CarbonKit: Designing A Personal Carbon Tracking Platform
August 14, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Laura Guzman, Stephen Makonin, Roger Alex Clapp
arXiv ID
1608.04162
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.SE
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Ubiquitous technology platforms have been created to track and improve health and fitness; similar technologies can help individuals monitor and reduce their carbon footprints. This paper proposes CarbonKit, a platform combining technology, markets, and incentives to empower and reward people for reducing their carbon footprint. We argue that a goal-and-reward behavioral feedback loop can be combined with the Big Data available from tracked activities, apps, and social media to make CarbonKit an integral part of individuals daily lives. CarbonKit comprises five modules that link personal carbon tracking, health and fitness, social media, and economic incentives. Protocols for safeguarding security, privacy and individuals control over their own data are essential to the design of the CarbonKit. Initially CarbonKit would operate on a voluntary basis, but such a system can also serve as part of a mandatory region-wide initiative. We use the example of the British Columbia to illustrate the regulatory framework and participating stakeholders that would be required to support the CarbonKit in specific jurisdictions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted