A Conceptual Sustainability Assessment Framework for Urban Micromobility Systems

Abstract

Urban micromobility systems are increasingly deployed to support sustainable transportation goals; however, their overall sustainability performance remains inconsistently assessed across environmental, social, economic, and operational dimensions. This study proposes a conceptual framework for evaluating the sustainability of urban micromobility systems, with a particular focus on e-scooters. It clarifies and restructures fragmented indicators into distinct, non-overlapping sustainability dimensions. The framework is structured around five impact areas: Environment, Economy, Users, Transport Performance, and Safety, complemented by two enabling components, namely the legal framework and business model, which are conceptualized as preconditions for system feasibility rather than performance dimensions. Building on existing sustainability assessment literature, the framework consolidates established indicators while introducing micromobility-adapted and context-specific indicators, such as service availability and operational characteristics, to better capture the distinctive features of shared micromobility systems. The resulting framework provides a structured and flexible tool for researchers, planners, and policymakers, emphasizing that micromobility sustainability depends not only on measured impacts, but also on governance, operational design, and local implementation conditions.

1. Introduction

Transportation systems are fundamental to sustainable development, as they shape cities’ environmental quality, economic vitality, and social equity outcomes [1]. Traditional transport planning paradigms have increasingly shifted toward sustainability, requiring structured frameworks and indicator systems that support evidence-based decision making and performance measurement across multiple impact dimensions. Sustainability can be incorporated into transportation planning through frameworks supported by indicator sets that account for differences across vehicle types and operational characteristics [2]. Such frameworks are necessary to clarify what should be measured, what outcomes are expected, and which indicators are appropriate for evaluating performance [3]. Structured metrics can guide planning decisions and support compliance with strategic goals [2,4]. However, the most common sustainability assessment methodology is the sustainability indicator system [5]. These indicator-based approaches are necessary to ensure that strategic goals, such as emissions reduction or equitable access, are meaningfully operationalized in planning and policy processes [6,7].

In this evolving landscape, micromobility, and particularly shared electric scooters (e-scooters) and bicycles, has become a prominent element of urban transport systems worldwide. In this study, urban micromobility systems are defined as lightweight, low-speed transport modes, typically operating in urban environments, which are often electrically powered and provided through shared services (e.g., e-scooters and bicycles). Unlike conventional non-motorized transport modes, such as privately owned bicycles or walking, micromobility systems are characterized by their integration of digital platforms, fleet management operations, and business models, which introduce additional environmental, economic, and governance considerations that must be accounted for in sustainability assessments.

E-scooters are selected as the primary focus of this study due to their rapid spread in urban areas worldwide [8] and their prominent role in contemporary micromobility systems. Compared to other micromobility modes, e-scooters have attracted significant attention from policymakers and researchers due to their distinct operational characteristics, including shared fleet models, intensive redistribution practices, and relatively high safety concerns [9]. These features introduce a wider range of environmental, economic, operational, and governance-related impacts that lead to diverse and often conflicting findings and that must be considered simultaneously [10].

Micromobility is often promoted as a way to replace short car trips, increase accessibility, and reduce pollution, highlighting its potential contributions to sustainable transportation goals [11]. However, whether micromobility delivers net sustainability benefits remains an open question. Recent life-cycle assessments (LCA) have shown that shared micromobility can have significant Greenhouse Gas emissions (GHG) depending on usage patterns, materials, and operational models, underscoring the need for comprehensive impact analysis rather than assumptions of inherent sustainability [12,13]. Meanwhile, user choice dynamics, including perceptions of safety, cost, and convenience influence adoption and mode substitution patterns, further complicating assessments of sustainable outcomes [14].

Moreover, structural enablers and barriers, such as infrastructure readiness, safety conditions, and regulatory frameworks, significantly affect micromobility uptake and its integration with existing transport networks [15]. Systematic reviews also point to the need for inclusive sustainability evaluation that incorporates energy systems, policy contexts, and infrastructure design in addition to user behavior and modal integration [16,17]. High-level policy analyses, such as the International Transport Forum’s Greener Micromobility report [18], further emphasize the growing evidence in realizing the environmental potential of micromobility services.

Although micromobility is often associated with sustainability benefits, the literature presents mixed and sometimes conflicting findings. The benefits are conditional and strongly dependent on modal shift, vehicle lifespan, utilization rates, and operational practices such as charging and rebalancing. For example, shared e-mopeds and shared e-bicycles in Barcelona increased GHG emissions due to unfavorable modal shift patterns [19]. LCA studies have found that production, battery manufacturing, and low vehicle lifetime mileage lead most environmental challenges [13,20,21,22]. Additionally, emissions from collection, redistribution, and charging vehicles may overwhelm the benefits [23]. Dockless bikes often suffer from low utilization and high redistribution needs, raising emissions [24].

Despite this expanding literature, existing assessment approaches often remain fragmented, focusing on isolated impacts, single modes, or life-cycle performance, while giving limited attention to integrated frameworks that can support multi-dimensional decision making. Systematic reviews and bibliometric studies have highlighted that research on micromobility sustainability is still developing across disciplines, with governance and policy frameworks identified as underexplored in many cases [25,26]. Policy-oriented reports call for harmonized data, coordinated regulation, and integrated governance approaches, suggesting that comprehensive, multi-dimensional evaluation frameworks are yet to be fully established [18,27]. While environmental and social impacts are commonly assessed, dimensions such as safety, transport performance, governance, and business models are frequently omitted or treated separately rather than as part of integrated frameworks, limiting planners’ ability to evaluate alternatives holistically [16,26].

To address the underexplored areas identified in the literature, this study proposes an impact-based sustainability framework for micromobility, tailored in this study to e-scooters. A literature synthesis supports the identification of sustainability dimensions, its structure, and the selection of appropriate indicators. The resulting framework is organized around five impact areas: Environment, Economy, Users, Transport performance, and Safety, and two enabling components: Legislation and Business models. Conceptually, the framework is represented as a flower, with petals corresponding to impact areas and the soil representing the implementation area. Both impact areas and enablers are further detailed through criteria and Key Performance Indicators (KPIs), providing a valuable tool for researchers, planners, and decision-makers. Building on existing sustainability assessment literature, the proposed framework restructures established indicators within an integrated framework, while also adapting and extending selected transport-based indicators to capture micromobility-specific characteristics, such as service availability and operational conditions. This structured organization allows cities to choose, compare, and regulate micromobility systems by explicitly revealing trade-offs across sustainability dimensions and by clarifying how regulatory and business model configurations influence observed impacts.

