# Digital Transition and the Environment: Towards an Approach to Assess Environmental Impacts of Digital Services Using Life Cycle Assessment

Mohammed Ziani, Nicolas Maranzana, Adélaïde Feraille, Myriam Saade

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Mohammed Ziani, Nicolas Maranzana, Adélaïde Feraille, Myriam Saade. Digital Transition and the Environment: Towards an Approach to Assess Environmental Impacts of Digital Services Using Life Cycle Assessment. 19th IFIP International Conference on Product Lifecycle Management (PLM), Jul 2022, Grenoble, France. pp.577-586, 10.1007/978-3-031-25182-5_56. hal-05420953

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# Digital Transition and the Environment:
## Towards an Approach to Assess Environmental
## Impacts of Digital Services Using Life Cycle
## Assessment

Mohammed Ziani<fnref n="1" />,<fnref n="2" />,<fnref n="3" />✉, Nicolas Maranzana<fnref n="3" />, Adélaïde Feraille<fnref n="2" />,
and Myriam Saade<fnref n="2" />

**Abstract.** The adoption of digital technologies in the construction sector is seen by many as a catalyst for the ecological transition, through the optimization of resource and energy use. However, the environmental impact of the digital infrastructure supporting projects is not well known. In the example of life cycle management of linear infrastructures, the increased use of IoT (Internet of Things) sensors for maintenance/operation has been followed by a diversification of technologies for Building Information Modeling (BIM), Building Life cycle Management (BLM), equipment and exchange formats. The variety of computer terminals, hardware storage and communication equipment are extended by that of software tools and free or proprietary solutions. The main objective of this paper is to establish a methodology based on environmental Life Cycle Assessment (LCA) to quantify the environmental impacts of a digital service in Infrastructure Life-cycle Management (ILM). Relying on a case study of a bridge monitoring system implemented for the A71 near Orléans, France, we propose to model the assessed system as four interacting sub-systems: data acquisition, network and communication, consultation, and data storage. Based on such modelling and related data, a preliminary assessment highlights the contribution of data storage in the environmental impacts of the overall system.

**Keywords:** Life Cycle Assessment (LCA) · Ecological transition · Internet of Things (IoT) · Infrastructure Lifecycle Management (ILM)

# 1 Introduction

The Architecture, Engineering, and Construction (AEC) services sector has experienced a spectacular advance in Information and Communication Technologies (ICT), among other sectors [1]. Many examples in France demonstrate the importance of technological

<footnote n="1">¹ Université Gustave Eiffel, MAST-EMGCU, Cité Descartes, Boulevard Newton,
77447 Marne-La-Vallée, France</footnote>

<footnote>m_ziani@gmx.fr</footnote>

<footnote n="2">² Ecole des Ponts ParisTech, MSA NAVIER, 77455 Marne La Vallée, France</footnote>

<footnote n="3">³ Arts et Métiers Institute of Technology, LCPI, HESAM Université, 75013 Paris, France</footnote>
and digital change in the management of engineering structures, the specific case of bridges being for example studied in the EMGCU laboratory, with the implementation of demonstrators on real bridges (mixed or prestressed structures) with an integrated measurement system using Internet of Things (IoT).

The current ecological crisis is however pushing infrastructure researchers and stakeholders to assess the environmental impact of digital technologies [2]. Promoted as a mean to increase the energy efficiency of buildings, decrease construction time, optimize the use of natural resources [3], or improve the recovery of building materials [4], digital services supporting civil engineering projects consume resources and energy [5]. Their environmental footprint is currently not well known, and methodologies to assess such footprint poorly developed. This article aims to propose an approach for the evaluation of the environmental impact of digital services, based on environmental Life Cycle Assessment (LCA). After presenting the state of the art (Sect. 2), the methodological approach is presented (Sect. 3) and applied to a case study (Sect. 4), a bridge monitoring system implemented for the A71 near Orléans, France. Results are presented in Sect. 5, followed by a conclusion (Sect. 6).

# 2 Research Background

## 2.1 Literature Review

The bibliographic review was based on French and European scientific publications and grey literature, including reports from consulting firms, professional federations, think tanks and public authorities (Table 1).

