# Can public investment in transport influence densification and land use? Evidence from the tramway of Dijon (France)

Jean Dubé, Julie Le Gallo, Marie-Pier Champagne, Mohamed Hilal, Diègo Legros

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## ► To cite this version:

Jean Dubé, Julie Le Gallo, Marie-Pier Champagne, Mohamed Hilal, Diègo Legros. Can public investment in transport influence densification and land use? Evidence from the tramway of Dijon (France). Transport Policy, 2026, 176, pp.103927. 10.1016/j.tranpol.2025.103927. hal-05392358

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# Can public investment in transport influence densification and land use? Evidence from the tramway of Dijon (France)

Jean Dubé<sup>a,*</sup> <sup>●</sup>, Julie Le Gallo<sup>b,●</sup>, Marie-Pier Champagne<sup>a,●</sup>, Mohamed Hilal<sup>c,●</sup>, Diègo Legros<sup>d,●</sup>

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

### Keywords:

- Public transport

- Tramway

- Densification

- Causal analysis

## ABSTRACT

Investment in public transport offers alternatives to reduce car dependency as well as many negative externalities associated with solo car usage. Yet, to be fully effective, public transport infrastructure must be able to facilitate economic activities and households' concentration. By increasing building density around stations and corridors, transport land-use feedback cycle can be engaged. The paper aims to evaluate the existence and extent of the causal relationship between public investment and densification, leveraging the implementation of a tramway service in the metropole of Dijon (France). The analysis decomposes the impact by direction as well as by the distance to corridor and city center. The results suggest that investment in public transport systems can significantly affect population and/or employment densification patterns, but that success is largely linked to the political willingness to stimulate densification through facilitating private investment.

## 1. Introduction

Cities have mainly developed and expanded with road systems over the last century (Duranton and Turner, 2011; Baum-Snow, 2007), especially after World-War II. Yet, the prominent use of roads and highways has led to car dependency (Newman and Kenworthy, 1989) and has generated major negative environmental and health externalities (Naess, 2022; van Wee and Handy, 2016; Boarnet, 2011; Banister, 2008; Bertolini and le Clercq, 2003), as well as social cohesion issues related to gentrification and socio-spatial segregation (Talen and Koschinsky, 2013).

While the decision to travel by car is highly related to individual factors, the characteristics of the built environment also appear to be significant determinants (Litman and Steele, 2018; Ewing and Cervero, 2010). Dense and mixed urban forms influence travel mode choice and energy consumption (Niemeier et al., 2011; Ewing & Cervero, 2001, 2010; Brownstone and Golob, 2009). Therefore, investment in public transport is considered as an interesting vector to influence city transformation, through built environment, and affect modal shift share

(Banister, 2008). As such, the transit-oriented development (TOD) concept (Calthorpe, 1993) has been considered as an interesting way of influencing the city structure to promote good urbanistic practices and to stimulate the use of public transport.

Overall, the literature suggests that economic benefits at the macro, meso and micro levels associated with public transport investment usually outweigh costs (Banister and Thurstain-Goodwin, 2011; Berghauser Pont et al., 2021; Ahlfeldt et al., 2018; Wardip, 2011). At the macro-level, investment in public transport can affect output and productivity by providing advantages from agglomeration economies (Duranton and Puga, 2004). At the meso-level, it can impact labor markets, mainly through participation to the labor force and influence time allocation of workers between employment, leisure activities and networking (Moretti, 2004). At the micro-level, the impacts are internalized into land and real estate values (Debrezion et al., 2007; Mohammad et al., 2013).

Yet, these impacts not only appear to be highly place-dependent (Gospodini, 2005), but they are also highly heterogeneous within cities. This heterogeneity can be related to specific characteristics of cities,

<footnote><sup>a</sup> Université Laval, Québec, Canada</footnote>

<footnote><sup>b</sup> Institut Agro Dijon, Dijon, France</footnote>

<footnote><sup>c</sup> INRAE, Dijon, France</footnote>

<footnote><sup>d</sup> Laboratoire d'économie de Dijon (LEDi), Université de Bourgogne, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: jean.dube@esad.ulaval.ca (J. Dubé), julie.le-gallo@institut-agro.fr (J. Le Gallo), marie-pier.champagne@fsa.ulaval.ca (M.-P. Champagne), mohamed.hilal@inrae.fr (M. Hilal), diego.legros@u-bourgogne.fr (D. Legros).</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** The transport land use feedback cycle and the role of transport infrastructure (sprawl vs densification)
Inspired from Duffhures and Bertolini (2016)</figcaption></figure>

such as distance to the city center, presence of multiple centers, and distance to the station or lines (see Dubé et al., 2024 for a discussion). It could also be related to other local characteristics, such as population and employment densities, land use mix, parking availability and walking facilities, making the impact different between neighborhoods of the same city (see Dubé et al., 2018a for an analysis on single-family house prices).

In this context, three questions emerge. First, can public transport investment affect land use change and the spatial distribution of households and economic activities within a city through a densification process along the transit corridors? Second, is the implementation of public transport enough to affect spatial distributions, or are they conditioned by specific public actions to maximize the potential impact? Third, is the effect similar throughout the city? The answer to those questions raises difficult methodological questions, as the locations selected to implement these public transports are not chosen randomly by public authorities but depend on the characteristics of places. Moreover, an econometric analysis that would simply relate some measure of densification to the existence, or not, of a public transport would probably be affected by unobserved confounders. Therefore, most recent empirical analysis investigating these questions resorts to causal inference evaluation methods (Imbens, 2017; Antonakis et al., 2010).

The paper proposes investigating these questions on the basis of a specific case study, which is the impact of the implementation of a new tramway service on densification. More precisely, by analyzing the location of new units devoted to residential and economic activities within the metropole of Dijon (France). Dijon is particularly well suited to inform on these questions as the implementation of a new tramway service, divided into three new corridors, allows to set up a quasi-experimental design. Based on a difference-in-difference specification, locations close to the new tramway are defined as the treated locations and locations close to preexisting bus lines, that remained unchanged throughout the whole study period, as the control locations. Using administrative datasets based on buildings construction year and main vocation, a panel event study is implemented to uncover potential anticipation effects and temporal heterogeneity of the impact. Furthermore, to evaluate whether this impact is spatially homogeneous, the treatment effect along the new corridors is decomposed based on distance to the city center and distance to the line. As the three corridors finish in three different municipalities belonging to the metropole of Dijon with very different public support strategies accompanying the implementation of a tramway along the corridors, the decomposition allows to evaluate whether this investment suffices to impact densification.

The results suggest that the tramway infrastructure in the metropole of Dijon has a generally positive effect on new construction along the line and the stations. However, the results also underline that not only is

the impact heterogeneous within the city, but that it is closely linked to public intervention, implication and choices to stimulate and encourage local private investment.

The contribution of the paper in the literature is threefold. First, it is one of the rare studies to investigate the causal effect of change in public transport supply on densification. It specifically aims at testing the validity of the transport land use (TLU) feedback loop as an endogenous mechanism to influence land use and built environment. Second, it decomposes the impact for the same service based on distinct geographical sectors and public authorities of the city based on the directions of the tramway line. Third, it offers a flexible methodological framework to test for spatial and temporal heterogeneity of the causal effects of the implementation of a new public transport service.

The remainder of the paper is divided into seven (7) sections. Section 2 proposes a short review about how investment in public transport can affect built environment through densification, or intensification, reduce car dependence through transit-oriented development (TOD) strategies and start/influence the transport land use feedback cycle. Section 3 exposes the methodological framework used to investigate the causal link between the development of new public transport and densification (or intensification<fnref n="1" />). Section 4 discusses the empirical case study and Section 5 presents the data used to investigate the causal relationship. Section 6 synthesizes the empirical results and a discussion with policy implication of the study is proposed in Section 7. Section 8 provides a conclusion.

## 2. On the role of public transport in sustainable mobility

According to Banister (2008), four actions are needed to lead to more sustainable mobility: i) reducing the travel need; ii) encouraging public and active transport mode; iii) reduce trip length; and iv) improve transportation system efficiency. As underlined by the urban economic model of Alonso (1964), Muth (1969) and Mills (1970, 1972), transport costs play an important role in (re)location decision of economic agents by changing accessibility to employment centers. As such, investment in public transport is likely to change travel conditions, which might shape transport and housing costs, and directly affect local attractiveness.

Winning conditions to influence transport choice and spatial distribution of economic activities and households have been identified by the 3Ds model (Cervero and Kockelman, 1997), and thereafter extended to the 5Ds model (Ewing and Cervero, 2010). In the 5Ds model, sustainable mobility emphasizes the role of density, diversity, design, destination

<footnote n="1">¹ In the paper, the terms densification and intensification are used as synonyms. They both point to a process of building additional units within developed neighborhoods.</footnote>
accessibility, and distance to transit (see also Kang, 2018). Local conditions not only affect travel mode, but also the willingness to be located close to specific infrastructures, and, thus, the spatial distribution of human activities.

Changing the spatial and built environment is an idea closely related to the development of the transit-oriented development (TOD) concept, first proposed by Calthorpe (1993). Pedestrian- and transit-friendly neighborhoods are more likely to influence individual decisions of travel mode choice. Density is indicated as an important determinant of commuter satisfaction (Mouratidis et al., 2019). In analyzing 12 different European cities, Gospodini (2005) concludes that “[...] transport infrastructure projects can work as a catalytic means of urban development, redevelopment and regeneration.” [...] (p. 1109). This finding is supported by Park et al. (2021) who find, for eight U.S. metropolitan areas, that people located close to a station surrounded by dense, mix land-use, walkable and transit-friendly environment are more likely to walk, use public transport and reduce driving (see also Handy, 2005; Pikora et al., 2003). Based on over 330 published articles, Ibraeva et al. (2020) conclude that “[…], proximity to a station offering TOD features (density, land use mix and pedestrian-friendly design) increases the use of transit.” [...] (p. 128).

Similar conclusions are drawn from several meta-analyses related to the impact of built environment and travel behavior (Stevens, 2017; Ewing and Cervero, 2010). In fact, most of the research shows that densification of the built environment surrounding public transport infrastructure generates a (local) positive economic impact (Berghauser Pont et al., 2021). Stated otherwise, public transport investment can be seen as an important toolkit to reduce potential negative impacts related to high car usage (Ellis-Young and Doucet, 2021; Jones and Jey, 2016). Some meta-analyses exploring the impact of proximity to public service suggest that the impact is highly related to rail services (Debrezion et al., 2007; Mohammad et al., 2013). Policy based on public transport systems can then be seen as the first step that drives the transport land-use (TLU) feedback cycle (Fig. 1).