The remainder of this paper is organized as follows. Section 2 provides the background for the study, including the sustainable development and policy context for micromobility (Section 2.1), a synthesis of transport sustainability impact frameworks (Section 2.2). Section 3 outlines a focused review of micromobility and e-scooter assessment studies, from which the indicator set is derived. Section 4 presents the proposed conceptual sustainability assessment framework, describing its logic, the five impact dimensions and two enabling components, and the associated criteria and KPIs. Section 5 discusses the main implications of the framework for planning and regulation, outlines limitations, and identifies directions for future research.

2. Background

The three pillars that underpin sustainability—environmental, social and economic—embrace a wide range of parameters that often overlap and extend across different contexts. Assessing whether an outcome is sustainable relies on performance measures that capture these environmental, social and economic dimensions. The need to depict the dimensions or the fundamental components of sustainability and the ways they interact has led to the development of several conceptual models. Most, however, are built around the same three foundational dimensions: environment, society and economy.

Assessing the sustainability of a transportation system requires that its key parameters be clearly defined and measurable. These parameters must indicate how the system can become more sustainable and the conditions necessary to achieve that goal. A coherent way to present this assessment process is through a structured framework.

2.1. Sustainable Development and Micromobility

Sustainable development constitutes a central policy objective shaping transportation planning at global, regional, and local levels. Although transport is not represented by a standalone Sustainable Development Goal (SDG), it is widely recognized as a critical enabler for achieving multiple SDGs, particularly those related to health, energy, infrastructure, cities, and climate action. Transport-related contributions are reflected across SDG 3 (Good Health and Well-Being), SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), where sustainable mobility systems are expected to reduce emissions, improve accessibility, and enhance urban quality of life [28].

Within this policy context, micromobility has gained attention as a potential contributor to sustainable urban transport strategies, particularly for short trips and first- and last-mile connectivity. Existing research indicates that micromobility can support SDG-related objectives by reducing car dependency, enhancing accessibility, and complementing public transport systems when appropriately integrated into urban networks. For example, Olabi et al. [29], assessed the role of shared micromobility systems in advancing SDG targets related to health and well-being (SDG 3), decent work and economic growth (SDG 8), sustainable cities and communities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13). However, the literature also consistently shows that these contributions are highly context-dependent, varying significantly across cities as a function of governance arrangements, infrastructure provision, regulatory frameworks, and operational models.

At the policy level, European and national initiatives increasingly promote micromobility as part of clean and sustainable urban mobility strategies, linking the integration of bicycles and light micromobility modes to decarbonization, improved connectivity, and modal shift objectives within long-term urban development agendas [30,31]. At the same time, the literature emphasizes that micromobility outcomes are highly sensitive to governance conditions: policies that support multimodal integration, equitable access, and adaptive regulation are more likely to realize sustainability benefits, whereas fragmented or overly restrictive frameworks may limit positive impacts or exacerbate inequalities in transport access [32].

This variability highlights a key challenge for urban decision-makers: the sustainability relevance of micromobility cannot be assumed a priori but must be evaluated in relation to local priorities and institutional conditions. Accordingly, in this study, the SDGs are not treated as an additional assessment layer or set of indicators. Instead, they serve as a normative policy reference that informs stakeholder-defined priorities within the proposed framework. Cities may emphasize different sustainability dimensions, such as safety, accessibility, or climate impacts, depending on their alignment with specific SDG targets, which are incorporated ex-ante through the prioritization process rather than directly measured.

2.2. Transport Impact Frameworks

Based on literature findings, several sustainability frameworks have been developed to assess impacts in the transport sector. Each framework utilizes different impact areas and indicators to account for transport mode characteristics (i.e., urban vehicles, freight, public transport, etc.) and support the community sustainability goals. For example, Zietsman et al. [33] used indicators, which grouped them under three aforementioned sustainability dimensions. Opposed to the three-dimensions approach, Mitropoulos et al. [34] developed a long-term integrated sustainability framework consisting of four dimensions (Environment, Technology, Energy and Economy) and three controllers (users, legal framework and local constraints) to assess the impacts of vehicle technologies. Based on these dimensions a set of indicators was developed for assessing the sustainability performance of five urban transportation vehicles, by considering impacts during their lifecycle [2,35]. Indicators were aggregated onto a single sustainability index to measure the overall sustainability per vehicle type [2].

Onat’s et al. [36] broadened the existing Life Cycle Sustainability Assessment (LCSA) framework by considering macrolevel environmental, economic, and social impacts. Onat et al. [37] added in the aforementioned framework the uncertainty to address both methodological challenges and uncertainties in transportation sustainability research. In both studies, a case study was performed assessing impacts for Internal Combustion Vehicles (ICVs), Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Battery Electric Vehicles (BEVs) quantifying dynamically seven sustainability impact categories. Additionally, Onat et al. [38] developed a hybrid multi-regional input-output based LCSA model to quantify fourteen sustainability indicators representing the three pillars of sustainability applied in four different electric vehicle technologies. This study focused on the vehicle’s operation as it was considered the most influential phase in terms of environmental impacts during its life cycle [38].

While these studies emphasize life-cycle–based evaluation, Jeon et al. [5] highlight that sustainability assessment can also be integrated at the planning stage to guide decision-making, and support policies that shape regional sustainability outcomes. Their work evaluated several performance measures and aggregated them into indices representing four dimensions of sustainability: transport system effectiveness, environmental, economic, and social impacts, to enable visualization and assessment of tradeoffs for the competing alternatives.

Logistic measures were assessed by Nathanail [39] using an integrated evaluation framework grounded in lifecycle analysis. Thus, the resulting multi-stakeholders multicriteria decision support platform consisted of sustainability disciplines, criteria and indicators, for assessing urban freight transport measures.

Building on this broader line of sustainability evaluation, Buenk et al. [40] and Latinopoulos et al. [41] proposed structured evaluation frameworks tailored to (micro) transit systems and micromobility, respectively. Buenk et al. [40] developed a comprehensive framework grounded in a systematic literature review, identifying 12 thematic areas and 50 sustainability indicators relevant to transport systems. Their approach combined expert input with weighting techniques to validate the framework for (micro) transit applications. The resulting tool is intended to support short-term decision-making in the design of urban transport services, monitor long-term progress toward sustainability goals, and guide policy development across diverse transport contexts.

Similarly, Latinopoulos et al. [41] focused on the rapidly expanding domain of e-scooters, synthesizing lessons from successful and unsuccessful deployments to propose an evaluation framework for micromobility systems. Their framework organizes operational, regulatory and user-related aspects into dimensions that help planners and policymakers assess system performance and anticipate challenges. A case study in Paris demonstrated the framework’s applicability and highlighted insights for designing or optimizing e-scooter systems to ensure their safe and sustainable integration into urban mobility networks.

