Scenario-Based Analysis of the Future Technological Trends in the Automotive Sector in Southeast Lower-Saxony

Abstract

The automotive industry faces radical technological change, driven by the adoption of electrification, automation, and digitalization. As a leading industrial hub with key OEMs and suppliers, such as Volkswagen, Southeast Lower Saxony is disproportionately impacted by this structural transformation. As a consequence of these trends, the region’s automotive base faces economic uncertainties, local regulatory lag, and technological disruptions. In this study a scenario planning methodology is conducted, to identify three potential mobility futures for 2035: a Best-Case scenario, where innovation and favorable policies enable a stable growth environment for the local automotive industry; a Trend scenario, marked by incremental yet uneven progress, while maintaining the current status quo; and a Worst-Case scenario, defined by economic stagnation and regulatory impediments, leading to a slow degradation of the regional automotive industry. The scenarios are then evaluated based upon their impact and probability of occurrence, while individual impact factors were also prepared and categorized to support future decision-making on a topical basis. This study offers an overview of potential scenarios for the Southeast Lower Saxon automotive industry, supporting the strategic decision-making.

1. Introduction

The automotive cluster in Southeast Lower Saxony stands at a critical inflection point. As the home of major original equipment manufacturers (OEMs), suppliers, and renowned research institutions, the region has long served as a cornerstone of Germany’s mobility sector. However, the convergence of electrification, automation, and digitalization is fundamentally restructuring the industry [1,2]. These disruptive forces are dismantling traditional manufacturing processes, altering employment structures, and reconfiguring supply chains, presenting a complex mix of challenges and opportunities for both corporate entities and policymakers. Consequently, proactive adaptation is essential to sustain the region’s economic resilience and industrial leadership.

To navigate these uncertainties, this study employs scenario planning—a methodology that eschews deterministic forecasting in favor of constructing multiple plausible futures [3]. By rigorously analyzing distinct trajectories (Best-Case, Worst-Case, and Trend), regional stakeholders can derive actionable insights into potential risks and strategic pivots [4].

This research examines the region’s trajectory through an expert-led scenario analysis conducted at the Technical University of Braunschweig. The study utilizes data derived from a specialized workshop comprising a panel of doctoral and postdoctoral researchers from the Institute for Automotive Engineering and the Institute of Engineering Design. The panel possessed interdisciplinary expertise spanning electric mobility, autonomous systems, technology management, and SME adoption, with the majority bringing significant prior industrial experience. The scenario framework was developed through a structured consensus process: influence and relevancy matrices were defined via moderated brainstorming, quantified individually, and aggregated to ensure statistical robustness.

The paper proceeds by first outlining the current status of the sector and the implications of recent technological shifts. It then details the theoretical underpinnings of scenario-based analysis before applying this framework to the specific transformation pressures facing Southeast Lower Saxony. By delineating these divergent futures, this work aims to equip businesses, policymakers, and researchers with the foresight necessary to navigate the region’s evolving automotive landscape.

2. State of the Art

The State of the Art provides an overview of the core topics of this study These are the basics of scenario management, the current trends of the automotive industry and the specific situation of southeast Lower Saxonies automotive industry.

2.1. Scenario Management

A scenario is a comprehensible projection of a potential future state, derived from a complex interplay of influencing variables [5]. The future is conceptualized as a funnel, leading to multiple scenarios based on the principle of multiple possible futures, where diverse developments per factor are considered [6]. The application of scenarios in strategic leadership is termed Scenario Management, which extends beyond mere scenario construction [7]. Scenarios typically depict the developmental trajectories of a specific domain, referred to as the scenario-field, which defines the scope addressed by the constructed scenarios [6]. The Scenario field and the enclosed scenarios are represented by a “Scenario funnel”. Instead of predicting one outcome, scenario planning develops a set of alternative futures, allowing decision-makers to explore different “what-if” situations. This offers organizations the opportunity to be more flexible and better prepared to handle surprises, including technological discontinuities or disruptive events [4].

A scenario field may comprise solely external, uncontrollable environmental factors. For instance, a machine tool manufacturer might use such environmental scenarios to anticipate market developments over the next decade and derive implications for required machine functionalities. Alternatively, a scenario field may include only internal control variables—elements of the design domain—constituting what are termed design-field scenarios, useful for generating coherent product concepts or strategy variants. When both external and internal factors are included, the scenario field can represent an entire system of environment and design domain, referred to as system scenarios, which encompass both boundary conditions and strategic options [6].

Scenario Management is structured into five sequential phases. Phase 1, Scenario Preparation, involves defining project objectives, establishing the project organization, and analyzing the design domain. In Phase 2, Scenario Field Analysis, the scenario field is characterized through relevant influencing factors, with key factors identified based on their interdependencies and impact. Phase 3, Projection Development, constitutes the core of the process, in which alternative future developments (future projections) are formulated for the key factors. Phase 4, Scenario Construction, synthesizes consistent scenarios by evaluating the pairwise compatibility of these projections; each scenario represents a coherent combination of projections. Finally, Phase 5, Scenario Transfer, examines the implications of the scenarios for the design domain and derives strategic recommendations based on alternative future developments. This phased model provides a robust methodological framework for scenario generation and its application in strategic management, technology planning, and product development [6].

The Scenario Management Technique applied in this paper is based on the approach described by Gausemeier et al. in [7], all further Scenario Management relevant steps are conducted in accordance with the described process, except for which steps a deviation from the original method is explicitly stated in this text.

2.2. Technological Transformation of the Automotive Industry

Over the past decade, the automotive industry has undergone a profound transformation driven by converging technological, environmental, and regulatory forces. This period has seen a simultaneous push toward electrification, the emergence of increasingly autonomous vehicles, greater digital connectivity, innovation in manufacturing and supply chains, a heightened focus on sustainability and decarbonization, and significant policy and regulatory shifts [8].

Electrification, marked by the transition from internal combustion engines to electric drivetrains, has been central to recent automotive industry shifts. Once niche, electric vehicles (EVs) experienced rapid growth in the 2020s, with global sales exceeding 10 million in 2022 and a global fleet surpassing 26 million—over fivefold the 2018 figure—despite comprising only ~2% of the total vehicle stock. This acceleration, particularly in China, Europe, and the U.S., is largely policy-driven, including fiscal incentives, infrastructure investments, and stringent emissions regulations aimed at phasing out Internal Combustion Engine (ICE) vehicle sales by the 2030s, thereby intensifying pressure on manufacturers to adopt low-emission EV technologies [9]. Technological advances over the past decade have significantly enhanced EV performance, with improvements in lithium-ion batteries, emerging solid-state technologies, and power electronics increasing range, durability, and cost efficiency. Standardization of fast-charging protocols and global expansion of charging infrastructure have further supported adoption. Intensified R&D has driven notable progress across battery systems and charging technologies, while also generating new employment opportunities within the sector [10]. Electrification is restructuring the automotive value chain by displacing ICE components with electric powertrains, disrupting traditional supplier networks while elevating battery manufacturers and prompting vertical integration. This transition reduces labor intensity and shifts workforce demands toward electrical and software engineering, necessitating reskilling. Although presenting structural and employment challenges, EV adoption is central to decarbonizing road transport, positioning electrification as both a technological and environmental imperative [11].

