Energy Efficiency最新文献

The global economies are rapidly taking action to reduce climate change issues. However, RCEP economies are still paying attention to economic stability, while efforts are needed to explore their influencing factors of environmental quality, which are hardly explored in the literature. To fill the gap, this study examines the influence of financial expansion and energy efficiency on carbon emissions in RCEP countries. Covering the extended period from 1990 to 2021, this study employs various diagnostic tests such as the normality test, slope homogeneity, and panel cross-section dependency. The cointegration of all the variables is found to exist. By applying the non-parametric techniques (method of moment quantile regression), the results indicate that economic growth is the only significant factor of environmental deterioration in the region. Whereas, energy efficiency and technological advancement significantly reduce the carbon emissions level. On the contrary, the financial expansion is found asymmetrically affecting the emissions level, which reduces emissions in the lower and medium quantiles while increasing the carbon level in the higher quantiles. The robustness of the results is validated by using quantile regression. This study suggests increased investment in energy efficiency and technological innovation. Also, the results suggested the expansion of the green financial system, which could be a promising tool for the environment and sustainable development.

This paper highlights the significance of optimizing district energy systems with solar prosumers from an exergy-based perspective to minimize carbon dioxide emission responsibilities. As a case study, the Dezonnet solar district energy project in Haarlem, the Netherlands, which incorporates solar prosumers with traditional rooftop photovoltaic-thermal panels, and heat pumps, integrated with a district heating network featuring a seasonal central thermal storage aquifer, is critically examined. This paper shows that the project has carbon dioxide emission responsibilities that can only be revealed by the Second Law of Thermodynamics. A novel extension of this law relates carbon dioxide emission responsibilities with major exergy destructions. As an alternative solution, a solar/green hydrogen house concept is presented, which encompasses advanced, pumpless photovoltaic-thermal panels with heat pipes, solar flat-plate panels, and thermo-electric generator layers in a sandwiched construction with phase-change thermal storage blocks. On-site seasonal thermal storage systems utilizing phase change material and biogas generation replace the large-scale district aquifer. New optimization constraints as well as the objective function of minimum exergy destruction and corresponding emission responsibility equations are presented. Sample studies indicate that the alternative solution may reduce carbon dioxide emissions responsibility by up to 95%. This paper concludes that smaller districts or individual solar homes with advanced technologies are preferable.

Building energy performance assessments are essential in High-Performance Building Design (HPBD) in order to reduce energy consumption and carbon emissions. With advancement in data analytics, rapid and accurate machine learning-based building energy consumption prediction models have emerged. These models can be used by non-professionals as an alternative to time-consuming energy simulation software, offering benefits in HPBD. Therefore, the main objective of the present study is to develop a prediction model using data generated by physics-based simulations of a typical open-plan office space. The model predicts annual energy consumption, CO₂ emissions, and percentage of comfort hours during the design phase. Various configurations of Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forrest (RF), and K-Nearest Neighbors (KNN) algorithms were trained and tested with the generated data via the DesignBuilder software. The technical parameters considered as inputs include U-values of envelope constructions, Window to Wall Ratio (WWR), orientation, and Heating, Ventilation, and Air-Conditioning (HVAC) systems. The results indicate that there is no clear linear relationship between individual inputs and the target indicators. However, ANN, with its ability to handle non-linear relationships, performed the best, achieving a maximum Coefficient of Determination (R²) value of 0.997 for predicting percentage of comfort hours and outperforms the other algorithms. Furthermore, the results show that RF is the next best algorithm, with 0.96 ≤ R²_Test ≤ 0.98 for predicting the various target variables. SVM with Radial Basis Function (SVM-RBF) follows, with 0.89 ≤ R²_Test ≤ 0.95. Contrary to ANN, SVM, and RF algorithms with high abilities to learn complex pattern between various independent parameters and the target variable, KNN exhibits the poorest performance, with 0.88 ≤ R²_Test ≤ 0.91. Additionally, it is observed that with a maximum time cost of 619 s, ANN with three layers is able to learn the relationships between the inputs and target indicators at a convenient speed. Since knowledge-based decision making in the early design stages is crucial for achieving the optimum solutions to reduce energy consumption and related CO₂ emissions while ensuring occupants’ comfort and minimizing future modifications and costs, high-speed and accurate prediction methods for design stage evaluation are essential.

Since the outbreak of the COVID-19 pandemic, the global oil market has experienced historic turbulence, and the extreme jump behavior of oil prices deserves relentless attention. This study analyzed how oil price shocks impact China’s transportation sector at the aggregate and subsector levels from the perspective of jump behavior in oil prices. We applied the ARMA-EGARCH-ARJI model to characterize the global oil price volatility and verified that it contains asymmetric clustering volatility and dynamic jumps. By using the ARMA-EGARCH-X model, we investigated the impacts of oil price jumps on the transportation sector successively considering the hysteresis and asymmetry. We found that the impacts of oil price changes on China’s transportation sector mainly come from unexpected shocks, in which the spillover effect of oil price volatility stems from the jump component rather than the clustering volatility. Hysteresis exists in the risk spillovers of oil price jumps on sector volatility. Furthermore, the sector’s responses to the jump shocks exhibit the asymmetric effect, that the inhibitory effect of the downward jumps on the sector returns is stronger than that of the upward jumps. Finally, diversities are displayed in the impacts of oil price jumps on China’s five transportation subsectors. This study has practical significance for the transportation sector and provides implications for market participants and policy-makers.