In line with the growing interest in micromobility, recent research has also examined the sustainability performance of shared bicycle systems through life-cycle–based approaches. A study of Capital Bikes in Washington D.C. [21] compared dockless and docked schemes, showing that infrastructure installation dominates emissions in docked systems, whereas fleet management and maintenance drive impacts in dockless ones. Manufacturing was identified as the largest overall emission source, and achieving net environmental benefits requires long-term bicycle use. The findings highlight the need for proactive maintenance, extended equipment lifetimes and user incentives to maximize the sustainability of shared bicycle programs.

Table 1. Sustainability dimensions and transport systems examined in selected studies.

Study	Sustainability Dimensions/Impact Areas							Transport Modes Examined
	Environment	Economy	Society	Users	Energy	Technology	System Effectiveness
Mitropoulos et al., [34]	✓	✓		✓	✓	✓		Urban transport vehicles
Mitropoulos et al., [42]	✓	✓		✓	✓	✓		Urban transport vehicles, buses
Jeon et al., [5]	✓	✓	✓				✓	Private vehicles, public transit, freight
Mitropoulos and Prevedouros, [35]	✓	✓		✓	✓	✓		Private car, car-sharing, bus
Onat et al., [36]	✓	✓	✓					Alternative vehicle types
Mitropoulos and Prevedouros, [2]	✓	✓		✓	✓	✓		Urban transport vehicles
Onat et al., [37]	✓	✓	✓					Alternative vehicle types
Nathanail, [43]	✓	✓	✓	✓	✓		✓	Urban freight transport
Onat et al., [38]	✓	✓	✓					Electric vehicles
Buenk et al., [40]	✓	✓	✓				✓	Micromobility
Latinopoulos et al., [41]	✓		✓	✓			✓	Micromobility
Chen et al., [21]	✓							Bike sharing

summarizes the sustainability dimensions and impact areas addressed in selected studies, along with the transport modes under examination. The comparison shows that while several frameworks incorporate multiple sustainability dimensions, none integrates all the identified impact areas within a single, unified structure. This mapping underscores the fragmented nature of existing assessment approaches and reveals variations in how sustainability and mobility performance are conceptualized across studies.

To further examine the strengths and limitations of existing approaches,

Table 2. Comparative analysis of existing transport sustainability assessment frameworks.

Study	Advantages	Disadvantages	Applicable Scenarios	Transport Modes Examined
Mitropoulos et al., [34]	Multi-criteria evaluation; Entire life cycle approach; Decision support tool; Multi-stakeholder integration.	Need for detailed data; Complex implementation and resource intensive.	Evaluation and comparison of measures and policies in cities.	Urban transport vehicles
Mitropoulos et al., [42]	Entire life cycle approach; Multi-dimensional assessment.	Need for detailed data.	Five types of urban transportation vehicles (ICEV, HEV, EV, DB, HDEB).	Urban transport vehicles, buses
Jeon et al., [5]	Multi-dimensional assessment; Decision support tool.	Does not capture interdependencies among dimensions; Relies on available data sources.	Atlanta; Suitable for regional transportation planning.	Private vehicles, public transit, freight
Mitropoulos and Prevedouros, [35]	Entire life cycle approach; Multi-dimensional assessment.	Relies on data generated with multiple tools and model assumptions.	Road vehicles; Six types ((shared) ICEV, (shared) HEV, Diesel bus, Hybrid bus).	Private car, car-sharing, bus
Onat et al., [36]	Casual loop modeling (dynamic perspective).	Integrates only the 3 fundamental dimensions; lack of appropriate data and integration difficulties with dynamic modeling; Limited indicators used.	US passenger transportation system (ICVs, HEVs, PHEVs, and BEVs).	Alternative vehicle types
Mitropoulos and Prevedouros, [2]	Entire life cycle approach; Decision support tool; Robust across weighting scenarios.	Relies on data and model assumptions; Results vary by region and may need local adjustment.	Road vehicles; Nine types (ICEV, HEV, FCV, EV, GPT, GSUV, DB, DBRT).	Urban transport vehicles
Onat et al., [37]	Holistic and dynamic assessment; Incorporates uncertainties.	Integrates only the 3 fundamental dimensions; Still under development.	Passenger vehicles in the US (ICVs, HEVs, PHEVs, EVs).	Alternative vehicle types
Nathanail, [43]	Holistic multi-stakeholder approach; Multi-dimensional assessment; Entire life cycle approach; Allows for adaptation.	Need for detailed data and coordination among involved stakeholders.	Urban freight transport policy and solutions assessment.	Urban freight transport
Onat et al., [38]	Entire life cycle approach; Allows for expansion.	Integrates only the 3 fundamental dimensions; Focus on SUVs, limiting transferability.	Qatar, where electricity generation is natural gas based.	Electric vehicles
Buenk et al., [40]	Decision support tool; Allows for customization; Use of weighted indicators.	Integrates only the 3 fundamental dimensions; Large array of indicators poses challenges; Potential emphasis bias.	Urban microtransit systems; No empirical application or case study.	Micromobility
Latinopoulos et al., [41]	Holistic assessment; Decision support tool; Allows for adaptation.	Requires access to detailed data; Excludes system operations’ elements.	Dockless e-scooters in Paris.	Micromobility
Chen et al., [21]	Entire life cycle approach.	Environmental impacts only; Direct applicability to other urban contexts is limited.	Dock-based (piling) and dockless bike sharing modes in Washington D.C.	Bike sharing

presents a comparative analysis of representative frameworks, highlighting their key advantages, disadvantages, and applicable contexts.

3. Assessment of Micromobility Systems

Shared mobility and micromobility systems have developed rapidly in recent years, through both sharing schemes and privately owned options [44]. Among these modes, e-scooters are widely used for short urban trips, especially for first- and last-mile connections [13,45]. Due to these characteristics, they are considered a promising alternative to conventional motorized travel, with the potential to mitigate traffic congestion by substituting car trips [45,46,47].

This growing adoption has increased the need to evaluate the e-scooter impacts across environmental, social, and economic dimensions, and numerous studies have quantified these impacts [8,48,49,50,51,52]. The majority of existing studies examine environmental impacts during their life cycle [13,23,53,54,55,56], while fewer explore the potential of e-scooters as a substitute to other modes [1,23,41,57,58,59,60] or report a case study/pilot findings [1,41,59,61]. Given their relatively recent introduction as mobility solution, their effects are mainly reported in cases.

Commonly used environmental indicators for e-scooters include GHG emissions, global warming potential, energy consumption, resource depletion and fine particulate matter formation. From a social perspective, research has mainly examined user behavior and travel choices, particularly modal shift patterns from private cars to e-scooters, which may contribute to improved quality of life through reduced environmental impacts.

By comparison, economic and transport system performance impacts remain underexplored in the literature. The limited attention to economic aspects may be partly explained by the relatively low cost associated with e-scooter usage. Additionally, safety emerges as a key area of concern in the literature and is treated in this study as a distinct impact category, unlike previous frameworks where it has been grouped under economic [34] or social dimensions [5,38]. Existing research has extensively investigated safety-related issues, including crash characteristics (e.g., lighting and weather conditions, alcohol involvement), injury patterns, helmet usage, interactions with pedestrians and vehicles, as well as the frequency and severity of incidents and instances of non-compliant riding behavior.

