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DOI: 10.1111/jiec.13119
RESEARCH AND ANALYSIS
Reducing the carbon footprint of ICT products through
material efficiency strategies
A life cycle analysis of smartphones
Mauro Cordella1,2Felice Alfieri1Javier Sanfelix3
1European Commission, Joint Research
Centre, Seville, Spain
2TECNALIA, Basque Research and Technology
Alliance (BRTA),Derio, Spain
3European Commission, Directorate General
for Research and Innovation, Brussels, Belgium
Correspondence
Mauro Cordella, TECNALIA, Basque Research
and TechnologyAlliance (BRTA),Astondo
Bidea, Edificio 700, 48160 Derio, Spain.
Email: mauro.cordella@tecnalia.com
Editor Managing Review: Niko Heeren.
Funding information
European Commission, Grant/Award
Number: Administrative Agree-
ment N. 070201/2015/SI2.719458 (signed
between DG ENV and DG JRC)
Abstract
With the support of a life cycle assessment model, this study estimates the carbon foot-
print (CF) of smartphones and life cycle costs (LCC) for consumers in scenarios where
different material efficiency strategies are implemented in Europe. Results show that
a major contribution to the CF of smartphones is due to extraction and processing of
materials and following manufacturing of parts: 10.7 kg CO2,eq/year, when assuming a
biennial replacement cycle. Printed wiring board, display assembly, and integrated cir-
cuits make 75% of the impacts from materials. The CF is increased by assembly (+2.7 kg
CO2,eq/year), distribution (+1.9 kg CO2,eq/year), and recharging of the device (+1.9 kg
CO2,eq/year) and decreased by the end of life recycling (−0.8 kg CO2,eq/year). However,
the CF of smartphones can dramatically increase when the energy consumed in com-
munication services is counted (+26.4 kg CO2,eq/year). LCC can vary significantly (235–
622 EUR/year). The service contract can in particular be a decisive cost factor (up to
61–85% of the LCC). It was calculated that the 1:1 displacement of new smartphones
by used devices could decrease the CF by 52–79% (excluding communication services)
and the LCC by 5–16%. An extension of the replacement cycle from 2 to 3 years could
decrease the CF by 23–30% and the LCC by 4–10%, depending on whether repair oper-
ations are required. Measures for implementing such material efficiency strategies are
presented and results can help inform decision-makers about how to reduce impacts
associated with smartphones.
KEYWORDS
climate change, industrial ecology, life cycle assessment (LCA), life cycle costs (LCC), material
efficiency, smartphone
1INTRODUCTION
Although the climate change threat due to anthropogenic emissions of greenhouse gases (GHG) was raised by the scientific community 30 years
ago (IPCC, 1992), it has been only partially reflected in effective interventions under the frameworks of the Kyoto Protocol (United Nations, 1997)
and the Paris Agreement (United Nations, 2015).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2021 European Commission, Joint Research Centre (JRC-Seville). Journal of Industrial Ecology published by Wiley PeriodicalsLLC on behalf of Yale University
448 wileyonlinelibrary.com/journal/jiec Journal of Industrial Ecology 2021;25:448–464.
CORDELLA ET AL.449
FIGURE 1 Material efficiency aspects in the life cycle of a product (Cordella et al., 2020a)
The European Commission has reinforced its commitment to tackle environmental challenges through the “European Green Deal” (European
Commission, 2019a), which includes measures on energy efficiency and circular economy performance of the information and communication tech-
nologies (ICT) sector.
The contribution of the ICT sector to the global GHG emissions was about 1.4% in 2007 and could exceed 14% in 2040. In particular, the contri-
bution from smartphones is increasing so rapidly that it could soon become greater than desktops, laptops, and displays. The main reasons for this
growth are the high market penetration of smartphones and their short replacement cycles (2 years on average) (Belkhir & Elmeligi, 2018).
In the European Union (EU), ICT products fall within the scope of Ecodesign Directive (European Union, 2009) and Energy Label Regulation
(European Union, 2017). These set out a regulatory framework for improving the energy efficiency of energy-related products (European Commis-
sion, 2016), with a current shift toward the more systematic consideration of material efficiency aspects (European Commission, 2019b). Material
efficiency could be defined as the ratio between the performance of a system and the input of materials required (Cordella et al., 2020a). As shown
in Figure 1, material efficiency can be improved along the life cycle of products by strategies that aim to minimize material consumption, waste
production, and their environmental impacts (Allwood et al., 2011; Huysman et al., 2015). In practice, this could be achieved by designing products
that are more durable and easier to repair, reuse, or recycle (European Commission, 2015).
The relevance of material efficiency strategies for mitigating climate change impacts depends on the relative impacts associated with each life
cycle stage of a product (Iraldo et al., 2017; Sanfelix et al., 2019; Tecchio et al., 2016), which can be quantified through life cycle assessment (LCA)
(ISO, 2006a;ISO,2006b).
The analysis of LCA studies can provide indications about the environmental impacts of smartphones (Cordella & Hidalgo, 2016). For exam-
ple, Andrae (2016), Ercan et al. (2016), and Clément et al. (2020) analyzed the Bill of Materials (BoM) of specific devices and their life cycle GHG
450 CORDELLA ET AL.
emissions (hereafter referred to as carbon footprint, CF). Manhart et al. (2016) analyzed resource efficiency aspects in the ICT sector, reporting the
CF of different devices. CF results are also shared by some manufacturers (e.g., Apple (2019); Huawei (2019)).
In terms of scenarios of use, Ercan et al. (2016) analyzed the effects of different use intensities of smartphones. An assessment of CF mitigation
effects of remanufacturing, reuse, and recycling is provided in Andrae (2016), while repair and refurbishment scenarios were assessed by Proske
et al. (2016). A comparative assessment of end of life (EoL) repurposing (vs. refurbishment) was carried out by Zink et al. (2014) . Furthermore,
Suckling and Lee (2015) provided a comparison of the CF associated with the EoL collection of old phones for reuse, remanufacturing, and recycling.
Economic considerations for EoL scenarios can also be found in the literature (Clift & Wright, 2000; Geyer & Doctori Blass, 2010; Gurita et al.,
2018). Furthermore, recent studies go beyondattributional LCA approaches by discussing rebound effects that could happen at macro-scale (Makov
& Font Vivanco, 2018; Makov et al., 2018;Zink&Geyer,2017; Zink et al., 2014).
This study aims to build upon the existing LCA literature for smartphones and expand it by providing a broad and critical analysis of material effi-
ciency strategies and their effect on CF and life cycle costs (LCC) for consumers. Measures are also identified to assist decision-makers in mitigating
impacts of smartphones in a cost-effective way.
2MATERIALS AND METHODS
2.1 Life cycle analysis of material efficiency strategies for smartphones
An attributional LCA was carried out for the analysis of material efficiency strategies. The aim was not to compare specific devices but to produce
general considerations for the EU. A number of scenarios were assessed that involve different technological and behavioral practices:
I. Baseline scenario (purchase, use and disposal of new smartphones);
II. Extended use scenarios with/without repair operations;
III. Scenarios involving the purchase of remanufactured or second-hand devices (both referred to also as “used devices” in this paper)1;
IV. Scenarios involving lean design concepts.
Ta b l e 1and the following sections provide an overview of analyzed scenarios and modeling assumptions.
2.1.1 Reference indicators
TheCF,expressedasCO
2,eq, was calculated based on the 100-year global warming potentials (GWP) of GHG emissions (IPCC, 2013). Although GWP
correlates to a number of environmental indicators (Askham et al., 2012; Huijbregts et al., 2006), a broader metric (covering impact categories such
as resource scarcity, biodiversity, and toxicity) would allow for a more comprehensive sustainability assessment (Moberg et al., 2014). Additional
environmental considerations are addressed qualitatively while discussing results. It is anticipated that the use of broader metric for the assessment
of smartphones (Ercan et al., 2016; Moberg et al., 2014; Proske et al., 2016) confirmed the importance played by manufacturing processes and
extraction of materials (e.g., cobalt, copper, gold, silver).
The quantitative assessment also included economic considerations about the LCC for consumers (COWI & VHK, 2011), expressed as EUR 2019
and calculated according to Equation (1). The formula was obtained by considering the present value factor equal to 1 (Boyano Larriba et al., 2017).
LCC =PP +
N
∑
1
OE +MRC +ELC,(1)
where:
∙LCC: life cycle costs for end users;
∙PP: purchase price;
∙OE: annual operating expenses for each year of use;
∙N: reference time in years;
∙MRC: maintenance and repair costs (when applicable);
∙ELC: end of life costs/benefits.
1Definitions used for lifetime extension processes (value-retention processes) vary widely (IRP, 2018). In this work, remanufacturing and refurbishment are used interchangeably to indicate the
“modification of an object that is a waste or a product to increase or restore its performance and/or functionality or to meet applicable technical standards or regulatory requirements, with the
result of making a fully functional product to be used for a purpose that is at least the one that was originally intended.” However,while remanufacturing is typically used for an industrial process to
make “as-new”products that carry a legal warranty, refurbishment requires operations that exceed repair but are less structured, industrialized and quality focused than remanufacturing (e.g., data
wiping and upgrade, repair for functionality,aesthetic touch-ups). Refurbishment is defined as “comprehensive” when happening within industrial or factory settings (IRP, 2018).
CORDELLA ET AL.451
TAB L E 1 Scenarios considered for the assessment of material efficiency aspects in the life cycle of smartphones
Scenario Key assumptions for the CF assessment Additional consideration for the LCC assessment
Baseline (BL) Replacement cycle: smartphones are replaced with a new
device (the same model) every 2 years; new devices are
bought and allocated to cover the reference lifetime (i.e.,
2.25 units for a period of 4.5 years).
EOL: the old product is kept unused at home.
Other system aspects: impact associated to data consumption
during the use phase are not considered. For sensitivity
analysis, BL+also consider:
- Impact associated to the usage of communication networks
during the use-phase;
- End-of-Life recycling with pre-treatment for battery
recovery.
Costs associated to the mobile contract service are included.
For sensitivity analysis, the following scenarios are
considered:
- BL, where an average product is considered;
- BL-HE, where a high-end product is considered;
- BL-LE, where a low-end product is considered.
Extended use (EXT) Replacement cycle: compared to BL, replacement cycle
increased to 3 (EXT1) and 4 years (EXT2), which results in
the need of less devices along the reference lifetime (i.e.,
1.5 and 1.125 units, respectively).
Other assumptions: as BL.
The following scenarios are considered:
- EXT1 and EXT2: as BL, with replacement cycle increased to 3
and 4 years, respectively;
- EXT1-HE and EXT2-HE: as BL-HE, with replacement cycle
increased to 3 and 4 years, respectively.
Battery change (BC) Replacement cycle: compared to EXT1 and EXT2, replacement
cycle is the same (i.e., 3 years for BC1 and 4 years for BC2)
with the change of the battery.
Other assumptions: as EXT1 and EXT2.
The following scenarios are considered:
- BC1a: as EXT1, with change of the battery made by the user;
- BC1b: as EXT1, with change of the battery made by a
professional repairer;
- BC2: as EXT2, with change of the battery made by the user.
Display change (DC) Replacement cycle: compared to EXT1 and EXT2, replacement
cycle is the same (i.e., 3 years for DC1 and 4 years for DC2)
with the repair (change) of the display.
Other assumptions: as EXT1 and EXT2.
The following scenarios are considered:
- DC1a: as EXT1, with repair of the display by the user;
- DC1b: as EXT1, with repair of the display by a professional
repairer;
- DC2: as EXT2, with repair of the display by the user.
Battery change +
display change
(BC-DC)
Replacement cycle: compared to EXT1 and EXT2, replacement
cycle is the same (i.e., 3 years for BC-DC1 and 4 years for
BC-DC2) with battery change and the repair (change) of
the display.
Other assumptions: as EXT1 and EXT2.
Not assessed directly.
Remanufacture (RM) Replacement cycle: remanufactured smartphones bought by
users every 2 years, to cover the reference lifetime (i.e.,
2.25 units for a period of 4.5 years).
Remanufactured device impacts: due to battery change, display
change, energy for manufacturing and transport.
EOL: the old product is kept unused at home.
As BL, with purchase price of the product calculated as the
cost of battery change and display repair.
Reuse (RU) Replacement cycle: reused smartphones bought by users
every 2 years, to cover the reference lifetime (i.e., 2.25
units for a period of 4.5 years).
Reused device impacts: due to battery change, display change,
and transport.
EOL: the old product is kept unused at home.
As BL, with purchase price of the product calculated as one
third of the original price and a margin of 40%.
Lean design (LD) Device manufacturing impacts: reduction of materials used for
housing: −10% by weight (LD1), −20% by weight (LD2),
−30% by weight (LD3).
Other assumptions:asBL.
Not assessed directly.
2.1.2 Functional unit and reference flow
Smartphones are multi-functional devices that provide different types and levels of performance. The assessment of specific devices should refer
to a functional unit (FU) that covers both quantitative and qualitative aspects (ETSI, 2019), and only products with similar characteristics should be
compared, which is beyond the scope of this study.
