Sustainable mobility indicators for Indian cities: Selection methodology and application

Ecological Indicators 79 (2017) 310–322

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Sustainable mobility indicators for Indian cities: Selection methodology and application

MARK

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Deepty Jaina, , Geetam Tiwarib a Transportation Research and Injury Prevention Programme (TRIPP), Indian Institute of Technology Delhi (IIT Delhi), Room 815, 7th Floor Main Building, Hauz Khas, New Delhi 110 016, India b Department of Civil Engineering, Indian Institute of Technology Delhi (IIT Delhi), Room 815, 7th Floor Main Building, Hauz Khas, New Delhi 110016, India

A R T I C L E I N F O

A B S T R A C T

Keywords: Causal chain framework Causal network framework Criteria based selection Sustainable mobility Indicators Indian cities

Various indicators of sustainable mobility have been developed. It is difficult to select the most relevant indicators that are useful in a specific context, and that are measurable and achievable at the same time. Indicator selection frameworks – criteria based; causal chains and causal networks have been proposed and used in the past. All three frameworks have certain limitations and strengths. In this study we have proposed a systematic approach of selecting sustainable mobility indicators for Indian cities by combining – criteria based, causal chain and causal network frameworks. The methodology involves both subjective judgments for evaluation of indicators against a set of criteria and objectivity during development and assessment of causal network. The method results in identifying 20 relevant factors for which 32 indicators are shortlisted. Further work is required to develop measurable indicators related to accessibility to the disadvantaged, speed limit restriction and street lighting. These have not been discussed in detail in the existing literature. The 20 factors are classified as root nodes, central and end-of-the-chain nodes that helps in identifying levers of attaining sustainable mobility in Indian cities. The developed causal network is evaluated for its ability to address all sectors associated with sustainable mobility. The causal network has low density and centralization index and therefore accounts for multiple factors. The shortlisted indicators are proposed for preparing low carbon mobility plan (LCMP) for three medium size Indian cities. The indicators are checked for data availability and ease of measurability based on the data collected for preparing the three LCMPs. The analysis shows that the data are available from secondary sources like census to measure root node indicators, whereas central indicators require conducting primary surveys and specific models are required to measure end-of-the-chain indicators. Based on the position of indicators within causal network, it is interpreted that pricing policy, urban form and infrastructure are the levers of sustainable mobility. The indicators of energy consumption, emissions and accessibility are the sustainable mobility targets that we want to achieve.

1. Introduction Indicators are widely used to evaluate progress, projects, and policies toward set goals and objectives. Organization for Economic Cooperation and Development Countries (OECD) define indicators as statistical measures of social, environmental and economic sustainability (Haghshenas and Vaziri, 2012). Indicators help in evaluating, simplifying, study trends, communicate issues and compare across places and situations (Boyko et al., 2012; DETR, 2000; Toth-Szabo and Várhelyi, 2012). A set of appropriate indicators allow decision makers to monitor status and understand consequences of the actions and inactions (Boyko et al., 2012; Gudmundsson and Sørensen, 2012;

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Henning et al., 2011; Rametsteiner et al., 2011). Even though a strong need of indicators to assess policy options is realized (Litman, 2007; Moussiopoulos et al., 2010; Zachariadis, 2005), their use in practice is missing (Gudmundsson and Sørensen, 2012). The non-motorized transport (NMT) and public transport (PT) share is high in Indian cities (Wilbur Smith Associates, 2008) resulting in comparatively low per capita CO2 emissions from transport sector (International Energy Agency, 2014). The existing infrastructure for NMT and PT is in poor condition or missing, NMT and PT users face high risk from traffic crash and discomfort. The majority of the NMT and PT users belong to low income groups who cannot afford other modes of transport and are therefore likely to shift to private motorized

Corresponding author. Present address: Department of Policy Studies, TERI University, Plot No. 10, Vasant Kunj Institutional Area, New Delhi 110070, India. E-mail addresses: [email protected] (D. Jain), [email protected] (G. Tiwari).

http://dx.doi.org/10.1016/j.ecolind.2017.03.059 Received 11 May 2016; Received in revised form 15 December 2016; Accepted 28 March 2017 1470-160X/ © 2017 Elsevier Ltd. All rights reserved.

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availability of measured indicators. In STPI-phase III Project, the indicators were selected that could also be studied over time (Gilbert et al., 2002; Gilbert and Tanguay, 2000). Castillo and Pitfield (2010) and Moussiopoulos et al. (2010) also mention the need for being able to forecast indicators. This helps in studying trends and estimate progress toward the set goals under identified scenarios. Litman (2009), Bojkovic et al. (2010) and Toth-Szabo and Várhelyi (2012) discuss the need to avoid conflicting indicators in the final set. For example, increase in mobility and reducing emissions are two conflicting targets (Toth-Szabo and Várhelyi, 2012). The selected indicators should provide unambiguous, specific information that can be used for decision making to achieve the set goals. They should be clear and easily understood by its intended users (Bojkovic et al., 2010; Castillo and Pitfield, 2010; Dale and Beyeler, 2001; Haghshenas and Vaziri, 2012; Joumard and Gudmundsson, 2010; Litman, 2009; Moussiopoulos et al., 2010; World Bank, UNEP, UNDP, and FAO, 1998). Studies also emphasize the role of policy or target relevant indicators (Castillo and Pitfield, 2010; Dizdaroglu, 2015; Gilbert et al., 2002; Haghshenas and Vaziri, 2012; Henning et al., 2011; Joumard et al., 2011; Lin et al., 2009; Moussiopoulos et al., 2010; Toth-Szabo and Várhelyi, 2012). Indicators that are not policy relevant may provide wrong interpretations and result in misguiding decision makers. Castillo and Pitfield (2010) highlight the need of using indicators for which transport impacts can be isolated. Gilbert et al. (2002) give an example of selecting indicator of emission from transport sector as opposed to using air quality index. The former provides information on the impacts of transport sector while in later the influence of transport sector is unknown. The study by Lin et al. (2009) and joint report by World Bank, UNEP, UNDP, and FAO (1998) specify the need for selecting indicators that can be controlled by management and policy actions. There is a growing body of research, which identifies the need of context specific indicators. Such indicators provide understanding of local community needs and reflect changes in urban structure and transport sector of cities (Boyko et al., 2012; Haghshenas and Vaziri, 2012; Joumard et al., 2011; Toth-Szabo and Várhelyi, 2012). As per Toth-Szabo and Várhelyi (2012) indicators should reflect the value systems of people. The authors therefore have not included poverty related indicators of mobility for Sweden in their final set. Moussiopoulos et al. (2010) included indicators related to sea environment and tourism to measure urban sustainability in Thessaloniki, Greece. Another important criterion identified in literature is comprehensiveness. This criterion is used to evaluate ability of indicator set to measure different dimensions associated with the system under study (Bojkovic et al., 2010; Dale and Beyeler, 2001; Gilbert et al., 2002; Lin et al., 2009; Litman, 2009; Nicolas et al., 2003). Table 1 presents the summary of criteria used for selecting sustainability indicators arranged in chronological order. As the table shows, earlier studies have used criteria related to data availability, measurability and interpretability for selecting indicators. However, recent studies emphasize the need for context specific interventions. Haghshenas and Vaziri (2012), Toth-Szabo and Várhelyi (2012) and Wang et al. (2009) highlight the need for uncorrelated indicators to avoid double counting. The duplicity in information revealed through the indicators results in giving over-emphasis on few issues instead of providing a comprehensive understanding of the system. In contrast to this, Rowley et al. (2012) argue that it is difficult to select a set of mutually independent indicators. The study provides an argument for the need to consider cause-effect chain relationships during indicator selection.

