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The Climate Effect of Digitalization in Production and Consumption in OECD Countries

Authors:
  • Institute for Ecological Economy Research, Berlin, Germany; Humboldt University Berlin; Technical University Berlin

Abstract and Figures

How does increasing digitalization affect the environment? A number of studies predict that digitalization will ultimately reduce environmental degradation but seem to overestimate the emission-reducing effects of digitalization through increases in resource efficiency, while underestimating substantial rebound effects and negative environmental impact of the construction and maintenance of complex digital infrastructures. Additionally, the environmental benefits of decreasing consumption of one-time usage goods may be outweighed by the environmental costs of the production of ICTs and the increasing use of digital technologies. This paper analyzes the relationship between degrees of countries' level of digitalization and environmental indicators by use of a panel data set of 37 economies. It is the first paper to differentiate between emissions associated with a country's production and those connected to a country's consumption, accounting for emissions related to exports and imports. The level of digitalization in production is approximated by companies' investments in digital technologies. The chosen indicator to measure consumers' proclivity to digital technologies is online shopping behaviour. We address the problem of changes in unobserved heterogeneity by using the recently developed Group Fixed Effects estimator. Results indicate that the beneficial environmental effects of digitalization on reducing climate gas emissions slightly outweigh the undesired environmental effects, both in production and consumption. Ultimately, we find that increases in digitalization have a net positive effect on the natural environment.
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The Climate Effect of Digitalization in
Production and Consumption in OECD
Countries
Thomas Kopp1and Steffen Lange2
1The University of G¨
ottingen, Platz der G¨
ottinger 7, 37073 G¨
ottingen, Germany
2Institute for Ecological Economy Research, Potsdamer Str. 105, 10785 Berlin, Germany
Abstract—How does increasing digitalization affect the
environment? A number of studies predict that digital-
ization will ultimately reduce environmental degradation
but seem to overestimate the emission-reducing effects of
digitalization through increases in resource efficiency, while
underestimating substantial rebound effects and negative
environmental impact of the construction and maintenance
of complex digital infrastructures. Additionally, the envi-
ronmental benefits of decreasing consumption of one-time
usage goods may be outweighed by the environmental costs
of the production of ICTs and the increasing use of digital
technologies.
This paper analyzes the relationship between degrees of
countries’ level of digitalization and environmental indica-
tors by use of a panel data set of 37 economies. It is the
first paper to differentiate between emissions associated
with a country’s production and those connected to a
country’s consumption, accounting for emissions related
to exports and imports. The level of digitalization in
production is approximated by companies’ investments
in digital technologies. The chosen indicator to measure
consumers’ proclivity to digital technologies is online shop-
ping behaviour. We address the problem of changes in
unobserved heterogeneity by using the recently developed
Group Fixed Effects estimator.
Results indicate that the beneficial environmental effects
of digitalization on reducing climate gas emissions slightly
outweigh the undesired environmental effects, both in pro-
duction and consumption. Ultimately, we find that increases
in digitalization have a net positive effect on the natural
environment.
Keywords: ecological footprint, digitalization, environ-
mental throughput, industry 4.0.
I. INTRODUCTION
There is a widely-held consensus among politicians and
economists that increases in digitalization will have a
net-positive effect on the environment. The German
Department of Trade and Industry claims that digital-
ization improves an economy’s ecological sustainabil-
ity by increasing resource and energy use efficiencies
(ENERGIE, 2015). According to the Association of Ger-
man Engineers, digitalization may result in increases
in resource efficiency of up to 25% (RESSOURCENEF-
FIZ IE NZ, 2017). And the ’Global e-Sustainability Initia-
tive’, an international network of IT companies, argues
that digitalization has the potential to decrease global
carbon emissions by an impressive 20% (GESI and
ACCENTURE, 2015).
Looking closer at these studies reveals that such pre-
dictions are founded upon weak empirical bases. Some
publications simply postulate the likely potential of digi-
talization to decrease environmental pressures with little
subsequent quantitative analysis (FORSCHUNG, 2014;
BUNDESREGIERUNG, 2014; WISSENSCHAFT, 2013).
Others, while based on more concrete empirical cal-
culations, nevertheless overestimate positive effects and
underestimate negative ones (GESI and ACCENTURE,
2015), for a discussion see HI LTY and BIE SE R (2017).
