A Bibliometric and Systematic Review of Carbon Footprint Tracking in Cross-Sector Industries: Emerging Tools and Technologies
Abstract
1. Introduction
2. Materials and Methods
2.1. Protocol Development
2.1.1. Research Question Formulation
- What are the emerging technologies to monitor and track the carbon footprints in industries? RQ1
- How can these technologies improve the efficiency, accuracy, and scalability of carbon footprint tracking in comparison with traditional methods? RQ2
- What could be the barriers and opportunities in the utilization of these technologies? RQ3
2.1.2. Search Strategy
2.1.3. Eligibility Criteria for Systematic Review
2.2. Bibliometric Analysis
2.3. Scientometric Analysis
3. Results
3.1. Selection of Studies
3.2. Mann–Kendall Test
3.3. Hurst Exponent Test
3.4. Journal Source Analysis
3.5. Bradford Zone Analysis
3.5.1. Bradford’s Law of Scattering
3.5.2. Verification of Bradford’s Law: Leimkuhler’s Model
3.6. Keyword Analysis
3.6.1. Co-Occurrence Analysis
3.6.2. Trend Map
- (I)
- Emerging Trend
- (II)
- Established Topics
- (III)
- Newer Topics
3.7. Country Analysis
3.8. Author Analysis
4. Comprehensive Analysis
4.1. Life Cycle Assessment (LCA)
- Eco-Concrete Based on Hemp: In 2024, Isopescu et al. applied the LCA approach by leveraging the SimaPro 9.5 and standards ISO 14040 and ISO 14044 to estimate the carbon footprint and reduction possibility and showed the negative footprint value of −20.3168 kgCO2 [101]. They emphasized this is due to recycling and CO2 sequestration.
- High-Concentration Photo-Voltaics (HCPV): In 2017, Hu et al. used LCA to assess the carbon emissions of HCPV systems [98]. They divided the HCPV systems into stages, categories, items, and inventory data. They used the SimaPRo 8.02 software, integrating INER, Ecoinvent, and CFCP data sources, and found out that material input and manufacturing stages account for 93% of the total carbon emissions with an Energy Payback Time (EPBT) of 2.61 years.
- Residential Buildings: Kurian et al. 2021 performed the life cycle analysis of a residential building using the Building Information Modeling (BIM) LCA approach [97]. They divided the system boundaries into construction, transportation, operational, and destruction stages. They used the software One Click LCA for software computation and used the databases GaBI and Ecoinvent for manual calculation of the carbon footprints. They highlighted that cement is the highest contributor to emissions during the operational stage (83.42%), followed by construction stage emissions (66.6%).
- Shoe Manufacturing: Serweta et al. in 2019 used the LCA approach to calculate the CF in footwear manufacturing [99]. They divided the manufacturing process into eight different stages and used the following empirical relation for each stage:
- Electric and Hybrid Buses: In 2022, Garcia, et al. conducted an LCA study on electric and hybrid buses to evaluate the carbon footprint (CF) at various stages. The emission values were evaluated by calculating the weight of each part and multiplying it by the corresponding emission value in the GREET database [96]. The study found 40% less CO2 emissions in hybrid buses and 60% less CO2 emissions in electric buses as compared to diesel.
4.2. Blockchain-Based Carbon Tracking
- Transparent Carbon Tracking: In 2023, Lee et al. implemented a blockchain-based system for carbon tracking in multi-tier supply chain systems [91]. This proved to improve the traceability data immutability and the transfer of data across the supply chains.
- Carbon Capture and Storage (CCS): In 2023, Aristia et al. proposed a blockchain-based architecture involving various stakeholders such as industrial plants, technical experts, government, the public, and other interested parties to incorporate accountability and transparency in industries [92].
- Security Accounting of Emissions: He et al. in 2024 proposed a blockchain-based carbon emission security accounting scheme (BCESAS), which is a cross-chain carbon emission accounting model, and tested the model using three blockchains theoretically, as well as experimentally to prove the privacy, correctness, and integrity of BCESAS [93].
4.3. ML and AI
- Chemical Industry: In 2024, Zhang et al. proposed a transformer-based framework named FineChem 2 to subdue the limitations of pre-LCA tools in the evaluation of carbon footprints in chemicals [88]. They took 100+ sets of organic chemicals for the experimentation, overcoming other tools by 55% in terms of predictive accuracy.
- Construction Industry: Tavares et al. in 2022, used k-nearest neighbors and random forest ML models to optimize the composition in ultra-high-performance concrete (UHPC) and found them to be more eco-efficient with reduced carbon footprints [86]. They also leveraged the ML model to estimate the global warming potential (GWP) of different concrete mixtures.
- Transportation Industry: In 2022, Wei et al. used super-learner models based on XGBoost, LightGBM, RF, CarBoost, and MOVES—a method for real-time monitoring of CO2 and NOx in diesel trucks [91]. They achieved high accuracy of prediction (R2 = 0.94 and R2 = 0.84) on validation tests.
4.4. Internet of Things: Real-Time Monitoring
- Urban Planning: Zhang and his team in 2021 designed a smart carbon tracking system using IoT sensors and cloud architecture in designing smart, small cities [74]. They used the smart carbon monitoring platform (SCMP) to integrate long-term data, such as GDP-based energy consumption, and short-term data, such as population density, energy usage, and traffic congestion, to find the predicted emissions at street and block levels using correlation coefficients.
- Concrete Manufacturing: In 2022, Kim and the team used Arduino-based sensors and IoT-enabled carbon monitoring in real time using a testbed setup in the Remicon manufacturing process at each stage [75].
4.5. Other Technologies
- Implications of Land Use in Carbon Emissions: Luo et al. [103] discussed how changes in land utilization affect the quantification of carbon emissions. They performed a bibliometric analysis; collected carbon emissions data from major Chinese cities, land cover change (LULC) data, and land use; and standardized the time series from 2001 to 2002. They also leveraged statistical analysis methods such as calculating the coefficient of variation (CV) and correlation analysis between carbon emissions and intensity indicators such as population, gross domestic product (GDP), and built area. Their empirical research found that land use planning, carbon reduction tools, and urban form control can effectively decrease the carbon emission intensity. Likewise, Ahmad et al. [104] quantified carbon emissions from deforestation and forest degradation in the temperate region of the Hindukush Himalaya and provided a detailed understanding of carbon dynamics in that region. They identified changes in land use over the study period using satellite imagery, applied allometric equations to estimate carbon stocks before and after the changes in land use, and estimated 206 kMg C (kilomicrograms of Carbon) from deforestation, 1757 kMg C from degradation, and 221 kMg C from wood harvesting. Cai et al. [105] developed a 30 m high-resolution carbon emission dataset for the Greater Bay Area (GBA). They leveraged the Normalized Difference Vegetation Index (NDVI) to quantify emissions from vegetated areas and to downscale carbon emissions data. They also performed K-means clustering and spatial autocorrelation analyses to identify high-density carbon emission zones. Cai et al. [106] used the Low-Emissions Analysis Platform (LEAP) model and scenario simulation, such as baseline and CCUS (Carbon Capture, Utilization, and Storage), to explore different ways to achieve carbon neutrality and emissions peaks.
