causal loops to analyse resource efficiency in cities

Causal loops Diagrams (CLD)to analyse resource efficiency in cities

Raquel Ubach – [email protected] Autonomous University of Barcelona (UAB) / European Topic Centre on Spatial

Information and Analysis (ETCSIA)

PLEEC WP4 Seminar on Causal loop diagrams22 - 23 June 2015 @ University of Copenhagen,

Department of Geosciences and Natural Resource Management

Task Background

• In 2013 – Contribution to EEA’s report on resource efficiency in cities:– Identification of main challenges for EU cities on

resource efficiency focused on two aspects: energy and water

Task Background

• In 2013 – Contribution to EEA’s report on resource efficiency in cities:– Identification of main challenges for EU cities on

resource efficiency focused on two aspects: energy and water

• In 2014 – Continued this contribution to EEA’s report on resource efficiency in cities:– Deeper analysis of two specific issues:

a) Energy use due to transport in relation with city morphology, in particular with commuters.

b) Water use and city morphology

Task Background

• Requirements:– To present main implied variables and their

relation in a simple and visual way -> by means of a Causal Loop Diagram

What are causal loop diagrams?

Task Preparation

• Research question:1. What is the effect of urban morphology on the

efficient consumption of energy in urban areas, in terms of transport and commuting?

2. Which are the consequences of urban compactness in the water system, especially in terms of water demand, transport, use and reuse towards a sustainable management?

What is the effect of urban morphology on the efficient consumption of energy in urban areas, in terms of transport and commuting?

Task Preparation

• Choosing the tool:– Insight Maker– Consideo

Task Preparation

• Insight Maker– https://insightmaker.com/– A web-based, general-purpose simulation and modeling tool– It has been designed to make modeling and simulation

accessible to a wider audience of users– It’s free and open source

https://insightmaker.com/

1st results

9 11

164

== 40 variables

Tool limitations

• Good for other analysis: System Dynamics, Agent-Based Modeling, and imperative programming

• Limited for CLD -> only good for diagrams

Task Preparation 2• Consideo Modeler

– http://www.consideo.com– It allows a simple use and fast modeling– Focused on visualizing and analyzing cause and effect relationships– New tools (Imodeler web app and desktop) are not free of charge,

but there is available an old desktop version for free.

http://www.consideo.com/

Consideo diagram results

== 26 variables in 4 thematic aspects7 5

95


== 14 variables in 4 thematic aspects6 2

42


Urban morphology described by 4 variables


urban sprawl has a negative direct impact on the compactness of the city


territorial planning and policy–making have the capacity to model the shape of urban areas in the long term (restrictions to certain types of urbanization (e.g. land price regulations), promoting the re-emergence of inner-city deprived areas, etc.)


Two feedback loops affecting the use of private vehicles 1 - traffic congestion discourages the use of private transport by incrementing travel time


Two feedback loops affecting the use of private vehicles 2 - defined by the negative effect of traffic congestion over private car use by reducing road capacity; consequently, this reduction acts as a disincentive to urban sprawl.


The relation between traffic congestion and road capacity will be crucial to tackle urban sprawl


Urban morphological factors have direct or indirect impact on ‘trip distance’


Compactness has a final effect on the fossil fuel consumptionMajor compactness of European cities allowed a two times lower fuel consumption per capita than Australian cities and four times lower than American ones (Newman and Kenworthy, 1989).


Population density presents different effects on transport cost and price for public transport system and for private vehicles.


In the case of public transport, major population density brings reduced costs due to the economy of scale. More people using the system reduce the total cost.


In the case of private vehicles, as more density makes more difficult to drive and to park, increasing the cost and access.


Trip distance is a key factor favouring different modal transport options for commuters. In general terms, for smallest distances commuters prefer to walk or cycle. As distance increases, there is a preference for cycling and public transport (metro, bus or train).


Trip distance is a key factor favouring different modal transport options for commuters. But for longer distances, private car or public transport (bus or train) are the most used.


Trip distance is not the unique factor determining one modal choice or another, as travel time is another variable that defines mobility patterns. Population normally estimates accessibility by time, considering the total time used for a certain trip.


Land uptake affects biodiversity since it takes builds up on natural or semi-natural areas, increasing the urban land. This decrease of natural habitats directly reduces the living space of a number of species. It also produces a fragmentation of landscapes that support and connect natural habitats


In the proposed model, territorial planning is addressed to halt the fossil fuel consumption; therefore road capacity is not favoured in order to reduce land take and the derived environmental impacts.


Fuel resources are generally imported from other countries. Europe is still highly dependent on imported fossil fuels (gas, solid fuels and oil) reaching in 2010 the 53,8% (values for EU27) of the total gross inland consumption (indicator ENER 36)

Consideo Insight Matrix

The insight matrix is related shows the sum of direct and indirect influences, both positive and negative on a given factor

Consideo Insight MatrixInsight matrix for ‘Fossil fuel consumption per capita’ – in the short term

Urban sprawl has the strongest positive impact (impact value: 4,62); this means that the fuel consumption per capita will always increase significantly as the urban expansion continues developing.


Modal split (number 8) and the perception improvement (number 9) have also an increasing impact. As far as there is a good accessibility to the public transport system, reducing travel time, together with a positive users' perception, there will be a continued increase in use of public transport and, consequently, an increase of fossil fuel consumption.


Road capacity (number 14) shows that an improvement in road infrastructure will appeal more users as travel time can be reduced; consequently, increases in private car use together with increases in fuel consumption are envisaged.


Travel time by car and private transport cost and price are the factors with the highest negative impact (-6,875). Increases of these factors have a higher impact on reducing fossil fuel consumption. Therefore, both factors will be crucial for discouraging fossil fuel consumption.


The following factors that present an important decreasing effect on the consumption of fuel are the compactness and public transport cost and price

Consideo Insight MatrixInsight matrix for ‘Fossil fuel consumption per capita’

Short term

Long term

Urban sprawl is still the factor with the highest impact increasing the fuel consumption per capita in the long term.

The total effect of all the relations between variables has increased from 4,62 to 7,27.

Though it is expected to be reduced over a longer period of time by balancing feedback loops

Which are the consequences of urban compactness in the water system, especially in terms of water demand, transport, use and reuse towards a sustainable management?

causal loops to analyse resource efficiency in cities

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