 Access to infrastructure and logistics 0.025 0.041 −0.02 Existing assets at location 0.012 0.012 0.00 Agglomeration and integration benefits 0.061 0.047 0.01
7790 Business Strategy and the Environment, 2025 SC Factors Chemistry global weights Steel global weights Delta global weights 04 Proximity to raw material suppliers 0.025 0.013 0.01 Reliable raw material supply 0.114 0.075 0.04 Proximity to energy supplier (incl. GH2) 0.023 0.033 −0.01 Reliable energy supply (incl. GH2) 0.093 0.147 −0.05 Access to bridging energy 0.024 0.037 −0.01 05 Proximity to next production step 0.075 0.079 0.00 Proximity to secondary production steps 0.019 0.007 0.01 Proximity to customers of final products 0.024 0.012 0.01 Flexible delivery of final products 0.018 0.015 0.00 Size of local market of final products 0.064 0.019 0.05 06 Stable legal and political system 0.022 0.019 0.00 Industrial friendly political attitude 0.016 0.039 −0.02 Low climate action regulations 0.003 0.007 0.00 Reliable production certification 0.007 0.025 −0.02 07 Pollution and environmental constraints 0.016 0.010 0.01 Affected communities' constraints 0.007 0.004 0.00 Quality of life for employees 0.005 0.006 0.00 Occupational health and safety standards 0.011 0.016 0.01 Note: A delta larger than 3% (0.03) is highlighted in gray.