Parallel to electrification, autonomous vehicle (AV) development has advanced markedly since the mid-2010s, driven by progress in AI, sensor technologies, and computational capabilities. Real-world trials of AVs, defined by SAE J 3016 levels from 0 to 5, [12] have reached Level 4 autonomy in constrained settings, while Level 5 remains theoretical. AV systems integrate interdisciplinary innovations across computer vision, sensor fusion, real-time decision-making, and control engineering [13]. Artificial intelligence—particularly machine learning and deep neural networks—has been pivotal in advancing AV capabilities, enabling perception, prediction, and path planning. These developments have introduced new challenges in safety, cybersecurity, and ethics. Concurrently, the rise of software-defined vehicles and over-the-air updates underscores the convergence of autonomy and connectivity, reflected in the emergence of connected and automated vehicles (CAVs) [14]. Since the late 2010s, AVs have been deployed in limited settings, with commercial applications emerging via Level 2 and early Level 3 systems. Full Level 5 autonomy remains unrealized due to persistent challenges in safety validation, regulatory uncertainty, and public acceptance. Key obstacles include rare-event reliability, liability frameworks, and societal concerns over ethical decision-making and data privacy [13]. Autonomous driving aligns with broader Mobility-as-a-Service (MaaS) trends, supporting shared transport models. A prolonged coexistence of human-driven and automated vehicles is expected, with full adoption contingent on advances in AI, safety, regulation, and public acceptance [13,15,16].

Over the past decade, vehicles have evolved into highly connected, software-defined systems, integrating advanced telematics, V2X communication, and centralized computing architectures. Connectivity enables real-time data exchange for safety, efficiency, and new mobility services, while over-the-air updates and infotainment redefine user experience. These developments underpin autonomous driving and Mobility-as-a-Service models. Concurrently, rising software content and cybersecurity concerns have driven regulatory responses. The emergence of 5G is set to further enhance vehicle-to-everything integration, solidifying the convergence of digitalization and mobility [17].

Technological advancements in EVs and electronics have coincided with major shifts in automotive manufacturing, driven by Industry 4.0. Automakers now employ intelligent robotics, IIoT sensors, AI-driven analytics, and digital twins to enhance real-time monitoring, predictive maintenance, and production efficiency. These tools enable flexible, high-precision manufacturing capable of handling increased product complexity, such as concurrent assembly of electric and conventional vehicles [18,19]. Another trend gaining popularity in manufacturing processes within the automotive sector is additive manufacturing [20]. Electrification is reshaping the automotive supply chain, shifting focus from combustion-related components to batteries, electric motors, and power electronics. This transition is driving global value chain realignment, intensifying competition among production sites, displacing traditional suppliers, and reducing labor demand due to simpler assembly processes and increased automation [11].

Sustainability now drives automotive transformation, driven by the imperative to decarbonize transport. As of 2021, transport contributed 37% of global end-use CO₂ emissions, with road vehicles accounting for approximately 76% of this total. Given their reliance on fossil fuels, road vehicles are key targets for climate mitigation. Following the 2015 Paris Agreement, global climate policies have pressured OEMs to reduce emissions, prompting widespread commitments to carbon neutrality—many by 2040—often guided by Science-Based Targets aligned with Paris goals [21]. Electrification remains central to automotive decarbonization, as EVs significantly reduce lifetime CO₂ emissions, especially with low-carbon electricity. Full environmental benefits depend on clean energy supply and mitigation of battery production emissions. Life-cycle assessments highlight the need for battery recycling and reuse to reduce raw material demand and support a circular battery economy [22,23]. Automakers are addressing full life-cycle emissions—including Scope 1, 2, and 3—through comprehensive decarbonization strategies. Most large OEMs have adopted carbon management practices, though regional disparities persist. Measures include sustainability reporting, renewable energy use in manufacturing, and adoption of low-carbon materials [21]. Decarbonization faces key challenges, notably reliance on critical raw materials such as lithium, cobalt, and rare earths. Accelerated EV production intensifies demand, raising concerns over environmental impact and supply security, prompting research into material alternatives and responsible sourcing practices [24,25].

Stricter emissions and fuel economy regulations have been pivotal in accelerating electrification. The EU’s CO₂ targets, U.S. CAFE and EPA standards, and China’s NEV credit system have collectively pressured automakers to adopt low- and zero-emission technologies. Policy alignment with technological maturity has been a key driver of EV adoption [26,27].

In addition to regulatory measures, many governments have supported EV adoption through financial incentives—such as rebates, tax credits, and exemptions—and infrastructure investments. These efforts, exemplified by Norway’s high EV market share, have been critical in overcoming early adoption barriers and expanding charging networks via public–private initiatives [28,29]. Autonomous vehicle regulation has advanced gradually, with various jurisdictions—such as U.S. states, Germany, Singapore, and the UK—enacting laws to support AV testing and limited deployment. International bodies like UNECE have introduced standards for Level 3 systems. However, a unified global framework is absent, and regulatory fragmentation persists. Key challenges include defining legal responsibility, certifying AV safety, and establishing consistent standards—critical prerequisites for broader adoption [30,31].

Trade policies and industrial strategies have supported the automotive transition by promoting domestic EV and battery production through tariffs and local content rules. The U.S. Inflation Reduction Act, for example, ties EV tax credits to North American assembly and allied-material sourcing, aiming to strengthen local industry and supply chain security. Similarly, the EU and China have invested in R&D and public–private initiatives to drive innovation in electric and connected mobility [32,33]. Over the past 10–15 years, automotive policy has become increasingly interventionist, aiming to accelerate the shift to cleaner technologies. This includes stricter environmental regulations, active support for EVs (and emerging support for AVs), and evolving frameworks addressing data, safety, and infrastructure. Literature consistently highlights policy as a critical enabler of sustainable and safe mobility transitions [13].

Over the past decade, electrification, digitalization, and automation have emerged as dominant trends, reshaping production, investment, and labor demands. Looking ahead, the industry’s future will be increasingly shaped by the integration of green and digital technologies, evolving trade patterns, and the realignment of supply chains. To navigate this transition, coordinated policy support will be essential—particularly in fostering innovation, securing critical raw materials, and ensuring inclusive industrial adaptation across regions and firm sizes [8].

2.3. Southeast Lower Saxonies Mobility Sector

The introduction and analysis of the Southeast Lower Saxon automotive Sector is based on the Prognos Report, which analyzes the current status quo. The insights provided in this section are a focused extract of relevant information from the original report [34].

