ETH Zurich : Computer Science : Pervasive Computing : Distributed Systems : Education : DR HS2020

Digitalisation and the Rebound Effect – Seminar HS2020

Time and place

Introduction session

Maximum number of participants and prioritization

Special Corona measures

The seminar aims for, and starts with, the physical presence of attendees. All of the social distancing and other measures listed in ETH's 'Schutzkonzept Lehre' (and in particular the compulsory wearing of masks) will be respected. Outside noise level permitting, the seminar will be held with open windows for better ventilation. According to the instruction of the rector, there will be no break, so we finish latest at 15:45, to allow for more ventilation between classes.

Anyone with flu-like symptoms should of course not attend the seminar; virtual participation will be arranged. If required by the dynamic circumstances, the seminar will switch to either a hybrid mode (in which participants can attend both remotely or in person) or fully virtual mode.

Objective

The goal of the seminar is to learn about the impact of digitalisation on energy consumption, greenhouse gas emissions, and environmental sustainability in general, with special emphasis on the subtler implications of rebound effects.

Furthermore, students will also be familiarized with reviewing scientific literature, delivering a scientifically sound presentation respecting the allocated time, and producing a scientific report.

Organisation

The seminar consists of talks given by students on selected topics, followed by subsequent discussions led by the instructor.

Seminar attendees select a specific topic within the broader context of current research and prepare an oral presentation. As a starting point, the students are assigned 3-5 important papers in their topic but they have to collect complementary materials and compile them together. Oral presentations must be planned for 30-40 minutes. Each presentation will be followed by a technical discussion as well as a short feedback session on the quality/style of the presentation. Each student also has to write a short essay on the selected topic. Essays must be composed using a given template and must be 3-4 pages long (including figures, tables, and references). The essay is due in 3 weeks after the presentation. The quality of this essay will be evaluated and considered for the final grade.

In addition to studying the selected papers only, the students should do independent literature research, and should summarize the whole topic in their presentations and essays.

The seminar will be held in English. Presentations and reports must be in English.

Templates

Grading

• 60%: quality of the presentation – includes content (topic coverage, problem emphasis, limitations), organization/structure, slides, delivery (enthusiasm, clarity of talk), length (w.r.t. the allocated time) and the answers to questions from the audience.
• 30%: quality of the essay – includes content, structure, and language.
• 10%: involvement in the seminar (i.e., participation in discussions and feedback sessions after each presentation).

Schedule

Topics

1) How can digital systems help saving energy and carbon?

This introductory talk defines digitalisation and presents societal and economic sectors in which digitalisation can lead to energy savings. It further highlights several important mechanisms by which these energy savings can be brought about, and adresses the challenges in quantifying them.

Suggested starting literature:

• International Energy Agency. Digitalisation & Energy, report, 2017 (chapters 1, 2, and 3)
• Global e-Sustainability Initiative. #SMARTer 2030, report, 2015
• Lorenz M. Hilty, Bernard Aebischer and Andrea E. Rizzoli. Modeling and evaluating the sustainablity of smart solutions, Environmental Modelling & Software 56, pp. 1–5, 2014
• Vlad C. Coroama and Mattias Höjer. Assessing GHG Benefits Induced by ICT Services in Practice: A Case Study and Resulting Challenges, Proceedings of ICT for Sustainability (ICT4S) 2016, pp. 29–35, 2016
• Andy Stephens and Veronika Thieme. Framework for Assessing Avoided Emissions. Accelerating innovation and disruptive low- and zero-carbon solutions. Part 2: Draft methodology for calculating avoided emissions, report, 2018
• Vlad C. Coroama, Pernilla Bergmark, Mattias Höjer, and Jens Malmodin. A Methodology for Assessing the Environmental Effects Induced by ICT Services: Part I: Single Services, Proceedings of ICT for Sustainability (ICT4S) 2020, pp. 36–45, 2020



2) Rebound effects

A phenomen first noticed in energy markets, efficiency gains are often undone by rebound effects. This talk reveals the concept of rebound effects, and introduces several types of rebound. It further addresses the relevance of rebound effects in the context of digitalisation.

