Energy Efficiency最新文献

Taking into account the contributions of economic performance (GDP), urbanization (URB), industrial structure (IND), and renewable energy consumption (REC), this paper examines the impact of green technology innovation (GTE), energy efficiency (EF), and environmental regulation (ER) on CO2 emissions in Chinese provinces from 2010 to 2020. Using the GMM method for the initial estimation, the MMQR as 2nd generation test for robustness and innovative panel causality presented by the JKS test, we have found: 1) a one percent boom in GDP is linked with a 0.08% upward push in CO₂ emissions throughout 30 provinces in China. 2) the renewable energy and energy efficiency data seems to effectively decrease CO₂ emissions, with a more pronounced impact observed at the upper quantile. 3) The environmental policy is limited across all quantiles. The study examines novel implications regarding sustainable development and carbon neutrality objectives.

This perspective paper argues how a social network approach can contribute to creating a more comprehensive picture of how individual and community characteristics influence participation in community energy initiatives (CEIs). We argue how social network theory and methods for social network analysis can be utilized to better understand participation. Further, we show how this can potentially aid the implementation of interventions aimed at attracting more participants with more diverse socio-demographic backgrounds. Importantly, we argue that the structure of community social networks connecting (potential) participants could importantly influence whether and how individual and community properties affect CEI participation. Our aim is conveying the social network approach to the field of community energy researchers and stakeholders who might not be familiar with it. We discuss empirical evidence on the effect of network characteristics on CEI participation and the connection between research on CEIs and adjacent fields as a foundation for our claims. We also illustrate how a social network approach might help to overcome biased participation and low participation numbers, by providing social scientists with a tool to give empirically grounded advice to CEIs. We conclude by looking at avenues for future research and discuss how the context of CEIs might yield new theoretical insights and hypotheses.

As long-distance flights increase, the widespread use of electric heating for hot water in domestic civil aircraft will pose a challenge to the aircraft's energy systems. Moreover, the aircraft environmental control system operates under variable environmental conditions during aircraft take-off, leading to changes in system performance and outlet parameters. In this paper, mathematical models of the new aircraft environmental control system are established during aircraft take-off, and the main factors affecting the performance of systems are discussed. Results show that hot water with an average temperature of 61 °C can be provided by the new system during aircraft take-off. In the new multi-functional system, the bleed air supply volume during aircraft take-off is less than that of the conventional system, and the system energy loss is also less. When the aircraft just takes off, the condenser accounts for the most significant portion of the system exergy loss. However, the exergy loss in the secondary heat exchanger is the largest, as the aircraft altitude increases. Compared with the conventional system, the exergy efficiency of the new system is 8.85% higher at a 4-5 km level flight, and it’s 3.21% higher at a 9-10 km level flight.

Carbon-footprint reduction of key industrial buildings is addressed, by proposing methodologies for continuously monitoring telecommunication (TLC) central offices (COs). Main aim is classifying sites according to their efficiency and reliability, via the diagnosis of anomalous electricity consumptions. Such a goal is achieved through the definition of new key performance indicators (KPIs) based on TLC and cooling energy demand, improving the outcomes of pre-existing methods. While the reliability index and index of cluster reliability are specifically proposed to evaluate and physically assess the impact of climate control (CLC, i.e., the parasitic quota) electricity consumption with respect to the TLC one, the here introduced coefficient of variation of telecommunication energy allows for a more solid evaluation of energy measurements reliability. Another target of this study is to extend the afore-mentioned KPIs-based analysis to multi-annual periods of monitoring, thus allowing successfully meeting the currently in-force ISO 50001 standard. Specific central offices were then selected and analyzed to verify the results physical meaning. The method was proven effective in classifying central offices belonging to climate-homogenous fleets, according to the reliability level estimated over a triannual timeframe. Positive impacts in terms of attainable energy saving through improved thermal management, as well as methodology extendibility to other industrial sectors are finally presented and discussed.

It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

Efficient use of energy and other resources, as the basic postulates of the circular economy, is a prerequisite for the green transition to more sustainable cities in the future. The main scientific goal of the paper is the development of a new approach to city governance when it comes to the inefficient use of energy, predominantly fossil fuels, mainly in developing and poor countries. Energy efficiency problems faced by these countries require the introduction of urgent, applicable, and realistically achievable solutions. A prerequisite for adequate analysis and modeling of energy efficiency performance, measures, policies, outcomes, and impacts is the introduction and functioning of the big data management system, which should begin with data mining. On the other hand, adequate data collection has been neglected in many of these countries. The study shows a way to reduce this gap, but in accordance with realistic and limited possibilities for countries with less favorable conditions. In that respect, a conceptual model for the Analytical Service for facilitating energy efficiency in city governance was developed and presented as a driver that can enable cities to manage energy more efficiently. The model is based on an interdisciplinary approach and on the needs of cities in the Republic of Serbia. However, it is designed to allow upgrading in accordance with the capabilities and resources of cities, primarily applicable in developing and poor countries.