<table><caption>Table 1. Public data</caption><thead><tr><td>Knowledge base</td><td>Government digital services (GDS)</td><td>LCA data</td><td>Manufacturer data</td></tr></thead><tbody><tr><td>ADEME</td><td>GreenIT.fr</td><td>Base Carbone</td><td>Dell</td></tr><tr><td>ARCEP</td><td>Bureau Veritas</td><td>Négaoctet</td><td>HP</td></tr><tr><td>CNum</td><td>fing.org, EcoInfo</td><td>Ecoinvent v3.6</td><td>Lenovo</td></tr><tr><td>EPLCA</td><td>Boavizta, APL-datacenter</td><td>ILCD</td><td>Seagate</td></tr><tr><td>scorelca.org</td><td>The Shift Project</td><td></td><td></td></tr></tbody></table>

Several studies and projects carried out over the last ten years have focused on specific issues, such as energy consumption in data centers, premature obsolescence of terminals or electronic waste management. Other studies have focused on the entire life cycle of digital equipment (Table 2), but with a single-criteria approach.

The LCA studies of the main equipment essential to the operation of networks and data transmission on the transport network are presented in the report EcoInfo - RENATER (EcoInfo - RENATER, 2021) and the study [13]. The institute "Efficacity", has developed an estimation on the consumption of the IT room part.
<table><caption>**Table 2.** LCA studies</caption><tr><td>Datacenter</td><td>Whitehead et al., 2015</td><td>[6]</td></tr><tr><td>RENATER network</td><td>(EcoInfo - RENATER, 2021)</td><td>[7]</td></tr><tr><td>Software</td><td>(GREENSPECTOR, n.d.)</td><td>[8]</td></tr><tr><td>USB key</td><td>(Guern and Farrant, 2011)</td><td>[9]</td></tr><tr><td>Workstation</td><td>(Anders S.G. {Citation}, 2012)</td><td>[10]</td></tr><tr><td>cloud computing</td><td>(Itten et al., 2020)</td><td>[11]</td></tr><tr><td>Semiconductors</td><td>(Sarah B. Boyd, 2012)</td><td>[12]</td></tr></table>

## 2.2 Key Technologies

### 2.2.1 Digital Service

Definition from the Alliance GreenIT [14, 15]: “A digital service is constituted of a set of software, hardware, networks, infrastructures, and other digital services. It fulfills a functional unit such as “book a train ticket”, “send an e-mail to friends”, etc.”

### 2.2.2 Internet of Things (IoT)

With the rapid development of IoT technology, it has become a promising technology that can make product management more flexible and efficient. According to ARCEP's [16, 17] definition, “The Internet of Things is a set of connected objects and network technologies that combine physical objects that have connected sensors, wired or wireless digital communication networks, remote storage spaces for the data collected and data processing applications that engage decision-making processes”.

### 2.2.3 Building Information Modeling (BIM)

Is an approach to building construction and operation, whose development is being extended to infrastructures and, more broadly, to civil engineering works. It is at the heart of the work process around a digital model of the structure or a digital twin for the operation/maintenance of infrastructures. The BIM approach improves data management, information quality and cost control in the construction process.

### 2.2.4 Infrastructure Lifecycle Management (ILM))

The life cycle of data consists of four phases: The creation, the hot, warm and cold phase. Information Life cycle Management (ILM) is a comprehensive approach to managing the flow of an information system's data and associated metadata from creation and initial storage to the time when it becomes obsolete and is deleted [18].

### 2.2.5 Building Lifecycle Management Platform (BLMP)

As a methodology Based on BLM and BIM technology and Industry Foundation Classes (IFC) standard, The Building Lifecycle Information Management Platform Based on
BIM presents a practical and effective way to realize information creating, exchange, sharing and integration management of all participants of the construction project [19]. The BLMP framework divides Collaboration Workspace into three functions, which are cooperative service management platform, collaborative work information management platform, and cooperative design work platform [20, 21].

## 3 Research Methodology

As a methodology for assessing environmental impacts related to products and their potential impacts, Life Cycle Assessment (LCA) has been developed as an important tool for evaluating the energy and environmental performance of products over their life cycle [22]. Life cycle assessment is a widely recognized method for evaluating environmental impacts [23]. LCA is defined by the ISO 14040 as the compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product system throughout its life cycle.

