key: cord-0814341-nyr2b3ub
authors: Ani, Melkonyan; Jennifer, Koch; Fabian, Lohmar; Vasanth, Kamath; Munteanu, Victoria; Alexander Schmidt, J.; Bleischwitz, Raimund
title: Integrated Urban Mobility Policies in Metropolitan Areas: A System Dynamics Approach for the Rhine-Ruhr metropolitan region in Germany
date: 2020-07-01
journal: Sustain Cities Soc
DOI: 10.1016/j.scs.2020.102358
sha: f2a7a903500514f4179679117e0c0930f75008dc
doc_id: 814341
cord_uid: nyr2b3ub

In today’s world, urban systems play an important role in sustainable economic development. In particular, urbanisation trends and the increasing demands of urban mobility place additional pressure on existing transportation infrastructure, and this creates new challenges for urban planners in terms of developing integrated and sustainable urban mobility policies. Here, we take a novel and holistic approach to analysing transformative pathways towards sustainable urban mobility, considering the complex dynamics in metropolitan regions. To achieve this, we develop a toolset to assess the impact of potential measures to be taken by decision makers. Our innovative approach is based on the introduction of a new system framework to link the interrelated sector parameters of mobility systems by considering the effects of innovative mixed methods (both qualitative and quantitative) on scenario development and evaluation on the basis of global trends at the macro scale and their specific influences on the mobility sector at the local scale. To this end, we used a participatory modelling approach to develop scenarios and evaluate them as integrated simulation runs via a comprehensive and holistic system dynamics (SD) model. Thus, we estimated dynamic interdependencies between all of the factors relating to the mobility sector and then assigned business decision-making criteria to the urban systems. Furthermore, we introduced a sustainable net present value framework to estimate the sustainability outcomes of government investment in urban mobility infrastructure. A case study relating to the Rhine-Rhine-Ruhr metropolitan region in Germany was applied in order to simulate four scenarios co-created with stakeholders involved in our study, namely, Smart City, Sustainable/Healthy City, Deurbanisation and Business-as-Usual (BaU), which served as a solid basis from which to quantify path dependencies in terms of policy implementation. At the same time, recommendations were derived for sustainable mobility transformation within metropolitan regions.

Rapid urbanisation and economic growth in recent years have caused a significant increase in urban transportation and mobility demands, and this, in turn, has fostered the development of efficient, integrated, just-in-time and system-based sustainable mobility concepts (Ambrosino et al., 2016; Jittrapirom et al., 2017) . Studies have shown that the transportation sector and urban areas are responsible for about 24% and 67% of energy-related greenhouse gas emissions, respectively (Ashnani et al., 2015; IPCC, 2015) , and this is likely to play a crucial role in whether or not the European Union will reache its target of a 60% reduction in greenhouse gas emissions by 2050 (Pichler et al., 2017; Gota et al., 2019) . In order to overcome the prevailing issues relating to inefficiency and non-sustainability, urban mobility needs to change, and this will require enhanced transparency, coordination, competitiveness, cooperation and creative effort in terms of redesigning our communities, businesses and governance systems. A major change will also require links to existing solutions, such as the 'Smart City' concept (Neirotti et Against this background, our objective is to develop a toolset for designing sustainable urban mobility solutions, while simultaneously exploring potential transformation pathways towards achieving net-zero greenhouse gas (GHG) emissions in metropolitan areas. In this context, we define transformation as improved efficiency and sustainability of the entire urban mobility J o u r n a l P r e -p r o o f 4 ecosystem by exploring its innovation potential within metropolitan areas (for example, new sharing economy business models, inclusive governance approaches and the sustainability impact of advanced digital technologies). To achieve this goal, we developed a system dynamics (SD) model for the mobility sector in metropolitan areas and applied it to a case study of the Rhine-Ruhr metropolitan region in Germany. Research has shown that using a systematic participatory approach whereby stakeholders are included in the scenario cocreation and modelling process has been successful in identifying more appropriate integrated solutions likely to gain a higher level of acceptance in communities (Voinov & Bousquet, 2010; Voinov et al., 2016) . Thus, in conjunction with stakeholders from the Rhine-Ruhr region, we started the modelling process by co-creating narratives of urban mobility scenarios and evaluating the descriptive factors of these narratives using the social, technological, economic, environmental and political (STEEP) method. Based on these narratives, a holistic SD model was created, which represented the causal interdependencies between the relevant factors influencing the mobility system in metropolitan areas. We then applied the SD model to estimate dynamic changes within the system boundaries for current and potential future regional developments, relying on a systematic literature analysis (a scientific estimation of the future development of system-relevant variables) as well as other mathematical model estimations (developed by the project partners cooperating with us).

