# Decarbonization in Manufacturing: A Systematic Review for Life Cycle Assessment with Sensor Data Integration

Samuel Quintero-Herrera, Damien Evrard

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Samuel Quintero-Herrera, Damien Evrard. Decarbonization in Manufacturing: A Systematic Review for Life Cycle Assessment with Sensor Data Integration. 32nd CIRP Conference on Life Cycle Engineering (LCE2025), Apr 2025, Manchester (UK), United Kingdom. pp.960-965, 10.1016/j.procir.2025.01.098. hal-05315559

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32nd CIRP Conference on Life Cycle Engineering (LCE 2025)

# Decarbonization in Manufacturing: A Systematic Review for Life Cycle Assessment with Sensor Data Integration

Samuel Quintero-Herreraª*, Damien Evrardª

ªUniv. Grenoble Alpes, CNRS, Grenoble INP, G-SCOP, 38000 Grenoble, France

* Corresponding author. E-mail address: samuel.quintero-herrera@grenoble-inp.fr

## Abstract

Integrating Industry 4.0 technologies in production systems is a gateway for enhanced control and refined understanding of CO₂ emissions. These technologies drive substantial changes that improve production efficiency, product quality, cost-effectiveness, and decarbonization. In this context, sensors are fundamental, providing vast amounts of data to aid decarbonization current challenges in real-time management for dynamic CO₂ emission analysis. Dynamic life cycle assessment (LCA) emerges as a viable alternative to current asynchronous approaches to address these challenges. This study presents the main findings of a systematic literature review of LCA studies that integrate sensor data in manufacturing. The Web of Science search engine was utilized; twenty-nine relevant studies were identified. The review focused on how sensor data is treated, the dynamic parameters associated with sensors, and their integration in the LCA methodology. The findings indicate that this approach is primarily applied to discrete processes, such as those in the machinery and construction materials industries, with a predominant use of cradle-to-grave system boundaries. The life cycle inventory includes dynamic parameters using sensors for measuring operational conditions, energy consumption, and emissions. Despite its potential, the effective integration of temporal information with LCA is not yet evident in production systems, a standardized methodology to solidify the role of real-time data in LCA should be implemented.

© 2025 The Authors. Published by Elsevier B.V.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Peer-review under responsibility of the scientific committee of the International Academy for Production Engineering (CIRP)

Keywords: dynamic life cycle assessment, production systems, decarbonization, sensors

## 1. Introduction

Industry 4.0 integrates core technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing, robotics, and cyber-physical systems to transform production systems (PS) [1]. In manufacturing, these technologies are driving huge changes to permit efficiency in production, quality improvement of produced products, and cost-effectiveness through predictive maintenance, machine health tracking, and optimized production schedules. However, manufacturing process generates vast amounts of data which is great opportunity to implement strategic pathways that reduce greenhouse gas (GHG) emissions. The data from Industry 4.0 is

valuable and can provide insights into enhanced production efficiency, traceable materials, energy consumption, and emissions during the production process [1]. The exploitable information includes operational conditions, energy and material consumption, emissions, waste generation, and maintenance [2] However, integrating this data to evaluate the environmental impact of PS presents challenges such as data quality enhancement, infrastructure requirements, handling unstructured data, ensuring security and privacy, and incorporating real-time data into life cycle assessment (LCA) models to effectively support decarbonization efforts.

To manage these data and infrastructure challenges, manufacturers are increasingly adopting the latest technologies
to automate and digitalize processes. The market size for digital transformation is projected to increase from USD 944 billion in 2024 to around 9,149 billion by 2033 [3].

The main challenge for LCA models is the amount and quality of data. Part of the data driving Industry 4.0 innovations is collected through sensors deployed across the PS. This data collection encourages LCA models to evolve by integrating IoT technologies for real-time analysis of inventory data, essential for tracking and reducing Scope 1 and Scope 2 emissions [4].

Key sensors utilized in decarbonization efforts include temperature sensors, energy meters, humidity sensors, pressure transducers, and gas analyzers. For instance, temperature sensors can control ambient conditions in the plant [5], while energy sensors monitor and optimize electrical consumption, contributing to sustainable manufacturing practices [6].