According to Duffhures and Bertolini (2016), accessibility and available infrastructure have a clear influence on the location of population and economic activities, which, in turn, affect land use changes and the way economic agents are moving within the city (see also Wegener and Fürst, 1999; Bertolini et al., 2012). Public investment might then be an important vector to stimulate spillover effects related to employment and population relocation through densification, or intensification, along new transport infrastructures, while densification influences the spatial distribution of economic agents, which, in turn, influence modal shift (Cervero and Day, 2008; Cervero, 1998).

While public investment might be a necessary initial condition to ensure that a TLU loop starts, it might, however, not be a sufficient condition. In particular, disconnection between private and public interests might affect the success of densification projects (Herdt and Jonkman, 2023). Kaniewska et al. (2024) suggest that “[...] top-down regulatory measures seem to have a very limited impact [...] and are less effective than bottom-up voluntary programs, policies, and actions.” (p. 13). Some authors show that a TOD project can sometimes lead to transit-adjacent development (TAD), consolidating car-use habits by providing low diversity and not much pedestrian- and transit-friendly environments, even when densification occurs (Dittmar and Ohland, 2012; Cervero and Duncan, 2008; Belzer and Autler, 2002). In some cases, investment in transport infrastructure might generate more cost than benefits if not consistent with urban transport policy (Ferreira and Batey, 2011; Melia et al., 2011, 2018; Niemeier et al., 2011).

The success of the TLU feedback cycle also depends on the local institutional framework and political climate, which can positively affect urban development, redevelopment or regeneration following the introduction of new public transport services (Gospodini, 2005). Banister and Berechman (2001) point out that the economic impact of transport investment on local growth highly depends on the investment conditions, as well as the political and institutional conditions. They

argue that it is only when both conditions are satisfied that the wider economic effects and spillovers operate. These conclusions are supported by Cai et al. (2023) who underline the role of providing places for affordable housing, office space, small business, and mixed-use buildings. These specificities might explain why the impact appears to be scale and place dependent in the literature.

From a public policy perspective, the TLU feedback cycle is effective if it can, initially, stimulate land use change, built environment and spatial distribution of households and economic activities. Densification will then influence transport infrastructure supply and accessibility. Showing that the correlation between densification and the use of public transport is important (Bassolas et al., 2019). However, correlation does not necessarily involve causality. Consequently, recent empirical analysis uses sound identification strategies to control for confounding factors and simultaneity issues and be able to derive a causal impact of public infrastructures on density and land-use.

In particular, the causal importance of land use change has been investigated by Lee and Guhathakurta (2018) using a quasi-experimental framework. By comparing the travel distance from recent movers into suburbs to travel distance of similar residents that do not experience important land-use change after moving to Atlanta, the study shows that increasing residential density and land-use mix in older suburban areas contributes to reducing car travel distance. Bothe et al. (2018) show that the implementation of a new metro service in Copenhagen (Denmark) has strongly affected employment growth as compared to other sectors in the city. The impact appears to be especially important within the city center. This conclusion is also supported by Jaczewska et al. (2022) studying the post-socialism transition in Poland, who conclude that: “[...] accessibility strategies turn out to be more feasible and bring essential long-term economic benefits over disadvantages in urban mobility.” (p. 12).<fnref n="2" />

So far, not much has been said about how investment in public transport might influence the densification or intensification process. As a matter of policy evaluation, it is important to investigate whether investment in public transport might act as a (causal) intervention to stimulate densification.

## 3. Methodology

To investigate whether public transport investment can affect density and the built environment through densification around new stations, the paper proposes to focus on the appearance of new units devoted to residential or economic activities before and after the implementation of a new tramway service.

Empirically, the difference-in-differences (DiD) estimator is a helpful tool to identify the causal impact of a specific change (Akbari et al., 2023; Butts, 2022; Murray and Bardaka, 2022; Dubé et al., 2014). The basic formulation of the DiD estimator in a panel data context with two-way fixed-effect (TWFE) specification can be formulated as follows:

$$y_{it} = \alpha + \sum_{k=1}^{K} x_{kit}\beta_{k} + [D_{i} \times D_{t}]\delta + \mu_{i} + \lambda_{t} + \varepsilon_{it} \quad (1)$$

where $y_{it}$ is the outcome of interest for observation i collected at time t, $x_{kit}$ is the kth time-varying characteristic, $D_i$ is a dummy variable identifying the observations within the treatment group (1 if within the treatment group, 0 otherwise), $D_t$ is a dummy variable identifying the observations collected after the treatment occurs, i.e., when $t \ge t^*$ (1 if observed after $t^*$, 0 otherwise), and $\varepsilon_{it}$ is an error term assumed to be independent and identically distributed (iid). The parameter of interest,

<footnote n="2">² The authors also suggest that an important decision needs to be made in favor of public transport even if such actions: “[...] exceeds budgetary capacities of the cities but also unpopular decisions restricting the freedom to use private cars.” (p. 12).</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 2. Interpretation of ESA coefficients.</figcaption></figure>

isolating the average treatment effect (ATE), is δ, identifying the specific effect related to a treatment group after the treatment occurs. The spatial, μᵢ, and temporal, λᵢ, fixed effects allow to mitigate the omission variable bias (OVB) linked to time-invariant individual characteristics and to common shocks.<fnref n="3" />

The DiD relies on an important premise: the parallel trend assumption (PTA). The PTA states that before the treatment, control and treatment groups follow a similar path. It implies that in the absence of treatment, the two groups would have evolved in the same way after the change. The validity of the assumption is assessed with the pre-treatment periods (Kearney and Levine, 2015; Roth, 2019; Rambachan and Roth, 2023; Roth et al., 2022).

Two strategies are mobilized to test for such assumption. On the one hand, one can use a placebo analysis: estimation is made on the observations collected before the change occurred by fixing a fictive date of treatment and testing for the significance of the ATE coefficient. If the coefficient related to the interaction variable is not statistically significant, it suggests that the PTA assumption cannot be rejected. On the other hand, estimation can be made on the whole sample by introducing additional variables proper to treatment observations before the change occurs. A test for the joint significance of all coefficients associated with these variables is conducted. The non-rejection of the null assumption, i. e., that all coefficients are non-statistically significant, suggests that PTA holds.

Heterogeneity of the ATE can be investigated with the DiD framework. To do so, the specification introduces additional interaction terms through cross-product variables based on specific characteristics, such as: i) the direction of the extension, or the lines, Dⱼ, where j = 1, ..., J is the number of distinct lines (equation (2)); ii) the distance to city center, dᵢCBD (equation (3)); iii) the distance to the nearest station, min(dᵢs), or to the line, min(dᵢl) (equation (4)); and iv) a combination of the three specificities (equation (5)).

$$y_{it} = \alpha + \sum_{k=1}^{K} x_{kit} \beta_k + \sum_{j=1}^{J} D_j [D_i \times D_t] \delta_j + \mu_i + \lambda_t + \varepsilon_{it} \quad (2)$$

$$y_{it} = \alpha + \sum_{k=1}^{K} x_{kit} \beta_k + (D_i \times D_t)(\delta + d_{iCBD} \delta_{CBD}) + \mu_i + \lambda_t + \varepsilon_{it} \quad (3)$$

$$y_{it} = \alpha + \sum_{k=1}^{K} x_{kit} \beta_k + (D_t \times D_i)(\delta + \min(d_{is})\delta_s) + \mu_i + \lambda_t + \varepsilon_{it} \quad (4)$$

$$y_{it} = \alpha + \sum_{k=1}^{K} x_{kit} \beta_k + \sum_{j=1}^{J} D_j (D_i \times D_t) (\delta_j + d_{iCBD} \delta_{jCBD}) + \min(d_{is}) \delta_{js} \\ + d_{iCBD} \times \min(d_{is}) \delta_{jCBD,s} + \mu_i + \lambda_t + \varepsilon_{it} \quad (5)$$

The last specification allows to retrieve a 2-D specification of the ATE, with the impact varying with all three components: distance to city center, distance to nearest station/line and by direction (see Dubé et al., 2024).

The DiD approach has recently been extended to event studies analysis using a two-way fixed effect model with pooled cross-sectional data (Wooldridge, 2021), allowing to investigate temporal heterogeneity of the ATE. Event study analysis (ESA) relies on a similar specification of the DiD but decomposes the ATE over time (Goodman-Bacon, 2021; Sun and Abraham, 2020; de Chaisemartin and D'Haultfoeuille, 2020). The pretreatment, usually identified as lag components, allows to test for PTA, while the post treatment component allows to decompose the ATE over time. ESA usually standardizes the treatment date to 0 (τ = 0) after rescaling the temporal variable, τ = t - t*, where t* is the period when the change occurs.<fnref n="4" />

The main advantage of the ESA framework is that it enables to explicitly account for anticipation effects related to the announcement of the project and its construction (Cervero and Duncan, 2002; Bae et al., 2003; McMillen and McDonald, 2004; Agostini and Palmucci, 2008; Devaux et al., 2017; Dubé et al., 2018a,b). More specifically, the ESA model specification is given by extending the DiD (equation (6)).

$$y_{it} = \alpha + \sum_{k=1}^{K} x_{kit} \beta_k + \sum_{t=1}^{T-2} (D_i \times D_t) \delta_t^{-} + \sum_{t=T}^{T} (D_i \times D_t) \delta_t^{+} + \mu_i + \lambda_t + \varepsilon_{it} \quad (6)$$

where the interaction term (Dᵢ × Dₜ) allows to isolate the difference in outcome yᵢₜ for the observation part of the treatment group at time t. The time period before the change, t = t* - 1, is the reference period and explicitly set to 0. The other estimated coefficients, δᵢ⁻ and δᵢ⁺, are then compared to this reference period.

In the ESA specification, the PTA implies that all coefficients related to the before time periods are equal (not statistically different) to the

<footnote n="3">³ The dummy variables Dᵢ and Dₜ, without interaction, are not included in the specification as they are perfectly correlated with the spatial and temporal fixed effects, respectively. The two individual dummy variables are subsets of individual and temporal fixed effect variables.</footnote>

<footnote n="4">⁴ Note that this expression can easily be extended to the case where the treatment date is different within observations, with τ = t - tᵢ, and tᵢ is the treatment period specific to observation i.</footnote>
<figure><img src="image_7.png" /><figcaption>a. The 23 municipalities of Dijon Métropole</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 3. Dijon Métropole.</figcaption></figure>

reference period, i.e., $\delta_1^{-} = \dots = \delta_{t-2}^{-} = 0$. This set of coefficients is usually labelled as “lead” coefficients. This assumption implies that there is no difference in the outcome variable, $y_t$, between the treatment and the control group before the change occurs. Anticipation effect might be detected if the set of lead coefficients is equal to a specific value, $\kappa$, i.e., $\delta_1^{-} = \dots = \delta_{t-2}^{-} = \kappa$. In such a case, a test for the validity of the PTA is given by checking if the set of coefficients is statistically equal to a specific value, $\kappa$.