3.1. Indicator Screening Method

To identify relevant KPIs for assessing e-scooter and micromobility systems, a structured literature search was conducted across multiple academic databases, including Scopus, Google Scholar, SAGE, and MDPI. The search was limited to peer-reviewed journal articles published in English between 2001 and 2025.

A combination of keywords was used to capture relevant studies. Core terms included “electric scooter”, “e-scooter”, “scooter”, and “micromobility”, which were combined with additional terms related to assessment objectives, such as “impact”, “evaluation”, “assessment”, “comparison”, and “sustainability”. These combinations resulted in search strings such as “e-scooter impacts”, “micromobility sustainability assessment”, and “e-scooter evaluation”. The search targeted keywords appearing in the title, abstract, and author-defined keywords of each study.

To ensure relevance, a set of eligibility criteria was applied. Studies were included if they: (i) examined micromobility systems, particularly e-scooters; (ii) proposed, applied, or discussed performance indicators or impact assessment approaches; and (iii) contributed to at least one sustainability-related dimension. Additional filtering was applied based on subject areas, including Engineering, Environmental Science, Energy, Computer Science, Mathematics, and Economics.

Given the conceptual objective of this study, the review does not aim to be exhaustive in a systematic review sense. Instead, studies were selectively retained based on their relevance and contribution to the development of the framework. In particular, emphasis was placed on studies introducing distinct dimensions, novel indicators, or context-specific assessment approaches, while studies repeating already well-established indicators without additional insights were not prioritized.

In addition to the literature-based screening process, the selection and organization of indicators were conceptually based on the SDGs. Specifically, SDGs provide a high-level policy context that guides the identification of relevant sustainability dimensions and the relative importance of specific impact areas, such as safety, accessibility, and environmental performance.

3.2. Indicators for E-Scooters’ Evaluation

The indicators of the examined studies are presented in detail in the following tables (

Table 3. Environmental identified KPIs.

Indicator	Unit of Measure	Study
Climate change	g. CO2 eq./PKT 1	[54]
Energy	MJ. eq./PKT	[54]
Resource damage	$/PKT	[54]
Human health damage	DALY/PKT	[54]
Ecosystem damage	species. year/PKT	[54]
Energy	% of total energy used by all passenger trips	[62]
CO2 equivalent savings (GWP) 2	% of kgCO2	[57]
Avg. energy consumption per trip	% of SoC 3	[63]
Energy consumption per pkt	% of SoC	[63]
Energy loss in idle status	% of SoC	[63]
GHG emissions	g. CO2 eq./PKT	[55]
GHG emissions	% per lifecycle stage	[55]
Global Warming	g. CO2 eq./PKT	[23]
Fine particulate matter formation	g. PM2.5 eq./PKT	[23]
Mineral resource scarcity	g. Cu eq./PKT	[23]
Fossil resource scarcity	g. oil eq./PKT	[23]
Global Warming	% of kg CO2 eq./PKT per LCA stage	[23]
Fine particulate matter formation	% of kg PM2.5 eq./PKT per LCA stage	[23]
Mineral resource scarcity	% of kg Cu eq./PKT per LCA stage	[23]
Fossil resource scarcity	% of kg oil eq./PKT per LCA stage	[23]
Global warming	g. CO2 eq./PKT	[13]
Respiratory effects	% of the PM2.5 eq.	[13]
Acidification	% of SO2 eq.	[13]
Eutrophication	% of g N eq.	[13]

¹ PKT: Passenger Kilometer Traveled. ² GWP: Global Warming Potential. ³ SoC: State of Charge.

,

Table 4. Social/Users identified KPIs.

Indicator	Unit of Measure	Study
Utilization rate	trips per day	[59]
E-scooters repositions per day	%	[59]
Modal shift ratio from walking to e-scooter	% trips	[59]
E-scooter complement bus system	% trips	[59]
Modal shift ratio from bus to e-scooter	% miles	[59]
E-scooter distance when used as a bus complement	% distance	[59]
Trips serving first/last mile connections	% trips	[59]
Modal shift ratio from carsharing to e-scooter	% trips	[1]
Impact of shared e-scooters on weekday bus ridership (utilitarian trips)	% of shared e-scooter trips	[64]
Impact of shared e-scooters on weekday bus ridership (social trips)	% of shared e-scooter trips	[64]
Net impact of shared e-scooters on weekday bus ridership	% of shared e-scooter trips	[64]
Bike sharing usage	% of average weekly usage	[60]
Bike sharing usage frequency	% of trips per week and membership	[60]
Bike sharing usage intensity	% of trips per week and trip duration	[60]
Change in bike sharing usage over time	% of trips per week and different time periods	[60]
Motivation for riding e-scooters	% of total respondents	[58]
Trip purpose	% of total respondents	[58]
Travel mode alternative to e-scooters	% of total respondents	[58]
Usage of e-scooters for commuting	% of the respondents	[57]
Commuting distance (unimodal or intermodal)	% of respondents who use e-scooters for commuting	[57]
Potential e-scooter users	% of respondents who have replaced their commuting mode with e-scooter	[57]
Modal shift ratio from any mode to e-scooter	% of the respondents	[57]
E-scooter usage for longer than 45 min trips	% of the respondents	[57]
Time savings	%	[57]
Modal shifts from other modes to dockless e-scooters	% users	[23]
Modal shifts from other modes to personal e-scooters	% users	[23]
Number of the real trips over 28 days	trips	[65]
Sharing frequency per scooter per day	frequency	[65]
Overall repositioning ratio	%	[65]
Repositioning ratio for rebalancing	%	[65]
Repositioning ratio for charging	%	[65]
Reasons for trying e-scooters for the first time	% of the respondents	[13]
Modal shifts from e-scooter to other modes	% of the respondents	[13]
Trip purpose	% of e-scooter users	[41]
Time of use	% of e-scooter users	[41]
Travel time	% of e-scooter users	[41]
Modal shifts from other modes to e-scooters	% users	[41]
Infrastructure where non-users think people should ride e-scooters	% of non-users	[41]
Infrastructure where e-scooter users actually ride	% of e-scooter users	[41]
Location of e-scooter parking	% of e-scooter users	[41]
Change in total distance traveled in the network	% of mobility consumption on a km basis	[55]
Modal shifts from other modes to e-scooters	%	[55]

,

Table 5. Economic identified KPIs.

Indicator	Unit of Measure	Study
Cost savings	% per km	[57]

,

Table 6. Transport performance identified KPIs.

