ICT for Sustainable Cities: How ICT can support an environmentally sustainable development in cities
ICT for Sustainable Cities:
How ICT can support an environmentally sustainable
development in cities.
Authors
Anna Kramers1, Mattias Höjer1, Nina Lövehagen2, Josefin Wangel1
1 KTH Royal Institute of Technology,
Environmental Strategies Research and Centre for Sustainable Communications,
Lindstedsvägen 3, SE-100 44 Stockholm
+ 4687906000
kramers@ kth. se, hojer@ kth. se, wangel@ kth. se
2 Ericsson AB,
Torshamnsgaten 23 SE-164 83 Stockholm
+ 46107190000
nina. lovehagen@ ericsson. com
ABSTRACT
In this article we focus on the opportunities to use ICT to help
cities reach their environmental targets and specifically how ICT
can support reduction of energy use. We have developed an
analytical framework to be able to identify ICT solutions
opportunities that can support cities to decrease the energy use
that origin from the inhabitants’ consumption in order to reach
climate targets. We use a consumption perspective on energy and
allocate all energy to the final consumers that are the individuals
living in the city. The analytical framework can be used by city
administrations and ICT solution companies for identification and
mapping of ICT applications and solutions with opportunities for
sustainable development in cities.
Keywords
Smart cities, energy use, ICT
INTRODUCTION 1.
Several initiatives have highlighted how ICT can be used to reach
cities climate targets by lowering energy use and greenhouse gas
(GHG) emissions from other sectors. There are proposals such as
dematerialization and demobilization, as well as whole concepts
for smart logistics and smart cities [1, 2].
ICT4S 2013: Proceedings of the First International Conference on
Information and Communication Technologies for Sustainability, ETH
Zurich, February 14-16, 2013. Edited by Lorenz M. Hilty, Bernard
Aebischer, Göran Andersson and Wolfgang Lohmann.
DOI: http://dx. doi. org/10.3929/ethz-a-007337628
It has been argued that decoupling of material resources into
dematerialized immaterial resources such as services is a
condition for sustainable development [3]. Within the field of ICT
it is the software that represents the immaterial resources and the
services provided represents the value that could become the
paradigm for the decoupled economy of the future [4].
ICT can, according to Hilty et al.[4], be viewed as an enabling
technology to improve or be substituted for processes in other
sectors. ICT can optimize the design, production, use and end-of
life treatment of other products as well as optimization and/or
modification of demand for other products by substitution or
induction by enabling distributed forms of production.
Cities with strong environmental profiles as well as
telecommunication industries seek to understand how to best
utilize ICT as an enabler to reach climate targets. Cities need to
better understand how to direct investments in ICT to provide
greater benefits for environment and society. ICT solution
providers are interested in emphasizing how they can provide
enabling technologies, which is demanded by their customers–
the cities.
The concept “smart city” has been used during the last 20 years
and has been seen as a strategic concept to gather modern urban
production factors in a common framework [5]. The adjective
“smart” and the concept “smart city” are used to highlight the
importance and potential of ICT supporting the city to get a
competitive profile and implies a positive urban-based
technological innovation and change via ICT [5, 6].
Mitchell has defined five main principles for how ICT can
contribute to reduce environmental impacts [1]. The first
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opportunity is dematerialization , where physical things have been
replaced by virtual. The second is demobilization , where travel is
totally or partially replaced with telecommunications. The third
opportunity is mass customization , with less consumption of
scarce resources through intelligent adaptation or personalization.
The fourth opportunity, intelligent operation , involves more
intelligence in operations of for instance water, fuel and electric
power. The fifth opportunity is soft transformation , where existing
physical infrastructure are transformed to meet requirements from
the information paradigm. The principles can be applied to
product design, architecture, urban design and planning and
regional, national and global strategy [1].