The set of coefficients $\delta_t^+$ allows to isolate for the ATE for each time period after the treatment, i.e., for $t \ge t^*$, when PTA is not rejected. This set of coefficients is usually labelled as “lag” coefficients in the literature. When anticipation effect is detected, the ATE can be retrieved by adding the value of the constant term, i.e., $ATE_t = \delta_t^+ + \kappa, \forall t \ge t^*$ (Fig. 2).

As for the DiD, the ESA specification can be extended to account for heterogeneity of the impact based on specific characteristics.<fnref n="5" /> The impact can be extended to vary for each time period, but it can also vary according to spatial characteristics. Depending on the choice of the specification, the number of parameters to be retrieved may be large. The limit of the possibilities is thus related to the number of observations available within the treatment group.

## 4. Case study: tramway in Dijon (France)

The case study is based on Dijon Métropole, which is an

<footnote n="5">⁵ It should be noted that the list of explanatory variables in the ESA needs to be time-varying. Otherwise, there is a perfect correlation (perfect collinearity) with the individual fixed effect. The estimation of the model can also introduce a grouped cluster variance-covariance matrix based on individual units when the number of observations is larger than 42 (Angrist and Pischke, 2009).</footnote>
<figure><img src="image_9.png" /><figcaption>Fig. 4. Evolution of public transport supply in Dijon, 2004–2018.</figcaption></figure>

intermunicipal group of 23 French municipalities, the lowest-tier juris-
diction in France, created in 2017 (Fig. 3a). Dijon Métropole hosts about
250,000 inhabitants and is located in the department of Côte d’Or
(Fig. 3b), the middle-tier jurisdiction, which is located almost half-way
between Paris, about 310 km south-east of the largest metropolitan area
in France, and Lyon, the second largest metropolis, located about 190
km to the south (Fig. 3c). Dijon Métropole exercises the powers trans-
ferred to it by the member municipalities. In particular, it exercises a set
of competences in terms of economic, social and cultural development
and planning as well as in terms of planning of the metropolitan area.
The main municipality within Dijon Métropole is Dijon, which is the
capital of the Région Bourgogne Franche-Comté. Dijon has a population
of about 155,000 inhabitants. Its historical city center hosts the location
of the ducal castle, but also an important concentration of commercial,
administrative and other economic activities.

The public transport system in Dijon is managed, since 2004, by Divia
Mobilité, an agency under the supervision of Dijon Métropole. Divia is
responsible for the whole public transport service supply in Dijon
Métropole. When the restructuring took place in 2004, a new service
was introduced: the Lianes. The Lianes service represents six (6) major
lines of bus rapid transit (BRT) service and aims to develop a major
restructuring of public transport service in Dijon Métropole. At that
time, the Lianes service represented about 80 % of the total frequenta-
tion of public transport service.

To improve the service, in 2008 the public authority decided to
implement a new public transport system: the tramway. The choice of
the tramway was confirmed in November 2008 and the construction of
two lines began in October 2010. The service was opened to users at the
end of 2012.

On the one hand, the lines aimed at replacing part of the existing

Lianes routes. The tramway operates on two different lines. Line T1 runs from east to west, from the train station to the municipality *Quétigny*. It largely replaces the Lianes L1 route, as well as certain sections of the L3 and L5 routes. Line T2 runs north to *Valmy* and south to the *Chenôve* municipality. The two lines provide three distinct corridors: the northern route largely replaces the original L4 route, while the southern section largely replaces the L2 line (Fig. 4). The new tram routes have not resulted in significant time savings for public transportation users. However, they do offer the advantage of being more pleasant, comfortable, and reliable (Dubé et al., 2018b).

On the other hand, the introduction of the tramway service opened
the possibility for Divia to develop new Lianes lines. Modifying existing
routes to avoid duplicating tramway services, as well as adding certain
routes and removing less-used ones, has helped improve bus services.
The reorganization of public transit lines has also had an impact on car
users, since the number of lanes dedicated to traffic has decreased on
several streets, in or near the city center, where historic buildings reduce
the space between buildings.

The introduction of the tramway service has resulted in a substantial
increase of the modal share of public transport. The first year, the fre-
quency of public transport rose by about 20 %, while a second increase
of about 9 % was noted during the second and the third year. The change
in the service was capitalized in housing values and resulted in a sub-
stantial rise in the apartment sale price within the city center (Dubé
et al., 2018b).

The tramway implementation represents an interesting quasi-natural
experiment, as it is an upgrade of the Lianes service, while part of the
original Lianes service remains intact. The environment surrounding the
former (resp. the latter) is the treatment group (resp. the control group).
In this way, the comparison of changes along the lines in terms of density
<table><caption>**Table 1**
Number of units in Dijon, 2004–2018.</caption><thead><tr><th rowspan="2">Development</th><th colspan="2">Residential activities</th><th colspan="2">Economic activities</th></tr><tr><th>Tramway</th><th>Lianes</th><th>Tramway</th><th>Lianes</th></tr></thead><tbody><tr><td>Not developed</td><td>1974</td><td>2695</td><td>371</td><td>448</td></tr><tr><td>Developed</td><td>6883</td><td>23,396</td><td>595</td><td>1484</td></tr><tr><td><strong>Total</strong></td><td><strong>8857</strong></td><td><strong>26,091</strong></td><td><strong>966</strong></td><td><strong>1932</strong></td></tr></tbody></table>

and land use, for the tramway and Lianes, provides insightful results about how the substitution of one service by another can affect land use changes and densification.

## 5. Data

To investigate the question of whether the implementation of a new public transport service causes densification (or intensification), the analysis exploits data from the land registers (*fichiers fonciers*) to identify new constructions appearing along the lines. The analysis is done for both services: the tramway, and the existing Lianes. The files contain information on all buildings, with the exact location, the construction year, the main vocation of the building - residential or economic activity -, the number of floors, the total living area, and the total number of rooms. Based on the construction year, the historical evolution of new construction along the lines has been built for 2004–2018 (Aïkous et al., 2023; Dubé et al., 2017, 2023). The date of construction is therefore used to indicate whether the plot has been developed or redeveloped (yes or no). The dependent variable therefore represents the persistence of the last construction listed.

A limit of this panel data set, which rebuilds backward historical construction, is that it assumes that the parcel was vacant before the construction year. However, some reconversion might be possible, i.e., destroying existing units to build others. This pattern might positively bias the ATE if the number of units replaced is important. However, while reconversion appears to be more important in North America and Australia, where only a small fraction of the total stock faces major change (Dubé et al., 2018c), this development pattern appears to be marginal in Europe, where most of the existing buildings are renovated instead of being demolished and rebuilt.

The total number of individual units, i.e. the total number of individual buildings or vacant parcels, is 256,786. These units represent the parcels that host residential or economic activity in 2018 for the whole Dijon metropolitan area. A total of 214,672 parcels devoted to residential or economic activities were already present in 2004, meaning that 42,114 units were built between 2004 and 2018.

More specifically, the analysis is limited to parcels located within 150 m from the existing Lianes and tramway lines, excluding the new Lianes lines as well as the new corridors on existing lines. This distance is selected to study the densification effect within direct proximity of the public transport service as the impact might be limited to a few streets along or near the corridors. The paper limits the analysis to 150m because real estate price changes are usually very local, lying between 800m for residential uses and 400m for commercial uses (Debrezion et al., 2007; Higgins and Kanaroglou, 2016; Mohammad et al., 2013; Banister and Thurstain-Goodwin, 2011), while the trade-off between negative and positive externalities is even more local, i.e. within 150m (Dubé et al., 2025; Yao et al., 2020). It is also motivated by the fact that using a too large buffer might generate a conflict between the two zones: the one where Lianes are still operating (control) and the one where the tramway is in service (treatment).

The analysis excludes the parcels located within 750 m of the city center, as both services (tramway and Lianes) operate in its vicinity, availability is highly limited, and the city center experimented with pedestrianization over the years. It also excludes the parcels located over 5.25 km from the city center, since the length of the tramway is limited to this distance.

<table><caption>**Table 2**
Number of units in Dijon according to location, 2004–2018.</caption><thead><tr><th rowspan="2">Corridor</th><th colspan="2">Residential activities</th><th colspan="2">Economic activities</th></tr><tr><th>Not developed</th><th>Developed</th><th>Not developed</th><th>Developed</th></tr></thead><tbody><tr><td><strong>Tramway</strong></td><td>1974</td><td>6883</td><td>371</td><td>595</td></tr><tr><td>Quétigny (East)</td><td>88</td><td>1371</td><td>49</td><td>147</td></tr><tr><td>Chenôve (South)</td><td>926</td><td>2354</td><td>67</td><td>203</td></tr><tr><td>Valmy (North)</td><td>960</td><td>3158</td><td>255</td><td>245</td></tr><tr><td><strong>Lianes</strong></td><td>2695</td><td>23,396</td><td>448</td><td>1484</td></tr><tr><td><strong>Total</strong></td><td><strong>4669</strong></td><td><strong>30,279</strong></td><td><strong>819</strong></td><td><strong>2079</strong></td></tr></tbody></table>

<table><caption>**Table 3**
Description of the time-varying explanatory variables.</caption><thead><tr><th>Variables</th><th>Definition</th></tr></thead><tbody><tr><td>Δhouses<sub>t-1</sub></td><td>Number of new house units within t-2 and t-1</td></tr><tr><td>Δapartment<sub>t-1</sub></td><td>Number of new apartment units within t-2 and t-1</td></tr><tr><td>Δeconomic<sub>t-1</sub></td><td>Number of new economic activity units within t-2 and t-1</td></tr><tr><td>No house<sub>t-1</sub></td><td>No house located within 100m. in t-1</td></tr><tr><td>No apartment<sub>t-1</sub></td><td>No apartment located within 100m. in t-1</td></tr><tr><td>No economic<sub>t-1</sub></td><td>No economic activity units located within 100m. in t-1</td></tr><tr><td>No vacant<sub>t-1</sub></td><td>No vacant units available within 100m. in t-1</td></tr></tbody></table>

Note: Calculations are based on a 100m. radius in the previous year.

After eliminating the observations not meeting these criteria, a total of 34,948 residential units and 2898 economic activity units in 2018 are mobilized (Table 1). The majority of the individual units are developed and located along the existing Lianes lines. About the same number of units were developed between 2004 and 2018 along both the tramway lines and the existing Lianes lines.

