Comparing the Environmental Footprint of Web Stacks in the RealWorld Apps
Abstract
As concerns about the environmental impact of software continue to grow, recent studies have explored how software consumes energy and emits greenhouse gases, often using isolated benchmarks or specific tasks. However, the environmental implications of implementing the same application across different technology stacks remain underexplored. In this paper, we investigate the energy consumption, execution time, and carbon dioxide emissions of a benchmark web application implemented using three distinct technology stacks. Through 32 usage scenarios and the analysis of 10 efficiency-related metrics, our results reveal no statistically significant differences among the stacks regarding their environmental footprint. These findings suggest that replacing the entire technology stack may not be as impactful as optimizing specific application functionalities when aiming for more sustainable software.
Keywords:
Web application technology stacks, energy consumption, execution time, carbon dioxide emission
References
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Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson Correlation Coefficient. Springer, 1–4. DOI: 10.1007/978-3-642-00296-0_5
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Semen Andreevich Budennyy, Vladimir Dmitrievich Lazarev, Nikita Nikolaevich Zakharenko, Aleksei N Korovin, OA Plosskaya, Denis Valer’evich Dimitrov, VS Akhripkin, IV Pavlov, Ivan Valer’evich Oseledets, Ivan Segundovich Barsola, et al. 2022. Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai. Advanced Studies in Artificial Inteligence and Machine Learning 106, Suppl 1 (2022), 118–128. DOI: 10.1134/S1064562422060230
Code Carbon. 2025. Track and reduce CO2 emissions from your computing. [link]
Songtao Chen, Upendar Rao Thaduri, and Venkata Koteswara Rao Ballamudi. 2019. Front-end development in react: an overview. Engineering International 7, 2 (2019), 117–126. DOI: 10.18034/ei.v7i2.662
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Charles Eckert, Xiaowei Wang, Jingcheng Wang, Arun Subramaniyan, Ravi Iyer, Dennis Sylvester, David Blaaauw, and Reetuparna Das. 2018. Neural cache: Bitserial in-cache acceleration of deep neural networks. In ACM/IEEE 45Th annual international symposium on computer architecture. 383–396. DOI: 10.1109/ISCA.2018.00040
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Alberto Gordillo, Coral Calero, Mª Ángeles Moraga, Félix García, João Paulo Fernandes, Rui Abreu, and João Saraiva. 2024. Programming languages ranking based on energy measurements. Software Quality Journal 32, 4 (2024), 1539–1580. DOI: 10.1007/s11219-024-09690-4
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GroqCloud. 2025. Fast LLM inference, OpenAI-compatible. Simple to integrate, easy to scale. [link]
Guimarães and Andrade. 2025. Online Appendix. [link].
Udit Gupta, Young Geun Kim, Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee, Gu-Yeon Wei, David Brooks, and Carole-Jean Wu. 2021. Chasing Carbon: The Elusive Environmental Footprint of Computing. In International Symposium on High-Performance Computer Architecture. 854–867. DOI: 10.1109/HPCA51647.2021.00076
Andreas Haas, Andreas Rossberg, Derek L Schuff, Ben L Titzer, Michael Holman, Dan Gohman, Luke Wagner, Alon Zakai, and JF Bastien. 2017. Bringing the web up to speed with WebAssembly. In ACM SIGPLAN conference on programming language design and implementation. 185–200. DOI: 10.1145/3062341.3062363
Abram Hindle, Alex Wilson, Kent Rasmussen, E Jed Barlow, Joshua Charles Campbell, and Stephen Romansky. 2014. Greenminer: A hardware based mining software repositories software energy consumption framework. In Proceedings of the 11th working conference on mining software repositories. 12–21. DOI: 10.1145/2597073.2597097
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Intel. 2024. Running Average Power Limit (RAPL) Energy Reporting. [link]. Accessed: 2024-01-27.
Congfeng Jiang, Tiantian Fan, Honghao Gao, Weisong Shi, Liangkai Liu, Christophe Cérin, and Jian Wan. 2020. Energy aware edge computing: A survey. Computer Communications 151 (2020), 556–580. DOI: 10.1016/j.comcom.2020.01.004
Eva Kern, Lorenz M Hilty, Achim Guldner, Yuliyan V Maksimov, Andreas Filler, Jens Gröger, and Stefan Naumann. 2018. Sustainable software products—Towards assessment criteria for resource and energy efficiency. Future Generation Computer Systems 86 (2018), 199–210. DOI: 10.1016/j.future.2018.02.044
Keith Kirkpatrick. 2023. The carbon footprint of artificial intelligence. Commun. ACM 66, 8 (2023), 17–19. DOI: 10.1145/3603746
KVision. 2025. Object oriented web framework for Kotlin/JS. [link]. accessed: 2025-03-27.
Loïc Lannelongue, Jason Grealey, and Michael Inouye. 2021. Green algorithms: quantifying the carbon footprint of computation. Advanced science 8, 12 (2021). DOI: 10.1002/advs.202100707
Valérie Masson-Delmotte, Panmao Zhai, Anna Pirani, Sarah L Connors, Clotilde Péan, Sophie Berger, Nada Caud, Y Chen, L Goldfarb, MI Gomis, et al. 2021. Climate change 2021: the physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change 2, 1 (2021). DOI: 10.1017/9781009157896
Medium. 2025. Medium: Where good ideas find you. [link]
Antonio Melé. 2024. Django 5 By Example: Build powerful and reliable Python web applications from scratch. Packt Publishing Ltd.