The FU considered in this study is the use of smartphones by a European consumer during a reference time of 4.5 years. This was chosen, based
on data from Prakash et al. (2015), as a proxy for the potential time during which smartphones can be used. This is comparable with average lifespan
452 CORDELLA ET AL.
data for mobile phones reported by Bakker et al. (2014) (4.5 years in 2005, Dutch data) and Makov et al. (2018) (4.5 and 5.6 years for two brands in
2015–2016, US data), which cover the first use cycle and possible subsequent use cycles of the product before the EoL disposal by the final owner.
The reference flow is the number of smartphone devices purchased, used, and disposed by the consumer during this period. The reference
flow is determined by the replacement cycle of smartphones (see Section 2.2.2): for a replacement cycle of Xyears, the reference flow is equal to
4.5 divided by X.
2.1.3 Assessed scenarios and system boundaries
The scenarios assessed in this study are reported in Table 1. For each scenario, the system boundaries cover the cradle-to-grave analysis of a generic,
virtual product.
As a baseline (BL), the following stages are considered:
1. Production of parts (extraction, processing and transportation of materials, manufacturing of parts);
2. Smartphone manufacturing (transportation of parts, device assembly);
3. Distribution and purchase (transportation of smartphones to points of sale);
4. Use (energy for battery recharging);
5. EoL replacement (old unused device being kept at home).
Additional scenarios integrate the following aspects: system impacts associated with communication services and EoL recycling (BL+), extended
use (EXT), battery change (BC), repair and change of the display (DC), remanufacture (RM), reuse (RU), lean design (LD). Services and material goods
necessary to support the business (e.g., research and development, marketing) are excluded from the assessment.
2.2 Carbon footprint modeling
LCA studies published from 2014 onward were screened to identify relevant sources of data for the analysis (Cordella& Hidalgo, 2016). The CF was
calculated based on such information and life cycle inventory (LCI) datasets (cut-off system models) from Ecoinvent 3.5 (Wernet et al., 2016), with
proxies used in the presence of data gaps. Assumptions made to handle existing data limitations were discussed with expertsin the sector (Cordella
et al., 2020b), and results compared with those of other studies (see Table 2). The GHG emission factors used for the assessment are provided in
Supporting Information S1.
2.2.1 Production of parts and manufacturing of the device
An average smartphone was considered to have a displaysize of 75.53 cm2and a weight of about 160 g, including 39 g for the battery and excluding
accessories and packaging (Manhart et al., 2016). Additional materials are necessary for packaging (cardboard and plastic materials), documenta-
tion, and accessories such as head set, USB cable, charger. The BoM of the virtual product is reported in Supporting Information S1.
Scenarios RM and RU, which involvethe purchase of remanufactured or second-hand devices, include a change of battery and display. The weight
of materials used for the housing and display of smartphones were proportionally decreased in the LD scenarios, without investigating how this can
affect other geometrical design characteristics (e.g., display size).
The assembly of one unit of smartphones was considered to happen in China and require 4.698 kWh (Proske et al., 2016). The same energy
consumption value (worst-case assumption) was considered for the remanufacturing of the device in industrial settings. However, when fewer
refurbishment operations are needed (e.g., clean-up and software update), the energy intensity of the remanufacturing process could be lower, for
example, 0.033 kWh per device (Skerlos et al., 2003). Section 3.1.4 shows a sensitivity analysis on this parameter, which provides an uncertainty
range for RM.
Regarding the transport of parts to the assembling factory, it was considered that housing and packaging materials are transported by lorry for
1000 km and 100 km, respectively, while other components (mostly electronics) are transported for 1000 km by flight and 100 km by lorry. Such
assumptions aimed to reflect the geographical availability of parts and materials and the ease/difficulty of procuring them.
2.2.2 Distribution and use
The following means of transport were considered for the distribution of smartphones: 8000 km by flight (distance between Beijing and Brussels)
and 600 km by heavy truck (transport distance proxy within Europe).
CORDELLA ET AL.453
TAB L E 2 Carbon footprints and key parameters from LCA studies on smartphones
Parametera) BL (this study)
Andrae
(2016)
Ercan et al.
(2016)
Proske et al.
(2016) Apple (2019)f) Huawei (2019)g)
CF, over the reference lifetime (kg CO2,eq )b) 77.2 39.2 56.7 43.9 45.0−79.0
(average: 61.2)
50.0−84.5
(average: 61.9)
CF contribution due to EOL recycling
(kg CO2,eq)c)
Not considered 0.4 −0.3 −1.0 ∼0.2 ∼0.1
Reference lifetime (years) 4.5 2 3 3 3 2
Replacement cycle (years) 2 2 3 3 3 2
Reference flow (smartphone units) 2.25 1 1 1 1 1
Weight of one device (g)d) 160 223 152 168 112−208
(average: 159)
142−232
(average: 163)
CF contribution due to the manufacturing of
one device (kg CO2,eq)e)
26.7 38.3 49.8 36.0 24.8–63.2
(average: 45.3)
41.0–70.4
(average: 51.4)
CF, adjusted to BL conditions for thisstudy
(kg CO2,eq)
- Over 4.5 years 77.2 87 123 97 66.8−117.3
(average: 90.9)
112.3−189.8
(average: 139.0)
- Normalized to 1 year of use 17.2 19.4 27.3 21.5 14.9−26.1
(average: 20.2)
25.0−42.2
(average: 30.9)
- Normalized to BL 100% 113% 159% 125% 86−152%
(average: 117%)
146−246%
(average: 180%)
Notes:
a) A full comparison of results is not possible since they depend on modeling assumptions and datasets used in different studies.
b)Communication services excluded.
c)Positive numbers indicate burdens, negative numbers indicate net savings.
d)Accessories and packaging excluded.
e)Including extraction and processing of raw materials, manufacturing of parts and assembling of the device.
f)Based on the analysis of 15 models (additional information reported in the Supporting Information).
g)Based on the analysis of 32 models (additional information reported in the Supporting Information).
The use of smartphones directly implies electricity consumption for the battery recharging cycles. The duration and frequency of recharg-
ing cycles can vary depending on technical characteristics of devices as well as user behavior (Falaki et al., 2010). An electricity consumption of
4.9 kWh/year was calculated by Proske et al. (2016) considering a battery capacity of 2420 mAh, 3.8 V of voltage, 69% of recharge efficiency, and
365 charge cycle per year. According to Andrae (2016), energy consumption is 1.538 times the battery capacity and can be 2–6 kWh/year, which is
similar to the 3–6 kWh/year estimated by Manhart et al. (2016). Ercan et al. (2016) instead quantified that the annual electricity demand of a smart-
phones can range from 2.58 kWh (1 recharge every 3 days) to 7.74 kWh (1 recharge per day). Based on the available information it was assumed
that the average electricity consumption directly associated with the use of smartphones is 4 kWh/year.
