Search

Understanding the factors that drive the use of residential electricity in India is essential for designing policies that can reduce its expected growth. This study empirically examines the magnitude and causes of variation in electricity use in a sample of Indian dwellings with and without air conditioning (AC), using an interview-based survey approach applied in 41 dwellings located across two cities representing the composite climate of India. Statistical analysis was used to unpack the relationship between electricity use and socio-technical factors. Despite the small sample size and having the same climatological realm, there was a wide variation observed in electricity use by income groups and the presence of ACs. The mean annual residential electricity consumption (REC) was observed to be highest in high-income group dwellings (5,618 kWh/year), followed by middle-income group dwellings (3,870 kWh/year) and lowest in low-income group dwellings (2,169 kWh/year). The mean REC of AC dwellings (4,208 kWh/year) was found to be nearly double that of non-AC dwellings (2,260 kWh/year) with significant seasonal variation. Multivariate regression analysis revealed that the presence of ACs, household size (number of occupants), annual non-AC appliance hours, dwelling size (number of rooms) and income group accounted for 80% of the variability in REC across the study sample. Residential energy policy in India should consider these key factors that drive REC. To be effective, such policy programmes need to be customised for different income groups, adopting incentives as well as energy awareness campaigns to influence both purchasing and habitual behaviours.

This study examines the complex and time-varying relationship between residential energy demand (including electricity, geothermal, and solar energy) and climate change using wavelet analyses with monthly USA data from January 1990 to March 2023. The results show that residential energy demand and climate change indicators exhibit a time-varying interrelationship with cyclical and lag effects. Specifically, before 2021, a positive correlation between residential electricity demand and carbon dioxide (CO2) emissions in short-term frequencies was found, but the relationship reversed thereafter, with an increase in CO2 levels influencing and decreasing residential electricity demand. In the long run frequencies, the link between residential power consumption and CO2 emissions shifted over time, exhibiting inconsistent co-movement. The co-movements between residential geothermal and CO2 show predominantly positive correlations, with CO2 leading the relationship in the short run, while geothermal leads the co-movements in the long run. In both short and long-term frequencies, the dependency and co-movement between residential solar and CO2 are mixed, with residential solar leading to positive correlations and CO2 leading to negative correlations. Therefore, improved insulation, energy-efficient windows, and high-efficiency heating systems can all assist in reducing heat loss and the total energy demand for domestic heating and subsequently low CO2 emissions.

In order to further tap the potential of multi-energy complementarity of energy Internet, improve the operating income of energy system and reduce the operating cost, an optimization method of load-source coordination of multi-energy hub considering consumption benefit was proposed. Considering the constraints of multi-energy system, the residential energy hub model was constructed according to minimizing the total operating cost of residential energy hub, and the industrial energy hub model was constructed with the goal of maximizing the total operating income of industrial energy hub. According to the constraints of the above two-layer model, the problems existing in the coordinated optimization of load and source are obtained, and the multi-objective mixed integer linear programming model was designed by using the multi-objective weighted optimization algorithm to realize the coordinated optimization of load and source. The simulation results show that the designed optimization method of load-source coordination of multi-energy hub considering consumption benefit can promote the consumption of renewable energy, reduce the operating cost of multi-energy hub, effectively reduce the overhead and improve the operating income of energy system, which provides a new technical way for the economic operation and load-source coordination optimization of multi-energy hub. [ABSTRACT FROM AUTHOR]

Universal access to clean fuels in household use is one explicit indicator of sustainable development while currently still billions of people rely on solid fuels for daily cooking. Despite of the recognized clean transition trend in general, disparities in household energy mix in different activities (e.g. cooking and heating) and historical trends remain to be elucidated. In this study, we revealed the historical changing trend of the disparity in household cooking and heating activities and associated carbon emissions in rural China. The study found that the poor had higher total direct energy consumption but used less modern energy, especially in cooking activities, in which the poor consumed 60 % more energy than the rich. The disparity in modern household energy use decreased over time, but conversely the disparity in total residential energy consumption increased due to the different energy elasticities as income increases. Though per-capita household CO2 and Black Carbon (BC) emissions were decreasing under switching to modern energies, the disparity in household CO2 and BC deepened over time, and the low-income groups emitted ∼ 10 kg CO2 more compared to the high-income population. Relying solely on spontaneous clean cooking transition had limited impacts in reducing disparities in household energy and carbon emissions, whereas improving access to modern energy had substantial potential to reduce energy consumption and carbon emissions and its disparity. Differentiated energy-related policies to promote high-efficiency modern heating energies affordable for the low-income population should be developed to reduce the disparity, and consequently benefit human health and climate change equally.

