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This study breaks new ground by assessing the influence of individual and collective errors in weather forecast variables and errors in model parameters on the prediction error of greenhouse power and gas demand. To achieve this a sample-based and a Polynomial Chaos based sensitivity analysis using higher order sensitivity indices is proposed. This is accompanied by a sensitivity analysis of the impact of reducing individual weather forecast errors on greenhouse energy demand prediction error. The findings of this study show that weather forecast errors have a far greater role in creating mean gas (27 W m − 2 ) and power (24 W m − 2 ) prediction uncertainty than parametric errors (5.7 W m − 2 and 4.6 W m − 2 ). In addition, and crucially, weather forecast and parameter errors were found to be independent factors. Reducing weather forecast error exhibited large diminishing returns with the reduction in prediction error. For instance, a scenario where the forecast error of all variables is reduced by 80–90 % resulted in only a 50 % decrease in gas and electrical power prediction error. The radiation forecast errors emerged as the primary contributor to power demand prediction errors, exhibiting the potential to reduce the power demand prediction error by approximately 60 %. Reductions of forecast errors in wind and outdoor air temperature were identified as the predominant contributors, offering a respective potential for a 17 % reduction in gas demand prediction error. • A gas demand and electrical power uncertainty analysis was performed. • This analysis considered both weather forecast and model parameter error. • Weather forecast error had a greater impact than parametric error. • An 80 % forecast error reduction creates a 50 % decrease in energy prediction error. [ABSTRACT FROM AUTHOR]

Within the modern greenhouse horticultural sector energy usage is planned using mathematical models that simulate the greenhouse's future performance. These models contain parameters whose values can be inaccurate which create errors in model predictions. This reduces the effectiveness of energy management and planning done using these models. This study proposes and demonstrates an algorithm to quantify the impact of parameter errors on greenhouse gas and electric power prediction uncertainty. The proposed algorithm introduces a Polynomial Chaos Expansion as a method for the sensitivity analysis in the domain of greenhouse horticulture. Contrary to commonly used sensitivity analyses, this approach introduces the analysis of higher order interactions into the domain of greenhouse horticultural research. It was found that for both electric power and gas production the HPS lamp power rating was the most influential individual parameter. Moreover, this study found that for power demand the uncertainty in parameters relating to the lamp system were far more impactful than those related to the crop or greenhouse structure, with a respective coefficient of variation of 24 %, 5 % and 5 %. This study makes a notable and novel conclusion that for parameters related to the greenhouse structure, larger groups of parameters were responsible for prediction uncertainty through higher order interactions of second to sixth order. These results reinforce the importance of future greenhouse research considering the impact of higher order parameter interactions on prediction uncertainty using the algorithm proposed in this study. • An electrical power and gas demand uncertainty analysis was performed. • A polynomial chaos expansion was used as part of an uncertainty analysis. • The proposed algorithm shows groups of parameters impacting prediction uncertainty. • Lamp intensity is the key factor for power and gas demand prediction uncertainty. [ABSTRACT FROM AUTHOR]

Condensation frequently manifests in environmentally controlled agricultural structures (such as greenhouses) due to the utilisation of specific covering materials and the often meticulous management of high relative humidity to facilitate optimal plant growth. The formation of condensation can significantly influence the thermal and optical characteristics of the covering materials, which also affects the covering insulation and the growth of plants. This study was conducted to investigate the impacts of surface condensation on the thermal performance of various greenhouse covering types. These options feature low-emissivity (Low-E) coatings, infrared reflective (IR) additives, UV protective films, and anti-condensation additives. The physical properties, specifically emissivity and wettability, were examined to understand their roles in modifying the thermal transfer dynamics of greenhouse coverings. A scale physical model was made to test the thermal performance of the coverings with and without condensation. The results indicated the effect of condensation on the overall heat transfer coefficient (U-factor). Especially when the surface exhibited characteristics of low emissivity, it resulted in a notable near-tripling of the U-factor. These widely accepted and advocated strategies of Low-E and IR reflective products may inadvertently lead to enhanced operational energy consumption when considering the influence of condensation. Notably, this research has also devised a regression function that establishes a correlation between the contact angle and the condensation heat transfer coefficient. This tool facilitates swift analysis and estimation of the effects of condensation on the U-factor. In the end, the potential and future work on condensation effects is also discussed. • Examined condensation effects on thermal performance of typical greenhouse glazings. • Verified the double-film theory for glazing's condensation heat transfer calculation. • Validated key coefficients in the analytical model of condensation heat transfer. • Analysed material surface properties' effects on heat transfer during condensations. • Built a simplified model linking surface wettability and condensation heat transfer. [ABSTRACT FROM AUTHOR]

Currently the Dutch greenhouse horticultural sector has a high energy demand. The present use of weather forecasts can exacerbate this high energy consumption by contributing to suboptimal prediction and trading of the greenhouse's power demand. This study investigates the role of weather forecast errors on energy prediction power and trading uncertainty in greenhouse horticulture. This was done using an uncertainty analysis and computer model of a tomato producing Venlo style greenhouse in Bleiswijk, The Netherlands. This greenhouse model was used to predict the greenhouse's gas and electrical power demand. The study concluded that errors in the weather forecast of outdoor radiation, temperature and wind speed caused an overestimation of greenhouse energy demand. A sensitivity analysis showed that the radiation forecast error had the greatest impact on predicted greenhouse power demand errors with a mean relative error of 6.1 %. Predicted gas demand errors were most dependent on the outside wind speed forecast mean relative error (18.0 %) and temperature forecast error (17.2 %). A power trading uncertainty analysis was done to investigate the impact of predicted energy demand errors on the cost of buying power on the Dutch imbalance and Amsterdam Power Exchange day-ahead market. This cost analysis found that the volume of initial power trading was greater than corrective trading. Additionally, the higher volatility in short term power prices resulted in higher corrective power costs per unit of power than if the power demand had been initially predicted with more accuracy. • The study shows that weather forecasts create energy demand uncertainty. • A data-based sensitivity analysis was done for input data weather variables. • The greenhouse power costs are calculated using imbalance and day-ahead markets. • As the weather forecasts lengthen, the energy prediction error and power costs increase. • The radiation forecast has the greatest impact on the predicted power error. [ABSTRACT FROM AUTHOR]

Accurately identifying the historical causes of carbon emissions in the process of national economic development is an important basis for developing countries to achieve carbon emission reduction. This paper explores the intrinsic institutional causes of China's high CO2 emission growth based on the characteristic economic growth target system of China, and attempts to empirically test the environmental effects behind this system. The results of the study show that the setting of absolute economic growth targets significantly increases the carbon dioxide emissions of cities under horizontal competition, and the setting of relative economic growth targets exacerbates the above carbon emission effect under vertical competition. In addition, the heterogeneity analysis shows that the carbon emission effect of setting economic growth targets is stronger in resource-dependent cities and cities with a lower level of economic development. Mechanism tests show that economic growth targets not only significantly increases total fossil energy consumption and reduces energy efficiency at the firm level, but also leads to the increase of energy consumption and the reduction of energy efficiency at the industry level. The findings of this study provide an intrinsic institutional explanation for China's high carbon emissions and provide useful guidance for the design of mechanisms to achieve large-scale carbon emission reductions in developing countries. • This paper investigates the impact of economic growth targets on carbon emissions. • How economic growth targets setting system affects carbon emissions remains underexplored. • There are significant differences in the impacts of EGT setting regimes on carbon emissions in cities with different types of development. • The economic growth targeting system influences regional carbon emissions through two pathways: energy consumption levels and energy efficiency. [ABSTRACT FROM AUTHOR]