- Battery Production: In 2024, Rietdorf et al. integrated digital twins (DTs), life cycle assessment (LCA), and a user interface for real-time CF monitoring [89].
- Data Centers: In 2022, Cao et al. performed a detailed survey and demonstrated a roadmap to evaluate the carbon footprint of data centers at different granular levels of time. They employed carbon usage effectiveness (CUE) and power usage effectiveness (PUE) metrics to estimate the CO2 emissions [80].
- Residential Carbon Footprint: In 2023, Arsiwala and her team leveraged IoT for real-time monitoring of carbon emissions, integrating the Azure Digital Twin (ADT) and Stochastic Gradient Descent (SGD) machine learning algorithms. The study revealed that the use of digital twins in CF tracking and monitoring enhances the accuracy of prediction in building management systems [69].
4.6. Future Research Directions
5. Limitations
6. Conclusions
- (a)
- Life Cycle Assessment: With the integration of LCA software and refined methodologies, this finds its extensive use in construction, electronics, and manufacturing industries.
- (b)
- ML/AI: Rapidly gaining more attention for its dynamic, predictive, and real-time monitoring and effective tracking solutions, especially in the chemical and transportation industries.
- (c)
- Internet of Things: Even with integration barriers related to standardization and data management, IoT’s role is rising mainly in concrete industries.
- (d)
- Blockchain: Rising as a promising technology in secure carbon accounting, with most applications in supply chain systems and industrial plants.
- (e)
- Other Technologies: Several other tools, such as advanced data analytics and geospatial tools, are emerging, highlighting the evolution toward efficient carbon management.
- (a)
- The lack of industry-wide standard methodologies for carbon footprint monitoring.
- (b)
- Challenges in the integration of AI and blockchain, such as a higher risk of data breaches and illegal access.
- (c)
- Challenges in incorporating life cycle assessment (LCA) with real-time tracking of dynamic emissions.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Dwivedi, Y.K.; Hughes, L.; Kar, A.K.; Baabdullah, A.M.; Grover, P.; Abbas, R.; Andreini, D.; Abumoghli, I.; Barlette, Y.; Bunker, D.; et al. Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action. Int. J. Inf. Manag. 2022, 63, 102456. [Google Scholar] [CrossRef]
- Omer, A.M. Energy, environment and sustainable development. Renew. Sustain. Energy Rev. 2008, 12, 2265–2300. [Google Scholar] [CrossRef]
- Gomez-Echeverri, L. Climate and development: Enhancing impact through stronger linkages in the implementation of the Paris Agreement and the Sustainable Development Goals (SDGs). Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2018, 376, 20160444. [Google Scholar] [CrossRef] [PubMed]
- Dzebo, A.; Janetschek, H.; Brandi, C.; Iacobuta, G. Connections between the Paris Agreement and the 2030 Agenda: The Case for Policy Coherence. 2019. Available online: https://www.sei.org/wp-content/uploads/2019/08/connections-between-the-paris-agreement-and-the-2030-agenda.pdf (accessed on 12 December 2024).
- Johnsson, F.; Karlsson, I.; Rootzén, J.; Ahlbäck, A.; Gustavsson, M. The framing of a sustainable development goals assessment in decarbonizing the construction industry—Avoiding ‘Greenwashing’. Renew. Sustain. Energy Rev. 2020, 131, 110029. [Google Scholar] [CrossRef]
- ISO 14064; International Standard for GHG Emissions Inventories and Verification. Jay Wintergreen and Tod Delaney First Environment, Inc.: Boonton, NJ, USA, 2007. Available online: https://gaftp.epa.gov/AIR/nei/ei_conference/EI16/session13/wintergreen.pdf (accessed on 12 December 2024).
- Weisser, D. A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies. Energy 2007, 32, 1543–1559. [Google Scholar] [CrossRef]
- Agbelusi, J.; Arowosegbe, O.; Alomaja, O.; Odunfa, O.; Ballali, C. Strategies for minimizing carbon footprint in the agricultural supply chain: Leveraging sustainable practices and emerging technologies. World J. Adv. Res. Rev. 2024, 2024, 2625–2646. [Google Scholar] [CrossRef]
- Hertwich, E.G.; Wood, R. The growing importance of scope 3 greenhouse gas emissions from industry. Environ. Res. Lett. 2018, 13, 104013. [Google Scholar] [CrossRef]
- Kim, D.; Kim, K.-T.; Park, Y.-K. A Comparative Study on the Reduction Effect in Greenhouse Gas Emissions between the Combined Heat and Power Plant and Boiler. Sustainability 2020, 12, 5144. [Google Scholar] [CrossRef]
- Nordenstam, L.; Ilic, D.D.; Ödlund, L. Corporate greenhouse gas inventories, guarantees of origin and combined heat and power production—Analysis of impacts on total carbon dioxide emissions. J. Clean. Prod. 2018, 186, 203–214. [Google Scholar] [CrossRef]
- Dragomir, V.D. The Disclosure of Industrial Greenhouse Gas emissions: A Critical Assessment of Corporate Sustainability Reports. J. Clean. Prod. 2012, 29–30, 222–237. [Google Scholar] [CrossRef]
- Brander, M.; Gillenwater, M.; Ascui, F. Creative accounting: A critical perspective on the market-based method for reporting purchased electricity (scope 2) emissions. Energy Policy 2018, 112, 29–33. [Google Scholar] [CrossRef]
- Wiedmann, T.; Chen, G.; Owen, A.; Lenzen, M.; Doust, M.; Barrett, J.; Steele, K. Three-scope carbon emission inventories of global cities. J. Ind. Ecol. 2020, 25, 735–750. [Google Scholar] [CrossRef]
- Onat, N.C.; Kucukvar, M.; Tatari, O. Scope-based carbon footprint analysis of U.S. residential and commercial buildings: An input-output hybrid life cycle assessment approach. Build. Environ. 2014, 72, 53–62. [Google Scholar] [CrossRef]
- Matthews, H.D.; Wynes, S. Current global efforts are insufficient to limit warming to 1.5 °C. Science 2022, 376, 1404–1409. [Google Scholar] [CrossRef]
- Kriegler, E.; Luderer, G.; Bauer, N.; Baumstark, L.; Fujimori, S.; Popp, A.; Rogelj, J.; Strefler, J.; van Vuuren, D.P. Pathways limiting warming to 1.5 °C: A tale of turning around in no time? Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2018, 376, 20160457. [Google Scholar] [CrossRef]
- IPCC. Climate Change 2022: Mitigation of Climate Change; IPCC: Geneva, Switzerland, 2022; Available online: https://www.ipcc.ch/report/ar6/wg3/ (accessed on 18 December 2024).