modes as and when they can afford it (Tiwari and Jain, 2013; Tiwari and Jain, 2008). To curb the increasing emission levels from urban transport it is required to retain the existing NMT and PT share in Indian cities (Jain and Tiwari, 2016). This requires sustainable mobility planning that ensures safe accessibility to all users of transport system irrespective of their socio-economic background (gender, income and caste) and mode used in a way that does not compromise with the health of the environment (UNEP, 2014). This definition draws focus on attaining social and environment sustainability. Planning for sustainable mobility requires assessing various aspects of transport using indicators as both cause and concern to identify issues, study trends and propose strategies. Various institutes and authorities have developed sustainable mobility indicators for efficient planning. Even though consensus on meeting the ‘triple bottom line’ exists i.e. environmental, social and economic sustainability; yet different indicator sets have been used to evaluate transport systems (Miranda and Silva, 2012; Richardson, 2005). It is required to select limited indicators from existing long list that not only provides a holistic view of the system but also helps in meeting the planning targets (Castillo and Pitfield, 2010; Dale and Beyeler, 2001; Fusco, 2001). This requires answering several questions. How to decide what is the optimal number of indicators? What is the importance of each indicator in total indicator set? Do the selected indicators provide a complete picture of the system? Transport is a complex system having many interacting sub-systems. Selection of indicator set should therefore take into account these interactions and consider feedbacks and rebound effects (Richardson, 2005). Accounting for dynamic interactions during indicator selection process can also help in avoiding double counting (Litman, 2009). Various indicator sets are used for mobility planning, however, the integrated approach of selection that considers these interactions is lacking (Huang and Lo, 2011; Moussiopoulos et al., 2010). Two more issues that require attention are – how to account for relationship between indicators and how to avoid double counting. In the study, indicator selection method is developed to address these issues. Indicator selection frameworks can be classified as criteria based, causal chains and causal networks. The three frameworks have certain limitations and strengths. In this study, we have explored the potential of each of the three frameworks and developed a methodology by combining them to select indicators of sustainable mobility in Indian cities. Later, the shortlisted indicators are evaluated based on the robustness of causal network and data availability and measurability. 2. Indicator selection frameworks 2.1. Criteria based framework In this approach, indicators are rated against selected criteria deemed important by expert group and stakeholders. Using appropriate aggregation methodology indicators are ranked and selected. Some of the key criteria used for selecting indicators are data availability; measurability; utility; sensitivity; transparency and interpretability (Table 1). The majority of the studies emphasize importance of data availability to measure indicators using scientifically sound and acceptable method (Bojkovic et al., 2010; Castillo and Pitfield, 2010; Dale and Beyeler, 2001; Gilbert et al., 2002; Gilbert and Tanguay, 2000; Haghshenas and Vaziri, 2012; Henning et al., 2011; Joumard et al., 2011; Lin et al., 2009; Moussiopoulos et al., 2010; Nicolas et al., 2003). Application of this criterion ensures reliability of the information delivered and allows regional and temporal comparison. In the project Sustainable Transport Performance Indicators (STPI) – Phase III, indicators for which data was available from federal government sources were selected (Gilbert et al., 2002; Gilbert and Tanguay, 2000). Castillo and Pitfield (2010) discuss speed of data availability as an important criterion for selection of indicators. This shall enable short time lag between changes in the phenomenon under study and the

2.2. Causal chain frameworks Causal chain frameworks account for linear relations between indicators of interest. Pressure, State and Response (PSR) framework 311

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Table 1 Commonly used criteria for selection of indicators. Criteria

Brief description

Literature review

Achievable

Represent issues that can be controlled through policy and strategic actions. Indicator can be measured in theoretically sound and easily understood manner.

World Bank, UNEP, UNDP, and FAO (1998); Joumard et al. (2011)

Measurable

Policy relevant

Provide relevant information to decision makers for changing policies to attain the desired goals.

Specific/interpretability

Indicator should be easily understood by intended users and useful for decision makers.

Time series data and ability to be forecasted Sensitive

The indicators can be forecasted using accepted methods to identify possible changes. The selected indicators should be sensitive to the stresses on the system under study. Indicator set should provide holistic view of the system overarching both causes and impacts. Data to measure indicators should be easily available at reasonable cost from reliable sources.

Comprehensive Data availability

Coherent Local priorities Speed of data availability Quantitative

Indicators with controversial desired directions should be avoided. Selected indicators should reflect area characteristics and local community needs. There should be minimum time lag between data collected and changes in phenomenon under study. Indicators should present issues in quantified manner.