The dependent variable of the analysis consists of a dummy variable taking a value of 1 when the units, residential or economic activity, are built at the given year, and 0 otherwise. Focusing on the three different directions of the tramway corridor, the number of residential parcels not developed in 2003 are equally distributed between Chenôve and Valmy, representing about 20 % of the total, while it has been relatively marginal for Quétigny, at about 2 % (Table 2). Regarding economic activities, the number of undeveloped parcels in 2003 is similar for Quétigny and Chenôve, representing about 25 % of the total, with 49 and 67 units, respectively. The line going to Valmy represents more than 50 % of the total undeveloped commercial units, with 255 parcels (Table 2).

For the explanatory variables, a set of time-varying local indexes are developed using the relative location of each building (Dubé et al., 2014) and the construction year. For all parcels, a radius of 100 m is used to calculate, for each year, the total number of units within the buffer for: i) residential units; ii) economic activity units; and iii) vacant parcels.<fnref n="6" /> To mitigate endogeneity and simultaneity bias, the counts are introduced using a temporal lag, i.e., for the previous year (Table 3).

The global correlations between the explanatory variables are relatively low (Table 4). For the residential units, the higher correlation, in absolute value, is less than 0.3 (top of Table 4). For the economic activity units, the correlations are a little higher, but remain well under control, with correlations never higher than 0.45 in absolute value (bottom of Table 4).

<footnote n="6">⁶ The counts are obtained based on the general function $N_{ita} = 1(a_j = \bar{a}; d_j \le d_c)$, where $N_{ita}$ denotes the number of units around observations i at time t within a specific activity a, $a_j$ is a categorical variable identifying the main activity of observation j, $\bar{a}$ is a specific activity, $d_j$ is the distance between observations j and a given observation i, $d_c$ is a critical distance cut-off value set to 150 m, and 1 is a general function returning a value of 1 if the constraints are respected, and 0 otherwise.</footnote>
<table><caption>**Table 4**
Correlation table between the explanatory variables according to the type of units.</caption><thead><tr><th>Residential units</th><th>x<sub>1</sub></th><th>x<sub>2</sub></th><th>x<sub>3</sub></th><th>x<sub>4</sub></th><th>x<sub>5</sub></th><th>x<sub>6</sub></th><th>x<sub>7</sub></th></tr></thead><tbody><tr><td>x<sub>1</sub>: Δhouses<sub>t-1</sub></td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>x<sub>2</sub>: Δapartment<sub>t-1</sub></td><td>0.045</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>x<sub>3</sub>: Δeconomic<sub>t-1</sub></td><td>0.001</td><td>0.137</td><td>1.000</td><td></td><td></td><td></td><td></td></tr><tr><td>x<sub>4</sub>: No house<sub>t-1</sub></td><td>-0.056</td><td>-0.006</td><td>0.030</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>x<sub>5</sub>: No apartment<sub>t-1</sub></td><td>0.005</td><td>-0.028</td><td>-0.007</td><td>-0.014</td><td>1.000</td><td></td><td></td></tr><tr><td>x<sub>6</sub>: No economic<sub>t-1</sub></td><td>0.008</td><td>-0.043</td><td>-0.025</td><td>0.001</td><td>0.273</td><td>1.000</td><td></td></tr><tr><td>x<sub>7</sub>: No vacant<sub>t-1</sub></td><td>0.002</td><td>-0.040</td><td>-0.006</td><td>0.234</td><td>0.051</td><td>0.076</td><td>1.000</td></tr><tr><td>Economic activity units</td><td>x<sub>1</sub></td><td>x<sub>2</sub></td><td>x<sub>3</sub></td><td>x<sub>4</sub></td><td>x<sub>5</sub></td><td>x<sub>6</sub></td><td>x<sub>7</sub></td></tr><tr><td>x<sub>1</sub>: Δhouses<sub>t-1</sub></td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>x<sub>2</sub>: Δapartment<sub>t-1</sub></td><td>0.070</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>x<sub>3</sub>: Δeconomic<sub>t-1</sub></td><td>0.003</td><td>0.044</td><td>1.000</td><td></td><td></td><td></td><td></td></tr><tr><td>x<sub>4</sub>: No house<sub>t-1</sub></td><td>-0.052</td><td>-0.001</td><td>0.085</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>x<sub>5</sub>: No apartment<sub>t-1</sub></td><td>-0.040</td><td>-0.091</td><td>-0.015</td><td>0.453</td><td>1.000</td><td></td><td></td></tr><tr><td>x<sub>6</sub>: No economic<sub>t-1</sub></td><td>-0.002</td><td>-0.042</td><td>-0.038</td><td>0.205</td><td>0.293</td><td>1.000</td><td></td></tr><tr><td>x<sub>7</sub>: No vacant<sub>t-1</sub></td><td>0.019</td><td>-0.004</td><td>0.052</td><td>0.232</td><td>0.110</td><td>0.016</td><td>1.000</td></tr></tbody></table>

Note: NT = 526,830.

Note: NT = 44,175.

<table><caption>**Table 5**
Estimation results for residential units.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">Model (1)</th><th colspan="2">Model (2)</th><th colspan="2">Model (3)</th><th colspan="2">Model (4)</th></tr><tr><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th></tr></thead><tbody><tr><td>Spatial Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Temporal Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Other Explanatory Time-varying Variables</td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Lead 4</td><td>-0.0210</td><td>***</td><td>-0.0210</td><td>***</td><td>-0.0209</td><td>***</td><td>-0.0208</td><td>***</td></tr><tr><td>Lead 3</td><td>-0.0140</td><td>***</td><td>-0.0141</td><td>***</td><td>-0.0140</td><td>***</td><td>-0.0139</td><td>***</td></tr><tr><td>Lead 2</td><td>-0.0101</td><td>***</td><td>-0.0101</td><td>***</td><td>-0.0101</td><td>***</td><td>-0.0100</td><td>***</td></tr><tr><td>Lead 1</td><td>Reference</td><td></td><td>Reference</td><td></td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>ATE</td><td>0.0505</td><td>***</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATE × Valmy</td><td></td><td></td><td>0.0680</td><td>***</td><td></td><td></td><td></td><td></td></tr><tr><td>ATE × Chenóve</td><td></td><td></td><td>0.0629</td><td>***</td><td></td><td></td><td></td><td></td></tr><tr><td>ATE × Quétigny</td><td></td><td></td><td>-0.0265</td><td>***</td><td></td><td></td><td></td><td></td></tr><tr><td>Lag 0</td><td></td><td></td><td></td><td></td><td>0.0085</td><td>***</td><td>0.0338</td><td>***</td></tr><tr><td>Lag 1</td><td></td><td></td><td></td><td></td><td>0.0162</td><td>***</td><td>0.0416</td><td>***</td></tr><tr><td>Lag 2</td><td></td><td></td><td></td><td></td><td>0.0258</td><td>***</td><td>0.0512</td><td>***</td></tr><tr><td>Lag 3</td><td></td><td></td><td></td><td></td><td>0.0382</td><td>***</td><td>0.0634</td><td>***</td></tr><tr><td>Lag 4</td><td></td><td></td><td></td><td></td><td>0.0291</td><td>***</td><td>0.0544</td><td>***</td></tr><tr><td>Lag 5</td><td></td><td></td><td></td><td></td><td>0.0366</td><td>***</td><td>0.0616</td><td>***</td></tr><tr><td>Lag 6</td><td></td><td></td><td></td><td></td><td>0.0575</td><td>***</td><td>0.0827</td><td>***</td></tr><tr><td>Lag 7</td><td></td><td></td><td></td><td></td><td>0.0586</td><td>***</td><td>0.0842</td><td>***</td></tr><tr><td>Lag 8</td><td></td><td></td><td></td><td></td><td>0.0949</td><td>***</td><td>0.1205</td><td>***</td></tr><tr><td>Lag 9</td><td></td><td></td><td></td><td></td><td>0.0801</td><td>***</td><td>0.1061</td><td>***</td></tr><tr><td>Lag 10</td><td></td><td></td><td></td><td></td><td>0.1120</td><td>***</td><td>0.1373</td><td>***</td></tr><tr><td>ATE × dCBD</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.1630</td><td>***</td></tr><tr><td>ATE × log(dCBD)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.4096</td><td>***</td></tr><tr><td>ATE × dLine†</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.1643</td><td>***</td></tr><tr><td>ATE × log(dCBD) × dLine†</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.1040</td><td>***</td></tr><tr><td>Number of observations (NT)</td><td>524220</td><td></td><td>524220</td><td></td><td>524220</td><td></td><td>524220</td><td></td></tr><tr><td>R-squared</td><td>0.2510</td><td></td><td>0.2534</td><td></td><td>0.2568</td><td></td><td>0.2628</td><td></td></tr><tr><td>RMSE</td><td>0.1344</td><td></td><td>0.1342</td><td></td><td>0.1339</td><td></td><td>0.1334</td><td></td></tr><tr><td>F statistic</td><td>819.08</td><td></td><td>768.64</td><td></td><td>592.87</td><td></td><td>544.82</td><td></td></tr><tr><td>AIC</td><td>-616370</td><td></td><td>-618056</td><td></td><td>-620410</td><td></td><td>-624655</td><td></td></tr></tbody></table>

Legend: ***p < 0.001; **p < 0.01; *p < 0.05.

Note: † indicates that the distance is transformed using a Gaussian function: $d_L^* = (1 - (d_L/d_{max})^2)^2$.

Where: $d_L^*$ is the resulting transformation, $d_L$ is the distance to the line; $d_{max}$ is the maximum distance used (150m.).

## 6. Results

Linear Probability Models (LPMs) are estimated. While the estimation of models using a dummy variable on the left-hand side is usually done using a probit or logit model, Wooldridge (2001) shows that such specification cannot be used to retrieve ATE. DiD analysis, and ESA, with a discrete choice model is forbidden for two reasons: i) "[...] the

consistency of the parameter relies on the assumption of serial independence in the idiosyncratic errors; and ii) the model does not identify average treatment effects unless T is large enough to provide estimates of the unobserved heterogeneity. And even then, inference is very challenging" (p. 75).

Wooldridge (1999, 2001) argues that LPM works well when the values of the explanatory variables are close to the means. Wooldridge
<figure><img src="image_10.png" /><figcaption>Fig. 5. Global ATE with respect to distance to line and city center, residential units.</figcaption></figure>

(1999) also states that “[...] it is still acceptable in applied work to present a standard OLS analysis of a linear probability model” (p. 236). Wooldridge points out that the problem of using an LPM is “[...] not serious when we are obtaining estimates of the partial effects of the explanatory variables for the middle ranges of the data” (p. 241).<fnref n="7" /> The same approach has been used by Aikous et al. (2023) when investigating the impact of the extension of a highway on the probability of construction on close vacant parcels.

