Calling for change? Innovation, diffusion, and the energy impacts of global mobile telephony

Abstract

Few technologies in history diffused as intensively and fast as mobile phones, to the point where they have become the most democratic technology. The article analyzes historical patterns of mobile phone growth and their effects in energy needs. Through an empirical analysis employing diffusion models on data for 227 countries between 1980 and 2010, it is concluded that global demand may saturate at around one subscription per person and the diffusion of mobile-broadband connection has contributed to sustain growth. Demand has already showed signs of saturation in developed countries, while there is still potential for growth in developing countries. Impacts on energy consumption are assessed with the help of a field trial. Even though the energy consumed in phone charging was not very significant (6-8TWh) in 2010, it becomes substantially higher when infrastructural needs are included (93TWh). The actual trends suggest that mobile communication might have a sizeable direct effect on energy consumption-although the net impact on energy demand is more difficult to estimate. This can become an issue in developing countries, where the adoption of mobile phones is catching-up rapidly with the world average, in a context of generalized increasing electricity demand.

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Methodological issues in measuring international inequality in technology ownership and infrastructure service use

“…The literature shows many examples of the Gini coefficients and Lorenz curves being used for assessing the inequality of domains other than income, such as: house size (Kohler et al 2017), happiness (Bennett and Nikolaev 2017) solid waste arising from material resource use (Druckman and Jackson 2008), historical (Groot 2010) and future (Zimm and Nakicenovic 2019) carbon emissions, material indicators (e.g. domestic extraction, domestic material consumption and material footprint) and indicators measuring the intensity with which human society uses terrestrial ecosystems (Teixidó-Figueras et al 2016), education (Sauer and Zagler 2014;Vinod, Yan, and Xibo 2001), health (Williams and Cookson 2000), spatial inequity in transportation (Jang et al 2017), internet bandwidth (Hilbert 2016), mobile phones, radios and bikes (Bento 2016), energy (Wu, Zheng, and Wei 2017) and electricity (Jacobson, Milman, and Kammen 2005).…”

“…Everyone has income, but not everyone owns goods or has access to infrastructures. If national averages of consumption or ownership are used in international inequality assessments, the population without access is overlooked, leading to underestimating international inequality (see Hilbert 2016 andBento 2016). Accounting for non-access can also lead to different country rankings.…”

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