We use a randomized controlled trial in Bangladesh to test three types of non-price energy conservation strategies that influence electricity consumption of households: (i) advice on electricity conservation methods (knowledge treatment); (ii) (median) electricity consumption of others in the suburb (suburb comparison); and (iii) (median) electricity consumption of neighbors (neighbor comparison). We find that providing advice on saving energy could reduce households' electricity consumption and bills significantly. The effects are stronger for advice on electricity conservation methods than neighbor and suburb comparisons. The effects of providing information about own electricity consumption relative to neighbors' electricity consumption is similar to the effects of giving information about own electricity consumption relative to electricity consumption of households in the same suburb. The effects among households who were inefficient users in neighbor and suburb comparison groups are almost as strong as those in the knowledge treatment group. The effects across all treatment groups become stronger over time as they receive repeated information. [ABSTRACT FROM AUTHOR]

Cache memories play a significant role in the performance, area, and energy consumption of modern processors, and this impact is expected to grow as on-die memories become larger. While caches are highly effective for cache-friendly access patterns, they introduce unnecessary delays and energy wastage when they fail to serve the required data. Hence, cache bypassing techniques have been proposed to optimize the latency of cache-unfriendly memory accesses. In this scenario, we discuss HBPB , a history-based preemptive bypassing technique that accelerates cache-unfriendly access through the reduced latency of bypassing the caches. By extensively evaluating different real-world applications and hardware cache configurations, we show that HBPB yields energy reductions of up to 75% and performance improvements of up to 50% compared to a version that does not apply cache bypassing. More importantly, we demonstrate that HBPB does not affect the performance of applications with cache-friendly access patterns. [ABSTRACT FROM AUTHOR]

This study evaluated the energy efficiency associated with drying by refractance window™ and its effect on the physical and chemical properties of beetroot in two different physical states, in semisolid (puree type) and solid (slices) were compared. The refractance window™ drier was compared to a tray dryer at 70 °C on the food surface. The drying time was calculated through the kinetics in both dryers in both physical states of the beetroot determining the moisture content at the beginning and the end of the drying process after which water activity and color retention were determined. Total polyphenol and flavonoid contents respectively, and antioxidant activity were evaluated. Overall energetic efficiency for both dryers was calculated. The beetroot dried in semisolid state using the refractance window™ technology retained color and showed no differences when compared with fresh beetroot. No significant differences in total polyphenol content and total flavonoid content when compared with the fresh sample after application of the refractance window™ were found. The overall energetic efficiency was higher after using refractance window™ drying in beetroot in the two physical states and produced greater retention of quality parameters when compared with the tray dryer. • Study of beet drying in RW™ by batch without pretreatment. • Evaluation of beet drying in two physical states: puree and slices. • Determination of overall energy efficiency of RW™ batch dryer. • Comparison of overall energy efficiency with a convective dryer. • Evaluation of bioactive substances in beet puree and slices by RW™ drying. [ABSTRACT FROM AUTHOR]

The findings related to strict environmental policies and energy poverty have been found contradictory. Strict environmental regulations may protect the environment by enhancing renewable energy resources but at the same time, major dependent sectors and consumers reliance on non-renewable energy resources face the problem of energy poverty. Moreover, environmental policies either soft are strict and depend on stakeholders' preferences, and such policies are implemented through institutions. Considering these aspects, the current study examines the impact of environmental policy stringency on energy poverty and further examines the role of institutions in bridging the gap between environmental policy stringency and energy poverty for the selected 31 countries from 1996 to 2020. For empirical analysis, Pooled OLS, random effect model, and system generalized methods of moments (GMM) are applied. To check the robustness of baseline models, spatial lag, spatial error, and feasible generalized methods of moments are applied. Furthermore, to examine the mediating role of institutions, we applied the structural equation modeling technique. Empirical analysis shows that an increase in environmental policy stringency significantly increases energy poverty, while institutional proxies significantly decrease energy poverty. The interactive effects of institutional proxies indicate declining effects on energy poverty. More importantly, institutions act as important mediators between environmental policies and energy poverty. Based on the findings, this study recommends soft environmental policies to maintain a balance between a sustainable environment and minimum energy poverty. This study further recommends authorizing and strengthening the institutions to formulate and regulate balanced environmental policies for environmental safety along with reduced energy poverty. This study further recommends increases in urbanization, personal remittances, and enhanced energy efficiency to minimize energy poverty. • Environmental policy stringency and energy poverty. • Institutional quality and energy poverty. • Energy efficiency in eradicating energy poverty. • Role of institutions through environmental policy stringency on energy poverty. • Energy efficiency, urbanization and remittances as determinants of energy poverty. [ABSTRACT FROM AUTHOR]

i) This study examines the determinants of environmental quality. It is not possible to fully analyze the complex network that emerges from the set of interactions of these determinants, both with each other and with environmental security. Indeed, a number of variables and relationships hidden in the background of the puzzle such as 'game theoretical interactions between economies on energy security', characterize this network. However, this study, which includes energy security and environmental quality simultaneously, may open the door to revealing the key patterns of the current network. ii) This study, which investigates the network between environmental problems and energy security, provides empirical evidence that these two variables may well evolve by positively affecting each other under some conditions. iii) Using the current and sophisticated econometric methods such as CDw + based on Juodis and Reese (2022) test and CS-ARDL Model, over a panel of top 20 energy-using countries in the period 1980–2018, the empirical analysis of the article shows that an increase in energy security risk positively affects environmental quality in aggregate by motivating increased energy efficiency, triggering environmental awareness and regulations, and stimulating research and development activities for clean energy etc. Technologies. Therefore, this study concludes that potential policies and reforms, including reducing fossil fuel consumption, increasing energy efficiency in distribution and consumption, encouraging investments in clean energy are of key importance in making energy security sustainable in the long term by increasing environmental quality. • The study coherently and thoroughly discusses the determinants of environmental quality. • Data from the top 20 energy-consuming countries are used. • The CS-ARDL and DCCE estimator is used. • It is found that an increase in energy security risk positively affects environmental quality in aggregate. • Interdisciplinary policy recommendations are made. [ABSTRACT FROM AUTHOR]

The enlarge in economic activities and the urban population at the global level has brought about an increase in the demand for energy, food, and natural resources, as well as an exacerbation in global climate change concerns. In this respect, it is important to ensure the balance between global climate change and global economic activities. Therefore, a wide literature has emerged that searches for alternative solutions to improve climate change and carbon dioxide (CO 2) emissions. The majority of existing studies emphasize the importance of renewable energy sources in environmental improvement efforts. Few studies highlight the importance of forestation in environmental improvement efforts, highlighting the non-linear effects of forestation. To fill this gap, this study uses panel data from 181 countries between 1990 and 2022 and evaluates the non-linear impact of economic growth, forest extent, energy efficiency, and urban growth on per capita CO 2 emissions using a dynamic panel threshold and dynamic panel quantile threshold methods. Furthermore, we extend the model and conduct robustness tests examining the non-linear threshold effects of renewable and non-renewable energy consumption on per capita CO 2 emissions. Our findings provide pieces of evidence that forest extents are an alternative solution to renewable energy use and energy efficiency in environmental improvement efforts. • The potential nonlinear relationship between CO2 emissions, forest extent, and energy efficiency is examined for a global panel. • The empirical methodology is based on a combination of dynamic panel threshold and dynamic panel quantile regression. • Forest extent, renewable energy and energy efficiency has a marginal non-linear mitigating effect on pollution. • Economic growth, urban growth and non-renewable energy consumption hasa non-linear marginal enhancing effect on pollution. [ABSTRACT FROM AUTHOR]