Various specifications are used to evaluate the impact of the development of the tramway on the probability of densification, or intensification, with new residential and economic activity units. The first two specifications are based on the classic DiD specifications. The first assumes a single and homogenous ATE for all time periods and both lines (Model 1). The second decomposes the ATE by lines, with the three different directions of the tramway, i.e., east (Quétigny), south (Chenóve) and north (Valmy) corridors (Model 2). The third proposes the ESA specification, i.e., decomposing the ATE over years (Model 3). The fourth introduces an additional heterogeneity specification, with the effect varying according to, on the one hand, the distance to the nearest station and, on the other hand, the distance to the city center (Model 4). The fifth proposes a decomposition of the ATE based on a temporal variation for the three corridors (Model 5). Finally, the sixth model decomposes the ATE with temporal and spatial variation for the three corridors (Model 6). All specifications are estimated for each type of the units, residential and economic activities, to investigate the heterogeneity effect by type of activities and to explore a potential complementarity of the effect between the type of units. All models are estimated using a cluster variance-covariance matrix to account for potential serial autocorrelation among the observations (Clark and Tapia-Schythe, 2021).

## 6.1. Impact on the construction of residential units

For the residential activity units (Table 5), the estimated model has an $R^2$ statistic that varies between 0.25 and 0.28, according to the specifications. The models are estimated using 34,948 individual residential units.

The first specification suggests that the PTA does not hold ($p = 0.0000$) due to a potential anticipation effect before the tramway project was announced (Model 1) - Table 5). The negative statistically significant anticipation effect varies between 1 and 2 percentage points and is statistically significant. This anticipation effect might be explained by the fact that the project was under study since 2006. Combining the two effects, i.e., the anticipation effect and the treatment effect, the first model suggests that the probability of building new residential units is between 6 and 7 percentage point higher along the tramway corridors than along the existing Lianes corridors.

Decomposing the effect by corridors (Model 2) – Table 5), the results suggest a mean negative anticipation effect varying between 1 and 2 percentage points, with the rejection of the PTA ($p = 0.0000$). Combining the anticipation effect with the ATE by corridor leads to a more nuanced conclusion. The impact on construction appears non-statistically significant in the east (Quétigny) corridor while it is statistically significant and varies between 7 and 9 percentage points for the two other corridors.

For the two other specifications, based on the ESA, the results reveal, once again, a statistically significant anticipation effect varying between 1 and 2 percentage points, with the PTA statistically significant ($p = 0.0000$), while the coefficients share similar confidence intervals. For the third specification (Model 3) – Table 5), the ATE rise over time, starting from an increase of about 1–3 percentage points at the announcement, reaching an increase of 3–5 percentage points in the probability of construction during the construction of the tramway and a higher probability of construction of about 10–13 percentage points after 4–6 years of operation.

Turning to a specification introducing the possibility that the ATE varies over space, the results suggest a stronger mean temporal increase in the probability of construction with an anticipation effect and rejection of PTA ($p = 0.0000$) with coefficients associated with lag periods

<footnote n="7">⁷ Wooldridge (2001) reinforces his point by stating that “[...] the LPM often seems to give good estimates of the partial effects on the response probability near the center of distribution of x.” (p. 455), that “[...] the LPM should be seen as a convenient approximation to the underlying response probability,” (p. 454) and that “the case for the LPM is even stronger if most of the x are discrete and take on only few values.” (p. 456).</footnote>
being similar, about 2 percentage points higher than the third specification (Model (4) – Table 5). However, the higher ATE is partly cancelled by the fact that the mean effect varies according to the distance to the city center, defined by the historical and central business district, and to the line. Based on the estimated coefficients, a complex 2-dimension (2-D) graph can be built to see how the ATE varies according to the relative position over space (Fig. 5).<fnref n="8" /> By combining the two sets of coefficients, i.e., those by years and those for the spatial variation, the results suggest that the impact is increasing with the distance to the center, reach a maximum impact at 2 km, and decreasing thereafter. The impact is negative after 4.5 km. The ATE is higher for short distances to the line, suggesting that most of the impact on new construction is localized along the line and in the middle of the corridors.

With the fifth specification, the impact is statistically significant for two lines: the north (Valmy) and the south (Chenôve) corridors (Model (5) – Table 6). The ATE appears to be slightly negative, when significant, for the east corridor (Quétigny). Focusing on the north corridor, an anticipation effect varying between 3 and 5 percentage points violates the PTA. Combining the anticipation effect with the time-varying ATE suggests a positive and statistically significant higher probability of new construction of about 4–7 percentage points at the announcement. The impact reaches 7 to 11 percentage points during the construction and about 8–13 percentage points when the tramway was in operation. For the southern corridor, the anticipation effect is negligible, i.e., about half a percentage point, but statistically significant. During the announcement, the ATE suggest that the change raises the probability of new construction between 1 and 3 percentage points, while this change translates into a higher probability of construction of about 3–4 percentage points during the construction. Once in operation, the probability of residential construction appears to be between 5 and 22 percentage points higher along the line.

With the sixth specification, the ATE is decomposed both temporally and spatially by lines (Model (6) – Table 6). The results are qualitatively similar to the fifth specification, i.e., there exists a more important anticipation effect, with the rejection of PTA, along the northern corridor (Valmy), with an impact starting from the announcement and prevailing many years after the tramway is in operation. Once again, the PTA does not hold for both lines, underlying the presence of anticipation effect. In this case, the coefficients associated with the distance to city center suggest a concave relation, with an increasing probability of new construction that reaches a maximum about 2 km away from the center, and then decreases until the end of the line. The positive and statistically significant coefficient related to the distance to the line suggests that the relation is decreasing from the line (Fig. 6). Combining the different

<table><caption>**Table 6**
Number of units in Dijon according to location, 2004–2018.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Model (5)</th><th colspan="2">Model (6)</th></tr><tr><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th></tr></thead><tbody><tr><td>Spatial Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Temporal Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Other Explanatory Time-varying Variables</td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Lead 4 × Valmy</td><td>-0.0515</td><td>***</td><td>-0.0513</td><td>***</td></tr><tr><td>Lead 3 × Valmy</td><td>-0.0369</td><td>***</td><td>-0.0365</td><td>***</td></tr><tr><td>Lead 2 × Valmy</td><td>-0.0288</td><td>***</td><td>-0.0286</td><td>***</td></tr><tr><td>Lead 1 × Valmy</td><td>Reference</td><td></td><td></td><td></td></tr><tr><td>Lag 0 × Valmy</td><td>0.0131</td><td>***</td><td>0.1020</td><td>***</td></tr><tr><td>Lag 1 × Valmy</td><td>0.0181</td><td>***</td><td>0.1073</td><td>***</td></tr><tr><td>Lag 2 × Valmy</td><td>0.0417</td><td>***</td><td>0.1303</td><td>***</td></tr><tr><td>Lag 3 × Valmy</td><td>0.0564</td><td>***</td><td>0.1450</td><td>***</td></tr><tr><td>Lag 4 × Valmy</td><td>0.0461</td><td>***</td><td>0.1348</td><td>***</td></tr><tr><td>Lag 5 × Valmy</td><td>0.0522</td><td>***</td><td>0.1403</td><td>***</td></tr><tr><td>Lag 6 × Valmy</td><td>0.0549</td><td>***</td><td>0.1431</td><td>***</td></tr><tr><td>Lag 7 × Valmy</td><td>0.0705</td><td>***</td><td>0.1590</td><td>***</td></tr><tr><td>Lag 8 × Valmy</td><td>0.0512</td><td>***</td><td>0.1406</td><td>***</td></tr><tr><td>Lag 9 × Valmy</td><td>0.0754</td><td>***</td><td>0.1640</td><td>***</td></tr><tr><td>Lag 10 × Valmy</td><td>0.0696</td><td>***</td><td>0.1583</td><td>***</td></tr><tr><td>ATE × dCBD × Valmy</td><td></td><td></td><td>-0.2532</td><td>***</td></tr><tr><td>ATE × log(dCBD) × Valmy</td><td></td><td></td><td>0.4883</td><td>***</td></tr><tr><td>ATE × dLine† × Valmy</td><td></td><td></td><td>0.1924</td><td>***</td></tr><tr><td>ATE × log(dCBD) × dLine† × Valmy</td><td></td><td></td><td>0.0211</td><td></td></tr><tr><td>Lead 4 × Chenôve</td><td>0.0046</td><td>***</td><td>0.0049</td><td>***</td></tr><tr><td>Lead 3 × Chenôve</td><td>0.0070</td><td>***</td><td>0.0069</td><td>***</td></tr><tr><td>Lead 2 × Chenôve</td><td>0.0073</td><td>***</td><td>0.0073</td><td>***</td></tr><tr><td>Lead 1 × Chenôve</td><td>Reference</td><td></td><td></td><td></td></tr><tr><td>Lag 0 × Chenôve</td><td>0.0074</td><td>***</td><td>0.4571</td><td>***</td></tr><tr><td>Lag 1 × Chenôve</td><td>0.0243</td><td>***</td><td>0.4742</td><td>***</td></tr><tr><td>Lag 2 × Chenôve</td><td>0.0219</td><td>***</td><td>0.4719</td><td>***</td></tr><tr><td>Lag 3 × Chenôve</td><td>0.0372</td><td>***</td><td>0.4869</td><td>***</td></tr><tr><td>Lag 4 × Chenôve</td><td>0.0284</td><td>***</td><td>0.4782</td><td>***</td></tr><tr><td>Lag 5 × Chenôve</td><td>0.0414</td><td>***</td><td>0.4910</td><td>***</td></tr><tr><td>Lag 6 × Chenôve</td><td>0.0962</td><td>***</td><td>0.5459</td><td>***</td></tr><tr><td>Lag 7 × Chenôve</td><td>0.0725</td><td>***</td><td>0.5234</td><td>***</td></tr><tr><td>Lag 8 × Chenôve</td><td>0.2011</td><td>***</td><td>0.6510</td><td>***</td></tr><tr><td>Lag 9 × Chenôve</td><td>0.1258</td><td>***</td><td>0.5786</td><td>***</td></tr><tr><td>Lag 10 × Chenôve</td><td>0.2148</td><td>***</td><td>0.6645</td><td>***</td></tr><tr><td>ATE × dCBD × Chenôve</td><td></td><td></td><td>-0.1399</td><td>**</td></tr><tr><td>ATE × log(dCBD) × Chenôve</td><td></td><td></td><td>0.0050</td><td></td></tr><tr><td>ATE × dLine† × Chenôve</td><td></td><td></td><td>-0.4248</td><td>***</td></tr><tr><td>ATE × log(dCBD) × dLine† × Chenôve</td><td></td><td></td><td>0.3357</td><td>***</td></tr><tr><td>Lead 4 × Quétigny</td><td>0.0077</td><td>***</td><td>0.0077</td><td>***</td></tr><tr><td>Lead 3 × Quétigny</td><td>0.0038</td><td>*</td><td>0.0038</td><td>*</td></tr><tr><td>Lead 2 × Quétigny</td><td>0.0039</td><td>**</td><td>0.0039</td><td>**</td></tr><tr><td>Lead 1 × Quétigny</td><td>Reference</td><td></td><td></td><td></td></tr><tr><td>Lag 0 × Quétigny</td><td>-0.0019</td><td></td><td>-0.0206</td><td>**</td></tr><tr><td>Lag 1 × Quétigny</td><td>-0.0067</td><td>***</td><td>-0.0254</td><td>**</td></tr><tr><td>Lag 2 × Quétigny</td><td>-0.0096</td><td>***</td><td>-0.0282</td><td>***</td></tr><tr><td>Lag 3 × Quétigny</td><td>-0.0109</td><td>***</td><td>-0.0295</td><td>***</td></tr><tr><td>Lag 4 × Quétigny</td><td>-0.0176</td><td>***</td><td>-0.0362</td><td>***</td></tr><tr><td>Lag 5 × Quétigny</td><td>-0.0194</td><td>***</td><td>-0.0381</td><td>***</td></tr><tr><td>Lag 6 × Quétigny</td><td>-0.0228</td><td>***</td><td>-0.0414</td><td>***</td></tr><tr><td>Lag 7 × Quétigny</td><td>-0.0060</td><td></td><td>-0.0244</td><td>**</td></tr><tr><td>Lag 8 × Quétigny</td><td>-0.0202</td><td>***</td><td>-0.0383</td><td>***</td></tr><tr><td>Lag 9 × Quétigny</td><td>-0.0080</td><td></td><td>-0.0265</td><td>**</td></tr><tr><td>Lag 10 × Quétigny</td><td>-0.0001</td><td></td><td>-0.0184</td><td>*</td></tr><tr><td>ATE × dCBD × Quétigny</td><td></td><td></td><td>-0.0017</td><td></td></tr><tr><td>ATE × log(dCBD) × Quétigny</td><td></td><td></td><td>0.0059</td><td></td></tr><tr><td>ATE × dLine† × Quétigny</td><td></td><td></td><td>0.0590</td><td>***</td></tr><tr><td>ATE × log(dCBD) × dLine† × Quétigny</td><td></td><td></td><td>-0.0398</td><td>***</td></tr><tr><td>Number of observations</td><td>524220</td><td></td><td>524220</td><td></td></tr><tr><td>R-squared</td><td>0.2668</td><td></td><td>0.2759</td><td></td></tr><tr><td>RMSE</td><td>0.1330</td><td></td><td>0.1322</td><td></td></tr><tr><td>F statistic</td><td>346.07</td><td></td><td>306.90</td><td></td></tr><tr><td>AIC</td><td>-627480</td><td></td><td>-633968</td><td></td></tr></tbody></table>