The automotive sector in Southeast Lower Saxony, centered in the Braunschweig-Wolfsburg region, constitutes a critical industrial and technological node within the German and European mobility landscape. Hosting approximately 182,000 employees—equivalent to 31% of the regional labor force of 591,000—the mobility sector accounts for a disproportionate share of regional gross value added. The regional GDP, exceeding €60 billion, has expanded by 36% since 2010, underpinned by a dense concentration of OEMs (e.g., Volkswagen AG), tier-1 suppliers (e.g., Bosch, Siemens, Continental), and engineering service providers (e.g., IAV, Bertrandt). This industrial agglomeration has historically provided economic stability but now constitutes a structural vulnerability in the context of accelerated sectoral transformation [34].

Labor market dynamics reflect this structural bifurcation. While employment in ICT (+18%) and technical R&D professions (+21%) has increased significantly between 2011 and 2021, traditional manufacturing occupations such as metalworking and vehicle assembly have stagnated or declined. Occupations with high routine intensity face substitution risks exceeding 60%, exposing the region to significant automation-induced displacement. Simultaneously, the demand for digitally and ecologically skilled labor exceeds supply, especially in SMEs, which often lack internal capacity for workforce development. Despite coordinated upskilling programs, such as those promoted through regional transformation labs, coverage remains uneven across firm size and subregion [34].

Demographically, the region has experienced moderate population growth (+1.9% from 2011–2021), reaching 1.14 million inhabitants. However, this growth derives exclusively from positive net migration, particularly among cohorts aged 18–30 and families with children. Natural population change remains consistently negative, with annual excess mortality of ~4000. The share of residents aged ≥ 65 rose from 20.0% to 22.9%, while the working-age population declined from 64.9% to 63.3%. These trends elevate the regional dependency ratio and constrain future labor supply, further exacerbating recruitment challenges in high-demand technical occupations [34].

The region’s research and innovation infrastructure partially offsets these structural risks. Over 18,000 individuals are employed in corporate R&D, complemented by ~6000 in academic and extra-university research institutions. This R&D intensity exceeds national and state benchmarks, with a regional growth rate of 15.4% in the corporate research workforce from 2015–2022. Key facilities—including the Testfeld Niedersachsen, the Wasserstoff Campus Salzgitter, Niedersächsisches Forschungszentrum Fahrzeugtechnik (NFF) and AIM (Anwendungsplattform Intelligente Mobilität)—exemplify applied innovation capacity. Nevertheless, innovation diffusion remains asymmetric: while large firms dominate patent production and project coordination, SMEs frequently lack integration into regional or transregional innovation networks [34,35].

Intra-regional disparities further complicate transformation trajectories. Urban nodes such as Braunschweig and Wolfsburg concentrate high-value employment, research institutions, and infrastructure, whereas peripheral districts (e.g., Goslar, Helmstedt) face demographic decline, industrial stagnation, and youth outmigration. For instance, Wolfsburg expanded employment in digital mobility fields by over 20% between 2011 and 2021, while Helmstedt experienced a net employment contraction. Such spatial asymmetries necessitate differentiated policy regimes capable of aligning infrastructural investment, labor integration, and institutional access across urban-rural divides [34].

Despite substantial technological assets, entrepreneurial intensity remains weak. Startup density, measured as new business registrations per 10,000 working-age inhabitants, lags significantly behind benchmark regions such as Munich or Berlin. This deficit reflects limited early-stage financing, underdeveloped incubation infrastructure, and absorptive pressures from incumbent firms. University-linked entrepreneurship initiatives and applied research hubs are emerging, yet lack critical mass. Absent a robust entrepreneurial ecosystem, translational efficiency from research to commercial innovation remains suboptimal [34].

In summary, the Braunschweig-Wolfsburg region represents a paradigmatic case of a high-capacity industrial system under structural stress. The convergence of demographic contraction, occupational mismatch, and spatial inequality—against a backdrop of global mobility transformation—demands a holistic regional strategy. Such a strategy must consolidate the region’s technological lead while broadening innovation participation, enabling SME integration, and ensuring workforce adaptability through systemic upskilling and managed migration. Sustained competitiveness will depend less on technological invention per se than on the institutional capacity to diffuse and embed innovation across the regional socio-economic fabric.

3. Scenario Management Method

This chapter provides a comprehensive overview of Scenario Management process introduced in Section 2. It showcases step-by-step, how the method described by Gausemeier et al. has been implemented for the specific Use Case of this Paper [7].

3.1. Scenario Preparation

The scenario preparation phase encompasses objective definition, establishment of the project structure, and delineation and initial analysis of the design domain. Primarily, project goals are specified with regard to intended scenario applications, supported decision processes, and targeted strategic approaches. This objective setting is intrinsically linked to the systematic assessment of the design domain. Within the domain analysis, the current state is descriptively characterized, yielding present challenges [6].

The Aims-and-Scope Phase of the scenario management process defines the overall purpose and intended use of the scenario project. It clarifies what the development and application of the scenarios should achieve, which strategic or operational decisions they are meant to support, and what type of strategy is to be derived. Establishing these objectives provides a focused framework that guides all subsequent steps of scenario development [6].

The Design Field Definition Phase, describes that the Technological design fields are characterized by the collective exploration of development opportunities within a significant technological domain, typically involving multiple firms. In contrast, global design fields operate at a higher hierarchical level, addressing overarching questions concerning the future of mobility, the economic development of a region, and the innovation capacity of the manufacturing sector. This dual-focus design field thus facilitates an integrated analysis of both domain-specific technological trajectories and broader systemic transformations [6].

The hybrid nature of the Design Field Characterization Phase of this paper necessitates a correspondingly dual-layered characterization of the design field, integrating both global technological trends in the automotive sector and the specific structural and economic features of the automotive industry in Southeast Lower Saxony [6]. Section 2.2 provides an in-depth analysis of global technological developments, while Section 2.3 details the regional context, with particular emphasis on its automotive domain.

3.2. Scenariofield Analysis

The objective of this phase is to identify the influencing variables relevant or particularly characteristic for the development of the scenario field—so-called key factors [6].

The Scenariofield Analysis begins with the identification of Areas of Influence. The areas of influence are categories, which directly surround the examined design field and form the basis for the derivation of the influencing factors [6].

This is followed by the Identification of the Influencing Factors, using the workshop framework outlined previously, incorporating the domain-specific insights presented in Section 2.2 and Section 2.3, as well as the participants’ own technological expertise and general knowledge [6].

The Direct Influence Analysis captures the immediate interrelations and mutual impacts among influencing factors [6]. This is operationalized through an influence matrix, originally developed by Duperrin and Godet, in which all factors are systematically juxtaposed [36]. For each pair of factors, the degree and immediacy of influence exerted by one factor on the other—reciprocally—is assessed using a four-level scale. Crucially, the evaluation is restricted to direct influences; indirect effects are addressed in a subsequent analytical step [6].