The real-time data can be incorporated into LCA through dynamic life cycle assessment (DLCA), an approach that considers temporal variations, incorporates real-time data, and integrates adaptive management practices compared to traditional static LCA [2]. However, the effective dynamic integration between IoT technologies and LCA remains a challenge. There is no clear understanding of how dynamic parameters such as operational conditions and material consumption can influence the environmental impact of PS. By including real-time environmental evaluation, monitoring indicators are generated, which allow informed and immediate decisions to adjust production parameters, thereby advancing decarbonization goals.

This article aims to develop an effective and comprehensive literature review on the use of sensor data in LCAs of PS. Some of the questions this research aims to answer are: How can sensor data be considered in each stage of the LCA? What are the main challenges in measuring dynamic parameters for LCA using sensors? What is the treatment of sensor data for incorporation into DLCA? The following sections will detail the findings of this systematic review of LCA that incorporate real-time data, types of sensors used in environmental assessments, and the data management of sensor data. Then, the results are presented and discussed. Finally, some recommendations and perspectives are proposed.

## 2. Literature search methodology

To identify environmental assessment studies in PS integrating sensor data, a literature review was developed following the guidelines set by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [7]. The methodology includes clear identification, comprehensive screening, defined eligibility, detailed inclusion, thorough synthesis, and transparent reporting (Figure 1). The search was conducted in February 2024, considering English articles from the last 20 years in the Web of Science search engine.

The search terms for screening the scientific articles were ("life cycle assessment" OR LCA OR "life cycle analysis"), and (sensor* OR detector*), and ("discrete" OR "manufacturing" OR "real time" OR "dynamic*"), and ("environmental impact*" OR sustainability OR "carbon footprint" OR "greenhouse gas*" OR decarboni?ation OR GHG) in titles, abstracts, and

keywords, resulting in a total of 54 potentially relevant articles. Review articles and grey literature were excluded, leaving 46 articles.

The screening also included screening the articles based on

<figure><img src="image_2.png" /><figcaption>Figure 1. PRISMA Flow diagram of the literature review</figcaption></figure>

the scope, excluding those outside manufacturing or PS. Articles with static LCA, meaning their inventory did not include dynamic components connected to sensor data, particularly related to real-time data collection, were also excluded. Articles addressing methodological frameworks and simulation data inventories of PS were considered for the study.

Given the criteria used for this study, a snowballing approach was applied, considering articles cited within the previously identified articles, based on their potential relevance and citation context within the identified literature. A total of 29 articles were selected for further analysis.

Six criteria were considered to extract the information from the selected articles:

* **Identification:** Including title, DOI, journal, and industry sector based on how production is carried out.

* **Goal and Scope:** Including study type, LCA scope, functional unit, and system boundary.

* **Inventory Analysis:** Classifying the life cycle inventory in dynamic parameters such as operational conditions, raw materials consumption, raw materials conditions, energy consumption, emissions, waste and treatment, and ambient conditions. Sensors used and temporal resolution were also included.

* **Life Cycle Inventory Assessment:** Including tools and software, impact assessment methods, and databases used in the LCA.

* **Interpretation:** Including data quality, policy implications, and contributions to the decarbonization of PS.

* **Data Management:** Including the treatment and management of sensor data for use in LCA, specifically associated with acquisition, transmission, and storage.

Finally, the qualitative criteria of the extracted information were subcategorized to facilitate the interpretation and analysis of the results.
## 3. Results and discussion

Table 1 presents the list of articles identified in this study, classifying each research according to the type of industrial process based on the Global Industry Classification Standard (GICS). Additionally, manufacturing processes were categorized into discrete processes (production by batches or individual units) and continuous processes (uninterrupted and homogeneous production).