Furthermore, it was considered as BL that smartphones are used for 2 years (Belkhir & Elmeligi, 2018; Prakash et al., 2015), before being replaced
with new devices. This does not mean that the device performance is necessarily compromised after 2 years. The decision to replace a smartphone
is often based on perceived functional obsolescence when compared to new models on the market (Makov& Fitzpatrick, 2019; Watson et al., 2017).
The replacement cycle was extended in other scenarios, resulting in the need for fewer device units over the reference time of 4.5 years (see
Section 2.1.2), as indicated in Table 1. In some scenarios, this was associated with a repair operation. Replacements of battery and/or display are
analyzed since these parts are frequently impacted by loss of performance, failures, and breakages (OCU, 2018,2019). Cordella et al. (2020b)
estimated that the likelihood of replacing the display or the battery during the lifespan of a smartphone could be up to 24% and 50%, respectively.
These proxies were used to build an average EU scenario (see Section 3.3).
2.2.3 End of life
Based on the literature (Ellen MacArthur Foundation, 2012; Ercan, 2013; Manhart et al., 2016), it was estimated that about 49% of devices are
kept unused at home once they reach their EoL; 36% find a second use (either as donation or through second-hand markets); 15% are collected and
recycled/remanufactured. As BL, it was assumed that devices are kept unused at home.
454 CORDELLA ET AL.
With respect to recycling, impacts can vary depending on characteristics of product recycling process (Geyer & Doctori Blass, 2010), as well as
on assumptions made and data used. For example, Proske et al. (2016) estimated that the recycling of battery, copper, and other precious materials
from a smartphone of 168 g yields a net saving of 1140 g CO2,eq (calculated as “burdens from impacts” minus “credits from avoided impacts”).
Andrae (2016) instead reported that the recycling of a smartphone of about 220 g result in the emission of 400 g of CO2,eq. A net saving of about
2150 g of CO2,eq would result by taking the full recovery of precious metals into account (calculated based on Manhart et al. (2016)andAndrae
(2016)). Although technologically feasible, a full recovery of precious metals (e.g., magnesium, tungsten, rare earth elements, tantalum) may not be
economically viable (Manhart et al., 2016).
The typical recycling process for smartphones consists of mechanical and manual operations for the separation of materials, including plastics,
and the recovery of batteries, copper, precious metals (gold, silver, platinum), aluminum, and steel (Manhart et al., 2016).
To provide an indication of the potential benefits associated with the recycling of smartphones (Cordella et al., 2020b), the estimate from Proske
et al. (2016) was rescaled to 160 g (device weight considered in this study), and credits were assigned to the recovery of materials and energy from
the housing (display excluded). It was assumed that:
∙Recycled materials can fully displace primary materials, which is not necessarily the case in real markets (Palazzo et al., 2019), as also discussed
in Section 3.1.3.
∙Aluminum and steel can be completely recycled at the EoL, and their recycling avoids the production of new materials, while emitting 1.01 and
0.85 g of CO2,eq per gram of aluminum and steel recycled, respectively.
∙Plastics are incinerated, which avoids 0.094 Wh of electricity and produces 1.04 g of CO2,eq per gram of plastic incinerated.
As a net result, it was estimated that the recycling of a smartphone could lead to the saving of 1640 g of CO2,eq.
2.2.4 Communication services
Beside battery recharging, energy is also needed for the operation of communication services such as mobile networks, fixed access networks (e.g.,
wi-fi), and core networks (e.g., data center and transmission infrastructures). Ercan et al. (2016) estimated that the energy used for operatingmobile,
wireless, and core networks correspond to 28.7 kWh/year for a light user, 33.3 kWh/year for a representative user, and 49 kWh/year for a heavy
user. According to Andrae (2016), the electricity consumption for operating networks and data center infrastructures is 1.16 kWh/GB. Considering
an average consumption of 4 GB per month (Transform Together, 2018), the annual consumption of electricity would be 55.7 kWh. This figure, which
is close to the heavy user estimation by Ercan et al. (2016), was considered in this study,also to reflect the trend toward increased data consumption
(Transform Together, 2018).
2.3 Life cycle cost modeling
2.3.1 Purchase price for new products and operating costs
A business model in which users are owners of smartphone devices was considered, which is a common scenario in the EU. It was estimated that
the average purchase price for a new smartphone in the EU is 320 EUR. This changes to less than 130 EUR and more than 480 EUR for low- and
high-end products, respectively (Cordella et al., 2020b).
The LCC effects of lean design concepts or changes in material composition of devices were not assessed. However, almost 70% of the purchase
price of smartphones is independent of parts and materials (Benton et al., 2015).
Operating costs include electricity consumption to recharge the battery and mobile service contract. They were considered equal to 0.2113
EUR/kWh (Eurostat, 2019), and 31.80 EUR/month (DG Connect, 2018) for a service contract including 5 GB of data, 100 calls, and 140 SMS. The
service contract cost decreases to 14.11 EUR for 100 MB, 30 calls, and 100 SMS (as EU average in 2017).
Product–service systems (PSS) appears a less common scenario for smartphones (Poppelaars et al., 2018) and were not directly assessed. The
main advantage of PSS business models is the enhanced possibility for service providers of collecting and reprocessing used devices. From a con-
sumer perspective, the LCC considerations provided in Section 3.2 can address the discussion of PSS business models. When the acquisition of a
smartphone is associated to a contract subscription with a telecommunication service provider, the product purchase cost is integrated in the sub-
scription costs and the consumer has the full ownership of the smartphone. Furthermore, smartphone contracts and replacement cycles havesimilar
lengths (Prakash et al., 2015), typically up to 2 years in the EU. Subscription contracts can vary when users do not own the device: for example, a
1-year subscription can cost from 15 EUR/month for low-end devices up to 100 EUR/month for high-end devices (Grover, 2021).
CORDELLA ET AL.455
2.3.2 Cost of more durable devices
The replacement cycle could be extended when more reliable and resistant devices are used (see Section 3.4). This is generally the case for high-
end devices and specific market segments (e.g., rugged smartphones), and to a lesser extent for medium-price devices (Cordella et al., 2021). The
purchase prices of average and high-end devices were considered in the assessment of extended use scenarios (EXT), where no repair operation is
needed because of enhanced design characteristics.
