The Environmental Cost of Learning: CO2 Emission Comparisons of Virtual Reality, Online, and Alternative Distance Education

The Environmental Cost of Learning: CO2 Emission Comparisons of Virtual Reality, Online, and Alternative Distance Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Environmental Cost of Learning: CO2 Emission Comparisons of Virtual Reality, Online, and Alternative Distance Education Xavier Monnet, Gizella Melania Istrate, Frank Van Gelder, Gergő József Szőllősi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8700884/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose: Environmental sustainability has become a critical concern across various sectors, including education and healthcare. Professional training programmes, particularly in intensive care medicine, are essential for maintaining competency but often lack consideration of their ecological impact. This study was conducted by European Society of Intensive Care Medicine (ESICM) and investigated the carbon footprints of three distinct training modalities: in-person alternative distance training, online training, and virtual reality (VR) training. Methods: Data from 116 participants in ESICM training programs were used to estimate CO₂ emissions for each training modality, considering travel distances, transportation modes, and standardized emission factors. Results: CO₂ emissions were significantly lower for both online (median: 43 kg per participant, interquartile range: 32–64 kg) and VR training (median: 43 kg, interquartile range: 28–56 kg) compared to in-person training (median: 429 kg, interquartile range: 345–490 kg; p<0.001 for both comparisons). No significant difference was found between online and VR training (p=0.893) in terms of CO₂ emission. Conclusion: The findings highlight the environmental benefits of digital education modalities, contributing to a significant reduction in CO₂ regarding online and VR training compared to in-person alternatives. Carbon footprint Sustainability Online training Virtual reality training Intensive care Figures Figure 1 Introduction Environmental sustainability has become a global concern, influencing policy decisions, business strategies, and individual behaviour. The education and healthcare sectors are not immune to this growing awareness, as organizations and institutions explore ways to reduce their carbon footprints while maintaining and increasing high standards of service delivery. The European Society of Intensive Care Medicine (ESICM) addresses the significant environmental impact of Intensive Care Units (ICUs), including energy efficiency, waste reduction and sustainable procurement practices, while maintaining high quality of patient care. It highlights the importance of integrating sustainability into clinical practice, research, education, and organizational policies, urging ICU stakeholders to collaboratively foster a resilient and environmentally responsible healthcare system [ 1 ]. In line with these recommendations, research has been conducted on the ecological impact of training methods, which are necessary for continuous skill development and maintaining competency in intensive care medicine [ 2 – 5 ]. Previous literature on the CO₂ footprint of educational and professional training programmes is very limited and detail remain underexplored. Although studies have examined the environmental impact of general educational systems, there is a notable lack of research focusing on professional training in healthcare. This is particularly the case in real-world scenarios involving online, virtual reality (VR) [ 6 , 7 ], and alternative distance education modalities. Given the global accessibility and strategic circular economy opportunities these training approaches offer, it is crucial to understand their ecological impact to inform sustainable educational practices. The Virtual reality training in Intensive Care To Optimize knowledge & skills Retention In Achieving better clinical practice (VICTORIA) study, conducted by ESICM, offers a novel approach to training that incorporates various educational technologies, including online and VR platforms [ 8 , 9 ]. While their effectiveness has been proven, especially for immediate response in healthcare emergencies [ 10 , 11 ], their environmental impact remains unclear. The present analysis aims to address this gap by conducting a comparative analysis of CO₂ emissions associated with three distinct training modalities: in-person alternative distance training, online training, and VR training. By quantifying the CO₂ footprints of these modalities using data from the VICTORIA study, this study seeks to provide actionable insights into the environmental sustainability of professional training programmes in the intensive care field. Methods Study design and setting This sub-study is part of a larger multiple-method study that was designed to assess training effectiveness in intensive care medicine and employed a two-arm intervention design to evaluate two educational modalities (the VICTORIA Study). Both educational modalities, the online and the VR training programmes encompassed an identical curriculum structured into five modules: Antimicrobial Stewardship, Hemodynamic Monitoring, Mechanical Ventilation in ARDS Patients, Renal Replacement Therapy, and Veno-Venous Extracorporeal Membrane Oxygenation (VV-ECMO). The same group of subject-matter experts contributed to the development of the VR modules and facilitated the online sessions to ensure alignment of educational objectives across both modalities. Despite the shared content and instructional personnel, the delivery formats differed. The online training was conducted as a single, 8-hour, expert-led live session via the ZOOM platform, fostering synchronous interaction. In contrast, the VR training was designed for asynchronous learning, accessible over a two-week period. Participants could engage with the content independently and had the opportunity to interact with instructors through a moderated online forum for addressing questions and discussions. This sub-study focused on these two already implemented interventions and one hypothetical scenario: 1) implemented VR-based training, 2) implemented online training, 3) hypothetical 3-day in-person training held in Brussels for all participants, considering travels by car, public transport [12], flights, and return via the same route from country of origin. Study participants were purposively selected from European countries based on the structure of their intensive care training programmes, ensuring diverse representation. A total of 141 participants were randomized into one of two intervention arms, of these, 67 individuals were assigned to online conventional training, while 74 were allocated to web-based self-paced VR training. The final number of participants who completed the pre-test, intervention and post-test was 57 in the online training group and 59 in the VR training group, amounting to 116 participants from18 European countries. Ethical approval was obtained from Veritas Independent Review Board (Reference number: 2024-3511-17603-3). Hence, Data Collection and Estimation To evaluate the predicted CO₂ footprint of the three different educational modalities, an analysis was conducted to estimate and compare CO₂ emissions associated with each scenario. The analyses were standardized to calculate the total CO₂ equivalent (kg CO₂) for each scenario [13], enabling an accurate evaluation. The predictions were based on calculated emissions per individual for all scenarios. CO₂ emissions were predicted for each training scenario using detailed travel and activity data, considering each participant’s city and country of origin relative to Brussels. Emissions were calculated based on a combination of direct travel distances, corrections for transport mode-specific deviations, and standardized emission factors. Point-to-point