World Bank, UNEP, UNDP, and FAO (1998), Dale and Beyeler (2001), Nicolas et al. (2003), Lin et al. (2009), Castillo and Pitfield (2010), Moussiopoulos et al. (2010), Joumard et al. (2011), Haghshenas and Vaziri (2012) World Bank, UNEP, UNDP, and FAO (1998), Gilbert et al. (2002), Lin et al. (2009), Castillo and Pitfield (2010), Moussiopoulos et al. (2010), Henning et al. (2011), Joumard et al. (2011), Haghshenas and Vaziri (2012), Toth-Szabo and Várhelyi (2012) World Bank, UNEP, UNDP, and FAO (1998), Dale and Beyeler (2001), Litman (2009), Bojkovic et al. (2010), Castillo and Pitfield (2010), Moussiopoulos et al. (2010), Joumard et al. (2011), Haghshenas and Vaziri (2012) Dale and Beyeler (2001), Gilbert et al. (2002), Castillo and Pitfield (2010), Moussiopoulos et al. (2010) Dale and Beyeler (2001), Bojkovic et al. (2010), Joumard et al. (2011), Dizdaroglu (2015) Dale and Beyeler (2001), Gilbert et al. (2002), Nicolas et al. (2003), Litman (2009), Lin et al. (2009), Bojkovic et al. (2010) Gilbert et al. (2002), Nicolas et al. (2003), Litman (2009), Bojkovic et al. (2010), Castillo and Pitfield (2010), Moussiopoulos et al. (2010), Henning et al. (2011), Joumard et al. (2011), Haghshenas and Vaziri (2012) Litman (2009), Bojkovic et al. (2010), Toth-Szabo and Várhelyi (2012) Moussiopoulos et al. (2010), Joumard et al. (2011), Boyko et al. (2012), Haghshenas and Vaziri (2012), Toth-Szabo and Várhelyi (2012) Castillo and Pitfield (2010) Dizdaroglu (2015)

Causal network frameworks consider all unidirectional and bidirectional, non-linear interactions between different indicators (Kok, 2009). It can be developed either by combining cognitive maps prepared by individual stakeholders (Kontogianni et al., 2013) or developing one cognitive map during a single workshop involving multiple stakeholders (Shiau and Liu, 2013). It helps in classifying indicators as root-cause, central and end-of-the-chain nodes and therefore identifies levers for attaining sustainability (Wolfslehner and Vacik, 2011). Relationships between indicators can be quantified using mathematical models like Analytical Hierarchy Process (AHP) and Bayesian network models (Niemeijer and de Groot, 2008). These techniques are also useful to account for duplication of indicators at aggregation stage. However, quantification requires large dataset collected over time that may not be possible in many countries. Richardson (2005) developed a root-cause analysis framework to identify agents of change for achieving the aim of sustainable transport. The framework was developed to study interactions between factors of passenger and freight transport systems. However, it was not used to select indicators. Kontogianni et al. (2013) developed causal network framework to understand expert and ‘non-experts’ perceptions on the potential costs and benefits of hydrogen fuel. The study was used to identify essential factors for promoting hydrogen fuel in the market. Shiau and Liu (2013) used AHP for selecting key transport indicators and developed causal maps to study relationship between the selected set of indicators for Taipei city. Causal network frameworks have been used for selecting ecological indicators, however, their application in transport sector is limited (Lin et al., 2009; Niemeijer and de Groot, 2008). Developing cognitive maps is both time and money consuming while adding the risk of missing important linkages (Kok, 2009). The frameworks help in studying relationships between factors of interest and identifying crucial factors for attaining sustainability. However, it cannot be used for selecting most appropriate indicators for each factor (Lin et al., 2009). This requires combining the causal network with criteria based framework.

developed by Government of Canada in 1970s and Driving Force, Pressure, State, Impact and Response (DPSIR) framework developed by OECD in 1994 are some of the examples (Gilbert and Tanguay, 2000; Lin et al., 2009). PSR framework is used to analyze interactions between environment related indicators (Meyar-Naimi and Vaez-Zadeh, 2012). The framework represents linkages between human activities like travel pattern that create pressure resulting in changing state of environment (air quality or land depletion). Strategic responses are the attempts that can be made to release pressures like maximum allowed noise levels and gasoline price. DPSIR framework includes non-environmental indicators as well and helps in understanding cross-sectoral trends (Jesinghaus, 1999). In the framework, two categories for pressure i.e. driving force and pressures and two categories for state i.e. state and impact are defined. The framework has been used in health and environmental sectors (Meyar-Naimi and Vaez-Zadeh, 2012). Linkages in causal chain framework formulate a feedback system that can be monitored at different stages. Although causal chain framework helps in developing better understanding of causality, however, its application in transport sector is argued. Gilbert and Tanguay (2000) find these frameworks “insufficiently policy relevant” and give lesser attention to trends and their causes. Such frameworks do not consider transport activity exclusively and it is placed with all other factors that have impact on transport activity. Niemeijer and de Groot (2008) find that causal chains account for one–one relationship while in reality system interaction is both one–many and many–many. The frameworks therefore induce a narrow, issue-centered perspective of system, resulting in over-simplification of reality. 2.3. Causal network frameworks There is a need to account for complex, multi trajectory and nonlinear inter-relations between indicators (Meyar-Naimi and VaezZadeh, 2012). This requires developing multiple causal chains. Here each indicator shall form part of more than one causal chains resulting in inter linkages between causal chains (Niemeijer and de Groot, 2008). 312

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Table 2 Comparison of indicator selection frameworks.

Brief description Inter-relationships Examples Pros

Cons

Criteria based

Causal chains

Causal networks

Ranking indicators based on criteria identified by community or stakeholders No relationships

Indicators are selected based on their linkage with each other in cause-effect relation. One to one and one to many.

Evaluative and Logical Approach to Sustainable Transport Indicator Compilation (ELASTIC) • Reflect community priority and needs.

Pressure-state-response (PSR), Drivers-pressure-stateimpact-response (DPSIR). • Accounts linear relationship. • Identifies origins and consequences of problems. • Allows system thinking and system dynamics to assess environmental policies. • Issue centered perspective. • Non-linear relationships are not accounted. • Cannot be used to predict change in one or more indicators on the basis of change in other indicators.

Multiple interactions between different indicators are accounted. One to one, one to many and many to many. Root-cause analysis, e-DPSIR.

