=Paper=
{{Paper
|id=Vol-2382/ICT4S2019_paper_3
|storemode=property
|title=The Climate Effect of Digitalization in Production and Consumption in OECD Countries
|pdfUrl=https://ceur-ws.org/Vol-2382/ICT4S2019_paper_3.pdf
|volume=Vol-2382
|authors=Steffen Lange,Tom Kopp
|dblpUrl=https://dblp.org/rec/conf/ict4s/LangeK19
}}
==The Climate Effect of Digitalization in Production and Consumption in OECD Countries==
https://ceur-ws.org/Vol-2382/ICT4S2019_paper_3.pdf
The Climate Effect of Digitalization in
Production and Consumption in OECD
Countries
Thomas Kopp1 and Steffen Lange2
1
The University of Göttingen, Platz der Göttinger 7, 37073 Göttingen, Germany
2
Institute for Ecological Economy Research, Potsdamer Str. 105, 10785 Berlin, Germany
Abstract—How does increasing digitalization affect the Department of Trade and Industry claims that digital-
environment? A number of studies predict that digital- ization improves an economy’s ecological sustainabil-
ization will ultimately reduce environmental degradation ity by increasing resource and energy use efficiencies
but seem to overestimate the emission-reducing effects of
digitalization through increases in resource efficiency, while (E NERGIE, 2015). According to the Association of Ger-
underestimating substantial rebound effects and negative man Engineers, digitalization may result in increases
environmental impact of the construction and maintenance in resource efficiency of up to 25% (R ESSOURCENEF -
of complex digital infrastructures. Additionally, the envi- FIZIENZ, 2017). And the ’Global e-Sustainability Initia-
ronmental benefits of decreasing consumption of one-time tive’, an international network of IT companies, argues
usage goods may be outweighed by the environmental costs
of the production of ICTs and the increasing use of digital that digitalization has the potential to decrease global
technologies. carbon emissions by an impressive 20% (G E SI and
ACCENTURE, 2015).
This paper analyzes the relationship between degrees of
countries’ level of digitalization and environmental indica- Looking closer at these studies reveals that such pre-
tors by use of a panel data set of 37 economies. It is the dictions are founded upon weak empirical bases. Some
first paper to differentiate between emissions associated
publications simply postulate the likely potential of digi-
with a country’s production and those connected to a
country’s consumption, accounting for emissions related talization to decrease environmental pressures with little
to exports and imports. The level of digitalization in subsequent quantitative analysis (F ORSCHUNG, 2014;
production is approximated by companies’ investments B UNDESREGIERUNG, 2014; W ISSENSCHAFT, 2013).
in digital technologies. The chosen indicator to measure Others, while based on more concrete empirical cal-
consumers’ proclivity to digital technologies is online shop-
culations, nevertheless overestimate positive effects and
ping behaviour. We address the problem of changes in
unobserved heterogeneity by using the recently developed underestimate negative ones (G E SI and ACCENTURE,
Group Fixed Effects estimator. 2015), for a discussion see H ILTY and B IESER (2017).
Results indicate that the beneficial environmental effects The scientific literature on the environmental effects of
of digitalization on reducing climate gas emissions slightly ICT usually differentiates between effects on different
outweigh the undesired environmental effects, both in pro- levels. Most taxonomies have in common that they
duction and consumption. Ultimately, we find that increases
in digitalization have a net positive effect on the natural include first higher order effects (RØPKE, 2012; H ORNER
environment. et al., 2016a; P OHL et al., 2019). The definition of first
order effects is similar throughout the literature. It entails
Keywords: ecological footprint, digitalization, environ- the energy, resource use and emissions associated with
mental throughput, industry 4.0. the production life cycle. This entails the production, use
phase and disposal of ICT.