Numerous empirical researches have investigated the association between exchange rates and energy consumption. However, existing studies often overlook the innovative aspect of exploring implications across various stages of energy demand, which sets our study apart, especially amidst significant fluctuations. Addressing this gap, our study uniquely examines both short- and long-term influences of exchange rates on energy demand in the emerging seven (E7) economies: Brazil, Mexico, Russia, India, Turkey, China, and Indonesia. We shed light on this innovative aspect of our research by utilizing quarterly data from Q1 1987 to Q4 2020 and employing the Quantile Autoregressive Distributed Lag (QARDL) model. Notably, our approach demonstrates an innovative methodology. While standard Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL approaches identify energy demand and exchange rate cointegration solely in India, the QARDL method reveals a deeper pattern that showcases the innovative nature of our investigation. Recognizing diverse energy demand levels emphasizes the innovative need to tailor strategies. Neglecting these disparities could precipitate undesired consequences, amplifying the relevance of our study's innovative approach to shaping effective policies.

The Energy Efficiency Obligation Scheme (EEOS) is a market-based policy mechanism that requires energy companies that sell energy to end-users to carry out energy efficiency measures on end-users. Within the scope of this study, EEOS’ implementations and studies are reviewed to contribute to setting up an obligation scheme in Turkey. By reviewing the existing literature, studies are grouped and summarized. Also, existing EEOS experiences were presented. Critical points, success parameters, and lessons learned from the EEOS were investigated, and the review results were discussed for possible adoption of Turkish EEOS. Finally, the study was concluded by proposing future research topics for Turkey.

Domestic hot water production is the second most important energy use in the European residential sector, nowadays accounting for 14% of the sector’s total final energy consumption. Despite its importance, the energy efficiency improvement rates for domestic hot water are lower than for other residential energy services, hence calling for energy-saving measures. One key measure is to install flow restrictors. Their advantages are the low upfront cost, easy installation, and suitability for integration into energy efficiency programs. Focusing on flow restrictors, this paper presents different methods for quantifying the energy savings using ex-ante and ex-post approaches: deemed savings (DES), dedicated measurements (DMs), and monthly and yearly billing analysis (SMBA and ABA). These methods were tested using information based on measurements (water flow, temperatures), historical billing analysis, a survey among inhabitants, and interviews with field experts. While measurements made at individual faucets or showerheads show significant water savings (20% and 33% respectively), energy savings associated with hot water production in the boiler (final energy) are significantly lower (around 10%) but far from being negligible. The main reasons for the difference are thermal losses related to hot water distribution in central heating systems, usages not affected by flow restrictors, and inhabitants removing them. We conclude that flow restrictors offer promising potential for short- to medium-term implementation. Given the simplicity of this solution, we recommend including it systematically in energy efficiency programs, as well as implementing a ban on fixtures with flow rates beyond a predefined level.

This research tested the energy consumption and efficiency of five electric tabletop appliances (cooktops) used in home cooking, specifically induction, infrared, resistance plate, resistance coil, and electric pot. The water boiling test with a cold start was used to determine the energy consumption and the efficiency for each appliance with variable volumes of water—500, 1000, 1500, and 2000 mL—and two pot sizes of 3 and 5 quarts. The effect of cold vs. hot start was evaluated with one water volume, 1000 mL, on all appliances. The electric pot was studied with and without insulation to determine if it is worth insulating the pot that comes un-insulated from the factory. Also, the appliances were subjected to simulated simmering tests to determine which device was more efficient in slow cooking. Of the five appliances tested, the induction cooktop and the electric pot were the most energy efficient and took the least time to boil water. Moreover, the energy efficiency and heating time were similar for the induction matched with the smaller pot and the electric pot. Induction lost efficiency by approximately ten percentage points when used with the large pot, thus reinforcing the importance of matching the pot size with the heating element. During the simmering experiments, induction proved easier than resistance coil to control the temperature in the culinary simmering band, from 85 to 100 °C; however, the energy consumption difference between induction and resistance coil was unclear.

Buildings constitute one of the main GHG emitting sectors, and energy efficiency is a key lever to reduce emissions in the sector. Global climate policy has so far mostly focused on economy-wide emissions. However, emission reduction actions are ultimately sectoral, and opportunities and barriers to achieving emission reductions vary strongly among sectors. This article therefore seeks to analyse to what extent more targeted global governance may help to leverage mitigation enablers and overcome barriers to energy efficiency in buildings. To this end, the article first synthesises existing literature on mitigation enablers and barriers as well as existing literature on how global governance may help address these barriers (“governance potential”). On this basis, the article analyses to what extent this governance potential has already been activated by existing activities of international institutions. Finally, the article discusses how identified governance gaps could be closed. The analysis finds that despite the local characteristics of the sector, global governance has a number of levers at its disposal that could be used to promote emission reductions via energy efficiency. In practice, however, lacking attention to energy efficiency in buildings at national level is mirrored at the international level. Recently, though, a number of coalitions demanding stronger action have emerged. Such frontrunners could work through like-minded coalitions and at the same time try to improve conditions for cooperation in the climate regime and other existing institutions.

Industrial processes are often accompanied by energy losses. These energy losses can be in the form of exhaust gas or effluents occurring at different temperature levels. All these losses are being curbed, but with an economic disadvantage. However, with the application of various waste heat recovery technologies such as heat recovery steam generator (HRSG) and waste heat boiler (WHB), this lost energy, mainly associated with the exhaust gas, can be recaptured and reused, thus resulting in better energy efficiency. This is also a firm step towards the protection of the environment and other associated gains like small plant size, less power consumption, less water consumption, etc. In this regard, the present paper revisits and reviews the current technology and practices in the context of HRSG and WHB. The future technological needs in this field have also been highlighted.