In 2008, the IT and telecom sector published a report together
with the Climate Group [7] that focused emissions reductions
from four different sectors; buildings, transport, power and
industry [2]. Five major opportunities for reduction of GHG
emissions and calculated potential emissions savings from each of
these sectors were identified. The opportunities were smart-grids,
-logistics,-buildings,-motors and dematerialization. The
reduction potential of these opportunities was estimated to be 15%
of total GHG emissions in a business as usual scenario 2020,
which represents five times the sector’s own emissions [2].
This paper focuses on the opportunities to use ICT to help cities
reach their environmental targets and specifically the climate
targets. Climate targets are here understood as comprising targets
on energy use and GHG emissions [8], and we focus on how ICT
can support the reduction of energy use in this article.
The purpose of this paper is to explore to what extent ICT can
support an environmentally sustainable development in cities
using a consumption perspective on energy. This is done through
the development of a framework for identification and mapping of
ICT applications and solutions with opportunities to decrease
energy use.
DEFINITIONS OF A SMART CITY 2.
The concept “smart city” can be understood as highlighting the
importance and potential of ICT supporting the city to get a
competitive profile [5]. Could it also be seen as a concept for an
environmentally sustainable city? We are investigating different
definitions of the smart city as well as ICT solutions for
environmentally sustainable cities. Forrester [9] focus on the main
infrastructures that cities provide to its citizens meaning that it is
the combination of the “smart computing”(use of software
systems, server infrastructure, network infrastructure and client
devices) within seven critical infrastructure components and
services (city administration, education, healthcare, public safety,
real estate, transportation and utilities) that makes a city smart.
The Climate Group et al. have focused on ICT for the cities own
administration and have defined the smart city to be a city that
uses data, information and communication technologies
strategically to provide efficient services to citizens, monitor
policy outcomes, manage and optimize existing infrastructure,
employ cross-sector collaboration and enable new business
models [10].
Nam and Pardo [11] frame the smart city by different clusters that
can be divided into three dimensions: technology (infrastructures
of hardware and software), people (creativity, diversity and
education) and institutions (governance and policy). According to
Nam and Pardo [11] the technology dimension can be clustered in
six different definitions, the digital city, the intelligent city, the
ubiquitous city, the wired city, the hybrid city and the information
city. The human dimension “people” are described in four
clusters, which are the creative city, the learning city, the humane
city and the knowledge city. The institution dimension has two
different definitions the smart community and the smart growth.
Visions about the future smart city includes solutions for smart
transportation, smart environment, smart health care, smart
energy, smart education, smart safety etc.[11].
Maeng and Nedovic-Budic [12] have gathered metaphors of the
ICT based city from literature that affects the urban form and
economics of cities. They found twelve useful metaphors which
are Electronic cottage; Technoburb, Wired City, Informational
city, Intelligent city, Invisible city, Telecity, City of bits, E-topia,
Digital places, Network cities and Ubiquitous city.
The Intelligent Community Forum (ICF) has listed five successful
factors for an intelligent community, which they use to rank the
level of smartness of different cities each year [13]. The success
factors according to ICF are broadband connectivity, knowledge
workforce, digital inclusion, innovation and marketing and
advocacy.
METHOD 3.
To be able to understand to what extent a city (or another object
of study) is environmentally sustainable, there is a need to define
what is meant by a “city”, ie to define the system. In order to do
so, we here use four so called methodological considerations,
identified as crucial when setting climate targets for cities [14].
The considerations can be seen as a way to identify the most
important choices that needs to be dealt with when defining the
system boundaries. The considerations concern the temporal
scope, the object ie the spatial boundaries and activities included,
the unit typically energy or GHG-emissions and the range of the
target. The range is divided into two different perspectives, if a
consumer or producer perspective is used and to what extent a
lifecycle perspective is taken [14].
In this paper we use a consumer perspective elaborated by Höjer
et al.[15] where the city residents’ activities have been divided
into six household functions. The energy use by city residents in
Stockholm in the year 2000 was distributed over the different
household functions [15]. The system for distributing energy
between the functions was comprehensive in the way that all
energy used for Stockholmers’ consumption was allocated to the
functions. The six household functions were personal, housing,
food, care, common and support.