I. I NTRODUCTION However, the entire environmental effects of ICT in-
volve additional mechanisms (A RVESEN et al., 2011;
There is a widely-held consensus among politicians and H AKANSSON and F INNVEDEN, 2015). Such higher order
economists that increases in digitalization will have a effects are manifold. Which effects are incorporated
net-positive effect on the environment. The German into the analysis and how they are systemized varies
II Literature review
throughout the literature. Some of these effects tend to macroeconomic shocks parallel to digitalization makes
have positive and some to have negative environmental it even more complicated to measure each of the three
consequences. A recent list of effects includes substi- mechanisms as along with the overall effect of digi-
tution effects, optimization effects, beneficial effects, talization. Indeed, it is difficult to clearly separate the
direct rebound effects, indirect rebound effects, induction environmental effects caused by digitalization from those
effects, sustainable lifestyles and practices, transforma- effects caused by other key factors, like the continuing
tional rebound effects, induction effects and systemic globalization of world trade, economic policies aimed at
transformation and structural economic change (P OHL climate change and environmental threats, urbanization,
et al., 2019). Several authors do not further categorize and population growth, among others.
these higher order effects (B ÖRJESSON R IVERA et al.,
Due to these challenges in measuring the environmental
2014; P OHL et al., 2019). However, there exist also
effects of digitalization directly, an alternative method
several categorizations amongst the higher order effects.
– complementary to the existing ones in the literature
B ERKHOUT and H ERTIN (2004) differentiate between
– is to compare economies within the same historic
indirect and systemic effects and H ORNER et al. (2016a)
setting through a differences in differences approach.
between application and systemic effects. Many authors
Do economies experiencing faster digitalization increase
differentiate between two levels of higher order effects
or decrease their environmental throughput compared to
(i.e., second and third order). This categorization has
economies with slower digitalization over the same time
first been introduced by B ERKHOUT and H ERTIN (2001).
period?
H ILTY and A EBISCHER (2015) see the life cycle effects
of production, use and disposal at the first order level, Nearly no studies exist on the macro level to allow for
referring to them as direct effects. At the second order, capturing both negative and positive effects of digital-
so-called enabling effects include process optimization, ization on biosphere use with an explicit differentiation
media substitution, and externalization of control. Me- between production and consumption effects. One of few
dia substitution means that with increased digitalization existing studies providing such evidence is S CHULTE
information is distributed through new forms of media, et al. (2016), who assess the effects of increasing ICT use
for example replacing books by tablets or Kindles, or re- on energy consumption in production through a cross-
placing audio playback devices with streaming services. country panel data analysis. Our approach adds to the
Externalization of control captures all processes that are existing body of knowledge by explicitly differentiating
out of the hands of consumers or businesses using a between the environmental effects of increasing digital-
specific technology, such as the need to regularly acquire ization caused by production and consumption.
new hardware due to software update cycles. Third
order effects are labelled systemic effects and encompass II. L ITERATURE REVIEW
rebound effects, emerging risks and transition towards
sustainable patterns of production and consumption. The life-cycle impacts of ICT use have been subject to a
number of investigations. The production and application
Accurately measuring the effect of digitalization on
of ICTs themselves require a substantial input of raw
the environment using these different classifications of
materials and energy. However, existing studies on the
effects is a challenging task (H EIJUNGS et al., 2009;
natural resource costs associated with the production and
F INKBEINER et al., 2014; M ILLER and K EOLEIAN,
employment of ICTs are limited.
2015). It would be possible to investigate the life-cycle
impacts, as well as the productivity increases, on a According to a recent study by M ALMODIN et al. (2018),
microeconomic scale by estimating changes in environ- the use of ICTs accounts for 0,5% of global material
mental productivity for the production of specific goods use. However, these numbers vary highly concerning the
and services, or by measuring the energy and resources specific material being considered. Indeed, ICT accounts
used to produce and use ICTs. However, even this is for 80-90% of depletion of certain materials, such as
a difficult task (H ILTY, 2015). Estimating the effect of indium, gallium and germanium. Several studies have
digitalization on labor productivity and economic growth investigated ICT’s effects on the demand for energy.
is even more difficult, as various other factors come A NDRAE (2015) estimate that the use of ICTs accounted
into play. Digitalization takes place within a certain for 8% of global energy use in 2010 and expect this share
historic situation of the world economy. The fact that to rise strongly in the coming years. VAN H EDDEGHEM
economies undergo a multitude of transformations and et al. (2014) estimate that the share of global energy
2
III Methodology
consumption due to a certain subset of ICTs (communi- and consumers have more income to spend on other
cation networks, personal computers, and data centres) goods and services. This implies that the energy and
grew from 3.9% to 4.6% in 2012 alone. These numbers natural resources saved through increased productivity
certainly show that such direct effects of ICTs must be can be used for other productive purposes, either to
considered when estimating whether growing levels of produce more of the same goods and services or to
digitalization serve to increase or decrease environmental produce additional other goods and services. Many au-
throughput over time. thors have found evidence for rebound effects in different
areas of the digital economy, including C OROAMA et al.
Many examples of increases in environmental produc-
(2012), M OKHTARIAN (2009), and A RNFALK et al.
tivity through increased ICT use have been observed.
(2016) for virtual meetings and video conferencing, and
More efficient movement of robots can decrease their
B ÖRJESSON R IVERA et al. (2014a) for production of
energy use in manufacturing.1 In one of the first studies
ICT hardware. Another example is in the increasing
on the subject, L ENNARTSON and B ENGTSSON (2016)
efficiency of processing units. The so called “Koomey’s
find that improvements in robotic movement efficiency
law” states that the energy efficiency of processing units
are associated with a decrease in energy consumption of
doubles every 1.5 years (KOOMEY et al., 2011). If the
up to 40%. C OROAMA et al. (2012) find that replacing
amount of processing units would stay the same, energy
in-person business meetings with virtual conferences
use by such units would decrease along a logarithmic
could decrease the carbon footprint of such meetings by
pattern with a half-life of 1.5 years, quickly approaching
37% - 50%. Electronic invoicing can decrease energy
very low levels. But at the same time, the amount of
use compared with traditional invoicing methods and
computations of the processing units produced and used
switching to online newspapers and magazines rather
grows over time. The bottom line is that the growth in the
than consuming conventional print publications can have
number of processing units is higher than the rate of in-
a substantial and far-reaching positive environmental
crease in productivity. This impressive growth can at least
impacts as well (M OBERG et al., 2010). Various studies
partly be explained by technological developments. The
have investigated the environmental effects of online
newer, more efficient units allow for media substitution,
vs. offline retailing at the micro level (H ORNER et al.,
resulting in more aggregate use. For example, with the
2016b; M ANGIARACINA et al., 2015; L OON et al., 2015).
larger and more energy-intensive processing units in the
Which one is more efficient depends on various factors
1990s, it was simply not feasible to invent a functional
such as population density and the specific conditions
smartphone.
of delivery, implying that online shopping can actually
be more environmentally harmful than traditional retail. In summary, the existing research suggests that digi-
The same is true for media substitution regarding online talization has the potential to increase environmental
video streaming compared to renting DVDs (S HEHABI productivity in many economic areas. But even if this
et al., 2014). holds true, the production and maintenance of a digital
infrastructure (first order effects) and potential rebound
In addition, rebound effects can also be observed. In
and other higher order effects could outweigh the benefits
a nutshell, rebound effects refer to the phenomenon
described by the first mechanism, possibly leading to
where an increase in production efficiency leads to a
an increase in total environmental throughput after all.
lowering of consumer prices. This, in turn, leads to
Only by an aggregated analysis that takes all mechanisms
higher demand, resulting in an expansion of total pro-
into account can we investigate whether digitalization
duction. The corresponding increase in natural resource
increases or decreases environmental throughput.
use may overcompensate the ecological efficiency gains,
leading to a net-increase of biosphere use (B ERKHOUT
et al., 2000). An overview of the literature on rebound III. M ETHODOLOGY
effects in regards to ICT usage is provided by G OSSART
(2014). If digitalization helps to increase energy and A. Estimation method
resource productivities, the costs for energy and other
resources decrease. Economically speaking, this means This analysis relies on the recently introduced Group
that the production function shifts downwards, a new Fixed Effects (GFE) estimator. It was developed by B ON -
HOMME and M ANRESA (2015) and has been used by
equilibrium of lower price and higher quantity is reached,
few studies so far (G RUNEWALD et al., 2017; KOPP and
1 Note that this refers to robot steerage, not the introduction of robots. D ORN, 2018). Its development has been motivated by
3
III Methodology
problems with conventional panel fixed-effect analysis, B. Identification strategy
which implicitly assumes unobserved heterogeneity be-
tween countries to stay constant over time. To tackle this The effects of digitalization can be decomposed into
issue, in the first stage the GFE assembles all countries those effects related to the production of goods and
into groups, according to the changes in the observables. those related to the consumption of goods. The former
In the second stage the panel estimation is exercised, includes increased technical efficiency due to the use of
supplemented by dummy variables for each of the groups ICT in production processes, while the latter refers to
instead of individual country effects. The GFE also changing consumption patterns, such as the switch from
solves the problem of low degrees of freedom in fixed- conventional analogous and offline practices to digital
effect panel estimations, which require a big number of and potentially online ones. To allow for a differentiation
dummy variables (one dummy per section, e.g. country). between the effects of these two areas we approach
Since the GFE bundles all countries within a relatively the question from two sides, first from the production
small number of groups (all literature reviewed that perspective and then from the consumption perspective.