Suggested starting literature:

• Blake Alcott. Jevons' paradox, Ecological Economics, 54 (1), pp. 9–21, 2005
• J. Daniel Khazzoom. Economic Implications of Mandated Efficiency in Standards for Household Appliances, The Energy Journal, 1 (4), pp. 21–40, 1980
• Steve Sorrell. Jevons’ Paradox revisited: The evidence for backfire from improved energy efficiency, Energy Policy, 37 (4), pp. 1456–1469, 2009
• Peter H. G. Berkhout, Jos C. Muskens and Jan W. Velthuijsen. Defining the rebound effect, Energy Policy, 28 (6–7), pp. 425–432, 2000
• Mathias Binswanger. Technological progress and sustainable development: what about the rebound effect?, Ecological Economics, 36 (1), pp. 119–132, 2001
• Eric Williams. Environmental effects of information and communications technologies, Nature, 479, pp. 354–358, 2011
• Nathaniel C Horner, Arman Shehabi, and Inês L Azevedo. Known unknowns: indirect energy effects of information and communication technology, Environmental Research Letters, 11 (10), 20 pages, 2016



3) Direct energy consumption of ICT

Even before considering rebound effects, the energy savings induced by digitalisation have to be weighted against the direct energy consumption of the technologies enabling them. Before diving into individual digitalisation technologies, this talk thus provides an overview of the energy consumption of information and communication technologies, addressing both today's values and expected future developments.

Suggested starting literature:

• Ward Van Heddeghem, Sofie Lambert, Bart Lannoo, Didier Colle, Mario Pickavet and Piet Demeester. Trends in worldwide ICT electricity consumption from 2007 to 2012, Computer Communications, 50, pp. 64–76, 2014
• Ralph Hintemann and Simon Hinterholzer. Energy Consumption of Data Centers Worldwide – How will the Internet become Green?, Proceedings of ICT for Sustainability (ICT4S), 2019
• Eric Masanet, Arman Shehabi, Nuoa Lei, Sarah Smith, and Jonathan G. Koomey. Recalibrating global data center energy-use estimates, Science, 367 (6481), pp. 984–986, 2020
• Vlad C. Coroama and Lorenz M. Hilty. Assessing Internet energy intensity: A review of methods and results, Environmental Impact Assessment Review, 45, pp. 63–48, 2014
• Vlad C. Coroama, Daniel Schien, Chris Preist and Lorenz M. Hilty. The Energy Intensity of the Internet: Home and Access Networks, ICT Innovations for Sustainability, pp. 137–155, 2015
• Daniel Schien, Vlad C. Coroama, Lorenz M. Hilty and Chris Preist. The Energy Intensity of the Internet: Edge and Core Networks, ICT Innovations for Sustainability, pp. 157–170, 2015
• The Shift Project. Lean ICT: Towards digital sobriety, report, 2019
• Joshua Aslan, Kieren Mayers, Jonathan G. Koomey and Chris France. Electricity Intensity of Internet Data Transmission: Untangling the Estimates, Journal of Industrial Ecology, 22 (4), pp. 785–798, 2017
• Lutz Stobbe, Marina Proske, Severin Beucker, Ralph Hintemann and Klaus-Dieter Lang. Energy Efficiency of ICT: Further Improvement through Customized Products, Proceedings of Electronics Goes Green (EGG) 2016
(longer version in German: Entwicklung des IKT-bedingten Strombedarfs in Deutschland, report, 2015



4) Sharing economy

The first domain in which we look at both the possible induced energy savings but also the potential counter-acting rebound effects, is the sharing economy. The sharing economy entails the promise to potentially save large amounts of energy and resources in the production of goods. Due to their shared nature, much less goods would need to be produced than if they were each individually used. As mountains of thrown-away bicycles from failed sharing schemes testify, however, this is not necessarily so. Other 'sharing' schemes, such as AirBnb or Uber, do not in fact promote sharing but just provide an easier access to services, and might thus contribute to an increase in demand.