In this paper, we present an Indirect Influence Analysis aimed at visualizing how one factor can affect another not only directly, but also through an intermediate factor. While the indirect influence is typically weaker than the direct one, it still provides valuable insight into the complex interdependencies between variables. To model this, we consider all possible paths that connect a source factor to a target factor via a single intermittent factor. For each such path, we calculate the indirect influence by taking the minimum of the two influence values: from the source to the intermittent factor, and from the intermittent factor to the target. From the unification of the direct and indirect influence matrices, five key metrics are derived to identify potential key influencing factors [6]. The Active Sum (AS) quantifies the direct impact a factor exerts on others (row total), while the Passive Sum (PS) reflects the degree of influence a factor receives (column total). The Impulse Index (IPI), as the AS/PS ratio, indicates a factor’s capacity to influence without being reciprocally affected—high values denote impulsive, low values reactive factors. The Dynamic Index (DI), computed as AS × PS, measures systemic embeddedness, distinguishing dynamic (high DI) from buffering (low DI) factors.

While the Influence Analysis reveals the systemic behavior of influencing factors, it does not quantify their specific impact on the object of study. This limitation is addressed through a Relevance Analysis, which employs pairwise comparisons in a relevance matrix to determine whether factor i (row) is more significant to the study than factor j (column) [6]. To ensure methodological simplicity, a binary evaluation is used (0 = no/1 = yes). The row sums of these binary assessments yield relevance scores, which form the basis for ranking the factors by their importance to the design field [6]

The characteristic values derived from influence and relevance analyses (e.g., Active Sum, Passive Sum, Relevance Sum) are visualized in the so-called System Grid, during the Key Factor Selection step, which serves as the basis for identifying the key influencing factors—those that most strongly shape the future of the object of study. In this diagram, the Active Sum of each factor is plotted against its Passive Sum, incorporating indirect influences. In practice, both axes are rank-scaled to enhance clarity. A comparison of the active influence values with the summarized influence values for each factor, is the hidden-driver diagram, where factors can be identified, which do not have high direct influence, but may be indirectly significant, regarding their influence [6].

The selected key factors are further processed for subsequent analysis during the Key Factor Preparation. Each is defined and accompanied by a precise, evidence-based description of its current state, derived from indicators—quantifiable, time-dependent variables that enable assessment of the factor’s development [6].

3.3. Projection Development

The projection development marks the actual “look into the future.” For each key factor, multiple possible developments are described. This requires the determination of a temporal horizon. [6] The creation of the Projection Development starts with the Identification of Possible Future Projections also demands both analytical and creative skills: analytically, future projections of key factors with quantitatively measurable or qualitative attributes can be assessed [6]. The large number of possible projections must be clustered and compiled into fewer, more understandable clusters. Methodically, the aim of the workshop participants was, to provide a negative, a positive and a trends projection for each key factor, ensuring variability and the coverage of all possible developments. Following the creation of future projection clusters, they must be formulated and justified in a manner that ensures quick and easy comprehension by uninvolved stakeholders. Each projection should begin with a concise, descriptive label to enhance usability within the project and to facilitate engagement and adoption in discussions. In addition to the short label, a detailed description and rationale are required [6].

3.4. Scenario Development Method

A scenario is, in principle, a coherent combination of compatible future projections. The credibility of such future narratives critically depends on their internal consistency, i.e., the absence of contradictions between individual projections. [6] To achieve this consistency, it is important to conduct a Pairwise Consistency Analysis, otherwise called Cross-Impact Balancing. During the pairwise consistency assessment, all combinations of two future projections are evaluated based on whether, assuming one projection is true, the other projection can also be true. Each pair is assessed on a scale ranging from −3 (complete inconsistency) to +3 (strong mutual support). To gain further insight into the acquired data, two different approaches for the consistency matrix have been designed. The first “symmetric” method, originally proposed by Gausemeier et al., treats the relations between the individual projections as symmetric, arguing that the consistency of two projections is the same, when viewing any one relative to the other. The second “directional” method departs from the approach proposed by Gausemeier et al. by treating the relationships between projections as directional rather than symmetric. Specifically, one projection is considered dominant, and the consistency of the second projection is evaluated from the perspective of this dominant scenario. This modification may grant further insight, as the projections differ significantly in their levels of influence on regional technological development. Consequently, a projection may have little to no impact when viewed in one direction, yet exert a substantial influence in the opposite direction. Projections belonging to the same key factor are not evaluated against each other [6]. The consistent projection bundles are grouped based on their similarity, forming clusters referred to as raw scenarios. These are later described in narrative form. The clustering is performed using Multi-dimensional Scaling, while the number of Scenarios is then determined by a Scree-Plot analysis [6].

3.5. Scenario Description

The final step of scenario construction focuses on transforming the identified raw scenarios into coherent prose descriptions by using the automatically generated list of factor projections and their frequencies. Based on this distribution, projections are classified as distinct, dominant, or alternative scenario characteristics, which determines how they shape the narrative. The scenario texts are then composed by systematically combining the previously formulated textual building blocks in accordance with these classifications [6].

3.6. Scenario Transfer

The developed scenarios broaden the perspective on potential future developments and thus provide a robust foundation for strategic formulation. The application of scenarios within the strategic management process is referred to as scenario transfer. Scenario transfer entails the analysis of scenarios, which is essential for identifying future success potentials as well as possible threats to today’s established business models. [6]

In strategic planning, the Reference Scenario Selection involves choosing one focal future from among several plausible scenarios, typically favoring the scenario that combines high impact on the current business with a comparatively high likelihood of occurrence. While organizations may pursue robust strategies that perform acceptably across most scenarios, such approaches often entail inefficient resource use; hence, focused strategy development is generally recommended. The portfolio used for scenario assessment positions scenarios along two dimensions, impact magnitude and probability of occurrence, thereby identifying those most relevant for strategic design. Scenarios with high impact and reasonable likelihood guide the reference choice, whereas low-impact, low-probability scenarios are deemed strategically irrelevant, and intermediate cases require nuanced evaluation. Once a reference scenario is selected, continuous premises controlling based on defined indicators is essential to monitor environmental changes and validate the assumptions underpinning the chosen strategy [6].

The Opportunities–Threats Matrix supports the systematic identification and evaluation of strategic options by analyzing the chances and risks derived from the scenarios along two dimensions: their significance for the strategic domain and their likelihood of occurrence. This assessment highlights that opportunities and threats differ not only in their potential impact but also in the reaction time required to address them effectively. By mapping these factors, four characteristic types of strategic responses emerge, ranging from immediate action on high-impact, high-probability issues to minimal resource allocation for low-impact, low-probability developments. The matrix thereby provides a structured basis for prioritizing proactive initiatives, preparing contingency plans, integrating minor yet probable developments into ongoing planning, and avoiding unnecessary resource commitments [6].

The Effects Analysis Table systematically examines how each scenario affects the relevant strategic domain, making it particularly valuable for market- and environment-oriented scenario work. Its central tool, the impact matrix, maps scenarios against key action areas and evaluates the implications of each scenario–area combination by asking how the respective future development would influence that domain. To capture these effects comprehensively, project teams must mentally immerse themselves in each scenario, often aided by a designated “scenario advocate” who ensures that discussions remain aligned with the scenario logic. The resulting matrix enables teams to derive strategic directions by identifying scenario-specific opportunities and threats, which can then be translated into actionable strategic thrusts, including decisions such as the selection of appropriate production technologies [6].