Existing grids have been designed with traditional large centralized generation in mind; however, with the ever-increasing utilization of renewable distributed energy resources, the challenges of proper grid management have intensified. Demand-side energy flexibility is seen as one potential way to alleviate these challenges. Presently, residential demand-side energy flexibility has remained a largely untapped resource since individual prosumers are too small to provide enough capacity, thus necessitating the need for an aggregator. In view of the aforementioned, this paper conducts a literature review on the aggregated residential demand-side energy flexibility. The paper gives an overview of characterization methods of energy flexibility. The sources of residential energy flexibility are identified and categorized based on their flexibility characteristics. In addition, the quantification methods and parameters of energy flexibility are analyzed. Moreover, the forecasting methods of energy flexibility in the context of different flexibility sources are outlined. Additionally, an overview of existing markets and potential new emerging flexibility markets is given. The challenges and barriers faced by the aggregators attempting to enter flexibility markets are examined. Finally, the paper is concluded by providing a discussion of the key findings that summarize the current research directions and highlight the gaps for future development of aggregated energy flexibility.

Copyright of Revista de Administração de Roraima is the property of Revista de Administracao de Roraima and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Demand response programs are promising approaches to balance supply and demand and reduce or shift peak hour loads. However, the impacts of residents’ behavioral patterns and social–psychological factors on these programs are poorly understood.Method: We used an extended model of the theory of planned behavior to understand how social–psychological factors, demographics, and household characteristics influence the demand response behaviors (i.e., adjusting A/C and shifting the time of use) of 826 US residents who participated in our study.Results: We found that low-income households (43.7%) reported as more likely to respond to the behavior of raising A/C thermostat settings when no one is home compared to higher-income residents. Thermal comfort needs (β = 0.34) and participants’ age (β = 0.02) increased the financial incentives requested to accept higher A/C temperatures while someone is home. Thermal comfort needs (β = 0.36) and energy-saving habits (β = −0.11) are the significant predictors of increased incentives requested for higher A/C temperatures when no one is home. Older adults request higher financial incentives for shifting washer and dryer usage than younger residents. Finally, attitudes toward energy saving are the strongest predictor of electricity curtailment behavioral intention, followed by perceived behavioral control, subjective norms, cost and environmental concerns, and energy-saving habits.Discussion: This finding suggests the potential of social–psychological variables in shaping energy consumption behaviors. This study provides insights for designing effective demand response programs and broadly analyzing energy behavioral patterns.

• ML-based UBEMs were developed for Chicago with five typical algorithms. • Different hyperparameter tuning methods were evaluated. • Random search method and suggested search space generally performed better. • Minimal gains were achieved from expanding search budget beyond 96 model runs. • Repeated cross-validation mitigated randomness in data splitting. In recent years, many studies have applied machine learning (ML) in urban building energy models (UBEMs) to understand building energy issues in cities. However, previous studies had limited in-depth discussions on hyperparameter tuning, an essential aspect of model training. To address this gap, this study examines the impact of different hyperparameter tuning strategies on the development of ML-based UBEMs, specifically for residential buildings in Chicago. Utilizing a comprehensive dataset that encompasses building, urban, household, and energy management factors, and annual electricity usage across 2,078 residential block groups in Chicago for the year 2010, this study employs five widely used supervised machine learning (SML) algorithms. Through three experiments, the study found that gradient boosting decision trees models generally outperform others. However, hyperparameter tuning can significantly enhance the performance of all tested SML models. The comparison of three search methods, grid search, random search, and Bayesian search, revealed that they have similar tuning performance, but random search stands out for its effectiveness, speed, and flexibility. Moreover, the study demonstrates that the suggested search space for hyperparameters yields better results compared to the commonly used search space in most cases. Interestingly, altering the search budget appeared to have minimal impact on the tuning effectiveness. Employing repeated cross-validation methods helped mitigate the issues associated with random data splits in the training process, leading to more reliable tuning outcomes. Further investigations into the scalability of training datasets and the evaluation of models using alternative accuracy metrics and considerations of model inductive bias provided additional insights. These findings informed recommendations regarding hyperparameter tuning strategies, optimal sample sizes, and model evaluation techniques. Overall, this research contributes to a deeper understanding of the role of hyperparameter tuning in the development of ML-based UBEMs, offering valuable insights for energy-efficient urban planning and policymaking toward sustainable city development. [ABSTRACT FROM AUTHOR]

The paper seeks to analyze residential energy use patterns and the price and energy expenditure responsiveness of household demand for residential energy in Ghana, using GLSS 7 data, the multivariate probit model, and the Quadratic Almost Ideal Demand System (QUAIDS) model. The study focuses on four main fuels; firewood, charcoal, and others (FCO), LPG, electricity, and kerosene. The results show that the demand for all household energy fuels in Ghana was significantly influenced by price, income, and social demographic factors. Household demand for electricity and LPG fuels are income elastic with values greater than 1, while FCO and kerosene fuels are income inelastic. The own-price elasticity also shows modern fuel (LPG and electricity) as the most responsive fuel to a price increase, even though all energy fuels are price inelastic and negative. The complementary cross-price elasticity of all fuels estimated must be carefully interpreted in the context of the cultural factors, socioeconomic factors, and the unique purposes of each fuel. After a robustness check was done in correcting for bias arising from zero energy expenditure, the study finds that the own-price elasticity for modern energy fuel (LGP and ECG) reduced slightly, even though the demand for all energy fuel remains negative and price inelastic. The study includes implications for policies geared towards reducing the price of residential energy fuels and policies to ensure clean and efficient residential energy fuels are readily accessible for consumption.