distances to Brussels were computed using geospatial coordinates based on the information provided by the subjects (both residential and hospital addresses), adjusted for travel mode variability [6, 12–16] to estimate real distances taken: car trips were adjusted by adding 45% for short distances and 25% for distances over 100 km, while flights and high-speed rail were adjusted by 7.5% to account for indirect routes [17]. Emission factors were sourced from authoritative databases [15–18] , ensuring accuracy and comparability across all calculations. Scenario 1: VR training at the intensivist’s local hospital In this decentralized model, the intensivist commutes daily from home to the hospital and back over the three-day period, resulting in six car travel legs in total. Emissions included six local commutes by car (home-hospital-home over three days, including pre-test, study of VR resources and post-test). Although this approach drastically reduces emissions from long-distance air travel and hotel stays, an additional source of emissions must be considered, in association with the production of the VR content. Scenario 2: Online training at the intensivist’s local hospital Scenario 2 is methodologically aligned with Scenario 1, as both deliver equivalent educational content. No additional emissions were attributed to live instruction in Scenario 2, given that the clinical cases and instructional material were identical to those presented in the VR training with the exception that here no VR was used. Scenario 3 : Hypothetical in-person training in Brussels, Belgium (at the ESICM office). The third scenario involves a traditional, in-person training in Brussels, Belgium, including travel and training time. Emissions were calculated over six travel segments: home to airport (car), airport to Brussels (air), ground transport to and from the training venue (ZIP code 1000), and return travel following the same route. Hotel accommodation emissions were also included based on a three-night stay [18]. For all scenarios, total CO₂ equivalent (CO₂e) emissions per participant were calculated. Travel distances were derived using ZIP-code-based coordinates and adjusted with correction factors: 1.45 for car travel and 1.075 for air travel. Emission factors applied were: 0.192 kg CO₂e/km (car), 0.100 kg CO₂e/km (air travel), and 0.005 kg CO₂e/km (high-speed rail). Hotel stays for the in-person scenario added 20.4 kg CO₂e per day per participant. The use of the internet was excluded from this study. Including it would have required modeling background processes such as energy consumption of servers, data transmission, and online booking platforms. Furthermore, internet use is a prevalent background activity shared across numerous functions, making allocation to this specific study highly uncertain. This exclusion represents a limitation that should be considered in result interpretation. This harmonized methodology enables an evidence-based evaluation of the environmental impact of centralized versus decentralized training models, contributing to the sustainability goals of the VICTORIA Project. CO₂ Emission Modelling The data model for the VICTORIA project’s ecological footprint analysis was using standardized emission factors and consistent data sources. Each scenario was broken down into transport-related “legs” (e.g., home to airport, plane travel, car trips), with emissions calculated per leg based on mode of transport and distance. To improve accuracy, correction factors were applied: a multiplier of 1.45 was used to adjust ZIP code-based distances for car travel to better reflect real-world routes rather than straight-line (point-to-point) distances, and 1.075 was applied to flight distances to approximate actual flight paths over great circle routes. The scenario 1 also included emissions from producing the VR content, involving travel by 21 intensivists to filming sites (Leuven, Ghent, and Paris) with transport modes assigned based on location. Emission values were derived using recognized conversion factors, ensuring comparability, and the total CO 2 equivalent emissions per scenario were summed for direct comparison. The comparison resulted in a total CO 2 equivalent (kg CO 2 ) for each scenario. Statistical Analysis Descriptive statistics were calculated to summarize the predicted CO₂ emissions for each training modality. Descriptive metrics included median, interquartile range, and minimum and maximum predicted values. Pairwise comparisons between groups were conducted using Dunn’s test with Bonferroni corrections to identify significant differences between the pairs regarding CO₂ emissions across the three modalities. Statistical analysis was performed using Stata Statistical Software (version 13.0, Stata Corp, College Station, Texas, United States of America). Results Median travel distances to Brussels varied across countries, reflecting the geographical diversity of the participants. The shortest median distance was observed for Belgium (84 km), followed by France (320 km) and the United Kingdom (400 km). In contrast, participants from Malta (2014 km), Finland (1947 km), and Portugal (1873 km) had the longest travel distances. The overall median travel distance was 1168 km (interquartile range: 781–1498 km), with values ranging from 32 km to 2045 km depending on country of origin (Table 1). In scenario 1 (VR training), the predicted total median emission was 43 (28-56) kg, ranging from a minimum of 3 kg to a maximum predicted value of 295 kg. France (248 kg) and Belgium (94 kg) showed the highest median CO₂ emissions, whereas the lowest values were recorded in Romania (3 kg) and Malta (26 kg). In scenario 2 (Online training), the predicted total median CO₂ emission was 43 (32-64) kg, ranging from 3 kg to 280 kg. Furthermore, Belgium (138 kg) and the Czech Republic (71 kg) showed the highest median CO₂ emissions and the lowest predicted values were observed in Romania (3 kg) and Malta (26 kg). The total predicted median CO 2 emission for scenario 3 (Hypothetical in-person training) was 429 (345-490) kg. The minimum predicted value was 196 kg, and the maximum predicted value was 606 kg, depending on the country of origin. The highest median CO₂ emissions were observed for Finland (606 kg) and Malta (590 kg), while the lowest were estimated for Belgium (216 kg) and France (257 kg). Several countries showed significant differences in CO₂ emissions between digital modalities (scenarios 1 and 2) and in-person training (Scenario 3). Specifically, median emissions in Austria, Belgium, Croatia, Germany, Ireland, Italy, Malta, Poland, Portugal, Romania, Slovenia, and Spain were significantly (p<0.05) lower for both VR and online training compared to hypothetical physical attendance. This indicates that hypothetical in-person training consistently resulted in higher emissions relative to VR and online trainings in multiple settings. At the overall level, both VR and online training resulted in significantly lower CO₂ emissions compared to in-person training (p<0.001 for both comparisons) (Figure 1). There was no significant difference in emissions between VR and online training (p=0.893), and these trends were consistent across all countries, regardless of geographic distance. Discussion Our results show that hypothetical in-person training consistently produced substantially higher CO₂ emissions compared to VR and online training. There was no significant difference in emissions between the two digital modalities, which is expected as both are remote, online approaches. This finding was consistent across all participating countries. Framed within the Life Cycle Assessment (LCA) methodology [ 19 ], this study represents a partial LCA restricted to the use phase of training delivery. The analysis focused on travel and accommodation, with the functional unit defined as one training programme per participant. While this