2.3.3 Repair costs
Battery or display replacement was considered in the repair scenarios (BC/DC). It was assumed that the replacement costs are 20 EUR for the
battery and 87 EUR for the display, when done by the user. When the replacement involves professional repairers, costs increase to 69 EUR for
the battery and 201 EUR for the display (Cordella et al., 2020b). Design concepts integrating reparability aspects could stimulate a reduction in the
repair costs. However, the influence of such aspects on LCC was not directly assessed.
2.3.4 Purchase price for used devices
The value of electronic devices drops over time (Culligan & Menzies, 2013). Makov et al. (2018) calculated residual values of smartphone models
from two brands. Average residual values were about 50–60%of the original price after 1 year, and 40–30% after 2 years.
In this study, the purchase price of second-hand device (RU scenario) was set equal to 149 EUR, considering that the product value drops to one
third of the original value, and that a 40% margin is applied. The purchase price of remanufactured products (RM scenario) was estimated as the sum
of costs for the replacement of battery and display by professionals: 270 EUR, corresponding to a market value loss of 16% (for an “as-new” device
but “old” model). However, the purchase price of remanufactured and new products could be the same in case remanufacturing results in “as-new”
devices with upgraded performance.
2.3.5 End of life costs and benefits
Economic benefits from the re-sale of old devices were not considered in the assessed scenarios but their possible effects are discussed in Sec-
tion 3.2.
Fees associated with WEEE services at the EoL were integrated in the product purchase price (Boyano Larriba et al., 2017).
The EoL recycling can also generate profit depending on factors such as collection rates, mass flow and design of devices, recycling technique
efficiency, as well as content and market value fluctuations of materials (Geyer & Doctori Blass, 2010; Renner & Wellmer, 2020). The profitability
could improve through separate processing of smartphones (Gurita et al., 2018), although mobile phones may not be an important source of income
for recyclers (Clift and Wright, 2000; Geyer & Doctori Blass, 2010). In any case, the relatively small profits from the recovery of materials are not
expected to affect the price of smartphones (Benton et al., 2015).
3RESULTS AND DISCUSSION
3.1 Carbon footprint
3.1.1 Baseline scenario (system aspects excluded)
In the scenario BL, smartphones are replaced every 2 years in a reference time of 4.5 years, which results in manufacturing and using 2.25 device
units. Old devices are kept unused at home and the usage of communication services is not considered. The storage of old devices at home is a
worst-case scenario leading over time to the piling-up of a stock of unused devices. In reality, some devices are sold or recycled at the end of the first
useful life (see Sections 3.1.3 and 3.3).
A CF of 77.2 kg CO2,eq over 4.5 years (equivalent to 17.2 kg CO2,eq/year) was quantified. Figure 2shows the breakdown of the CF by life cycle
stage: the main contribution comes from the BoM (62%), followed by device assembly (16%), distribution (11%), and use (11%).
456 CORDELLA ET AL.
FIGURE 2 Carbon footprint results for the baseline scenario(s) (reference: 4.5 years, 2.25 smartphone units)
FIGURE 3 Carbon footprint associated to the Bill of Materials of one smartphone unit and contribution of different parts
To understand the plausibility of the CF result for BL, this was compared with other studies, as reported in Table 2. Results are also in line with
the literature in highlighting the important contribution of materials and manufacturing processes to the CF of smartphones (78% for BL). Figure 3
shows the breakdown of the CF associated to materials for different parts of smartphones. The significant contribution of integrated circuit (IC),
printed wiring board (PWB), display, and camera is notable. The importance of IC, PWB, and display is confirmed by other studies (Andrae, 2016;
Clément et al., 2020; Ercan et al., 2016; Manhart et al., 2016; Proske et al., 2016). The importance of the camera unit was also highlighted by Proske
et al., 2016. A smaller contribution is instead quantified for the battery. This is comparablewith the results from Andrae (2016), although lower than
indicated in Ercan et al. (2016) and Proske et al. (2016).
Absolute results can vary depending on design characteristics, user behavior, system aspects, as well as modeling approach, assumptions, and
data used in different studies (Manhart et al., 2016; Clément et al., 2020). In particular, GHG emissions for IC are lower than in Andrae (2016), Ercan
et al. (2016),andProskeetal.(2016). Given the lack of primary data, it was necessary to consider proxies for the BoM. The deviation observed for
IC and PWB depends on weights and LCI datasets considered for these parts (see Supporting Information S1). However, the deviation is lessened
when IC and PWB are considered together and results converge in the identification of priority parts, at least qualitatively.
As calculated in Proske et al. (2016), materials and assembly of the device are dominant contributors to the life cycle impacts of smartphones
also for other impact categories (i.e., abiotic depletion potential, human toxicity, and ecotoxicity). Environmental impacts are due to manufacturing
processes and the extraction and sourcing of materials (Moberg et al., 2014): the acquisition of gold and other metals (e.g., palladium) can contribute
CORDELLA ET AL.457
to about 10% of the CF (Andrae, 2016), while cobalt, copper, gold, and silver are important for resource scarcity, eutrophication, and human toxicity
(Ercan et al., 2016).
3.1.2 Inclusion of system aspects in the baseline scenario
Figure 2shows the effects of including EoL recycling and usage of communication services in the scenario BL+.
Recycling reduced the CF by 5% compared to BL, thanks to recovery of materials and energy. State-of-the-art practices were considered in the
modeling. The application of more advanced recycling technologies could allow a more efficient recovery of materials, with CO2,eq saving that could
be ∼30% higher. On the contrary, the EoL could even result in environmental burdens if materials are not recovered (see Section 2.2.3).
It should also be observed that the approach followed in this work is to assume the 1:1 displacement of primary material by recycled material.
The displacement of primary materials in EoL modeling has been object of extensive discussion. Recent studies integrating consequential LCA con-
siderations (Palazzo et al., 2019,2020;Zink&Geyer,2017) indicate that recycled materials do not necessarily replace primary materials of the
same type. Therefore, actual benefits associated with the recycling of smartphones could be lower than calculated in this study.
Benefits of recycling would be better depicted through indicators relating to the scarcity of materials: according to Proske et al. (2016), recycling
of smartphones can reduce impacts associated with materials and manufacturing stage by 3% for GWP, 6% for ecotoxicity, 9% for abiotic depletion
of fossil fuels, 10% for human toxicity, and 59% for abiotic depletion of elements. Furthermore, recycling is important because smartphones includes
CRM (e.g., cobalt, rare earths) and minerals from conflict-affected and high-risk areas (e.g., gold) (Manhart et al., 2016).