<table><caption>Table 1. Articles identified in the literature review by industry</caption><thead><tr><th>Ref</th><th>Continuous</th><th>Discrete</th><th>Both</th></tr></thead><tbody><tr><td>[8]</td><td></td><td>Renewable Energy</td><td></td></tr><tr><td>[9]</td><td></td><td>Agricultural Equipment</td><td></td></tr><tr><td>[10]</td><td></td><td></td><td>Various Industries</td></tr><tr><td>[11]</td><td>Metals & Mining</td><td></td><td></td></tr><tr><td>[12]</td><td></td><td>Construction Materials</td><td></td></tr><tr><td>[13]</td><td></td><td>Renewable Energy</td><td></td></tr><tr><td>[4]</td><td></td><td>Industrial Machinery</td><td></td></tr><tr><td>[14]</td><td></td><td>Automotive</td><td></td></tr><tr><td>[15]</td><td></td><td>Education</td><td></td></tr><tr><td>[16]</td><td></td><td></td><td>Information</td></tr><tr><td>[17]</td><td></td><td></td><td>Technology</td></tr><tr><td>[18]</td><td></td><td></td><td>Various Industries</td></tr><tr><td>[19]</td><td></td><td></td><td>Information</td></tr><tr><td>[20]</td><td></td><td>Waste Management</td><td></td></tr><tr><td>[21]</td><td></td><td></td><td>Various Industries</td></tr><tr><td>[22]</td><td></td><td>Industrial Machinery</td><td></td></tr><tr><td>[23]</td><td></td><td>Industrial Machinery</td><td></td></tr><tr><td>[24]</td><td></td><td></td><td>Industrial Machinery</td></tr><tr><td>[25]</td><td></td><td>Industrial Machinery</td><td></td></tr><tr><td>[26]</td><td>Utilities</td><td></td><td></td></tr><tr><td>[27]</td><td></td><td>Industrial Machinery</td><td></td></tr><tr><td>[6]</td><td>Waste Management</td><td></td><td></td></tr><tr><td>[28]</td><td></td><td></td><td>Various Industries</td></tr><tr><td>[29]</td><td></td><td></td><td>Various Industries</td></tr><tr><td>[30]</td><td>Metals & Mining</td><td></td><td></td></tr><tr><td>[31]</td><td></td><td></td><td>Construction Materials</td></tr><tr><td>[32]</td><td></td><td>Industrial Machinery</td><td></td></tr><tr><td>[33]</td><td></td><td></td><td>Various Industries</td></tr><tr><td>[34]</td><td></td><td>Industrial Machinery</td><td></td></tr></tbody></table>

The most recurrent discrete process identified is related to the production of industrial machinery. However, a significant portion of the studies is not directly tied to a specific case study but is presented as a methodological framework. This allows the "Various Industries" category to extend to both discrete and continuous processes with certain adjustments. The most consulted journals in this research include the *Journal of Cleaner Production*, *Procedia CIRP*, and *The International Journal of Life Cycle Assessment*.

There are case studies encompassing both discrete and continuous processes. For example, in waste management, one study focuses on urban waste collection using a city-scale wireless sensor network [6] (discrete process), while others examine wastewater treatment plants (continuous processes) [20], [29]. Other case is the information and technology cases which describe approach to collect data [16] and information and communication technology (ICT) based toolset for lifecycle sustainability [18]. The following sections detail how the different investigations addressed the phases of the LCA.

### 3.1 Goal and scope

The main characteristics of the identified articles focus on the inclusion of temporal aspects in LCA, although 9 of the articles make use of DLCA. The other LCA approaches considered are attributional (~30%) and comparative studies.

The decarbonization efforts is a primary outcome of integrating LCA in some articles. This objective is addressed from different perspectives, such as the optimization of environmental performance [32] and the integration of emerging technologies to tackle climate change challenges [4]. Strategies include enhancing energy efficiency, adopting renewable energy sources, and optimizing production processes to minimize both Scope 1 (direct emissions from owned or controlled sources) and Scope 2 (indirect emissions from the generation of purchased energy) emissions. For instance, studies focusing on industrial machinery [4], [25], [32] explore the use of advanced technologies to enhance energy efficiency, thereby reducing Scope 2 emissions, while waste management studies [6], [20] implement process optimizations to lower Scope 1 emissions from waste treatment processes.

Other approaches to the goal and scope of the analyzed articles aim to shift traditional LCA approaches from static, retrospective assessments to dynamic [8], [9], [10], real-time, or predictive assessments [11], [12]. These studies strive to improve the timeliness, accuracy, and adaptability of LCA by ensuring that data is collected and analyzed in real time. Additionally, models and algorithms are proposed to predict environmental impacts based on real-time data and evolving conditions. In addition, digital technologies are becoming crucial in LCA frameworks. Their inclusion allows for more granular and precise data collection [13], enabling more accurate assessments and the possibility of continuous monitoring of environmental impacts through sensors [9].