Background: Urban tree canopy (UTC) is often proposed as a mitigation strategy for simultaneously decreasing carbon emissions and urban heating in cities. Not only can trees reduce outdoor temperatures through shading and transpiration, but research also suggests that microclimate regulation by trees surrounding buildings can lead to cooler indoor temperatures and a subsequent decrease in summertime energy use. Methods: We analyzed summertime cooling electricity consumption for 21,048 single-family homes in a semi-arid city in northern Colorado, USA. Using Pearson’s correlation coefficients and multiple linear regression models, we evaluated the potential impact of UTC on cooling electricity use in 16 different zones around each house. We hypothesized that trees closer to the home, and trees located on the west and south sides of homes, would have the greatest impact on cooling electricity use. Results: UTC in all 16 zones around residential buildings was associated with negative correlation coefficients, indicating that UTC may be having an impact on energy use. Our regression results showed that UTC on the east side of single-family homes had the greatest effect. Conclusions: Although our results indicated that trees in landscapes around residential buildings can lead to some decreases in household-level energy consumption, the reductions in electricity usage were not as substantial as previous studies have predicted. Past research has shown that tree location matters, and our results indeed show that where UTC is located in reference to a building can change how much impact trees have on energy use. However, our results also show that trees on the east side of buildings have the most impact on household energy consumption in a semi-arid city in Colorado during the summer months. These results directly contradict predictions offered by popular ecosystem service models that show trees on the west and south sides of buildings as having the most impact on energy use in the Northern Hemisphere. Furthermore, many studies have suggested that the energy benefits provided by urban trees outweigh their carbon sequestration potential, and our results indicated this assumption may not hold true in all cities. [ABSTRACT FROM AUTHOR]

Global warming is one of the most important environmental problems in the world. One might state that the most potential considerable cause of global warming is the increase in greenhouse gas emissions in the atmosphere. The commonly suggested method used to struggle with this potential problem is to consume renewable energy sources instead of fossil fuels. In today's World, where the rate of urbanization is very high, the research on the effect of the energy demand of the residential sector on global warming has become a crucial research topic in the energy and climatic literature. Among different solar sectors, the residential sector is a major energy consumer. Considering the residential, commercial, and industrial solar sectors, the residential solar sector has become a major component of total solar energy. It accounts for approximately 43% of total solar. On the other hand, to the best of my knowledge, there exists no empirical study in the literature considering the influence of residential solar consumption on environmental quality. Thus, determining the effect of the most used energy source, residential solar, energy consumption on the environmental quality has arisen the key research problem of this paper. This research analyzes the effects of residential solar energy usage on CO 2 emissions for the period 1989:1–2020:1 in the USA. It has been observed throughout Morlet wavelet analyses that the use of residential solar energy has a strong effect on mitigating CO 2 emissions in the 1–2 year frequency. Eventually, this paper provides relevant potential energy policies that can enhance the efficiency of residential solar energy consumption. Partial wavelet coherence (RSolar-CO 2 || Coal, Hydro, IP), 1989:1–2020:1. [Display omitted] • Considers the impact of residential solar consumption (RSolar) on CO 2 emissions. • Follows the data for the USA for the monthly period 1989:1–2020:1. • Launches wavelet models in which RSolar, CO 2 , Coal, Hydro, and IP are employed. • Reaches the negative impact of RSolar on CO 2 in the USA for the period 1989:1–2020:1 at 1-2 year-cycle period. • Through potential energy policies, residential solar energy consumption efficiency should be increased. [ABSTRACT FROM AUTHOR]

Building characteristics, household compositions, lifestyles, and home equipment are recognized as the main factors influencing residential energy consumption, which has been a subject of extensive exploration for many years now. However, the quantitative correlation models between the above factors and residential end-use energy have not been fully studied. This paper aims to explore the determinants of residential end-use energy consumption by a comprehensive analysis based on the factors of building characteristics, household compositions, lifestyles, and home equipment. For this purpose, we investigated and collected the building information of 66 households and obtained the data through an installed measurement system of the annual residential end-use energy from July 2019 to June 2020. Subsequently, six multiple regression models were used to quantitatively analyze the valid determinants of each end-use energy. The main results were as follows: for cooling energy consumption, the greatest effective variable was FM_no (22–59, number of family members aged 22 to 59); the most influential variable was found to be FM_no (number of family members) for DHW and appliances energy consumption; for lighting and cooking energy consumption, the most effective variables were AREA (floor area) and Cooking (average daily cooking hours), respectively. Moreover, the order of influence of building characteristics, household compositions, lifestyles, and home equipment over each end-use energy consumption is as follows: households > equipment > lifestyles for cooling and DHW, households > buildings > equipment for lighting, equipment > lifestyles for appliances and cooking. [ABSTRACT FROM AUTHOR]