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used. [Display omitted] • Unique three-path-ANN system to simulate catalytic biomass combustion. • High accuracy with MRE of 3.54 % versus unbiased test data. • Bioenergy production from straw, hemp and wood pellets. • Complexed system to forecast 11 different parameters. • Case study indicates optimal catalyst – feedstock pairs. [ABSTRACT FROM AUTHOR]

The development of digital finance provides new opportunities for improving energy efficiency and promoting green development. This paper calculates green total factor energy efficiency (GTFEE) using the super-efficiency SBM model and examines the impact of digital finance on GTFEE. Digital finance has a significant positive impact on GTFEE. Under a bank-dominated financial structure, the positive impact of digital finance on GTFEE is quite significant. In regions with intense banking competition, a large amount of green credit, and lower resource dependence, digital finance is conducive to enhancing GTFEE. Optimizing the allocation efficiency of production factors is an essential mechanism for digital finance to encourage improvements in GTFEE. Digital finance alleviates distortions in factor markets and enhances the matching of the marginal output and the price of capital, labor, and energy factors, thereby facilitating improvements in GTFEE. Further analysis indicates that digital finance has a significant, positive spatial spillover effect on GTFEE, enhancing GTFEE levels in both local and neighboring regions. This study enriches the research on the relationship between digital finance and energy efficiency and provides theoretical foundations and policy references for how digital finance can better serve the green transition of the economy. • This paper analyzes the impact of digital finance on green total factor energy efficiency (GTFEE). • Digital finance alleviates distortions in factor markets, thereby promoting GTFEE. • Digital finance's impact on GTFEE depends on financial structure, banking competition, green credit and resource dependence. • Digital finance has a significant positive spatial spillover effect on GTFEE. [ABSTRACT FROM AUTHOR]

The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' energy usage is commonly acknowledged as encouraging energy efficiency and enabling well-informed decision-making, ultimately leading to decreased energy consumption. Implementing eco-friendly architectural designs is paramount in mitigating energy consumption, particularly in recently constructed structures. This study utilizes clustering analysis on the original dataset to capture complex consumption patterns over various periods. The analysis yields two distinct subsets that represent low and high consumption patterns and an additional subset that exclusively encompasses weekends, attributed to the specific behavior of occupants. Ensemble models have become increasingly popular due to advancements in machine learning techniques. This research utilizes three discrete algorithms, namely Artificial Neural Network (ANN), K-nearest neighbors (KNN), and Decision Trees (DT). In addition, the application employs three more machine learning algorithms bagging and boosting: Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting Trees (GBT). To augment the accuracy of predictions, a stacking ensemble methodology is employed, wherein the forecasts generated by many algorithms are combined. Given the obtained outcomes, a thorough examination is undertaken, encompassing the techniques of stacking, bagging, and boosting, to conduct a comprehensive comparative study. It is pertinent to highlight that the stacking technique consistently exhibits superior performance relative to alternative ensemble methodologies across a spectrum of heterogeneous datasets. Furthermore, using a genetic algorithm enables the optimization of the combination of base learners, resulting in a notable enhancement in prediction accuracy. After implementing this optimization technique, GA-Stacking demonstrated remarkable performance in Mean Absolute Percentage Error (MAPE) scores. The improvement observed was substantial, surpassing 90 percent for all datasets. In addition, in subset-1, subset-2, and subset-3, the achieved R2 scores were 0.983, 0.985, and 0.999, respectively. This represents a substantial advancement in forecasting the energy consumption of residential buildings. Such progress underscores the potential advantages of integrating this framework into the practices of building designers, thereby fostering informed decision-making, design management, and optimization prior to construction. [Display omitted] • Due to the COVID-19, rising residential energy consumption has become a challenge. • Applying k-means clustering to detect patterns, emphasizing various demand periods. • Optimizing model performance using Recursive Feature Elimination with Random Forest. • Identifying the best ensemble technique for practitioners in a specific domain. • Refining ensemble models with genetic algorithms significantly boosts accuracy. [ABSTRACT FROM AUTHOR]

Undesirable outputs can be challenging to avoid in the production of goods and services, often overlooked. Pollution is generally regarded as a negative externality and is taken into account during the production process. The novelty of this study lies in introducing CO2 as an economic "bad" in the energy sector's efficiency measure through a stochastic data envelopment analysis (DEA) cross-efficiency model. Unlike pollution and economic goods, where increased production leads to more pollution, CO2 is weakly disposable, meaning that higher CO2 values lead to a decrease in the number of good outputs produced. The study proposes a new stochastic model based on an extension of the cross-efficiency model and applies it to measure the energy efficiency of 32 thermal power plants in Angola in the presence of undesirable outputs. This will help promote better environmental management. The study's findings offer vital policy insights for the energy sector. The introduction of new stochastic models enables more accurate efficiency measurement under uncertain conditions, aiding policymakers in resource allocation decisions. Additionally, the adoption of stochastic cross-efficiency methods enhances performance assessments, facilitating targeted interventions for underperforming units. These findings contribute to evidence-based policymaking, promoting sustainability and competitiveness within the energy sector. • A novel stochastic DEA model is presented. • The energy sector efficiency is assessed. • Undesirable output, CO 2 emission, is modeled in the proposed method. [ABSTRACT FROM AUTHOR]

The demand of gluten-free bakery products is worldwide increasing. Simultaneously, ohmic heating is an alternative heating technology that still deserves further research and development. Therefore, the aim of this work was assessing different technological and quality characteristics of gluten free sponge cake baked by ohmic heating. A custom ohmic system was designed and built and different electric field strengths (1500, 2000 and 2500 V/m) were tested. Conventional baking was performed for comparison purposes. The conventional baking time was 24 min, whilst the ohmic heating time ranged from 3.51 min at 2500 V/m to 7.93 min at 1500 V/m. The ohmic system achieved significantly higher energy efficiencies, reaching nearly 60%, compared to only 4.1% for conventional baking. Additionally, specific energy consumption was significantly lower for ohmic heating. Weight loss was higher for ohmically processed samples, showing larger number of pores in their inner spongy structure. Ohmic heating resulted in a larger volume expansion and a cake with lower density. The remaining quality characteristics, such as color, hardness, cohesiveness, and elasticity, were not significantly different. Results demonstrated that ohmic heating is a suitable technology for baking gluten-free sponge cakes and that it is a promising technology for fast industrial baking of such products. • Ohmic baking of sponge cake is faster that conventional baking. • Heating rate can be easily controlled changing the electric field. • Ohmic baking is more energy efficient than conventional baking. • Ohmic baking improves the volume expansion of gluten free sponge cake. [ABSTRACT FROM AUTHOR]

• GPUs may be safely undervoltage, allowing for non-conventional DVFS configurations. • A benchmark suit characterizes GPU components regarding undervoltage limitations. • ALU and DRAM-Cache controller are the most sensitive components to voltage drops. • High energy efficiency gains are obtained with decoupled frequency-voltage pairs. • Accuracy of Deep Neural Networks (DNN) applications is not compromised. The use of GPUs to accelerate DNN training and inference is already widely adopted, allowing for a significant performance increase. However, this performance is usually obtained at the cost of a consequent increase in energy consumption. While several solutions have been proposed to perform voltage-frequency scaling on GPUs, these are still one-dimensional, by simply adjusting the frequency while relying on default voltage settings. To overcome this limitation, this paper introduces a new methodology to fully characterize the impact of non-conventional DVFS on GPUs. The proposed approach was evaluated on two devices, an AMD Vega 10 Frontier Edition and an AMD Radeon 5700XT. When applying this non-conventional DVFS scheme to DNN training, the obtained results show that it is possible to safely decrease the GPU voltage, allowing for a significant reduction of the energy consumption (up to 38%) and of the EDP (up to 41%) on the training procedure of CNN models, with no degradation of the networks accuracy. [ABSTRACT FROM AUTHOR]