- Akhtar, I.; Kirmani, S.; Jameel, M.; Alam, F. Feasibility Analysis of Solar Technology Implementation in Restructured Power Sector With Reduced Carbon Footprints. IEEE Access 2021, 9, 30306–30320. [Google Scholar] [CrossRef]
- Xie, J.; Fu, J.; Liu, S.; Hwang, W. Assessments of carbon footprint and energy analysis of three wind farms. J. Clean. Prod. 2020, 254, 120159. [Google Scholar] [CrossRef]
- Ball, P.J. A Review of Geothermal Technologies and Their Role in Reducing Greenhouse Gas Emissions in the USA. J. Energy Resour. Technol. 2020, 143, 1–47. [Google Scholar] [CrossRef]
- Idroes, G.M.; Syahnur, S.; Majid, A.; Idroes, R.; Kusumo, F.; Hardi, I. Unveiling the Carbon Footprint: Biomass vs. Geotherm. Energy Indonesia. Ekon. J. Econ. 2023, 1, 10–18. [Google Scholar] [CrossRef]
- Wang, J.; Chen, X.; Liu, Z.; Frans, V.F.; Xu, Z.; Qiu, X.; Xu, F.; Li, Y. Assessing the water and carbon footprint of hydropower stations at a national scale. Sci. Total Environ. 2019, 676, 595–612. [Google Scholar] [CrossRef]
- Bello, M.O.; Solarin, S.A.; Yen, Y.Y. The impact of electricity consumption on CO2 emission, carbon footprint, water footprint and ecological footprint: The role of hydropower in an emerging economy. J. Environ. Manag. 2018, 219, 218–230. [Google Scholar] [CrossRef] [PubMed]
- Adhikari, N.; Adhikari, N.; Poudel, S.K.; Gurung, S.; Subedi, S.; Bastakoti, D. Study on Effect of Flow Rate and Number of Blades on Sizing of Archimedes Screw Turbine. In Proceedings of the 11th IOE Graduate Conference, Pokhara, Nepal, 9–10 March 2022; Available online: http://conference.ioe.edu.np/ioegc11/papers/ioegc-11-024-11052.pdf (accessed on 17 February 2025).
- Xu, X.; Bi, R.; Song, M.; Dong, Y.; Jiao, Y.; Wang, B.; Xiong, Z. Organic substitutions enhanced soil carbon stabilization and reduced carbon footprint in a vegetable farm. Soil Tillage Res. 2023, 236, 105955. [Google Scholar] [CrossRef]
- Suresh, P.; Paul, A.; Kumar, B.A.; Ramalakshmi, D.; Dillibabu, S.P.; Boopathi, S. Strategies for Carbon Footprint Reduction in Advancing Sustainability in Manufacturing. In Environmental Applications of Carbon-Based Materials; Advances in Chemical and Materials Engineering Book Series; IGI Global: Hershey, PA, USA, 2024; pp. 317–350. [Google Scholar] [CrossRef]
- Liu, C.; Cutforth, H.; Chai, Q.; Gan, Y. Farming tactics to reduce the carbon footprint of crop cultivation in semiarid areas. A review. Agron. Sustain. Dev. 2016, 36, 1–16. [Google Scholar] [CrossRef]
- Scolaro, E.; Beligoj, M.; Estevez, M.P.; Alberti, L.; Renzi, M.; Mattetti, M. Electrification of Agricultural Machinery: A Review. IEEE Access 2021, 9, 164520–164541. [Google Scholar] [CrossRef]
- Knobloch, F.; Hanssen, S.V.; Lam, A.; Pollitt, H.; Salas, P.; Chewpreecha, U.; Huijbregts, M.A.J.; Mercure, J.-F. Net emission reductions from electric cars and heat pumps in 59 world regions over time. Nat. Sustain. 2020, 3, 437–447. [Google Scholar] [CrossRef]
- Chowdhury, N.I.; Gopalakrishnan, B.; Adhikari, N.; Li, H.; Liu, Z. Evaluating Electrification of Fossil-Fuel-Fired Boilers for Decarbonization Using Discrete-Event Simulation. Energies 2024, 17, 2882. [Google Scholar] [CrossRef]
- Xia, X.; Li, P.; Xia, Z.; Wu, R.; Cheng, Y. Life cycle carbon footprint of electric vehicles in different countries: A review. Sep. Purif. Technol. 2022, 301, 122063. [Google Scholar] [CrossRef]
- Kumar, S.; Tirlangi, S.; Kumar, A.; Imran, M.; Hp, J.S.P.; Koshariya, A.K.; Sathish, T.; Ubaidullah, M.; Ayub, R.; Reddy, V.R.M.; et al. A review on the contribution of nanotechnology for biofuel production from algal biomass: A bridge to the reduction of carbon footprint. Sustain. Energy Technol. Assess. 2023, 60, 103498. [Google Scholar] [CrossRef]
- Zamboni, A.; Murphy, R.J.; Woods, J.; Bezzo, F.; Shah, N. Biofuels carbon footprints: Whole-systems optimisation for GHG emissions reduction. Bioresour. Technol. 2011, 102, 7457–7465. [Google Scholar] [CrossRef]
- Colella, W.G.; Jacobson, M.Z.; Golden, D.M. Switching to a U.S. hydrogen fuel cell vehicle fleet: The resultant change in emissions, energy use, and greenhouse gases. J. Power Sources 2005, 150, 150–181. [Google Scholar] [CrossRef]
- Mills, E.; Jacobson, A. From carbon to light: A new framework for estimating greenhouse gas emissions reductions from replacing fuel-based lighting with LED systems. Energy Effic. 2011, 4, 523–546. [Google Scholar] [CrossRef]
- Yildiz, O.F.; Yilmaz, M.; Celik, A. Reduction of energy consumption and CO2 emissions of HVAC system in airport terminal buildings. Build. Environ. 2022, 208, 108632. [Google Scholar] [CrossRef]
- Sozer, H. Improving energy efficiency through the design of the building envelope. Build. Environ. 2010, 45, 2581–2593. [Google Scholar] [CrossRef]
- Seyedabadi, M.R.; Eicker, U.; Karimi, S. Plant selection for green roofs and their impact on carbon sequestration and the building carbon footprint. Environ. Chall. 2021, 4, 100119. [Google Scholar] [CrossRef]
- Franchetti, M.J.; Apul, D. Carbon Footprint Analysis. Google Books. 2025. Available online: https://books.google.com/books?hl=en&lr=&id=UIUmD75qyzUC&oi=fnd&pg=PP1&dq=traditional+carbon+footprint+tracking+using+spreadsheets&ots=vznHjENNiO&sig=QAn3UzX7muH8oUxpL2b9EMU3C_Q#v=onepage&q&f=false (accessed on 27 January 2025).