Housing is an established channel for U.S. climate policy. Local and national environmental policymakers have attempted to mitigate the contributions of home energy use and residential sprawl to the greenhouse gas emissions that cause global climate change for almost a half-century. More recently, hazard and planning officials are also exploring how to adapt housing to the multiple environmental effects that are already being realized as a consequence. But how is contemporary housing policy responding to the opportunities and needs of the climate crisis? The wide terrain of housing policy—including local land-use regulations, public subsidies for affordable housing production and maintenance, direct aid to households for their housing costs, the enforcement of fair housing laws, and the promotion of secure and affordable lending institutions among many relevant policy interventions—has struggled with integrating climate mitigation and adaptation strategies for a host of reasons. Resource constraints abound. Housing policymakers continue to focus on other persistent challenges such as the housing affordability crisis. The challenge of integrating climate response in this already complex social, economic, and environmental system may even be overwhelming. This special issue of Housing Policy Debate explores the ways in which the range of contemporary housing-relevant policy addresses climate change or, as the submissions suggest, ignores it. [ABSTRACT FROM AUTHOR]

The paper seeks to analyze residential energy use patterns and the price and energy expenditure responsiveness of household demand for residential energy in Ghana, using GLSS 7 data, the multivariate probit model, and the Quadratic Almost Ideal Demand System (QUAIDS) model. The study focuses on four main fuels; firewood, charcoal, and others (FCO), LPG, electricity, and kerosene. The results show that the demand for all household energy fuels in Ghana was significantly influenced by price, income, and social demographic factors. Household demand for electricity and LPG fuels are income elastic with values greater than 1, while FCO and kerosene fuels are income inelastic. The own-price elasticity also shows modern fuel (LPG and electricity) as the most responsive fuel to a price increase, even though all energy fuels are price inelastic and negative. The complementary cross-price elasticity of all fuels estimated must be carefully interpreted in the context of the cultural factors, socioeconomic factors, and the unique purposes of each fuel. After a robustness check was done in correcting for bias arising from zero energy expenditure, the study finds that the own-price elasticity for modern energy fuel (LGP and ECG) reduced slightly, even though the demand for all energy fuel remains negative and price inelastic. The study includes implications for policies geared towards reducing the price of residential energy fuels and policies to ensure clean and efficient residential energy fuels are readily accessible for consumption. [ABSTRACT FROM AUTHOR]

In this study, we analyse the role of forced lockdowns on electricity consumption behaviour and its effect on momentary transition in electricity use. Electricity consumption data for residential, commercial and industrial consumers within the Lagos metropolis representing 259 electrical feeder locations were collected and analysed under three scenarios: first, we analyse a business-as-usual scenario without a lockdown; secondly, we analyse the case of a partial lockdown; and finally, we analyse the case of a total lockdown. The study revealed that aside government announcement of the lockdown, certain social practices triggered changes in electricity consumption and use leading to momentary energy transition. Within the residential sector, increased cooking, home laundry, showering, and some professional practices that moved to the homes impacted on higher electricity consumption. Reduced manufacturing practices limited to those involved in food, personal care and pharmaceutical products led to a reduction in electricity use within the industrial sector, while reduced electricity use in the commercial sector was triggered mainly by a scaling down of trading services to essentials. The study concludes by highlighting the impact of changes in electricity demand and consumption under these scenarios and its implications for energy transition and electricity planning.

The increased use of renewable energy in electricity generation has not been sufficient to reduce the associated emissions. Therefore, a better understanding of the evolution of electricity consumption may be advisable to undertake suitable energy policies. This paper contributes to this understanding by analyzing the non-linear income and temperature effects on electricity demand in the residential sector in Andalusia (Spain). To this end, panel data OLS, PCSE, and quantile regression analysis methods have been performed to provide a complete picture of the relationship between the studied variables. The results show evidence to support an inverted N-shaped relationship, with respect to income, without reaching the upper threshold point. The results also show that electricity elasticity, with respect to income, tends to increase with residential electricity consumption. It is also observed that temperature values, increasing above 22°C and temperatures decreasing below 15°C progressively, increase residential electricity consumption, the elasticity with respect to temperatures being higher for municipalities with lower residential electricity consumption. Reinforcing energy efficiency measures in the municipalities with the highest electricity consumption is recommended. [ABSTRACT FROM AUTHOR]