Another characteristic to highlight is related to system boundaries. Most studies are cradle-to-grave assessments, followed by cradle-to-gate, and gate-to-gate which is logical considering that these are manufacturing processes. In studies that incorporate dynamic aspects in LCA, system boundaries may change over time, capturing variations in processes, technologies, and materials used throughout the life cycle. To define the system boundaries in manufacturing processes utilizing sensors, it is essential to consider inherent variations, periods of occurrence, or evolution within the temporal scope of model components. Some of these aspects are discussed below.

An additional relevant factor is how LCA studies include the functional unit (FU). Generally, studies do not consider the temporal aspect when describing their FU. From the studies that explicit the FU, 6 articles (~20%) use a mass-based unit, another 20% use a volumetric unit, and the remaining are unitary representations of the processes.

### 3.2 Life cycle inventory

The life cycle inventory (LCI) was categorized into eight key areas. This classification provides a general overview of how sensor data is managed in LCI for PS. Notably, this approach enables the evaluation of time-sensitive factors such as energy
consumption, emissions, and resource use. The life cycle inventory considers materials, emissions, and energy according to ISO 14040.

Related to materials, the classification includes:

*   **Operational conditions:** include operational components specification [9], [11], [14], [15], [20], [23], [25], [26], [27], [31], production parameters specification [9], [15], [18], [20], [34], time-dependent operations [17], [29], external factors [29], [31]. some examples include production parameters such as fuel consumption [9], [15]; or time-dependent operations such as time-dependent production and supply functions [17] or production rate changes [29].

*   **Raw materials consumption:** include direct measurement [11], [31], materials in production [6], [20], [25], [26], [29], [30], recycled or sustainable materials [12], and time-dependent raw materials [17], [31]. These parameters are fundamental for LCA and can include direct measurements of resource consumption [11], or listing specific materials used in production, such as electronic components [20], methanol or iron chloride [29], and steel scraps, pig iron, hot briquetted iron, and iron alloys [30]. Some studies also consider circular economy strategies, such as using recycled materials like recycled paper and textile fibers in panels [12].

*   **Raw materials conditions:** include environmental conditions affecting materials [12], [31], monitoring material conditions [6], and temporal parameters affecting material conditions [31]. These parameters are crucial due to the use of sensors to measure aspects like environmental humidity or rate of compression, which can affect material properties and, consequently, the environmental impact [31].

*   **Waste and treatment:** include waste minimization strategies [9], [17], [29], [31], recycling and reuse initiatives [9], [12], [20], [25], [29], [30], [33], waste monitoring and reporting [14], [17], [18], [6], [23], [33], waste management practices [11], [15], [21], [23], [27], [31], and other waste treatment practices [32].

Related to emissions, the classification includes:

*   **Emissions:** include total emissions [9], [11], [15], [18], [20], [25], [26], [27], [29], [30], [33], emission monitoring and management [12], [13], [14], [18], [20], [21], [23], [25], [6], [27], [32], [33], [34], and emission reduction targets [17], [31]. To quantify emissions, some works propose the installation of emission monitoring systems through sensors [14], [33]. Emission Monitoring and Management enables real-time tracking of pollutants, which is crucial for dynamic LCA and for implementing emission reduction strategies. In general, monitoring and reporting strategies are fundamental for dynamic parameters utilized in LCA. In an inventory with temporal variations, time-differentiated data describe elementary flows, reflecting changes in the environmental performance of a product or system over time [2].

Related to energy, the classification includes:

*   **Energy consumption:** total energy consumption [8], [12], [20], [26], [33], energy efficiency measures [8], [13], [20], [25], [30], [32], energy monitoring and management [8], [9], [11], [14], [15], [17], [18], [23], [25], [27], [6], [29], [30], [31], [33], [34], energy-related technologies [13], [21], [27], [32]. The use of sensors/meters to quantify electricity consumption is usual. The installation of sensors allows for real-time monitoring and management of energy use, leading to opportunities for optimization and reduction of environmental impacts.

