Energy Efficiency最新文献

Solar cooling systems are considered as an alternative to conventional mechanical compression air conditioning systems. The use of these solar cooling systems contributes to the achievement of climate change objectives. This article provides a study of a single-effect LiBr/H2O absorption cooling system with a wet cooling tower driven by a combined double-acting collector for daytime solar heating and nighttime radiative cooling for efficient use of this system in hot climate regions such as Mali. The operation of a type of double-acting collector and cooling tower was studied throughout the year. The solar energy absorbed by the double-acting collector on the first day of operation heats the water in the storage tank 1 to 80 °C after 6 to 7 h of operation, and on subsequent days this duration is reduced by 3 to 4 h depending on the weather conditions. The operating time of the solar absorption cooling system studied is on average 8 h per day for the whole year. The water in the cold-water tank, cooled by night radiation into space, ensures operation without turning on the cooling tower for an average of 73 min per day. The annual water saving for the water losses in the cooling tower due to the night operation of the combined double-acting collector is estimated at approximately 35 m³. The yearly operation of the wet cooling tower of this absorption cooling system powered by a double-acting combined collector makes it possible to save approximately 15.4% of water losses and approximately 15.2% of energy thanks to the nocturnal radiative cooling of the water by the combined collector.

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There is a significant research gap on the efficiency and sustainability of oil-exporting developing countries. This study employs a slacks-based-measure data-envelopment-analysis (SBM-DEA) model to optimize oil well benefits while minimizing undesirable outputs, such as carbon emissions and energy degradation. Furthermore, we apply a Malmquist Productivity Index (MPI) to assess and compare dynamic energy productivity efficiency among Latin American and African countries. Our analysis of 14 countries in these regions from 2006 to 2020 demonstrates that Equatorial Guinea, Gabon, Peru, and Bolivia exhibit higher energy efficiency than counterparts like Angola, Algeria, Mexico, Ecuador, and Colombia. Notably, our findings suggest that countries with higher extraction rates tend to be less efficient, resulting in greater environmental impact relative to economic benefits from extraction. In conclusion, this study underscores the importance of energy efficiency policies, which can significantly mitigate Greenhouse Gas (GHG) emissions and resource depletion at the national level while enhancing industry sustainability. We recommend that governments implement policies aimed at reducing energy consumption within the oil sector, including the reduction of electricity subsidies. Adopting realistic energy extraction costs is crucial in facilitating the transition towards renewable energy sources.

This research explores energy poverty and its contextual factors in the Oita and Tokyo prefectures of Japan. The main objective of this research is to examine the household’s energy poverty variations from an affordability perspective due to the unexpected rise in energy prices following the Russia-Ukraine conflict and other influential factors. The study utilizes the Ten-percent rule and K-means clustering algorithm along with fuzzy numbers to propose the Energy Poverty Possibility Indicator. The proposed indicator is used to compare the intensity of energy poverty between Oita and Tokyo households in terms of three classes via 1,298 refined samples collected between 2021 and 2022. Results indicate that sudden spikes in energy costs widen the energy poverty gap to varying degrees across different regions. The results of the sensitivity analysis demonstrated that, in the current energy dilemma, the most effective plan to reduce the energy poverty burden in Tokyo (Oita) is to limit the increase of electricity and gas prices to 14% (9%) and 3% (21%), respectively, along with providing 5500 (9500) Japanese yen monthly in government financial support.

In the context of an energy crisis, efficient energy management has become an unavoidable issue for sustainability, regardless of the domain under consideration. Smart grids are no exception; they aim to motivate energy optimization according to billing strategies and users’ comfort. In this paper, two optimization problems (OP) are proposed involving billing strategies and users’ flexibility. A single-centralized OP aims to minimize the total energy provided by a company, while a distributed OP targets minimizing individual user costs independently, involving users’ flexibilities, different billing strategies, and a variable number of users, with random appliances assigned during simulations. The resolution was carried out using the Non-dominated Sorting Algorithm II and Multi-Criteria Analysis, with a Game-based algorithm also utilized. Additionally, simulations were performed under three billing mechanisms. The findings show that costs decrease exponentially with user participation. Similarly, both individual user costs and total costs at the energy provider level were minimized as users’ flexibilities increased. The Peak-to-Average-Ratio is minimized and exhibits a bimodal behavior when observed as a random variable. Regarding the interplay of billing mechanisms, simulation results demonstrate that the smart billing mechanism proposed by the authors outperforms other billing models proposed in the literature for both consumers and utility companies.

Educational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables.

