Climate Implications of a Collaborative Economy Scenario for Transportation and the Built Environment
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DEGREE PROJECT IN ENVIRONMENTAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2016 Climate Implications of a Collaborative Economy Scenario for Transportation and the Built Environment NICOLAS FRANCART KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT
TRITA INFRA-FMS-EX-2016:06 www.kth.se
Title: Climate Implications of a Collaborative Economy Scenario for Transportation and the Built Environment Titel: Klimat Innebörd av en Kollaborativ Ekonomi Scenario för Transporter och den Byggda Miljön Degree project in Strategies for sustainable development, Second Cycle AL250X, 30 credits Author: Nicolas Francart Supervisor: Tove Malmqvist Examiner: Göran Finnveden Division of Environmental Strategies Research (fms) Department of Sustainable Development, Environmental Science and Engineering School of Architecture and the Built Environment KTH Royal Institute of Technology
Climate Implications of a Collaborative Economy Scenario for
Transportation and the Built Environment
Nicolas Francart
June 13, 2016
Summary
In a context of increasingly ambitious climate objectives after the Paris Agreement in 2015, this thesis investigates
a scenario for sustainable development in Sweden in 2050 in terms of greenhouse gases emissions. The scenario
is built around the idea of a development of collaborative economy in a context of low growth or degrowth. The
concept of “collaborative economy” encompasses the sharing of services and underused and unwanted goods
between individuals, a focus on the access to services rather than the ownership of products, and new ways of
sharing space and time (cohousing, time banks, etc). The present study focuses on the implications of the Collab-
orative Economy scenario for transports and the built environment at a municipal scale, and aims at modeling the
corresponding greenhouse gases emissions. A literature review was carried out to identify the main aspects of the
scenario and exemplify the changes it entails. Two spreadsheet models were then developed for transports and
the built environment, estimating greenhouse gases emission levels based on a range of assumptions elaborated
from the literature review. The municipality of Malmö was used as a case study. Overall, the results of the models
and the sensitivity analysis indicate a rather weak influence of collaborative economy strategies on greenhouse
gases emissions. Strategies related to changes in the energy mix for heating, materials used in construction, fuels,
etc seem to be much more impactful. However, such strategies only impact greenhouse gases emissions, whereas
collaborative economy strategies can have other benefits. In particular, cohousing can increase social capital and
foster sharing, which in turn could decrease energy and material use for the production of goods. Ridesharing, re-
mote working among others, can decrease congestion and the daily distance traveled. Most of these strategies also
provide energy savings, improving the resilience of the system and freeing the energy supply for other purposes.
Sammanfattning
Inom ramen för Parisavtalet undersöker denna avhandling ett scenario för hållbar utveckling i Sverige år 2050
när det gäller utsläppen av växthusgaser. Scenariot är baserat på en utveckling av kollaborativ ekonomin inom
ramen för låg tillväxt eller nerväxt. Begreppet "kollaborativ ekonomi" omfattar fördelningen av tjänster och un-
derutnyttjade och oönskade varor mellan individer, fokus på tillgång till tjänster snarare än ägandet av produkter
och nya sätt att dela utrymme och tid (kollektivhus, tidsbanker, etc). Avhandlingen fokuserar på konsekvenserna
av scenariot för transporter och den byggda miljön på kommunal nivå, och modellerar motsvarande utsläppen av
växthusgaser. En litteraturöversikt genomfördes för att identifiera de viktigaste aspekterna av scenariot och exem-
plifiera vad det innebär för kommunen. Sedan utvecklades två modeller för transporter och den byggda miljön,
och utsläpp av växthusgaser uppskattades baserat på förutsättningar från litteraturöversikten. Malmö kommun
användes som en fallstudie. Resultaten av modellerna och känslighetsanalysen visar på en svag påverka av kollab-
orativ ekonomi strategier på utsläppen av växthusgaser. Förändringar i energimixen för uppvärmning, material
som används i byggandet, bränslen, etc verkar vara mycket mer effektfulla. Men sådana strategier påverkar bara
utsläppen av växthusgaser, däremot kan kollaborativ ekonomi strategier ha andra fördelar. Kollektivhus kan ex-
empelvis öka det sociala kapitalet och främja fördelning, vilket kan minska energi- och materialanvändning för
produktion av varor. Samåkning, distansarbete m.m. kan minska trängsel och behovet av transporter. De flesta
av dessa strategier minskar också energianvändningar, vilket förbättrar systemets resiliens och kan frigöra energi
för andra ändamål.