• Inter-relationships between indicators is not accounted. • No clear criteria why a particular indicator is relevant or not relevant.

• Accounts complexities of the real world. • Inter-relations between indicators can be quantified. • Useful to predict future developments. • Time and money consuming, risk of missing important linkages. • Does not allow evaluation of long list of indicators. • Quantification of inter-relations is data intensive.

2.4. Discussion Pros and cons of the three indicator selection frameworks are summarized in Table 2. Each of the three frameworks has certain limitations where other framework provides an edge over it.

3. Methodology The literature review emphasize that causal network enables accounting for interactions between indicators during selection process. However, developing robust causal network is difficult and it cannot be used for selecting the final indicator set. Lin et al. (2009) and Niemeijer and de Groot (2008) have used multiple steps for developing causal network and selecting ecological indicators. The first step requires classifying indicators into factors based on the broad processes or dimensions measured by them. In the second step, factors are classified into categories defined by causal chains. The third step involves linking these factors into directional maps resulting in causal network. Multiple indicators measure each factor in a causal network. Lin et al. (2009) applied a list of criteria to select indicators within each factor while Niemeijer and de Groot (2008) used system boundaries for selecting relevant indicators and factors. We have combined the three indicator selection frameworks for selecting sustainable mobility indicators for Indian cities. This helps in realizing the respective potentials of each of the framework and accounting for the respective limitations during the selection process. Causal chains are developed that contribute to the formation of causal network. Causal network helps in identifying relevant factors and their respective position. Criteria based evaluation is applied to select indicators within each identified factor. The proposed indicator selection method is a three-step process (Fig. 1). STEP 1: Develop causal chains

Fig. 1. Methodology for selecting sustainable mobility indicators.

(a) Indicators within each factor are rated based on the five criteria – specific, measurable, achievable, relevance (policy and local) and data availability. 4. Selecting sustainable mobility indicators for Indian cities 4.1. STEP 1: Develop causal chain frameworks Given the complexity of transport systems, it is difficult to develop a robust causal network that accounts for all the factors and linkages in one-step. Therefore, smaller causal chains are developed that are later linked to form causal network.

(a) List indicators from literature and group them on the basis of dimension measured by them. (b) Link identified factors into multiple causal chains.

4.1.1. Identifying factors A list of 184 indicators has been identified from the literature and using them directly to develop causal chains is cumbersome. Therefore, the 184 indicators are grouped into smaller number of factors for better understanding of the complex system of sustainable mobility. The classification of initial list of indicators is based on the theoretical foundation that sustainable mobility encompasses the three pillars of sustainability (social, environmental and economy) along with travel

STEP 2: Develop causal network (a) Link causal chains on the basis of common factors between them. (b) Classify factors as transmitter (root-cause), ordinary (central) and receiver (end-of-the-chain). STEP 3: Evaluate indicators using criteria

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Table 3 Indicator classification into 31 factors. Broad categories

Factors

No. of indicators

Description

Social

Congestion Noise Security Travel time Internet availability

1 1 2 2 3

Social equity Trip length

4 4

Urban form

8

Safety

9

Health hazard Affordability and household expenditure Accessibility

10 14

Traffic condition in city and its relative impact on society in terms of travel time delay. Noise levels in city that has impact on human health. Crimes associated with the use of transport system. Accessibility to opportunities and effectiveness of transport system by different socio-economic groups. Accessibility to opportunity while reducing the need of moving for different socio-economic profile of people. Index of extent to which transport contributes to social polarization – vertical and horizontal equity. Actual distance traveled by people to access different activities; shall also emphasize on the social equity. All the indicators related to urban development pattern and city structure of city having impact on travel behavior of different users. Safety aspect of accessing opportunities. Identifies vulnerable users and users that impose negative externalities. Health consequences of transport system to different people. Household expenditure on transport with regard to the household income.

Modal share Infrastructure

16 29

Possibility of people to access opportunities within specified distance and time using different transport modes. Presents travel behavior of people belonging to different socio-economic profile. Quality of infrastructure – walk, bicycle, public transport and personal motorized modes.

Environment

Ecological footprint Environment related Policies Land consumption Public awareness Waste Energy consumption Emissions

1 1 3 3 3 6 13

Natural resources used in terms of total land area required to support the human settlement. Effort of government authorities toward reducing the negative impacts on environment. Amount of land used for different purpose to meet transport needs in a region. Public involvement in planning and their awareness on the issues related to sustainable mobility. All the indicators that measure amount of waste produced by transport sector. Indicates energy efficiency of transport sector. Pollutants emitted by transport sector having impact on air quality.

Economy

Revenue City Economy Investment

2 3 4

Pricing policies

6

Cost imposed over society (monetary)

10

Economic efficiency of transport system like public transport revenue service kilometers. Concerns all the indicators of economy affected by transport sector like GDP and employment Investment made by government authorities for development of transport infrastructure and encouraging the use of sustainable modes of transport. It relates to the cost that every passenger needs to pay for using different types of modes. It also highlights on the amount of external cost paid by respective mode users. Monetized value of negative externalities of transport sector.

Trip rate Occupancy Passenger km Fleet Vehicle kilometer

1 2 2 3 3

Indicates amount of mobility in city. Efficiency with which a transport mode is used. Extent to which different transport modes are used (includes both walk and bicycle). Type and size of fleet. Extent to which different transport modes are used (only for motorized modes).