The paper is structured as follows: First, in Section 2.1, we discuss the theoretical background and position of our paper within the literature on global urbanisation, which is one of the main explanatory trends for mobility turnover. The effect of urbanisation on the economy is then analysed in Section 2.2 from an ecological economics perspective, and alternative urban economic indicators are defined. Sections 2.3 and 2.4 deal with innovative mobility concepts as best case examples and also with the history of dynamic modelling in the mobility sector; additionally, these sections highlight the contribution of our paper to the body of international literature. In Section 3, which focusses on the research design, we provide a graphic J o u r n a l P r e -p r o o f 5 illustration of the concept and structure of the paper. In Sections 3.1 and 3.2, we describe the scenario development method, STEEP, and the SD modelling technique, respectively. To test the model, we applied it to a case study of the Rhine-Ruhr metropolitan region in Germany, as described in Section 3.3, and this provides the input data for the SD model. The results of scenario development and the scenario-based simulation runs are presented in Section 4. We then discuss the results and provide some concluding remarks in Section 5. Also in this section, we discuss the potential for implementing our model framework in other metropolitan areas and address the limitations of the model.

In recent times, rapid urbanisation rates have led to increased mobility demands, which has hastened the need to develop sustainable, integrated and holistic urban mobility strategies that can be designed and implemented on the basis of sustainable and inclusive urban governance (Hodson et al., 2017) .

To address this need, we contextualise our research by reviewing international scientific literature on the sustainable urban systems that relate to the mobility patterns. In this paper we used the system components, which are described in the literature as follows:

 socially inclusive and transformative cities (Nevens et This literature review is then used to identify the trends, factors and parameters of an urban system, which have the greatest impact on sustainable mobility transitions and their related dynamics based on ecological economic theories. These trends, factors and parameters are then combined with the co-created narratives (using the STEEP method) and serve as the basis for the SD model development.

Therefore, one part of our theoretical contribution is a review of the sustainability factors relating to open/social innovation (for example, sharing economy business models), sustainable governance, as well as advanced technologies and their acceptance by users, all of which serve as a basis for estimating the effects of scenario-based simulation runs on efficient urban mobility policies. A further contribution is linking the different advanced qualitative and quantitative approaches used in international literature to run scenario-based simulations.

In essence, urban systems are the engines of economic, social and cultural development. The increase in urban land cover during the first three decades of the twenty-first century is expected to be higher than the cumulative level of urban expansion in all of human history (IPCC, 2014) . Currently, 55% of the world's population lives in urban areas, and this figure is projected to rise to 68% by 2050 (UN, 2018).

The term urbanisation describes more than simply an increase in the urban population, and in recent years, new frameworks have been proposed to capture the multiple dimensions of the concept of urbanisation (Boone et al., 2014) . Instigators of urbanisation include increased birth rates in urban areas and people moving from rural areas, causing cities to grow in population and physical size. Population growth rates are often proportionally lower than the increase in developed urban land, which typically indicates an expansive pattern of urban growth (Seto, 2011). This phenomenon-referred to as urban sprawl-remains a complex and elusive concept (Galster et al., 2001) . However, key attributes of urban sprawl include J o u r n a l P r e -p r o o f 7 extension of the city area beyond walkable range (Rahman, 2016) , a decline in urban densities (Ewing et al., 2016) , increased consumption of land resources by urban dwellers (Huang et al., 2010) , ongoing suburbanisation (Koch et al., 2019) and fragmentation of open spaces as well as built-up areas (Oueslati et al. 2015; Dorning et al., 2014) . Numerous studies have identified the primary factors that drive urban sprawl, including a rise in household incomes, individual preferences, technological progress in the automobile industry, affordability of vehicles and a decline in commuting costs (Deng et al., 2008; Patacchini and Zenou, 2009; Seto, 2011; Oueslati et al., 2015) . In this sense, our Deurbanisation scenario is strongly tied to the idea of urban sprawl, assuming increased housing costs in city centres and a good mobility infrastructure to allow convenient commuting.

Studies have shown that increased urbanisation rates are frequently associated with economic growth, specifically with the formation of agglomeration economies, increased trade volumes, technological development and productivity gains, as well as socio-ecological benefits such as a reduction in poverty, inequality and pollution (Brülhart and Sbergami, 2009; World Bank, 2009; Sekkat, 2017; Frick and Pose, 2018) . However, the resulting overall positive urban economic effects are uncertain, since urban diseconomies of scale, such as congestion, social inequality, unemployment, the digital divide, political and social conflicts, are often neglected (Frick and Pose, 2018) . Hence, strategic urban planning must respond to the current challenges associated with social equity, mobility patterns, global competitiveness and energy efficiency (Seto, 2011; OECD, 2018). It is estimated that transformative urban change in transportation systems has the potential to reduce GHG emissions by up to 1.5 billion CO2eq by 2030 (New Climate Economy, 2016). Moreover, it is essential to develop inclusive concepts of urbanisation that guarantee access to infrastructure, social services, housing, J o u r n a l P r e -p r o o f 8 education, health care, fair employment and a safe environment for all residents (Palanivel, 2017 as well as net present value of public investments), instead of estimating only economic profitability and digital enhancement of these concepts (Kresl and Singh, 1999; EU, 2018) .

However, while considerable work has been done at the community, national and international level to identify suitable indicators, there is no consensus on a universal measure of sustainable urban development (Rodrigues and Franco, 2009 ). Gross Domestic Product (GDP) is frequently used because of its transparency and replicability (Michael et al., 2014) , but given the non-linear interactions between the economic, socio-environmental and infrastructural components within urban systems, there is a need for interdisciplinary approaches to measuring sustainability in those systems.

In the research presented here, we used the idea of the Genuine Progress Indicator (GPI) and its descriptors to model the urban mobility ecosystem. The GPI concept is designed to account for income, the three dimensions of sustainability, and other aspects of capital relevant to human, social, built and natural welfare (Huang et al., 2015) . Of the twenty monetarily assessed components of GPI, there are multiple parameters that are highly influenced by mobility patterns, and those parameters account for, among other things, the cost of travel between home and the workplace, the cost of traffic accidents, damages relating to air pollution, the cost of noise damage, substitution costs generated by the exploitation of nonrenewable resources, and the cost of damages from GHG emissions (Cobb et al., 2001 ). An example for Germany displays clearly the difference between the GDP and GPI indicators: if J o u r n a l P r e -p r o o f 9 GDP has risen steadily, GPI peaked around 2000 and has fallen ever since (Diefenbacher et al., 2016) . The same trend was observed in the state of North Rhine-Westphalia, where a study between the years 1999 and 2013 revealed that the components accounting for the cost of travel between the home and the workplace and the cost of traffic accidents each contributed to a 5% decrease in GPI (Rodenhäuser et al., 2013) .

Nevertheless, efficiency assessments that have been carried out regarding public government investment still lack a social and environmental dimension, particularly concerning CO2 savings. In this context, Zore et al. (2017) proposed a framework on sustainability net present value (SNPV), which takes into account economic, social and ecological aspects for assessing investments made by companies. Specifically, SNPV represents the responsibility of companies for preventing pollution and relies on a fair consideration of favourable/adverse environmental effects from a global/ideal viewpoint. Relying on this concept, we propose a sustainable net present value (SNPV) framework to estimate the sustainability outcomes of government investment in urban mobility infrastructure, utilising some of the descriptive parameters of GPI, such as cost of underemployment and cost of air pollution and environmental damage (Costanza et al., 2004) . Based on this innovative framework, a rescaling of governments' primary concern with greenhouse gases towards wider and more comprehensive economic and social isses could help to root environmental accounting more deeply in industrial and market structures (Jordan and Bleischwitz, 2020).

A number of factors today place additional pressure on urban mobility systems and drive the need for innovative mobility solutions; these include legal requirements, new participants entering the sector, the emergence of disruptive technologies, and strict national emission targets. As with organisational innovations and societal changes, the development of new mobility concepts is heavily influenced by technological innovations (Kamargianni et al., J o u r n a l P r e -p r o o f 10 2016), for example, the replacement of combustion engine cars with electrical (EV) or alternative vehicles (Gass et al., 2014) . Similarly, the main driver of new mobility solutions within the transportation sector is digital innovation, which satisfies stakeholders' requirements for changing mobility patterns, regulations, competition and investment structures (Pangbourne et al., 2018) . Furthermore, digitisation provides more transparency and process efficiency, and it can also facilitate cost reduction, rapid co-development of innovative business models, increased communication, and sustainability (Gunasekaran et al., 2017) . Considering rapidly emerging disruptive technologies, two main aspects can be observed within the context of new urban mobility solutions (Bouteil, 2019), as follows:

A taxonomy of diversified new mobility services for eleven categories has recently been established: traditional ride sharing (carpooling); car sharing; bike sharing; microtransit; employee buses; sequential sharing; concurrent sharing; taxi apps (E-Hail); aggregator apps; parking/navigation apps; and mobile payment for transportation services.

The second important aspect of new mobility concepts is the entrance of new players to the sector. Table 1 shows the top eight start-ups specialising in new mobility services. These start-ups have received massive funding from corporate stakeholders in the ICT industry, highlighting the ongoing digitisation of the mobility sector (Bouteil, 2019). scenario, which applies the social component of the sharing society to achieve mobility transformation. However, even though the relationship between the smart city concept and sustainable urban transport has been analysed from a CO2-saving perspective in international literature (Zawieska and Pieriegud, 2018), a holistic approach to the complex dynamics in other sectors (for example, the energy sector) and the impact of sharing mobility concepts on the environment has not yet been considered. This gap is addressed by the given paper.

The flow of high-quality information on mobility solutions between urban planners, the industrial sector (mobility/technology/infrastructure providers) and consumers can be improved by advanced tools and platforms, which serve to enable the flow of information and support the decision-making process in terms of mobility planning. One method for improving information flow and decision support is the system dynamics (SD) approach. This has been successfully applied for decision support in participatory settings in a wide range of disciplines (Tako and Robinson, 2012) and has a high potential for transition research and applications in disruptive areas, such as urban mobility and transport (Marsden and Docherty, 2013 ). In addition, SD has been applied to explore the interactions between societal, technological, managerial, urban and ecological systems, all of which are driven by, and driving, changes in the behaviour patterns of the stakeholders involved in urban mobility (Bernardino and Hoofd, 2013) .

For the research presented here, we chose the SD approach for the following reasons: Qualitative data is one of the most important sources when it comes to modelling participatory decision-making processes (Forrester, 1961; Groesser and Schaffernicht, 2012) . In this context, the SD approach can be applied to describe qualitative research as narrative scenarios, and it can also be used to develop mid-term hypotheses (Schwaninger and Grosser, 2008) and therefore identify a trade-off between quantitative and qualitative data. Moreover, it can make use of the other data sources, such as qualitative analyses carried out using the STEEP method or other quantitative data inputs (for example, agentbased modelling).

3. The SD approach offers a structured method of public/stakeholder involvement (using mental maps such as causal loop diagrams); it also functions as a tool to focus on a problem and the related policy levers, aims to identify issues with the structure of a system, and offers opportunities to learn about and document the policy process (Stave, 2002) . 

In order to develop a toolset for developing and assessing sustainable urban mobility patterns, This innovative approach can be applied as a framework in other metropolitan areas around the world, using local specifications and data, as well as the political and societal requirements for mobility transformation in those areas.

J o u r n a l P r e -p r o o f Figure 1 : Simulation-based scenario assessment framework developed in this paper

The co-creation of scenarios was implemented based on a participatory approach during a series of workshops attended by the relevant stakeholders from the region, including citizens, policy makers from the city governments of Bottrop and Essen, transport association 1 and experts with extensive experience in the mobility sector of the region. Within the workshops, global trends that could have a significant impact on the region were discussed in detail and served as a basis for the scenarios, in conjunction with the application of the STEEP method.

The aim of this method is to identify external factors that are outside the control of the decision maker but have a significant impact on transport and mobility operation within a complex system of interactions. With its emphasis on the mixed-method approach, STEEP comprises qualitative techniques, such as participatory and brainstorming techniques, while also utilising several statistical tools, for example, CIB (cross-impact balance analysis) and Table 3 , provided a solid basis for carrying out the simulation runs following development of the complex and holistic SD model.

In essence, the development of an SD model combines the use of qualitative methods (causalloop diagrams, CLD) and quantitative methods (stock-flow diagrams, SFD) (Bossel, 2007) .

Qualitative methods are used to make initial statements about the underlying system behaviour and to create logical models, while quantitative methods are used to formulate predictive mathematical models. Hence, the first step in developing an SD model is to map The second stage of developing an SD model involves extracting the stocks and flows from the CLD and translating them to an SFD (Bossel, 2007) . To do this, an in-depth study of the interactions and relationships between the system components is necessary, and this process can lead to an increase in the number of variables involved initially, making system representation more complex.

The following questions must be answered to decide whether a parameter is a stock: (1) 3) the newly developed SNPV framework (relying on GPI indicators); 4) sharing economy models in the mobility sector (the societal requirements to use these concepts were estimated by the focus groups, while the political requirements were set following interviews conducted with government representatives of four cities in the region).

The MURMO and MATSim models and their precise descriptions and development processes are not within the scope of this paper (see references above); however, shows the output parameters of these models serving as input parameters for our SD model.

The innovativeness of the SD model in our paper is based on a new system framework that links together the interrelated parameters of the mobility and energy sectors, while simultaneously considering advanced approaches relating to ecological economics at the macro scale (alternative indicators of urban economics) as well as new socio-economic dynamics in sharing economy models at the local scale.