employs the GFE estimator relies on less than ten groups
(B ONHOMME and M ANRESA, 2015; G RUNEWALD et al., To measure production-side effects, we measure all re-
2017; KOPP and D ORN, 2018)), the number of covariates sources that are used in one country’s industrial pro-
decreases strongly. duction and investigate how deeply resource use in that
country is affected by the country’s level of industrial
Four control variables are included, following
digitalization. This level of digitalization is captured by
G RUNEWALD et al. (2017) and KOPP and D ORN
the total yearly investments of all firms in information
(2018): the share of the population living in urban areas,
and communication technology. Environmental through-
as well as the shares of the GDP being generated in
put is proxied by each country’s CO2 emissions.
the agriculture, the manufacturing, and service sectors,
respectively. This leads to the following equation to The analysis on the consumption side considers all
be estimated, for both production and consumption resources used during the production of the goods con-
analyses: sumed in one certain country (even if produced abroad)
and associates them with a measure of digitalization on
ln CO2 = ln Digi + ln GDP the consumer side in one country. Defining an aggregate
X
+ ln GDP Digi + X measure to account for all aspects of digitalization on the
4 (1) consumer side is complicated, as it encompasses several
+ GF E + c + "; i
dimensions. This means, generally speaking, that new or
i=1 additional products and services are consumed that were
not previously imagined to complement or substitute
where CO2 stands for climate gas emissions and Digi existing ones. This also includes the purchasing process
for the level of digitalization.2 GDP denotes each itself, which is involved in every purchasing act, and
country’s GDP. GDP Digi is a cross term capturing might therefore serve as an effective proxy for the
interaction effects between GDP and the measure of consumers’ openness to new technology and willingness
digitalization on the outcome variable.3 X is the vector to use them. This paper therefore proxies digitalization
of control variables (x1 ; x2 ; x3 ; x4 ) and the vector of on the consumption side by the share of individuals who
the respective coefficients (1 ; 2 ; 3 ; 4 ). GF Ei stands ordered consumer articles online during the last three
for the coefficients of the GFE-groups, of which one is months.4 The environmental throughput caused by the
omitted from the estimation due to collinearity. c is a consumption of goods in a country is proxied by the
constant and " an error term. The dependent and the sub-index CO2 emissions of the ecological footprint. The
key explaining variables are described in the following critical difference between the two measures of biosphere
sections. use is that the former captures the CO2 emissions pro-
duced within the countries while the latter also accounts
2 The identification of the digitalization effect is laid out in the next
for emissions imported and exported through trade.
section.
3 This 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 4 As this decision is controversial some critical reflections and ideas
biosphere use in richer countries systematically different than it does on alternatives to this measure are provided in the “Outlook” section
in less well-off countries. IV-C.
4
IV Results, discussion, and outlook
C. Data and exported. Since the database provides the EF in the
form of “global hectares”, it was converted back to CO2
On the production side the key explanatory variable, emissions, based on average sequestration capacity of
the digitalization in a country’s production, is the sum forests, which is the measure used to construct the EF in
of all investments made by all companies into ICT the first place. The control variables are the same as for
infrastructure and software that is used for more than the production side. Descriptive statistics of all variables
one year. This variable is provided by the OECD (2017). entering the consumption side regression are provided
The dependent variable is CO2 emissions generated in Table (II). The values diverge slightly from the ones
by all production processes carried out in one country, provided in table (I); because – due to different data
proxying the environmentally detrimental output caused availabilities – the countries included in the analysis vary
by production. This variable, as well as the controls, were slightly.
taken from the World Development Indicators, provided
by the World Bank. Descriptive statistics of all variables TABLE II: Summary statistics of all variables entering
entering the production side regression are provided in the consumption side regression.
