Soft City Culture and Technology

Skelton, Carl

doi:10.1007/978-1-4614-7251-3

Cited by 4 publications

(2 citation statements)

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“…In addition, another future goal of this platform is to empower and engage young communities with the goal of encouraging them to reduce their energy usage. For these reasons and for others related to the growing interest and recognition of gaming in urban planning (Skelton, 2013), we have decided to use a well-known game engine as the main platform for hosting EvoEnergy. To the best of our knowledge, we are the first to utilize the capabilities of game engines to support urban energy planning decisionmaking.…”

Section: Identify Residential Areas With Potential For Co 2 Emission mentioning

confidence: 99%

Impact of household transitions on domestic energy consumption and its applicability to urban energy planning

Medjdoub¹,

Chalal²

2017

Front. Eng

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The household sector consumes roughly 30% of Earth's energy resources and emits approximately 17% of its carbon dioxide. As such, developing appropriate policies to reduce the CO 2 emissions, which are associated with the world's rapidly growing urban population, is a high priority. This, in turn, will enable the creation of cities that respect the natural environment and the well-being of future generations. However, most of the existing expertise focuses on enhancing the thermal quality of buildings through building physics while few studies address the social and behavioral aspects. In fact, focusing on these aspects should be more prominent, as they cause between 4% and 30% of variation in domestic energy consumption. Premised on that, the aim of this study was to investigate the effect in the context of the UK of household transitions on household energy consumption patterns. To achieve this, we applied statistical procedures (e.g., logistic regression) to official panel survey data comprising more than 5500 households in the UK tracked annually over the course of 18 years. This helped in predicting future transition patterns for different household types for the next 10 to 15 years. Furthermore, it enabled us to study the relationship between the predicted patterns and the household energy usage for both gas and electricity. The findings indicate that the life cycle transitions of a household significantly influence its domestic energy usage. However, this effect is mostly positive in direction and weak in magnitude. Finally, we present our developed urban energy model "EvoEnergy" to demonstrate the importance of incorporating such a concept in energy forecasting for effective sustainable energy decision-making.

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Section: Identify Residential Areas With Potential For Co 2 Emission mentioning

confidence: 99%

Impact of household transitions on domestic energy consumption and its applicability to urban energy planning

Medjdoub¹,

Chalal²

2017

Front. Eng

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“…In its roll-out, the soft cab has come bundled with the move to supplant traditional regulatory models with softwareenabled protocological control (or "regulation by software" (Grimmelmann 2005); the complex, sedimented cultural image of the taxicab is to be cleared away, replaced with a purified site more in tune with the depoliticizing aesthetic of the "soft city" (Harvey 1990;cf. Raban 1988 andSkelton 2013).…”

Section: Ridesharing In Soft Cabsmentioning

confidence: 99%

Wheels in the Head: Ridesharing as Monitored Performance

Anderson

2016

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For-profit "ridesharing" services (or soft cabs) offer on-demand rides much like taxicabs, but are distinguished by an affective framing which emphasizes that drivers are "friends with cars, on demand" rather than "cabdrivers." This reframing is achieved through the insertion of smartphones as social interfaces between drivers and passengers, restructuring social interaction through an allegorithm (the productive co-deployment of a socially relevant allegorical script and a software-mediated algorithm). Much of the affective labor of ridesharing drivers consists in maintaining this affective framing and internalizing the logic by which their performances are monitored through the work platform. In this article the writings and videos of three ridesharing drivers will be drawn on to illustrate the ways drivers develop and evaluate their own performances as ridesharing drivers.

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