4. Results

This chapter provides a comprehensive overview of the Results of the Scenario Analysis and aims to explain the background and decisions made during the Scenario Management Process.

4.1. Scenario Preparation Results

The objective of this Scenario Management process is the systematic derivation of plausible future scenarios and key influencing factors for the mobility sector in Germany. These scenario-based insights will serve as the analytical foundation for a region-specific transformation strategy, intended to reconceptualize the current and future state of regional mobility. The resulting strategy is designed to equip stakeholders from economic, political, and scientific domains with a structured basis for shaping and facilitating the regional automotive transition. The analytical scope spans the German mobility sector within the broader European context, with a temporal horizon extending to 2035. It integrates projections of technological trajectories, regulatory developments, societal value shifts, and macroeconomic conditions relevant to the automotive industry.

The Design Field selected for this Scenario Management process represents an adaptation of the “global” type of scenario analysis, as defined be Gausemeier et al., aiming to determine the future situation, potential and challenges for actors within the field of mobility within Germany [6]. The hybridization of design fields is essential to accommodate the dual objective of this Scenario Management process: generating detailed technological scenarios for emerging mobility technologies, while simultaneously capturing the region-specific characteristics of Southeast Lower Saxony. This necessitates the integration of a granular, technology-focused perspective with the broader systemic viewpoint inherent to global design fields. Summarizing these insights, the design field of this study, can be formulated as: “Future state of the southeast Lower Saxon automotive sector, with a particular focus on the management of the technological transition within its automotive industry”.

4.2. Scenariofield Analysis Results

In this application of the Scenario Management Process, the areas of influence are grouped according to the STEEP (Social, Technological, Economic, Environmental, Political) logic. Within the automotive technologies, the following areas of influence are considered: “vehicle concepts”, “vehicle digitization”, “automated and connected driving”, “new drivetrain technologies” and “utilization of ai in a vehicular context”. There are also three socioeconomic areas of influence: “society”, “economics” and “politics and regulation”. The categorization was derived through a series of expert workshops involving representatives from the Institute for Engineering Design and the Institute for Automotive Engineering at TU Braunschweig. Each workshop engaged a minimum of seven interdisciplinary experts with 2–10 years of relevant experience. Concepts and formulations were iteratively refined through open discourse until consensus was achieved or a majority-supported position emerged. The Direct Influence Analysis process was executed within the expert group workshop framework detailed in Section 1.

Given a total of 50 factors, there are 48 potential intermediaries for any given pair. The total indirect influence is then obtained by summing these minimum values and applying a damping factor to reflect the reduced strength of indirect relationships. Influences involving two or more intermediate steps (i.e., third-order or higher) have not been considered in this analysis. The factor applied to the indirect influence matrix has been 1/480, taking into account the normalization through 48 factors and an impact value of 10% compared to direct influence. The complete relevance matrix, filled out within the expert group workshop framework detailed in Section 1.

The Active-Passive Grid Diagram, shown in Figure 1, unifies the influence and relevance tables, into one comprehensive diagram. The diameter of each plotted sphere represents the outcome of the relevance analysis: larger diameters indicate greater relevance to the study object. To identify key factors, the System Grid is scanned top-down by descending Active Sum—or diagonally from top right to bottom left in highly dynamic, short-term scenarios based on the Dynamic Index. Key factors are those with both a large sphere diameter (high relevance) and high vertical positioning (strong activity within the network of influencing factors) [6]. An overview of the key factor selection values, in the form of the active-passive grid, is provided in Figure 1.

During the expert workshop, the data was thoroughly examined to support the selection of key factors, with discussions centered on both quantitative indicators—such as active and passive sum values—and the overall relevance of each factor within the broader context. The selection process was primarily data-driven, ensuring that the most influential and impactful factors were identified. However, expert judgment also played a crucial role; certain factors, such as “Control concepts,” were incorporated despite not ranking highly in the data analysis. These were included because the experts unanimously recognized their foundational importance and their critical influence on the functioning and interdependencies of other key factors. This combined approach ensured a robust and contextually informed selection of factors.

4.3. Projection Development Results

For this paper, the identification of possible future projections has been conducted during the expert group workshop, where relevant future scenarios have been identified and documented. Based on these projections, the consistency matrices have been created as described in Section 3.3. During the workshop the consistency tables have been filled independently by participants, the final matrix has been created by filling each cell with the median value of the participants tables.

4.4. Scenario Development

Based on the consistency matrices, the Scree-Diagrams for this analysis are shown in Figure 2, clearly showing, that to achieve a reasonable information retention, three scenario-clusters are required, over both matrices.

Based on the consistency analysis, all scenarios with thresholds of 0, −1 and −2 have been investigated, aiming to understand, how the creation of scenario-clusters changes with decreasing thresholds, while also aiming to identify the ideal clustering of scenarios. The results of the multidimensional screening (MDS) are shown in Figure 3. Hereby the threshold represents the lowest number of consistencies among all possible scenarios, whereby decreasing thresholds mean decreasing consistency among the scenarios.

The analysis of the different clusters reveals that there is no difference for both the symmetric and the directional consistency analysis, for the thresholds of 0 and −1, resulting in the same number of consistent scenarios. It is also visible, that the threshold of −2, for both cases, introduces a large number of inconsistent scenarios into the clustering, making the clustering loose the clear distinction between elements within or outside of the clusters, diminishing the relevance of the results.

4.5. Scenario Description Results

This Section describes the three main identified scenario clusters.

4.5.1. Scenario 1: Innovation Standstill and Societal Withdrawal

Economic Environment

The spread of autonomous vehicles remains absent. Despite early enthusiasm, there is now a stagnation in the spread and integration in traffic spaces. Neither in urban nor rural regions have there been any significant establishment of the technology—pilot projects often fail, and a nationwide rollout remains distant. Security solutions such as distributed security architectures through multiparty computation would be theoretically promising, but in practice they fail due to the lack of standards and the unwillingness of manufacturers to cooperate. This leads to fragmentation and high implementation costs, deterring many providers. Even automated parking, once celebrated as an “entry-level innovation,” is not progressing. Technical and regulatory challenges cause significant delays. Widespread operation remains absent. At the same time, costs are rising. Vehicle costs increase due to complex technological requirements, lack of economies of scale, and increasing regulatory burdens. Autonomous driving remains an expensive niche product for large corporations or government-funded pilot regions. Society shows itself to be resistant. Low to minimal acceptance of autonomous vehicles extends across all population groups. Uncertainties about safety, data protection, and loss of control dominate the debate. Many consider autonomous vehicles “unnatural” and distrust the technology. Political power plays dominate global affairs—international cooperation has become the exception. Technological exchange is geopolitically charged, and trust between economic powers is low.