scope highlights travel as the dominant contributor to emissions [ 20 , 21 ], future work should extend to other life cycle stages, such as the VR and other online resource production, energy demands of digital infrastructures, and the production and disposal of VR hardware. These findings carry important implications for the design of sustainable training programmes in intensive care. They suggest that VR and online modalities can be prioritized to reduce environmental burdens while preserving accessibility and flexibility provided that educational effectiveness and feasibility are preserved. Evidence from other fields supports this conclusion [ 21 , 22 ]. Together with our results, these findings strengthen the evidence base that remote training and education can generate significant environmental savings whilst maintaining and enhancing our educational role. It is important, however, to recognize that sustainability in education cannot be assessed on environmental grounds alone. The Environmental, Social and Governance (ESG) framework emphasizes the integration of all its dimensions. In the training context, this includes evaluating work–life balance, time away from clinical duties, financial implications for hospitals, and the quality of learning outcomes [ 21 , 23 , 24 ]. Digital education may also mitigate workforce strain, particularly in periods of staff shortage, by minimizing travel and time away from care. Initiatives such as C19_SPACE [ 10 ] illustrate how large-scale online and VR-based training can provide both ecological benefits and scalable educational opportunities. At the same time, the concept of opportunity cost in healthcare training must be acknowledged: diverting clinical professionals from patient care for education carries substantial societal implications, a challenge highlighted during the COVID-19 pandemic [ 25 ]. Strengths of this study include being among the first to quantify CO₂ emissions associated with professional education in intensive care, and to directly compare in-person, VR, and online modalities. The inclusion of participants from multiple European countries increases external validity, and situating the results within the ESG and Sustainable Development Goals (SDG) frameworks enhances their policy and practice relevance. Limitations include the hypothetical nature of the in-person training scenario, which was based on assumptions regarding travel modes and routes that may not fully reflect real-world behaviours (limited multimodal transport). Not all LCA life cycle stages were included leaving space for future research. Emission factors were derived from averages and may not capture variability due to vehicle type, travel class, or accommodation standards. Additional CO₂ emissions associated with deploying a multidisciplinary expert team for VR filming were not included in the core calculations, as this filming represented a one-time production activity rather than a recurring feature of the training delivery. The short-term carbon footprint of VR may appear higher than that of online training. However, once developed, VR training resources can be reused over extended periods and accessed by an unlimited number of users, which may help to mitigate their initial environmental burden in the long term. Finally, this sub-study did not assess training effectiveness across hybrid modalities (online and face to face), which remains an essential component of future research to guide educational strategists in selecting best modalities according to needs while remaining grounded in high-quality, evidence-based and expert-delivered content. Conclusions This study highlights the environmental benefits of online and VR training modalities in professional education, particularly in intensive care medicine. By comparing the CO₂ emissions associated with alternative distance training, online training, and VR training, the findings underscore the potential for substantial reductions in carbon footprints when leveraging virtual and digital education platforms. These insights provide valuable guidance for seeking to minimize the ecological impact of professional training programmes., in line with ESICM’s sustainability initiatives and the United Nations’ SDGs, in particular SDG #17 [ 26 ] underlining the importance of global partnership for sustainable developments. Declarations Disclosures and declarations Author contributions GMI and XM designed the study, and PP, MO, and JDW reviewed and advised. Data collection and study coordination were performed by AB, while GJSZ and FVG provided methodological and statistical expertise. The first draft of the manuscript was written by GMI, and all authors commented on previous versions. All authors read and approved the final manuscript. Funding This research study is supported by the European Society of Intensive Care Medicine. Availability of data and materials No individualized data will be shared; only aggregated data will be available for sharing. Conflict of interest The authors declare no conflict of interest. Ethics approval and consent to participate Ethical approval was obtained from Veritas Independent Review Board (Reference number: 2024-3511-17603-3). Participation was optional, and each participant had the freedom to withdraw from the study at any time. Before the study began, written informed consent was obtained from all participants. References De Waele JJ, Hunfeld N, Baid H, et al (2024) Environmental sustainability in intensive care: the path forward. An ESICM Green Paper. Intensive Care Med 50:1729–1739. https://doi.org/10.1007/s00134-024-07662-7 Bion J, Rothen HU (2014) Models for intensive care training. A European perspective. Am J Respir Crit Care Med 189:256–262. https://doi.org/10.1164/rccm.201311-2058CP Póvoa P, Martin-Loeches I, Duska F, CoBaTrICe Collaboration (2022) Updated competency-based training in intensive care: next step towards a healthcare union in Europe? 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Geographic Distribution of Participants and Estimated CO₂ Emissions. Country of origin Median distance to Brussels (km) Median CO2 emission (kg) with interquartile ranges SCENARIO 1 (VR training) n=59 SCENARIO 2 (Online training) n=57 SCENARIO 3 (Hypothetical in-person training) n=116 Austria (n=7) 1036 (1007-1036) 56 (56-56) * 56 (56-56) * 399 (390-399) Belgium (n=14) 84 (32-98) 94 (61-132) * 138 (61-158) * 216 (198-221) Croatia (n=8) 1246 (1138-1420) 32 (32-32) * 32 (32-32) * 458 (147-525) Czech Republic (n=2) 827 (-) - 71 (-) 364 (-) Finland (n=1) 1947 (-) 53 (-) - 606 (-) France (n=5) 320 (313-320) 248 (200-295) 35 (35-280) 257 (254-257) Germany (n=3) 735 (-) 62 (-) * - 339 (-) Ireland (n=3) 872 (-) 55 (55-55) * 55 (-) 364 (-) Italy (n=16) 789 (785-803) 43 (43-43) * 43 (43-43) * 355 (350-361) Malta (n=11) 2014 (2014-2020) 26 (26-31) * 26 (26-42) * 590 (590-592) Norway (n=2) 1266 (-) 55 (-) 55 (-) 450 (-) Poland (n=4) 1198 (-) - 64 (-) * 429 (-) Portugal (n=4) 1873 (-) 28 (-) * 28 (-) * 563 (-) Romania (n=17) 1498 (1498-1925) 3 (3-3) * 3 (3-3) * 489 (489-575) Slovenia (n=8) 1124 (1064-1270) 48 (48-48) * 48 (48-48) * 438 (415-478) Spain (n=8) 1199 (1199-1449) 37 (37-37) * 37 (37-79) * 430 (430-480) Sweden (n=1) 1444 (-) 27 (-) - 361 (-) United Kingdom (n=2) 400 (-) 39 (-) 39 (-) 274 (-) Total median and interquartile ranges 1168 (781-1498) 43 (28-56) * 43 (32-64) * 429 (345-490) *Significant difference (p<0.05) when compared with Scenario 3 Supplementary Files STROBEchecklistcohort20012026.