Historical trends in household energy mix and disparities are examined revealing adoption of clean modern energies can effectively reduce energy consumption and associated carbon emission inequalities. [Display omitted] Universal access to clean fuels in household use is one explicit indicator of sustainable development while currently still billions of people rely on solid fuels for daily cooking. Despite of the recognized clean transition trend in general, disparities in household energy mix in different activities (e.g. cooking and heating) and historical trends remain to be elucidated. In this study, we revealed the historical changing trend of the disparity in household cooking and heating activities and associated carbon emissions in rural China. The study found that the poor had higher total direct energy consumption but used less modern energy, especially in cooking activities, in which the poor consumed 60 % more energy than the rich. The disparity in modern household energy use decreased over time, but conversely the disparity in total residential energy consumption increased due to the different energy elasticities as income increases. Though per-capita household CO 2 and Black Carbon (BC) emissions were decreasing under switching to modern energies, the disparity in household CO 2 and BC deepened over time, and the low-income groups emitted ∼ 10 kg CO 2 more compared to the high-income population. Relying solely on spontaneous clean cooking transition had limited impacts in reducing disparities in household energy and carbon emissions, whereas improving access to modern energy had substantial potential to reduce energy consumption and carbon emissions and its disparity. Differentiated energy-related policies to promote high-efficiency modern heating energies affordable for the low-income population should be developed to reduce the disparity, and consequently benefit human health and climate change equally. [ABSTRACT FROM AUTHOR]

Building characteristics, household compositions, lifestyles, and home equipment are recognized as the main factors influencing residential energy consumption, which has been a subject of extensive exploration for many years now. However, the quantitative correlation models between the above factors and residential end-use energy have not been fully studied. This paper aims to explore the determinants of residential end-use energy consumption by a comprehensive analysis based on the factors of building characteristics, household compositions, lifestyles, and home equipment. For this purpose, we investigated and collected the building information of 66 households and obtained the data through an installed measurement system of the annual residential end-use energy from July 2019 to June 2020. Subsequently, six multiple regression models were used to quantitatively analyze the valid determinants of each end-use energy. The main results were as follows: for cooling energy consumption, the greatest effective variable was FM_no (22–59, number of family members aged 22 to 59); the most influential variable was found to be FM_no (number of family members) for DHW and appliances energy consumption; for lighting and cooking energy consumption, the most effective variables were AREA (floor area) and Cooking (average daily cooking hours), respectively. Moreover, the order of influence of building characteristics, household compositions, lifestyles, and home equipment over each end-use energy consumption is as follows: households > equipment > lifestyles for cooling and DHW, households > buildings > equipment for lighting, equipment > lifestyles for appliances and cooking.

Residential thermostats control a substantial portion of both fuel and electrical energy--9% of the total energy consumption in the U.S. Consumers install programmable thermostats to save energy, yet numerous recent studies found that homes with programmable thermostats can use more energy than those controlled manually depending on how--or if--they are used. At the same time, thermostats are undergoing a dramatic increase in capability and features, including control of ventilation, responding to electricity price signals, and interacting with a home area network. These issues warrant a review of the current state of thermostats, evaluating their effectiveness in providing thermal comfort and energy savings, and identifying areas for further improvement or research. This review covers the evolution in technologies of residential thermostats; we found few standards and many features. We discuss studies of how people currently use thermostats, finding that nearly half do not use the programming features. The review covers the complications associated with using a thermostat. Finally, we suggest research needed to designdand especially test with usersdthermostats that can provide more comfortable and economical indoor environments

Residential coal combustion has played an important role in the domestic energy supply of Northern China for many decades and will do so for the foreseeable future, although it is also an important contributor to severe air pollution. Meeting the daily cooking and spacing-heating demands of rural residents in an eco-friendly manner necessitates cleaner-burning technologies for residential coal combustion. Several reports have suggested that appropriately sized coal be beneficial for optimizing the performance of domestic coal-fired stoves. The effects of coal size (<1.6 cm, 1.6–2.0 cm, 2.0–2.5 cm and >2.5 cm) on fine particulate matter (PM 2.5) and sixteen U.S. Environmental Protection Agency (EPA) priority polycyclic aromatic hydrocarbons (16-PAHs) emissions from a natural cross-draft stove, operating in different phases (ignition, high power heating, low power heating, ramping up and high power cooking) were analyzed in this study. Results indicated that decreasing the coal size enhanced thermal efficiency and reduced pollutant emissions. When the coal size decreased from >2.5 cm to <1.6 cm, the average emission factor (EF) of PM 2.5 over a complete combustion sequence decreased from 3.12 to 1.42 mg/MJ net , and the EF of PM-bound total PAHs decreased from 44.9 to 10.9 μg/MJ net. The corresponding toxic equivalent quantity (TEQ) decreased from 1.25 to 0.38 μg/MJ net. Emissions and energy efficiencies varied markedly between the various combustion phases, adequate air supply during the high power heating and cooking phases reduced the EFs of PM 2.5 and PAHs, while the low power heating phase produced relatively more pollutants due to a fuel-rich condition. Image 1 • Effect of coal size on thermal and emission performances of a cross-draft stove. • Coal combustion tested in different combustion phases. • Emission factors of PM 2.5 and PM-bound PAHs (16 U.S. EPA priority PAHs). • Determination of toxic equivalent quantity of different-size coal burning in stove. • The importance of crushing and sizing coal on emission reduction from households. [ABSTRACT FROM AUTHOR]