- Kendall, A. Time-adjusted Global Warming Potentials for LCA and Carbon Footprints. Int. J. Life Cycle Assess. 2012, 17, 1042–1049. [Google Scholar] [CrossRef]
- Rodrigo, M.N.N.; Perera, S.; Senaratne, S.; Jin, X. Potential Application of Blockchain Technology for Embodied Carbon Estimating in Construction Supply Chains. Buildings 2020, 10, 140. [Google Scholar] [CrossRef]
- Hammond, G.P.; Jones, C.I. Embodied energy and carbon in construction materials. Proc. Inst. Civ. Eng. Energy 2008, 161, 87–98. [Google Scholar] [CrossRef]
- Symons, K. Book Review: Embodied Carbon: The Inventory of Carbon and Energy (ICE). A BSRIA Guide; ICE Virtual Library: Online, 2011; Volume 164, p. 206. [Google Scholar] [CrossRef]
- Ecoinvent Database. ecoinvent. Available online: https://ecoinvent.org/database/ (accessed on 15 February 2025).
- AusLCI. 2023. Available online: https://www.auslci.com.au/ (accessed on 21 January 2025).
- Aoife. The GreenBook Updated with Great New Features | the Footprint Company. The Footprint Company, 4 July 2021. Available online: https://www.tsariley.com/service/the-greenbook/ (accessed on 27 January 2025).
- SimaPro | The World’s Leading LCA Software. SimaPro. Available online: https://simapro.com/ (accessed on 21 January 2025).
- GaBi (GaBi Education License)—LIFE-C. LIFE-C, 7 July 2023. Available online: https://life-c.eu/programs/gabi-gabi-education-license/ (accessed on 27 January 2025).
- Built Environment Carbon Database. Available online: https://www.becd.co.uk/ (accessed on 9 January 2025).
- Ramboll Launches Open Access Carbon Database for Buildings—Ramboll Group. 2024. Available online: https://www.ramboll.com/co2mpare (accessed on 27 January 2025).
- World’s fastest Building Life Cycle Assessment Software—One Click LCA. One Click LCA® Software. Available online: https://oneclicklca.com/ (accessed on 14 February 2025).
- EC3 Tool. Carbon Leadership Forum, 6 October 2021. Available online: https://carbonleadershipforum.org/ec3-tool/ (accessed on 19 February 2025).
- Free Embodied Carbon Calculator | One Click LCA. 2025. Available online: https://oneclicklca.com/resources/planetary (accessed on 27 January 2025).
- Download Our FREE Embodied Carbon Calculator | Mesh Energy. Available online: https://www.mesh-energy.com/resources/embodied-carbon-calculator-v2 (accessed on 28 December 2024).
- Mann, H.B. Nonparametric Tests Against Trend. Econometrica 1945, 13, 245. [Google Scholar] [CrossRef]
- Hurst, H.E. Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 1951, 116, 770–799. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An Updated Guideline for Reporting Systematic Reviews. Br. Med. J. 2021, 372, 71. [Google Scholar] [CrossRef]
- van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef]
- Aria, M.; Cuccurullo, C. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
- McBurney, M.K.; Novak, P.L. What is bibliometrics and why should you care? In Proceedings of the IEEE International Professional Communication Conference, Shanghai, China, 20–24 May 2019. [Google Scholar] [CrossRef]
- Hasan, M.R.; Wuest, T. A Review of Sustainable Composites Supply Chains. IFIP Adv. Inf. Commun. Technol. 2022, 663, 448–455. [Google Scholar] [CrossRef]
- Delavari, V.; Shaban, E.; Janssen, M.; Hassanzadeh, A. Thematic mapping of cloud computing based on a systematic review: A tertiary study. J. Enterp. Inf. Manag. 2019, 33, 161–190. [Google Scholar] [CrossRef]
- Zhong, B.; Wu, H.; Li, H.; Sepasgozar, S.; Luo, H.; He, L. A scientometric analysis and critical review of construction related ontology research. Autom. Constr. 2019, 101, 17–31. [Google Scholar] [CrossRef]
- Design Trend Mann-Kendall. Available online: https://vsp.pnnl.gov/help/vsample/design_trend_mann_kendall.htm (accessed on 29 December 2024).