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 13 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 27 Jan, 2026 First submitted to journal 26 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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business, university of Antwerp","correspondingAuthor":false,"prefix":"","firstName":"Roel","middleName":"","lastName":"Gevaers","suffix":""},{"id":582334982,"identity":"56c711a0-17f1-4e97-b9f0-627f2f18df79","order_by":5,"name":"Anita Barth","email":"","orcid":"","institution":"European Society of Intensive Care Medicine","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"","lastName":"Barth","suffix":""},{"id":582334983,"identity":"37bb387f-eaee-45f7-9cdc-55617ea69afc","order_by":6,"name":"Pedro Povoa","email":"","orcid":"","institution":"NOVA University Lisbon NOVA Medical School: Universidade Nova de Lisboa Nova Medical School","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Povoa","suffix":""},{"id":582334984,"identity":"17a96306-e3c5-4365-bd4a-855582df116e","order_by":7,"name":"Marlies Ostermann","email":"","orcid":"","institution":"Department of critical care, King's college London, Guy' and St Thomas' NHS foundation trust, London","correspondingAuthor":false,"prefix":"","firstName":"Marlies","middleName":"","lastName":"Ostermann","suffix":""},{"id":582334985,"identity":"31feb472-d180-44cd-ade2-4fac7b18dde6","order_by":8,"name":"Jan de Waele","email":"","orcid":"","institution":"Department of intensive care medicine, Gent university","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"de Waele","suffix":""}],"badges":[],"createdAt":"2026-01-26 13:41:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8700884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8700884/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101639071,"identity":"82840ff2-6bc5-4b0f-aacc-aa5474f2d535","added_by":"auto","created_at":"2026-02-02 07:16:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44655,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of predicted CO₂ emissions (kg) across training modalities.\u003c/p\u003e\n\u003cp\u003e* Indicate statistically significant differences between groups (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8700884/v1/37a454ebdc523081aa0f4b73.png"},{"id":101639084,"identity":"06e4b990-7891-4f83-bbbc-066b5307f7f9","added_by":"auto","created_at":"2026-02-02 07:16:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":593237,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8700884/v1/8c46b6ae-64e1-42b3-9f94-2d44e4dc8b19.pdf"},{"id":101639070,"identity":"95d7f4c1-0894-4557-bebd-806d0f336552","added_by":"auto","created_at":"2026-02-02 07:16:53","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":33640,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcohort20012026.docx","url":"https://assets-eu.researchsquare.com/files/rs-8700884/v1/183a1fb1372893243e0e055e.docx"}],"financialInterests":"","formattedTitle":"The Environmental Cost of Learning: CO2 Emission Comparisons of Virtual Reality, Online, and Alternative Distance Education","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnvironmental sustainability has become a global concern, influencing policy decisions, business strategies, and individual behaviour. The education and healthcare sectors are not immune to this growing awareness, as organizations and institutions explore ways to reduce their carbon footprints while maintaining and increasing high standards of service delivery.\u003c/p\u003e \u003cp\u003eThe European Society of Intensive Care Medicine (ESICM) addresses the significant environmental impact of Intensive Care Units (ICUs), including energy efficiency, waste reduction and sustainable procurement practices, while maintaining high quality of patient care. It highlights the importance of integrating sustainability into clinical practice, research, education, and organizational policies, urging ICU stakeholders to collaboratively foster a resilient and environmentally responsible healthcare system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In line with these recommendations, research has been conducted on the ecological impact of training methods, which are necessary for continuous skill development and maintaining competency in intensive care medicine [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious literature on the CO₂ footprint of educational and professional training programmes is very limited and detail remain underexplored. Although studies have examined the environmental impact of general educational systems, there is a notable lack of research focusing on professional training in healthcare. This is particularly the case in real-world scenarios involving online, virtual reality (VR) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and alternative distance education modalities. Given the global accessibility and strategic circular economy opportunities these training approaches offer, it is crucial to understand their ecological impact to inform sustainable educational practices.\u003c/p\u003e \u003cp\u003eThe Virtual reality training in Intensive Care To Optimize knowledge \u0026amp; skills Retention In Achieving better clinical practice (VICTORIA) study, conducted by ESICM, offers a novel approach to training that incorporates various educational technologies, including online and VR platforms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While their effectiveness has been proven, especially for immediate response in healthcare emergencies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], their environmental impact remains unclear.\u003c/p\u003e \u003cp\u003eThe present analysis aims to address this gap by conducting a comparative analysis of CO₂ emissions associated with three distinct training modalities: in-person alternative distance training, online training, and VR training. By quantifying the CO₂ footprints of these modalities using data from the VICTORIA study, this study seeks to provide actionable insights into the environmental sustainability of professional training programmes in the intensive care field.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis sub-study is part of a larger multiple-method study that was designed to assess training effectiveness in intensive care medicine and employed a two-arm intervention design to evaluate two educational modalities (the VICTORIA Study). Both educational modalities, the online and the VR training programmes encompassed an identical curriculum structured into five modules: Antimicrobial Stewardship, Hemodynamic Monitoring, Mechanical Ventilation in ARDS Patients, Renal Replacement Therapy, and Veno-Venous Extracorporeal Membrane Oxygenation (VV-ECMO). The same group of subject-matter experts contributed to the development of the VR modules and facilitated the online sessions to ensure alignment of educational objectives across both modalities. Despite the shared content and instructional personnel, the delivery formats differed. The online training was conducted as a single, 8-hour, expert-led live session via the ZOOM platform, fostering synchronous interaction. In contrast, the VR training was designed for asynchronous learning, accessible over a two-week period. Participants could engage with the content independently and had the opportunity to interact with instructors through a moderated online forum for addressing questions and discussions. This sub-study focused on these two already implemented interventions and one hypothetical scenario: 1) implemented VR-based training, 2) implemented online training, 3) hypothetical 3-day in-person training held in Brussels for all participants, considering travels by car, public transport [12], flights, and return via the same route from country of origin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy participants were purposively selected from European countries based on the structure of their intensive care training programmes, ensuring diverse representation. A total of 141 participants were randomized into one of two intervention arms, of these, 67 individuals were assigned to online conventional training, while 74 were allocated to web-based self-paced VR training. The final number of participants who completed the pre-test, intervention and post-test was 57 in the online training group and 59 in the VR training group, amounting to 116 participants from18 European countries.