The Rhine-Ruhr region, located in the state of North Rhine-Westphalia in Germany, is a metropolitan urban complex of closely located cities with a post-industrial character. The individual cities are set apart by social, economic and spatial variances that define the lifestyle, design and mobility of the area. Due to economic and structural changes in the region, the dense transport infrastructure that drove industrialisation processes was abandoned or shut down over time, and new connections emerged to fulfil the needs of modern society.

In the intervening period, the Rhine-Ruhr region has developed a unique, car-oriented mobility infrastructure featuring dense highway and road networks, waterways, railways and multimodal hubs of European importance for freight transportation (IHK, 2013). However, J o u r n a l P r e -p r o o f 19 infrastructural capacity reached its limits many years ago, causing environmental problems for the region (IHK, 2013). Furthermore, dependency on private motor vehicles has created congestion issues and challenges relating to the environment, energy consumption, public health, and social and spatial segregation (Frank, 2000) . In 2012, more than every other trip undertaken in the Ruhr area was made using a private motor vehicle, accounting for 53% of all trips, while 23% of trips were made on foot, 8% by bicycle and 16% by public transport (Grindau and Sagolla, 2012) . These proportions are almost equal to the average modal split in Germany, even though the urban structure of the Rhine-Ruhr region lends itself to car-free mobility (Müller, 2017) .

Innovative mobility solutions to increase efficiency in the transportation system can be Huawei to modernise their digital infrastructure. This cooperation is even more relevant for Duisburg, since its inland port, which is the biggest in Europe, is an essential part of the 'Belt and Road Initiative' (Hunag, 2016) . The city of Dortmund follows a different approach to digitisation; in addition to technological solutions, its Smart City approach is based on citizen involvement and focusses on holistic urban development rather than just on infrastructure. (Table A. 1), and these were then used for the base case scenario ( Table 3) These sources are also differentiated in Table A .1 in the Appendix. In addition, the Table A.1 also displays equations describing the interdependencies between the parameters, which were derived through a systematic literature review.

These comprehensive and novel analyses, when integrated into the holistic SD model, transform regional dynamic processes into a decision support tool for policymakers in the Ruhr Region, allowing them to test the impact of decisions on the system components or on the system as a whole. This tool development process can also be applied elsewhere in the world.

We developed a comprehensive system dynamics simulation model for the mobility sector, which aimed to provide a 'virtual laboratory' for testing potential transformation pathways towards net-zero greenhouse gas (GHG) emissions in metropolitan areas. The model and the corresponding test case were designed in conjunction with stakeholders from the Rhine-Ruhr metropolitan region in Germany using STEEP, a development and evaluation method for participatory scenarios, and the results of the process are presented in Section 4.1 below.

The results of the intensive literature analysis on current trends influencing urban mobility were discussed with the stakeholders during the workshop series, with a view to prioritising the trends with the greatest influence on mobility in the Rhine-Ruhr region.

The results of the trend analysis are presented in Table 2 . Mainly, these trends relate to (Table 2) . These trends and their impact on mobility were described verbally by the stakeholders so that the key attributes or factors relating to each trend could be identified. The factors were then classified into five clusters based on the STEEP method, which are described in Table 2 below. In relation to all four of the trends clustered using the STEEP method, all possible related portfolios were created using the logic described above, which served as a basis for the next step, namely, scenario development. The optional future states for each key parameter (options A to D in the previous example) were checked pair-wise with the other options for all four trends by applying an evaluation range of -2 to +2. If the coexistence of two states was estimated to be unrealistic, -2 was attached, and +2 indicated common occurrence in two portfolios of randomly chosen factors. This analysis caused the development of a 16 x 16 matrix (four trends with four options each). The options estimated as mutually exclusive combinations (-2 and -1) were eliminated to create consistent scenarios, and the remaining 

Shared mobility business models (Table 3) .  New digital technologies lead to more remote working job models and less commuting distances:

 Local actors are better off: Daily distance by car (Environment)

The SD model developed and applied for the example of the Rhine-Rhine-Ruhr metropolitan region is presented in Figure 3 . It comprises major socio-demographic, economic, environmental, technological and mobility dynamics. We validated the model for the output Within mobility dynamics, we defined the modal split as the ratio between public and private transport usage as a dependent variable from the perspective of public transport attractiveness, which can be defined based on fuel price (1.5 euro/litre) and

parking fees (2 euro/hour). In addition to economic parameters and the distance to be travelled, we defined the modal split as being dependent on a sharing society, which was identified as the percentage of the population that is receptive to changing its mobility patterns in terms of public transport or sharing business model concepts such as Uber (Table   1 ). Moreover, we estimated the impact of sharing concepts on CO2 emissions (six million tonnes of CO2 from the mobility sector) as being dependent on the sharing society (20%), the motorisation rate (40%), emissions/car (0.14 kg CO2/car), and the number of cars substituted by a shared car (3).

