capbtabboxtable[][]

# The built environment and induced transport CO2 emissions: A double machine
learning approach to account for residential self-selection

Florian Nachtigall
Technical University of Berlin
Berlin, Germany
<EMAIL_ADDRESS>
Felix Wagner
Technical University of Berlin
Berlin, Germany
Peter Berrill
Technical University of Berlin
Berlin, Germany
Felix Creutzig
Technical University of Berlin
Berlin, Germany

###### Abstract

Understanding why travel behavior differs between residents of urban centers
and suburbs is key to sustainable urban planning. Especially in light of rapid
urban growth, identifying housing locations that minimize travel demand and
induced CO2 emissions is crucial to mitigate climate change. While the built
environment plays an important role, the precise impact on travel behavior is
obfuscated by residential self-selection. To address this issue, we propose a
double machine learning approach to obtain unbiased, spatially-explicit
estimates of the effect of the built environment on travel-related CO2
emissions for each neighborhood by controlling for residential self-selection.
We examine how socio-demographics and travel-related attitudes moderate the
effect and how it decomposes across the 5Ds of the built environment. Based on
a case study for Berlin and the travel diaries of 32,000 residents, we find
that the built environment causes household travel-related CO2 emissions to
differ by a factor of almost two between central and suburban neighborhoods in
Berlin. To highlight the practical importance for urban climate mitigation, we
evaluate current plans for 64,000 new residential units in terms of total
induced transport CO2 emissions. Our findings underscore the significance of
spatially differentiated compact development to decarbonize the transport
sector.

##  1 Introduction

The built environment plays a critical role in facilitating or hindering the
decarbonization of urban transport [1]. The location and compactness of new
residential development shape and constrain residents’ future travel behavior.
As such, they limit the mitigation potential of mobility-related lifestyle
changes and potentially lock in transport-related emissions for decades [2].
Urban sprawl and suburban development, in particular, have been widely
criticized for increasing car dependence and travel demand, hindering the
decarbonization of urban transport [3]. Many studies have observed that
residents of urban (central, higher-density, mixed-use) neighborhoods tend to
drive less and instead walk [4, 5, 6, 7], bike [4], and use public transit
more [8, 9] than residents of suburban (non-central, lower-density, single-use
residential) neighborhoods. Yet, it is ambiguous to what extent these
differences can be attributed to the built environment itself, as opposed to
pre-existing differences in travel preferences between residents of urban and
suburban neighborhoods. These differences are a result of a process known as
residential self-selection [10], in which people choose their place of
residence based on, among other things, locally available transport options
matching their pre-existing travel preferences. Failing to account for
residential self-selection and the resulting differences in travel preferences
among residents of different neighborhoods can lead to falsely attributing
observed differences in travel behavior to the built environment alone,
overestimating its impact and drawing biased conclusions [11].

Two strands in literature aim to uncover the built environment’s influence on
travel behavior, one focusing on the nonlinearity of the relationship using
machine learning methods, in particular decision tree ensembles [12, 13], the
other focusing on disentangling the influence from the confounding effect of
residential self-selection using statistical methods such as statistical
control, propensity score matching, and sample selection [14]. We aim to bring
both approaches together.

We explore the potential of the causal inference method double machine
learning (DML) [15] to examine the non-linear effect of the built environment
on travel behavior from mobility survey data while accounting for confounding
factors. We assess the built environment’s impact across multiple, continuous
dimensions, specifically the 5Ds [16], density, diversity, design, destination
accessibility, and distance from transit, which all are presumed to have
independent effects on travel behavior [11]. We control for the non-linear
influence of socio-demographics and travel-related attitudes that are
responsible for residential self-selection [14]. We examine the impact on
travel behavior in terms of travel-related emissions to account for changes in
both, travel distances and mode choice, allowing for a comprehensive
assessment of the climate-related impacts of the built environment. We
demonstrate our approach using the city of Berlin, Germany, as a case study
and discuss local implications for low-carbon urban planning. Our research
questions are the following:

  1. 1.

What is the isolated effect of the built environment on travel behavior and
induced CO2 emissions when accounting for residential self-selection for each
neighborhood in Berlin?

  2. 2.

How does the effect decompose into the 5Ds and how is it moderated by socio-
demographics and travel-related attitudes?

  3. 3.

What are the induced transport CO2 emissions of currently planned housing
projects in Berlin and how can the spatial allocation be improved to most
effectively reduce emissions?

##  2 Methods

###  2.1 Preprocessing

Data sources. Representative travel behavior per neighborhood is obtained from
travel diaries of the German national mobility survey "Mobilität in Städten –
SrV 2018" [17], which includes more than 100,000 trips for Berlin. To describe
the built environment, we use only publicly available data from OpenStreetMap
[18] on the street network and points of interest, from the public transit
operator VBB on transit accessibility (GTFS data) [19] and from Berlin’s open
data portal [20] on population density, land-use, street greenery, street
space allocation, and election results. See figure 4 for a visualization of
the built environment of Berlin. Lastly, to convert mode-specific travel
kilometers to emissions, we use emissions factors from the International
Transport Forum (ITF) [21] (see table 2).

Unit of analysis. We approximate the representative average travel behavior
per neighborhood by averaging the surveyed and preprocessed travel behavior of
all households. We assume that neighborhoods, in our case zip code areas, are
sufficiently homogeneous in terms of their built environment to be able to
detect a consistent impact on the travel behavior of its residents. This
approach has the benefit of reducing noise related to sampling from a wide
range of individual daily travel patterns and only examining to what degree
the average travel behavior differs between neighborhoods.

###  2.2 Causal inference

Confounding effects. Travel preferences confound the influence of the built
environment on travel behavior because of residential self-selection. The
relationship is visualized as a directed acyclic graph (DAG) in figure 1. The
confounding effect can be accounted for by controlling for socio-demographic
traits and travel-related attitudes [14]. Here, we use information on age,
income, education, and household size from the mobility survey to describe the
socio-demographic composition of neighborhoods. Because travel-related
attitudes are not directly captured in the survey, we use information on
ownership of transport means as proxies, specifically car, bike, driving
license, and transit subscription ownership. While this is not a comprehensive
characterization of travel-related attitudes, it has the advantage of
capturing the temporal lag of travel-related attitudes through vehicle
ownership, particularly car ownership [22], that not only locks in travel
behavior but also influences residential choice [23, 24]. Complementary to
transport ownership, we capture general attitudes towards sustainable mobility
and other environmental issues according to recent election results where the
provision of sustainable transport infrastructure was in the public focus.
Refer to table 3 for a brief description of all attributes considered with
respect to travel preferences.

Figure 1: Directed acyclic graph (DAG). Travel preferences confound the effect
of the built environment on travel behavior via residential self-selection.
Operationalization of concepts as specified in the grey text.

Treatment encoding. To examine how the built environment of a specific
neighborhood shapes travel behavior, we first determine how the local built
environment is different from the city average. We characterize the difference
along the 5Ds, density, diversity, design, destination accessibility, and
distance from transit, for each of which we engineer at least one feature,
guided by previous studies (see table 4). To quantify how this difference
impacts travel behavior, we define the treatment level as the difference
between the neighborhood built environment and the city average built
environment.

Model selection. Given continuous treatment along multiple treatment
dimensions, we choose a CausalForest-based DML estimator to estimate the
causal non-linear effect of the built environment on travel behavior. DML [15]
consists of two stages: First, to account for the confounding effect, the
outcome and treatment are being predicted from the controls using any
appropriate machine learning model. Then, the treatment residuals are used to
fit the outcome residuals yielding a debiased treatment effect estimate. The
controls are also included to account for any moderating effects in explaining
the heterogeneity of the treatment effect. We use the DML open source
implementation of the EconML package [25]. For the first stage, we use XGBoost
[26] to model the non-parametric relationship between travel preferences and
travel behavior and the built environment. We choose hyperparameters based on
a random search using 5-fold cross validation, resulting in 1000 tree
estimators, a tree depth of 6, and a learning rate of 0.01. For the final
model, we use a CausalForest [27] with 100 tree estimators.

Effect heterogeneity & composition. To analyze the heterogeneity of the built
environment’s effect on travel behavior, we calculate SHAP values [28] for the
constant marginal effect estimation of the final stage model. This allows us
to examine the moderating influence of confounding socio-demographic traits
and travel-related attitudes on the effect. We are interested in the subgroups
of households for which the built environment may have a particularly large or
small effect. To avoid reporting of spurious correlations that the model
picked up, we repeat the effect estimation 10 times, for all 5D dimensions
combined and each individually, and only report moderators with a clear and
consistent influence across all iterations. Further, we decompose the total
effect of the built environment into the 5D dimensions and discuss the main
drivers for low-carbon urban development. To make the 5D dimensions comparable
in terms of their contribution to the overall marginal effect, we standardize
all built environment characteristics and ensure consistent direction of
feature values.

###  2.3 Urban planning case study.

To highlight the practical importance for urban climate mitigation, we
evaluate current plans for 64,000 new residential units in Berlin in terms of
total induced transport emissions. We compare the induced emissions of three
alternative densification scenarios: (1) transit-oriented development focused
on neighborhoods whose residents have an average walk time of less than 7
minutes to the nearest rail station, (2) transit-oriented development focused
on centrally located neighborhoods, specifically neighborhoods connected by or
located within the commuter rail line that circles central Berlin, the so-
called “Ringbahn”, and (3) densification of low-emission neighborhoods where
the built environment has the largest reducing impact on emissions according
to our estimations. We assume that the housing units will be evenly
distributed among the targeted neighborhoods for all three scenarios. We
characterize the potential emissions savings from each strategy compared to
the average household emissions. We assume that the built environment remains
unchanged, i.e., we do not consider potential changes related to the new
residential developments.

##  3 Results

Isolated effect. The built environment has a considerable effect on travel
behavior and related emissions after controlling for residential self-
selection. In the center of Berlin, the built environment facilitates a 40%
decrease and in the outskirts a 50% increase in travel-related emissions
compared to the city average (see figure 2). Overall, half of the observed
differences in neighborhood emissions are due to the built environment
according to our CausalForest-based DML estimator. The remaining residuals may
partly be due to residential self-selection and an insufficient
characterization of the built environment. Overall our features explain the
variation of neighborhood emissions with a coefficient of determination (R2)
of $0.85$, suggesting a good characterization of travel preferences and the
built environment. The nuisance scores of the first stage DML models indicate
a high degree of confounding, with the R2 being $0.76$ and $0.56$ for fitting
the target and treatment, respectively.

Effect heterogeneity. The estimated effect of the built environment is
moderated by socio-demographics and travel-related attitudes. The household
size, income, age and car ownership are positively associated with the impact
of distance to the city center on travel-related emissions meaning that the
impact of the built environment may be larger for households that are
relatively large, old, high-income, or highly car-owning (see figure 5). On
the other hand, environmentally friendly attitudes are associated with a lower
effect of the built environment. The other confounding factors do not exhibit
a consistent impact on the heterogeneity across multiple iterations with
different seeds and are thus excluded from our results.

Effect composition. From the 5Ds, destination accessibility has the largest
effect on travel-related emissions. 73.7% of the built environment’s influence
is determined by the distance to the center, to the subcenter and the local
neighborhood job accessibility (see table 2). The second most important D is
density, with the adjusted population density being responsible for 15.2% of
the total effect. While design and distance from transit have a small effect,
6.4% and 4.3% respectively, diversity in terms of the mixed-use land share has
no meaningful impact on travel-related emissions according to our approach.

Figure 2: Spatial heterogeneity of the built environment’s effect on travel-
related CO2 emissions.

5D |  Feature name |  Effect share
---|---|---
Destination accessibility |  Distance to center |  51.2%
Distance to subcenter |  15.2%
POI density index |  11.1%
Density |  Population density |  11.4%
Diversity |  Land use |  0.3%
Design |  Car-friendliness index |  -
Walkability index |  6.4%
Distance to transit |  Transit accessibility index |  4.3%

Table 1: Decomposition of the built environment’s overall constant marginal
treatment effect.

Urban planning case study. Due to location-dependent built environment
effects, the planned settlements in Berlin are expected to increase average
household travel-related emissions by 16.8% above the city average (see figure
3). Alternative residential development strategies that focus on densifying
the center and transit-oriented development may lead to significantly lower
emissions. We estimate that emissions can be reduced by up to 30.9% if
residential planning prioritizes the 20 neighborhoods with the most
sustainable built environment according to the model. If all neighborhoods
with a good transit accessibility are targeted, specifically within 7 minutes
of walking to the nearest rail station, emissions of future residents are
expected to be 14% below the city average.

Figure 3: Induced transport CO2 emissions of planned settlements 70% above the
theoretical optimum. Left: New settlement locations are marked by circles,
with color representing future residents’ expected average emissions and size
indicating planned housing units. Neighborhoods with the largest reducing
effect on emissions, targeted by the “Optimum” policy, are marked in light
pink. Right: Comparison of different residential planning strategies,
including transit-oriented development (TOD) and a densification according to
the model (“Optimum”), in terms of average household travel-related emissions
relative to the city average.

##  4 Discussion & Conclusion

With the need to reduce transportation emissions and curb global boiling,
growing cities face the urgent question of where to locate new residents to
minimize travel demand and related emissions. Thus, having a spatially
explicit estimate of the built environment’s impact on transport emissions at
potential new residential development planning sites is indispensable for
evidence-based low-carbon urban planning.

In Berlin, the impact of the built environment on travel-related emissions is
tremendous. Household emissions differ by a factor of two between the city
center and the outskirts because of the built environment. This disparity is
mainly due to the different accessibility of destinations (74%). We find that
the effect of the built environment is largest for households that are
relatively large, old, high-income, or car-owning. According to our
calculations, the induced emissions of the currently planned 66,000
residential units are 70% above the optimum. Alternative compact or transit-
oriented development strategies would lead to significantly lower emissions.

While we believe that the overall effect magnitude is robust, we want to
emphasize that the precise estimates of induced emissions are subject to
uncertainty for three main reasons. First, causal inference assumptions are
partially violated, most importantly we do not account for spatial
interference of treatments and spatial confounding effects [29]. Second,
travel-related attitudes are likely affected to some degree by the built
environment and past travel behavior and thus interdependent instead of being
an exogenous predisposition [30, 31]. Third, by aggregating travel behavior
and built environment characteristics for each zip code area, we mask
heterogeneous distributions and nuances in the disaggregated data, potentially
leading to a biased estimate (MAUP effect [32, 33]).

In conclusion, double machine learning has the potential to greatly facilitate
and scale the estimation of travel demand induced by the built environment.
Although the effect estimates are subject to some uncertainty, it can provide
cities with a cost-effective tool to establish a starting point for evidence-
based sustainable urban planning.

## Acknowledgments and Disclosure of Funding

This work received funding from the CircEUlar project of the European Union’s
Horizon Europe research and innovation program under grant agreement
101056810.

## References

  * [1] P. Jaramillo, S. Kahn Ribeiro, P. Newman, S. Dhar, O.E. Diemuodeke, T. Kajino, D.S. Lee, S.B. Nugroho, X. Ou, A. Hammer Strømman, and J. Whitehead.  Transport.  In P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, and J. Malley, editors, Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, book section 10. Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022.
  * [2] Felix Creutzig, Peter Agoston, Jan C Minx, Josep G Canadell, Robbie M Andrew, Corinne Le Quéré, Glen P Peters, Ayyoob Sharifi, Yoshiki Yamagata, and Shobhakar Dhakal.  Urban infrastructure choices structure climate solutions.  Nature Climate Change, 6(12):1054–1056, 2016.
  * [3] Frans Dieleman and Michael Wegener.  Compact city and urban sprawl.  Built environment, 30(4):308–323, 2004.
  * [4] Veerle Van Holle, Benedicte Deforche, Jelle Van Cauwenberg, Liesbet Goubert, Lea Maes, Nico Van de Weghe, and Ilse De Bourdeaudhuij.  Relationship between the physical environment and different domains of physical activity in european adults: a systematic review.  BMC public health, 12(1):1–17, 2012.
  * [5] Gavin R McCormack and Alan Shiell.  In search of causality: a systematic review of the relationship between the built environment and physical activity among adults.  International journal of behavioral nutrition and physical activity, 8:1–11, 2011.
  * [6] David W Barnett, Anthony Barnett, Andrea Nathan, Jelle Van Cauwenberg, and Ester Cerin.  Built environmental correlates of older adults’ total physical activity and walking: a systematic review and meta-analysis.  International journal of behavioral nutrition and physical activity, 14(1):1–24, 2017.
  * [7] Ester Cerin, Andrea Nathan, Jelle Van Cauwenberg, David W Barnett, and Anthony Barnett.  The neighbourhood physical environment and active travel in older adults: a systematic review and meta-analysis.  International journal of behavioral nutrition and physical activity, 14(1):1–23, 2017.
  * [8] Laura Aston, Graham Currie, Alexa Delbosc, Md Kamruzzaman, and David Teller.  Exploring built environment impacts on transit use–an updated meta-analysis.  Transport reviews, 41(1):73–96, 2021.
  * [9] Anna Ibraeva, Gonçalo Homem de Almeida Correia, Cecília Silva, and António Pais Antunes.  Transit-oriented development: A review of research achievements and challenges.  Transportation Research Part A: Policy and Practice, 132:110–130, 2020.
  * [10] Xinyu (Jason) Cao, Patricia L. Mokhtarian, and Susan L. Handy.  Examining the impacts of residential self-selection on travel behaviour: A focus on empirical findings.  29(3):359–395.  Publisher: Routledge _eprint: https://doi.org/10.1080/01441640802539195.
  * [11] Xiaodong Guan, Donggen Wang, and Xinyu Jason Cao.  The role of residential self-selection in land use-travel research: a review of recent findings.  40(3):267–287.  Publisher: Routledge _eprint: https://doi.org/10.1080/01441647.2019.1692965.
  * [12] Mahdi Aghaabbasi and Saksith Chalermpong.  Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: a systematic review.  Travel behaviour and society, 33:e100640–e100640, 2023.
  * [13] Jason Cao and Tao Tao.  Using machine-learning models to understand nonlinear relationships between land use and travel.  Transportation Research Part D: Transport and Environment, 123:103930, 2023.
  * [14] Patricia L Mokhtarian and Xinyu Cao.  Examining the impacts of residential self-selection on travel behavior: A focus on methodologies.  Transportation Research Part B: Methodological, 42(3):204–228, 2008.
  * [15] Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins.  Double/debiased machine learning for treatment and structural parameters, 2018.
  * [16] Reid Ewing and Robert Cervero.  Travel and the built environment: A meta-analysis.  Journal of the American planning association, 76(3):265–294, 2010.
  * [17] Stefan Hubrich, Frank Ließke, Rico Wittwer, Sebastian Wittig, and Regine Gerike.  Methodenbericht zum forschungsprojekt.„mobilität in städten–srv 2018 “.  2019\.
  * [18] OpenStreetMap contributors.  Planet dump retrieved from https://planet.osm.org .  https://www.openstreetmap.org, 2017.
  * [19] VBB Verkehrsverbund Berlin-Brandenburg GmbH.  GTFS data berlin-brandenburg (VBB).
  * [20] Offene daten berlin | offene daten lesbar für mensch und maschine. das ist das ziel.
  * [21] P Cazzola and P Crist.  Good to go? assessing the environmental performance of new mobility.  2020\.
  * [22] Veronique Van Acker, Patricia L Mokhtarian, and Frank Witlox.  Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes.  Transport Policy, 35:88–99, 2014.
  * [23] Tao Lin, Donggen Wang, and Xiaodong Guan.  The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination?  65:111–122.
  * [24] Joachim Scheiner.  Residential self-selection in travel behavior: Towards an integration into mobility biographies.  Journal of Transport and Land Use, 7(3):15–29, 2014.
  * [25] Microsoft Research.  EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation.  https://github.com/microsoft/EconML, 2019.  Version 0.x.
  * [26] Tianqi Chen and Carlos Guestrin.  Xgboost: A scalable tree boosting system.  In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016.
  * [27] Susan Athey, Julie Tibshirani, and Stefan Wager.  Generalized random forests.  2019\.
  * [28] Scott M Lundberg and Su-In Lee.  A unified approach to interpreting model predictions.  Advances in neural information processing systems, 30, 2017.
  * [29] Brian J Reich, Shu Yang, Yawen Guan, Andrew B Giffin, Matthew J Miller, and Ana Rappold.  A review of spatial causal inference methods for environmental and epidemiological applications.  International Statistical Review, 89(3):605–634, 2021.
  * [30] Petter Næss.  Residential self-selection and appropriate control variables in land use: Travel studies.  29(3):293–324.  Publisher: Routledge _eprint: https://doi.org/10.1080/01441640802710812.
  * [31] Joachim Scheiner.  Transport costs seen through the lens of residential self-selection and mobility biographies.  65:126–136.
  * [32] A Stewart Fotheringham and David WS Wong.  The modifiable areal unit problem in multivariate statistical analysis.  Environment and planning A, 23(7):1025–1044, 1991.
  * [33] Ming Zhang and Nishant Kukadia.  Metrics of urban form and the modifiable areal unit problem.  Transportation Research Record, 1902(1):71–79, 2005.
  * [34] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay.  Scikit-learn: Machine learning in Python.  Journal of Machine Learning Research, 12:2825–2830, 2011.

