Energy Efficiency最新文献

This paper explores the interaction between the energy costs/GDP ratio, energy prices, energy efficiency, “quality of energy’’, and economic growth. The relationships between the first three were formulated by the author back in 2007 in the form of three laws of energy transitions. The paper provides additional empirical evidence and theoretical support to these laws and looks into their implications for economic growth and climate mitigation policies. It argues for launching effective energy costs accounting at the national level to support such policies. It also argues that escalation of energy prices driven only by the growing share of higher quality energy resources does not impede, but stimulates economic growth. The paper shows, that improving energy efficiency results in the removal of the ‘limits of growth’ – affordability, resource and environmental limitations; but as it faces the ‘limits of change’, the trade-off between maximizing economic growth and minimizing GHG emissions is inevitable.

Energy demand flexibility is important for a sustainable transition of energy systems, and dynamic electricity pricing, like real-time pricing (RTP), is a popular instrument to incite households to time-shift energy demand. However, the effect of such pricing schemes varies substantially across contexts and depends on the expectations that dynamic price signals integrate into everyday routines and rhythms. This paper investigates the flexibility of the everyday routines and rhythms of households. Based on visits to 11 Danish households and interviews with electricity customers new to RTP, the study provides three insights. First, households expressed confusion over terminology for dynamic pricing products and low interest in flexible energy demand. Second, everyday practices (already) appeared flexible but for reasons other than energy demand flexibility. Third, energy habits were important for providing meaning to everyday practices. The study shows that energy flexibility is not just about energy demand, but rather about all the other activities that form routines and rhythms in everyday life. We suggest (1) designing products that reflect ‘natural’ or ‘societal’ daily rhythms, (2) focusing on the engaged households, and (3) being aware that for some households, changes in pricing, including new payment schemes, might put extra pressure on already harried everyday routines.

The global economic recovery from the COVID-19 pandemic and the Russian invasion of Ukraine have significantly increased global energy prices, resulting in higher household burdens for citizens. This study uses fuel poverty indicators to determine household vulnerability to increase in energy price in Japan. A web questionnaire was conducted asking about household income and fuel bills in 2021 and 2022, using Kobe City, Japan, as a case study. As the result, the percentage of fuel-poor households in 2022 was 5.39%, an increase of 0.23% over 2021. This suggests that fuel-poor households are increasing in response to increasing energy prices. The 2022 fuel poverty rate for elderly households was 1.48%, and it was 3.91% for households other than the elderly. Analysis result regarding the characteristics of households determined to be fuel-poor indicated that single-parent households were more likely to fall into fuel poverty than average households. A model combining binomial logistic regression and multiple regression analyses was derived to measure the effect of fuel poverty measures based on the questionnaire survey results. The study results of measures to reduce the time spent using climatization equipment during the daytime in summer by implementing energy sharing showed that all households and single-parent households could move out of fuel poverty. However, elderly households could not move out of fuel poverty, and an additional condition for breaking out of fuel poverty was an increase in the annual household income of elderly households by 0.2 million JPY (Japanese Yen).

The energy crisis following the Russian invasion of Ukraine has prompted affected countries to increase efforts to save energy. Despite substantial energy conservation observed across sectors, research has inadequately explored the reasons behind these savings. This study bridges this gap by analyzing the discourse on energy conservation during the crisis in Germany, using Twitter data. We analyse a set of 136,837 tweets from January 2022 until May 2023. The analysis consists of two approaches based on natural language processing (NLP): First, we conduct word counts within the days with high tweet activity related to energy saving to identify specific triggers. For a broader quantitative analysis, we then use text classification to categorize the data set into different aspects as well as different stances and investigate how these evolve during the crisis. Our results show a heightened focus on energy conservation in the Twitter debate during the crisis, reflected in an increase of the average monthly tweets from 730 (before 2022) to 8,552 in 2022/2023. Regarding the motivations, ethical concerns linked to the war play a larger role in the initial months, while other factors such as energy costs, solidarity aspects, and climate change are consistently present throughout the debate. Additionally, we note significant negative discourse regarding the policy measures enacted during the crisis, indicating that policy backlash is a critical issue, at least in the realm of the Twitter debate. Given the vital role of energy efficiency in combating climate change, comprehending the motivations and de-motivations behind voluntary energy conservation during the crisis offers valuable insights. This understanding can inform future policies aiming to encourage energy savings.

This study investigates the effectiveness of an Optimal Time Recommendation model (OTR) in encouraging citizens to shift the usage of their home appliances, such as dishwasher to off-peak hours. The research was conducted at the HSB Living Lab + in Gothenburg city, involving 74 participants from diverse social groups, including students, one-person households, couples, and families with kids. The study employed a mixed-methods approach, combining surveys, interviews, and data from self-reporting QR-code or iPad-based web-interface. Participants were provided with personalised recommendations generated by the OTR model, which considered factors such as energy demand, grid load, electricity pricing and level of CO2. The recommendations aimed to assist users in identifying the optimal time slots for operating their home appliances during off-peak, motivated by the lower price, lower CO2 emission or both. Results indicated a positive response from participants across all social groups. Most participants reported an increased awareness of their energy consumption patterns and a willingness to adopt delay shifting practices. However, some frictions and obstacles to adopt shifting time of the behaviour were highlighted as well. The findings from this case study contribute to the existing knowledge on flexibility and Demand-Side Management (DSM). These findings can inform home appliances producers to increase the delay start function usability, policymakers to emphasise the eco-design of the white goods, and researchers in developing effective strategies to encourage energy conservation practices on a larger scale.

The renewable energy transition requires more flexible electricity consumption. This article follows up on Norwegian plans to achieve this through demand response and a piloting of smart home technology targeting indoor heating, which we call Smart Energy Technology (SET). Based on 17 in-depth interviews with participants in a technology pilot project, we map the work required to keep the SET system running, how it impacted electricity-consuming household practices, and analyse why use of the system varied widely among participants. We show that the system in question was too complex for other than exceptionally skilled and motivated users, who engaged in extensive “digital housekeeping”. Other users were navigating a complex Norwegian electricity market using technology they found difficult to operate. This was linked with limited system use, which lowered the energy efficiency contribution of the smart system. Smart systems and their use are highly gendered and have the potential both to challenge and reinforce gendered divisions of labour. More research into the gendered impacts of smart systems in Norway is needed. Simpler and more user-friendly systems are necessary for future pilot projects, more hands-on training for users in such pilots is required, and the daily work required to operate complex smart systems should be recognised more clearly by both smart system developers and policy makers.

In light of India's current energy crisis, it has been determined that the coal mining industry may be used to implement Demand Side Management (DSM) applications. This study explores the possibility of energy savings and reduction in energy costs associated with the installation of (Variable Speed Drives) VSDs on primary ventilation fans in below-ground coal mines. According to Ventilation-on-Demand (VOD), where air volume is changed according to demand at various time intervals, a nonlinear constraint optimization model is created to reduce energy costs and achieve better energy efficiency. To achieve accurate results at the fan's operating point, this model is also constructed to adhere to the affinity law of the fan. Tellegen's theorem and Kirchhoff's law are applied to model the ventilation system of the underground mine. The ventilation network was used to investigate energy and financial savings with various airflow scenarios. Load management is accomplished by figuring out the ideal start time for the mining schedule in accordance with the Time of Use (TOU) tariffs. A critical analysis of the Tandsi underground coal mine is presented to explain the impacts of the optimization model. The study found that implementing DSM solutions could result in a total annual energy savings of 118625 kWh, or a reduction in energy costs of USD 22995.

Automatic dishwashing is an example of a complex socio-technical system with an interplay between a variety of factors and participants, including manufacturers, technology, regulators, consumer practices, cultural norms and infrastructure. Understanding this system is a prerequisite to find the right levers to optimise it. In this context, automatic dishwashing in Australia is studied by modelling the installed base of dishwashers in Australian households and recording the consumer usage of dishwashers in detail in a representative way. The information from both combined allows one to estimate the actual average use of resources (energy, water) and programme duration of dishwashers. Analysing the consumer behaviour opens the way to find optimisations of the consumer behaviour which would allow a saving of resources (more than 20 % on electrical energy) without deteriorating the performance and practical use of the dishwashers. Moreover, the comparison of the Australian socio-technical system around automatic dishwashers with the European system allows one to find a possible saving of another 20 % on energy caused by differences in the regulatory framework for those appliances.

To mitigate the impact of aviation on climate change, the International Civil Aviation Organization (ICAO) has committed to reducing carbon dioxide (CO₂) emissions from international aviation by 5% by 2030 and to achieving net-zero carbon emissions by 2050. It is therefore essential to assess the environmental efficiency of airlines to urge them to adjust their actions promptly to meet the desired strategic objectives. This paper focuses on assessing the environmental efficiency of airlines using a network structure and constructs a three-stage airline network structure. Then, a non-convex meta-frontier DEA model with unified natural disposability and managerial disposability is proposed to assess the overall efficiency of 85 airlines for 2018–2019, based on data from 85 airlines worldwide for 2018–2019. The new model not only automatically determines whether an airline's inputs are natural or managerial disposable, but also measures the environmental efficiency of each region, while optimizing overall efficiency. Firstly, Ryanair has the highest overall efficiency among the 85 airlines in 2018–2019, and this increased efficiency is closely related to its excellent performance in handling carbon dioxide emissions. Secondly, the airline efficiency rankings obtained from the non-convex meta-frontier model and the convex meta-frontier model proposed in this paper are largely in line with each other.