• Frequency constraints lower scheduling difficulty significantly. • Problem formulation is key when aiming for optimal solutions. • Core partitioning is more effective than partitioning along the time axis. • Fully heuristic scheduling approach is viable for larger task sets. Static scheduling of independent, moldable tasks on parallel machines with frequency scaling comprises decisions on core allocation, assignment, frequency scaling and ordering, to meet a deadline and minimize energy consumption. Constraining some of these decisions reduces the solution space, i.e. may increase energy consumption, but may also open the path to new, near-optimal approaches. We investigate how constraints of different steps influence energy consumption, starting with an unrestricted scheduler for moldable tasks. The constraints are partly derived from existing schedulers, but also generalized in a systematic way. We present integer linear programs for all scheduling variants. We compare energy consumption of schedules for a benchmark suite of synthetic task sets of different sizes and for task sets derived from real applications. In addition, we check how close the results are to the optimum results when the ILP solver meets a timeout. Our results indicate that constraints on task execution order, which avoid explicit representation of task order in ILPs, are mostly responsible for near-optimal energy consumption among large task sets. Furthermore, we find that for all steps except allocation, non-optimal fast heuristics can be used without sacrificing too much energy for the resulting schedule. Finally, we can show that an ILP for a new scheduler, for which also a heuristic version exists, is comparable in quality to more complicated schedulers. [ABSTRACT FROM AUTHOR]

We study the effect of an employer's communication concerning energy-efficient behavior on a firm's employees' behavioral responses. The management aims to enhance energy-efficient behavior performance by irregularly sending online messages to remote employees. We break down each message, analyzing the positive or negative emotions conveyed, collective or individual orientation, and ease of language. Comparing employees' daily energy efficiency after receiving a message to their daily performance without a previous message, we find that messages generally result in an immediate increase in efficiency. Messages conveying negative emotions and those making a distinction between the manager and the employees have a smaller effect than messages conveying positive emotions and emphasizing a collective orientation that includes both the management and employees. Additionally, shorter messages are more likely to induce improved efficiency. Sending messages significantly impacts driver performance for up to six days after the message, resulting in economically relevant cost reductions. [ABSTRACT FROM AUTHOR]

• The Sigmoid algorithm applies the well-known sigmoid function to load balancing. • The algorithm uses all the resources of a system to their fullest requiring no effort. • It adapts to different hardware configurations and application behaviours. • Sigmoid successfully scales to adapt to the systems of the future. A challenge that heterogeneous system programmers face is leveraging the performance of all the devices that integrate the system. This paper presents Sigmoid, a new load balancing algorithm that efficiently co-executes a single OpenCL data-parallel kernel on all the devices of heterogeneous systems. Sigmoid splits the workload proportionally to the capabilities of the devices, drastically reducing response time and energy consumption. It is designed around several features; it is dynamic, adaptive, guided and effortless, as it does not require the user to give any parameter, adapting to the behaviour of each kernel at runtime. To evaluate Sigmoid's performance, it has been implemented in Maat, a system abstraction library. Experimental results with different kernel types show that Sigmoid exhibits excellent performance, reaching a utilization of 90%, together with energy savings up to 20%, always reducing programming effort compared to OpenCL, and facilitating the portability to other heterogeneous machines. [ABSTRACT FROM AUTHOR]

Modern GPPs implement specialized instructions in the form of ISA extensions aiming to increase the performance of emerging applications. These extensions impose a significant overhead in the area and power of the processor because of their specific datapaths (e.g. hardware for SIMD and FP instructions may represent more than half of the core area). Considering that some devices (e.g., edge computing), must be as energy- and area-efficient as possible, and the sporadic usage of specialized instructions in many applications, we propose PHISA multicores. PHISA is composed of heterogeneous cores of the same single base ISA, but asymmetric functionality: some of the cores do not fully implement the costly instruction extensions, making room for the designers to add more efficient cores. We show that PHISA increases performance in (32%) and reduces energy consumption in (82%) compared to full-ISA systems with the same power budget, in multi-workload environments. • A heterogeneous multicore design composed of cores with partial-ISA implementation. • Partial-ISA replaces resource-expensive ISA extensions for extra core count. • Schedulers that keep execution transparency between full- and partial-ISA cores. • Evaluation in several different scenarios, configurations and optimization goals. • Partial-ISA shows better performance and energy efficient than traditional designs. [ABSTRACT FROM AUTHOR]

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening—energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process. • Green innovation resilience can promote energy efficiency. • Early digital economy development can lead to resource grabbing and technology constraints effects. • Pursuing the policy orientation of "digital greening—energy efficient". [ABSTRACT FROM AUTHOR]

This study delves into the critical nexus between wastewater treatment, energy consumption, and greenhouse gas emissions. Wastewater treatment is a linchpin of sustainable development, yet its energy-intensive processes contribute significantly to greenhouse gas emissions. The research focuses on wastewater treatment plants (WWTPs) in Mexico City (CDMX) and the Metropolitan Area of Barcelona (AMB), exploring the disparities between a developed country and a developing country. The study examines how factors such as water treatment technologies and electricity sources influence carbon emissions. The AMB exhibits superior performance by treating all wastewater, cogenerating energy from the biomass contained in the wastewater and generating 10% fewer emissions, in stark contrast to CDMX, which does not capture the CH 4 produced during water treatment, on top of only treating the water of 14% of the city's agglomeration. It underscores the critical implications of WWTP efficiency on climate change and progress toward UN Sustainable Development Goals. Given the limited attention to the Global South, this research serves as a vital contribution to the discourse on sustainability and development. • Wastewater treatment plants (WWTPs) are a notable source of GHG, mainly CO2, CH4, and N2O. • Barcelona WWTPs treat four times the treated flow as Mexico City ones. • Barcelona WWTPs produce 10% less GHGs per cubic meter of flow than Mexico City ones. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology represents a disruptive innovation that has garnered significant interest among researchers for its potential applications in ecological and environmental management. While many studies have investigated the impact of AI on carbon emissions, relatively few have delved into its relationship with air pollution. This study sets out to explore the causal mechanisms and constraints linking AI technologies and air pollution, using provincial panel data collected from 2007 to 2020 in China. Furthermore, this study examines the distinct pathways through which AI technology can ameliorate air pollution and reduce carbon emissions. The findings reveal the following key insights: (1) AI technologies have the capacity to significantly reduce air pollution, particularly in terms of PM 2.5 and SO 2 levels. (2) AI technologies contribute to enhanced air quality by facilitating adjustments in energy structures, improving energy efficiency, and strengthening digital infrastructure. Nonetheless, it is important to note that adjusting the energy structure remains the most practical approach for reducing carbon emissions. (3) The efficacy of AI in controlling air pollution is influenced by geographical location, economic development level, level of information technology development, resource dependence, and public attention. In conclusion, this study proposes novel policy recommendations to offer fresh perspectives to countries interested in leveraging AI for the advancement of ecological and environmental governance. • Verified that AI technologies can significantly improve air quality. • AI technologies can reduce air pollution through energy structure, energy efficiency, and digital infrastructure. • The mechanisms by which AI technologies affect air quality and CO 2 emissions differ significantly. • The impact of AI technologies on air quality is constrained by geographic location, economic development, resource dependence, information technology development, and public attention. [ABSTRACT FROM AUTHOR]