Additionally, the classification includes:

*   **Ambient conditions:** consider the external factors [29], [31]

*   **Technology evolution:** consider advanced machinery and equipment [10], [14], [18], [20], [21], [23], [27], [29], [30] and information and communication technologies [8], [12], [13], [18], [25], [32].

### 3.3 Life cycle inventory assessment methods

Figure 2 presents the different software and databases used in the literature review. Several software tools and databases are used in the identified studies to model LCA. Some works present custom-developed models specific to certain operations. For instance, one author [15] developed a code called LiveLCA to subscribe and receive process information and integrate it to perform an LCA.

<figure><img src="image_3.png" /><figcaption>Figure 2. LCA software and databases used in studies identified</figcaption></figure>

Excluding the "not specified" databases, the most used is Ecoinvent in its various versions, indicating its acceptance as a comprehensive and reliable source for life cycle inventory data. Regarding the impact assessment methods, several standouts such as Global Warming Potential (GWP) [12], [32], [34], Eco-Indicator 99 [9], [18], CML 2001 [11], [27], IMPACT 2002+ [8], and ReCiPe [6], [26].

The integration of dynamic aspects in emission factors has been proposed by [31] through the implementation of a framework called Temporalis, which includes dynamic impact assessment functions producing characterization factors or temporal characterization distributions This model has been implemented in Brightway2, an open-source software package written in Python and serves as the basis for dynamic modeling, where characterization factors are adjusted to reflect differences in temporal scale. However, in the literature review, concrete applications of this methodology in manufacturing processes
are not observed, indicating an area of opportunity to establish
a methodology that integrates the dynamic system boundary, the
dynamic inventory, and dynamic modeling/characterization in
PS with sensor data integration.

## 3.4 Sensors used in LCA studies

Sensors play a role in enhancing the accuracy and real-time
capabilities of LCA studies by providing detailed and
continuous data. However, specifying sensor details is often not
emphasized in LCA studies. Despite this, their use is
widespread across various applications. For example, [13]
included power meters and real-time IoT sensors in their
framework. These modules comprise power meters,
programmable logic controllers (PLCs), RFID readers, and
wireless sensor networks integrated into an IoT node. This setup
allows capturing carbon emission and product manufacturing
information in real time, and the calculation of characterization
factors can be scheduled at any time frame in the manufacturing
line. Several types of sensors and measurement devices
identified in the studies include:

*   **Energy meters, light barriers, thermometers:** Used for monitoring energy consumption and environmental conditions [15],

*   **RFID technology:** Facilitates tracking of materials and products throughout the manufacturing process [4], [13], [16], [21], [22],

*   **Position and movement sensors:** Monitor the movement of components and products, enhancing process control [16].

*   **Thermocouples, thermal flux meters, infrared thermal cameras:** Measure temperature-related parameters, crucial for processes where thermal conditions impact product quality [12].

*   **Continuous Emission Monitoring Systems** [25]: Provide real-time data on emissions, enabling compliance with environmental regulations and dynamic assessment of environmental impacts. For example, oxygen sensors [26].

*   **Air and flow meters:** Monitor resource flows contributing to a comprehensive environmental performance evaluation [27], [28].

*   **Sensors for temperature, humidity, moisture content, electricity consumption:** Collect data on environmental and operational conditions that affect both process efficiency and environmental impact [32].

Despite the variety of sensors used, detailed information on
sensor specifications, data accuracy, and calibration methods is
often lacking in LCA studies. These sensor technologies are
integral to implementing decarbonization pathways by
providing the necessary data to monitor and reduce emissions.
For example, Continuous Emission Monitoring Systems
directly track Scope 1 emissions [25], [26], while energy meters
and electricity consumption sensors help manage and reduce
Scope 2 emissions by optimizing energy use [15], [32]. These
systems facilitate compliance with environmental regulations
and support the development of targeted emission reduction
strategies.

## 3.5 Data management of sensor data

The management of data from sensors to be considered in LCA studies is fundamental to ensure that data collected from sensors is accurate, reliable, and usable. The data management process can be broadly analyzed into three stages: Acquisition, Transmission, and Storage [2].

*   **Data acquisition:** involves the collection of data from various sensors installed in manufacturing processes. The identified studies employ a range of sensors to capture operational parameters, energy consumption, emissions, and environmental conditions discussed in Section 3.2.

*   **Data Transmission:** refers to the methods and protocols used to transfer data from sensors to data processing and storage systems. It is made via communication protocols such as MTConnect® [22], [25], [27], MQTT over TCP/IP [32], and OPC-UA and OPC-XML [25]. Other methods for data transmissions include the wireless sensor networks [20], [21], and IoT platforms [4], [8], [13], [33].

*   **Data storage:** involves recording and maintaining the data collected from sensors. Some studies identified cloud-based storage such as the EnBox presented by [24] which uses cloud storage to update the LCI database with real-time energy consumption data. Other propositions store data in local servers or databases [15] with formats such as XML, JSON, and SQL [24], [25], [27].

Some studies mention the use of specialized data
management systems or middleware to handle data storage and
retrieval [22], [27], [32]. However, challenges such as
standardization of communication protocols, automation of data
cleaning, or data security and confidentiality remain areas of
opportunity. Effective data management is crucial for
supporting decarbonization pathways, as it ensures the
reliability of data used to track and reduce Scope 1 and Scope 2
emissions. Proper acquisition, transmission, and storage
protocols enable accurate real-time monitoring and
management of emissions, facilitating timely interventions and
strategic decision-making to achieve decarbonization targets.

## 4. Conclusions and perspectives

This comprehensive literature review highlights the
significant advancements and ongoing challenges in integrating
sensor data into LCA studies within the context of Industry 4.0.
The key findings from the analysis of 29 articles include the
identification and classification of dynamic variables,
identification and characterizing the sensors used, and
remarking on the data integration into DLCA. Notably, real-
time sensor data support decarbonization pathways by tracking
both Scope 1 and Scope 2. Future research should implement a
formalized methodology based on the identified technologies to
incorporate real-time sensor data, dynamically adjust system
boundaries, and account for temporal variations in
manufacturing processes. This approach is key to solidifying the
role of real-time data in achieving sustainable manufacturing
and decarbonization targets.
Acknowledgments

This research was funded, in whole or in part, by the National Research Agency (ANR) of France under the project ANR-22-PESP-0002. A CC BY 4.0 license has been applied. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] B. R. Bakshi and J. A. Paulson, "Sustainability and Industry 4.0: Obstacles and Opportunities," in 2022 American Control Conference (ACC), IEEE, Jun. 2022, pp. 2449–2460. doi: 10.23919/ACC53348.2022.9867815.

[2] T. P. da Costa, D. M. B. da Costa, and F. Murphy, "A systematic review of real-time data monitoring and its potential application to support dynamic life cycle inventories," Environ Impact Assess Rev, vol. 105, p. 107416, Mar. 2024, doi: 10.1016/j.eiar.2024.107416.

[3] Precedence Research, "Digital Transformation Market Size and Growth 2024 to 2033." Accessed: Sep. 23, 2024. [Online]. Available: https://www.precedenceresearch.com/digital-transformation-market

[4] S. Mishra and S. P. Singh, "Carbon management framework for sustainable manufacturing using life cycle assessment, IoT and carbon sequestration," Benchmarking: An International Journal, vol. 28, no. 5, pp. 1396–1409, May 2021, doi: 10.1108/BIJ-01-2019-0044.

[5] V. Apostolopoulos et al., "An integrated life cycle assessment and life cycle costing approach towards sustainable building renovation via a dynamic online tool," Appl Energy, vol. 334, p. 120710, Mar. 2023, doi: 10.1016/j.apenergy.2023.120710.

[6] D. Torregrossa, A. Marvuglia, and U. Leopold, "A novel methodology based on LCA + DEA to detect eco-efficiency shifts in wastewater treatment plants," Ecol Indic, vol. 94, pp. 7–15, Nov. 2018, doi: 10.1016/j.ecolind.2018.06.031.

[7] M. J. Page et al., "The PRISMA 2020 statement: An updated guideline for reporting systematic reviews," Mar. 29, 2021, BMJ Publishing Group. doi: 10.1136/bmj.n71.

[8] J. An, Z. Zou, G. Chen, Y. Sun, R. Liu, and L. Zheng, "An IoT-Based Life Cycle Assessment Platform of Wind Turbines," Sensors, vol. 21, no. 4, p. 1233, Feb. 2021, doi: 10.3390/s21041233.

[9] J. Ma and H. M. Kim, "Predictive usage mining for life cycle assessment," Transp Res D Transp Environ, vol. 38, pp. 125–143, Jul. 2015, doi: 10.1016/j.trd.2015.04.022.

[10] A. D. Brucker and S. Yalman, "Confidentiality Enhanced Life-Cycle Assessment," in University of Exeter, vol. 436, A. Marrella and B. Weber, Eds., 2022, pp. 434–446. doi: 10.1007/978-3-030-94343-1_33.

[11] A. Astarita, M. De Luca, and C. Sinagra, "Impact of rolling processes in the production of aluminum packaging assessed through LCA," Int J Life Cycle Assess, vol. 28, no. 12, pp. 1756–1772, Dec. 2023, doi: 10.1007/s11367-023-02220-7.

[12] P. Ricciardi, E. Belloni, and F. Cotana, "Innovative panels with recycled materials: Thermal and acoustic performance and Life Cycle Assessment," Appl Energy, vol. 134, pp. 150–162, Dec. 2014, doi: 10.1016/j.apenergy.2014.07.112.

[13] M. Tu, W.-H. Chung, C.-K. Chiu, W. Chung, and Y. Tzeng, "A novel IoT-based dynamic carbon footprint approach to reducing uncertainties in carbon footprint assessment of a solar PV supply chain," in 2017 4th International Conference on Industrial Engineering and Applications (ICIEA), IEEE, Apr. 2017, pp. 249–254. doi: 10.1109/IEA.2017.7939216.

[14] Y. Pigné et al., "A tool to operationalize dynamic LCA, including time differentiation on the complete background database," Int J Life Cycle Assess, vol. 25, no. 2, pp. 267–279, Feb. 2020, doi: 10.1007/s11367-019-01696-6.

[15] J. Hagen, L. Büth, J. Haupt, F. Cerdas, and C. Herrmann, "Live LCA in learning factories: real time assessment of product life cycles environmental impacts," Procedia Manuf, vol. 45, pp. 128–133, 2020, doi: 10.1016/j.promfg.2020.04.083.

[16] G. van Capelleeven, J. Pohl, A. Fritsch, and D. Schien, "The Footprint of Things: A hybrid approach towards the collection, storage and distribution of life cycle inventory data," pp. 350–334. doi: 10.29007/8pnj.

[17] L. Tiruta-Barna, Y. Pigné, T. Navarrete Gutiérrez, and E. Benetto, "Framework and computational tool for the consideration of time dependency in Life Cycle Inventory: Proof of concept," J Clean Prod, vol. 116, pp. 198–206, Mar. 2016, doi: 10.1016/j.jclepro.2015.12.049.

[18] D. Cerri and S. Terzi, "Proposal of a toolset for the improvement of industrial systems' lifecycle sustainability through the utilization of ICT technologies," Comput Ind, vol. 81, pp. 47–54, Sep. 2016, doi: 10.1016/j.compind.2015.09.003.

[19] D. Beloin-Saint-Pierre, R. Heijungs, and I. Blanc, "The ESPA (Enhanced Structural Path Analysis) method: A solution to an implementation challenge for dynamic life cycle assessment studies," International Journal of Life Cycle Assessment, vol. 19, no. 4, pp. 861–871, Apr. 2014, doi: 10.1007/s11367-014-0710-9.

[20] J. Bonvoisin, A. Lelah, F. Mathieux, and D. Brissaud, "An integrated method for environmental assessment and ecodesign of ICT-based optimization services," J Clean Prod, vol. 68, pp. 144–154, Apr. 2014, doi: 10.1016/j.jclepro.2014.01.003.

[21] Fei Tao, Ying Zuo, Li Da Xu, Lin Lv, and Lin Zhang, "Internet of Things and BOM-Based Life Cycle Assessment of Energy-Saving and Emission-Reduction of Products," IEEE Trans Industr Inform, vol. 10, no. 2, pp. 1252–1261, May 2014, doi: 10.1109/TII.2014.2306771.