1
Contents
I Background 5
1 Introduction: Project Scope and Goals 5
1.1 The Beyond GDP Growth project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Objective and scope of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Outline of the report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Generalities about collaborative economy 6
2.1 The concept of collaborative economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Critics and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 The Collaborative Economy scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Transports in the Collaborative Economy scenario 8
3.1 Ridesharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Ridesourcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.3 Carsharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.4 Bikesharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.5 Decentralized concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.6 Using ICTs in Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Built environment in the Collaborative Economy scenario 13
4.1 Cohousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1.1 Principle and origins of cohousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1.2 Climate benefits of cohousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Sharing office space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 Improvements in construction processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.4 Embedding ICTs in buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5 Setting Climate Goals 15
5.1 What quota should be attributed to transports and the built environment? . . . . . . . . . . . . . . . 15
5.2 Does Malmö deserve a higher or lower emission quota? . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5.3 Comments on carbon capture and storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
II Methods 19
6 General approach 19
7 Emissions from the Built Environment 20
7.1 Overview of the spreadsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
7.2 The “Demography” table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
7.3 The “Energy” table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
7.4 The “Housing exploitation” table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
7.4.1 Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
7.4.2 Heating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
7.4.3 Appliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
7.5 Offices exploitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
7.5.1 Lighting in offices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
7.5.2 Heating offices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
7.5.3 Using appliances in offices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
7.6 Construction and renovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
7.6.1 Construction of new buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
7.6.2 Renovation of the existing building stock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
7.7 The “Results” table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2
8 Emissions from Transports 29
8.1 Overview of the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
8.2 Comparing the current and future situations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
8.2.1 Demand for different modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
8.2.2 Modal characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
8.2.3 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
8.2.4 Built environment characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
8.3 Scenario-specific factors of influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
8.4 Demand for each transport mode in 2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
8.5 Taking into account plane travel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
8.6 Calculating greenhouse gases emissions in 2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
III Results & Discussion 38
9 Results and analysis for the Built Environment 38
9.1 Results of the model with the presented assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
9.2 Sensitivity analysis for the Built Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
9.2.1 Influence of cohousing on greenhouse gases emissions . . . . . . . . . . . . . . . . . . . . . . 39
9.2.2 Influence of the production of district heat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
9.2.3 Influence of construction processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
10 Results and analysis for Transports 44
10.1 Results of the model with the presented assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
10.2 Sensitivity analysis for transports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
10.2.1 Influence of travel times and costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
10.2.2 Influence of built environment characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
10.2.3 Influence of collaborative economy strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
10.2.4 Influence of air traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
IV Discussion 50
10.3 Modeling emissions from the built environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
10.3.1 Insights from the Built Environment model and the sensitivity analysis . . . . . . . . . . . . 51
10.3.2 Emission quota for the Built Environment and strategies to reach it . . . . . . . . . . . . . . . 51
10.3.3 Validity of the Built Environment model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
10.4 Modeling emissions from transports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
10.4.1 Insights from the Transportation model and the sensitivity analysis . . . . . . . . . . . . . . 52
10.4.2 Emission quota for Transportation and strategies to reach it . . . . . . . . . . . . . . . . . . . 52
10.4.3 Validity of the Transportation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
10.4.4 Approach chosen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
10.5 Adapting the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.5.1 Adaptation to another scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
10.5.2 Adaptation to another area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
10.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
References 56
V Appendices 63
1 Model parameters & background calculations 63
1.1 Setting up model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
1.1.1 Floor area per person . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
1.1.2 Working habits and office space per employee . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
1.1.3 Improvement of the building envelope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
1.1.4 Present and future energy needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
1.1.5 Energy Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
1.1.6 Characteristics of vehicles in 2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
1.2 Methodological details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3
1.2.1 Methodology for calculating the electricity use of appliances . . . . . . . . . . . . . . . . . . . 67
1.2.2 Representing the land use mix with the Shannon entropy index . . . . . . . . . . . . . . . . . 68
1.2.3 Adjusting the emissions from refurbishment processes . . . . . . . . . . . . . . . . . . . . . . 68
1.3 Values and references for travel demand elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
1.3.1 Effect of collaborative economy strategies on the demand for each . . . . . . . . . . . . . . . 71
2 List of symbols 72
4
Part I
Background
1 Introduction: Project Scope and Goals
Climate change is a major environmental challenge, requiring all nations to cooperate and make ambitious efforts
to tackle it. The latest international agreement aimed at limiting global warming was signed by 177 countries
during the 21st Conference of the Parties (COP21), which took place in Paris in 2015. The agreed-upon objective is
to limit the increase in global average temperature to well below 2°C, and pursue efforts to limit it further to 1,5°C
(Conference of the Parties, 2015). This will require every country to set up an extensive long-term strategy to limit
greenhouse gases emissions. The purpose of this study is to contribute to this reflexion, by investigating a scenario
for Sweden in 2050 in terms of greenhouse gases emissions.
1.1 The Beyond GDP Growth project
The present master thesis was conducted within the “Beyond GDP Growth” project at KTH Stockholm, in the
division of environmental strategies research (FMS). The “Beyond GDP Growth” project is a backcasting study
addressing the issue of how to reach different sustainability goals in Sweden in 2050. Its aim is to “explore scenar-
ios for developments beyond traditional GDP growth, understand the implications of such development within
the field of planning and building, and subsequently develop strategies and policies that can be used for sustain-
able urban planning and building in Sweden.” (Beyond GDP Growth research team, 2014). In particular, this
entails the development of a number of backcasting scenarios, i.e. images of the future that are not necessarily
realistic but aim at exploring very different perspectives.
First of all, four sustainability targets were defined regarding greenhouse gases emissions, land use, citizen
participation and access to welfare and resources (Fauré et al., 2016). Then, scenarios are developed to describe
hypothetical situations in Sweden in 2050, with a focus on the operationalization of these four targets in a context
of low growth or degrowth. These images do not necessarily represent a likely future situation, but they rather
aim at illustrating very different developments from the present situation and identify opportunities and desirable
aspects in each scenario to reach the project’s targets. Four scenarios are being investigated further (Svenfelt et al.,
2016):
Circular economy in the welfare state: This scenario assumes to some extent that the present dynamics continue
and no radical societal change happens. In particular, there is limited effort towards sustainable develop-
ment. Mostly, improvements rely on a more efficient technology, a service-oriented economy and a strong
focus on material productivity: products and buildings are designed to be reused, repaired and recycled,
and flows are circular. Activity is centralized in large cities, with a strong role of the State and of companies.
Automation for quality of life: This scenario assumes an extensive international collaboration and efforts to-
wards sustainable development, mainly through technological improvement. Economic growth is volun-
tarily limited and the focus switched to well-being and the repartition of resources. Automation is so
widespread that working hours have drastically decreased, together with consumption. Paid work lost
its importance, but voluntary work and personal engagement have become the norm. Technology is om-
nipresent in people’s lives, and large corporations and technological innovators are very influential. The
electricity demand increases, but so does resource productivity.
Local self-sufficiency: This scenario assumes a relative failure of governments and international institutions to
solve social and environmental problems. Therefore, trust and power have shifted from national and global
levels to the local level. Society is decentralized and organized around self-sufficient, small to medium-sized
communes. The level of consumption is low and life is organized around local food production and the
reparation of necessary goods for the community. The civil society and citizen engagement play a key role.