Flexible dielectric materials such as poly(vinylidene fluoride)-based nanocomposites with high energy density are employed for applications in modern electronic and electric systems. In this study, we improve traditional methods by optimizing the interfacial structure, achieving a 34% increase in energy density without reduced discharge efficiency. Herein, a simple solution-cast method is used to prepare poly(vinylidene fluoride- co -trifluoroethylene) nanocomposites filled by γ-methacryloyl-propyltrimethoxysilane (MPMS) grafting barium titanate nanoparticles, forming a class of cross-linking networks by irradiation. More additional interfaces arising from irradiation cross-linking give rise to high discharge energy density, and the small crystalline domain and cross-linking network enhance the charge-discharge efficiency. Furthermore, we find two types of cross-linking centers on the network. One is more beneficial to energy density, and the other is more beneficial to efficiency. Regulating two types of cross-linking centers can balance efficiency and energy density. In summary, this work provides a promising strategy for exploiting advanced flexible dielectric materials to meet application requirements. [ABSTRACT FROM AUTHOR]

Propane heating is both a financial and an environmental burden for broiler houses located in cold climate regions. Heat recovery can reduce propane consumption significantly, by preheating fresh air inflow with stalled air outflow. While there have been many studies on the improvement of broiler houses direct ventilation, little attention has been given to the integration of heat exchangers. In this study, an existing broiler house (800 m2) equipped with two air-to-air ductless heat exchangers (0.38 m3s-1 each) was simulated. Computational fluid dynamics (CFD) software OpenFOAM was used to create a 3D steady-state buoyant simulation with RNG k - ε turbulence model. The CFD model was validated with experimental data from Nielsen (1976). In the reference configuration, the two heat exchangers were aligned in parallel, positioned against the northern wall. Three alternative heat exchangers configurations were simulated and analysed in their ability to provide adequate, uniform velocity, temperature and air age at bird height. No configuration was found to avoid excessive air velocities entirely. One configuration (C1) reduced the air age standard deviation from 477 to 179 s. It also increased the surface with adequate air quality from 55% (C0, reference case) to 72% of the floor area. Despite using the same heating power, temperature differences of up to 2 °C were observed in the mean air temperature of broiler zones of different configurations. These results confirm that ventilation performance can be improved by a careful analysis of heat exchangers position and its effects on airflow patterns. • Heat exchangers save fuel but achieving uniform housing conditions is a challenge. • One configuration (C1) reduced the air age standard deviation from 477 to 179 s. • Secondary vortex with low air quality can form with some configurations. • Excessive wind chill seems hard to eliminate with ductless heat exchangers. [ABSTRACT FROM AUTHOR]

Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-core system while adjusting resources to expend a minimum of energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core last-level cache partitions and the per-core voltage–frequency settings to save energy while respecting QoS requirements of every application in multi-programmed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and Dynamic Voltage–Frequency Scaling (DVFS) settings at runtime at negligible overhead. We show that the energy of 4-core and 8-core systems can be reduced by up to 18% and 14%, respectively, compared to a baseline with even distribution of cache resources and a fixed mid-range core voltage–frequency setting. The energy savings can potentially reach 29% if the QoS targets are relaxed to 40% longer execution time. [ABSTRACT FROM AUTHOR]

Conventional capacitive deionization (CDI) with carbon electrodes is a desalination process based on the formation of electrical double layer. Intercalation capacitive deionization (ICDI), a category of CDI based on intercalation materials as electrodes, achieves desalination by inserting ions into the crystal lattice of the electrode when a voltage is applied. It has been proven numerically that a thermodynamically reversible CDI cycle always consumes electrical work that equals the Gibbs free energy of the separation. We conducted a thermodynamic analysis of a four-stage reversible cycle for both symmetric and asymmetric ICDI using Frumkin isotherm to describe the electrode-solution chemical equilibrium. We provided both analytical and numerical proof showing the electrical work to complete a four-stage ICDI cycle is exactly identical to the Gibbs free energy of separation. Our thermodynamic analysis also shows ICDI is typically more energy efficient than CDI if constant voltage charging and discharge are performed to complete the same separation. [ABSTRACT FROM AUTHOR]

A Cognitive Radio Sensor Network (CRSN) is a distributed network of sensor nodes, which senses event signals and collaboratively communicates over dynamically available spectrum bands in a multi-hop mode. All nodes participating in CRSN have to be cognitive of the network environment and autonomous in decision making for resolving issues related to throughput maximization, delay, and energy minimization. Clustering in CRSN is proven to tackle such issues and enlarges the network's lifetime. However, the existing clustering algorithms designed for WSNs do not consider the CR functionalities and challenges, and CR based networks work on the assumption of unlimited energy. This paper proposes an energy and spectrum aware unequal cluster based routing (ESUCR) protocol intending to resolve the issues of clustering and routing in CRSN. In ESUCR, cluster formation is mainly performed considering the residual energy of the secondary users (SUs) and relative spectrum awareness such that the common data channels for clusters are selected based on the appearance probability of PUs. ESUCR performs energy-efficient channel sensing by deciding the channel state with the statistic previous channel states. The premature death of cluster heads (CHs) is minimized by selecting and rotating the CHs based on intra-cluster channel stability, energy, distance, and neighbor connectivity. During event detection, ESUCR performs energy-efficient data routing towards the sink node by employing hop by hop forwarding through the CHs and primary/secondary gateways. The performance of the proposed ESUCR protocol is proved through extensive simulations and compared to those of the state-of-the-art protocols under a dynamic spectrum-aware data transmission environment. • An optimal channel selection technique based on an intelligent channel ranking. • Energy-efficient spectrum sensing technique. • A cluster head selection and rotation technique for achieving energy balance. • A gateway and relay selection technique for performing inter-cluster data forwarding. [ABSTRACT FROM AUTHOR]

Due to new government legislation, customers' environmental concerns and continuously rising cost of energy, energy efficiency is becoming an essential parameter of industrial manufacturing processes in recent years. Most efforts considering energy issues in scheduling problems have focused on static scheduling. But in fact, scheduling problems are dynamic in the real world with uncertain new arrival jobs after the execution time. This paper proposes an energy efficient dynamic flexible flow shop scheduling model using the peak power value with consideration of new arrival jobs. As the problem is strongly NP-hard, a priority based hybrid parallel Genetic Algorithm with a predictive reactive complete rescheduling strategy is developed. In order to achieve a speedup to meet the short response in the dynamic environment, the proposed method is designed to be highly consistent with the NVIDIA CUDA software model. Finally, numerical experiments are conducted and show that our approach can not only solve the problem flexibly, but also gain competitive results and reduce time requirements dramatically. • An energy efficient dynamic flexible flow shop scheduling (EDFFS) model is studied. • A parallel Genetic Algorithm (GA) with a predictive reactive complete rescheduling approach is developed. • The parallel GA is highly consistent with the hierarchy of threads and memory of CUDA. • The parallel GA obtains competitive results and reduces time requirements dramatically. • The proposed method is flexible to solve the EDFFS problem and overcomes the traditional static approach. [ABSTRACT FROM AUTHOR]

Improving environmental performance of energy- and carbon-intensive sectors represented by the iron and steel (IS) industry is of utmost importance to address the challenges of resource depletion and climate change worldwide. This article adopts a global-super-Epsilon-Based Measure (EBM) model with undesirable output for IS energy efficiency estimation, identifies efficiency determinants based on Technology-Organization-Environment (TOE) framework, and analyzes various pathways for efficiency improvement by grouping Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA). Empirical testing using statistical data of the G20 economies during 2010–2020 demonstrates that: 1) energy efficiency in the IS industry in G20 countries has risen amidst fluctuations, with developed countries performing more efficiently than developing countries; 2) individual factors do not constitute a compulsory condition to achieve high energy efficiency in the IS industry; 3) three different paths to achieve high energy performance are found, that is, technology-structure driven, regulation-economy-technology driven, and regulation-technology-production driven. Heterogenous policy recommendations for efficiency gains in the IS sector of different countries with divergent features are proposed accordingly. • An adjusted EBM model with undesirable output is constructed to estimate energy efficiency of the IS industry. • TOE framework is adopted to identify determinants of efficiency change. • NCA and fsQCA are combined to observe various configurations for high efficiency level. • Divergent pathways for efficiency gains against different backgrounds are summarized. [ABSTRACT FROM AUTHOR]