The scientific literature on the environmental effects of
ICT usually differentiates between effects on different
levels. Most taxonomies have in common that they
include first higher order effects (RØP KE, 2012; HORNER
et al., 2016a; POHL et al., 2019). The definition of first
order effects is similar throughout the literature. It entails
the energy, resource use and emissions associated with
the production life cycle. This entails the production, use
phase and disposal of ICT.
However, the entire environmental effects of ICT in-
volve additional mechanisms (ARVESEN et al., 2011;
HAKANSSON and FINNVEDEN, 2015). Such higher order
effects are manifold. Which effects are incorporated
into the analysis and how they are systemized varies
II Literature review
throughout the literature. Some of these effects tend to
have positive and some to have negative environmental
consequences. A recent list of effects includes substi-
tution effects, optimization effects, beneficial effects,
direct rebound effects, indirect rebound effects, induction
effects, sustainable lifestyles and practices, transforma-
tional rebound effects, induction effects and systemic
transformation and structural economic change (POHL
et al., 2019). Several authors do not further categorize
these higher order effects (B ¨
ORJESSON RIVERA et al.,
2014; POHL et al., 2019). However, there exist also
several categorizations amongst the higher order effects.
BERKHOUT and HE RTI N (2004) differentiate between
indirect and systemic effects and HORNER et al. (2016a)
between application and systemic effects. Many authors
differentiate between two levels of higher order effects
(i.e., second and third order). This categorization has
first been introduced by BERKHOUT and HERTIN (2001).
HILTY and A EBISCHER (2015) see the life cycle effects
of production, use and disposal at the first order level,
referring to them as direct effects. At the second order,
so-called enabling effects include process optimization,
media substitution, and externalization of control. Me-
dia substitution means that with increased digitalization
information is distributed through new forms of media,
for example replacing books by tablets or Kindles, or re-
placing audio playback devices with streaming services.
Externalization of control captures all processes that are
out of the hands of consumers or businesses using a
specific technology, such as the need to regularly acquire
new hardware due to software update cycles. Third
order effects are labelled systemic effects and encompass
rebound effects, emerging risks and transition towards
sustainable patterns of production and consumption.
Accurately measuring the effect of digitalization on
the environment using these different classifications of
effects is a challenging task (HE IJ UN GS et al., 2009;
FINKBEINER et al., 2014; MI LLER and KEOLEIAN,
2015). It would be possible to investigate the life-cycle
impacts, as well as the productivity increases, on a
microeconomic scale by estimating changes in environ-
mental productivity for the production of specific goods
and services, or by measuring the energy and resources
used to produce and use ICTs. However, even this is
a difficult task (HI LTY, 2015). Estimating the effect of
digitalization on labor productivity and economic growth
is even more difficult, as various other factors come
into play. Digitalization takes place within a certain
historic situation of the world economy. The fact that
economies undergo a multitude of transformations and
macroeconomic shocks parallel to digitalization makes
it even more complicated to measure each of the three
mechanisms as along with the overall effect of digi-
talization. Indeed, it is difficult to clearly separate the
environmental effects caused by digitalization from those
effects caused by other key factors, like the continuing
globalization of world trade, economic policies aimed at
climate change and environmental threats, urbanization,
and population growth, among others.
Due to these challenges in measuring the environmental
effects of digitalization directly, an alternative method
– complementary to the existing ones in the literature
– is to compare economies within the same historic
setting through a differences in differences approach.
Do economies experiencing faster digitalization increase
or decrease their environmental throughput compared to
economies with slower digitalization over the same time
period?
Nearly no studies exist on the macro level to allow for
capturing both negative and positive effects of digital-
ization on biosphere use with an explicit differentiation
between production and consumption effects. One of few
existing studies providing such evidence is SCH ULT E
et al. (2016), who assess the effects of increasing ICT use
on energy consumption in production through a cross-
country panel data analysis. Our approach adds to the
existing body of knowledge by explicitly differentiating
between the environmental effects of increasing digital-
ization caused by production and consumption.
II. LITERATURE REVIEW
The life-cycle impacts of ICT use have been subject to a
number of investigations. The production and application
of ICTs themselves require a substantial input of raw
materials and energy. However, existing studies on the
natural resource costs associated with the production and
employment of ICTs are limited.
According to a recent study by MALMODIN et al. (2018),
the use of ICTs accounts for 0,5% of global material
use. However, these numbers vary highly concerning the
specific material being considered. Indeed, ICT accounts
for 80-90% of depletion of certain materials, such as
indium, gallium and germanium. Several studies have
investigated ICT’s effects on the demand for energy.
ANDRAE (2015) estimate that the use of ICTs accounted
for 8% of global energy use in 2010 and expect this share
to rise strongly in the coming years. VAN HED DE GH EM
et al. (2014) estimate that the share of global energy
2
III Methodology
consumption due to a certain subset of ICTs (communi-
cation networks, personal computers, and data centres)
grew from 3.9% to 4.6% in 2012 alone. These numbers
certainly show that such direct effects of ICTs must be
considered when estimating whether growing levels of
digitalization serve to increase or decrease environmental
throughput over time.
Many examples of increases in environmental produc-
tivity through increased ICT use have been observed.
More efficient movement of robots can decrease their
energy use in manufacturing.1In one of the first studies
on the subject, LE NNART SO N and BENGTSS ON (2016)
find that improvements in robotic movement efficiency
are associated with a decrease in energy consumption of
up to 40%. COROA MA et al. (2012) find that replacing
in-person business meetings with virtual conferences
could decrease the carbon footprint of such meetings by
37% - 50%. Electronic invoicing can decrease energy
use compared with traditional invoicing methods and
switching to online newspapers and magazines rather
than consuming conventional print publications can have
a substantial and far-reaching positive environmental
impacts as well (MOBERG et al., 2010). Various studies
have investigated the environmental effects of online
vs. offline retailing at the micro level (HORNER et al.,
2016b; MANGIARACINA et al., 2015; LO ON et al., 2015).
Which one is more efficient depends on various factors
such as population density and the specific conditions
of delivery, implying that online shopping can actually
be more environmentally harmful than traditional retail.
The same is true for media substitution regarding online
video streaming compared to renting DVDs (SHEHAB I
et al., 2014).
In addition, rebound effects can also be observed. In
a nutshell, rebound effects refer to the phenomenon
where an increase in production efficiency leads to a
lowering of consumer prices. This, in turn, leads to
higher demand, resulting in an expansion of total pro-
duction. The corresponding increase in natural resource
use may overcompensate the ecological efficiency gains,
leading to a net-increase of biosphere use (BERKHOUT
et al., 2000). An overview of the literature on rebound
effects in regards to ICT usage is provided by GOS SA RT
(2014). If digitalization helps to increase energy and
resource productivities, the costs for energy and other
resources decrease. Economically speaking, this means
that the production function shifts downwards, a new
equilibrium of lower price and higher quantity is reached,
1Note that this refers to robot steerage, not the introduction of robots.
and consumers have more income to spend on other
goods and services. This implies that the energy and
natural resources saved through increased productivity
can be used for other productive purposes, either to
produce more of the same goods and services or to
produce additional other goods and services. Many au-
thors have found evidence for rebound effects in different
areas of the digital economy, including COROAM A et al.
(2012), MOKHTARIAN (2009), and AR NFALK et al.
(2016) for virtual meetings and video conferencing, and
B¨
OR JE SS ON RIVERA et al. (2014a) for production of
ICT hardware. Another example is in the increasing
efficiency of processing units. The so called “Koomey’s
law” states that the energy efficiency of processing units
doubles every 1.5 years (KOO ME Y et al., 2011). If the
amount of processing units would stay the same, energy
use by such units would decrease along a logarithmic
pattern with a half-life of 1.5 years, quickly approaching
very low levels. But at the same time, the amount of
computations of the processing units produced and used
grows over time. The bottom line is that the growth in the
number of processing units is higher than the rate of in-
crease in productivity. This impressive growth can at least
partly be explained by technological developments. The
newer, more efficient units allow for media substitution,
resulting in more aggregate use. For example, with the
larger and more energy-intensive processing units in the
1990s, it was simply not feasible to invent a functional
smartphone.
In summary, the existing research suggests that digi-
talization has the potential to increase environmental
productivity in many economic areas. But even if this
holds true, the production and maintenance of a digital
infrastructure (first order effects) and potential rebound
and other higher order effects could outweigh the benefits
described by the first mechanism, possibly leading to
an increase in total environmental throughput after all.
Only by an aggregated analysis that takes all mechanisms
into account can we investigate whether digitalization
increases or decreases environmental throughput.