We used the four methodological considerations defined by
Kramers et al.[14] as a basis for setting the system boundaries of
the environmental impacts of the city. For this paper’s specific
purpose, we defined the system boundaries by stating in what way
we handle each methodological consideration.
To get an overview of ideas on how different ICT applications
and solutions can support an environmentally sustainable
development in cities we investigated the main proposals from
businesses and previous research, including definitions of the term
smart city, in literature and through seminars. Participants
included major ICT companies, city officials and a neighborhood
community as well as from researchers from academia.
The five main ICT opportunities to support cities to become
environmentally sustainable developed by Mitchell [1] and the
household functions elaborated by Höjer [15] were then used to
develop a matrix to be used as an analytical framework for
identifying new ICT application and solution opportunities.
For each household function we went through the five ICT
opportunities by Mitchell to identify already implemented and
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existing solutions, ideas or pilots that are underway, new
opportunities and lastly areas where we did not find any use of
ICT to reduce energy.
Lastly we discuss the potential reduction of energy use in cities by
the ICT opportunities we have identified, based on the findings
and complemented with own speculations regarding what could
be done in a situation that is seen as pressing for energy
reductions.
DEFINING SYSTEM BOUNDARIES FOR 4.
THE SUSTAINABLE CITY
By setting clear and transparent system boundaries it is possible to
understand which of the environmental impacts generated by the
city that is included. System boundaries can be set differently
dependent of the purpose of the measurement of environmental
impact of the city.
As previously mentioned we here try out to use the
methodological considerations in Kramers et al.[14] to define
system boundaries for an environmentally sustainable city,
exemplified by suggested targets for Stockholm.
There are four major considerations to make.
The first consideration is the temporal scope of the target, ie
what future point in time it aims at, and from which year, if there
is a reference year. For the temporal scope we use 2050, with
reference year 2000, the same years as in Höjer et al, 2011 [15].
Long-term targets are often discussed for 2050, even though not
even the 2050-targets are always final targets, as mentioned by
eg Åkerman and Höjer [16].
The second consideration is about the object of target , ie the
spatial boundaries and activities included. In this paper, we
propose to follow the same geographical limits as in Höjer [15].
Therefore, we here chose the 26 municipalities, comprising the
Greater Stockholm labour market as the geographical scope. We
include all activities in society in the target.
The third consideration is about the unit of target , typically energy
use or GHG-emissions. The unit of the target in this first attempt
to use the methodological considerations is energy and it is set to
te same as in Höjer et al, 2011 [15], ie a reduction by 60% per
capita living in the city. Thus an increased population in the city
means that more energy can be used within the geographical
borders in question.
The fourth consideration is about the range of the target . The
range is divided into two different perspectives, if a consumer or
producer perspective is used and to what extent a lifecycle
perspective is taken [14]. For the range of the environmental
impacts we are using a consumption perspective and only include
the emissions from city residents living in the city and not from
visitors. The consumption perspective we are using in this article
is further highlighted by using the household functions suggested
in Höjer et al, 2011 [15], where all energy use is allocated to one
of the six categories personal, housing, food, care, common and
support (see also Section 5 for more details regarding these
functions). These are developed so as to comprise all energy use
related to the consumption of residents living in a specific area.
Altogether, this means that if the same considerations were set up
for all geographical areas in the world, the targets would cover the
total global energy use. Table 1 illustrates the baseline situation
for Stockholm 2000 with our methodological considerations.
Table 1. Energy use per household function,
Stockholm 2000 [15]
The choices we make in this paper can be compared to how the
current policy of the City of Stockholm looks like in the area.
According to the Stockholm Environment Program 2012-2015
[17], the City uses in principle two target years: 2015 and 2050.
Both of those are absolute, thus, they have no reference year.
However, the City indirectly relates the 2020-targets to EU-targets
and national targets both stated with 1990 as reference year. The
object of target is the Municipality of Stockholm and only
heating, electricity and transport within the municipality are
included. The City does not use any target on energy in their
environmental program. Instead, they stick to CO2e/per capita.