Using a panel of Romanian firms in the manufacturing sector between 2010 and 2019, this paper assesses the impact of utility costs (electricity, gas, water and waste) increases on the economic performance of firms (in terms of employment, exports and productivity). By means of static (fixed effects) and dynamic (GMM) specifications, it finds a relatively moderate, yet negative impact in the case of all variables, under a variety of specifications. Energy costs seem to have a larger contemporaneous effect, which suggests that firms might in the longer-run switch to less energy intensive production processes. Moreover, the more energy intensive sectors are generally also the most impacted in terms of employment and productivity.

In this paper we re-examine the relationship between global Gross Domestic Product (GDP), Primary Energy Use (PEU) and Economic Energy Efficiency (EEE) to explore how investment in energy efficiency causes rebound in energy use at the global scale. Assuming GDP is a measure of final useful work, we construct and fit a biophysics-inspired nonlinear dynamic model to global GDP, PEU and EEE data from 1900—2018 and use it to estimate how energy efficiency investments relate to output growth and hence economy-wide rebound effects. We illustrate the effects of future deployment of enhanced energy efficiency investments using two scenarios through to 2100. The first maximizes GDP growth, requiring energy efficiency investment to rise ~ twofold. Here there is no decrease in PEU growth because economy-wide rebound effects dominate. The second scenario minimizes PEU growth by increasing energy efficiency investment ~ 3.5 fold. Here PEU and GDP growth are near fully decoupled and rebound effects are minimal, although this results in a long run, zero output growth regime. We argue it is this latter regime that is compatible with the deployment of enhanced energy efficiency to meet climate objectives. However, while output growth maximising regimes prevail, efficiency-led pledges on energy use and emissions reduction appear at risk of failure at the global scale.

In the context of the energy transition policy that came into force in Spain in 2019, it is necessary for households progressively to substitute dirty energy heating sources with clean ones. This means replacing energy heating carriers that use carbon energy sources with others that use electricity, that is the cleaner energy source, specifically in Spain where electricity mainly comes from renewable sources. This replacement must be based on the use of modern and efficient electric heating appliances. This can involve a substantial economic effort for certain households, that are already vulnerable. This paper proposes a multinomial model to determine which variables explain households’ energy heating sources use and applies this model to microdata, from the Spanish Household Budget Survey, for 2016-2019. Results show that it is likely that energy-poor households use gasoil or coal and electricity for heating. It is also more probable that households living in rural areas and older buildings use these sources. Households renting their dwelling and living in warm regions are more likely to use electricity, whereas those living in cold regions, urban areas, with woman heads are more likely to use gas. Households owning the dwelling, with older heads and residing in larger houses are more likely to use gasoil or solid fuels. From these results, implications are derived to inform public policy regarding just energy transition.

The efficiency of energy utilization in autonomous electric vehicles greatly impacts their longitudinal motion control. However, the complexity of driving scenes poses challenges to this control. This study introduces a hybrid approach that combines the improved coot optimization algorithm with adaptive reinforcement equilibrium learning to enhance both energy efficiency and speed control in autonomous electric vehicles. The primary innovation lies in optimizing and managing the powertrain efficiency operating point distribution to increase energy utilization efficiency. In the first phase, the improved coot optimization handles vehicle energy utilization efficiency by optimizing operational point transfers. The system normalizes motor torque and velocity to maximize efficiency within constrained conditions. Subsequently, in the second phase, adaptive reinforcement equilibrium learning effectively predicts vehicle speed control on irregular pathways. The proposed technique is implemented on the PYTHON platform to evaluate performance. The analysis also investigates two specific operating conditions: New European Driving Cycle (NEDC) and World Light-Duty Vehicle Test Cycle (WLTC). The findings demonstrate that the proposed strategy effectively optimizes vehicle powertrain efficiency operating point distribution, resulting in improved energy consumption outcomes. The energy utilization efficiency of the proposed approach is 90%, 93%, 95%, 96%, and 98.4%, respectively, at time 100 s, 200 s, 300 s, 400 s, and 500 s.