III. METHODOLOGY
A. Estimation method
This analysis relies on the recently introduced Group
Fixed Effects (GFE) estimator. It was developed by BO N-
HOMME and MA NR ES A (2015) and has been used by
few studies so far (GRU NE WALD et al., 2017; KOPP and
DOR N, 2018). Its development has been motivated by
3
III Methodology
problems with conventional panel fixed-effect analysis,
which implicitly assumes unobserved heterogeneity be-
tween countries to stay constant over time. To tackle this
issue, in the first stage the GFE assembles all countries
into groups, according to the changes in the observables.
In the second stage the panel estimation is exercised,
supplemented by dummy variables for each of the groups
instead of individual country effects. The GFE also
solves the problem of low degrees of freedom in fixed-
effect panel estimations, which require a big number of
dummy variables (one dummy per section, e.g. country).
Since the GFE bundles all countries within a relatively
small number of groups (all literature reviewed that
employs the GFE estimator relies on less than ten groups
(BONHOMME and M ANRES A, 2015; GRUNEWALD et al.,
2017; KOP P and DORN, 2018)), the number of covariates
decreases strongly.
Four control variables are included, following
GRUN EWALD et al. (2017) and KOPP and DORN
(2018): the share of the population living in urban areas,
as well as the shares of the GDP being generated in
the agriculture, the manufacturing, and service sectors,
respectively. This leads to the following equation to
be estimated, for both production and consumption
analyses:
(1)
where stands for climate gas emissions and
for the level of digitalization.2denotes each
country’s GDP. is a cross term capturing
interaction effects between GDP and the measure of
digitalization on the outcome variable.3is the vector
of control variables and the vector of
the respective coefficients . stands
for the coefficients of the GFE-groups, of which one is
omitted from the estimation due to collinearity. is a
constant and an error term. The dependent and the
key explaining variables are described in the following
sections.
2The identification of the digitalization effect is laid out in the next
section.
3This allows for the possibility that the effect of one of the variables
depends on the state of the other, i.e. that digitalization may affect
biosphere use in richer countries systematically different than it does
in less well-off countries.
B. Identification strategy
The effects of digitalization can be decomposed into
those effects related to the production of goods and
those related to the consumption of goods. The former
includes increased technical efficiency due to the use of
ICT in production processes, while the latter refers to
changing consumption patterns, such as the switch from
conventional analogous and offline practices to digital
and potentially online ones. To allow for a differentiation
between the effects of these two areas we approach
the question from two sides, first from the production
perspective and then from the consumption perspective.
To measure production-side effects, we measure all re-
sources that are used in one country’s industrial pro-
duction and investigate how deeply resource use in that
country is affected by the country’s level of industrial
digitalization. This level of digitalization is captured by
the total yearly investments of all firms in information
and communication technology. Environmental through-
put is proxied by each country’s emissions.
The analysis on the consumption side considers all
resources used during the production of the goods con-
sumed in one certain country (even if produced abroad)
and associates them with a measure of digitalization on
the consumer side in one country. Defining an aggregate
measure to account for all aspects of digitalization on the
consumer side is complicated, as it encompasses several
dimensions. This means, generally speaking, that new or
additional products and services are consumed that were
not previously imagined to complement or substitute
existing ones. This also includes the purchasing process
itself, which is involved in every purchasing act, and
might therefore serve as an effective proxy for the
consumers’ openness to new technology and willingness
to use them. This paper therefore proxies digitalization
on the consumption side by the share of individuals who
ordered consumer articles online during the last three
months.4The environmental throughput caused by the
consumption of goods in a country is proxied by the
sub-index emissions of the ecological footprint. The
critical difference between the two measures of biosphere
use is that the former captures the emissions pro-
duced within the countries while the latter also accounts
for emissions imported and exported through trade.
4As this decision is controversial some critical reflections and ideas
on alternatives to this measure are provided in the “Outlook” section
IV-C.
4
IV Results, discussion, and outlook
C. Data
On the production side the key explanatory variable,
the digitalization in a country’s production, is the sum
of all investments made by all companies into ICT
infrastructure and software that is used for more than
one year. This variable is provided by the OECD (2017).
The dependent variable is emissions generated
by all production processes carried out in one country,
proxying the environmentally detrimental output caused
by production. This variable, as well as the controls, were
taken from the World Development Indicators, provided
by the World Bank. Descriptive statistics of all variables
entering the production side regression are provided in
Table (I).