Inspired by Denmark's ambitious renewable energy initiatives and its commitment to achieving a substantial 70 percent reduction in greenhouse gas (GHG) emissions by 2030 and achieving net-zero emissions by 2050, this study delves deeper into examining the roles of energy source efficiency, renewable energy utilization, and environment-related technologies spanning the years from 1990 to 2021. A comprehensive array of wavelet tools, including wavelet coherence, wavelet-based ordinary least squares (WBOLS), Continuous Wavelet Transform (CWT), Granger causality, and wavelet correlation, was employed to dissect these dynamics. The primary findings underscore the potential for enhancing environmental sustainability through these key indicators. For instance, employing the WBOLS method reveals that a percent increase in renewable energy consumption translates into an approximate reduction of ∼0.02%, ∼0.03%, and ∼0.54% in GHG emissions in the short-, medium-, and long-term, respectively. Similarly, improvements in energy efficiency yield remarkable outcomes. A one percent increase in the efficiency of natural gas utilization leads to GHG emission reductions of ∼0.44%, ∼0.19%, and ∼0.83% in the short-, medium-, and long-term, respectively. Moreover, a 1 percent enhancement in coal energy efficiency results in GHG emission reductions of ∼0.23%, ∼0.19%, and ∼0.91% in the short-, medium-, and long-term, respectively. Furthermore, the study indicates that a surge of 1% in innovation through environment-related technologies corresponds to GHG emission reductions of ∼0.56%, ∼0.10%, and ∼0.02% in the short-, medium-, and long-term, respectively. The results are notably substantiated by the CWT Granger causality approach. Considering the somewhat modest impact of innovation on GHG emissions, especially in the long-term, the study recommends a deliberate emphasis on the design and formulation of environmentally-related innovations that prioritize attributes such as reliability, durability, and adaptability. • Examined Denmark's 2030; 2050 net-zero emission prospects from GHG) emission perspective. • Deployed wavelet-based empirical approaches. • GHG emission is mitigated by renewable energy in the short-, medium-, and long-term. • Natural gas energy efficiency decreases in GHG emissions in the short-, medium-, and long-term. • Coal efficiency decreases in GHG emissions in the short-, medium-, and long-term. [ABSTRACT FROM AUTHOR]

Whether constructing more transportation infrastructure can be helpful for the achievement of energy conservation is a long-running and debatable issue. To answer this question, the relationship between transportation infrastructure and energy efficiency must first be clarified. Nonetheless, the existence of the endogeneity problem poses a challenge to defining the relationship. In this paper, an endogenous stochastic frontier analysis method is used to investigate the influence of transportation infrastructure on energy efficiency. Based on the prefecture-city level panel data in China, we find that after addressing the endogeneity problem, the impact of transportation infrastructure on energy efficiency increases dramatically. Moreover, this impact is more pronounced in small-scale cities compared to large and medium-scale cities. Regardless of the measurement of transportation infrastructure, instrumental variable, or production function form, we get the similar conclusions, demonstrating the robustness of our findings. Additional simulation analysis shows that the energy conservation potential would be 1222–2935 million kilowatt hours if the level of transportation infrastructure could be optimized. We recommend accelerating the transportation infrastructure construction, particularly in the small-scale cities so as to boost the energy efficiency and achieve energy conservation targets. • The relationship between energy efficiency and transportation infrastructure is investigated. • The endogeneity of transportation infrastructure is addressed. • A novel framework to analyze the relationship between energy efficiency and transportation infrastructure is proposed. • Accelerating the construction of transportation infrastructure to increase energy efficiency is proposed. [ABSTRACT FROM AUTHOR]

Improving energy efficiency can go a long way in helping China address environmental problems it currently faces and help deliver on its pledge of achieving carbon neutrality by 2060. At the same time, innovative production technologies based on digital solutions continue to attract significant attention, owing to their potential to provide environmentally sustainable development opportunities. This study explores whether the digital economy can improve energy efficiency by facilitating input reallocation and promoting better information flows. We rely on a panel of 285 Chinese cities for the period 2010–2019 and a so-called slacks-based efficiency measure incorporating socially undesirable outputs to obtain energy efficiency from the decomposition of a productivity index. Our estimation results demonstrate that the digital economy can promote better energy use efficiency. More specifically, a 1-percentage point increase in the size of the digital economy leads to an average increase of around 14.65 percentage points in energy efficiency. This conclusion still holds under a two-stage least-squares procedure used to mitigate endogeneity. The efficiency-enhancing impact of digitalization is heterogeneous and depends on factors such as resource endowment, city size, and geographical location. Additionally, our results suggest that digital transformation within a particular region has an adverse effect on energy efficiency in that region's neighboring areas due to negative spatial spillover effects. These negative spillovers outweigh the positive direct effect on energy efficiency that can be attributed to a growing digital economy. • Energy efficiency is crucial to achieve a "two-carbon" target. • Digital technology offers opportunities for low-carbon development. • The digital economy has a positive impact on enhancing energy efficiency. [ABSTRACT FROM AUTHOR]

We study the transition to a carbon-free economy in a model with a polluting non-renewable resource and a clean renewable resource. Transforming primary energy into ready-to-use energy services is costly and more efficient energy transformation rates are more costly to achieve. Renewable energy competes with food production for land and the food productivity of land can be improved at some cost. To avoid catastrophic climate damages, the pollution stock is mandated to stay below a given cap. When the economy is not constrained by the cap, the efficiency of energy transformation increases steadily until the transition toward the ultimate green economy; when renewable energy is exploited, its land use rises at the expense of food production; food productivity increases together with the land rent but food production drops; the food and energy prices increase and renewables substitute for non-renewable energy. During the constrained phase, the economy follows a constant path of prices, quantities, efficiency rates, food productivity and land rent, a phenomenon we call the 'ceiling efficiency paradox'. [ABSTRACT FROM AUTHOR]

We build novel welfare-based price indices for major household appliances that leverage changes in same-model prices and how consumers substitute between exiting, continuing and new models. We then evaluate how minimum energy efficiency requirements and changing criteria for Energy Star™ labels affected these indices in the U.S. between 2001 and 2011, a period of time when some appliances experienced standard changes while others did not. We find that prices declined while quality and consumer welfare increased, especially when standards become more stringent. We also find that much of the price index decline can be attributed to standards-induced innovation, or cannibalism, not to inter-manufacturer competition. Our results also add to a growing body of evidence that the Consumer Price Index exaggerates inflation due to inadequate account of quality and substitution to new goods. [ABSTRACT FROM AUTHOR]

More than half of all energy produced by electric utilities is lost in the form of waste heat. However, when manufacturing facilities choose to produce their own electricity, this waste heat is captured by Combined Heating and Power (CHP) technologies and used in the production process. As a result, manufacturers' pollution footprint can be dramatically reduced by choosing to produce electricity onsite rather than purchasing it from a utility. This paper uses Census microdata to study manufacturers' decision to produce electricity onsite and examines how plants adjust onsite generation when they are subject to environmental regulations. Environmental regulations will backfire if they cause manufacturers to produce less electricity onsite and shift to electricity from less efficient, offsite electric utilities. We find that manufacturing plants subject to NOx command-and-control regulations decrease onsite electricity generation, increase electricity purchases from off-site utilities and see declines in their energy efficiency. However, manufacturers subject to cap-and-trade see no decline in onsite generation and experience improvements in energy efficiency. These findings demonstrate the importance of instrument selection and identify a new pathway through which emissions leakage may occur. • NOx regulations reduce distributed generation by 2.0–3.3 percentage points. • NOx cap-and-trade instruments increase energy productivity by 21%. • Cap-and-trade instruments reduce CO 2 emissions by 20%. • Command-and-control increases CO 2 by 8%. [ABSTRACT FROM AUTHOR]