Suggested starting literature:

• Harald Heinrichs. Sharing Economy, Gaia, 22 (4), pp. 228-231, 2013
• Chris J. Martin. The sharing economy: A pathway to sustainability or a nightmarish form of neoliberal capitalism?, Ecological Economics, 121, pp. 149-159, 2016
• Maria J. Pouri and Lorenz M. Hilty. Conceptualizing the Digital Sharing Economy in the Context of Sustainability, Sustainability, 10 (12), 2018
• Raza Hasan and Mehdi Birgach. Critical success factors behind the sustainability of the Sharing Economy, Proceedings of the 14th IEEE International Conference on Software Engineering Research, Management and Applications (SERA), 2016.



5) Autonomous vehicles

Autonomous vehicles can bring about numerous potential traffic benefits. Autonomous taxis, for example, could considerably reduce vehicle emissions, while platooning (coordinated travel in close proximities on highways) can substantially reduce the average fuel consumption by coordinating driving speed and behavior, and by minimizing the distance between vehicles to reduce wind resistance. Finally, the emergence of autonomous vehicles could boost the market for sharing such vehicles to the detriment of private car ownership. The time spent in an autonomous vehicle, however, is likely to be more enjoyable or productive than when driving one’s self. The time can be used for socializing or work. This is likely to increase the appeal of car rides, which might lead to more frequent and longer trips. Car rides would also become more attractive as compared to other modes of transport, in particular public transport, leading to a partial substitution of the former for the latter.

Suggested starting literature:

• Jeffery B. Greenblatt and Samveg Saxena. Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles, Nature Climate Change 5, pp. 860–863, 2015
• Austin Brown, Jeffrey Gonder and Brittany Repac. An Analysis of Possible Energy Impacts of Automated Vehicles, In: Gereon Meyer and Sven Beiker (Eds.), Road Vehicle Automation, pp. 137–153, Springer, 2014.
• Lawrence D. Burns. A vision of our transport future, Nature 497, pp. 181-182, 2013
• Joschka Bischoff and Michal Maciewski. Simulation of City-wide Replacement of Private Cars with Autonomous Taxis in Berlin, Procedia Computer Science, 83, pp. 237–244, 2016
• Corey D. Harper, Chris T. Hendrickson, Sonia Mangones and Constantine Samaras. Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions, Transportation Research Part C: Emerging Technologies, 72 (1), pp. 1-9, 2016
• Christina Pakusch, Gunnar Stevens, Alexander Boden and Paul Bossauer. Unintended Effects of Autonomous Driving: A Study on Mobility Preferences in the Future, Sustainability, 10 (7), 2018



6) Machine learning

It is also worthwhile to look at the energy-saving potential of machine learning beyond autonomous vehicles. Neural networks can, for example, improve electric load forecasting in smart grids, supporting a better load scheduling and integration of renewables, and reduce excessive electricity production. Machine learning can further support smart heating and demand-side management in households, improve the efficiency of freight operations or for more general sustainability aims, such as environmental monitoring.

Suggested starting literature:

• Ni Ding, Clémentine Benoit, Guillaume Foggia, Yvon Bésanger, Frédéric Wurtz. Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems, IEEE Transactions on Power Systems, 31 (1), pp. 72–81, 2016
• Salah Bouktif, Ali Fiaz, Ali Ouni, Mohamed Adel Serhani. Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches, Energies, 11 (7), 2018
• Fabiano Pallonetto, Mattia De Rosa, Federico Milano, Donal P.Finn. Demand response algorithms for smart-grid ready residential buildings using machine learning models, Applied Energy 239, pp. 1265-1282, 2019
• Rich Evans and Jim Gao. DeepMind AI reduces energy used for cooling Google data centers by 40%, report, 2016
• Vincent Becker, Wilhelm Kleiminger, Vlad C. Coroamă, Friedemann Mattern. Estimating the savings potential of occupancy-based heating strategies, Energy Inoformatics 1, 2018
• Andreas Froemelt, René Buffat, Stefanie Hellweg. Machine learning based modeling of households: A regionalized bottom-up approach to investigate consumption-induced environmental impacts, Journal of Industrial Ecology 24 (3), pp. 639-652, 2019