- Lundberg, J. Lifting the crown—Citation z-score. J. Informetr. 2007, 1, 145–154. [Google Scholar] [CrossRef]
- Bhargav, P.N.V.; Kishore, A.; Doraswamy, M. Application of Bradford’s Law of Scattering in the Field of Production Engineering Literature: A Bibliometric Analysis of Ph.D. Theses. Int. J. Res. Libr. Sci. 2020, 6, 24. [Google Scholar] [CrossRef]
- Lamas, P.F.; Lopes, S.I.; Caramés, T.M.F. Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case. Sensors 2021, 21, 5745. [Google Scholar] [CrossRef]
- Alharbi, H.A.; Aldossary, M. Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based Smart Agriculture Environment. IEEE Access 2021, 9, 110480–110492. [Google Scholar] [CrossRef]
- Arsiwala, A.; Elghaish, F.; Zoher, M. Digital twin with Machine learning for predictive monitoring of CO2 equivalent from existing buildings. Energy Build. 2023, 284, 112851. [Google Scholar] [CrossRef]
- Patsavellas, J.; Salonitis, K. The Carbon Footprint of Manufacturing Digitalization: Critical literature review and future research agenda. Procedia CIRP 2019, 81, 1354–1359. [Google Scholar] [CrossRef]
- Ihoume, I.; Tadili, R.; Arbaoui, N.; Krabch, H. Design of a low-cost active and sustainable autonomous system for heating agricultural greenhouses: A case study on strawberry (Fragaria vulgaris) growth. Heliyon 2023, 9, e14582. [Google Scholar] [CrossRef]
- IRIE, H.; YAMADA, T. Decision support model for economical material carbon recovery and reduction by connecting supplier and disassembly part selections. J. Adv. Mech. Des. Syst. Manuf. 2020, 14, JAMDSM0024. [Google Scholar] [CrossRef]
- Zhang, H.; Zhang, J.; Wang, R.; Huang, Y.; Zhang, M.; Shang, X.; Gao, C. Smart carbon monitoring platform under IoT-Cloud architecture for small cities in B5G. Wirel. Netw. 2021, 30, 3837–3853. [Google Scholar] [CrossRef]
- Kim, B.; Jeong, J. Real-Time Low-Carbon Prediction in Ready-Mixed Concrete Production Process for Smart Manufacturing. Procedia Comput. Sci. 2022, 203, 205–212. [Google Scholar] [CrossRef]
- Xue, Z.; Zhu, W.; Li, L.; Jiang, C.; Yan, C.; Wang, Y.; Gao, J.; Luo, J. Carbon emissions assessment of cement mixing piles for soft loess improvement and carbon emission reduction using white mud-cement composite material. Case Stud. Constr. Mater. 2024, 21, e03397. [Google Scholar] [CrossRef]
- Li, Z.; Fei, J.; Du, Y.; Ong, K.-L.; Arisian, S. A near real-time carbon accounting framework for the decarbonization of maritime transport. Transp. Res. Part E Logist. Transp. Rev. 2024, 191, 103724. [Google Scholar] [CrossRef]
- Wan, Z.; el Makhloufi, A.; Chen, Y.; Tang, J. Decarbonizing the international shipping industry: Solutions and policy recommendations. Mar. Pollut. Bull. 2018, 126, 428–435. [Google Scholar] [CrossRef]
- Rashid, K.; Hameed, R.; Ahmad, H.A.; Razzaq, A.; Ahmad, M.; Mahmood, A. Analytical framework for value added utilization of glass waste in concrete: Mechanical and environmental performance. Waste Manag. 2018, 79, 312–323. [Google Scholar] [CrossRef]
- Cao, Z.; Zhou, X.; Hu, H.; Wang, Z.; Wen, Y. Towards a Systematic Survey for Carbon Neutral Data Centers. arXiv 2021. [Google Scholar] [CrossRef]
- Xue, L.; Prass, N.; Gollnow, S.; Davis, J.; Scherhaufer, S.; Östergren, K.; Cheng, S.; Liu, G. Efficiency and Carbon Footprint of the German Meat Supply Chain. Environ. Sci. Technol. 2019, 53, 5133–5142. [Google Scholar] [CrossRef] [PubMed]
- Jin, H.; Frost, K.; Sousa, I.; Ghaderi, H.; Bevan, A.; Zakotnik, M.; Handwerker, C. Life cycle assessment of emerging technologies on value recovery from hard disk drives. Resour. Conserv. Recycl. 2020, 157, 104781. [Google Scholar] [CrossRef]
- Wei, N.; Zhang, Q.; Zhang, Y.; Jin, J.; Chang, J.; Yang, Z.; Ma, C.; Jia, Z.; Ren, C.; Wu, L.; et al. Super-learner model realizes the transient prediction of CO2 and NOx of diesel trucks: Model development, evaluation and interpretation. Environ. Int. 2022, 158, 106977. [Google Scholar] [CrossRef]
- Savazzi, S.; Rampa, V.; Kianoush, S.; Bennis, M. An Energy and Carbon Footprint Analysis of Distributed and Federated Learning. IEEE Trans. Green Commun. Netw. 2022, 7, 248–264. [Google Scholar] [CrossRef]
- Aryai, V.; Goldsworthy, M. Day ahead carbon emission forecasting of the regional National Electricity Market using machine learning methods. Eng. Appl. Artif. Intell. 2023, 123, 106314. [Google Scholar] [CrossRef]
- Tavares, C.; Grasley, Z. Machine learning-based mix design tools to minimize carbon footprint and cost of UHPC. Part 2: Cost and eco-efficiency density diagrams. Clean. Mater. 2022, 4, 100094. [Google Scholar] [CrossRef]
- Taslakyan, L.; Baker, M.C.; Shrestha, D.S.; Strawn, D.G.; Möller, G. CO2e footprint and eco-impact of ultralow phosphorus removal by hydrous ferric oxide reactive filtration: A municipal wastewater LCA case study. Water Environ. Res. 2022, 94, e10777. [Google Scholar] [CrossRef]
- Zhang, D.; Wang, Z.; Oberschelp, C.; Bradford, E.; Hellweg, S. Enhanced Deep-Learning Model for Carbon Footprints of Chemicals. ACS Sustain. Chem. Eng. 2024, 12, 2700–2708. [Google Scholar] [CrossRef]
- Rietdorf, C.; Torolsan, K.; Favier, M.; Krishna, S.; Henke, A.; Wahl, K.; Oberle, M.; Defranceski, M.; Koch, D.; Schwarz, J.; et al. Leveraging Digital Twins for Real-Time Environmental Monitoring in Battery Manufacturing. Procedia CIRP 2024, 130, 749–754. [Google Scholar] [CrossRef]
- Alzoubi, Y.I.; Mishra, A. Green blockchain—A move towards sustainability. J. Clean. Prod. 2023, 430, 139541. [Google Scholar] [CrossRef]
- Wei, A.; Toyoda, K.; Yeow, I.; Yeo, Z.; Sze, J.; Lu, W.F. Blockchain-enabled carbon emission management system in a multi-tier supply chain. Procedia CIRP 2023, 116, 233–238. [Google Scholar] [CrossRef]
- Aristia, G.; Salehin, K. Transparent carbon capture and storage using blockchain technology. E3S Web Conf. 2024, 475, 01003. [Google Scholar] [CrossRef]
- He, Y.; Wang, S.; Zhou, Z.; Xiao, K.; Xie, A.; Wu, B. A Blockchain-based carbon emission security accounting scheme. Comput. Netw. 2024, 243, 110304. [Google Scholar] [CrossRef]
- Nechetnyy, N.; Balassem, Z.A.; Subbarayudu, Y.; Goyal, W.; Singh, M.; Mittal, V.; Sobti, S.; Sharma, G.; Nagaraju, K.C. Analysis of Carbon Footprint Reduction in Supply chains using Blockchains. E3S Web Conf. 2024, 581, 01017. [Google Scholar] [CrossRef]
- Gupta, U.; Kim, Y.G.; Lee, S.; Tse, J.; Lee, H.-H.S.; Wei, G.-Y.; Brooks, D.; Wu, C.-J. Chasing Carbon: The Elusive Environmental Footprint of Computing. IEEE Xplore, 1 February 2021. Available online: https://ieeexplore.ieee.org/abstract/document/9407142 (accessed on 29 January 2022).