Activity

15

security and mode choice.

activity and each pillar is measured by multiple dimensions. For each dimension various indicators have been developed. The indicators are first classified into four broad groups (social, environment, economic and travel activity) as done in the STPI-phase III project (Gilbert et al., 2002). Later, within each group, the indicators are classified into factors based on the commonality in dimension measured by them (Bojkovic et al., 2010). However, due to the complexity of the system, one indicator can be related to multiple aspects of sustainability (Bojkovic et al., 2010; Lin et al., 2009). Bojkovic et al. (2010) quote an example of affordability and accessibility indicators that can be associated with both social and economic aspects. Similarly, the indicator of energy consumption is associated with both economic and environmental impacts. Considering this, indicators are grouped into factors based on the dimension measured by them. For example, speed, street lighting and footpaths are grouped in one factor of infrastructure. Thirty-one factors are defined using this approach (Table 3). These factors are later used to develop causal chains and causal network. Such classification of indicators into factors has both advantages and disadvantages. It is likely to result in overpassing the detailed information that may be necessary for understanding the system complexities; however, it provides simplified and manageable information for developing causal chains and causal network. Later in the study, some of the factors are further disaggregated to represent independent aspects. For example, the infrastructure factor is disaggregated based on the identified relation between the individual aspects and other factors like safety,

4.1.2. Determining relationship between factors PSR and DPSIR frameworks limit the number of allowed linkages in a causal chain. These frameworks require classifying indicators as pressure, state and response. Since, one indicator shall become part of multiple causal chains; therefore, status of indicators may vary in different causal chains (Niemeijer and de Groot, 2008). To overcome these limitations, each factor is linked with other factors in two-way cause–effect relationships determined on the basis of literature review. Both direct and indirect consequences are accounted in the form of direct links that will be resolved in the next step. This results in developing multiple causal chains for each factor. An example of noise factor is shown in Fig. 2. Direction of the arrows shows the cause and effect relationship. Factors like vehicle kilometer and occupancy have impact on noise that has impact on health. 4.2. STEP 2: Developing causal network framework Causal chains for 31 factors developed in step 1 are linked into causal network using Pajek 3.12 as shown in Figs. 3 and 4. Pajek 3.12 is the freeware developed for analyzing and visualizing large networks having thousands of vertices. The tool is used for studying collaboration networks, internet networks, citation networks, diffusion networks, etc. (Batagelj and Mrvar, 2011). It helps in abstracting smaller networks from large networks for detailed analysis; analyze network using 314

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Fig. 2. Causal chain for noise variable.

39 factors. For example, infrastructure was split into multiple factors as speed, traffic volume and capacity.

algorithms and classify factors based on their position in the network. For example in Fig. 4, factors like modal share, infrastructure and urban form are central to the causal network having multiple incoming and outgoing links followed by travel time, trip rate, revenue and investment.

4.2.2. Classification of factors Thirty-nine factors are classified based on their position in the causal network as transmitter, ordinary and receiver (Kontogianni et al., 2013) that are also termed as root nodes, central nodes and end-of-the-chain nodes by Niemeijer and de Groot (2008), respectively. Root nodes have influence on many other factors. These therefore have more than average outgoing arcs and are the source of multiple issues. Central or ordinary factors have many incoming and outgoing arcs and are influenced by many factors and influence many other factors. Endof-the-chain nodes are the goals that need to be achieved and thus have more than average incoming arcs. Classification of factors helps in identifying levers for attaining the sustainability goals. Those factors that cannot be classified as any of these nodes need to be studied in detail for inclusion or exclusion in the network. There are two methods to classify factors – degree and proximity

4.2.1. Resolve causal network using partitions The developed causal network includes indirect links as direct link. For example, modal share impacts vehicle kilometer that have impact on noise. In the network, modal share that is indirectly linked with noise is accounted as a direct link (Fig. 5). Such indirect links are removed by creating ‘Network Partitions’ for each factor based on the input degree of vertices/factors (Table 4). Seventeen partitions are created where zeroth partition consists of those factors that have no factor affecting them while first partition has one impacting factor and so on. Starting from partition ‘1’ the indirect links accounted as direct links are removed. As an example, direct link between modal share and noise is removed. In the process, some factors are split resulting in total

Fig. 3. Example of formation of causal network using Pajek 3.12.

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Fig. 4. Complete causal network including both direct and indirect links.

prestige index (de Nooy et al., 2005). ‘Degree’ accounts for local effect only and therefore considers outgoing and incoming arcs from the immediate neighborhood only. ‘Proximity prestige index’ is computed considering both direct and indirect effects where direct neighbors contribute more than the indirect neighbors. Maximum proximity prestige index can be attained if a factor is directly connected to all other factors (de Nooy et al., 2005). In the study, proximity prestige index is used for classification of the factors. Three types of proximity prestige index are measured i.e. input, output and all. Input-proximity prestige index is the measure of incoming links; output-proximity prestige index accounts for outgoing links; and all-proximity prestige index accounts for both incoming and outgoing links. Factors are classified based on the average value obtained for the three proximity prestige index (Table 5). All the factors that have input-proximity prestige index more than the average (0.13) and that do not overlap with output classifications are classified as end-of-the-chain nodes. Factors having output-proximity prestige index more than average (0.133) and not overlapping with the input factors are classified as root nodes. Factors having all-proximity prestige index more than 0.39 are classified as central factors. Central factors have more than the average input and output proximity prestige index. In the process, 32 factors are classified in one of the three groups. Of the remaining seven factors, speed and congestion have more than the average input and output proximity index and less than the average of all-proximity index. These factors are classified as central factors. The aim of the research is to identify the sustainable mobility indicators for Indian cities that can be used by the analysts and decision makers to identify the intervention areas to achieve the desired aims. Therefore, the long term impacts that are difficult to quantify are not included in the proposed causal network. For example, impact of transport infrastructure on urban form is established in the literature

Table 4 Network partitions. Cluster – number of impacting factors

Factors in each cluster

0

Environment policies; urban form; public awareness Occupancy; pricing policy Trip rate; security; waste; fleet type; trip length Land consumption; economy Investment; passenger km Travel time; infrastructure Congestion; revenue; safety Noise; vehicle km; expenditure; affordability Accessibility Modal share Health hazard Cost imposed; energy consumption Equity Emissions Ecological footprint

1 2 3 4 5 6 7 8 9 10 13 14 15 16

(Adhvaryu, 2010; Braimoh and Onishi, 2007; Cervero, 2003; Clarke and Gaydos, 1998; Hu and Lo, 2007). However, it is a long-term impact and difficult to quantify (Adhvaryu, 2010; Litman, 2011). Therefore, this link is not considered in the existing study. Although, including such links is likely to result in changing the relative classification of the factors. To check for the consistency incoming links were added to urban form from both investment and infrastructure. This has not resulted in changing the relative position of the urban form factor within the identified causal network (input proximity prestige index = 0.095, output proximity prestige index = 0.25, arithmetic mean value = 0.1434 and 0.1358, respectively).