The inclusion of communication services (i.e., mobile networks, fixed access networks, data centers, and transmission infrastructure) resulted in
the increase of the CF from 77.3 to 192.4 kg CO2,eq (2.5 times BL). This is due to the GHG emissions associated with the energy used for operating
networks and data centers: 55.7 kWh/year, compared to 4 kWh/year for battery recharging and 4.7 kWh consumed for the manufacturing of a
device. The impact of communication services is particularly relevant in case of internet content consumption with high bit rate (e.g., for video
streaming) (Schien et al., 2013), which requires a large transmission of data. The CF would increase considerably (1.8 times BL) also when data
consumption is halved.
The energy intensity of communication services is expected to decrease in the future since mobile-access network energy efficiency has
improved by 10-30% annually in recent years (IEA, 2020). However, the energy efficiency increase associated with newer network technolo-
gies may not continue with 5G (Pihkola et al., 2018), and the simultaneous growth of data traffic could offset expected efficiency improvements
(Ericsson, 2020; Lange et al., 2020; Montevecchi et al., 2020). In fact, data traffic volumes over mobile networks are increasing, and at a more dra-
matic rate than fixed-line traffic, mainly due to the increased consumption of video-streaming services (Cisco, 2015; Ericsson, 2020; Morley et al,
2018).
The significant and “hidden” contribution of networks and data centers (Andrae, 2016; Ercan et al., 2016; Suckling & Lee, 2015) calls for a sys-
tem approach in the analysis and mitigation of the impacts associated to ICT products and related communication networks (Schien et al., 2013;
Coroama & Hilty, 2014).
3.1.3 Comparison between scenarios implementing material efficiency strategies
Ta b l e 3and Figure 4provides CF results for BL and material efficiency scenarios described in Table 1.
The CF can significantly decrease by extending the average replacement cycle of devices from 2 to 3 years (EXT1: −30%) or 4 years (EXT2:
−44%). The CF reduction was associated with fewer device units (and thus parts and materials) being allocated to the reference time of 4.5 years
(2.25, 1.5, and 1.125 units in case of replacement cycles of 2, 3, and 4 years, respectively).
In case of battery or display change in the first 2 years of use, the CF increased by 1% (BC) and 9% (DC) compared to BL, respectively. When
the change of battery comes with an extension of the replacement cycle to 3 years (BC1) or 4 years (BC2), the CF decreased by 29% and 44%,
respectively. The change of battery would not cause significant increase in the GHG emissions. In case of display change and replacement cycle
of 3 years (DC1) or 4 years (DC2), the CF decreased by 23% and 40%, respectively. The CF decreased because the impacts associated with the
manufacture of additional parts are compensated by the benefits of using smartphone devices longer.
The CF decreased to about half of BL when considering the purchase of remanufactured devices (RM), and the same energy consumption for
producing new and remanufactured devices (4.7 kWh per device). The CF decrease could be more significant if less energy were needed for the
reprocessing of devices. For example, the CF could decrease by 70% (compared to BL) considering 0.033 kWh for the refurbishment of a device
(Skerlos et al., 2003). The CF could decrease even more (byabout 80%) when refurbishment is not needed for the acquisition of second-hand devices
(RU).
Environmental savings are possible under the assumption that the purchase of used devices, inclusive of both remanufactured and second-hand
smartphones, perfectly replace the sale of new smartphone units. Actual benefits depend on “what” and “how much” is displaced (Zink et al., 2014).
458 CORDELLA ET AL.
TAB L E 3 Carbon footprint results for different scenarios implementing material efficiency strategies
Greenhouse gas emissions (kg CO2,eq)
Scenario 4.5 years 1 year Relative (%)
BL: baseline (2-year replacement cycle) 77.3 17.2 100
BL+:asBL+system aspects 192.4 42.8 249
EXT1: as BL with replacement cycle increased to 3 years 54.4 12.1 70
EXT2: as BL with replacement cycle increased to 4 years 42.9 9.5 56
BC: as BL with battery change 77.9 17.3 101
BC1: as EXT 1 with battery change 54.8 12.2 71
BC2: as EXT 2 with battery change 43.2 9.6 56
DC: as BL with display change 84.5 18.8 109
DC1: as EXT1 with display change 59.2 13.2 77
DC2: as EXT2 with display change 46.5 10.3 60
BC-DC: as BL with battery and display change 85.1 18.9 110
BC-DC1: as EXT1 with battery and display change 59.6 13.2 77
BC-DC2: as EXT2 with battery and display change 46.8 10.4 61
RM: Purchase of remanufactured device 37.0 8.2 48
RU: Reuse (purchase of second-hand device) 16.3 3.6 21
LD1: as BL with 10% lighter housing and display 75.5 16.8 98
LD2: as BL with 20% lighter housing and display 73.7 16.4 95
LD3: as BL with 30% lighter housing and display 71.9 16.0 93
Note: Absolute values calculated over 4.5 years, normalized to 1 year, and expressed in relative terms with reference to the BL.
FIGURE 4 CF and LCC results for selected scenarios implementing material efficiency strategies (underlying data used to create this figure
are provided in Supporting Information S2). Legend: BC1a (battery change by user, smartphone replacement cycle: +1 year), BC1b (battery change
by professional repairer, smartphone replacement cycle: +1 year), BC2 (battery change by user, smartphone replacement cycle: +2years),BL
(baseline), BL-HE (baseline, high-end device), BL-LE (baseline, low-end device), DC1a (display change by user, smartphone replacement cycle: +1
year), DC1b (display change by professional repairer, smartphone replacement cycle: +1 year), DC2 (display change by user, smartphone
replacement cycle: +2 years), EXT1 (extended use, smartphone replacement cycle: +1 year), EXT1-HE (extended use, high-end device, smartphone
replacement cycle: +1 year), EXT2 (extended use, smartphone replacement cycle: +2 years), EXT2-HE (extended use, high-end device, smartphone
replacement cycle: +2 years), LD3 (lean design, housing: −30% by weight), RM (remanufacture), RU (reuse). Note: System aspects excluded from
the assessment of the CF; higher service contract costs considered for the assessment of LCC
In the past, used smartphones were often sent to developing countries, where consumers owned no smartphone (Zink & Geyer, 2017). Zink et al.