- García, A.; Monsalve-Serrano, J.; Sari, R.L.; Tripathi, S. Life cycle CO2 footprint reduction comparison of hybrid and electric buses for bus transit networks. Appl. Energy 2022, 308, 118354. [Google Scholar] [CrossRef]
- Kurian, R.; Kulkarni, K.S.; Ramani, P.V.; Meena, C.S.; Kumar, A.; Cozzolino, R. Estimation of Carbon Footprint of Residential Building in Warm Humid Climate of India through BIM. Energies 2021, 14, 4237. [Google Scholar] [CrossRef]
- Hu, A.H.; Huang, L.H.; Lou, S.; Kuo, C.-H.; Huang, C.-Y.; Chian, K.-J.; Chien, H.-T.; Hong, H.-F. Assessment of the Carbon Footprint, Social Benefit of Carbon Reduction, and Energy Payback Time of a High-Concentration Photovoltaic System. Sustainability 2016, 9, 27. [Google Scholar] [CrossRef]
- Serweta, W.; Gajewski, R.; Olszewski, P.; Zapatero, A.; Ławińska, K. Carbon Footprint of Different Kinds of Footwear—A Comparative Study. Fibres Text. East. Eur. 2019, 27, 140–149. [Google Scholar] [CrossRef]
- Lang, S.; Engelmann, B.; Schiffler, A.; Schmitt, J. A Simplified Machine Learning Product Carbon Footprint Evaluation Tool. Clean. Environ. Syst. 2024, 13, 100187. [Google Scholar] [CrossRef]
- Isopescu, D.N.; Adam, L.; Nistorac, A.; Bodoga, A. Carbon Footprint Assessment: Case Studies for Hemp-Based Eco-Concrete Masonry Blocks. Buildings 2024, 14, 3150. [Google Scholar] [CrossRef]
- Abulibdeh, A. Spatial and temporal assessment of the carbon footprint of water and electricity consumption in residential buildings in Qatar. J. Clean. Prod. 2024, 445, 141262. [Google Scholar] [CrossRef]
- Luo, H.; Liu, Z.; Li, Y.; Meng, X.; Yang, X. Characterizing and predicting carbon emissions from an emerging land use perspective: A comprehensive review. Urban Clim. 2024, 58, 102141. [Google Scholar] [CrossRef]
- Ahmad, A.; Liu, Q.-J.; Nizami, S.M.; Mannan, A.; Saeed, S. Carbon emission from deforestation, forest degradation and wood harvest in the temperate region of Hindukush Himalaya, Pakistan between 1994 and 2016. Land Use Policy 2018, 78, 781–790. [Google Scholar] [CrossRef]
- Cai, Y.; Su, S.; Zhang, P.; Chen, M.; Wang, Y.; Xie, Y.; Tan, Q. Quantifying high-resolution carbon emissions driven by land use change in the Guangdong-Hong Kong-Macao Greater Bay Area. Urban Clim. 2024, 55, 101943. [Google Scholar] [CrossRef]
- Cai, L.; Luo, J.; Wang, M.; Guo, J.; Duan, J.; Li, J.; Li, S.; Liu, L.; Ren, D. Pathways for municipalities to achieve carbon emission peak and carbon neutrality: A study based on the LEAP model. Energy 2023, 262, 125435. [Google Scholar] [CrossRef]
Name | Type | Last Updated |
---|---|---|
Built Environment Carbon Database (BECD) [50] | Database | 2023 |
CO2mpare by Ramboll [51] | Database | 2023 |
One Click LCA [52] | Database | 2023 |
Embodied Carbon in Construction Calculator (EC3) [53] | Tool | 2022 |
One Click LCA Planetary [54] | Tool | 2023 |
Mesh Embodied Carbon Calculator v8.0 [55] | Tool | 2023 |
S.N. | Database | Trend | Z-Value | p-Value | Significance |
---|---|---|---|---|---|
1. | Scopus | Increasing | 5.146665 | 2.65 × 10−7 | Significant |
2. | Web of Science | Increasing | 5.047691 | 4.47 × 10 −7 | Significant |
3. | GreenFILE | Increasing | 4.858104 | 1.185 × 10−6 | Significant |
Database | Hurst Exponent Values | Remarks | Interpretation |
---|---|---|---|
Scopus | 0.617 | Persistent | Steady and upward trend |
Web of Science | 0.687 | Persistent | Stronger persistence than other databases, more chances of continued growth |
GreenFILE | 0.601 | Persistent | Moderate persistence, stable and upward trend |
Zone | Number of Sources | Number of Documents | Percentage of Journals |
---|---|---|---|
1 (Core) | 13 | 455 | 2.21 |
2 (Secondary) | 99 | 456 | 16.83 |
3 (Peripheral) | 416 | 456 | 80.96 |
Total | 588 | 1367 | 100 |
Author | h_Index | g_Index | m_Index | TC | NP | PY_Start |
---|---|---|---|---|---|---|
LI X | 7 | 9 | 0.778 | 143 | 9 | 2017 |
LIU Y | 7 | 12 | 0.7 | 306 | 12 | 2016 |
HUANG Y | 6 | 7 | 0.75 | 242 | 7 | 2018 |
LI J | 6 | 13 | 0.6 | 194 | 17 | 2016 |
WANG Y | 6 | 9 | 1 | 107 | 18 | 2020 |
ZHANG H | 6 | 8 | 0.857 | 107 | 8 | 2019 |
CHEN Y | 5 | 5 | 1.25 | 71 | 5 | 2022 |
LI Y | 5 | 11 | 0.714 | 131 | 15 | 2019 |
WANG H | 5 | 10 | 0.833 | 126 | 10 | 2020 |
WANG S | 5 | 9 | 0.833 | 220 | 9 | 2020 |
Paper | Authors | RQ Addressed | Methodology | Objectives | Key Findings | Technologies Used in Carbon Footprint Tracking |
---|---|---|---|---|---|---|
1 | Fraga-Lamas et al. (2021) [68] | RQ1, RQ2 | Review of literature and energy consumption analysis | To examine Green IOT (G-IOT) and Edge AI as sustainable digital transition technologies | G-IoT and Edge AI as key enablers for carbon footprint reduction | G-IoT and Edge AI |
2 | Alharbi and Aldossary (2021) [69] | RQ1, RQ2 | Heuristic algorithms and mathematical modeling (MILP) | To create a smart agricultural edge-fog cloud architecture | Suggested architecture reduced CO2 emissions by 43% | Edge computing, cloud computing, IoT, MILP optimization model |
3 | Arva Arsiwala et al. (2023) [70] | RQ1, RQ2, RQ3 | Use of machine learning to create digital twins | To use the digital twin to monitor and predict CO2 emissions in buildings | Digital twins improve the accuracy of CO2 emissions predictions | Digital twin, machine learning, IoT sensors |
4 | Patsavellas et al. (2019) [71] | RQ1, RQ2, RQ3 | Review of literature and energy consumption analysis | To assess the carbon footprint in digital manufacturing | Identified carbon emissions in IOT and data centers and proposed sleep/wake protocols | IIoT, IoT, cloud computing, edge computing |