\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from Veritas Independent Review Board (Reference number: 2024-3511-17603-3). Hence,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the predicted CO₂ footprint of the three different educational modalities, an analysis was conducted to estimate and compare CO₂ emissions associated with each scenario. The analyses were standardized to calculate the total CO₂ equivalent (kg CO₂) for each scenario [13], enabling an accurate evaluation. The predictions were based on calculated emissions per individual for all scenarios.\u003c/p\u003e\n\u003cp\u003eCO₂ emissions were predicted for each training scenario using detailed travel and activity data, considering each participant’s city and country of origin relative to Brussels. Emissions were calculated based on a combination of direct travel distances, corrections for transport mode-specific deviations, and standardized emission factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePoint-to-point distances to Brussels were computed using geospatial coordinates based on the information provided by the subjects (both residential and hospital addresses), adjusted for travel mode variability [6, 12–16] to estimate real distances taken: car trips were adjusted by adding 45% for short distances and 25% for distances over 100 km, while flights and high-speed rail were adjusted by 7.5% to account for indirect routes [17]. Emission factors were sourced from authoritative databases [15–18] , ensuring accuracy and comparability across all calculations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario 1:\u003c/strong\u003e VR training at the intensivist’s local hospital\u003c/p\u003e\n\u003cp\u003eIn this decentralized model, the intensivist commutes daily from home to the hospital and back over the three-day period, resulting in six car travel legs in total. Emissions included six local commutes by car (home-hospital-home over three days, including pre-test, study of VR resources and post-test). Although this approach drastically reduces emissions from long-distance air travel and hotel stays, an additional source of emissions must be considered, in association with the production of the VR content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario 2:\u003c/strong\u003e Online training at the intensivist’s local hospital\u003c/p\u003e\n\u003cp\u003eScenario 2 is methodologically aligned with Scenario 1, as both deliver equivalent educational content. No additional emissions were attributed to live instruction in Scenario 2, given that the clinical cases and instructional material were identical to those presented in the VR training with the exception that here no VR was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario 3\u003c/strong\u003e: Hypothetical in-person training in Brussels, Belgium (at the ESICM office).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe third scenario involves a traditional, in-person training in Brussels, Belgium, including travel and training time. Emissions were calculated over six travel segments: home to airport (car), airport to Brussels (air), ground transport to and from the training venue (ZIP code 1000), and return travel following the same route. Hotel accommodation emissions were also included based on a three-night stay [18].\u003c/p\u003e\n\u003cp\u003eFor all scenarios, total CO₂ equivalent (CO₂e) emissions per participant were calculated. Travel distances were derived using ZIP-code-based coordinates and adjusted with correction factors: 1.45 for car travel and 1.075 for air travel. Emission factors applied were: 0.192 kg CO₂e/km (car), 0.100 kg CO₂e/km (air travel), and 0.005 kg CO₂e/km (high-speed rail). Hotel stays for the in-person scenario added 20.4 kg CO₂e per day per participant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe use of the internet was excluded from this study. Including it would have required modeling background processes such as energy consumption of servers, data transmission, and online booking platforms. Furthermore, internet use is a prevalent background activity shared across numerous functions, making allocation to this specific study highly uncertain. This exclusion represents a limitation that should be considered in result interpretation.\u003c/p\u003e\n\u003cp\u003eThis harmonized methodology enables an evidence-based evaluation of the environmental impact of centralized versus decentralized training models, contributing to the sustainability goals of the VICTORIA Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCO₂ Emission Modelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data model for the VICTORIA project’s ecological footprint analysis was using standardized emission factors and consistent data sources. Each scenario was broken down into transport-related “legs” (e.g., home to airport, plane travel, car trips), with emissions calculated per leg based on mode of transport and distance. To improve accuracy, correction factors were applied: a multiplier of 1.45 was used to adjust ZIP code-based distances for car travel to better reflect real-world routes rather than straight-line (point-to-point) distances, and 1.075 was applied to flight distances to approximate actual flight paths over great circle routes. The scenario 1 also included emissions from producing the VR content, involving travel by 21 intensivists to filming sites (Leuven, Ghent, and Paris) with transport modes assigned based on location. Emission values were derived using recognized conversion factors, ensuring comparability, and the total CO\u003csub\u003e2\u003c/sub\u003e equivalent emissions per scenario were summed for direct comparison. The comparison resulted in a total CO\u003csub\u003e2\u003c/sub\u003e equivalent (kg CO\u003csub\u003e2\u003c/sub\u003e) for each scenario.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were calculated to summarize the predicted CO₂ emissions for each training modality. Descriptive metrics included median, interquartile range, and minimum and maximum predicted values. Pairwise comparisons between groups were conducted using Dunn’s test with Bonferroni corrections to identify significant differences between the pairs regarding CO₂ emissions across the three modalities. Statistical analysis was performed using Stata Statistical Software (version 13.0, Stata Corp, College Station, Texas, United States of America).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMedian travel distances to Brussels varied across countries, reflecting the geographical diversity of the participants. The shortest median distance was observed for Belgium (84 km), followed by France (320 km) and the United Kingdom (400 km). In contrast, participants from Malta (2014 km), Finland (1947 km), and Portugal (1873 km) had the longest travel distances. The overall median travel distance was 1168 km (interquartile range: 781–1498 km), with values ranging from 32 km to 2045 km depending on country of origin (Table 1).