TABLE I: Summary statistics of all variables entering
the production side regression.
mean sd min max
6.56 1.33 4.65 9.96
lnICTinvest 2.73 0.39 1.03 3.48
lnGDP 23.10 1.20 21.03 26.04
Urban 77.56 7.58 57.92 88.91
Manu 17.52 4.15 9.06 27.80
Agri 2.95 2.08 0.55 11.68
Serv 61.39 6.05 44.60 76.38
318 observations
So on the production side the following model is esti-
mated:
(2)
where subscript indicates the production side coeffi-
cients.
On the consumption side the key explanatory variable is
the share of people who used the internet to purchase
goods or services during the last three months. The
data were provided by EuroStat, the statistics service
of the European Commission (EU RO STAT, 2018). We
generated the dependant variable based on the carbon
sub-index of the ecological footprint (EF), provided by
the Ecological Footprint Network (LI N et al., 2016).
Unlike other accounts of emissions the EF captures not
only the ones produced in one country, but also accounts
for the ecological backpack carried by all goods imported
and exported. Since the database provides the EF in the
form of “global hectares”, it was converted back to
emissions, based on average sequestration capacity of
forests, which is the measure used to construct the EF in
the first place. The control variables are the same as for
the production side. Descriptive statistics of all variables
entering the consumption side regression are provided
in Table (II). The values diverge slightly from the ones
provided in table (I); because – due to different data
availabilities – the countries included in the analysis vary
slightly.
TABLE II: Summary statistics of all variables entering
the consumption side regression.
mean sd min max
lnEFP 5.77 1.36 2.43 7.88
lnOnlineShopping -1.57 0.85 -3.91 -0.33
lnGDP 22.00 1.52 18.03 24.39
Urban 72.62 12.84 49.69 97.82
Manu 13.72 4.48 4.08 24.83
Agri 2.36 1.68 0.25 9.03
Serv 62.75 7.03 42.92 78.31
177 observations
The consumption side is estimated as follows:
(3)
where subscript indicates the consumption side coef-
ficients.
The countries entering the analysis, their descriptives
and group assignments are displayed in Table (V) in
the appendix. For the production side, the panel covers
the years 1990-2009 and for the consumption side 2008-
2014. The number of observations per group is displayed
in Table (IV) in the appendix.
IV. RES ULTS,DISCUSSION,A ND O UT LOOK
A. Results
Results of both regressions are displayed in table (III).
The inclusion of the interaction terms impedes a straight-
forward interpretation by simply observing the estimated
5
IV Results, discussion, and outlook
coefficients. To facilitate an intuitive interpretation, fig-
ures (1) and (2) visualize the effect of digitalization
within the range of the GDP and digitalization levels
in the data in the form of heatmaps.
TABLE III: Regression results.
(1) (2)
VARIABLES
lnICTinvest -3.835***
(0)
lnGDP ICTinvest 0.171***
(0)
lnOnlineShopping -2.055***
(6.88e-05)
lnGDP OnlineShopping 0.0826***
(0.000356)
lnGDP 1.577*** -0.190
(0.000139) (0.706)
lnGDP -0.0195** 0.0247**
(0.0456) (0.0331)
Urban 0.0290*** 0.00841***
(0) (0.00283)
Manu 0.00705* 0.0412***
(0.0888) (4.63e-06)
Agri 0.0669*** 0.157***
(0) (4.83e-08)
Serv -0.0231*** 0.00433
(2.88e-09) (0.549)
Assignment1 -0.438*** 0.742***
(0) (3.61e-09)
Assignment2 0.448*** 0.879***
(0) (0)
Assignment3 0.734*** 0.801***
(0) (0)
Assignment4 0.253*** 0.424***
(0) (1.64e-05)
-21.12*** -4.855
(6.41e-06) (0.395)
Observations 318 177
0.989 0.957
pval in parentheses
*** p 0.01, ** p 0.05, * p 0.1
B. Discussion
Figure (1) shows that the effects of digitalization in
production on emissions depend on the level of GDP
in a given country. In the lower quartile of the GDP
distribution, increasing levels of digitalization lead to
a reduction in environmental throughput, while for the
upper quartile the opposite is true. At the sample mean
the effect is more ambiguous.
On the consumption side (Figure 2) the effects appear
clearer: digitalization leads to a reduction in environmen-
tal throughput. However, the effect grows weaker as GDP
increases.