Collaborative economy: This scenario assumes a successful cooperation towards sustainable development at the
local, national and international levels. Goods and services are shared instead of being consumed and society
revolves around collaborative lifestyles. Sharing allows people to decrease the environmental impact of their
lifestyle without decreasing their quality of life (or even improving it through more social contact).
The present master thesis will investigate this last scenario in terms of its impact on greenhouse gases emissions.
The scenario is described further in section 2.3.
5
1.2 Objective and scope of the study
The collaborative economy offers opportunities to reduce greenhouse gases emissions, by allowing for a more
efficient use of space and goods, a reduction in material waste and a less consumerist lifestyle. It appears to be
a possible pathway towards a low-carbon economy. However, its implications for society are vague and there is
no guaranty that the range of these savings would be significant enough to reach an emission level as low as the
targets of the Paris Agreement.
It is therefore relevant to develop further the Collaborative Economy scenario by suggesting how it could
influence urban landscapes and people’s lifestyles. This will be performed in a quantitative way, in order to assess
whether the scenario’s assumptions would allow reaching the “Beyond GDP Growth” project’s target in terms of
per capita greenhouse gases emissions1 . To serve a more immediate purpose and help with decision making at
the municipal level, this study will provide insights regarding strategies and policies that could allow reaching
sustainability targets without relying on economic growth.
Regarding the geographical scope of the study, the Collaborative Economy scenario assumes among other
hypotheses a strongly reduced role of the state and a strongly increased role of the civil society. Therefore, it
seems relevant to study it further with a focus on the local scale. The study will thus be based on the case of
Malmö, which has already been used as a case study municipality for the Beyond GDP Growth project. Regarding
the conceptual scope, limits have to be set due to the limited time available. The study will therefore be limited
to emissions related to transports and the built environment, representing around 60% of the emissions related
to private consumption (Naturvårdsverket, 2011). Other emissions, linked with the consumption of goods and
services, seem more difficult to assess for that scenario due to complex changes in behaviors and the notion of
property, and limited assumptions about the type of products consumed or the average diet. What’s more, the
built environment and transportation are somewhat intertwined (urban planning and architecture influence and
are influenced by the available public transportation).
In a nutshell, a first research question for the present study is : “How to model the Collaborative Economy
scenario’s implications for transports and the built environment at a municipal scale?”. A second research ques-
tion, related to the project’s environmental goals, is : “To what extent are strategies related to the Collaborative
Economy scenario relevant to decrease greenhouse gases emissions?”.
1.3 Outline of the report
This report will start with a literature review of key aspects of the Collaborative Economy scenario for transporta-
tion and the built environment. The second part will describe the methodology used to model the emissions from
transports and the built environment under the scenario (using a spreadsheet model based on LibreOffice Calc,
Microsoft Excel or any similar software). The third part will present the results and predictions obtained once
the spreadsheet model was implemented, as well as a sensitivity analysis of the results. Finally, the last part will
discuss the insights obtained on different strategies to reduce greenhouse gases emissions, the validity of the ap-
proach chosen, and examine how to adapt this approach to another scenario (e.g. circular economy instead of
collaborative economy) or another area (e.g. a rural city).
2 Generalities about collaborative economy
This section will explain briefly the concept of collaborative economy, and the assumptions related to the Collab-
orative Economy scenario.
2.1 The concept of collaborative economy
A collaborative economy can be defined as “An economy built on distributed networks of connected individuals
and communities versus centralized institutions” (Botsman, 2013). This broad definition encompasses collabora-
tive consumption, production, education and finance. The primary focus of this study will be on collaborative
consumption, that can be further subdivided into three activity categories (Botsman, 2013):
• Redistribution markets, representing the reuse of unwanted and underused goods (e.g. second-hand shops
or free giveaways via networks like Freecycle)
• Product service systems, representing the access to the benefits of a product without needing to own it, by
having a number of persons using the same product (e.g. carsharing, cohousing with common facilities, etc)
1 Which are in line with the most ambitious objective of the Paris Agreement of limiting global warming to 1,5°C
6
• Collaborative lifestyles, representing new ways of sharing space, skills and money (e.g. ridesharing, sharing
tasks in cohousing or on websites such as Taskrabbit)
A central concept of all these categories is that “unused value is wasted value”, whether it’s the remaining value
of a discarded product, the unused value of a car driving with a single passenger or of a washing machine used
by one person only. The term “sharing economy” is sometimes used to emphasize the sharing and exploitation of
underutilized assets to exploit this unused value (whereas “collaborative economy” is used to emphasize the fact
that it relies on networks of individuals). Collaborative economy has been developing rapidly recently, driven by
the development of Information and Communication Technologies (ICT) such as social networks and smartphones
providing an internet access virtually everywhere. It is seen as a solution to increase the power of individuals (as
opposed to large scale organizations and companies) and reduce the environmental impact and the cost of our
lifestyles (as the prices of raw materials become higher and more volatile). This concept has also been seen as
a business opportunity by a number of companies in almost all sectors of the economy (Cohen and Kietzmann,
2014), including shopping (e.g. Rent the Runway), professional services (e.g. Upwork), transportation (e.g. Uber,
Lyft), housing (e.g. Airbnb) and funding (e.g. Kickstarter). Some of them have been characterized by a very quick
growth and huge investments (O’Brien, 2015).
2.2 Critics and limitations
There is an ongoing debate about terminology and what these concepts encompass. In particular, a distinction has
been made between the concepts of “sharing economy” and “access economy” (Cartagena, 2014; Eckhardt and
Bardhi, 2015). According to the critics, the main driver of activities like ride-sharing or couchsurfing is not social
relationships or trust, but rather low costs : it emerged in part because of the economic recession, and the most
successful actors are the ones that emphasize this aspect and keep anonymous and distant relationships between
users (for instance Uber as opposed to Lyft, according to Eckhardt and Bardhi). Therefore, the successful initiatives
improve asset exploitation, but not necessarily trust or social capital. Moreover, these “sharing” activities retain a
structure of market capitalism, with dominant actors reaping a significant part of the profit, that might thus not
trickle down.