5 | Ihoume et al. (2023) [72] | RQ1, RQ2, RQ3 | Experimental investigation with the autonomous solar system | To design an environmentally friendly solar heating system | Reduced carbon emissions by yielding water saving and increasing agricultural efficiency. | Real-time monitoring, IoT |
6 | Irie et al. (2020) [73] | RQ1, RQ2 | Epsilon- constraint method, 3D- CAD model, life cycle inventory | To provide decision support models for low-cost carbon reduction and recovery | Connecting the right disassembly and supplier parts reduces CO2 emissions and expenses | Epsilon constraint programming, IOT, and LCI databases |
7 | Zhang et al. (2021) [74] | RQ1, RQ2, RQ3 | Real-time monitoring, GIS-based simulation, IOT cloud platform | To use cloud architecture and IOT to create a smart carbon monitoring platform | Combined real and long-term data to track carbon emissions at the street level | IoT, GIS, real-time monitoring |
8 | Kim et al. (2022) [75] | RQ1, RQ2, RQ3 | Real-time monitoring using a testbed setup | To suggest a system that computes real-time carbon emissions in concrete manufacturing | Suggested an IoT-based carbon emissions tracking system | IoT, cloud computing |
9 | Xue et al. (2024) [76] | RQ1, RQ2, RQ3 | Monte Carlo simulation, life cycle assessment (LCA), Emissions Assessment Model (SEEAM) | Reducing carbon emissions in the cement mixing process | Mixing cement with 50% white mud gave a 576% reduction in carbon footprint | IoT, SEEAM, Monte Carlo simulation, white mud substitution |
10 | Zhijun Li et al. (2024) [77] | RQ1, RQ2, RQ3 | Real-time carbon accounting using machine learning models | To suggest an NRT carbon monitoring system for ships | Suggested a data-driven ML framework to predict carbon emissions with a cumulative error of 5.83% | Cloud computing, IoT, AIS |
11 | Zheng Wan et al. (2018) [78] | RQ1, RQ2, RQ3 | Policy evaluations of technical, market-based, and operational approaches to lower carbon emissions | To assess the potential of various decarbonizing solutions | Proposed emissions trading systems (ETSs) and pointed out the loopholes in the Energy Efficiency Design Index (EEDI) | Data analytics, GIS, sensors, emissions trading systems (ETSs) |
12 | Khurram Rashid et al. (2018) [79] | RQ2, RQ3 | TOPSIS and AHP to enhance the use of glass in concrete | To test the environmental and mechanical performance of glass-based concrete instead of coarse-based concrete | Showed fewer carbon footprints with the use of glass and a marginal change in the compressive strength of the concrete | Multi-criteria decision making (TOPSIS, AHP), data analytics |
13 | Zhiwei Cao et al. (2022) [80] | RQ1, RQ2, RQ3 | Comprehensive survey related to data centers | To suggest a roadmap for carbon-neutral data centers | Suggested waste heat recovery, AI-driven digital twin framework, and energy efficiency improvements as footprint-reducing tools | IoT, digital twins, AI, data analytics |
14 | Li Xue et al. (2019) [81] | RQ1, RQ2, RQ3 | Scenario Analysis and Material Flow Analysis | To test the carbon footprint in the German meat supply chain | Shown the tracking of carbon emissions using MFA and suggested some mitigation measures | Data analytics, MFA |
15 | Hongyue Jin et al. (2020) [82] | RQ1, RQ2, RQ3 | Life cycle assessment (LCA) | To evaluate and compare new technologies in HDD value recovery | Highlighted the benefits of magnet-to-magnet recycling and HDD reuse for carbon emissions reduction | Data analytics, LCA |
16 | Ning Wei et al. (2022) [83] | RQ1, RQ2 | Machine Learning Super-Learner model (LightGBM, CatBoost, RF, XGBoost) | To develop a transient emission model for NOx and CO2 emissions in diesel trucks | Identified key factors that affect carbon emissions and achieved CO2 model accuracy of (R2 = 0.94) | Machine learning (XGBoost, LightGBM, Super-Learner), data analytics |
17 | Stefano Savazzi et al. (2023) [84] | RQ1, RQ2, RQ3 | Analysis of carbon footprints of federated learning and distributed systems | To calculate the carbon and energy prints of federated, centralized, and decentralized learning approaches | Demonstrated learning paradigms such as federated learning (FL) significantly reduce carbon footprints as compared to centralized learning. | IoT, edge computing, data analytics, federated learning |
18 | Vahid Aryai et al. (2023) [85] | RQ1, RQ2, RQ3 | Forecasting using PSO-Optimized ERT | To leverage machine learning to predict emissions in the Australian NEM | ERT model outperformed LSTM and ELM in the forecasting of emissions | Data analytics, LSTM, ELM, PSO, ERT, and multi-layer perceptrons |
19 | Cesario Tavares et al. (2022) [86] | RQ1, RQ2, RQ3 | Mixed design with machine learning | To use density diagrams to evaluate the environmental cost of ultra-high-performance concrete (UHPC) | Machine learning models with orthogonal arrays optimized for carbon footprint | Data analytics, machine learning (random forest, k-nearest neighbors) |
20 | Lusine Taslakyan et al. (2022) [87] | RQ1, RQ2, RQ3 | Life cycle assessment (LCA) | To track the carbon footprint in wastewater treatment | Found 0.02 kg/m3 of carbon footprint with 99% removal of phosphorus and identified major contributors | Data analytics, LCA |
21 | Dachuan Zhang et al. (2024) [88] | RQ1, RQ2, RQ3 | A transformer-based deep learning model, FineChem2 | To predict the carbon footprint of chemical products | The use of FineChem 2 surpasses the existing methods by 55% in terms of predictive accuracy | Data analytics, machine learning (transformer-based) |