The aim is set to 3 tons CO2/capita by 2015 aiming at a fossil free
Stockholm by 2050. The range of the City’s target is the whole
life cycle for fuels and electricity production. However, they do
not include energy use for the citizens’ consumption of goods, nor
their travel beyond the city borders, but they bring in transport
within the city limits by others than the citizens.
The main differences between the City’s considerations, and ours,
can be summarized as follows:
-The city uses CO2e instead of energy. This means that
change of fuels in power plants or for cars, can be a way
of reaching their target, but does not help with our
methodological considerations.
-The city only includes some activities, whereas we put
forward a comprehensive system of activities.
-The city limits most of the activities to things happening
within the city border, whereas we include all activities
by the city’s citizens.
Looking at Table 1 can make a very rough quantitative
measurement of the difference in scope between the two sets of
considerations. Energy use for “Personal” consists of mainly long-
distance travel, and to some extent of consumption of personal
products, and heating of holiday houses. Thus, most of this energy
use is not covered by the City’s delimitation. The same goes for
most of the energy use for food. For the other parts, the City’s
target covers most, but not all, energy use–such things as heating
of houses (Housing) and public buildings (Care and Common)
and commuting (Support). Thus, altogether it seems like the
City’s target covers about 50-60% of the energy use caused by all
Stockholmers. Therefore, such a target does not only imply that a
large portion of the energy use remains unattended but also risks
resulting in that a whole range of measures are overlooked.
ICT FOR ENVIRONMENTALLY 5.
SUSTAINABLE CITIES
To support the identification of ICT applications that can support
the reduction of energy use in cities we developed an analytical
framework, presented in Table 2. The analytical framework was
developed by combining the household functions elaborated by
Höjer et al [15], with the ICT opportunities for reducing energy
use in cities identified by Mitchell [1]. To also address solutions
aimed at persuasion or “user awareness and decision support”[18]
these were included in “intelligent operation”.
The analytical framework was used to categorize ICT solutions
that emerged from the literature studies and seminars into:
already implemented solutions (i), pilot solutions (p), and new
opportunities for ICT application (o). Furthermore also areas
Function: Personal Housing Food Care Common Support
Energy use 35% 32% 13% 11% 5% 4%
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where ICT solutions were deemed to have little or no potential to
make use of to decrease energy use were identified (na)
Table 2. Analytical framework to explore the potential of ICT
solutions to decrease energy use. The matrix shows energy use
per household function in Stockholm 2000 [15] combined with
the identified ICT opportunities. The latter divided into new
opportunities (o), pilots (p) and existing ICT solutions (i) as
well as where ICT solutions not is applicable for energy
decrease (na).
In the following sections ICT application solutions are identified,
presented and discussed for each household function.
5.1 Personal
The category Personal comprises the largest part of householders’
energy use. This household function category includes activities
such as sleep, clothing, hygiene, recreation, entertainment, certain
types of trips and holiday homes. It also includes durable and
semi-durable goods such as televisions, computers, sound systems
and discs, videotapes, books and clothes; consumables such as
tobacco, wine, soap and makeup; and services such as restaurant
visits and pedicure [15]. Many of the activities, goods and
services included in the category Personal serve as lifestyle
markers. Within this category the activity leisure travel with car
stands for the largest part of the energy use followed by holiday
air travel. Goods such as cars, mobile phones, lap tops etc. use a
lot of the energy from Personal.
To demobilize leisure travel would not be easy, since the whole
idea with the activity is to physically go somewhere else, to meet
with friends and family and take part of different activities or to
spend a weekend in Paris. It is difficult to demobilize a soccer
game competition or a visit to a countryside house. Therefore the
opportunity within Personal lies more in dematerialization , mass
customization and intelligent operation .
Many of the durable goods included in Personal are already
becoming dematerialized by the use of different ICTs.