Specific concerns have also been raised about cohousing. Intentional communities tend to be homogeneous in
terms of race, religion, income and education (Meltzer, 2001). While this tends to improve social relationships and
cooperation within the community, it is also a factor of social exclusion and communitarianism, as seen for instance
in the “gated communities” in the United States (Williams, 2005, 2008). There can therefore be a trade-off between
social integration and social capital. More than a simple communitarian trend, affordability by itself can be a
barrier and reinforce homogeneity. Recent experiments are actively trying to tackle this problem by integrating
diversity at the core of their concept and by developing new ownership and management models like Mutual
Home Ownership Societies (Chatterton, 2013).
2.3 The Collaborative Economy scenario
The Collaborative Economy scenario developed within the Beyond GDP Growth project assumes a successful co-
operation towards sustainable development at the local, national and international level. It is therefore optimistic,
assuming a decrease in social inequalities and improvements in resource- and energy efficiency until 2050. Invest-
ments and economic incentives allow for an extensive exploitation of renewable energy and the Swedish energy
mix has become fossil- and nuclear-free. Greenhouse gases emissions are strictly regulated to ensure that global
warming is limited to a global average increase in temperature of 1,5°C compared to pre-industrial levels (Fauré
et al., 2016).
Consumerism is on the decline due to voluntary changes in behaviors as well as a decrease in the availability of
capital and material resources. The Swedish economy has seen the rise of digitization and informal economy, with
consumers becoming co-producers of value as well (or “prosumers”) and exchanging with one another. Unpaid
work, exchanges of services and time banks (where individuals earn time credits when helping the community,
that they can later spend in exchange for services like child care, private lessons, etc) are widespread. Informa-
tion and communication technologies (ICTs) make sharing easier by linking people together, and digital services
replace to some extent material products. What matters is access to goods and services and use value, rather than
ownership and exchange value. Sharing and joint ownership of products, appliances, and even industrial equip-
ment, have therefore become common (as well as products under “open source” or Creative Commons licenses),
and products are designed to last and be easily repaired. As living in a community becomes common, social influ-
ence shifts to some extent from the individual to the group he or she belongs to. Moreover, the civil society tends
to play a much more important role than today : personal engagement in associations and informal networks is
common and a lot of decisions happen at the local level. The role of the State has been reduced to a minimum, but
7it still ensures a certain level of welfare. On the other hand, the individual’s engagement in his or her community
and the services provided in that community are very meaningful.
The importance of the community in one’s life naturally lead people to live among their community, leading
to a rise in cohousing and the sharing of common premises. Priority has been given to the renovation of exist-
ing buildings rather than the construction of new buildings. Joint ownership of buildings and cooperatives are
widespread. Construction processes have also improved thanks to the development of 3D-printing and recycling,
and smart meters are commonly embedded in buildings. Since services and products are exchanged at a local level
and ICTs allow for an easy access to information (e.g. online courses) and communication from a distance (e.g.
virtual meetings), people tend to live in dispersed but dense clusters. This type of urban development is termed
“decentralized concentration”. ICTs also allow for improvements in transports, compensated to some extent by
a rebound effect: people tend to travel less for work-related purposes, but more for leisure. The development of
collaborative economy has lead to a widespread use of carsharing and ridesharing, allowing people to cope with
increases in the cost of fuels and vehicles (Svenfelt et al., 2016).
3 Transports in the Collaborative Economy scenario
In the last decades, greenhouse gases emissions related to transportation have increased, in particular in rural
areas. More than the frequency of trips, it is the average length that has steadily increased : while short trips have
decreased in frequency, the increase in long trips has more than compensated the possible reduction in emissions
(Banister, 1997). The present study will focus only on personal transports and exclude the transport of goods due
to time constraints. From a life cycle perspective, the environmental impacts of the transportation of raw materials
and the delivery of products are taken into account in the impact of the product itself. Therefore, the transportation
of goods could be handled altogether with the consumption of goods and services, which is outside of the scope
of the present study.
The collaborative economy offers a variety of solutions that could impact emissions from transportation. Among
these, initiatives that change behaviors related to car use are of particular significance, due to the unused (and
therefore wasted) value related to car use (be it because cars are parked and unused most of the time or because
they drive with a low occupancy). Since a variety of terms, such as carpooling, carsharing, ridesharing or lift-
sharing, are sometimes used in ambiguous ways, the following definitions will be used throughout the study
:
• Ridesharing is defined as the sharing of car journeys, planned or ad-hoc, so that several persons travel in the
same car. In that case, the driver barely changes his or her path to take the passengers to their destination,
and uses the service to cover part of the costs of the trip (ex: BlaBlaCar).
• Ridesourcing is defined as a service outsourcing rides to drivers (professional or not). Here, the drivers
provide a service somewhat similar to a taxi: they do not necessarily share a destination with the passengers
and drive for profit (ex: Uber).
• Carsharing is defined as the use of a car by several persons or households, who take turns using the car to
maximize its use value and avoid buying several cars. The car can be owned by one of the carsharers, but
most often the service is provided by a company with a pool of dedicated cars.
This section will describe more in detail aspects of the Collaborative Economy scenario that are relevant for trans-
ports. First, it will deal with initiatives related to the sharing of rides and vehicles, i.e. ridesharing, ridesourcing,
carsharing and bikesharing. Then, it will describe aspects of the scenario that are relevant for transports, but not
directly related to collaborative economy, such as decentralized concentration and information technologies.