Indicator	Unit of Measure	Study
Maximum vehicle queue length	% change in length with increasing e-scooter share (from 0 to 50%)	[41]
Maximum vehicle delay	% change in delay with increasing e-scooter share (from 0 to 50%)	[41]

and

Table 7. Safety identified KPIs.

Indicator	Unit of Measure	Study
Weather condition	% of e-scooter crashes	[66]
Lighting condition	% of e-scooter crashes	[66]
Alcohol presence in e-scooter rider	% of e-scooter crashes	[66]
Alcohol presence in motorist	% of e-scooter crashes	[66]
E-scooter crash distance from home in miles	% of e-scooter crashes	[66]
General crash location	% of e-scooter crashes	[66]
Accident type	% of reported injuries associated with e-scooters	[67]
Location of injuries occurring	% of e-scooter riders	[68]
Circumstances of injuries (involved vehicle or other road user)	% of e-scooter riders	[68]
Circumstances of injuries (did not involve vehicle or other road user)	% of e-scooter riders	[68]
Trip characteristics	% of e-scooter riders	[68]
Illegally operated shared e-scooters	% of shared e-scooters	[69]
Riding location	% of e-scooters	[69]
Conflicts	% of shared e-scooters ridden on the footpath	[69]
Illegal riding	% of shared e-scooters	[70]
Helmet use	% of shared e-scooter riders	[70]
Type of conflict	% of shared e-scooter riders	[70]
Location of crash	% of shared e-scooter riders	[70]
Time period of crash	% of shared e-scooter riders	[70]
Interactions involving at least one pedestrian on footpaths	% of shared e-scooter riders	[70]
Conflict rate when there were pedestrians within 1 m	%	[70]
E-scooter vibration on different facilities	events/mile	[71]
E-scooter crashes—Gender	% of reported crashes	[45]
E-scooter crashes—Age	% of reported crashes	[45]
E-scooter crashes—Place	% of reported crashes	[45]
E-scooter crashes—Severity	% of reported crashes	[45]
E-scooter crashes—Collision type	% of reported crashes	[45]
E-scooter crashes—Day/Night	% of reported crashes	[45]
Age distribution under different levels of severity	age groups	[45]
Severity of crashes—Day/Night	% of crashes	[45]
Severity of crashes—Male/Female	% of crashes	[45]
Collision type—Male/Female	% of crashes	[45]
Location of injuries occurring	% of e-scooter riders	[45]
Scooter-related injuries	%	[72]
Injury type	% of reported injuries	[72]
Accident location	% of accidents	[72]
Wearing helmet at the time of the crash	number	[72]
Injured individuals associated with privately owned e-scooters	%	[72]

).

The distribution of indicators across the five impact areas reveals a clear imbalance in the existing literature. Environmental impacts are extensively examined, primarily through life cycle assessment metrics such as GHG emissions, energy consumption, and resource use. Safety also emerges as a dominant dimension, with numerous studies focusing on crash characteristics, injury severity, rider behavior, and conflicts with other road users. The Social/Users dimension is moderately represented, largely emphasizing modal shift, usage patterns, and behavioral aspects. In contrast, the Economic and Transport Performance dimensions receive comparatively limited attention, with only a small number of indicators addressing cost efficiency or system-level operational effects within the broader transport network. This uneven emphasis suggests that current assessment approaches tend to prioritize environmental and safety concerns, while economic viability and network integration remain underexplored. Such fragmentation underscores the need for a more comprehensive and balanced structure capable of integrating multiple dimensions within a unified sustainability assessment framework.

The SDGs provide an overarching normative framework within which urban mobility policies, including micromobility initiatives, are increasingly formulated. While the SDGs do not prescribe specific transport performance metrics, they articulate policy objectives that shape the prioritization of sustainability dimensions at the urban scale. Goals related to health and well-being (SDG 3), energy and emissions (SDGs 7 and 13), resilient infrastructure and innovation (SDG 9), and inclusive and sustainable cities (SDG 11) collectively frame expectations regarding accessibility, safety, environmental performance, and social equity in transport systems.

In this context, the indicators identified can be understood as operational expressions of these broader sustainability objectives. The selection and use of indicators therefore reflect not only data availability and methodological guide, but also the underlying policy priorities embedded within SDG-oriented planning frameworks.

Importantly, the relationship between SDGs and micromobility indicators is neither fixed nor universal. Different cities may emphasize distinct sustainability objectives depending on local challenges, governance structures, and development trajectories.

Accordingly, in this study the SDGs are treated as a normative reference that informs stakeholder-defined priorities ex-ante, while the indicator set synthesized from the literature provides the empirical basis for assessing micromobility performance.

4. Conceptual Sustainability Framework

In developing a conceptual sustainability framework for urban transportation modes, the generic structural components of a system and potential implementation constraints were considered [34]. It builds on the policy objectives outlined through the SDGs and the indicator taxonomy presented in Section 3. The following section introduces the conceptual assessment framework and defines the key dimensions and enabling conditions required for a comprehensive system assessment. The framework is mode-agnostic, as the impact areas and enabling components reflect systemic properties of micromobility services rather than vehicle-specific characteristics.

The framework comprises five fundamental dimensions—Environment, Users, Safety, Economy, and Transport performance—together with two enabling components: the Business model and the Legal framework. These enabling components are incorporated to reflect the institutional and operational conditions that influence system deployment and long-term viability.

While sustainability assessments traditionally rely on the three-pillar approach (environment, economy, and society), literature indicates the need for a more differentiated categorization in the case of micromobility. “Safety” emerges as a distinct impact area, as nearly half of the reviewed studies highlight safety concerns and the vulnerability of e-scooter users in traffic accidents. Similarly, “Transport performance” is considered separately to emphasize the role of e-scooters within the broader transportation network and to capture indicators related to system efficiency and modal integration.

The “Users” dimension is conceptualized as a social and behavioral impact area, capturing how individuals perceive, adopt, and interact with micromobility systems. It reflects user behavior, modal substitution patterns, availability perceptions, and usage characteristics, rather than system supply or network performance. This distinction allows behavioral responses to be assessed separately from transport performance indicators, which describe network-level efficiency and accessibility. Finally, the “Environment” dimension captures environmental externalities associated with micromobility systems, including energy use and emissions. The “Economy” dimension reflects economic implications for users, operators, and public authorities.

In addition to the impact dimensions, the framework includes two enabling components: the “Legal framework” and the “Business model”. These are treated as contextual preconditions that determine whether a micromobility system can be implemented and meaningfully assessed within a given urban setting. The legal framework defines the regulatory permissions, constraints, and obligations governing micromobility operations, while the business model describes the organizational and operational structure through which services are provided. Together, these enablers shape feasibility, scale, and mode of deployment, thereby conditioning how sustainability impacts can emerge and be interpreted.