\u003c/p\u003e\n\u003cp\u003eIn scenario 1 (VR training), the predicted total median emission was 43 (28-56) kg, ranging from a minimum of 3 kg to a maximum predicted value of 295 kg. France (248 kg) and Belgium (94 kg) showed the highest median CO₂ emissions, whereas the lowest values were recorded in Romania (3 kg) and Malta (26 kg). In scenario 2 (Online training), the predicted total median CO₂ emission was 43 (32-64) kg, ranging from 3 kg to 280 kg. Furthermore, Belgium (138 kg) and the Czech Republic (71 kg) showed the highest median CO₂ emissions and the lowest predicted values were observed in Romania (3 kg) and Malta (26 kg). The total predicted median CO\u003csub\u003e2\u003c/sub\u003e emission for scenario 3 (Hypothetical in-person training) was 429 (345-490) kg. The minimum predicted value was 196 kg, and the maximum predicted value was 606 kg, depending on the country of origin. The highest median CO₂ emissions were observed for Finland (606 kg) and Malta (590 kg), while the lowest were estimated for Belgium (216 kg) and France (257 kg). Several countries showed significant differences in CO₂ emissions between digital modalities (scenarios 1 and 2) and in-person training (Scenario 3). Specifically, median emissions in Austria, Belgium, Croatia, Germany, Ireland, Italy, Malta, Poland, Portugal, Romania, Slovenia, and Spain were significantly (p\u0026lt;0.05) lower for both VR and online training compared to hypothetical physical attendance. This indicates that hypothetical in-person training consistently resulted in higher emissions relative to VR and online trainings in multiple settings. At the overall level, both VR and online training resulted in significantly lower CO₂ emissions compared to in-person training (p\u0026lt;0.001 for both comparisons) (Figure 1). There was no significant difference in emissions between VR and online training (p=0.893), and these trends were consistent across all countries, regardless of geographic distance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show that hypothetical in-person training consistently produced substantially higher CO₂ emissions compared to VR and online training. There was no significant difference in emissions between the two digital modalities, which is expected as both are remote, online approaches. This finding was consistent across all participating countries.\u003c/p\u003e \u003cp\u003eFramed within the Life Cycle Assessment (LCA) methodology [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], this study represents a partial LCA restricted to the use phase of training delivery. The analysis focused on travel and accommodation, with the functional unit defined as one training programme per participant. While this scope highlights travel as the dominant contributor to emissions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], future work should extend to other life cycle stages, such as the VR and other online resource production, energy demands of digital infrastructures, and the production and disposal of VR hardware.\u003c/p\u003e \u003cp\u003eThese findings carry important implications for the design of sustainable training programmes in intensive care. They suggest that VR and online modalities can be prioritized to reduce environmental burdens while preserving accessibility and flexibility provided that educational effectiveness and feasibility are preserved. Evidence from other fields supports this conclusion [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Together with our results, these findings strengthen the evidence base that remote training and education can generate significant environmental savings whilst maintaining and enhancing our educational role.\u003c/p\u003e \u003cp\u003eIt is important, however, to recognize that sustainability in education cannot be assessed on environmental grounds alone. The Environmental, Social and Governance (ESG) framework emphasizes the integration of all its dimensions. In the training context, this includes evaluating work\u0026ndash;life balance, time away from clinical duties, financial implications for hospitals, and the quality of learning outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Digital education may also mitigate workforce strain, particularly in periods of staff shortage, by minimizing travel and time away from care. Initiatives such as C19_SPACE [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] illustrate how large-scale online and VR-based training can provide both ecological benefits and scalable educational opportunities. At the same time, the concept of opportunity cost in healthcare training must be acknowledged: diverting clinical professionals from patient care for education carries substantial societal implications, a challenge highlighted during the COVID-19 pandemic [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStrengths of this study include being among the first to quantify CO₂ emissions associated with professional education in intensive care, and to directly compare in-person, VR, and online modalities. The inclusion of participants from multiple European countries increases external validity, and situating the results within the ESG and Sustainable Development Goals (SDG) frameworks enhances their policy and practice relevance.\u003c/p\u003e \u003cp\u003eLimitations include the hypothetical nature of the in-person training scenario, which was based on assumptions regarding travel modes and routes that may not fully reflect real-world behaviours (limited multimodal transport). Not all LCA life cycle stages were included leaving space for future research. Emission factors were derived from averages and may not capture variability due to vehicle type, travel class, or accommodation standards. Additional CO₂ emissions associated with deploying a multidisciplinary expert team for VR filming were not included in the core calculations, as this filming represented a one-time production activity rather than a recurring feature of the training delivery. The short-term carbon footprint of VR may appear higher than that of online training. However, once developed, VR training resources can be reused over extended periods and accessed by an unlimited number of users, which may help to mitigate their initial environmental burden in the long term. Finally, this sub-study did not assess training effectiveness across hybrid modalities (online and face to face), which remains an essential component of future research to guide educational strategists in selecting best modalities according to needs while remaining grounded in high-quality, evidence-based and expert-delivered content.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the environmental benefits of online and VR training modalities in professional education, particularly in intensive care medicine. By comparing the CO₂ emissions associated with alternative distance training, online training, and VR training, the findings underscore the potential for substantial reductions in carbon footprints when leveraging virtual and digital education platforms. These insights provide valuable guidance for seeking to minimize the ecological impact of professional training programmes., in line with ESICM\u0026rsquo;s sustainability initiatives and the United Nations\u0026rsquo; SDGs, in particular SDG #17 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] underlining the importance of global partnership for sustainable developments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures and declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGMI and XM designed the study, and PP, MO, and JDW reviewed and advised. Data collection and study coordination were performed by AB, while GJSZ and FVG provided methodological and statistical expertise. The first draft of the manuscript was written by GMI, and all authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research study is supported by the European Society of Intensive Care Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo individualized data will be shared; only aggregated data will be available for sharing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from Veritas Independent Review Board (Reference number: 2024-3511-17603-3). Participation was optional, and each participant had the freedom to withdraw from the study at any time. Before the study began, written informed consent was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDe Waele JJ, Hunfeld N, Baid H, et al (2024) Environmental sustainability in intensive care: the path forward. An ESICM Green Paper. Intensive Care Med 50:1729\u0026ndash;1739. https://doi.org/10.1007/s00134-024-07662-7\u003c/li\u003e\n \u003cli\u003eBion J, Rothen HU (2014) Models for intensive care