Exploring the role of ICT on household behavioural energy efficiency to mitigate global warming

Bastida, Leire; Cohen, Jed; Kollmann, Andrea; Moya, Ana M. Contreras; Reichl, Johannes

doi:10.1016/j.rser.2019.01.004

Cited by 185 publications

(96 citation statements)

References 43 publications

(8 reference statements)

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“…On the one hand, early studies, including those of Walker (1986) and Chen (1994), generally concluded that ICT and energy were substitutes for each other and that ICT would reduce energy use. Empirically, some studies find that the application of ICT can promote energy efficiency and productivity and thereby contribute to reducing energy intensity (Røpke and Christensen, 2012;Corbett, 2013;Cai et al, 2013;Wang and Han, 2016;Bastida et al, 2019;Avom et al, 2020). For example, Røpke and Christensen (2012) pointed out that ICT has great potential for reducing energy consumption but that this depends on economic and political conditions.…”

Section: Technologies and Energy Intensitymentioning

confidence: 99%

Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Liu

Yang

Fujii

et al. 2021

Economic Analysis and Policy

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The continued development of artificial intelligence (AI) has changed production methods but may also pose challenges related to energy consumption; in addition, the effectiveness of AI differs across industries. Thus, to develop efficient policies, it is necessary to discuss the effect of AI adoption on energy intensity and to identify industries that are more significantly affected.Using data on industrial robots installed in 16 Chinese industrial subsectors from 2006 to 2016, this paper investigates both the effect of AI on energy intensity and the channel through which this effect is transmitted. The empirical results show, first, that boosting applications of AI can significantly reduce energy intensity by both increasing output value and reducing energy consumption, especially for energy intensities at high quantiles. Second, compared with the impacts in capital-intensive sectors (e.g., basic metals, pharmaceuticals, and cosmetics), the negative impacts of AI on energy intensity in labor-intensive sectors (e.g., textiles and paper) and technology-intensive sectors (e.g., industrial machinery and transportation equipment) are more pronounced. Finally, the impact of AI on energy intensity is primarily achieved through its facilitation of technological progress; this accounts for 78.3% of the total effect. To reduce energy intensity, the Chinese government should effectively promote the development of AI and its use in industry, especially in labor-intensive and technology-intensive sectors.

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Section: Technologies and Energy Intensitymentioning

confidence: 99%

Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Liu

Yang

Fujii

et al. 2021

Economic Analysis and Policy

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“…For this reason, the authors put the focus on how to get users' attention and educate them to increase customer awareness and participation in demand management. Accordingly, Bastida et al [139] analyses the role of the consumers' behaviour with respect to the potential of ICT-based interventions to decrease electricity usage in households. In this work, they addressed motivating, reinforcing and enabling factors to encourage lasting behaviours and analysed their impact on the efficacy of ICT approaches to mitigate CO 2 emissions.…”

Section: Discussionmentioning

confidence: 99%

Addressing Behavioural Technologies Through the Human Factor: A Review

et al. 2020

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Energy-efficiency related research has reached a growing interest in recent years due to the imminent scarcity of non-renewable resources in our environment and the impending impacts their usage have on our environment. Thus, facing the reduction of energy waste and management has become a pivotal issue in our society. To cope with energy inefficiency, the scientific research community has identified the promotion of people's behaviour change as a critical field to foster environmental sustainability. However, the body of literature shows a lack of systematic methods and processes to reach a common ground when designing technology for promoting sustainable behaviour change. Therefore, this paper contributes with a thorough review and analysis of state of the art. Firstly, theoretical works related to behaviour change are collected and studied to clarify their main concepts and theories. Secondly, the different technologies, processes, methods and techniques applied in the field are reviewed to find diverse strategies in the application of the previously explained theoretical domains. Moreover, a wide range of systems developed to improve energy efficiency through human behaviour change is analysed (from augmented objects to the Internet of Things, digital applications or websites). Finally, the detected research gaps are listed to guide future research when aiming to raise the awareness of individuals through Information and Communication Technologies. INDEX TERMS Behaviour change, Internet of Things, sustainability, sustainable behaviour change, ICT.

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“…The operation of flexible loads is executed at a fixed a priori cost. If a flexible load diverges at a given hour from the value of the original load, then that change is penalized by a cost (8). Therefore, the load deviation y REG is penalized either if it is positive or negative.…”

Section: The New Objective Functionmentioning

confidence: 99%

Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market

Marañón-Ledesma

Tomasgård

2019

Energies

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One of the challenges in the transition towards a zero-emission power system in Europe will be to achieve an efficient and reliable operation with a high share of intermittent generation. The objective of this paper is to analyse the role that Demand Response (DR) potentially can play in a cost-efficient development until 2050. The benefits of DR consist of integrating renewable source generation and reducing peak load consumption, leading to a reduction in generation, transmission, and storage capacity investments. The capabilities of DR are implemented in the European Model for Power Investments with high shares of Renewable Energy (EMPIRE), which is an electricity sector model for long-term capacity and transmission expansion. The model uses a multi-horizon stochastic approach including operational uncertainty with hourly resolution and multiple investment periods in the long-term. DR is modelled through several classes of shiftable and curtailable loads in residential, commercial, and industrial sectors, including flexibility periods, operational costs, losses, and endogenous DR investments, for 31 European countries. Results of the case study shows that DR capacity partially substitutes flexible supply-side capacity from peak gas plants and battery storage, through enabling more solar PV generation. A European DR capacity at 91 GW in 2050 reduces the peak plant capacities by 11% and storage capacity by 86%.

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Exploring the role of ICT on household behavioural energy efficiency to mitigate global warming

Cited by 185 publications

References 43 publications

Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel

Addressing Behavioural Technologies Through the Human Factor: A Review

Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market

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