22 | Chantal Rietdorf et al. (2024) [89] | RQ1, RQ2, RQ3 | Use of digital twin and real-time life cycle assessment | To evaluate the real-time carbon monitoring in battery manufacturing | Developed a model integrating digital twin and Life Cycle Analysis for PCF tracking | Sensors, data analytics, digital twin |
23 | Yehia Ibrahim Alzoubi et al. (2023) [90] | RQ1, RQ2 | Review of green blockchain technology | To figure out green blockchain networks with reduced carbon emissions | Highlighted 23 different blockchain networks that produce less CO2 emissions compared to conventional Proof-of-Work Systems | zk-SNARK, green blockchain, Proof of Stake (POS) |
24 | Amos Wei Lun Lee et al. (2023) [91] | RQ1, RQ2, RQ3 | Carbon emission management based on blockchain | To develop blockchain-based carbon emissions monitoring and control | Implemented a carbon monitoring system that records life cycle emissions for multi-tier supply chains | Life cycle assessment (LCA), blockchain |
25 | Gabriela Aristia et al. (2023) [92] | RQ1, RQ2, RQ3 | Carbon capture storage based on blockchain | To enhance efficiency in carbon capture using blockchain technology | Developed a blockchain-based CCS for tracking carbon emissions | IoT sensors, blockchain, LCA |
26 | Yunhua He et al. (2024) [93] | RQ1, RQ2, RQ3 | Carbon emission accounting using blockchain | To develop a blockchain-based system for carbon emission accounting | Proposed BCEAS for integrity and accuracy of carbon emissions tracking | Blockchain, homomorphic encryption |
27 | Nikita Nechetnyy et al. (2024) [94] | RQ1, RQ2, RQ3 | Carbon emission analysis | To improve blockchain operations using a lower carbon footprint | Highlighted that blockchain reduces transportation carbon emissions by 10% for the air, 12% for the sea, 13% for the road, and 15% for the rail | Data analytics and smart contracts |
28 | Udit Gupta et al. (2024) [95] | RQ1, RQ2, RQ3 | Life cycle analysis and industry reports | To quantify the carbon emissions of computing systems | Found that manufacturing in mobile devices and data centers accounts for 74–86% of emissions | LCA |
29 | Antonio García et al. (2022) [96] | RQ1, RQ2, RQ3 | Life cycle analysis (LCA) | To assess the carbon footprint of electric buses in Spain | Hybrid and electric buses reduce CO2 emissions by 60% and 40% respectively | LCA, Greet Model |
30 | Rosaliya Kurian et al. (2021) [97] | RQ1, RQ2, RQ3 | Life cycle analysis (LCA) using BIM | To evaluate the carbon footprint of residential buildings in India | Cement produces 66.6% of carbon emissions, and the operational stage makes up 83.42% of the carbon emissions | BIM, LCA |
31 | Allen H. Hu et al. (2017) [98] | RQ1, RQ2, RQ3 | Life cycle analysis (LCA) | To evaluate the carbon footprint, Taiwan’s HCPV systems | With an EPBT of 2.61 years, 107.69 gCO2eq/kWh of carbon footprint was calculated | LCA, SimaPro, Ecoinvent |
32 | Wioleta Serweta et al. (2019) [99] | RQ1, RQ2, RQ3 | Life cycle analysis (LCA) | To calculate the carbon footprint in shoe manufacturing | 74.5% of the pollutants are from manufacturing, and 5% come from packaging | LCA, multivariate regression analysis |
33 | Silvio Lang et al. (2024) [100] | RQ1, RQ2, RQ3 | Carbon footprint assessment using machine learning (MINDFUL) | To use ML to ease the carbon footprint assessment | Used MINDFUL machine learning to minimize the PCF estimation | Machine learning (random forest, KNN) |
34 | Dorina Nicolina Isopescu et al. (2024) [101] | RQ1, RQ2, RQ3 | Life cycle assessment (LCA) | To evaluate the carbon footprint reduction potential of eco-concrete blocks | The life cycle scenario showed a negative carbon footprint of −20.32 kg of CO2-eq due to recycling and CO2 sequestration. | SimaPro, GWP100, LCA |
35 | Ammar Abulibdeh et al. (2024) [102] | RQ1, RQ2, RQ3 | Multiregional I/O life cycle assessment using spatial analytic tools | To evaluate the carbon footprint of Qatar’s residential structures | Villas are responsible for 83% of electricity-related CF (7 MtCO2) and 94% of water CF (0.06 MtCO2) | Micro-LCA, autocorrelation, Getis–Ord |
Technology | Common Applied Industries | Remarks |
---|---|---|
Life Cycle Assessment (LCA) |
| Widely used but mature; requires complementing |
Internet of Things (IOT) |
| Rising; dynamic, predictive |
Machine Learning (ML)/Artificial Intelligence (AI) |
| Real-time data; important in specific sectors |
Blockchain |
| Rising; transparency focus |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Adhikari, N.; Li, H.; Gopalakrishnan, B. A Bibliometric and Systematic Review of Carbon Footprint Tracking in Cross-Sector Industries: Emerging Tools and Technologies. Sustainability 2025, 17, 4205. https://doi.org/10.3390/su17094205
Adhikari N, Li H, Gopalakrishnan B. A Bibliometric and Systematic Review of Carbon Footprint Tracking in Cross-Sector Industries: Emerging Tools and Technologies. Sustainability. 2025; 17(9):4205. https://doi.org/10.3390/su17094205
Chicago/Turabian StyleAdhikari, Nishan, Hailin Li, and Bhaskaran Gopalakrishnan. 2025. "A Bibliometric and Systematic Review of Carbon Footprint Tracking in Cross-Sector Industries: Emerging Tools and Technologies" Sustainability 17, no. 9: 4205. https://doi.org/10.3390/su17094205
APA StyleAdhikari, N., Li, H., & Gopalakrishnan, B. (2025). A Bibliometric and Systematic Review of Carbon Footprint Tracking in Cross-Sector Industries: Emerging Tools and Technologies. Sustainability, 17(9), 4205. https://doi.org/10.3390/su17094205