Videotapes, records and books are now broadly available as media
files. Moreover, other goods such as phones, cameras, keys,
money, CD-players and navigation devices have been
dematerialized by way of being integrated in one and the same
device.
There are also other durable or semi durable goods in the
household that might be transferred to services performed by an
operator that have resources to dematerialize and/or mass
customize services based on demand. One example of where both
dematerialization and mass customization are used is cloud
computing. Cloud computing is a model for enabling on-demand
network access to a shared pool of resources such as servers,
storage, applications etc.[19]. Cloud computing allows for
computer-processing resources to reside in the cloud and thus
enables only having the displaying devices in the household. The
energy used for the cloud computing services would still be
allocated to the household but could, depending on the energy
performance of the cloud computing service, be lower than if all
households have their own computing devices. Another example
of a good could be both dematerialized and mass customized
through ICT is to shift the owning a private car to a subscription
to a mobility service, such as a car pool in which a range of
different cars are available to be booked and used for different
purposes. Similar solutions are public transportation access cards,
car renting services and City Bike systems. There is however a
great potential to use ICT to further integrate these systems, eg
by way of providing a common booking system, interface and
payment system. One example of such a system is the Dutch
system ‘Green Wheels’, but public transportation is not integrated
in that service [20].
Intelligent operation of personal household functions could for
example be used to make use of ICT to help travellers to find the
most environmentally friendly travel mode. Kramers [21] has
explored these opportunities by identifying new functionality for
advanced traveller information systems. Intelligent operation
could also be used to help reduce the energy use in households by
different technologies aiming at user awareness and decision
support, intelligent control of the households energy use through
eg standby management, energy management and trading, or by
integration technologies for both process and system integration
[18].
The presented ICTs could also contribute to a soft transformation
of the urban fabric through an optimized use of transportation
infrastructure, roads and parking places. The need for space in the
household can be reduced because of the need for storage of
durable and semi durable goods will decrease.
5.2 Housing
The energy use allocated to the household function Housing
consists of the residence and parts of its equipment such as
residential service, heating and lighting; furnishings such as
furniture, carpets and textiles; and domestic services such as
cleaning, maintenance and repair. The energy use for operation
and management of the housing as well as electricity for common
areas in multifamily houses is also included [15]. The by far
largest part of the energy use within the housing category comes
from heating. The energy use for heating is even greater than the
energy use for leisure travel.
Today the main focus on intelligent operation solutions in relation
to Housing is focusing on the electricity grid. As electricity is
used for more than one household function, saving effects from
ICT investments in the grid need to be allocated to all of these
functions. However, the same technologies that are used for the
smart grid and smart meter solutions could also be used to lower
or cut peaks in the use of district heating [22]. When looking at
the activities in Housing it becomes evident that ICT is not used to
any wider extent for the purpose of saving energy. Thus, the
potential for identifying further opportunities should be good.
Indoor-space can be dematerialized to some extent by sharing of
spaces but also by using a virtual world for certain activities. The
energy use for transportation in Housing is very low, meaning that
there is little potential for demobilization .
Mass customization is currently mainly used for demand
management of indoor lighting by sensors in buildings used for
business purposes. It could however also be used for management
of households lighting and for steering of heating and/or cooling
of indoor space.
Likewise as the durable and semi durable goods in the household
function Personal some goods connected to Housing such as
laundry machines, vacuum cleaners, drilling machines or trailers
ICT Opportunity
Household
functions
35 Personal i na o/p p o
32 Housing o na o o o
13 Food na na o/p o o
11 Care na i i o o
5 Common na na na na na
4 Support na i/o o/p p o
Soft trans-
formation
Energy
use%
Dematerali-
zation
Demobili-
zation
Mass
customi-
zation
Intelligent
operation
186
can also be shared between households or subscribed to from an
operator that supplies the service. The role of ICT in this would be
to facilitate booking of the service, and potentially to keep track of
the goods. The soft transformation that can take place because of
the different ICT solutions for the housing household function is a
more optimized use of heated and/or cooled space and reduced
number of heated spaces as well as reduced need for space to store
goods. Energy trading between buildings will put requirements on
how to arrange the buildings for more optimized use of the energy
system. If office buildings were located close to residential
buildings excess waste heat can be used to heat the residential
buildings [23]. Intelligent operation to optimize management and
operation of the whole building or apartment is already used in
parts of the building stock but could be much more widely used.