Table (I).
mean sd min max
TABLE I: Summary statistics of all variables entering lnEFP 5.77 1.36 2.43 7.88
lnOnlineShopping -1.57 0.85 -3.91 -0.33
the production side regression. lnGDP 22.00 1.52 18.03 24.39
Urban 72.62 12.84 49.69 97.82
mean sd min max Manu 13.72 4.48 4.08 24.83
ln CO2 6.56 1.33 4.65 9.96 Agri 2.36 1.68 0.25 9.03
lnICTinvest 2.73 0.39 1.03 3.48 Serv 62.75 7.03 42.92 78.31
lnGDP 23.10 1.20 21.03 26.04
Urban 77.56 7.58 57.92 88.91
177 observations
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 The consumption side is estimated as follows:
318 observations
ln CO2C = C ln OnlineShopping + C ln GDP
So on the production side the following model is esti-
+ C ln GDP OnlineShopping
mated:
X GF E + c + " ;
+ C XC (3)
4
ln CO2P = P ln ICTInvest + P ln GDP + i C C
i=1
X GF E + c + " ;
+ P ln GDP ICTInvest + P XP
4 (2)
where subscript C indicates the consumption side coef-
+ i P P ficients.
i=1
The countries entering the analysis, their descriptives
where subscript P indicates the production side coeffi- and group assignments are displayed in Table (V) in
cients. the appendix. For the production side, the panel covers
On the consumption side the key explanatory variable is the years 1990-2009 and for the consumption side 2008-
the share of people who used the internet to purchase 2014. The number of observations per group is displayed
goods or services during the last three months. The in Table (IV) in the appendix.
data were provided by EuroStat, the statistics service
of the European Commission (E URO S TAT, 2018). We IV. R ESULTS , DISCUSSION , AND OUTLOOK
generated the dependant variable based on the carbon
sub-index of the ecological footprint (EF), provided by A. Results
the Ecological Footprint Network (L IN et al., 2016).
Unlike other accounts of emissions the EF captures not Results of both regressions are displayed in table (III).
only the ones produced in one country, but also accounts The inclusion of the interaction terms impedes a straight-
for the ecological backpack carried by all goods imported forward interpretation by simply observing the estimated
5
IV Results, discussion, and outlook
coefficients. To facilitate an intuitive interpretation, fig- Fig. 1: Effects of lnICT-Investments and lnGDP on CO2 emissions
produced within the country.
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 ln CO2 ln EF P
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)
lnGDP2 -0.0195** 0.0247**
(0.0456) (0.0331) The shading indicates the size of the EF of the respective measure.
Urban 0.0290*** 0.00841*** The dots represent the distribution of lnICT-investments and ln GDP
(0) (0.00283) of all countries in our sample.
Manu 0.00705* 0.0412***
(0.0888) (4.63e-06)
Agri 0.0669*** 0.157***
Fig. 2: Effects of lnOnline-Shopping and lnGDP on CO2 emissions
(0) (4.83e-08)
(including imported emissions).
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
R2 0.989 0.957
pval in parentheses
*** p<0.01, ** p<0.05, * p<0.1
B. Discussion
The shading indicates the size of the EF of the respective measure.
The dots represent the distribution of ln Online Shopping and
Figure (1) shows that the effects of digitalization in ln GDP of all countries in our sample.
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 For both production and consumption, the effects of
upper quartile the opposite is true. At the sample mean digitalization on environmental throughput appear to
the effect is more ambiguous. be relatively small compared with the effects of GDP.
Nevertheless, the effect may still be substantial. To fully
On the consumption side (Figure 2) the effects appear understand the marginal effects, we differentiate the
clearer: digitalization leads to a reduction in environmen- parametrized equations with respect to their respective
tal throughput. However, the effect grows weaker as GDP measurements of digitalization, ICT investments, and
increases. online shopping behaviour.
6
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Differentiating equation (1) with respect to Digi yields V. C ONCLUSION
To the best of the authors’ knowledge, this paper is one of
@ ln CO2 1 the first to analyze the impact of increased digitalization
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digitalization measures on the consumption side.
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VI. A PPENDIX
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.
CO2 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