Society, Environment, and Politics

In the technical field, disillusionment prevails. Instead of breakthroughs, challenges due to costs and technological limits define the innovation climate. Further development has stalled, particularly in sensor technology and complex AI systems. Instead of regulatory support, fragmentation dominates. Strict and inconsistent laws block innovation: every country—sometimes even every region—sets its own requirements. Approval procedures are lengthy, internationally incompatible, and lead to the isolation of national markets. This regulatory situation is further aggravated by technological dead ends. Particularly in hardware, bottlenecks occur—computing power, energy efficiency, and reliability stagnate. Advances in computer vision are lacking, causing autonomous systems to fail at perceiving their environment. Under these conditions, stagnation and innovation brakes due to outdated regulations are the logical consequence. Bureaucratic processes act as a brake on any form of agility. Even successful test projects are blocked by outdated laws. In industry, this results in limited implementation of innovations. Many concepts remain in the lab or at the study stage. The transition to marketable products either does not occur or fails at an early stage. Infrastructure also lags behind. Delayed implementation of V2X communication prevents the establishment of connected traffic systems. Vehicles cannot communicate with traffic lights, roads, or other cars—reducing efficiency and safety. Another problem is human–machine interaction. The delayed introduction of human-behavior estimation means that autonomous vehicles cannot correctly predict the behavior of other road users. This significantly undermines the sense of safety.

Acceptance of and Need for Mobility

Society’s response to these developments is a clear withdrawal. The need for mobility is decreasing—not only for technology-related reasons but also out of frustration over constantly failing innovation promises. The economic consequences are tangible: instead of growth, there is a decline in economic dynamism. Investments flow into other sectors, technological leadership positions are lost. Germany loses competitiveness.

4.5.2. Scenario 2: Breakthrough in Innovation Centers

Economic Environment

The mobility sector is undergoing a phase of profound renewal. In leading regions of the world, new vehicle technologies are emerging that align with dedicated operational areas and specially designed infrastructures. This technological openness enables a rapid transformation toward automated mobility—independent of established legacy structures. To meet the challenges of connected mobility, manufacturers are increasingly adopting adaptive cybersecurity strategies. These allow for dynamic defense against new threats and strengthen trust in autonomous systems. At the same time, automated parking is being implemented on a large scale—particularly in urban centres. Intelligent parking infrastructures and communicating vehicles increase efficiency and reduce land use. A key driver of this development is falling costs: Vehicle costs are trending downward, supported by economies of scale, modular construction, and competitive pressure. As a result, automated mobility becomes accessible to broader segments of the population. In parallel, autonomous driving enjoys high acceptance. Pilot projects attract great interest, and the desire for increased safety, comfort, and efficiency promotes the uptake of related services. Economically, this results in a new division of labor along global supply chains, leading to rising prosperity and productivity. Efficiency gains through automation and data-driven optimization open new export opportunities, particularly for technology-oriented countries.

Society, Environment, and Politics

The social and political landscape shows itself to be innovation-friendly: a technological breakthrough is achieved that meets with broad market acceptance. This is based not least on stable framework conditions and the clear expectations of politics and society. At the European level, harmonized EU regulations are being established to simplify market access, standardize norms, and thus enable rapid scaling across national borders. In the field of vehicle sensor technology, a revolution is underway: high-performance hardware drives computer vision to a new level. Vehicles reliably recognize complex traffic situations in real time, significantly enhancing system safety and comfort. Politically, this transformation is accompanied by flexible regulation. Legislators and authorities enable the seamless integration of new vehicle technologies without lengthy approval processes. This accelerates innovation cycles and minimizes risks for investors. In practice, a clear picture emerges: autonomous systems are being deployed on a large scale—in both the public and private sectors. Logistics, passenger transport, and on-demand services benefit equally. Technological infrastructure is at the cutting edge: comprehensive networking through V2X communication is taking place. Vehicles exchange information with transport infrastructure, other road users, and central control systems—further enhancing the efficiency and safety of the overall system. Another milestone is the widespread integration of human-behavior estimation into mobility and safety systems. Vehicles can anticipate human behaviour—for example, from pedestrians, cyclists, or unpredictable road users—significantly improving road safety, especially in complex urban environments.

Acceptance and Need for Mobility

At the same time, people’s mobility needs are increasing. Urbanization, flexible work models, and individualized lifestyles are leading to more frequent use of multimodal and autonomous mobility services. This development is also reflected in economic growth. Rising demand boosts investment, creates jobs in new sectors, and strengthens especially those countries that invested early in autonomous technologies.

4.5.3. Scenario 3: Connected Transition Phase

Economic Environment

The development of mobility is characterized by a dual-track transformation: on one hand, the integration of autonomous vehicles into existing transport systems is progressing gradually; on the other hand, a major breakthrough is still lacking, as existing infrastructures continue to dominate. New technologies must laboriously establish themselves alongside conventional mobility solutions, leading to a delayed transformation. In the field of safety, a divided picture emerges: while adaptive cybersecurity strategies achieve partial progress, fragmented security approaches continue to dominate, causing implementation delays. Differing standards and a lack of interoperability hinder the safe deployment of autonomous systems. At the same time, there is a steady rollout of automated parking—particularly in urban zones and parking garages. This segment is becoming increasingly automated, even though regulatory issues elsewhere continue to slow progress. Costs remain a major challenge. Although vehicle prices are no longer rising, they remain consistently high. This leads to selective access to technology: autonomous vehicles remain, for now, a premium product. In society, ambivalent opinions about autonomous driving prevail. While tech-savvy users are open to autonomous services, many people remain uncertain about responsibility, ethics, and control. Widespread market acceptance has therefore not yet been achieved. Meanwhile, persistent geoeconomic tensions affect global supply chains and development partnerships. International technology transfer is slowing down, and dependencies on specific markets (e.g., semiconductor production) complicate long-term investment planning.

Society, Environment, and Politics

On the innovation side, progress is stable but slow. Technologies are improving continuously, but major breakthroughs are lacking. Added to this are technological and financial barriers that discourage smaller companies from entering the market. The regulatory side can hardly keep up with the pace of technological change. While there is no general resistance to progress, bureaucratic hurdles slow down processes without completely halting them. In practice, approval deadlines, testing procedures, and conflicts over jurisdiction lead to delays in the trial phase of new mobility services. Technologically, solid progress can be observed in sensor and hardware development. Systems continue to evolve steadily, increasing operational safety, but performance remains limited—revolutionary innovations are still missing. Politically, there is a gradual adaptation to regulatory challenges. Individual cities and regions are creating test areas, but a national or EU-wide strategy is lacking. Innovation depends heavily on regional political will and resources. This uncertainty is also reflected in implementation. Many ideas are realized but rarely scaled up. The patchwork of isolated initiatives prevents the emergence of scalable solutions. Nevertheless, some regions are managing the gradual introduction of V2X technologies. Vehicles communicate with infrastructure—but only in selected zones and with limited functionality. In the area of vehicle safety and behavioral recognition, efforts are underway toward the gradual introduction of human-behavior estimation. Initial applications in highly specialized contexts show potential, but widespread implementation remains a matter for the future.

