Harnessing smart city technology to reduce greenhouse gas emission

Photo: tpsdave / pixabay.com

Buildings account for nearly 40 % of total global energy consumption. However, at the same time buildings can, on average, be made up to 40 % more energy efficient (U.S. Green Building Council, n.d.). A natural first step in tackling greenhouse gas emission would therefore be to make the existing system of buildings smarter and more energy efficient by applying IT to its shortcomings.

In discussing shortcomings of the existing system, it is useful to consider both demand-side and supply-side issues. On the demand side, the existing system does not to a sufficient extent empower occupants to make informed decisions about their energy consumption. Occupants must be incentivised to reduce the demand for energy, for instance through gamification. This, however, is difficult to achieve as long as buildings are unable to provide granularity in energy consumption data. Another shortcoming is the inability to adjust to circumstances, e.g. the number of people present in a building, a factor which affects need for heating and ventilation. This challenge is emphasized by the fact that only 27 percent of office occupants occupy buildings that adjust automatically to these circumstances (IBM, 2010). Another shortcoming is the inability to adjust to weather conditions which, for instance, can affect the need for lighting and heating. On the supply side are issues too, for instance small water leakages from toilets, which can lead to large amounts of water being wasted on an accumulated scale (NYC Environment Protection, 2017).

READ MORE: How Copenhagen should use smart city technology to reach sustainability goal

Increased energy efficiency in buildings will not only have positive environmental implications. It will also yield economic benefits through cost reduction. With data granularity, sub-metering will also become possible, giving occupants complete transparency and control of their energy costs, thus assumingly incentivizing reduction of energy consumption. Additionally, monitoring the condition of building infrastructure such as water and heating pipes will allow for more timely failures responses and may in fact enable prediction of failures. Benefits can also be achieved by making the smart building data open, as open data allows for public participation in the provision of services and for new business models to arise. An example of a business arisen from open data from buildings is Helios, which offers tools using open data to calculate the benefits of retrofitting buildings (Gust, 2017). Open data will also help overcome the previously mentioned challenge of silo architecture and provide public transparency.

These achievements are, however, inhibited by hurdles with budget constraints and limited resources and expertise being the most frequently cited inhibitors (Black & Veath, 2017). Related to open data is the issue of whether whether data should be made available free of charge or not and managing implications of open access to data if data is in fact to become a new “natural” resource. Another data related issue is deciding on the appropriate data velocity, a challenge which is particularly evident for municipalities where lack of expertise and resources is one of the most widely recognized hurdles. When making buildings smarter, it is also of paramount importance to acknowledge that buildings only represent one system in the system of systems that makes up a city. Acknowledging that systems are interrelated is important because applying a reductionist view on cities leads to a siloed view which can inhibit positive gains. This is a widely accepted notion, for instance within IT architecture research (e.g. Weill & Ross, 2010).

Making buildings smarter also comes with risks. Though security concerns is not considered a smart city inhibitor (Black & Veath, 2017), cyber security is without a doubt an inherent risk of smart cities. One of the key enablers of smart city technology is Internet of Things, and one of the most prominent challenges of this emerging technology is the risk of data leakages and compromised privacy. However, for this issue, blockchain technology seems to be proving a suitable solution (IBM, 2017). Another risk of digitizing our buildings is exclusion of those who are not digitally adept, for instance elderly citizens or citizens with reduced cognitive capacity.

Despite potential risks, it should be clear to see that the potential gains of applying IT to make buildings smarter by far outweigh the risks. Greenhouse gas emission is simply too important a problem to be ignored, and through smart use of IT, we can come a long way in tackling the problem.

Let’s connect on LinkedIn if you’re also into smart city technology, AI / machine learning or blockchain!

Sources

“Buildings and climate change.” U.S. Green Building Council. U.S. Green Building Council, n.d. Web. 4 July 2017. <http://www.eesi.org/files/climate.pdf>.

“Buildings Energy Data Book.” Buildings Energy Data Book — Data.gov. Publisher Office of Energy Efficiency & Renewable Energy (EERE), 28 Aug. 2015. Web. 04 July 2017.

“Device democracy.” IBM — Device democracy — United States. N.p., 17 Feb. 2017. Web. 04 July 2017.

“Helios Exchange.” Gust. Gust, n.d. Web. 4 July 2017. <https://gust.com/companies/helios_exchange>.

Smart city/smart utility report 2017 — Strategic directions. Rep. Black & Veath, 2017. Web. 4 July 2017.

Smarter Buildings Survey — Consumers rank their office buildings. Rep. Foster City: IBM, 2010. Print.

“Toilet Leak Detection.” NYC Environmental Protection. New York City, n.d. Web. 04 July 2017.

Weill, Peter, and Jeanne W. Ross. IT governance: how top performers manage IT decision rights for superior results. Boston, MA: Harvard Business School Press, 2010. Print.

Kasper Groes Ludvigsen

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Technophile optimist writing about whatever I please

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