7) Teleworking (with special emphasis on the Corona situation)

Teleworking has the potential to greatly reduce work commute and therefore the energy use for personal transport. There are, nevertheless, also numerous possible sources of rebound, such as the increase of non-commute trips during working days or the number of weekend trips to compensate for activities not performed in conjunction with the work commute. Furthermore, teleworking and teleconferences in general can foster (business or private) relationships that would not have existed otherwise and that ultimately also induce physical travel. What happened, however, during the Corona lockdowns, when travel came almost to a complete halt – was there no rebound effect? Or were those travels simply postponed? Are there any hints towards the long-term consequences?

Suggested starting literature:

• H. Scott Matthews and Eric Williams. Telework Adoption and Energy Use in Building and Transport Sectors in the United States and Japan, Journal of Infrastructure Systems, 11 (1), pp. 21–30, 2005
• B. Koenig, D. Henderson, and P. L. Mohktarian. The Travel and Emissions Impacts of Telecommuting for the State of California Telecommuting Pilot Project, Transportation Research Part C: Emerging Technologies, 4 (1), pp. 13–32, 1996
• Christian Fuchs. The implications of new information and communication technologies for sustainability, Environment, Development and Sustainability, 10 (3), pp. 291–309, 2008
• Patricia L. Mokhtarian. A Synthetic Approach to Estimating the Impacts of Telecommuting on Travel, Urban Studies, 35 (2), pp. 215–241, 1998
• Kurt W. Roth, Todd Rhodes, and Ratcharit Ponoum. The energy and greenhouse gas emission impacts of telecommuting in the U.S., 2008 IEEE International Symposium on Electronics and the Environment, pp. 1-6, 2008



8) Skill rebound – a qualitatively new type of rebound?

Digitalisation can induce a democratisation of skills in various sectors. One such example are autonomous vehicles that lower the skill bar needed to 'drive' (in fact, be driven) by a car. This will surely have many positive consequences from a social sustain-ability perspective, and possibly for vulnerable groups in particular. At the same time, these positive consequences will probably be intimately connected to the increased consumption of the now more accessible car rides, thus inducind environmentally detrimental effects. A recent paper proposed that this phenomenon should be called "skill rebound", which generally refers to a lowering of the skill requirements needed to perform a specific activity that leads to an increase in that activity. Is this indeed a qualitatively different phenomenon from other types of rebound effects? Are there further strong examples beyond autonomous vehicles?

Suggested starting literature:

• Vlad C. Coroamă and Friedemann Mattern. Digital Rebound - Why Digitalization Will Not Redeem Us Our Environmental Sins, In ICT4S2019: Proceedings of the 6th International Conference on ICT for Sustainability, 2019
• Vlad C. Coroamă and Daniel Pargman. Skill rebound: On an unintended effect of digitalization, In ICT4S2020: Proceedings of the 7th International Conference on ICT for Sustainability, pp. 213–219, 2020
• Paul E. Ceruzzi. When Computers Were Human, IEEE Annals of the History of Computing 13 (3), pp. 237–244, 1991.
• Jennifer S. Light. When Computers Were Women, Technology and Culture 40 (3), pp. 455-483, 1999



9) Applications with little or no rebound

In all domains so far, digitalisation had the potential to induce energy and resource savings, but there the possibility of rebound effects was looming over each of them. This talk explores application domains that might have little or no rebound at all. Is there a pattern to them?