Acceptance and Need for Mobility

At the societal level, a stagnation in mobility needs is becoming apparent. The initial enthusiasm for new mobility concepts has waned, while new living and working models (home office, hybrid models) have not changed or relieved traffic as much as expected. Economically, the picture remains subdued. There is no significant growth, which also affects investments in new mobility concepts. Large-scale funding remains scarce, and many pilot projects remain stuck at the prototype stage.

4.6. Scenario Transfer Results

The selection of a main reference scenario, which forms the basis for future planning and the creation of a transformation strategy, as shown in Figure 4 and further explained in this section.

Scenario 1 occupies the lowest strategic relevance within the matrix, which aligns with its characterization as a stagnating and regressive development path. The scenario depicts a context of technological paralysis and societal withdrawal, in which autonomous mobility fails to progress beyond fragmented pilot projects. High regulatory barriers, rising costs, and the absence of societal trust create an innovation climate dominated by inertia. From a strategic management perspective, this scenario primarily serves as a risk-oriented baseline: it identifies systemic vulnerabilities such as the lack of standardization, geopolitical fragmentation, and declining public acceptance that could threaten competitiveness if unaddressed. Therefore, while Scenario 1 is of low strategic relevance for active development, it is crucial as a cautionary reference, warning organizations about the consequences of regulatory rigidity, insufficient cooperation, and societal disengagement.

Scenario 2 is positioned at the upper end of the strategic relevance scale, reflecting its potential as a target-oriented and transformative future. It represents an optimistic trajectory in which autonomous mobility achieves large-scale deployment supported by harmonized regulations, cost reductions, and broad societal acceptance. The scenario’s high relevance stems from its function as a strategic vision that embodies technological maturity, efficient governance, and socio-political alignment. By highlighting innovation clusters and adaptive cybersecurity strategies, Scenario 2 defines key success factors for realizing systemic transformation—namely regulatory harmonization, international cooperation, and market-driven scalability. Strategically, it serves as the guiding blueprint for policy and industry actors aiming to position themselves as global leaders in automated mobility, emphasizing the economic benefits of productivity growth and the societal gains of safety and inclusivity.

Scenario 3 occupies a medium position in the strategic relevance matrix, illustrating a gradual and uneven transition toward autonomous mobility. It combines partial progress—such as steady technological improvement and regional policy adaptation—with persistent structural challenges like regulatory inconsistency and limited scalability. From a strategic standpoint, this scenario is particularly relevant for identifying incremental development pathways and adaptive strategies under conditions of uncertainty. It underscores the need for long-term investment in infrastructure, coordinated standardization, and flexible governance to overcome stagnation risks. As a bridge between the stagnation of Scenario 1 and the breakthrough of Scenario 2, the connected transition scenario holds moderate but pragmatic strategic importance: it guides stakeholders in managing hybrid systems where human and automated mobility coexist, while progressively preparing for broader integration and market maturity.

Scenario 3, “Connected Transition Phase,” is strategically important as a reference for adaptive and incremental planning. It portrays a realistic and moderate development path in which progress toward autonomous mobility occurs gradually rather than through disruptive breakthroughs. This scenario acknowledges both the technological advancements already underway and the persistent structural barriers—such as regulatory fragmentation, high costs, and limited public acceptance—that constrain rapid transformation. As such, Scenario 3 offers a pragmatic foundation for strategy development, guiding organizations and policymakers in managing hybrid mobility systems where human-driven and autonomous vehicles coexist. It highlights the need for long-term investment, flexible regulation, and coordinated innovation policies, making it a valuable reference for developing resilient strategies under uncertainty and for preparing the gradual transition toward full automation.

The evaluation of opportunities and threats within the mobility transformation framework for South-East Lower Saxony (SON) is based on a multidimensional assessment of technological readiness, regional innovation potential, and systemic impact. The placement of each factor in the Chance–Danger Matrix, categorized as Wild Cards, Hot Topics, On the Edge, or No Resource Input, reflects both its strategic relevance and its expected influence on the design field of autonomous and digital mobility within the SON region, as shown in Figure 5. The matrix itself is created in a workshop setting as a qualitative transfer tool, where experts jointly assign topics through structured discussion/brainstorming rather than via a formal quantitative scoring, aggregation, or threshold scheme.

Factors such as Supply Chain Problems (1), Internet Expansion (8), Autonomous Chip Production (9), Economic Growth Through Automation (15), and Geopolitical Dependencies (18) were allocated to the Wild Card category due to their high potential to either accelerate or disrupt mobility innovation in SON. These developments are largely influenced by global or national trends beyond regional control, yet they exert substantial indirect impact on local innovation ecosystems. For instance, the Intel semiconductor investment in Saxony (9) could foster a resilient technological value chain across northern Germany, benefiting SON’s automotive suppliers through proximity and potential spillover effects in microelectronics. Conversely, global supply chain dependencies (1) and geopolitical tensions (18) represent major uncertainties that could disrupt the flow of raw materials and components crucial to vehicle electrification and automation. Economic growth through automation (15) serves as a wildcard opportunity since the regional automotive and logistics industries in SON could leverage automation to boost productivity, though its realization depends on broader macroeconomic stability and investment willingness. Nationwide 5G expansion (8), while initiated on the federal level, directly determines the feasibility of connected mobility infrastructures in SON, underlining the high-impact yet externally driven nature of this category.

Hot Topics represent factors with immediate strategic relevance that SON’s stakeholders, especially regional policymakers, automotive OEMs, and research institutions, can actively influence. Skilled Labor Shortage (2) and Technological Stagnation (19) are pressing threats, given the strong concentration of advanced manufacturing and R&D in the region. SON’s universities and technical colleges play a critical role in mitigating this through targeted qualification programs and interdisciplinary research initiatives. Similarly, Mobility Digitalization (4), High-Tech Profiling (5), and Infrastructure Modernization (13) are categorized as opportunities with high short-term leverage. SON benefits from an established innovation ecosystem, centered around Wolfsburg, Braunschweig, and Salzgitter, where digital services, AI-driven traffic management, and intelligent transport infrastructure can be integrated into existing urban mobility concepts. Public Skepticism (20) and Bureaucratic Hurdles (7), while threats, remain hot topics because they are partly addressable through regional governance and communication strategies. Enhanced Public Acceptance Campaigns (16) can therefore directly counterbalance skepticism, reinforcing SON’s leadership in testing and acceptance of automated mobility technologies.