The Eastern Mediterranean and Middle East (EMME) region hosts some of the world’s most influential and troubled cities. It is also a hotspot of climate change and socio-economic and political turbulence, which inflate the already flammable conditions and reinforce existing local vulnerabilities. Some of the most arduous challenges of cities relate to the built environment – although vital for human well-being, buildings rarely offer both sufficient and affordable shelter to their inhabitants. With energy performance regulations coming into effect during the past three decades, a considerable proportion of the worldwide building stock had already been constructed and is now ageing and inefficient. Harmonising the energy performance of buildings at a sufficient level requires common objectives and priorities, and the EMME region consists of nations with different governance and regulations. Scarce literature exists on the existing operational frameworks, and this study aims to offer an overview of the built environment policy scene in the EMME region, identifying gaps, good practices and prospects. The study draws from scholarly literature, national and international regulations and other document sources, as well as local experts. This work finds that although most EMME countries participate in and embrace international agreements, they act individually and not collectively, confirming our hypothesis that the policy agenda reflects the diverse characteristics of the region. By recognising standing failings and strengths, moving forward becomes a possibility through the adoption of integrated governance, common policy agendas and financing mechanisms to create sustainable urban centres inhabited by resilient and equitable communities.

Whether the energy-consuming right transaction system (ECRTS) can achieve a win–win development in economic growth and energy saving is a practical issue that needs to be tested urgently. The existing studies lack results that test the policy effect of ECRTS from the enterprise perspective. This paper uses the ECRTS implemented in 2016 as a natural experiment drawing on the relevant data of Chinese listed enterprises from 2009 to 2020. It adopts a difference-in-difference model to assess the effect of ECRTS on energy efficiency. The research finds that ECRTS improves energy efficiency of enterprises, and ECRTS can improve energy efficiency by promoting technological innovation and optimizing the efficiency of capital allocation. Further, ECRTS is more conducive to improving the energy efficiency of state-owned enterprises, enterprises with executives financial background, high pollution enterprises, and enterprises in regions with low resource endowments. Moreover, the improvement of energy efficiency can further enhance the value of enterprises after enterprises participate in ECRTS. The study provides a direct answer to the key question of whether the ECRTS policy can actually support the high-quality economic development.

There is a wide evidence base on various interventions to change energy behaviours in households, but limited evidence on their long-term effects. It is argued that interventions in individual behaviours are subject to attrition over time following the withdrawal of the intervention. Evaluation of these arguments is difficult since the conditions and composition of households change over time, making it hard to isolate the effects of a behavioural intervention several years later. Social practice theory argues for interventions that do not target individual attitudes or behaviours but focus instead on household practices (such as laundering and heating homes), and the underlying notions of cleanliness and comfort that are the causes of energy use in households. In contrast to behaviours, practices are also seen as socially shared, bundled with other practices, and embedded in social structures and processes. Practices are durable entities that persist over time; true practice change would hence entail durable intervention impacts. Our paper qualitatively investigates the long-term effects (2018–2023) of a practice-based living lab intervention that aimed to influence laundry and heating practices in Finnish households. We compare how the new or changed practices of 21 households, learned during the intervention, have persisted through societal changes (i.e., the Covid pandemic and the energy crisis), and whether these new or changed practices helped households to deal better with these crises. In this way, we contribute to, and also reframe, the problem of evaluating long-term effects, and critically consider the persistence of small-scale interventions in practices.

The rapid growth and expansion of the economy have given rise to several environmental concerns. The hotel industry's activities might be directed to ecological harms such as the diminution of usual resources, global warming, and the emission of various green hazards that lead to wildlife extinction and water, air, light, and industrial pollution. The hotel business can become more environmentally friendly by utilizing employee green service recovery performance to determine which issues are environmentally friendly or unfriendly and are motivated by eco-friendly dedication and sustainable human resource management (GHRM) practices. GHRM encourages employees to be committed to the environment and act sustainably, enabling hotels to enhance their environmental sustainability. The key benefit of this research is to make it easier to understand how integrating GHRM practices improves environmental performance by increasing employees' commitment and behavior in an environmentally friendly manner. The modern study aimed to inspect and assess the effect of green intellectual capital and green commitment on environmental performance with the assistance of employees and managers working in hotels in India. It also investigates how green management of human resources affects how environmentally responsible hotels are. Moreover, Structural Equation Modeling (SEM) will be implemented to continuously evaluate any proposed interlinkages between the latent construct. Empirical findings have revealed that managing sustainable human resources improves employees' commitment to the organization, their environmentally responsible conduct, and the environmental performance of hotels. Before the conclusion, a few of the study's shortcomings and suggestions are presented.

With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.