In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary 'agents of change' in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions. [ABSTRACT FROM AUTHOR]

As one of the measures to achieve the reduction in greenhouse gas emissions agreed to in the "Kyoto Protocol," an institutional scheme for determining energy efficiency standards for energy-consuming appliances, called the "Top-Runner Approach," was developed by the Japanese government. Its goal is to strengthen the legal underpinnings of various energy conservation measures. Particularly in Japan's residential sector, where energy demand has grown vigorously so far, this efficiency standard is expected to play a key role in mitigating both energy demand growth and the associated CO2 emissions. This paper presents an outlook of Japan's residential energy demand, developed by a stochastic econometric model for the purpose of analyzing the impacts of the Japan's energy efficiency standards, as well as the future stochastic behavior of income growth, demography, energy prices, and climate on the future energy demand growth to 2030. In this analysis, we attempt to explicitly take into consideration more than 30 kinds of electricity uses, heating, cooling and hot water appliances in order to comprehensively capture the progress of energy efficiency in residential energy end-use equipment. Since electricity demand, is projected to exhibit astonishing growth in Japan's residential sector due to universal increasing ownership of electric and other appliances, it is important to implement an elaborate efficiency standards policy for these appliances.

The use of biomass instead of fossil fuel and electricity generated mainly from fossil fuel in residential sector could contribute to carbon peaking and carbon neutrality as it is a renewable energy. Although biomass consumption in China is considerable, the current status and potential of biomass use in Chinese residential households, as well as the related impacts on household biomass use have not been fully understood. Here, by systematically reviewing literatures on biomass utilization in residential homes, it is found that the current use pattern of biomass in Chinese residential homes is inefficient, not only wasting energy, but also yielding amounts of air pollutants to affect climate change and human health. Biogas from biomass is being encouraged for decades by Chinese government, which also failed to achieve the expected goal since considerable household biogas plants (HBPs) were out of service. Fortunately, Biogas produced from middle to large biogas plants (MLBPs) keeps increasing with a proportion over 20% in China. How to use biomass effectively and clean is crucial for the achievement of carbon neutrality and the health of rural residents in China. In our view, molded biomass with improved stoves equipped with advanced technologies should be encouraged, not only in individual homes for cooking and space heating, but also using in central heating systems in villages. Biogas deserves another chance for development in the future, but the foci should be on the MLBPs instead of HBPs considering the social and economic factors at this stage. Last but not least, the future of biomass in residential homes will be bright only when the social consensus, government policies, technologies, subsidies, biomass industry development, and trainings to rural residents work together. This review could provide a full insight for promoting biomass as residential energies for both the policy makers and research community both in China and other countries. [Display omitted] • Residential biomass uses pattern and its environmental and health impacts was reviewed. • Biogas produced from MLBPs keeps increasing with a proportion over 20% in China. • Leading role of, but continuous decrease in, biomass in residential heat generation. • China's energy interventions are expected to provide lessons for other countries. [ABSTRACT FROM AUTHOR]

In addition to many recent actions taken to reduce emissions from energy production, industry, and transportation, a new campaign substituting residential solid fuels with electricity or natural gas has been launched in Beijing, Tianjin, and 26 other municipalities in northern China, aiming at solving severe ambient air pollution in the region. Quantitative analysis shows that the campaign can accelerate residential energy transition significantly, and if the planned target can be achieved, more than 60% of households are projected to remove solid fuels by 2021, compared with fewer than 20% without the campaign. Emissions of major air pollutants will be reduced substantially. With 60% substitution realized, emission of primary PM2.5 and contribution to ambient PM2.5 concentration in 2021 are projected to be 30% and 41% of those without the campaign. With 60% substitution, average indoor PM2.5 concentrations in living rooms in winter are projected to be reduced from 209 (190 to 230) μg/m³ to 125 (99 to 150) μg/m³. The population-weighted PM2.5 concentrations can be reduced from 140 μg/m³ in 2014 to 78 μg/m³ or 61 μg/m³ in 2021 given that 60% or 100% substitution can be accomplished. Although the original focus of the campaign was to address ambient air quality, exposure reduction comes more from improved indoor air quality because ∼90% of daily exposure of the rural population is attributable to indoor air pollution. Women benefit more than men. [ABSTRACT FROM AUTHOR]