Our approach, adapted to digital services, was carried out following the general methodological recommendations of ISO 14040:2006 and 14044:2006. It necessarily takes into account the following points (Fig. 1):

1. The 4 main steps, respectively: the definition of the objectives and the system, the analysis of the inventory, the evaluation of the impacts and the interpretation of the results.

2. All stages of the life cycle of the service and associated equipment: manufacturing, installation, distribution, use, end of life;

3. The three thirds of the digital architecture: user terminals, communication networks and data centers.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Proposed LCA approach, adapted to digital services.</figcaption></figure>
# 4 Case Study

## 4.1 Case Description

This study quantifies the environmental impacts of the digital equipment and infrastructures which support the monitoring system of a structure: **A71 Viaduct on the Loire River**, according to the four bricks illustrated below: data acquisition system; network and Communication system; consultation system; data Storage system. The description of the global architecture of the system is illustrated in Fig. 2. The components of the system and communication networks are simplified to make the result readable and understandable. The equipment listed in Fig. 2, are the components included in the present study, based on the experimental protocol set up in the framework of EMGCU Lab activities, to evaluate the services of the monitoring structures.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Diagram of the equipment needed for bridge monitoring - Bridge Monitoring System Architecture</figcaption></figure>
## 4.2 Function

The monitoring of structures implies to assess their structural health and to detect new developments early enough to avoid major problems. Sensor data is collected and read on a periodically basis by the nodes and sent to the gateway via the mesh network nodes. The gateway then sends the data to the EMGCU server, or to a third-party server. The data exchange flow consists of the following steps:

- Acquisition of input data from the sensors: bathymetry, water level, flow speed, camera, temperature, vibrations

- Transfer of input data to the calculation platform (local server)

- Calculation and centralization of the data on the local calculation platform,

- Transfer of the data on the transport network, on RENATER (Internet)

- Transfer of data to the storage platform (Data Center EMGCU)

- Analysis and exploitation of the data

The daily volume of data is estimated at 10 MB.

## 4.3 Functional Unit

Given the objective of the study, the Functional Unit (FU) is defined. The FU is the unit of reference used to link the inputs and outputs and the environmental performance of the system under study. The FU selected for this study is **one day of monitoring**.

## 4.4 System Boundary

The assessed system is modeled as 4 interacting sub-systems (Fig. 2.): data acquisition; network and communication; consultation; and data storage, and each subsystem is composed of several components. The system studied includes the following steps (Fig. 4):

* The manufacture of equipment: the manufacture of equipment takes into account the consumption of raw materials, the transport of raw materials and the energy consumption necessary for the production of equipment. The values of the coefficients used in the method come from measurements, from the Ecoinvent 3.7.1 database [24], from manufacturers or from the literature.

* The use phase: Operation and maintenance: For the reference scenario, the use phase requires the electricity consumption of the components of the monitoring system. As much as possible, we have relied on the real electrical consumption (especially for the sensors). When this information was missing, we relied on of the technical specifications of the equipment provided by the manufacturer and have reconstructed each equipment of the subsystems.
## 4.5 Inventory Data

Life Cycle Inventory emission factors, related to flows of raw material extracted and base material used, is extracted from the Ecoinvent 3.7 database. The energy mix considered is that for France. The model has been developed with OpenLCA [25] in line with the Ecoinvent database.

## 4.6 Life Cycle Impact Assessment

Impacts calculation was conducted using ReCiPe [26].

# 5 Results and Discussion

The following figure present the impact scores for different environmental categories related to the functional unit defined - one day of monitoring. Impact scores for eighteen impact categories are calculated considering the different life cycle stages of the monitoring system: production, assembly, transportation, use and end of life. The use phase includes all steps associated with the acquisition of measurement data transmitted to the storage system and the consultation of the data by EMGCU project beneficiaries. Specifically, the electricity consumption for the acquisition system is taken into account.

