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Paper ID:IJSREM65310

A Java-Based Desktop Application for Integrated Environmental Monitoring and Carbon Footprint Management

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65310

Volume:

10 |

6 |

Jun 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65310

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Aditya Khedkar

Aditya Khedkar

Department of Information Technology

Smt. Kashibai Navale College of Engineering, Vadgaon(Bk.)

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Naim Attar

Naim Attar

Department of Information Technology

Smt. Kashibai Navale College of Engineering, Vadgaon(Bk.)

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Chaitanya Jadhav

Chaitanya Jadhav

Department of Information Technology

Smt. Kashibai Navale College of Engineering, Vadgaon(Bk.)

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Atharava Joshi

Atharava Joshi

Department of Information Technology

Smt. Kashibai Navale College of Engineering, Vadgaon(Bk.)

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Mentors

PBG

Prof. B.G. Galande

Department of Information Technology

Smt. Kashibai Navale College of Engineering, Vadgaon(Bk.)

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Abstract

Climate change poses a significant threat to the environment due to increasing greenhouse gas emissions generated by everyday human activities. Many individuals are willing to contribute to sustainability but lack accessible tools to measure and understand their environmental impact. This paper presents EcoTrack, an innovative web application designed to help users monitor and reduce their carbon footprint by tracking daily activities such as travel, food consumption, and household appliance usage. The system includes a real-time carbon footprint calculator, visual analytics using dynamic charts, and an integrated AI-based chatbot that provides personalized sustainability suggestions and environmental guidance. EcoTrack also offers educational resources that promote awareness and encourage long-term eco friendly behavior. The architecture utilizes a web-based interface linked to a Java backend and Firebase cloud database, ensuring accessibility, scalability, and security. Preliminary testing indicates that EcoTrack effectively increases user engagement and awareness, enabling individuals to make more sustainable lifestyle decisions.

KEYWORDS

eco friendly behavior

Cite This Article

Aditya Khedkar, Naim Attar, Chaitanya Jadhav, Atharava Joshi, Prof. B.G. Galande, "A Java-Based Desktop Application for Integrated Environmental Monitoring and Carbon Footprint Management," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-9, Jun 2026. https://doi.org/10.55041/IJSREM65310

Full Article/Conclusion

Eventually, EcoTrack exemplifies the transformative potential of mobile applications in combating climate change by offering a com- prehensive and user-friendly platform for reducing individual carbon footprints. The app not only empowers users to make informed, sustainable choices but also incorporates gamifica- tion and rewards systems, incentivizing eco-friendly behavior. By allowing users to redeem rewards for purchasing eco-friendly products, EcoTrack not only encourages sustainable practices but also promotes the integration of sustainability into everyday life, underscoring the broader impact of mobile technology in fostering global environmental responsibility.   Moreover, EcoTrackgoes                     beyond traditional environmental applications by combining awareness, action, and motivation into a single integrated ecosystem. The system’s ability to provide real-time guidance, track sustainability progress, and support community-driven waste management makes it a valuable tool for both individuals and society. By promoting responsible behavior through education and rewards, EcoTrack encourages users to develop habits that positively impact the environment. As environmental challenges continue to grow, platforms like EcoTrack play a crucial role in supporting a greener future and inspiring a collective shift toward sustainable living.

References

[1]       (2021). A practical solution for pollution management and waste disposal issue in urban environments. Journal of Machine and Computing, 48-56. https://doi.org/10.53759/7669/jmc202101006 [2]       (2021). Untitled. World Journal of Environmental Research,10(2). https://doi.org/10.18844/wjer.v10i2 [3]       (2023). Interaction between waste management and energy generation systems in terms of material properties and environmental impact in the European Union. International Journal of Progressive Research in Engineering Management and Science. https://doi.org/10.58257/ijprems30525 [4]       (2023). Speedy ship- intracity logistic app. International Research Journal of Modernization in Engineering           Technology           and           Science. https://doi.org/10.56726/irjmets37539 [5]       Aarif, K., Yousuff, C., Hashim, B., Hashim, C., & Poruran, S. (2022). Smart bin: waste segregation system using deep learning-internet of things for sustainable smart cities. Concurrency and Computation Practice and Experience,  34(28).https://doi.org/10.1002/cpe.7378 [6]       Addo, H., Dun-Dery, E., Afoakwa, E., Elizabeth, A., Ellen, A., & Rebecca, M. (2017). Correlates of domestic waste management and related health outcomes in Sunyani, ghana: a protocol towards enhancing policy. BMC                             Public            Health,  17(1). https://doi.org/10.1186/s12889-017-4537-8 [7]       Afrianto, I., Irfan, M., & Atin, S. (2019). Aplikasi chatbot speak english media pembelajaran bahasa inggris berbasis android. Komputika Jurnal Sistem Komputer,     8(2),       99-109. https://doi.org/10.34010/komputika.v8i2.2273 [8]       Allesch, A. and Brunner, P. (2015). Material flow analysis as a decision support tool for waste management: a literature review. Journal of Industrial Ecology,           19(5),            753-764. https://doi.org/10.1111/jiec.12354 [9]       Alqahtani, A. (2019). Usability testing of Google Cloud applications: students' perspective. Journal of Technology and Science Education, 9(3), 326. https://doi.org/10.3926/jotse.585 [10]    Altikat, A., Gülbe, A., & Altikat, S. (2021). Intelligent solid waste classification using deep convolutional neural networks. International Journal of Environmental Science and Technology, 19(3), 1285- 1292. ttps://doi.org/10.1007/s13762-021- 03179-4

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Publication Date

6/24/2026

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