Abstract The increasing cost of fossil fuels and the need to reduce harmful emissions have been the main motivations in seeking more efficient and environmentally-friendly methods of power and heat generation. In this paper a comprehensive review of fuel cell-based micro-combined-heat-and-power systems is presented. The aim is to present all past findings and also show the recent progress of the technology, aiming at presenting current trends, limitations and perspectives for future planning and development. The configuration details of a basic system are given, with emphasis on the different fuel cell types, fuel processing methods, heat management and component integration options. Also further integration options are given, including energy storage, refrigeration cycles, micro-gas turbines, and renewable energy sources. System operation is distinguished between operating mode and operating strategy. Different modeling approaches are given, such as: steady-state and dynamic modeling, optimization methods, feasibility studies, and emission studies. A summary of the experimental activities are provided, including system characterization and degradation studies. Also a summary of the recent progress in commercialization activities of the technology is given. Finally, a critical review of the technology is provided, with a discussion on scenarios and future prospects. Highlights • A review of fuel cell-based micro-combined-heat-and-power technology is presented. • Past findings, recent progress, current trends, and bottlenecks are analyzed. • All proposed configuration details of basic and advanced systems are given. • Developed models with key findings, and experimental activities are provided. • Recent commercialization activities and future prospects are discussed. [ABSTRACT FROM AUTHOR]

Abstract Due to the growing concerns about global climate change and energy dependence, many countries have introduced regulations targeting the energy efficiency of the residential sector. However, whether these policies have been effective in reducing the total residential consumption of energy is still unclear. In this paper, we analyze the impact of residential energy efficiency policies on household energy consumption across Europe for the period 1980–2016. We examine the electricity and non-electricity energy consumption separately, as these are generally used for different purposes (appliances and heating) by households and are subject to different energy efficiency policies. We focus on two distinct types of regulations – mandatory energy efficiency labels for household appliances and building standards. After controlling for county-specific effects and changes in income, energy prices, demography and climate conditions over our sample period, our results offer compelling evidence that both the energy labeling requirements for appliances and the stricter building codes lead up to lower residential energy consumption. According to our estimation results, we should expect a 0.24% reduction in residential electricity use after governments introduce a mandatory disclosure of energy labels for an appliance group that represent ten percent of households' electricity use. Similarly, given that U-values proxy the thermal quality of the new dwellings, we find that a 0.1 unit decrease in the U-value requirement triggers a lasting 0.28% annual decrease in residential non-electricity energy consumption. These results can help policymakers to shape future regulations targeted on reducing our energy use in the years to come. Highlights • We analyze the impact of energy efficiency policies on households' energy demand. • Electricity demand decreases after the introduction of energy labeling policy. • Stricter building standards lead to a reduction in residential energy demand. • The effects are stronger with higher construction and appliance ownership rates. [ABSTRACT FROM AUTHOR]

Decomposition analysis provides a potentially powerful means for analyzing community greenhouse gas (GHG) emissions data. However, this form of analysis is typically conducted at larger geographical scales (i.e., national and state/provincial levels), which leaves questions around how to apply this methodology to local and regional contexts. This study explores the application of decomposition methodology to community data in order to elucidate how this form of analysis can be employed to inform local/regional planning and climate policy. The research involved developing decomposition models that focus on two particularly areas important for local climate action - transportation and residential energy. Each model consisted of five factors - population, population distribution, travel (transportation) or energy customer accounts (residential) intensity, vehicle (transportation) or home energy (residential) type, and emissions intensity. Using data from the BC Community Energy Emissions Inventory, the study examined effects of the different factors on emissions changes occurring between 2007 and 2012 in the Metro Vancouver Regional District (British Columbia, Canada). Results from the transportation analysis indicated that population growth and people's choices in vehicle type had the effect of increasing emissions in the MVRD, whereas travel and emissions intensity factors had mitigating effects. The residential energy analysis indicated that only population effects led to emissions-increases in the MVRD, whereas the other factors had mitigating effects. Community-scale analyses also were conducted to identify municipalities where emissions-increasing effects were experienced more dramatically, for example, travel intensity effects in smaller communities (e.g., White Rock), energy accounts intensity effects in Vancouver, and emissions intensity effects in the District of North Vancouver. The study demonstrated how decomposition analysis can provide regional and local governments with valuable insight on what is contributing to GHG emissions and where progress is being made, which in turn can help these governments focus climate policy and planning efforts to achieve progress toward mitigation. The paper begins with an introduction on the use of decomposition methodology in energy and GHG emissions studies, and its potential value for analyzing local emissions data. The next sections discuss the analytical approach designed for and employed in this study, as well as describing the data used for this research. The paper then presents the results of the analysis performed on the MVRD. Finally, it concludes with a discussion on the implications of the results of this study for regional and local policy and planning. [ABSTRACT FROM AUTHOR]