training. A European perspective. Am J Respir Crit Care Med 189:256\u0026ndash;262. https://doi.org/10.1164/rccm.201311-2058CP\u003c/li\u003e\n \u003cli\u003eP\u0026oacute;voa P, Martin-Loeches I, Duska F, CoBaTrICe Collaboration (2022) Updated competency-based training in intensive care: next step towards a healthcare union in Europe? Intensive Care Med 48:1093\u0026ndash;1094. https://doi.org/10.1007/s00134-022-06783-1\u003c/li\u003e\n \u003cli\u003eDuska F, Cecconi M, Intensive Care Fundamentals Taskforce Members of the European Society of Intensive Care Medicine (ESICM), ESICM (2022) We wish you a smooth takeoff! Launching \u0026ldquo;Intensive Care Fundamentals\u0026rdquo;: an ESICM educational initiative for newcomers to intensive care unit. Intensive Care Med 48:1778\u0026ndash;1780. https://doi.org/10.1007/s00134-022-06906-8\u003c/li\u003e\n \u003cli\u003eBalan C, Bubenek-Turconi S-I, Al-Haddad M Intensive Care Fundamentals in Romania. A Critical Step in Romanian Intensive Care Education. J Crit Care Med 10:279\u0026ndash;281. https://doi.org/10.2478/jccm-2024-0029\u003c/li\u003e\n \u003cli\u003eShiradkar S 1, Rabelo L 2, Alasim F 3, et al (2021) Virtual World as an Interactive Safety Training Platform. 219. https://doi.org/10.3390/info12060219\u003c/li\u003e\n \u003cli\u003eTaylor S, Hoang T, Aranda G, et al (1 AD) Immersive Collaborative VR Application Design: A Case Study of Agile Virtual Design Over Distance. Httpsservicesigi-Glob\u003c/li\u003e\n \u003cli\u003ePottle J (2019) Virtual reality and the transformation of medical education. Future Healthc J 6:181\u0026ndash;185. https://doi.org/10.7861/fhj.2019-0036\u003c/li\u003e\n \u003cli\u003eSivarajah RT, Curci NE, Johnson EM, et al (2019) A Review of Innovative Teaching Methods. Acad Radiol 26:101\u0026ndash;113. https://doi.org/10.1016/j.acra.2018.03.025\u003c/li\u003e\n \u003cli\u003eCecconi M, Barth A, Szőllősi GJ, et al (2024) The impact of the massive open online course C19_SPACE during the COVID-19 pandemic on clinical knowledge enhancement: a study among medical doctors and nurses. Intensive Care Med 50:1841\u0026ndash;1849. https://doi.org/10.1007/s00134-024-07652-9\u003c/li\u003e\n \u003cli\u003eSchaller SJ, Mellinghoff J, Cecconi M, on behalf of the C19_Space Taskforce members, ESICM (2022) Education to save lives: C19SPACE, the COVID19 Skills PrepAration CoursE. Intensive Care Med 48:227\u0026ndash;230. https://doi.org/10.1007/s00134-021-06591-z\u003c/li\u003e\n \u003cli\u003eCESGA\u0026reg; ET (2023) The Environmental Impact of High-Speed Rail vs. Air Travel: A Comprehensive Analysis. In: Medium. https://medium.com/@eddie.hc.tsui/the-environmental-impact-of-high-speed-rail-vs-air-travel-a-comprehensive-analysis-636b38ed3812. Accessed 29 Aug 2025\u003c/li\u003e\n \u003cli\u003eSmart Freight Centre. https://www.smartfreightcentre.org/en/our-programs/emissions-accounting/global-logistics-emissions-council/. Accessed 9 Mar 2025\u003c/li\u003e\n \u003cli\u003eJourquin B (2015) Impact of rest periods in road freight transport costs: A network model. https://doi.org/10.13140/2.1.5068.9289\u003c/li\u003e\n \u003cli\u003eEurope - Railway Station. https://data.opendatasoft.com/explore/dataset/europe-railway-station@public/table/. Accessed 9 Mar 2025\u003c/li\u003e\n \u003cli\u003eGeonames - All Cities with a population \u0026gt; 1000. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en\u0026amp;sort=name. Accessed 9 Mar 2025\u003c/li\u003e\n \u003cli\u003eQureshi MA, Hwang H-L, Chin S-M (2002) Comparison of Distance Estimates for Commodity Flow Survey: Great Circle Distances Versus Network-Based Distances. Transp Res Rec 1804:212\u0026ndash;216. https://doi.org/10.3141/1804-28\u003c/li\u003e\n \u003cli\u003eGlobal Energy Review: CO2 Emissions in 2021 \u0026ndash; Analysis. In: IEA. https://www.iea.org/reports/global-energy-review-co2-emissions-in-2021-2. Accessed 29 Aug 2025\u003c/li\u003e\n \u003cli\u003eMcGinnis S, Johnson-Privitera C, Nunziato JD, Wohlford S (2021) Environmental Life Cycle Assessment in Medical Practice: A User\u0026rsquo;s Guide. Obstet Gynecol Surv 76:417\u0026ndash;428. https://doi.org/10.1097/OGX.0000000000000906\u003c/li\u003e\n \u003cli\u003eSingh A, Hadfield J, Gale J, Shaw C (2022) Doctors\u0026rsquo; travel in the Anthropocene. N Z Med J 135:88\u0026ndash;93\u003c/li\u003e\n \u003cli\u003eSharma D, Rizzo J, Nong Y, et al (2024) Virtual Learning Decreases the Carbon Footprint of Medical Education. Dermatol Ther 14:853\u0026ndash;859. https://doi.org/10.1007/s13555-024-01120-4\u003c/li\u003e\n \u003cli\u003eHeller RF, Sun Y-Y, Guo Z, Malik A (2021) Impact on carbon emissions of online study for a cohort of overseas students: A retrospective cohort study. F1000Research 10:849. https://doi.org/10.12688/f1000research.55156.5\u003c/li\u003e\n \u003cli\u003eGualano MR, Sinigaglia T, Lo Moro G, et al (2021) The Burden of Burnout among Healthcare Professionals of Intensive Care Units and Emergency Departments during the COVID-19 Pandemic: A Systematic Review. Int J Environ Res Public Health 18:8172. https://doi.org/10.3390/ijerph18158172\u003c/li\u003e\n \u003cli\u003eGattrell WT, Barraux A, Comley S, et al (2022) The Carbon Costs of In-Person Versus Virtual Medical Conferences for the Pharmaceutical Industry: Lessons from the Coronavirus Pandemic. Pharm Med 36:131\u0026ndash;142. https://doi.org/10.1007/s40290-022-00421-3\u003c/li\u003e\n \u003cli\u003eEbm C, Istrate M, Van Gelder F, et al (2025) Return on investment of rapid ICU workforce upskilling: an economic and cost-effectiveness analysis. Intensive Care Med 51:1453\u0026ndash;1461. https://doi.org/10.1007/s00134-025-08033-6\u003c/li\u003e\n \u003cli\u003eGoal 17 | Department of Economic and Social Affairs. https://sdgs.un.org/goals/goal17. Accessed 6 Sept 2025\u003cem\u003e\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1. Geographic Distribution of Participants and Estimated CO₂ Emissions.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003eCountry of origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003eMedian distance to Brussels (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 360px;\"\u003e\n \u003cp\u003eMedian CO2 emission (kg) with interquartile ranges\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSCENARIO 1\u003c/p\u003e\n \u003cp\u003e(VR training)\u003cbr\u003e\u0026nbsp;n=59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eSCENARIO 2\u003c/p\u003e\n \u003cp\u003e(Online training)\u003cbr\u003e\u0026nbsp;n=57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSCENARIO 3 (Hypothetical in-person training)\u003c/p\u003e\n \u003cp\u003en=116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eAustria (n=7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1036 (1007-1036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e56 (56-56) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e56 (56-56) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e399 (390-399)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBelgium (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e84 (32-98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e94 (61-132) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e138 (61-158) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e216 (198-221)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCroatia (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1246 (1138-1420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e32 (32-32) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e32 (32-32) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e458 (147-525)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eCzech Republic (n=2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e827 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e71 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e364 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eFinland (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1947 