5.3 Food
Food includes energy use related to food items and the equipment
required for storage, purchasing and preparation of food, as well
as parts of the restaurant and café visits. Production of food is the
largest contributor to energy use in this category–what we eat is
important for the energy use [15]. The second largest portion
comes from the storage and cooking of food. The travel to buy
food is included in this category but is much smaller than the
other two activities.
Dematerialization and demobilization is not possible for this
category since food cannot be digitalized. Instead ICT can be used
to inform and build up knowledge of what we eat and how it
affects the environment. Mass customization and intelligent
operation , in combination enables ICT to tell us about the best
possible choices from environmental and availability point of
view. There are existing examples of these types of applications.
Two examples are Good Guide [24] in the US and Shopgun [25]
in Sweden. Intelligent operation could also be used to optimize
logistics of food transportation and to find the best possible place
for the trading point between delivery of goods and the consumer.
A lean production way of thinking of the food supply chain would
make it possible to both optimize the production as well as the
transportation of food. Maybe it would also in the long run
provide means for soft transformation of both to decrease the need
for heated and/or cooled spaces in groceries and less demand for
transport infrastructure.
5.4 Care
The category Care stands for an eighth of the households’ energy
use. The category is divided into three major parts: education,
social security and healthcare. Only 10% of the energy use in the
care category is related to the private households according to
Höjer et al [15]. The rest is mainly energy use in public buildings.
Being a service it may be hard to see how Care could be
dematerialized . However, Care includes numerous types of
material equipment that might be substituted by the help of ICT,
for instance the shift from analogous x-ray to digital. It is possible
to demobilize certain care services. Examples on these are remote
healthcare via sensors and mobile phones, education on distance
and security systems via surveillance equipment. An opportunity
for mass customization is to make use of ICT for more
personalized service to care takers according to their needs.
Intelligent operation can be used to manage and operate energy
use in buildings used by the care function. A soft transformation
can take place by making use of ICT to understand where to
locate care functions to best serve the households in the effort to
minimize the distance travelled.
5.5 Common
The category Common meets the basic needs of safety and
security. The energy use comes from buildings and public goods
used in the political system, military, police, judiciary, central and
local government administration and the county administration
and local government [15]. Most of the energy use in this category
is outside the individuals responsibility and therefore difficult for
the individual to reduce. Instead, the ICT-applications of
relevance here are such that can improve efficiency in use and
heating of buildings. Examples of the first are steering systems for
heating and cooling, and examples of the latter are various forms
of reforms for reduced use of space, such as distance work, e-
governance and mediated meetings.
5.6 Support
The category support includes paid work and commuting. The
energy use in the category support consists solely of energy use
from commuting to work [15], since energy use at work, is
allocated to other categories.
Travelling by car and/or motorcycle is responsible for 65% of
total energy use for commuting. The rest of the energy use for the
category support is shared between commuting via public
transportation/cycling and maintenance of the road infrastructure,
which have the same share, 17% of the total energy use.
Dematerialization of commuting through ICT is not possible and
therefore not applicable in the category support. Commuting is
different from leisure travel and can more easily be demobilized
and replaced by virtual means such as collaboration tools,
telephone or video-meetings. Commuting could be mass
customized and enabled by ICT in the same way as described for
leisure travel. Intelligent operation of commuting could likewise
as for leisure travel be used to make use of ICT to help travellers
to find the most environmentally friendly travel mode.
Since commuting is more easily demobilized, there are
possibilities for soft transformation of buildings and
infrastructure. There are possibilities to locate work hubs close to
where people are living and provide professional business
environments with all necessary equipment that can be shared
between different companies. The idea is not new but has very
rarely been implemented, but the opportunities can increase with a
population more used to communicating without being at the
same physical spot, and with a more pressing situation, caused by
eg demands on reduced energy use.