(2014) estimated that the displacement of new smartphones by used devices could be equal to 0–5%. At a macro-scale, this would increase the
overall consumption of the sector (Makov & Font Vivanco, 2018;ZinkandGeyer(2017). However, the market of used smartphones has recently
become more important also in developed countries (Watson et al., 2017). Worldwide shipments of used smartphones are expected to increase
from 175.8 million units in 2018 to 332.9 million units in 2023 (IDC, 2019), against a total shipment of smartphones that is stagnant at around
CORDELLA ET AL.459
TAB L E 4 Life cycle costs for different scenarios implementing material efficiency strategies
Life cycle costs (EUR 2019)
Higher service costs Lower service costs
Scenario 4.5 years 1 year Relative (%) 4.5 years 1 year Relative (%)
BL: baseline (2-year replacement cycle, average device) 2441 542 100 1486 330 100
BL-HE: as BL, with high-end device 2801 622 115 1846 410 124
BL-LE: as BL, with low-enddevice 2014 448 82 1058 235 71
EXT1: as BL with replacement cycle increased to 3 years 2201 489 90 1246 277 84
EXT1-HE: as BL-HE with replacement cycle increased to 3
years
2441 542 100 1486 330 100
EXT2: as BL with replacement cycle increased to 4 years 2081 462 85 1126 250 76
EXT2-HE: as BL-HE with replacement cycle increased to 4
years
2261 502 93 1306 290 88
BC1a: as EXT1 with battery change made by the user 2231 496 91 1276 284 86
BC1b: as EXT1 with battery change made by a
professional repairer
2305 512 94 1349 300 91
BC2: as EXT2 with battery change made by the user 2104 468 86 1148 255 77
DC1a: as EXT1 with display change by the user 2332 518 96 1376 306 93
DC1b: as EXT1 with display change by a professional
repairer
2503 556 103 1547 344 104
DC2: as EXT2 with display change by the user 2179 484 89 1224 272 82
RM: purchase of remanufactured device 2329 518 95 1373 305 92
RU: reuse (purchase of second-hand device) 2057 457 84 1102 245 74
Note: Absolute values calculated over 4.5 years, normalized to 1 year, and expressed in relative terms with reference to the BL.
1.4 billion units (Statista, 2019). This can be partly explained by the fact that used smartphones can offer similar features of new devices at lower
price (IDC, 2019), at least until 5G networks and 5G-compatible smartphones achieve broad market penetration.
Furthermore, some authors (Makov & Font Vivanco, 2018; Makov et al., 2018;Zink&Geyer,2017) pointed out that benefits from the sale of
used devices could be partially offset by other rebound effects associated with the re-spending of economic savings. Makov and Font Vivanco
(2018) estimated that at least one third of the emission savings resulting from smartphone reuse could be lost because of rebound effects.
Material design change was assessed in terms of lean design, where quantities of materials for housing and display are decreased by 10% (LD1),
20% (LD2), and 30% (LD3). It was estimated that the CF can decrease by 2−7%. The display size was maintained unvaried. Lean design could help
counterbalance the increase of impacts associated with larger display sizes by reducing the amount of materials used. However, the actual variation
of impacts also depends on inherent characteristics of used materials and their supply chains. For example, a substantial CF reduction can result
from the use of renewable energy along the value chain and the recovery of metal scraps (Clément et al., 2020). Materials can also have an impact
on the generation of manufacturing scraps, the recycling process, and the market of recycled materials (Cordella et al., 2020b).
3.2 Life cycle costs
Different LCC scenarios are shown in Table 4and Figure 4. When higher service contract costs were considered, a LCC of 2441 EUR over 4.5 years
(542 EUR/year) was calculated for BL. LCC are 3.4 times the purchase price of single device units, with the larger contribution due to the use phase
(70.5%), mainly because of the service contract. LCC were 15% higher (+81 EUR/year) in case of high-end product (purchase price: +50%) and 17%
lower (−92 EUR/year) for the low-end product (purchase price: −40%), under the assumption that devices are replaced every 2 years.
For longer replacement cycles of 3 or 4 years (EXT1, EXT2), LCC decreased by 10% (−54 EUR/year) and 15% (−81 EUR/year), respectively. If
the increased lifetime of the product is associated with high-end products (EXT1-HE, EXT2-HE), economic savings for consumers could be more
moderate or even offset by the higher purchase price.
In case longer replacement cycles come with the battery change (BC1a, BC1b, BC2) there could be still LCC savings for consumers, although the
intervention of professional repairers can lower them (BC1b). In case the longer replacement cycles come with the repair and change of the display
460 CORDELLA ET AL.
(DC1a, DC1b, DC2), there could be less or no LCC savings for consumers, due to higher repair costs. Facilitating the replacement of the displays by
users can help reduce LCC (DC1a, DC2). However, the service contract appeared a more significant factor.
Finally, a decrease of LCC by 5% (−25 EUR/year) and 16% (−85 EUR/year) was calculated for the purchase of remanufactured (RM) or reused
devices (RU) and higher service costs.
If lower service contract costs are considered (e.g., 14.11 EUR/month instead of 31.80 EUR/month), fluctuations over BL would be more signifi-
cant in all the scenarios due to the increased importance of the product-related costs.
No recovery of residual value through re-sale of old devices was considered so far. As discussed in Sections 3.1.1 and 3.3, a number of devices are
sold or recycled when approaching their EoL, which could allow recovering their residual value. Economic benefits from the re-sale of old devices
could be considerable for consumers, for example, up to 288 EUR over 4.5 years (64 EUR/year) for BL.
All in all, results indicate that the analyzed material efficiency strategies can be economically appealing for consumers. However, as discussed in
Section 3.1.3, it cannot be excluded that LCC saving can lead to re-spending rebound effects (Zink & Geyer, 2017).
3.3 Average EU scenario
Results reported above aim to analyze different scenarios of use and disposal for smartphones. Repair frequencies and EoL disposal routes (see
Section 2.2) were used to build an average EU scenario. Over a period of 4.5 years, this resulted in an average CF of 52 kg of CO2,eq (11.5 kg of
CO2,eq/year) and an average LCC of 2294 EUR (510 EUR/year) per user, which are 33% and 6% lower than in the BL, respectively. This supports the
importance of extending the replacement cycle of devices and promoting remanufacturing, reuse, repair, and recycling activities, with even more
evident benefits that could be observed with other metrics.
3.4 Technical measures to improve the material efficiency of smartphones
Results support the importance of material efficiency strategies for smartphones. In particular,considerable CF and LCC decreases were associated
with strategies oriented to extend the lifetime of device or its parts (see Figure 4).
This can be promoted through designs aimed at improving the reliability of the device, especially for electronics and parts as batteries and dis-
plays that could cause a premature replacement, as well as its resistance to accidental drops and its protection from water and dust (Cordella et al.,
2021).
A lifetime extension can be pursued also through repair, remanufacture, and re-sale of devices. Apart from ensuring the availability of quality-
compliant parts, these strategies can be enhanced through design-for-disassembly and modular design concepts (which would also enable hard-
ware and aesthetic upgrades), as well as the integration of functions for data transfer and deletion, password reset, and factory-setting restoration
(Cordella et al., 2020b; Peiró et al., 2017).