1) The coefficients related to the distance to city center ($d_{CBD}$) and its logarithmic transformation ($\log(d_{CBD})$) allow to retrieve a non-linear effect. The impact is concave (convex) when the coefficients of $d_{CBD}$ is positive (negative), while the coefficient of $\log(d_{CBD})$ is negative (positive). It can also identify a strictly increasing (decreasing) relation when the coefficient associated with the distance to city center is positive (negative) and the coefficient associated with the log of the distance ($\log(d_{CBD})$) is non-significant, or when the coefficient associated with the log of the distance is positive (negative) and the coefficient related to city center ($d_{CBD}$) is non-significant;

2) The coefficient related to the distance to the line, expressed with a Gaussian transformation, i.e., $d_L^* = (1 - (d_L/d_{max})^2)^2$ with $d_L$ the distance to the line and $d_{max}$ the maximum distance used (150m.), allows for an increasing (decreasing) relation when the coefficient is positive (negative);

3) The interaction term with the logarithm distance to city center and transformed distance to the line allows for a higher impact for units located close to the line and closer to (resp. further from) the CBD when the coefficient is negative (resp. positive).

Legend: ***p < 0.001; **p < 0.01; *p < 0.05.

Note: † indicate that the distance is transformed using a Gaussian function: $d_L^* = (1 - (d_L/d_{max})^2)^2$.

Where: $d_L^*$ is the resulting transformation, $d_L$ is the distance to the line. $d_{max}$ is the maximum distance used (150m.).

<footnote n="8">⁸ The shape of the spatially varying ATE depends on three specific combinations of the coefficients:</footnote>
<figure><img src="image_11.png" /><figcaption>Fig. 6. ATE effect for Valmy (north line) based on distance to line and city center, residential units.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 7. ATE effect for Chenôve (south line) based on distance to line and city center, residential units.</figcaption></figure>

coefficients returns a complex concave relation that is, most of the time, positive and statistically significant between 1 km and 3 km of the city center and within 100m. of the line. The probability of new residential construction at the end of the north line is about 20-45 percentage points lower as compared to the development along the existing Lianes lines.

For the south line (Chenôve), the ATE appears to be positive and varies between 46 and 67 percentage points. However, the high positive probability of construction of new residential units is partly cancelled out by the coefficients associated with the distance to the center, as well as with the negative coefficient associated with the distance to the line, suggesting that the impact is higher close to the line. The positive and

statistically significant coefficients related to the cross-product of the distance to the city center and the line reinforce that the ATE is higher for a large distance to the line and a small distance to the city center (Fig. 7). In the end, the probability of residential construction is higher close to the city center, but not directly along the line as compared to the Lianes lines.

For the east line (Quétigny), the impact appears to be negative and statistically significant, but weak, with a temporal ATE less than 4 percentage points in absolute value. Looking at the coefficients related to the different distance variables, the results show that the impact is higher along the line and close to the city center (Fig. 8). In the end, the impact appears to be positive and barely significant close to the CBD,
<figure><img src="image_13.png" /><figcaption>Fig. 8. ATE effect for Quétigny (east line) based on distance to line and city center, residential units.</figcaption></figure>

and negative and statistically significant when the distance to the CBD increases.

In the end, the results appear to be clearly related to the direction, the distance to city center and the distance to the line. Different patterns emerge for the three different directions, with the more important and statistically significant impact being measured for the northern direction in the middle of the line. The southern direction appears to provide statistically significant results, but mainly close to the city center. The pattern is similar for the eastern direction, but the impact appears to be less important.<fnref n="9" />

## 6.2. Impact on the construction of economic activity units

Turning to the economic activity units, the estimated model has an R² statistic that varies between 0.38 and 0.40. The models are estimated using 2898 individual economic activity units per year, for a total of 43,470 observations.

The first specification suggests that the PTA does not hold either (p = 0.0000), providing an anticipation effect of about 2.5–3.75 percentage points for economic activity (Model (1) – Table 7). Combined with the ATE, the first model suggests that the probability of having new construction of economic activity units is about 6.3–7.8 percentage points higher along the new tramway lines.

Turning to the second specification (Model (2) – Table 7), the results again suggest the presence of an anticipation effect, varying between 2.5 and 3.75 percentage points, as the PTA does not hold (p = 0.0000). However, interestingly, the ATE is only statistically significant for the north line (Valmy), suggesting a 10.5 to 12 percentage points increase in the probability of building new economic activity units along the north line. For the two other lines, the impact is limited to the anticipation effect, i.e., about a 2.5 to 3.75 percentage points' increase in the probability of new construction as compared to the Lianes lines.

For the third specification, the temporal varying ATEs suggest a small but statistically significant increase in the probability of construction when the announcement was made (Model (3) – Table 7). The

impact is estimated to be about 4.5–5.75 percentage points before, suggesting an anticipation effect. During the construction period, the increase in probability is estimated to be about 5.5–8 percentage points. It turns out to be higher when the service came into operation, and the increase in the probability of construction, of about 8.5–11.5 percentage points, is mainly concentrated in the three years following the introduction of the service.

In the fourth specification, the ATEs appear again to vary in time and in space with a statistically significant anticipation effect. The impact is also more important when the service opened (Model (4) – Table 7). Turning the attention to the coefficients related to the distance to city center and to the line, the results suggest that the impact is positive, statistically significant, and higher close to the line. The negative and statistically significant coefficients associated with the interaction of the distance to the city center and the line suggest that the impact is positive close to the CBD, that it decreases until 2.5 km and becomes flat close to the line but increases further away from the line (Fig. 9).

For the fifth specification, the results suggest that the ATE is not statistically significant for the east line (Quétigny) (Model (5) – Table 8). For the south line (Chenóve), the impact is limited to anticipation effect, with a 6.5 to 7.8 percentage points' higher probability of new construction. However, this effect turns out to be non-statistically significant from the beginning of the construction in 2008, except for the last two years available (2017 and 2018). For the north line (Valmy), the ATE is positive and statistically significant, with an anticipation effect. This result suggests that when the service is in operation, the probability of new construction appearing is about 10–17 percentage points' higher as compared to the Lianes routes.