Factors categorized as On the Edge, such as Technological Leap (3), Knowledge Integration (6), AI-Based Safety Systems (12), Cross-Sector Collaboration (14), EU Laws (10), Fragmented Legal Frameworks (17), High System Costs (21), and Regulatory Delay (22), reflect challenges and opportunities that evolve gradually and require sustained policy coordination. For SON, where automotive supply chains are dominated by medium-sized enterprises, technological leaps (3) pose a risk of overextension and competitive exclusion. However, fostering cross-sector collaboration (14) between industry, academia, and public agencies could help SMEs integrate new technologies incrementally. Knowledge integration (6) and AI-based safety systems (12) represent high-potential enablers for safer, data-driven mobility, aligning with SON’s research infrastructure, such as the TU Braunschweig’s automotive AI programs. In contrast, EU-level legislation (10) and fragmented legal frameworks (17) pose external regulatory constraints that SON must adapt to but cannot directly shape. The cost and regulation-related factors (21 and 22) underscore financial and administrative inertia, which could limit the scalability of pilot projects despite SON’s advanced technological base.

The No Resource Input category comprises factors of limited immediate strategic significance, such as Localized Pilot Projects (23), Incremental Sensor Improvements (24), Niche Market Fragmentation (25), and Limited Public Communication Efforts (26). These issues yield marginal returns relative to investment and resource allocation, warranting only minimal strategic focus. In SON, small-scale pilot projects and sensor upgrades are already underway but lack sufficient integration or scalability. Without coordinated policy and communication frameworks, their cumulative impact remains low. Niche market fragmentation (25), characterized by redundant innovation efforts among small firms, weakens regional cohesion and diverts resources from system-level innovation. Similarly, limited public communication (26) contributes to low public visibility of SON’s technological achievements, underscoring the need for targeted outreach to enhance the region’s perception as a European hub for sustainable mobility innovation.

The threats identified across the analyzed fields of action are highly relevant to the SON region, given its structural dependence on the automotive and manufacturing industries, medium-sized supplier networks, and limited diversification in high-tech sectors. Labor market vulnerabilities—such as skill shortages and potential outmigration of qualified professionals—pose significant risks to regional innovation capacity. Similarly, the gradual pace of digital infrastructure expansion and regulatory fragmentation could exacerbate existing disparities between urban and rural areas, limiting the region’s competitiveness in autonomous and connected mobility. The sustainability transition further introduces economic pressure on SMEs through compliance costs and raw material constraints, while insufficient R&D investment and slow technology adoption risk widening the innovation gap vis-à-vis leading European regions. Overall, these threats are empirically valid for SON’s industrial structure and demographic trends, underscoring the need for proactive governance, targeted upskilling strategies, and strengthened research-industry linkages to sustain long-term regional resilience and competitiveness.

5. Discussion

The scenario-based analysis of Southeast Lower Saxony’s automotive sector reveals the systemic interdependence of technological, economic, and socio-regulatory dynamics shaping regional transformation. The three derived scenarios, Innovation Standstill and Societal Withdrawal, Connected Transition Phase, and Breakthrough in Innovation Centers, collectively delineate a plausible spectrum of mobility futures toward 2035. Methodologically, the structured scenario management approach applied here, following Gausemeier et al., proved effective in managing complexity and uncertainty by systematically linking qualitative expertise with quantitative interrelation modeling.

The influence and relevance analyses demonstrated that factors such as workforce qualification, digital infrastructure, regulatory harmonization, and R&D intensity exert the highest leverage on regional resilience. These relationships are consistent with broader findings on industrial transitions, confirming that technological diffusion in automotive clusters depends less on invention itself than on institutional capacity for coordination and knowledge transfer. The scenarios thus provide analytically robust boundary conditions for policy and industry: while the Best-Case scenario illustrates the potential of coordinated innovation ecosystems, the Worst-Case warns of stagnation driven by regulatory fragmentation and skill shortages, and the Trend scenario captures the most credible path of gradual adaptation.

From a methodological standpoint, the scenario technique offers substantial strengths. Its explicit decomposition of complex systems into influencing variables enables transparent causal mapping and interdisciplinary discourse. The combination of direct and indirect influence matrices captures nonlinear dependencies, and the subsequent consistency analysis ensures internal logical coherence of projections. The participatory expert-workshop format further enhances contextual validity by embedding local knowledge into the analytical framework. However, these strengths are balanced by limitations intrinsic to exploratory foresight: subjective weighting of factors, static temporal framing, and potential regional bias constrain external generalizability. The resulting scenarios should therefore be interpreted as structurally valid but not probabilistic, they define plausible developmental corridors rather than forecasts.

Within this methodological framework, internal validity can be considered high due to the transparent multi-phase process and metric-based consistency testing, whereas external validity remains moderate, limited by context-specific assumptions. The method’s epistemic value thus lies not in prediction but in its capacity to structure uncertainty, reveal systemic interactions, and support anticipatory strategy formation. Interestingly, the application of two different consistency approaches did not lead to significant differences, especially for the highly consistent scenarios, further practice is needed to determine the benefits and difficulties of both approaches.

Overall, the study confirms that scenario management constitutes a scientifically rigorous yet adaptive framework for regional transformation analysis. It provides policymakers and industry leaders with a logically consistent foundation for decision-making under uncertainty, while clearly delineating the methodological limits of inference and confidence inherent to qualitative-quantitative foresight.

6. Conclusions

This paper presents a comprehensive analysis of the structural transformation within the German automotive sector, specifically examining its implications for SON through the application of scenario-based methodologies. Utilizing the scenario technique established by Gausemeier et al., the study identified and categorized 50 influencing factors, which were subsequently filtered through influence and relevancy matrices. The 15 most critical descriptors were synthesized to project the region’s development trajectory through 2035 across three distinct scenarios: Best-Case, Trend, and Worst-Case.

Collectively, these scenarios delineate a plausible “corridor of the future,” allowing regional stakeholders to evaluate strategic decisions against a spectrum of outcomes ranging from stagnation to dynamic leadership. The analysis highlights that the region’s future is heavily dependent on specific high-impact variables, most notably mobility demand, EU economic stability, vehicle approval and operating rights, and cybersecurity, alongside technological enablers such as computer vision and automated driving hardware and software. A key finding across all scenarios is that regulatory frameworks and software-centric competencies act as decisive co-determinants of success, equal in importance to pure technological progress. Finally, by introducing “Scenario Transfer Concepts,” this study bridges the gap between theoretical foresight and practical application, providing a robust foundation for resource allocation and strategic planning in SON.

7. Outlook

Looking ahead, additional techniques of scenario analysis could further enhance the understanding of the future mobility landscape in Southeast Lower Saxony. Scenario transfer would help translate insights from projections into actionable strategies, beginning with the selection of a reference scenario for benchmarking. An Opportunities–Threats Matrix would evaluate the risks and benefits tied to key mobility developments, while an Impact Analysis would assess the broader consequences of these factors, ensuring decisions are based on comprehensive insights. Retrospective future scenarios could also help evaluate the accuracy of past predictions, providing valuable feedback for future forecasting. In addition, the Delphi method could be used to gather expert opinions, offering a deeper understanding of uncertainties and trends. Trend analysis would identify emerging patterns, helping to track technological and societal shifts that could shape the region’s mobility landscape. Together, these methods would provide a more comprehensive view of the forces shaping the sector, allowing for better-informed decisions and strategic planning for the future of mobility in Southeast Lower Saxony.