Durability considerations go beyond the hardware and cover also software and firmware (OCU, 2017). Measures that could avoid the prema-
ture functional obsolescence of smartphones (e.g., not working applications, unavailability of security updates) include the installation of sufficient
capacity (memory) in the device, as well as the availability of update support (e.g., operating system [OS] and/or security updates) and compatible
open source OS. Furthermore, the battery management software plays a key role in preserving the battery (Cordella et al., 2021).
Complementary measures should aim at facilitating the recycling of smartphones (Kasulaitis et al., 2018), in particular by enhancing the collection
of devices and the separation of parts and materials (Geyer & Doctori Blass, 2010).
It should also be observed that potential trade-offs associated with specific design concepts should be evaluated carefully. For example, products
designed to be more resistant may see their repairability limited, and vice versa (Cordella et a., 2021), while leaner design concepts may reduce the
flow of recyclable materials and hinder EoL recycling (Geyer & Doctori Blass, 2010). Profitability of recycling is particularly affected by the price
volatility of materials, with recycling itself partly contributing to decrease the price of primary materials (Clift & Wright, 2000). This scenario may
change in future since the transition to a “green economy” may have a substantial impact on the market of materials and provide incentives for
recycling (Renner & Wellmer, 2020).
Other EoL alternatives (reuse and remanufacture) may be more profitable than smartphone recycling (Geyer & Doctori Blass, 2010). However,
as discussed in Section 3.1.3, environmental benefits achievable with the purchase of remanufactured and second-hand devices may be counterbal-
anced by possible rebound and market expansion effects.
Furthermore, consumers may find it difficult to understand the benefits associated with specific design options and that their replacement
choices can be driven by perceived (psychological) obsolescence and the desire of having new devices (Makov & Fitzpatrick, 2019; Watson et al.,
2017). To be effective, the technical measures described above should be accompanied by the sharing of information about correct use, mainte-
nance and disposal of smartphones and associated benefits (e.g., why and how preserving the battery life, applying protective accessories, collecting
unused smartphones at the EoL).
CORDELLA ET AL.461
4CONCLUSIONS
This study analyzed how different material efficiency scenarios can affect the CF of smartphones, and their LCC from the perspective of consumers.
Technical measures to implement material efficiency strategies for this device were then discussed. The following conclusions are drawn.
4.1 Material efficiency scenarios and system aspects
The analysis of a sample of smartphone devices suggested an apparent increase in display size and memory configuration for newer models, which
can partly contribute to increase the CF of smartphones. However,other circumstances that can more significantly contribute to the overall increase
of GHG emissions from smartphones are their growing market penetration and short replacement cycles.
Extending the replacement cycle of smartphones (beyond 2 years) avoids the need of new devices, parts, and materials. This appeared as a win–
win solution to reduce CF and LCC, as depicted in Figure 4. Appreciable savings could be possible also in case of battery/display replacement. An
extension of the replacement cycle could be supported through design concepts that enhance hardware reliability, repairability, and upgradability,
as well through the availability of appropriate software and firmware solutions.
Remanufactured and second-hand devices could potentially yield greater benefits. However, results are dependent on the 1:1 displacement
assumption made in the assessment, based on which devices, parts, and materials can be perfectly replaced by secondary ones, while rebound
effects could occur in real markets.
Since a large portion of smartphones is not properly collected at the EoL, enhancing their collection is vital for product and material value reten-
tion. Measures to facilitate the recycling of smartphones are complementary to those promoting an extension of the lifetime of the device and its
parts. Additional benefits could come with the adoption of leaner designs, although this strategy was not assessed thoroughly.
However, it should be highlighted that the analysis of material efficiency aspects is complicated by the presence of possible trade-offs, and that
the effective implementation of material efficiency strategies rely on behavioral aspects and comprehensive information of users.
Furthermore, although the focus of this work was on material efficiency aspects, it was interesting to observe how major contributions to CF and
LCC are beyond the physical device boundaries.
The CF of smartphones is determined to a large extent by the usage of communication services. This calls for the importance of addressing their
energy efficiency and informing users about the “hidden” impacts associated with the use of smartphones.
From a LCC perspective, the main cost for consumers is instead associated to service contracts. Apart from their economic relevance, service
contracts can also play an environmentally strategic role for smartphones since they can affect the amount of data that users exchange and how
frequently devices are replaced.
4.2 Application of results and future research perspectives
This study can be used by decision-makers as a base of information to reduce the impacts associated with the production and consumption of smart-
phones. For example, it could: (i) guide purchase decisions of consumers and public procurers; (ii) support product design and business development
activities of manufacturers and service providers; (iii) feed discussion on product testing and/or regulation, as currently happening at the EU level
(Ecosmartphone, 2020).
This study focused on virtual scenarios for the EU and a limited number of quantitative indicators. Future research could build on this study by
considering further scenarios (e.g., focusing on network systems and PSS business models) based on real case studies, and the adoption of broader
sustainability assessment metrics (Peña et al., 2021), also to understand the magnitude of possible trade-offs and rebound effects that could reduce
circular economy benefits at a macro-scale (Makov et al., 2018;Zink&Geyer,2017).
However, gathering information for smartphones was a challenging process. This was handled through a transparent description of available
information and assumptions made, as well as the critical analysis of the results, also through the consultation of ICT experts. Further effort and
collaboration between manufacturers, researchers, and policy makers is necessary to develop and make available relevant data for smartphones
(and other electronics), such as BoM and LCI datasets, failure and repair frequencies, user statistics.
ACKNOWLEDGEMENTS
This publication does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf
of the Commission is responsible for the use that might be made of this publication.
The authors would like to thank the experts involved in the development of this study for the input provided. The experts represented a
broad range of stakeholders (European Commission’s services, Member States, industry, repair and recycling sector, NGOs, consumer testing
462 CORDELLA ET AL.
organizations, and the scientific community). The authors would also like to thank the editorial board of the Journal of Industrial Ecology, the review-
ers involved in the review process, and Mr. Shane Donatello (proofreading) for helping improve the overall quality of the work.
FUNDING INFORMATION
This work has been financially supported by the European Commission through the Administrative Agreement N. 070201/2015/SI2.719458/
ENV.A.1, signed by DG ENV and DG JRC.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ORCID
Mauro Cordella https://orcid.org/0000-0001- 9121-1134
Feli ce Alfie ri https://orcid.org/0000-0003-1147-719X
Javier Sanfelix https://orcid.org/0000-0002-4465-2276
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SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section at the end of the article.
How to cite this article: Cordella M, Alfieri F, Sanfelix J. Reducing the carbon footprint of ICT products through material efficiency
strategies: A life cycle analysis of smartphones. J Ind Ecol. 2021;25:448–464. https://doi.org/10.1111/jiec.13119
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