The interconnections and complex dynamics described above are highly sensitive to political decisions, which are represented in the model by urban infrastructure investment. We quantify the sustainability of governmental decisions on urban investment as being dependent on the sustainable net present value of the investment (SNPV) (see Section 2.2). The SNPV represents the economic aspects of any investments made, such as interest rate and revenues.

Moreover, we defined revenues not only as government income from taxes (dependent on population dynamics), but also as alternative CO2 costs that can be saved through sustainable investment (for example, renewable energy sources, alternative vehicle engines or cycling networks). The sharing economy rates for the Deurbanisation scenario are consistently at the highest level, The Sustainable City scenario will also be at a high level due to the higher costs of parking fees, city tolls etc.

The dynamic increase of sharing society percentage within the Smart City Scenario can be explained by the fact that the SD model representation of a sharing society is dependent on population dynamics (such as age group) and on internet coverage, which enables shared mobility concepts to be applied; the latter is highest for the Smart City scenario. Figure 4 also presents the scenario simulation results with regard to total CO2 emissions.

Since the percentage of the sharing economy will be highest within the Deurbanisation scenario, the correlation between these two factors is presented for that scenario. The correlation between the percentage of the sharing society and total CO2 emissions is significantly positive, with an R 2 value of 0.95. This relates to the fact that despite their willingness to share mobility patterns within the Deurbanisation scenario, inhabitants will be travelling longer distances both for work and leisure purposes. The effect of urban expansion is also visible in an expanded view of the urban infrastructure (for example, impervious surfaces), which is displayed in Figure 5 . emissions from the transportation sector within the Smart City scenario (Fig. 4) .

The 

We introduced a new simulation tool that can be applied to design low-emission urban that differed regarding their assumptions on city planning and mobility concepts. One key innovation applied in our model was a novel combination approach using the scenario cocreation method STEEP. Another innovation within the model was the consideration of sharing society concepts, which allowed the model to simulate and compare the effects of innovative mobility concepts on indicators relevant to policymakers, such as land conversion, CO2 emissions and the sustainable net present value (SNPV) of public investment ( Table 4 ).

The SNPV also represents a novel component for which we used alternative urban economics indicators in the simulation. This simulation-based scenario assessment tool is of particular importance for the development of policy recommendations on public investment in order to achieve sustainable urban mobility turnover. The key outcomes of the paper are presented below.

1. One key outcome of our research is the SD simulation tool itself. While the SD model was designed in conjunction with policymakers from the Rhine-Rhine metropolitan region, it was implemented in a generic manner, which makes it transferrable to other metropolitan regions aiming to develop sustainable transportation and mobility. The research design presented in Figure 1 can be adopted by any other region. Even though the scenario narratives were co-created with the stakeholders, in the first instance, the logic behind the creation process is transferrable. After carrying out a trend analysis based on the literature, the trends and their descriptive parameters, as clustered in the STEEP framework, should be intensively discussed with stakeholders in the region. Scenarios could then be developed with local or regional stakeholders, following participatory approaches such as the storyline-and-simulation approach (Alcamo, 2001 ). This could be combined with existing approaches to stakeholder involvement in scenario planning for urban mobility (Chu et al., 2016) . Second, the scenarios used in this paper were generalised so that they Figure 7 ). The major difference between these two scenarios is visible in the total cumulative distance travelled, which is considerably higher for the Smart City scenario ( Figure 6 ) and is directly linked to the higher values for urban infrastructure expansion ( Figure 5 ). Both scenarios also display savings in CO2 emissions ( Figure 4) , which can be attributed to the high proportion of public transportation use ( Figure 5 ); for example, under the Sustainable City scenario, more than twice the distance travelled is accounted for by public means of transport as compared to private transportation. The CO2 emission savings strongly influence the SNPV, since we consider savings in payments of a CO2 tax of 50 euro/ton in our simulation model. 

Based on the boundary conditions and scenario assumptions tested, we summarised a set of policy recommendations that resulted from simulation runs. In general, our findings imply that to make the transition towards sustainable urban mobility, policymakers should focus on initiating pulling effects rather than pushing effects, while taking a proactive role in shaping future urban mobility. Even though this would require a large initial investment in public transportation infrastructure, our simulations show that within an interval of only three to four years, an increased sustainable net present value would result (Figure 7) . A significantly high positive SNPV can be attributed to savings in CO2 emissions and, as a result, savings in payments of CO2 taxes (100 euro/tonne), which might be implemented in the near future.

