Tools for Individuals

From Climate Change AI Wiki
This is the approved revision of this page; it is not the most recent. View the most recent revision.
Jump to navigation Jump to search

This page is about the intersection of individual decision-making and machine learning in the context of climate change. For an overview of human decision-making as a whole, please see the Wikipedia page on this topic.

Individuals and households constantly make decisions that affect their carbon footprint, and many wish to reduce their impact. ML can help quantify the climate impact of consumer products and actions, estimate the benefits resulting from personal behavior change, provide appliance-level residential energy use data, identify households with high potential for efficiency gain, and optimize appliances to operate when low-carbon electricity is available. ML can also help effectively inform people and provide them constructive opportunities by modeling consumer behavior and simplifying information on climate-relevant laws and policies.

Machine Learning Application Areas

Background Readings

General

  • Climate Change Needs Behavior Change: Making the case for behavioral solutions to reduce global warming (2018)[1]: a report on the foundations of climate change and the role for human behavior in slowing it down. Available here.

Individual and Household Consumption

  • Modeling of end-use energy consumption in the residential sector: A review of modeling techniques (2009)[2]: a review of the various modeling techniques used for modeling residential sector energy consumption.
  • Quantifying carbon footprint reduction opportunities for US households and communities (2011)[3] : this study uses consumption-based life cycle accounting techniques to quantify the carbon footprints of typical U.S. households and to quantify greenhouse gas and financial savings from 13 potential mitigation actions. Available here.
  • Residential load scheduling with renewable generation in the smart grid: A reinforcement learning approach (2018)[4]: this paper proposes of a comprehensive, Reinforcement Learning-based model with implementable solution considering consumer comfort, stochastic renewable power, and tariffs. Available here.

Behavior Change

  • Making carbon pricing work for citizens (2018) [5]: this paper synthesizes findings regarding the optimal use of carbon revenues from both traditional economic analyses and studies in behavioural and political science. Available here.
  • The future is now: reducing psychological distance to increase public engagement with climate change (2017)[6] : a study that looks at climate change communication intervention and how it can increase public engagement by reducing the psychological distance of climate change. Available here.
  • Predictive segmentation of energy consumers (2016)[7]: this paper proposes a predictive segmentation technique for identifying sub-groups in a large population that are both homogeneous with respect to certain patterns in customer attributes, and predictive with respect to a desired outcome. Available here.

Online Courses and Course Materials

Under construction

Community

Under construction

Major conferences

Major journals

Major societies and organizations

Libraries and Tools

  • Tomorrow Project - Calculates impact of your climate behaviors by using ML to read your data.
  • WattTime - Predicts marginal emissions cost of energy consumption in real time.
  • Ecological Footprint Explorer - an interactive tool to explore the Ecological Footprint and biocapacity for over 200 countries and regions, updated annually.

Data

Many of the existing datasets involve individual or household level information regarding energy consumption, transportation and consumer habits. Some useful datasets include:

References

  1. Katie Williamson, Aven Satre-Meloy, Katie Velasco, Kevin Green (2018). "Climate Change Needs Behavior Change: Making the Case for Behavioral Solutions to Reduce Global Warming" (PDF).CS1 maint: multiple names: authors list (link)
  2. Swan, Lukas G.; Ugursal, V. Ismet (2009-10). "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques". Renewable and Sustainable Energy Reviews. 13 (8): 1819–1835. doi:10.1016/j.rser.2008.09.033. Check date values in: |date= (help)
  3. Jones, Christopher M.; Kammen, Daniel M. (2011-05). "Quantifying Carbon Footprint Reduction Opportunities for U.S. Households and Communities". Environmental Science & Technology. 45 (9): 4088–4095. doi:10.1021/es102221h. ISSN 0013-936X. Check date values in: |date= (help)
  4. Remani, T.; Jasmin, E.A.; Ahamed, T.P. Imthias (2019-09). "Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach". IEEE Systems Journal. 13 (3): 3283–3294. doi:10.1109/JSYST.2018.2855689. ISSN 1932-8184. Check date values in: |date= (help)
  5. Klenert, David; Mattauch, Linus; Combet, Emmanuel; Edenhofer, Ottmar; Hepburn, Cameron; Rafaty, Ryan; Stern, Nicholas (2018-08). "Making carbon pricing work for citizens". Nature Climate Change. 8 (8): 669–677. doi:10.1038/s41558-018-0201-2. ISSN 1758-678X. Check date values in: |date= (help)
  6. Jones, Charlotte; Hine, Donald W.; Marks, Anthony D. G. (2017-02). "The Future is Now: Reducing Psychological Distance to Increase Public Engagement with Climate Change: Reducing Psychological Distance". Risk Analysis. 37 (2): 331–341. doi:10.1111/risa.12601. Check date values in: |date= (help)
  7. Albert, Adrian; Maasoumy, Mehdi (2016-09). "Predictive segmentation of energy consumers". Applied Energy. 177: 435–448. doi:10.1016/j.apenergy.2016.05.128. Check date values in: |date= (help)