Abstract Buildings constructed according to bioclimatic architectural principles in amenable climates have often experienced posterior interventions that have closed ventilation openings for the installation of air conditioning units. The present work sought to investigate the reasons for installing air conditioning equipment in buildings even under adverse economic conditions and with the awareness of their negative environmental implications. The Faculty of Architecture building at the Federal University of Bahia, located in the city of Salvador, Bahia State, Brazil, was the focus of the present investigation. It was determined that the lack of maintenance of the windows and window frames, and the closing of projected openings compromised natural ventilation. The study confirmed the adequacy of the architectural project in relation to the local climate, and in loco measurements likewise confirmed the efficiency of natural ventilation through the windows and other openings in the faculty room and classrooms examined. The results of the interviews concerning thermal comfort indicated that 53% of the users felt comfortable. Nonetheless, it was found that the building's windows and window frames were poorly maintained, compromising their ability to facilitate efficient natural ventilation and significantly diminishing the capacity for thermal regulation in the building. This study calls attention to the necessity of refining and improving the maintenance of university buildings to reduce the intensive use of artificial air conditioning in detriment to investments in projects that could lend priority to natural ventilation and the maintenance of good window operating conditions. Highlights • The bioclimatic architecture was adequate for the local climate. • Adequate window ventilation was demonstrated. • The physical conditions of the windows and user behavior must be considered. • The lack of window frame maintenance compromised user comfort. • Fifty-three percent of the users experienced thermal comfort. [ABSTRACT FROM AUTHOR]

Abstract Hardware transactional memory (HTM) is widely supported by commodity processors. While the effectiveness of HTM has been evaluated based on small-scale multi-core systems, it still remains unexplored to quantify the performance and energy efficiency of HTM for scientific workloads on large-scale NUMA systems, which have been increasingly adopted to high-performance computing. To bridge this gap, this work investigates the performance and energy-efficiency impact of HTM on scientific applications on large-scale NUMA systems. Specifically, we quantify the performance and energy efficiency of HTM for scientific workloads based on the widely-used CLOMP-TM benchmark. We then discuss a set of generic software optimizations, which effectively improve the performance and energy efficiency of transactional scientific workloads on large-scale NUMA systems. Further, we present case studies in which we apply a set of the performance and energy-efficiency optimizations to representative transactional scientific applications and investigate the potential for high-performance and energy-efficient runtime support. Highlights • We investigate the performance sensitivity of CLOMP-TM to various TM settings. • We quantify the impact of HTM on scientific workloads on large-scale NUMA systems. • We propose a set of generic software optimizations. • We present a case study to show the effectiveness of the proposed optimizations. • We present a case study to demonstrate the potential for efficient runtime support. [ABSTRACT FROM AUTHOR]

Abstract We analyze the effect of CEO education on environmental decision-making. Using a unique sample of Danish firms from 1996 to 2012, we find that CEO education significantly improves firms' energy efficiency. We seek to derive causality using health shocks: the hospitalization of highly educated CEOs induces a drop in energy efficiency, whereas the hospitalization of less educated CEOs does not have any significant effect. Exploring the mechanisms at play, we show that our results are largely driven by advanced education in business degrees. Moreover, we show that CEO education is associated with greater environmental awareness: highly educated CEOs exhibit greater concerns for climate change, as measured by a survey of social preferences, and drive more environmentally efficient cars. Taken together, our findings suggest that education shapes managerial styles giving rise to greater sustainability in corporate actions. [ABSTRACT FROM AUTHOR]

Abstract A lot of cloud computing and cloud database techniques are adopted in industry and academia to face the explosion of the arrival of the big data era. Meanwhile, energy efficiency and energy saving become a major concern in data centers, which are in charge of large distributed systems and cloud databases. However, the phenomenon of energy wasting is related to resource provisioning. Hot-N-Cold model is introduced in this paper, which uses workload predictions and DVFS(Dynamic Voltage and Frequency Scaling) to cope with the resource provisioning problem within energy aware cloud database systems. In this model, the resource provisioning problem is considered as two bounded problems. A nonlinear programming algorithm and a multi-phase algorithm are proposed to solve them. The experimental results show that one of the proposed algorithms has great scalability which can be applied to a cloud database system deployed on 70 nodes. Using Hot-N-Cold model can save up to 21.5% of the energy of the running time. Highlights • The energy efficiency in cloud database systems is discussed. • The resource-providing problem in cloud database systems is considered as two bounded problems which are studied in this work. • Under Hot-N-Cold model, nonlinear programming algorithm and multiphase algorithm are proposed to solve the bounded problems. • The simulation and real case experiments are designed and implemented to measure the proposed algorithms in aspect of performance and assurance. [ABSTRACT FROM AUTHOR]

A methodology was developed to study the equivalence between the results obtained using the models of distributed and lumped parameters on the description of the drying kinetics of grape seeds in thin layer. For the lumped model, using a modification of the Glueckauf and Coates model, the relative average error was 11.47%. Having verified equivalence, the kinetic parameters set in the thin layer model could be used in the deep bed model. The energy efficiency of the drying process of Cabernet Sauvignon grape seeds in deep bed was calculated, with and without the recirculation of exhaust air. In the process without recycling, the highest energy efficiency was 2.27%, obtained for the dryer operating under mild drying conditions imposed by the set of restrictions, 50 °C and 1 m s−1. The maximum energy efficiency of the drying process with recirculation of air was 12.07% for dryer operating at 99% recycling ratio, 80 °C and 1 m s−1. It has been observed that the use of lower drying air flows is recommended, both in conventional dryers and those operated with recirculation of exhaust air. When the dryer was operated without recycling the exhaust air, greater efficiencies have been achieved at lower operating temperatures. Moreover, when the device was provided with recirculation of the exhaust air, the higher the operating temperature of the process, the higher the efficiency. Highlights • Grape seed drying was modelled phenomenologically. • The equivalence between models with lumped or distributed parameters was analysed. • The influence of exhaust air recirculation on energy efficiency was evaluated. • Optimal conditions of operation were established. [ABSTRACT FROM AUTHOR]

Energy efficiency in irrigation distribution networks can be estimated by following the method proposed by Abadia et al (2008) [Biosystems Engineering, 101, 21–27], in which energy efficiency is quantified in terms of global energy efficiency (GEE), a parameter that is a compound of the pumping energy efficiency (PEE) and the supply energy efficiency (SEE). PEE is related to pump functioning, while SEE is related to the design and management of the distribution system. Calculation of SEE by this method is limited to irrigation networks that have a single water intake at an elevation below that of the irrigated area and a single supplied sector. Results from the method are confusing for networks with more than one water supply point or irrigation sectors supplied by intakes located at elevations higher than that of the irrigated area. This paper proposes a modification of the earlier method that is appropriate to any type of layout and configuration of the irrigation network, with one or several water intake points and supplied sectors. The new model is applied to four different types of collective pressurised irrigation network, and the results are compared with those of the previous model. The influence of different measures taken to improve the SEE and determine the variables that affect this efficiency was analysed. The results show that adequate SEE values can be obtained with the proposed model for all the situations analysed, and that it is possible to quantify the improvements in SEE with the model. Highlights • Knowing the energy efficiency of a network can improve design and energy management. • Previous Supply Energy Efficiency (SEE) definition unfit for some network designs. • New method proposed and applied to four distribution networks. • New method is coherent and appropriate for all networks designs. • New SEE definition allows for identification of badly designed or managed networks. [ABSTRACT FROM AUTHOR]