<figure><img src="image_5.png" /></figure>

Impact characterization Method: ReCiPe midpoint H. FPMF : Fine particulate matter formation, FRSc : Fossil resource scarcity, FAEx : Freshwater ecotoxicity, FAEt : Freshwater eutrophication, GW : Global warming, HCTx : Human carcinogenic toxicity, HnCTX : Human non-carcinogenic toxicity, IRad : Ionizing radiation, LU : Land use, MEx : Marine ecotoxicity, MEu : Marine eutrophication, MRSc : Mineral resource scarcity, OzFHH : Ozone formation, Human health, OzFTE : Ozone formation, Terrestrial ecosystems, SOzD : Stratospheric ozone depletion, TAc : Terrestrial acidification, TEc : Terrestrial ecotoxicity, WC : Water consumption.

<caption>**Fig. 3.** Environmental impact scores – contribution analysis to Monitoring System Impacts (by subsystem)</caption>
Results presented in Fig. 3 shows that for most impact categories, the consultation system is the most contributing subsystem, along with the data acquisition system. The most contributing processes are the production of screens for the consultation block, the production of electricity for the sensors and the production of the electronic components of the sensor system. The storage system has a low contribution, because we assumed that data are stored for a duration of one day.

## 5.1 Sensitivity Analysis Results

The sensitivity analysis focuses on the duration of data storage, considering a storage of 1 year and 10 years (the initial assumption being 1 day). The emissions table for the impact category/Global Warming (kg CO₂ eq) is given in Fig. 4. We find that the duration of the data storage has a strong influence on electricity consumption. By increasing the data storage duration, the overall impact scores increase.

<table><thead><tr><th>Lifetime</th><th>1 Jour</th><th>1 an</th><th>10 ans</th></tr></thead><tbody><tr><td>Impact Category / Global Warming (kg CO2 eq)</td><td>0,1</td><td>4</td><td>42</td></tr><tr><td></td><td>2</td><td>2,73</td><td>7,27</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Climate change impact score (kg CO₂ eq)</figcaption></figure>

**Fig. 4.** Sensitivity analysis - Climate Change for different data storage duration (kg CO₂ eq/FU)

## 5.2 Discussion

The results show that for the baseline scenario with 1 day of storage, the data storage system has a significantly lower impact than the other evaluated subsystems. The sensitivity results show that the impact increases significantly with increasing storage time. This demonstrates the importance of electricity consumption of data centers, which in our case is based on rudimentary assumptions. This parameter should be further explored.

While these preliminary results show the importance of data storage time, they also show the significant impact of the production of equipment, and in particular the consultation systems (screen). The hypotheses taken for the case study corresponding to
an experimental system, it would be necessary to carry out a complementary sensitivity study on the duration of use of the consultation system per FU, in order to get closer to realistic hypotheses corresponding to an operational situation.

The case study presented here therefore demonstrates that LCA is a valuable tool to evaluate the environmental impacts of digital services. It allows to highlight the effect of each practice on the environmental performance of digital services.

# 6 Conclusions

This study quantified the environmental impacts of complex digital equipment and infrastructure in the construction and Engineering Construction (AEC) sector, using a multi-criteria, multi-stage LCA approach. This real-life case study made it possible to test the methodological and theoretical approach to evaluate the environmental impacts of a digital service. The model, at this stage, has allowed having an idea on the environmental performance of the digital service in a case of 'BIMP' for the Management-Operation-Maintenance step.

The use of life cycle inventories is derived from different data sources (Impacts Base, Ecoinvent, manufacturers, etc.). The large amount of data collected on the system satisfies our initial desire to do a simplified LCA study.

Nevertheless, these first results can be considered as encouraging with regard to the work carried out by the digital and Green IT community on this subject. This work is only a beginning; it can be completed within the framework of future analyses that will be carried out by the research teams and the national community of the Digital LCA.

## 6.1 Limitations of the Study

The accurate and comprehensive determination of the environmental impacts of digital equipment and infrastructure is a complex task that faces many limitations due to data access and associated uncertainties. In addition, this study has identified a number of complementary works that can be carried out to allow a better understanding and analysis of the environmental impacts of the digital sector, Particularly, the approach should be extended to other infrastructures project phases (BIM in design, deconstruction, renovation...) and not only monitoring.

**Acknowledgments.** This research was funded by IREX (https://irex.asso.fr/). The case study was proposed by the EMGCU lab (https://emgcu.univ-gustave-eiffel.fr/).

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