Fig. 5. Close loop in causal network.

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Table 5 Proximity index for 39 factors.

and air and noise pollution. The classification of factors in the study is objective in nature. Using the proximity indices, five factors are not classified in any of the three groups. Proximity prestige index is based on the number of factors that are directly and indirectly influenced by other factors. Therefore, it

The relative position of each of the factors is shown in Fig. 6. Outer circle shows root-cause nodes, middle circle shows central factors and innermost circle shows end-of-the-chain factors. As shown in Fig. 6, pricing, environmental policies, urban form and infrastructure design help in reducing negative impacts of transport i.e. energy consumption 317

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sustainable mobility. Fuel used is a root-cause factor and both land consumption and waste are identified as end-of- the-chain factors. By this step, we have been able to involve subjective judgments in an objective approach. 4.3. STEP 3: Evaluating list of factors and indicators Of the identified 12 criteria in Section 2.1, five criteria are data specific while other criteria are policy specific. Of these, comprehensiveness, sensitivity and coherency are intrinsically built into the methodology of selecting indicators by using causal networks. Criteria of ability to be forecasted and speed of data availability are discussed in the literature. Indicators are used to understand existing situation, identify possible interventions and study trends. Some indicators cannot be forecasted like affordability of people belonging to different economic groups. These indicators however provide an understanding of the existing situation and identify the areas of intervention. Therefore, these criteria are not used for evaluating indicators. The initial list of 184 indicators is evaluated based on the remaining criteria – interpretability, measurability, achievable, relevant to the context, policy targets and transport sector and data availability. These criteria provide essential qualities that an indicator should have to evaluate sustainability mobility.

Fig. 6. Root nodes, central and end-of-the-chain.

shows that these five factors are less connected with other factors of sustainable mobility. However, it does not relate to the importance of these factors in determining sustainability of the system. It is required to study the five factors in detail and involve subjectivity to determine their relative importance and location within the causal network. For this purpose, system boundaries are defined that will also be used for evaluating indicators in the next step. The aim of the research is to select indicators that shall help in identifying issues related to attaining sustainable mobility in Indian cities and related interventions. Therefore factors that are policy relevant and do not present conflict with the sustainable mobility goals are considered for inclusion. Based on this definition, the remaining five factors are studied in detail –

• Achievable/controllable Indicators are proposed to be used to

•

(a) Transport capacity – Some of the examples of capacity indicators are – length of infrastructure per capita, parking capacity and capacity of transport network. As per Litman (2015) these indicators justify infrastructure expansion to increase mobility of motorized vehicles that result in restricting safe accessibility of pedestrians and bicyclists (Litman, 2015). These indicators, therefore, do not help in achieving the aims of sustainable mobility. (b) Employment rate – Contribution of transport sector to employment growth represents the economic pillar of sustainability (Bojkovic et al., 2010). However, the factor is a representation of sustainable transport and does not have impact on sustainable mobility. Therefore, it is excluded from the final set. (c) Fuel used – As discussed earlier, energy consumption, emissions and noise pollution are some of the key end-of-the-chain variables. Studies show that changing fuel type has significant impact on fuel economy and emission levels (Cse, 2008; Jain and Tiwari, 2016; Sperling et al., 2004) and is therefore included in the final set. (d) Land consumption – In urban areas, the use of large amount of land for transport purposes that also includes storage/parking of vehicles has implication on depletion of natural resources (Gilbert and Tanguay, 2000). One of such impact is unavailability of unpaved areas resulting in water run-off and change of natural drainage course. The indicators related to land consumption are therefore important to account for the negative impacts of transport system on natural resources. In addition, the factor indicates the state of development of various modes of transport and their relative importance in planning and therefore included for further steps. (e) Waste – It relates to the amount of waste produced by scrapping of vehicles and other transport related equipment. It is one of the crucial factors accounting for the externalities of transport systems (Awasthi et al., 2011; Gilbert and Tanguay, 2000).

•

•

•

Of the remaining five factors fuel used, land consumption and waste are identified as important factors that are relevant to the goals of 318

understand existing issues and identify appropriate interventions to achieve sustainable mobility in Indian cities. The selected indicators are therefore required to be achievable or controllable through policy actions. The indicators that are not controllable are rated as ‘0’. For example, trip rate cannot be directly controlled by policy actions. Data availability Data required for measuring indicators should be available from reliable sources or collected using primary surveys. The indicators here are rated on Likert scale from 0 to 5. 0 is assigned to the indicators for which data cannot be collected at all, 1 for complex models, 2 where simple models can be used to generate relevant information like spreadsheet models, 3 for surveys, 4 for data that needs to be compiled from secondary sources and 5 for data that is readily available for use like census. Measurable Indicators should be measurable in theoretically sound and easily understood manner. Indicators are rated based on the type of information delivered as quantitative (1), qualitative (0) and not measurable (0). Although qualitative information is also useful however it is exposed to biases during interpretation and are therefore less reliable (Joumard and Gudmundsson, 2010). Hence, qualitative indicators are assigned ‘0’ during evaluation. Relevant to context, policy targets and transport Here two subcriteria are defined – relevant to Indian context and relevant to sustainable mobility goals. Indian cities are characterized by heterogeneous group of users belonging to different socio-economic profiles having varied affordability for different modes. This has impact on their respective mode choice. The sustainable mobility indicators therefore need to reflect on different types of road users (Tiwari and Jain, 2012). Also, it is desirable that indicators should reflect the travel behavior of people, city structure and infrastructure quality in Indian cities. Therefore, the indicators of internet accessibility are rated as ‘0’ since the impact of internet availability on mobility in Indian cities is not well established. Indicators are also evaluated based on their relevance to the policy targets. This is similar to the boundary defined for the system in Section 4.2.2. Many of the listed indicators like revenues and length of infrastructure per person are indicators of transport sector sustainability. These indicators reflect less about sustainable mobility in cities. Specific/interpretability Indicators should deliver clear/unambiguous information that can be easily interpreted and used by stakeholders. These are rated as specific (1) and not specific (0).

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For all criteria except data availability, the listed indicators are rated on binary scale. Non-compensatory conjunctive method is used for short listing indicators where none of the shortlisted indicators attain the value of ‘0’ for any criteria. Based on the evaluation, 108 indicators are shortlisted from an initial list of 184 indicators. Based on the rating on data availability, a final list of indicators is selected. The impact of transport sector cannot be isolated for some of the indicators like air quality and noise. Cost imposed on society requires monetization of accidents, pollution and congestion that is debatable (Cascetta, 2009; Tudela et al., 2006) and hence is removed from further analysis. Indicators related to speeds, volume, congestion, vehicle kilometer and revenue are argued for their relevance to sustainable mobility planning (Litman, 2007). Trip rate and occupancy factor are not directly achievable and hence are eliminated from further analysis. Social equity is an index of affordability and accessibility and ecological footprint is an index of emissions, energy consumption and land consumption. These are composite indicators used to measure sustainability at macro level (Dizdaroglu, 2015; Singh et al., 2012). Litman (2009) states, that such index are less useful in achieving the desirable goals. Indicators related to waste cannot be measured because of the lack of appropriate data. Based on the defined criteria, 17 factors are identified irrelevant for short listing indicators. The eliminated factors were removed from the developed causal network and accordingly related links were altered.

development, presence of informal sector and varying densities. Therefore, urban form indicators need to be developed in detail to measure the complex structure of Indian cities. “Users” of the transport are a mix of people belonging to different socio-economic classes, with varied travel patterns. In Indian cities, a large proportion of population lives in slums, for example in Mumbai 54.1% of the population lives in slums, Kolkata 32.5% and in Delhi 18.7%. This group of people cannot afford personal motorized vehicles (cars and two-wheelers) for transportation and even subsidized bus systems are expensive for them for daily commute (Tiwari, 2002). Evaluation of a transport system thus needs to account for the different types of users and assess impact on the well-being of the society as a whole (Tiwari and Jain, 2012). Therefore, indicators for factors like health hazard, accessibility and safety need to be disaggregated by social classifications and measured for all modes including walk and bicycle. The indicators of urban form (density and diversity), infrastructure location (accessibility to PT within 10 min walking distance and Kernel density of roads and PT stops) and accessibility (total destinations within reach in a given time) can be measured at both city level and at spatially finer scales. It is required to identify the specific intervention areas to achieve the aims of sustainable mobility. The objective can be achieved by measuring these indicators at spatially finer scales like census wards and enumeration blocks. For example, measuring indicators at spatially finer scale can help in identifying the areas/zones within the city with low density, diversity and access to PT and specifying specific strategies to increase the same. Measuring indicators at spatially finer scales shall also enable modeling travel behavior for all modes of transport including walk and bicycle and estimate the impacts of policies/strategies on different users (Jain and Tiwari, 2017).

5. Final list of indicators

6. Evaluating indicator set

Thirty-two indicators are selected using the methodology described in Section 4 (Table 6). Other than these, specific indicators for accessibility of disadvantaged, speed limit restriction and street lighting need to be developed. Indian cities are characterized by mixed land use

First, the resulting causal network is assessed for its ability to measure different dimensions of sustainable mobility. Second, measurability and data availability for the shortlisted indicator set is evaluated based on the data collected for preparing LCMP for three Indian cities –

For example, presence of sustainable policies and public awareness does not provide clear information on sustainable mobility paradigm in cities while the indicator of percentage of subsidies granted measures the government effort in encouraging the use of PT in the city.

Table 6 Selected indicators. Level Root-cause

Factor Affordability Investment Infrastructure Land consumption Speed limit Street Lighting Infrastructure location

Urban form

Pricing policy

Indicators a

Affordability of public transport Percentage of population owning passes Expenditure on infrastructurea Streets with sidewalks Average number of interchanges per public transport trip Land devoted to transport facilities by modeb No indicator No indicator Percentage of HH within 10 min walking distance from public transport stopa Kernel density of roads and public transport Land use mix Density Income level heterogeneity

Level

Factor

Indicators

Central

Fleet type Trip length

Vehicle fleet size by fuel type, average ageb Trip length frequency Average trip lengthb Average trip length by trip purposea,b Average travel timea,b Modal share by trip purposea,b Average modal sharea,b Percentage of people feeling safe to walk/ cyclea, Risk exposure mode wiseb

Travel time Modal share Security Safety

Emission End-of-thechain

Tax burdenb Percentage of subsidies granted Other charges leviedb

Energy Expenditure Health

Accessibility

a b

To be measured across different socio-economic groups (income, gender, caste and others as applicable). To be measured for all modes including pedestrians and bicyclists.

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Risk imposed by modesb Overall safety GHG emissionsb Lifecycle emissions Per capita energy consumption by fuel typeb Percentage of income spend on transporta Percentage of population exposed to air pollutiona Percentage of population exposed to noise levels > 50 dba Accessibility for disadvantagedb Total destinations within reach in a given timea,b

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The causal network developed is not very dense with respect to the maximum number of possible linkages. This also relates to the in-degree and out-degree centralization index. The network has approximately same in-degree and out-degree centralization index. This represents that there are approximately same number of receiver and transmitter factors. Since, the centralization index lies within the required lower and upper bound (0, 0.93); the network has some centralized in-degree and out-degree factors. Therefore, it can be interpreted that the network is balanced enough to account for multiple types of factors (root-cause, end-of-the-chain and central) and not centralized around single or few central factors.

Rajkot, Udaipur and Vishakhapatnam during 2012–2013 (Arora et al., 2014; Munshi et al., 2014; UMTC, 2014). 6.1. Map analysis The resulting causal map can be evaluated using three measures i.e. centralization, density and hierarchy (Kontogianni et al., 2013). Since there are many loops and feedbacks in the existing causal network, it cannot be assessed using hierarchy. 6.1.1. Density index Density is the index of connectivity measured as proportion of number of lines in a simple network and the maximum possible number of lines (de Nooy et al., 2005). A complete causal network thus is a network with maximum density. A directed graph will have half the density of its undirected equivalent since there are as many possible edges between vertices.