To make use of ICT for transport of both leisure travel as well as
commuting and business travel could lead to more optimized
infrastructure with less demand for energy. A demand-based
mobility where city residents subscribe to mobility can lead to less
demand for both fuel and also for spaces used for private cars. A
more intelligent operation of public transportation and
information about it by the use of advanced traveler information
systems would be feasible. There are gains for unexploited
efficiency potentials in public transportation, seamless integration
of various systems for different travel modes, demand/supply co-
ordination and electronic payment for both dematerialization and
demobilization of ticket handling [21, 26].
CONCLUDING DISCUSSION 6.
In this paper we have put together thoughts on smart cities,
methodological considerations for setting climate targets and
household functions and collected ideas on how ICT-solutions can
be used to reduce energy use in cities.
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We conclude that the concept “Smart city” is in many cases used
as a place-marketing concept to attract investments, businesses,
residents and tourists. It promotes the city’s image and
attractiveness, but it often has little to do with environmental
concerns or solutions. Therefore, caution is called for when using
the smart city concept, so that it does not lead to the promotion of
environmentally negative effects, but instead focus on the
environmentally positive solutions.
The framework we developed can be used to find opportunities,
pilots and existing solutions for the use of ICT for energy
reductions. We suggest that areas with the greatest potential for
energy reductions can be identified by looking for combinations
of household functions with large energy use and many new
opportunities for ICT solutions. These areas are mass
customization, intelligent operation and soft transformation of
transport and heating of buildings. This correlates well with the
findings from a study made by Hilty et al.[26], where it was
found that ICT could increase energy use by 37% in the worst-
case scenario but it could also decrease energy use in the best-case
scenario by 17%[26].
When searching for figures on how much energy that can be
saved there is not much information. We have used energy use as
an indicator on ICTs contribution to environmental sustainability,
however there are other proposals such as, tons of kilometers of
freight transport= tkm, passenger transport measured in persons
times kilometer= pkm, energy use, GHG emissions and waste not
recycled [26]. Other indicators that could be useful are for
example heated space, kg of decreased material resources, number
of parking spaces and road kilometers.
Hilty et al.[26] found that the main decreasing effects of ICT on
energy consumption are to make use of ICT to shift from material
goods to services, to install intelligent heating systems, to use ICT
for production process control and for supply chain management.
Personalization and demand management characterize the ICT
potential mass customization. In this field there are opportunities
to use the concept persuasive computing to enable ICT to
automate, persuade or inform individuals of different alternatives
[27].
Cities need to thoroughly go through the opportunities and
investigate how they best can support the implementation of
different ICT solutions. Meanwhile, businesses need to learn how
to best design and implement ICT-solutions that decrease energy
use.
A special problem that is displayed in this paper is the risk for
mismatch between city’s climate targets and the opportunities
given by ICT-solutions. As was shown in Section 4, Stockholm
City’s climate targets only covers 50-60 percent of total energy
use. Therefore, they may miss important ICT-solutions. Those are
solutions related mainly to personal consumption of goods and
services. They can be either focused on direct energy savings, eg
by more efficient use of leisure houses. Or they can be focused on
collecting information on energy use of various activities.
It is highly debated if and how such information can have any
effect on actual energy use. There are also sensitive ethical issues
involved in which ways public authorities may affect its citizens.
This still needs to be investigated. In any case, the lack of
knowledge regarding activities’ energy use is a democratic
problem, since it blocks informed discussions regarding what can,
should and needs to be done and whose responsibility that is. ICT
has a great potential to highlight those issues. Therefore, it can
make energy use that until now has been seen as beyond the
jurisdiction of city administrations, to being something they
should and need to deal with.
ACKNOWLEDGEMENT
The authors would particularly like to thank VINNOVA–the
Swedish Governmental Agency for Innovation Systems–for
funding the project.
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