Suggested starting literature:

• Vlad C. Coroama, Lorenz M. Hilty and Martin Birtel. Effects of Internet-based multiple-site conferences on greenhouse gas emissions, Telematics & Informatics, 29 (4), pp. 362-374, 2012
• Verena Tiefenbeck, Lorenz Goette, Kathrin Degen, Vojkan Tasic, Elgar Fleisch, Rafael Lalive and Thorsten Staake. Overcoming Salience Bias: How Real-Time Feedback Fosters Resource Conservation, Management Science, 64 (3), pp. 1458-1476, 2018
• Lorenz M. Hilty. Why energy efficiency is not sufficient – some remarks on "Green by IT", Proceedings of the 26th Environmental Informatics Conference (EnviroInfo), pp. 13-20, 2012
• Masahito Takahashi and Hiroshi Asano. Japanese Vending Machine and Display Cooler Energy Use Affected by Principal-Agent Problem, In: Quantifying the Effects of Market Failures in the End-Use of Energy, pp. 108–119, International Energy Agency, 2006
• Joseph C. von Fischer et al. Rapid, Vehicle-Based Identification of Location and Magnitude of Urban Natural Gas Pipeline Leaks, Environmental Science & Technology, vol. 51, no. 7, pp. 4091-4099, 2017



10) Conflicting goals enabled by digitalisation

Digitalisation seems to often lead to tradeoffs and conflicting objectives between energy-saving and environmental goals on one side and personal privacy and further societal issues on the other. Advanced sensing technologies, for example, can lead to safer driving and more equitable insurance premiums, but also to massive privacy intrusions and unaffordable premiums for some. User classification using smart shower meters and machine learning works quite reliable, leading to both larger savings’ potential (through individualized feedback) but also to possible privacy intrusions. Autonomous vehicles relying on advanced sensing and machine learning will substantially lower the skills needed to ‘drive a car. Thereby, they are likely to achieve significant societal goals (such as better inclusion of the elderly or physically impaired), but at the same time induce a considerable amount of mileage, congestion, and substitution to the detriment of public transportation. Is there an inescapable pattern here? What distinguishes such digitally induced conflicts from other domains or digital applications that do not induce such tradeoffs?

Suggested starting literature:

• Vlad C. Coroamă. The Smart Tachograph – Individual Accounting of Traffic Costs and Its Implications, In Pervasive 2006: International Conference on Pervasive Computing, pp. 135-152, Dublin, Ireland, 2006
• Sebastian A. Günther, Carlo Stingl, Vlad C. Coroamă, Samuel Schöb, Thorsten Staake. Empowering personalized feedback on hot water usage: a field study with shower meters, In SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp. 763-766, 2020
• Vlad C. Coroamă and Friedemann Mattern. Zielkonflikte zwischen Umwelt- und Datenschutz: Von der Möglichkeit, Daten preiszugeben, um die Umwelt zu retten, In: Anja Höfner and Vivian Frick (Eds.), Was Bits und Bäume verbindet: Digitalisierung nachhaltig gestalten, pp. 58–60, oekom, 2019



11) Is the rebound of digitalisation unavoidable?

Previous presentations introduced several domains in which rebound effects seem to offset any efficiency gains brought about by digital technologies. We have also witnessed some domains with little or no rebound and considered the fate of other general-purpose technologies along humankind's history. Given all these prerequisites, the last presentation will reflect both whether unchecked rebound effects are ultimately unescapable, but also which policy measures might be effective in mitigating them.

Suggested starting literature:

• Tilman Santarius, Hans Jakob Walnum and Carlo Aall. From Unidisciplinary to Multidisciplinary Rebound Research: Lessons Learned for Comprehensive Climate and Energy Policies, Frontiers in Energy Research, 2018
• Edgar G. Hertwich. Consumption and the Rebound Effect: An Industrial Ecology Perspective, Journal of Industrial Ecology, 9 (1-2), pp. 85–98, 2004
• Kenneth Gillingham, Matthew J. Kotchen, David S. Rapson and Gernot Wagner. The rebound effect is overplayed, Nature 493, pp. 475-476, 2013
• David Font Vivanco, René Kemp, Ester van der Voet. How to deal with the rebound effect? A policy-oriented approach, Energy Policy 94, pp. 114-125, 2016
• Jack H. Townsend and Vlad C. Coroama. Digital Acceleration of Sustainability Transition: The Paradox of Push Impacts, Sustainability 10 (8), 2016



Contact

For further information please contact Vlad Coroama.