Hence, to reduce the total amount of CO2 emissions, we provide the following recommendations:

1. The percentage of renewable energy use in motorised vehicles with electrical engines should be at least 80% in order not to generate rebound effects. 

We introduced a new SD modelling approach that is transferable to different metropolitan areas, and we applied the model to the Rhine-Ruhr metropolitan region in order to facilitate the development of policy recommendations for sustainable and transformative mobility pathways. The key factor in the success of the approach is to make public transportation attractive by using new sustainable (agile and digital) pricing systems and by relying on environmental and ecological macroeconomic models (alternative urban economics indicators). The research presented here is a first attempt to model and detangle the complexities in urban mobility. However, there are some limitations to our modelling approach, such as the use of more complex and holistic mobility parameters (dynamic pricing systems or traffic accidents etc.), as well as macroeconomic parameters such as GDP by sector, which should then be more precisely adjusted using GPI parameters. This is in line with the research of Melo et al. (2020) , who studied the impact of the transportation infrastructure on economic growth. Their findings suggest that in the short run, there is no causality between the two variables at the national level; however, a unidirectional causality from economic development to infrastructure investment exists in the long run. These outcomes shed light on the fact that infrastructure investment per se is not sufficient on its own to boost economic activity. An investment package is needed, targeting not only infrastructure but also social and technological development. This delay of applied political changes and their impact on output variables within this paper is too quick. We applied changes in the same timestep, given the fact that it is almost impossible to get an access to the data on the delay caused in implementation of policy changes, representing modelling limitation.

Another limitation of our research is linked with the SD modelling approach, which is well suited to evaluating the temporal dynamics of a system but has limited capability for J o u r n a l P r e -p r o o f 42 analysing a system's spatial dynamics. As a result, model completeness is a limitation. The current model studied the effects of population dynamics, economic growth, infrastructure development and pollution on sustainable urban mobility. While the feedback loops captured specific issues such as development index decline, the impact of sharing mobility concepts on CO2 emissions and so on, these issues are not studied for their sensitivity in the SD model.

Another limitation is the use of assumptions based on expert opinion and the related mental models, even though they were generated using a STEEP method designed to develop advanced scenarios. As it is not possible to obtain empirical data for all variables, some level of subjectivity and assumptions cannot be ruled out. Even though these assumptions were tested during model testing, they are not empirically derived.

While we were able to consider some spatial characteristics in our simulation model (for example, travel distance or an increase in impervious surfaces), the SD approach does not allow the simulation of location and spatial configurations for these spatial characteristics. 

We wish to draw the attention of the Editor to the following facts which may be considered as potential conflicts of interest and to significant financial contributions to this work. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). She is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from (ani.melkonyan-gottschalk@uni-due.de). Signed by the corresponding author: 

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Aktualisierung und methodische Überarbeitung des Nationalen Wohlfahrtsindex 2.0 für Deutschland

Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies

Biodiversity and Ecosystem Services: Challenges and Opportunities A Global Assessment

Produced for the European Commission DG Environment by the Science Communication Unit

Urban sprawl as a risk factor in motor vehicle crashes

Land Use and Transportation Interaction: Implications on Public Health and Quality of Life

Urban concentration and economic growth

Wrestling Sprawl to the Ground: Defining and measuring an elusive concept

Cagliari and smart urban mobility: Analysis and comparison

Analysis of alternative policy instruments to promote electric vehicles in Austria

Decarbonising transport to achieve Paris Agreement targets

Global change and the ecology of cities

Nahverkehr -Lokales Verkehrswesen

Big data and predictive analytics for supply chain and organizational performance

Optimal and Long-Term Dynamic Transport Policy Design: Seeking Maximum Social Welfare through a Pricing Scheme

Greening cities -To be socially inclusive? About the alleged paradox of society and ecology in cities

The development of a participatory assessment technique for infrastructure: Neighborhood-level monitoring towards sustainable infrastructure systems

Reconfiguring Urban Sustainability Transitions, Analysing Multiplicity

Understanding China's Belt & Road Initiative: Motivation, framework and assessment

Defining and measuring urban sustainability: a review of indicators

The transition to an urbanizing world and the demand for natural resources

Verkehrspolitisches Positionspapier der Industrie-und Handelskammern im Ruhrgebiet. Industrie-und Handelskammern im Ruhrgebiet

IPCC (Intenational Panel on Climate Change)

Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

Mobility as a Service: A Critical Review of Definitions, Assessments of Schemes, and Key Challenges. Urban Planning 2/ 2

Legitimating the governance of embodied emissions as a building block for sustainable energy transitions