To illustrate the conceptual structure, the framework is presented through a flower analogy (Figure 1). The five petals correspond to the five sustainability dimensions, each considered equally important and collectively describe system performance. The water and fertilizer represent the enabling conditions, namely the Legal framework and Business model, which act as foundational and moderating factors influencing the feasibility, scale, and operational characteristics of micromobility systems. Assessment input data are represented by sunlight, while stakeholder-defined priorities are symbolized by carbon dioxide (CO₂), both of which are incorporated into the framework. Analogous to photosynthesis, these inputs are jointly used within the dimensions and enablers, thus transformed through the assessment process. The resulting outcomes, the system’s sustainability impact results, are represented by the oxygen released by the flower.

The “flower” representation (Figure 1) illustrates the conceptual completeness and balance among sustainability dimensions; however, it does not explicitly convey the hierarchical structure or the relationships between components. It is important to note that the five sustainability dimensions, together with the two enabling components, are conceptualized as equally important elements of the overall system assessment; therefore, the framework does not impose a predefined weighting scheme. Instead, the relative importance of each component can be defined by stakeholders during application, reflecting regional/local priorities and policy objectives.

The enabling components are not treated as impact dimensions, but rather as contextual preconditions that shape system feasibility and operation. They support the evaluation framework by linking measured sustainability performance with real-world implementation conditions, thereby enhancing its explanatory and policy-relevant value.

The critical role of these enabling conditions is evident in real-world deployments. In several cities, early e-scooter implementations occurred in the absence of clear regulatory frameworks, leading to operational disruptions, safety concerns, and in some cases market withdrawal or service suspension. For example, initial deployments in cities such as Athens, Greece were characterized by regulatory ambiguity regarding parking, road use, and liability, which limited effective system integration and long-term viability [73]. More broadly, international evidence shows that cities lacking coherent regulatory structures faced issues related to uncontrolled fleet expansion, sidewalk clutter, and safety risks, often resulting in restrictive policy responses or temporary bans [74].

At the same time, the sustainability performance of micromobility systems is highly dependent on viable business models. Studies have shown that early shared e-scooter systems experienced high operational costs due to low vehicle lifespans, inefficient rebalancing, and maintenance challenges, raising concerns about their long-term economic and environmental sustainability [75].

The framework can be applied using data from an existing or implemented micromobility system, as well as simulated data from an examined system. The framework does not require simultaneous application of all indicators; rather, it supports modular use depending on data availability, institutional capacity, and assessment objectives. This allows users to select a subset of indicators that are most relevant, non-overlapping, and feasible for their specific context. By integrating sustainability dimensions with enabling conditions, the approach acknowledges that measured performance alone does not guarantee successful implementation.

The importance of enabling conditions is evident in cases where micromobility systems are introduced without a clearly defined regulatory framework, leading to operational and safety challenges. Such experiences demonstrate how institutional gaps may constrain system development regardless of sustainability intentions.

Based on the interaction between sustainability dimensions and system characteristics, specific criteria and KPIs are identified and presented in

Table 8. Proposed KPIs.