The sixth specification provides interesting information about the spatial variability of the ATE, with, again, a statistically significant anticipation effect. For the north line, the negative and statistically significant temporal ATE are more than compensated by the spatial structure of the impact, as measured by the amplitude of the coefficients related to the distance to city center and to the corridor (Fig. 10). The probability of new construction is about 35 percentage points higher close to the city center and the line. It decreases in the middle of the line but increases at its end. At the end of the line, the ATE suggests a 20 to 46 percentage points higher probability of building new units. The results show that new economic activities units emerge close to the center or at

<footnote n="9">⁹ It should be noted that spatial autocorrelation is not detected among residuals of the different specifications.</footnote>
<table><caption>**Table 7**
Estimation results for economic activity units.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">Model (1)</th><th colspan="2">Model (2)</th><th colspan="2">Model (3)</th><th colspan="2">Model (4)</th></tr><tr><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th></tr></thead><tbody><tr><td>Spatial Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Temporal Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Other Explanatory Time-varying Variables</td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Lead 4</td><td>-0.0243</td><td>**</td><td>-0.0244</td><td>**</td><td>-0.0244</td><td>**</td><td>-0.0244</td><td>**</td></tr><tr><td>Lead 3</td><td>-0.0317</td><td>***</td><td>-0.0318</td><td>***</td><td>-0.0317</td><td>***</td><td>-0.0318</td><td>***</td></tr><tr><td>Lead 2</td><td>-0.0375</td><td>***</td><td>-0.0375</td><td>***</td><td>-0.0375</td><td>***</td><td>-0.0375</td><td>***</td></tr><tr><td>Lead 1</td><td>Reference</td><td></td><td>Reference</td><td></td><td>Reference</td><td></td><td>Reference</td><td></td></tr><tr><td>ATE</td><td>0.0390</td><td>***</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATE × Valmy</td><td></td><td></td><td>0.0791</td><td>***</td><td></td><td></td><td></td><td></td></tr><tr><td>ATE × Chenóve</td><td></td><td></td><td>-0.0035</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATE × Quétigny</td><td></td><td></td><td>0.0002</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lag 0</td><td></td><td></td><td></td><td></td><td>0.0202</td><td>**</td><td>-0.1294</td><td>**</td></tr><tr><td>Lag 1</td><td></td><td></td><td></td><td></td><td>0.0026</td><td></td><td>-0.1461</td><td>***</td></tr><tr><td>Lag 2</td><td></td><td></td><td></td><td></td><td>0.0305</td><td>***</td><td>-0.1178</td><td>**</td></tr><tr><td>Lag 3</td><td></td><td></td><td></td><td></td><td>0.0425</td><td>***</td><td>-0.1058</td><td>*</td></tr><tr><td>Lag 4</td><td></td><td></td><td></td><td></td><td>0.0629</td><td>***</td><td>-0.0854</td><td>*</td></tr><tr><td>Lag 5</td><td></td><td></td><td></td><td></td><td>0.0756</td><td>***</td><td>-0.0725</td><td></td></tr><tr><td>Lag 6</td><td></td><td></td><td></td><td></td><td>0.0635</td><td>***</td><td>-0.0842</td><td>*</td></tr><tr><td>Lag 7</td><td></td><td></td><td></td><td></td><td>0.0627</td><td>***</td><td>-0.0849</td><td>*</td></tr><tr><td>Lag 8</td><td></td><td></td><td></td><td></td><td>0.0161</td><td></td><td>-0.1317</td><td>**</td></tr><tr><td>Lag 9</td><td></td><td></td><td></td><td></td><td>0.0502</td><td>***</td><td>-0.0975</td><td>*</td></tr><tr><td>Lag 10</td><td></td><td></td><td></td><td></td><td>0.0084</td><td></td><td>-0.1392</td><td>**</td></tr><tr><td>ATE × dCBD</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.0224</td><td></td></tr><tr><td>ATE × log(dCBD)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.0871</td><td></td></tr><tr><td>ATE × dLine†</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.1897</td><td>***</td></tr><tr><td>ATE × log(dCBD) × dLine†</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.2052</td><td>***</td></tr><tr><td>Number of observations</td><td>43470</td><td></td><td>43470</td><td></td><td>43470</td><td></td><td>43470</td><td></td></tr><tr><td>R-squared</td><td>0.3807</td><td></td><td>0.3826</td><td></td><td>0.3826</td><td></td><td>0.3843</td><td></td></tr><tr><td>RMSE</td><td>0.1775</td><td></td><td>0.1773</td><td></td><td>0.1773</td><td></td><td>0.1771</td><td></td></tr><tr><td>F statistic</td><td>261.95</td><td></td><td>254.20</td><td></td><td>219.93</td><td></td><td>209.82</td><td></td></tr><tr><td>AIC</td><td>-26892</td><td></td><td>-27021</td><td></td><td>-27003</td><td></td><td>-27117</td><td></td></tr></tbody></table>

Legend: ***p < 0.001; **p < 0.01; *p < 0.05.

Note: † indicate that the distance is transformed using a Gaussian function: $d_L^* = (1 - (d_L/d_{max})^2)^2$.

Where: $d_L^*$ is the resulting transformation, $d_L$ is the distance to the line; $d_{max}$ is the maximum distance used (150m.).

<figure><img src="image_14.png" /><figcaption>**Fig. 9.** Global ATE with respect to distance to line and city center, economic activity units.</figcaption></figure>
<table><caption>**Table 8**
Estimation results for economic activity units.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Model (5)</th><th colspan="2">Model (6)</th></tr><tr><th>Coefficient</th><th>sign.</th><th>Coefficient</th><th>sign.</th></tr></thead><tbody><tr><td>Spatial Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Temporal Fixed Effects</td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Other Explanatory Time-varying Variables</td><td>Yes</td><td></td><td>Yes</td><td></td></tr><tr><td>Lead 4 × Valmy</td><td>-0.0140</td><td></td><td>-0.0139</td><td></td></tr><tr><td>Lead 3 × Valmy</td><td>-0.0229</td><td>*</td><td>-0.0229</td><td>*</td></tr><tr><td>Lead 2 × Valmy</td><td>-0.0261</td><td>**</td><td>-0.0257</td><td>**</td></tr><tr><td>Lead 1 × Valmy</td><td>Reference</td><td></td><td></td><td></td></tr><tr><td>Lag 0 × Valmy</td><td>0.0698</td><td>***</td><td>-0.4435</td><td>***</td></tr><tr><td>Lag 1 × Valmy</td><td>0.0216</td><td></td><td>-0.4822</td><td>***</td></tr><tr><td>Lag 2 × Valmy</td><td>0.0695</td><td>***</td><td>-0.4305</td><td>***</td></tr><tr><td>Lag 3 × Valmy</td><td>0.0918</td><td>***</td><td>-0.4078</td><td>***</td></tr><tr><td>Lag 4 × Valmy</td><td>0.0948</td><td>***</td><td>-0.4045</td><td>***</td></tr><tr><td>Lag 5 × Valmy</td><td>0.1505</td><td>***</td><td>-0.3489</td><td>***</td></tr><tr><td>Lag 6 × Valmy</td><td>0.0993</td><td>***</td><td>-0.3958</td><td>***</td></tr><tr><td>Lag 7 × Valmy</td><td>0.1160</td><td>***</td><td>-0.3789</td><td>***</td></tr><tr><td>Lag 8 × Valmy</td><td>0.0593</td><td>**</td><td>-0.4358</td><td>***</td></tr><tr><td>Lag 9 × Valmy</td><td>0.1306</td><td>***</td><td>-0.3645</td><td>***</td></tr><tr><td>Lag 10 × Valmy</td><td>0.0638</td><td>**</td><td>-0.4304</td><td>***</td></tr><tr><td>ATE × dCBD × Valmy</td><td></td><td></td><td>0.1825</td><td>***</td></tr><tr><td>ATE × log(dCBD) × Valmy</td><td></td><td></td><td>-0.0859</td><td>***</td></tr><tr><td>ATE × dLine × Valmy</td><td></td><td></td><td>0.4792</td><td>***</td></tr><tr><td>ATE × log(dCBD) × dLine × Valmy</td><td></td><td></td><td>-0.4375</td><td>***</td></tr><tr><td>Lead 4 × Chenóve</td><td>-0.0659</td><td>***</td><td>-0.0654</td><td>***</td></tr><tr><td>Lead 3 × Chenóve</td><td>-0.0690</td><td>***</td><td>-0.0686</td><td>***</td></tr><tr><td>Lead 2 × Chenóve</td><td>-0.0779</td><td>***</td><td>-0.0774</td><td>***</td></tr><tr><td>Lead 1 × Chenóve</td><td>Reference</td><td></td><td></td><td></td></tr><tr><td>Lag 0 × Chenóve</td><td>-0.0602</td><td>***</td><td>0.7062</td><td>*</td></tr><tr><td>Lag 1 × Chenóve</td><td>-0.0316</td><td>*</td><td>0.7346</td><td>*</td></tr><tr><td>Lag 2 × Chenóve</td><td>-0.0226</td><td></td><td>0.7433</td><td>*</td></tr><tr><td>Lag 3 × Chenóve</td><td>-0.0169</td><td></td><td>0.7492</td><td>*</td></tr><tr><td>Lag 4 × Chenóve</td><td>-0.0322</td><td></td><td>0.7336</td><td>*</td></tr><tr><td>Lag 5 × Chenóve</td><td>-0.0201</td><td></td><td>0.7452</td><td>*</td></tr><tr><td>Lag 6 × Chenóve</td><td>0.0217</td><td></td><td>0.7870</td><td>*</td></tr><tr><td>Lag 7 × Chenóve</td><td>-0.0341</td><td></td><td>0.7326</td><td>*</td></tr><tr><td>Lag 8 × Chenóve</td><td>-0.0426</td><td></td><td>0.7238</td><td>*</td></tr><tr><td>Lag 9 × Chenóve</td><td>-0.0667</td><td>*</td><td>0.7001</td><td>*</td></tr><tr><td>Lag 10 × Chenóve</td><td>-0.0685</td><td>*</td><td>0.6985</td><td>*</td></tr><tr><td>ATE × dCBD × Chenóve</td><td></td><td></td><td>0.1586</td><td></td></tr><tr><td>ATE × log(dCBD) × Chenóve</td><td></td><td></td><td>-1.0815</td><td>*</td></tr><tr><td>ATE × dLine × Chenóve</td><td></td><td></td><td>-0.8555</td><td>*</td></tr><tr><td>ATE × log(dCBD) × dLine × Chenóve</td><td></td><td></td><td>0.6886</td><td>*</td></tr><tr><td>Lead 4 × Quétigny</td><td>0.0049</td><td></td><td>0.0037</td><td></td></tr><tr><td>Lead 3 × Quétigny</td><td>-0.0046</td><td></td><td>-0.0060</td><td></td></tr><tr><td>Lead 2 × Quétigny</td><td>-0.0120</td><td></td><td>-0.0133</td><td></td></tr><tr><td>Lead 1 × Quétigny</td><td>Reference</td><td></td><td></td><td></td></tr><tr><td>Lag 0 × Quétigny</td><td>-0.0060</td><td></td><td>0.1510</td><td>**</td></tr><tr><td>Lag 1 × Quétigny</td><td>-0.0016</td><td></td><td>0.1552</td><td>**</td></tr><tr><td>Lag 2 × Quétigny</td><td>0.0073</td><td></td><td>0.1645</td><td>**</td></tr><tr><td>Lag 3 × Quétigny</td><td>0.0021</td><td></td><td>0.1594</td><td>**</td></tr><tr><td>Lag 4 × Quétigny</td><td>0.1142</td><td>***</td><td>0.2713</td><td>***</td></tr><tr><td>Lag 5 × Quétigny</td><td>0.0221</td><td></td><td>0.1868</td><td>**</td></tr><tr><td>Lag 6 × Quétigny</td><td>0.0401</td><td></td><td>0.2047</td><td>***</td></tr><tr><td>Lag 7 × Quétigny</td><td>0.0656</td><td>**</td><td>0.2302</td><td>***</td></tr><tr><td>Lag 8 × Quétigny</td><td>-0.0058</td><td></td><td>0.1593</td><td>*</td></tr><tr><td>Lag 9 × Quétigny</td><td>0.0104</td><td></td><td>0.1759</td><td>**</td></tr><tr><td>Lag 10 × Quétigny</td><td>-0.0154</td><td></td><td>0.1500</td><td>*</td></tr><tr><td>ATE × dCBD × Quétigny</td><td></td><td></td><td>-0.1630</td><td>**</td></tr><tr><td>ATE × log(dCBD) × Quétigny</td><td></td><td></td><td>0.2973</td><td></td></tr><tr><td>ATE × dLine† × Quétigny</td><td></td><td></td><td>-0.0322</td><td></td></tr><tr><td>ATE × log(dCBD) × dLine† × Quétigny</td><td></td><td></td><td>0.1915</td><td></td></tr><tr><td>Number of observations</td><td>43470</td><td></td><td>43470</td><td></td></tr><tr><td>R-squared</td><td>0.3869</td><td></td><td>0.3955</td><td></td></tr><tr><td>RMSE</td><td>0.1767</td><td></td><td>0.1755</td><td></td></tr><tr><td>F statistic</td><td>135.87</td><td></td><td>156.96</td><td></td></tr><tr><td>AIC</td><td>-27255</td><td></td><td>-27839</td><td></td></tr></tbody></table>

Legend: ***p < 0.001; **p < 0.01; *p < 0.05.

Note: † indicate that the distance is transformed using a Gaussian function: $d_L^* = (1 - (d_L/d_{max})^2)^2$.

Where: $d_L^*$ is the resulting transformation, $d_L$ is the distance to the line, $d_{max}$ is the maximum distance used (150m.).

the end of the line. For the north part (Valmy), this pattern is clearly related to the development of an important cluster of economic activities in the health sector around the terminus of the line.

For the south line (Chenóve), the high positive and statistically significant ATE hides the fact that the spatial variation of the impact highlights an important concentration of the impact close to the city center, but not along the line (Fig. 11). The statistically significant coefficients related to three distance coefficients suggest a high spatial variance of the ATE. The impact appears to be more distant from the line but also closer to the center.

For the east line (Quétigny), the coefficients indicate that the ATE is positive, statistically significant and relatively stable from a temporal perspective (Fig. 12). However, the ATE decreases according to the distance to the CBD. Combining all coefficients, the results suggest that the impact is mainly concentrated along the line and in the middle of the line, i.e., close to the hospital and the university. The number of new constructions of economic activity units is relatively higher around the tramway line as compared to the Lianes routes.

Overall, the analysis suggests that the impact on the construction of new economic activity units is mainly concentrated close to the city center, or at the end of the north corridor, where a new health center was introduced at the beginning of 2000 and a major commercial center (Toison d'Or) was developed in the 1990's. Most of the new commercial development is concentrated on the north corridor along the existing cluster.<fnref n="10" />

## 7. Discussion

Empirical analysis shows that investment in public transport might be useful to stimulate or accompany densification (or intensification). However, it also underlines the fact that the impact, within the same agglomeration, is clearly not homogenous. On the one hand, the results show a mixed densification process close to the city center for the south (Chenóve) and east (Quétigny) corridor. On the other hand, a complementary development occurs on the north (Valmy) corridor, with new economic activity units emerging at the end of the line, while new residential units are developed in the middle. The construction of new economic activity units is important around the city center. For the east line (Quétigny), new economic activities emerge in the middle of the line, i.e., close to the university campus.

Overall, the impact of this new public transport infrastructure is mainly localized within the administrative limit of the city of Dijon, as the impacts for the corridors with the terminus in the municipalities of Chenóve and Quétigny appear to be marginal or even non-significant. The important development that occurred before the opening of the tramway system in the north might have been a major move that invited the administration to promote location along the lines on this corridor. The city of Dijon also devoted efforts to converting the former barracks it acquired in the early 2000s. These sites have been used to develop housing, but also to host administrative activities. All those private and government investments can be part of a specific reconversion that has been fostered by the development of the tramway line. As a consequence, there might be some political/local reason why this heterogeneity is noted, as no similar investment has been made for the two other corridors.

Regarding the positive and significant effect of the two other corridors close from the city center, additional explanations can be put forward to explain this part. First, on the south corridor, the development of the Cité internationale de la gastronomie et du vin, was announced in

<footnote n="10">¹⁰ As with the residential units, Moran's I indices were calculated for the residuals of the different specifications, for each year, in order to check for possible spatial autocorrelation. Although mostly significant, the indices remain fairly low (between 0 and 0.06), suggesting that the model controls adequately for the presence of spatial patterns among the residuals.</footnote>
<figure><img src="image_15.png" /><figcaption>Fig. 10. ATE effect for Valmy (north line) based on distance to line and city center, economic activity units.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 11. ATE effect for Chenóve (south line) based on distance to line and city center, economic activity units.</figcaption></figure>

2012, while its official opening occurred in 2022. This project can be seen as an important investment that might attract visitors and potential investors. The Auditorium, close to the *Parc des expositions et des congrès*, has been officially opened in 1998 on the east corridor, close from the city center. The presence of important infrastructure to welcome shows and exhibitions may also have boost the attendance of the sectors, which might in turn have influenced the construction of new residential and commercial buildings.

With the development of the tramway service, the city of Dijon has devoted important energy and resources to facilitate and promote density along the tramway line for both sectors, residential and economic activity, especially by fostering present and past local investment. The mayor of Dijon was clearly convinced by the fact that the tramway might

affect mobility and the shape of the city.<fnref n="11" /> Consequently, some programs were put in place over the years to help restructuring the surroundings of the stations. Based on the other directions, there is no important evidence that the tramway itself has been able to stimulate densification, except for the densification (or intensification) related to economic activities close to the university campus, which is also located within the administrative limit of the municipality of Dijon. The efforts by the two other municipalities were not as much as for Dijon and no particular programs to accompany the setting of the tramway were implemented.

<footnote n="11">¹¹ Microsoft Word - Volume G1 - Resume non technique - 2009 - V4.doc (eib.org).</footnote>
<figure><img src="image_17.png" /><figcaption>Fig. 12. ATE effect for Quétigny (east line) based on distance to line and city center, economic activity units.</figcaption></figure>

Thus, the densification effect might result from a mix of private interest and public effort, as the results suggest that the impact is greater on the north line.

In the end, results confirm that the implementation of new public transport services can attract new residential and commercial construction and development (Champagne et al., 2022). However, they also underline that implication of public authorities can boost the impact on densification, as suggested by Banister and Berechman (2001) and Godspini (2005), and also consistent with the works of Berghauser Pont et al. (2021), Herdt and Jonkman (2023), and Kaniewska et al. (2024). Although initial change is necessary to start the loop, it alone does not seem sufficient to ensure success. Accompanying actions and public policies as well as combined effort (private and local) in investment provide essential support to optimize the chances to reshape local development and new residential and commercial construction along the line, as identified through residential and commercial construction for the case of Dijon. The TLU loop therefore can play a significant role in reshaping cities along new public transport lines.

## 8. Conclusion

This paper aims to identify the impact of the construction of a new tramway line on population and/or employment densification, or intensification, measured by the construction of new residential and economic activity units. To explore this question, the analysis proposes an empirical application based on the case of the Dijon metropole, where part of the bus rapid transit (BRT) service was replaced by a tramway service. While the impact on travel time is not major (Dubé et al., 2018b), the fact remains that the implementation of a rail service might shape travel habits for decades as the service is fixed. Thus, the replacement of some of the existing BRT (Lianes) routes and the continuity of the service in other parts of the city offer an interesting quasi-natural experiment framework to measure the average treatment effect (ATE) of the introduction of the tramway service on the construction of new residential and economic activity units along the transit lines.

To identify the average treatment effect (ATE), the paper proposes a difference-in-differences (DiD) with an event study analysis (ESA) specification to test for the presence of treatment effect, as well as to test

for the presence of spatial and temporal heterogeneity within the Dijon metropole. While the ATE appears to be globally positive and significant, the analysis allows uncovering important spatial and temporal variations. In addition to detecting significant anticipation effects, the parametric analysis proposed in the paper also allows to build 2-dimension (2-D) graphs illustrating how the impact varies according to the line under study as well as to the type of new construction units, residential or economic activity (see Dubé et al., 2024).

The results suggest three different patterns of the ATE. For the north line, the ATE of residential and economic activity appears to be complementary: the impact is stronger for economic activity close to the city center as well as at the end of the line, while it is mostly significant for residential units in the middle of the line. A densification pattern emerges along the line, but it is not based on a mixed-land use (diversity). For the south line, the impact is mainly concentrated close to the city center, within the administrative limit of Dijon, and close to the important redevelopment. For the east line, the impact for residential activity is concentrated close to the CBD, while the emergence of new economic activity buildings mainly appears close to the line and in the middle, close to the hospital center and the university campus.

The analysis clearly underlines the importance of public intervention in densification efforts along new public transport services. The fact that the ATE appears to be marginal or non-significant in the ends of the south and east lines suggests that densification does not automatically emerge from the implementation of new public transport services, such as the tramway. Combining effort from public and private sectors might help to consolidate densification within a city, as good coordination might be a key concept to reach specific goals.

The interpretation of the results of the study has, however, its own limitations. First, as the construction of new buildings is based on the construction years, it fails to account for possible renovation effect of the tramway. It solely focuses on the construction of the new buildings, which limits the densification effect that is captured. Second, the construction years might represent a lower bound for densification, as a single building might include many dwellings and commercial units. A promising avenue to investigate such questions in the future would be to use satellite images and digital twins. However, this avenue might be limited by the quality of images available, especially if changes occurred many years ago.
CRediT authorship contribution statement

**Jean Dubé:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Julie Le Gallo:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Conceptualization. **Marie-Pier Champagne:** Writing – review & editing, Visualization, Validation, Software, Methodology, Investigation, Conceptualization. **Mohamed Hilal:** Writing – review & editing, Visualization, Validation, Supervision, Software, Investigation, Formal analysis, Data curation, Conceptualization. **Diègo Legros:** Writing – review & editing, Visualization, Validation, Software, Methodology, Investigation, Conceptualization.

Declaration of competing interest

The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as hono- raria; educational grants; participation in speakers' bureaus; member- ship, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Data availability

The authors do not have permission to share data.

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