3.1 Ridesharing
Casual ridesharing has been practiced in cities such as San Francisco and Washington DC for decades. Passengers
queue at fixed pickup points during commute times, and drivers just drive by and pick them up. The main
motivations are time and money savings for both the passengers and the driver. Indeed, High-Occupancy Vehicles
(HOV) facilities have been set up to promote ride-sharing and reduce congestion : the drivers can thus enjoy
dedicated lanes or tollbooth bypasses (Levofsky and Greenberg, 2001). The efficiency of casual ridesharing and
HOV facilities in decreasing traffic and congestion has been widely discussed. While ridesharing is supposed to
reduce congestion, fuel consumption and pollution, it might actually in some cases take travelers away from public
transportation by making car travel more attractive. However, even if casual ride-sharing arguably increases car
traffic, it might still reduce the overall energy use, by allowing cars to drive more efficiently and by replacing
buses making empty returns (assuming the alternative to ride-sharing would be buses and not trains (Minett and
8Pearce, 2011)). The overall impact seems to be highly dependent on the available public transportation alternatives.
According to the California Department of Transportation, a 5% increase in ridesharing adoption rates by 2040
would lead to a 2,9% reduction in vehicles kilometers traveled (VKT), and requiring vehicles driving on HOV
lanes to have a minimum of 3 passengers instead of 2 would lead to a 0,8% reduction (Shaheen et al., 2015). The
International Energy Agency (2005) estimates that HOV lanes can reduce total VKT by 0,2 to 1,4% depending
on local conditions. Higher efficiencies are found for cases where the commute distance is long or where the
destinations are very centralized.
Recently, ridesharing has benefited from the development of the internet and smartphone applications. This
is especially true for long distance ridesharing, as internet services such as BlaBlaCar (a website matching drivers
and passengers for one-time, long distance ridesharing) have become broadly popular. It has been estimated that
1 vehicle kilometer traveled via BlaBlaCar in France avoids 0,04 km driven by car, 0,07 km flown by plane and 1,97
traveled by train, corresponding to a 12% reduction in greenhouse gases emissions on each trip, for all travelers
(6t - Bureau de recherche, 2015a). Long distance ridesharing seemed to have a low impact on car ownership (3%
of respondents shed a car because of their possibility to use BlaBlaCar, and 13% delayed a purchase). However,
the service exhibits a detrimental rebound effect : it strongly competes against train use and induces travel (21%
of respondents claimed they would travel less if not for BlaBlaCar).
Short distance regular ridesharing, on the other hand, hasn’t developed that much, but can potentially concern
a much larger share of the trips. Here, the development has been mainly informal, and people share rides with
their family or colleagues. This might however develop in the near future due to smartphone applications allowing
passengers to find a driver passing nearby up to a few minutes before departure. Estimations from ENGES (2007)
mention that, optimistically, 1,3% of drivers could take part in ridesharing (as drivers or passengers). A case
study of the city of Lyon in France showed that short distance ridesharing could decrease emissions related to
commuting by around 10% (INDIGO and EnvirOconsult, 2015). Giving employees incentives or bonuses to share
rides to work can eliminate up to 20% of the commute trips (International Energy Agency, 2005). However there
is probably a huge variation depending on the city considered for these results, especially regarding criteria such
as employment density, public transportation offer and social norms (trust, etc).
Overall estimates of the effect of ridesharing development in Europe yield the following results (International
Energy Agency, 2005):
• If one person is added to every car trip (extremely optimistic assumption), VKT could be reduced by 14,5%
• If one person is added to every commute trip (optimistic assumption), VKT could be reduced by 8%
• If one person is added to every trip on urban motorways, VKT could be reduced by 3,3%
• If vehicle traffic on urban motorways is reduced by 10%, VKT could be reduced by 0,8%
3.2 Ridesourcing
Due to their extremely fast growth and their popularity, it is almost guaranteed that ridesourcing and transporta-
tion network companies such as Uber or Lyft will impact mobility. However, it is still unclear what their climate
impact will be : they could both enable people to avoid owning a car and drive less, or take potential travelers
away from public transportation and biking and increase congestion during peak times. The first academic study
on the climate impact of ridesharing companies is currently being carried out and should be published in sum-
mer or autumn 2016 (Hawkins, 2015). Earlier results on the relationships between ridesourcing, taxis and public
transportation (Rayle et al., 2014; Shaheen et al., 2015; Shaheen and Chan, 2015; 6t - Bureau de recherche, 2015c)
showed that the amount of travel induced by ridesourcing could broadly vary. ( from 8% according to Rayle et al.
(2014), up to 27% according to 6t - Bureau de recherche (2015c)). Ridesourcing seemed to provide more mobility
in fewer vehicle kilometers compared to taxis. This is due to a higher occupancy of vehicles (Rayle et al., 2014).
It also led to a small but significant decrease in car ownership (-3%) and motorbike ownership (-7%) (6t - Bureau
de recherche, 2015c). For a similar adoption rate, ridesourcing has a lower impact on car ownership than carshar-
ing. However, its very large amount of users allows Uber to remove much more cars from circulation than any
carsharing initiative in France (6t - Bureau de recherche, 2015c). Ridesourcing customers tend to own less cars and
drive less compared to individual car and taxi users (Rayle et al., 2014; 6t - Bureau de recherche, 2015c). This can
indicate that ridesourcing helps reducing car ownership and car use, but it could also simply be due to the specific
population of ridesourcing users (e.g. young people living in the city, who tend to own less cars). Ridesourcing
can both compete with public transportation (ridesourcing users use less public transportation services) and com-
plement it. Indeed, most of the induced trips in 6t - Bureau de recherche (2015c) were made for leisure and night
time activities, which seemed to indicate a complementarity with public transportation, Uber being an alternative
when public transportation is unavailable.
93.3 Carsharing
Carsharing enables individuals to gain the benefits of using a car without owning one. Several forms exist (Sha-
heen et al., 2015): roundtrip carsharing, where people gain hourly access to a fleet of vehicles that they have to
drive back to the original location afterwards; one-way carsharing, where members can pick up a vehicle at one
location and leave it at another; peer-to-peer vehicle sharing, where members occasionally use cars owned by
other members; or fractional ownership, where members subscribe to a vehicle in exchange for paying part of the
operating and maintenance costs (mostly for luxury and recreational vehicles).