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e53 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e606 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eFrance (n=5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e320 (313-320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e248 (200-295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e35 (35-280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e257 (254-257)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eGermany (n=3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e735 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e62 (-) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e339 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eIreland (n=3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e872 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e55 (55-55) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e55 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e364 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eItaly (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e789 (785-803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e43 (43-43) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e43 (43-43) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e355 (350-361)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eMalta (n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2014 (2014-2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e26 (26-31) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e26 (26-42) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e590 (590-592)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNorway (n=2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1266 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e55 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e55 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e450 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePoland (n=4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1198 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e64 (-) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e429 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePortugal (n=4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1873 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e28 (-) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e28 (-) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e563 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eRomania (n=17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1498 (1498-1925)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e3 (3-3) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e3 (3-3) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e489 (489-575)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSlovenia (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1124 (1064-1270)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e48 (48-48) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e48 (48-48) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e438 (415-478)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSpain (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1199 (1199-1449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e37 (37-37) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e37 (37-79) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e430 (430-480)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSweden (n=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1444 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e27 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e361 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eUnited Kingdom (n=2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e400 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e39 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e39 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e274 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eTotal median and interquartile ranges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1168 (781-1498)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e43 (28-56) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e43 (32-64) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e429 (345-490)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant difference (p\u0026lt;0.05) when compared with Scenario 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"intensive-care-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"icme","sideBox":"Learn more about [Intensive Care Medicine](http://link.springer.com/journal/134)","snPcode":"134","submissionUrl":"https://www.editorialmanager.com/icme/default2.aspx","title":"Intensive Care Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carbon footprint, Sustainability, Online training, Virtual reality training, Intensive care ","lastPublishedDoi":"10.21203/rs.3.rs-8700884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8700884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eEnvironmental sustainability has become a critical concern across various sectors, including education and healthcare. Professional training programmes, particularly in intensive care medicine, are essential for maintaining competency but often lack consideration of their ecological impact. This study was conducted by European Society of Intensive Care Medicine (ESICM) and investigated the carbon footprints of three distinct training modalities: in-person alternative distance training, online training, and virtual reality (VR) training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData from 116 participants in ESICM training programs were used to estimate CO₂ emissions for each training modality, considering travel distances, transportation modes, and standardized emission factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCO₂ emissions were significantly lower for both online (median: 43 kg per participant, interquartile range: 32–64 kg) and VR training (median: 43 kg, interquartile range: 28–56 kg) compared to in-person training (median: 429 kg, interquartile range: 345–490 kg; p\u0026lt;0.001 for both comparisons). No significant difference was found between online and VR training (p=0.893) in terms of CO₂ emission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe findings highlight the environmental benefits of digital education modalities, contributing to a significant reduction in CO₂ regarding online and VR training compared to in-person alternatives.\u003c/p\u003e","manuscriptTitle":"The Environmental Cost of Learning: CO2 Emission Comparisons of Virtual Reality, Online, and Alternative Distance Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 07:16:48","doi":"10.21203/rs.3.rs-8700884/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2026-02-13T10:33:25+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-29T09:50:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T09:47:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T15:16:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Intensive Care Medicine","date":"2026-01-26T08:41:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"intensive-care-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"icme","sideBox":"Learn more about [Intensive Care Medicine](http://link.springer.com/journal/134)","snPcode":"134","submissionUrl":"https://www.editorialmanager.com/icme/default2.aspx","title":"Intensive Care Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"931e6772-5905-458e-a06c-1dad3b2b791d","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T09:42:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 07:16:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8700884","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8700884","identity":"rs-8700884","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}