Integrating work into life helps reduce residential greenhouse gas emissions.

Work from home (WFH) creates work-life integration by moving work into traditional life at home, but its influence on residential greenhouse gas (GHG) emissions remains unclear. In this study, an activity-based bottom-up model was developed to analyze the time-use patterns (activity durations and timeline of a typical day) of participants under WFH and traditional home life and to quantify their residential GHG emissions. Under WFH, participants generated an average of 9.03 kg CO2e/person/day, primarily attributed to space heating and cooling, cooking, grooming, work, and watching TV and movies. Notably, the GHG footprints varied across groups (8.08-9.93 kg CO2e/person/day) due to different work and household responsibilities and leisure time and varied with climate region (4.99-10.63 kg CO2e/person/day) because of emission factors of electricity, space heating and cooling, and cooking. Compared with traditional life at home (10.06 kg CO2e/person/day), WFH participants spent less time on almost all major activities (especially sleeping and watching TV and movies) to focus on work, enabling an 11.34% (1.02 kg CO2e/person/day) mitigation of GHG emissions. The reductions also varied by group and climate region, mainly associated with laundry, cooking, and watching TV and movies. Opportunities to reduce GHG emissions under WFH lie in targeting key activities, balancing the time spent on various activities, and developing group- and spatial-specific strategies. This study provides a systematic and high-resolution estimation of residential GHG emissions under WFH and traditional home life, with a complete system boundary, activity-specific considerations, and countrywide understanding. The findings reveal the environmental impact of work-life integration from the residential perspective and can aid residents and policymakers in utilizing decarbonization opportunities to advance low-carbon living under WFH.

Drinking Water in Rural China: Water Sources, Treatment, and Boiling Energy.

Access to safe drinking water is a major public concern in China. A national survey of 57 029 households was conducted to fill major knowledge gaps on drinking water sources, end-of-use treatment methods, and energy used to boil water. Herein, we show that surface water and well water were frequently used by >147 million rural residents living in low-income inland and mountainous areas. Driven by socioeconomic development and government intervention, the level of access to tap water in rural China increased to 70% by 2017. Nevertheless, the rate was considerably lower than that in cities and unevenly distributed across the country. Approximately 90% of drinking water was boiled, an increase from 85% a decade ago. The contribution of electricity, mainly electric kettles, to the boiling of water was 69%. Similar to cooking, living conditions and heating requirements are the main influencing indicators of energy used to boil water. In addition to socioeconomic development, government intervention is a key factor driving the transition to safe water sources, universal access to tap water, and clean energy. Further improvement in drinking water safety in poor and remote rural areas remains challenging, and more intervention and more investment are needed.

Energy and air pollution benefits of household fuel policies in northern China.

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.

How would the carbon tax on energy commodities affect consumer welfare? Evidence from China's household energy consumption system.

Although carbon tax policies can effectively restrain energy consumption and reduce pollution, they will also affect the welfare of residents through a price mechanism. We explore the impact of energy price increases that are caused by possible carbon tax policies on the welfare of residents in China with a quadratic almost ideal demand system (QUAIDS) model. The estimated elasticities show that the income elasticity of coal demand is -0.741 for urban residents, compared to 0.392 for rural residents. The cross-price elasticity shows that China's residential energy consumption has moved up the clean energy ladder. Based on the above reliable elasticity estimates, the welfare effects are analyzed in the residential consumption system. The overall welfare loss for residents increases with the level of carbon tax. A carbon tax on all energy sources is a regressive policy for China, and when the carbon tax rate reaches the world average, of 30 USD/tCO2e, the welfare loss for low-income and high-income residents is 1.55% and 0.62% respectively. However, the separate imposition of carbon taxes on different energy sources shows the heterogeneity of the welfare impacts of carbon taxes. At the national and urban levels, the distribution effects of carbon taxes are regressive for coal, LPG, and electricity, progressive for gasoline, and distributional neutral for natural gas. In rural areas, however, the welfare distribution effect of the carbon tax on diesel, LPG, and natural gas are progressive, and the welfare effects of carbon taxes on electricity show an inverted U-shaped distribution. Our findings are conducive to the development of a differentiated carbon tax policy by the Chinese government.