The climate impact of roundtrip carsharing has been the topic of a number of studies. It seems clear that
roundtrip carsharing reduces the number of vehicles on the road, vehicle kilometers traveled and greenhouse
gases emissions. Importantly, carsharing reduces car ownership : the average number of cars per household in
a related American study dropped from 0,47 to 0,24. Between 4 and 6 vehicles were shed for each carsharing
vehicle added, which rises to between 9 and 13 vehicles if avoided purchases are taken into account. What’s
more, carsharing vehicles are often more energy efficient than shed vehicles and consume in average 3 L less
fuel per 100 km (Martin et al., 2010). An European study found that between 4 and 7 vehicles were shed for
each carsharing vehicle, rising to between 6 and 10 if considering avoided purchases. It also found that the fuel
efficiency of carsharing vehicles was 17% better in average compared to shed cars (Rydén and Morin, 2005). Finally,
a French case study found that each carsharing vehicle could replace around 9 cars (without considering avoided
purchases), though it acknowledged that this result was higher than the literature (6t - Bureau de recherche, 2013).
However, there is no clear link between car ownership and car use. Studies often can’t separate the effects
of income and car ownership on car use, because income and car ownership are strongly colinear (Paulley et al.,
2006). A case study for London found no relationship between car ownership and peak traffic in residential areas
(WSP, 2011), whereas a case study for Oslo found that employees from households with one car per adult could
consume 130% more energy for commuting than employees without a car (Naess and Sandberg, 1996). It is likely
that switching from one to zero car in a household would reduce its emissions, but the range of the effect when
switching e.g. from two cars to one car is unclear. Therefore, it is not possible to conclude on emission levels based
on car ownership numbers. Carsharing does decrease car traffic (Martin and Shaheen, 2011) but there is no proof
that this is due to a reduction in car ownership.
Households that shed their car to adopt carsharing tend to drive less. The overall impact of roundtrip carshar-
ing as studied by Martin and Shaheen (2011) is a net reduction of vehicle kilometers traveled (VKT) of 27 to 43%,
and of greenhouse gases emissions of about 0,58 tCO2e per household and per year, or 0,84 tCO2e when taking
into account hypothetical avoided emissions that would have happened if the households didn’t choose carshar-
ing2 . Interestingly, a majority of the respondents (mostly those who didn’t own a car before joining carsharing)
actually increased their emissions slightly, but the emissions reduction from the other households more than com-
pensate for that increase, leading to an overall decrease in emissions. These results are consistent with Rydén and
Morin (2005), who showed a 28 to 45% decrease in VKT and a 40 to 50% decrease in CO2 emissions in an Euro-
pean study. Another case study of roundtrip carsharing companies in France showed decreases in VKT of 41% (6t
- Bureau de recherche, 2013). Carsharing has been reportedly competing slightly with public transportation, but it
also increased significantly walking and cycling, and led to an overall decrease in car use (Shaheen et al., 2015).
However, the impact of other modes of carsharing might well be different from the impact of roundtrip car-
sharing. Indeed, in a study by 6t - Bureau de recherche (2014), decreases in VKT of 42 to 45% were found for
roundtrip carsharing but one-way carsharing led to a decrease of only 11%. This is seemingly due to fact that
one-way carsharing doesn’t lead to a significant change in behavior and simply acts as a substitute car provider.
Moreover, one-way carsharing seems to directly compete against public transportation, whereas other modes of
carsharing actually act in a complementary and synergistic way (Rydén and Morin, 2005). However, one-way car-
sharing can increase flexibility and help solve the problem of first- and last-mile connectivity (Shaheen et al., 2015),
which allows it to reach a much broader audience, possibly leading to significant impacts overall (6t - Bureau de
recherche, 2014).
Peer-to-peer carsharing is expected to impact more households with a relatively low income in neighborhoods
where carsharing stations are not available. In that sense, it complements other modes of carsharing (Shaheen
et al., 2015). However, its environmental impact is uncertain : in this case, no new car is added to the road, but
2 The numbers from American case studies require some adaptation to be used even as an approximation for the Swedish case, as the baseline
kilometers driven per person and vehicle fuel efficiency are very different. In the USA, the average greenhouse gases emissions from car traffic
are around 257 gCO2e/km (United States Environment Protection Agency, 2014) whereas in Europe they are around 185 gCO2e/km (Hickman
and Banister, 2007). The average car in the USA drives about 18400 km/year (United States Environment Protection Agency, 2014), compared
to around 11000 km/year in Europe (Hickman and Banister, 2007). Overall, the yearly impact of an European car seem to be around 60% of
the impact of an American car. Furthermore, we need to consider the fact that the fuel efficiency of the vehicle fleet will improve significantly
before 2050. A fuel efficiency of 90 gCO2e/km would represent 35% of the current emission level of American cars. Therefore, assuming that
carsharing in Sweden would decrease the VKT in the same proportion as in the US, it would save between 0, 35 × 0, 6 × 0, 58 = 0, 12 tCO2e
and 0, 35 × 0, 6 × 0, 84 = 0, 18 tCO2e per household and per year.
10shared cars might not be more fuel-efficient and might get used faster due to the increase in mileage. A French
case study showed no apparent change in mileage or car ownership for peer-to-peer carsharing users, and a much
weaker interaction with public transportation compared to other types of carsharing. However, since it is a recent
and occasional practice, it might take time to see any visible effect (6t - Bureau de recherche, 2015b).
3.4 Bikesharing
Bikesharing systems provide users in a city with a fleet of bikes available in dedicated stations. This innovation
has spread rapidly in a large number of cities. Studies undertaken in large cities in the United States show that
bikesharing has the potential to reduce car traffic. 5,5% of users sold their vehicle or postponed a purchase and
50% reported they drive less. Bikesharing has a more complex interaction with public transportation : it reduced
bus and rail use in large cities due to cheaper and faster travels by bike, but it increased public transports use in
smaller cities, as it can improve first- and last-mile connectivity (Shaheen et al., 2015; Shaheen and Chan, 2015).