[22] F. Cerdas, S. Thiede, M. Juraschek, A. Turetskyy, and C. Herrmann, "Shop-floor Life Cycle Assessment," Procedia CIRP, vol. 61, pp. 393–398, 2017, doi: 10.1016/j.procir.2016.11.178.

[23] P. Smolek, I. Leobner, B. Heinzl, G. Gourlis, and K. Ponweiser, "A Method for Real-Time Aggregation of a Product Footprint During Manufacturing," Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 4, no. 4, pp. 360–378, Dec. 2016, doi: 10.13044/j.sdewes.2016.04.0028.

[24] J. Jayapal and S. Kumaraguru, "Real-Time Linked Open Data for Life Cycle Inventory," in Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, vol. 536, I. Moon, G. M. Lee, J. Park, D. Kiritsis, and G. Von Cieminski, Eds., Cham: Springer International Publishing, 2018, pp. 249–254. doi: 10.1007/978-3-319-99707-0_31.

[25] R. A. P. Filleti, D. A. L. Silva, E. J. da Silva, and A. R. Ometto, "Productive and environmental performance indicators analysis by a combined LCA hybrid model and real-time manufacturing process monitoring: A grinding unit process application," J Clean Prod, vol. 161, pp. 510–523, Sep. 2017, doi: 10.1016/j.jclepro.2017.05.158.

[26] Y. Majanne, T. Korpela, J. Judl, S. Koskela, V. Laukkanen, and A. Häyrinen, "Real Time Monitoring of Environmental Efficiency of Power Plants," IFAC-PapersOnLine, vol. 48, no. 30, pp. 495–500, 2015, doi: 10.1016/j.ifacol.2015.12.428.

[27] R. A. P. Filleti, D. A. L. Silva, E. J. Silva, and A. R. Ometto, "Dynamic System for Life Cycle Inventory and Impact Assessment of Manufacturing Processes," Procedia CIRP, vol. 15, pp. 531–536, 2014, doi: 10.1016/j.procir.2014.06.024.

[28] A. Barni, A. Fontana, S. Menato, M. Sorlini, and L. Canetta, "Exploiting the Digital Twin in the Assessment and Optimization of Sustainability Performances," in 2018 International Conference on Intelligent Systems (IS), Funchal - Madeira, Portugal: IEEE, Sep. 2018, pp. 706–713. doi: 10.1109/IS.2018.8710554.

[29] A. H. Shimako, L. Tiruta-Barna, A. B. Bisinella de Faria, A. Ahmadi, and M. Spérandio, "Sensitivity analysis of temporal parameters in a dynamic LCA framework," Science of The Total Environment, vol. 624, pp. 1250–1262, May 2018, doi: 10.1016/j.scitotenv.2017.12.220.

[30] D. Rovelli, C. Brondi, M. Andreotti, E. Abbate, M. Zanforlin, and A. Ballarino, "A Modular Tool to Support Data Management for LCA in Industry: Methodology, Application and Potentialities," Sustainability (Switzerland), vol. 14, no. 7, Apr. 2022, doi: 10.3390/su14073746.

[31] G. Cardellini, C. L. Mutel, E. Vial, and B. Muys, "Temporalis, a generic method and tool for dynamic Life Cycle Assessment," Science of The Total Environment, vol. 645, pp. 585–595, Dec. 2018, doi: 10.1016/j.scitotenv.2018.07.044.

[32] R. Kumar et al., "Live Life Cycle Assessment Implementation using Cyber Physical Production System Framework for 3D Printed Products," Procedia CIRP, vol. 105, pp. 284–289, 2022, doi: 10.1016/j.procir.2022.02.047.

[33] S. Kamalakkannan, A. K. Kulatunga, and L. A. D. A. D. Bandara, "The conceptual framework of IoT based decision support system for life cycle management," Procedia Manuf, vol. 43, pp. 423–430, 2020, doi: 10.1016/j.promfg.2020.02.192.

[34] D. Schneider, P. Jordan, J. Dietz, M. F. Zaeh, and G. Reinhart, "Concept for Automated LCA of Manufacturing Processes," Procedia CIRP, vol. 116, pp. 59–64, 2023, doi: 10.1016/j.procir.2023.02.011.