An Appliance Scheduling System for Residential Energy Management.

In this work, an Appliance Scheduling-based Residential Energy Management System (AS-REMS) for reducing electricity cost and avoiding peak demand while keeping user comfort is presented. In AS-REMS, based on the effects of starting times of appliances on user comfort and the user attendance during their operations, appliances are divided into two classes in terms of controllability: MC-controllable (allowed to be scheduled by the Main Controller) and user-controllable (allowed to be scheduled only by a user). Use of all appliances are monitored in the considered home for a while for recording users' appliance usage preferences and habits on each day of the week. Then, for each MC-controllable appliance, preferred starting times are determined and prioritized according to the recorded user preferences on similar days. When scheduling, assigned priorities of starting times of these appliances are considered for maintaining user comfort, while the tariff rate is considered for reducing electricity cost. Moreover, expected power consumptions of user-controllable appliances corresponding to the recorded user habits and power consumptions of MC-controllable appliances corresponding to the assigned starting times are considered for avoiding peak demand. The corresponding scheduling problem is solved by Brute-Force Closest Pair method. AS-REMS reduces the peak demand levels by 45% and the electricity costs by 39.6%, while provides the highest level of user comfort by 88%. Thus, users' appliance usage preferences are sustained at a lower cost while their comfort is kept impressively.

Spatial differentiation of carbon emissions from residential energy consumption: A case study in Kaifeng, China.

Effective strategies, policies and measures for carbon emission reduction need to be developed and implemented according to good understanding of both local conditions and spatial differentiation mechanism of energy consumption associated with human activities at high resolution. In the study, we first collected statistical yearbooks, high resolution remotely sensed imageries, and 3895 usable questionnaires for the urban areas of Kaifeng; then measured the carbon emissions from household energy consumption, using the accounting method provided in the IPCC GHG Inventory Guidelines; and finally applied both exploratory and explanatory statistical methods to characterize the spatial pattern of carbon emissions at high resolution, identify key influencing factors, and gain better understanding of the spatial differentiation mechanism of urban residential carbon emissions. Our study reached the following conclusions: (1) Central heating facilities with controllable flow are important for carbon emissions reduction, but its spatial distribution shows unfairness; (2) Spatial clusters of high carbon emission areas were primarily located in the outer suburbs of the city, validated to some extent the hypothesis that urban sprawl has a driving effect on the increasing urban residential carbon emissions; (3) Factors like size of residential area, family structure, life style, personal preference and behavior rather than household income have significant impacts on household carbon emissions, implying that effective control of residential areas, promotion of family life and low-carbon lifestyle, and effective guidance of proper behaviors and preferences will play a crucial role in reducing urban residential carbon emissions; and (4) Most of the identified influencing factors exhibit clear and specific spatial patterns and gradients of impact, implying that measures for urban residential carbon emission reduction should be adapted to location conditions. The study has generated a set of concrete evidences and improved understandings of the spatially differentiated mechanisms upon which the formation and deployment of any effective strategies, policies and measures for reducing urban residential carbon emissions should be based.

The impact of climate change on residential energy consumption in urban and rural divided southern and northern China.

In different regions of China, climate change has various influences on urban and rural residential energy consumption, which also shows that the research on it could be profoundly vital in order to formulate the energy-saving and emission-reducing policies. Based the provincial panel data from 2000-2016, the extended stochastic impacts by regression on population, affluence, and technology (extended STIRPAT) model was utilized to evaluate the impacts of climate change on residential energy consumption in different Chinese regions. The results show that: (1) during 2000 to 2016, the urban and rural energy consumption enlarged by 878.83 billion kWh and 488.98 billion kWh, respectively. In addition, electricity and oil have occupied more proportion in urban energy consumption, while coal still plays an important role in rural residential energy consumption (28.2%). (2) Heating degree day (HDD) and cooling degree day (CDD) have positive influences on urban and rural residential energy consumption in different areas, and the elastic coefficients are 0.028-0.371 and 0.066-0.158, respectively. (3) The elastic coefficient of CDD in urban areas of southern regions (0.158) is much larger than that in northern regions (0.068).

Modernizing our way out or digging ourselves in? Reconsidering the impacts of efficiency innovations and affluence on residential energy consumption, 2005-2015.

This study scrutinizes the impacts of efficiency innovations as well as affluence on residential energy consumption, which is a major driver of greenhouse gas emissions. The study draws on the ecological-modernization perspective, which is optimistic about how technological innovations and affluence can help societies overcome environmental challenges associated with production and consumption, and the political-economy perspective, which raises doubts about whether these factors are beneficial to the environment, given their tendency to drive more consumption. Analysis of nationally representative longitudinal data reveals mixed relationships between efficiency innovations and residential energy consumption: while some measures of efficiency innovations, generally those not requiring human-technology interactions, are negatively related to residential energy consumption, others are either unrelated to it or drive more consumption. These findings suggest efficiency innovations offer only minimal opportunities for conserving energy, and may depend on the nature of the innovation. Raising doubts about the potential for rising affluence to promote environmental protection, this study reveals positive relationships between our measures of affluence and residential energy consumption.

Buildings behaving badly: a behavioral experiment on how different motivational frames influence residential energy label adoption in the Netherlands.

Heating buildings contributes to approximately 36 % of Europe's energy demand and several EU member states have adopted mandatory energy labels to improve energy efficiency by promoting home weatherization investments. This paper focuses on the perception of the energy label for residential buildings in the Netherlands and the role of different frames (egoistic, biospheric and social norms and neutral frames) in motivating adoption of energy labels for housing. We used a behavioral email experiment and an online survey to investigate these motivational factors. We find that biospheric frames are weaker than the other three motivational frames in terms of engaging interest in the energy label, but that the biospheric frame results in higher willingness to pay (WTP) for the energy label. We also find that age (rather than income) correlates with higher WTP for home energy labels.

Household fuel and direct carbon emission disparity in rural China.

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 âˆ¼ 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.

Exploring the correlation between rising temperature and household electricity consumption: An empirical analysis in China.

This study investigates the nexus between rising temperatures and household energy consumption using data from respondents' electricity bills in the "China Residential Energy Consumption Survey." Our analysis reveals a significant correlation, with an 8.9 % increase in yearly energy consumption observed when the average temperature exceeds 32 °C. Additionally, we explore potential shifts in power usage due to global warming by integrating baseline estimates with daily temperature forecasts from eight contemporary climate models. Our findings project alarming trends: without interventions to curb greenhouse gas emissions, home electricity consumption could surge by 9.59-30.09 % in the medium term and by 9.77-47.70 % in the long run. By shedding light on these critical connections, our research underscores the urgent need for policy actions to mitigate the adverse impacts of climate change on energy consumption patterns.

Residents' participation in energy performance certification for collective action on climate change: the case of EnerGuide.

Introduction: Although governments have launched energy performance certification (EPC) programs to increase residential energy efficiency, residents' perception and acceptance of these programs have been little studied. This study contextualizes residents' intentions to participate in EPC programs in terms of their attributions to and attitudes toward climate change to find mediating factors and effects to help trigger collective action to reduce residential sector energy demand. Methods: This study employed a partial least squares structural equation modeling approach to empirically analyze a survey conducted on 400 residents of Edmonton, Canada, who participated in the Canadian energy efficiency rating and labeling program, EnerGuide. Results and Discussion: Using EnerGuide, a Canadian energy efficiency rating and labeling program, as an example, this study establishes a framework to explain that local residents' internal and external attributions to climate change elicit positive attitudes (need to take action), increasing their recognition of energy efficiency program benefits, which further promotes their EnerGuide program acceptance and participation intentions. This study also reveals the mediating effects between variables. Residents' attitudes toward climate change mediate the relationship between internal/external attributions and EnerGuide program acceptance, and they indirectly impact residents' program acceptance and participation intentions, with this effect moderated by energy efficiency program benefits. Residents' program acceptance also mediates the relationship between climate change attitudes/energy efficiency program benefits and the intention outcome. Implication: The study provides an example of the use of climate change discourse to motivate residents' energy efficiency program participation.

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model.

Integrating occupant behavior with residential energy use for detailed energy quantification has attracted research attention. However, many of the available models fail to capture unseen behavior, especially in unprecedented situations such as COVID-19 lockdowns. In this study, we adopted a hybrid approach consisting of agent-based simulation, machine learning and energy simulation techniques to simulate the urban energy consumption considering the occupants' behavior. An agent-based model is developed to simulate the in-home and out-of-home activities of individuals. Separate models were developed to recognize physical characteristics of residential dwellings, including heating equipment, source of energy, and thermostat setpoints. The developed modeling framework was implemented as a case study for the Central Okanagan region of British Columbia, where alternative COVID-19 scenarios were tested. The results suggested that during the pandemic, the daily average in-home-activity duration (IHD) increased by approximately 80%, causing the energy consumption to increase by around 29%. After the pandemic, the average daily IHD is expected to be higher by approximately 32% compared with the pre-pandemic situation, which translates to an approximately 12% increase in energy consumption. The results of this study can help us understand the implications of the imposed COVID-19 lockdown with respect to energy usage in residential locations.

Mind the Energy Performance Gap: testing the accuracy of building Energy Performance Certificates in Ireland.

Ireland's Climate Action Plan aims upgrade 500,000 homes to B2 Energy Performance Certificate (EPC) standard by 2030. Evidence of an Energy Performance Gap, where actual energy use differs from the EPC, could undermine progress towards such targets. This paper studies the energy performance gap for a general housing sample (n = 9923) over multiple years. It provides a novel comparison between whole-home energy use (electricity and gas) that accounts for fuel switching and removes potential rebound effects by excluding households that may have changed their behaviour following a retrofit. Results suggest that actual energy use is unresponsive to the EPC, with a range of 457 kWh/year observed across EPC-level averages for the entire sample. This difference equated to less than 5% of the sample average annual energy use observed. The Energy Performance Gap range features an average deficit of 17% below theoretical energy use. The least energy efficient dwellings feature an average difference ranging from - 15 to - 56% of the relevant EPC. Conversely, energy efficient houses display higher-than-theoretical energy use, with average surpluses ranging from 39 to 54% of the relevant EPC. Results sound a note of caution for policymakers that rely on a theoretical EPC to deliver real energy savings. Future EPCs could be improved by incorporating historical household energy usage to help improve models.

Residential energy environmental Kuznets curve extended with non-linear temperature effects: a quantile regression for Andalusian (Spain) municipalities.

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.

Saving energy in residential buildings: the role of energy pricing.

A zero-carbon society requires dramatic change everywhere including in buildings, a large and politically sensitive sector. Technical possibilities exist but implementation is slow. Policies include many hard-to-evaluate regulations and may suffer from rebound mechanisms. We use dynamic econometric analysis of European macro data for the period 1990-2018 to systematically examine the importance of changes in energy prices and income on residential energy demand. We find a long-run price elasticity of -0.5. The total long-run income elasticity is around 0.9, but if we control for the increase in income that goes towards larger homes and other factors, the income elasticity is 0.2. These findings have practical implications for climate policy and the EU buildings and energy policy framework. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10584-021-03164-3.

Exploring the effects of California's COVID-19 shelter-in-place order on household energy practices and intention to adopt smart home technologies.

To contain the spread of the novel coronavirus (COVID-19), local and state governments in the U.S. have imposed restrictions on daily life, resulting in dramatic changes to how and where people interact, travel, socialize, and work. Using a social practice perspective, we explore how California's Shelter-in-Place (SIP) order impacted household energy activities. To do so, we conducted an online survey of California residents (n = 804) during active SIP restrictions (May 5-18, 2020). We asked respondents about changes to home occupancy patterns and household energy activities (e.g., cooking, electronics usage) due to SIP restrictions, as well as perspectives toward smart energy technologies. Households reported increased midday (10am-3pm) occupancy during SIP, and this increase is related to respondent and household characteristics, such as education and the presence of minors in the home. Examining change in the frequency of household activities during SIP, presence of minors and increased midday occupancy proved important. Finally, we considered relationships to intention to purchase smart home technologies, with the presence of minors and increased activity frequency relating to greater intention to purchase. These findings demonstrate how household activities and occupancy changed under COVID restrictions, how these changes may be related to energy use in the home, and how such COVID-related changes could be shaping perspectives toward smart home technology, potentially providing insight into future impacts on household practices and electricity demand.

Residential stock data and dataset on energy efficiency characteristics of residential building fabrics in Ireland.

These data support the research article "Improving energy savings from a residential retrofit policy: a new model to inform better retrofit decisions" - (Mac Uidhir et al., 2019) [1]. This article presents 3 data sources which are utilised in conjunction with a detailed energy system model of the residential sector to explore policy pathways for residential retrofitting. Data is collected from the Central Statistics Office (CSO) and the Sustainable Energy Authority of Ireland (SEAI). The first SEAI dataset is compiled for Ireland in compliance with the EU Energy Performance of Buildings Directive (EPBD) [2]. Data is collected using the Dwelling Energy Assessment Procedure (DEAP) [3]. DEAP is used to produce energy performance certificates known as Building Energy Ratings (BER). A BER indicates a buildings energy performance across a 15-point energy efficiency scale, rated alphabetically from A1 to G, in units of kWh/m2 year. A BER is required for new buildings and the rent or sale of existing dwellings - therefore the database has consistently grown in size since its inception in 2006. The BER database contains 735,906 records of individual dwellings. The database includes detailed building fabric information across a range of different building types, year of construction, Main/Secondary space/water heating fuels, heating system efficiency, ventilation method and structure type (Insulated concrete form, Masonry, Timber or Steel Frame). The second SEAI dataset (PWBER) contains aggregated pre and post BER information for a sample of 112,007 dwellings retrofitted during the period 2010-2015; this database contains mean energy efficiency improvement (kWh/m2 year) for a range of retrofit combinations as they apply to nine distinct building archetypes. The third CSO dataset is compiled from census data, representing the frequency of building types by year of construction.

Energy transition in a lockdown: An analysis of the impact of COVID-19 on changes in electricity demand in Lagos Nigeria.

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.

Residential energy environmental Kuznets curve in emerging economies: the role of economic growth, renewable energy consumption, and financial development.

The aim of the present paper is to determine residential energy environmental Kuznets curve (EKC) in Next-11 and BRICS countries with the specific consideration on the role of economic growth, renewable energy consumption, and financial development from an era of 1990-2015. In order to determine the cross-section independence and to control the heterogeneity between cross-sections in the paper, we have applied unique and advanced techniques of econometrics panel data. Moreover, the following tests have been applied which are the CIPS unit root test, co-integration test, fully modified ordinary least square (FMOLS), and heterogeneous panel causality technique. The outcomes revealed that in the long run, all the variables are co-integrated. Moreover, there is a significant and positive influence of residential energy consumption, economic growth, and financial development on environmental degradation. However, in the reduction of carbon dioxide (CO2) emissions, essential role is performed by renewable energy. On the other hand, findings show great support for the residential energy EKC hypothesis in emerging countries.