Impact Area	Criterion	Indicator [SDG x]	Description	Measurement Approach	Reference
Environment	Climate change	CO2 equivalent (GWP) [SDG 13]	Total CO2 emissions produced	LCA, emission factors from literature/databases	[13,23,38,42,54,57]
		CH4 equivalent [SDG 13]	Total CH4 emissions produced
		CO equivalent [SDG 13]	The maximum daily 8 h average CO concentration
	Energy	Construction energy [SDG 7]	Energy consumption	Measured data, LCA-based energy consumption estimates	[42,54,62,63]
		Operating energy [SDG 7]
		Energy to collect and repositioning [SDG 7]
		Maintenance energy [SDG 7]
		Disposal energy [SDG 7]
	Human health damage	SOx [SDG 3]	The mixture of solid and liquid pollutant particles spread in the atmosphere	Measured data, LCA-based energy consumption estimates	[23,34,38,42,54]
		NOx [SDG 3]
		Particulate Matter PM10 [SDG 3]
		Particulate Matter PM2.5 [SDG 3]
		N2O [SDG 3]
	Ecosystem damage	Eutrophication N-eq. [SDG 15]	% of N-eq. and SO2-eq. from all lifecycle stages	Estimated through LCA methods using standardized environmental indicators	[13,54]
		Acidification SO2-eq. [SDG 15]
Economy	E-scooter lifecycle cost	Purchase cost [SDG 8]	It can be combined with other public transport incentives	Cost data from operators, the literature, and financial reports	[38,42,57]
		Operating cost [SDG 8]
		Collection and repositioning costs [SDG 8]
		Maintenance cost [SDG 8]
		Disposal cost [SDG 8, 12]
	Investment	Investment cost [SDG 9]	Total additional capital costs for setting up an initiative, demonstration, action or measure.	Project budgets or planning documents	[43]
	Subsidies	Purchase subsidy [SDG 9]	Portion of costs covered by taxpayers. Indicator values can be replaced with local data.	Public funding data or policy reports	[42]
	Infrastructure cost	Equipment/infrastructure cost [SDG 9]	Cumulative amount of money spent on acquisition of equipment and construction of infrastructure	Estimated from project budgets or planning documents	[43]
	Staff costs	Training cost [SDG 8]	Cumulative amount of money spent on staff/personnel training.		[43]
Users	The impact on transport modes	Active modes [SDG 11]	Modal shift ratio from walking or bicycling to e-scooter	Estimated through user surveys, travel diaries, or stated-preference studies	[57,59,60]
		Private vehicles [SDG 11]	Modal shift ratio from passenger cars or motorcycles to e-scooter		[13,23,55,57]
		Public transport [SDG 11]	Modal shift ratio from buses, subway, railway or tram to e-scooter		[57,59,64]
		Shared mobility [SDG 11]	Modal shift ratio from carsharing/carpooling/ride hailing to e-scooter		[1]
	First/last mile trip	Trips serving the first/last mile [SDG 11]	Percentage of e-scooter trips serving the first/last mile	Derived from trip data or user surveys	[59]
	Complement trips with public transport	E-scooter complement bus system [SDG 11]	Percentage of e-scooter trips in combination with public transport	Estimated using integrated mobility datasets, surveys	[59]
	Availability	Daytime availability [SDG 11]	Time during which a vehicle is not available to its potential users during the 19 h (5 am to 12 am) per day when 98.8% of total trips occur. Indicator values can be replaced with local data (It is expressed as an annual percentage).	Calculated from operator data	[42]
	E-scooter characteristics	Passenger capacity [SDG 11]	Maximum number of passengers per e-scooter	Obtained from technical specifications or operator data	[42]
		Charging frequency [SDG 7]	Time required to charge an e-scooter (From 20% to 80% SoC)		[42]
		Frequency of battery replacement [SDG 12]	The number of times the e-scooter battery needs to be replaced over its life cycle		[42]
Transport performance	Traffic congestion	Vehicle delay [SDG 11]	Road segment/junction level of service	Measured using traffic models, simulation tools, or traffic monitoring data	[41]
		Vehicle queue length [SDG 11]	% of length change		[41]
	Accessibility	Accessibility to public buildings/activity centers [SDG 11]	The ability to approach opportunities. It can be measured by the number of activities that are accessible to a given destination within a specified time period or distance.	Calculated using GIS-based accessibility analysis (time/distance-based metrics)	[38,42]
		Accessibility to public transport stations [SDG 11]
		Accessibility to public areas (parks)/Open spaces [SDG 11]
	Supply	E-scooter charging opportunities [SDG 9]	Available e-scooter charging areas	Derived from spatial data and operator service maps	[42]
		Network coverage [SDG 9]	Areas where e-scooters can move freely		[42]
	Demand	Passenger traveled kilometers [SDG 11]	Distance traveled by e-scooter users in km per day	Obtained from operator data or mobility datasets	[5]
		Frequency of use [SDG 11]	Usage frequency per e-scooter per day		[42]
		Trips [SDG 11]	Number of e-scooter trips per day		[65]
			Number of e-scooter trips during peak hours		[65]
		Repositioning [SDG 9, 11]	Overall repositioning ratio, Repositioning ratio for rebalancing and for charging	Calculated from fleet management data	[65]
		Equality of access [SDG 10]	Equality of access is measured by the proportion of people using vehicles by ethnicity/social group	Assessed using demographic data combined with usage patterns	[42]
	Quality/Level of Service	Reliability [SDG 11]	Available e-scooters (with battery SoC > 50%) per km	Derived from real-time availability data	[34,40]
	Convenience	Vibrations [SDG 11]	E-scooter vibration on different facilities (events/km)	Measured through technical tests or user perception surveys	[71]
		Cargo space [SDG 11]	Physical vehicle characteristics which maximize user comfort and convenience		[42]
	Parking	Space occupation (m2) [SDG 11]	Space occupation per e-scooter, per 10 e-scooters (including infrastructure—if applicable)	Estimated using spatial analysis or field observations	[57]
Safety	Accidents	Accident with a motor vehicle [SDG 3]	Number of accidents involving a privately owned motor vehicle (car or motorcycle)	Obtained from police reports, hospital data, or transport safety databases	[40,45,67,68]
		Accident with a pedestrian [SDG 3]	Number of accidents involving at least one pedestrian		[45,67,68,70]
		Accident with another e-scooter [SDG 3]	Number of accidents involving another e-scooter		[45,68]
		E-scooter accident [SDG 3]	Number of e-scooter accidents involving no other user (e.g., fall)		[45,68]
		Accidents with heavy vehicles [SDG 3]	Number of accidents involving heavy vehicles (>3.5 tons)		[68]
	Crash	Crash severity [SDG 3]	Number of crashes per severity (Light, Heavy, Fatal crash)	Classified using official accident severity scales	[45,69,72]
	Vandalism	Vandalism [SDG 11]	Number of e-scooter vandalism per year	Derived from operator reports or municipal records	[43]
Legislation	Legal framework	Strictness [SDG 16]	It refers to whether the (existing) legal framework is strict or flexible to allow, enforce or support the implementation of new modes.	Assessed qualitatively through policy analysis or expert judgment	[34,42]
		Adaptability [SDG 16]	It refers to the extent to which the existing legal framework can follow the trends and rules indicated by the market, in terms of new technologies, etc., affecting operating modes.		[34,42]
		Jurisdiction [SDG 16]	It refers to the transparency and clarity with which the various stakeholders involved in the implementation of an instrument allocate their responsibilities, authority and rights		[34,42]
	Acceptance of regulations	Conformity [SDG 16]	Public conformity with regulations	Measured using compliance rates or survey-based perception data	[43]
		Enforcement [SDG 16]	Ease to comply with new measures, rules and regulations		[43]
	Use	Vehicle driving practice requirement [SDG 3]	Intentions of e-scooter users to practice using the vehicle before starting the trip	Observed through field studies or user surveys	[43]
		Helmet use [SDG 3]	Correct helmet use; No helmet; On but not fastened		[70]
Business model	Customers	Customer trust/loyalty [SDG 8]	Number of customers who re-used the service	Measured through user surveys or platform feedback data	[76]
		Satisfaction [SDG 8]	Number of customer complaints; Number of billing errors		[76]
	Application technology	Website views [SDG 9]	Number of clicks/page views; Number of unique visitors; Number of repeat visitors	Obtained from platform analytics	[76]
		Service application [SDG 9]	Number of downloads; Frequency of errors; Average usage time per user		[76]
	Services	New services [SDG 9]	New service development time	Measured using operator performance data	[76]
		Quality of services [SDG 9]	Average response time to customer requests		[76]
		Workforce [SDG 8]	Work hours spent for service promotion		[76]
	Investment	Vehicle investment [SDG 9]	Number of new e-scooters purchased per period	Derived from company reports or operational data	[76]

, together with their descriptions and indicative measurement approaches. These indicators operationalize the framework and provide a structured basis for multi-dimensional assessment of micromobility systems. Indicators were also mapped to the SDGs based on their primary contribution to environmental sustainability, economic performance, social equity, and institutional effectiveness. Each indicator was assigned to the most directly relevant SDG. In this study, the SDGs are not used as direct evaluation indicators but rather as a strategic reference that informs the prioritization of sustainability dimensions and criteria.

The indicators are presented conceptually, without predefined units of measurement. This choice reflects the framework’s role as a decision-oriented assessment structure rather than a prescriptive evaluation tool, allowing cities to operationalize indicators using context-appropriate units, scales, and data availability. In practice, indicators can be quantified using a range of data sources and methods, including operational data from micromobility providers, transport system data, simulation outputs, and user-based surveys. In particular, subjective indicators (e.g., convenience, satisfaction) can be assessed through stated-preference or perception surveys. Regarding applicability, the proposed indicators are primarily tailored to e-scooter systems; however, the framework is readily adaptable to other urban micromobility modes. The indicator set can be selected, modified, or refined based on the specific characteristics of the system under evaluation and the objectives of the assessment.

5. Discussion and Conclusions

The proposed framework contributes to the ongoing discussion on sustainability assessment in urban mobility by integrating multiple impact dimensions within a unified structure. As highlighted in Table 1, existing studies tend to prioritize specific aspects of sustainability, such as environmental impacts, economic performance, or technological characteristics, while rarely incorporating all relevant dimensions simultaneously. By organizing five sustainability dimensions together with legal and business enablers, the framework adopts a broader systems perspective that links performance outcomes with implementation feasibility.