Fig. 1: Effects of lnICT-Investments and lnGDP on CO emissions
produced within the country.
The shading indicates the size of the EF of the respective measure.
The dots represent the distribution of lnICT-investments and
of all countries in our sample.
Fig. 2: Effects of lnOnline-Shopping and lnGDP on CO emissions
(including imported emissions).
The shading indicates the size of the EF of the respective measure.
The dots represent the distribution of and
of all countries in our sample.
For both production and consumption, the effects of
digitalization on environmental throughput appear to
be relatively small compared with the effects of GDP.
Nevertheless, the effect may still be substantial. To fully
understand the marginal effects, we differentiate the
parametrized equations with respect to their respective
measurements of digitalization, ICT investments, and
online shopping behaviour.
6
Bibliography
Differentiating equation (1) with respect to yields
(4)
On the production side, equation (4) yields -0.22 at the
sample mean. At this point in the data, an increase in ICT
investments by 10% (that is, by 1.65) would decrease
by 0.3664. This amounts to 5.6% of all CO
emissions caused in production, ceteris paribus.
The computation is the same on the demand side. At
the sample mean, an increase in Online Shopping by
10% would decrease caused through a country’s
consumption by 3.4%, ceteris paribus.
C. Outlook
The global effects of digitalization are widespread and
far-reaching in scope, encompassing multiple industries
and sectors (HILTY et al., 2014; WILLIAMS, 2011;
B¨
ORJESSON RIVERA et al., 2014b). This work specif-
ically focuses upon two aspects central to digitalisation.
On the production side, we focus on the life cycle
impacts reflected through the ever-increasing use of
digital hardware, proxied by firms’ ICT investments. On
the consumption side, digitalization manifests itself in a
general proclivity and openness of consumers towards
digitalization (second and third order effects), which
can be measured effectively through online shopping
behaviour.
However, one potential caveat of this study that must be
taken into account is the potentially questionable validity
of online shopping behaviour as a proxy for consumer
openness towards digital services. To account for this
potential weakness, the next step is to expand upon the
study by testing our results for robustness by adding more
dimensions on the consumer side, in order to provide a
more complete picture of consumer-side digitalization.
This can be achieved by executing our existing analysis
with an index composed of data on more second-order
effects, including media substitution, process optimiza-
tion, and externalization of control. These can include
data on internet penetration rates, average internet speed,
share of Netflix subscriptions vs. DVD rentals, quantity
of decentralised energy systems/smart grids, sales of
Amazon Kindles, inter alia. These data would then need
to be normalized and aggregated to an index.
V. CONCLUSION
To the best of the authors’ knowledge, this paper is one of
the first to analyze the impact of increased digitalization
on environmental throughput at the macroeconomic level.
It is the first paper that differentiates between consump-
tion and production side effects. We make use of a unique
dataset linking national CO emissions to digitalization
in production and net CO levels after trading to data
on digitalization levels in consumption. To answer the
research questions, we apply the newly developed Group
Effects estimator.
The results of this study provide the first evidence of
its kind that the CO -decreasing benefit of digitalization
might outweigh its environmental costs. Indeed, there
is evidence that the net effect of digitalization on CO
emissions and environmental throughput are in fact pos-
itive. However, supplemental research is required to test
the robustness of these results against a wider range of
digitalization measures on the consumption side.
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TABLE IV: Observations per group.
N production N consumption
1 52 41
2 73 152
3 46 35
4 117 77
5 75 52
10
VI Appendix
TABLE V: Mean values of key variables.