Malmö is already a very bike-friendly city, but the municipality plans to encourage further bike use by imple-
menting a bikesharing system during spring 2016 (Malmö stad, 2016). The impact of this initiative on traffic and
greenhouse gases emissions seems hard to predict. According to the studies above, it can be expected to reduce
car traffic and increase public transports use. However, Malmö is very different from an American city. Car traffic
is already lower, and bike traffic is high. Therefore, bikesharing could have a higher impact due to the popularity
and ease of biking in Malmö, or a lower impact due to the fact that a lot of people are already biking and that the
margin for improvement might be narrow.
3.5 Decentralized concentration
While urban planning by itself can not be considered as an issue related to collaborative economy, it is a critical fac-
tor shaping the transportation system in a city, and as such it is an important aspect of the Collaborative Economy
scenario. Examples of strategies related to urban planning that could reduce greenhouse gases emissions include:
Transit-oriented development (TOD): public transports stops are specifically designed to be easily accessible and
promote mixed land use in their surroundings, and urban development is clustered around such stops
Local jobs provision: making living areas dynamic and attractive will decrease the need to commute to and from
them or to drive for leisure or shopping.
Adaptation to slow modes: cycling and walking can be promoted by creating attractive areas for these modes
(i.e. pedestrian areas, cycling lanes, etc).
Size and density: these two aspects play a key role in reducing car travel, by grouping main activities around
transport nodes, reducing distances, allowing commuting by cycling, etc. While it has been proven that
the amount of car trips is lower in high density areas, there are however still unclear relationships between
population density and total travel, or between population density and car use for commuting (Banister and
Hickman, 2006).
A case study for London predicts that clever planning could save by itself 0,5 to 2,4 MtCO2e per year in London in
2030, i.e. 0,05 to 0,26 tCO2e per person and per year (Hickman and Banister, 2007). On the other hand, scenarios
built by Buttazzoni et al. (2008) assumed that clever planning using dedicated tools for sustainability could reduce
emissions from car traffic by 1 to 10%. However, clever urban planning mostly acts in interaction with other
measures, for instance as an enabling factor for efficient public transportation.
The collaborative economy scenario assumes that the dominant urban form will be decentralized concentration,
i.e. the development of dense polycentric urban areas. This urban form has a lot of advantages when it comes to
environmental sustainability. First of all, housing is mostly provided through multi-family residential buildings,
as opposed to single-family houses that tend to have a higher ecological footprint. The relatively high density
in residential areas and the short distance to the city center (since there is not a single megacity concentrating all
activities but rather several centers of moderate size dispersed in the urban area) allows for a modal shift towards
walking, cycling and public transportation (Holden, 2004; Banister et al., 1997). A case study for Oslo showed that
for commuting, employees from businesses located 2km from the city center consumed about 40% less energy for
commuting than employees from businesses located 12 km from the center (car ownership and income being held
constant) (Naess and Sandberg, 1996). Decentralized concentration would allow locating businesses as close as
possible to activity centers. This urban form also works as an enabling factor for other benefits of collaborative
11economy: decentralization allows for more local decision-making and density is a necessary condition for cohous-
ing and the sharing economy. Decentralization also means a higher level of self-sufficiency in the urban centers,
and especially a local provision of jobs that helps reducing commuting trips3 .
3.6 Using ICTs in Transportation
Information and Communication Technologies (ICTs) present an opportunity to drastically reduce greenhouse
gases emissions in a number of sectors by improving efficiency and encouraging sustainable behaviors. Regard-
ing transportation, ICTs have a complex impact (besides the fact that they work as an enabling technology for
ridesharing, carsharing, etc). On one hand, they allow for improvements in fuel efficiency by enabling more effi-
cient motors, route planning for both cars and trucks and decreased time for parking (Banister and Hickman, 2006;
Banister and Stead, 2004). On the other hand, they enable telecommuting (i.e. working from home or a nearby
office) and videoconferences that decrease the need for travel, but this might lead to increased urban sprawl and
longer trips for leisure and groceries. Importantly, ICTs induce a rebound effect : the reduction in GHG emissions
expected when ICTs substitute for transport is compensated to some extent by an increase in travel for leisure (due
to time and money savings), longer trips (caused by urban sprawl and the possibility for remote workers to live
further away from their workplace) and the latent demand of people who didn’t use their car due to congestion
but might do it if congestion decreases (Buttazzoni et al., 2008). There is also a possibility that monetary savings
would be used for other environmentally damaging activities. Gossart (2014) mentions rebound effects ranging
from 0 to 50% for energy savings, with an example showing a 30% rebound effect. Moreover, the development of
ICTs entails a higher electricity demand and the use of more and more servers and computers. Therefore, their
impact will be highly dependent on the context in which they develop and the policy that are implemented.
ICTs could thus allow for a direct substitution for transport, through remote working and virtual meetings
(Banister and Hickman, 2006; Banister and Stead, 2004). Despite the possible rebound effect discussed above, this
is expected to lead to a reduction in traffic and congestion if suitable policies are also implemented. Buttazzoni et al.
(2008) assume that telecommuters save 75% of their commuting emissions, corresponding to a 25% rebound effect
assuming they never drive to their office. They also present 3 scenarios regarding the adoption of telecommuting
(adopted by 5, 10 or 30% of workers in 2030). Another possibility for the teleworkers is to work not from home,
but from a telecentre close to home. A case-specific survey about a telecentre showed reductions in commuting
distance of 13% (Banister and Hickman, 2006). In 2005, 9,4% of Swedish employees were involved in telework at
least a quarter of the time and 0,4% were involved almost all the time4 (European Foundation for the Improvement
of Living and Working Conditions, 2010). Virtual meetings can also reduce the need for environmentally damaging
business trips. Scenarios from Buttazzoni et al. (2008) assume that they can replace 5, 15 or 30% of meetings and
that 30% of air travel is due to business trips.