Compared to the existing literature, the proposed framework uniquely extends beyond the traditional three sustainability pillars by explicitly incorporating safety and transport performance as distinct dimensions, while also integrating the legal framework and business model as enabling components. These elements are critical in determining whether micromobility systems can be effectively deployed and sustained in practice.

Specifically, the proposed framework differentiates itself from existing approaches in terms of: (i) the level of dimension integration, (ii) the explicit separation of impact dimensions and enabling conditions, (iii) the structured classification of indicators, (iv) its applicability across different planning contexts, and (v) its ability to support decision-making under real-world constraints. As illustrated in Table 1, existing frameworks typically focus on a limited subset of dimensions, most commonly environmental, economic, and social impacts [36,37,38], while rarely incorporating transport performance, safety, and governance-related aspects within a unified structure. Furthermore, enabling conditions such as regulatory frameworks and business models are generally omitted or treated implicitly, limiting their explanatory power in practical applications.

The development of the proposed framework is based on a structured literature review (Section 3.1), which defines the scope, databases, keyword combinations, and inclusion criteria used to identify relevant studies. The review focuses on peer-reviewed literature published between 2001 and 2025 and supports the identification and classification of sustainability indicators for e-scooters across multiple dimensions.

The framework is primarily intended to support strategic and policy-level decision-making, such as system selection, regulatory design, and comparative evaluation across cities, rather than real-time operational monitoring. For example, a city evaluating a proposed e-scooter scheme may observe strong environmental performance indicators but identify unsupportive regulatory conditions, implying that policy reform rather than operational adjustment is required prior to deployment. The explicit inclusion of enabling conditions represents an important conceptual shift. Micromobility systems may demonstrate positive environmental or transport-related impacts, yet their long-term viability depends on regulatory clarity and operational sustainability. The case of e-scooter deployment in Athens, Greece illustrates this interdependence, as the initial absence of a clearly defined legal framework led to operational and safety challenges. Key aspects such as road behavior, insurance obligations, and parking regulations were not clearly established prior to deployment, creating uncertainty and limiting effective system integration. This example highlights that sustainability performance alone does not guarantee successful implementation; institutional readiness and governance structures are equally critical for ensuring long-term system effectiveness.

The framework offers a structured tool for comprehensively assessing micromobility systems by linking performance dimensions with enabling conditions. It connects performance metrics with enabling conditions, allowing planners to explain why identical micromobility systems perform differently across cities. The framework can support municipalities, transport authorities, policymakers, and mobility operators in evaluating existing schemes or planning new deployments. By combining multi-dimensional indicators with contextual factors such as regulatory and business conditions, the framework facilitates evidence-based decision making and promotes more balanced and resilient urban mobility strategies. The accompanying set of KPIs operationalizes the framework and provides a structured basis for multi-dimensional assessment. Rather than prescribing specific policy solutions, it supports evidence-informed evaluation, allowing decision-makers to examine trade-offs across environmental, economic, social, safety, and performance dimensions within their local context. For municipalities, the framework provides a structured tool to assess whether micromobility systems are not only performing well but are also implementable under local regulatory and market conditions. This enables cities to distinguish between performance gaps that require operational adjustments and structural barriers that require regulatory or institutional action.

To address the dynamic nature of micromobility systems, the proposed framework is designed to be both flexible and adaptable. Indicators are not treated as exhaustive, but as a set that can be updated, refined, or extended as technologies, operational models, and policy contexts evolve or change among regions. For instance, emerging indicators related to battery circularity, data governance, or integration with autonomous systems can be incorporated without altering the overall structure of the proposed framework. As micromobility systems continue to mature and scale, greater emphasis is expected to be placed on long-term economic viability and network integration, particularly in relation to first- and last-mile connectivity.

The integration of multiple sustainability dimensions within a unified framework enables the identification of both synergies and trade-offs across impact areas. For instance, increased e-scooter usage may contribute to reduced greenhouse gas emissions and improved transport system efficiency, while simultaneously raising safety concerns due to higher exposure to traffic conflicts. Similarly, policies that enhance service availability and accessibility may support social inclusion but may also increase operational costs or energy consumption associated with fleet management. The proposed framework supports the explicit consideration of such relationships, allowing decision-makers to evaluate competing objectives through a multi-criteria perspective and to prioritize interventions based on local policy goals.

Within this context, the framework does not prescribe a single optimal solution but supports multi-criteria evaluation of alternative scenarios. Urban decision-makers can use the framework to assess how different policies or operational choices affect multiple sustainability dimensions simultaneously. For instance, a city may prioritize safety over users’ impacts, or environmental performance over economic cost, depending on local policy objectives. The framework can therefore be combined with established multi-criteria decision analysis (MCDA) approaches, such as weighting schemes or scoring methods [5,35], to support structured evaluation and prioritization of alternatives.

A key advantage of this framework lies in its ability to support the identification of synergies and trade-offs across sustainability dimensions. For example, increased e-scooter adoption may reduce greenhouse gas emissions and improve accessibility (environmental and transport performance benefits) but may simultaneously increase exposure to safety risks due to higher interaction with motorized traffic. Similarly, strategies that maximize service availability and coverage can enhance user accessibility and system effectiveness but may lead to higher operational costs and increased energy consumption due to fleet redistribution. Conversely, synergies may also emerge. The integration of micromobility with public transport systems can simultaneously improve accessibility, reduce car dependency, and decrease emissions, contributing positively to multiple sustainability dimensions.

Table 9. Proposed synergies and trade-offs across sustainability objectives.

Dimension Pair	Relationship	Example
Environment ↔ Transport Performance	Synergy	Modal shift reduces emissions and congestion
Environment ↔ Economy	Trade-off	Low emissions may require higher operational costs
Users ↔ Safety	Trade-off	Increased usage → higher exposure to accidents
Transport Performance ↔ Economy	Trade-off	Higher coverage → higher costs
Users ↔ Transport Performance	Synergy	First/last mile improves accessibility
Safety ↔ Accessibility	Trade-off	Expanding access may reduce safety if infrastructure lacking

presents the proposed synergies and trade-offs across sustainability objectives, illustrating how different dimension pairs may reinforce or counteract one another within shared micromobility systems.

Certain limitations should be acknowledged. The framework has been developed primarily for e-scooter systems, and although adaptable, some indicators may require adjustment for other micromobility modes. In addition, the framework does not impose a fixed weighting scheme, as the relative importance of dimensions may vary across cities. Data availability, particularly for business and user-related indicators, may also affect practical implementation.

Future research could focus on empirical application of the framework in different cities to assess its usability and robustness under varying regulatory and market conditions. Comparative case studies may help refine indicators, explore context-specific weighting approaches, and evaluate interactions between sustainability performance and enabling conditions. Further methodological development could also examine the integration of participatory or MCDA techniques to support stakeholder-informed assessments.