As the number of data centers is increasing worldwide, there are concerns about their growing electricity demand on power grids and its impact on the environment. These concerns can be mitigated at the urban scale by utilizing the waste heat generated by data centers and multi-energy hub integration. Using Combined Cooling, Heating, and Power (CCHP) and Photovoltaic (PV) systems within a data center, in combination with district energy network connections, can create energy interactions with residential customers in a multi-energy hub. However, connecting large energy consumers and distributed energy systems to the electrical distribution grid presents challenges due to the conventional design of the power grid with unidirectional electricity flow. There is a lack of understanding about the grid impact of data centers with multi-energy systems regarding electrical performance parameters, energy demand, and emissions. To address these open issues, we developed an hourly energy interaction model that includes a CCHP-PV-based data center and residential customers. In addition, the CCHP-PV-based data center was operated with a cost-optimized energy scheduling. We conducted six case studies to distinguish the specific effects of CCHP, PV, and district energy network connections. The model takes into account hourly outdoor temperatures, energy prices, and varying energy demands to capture the seasonal impact on electrical performance parameters and energy results. From an energy and environmental perspective, CCHP-PV systems can eliminate the data center's electricity demand from the grid, reduce residential energy demand, and decrease carbon dioxide equivalent emissions. This cleaner production and energy exchange further support the United Nations Sustainable Development goals. The experimental results showed a 43% reduction in power loss in the distribution grid and helped the data center's main electrical bus and residential bus against the voltage drop phenomenon. The CCHP-PV-based data center with district heating and cooling connection reduced the peak hour electricity demand on the grid by 39% and annual electricity consumption from the grid by 66%. Furthermore, Greenhouse Gas Protocols (GHG) Scope-2 emissions associated with the data center's grid electricity consumption and only residential electricity consumption were reduced by 77% and 36%, respectively. [Display omitted] • CCHP, PV, free cooling, and residual heat recovery for sustainable data centers. • A comprehensive model for the hourly energy flows of multi-energy systems. • Distribution grid electrical performance and emissions were evaluated. • Electrical parameters were analyzed with different cases. • Energy consumption and cost reductions were achieved with the optimized operation. [ABSTRACT FROM AUTHOR]

With expected further increases in renewable generation, researchers have been investigating energy storage as a means to improve grid resiliency and harness renewable resources. Energy storage has the potential to both shift renewable generation in time and level peaks in demand. In this paper, we study the impact of centralized energy storage and rooftop photovoltaics on the design and operation of a natural-gas powered Combined Heat and Power (CHP) plant as an integrated utility supplier for a residential neighborhood operating in island mode. Utilizing a novel simultaneous optimization approach for the plant design and operating strategy, the CHP plant is optimized to meet a time-dependent energy demand profile characteristic of residential energy use. This multi-scale optimization problem is solved using a tailored bilevel decomposition method. The results reveal the most economic energy storage method, while highlighting the need to decrease the cost of rooftop photovoltaic panels. [ABSTRACT FROM AUTHOR]

Building energy forecasting methodologies utilized by municipal governments tend to be geared heavily towards depicting broader qualitative representations of regional change and are in need of complementary data-driven models that can produce quantitatively reliable depictions of future energy consumption at the neighborhood-level. The current research demonstrates an application of a Machine Learning (ML) model in the form of an Extreme Gradient Boosting (XGBoost) algorithm for forecasting the energy use of commercial and residential buildings. The methodology serves to improve on municipal scenario planning by providing a more spatially granular representation of future energy use. In this way, city government and urban planners can more accurately set carbon emission benchmarks and target specific locales for sustainability initiatives. The second major contribution of the study is to demonstrate how scenario planning approaches can utilize existing Machine Learning techniques to compensate for gaps in the data. This work is developed through a case study of Philadelphia. The study begins with the construction of residential and commercial energy models for the year 2015 and corresponding models for the year 2045. The forecast models integrate regional socioeconomic trends from the Delaware Valley Regional Planning Commission (DVRPC) scenario of Enduring Urbanism. The commercial energy use model is developed from the DVRPC's open-source Geographic Information System (GIS) datasets, the Commercial Buildings Energy Consumption Survey (CBECS), and CoStar commercial real estate data. The residential model applies the Residential Energy Consumption Survey (RECS), the Public Use Microdata Sample (PUMS), and Census Bureau American Community Survey (ACS) estimates. A corresponding SHAP (SHapley Additive exPlanations) analysis is implemented to pinpoint feature contributions to the model's energy estimates. By using the PopGen software, the model's energy estimates could be analyzed at the household level, the smallest possible scale. To provide a useful resource for key stakeholders, the study aggregates model output by Traffic Analysis Zone (TAZ) and Public Use Microdata Area (PUMA) to display a detailed forecast of energy use. The results indicate that the DVRPC Enduring Urbanism trends in income and employment do not significantly affect energy consumption for the study area. However, features related to lower building intensity (e.g., lower square footage, fewer floors per building) were associated with reduced energy use in both models. Additionally, the study found residential buildings under the "single-family attached" zoning designation to correspond with higher energy estimates. [ABSTRACT FROM AUTHOR]