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Nov 5, 2026Conference paperRestrictedv1.0

The Indirect Link between Computing Efficiency Gains and Energy Savings: Focusing on the Jevons Paradox

Jeonghoon Bang

This study began from a question: devices use less electricity every year, yet digital devices and artificial intelligence are said to be driving up electricity demand. We examined whether improving energy efficiency actually reduces energy consumption. When efficiency improves, the cost of using the same service falls, and by the law of demand, usage and new applications increase, consuming the energy that was saved. This is called the rebound effect, and the case in which the added consumption exceeds the savings, so that total consumption rises, is called the Jevons paradox. We compared three cases using published statistics: coal in nineteenth-century Britain, internet data transmission networks, and artificial intelligence. Watt’s steam engine greatly improved the efficiency of coal use, but as steam engines spread across industries, Britain’s total coal consumption surged. Between 2015 and 2021, internet traffic grew by 440% and the electricity used by data transmission networks grew by 20–60%. Electricity per unit of data fell by about 70–80%, yet total electricity use still increased. The cost of using artificial intelligence of a given capability fell to about 1/280 in roughly two years, while weekly users of a leading service grew about ninefold. In all three cases, efficiency gains slowed the growth of consumption but did not reduce it. Energy efficiency is therefore a necessary but not a sufficient condition for sustainable energy use, and total usage must be managed alongside efficiency. To this end, we proposed an efficiency-linked carbon price, mandatory minimum software support periods, and energy labeling with energy-saving default settings for digital services.

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Jul 6, 2026Conference paperOpen accessv1.0

Electrical signals in neurons and artificial neural networks

Jeonghoon Bang, Mingyu Cho

This report analyzes the process by which the core technology underlying the mechanism of electrical signal transmission in human nerve cells (neurons) was developed and advanced into artificial neural networks. First, this report explains the structure of neurons, the definition of membrane potential, and the process and principles behind the generation of action potentials, and defines how these signals enable information to travel from one neuron to another. In particular, it clarifies that not all stimuli to a neuron result in a signal; rather, the concept of a stimulus exceeding a certain threshold is necessary, and this is summarized through the “all-or-nothing” principle. To analyze how electrical signals—which are merely simple neurological signals—evolved into artificial neural networks, this study defined the concept of weights through perceptron theory, as well as multi-perceptrons and the backpropagation algorithm. It then described the progression to deep learning in artificial intelligence, supported by scientific facts, and demonstrated how stimuli exceeding a neuron’s threshold are applied to modern AI technology through the use of activation functions such as the sigmoid, ReLU, and Softmax to demonstrate how stimuli exceeding a neuron’s threshold are applied in modern AI technology. Finally, the paper examined the areas of application for artificial neural networks and summarized, based on the development of core AI principles, how they are integrated into fields such as healthcare, autonomous vehicles, image recognition, weather forecasting services, and AI chatbots. Through the advancements in deep learning and AI—enhanced by development based on the emulation of human neurons—the importance and value of the convergent development of life sciences and engineering were confirmed.

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