## Appendix

Figure 4: Built environment of Berlin, Germany. The center and subcenters, based on points of interest density, are indicated as dark red circles. Population density of neighborhoods is color coded in blue. Natural areas according to Berlin land use data [20] are marked in green. The public transportation rail network is drawn in gray, with the exception of the so-called "Ringbahn", a commuter rail line that circles central Berlin, which is highlighted in orange. We consider neighborhoods that are located outside of the "Ringbahn" and not within walking distance to be suburban.  Mode | Emissions [g CO2/pkm]
---|---
Car (ICE) | 162
Moped (ICE) | 70
Transit | 65
Bike | 20
Foot | 0
Table 2: Emission factors of transport modes. Central estimates of life-cycle greenhouse gas emissions of urban transport modes per person km according to the International Transport Forum (ITF) [21]. Emissions factors are expressed CO2 equivalents and have partially been aggregated to match transport modes considered in this study (e.g. bus & metro → transit). Life-cycle emissions include a vehicle, fuel, and infrastructure component as well as operational services. ICE refers to internal combustion engine. Category | Variable name |  Description
---|---|---
Socio-demographics | income |  Average household income
hh_size |  Average number of persons living in a household
age |  Average age of adult (>18 years) residents
uni_share |  Share of people older than 25 with university degree
Proxies for travel-related attitudes | car_ownership |  Average number of private & company cars per household
bike_ownership |  Average number of bicycles owned per person
driving_license |  Average share of adults (>18 years) with driving license
transit_subscription |  Average share of people with monthly transit subscription (incl. children and people with disabilities with free ride tickets)
green_share |  Electoral share of the Green party in constituencies intersecting the neighborhood in the last regional elections
Table 3: Travel preferences. Overview of all socio-demographic traits and proxies for travel-related attitudes considered in the study including a brief description. Children are excluded from the calculation of average age, education, and driver’s license ownership to reduce the correlation between the variables and ensure that each variable describes a separate aspect. 5D’s of compact development | Feature name |  Description
---|---|---
Destination accessibility | Distance to center |  Distance to neighborhood with highest POI density
Distance to subcenter |  Least distance to any of the 10 neighborhoods with highest POI density
POI density index |  Local POI density for offices, schools, kindergarten, and universities
Density | Population density |  Population density of the built-up area
Diversity | Land use |  Share of mixed-use areas
Design | Car-friendliness index |  Provision of expressway kilometers per capita
Walkability index |  Intersection density in the built-up area
Distance from transit | Transit accessibility index |  Gravity model-based index describing the average spatio-temporal transit accessibility of a neighborhood
Table 4: Built environment characteristics. Overview of all built environment
characteristics considered in the study including a brief description and
mapping to the corresponding D of compact development. The street network and
points of interest (POIs) are extracted from OpenStreetMap. Density measures
are calculated using the built up area only. Indices are calculated using
sklearn’s StandardScaler [34].

Figure 5: Household size, income, age, car ownership, and environmental
attitudes consistently moderate treatment effect. SHAP dependence plot for the
final stage model predicting treatment heterogeneity. Selection of confounders
that have a consistent moderating effect on travel-related emissions.
Confounding variables have been standardized to facilitate comparison. A SHAP
value below 0 corresponds to an increased effect of the built environment for
such households, whereas a SHAP value above zero corresponds to a decreased
effect. Consistent zero SHAP values for all household characteristics would
imply that there is no effect heterogeneity.