ICTinvest assignment prod. EF OnlineShopping assignment cons. GDP
Australia 1367 18.54 4 1838 1.3e+10
Austria 289 11.32 2 391 0.36 2 5.5e+09
Belgium 399 544 0.30 2 7.1e+09
Bulgaria 197 267 0.10 5 9.0e+08
Canada 2318 16.75 2 3126 2.5e+10
Croatia 91 120 0.13 4 1.0e+09
Czechrepublic 526 728 0.17 3 3.8e+09
Denmark 232 20.51 5 306 0.57 4 4.5e+09
Estonia 82 106 0.17 1 3.6e+08
Finland 282 11.09 4 268 0.41 1 3.6e+09
France 1513 15.49 5 1963 0.39 2 3.4e+10
Greece 312 423 0.13 4 4.0e+09
Hungary 210 273 0.20 3 2.6e+09
Ireland 187 10.15 3 247 0.34 4 3.3e+09
Italy 1829 12.48 2 2359 0.11 2 3.2e+10
Japan 4973 13.27 5 6403 8.1e+10
Latvia 31 42 0.14 5 4.0e+08
Lithuania 62 79 0.11 5 6.8e+08
Luxembourg 37 49 0.51 2 7.0e+08
Malta 10 13 0.32 5 1.3e+08
Montenegro 10 13 0.04 6.8e+07
Netherlands 694 880 0.52 2 1.2e+10
Newzealand 130 20.56 4 160 1.7e+09
Norway 279 292 0.55 1 8.4e+09
Poland 1478 1948 0.20 2 8.8e+09
Portugal 201 276 0.12 3 3.5e+09
Romania 461 626 0.04 4 3.6e+09
Serbia 243 324 0.03 7.6e+08
Slovakia 168 237 0.23 2 1.7e+09
Slovenia 71 101 0.19 2 8.4e+08
Southkorea 1996 11.17 3 2641 1.2e+10
Spain 1282 13.78 4 1680 0.20 3 2.0e+10
Sweden 241 22.65 1 322 0.52 5 6.6e+09
Switzerland 163 16.66 1 229 0.62 7.2e+09
Turkey 1554 2061 0.05 4 1.6e+10
Unitedkingdom 2076 23.58 4 2669 0.63 2 3.1e+10
Unitedstates 20518 29.54 2 26680 1.8e+11
Blank spots in the table refer to non-observed values in the respective dataset.
11
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Background. There has been sustained and growing interest in characterizing the net energy impact of information and communication technology (ICT), which results from indirect effects offsetting (or amplifying) the energy directly consumed by ICT equipment. These indirect effects may be either positive or negative, and there is considerable disagreement as to the direction of this sign as well as the effect magnitude. Literature in this area ranges from studies focused on a single service (such as e-commerce versus traditional retail) to macroeconomic studies attempting to characterize the overall impact of ICT. Methods. We review the literature on the indirect energy effect of ICT found via Google Scholar, our own research, and input from other researchers in the field. The various studies are linked to an effect taxonomy, which is synthesized from several different hierarchies present in the literature. References are further grouped according to ICT service (e.g., e-commerce, telework) and summarized by scope, method, and quantitative and qualitative findings. Review results. Uncertainty persists in understanding the net energy effects of ICT. Results of indirect energy effect studies are highly sensitive to scoping decisions and assumptions made by the analyst. Uncertainty increases as the impact scope broadens, due to complex and interconnected effects. However, there is general agreement that ICT has large energy savings potential, but that the realization of this potential is highly dependent on deployment details and user behavior. Discussion. While the overall net effect of ICT is likely to remain unknown, this review suggests several guidelines for improving research quality in this area, including increased data collection, enhancing traditional modeling studies with sensitivity analysis, greater care in scoping, less confidence in characterizing aggregate impacts, more effort on understanding user behavior, and more contextual integration across the different levels of the effect taxonomy.
Conference Paper
This paper attempts to make a high-level estimate of the material footprints of the Information and Communication Technology (ICT) and Entertainment and Media (E&M) sectors for one year from four different perspectives while including the full life cycle of products and applying a top-down perspective. The four perspectives explored are: i) amount of materials used; ii) carbon footprint of the materials focusing on Raw Materials Acquisition (RMA) and End of Life Treatment (EoLT) stages; iii) material resource depletion; and iv) toxicity of materials. For the given assumptions, it is estimated that the sectors represent about 0,5% of the global annual usage of the selected materials, and for several materials (indium, gallium and germanium), ICT and E&M usage represents as much as 80-90% of the overall usage. Their use of materials represents about 0.9% of the carbon footprint for the selected materials, and about 0.1% of the total global carbon footprint, while the sectors material resource depletion potential is estimated to be between 13% and 48% of overall global depletion for the selected materials, depending on impact assessment method. Finally, the for toxicity of the selected materials, plus cement production, ICT and E&M are estimated to represent about 3.3%, based on ReCiPe. Toxicity and resource depletion results and the mass result for specific materials all indicate that the ICT and E&M sectors play a larger role than their average share of the total annual materials usage indicates, and gold and copper are identified as the most impacting materials. The applied top-down method provide only coarse estimates and further research is needed based on bottom-up methods.
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