Moreover, ICTs enable the development of Intelligent Transport Systems (ITS), for instance setting up conges-
tion charges with automated payment, providing travelers with real time information on public transportation
routes, or even adapting bus routes to the demand. This can act as an incentive to encourage people to use pub-
lic transportation. The overall effect is heavily dependent on the policy framework, and forecasts show a very
broad range of assumption regarding car traffic lost to public transportation thanks to ITS in 2030 (3, 20 or 40%
depending on the scenario in Buttazzoni et al. (2008)). It seems hard to predict the effect on the specific case of
Malmö and in 2050, since the interactions with other factors such as public transports offers and policies are com-
plex. However, recent development in ICTs have notably improved information access for travelers. By giving
information on travel time and conditions with different modes, they usually lead to a modal shift from personal
car to public transports. Shifts ranging from 1 to 7% among users have been reported (ENGES, 2007). The same
study assumes that 5 to 10% of the population uses multimodal information, however this number is expected to
increase drastically. Other solutions to allow travelers to optimize their trips include signaling available parking
places : typically, between 5 and 10% of VKT in urban areas are made while looking for a parking place, and
experiments with parking signals showed an overall reduction of 0,3% in VKT (ENGES, 2007).
Finally, it should be noted that ICTs enables a reduction in greenhouse gases emissions from the transportation
of goods. Indeed, they enable both a more efficient management of trucks fleets for freight transport and more
efficient deliveries with e-commerce. However, from a consumption perspective, these impacts are included in the
impacts of the related goods. Since food and other goods are not considered in this study, these impacts will be
ignored, but they should be taken into account in a more overarching study.
3 Interestingly, clever urban planning and density can help freeing space. While this appears to be of concern mostly when discussing land
use, there is also a link to be made with greenhouse gases emissions. Indeed, free space can be planted with trees that can absorb carbon dioxide.
Due to its large area of forests, Sweden’s greenhouse gases emissions from land use are estimated at -34 MtCO2e per year (International Energy
Agency, 2013).
4 The European averages are 7% and 1,7% respectively
124 Built environment in the Collaborative Economy scenario
The climate impact of buildings arises from construction and renovation processes as well as from the consump-
tion of heat and electricity during the use phase. The present study will encompass both the construction and
exploitation of dwellings and buildings related to tertiary business activities, like offices and supermarkets. The
construction and exploitation of factories and other infrastructures, like roads, will be out of scope. The heat and
electricity use of a factory should be taken into account when addressing the impact of the consumption of goods.
The study will take into account all sources of electricity use, including the use of appliances. However, the climate
impact of the production and disposal of appliances will not be considered. Again, this limitation of the scope is
due to time constraints and it should be noted that sharing appliances might significantly reduce the impact from
their production and disposal.5
Collaborative economy initiatives can reduce greenhouse gases emissions from the built environment through
the sharing of space and appliances. Reducing floor area per person in dwellings (e.g. by living in cohousing
communities) and offices (e.g. by sharing and using more efficiently office space) can allow for a reduction in heat
and electricity demand, as well as a reduction in the surface of buildings that have to be built. Sharing appliances
could as well help inhabitants reduce their electricity use without compromising on their comfort. This section
will detail aspects of the Collaborative Economy scenario that could impact emissions from the built environment.
First and foremost, the concept of cohousing and its potential benefits in terms of greenhouse gases emissions will
be described. Then, the exploitation of non-residential buildings will be handled. Finally, aspects of the scenario
that are relevant for the built environment but not directly related to collaborative economy (such as the use of
ICTs in buildings and the improvement of construction processes) will be described.
4.1 Cohousing
4.1.1 Principle and origins of cohousing
Cohousing dwellings usually have four common characteristics (Williams, 2008):
• Social contact design: the dwelling is explicitly designed to encourage social contact between inhabitants.
• Common facilities: private living areas are supplemented with common areas for daily use, such as a kitchen,
a workshop or a laundry room.
• Resident involvement: the residents are involved in the operation of the dwelling, in the recruitment of other
residents and sometimes even in the construction of the building.
• Collaborative lifestyles: the residents are somewhat interdependent, they support each other and share ma-
terial and non-material assets (e.g. time, skills, etc).
Modern cohousing originated in Denmark in the 1960s and 1970s as a form of social experimentation aimed at
building better social relationships and a sense of community. In Sweden, cohousing was promoted by the fem-
inist movement as a way to share chores more equally, thus giving women who wanted to work more free time.
Contrary to the Danish initiatives, cohousing in Sweden met a mostly vertical development and took place in
medium to high rise apartment blocks (as opposed to village-like communities). Most Swedish cohousing com-
munities were developed through public investment as part of a large political project, however the tendency
is changing and the current trend sees the development of privately owned communities (Sørvoll, 2013; Lietaert,
2007; Meltzer, 2001). Cohousing has been broadly praised for enhancing social relationships, empowering citizens,
and allowing for a less consumerist, less carbon-intensive lifestyle.
4.1.2 Climate benefits of cohousing
Sharing appliances and adopting a less consumerist lifestyle One major impact of cohousing on ownership is
that it allows dwellers to reduce their consumption of goods, mainly by sharing a large number of appliances. Av-
erage reductions in ownership of 25% for devices like washing machines and freezers, up to 75% for lawnmowers,
have been observed (Williams, 2008). Moreover, cohousing communities are not based on consumerist behav-
iors and frequently promote environmentally-friendly lifestyles. Living in such a community with a high level
of social capital will often encourage environmentally-friendly behaviors due to peer pressure and a change in
personal values towards less consumerism, but also simply because it makes setting up a local exchange and trad-
ing system much easier (Williams, 2005). However, even if cohousers can easily adopt environmentally-friendly
5 The Beyond GDP Growth project adopts a different perspective. When this report was written, the electricity use from appliances was
considered together with the impact from their production and disposal, and separately from the other impacts of buildings.
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