Abstract There is an ongoing debate whether energy efficiency retrofits save energy in practice. This paper quantifies the energy savings of the Energize Phoenix program in Phoenix, Arizona. Impacts of retrofits are analyzed using pre-post treatment billing data from January 2008 to April 2013, covering 201 residential buildings and 636 commercial buildings. There are five types of retrofits for residential buildings and six types for commercial buildings. Empirical results from fixed effects panel regression models indicate monthly energy savings of 12% for commercial buildings and 8% for residential buildings. The realized energy savings are 30–50% lower than those predicted by engineering models. We have also separated the energy savings provided by individual retrofits and retrofit bundles, and estimate how the effectiveness of retrofits varies based on building attributes, all of which should be considered when making energy efficiency decisions. Learning effects are also observed in this program, which means as the post-retrofit time increases, there is a possibility for improved learning and control of energy use. Highlights • Empirical estimates of the net energy savings of building energy retrofits. • Retrofits save 12% of energy for commercial buildings. • Retrofits save 8% of energy for residential buildings. • Realized energy savings are 30–50% lower than those predicted by engineering models. • Energy savings provided by each type of retrofit are disentangled. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) serves as a promising alternative for waste treatment and a potential solution to improve the energy supply security. The feasibility of AD has been proven in some of the technologically and agriculturally advanced countries. However, development is still needed for worldwide implementation, especially for AD process dealing with municipal solid waste (MSW). This paper reviews various approaches and stages in the AD of MSW, which used to optimise the biogas production and quality. The assessed stages include pre-treatment, digestion process, post-treatment as well as the waste collection and transportation. The latest approaches and integrated system to improve the AD process are also presented. The stages were assessed in a relatively quantitative manner. The range of energy requirement, carbon emission footprint and the percentage of enhancement are summarised. Thermal hydrolysis pre-treatment is identified to be less suitable for MSW (−5% to +15.4% enhancement), unless conducted in the two-phase AD system. Microwave pre-treatment shows consistent performance in elevating the biogas production of MSW, but the energy consumption (114.24–8,040 kW e h t −1 ) and carbon emission footprint (59.93–4,217.78 kg CO 2 t −1 waste) are relatively high. Chemical (∼0.43 kW e h m −3 ) and membrane-based (∼0.45 kW e h m −3 ) post-treatments are suggested to be a lower energy consumption approach for upgrading the biogas. The feasibility in terms of cost (scale up) and other environmental impacts (non-CO 2 footprint) needs to be further assessed. This study provides an overview to facilitate further development and extended implementation of AD. [ABSTRACT FROM AUTHOR]

Phasing out thermal power plants is vital to combatting climate change. Less attention has been given to provincial-level thermal power plants, which are implementers of the policy of phasing out backward production capacity. To improve energy efficiency and reduce negative environmental impacts, this study proposes a bottom-up cost-optimal model to explore technology-oriented low-carbon development pathways for China's provincial-level thermal power plants. Taking 16 types of thermal power technologies into consideration, this study investigates the impacts of power demand, policy implementation, and technology maturity on energy consumption, pollutant emissions, and carbon emissions of power plants. The results show that an enhanced policy combined with a reduced thermal power demand would peak carbon emissions of the power industry at approximately 4.1 GtCO 2 in 2023. Meanwhile, most of the inefficient coal-fired power technologies should be eliminated by 2030. Carbon capture and storage technology should be gradually promoted in Xinjiang, Inner Mongolia, Ningxia, and Jilin after 2025. Energy-saving upgrades on 600 MW and 1000 MW ultra-supercritical technologies should be emphatically carried out in Anhui, Guangdong, and Zhejiang. By 2050, all thermal power will come from ultra-supercritical and other advanced technologies. [Display omitted] • A technology-oriented bottom-up model with cost minimization was developed. • Carbon capture and storage technology should be gradually promoted after 2025. • Carbon emissions of the thermal power industry could peak at 4.1 GtCO 2 in 2023. • 98% of the inefficient coal-fired power technologies should be eliminated by 2030. • The mitigation potentials for both SO 2 and NO x will exceed 3 million tons by 2050. [ABSTRACT FROM AUTHOR]

COP27 has provided a new direction in overcoming the issue of climate change. In the world of growing environmental degradation and climate change issues, the South Asian economies are playing a vital role in tackling these issues. Still, the literature focuses on industrialized economies while ignoring the newly emerging economies. This study evaluates the technological factors' influence on carbon emission in the four small South Asian economies (Sri Lanka, Bangladesh, Pakistan, and India) from 1989 to 2021. This study found the long-run equilibrium relationship between the variables using second-generation estimating tools. Employing the non-parametric and robust parametric approach, this study found that economic performance and development are substantial emissions drivers. In contrast, energy technology and tech innovation are the region's key environmental sustainability factors. Further, the study found that trade positively yet insignificantly impacts pollution. This study suggests further investment in energy technology and technological innovation to improve the production of energy-efficient products and services in these emerging economies. • COP27 perspective of South Asian countries is analyzed. • The role of trade and economic growth is examined during 1989 and 2021. • Method of moment quantile regression and robust regression are used. • Economic growth and trade drives climate change and environmental degradation. • Technological innovation and energy technology reduces carbon emission. [ABSTRACT FROM AUTHOR]

This paper examines the causal impact of LEED (Leadership in Energy & Environmental Design) certification on energy consumption among federally owned buildings that were retrofitted over the period 1990–2019. Using a difference-in-differences propensity score matching approach, the paper has two findings. First, despite energy savings being an explicit federal goal, LEED-certified retrofits of federal buildings did not have statistically significant energy savings on average. Second, LEED buildings with higher energy scores had greater energy efficiency post-certification, and the improvements were economically meaningful. The absence of energy savings on average appears to be driven by three factors — trade-offs across energy and other areas in acquiring points for certification, possible changes in energy use after the official performance period for LEED certification ended, and improvements in the energy efficiency of all federal buildings. [ABSTRACT FROM AUTHOR]

In recent years, there have been substantial efforts to improve the efficiency of production resources, including energy use, to reduce the human footprint from economic activities. Increasing production capacity and incorporating new technologies that improve energy efficiency in the production process are two primary challenges faced by developing countries, where capital goods imports could play a key role in addressing both challenges. This paper contributes to the empirical literature by examining the relationship between energy intensity, economic structure, and capital goods imports in a set of 36 upper-middle income economies in the period 2000–2019. The empirical strategy recognizes the existing heterogeneity among the broad group of countries in the sample by implementing the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm, a state-of-the-art unsupervised machine learning technique which allows identification of clusters of countries and years. The results show the existence of ten clusters, where energy intensity has the most relevant positive associations with industry share, trade openness, and merchandise imports. Improvements in regulatory quality are associated with lower energy intensity. The direction and strength of the relationship between energy intensity and capital goods imports depend on the cluster; nonetheless, it is usually a weak relationship. Policy implications are discussed. [Display omitted] • The relationship between energy intensity, economic structure and capital goods imports in developing countries is examined. • The hierarchical density-based spatial clustering of applications with noise algorithm is implemented. • In several clusters, energy intensity relates to industry share, trade openness, merchandise imports and regulatory quality. • The association between energy intensity and capital goods imports is weak. • Associations between socioeconomic variables and energy intensity are country- and time-dependent. [ABSTRACT FROM AUTHOR]