D=

L N (N − 1)

6.2. Evaluating shortlisted indicators for data availability and measurability For the three medium size Indian cities (population between 1 and 4 million) – Rajkot, Udaipur and Vishakhapatnam LCMP are prepared (Arora et al., 2014; Munshi et al., 2014; UMTC, 2014). For preparing the LCMP, detailed data were collected using both secondary sources and primary surveys based on the methodology defined in the LCMP toolkit (UNEP et al., 2014). In all the three LCMP, existing situation is evaluated using the selected indicators and some of these indicators were also estimated in alternate scenarios. Annexure 1 provides a list of data collected for the three cities to measure the selected indicators. This information is used to evaluate the selected indicators based on the data availability and measurability. In Annexure 1, the indicators are grouped by factors and factors are grouped by their location in the developed causal network as rootcause, central and end-of-the-chain. The selected indicators are rated from 1 to 3 based on the difficulty level to measure them. Indicators for which data are available from secondary sources are rated as ‘1’. Those indicators that require conducting primary surveys are rated as ‘2’. The indicators for which model application is required are the most difficult to measure and are rated as ‘3’. As per the analysis, most of the rootcause indicators can be measured by using the data available from secondary sources except for infrastructure related indicators. Data for infrastructure related indicators like percentage of streets with sidewalks requires conducting primary surveys. To measure central indicators, primary surveys are required and measuring end-of-the-chain indicators requires developing models. The shaded cells in the Annexure 1 show the indicators that were measured in the LCMP. All the three LCMP have measured most of the root-cause and central indicators while end-of-the-chain indicators are not measured in any of them. This reflects the difficulty in measuring this group of indicators that requires developing extensive models. Both the root-cause and central indicators provide an understanding of current situation of mobility in the cities however, the link with the sustainable mobility targets is missing. This shall require developing detailed method to measure the end-of-the-chain indicators that can be adapted by local government authorities and consultancies involved in for preparing plans.

(1)

where L = number of lines, N = number of factors in the network. The density of the developed directed causal network is 0.089. This means 8.9% of all possible links are present in the network. Since, this measure is inversely proportional to the network size; the index is more useful for drawing comparison between different networks. However, it can be used in combination with centrality index to draw meaningful conclusions. 6.1.2. Centralization index Network centralization is related to the level of connectivity of a factor with other factors. Degree of centralization is thus the variation in the degree of vertices divided by the maximum variation in degree which is possible given the number of vertices in the network. A very centralized network is dominated by one or a few central nodes forming the crucial element in the network. If these nodes are removed or damaged, the network quickly fragments into unconnected sub-network (Rana, 2010). For directed networks, three types of degree of centralization are defined as in-degree, out-degree and total degree. For directed networks degree of centralization is computed as – n

Cx =

∑ j =1 [Cx (pj ) − Cx (pi )] (N − 1)2

(2)

where Cx(pi) is any centrality measure of factor i; Cx(pj) is the largest centrality measure in the network of factor j; N is the number of factors in the network. Butts (2006) shows that the degree of centralization of a network depends largely on the density and size of the network. As per the findings of Butts (2006), if number of factors in a network are more than 20 and density is less than 0.1, upper bound of the in and out degree centralization index tends to be 0.93 while lower bound being 0. Total Degree of centralization for the existing network is 0.21 and both in-degree and out-degree centralization is 0.26. Centralization index is within the limits defined by Butts (2006) however, the three values are relatively at the lower ends.

6.3. Conclusion and way forward The study aims to select a set of indicators to measure various aspects related to urban transport like development pattern, income heterogeneity and policy responses to plan for sustainable mobility in cities. It is one of the first attempts where an approach is developed by combining the three frameworks – criteria based, causal chain and causal network to select indicators. The proposed methodology includes both subjective and objective judgments for identification of indicators. We have identified 20 relevant factors from an initial list of 31 factors for which 35 indicators are shortlisted. Indicators need to be developed for speed limit restrictions, street lighting and accessibility for disadvantaged. With the help of proximity prestige index; root-cause, central and end-of-the-chain factors are identified. As per the classification, pricing policy, urban form, infrastructure attributes and location

6.1.3. Discussion In social network analysis, high centralization index represents ability of one or few individuals to lead. This is preferred when collective decision has to be made in a social network comprising of organizations, groups of society and individuals within society (Sueur et al., 2012). However, selecting indicators requires accounting to different dimensions of the system under study. Therefore, the requirements for social network analysis does not hold true for network analysis of indicators. 320

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it can be replicated in varying contexts. It can be further improved by involving stakeholders at various steps of indicator selection like – during identification of criteria, ranking indicators and receiving commentary on selected set of indicators and developed causal network. The existing causal network is developed on the basis of extensive literature review and the links between the factors are accounted as direct and equal. The developed causal network is general for Indian cities. The causality and the direction of links between the factors in causal network are likely to vary by cities. Further work is required to measure the indicators and establish causality between the identified factors for the city under study. This shall result in developing different sets of causal network with few or more nodes with respect to the city.

play an important role in achieving the aims of sustainable mobility. The developed causal network is evaluated using centralization index and density index. Low density and low centralization index reveals that the network is sparse enough to deal with multiple issues and accounts for all types of factors i.e. root-cause, end-of-the-chain and central factors. Measurability of the shortlisted set of indicators is validated on the basis of data collected for preparing LCMP for three Indian cities (Rajkot, Udaipur and Vishakhapatnam). The analysis shows that most of the root-cause and central indicators are measured for the three cities. Measuring these indicators requires using data from secondary sources and conducting primary surveys. However, in all the three LCMP end-of-the-chain indicators are not measured that requires developing models. Therefore, there is a need to develop detailed methodology to measure end-of-the-chain indicators that can be easily adapted by local authorities. There is also a need to check if the measured indicators in the three LCMPs have helped in adopting strategies/policies to achieve the aims of sustainability mobility. This will require detail analysis of the three LCMPs. Further work is required to engage with different stakeholders to assess usability and usefulness of these indicators. The methodology does not specify controlling points and therefore

Acknowledgements This work was partly funded by UNEP Risø Center on Energy, Climate and Sustainable Development Technical University of Denmark under the project “Promoting Low carbon Transport in India”. We acknowledge Dr. Anvita Arora and Ravi Gadepalli, ITRANS, Dr. Talat Munshi, CEPT University, Ahmedabad and Ranjan Dutta, from UMTC for providing data for analysis.

Annexure 1

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