A Critical Review of New Mobility Services for Urban Transport

Modeling landowner interactions and development patterns at the urban fringe

Competitiveness and the Urban Economy: Twentyfour Large US Metropolitan Areas

System dynamics mapping of acute patient flows

Uncovering landuse dynamics driven by human decision-making -A combined model approach using cellular automata and system dynamics

Security and the Smart City: A Systematic Review

Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies

Multiscale urban modeling; A de-urbanization scenario in the Ruhr area

Best Practice Factory for Freight Transport in Europe: Demonstrating how 'Good' Urban Freight Cases are Improving Business Profit and Public Sectors Benefits

Integrated Scenarios of Sustainable Food Production and Consumption in Germany

Multi-Agent Transport Simulation -MATSim Model

Towards a resilience management guideline -Cities as a starting point for societal resilience

Insights on disruptions as opportunities for transport policy change

Modelling the Effects of Parking Charge and Supply Policy Using System Dynamics Method

Scenario and strategy planning for transformative supply chains within a sustainable economy

What's the economic value of greening transport infrastructures? The case of the underground passages in Lisbon

Review of urban sustainability indicators assessment -Case study between Asian countries

Modal Split

Current trends in Smart City initiatives: Some stylised facts

Modeling structural change in spatial system dynamics: A Daisyworld example

Urban Transition Labs: co-creating transformative action for sustainable cities

The sustainable infrastructure imperative -Financing for Better Growth and Development

Port governance in China since 2004: Institutional layering and the growing impact of broader policies

Rethinking Urban Sprawl: Moving Towards Sustainable Cities

Determinants of urban sprawl in European cities

Rapid urbanisation: opportunities and challenges to improve the well-being of societies

The Case of Mobility as a Service: A Critical Reflection on Challenges for Urban Transport and Mobility Governance

Urban sprawl in Europe

Reducing Urban Greenhouse Gas Footprints

Detection of land use/land cover changes and urban sprawl in

An analysis of multi-temporal remote sensing data

A study of acceptable trip distances using walking and cycling in Bangalore

Valuing co-benefits to make low-carbon investments in cities bankable: The case of waste and transportation projects

Mobilität, Wirtschaftsverkehr und Logistik

ÖPNV und Radverkehr

Der Regionale Wohlfahrtsindex für Nordrhein-Westfalen 1999 -2013 und Leben in Nordrhein-Westfalen -subjektive Einschätzungen. Institut für interdisziplinäre Forschung (FEST)

Measuring cities' performance: proposal of a composite index for the intelligence dimension

Urban green infrastructure and urban forests: A case study of the Metropolitan Area of Milan

Cellular automata models for the simulation of real-world urban processes: A review and analysis

Interrelations between travel mode choice and trip distance: trends in Germany

Urban concentration and poverty in developing countries

Innovation or episodes? Multi-scalar analysis of governance change in urban regeneration policy in South Korea

Urban sustainable mobility and planning policies. A Spanish mid-sized city case

Implementing Mobility as a Service: Challenges in Integrating User, Commercial, and Societal Perspectives

A collaborative appraisal framework to evaluate transport policies for improving air quality in city centres

Heading towards a multimodal city of the future? Multi-stakeholder scenarios for urban mobility

Using system dynamics to improve public participation in environmental decisions

Enhancing urban mobility: Integrating ride-sharing and public transit

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Indicator: National Welfare Index

World Urbanization Prospects: The 2018 Revision. Population Division of the United Nations Department of Economic and Social Affairs

Modelling with stakeholders

Modelling with stakeholders-next generation

Constructing urban dynamic transportation planning strategies for improving quality of life and urban sustainability under emerging growth management principles

High-tech business location, transportation accessibility, and implications for sustainability: Evaluating the differences between high-tech specializations using empirical evidence from US booming regions

Smart city as a tool for sustainable mobility and transport decarbonisation

Geographical patterns of traffic congestion in growing megacities: Big data analytics from Beijing

The role of stakeholders and their participation network in decision-making of urban renewal in China: The case of Chongqing

Maximizing the Sustainability Net Present Value of Renewable Energy Supply Networks

The research activities of this study are connected to the project "NEMO" (New Emscher Mobility: Mobility Concepts that go beyond Car Traffic: 2017-2020). The authors express their gratitude to the Mercator Stiftung for providing financial support, as well as to the other project partners -Technical University Berlin, Institute of land and sea transport systems (ILS), working group of Prof. Dr. Kai Nagel and non-profit organization DIALOGIK (communication and cooperation research) for providing data from microsimulation and organizing workshops.

 Table A .1: List of variables used in the SD model along with the data units, the used data sources, and mathematical formulas representing the causal interdependencies among the parameters.

Unit Source