Luca Ardito Mohammad Rashid and Marco Torchiano. 2015. Energy Consumption Analysis of Algorithms Implementations. In International Symposium on Empirical Software Engineering and Measurement. 1–4. DOI: 10.1109/ESEM.2015.7321198
Anton Moiseev and Yakov Fain. 2018. Angular Development with TypeScript. Simon and Schuster.
Ruby on Rails. 2025. A web-app framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern. [link]. accessed: 2025-03-27.
Our World in Data. 2023. Our World in Data. [link] Disponível em: [link]. Acesso em: 01/04/2025.
Jonathan Oxer and Hugh Blemings. 2011. Practical Arduino: cool projects for open source hardware. Apress.
Showmick Guha Paul, Arpa Saha, Mohammad Shamsul Arefin, Touhid Bhuiyan, Al Amin Biswas, Ahmed Wasif Reza, Naif M. Alotaibi, Salem A. Alyami, and Mohammad Ali Moni. 2023. A Comprehensive Review of Green Computing: Past, Present, and Future Research. IEEE Access 11 (2023), 87445–87494. DOI: 10.1109/ACCESS.2023.3304332
Rui Pereira, Marco Couto, Francisco Ribeiro, Rui Rua, Jácome Cunha, João Paulo Fernandes, and João Saraiva. 2021. Ranking programming languages by energy efficiency. Science of Computer Programming 205 (2021). DOI: 10.1016/j.scico.2021.102609
Bambang Purnomosidi Dwi Putranto, Robertus Saptoto, Ovandry Chandra Jakaria, and Widyastuti Andriyani. 2020. A comparative study of java and kotlin for android mobile application development. In International Seminar on Research of Information Technology and Intelligent Systems. 383–388. DOI: 10.1109/ISRITI51436.2020.9315483
Python. 2025. Programming language that lets you work quickly and integrate systems more effectively. [link]. accessed: 2025-03-27.
Saurabhsingh Rajput and Tushar Sharma. 2024. Benchmarking emerging deep learning quantization methods for energy efficiency. In International Conference on Software Architecture Companion (ICSA-C). 238–242. DOI: 10.1109/ICSA-C63560.2024.00049
Raspberry Pi Foundation. 2025. Raspberry Pi: The official website of Raspberry Pi. [link].
React. 2025. The library for web and native user interfaces. [link]. accessed: 2025-03-27.
L. Ross and A. Christie. 2022. Energy Consumption of ICT. Disponível em: [link] Acesso em: 05/04/2025.
Francisco Ribeiro Jácome Cunha João Paulo Fernandes Rui Pereira, Marco Couto and João Saraiva. 2017. Energy efficiency across programming languages: how do energy, time, and memory relate?. In ACM SIGPLAN international conference on software language engineering. 256–267. DOI: 10.1145/3136014.3136031
Apitchaka Singjai and Uwe Zdun. 2022. Conformance assessment of Architectural Design Decisions on API endpoint designs derived from domain models. Journal of Systems and Software 193 (2022), 111433. DOI: 10.1016/j.jss.2022.111433
Lars St, Svante Wold, et al. 1989. Analysis of variance (ANOVA). Chemometrics and intelligent laboratory systems 6, 4 (1989), 259–272. DOI: 10.1016/0169-7439(89)80095-4
Diomidis Spinellis Stefanos Georgiou, Maria Kechagia. 2017. Analyzing Programming Languages’ Energy Consumption: An Empirical Study. In Brazilian Symposium on Programming Languages. 1–6. DOI: 10.1145/3139367.3139418
Dave Thomas, David Copeland, and Sam Ruby. 2020. Agile Web Development with Rails 6. The Pragmatic Bookshelf.
Craig Walls. 2015. Spring Boot in action. Simon and Schuster.
Wasmboy. 2025. Gameboy Emulator Library written in Web Assembly. [link].
Claes Wohlin, Per Runeson, Martin Höst, Magnus C Ohlsson, Björn Regnell, Anders Wesslén, et al. 2012. Experimentation in software engineering. Vol. 236. Springer.
Real World. 2025. Real World Example Apps. [link]
Rahulkrishna Yandrapally, Saurabh Sinha, Rachel Tzoref-Brill, and Ali Mesbah. 2023. Carving ui tests to generate API tests and API specification. In International Conference on Software Engineering (ICSE). 1971–1982. DOI: 10.48550/arXiv.2305.14692
Man Zhang, Asma Belhadi, and Andrea Arcuri. 2022. JavaScript instrumentation for search-based software testing: A study with RESTful APIs. In Conference on Software Testing, Verification and Validation. 105–115. DOI: 10.1109/ICST53961.2022.00022
Published
2025-09-22
How to Cite
GUIMARÃES, Allysson; ANDRADE, Rodrigo.
Comparing the Environmental Footprint of Web Stacks in the RealWorld Apps. In: BRAZILIAN SYMPOSIUM ON SOFTWARE COMPONENTS, ARCHITECTURES, AND REUSE (SBCARS), 19. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 24-35.
DOI: https://doi.org/10.5753/sbcars.2025.14107.
