Video-conferencing CO2 calculator
This project was made during CERN WEBFEST 2020 .
CERN LABORATORY :
CERN Webfest 2020 site :
The challenge that myself, with a team of 6 solved together :
https://webfest.cern/project/online-vs-person-co2-calculator
Special thanks to our Mentor Mr. Ben Krikler
Ben Krikler LinkedIn profile : https://www.linkedin.com/in/ben-krikler/
Ben krikler Github profile : https://github.com/benkrikler
Members:
1)Balaji G : https://www.linkedin.com/in/balaji-g-3bb1a1182/
2)Sampada Gaonkar : https://www.linkedin.com/in/sampada-gaonkar-287abb1b1/
3)Ankita Waghmare : https://www.linkedin.com/in/ankita-waghmare-07aab81b1/
4)Hannah Johnson
5)Saudamini Kulkarni : https://www.linkedin.com/in/saudamini-kulkarni-224b901a3/
6)Soni Pokharel
Description
It’s often assumed that moving an event online will produce less CO2 than an equivalent one held in person, but just how true is this? Research by RemotelyGreen so far suggests it can reduce emissions by 96%, but this depends on the length and location of the in-person event, and how far people have to travel, while computing hardware and server power can have sizeable implications for online events, particularly if the power doesn’t come from a sustainable source. Can we turn RemotelyGreen’s simple case study into a functioning model that can help encourage people understand the benefits or impacts of moving their event online?
Weekend Goals
A baseline open-source model that can receive inputs about the type of event (number of people, duration, types of activities (video, audio, screen sharing (?), VR (?)), and types of equipment used by each person and produce an estimate for the carbon footprint of such an event.
Identification of good sources of input datasets (i.e. accuracy, completeness, consistency, timeliness, validity, uniqueness, temporal, geographical and technology coverage) on which to develop and validate the model.
A way to represent the online event footprint in terms of an in-person event or equivalent environmental metric
PROCEDURE FOLLOWED :
Initially we had our target split ,in order to reduce the complexity.
Then we had our team split according to the revised targets.
Finally ,we managed to combine a total outcome from every piece of achievements.
OUR SUBMISSION : https://webfest.cern/node/289
ALL SUBMISSIONS : https://webfest.cern/projects
ABSTRACT :
So basically ,this project is a python implementation of the research paper -Comparison of the energy, carbon and time costs of videoconferencing and in-person meetings by Dennis Ong , Tim Moors, Vijay Sivaraman.
While video conferencing is often viewed as a greener alternative to physically travelling to meet in person, it has its own energy, carbon dioxide and time costs. Here we present the analysis of the total cost of videoconferencing, including operating costs of the network and videoconferencing equipment, lifecycle assessment of equipment costs, and the time cost of people involved in meetings. We compare these costs to the corresponding costs for in-person meetings, which include operating and lifecycle costs of vehicles and the costs of participant time.
END RESULT :
While the costs of these meeting forms depend on many factors such as distance travelled, meeting duration, and the technologies used, we find that videoconferencing takes at most 7% of the energy/carbon of an in-person meeting.
DATA SETS WORKED :
https://data.world/doe/hourly-energy-emission-factors
CO2 emissions from transport (% of total fuel combustion) | Data https://data.worldbank.org/indicator/EN.CO2.TRAN.ZS
CO2 emissions from electricity and heat production, total (% of total fuel combustion) https://data.worldbank.org/indicator/EN.CO2.ETOT.ZS
CO2 Datasets https://www.co2.earth/co2-datasets
https://www.kaggle.com/txtrouble/carbon-emissions
Electric power consumption (kWh per capita) | Data https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC
https://data.world/johnsnowlabs/estimates-emissions-of-co2-at-country-and-global-level
CMS: CO2 Emissions from Fossil Fuels Combustion, ACES Inventory for Northeastern USA https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1501
https://doi.org/10.3334/ORNLDAAC/1501
Car Fuel & Emissions 2000-2013 - dataset by amercader https://data.world/amercader/car-fuel-emissions-2000-2013
CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion) https://data.worldbank.org/indicator/EN.CO2.OTHX.ZS
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent