During the COVID-19 pandemic, the strictness of confinement measures varied widely across regions and countries, potentially influencing household water-use behaviour and leading to different consumption patterns between pre-pandemic and pandemic periods. This study investigates changes in residential water consumption in a mid-rise condominium in Goiânia, Brazil, by comparing data from the pre-pandemic and pandemic phases under conditions of a mild lockdown. Monthly water consumption records from 44 apartments were analysed from January 2018 to December 2022 using a multi-method statistical approach. The data were non-normally distributed, with most monthly consumption values ranging between 5 m³ and 25 m³. Fewer outliers were observed during the pandemic, along with a general downward trend in water consumption over time. Comparison of means using the non-parametric Kruskal-Wallis test suggested potential differences between the two periods; however, these differences were not statistically significant under the adopted significance threshold. The results diverge from patterns commonly reported in the literature. These can be attributed to the specific local context experienced during the COVID-19 pandemic, characterised by a short period of effective isolation, resistance to restrictive measures, low adherence to social distancing, and recurrent water scarcity in the region.

Assessing the impact of tiered water pricing policy on residential water consumption in China: A national evaluation and spatial heterogeneity analysis

Investigating the characteristics of residential hot-water consumption: A worldwide review.

ABSTRACT Accurate short‐term forecasting of residential natural gas consumption (NGC) is essential for operational planning and supply reliability. Most forecasting studies rely primarily on meteorological variables, often neglecting infrastructure expansion effects. This study introduces the subscription growth ratio (SGR) as a socioeconomic indicator to enhance daily residential NGC forecasting in Qazvin Province, Iran. Two datasets were developed: Dataset A containing meteorological variables and Dataset A + SGR incorporating subscription growth information. Forecasting performance was evaluated using expanding window cross‐validation across 52 sequential folds. Multiple regression models were implemented, including multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and XGBoost (XGB). Feature importance analysis confirmed that temperature variables dominate NGC variation; however, SGR ranked as the third most influential predictor, exceeding maximum temperature. Results show that incorporating SGR significantly improves nonlinear model performance, reducing MAE by approximately 20% in RFR and XGB and increasing adjusted R ² by about 38%. Paired hypothesis testing confirmed statistically significant improvements for SVR ( p < 0.001), RFR ( p = 0.000347), and XGB ( p = 0.012654), while MLR showed no significant improvement ( p = 0.767816). The findings demonstrate that infrastructure‐driven demand growth has a nonlinear influence on residential NGC and should be integrated into operational forecasting frameworks.

Abstract Residential consumption is a major source of greenhouse gas emissions, and rapid population aging is reshaping how these emissions are generated and distributed. In aging societies, later-life carbon outcomes are influenced not only by income and consumption needs but also by intergenerational support, housing conditions, digital access, and climatic exposure. China provides an important setting because population aging, strong family support systems, and large cross-city climatic differences coexist with substantial heterogeneity in household living conditions. However, micro-level evidence remains limited regarding how carbon-footprint heterogeneity varies across adjacent later-life cohorts and across these multiple dimensions. Here we show, by linking an environmentally extended multi-regional input–output framework to data for 15,243 individuals in 104 Chinese cities, that assigned per-capita household consumption-based carbon footprints differ systematically between adults aged 45–64 years and those aged 65 years and older, and that the nonlinear associations with key socioeconomic factors also vary across cohorts: income exhibits a U-shaped association, whereas assets and intergenerational support exhibit inverted-U associations, with these relationships differing in strength between the two groups. Model-implied scenario contrasts indicate that the largest predicted differentials arise across climate-related settings, reaching 407.95 kg-CO2/cap for cold-versus-temperate conditions and 190.87 kg-CO2/cap for hot-versus-temperate conditions. In addition, a reduction in durable-goods expenditure among high-income adults aged 45–64 years is associated with a predicted decline of 112.6 kg-CO2/cap, of which vehicle outlays account for the largest share. These findings suggest that later-life carbon heterogeneity is shaped not only by affluence but also by cohort-specific consumption patterns and climate-related living environments, thereby providing a more differentiated basis for mitigation design in aging societies.

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies.

ABSTRACT Conceptual model illustrating three steps: estimating future PCC from temperature, combining contextual data for water demand components, and integrating both via the water balance equation to generate OPT and PES scenarios. Urban areas face increasing pressures due to climate change, population growth, and aging infrastructures. In this challenging context, municipalities need to foresee future water demand to plan investments and ensure a reliable water supply. This study proposes a simple model to estimate future urban water demand and applies it to a city located in Southern Quebec, Canada. The model estimates future water demand under different climate projections based on Coupled Model Intercomparison Project Phase 6 models by considering residential consumption alongside factors such as leakage rates, industrial-commercial-institutional consumption, demographic growth, and conservation strategies. A combination of regression and scenario-based analysis is used to explore water demand under different future conditions in this data-scarce study area. Population growth and rising temperatures, particularly under high radiative forcing scenarios, are projected to increase water demand. However, the study finds that implementing effective conservation measures, such as reducing system leakage and introducing conservation measures, can significantly offset these increases. The study underscores the importance of integrating climate, demographic trends, and behavioral factors into long-term water planning and puts forth a simple approach for municipalities to anticipate urban water demand based on limited available data to support strategic decision-making.

The contributions of refined anthropogenic sources to PM2.5 air quality and health impacts in China from 2020 to 2030, and associated policy benefits.

Amid the rapid expansion of China’s emerging middle class and the upgrading of urban residential consumption, serviced apartments are transforming from functional accommodation products into lifestyle-oriented living spaces. Taking Ascott Serviced Residences in Shanghai as a case study, this research examines how marketing strategies can better align with the consumption psychology, behavioral preferences, and decision-making patterns of the new middle class. Drawing on STP theory, the 4P marketing mix, the VALS lifestyle model, and the five-dimensional experiential marketing framework, the study employs questionnaire surveys, empirical analysis, and competitive benchmarking to evaluate the effectiveness of Ascott’s current strategies. The findings reveal a mismatch between standardized product offerings and diversified, experience-driven user demands, as well as limitations in pricing flexibility, digital channels, and community engagement. Sensory and emotional experiences significantly influence initial stay decisions, while relational experiences drive repeat consumption. This study proposes a localized, experience-oriented marketing optimization framework, offering practical implications for serviced apartment brands seeking differentiated competition and sustainable brand value enhancement in emerging urban markets

Previous literature has focused on the impact of HSR on population mobility, but research exploring the impact of HSR on residents’ diverse consumption from the cultural diversity perspective of population mobility is scarce. In this article, data on the opening of high-speed railways in 260 cities in China from 2007 to 2015 were collected and matched with data on private car sales. We use the time-varying difference-in-differences (DID) method to examine the impact of high-speed railway opening on residents’ diverse consumption (car brand diversity and colour diversity). This paper reveals that the opening of high-speed rail has a significant positive effect on residents’ consumption of automobile brand diversity and colour diversity. Cultural diversity plays a partial mediating role in the impact of high-speed rail opening on diversity consumption. The level of intercity mobility plays a moderating role in the impact of high-speed rail opening on diverse consumption. Furthermore, the robustness of the results was validated by parallel trend tests, the propensity score matching–double difference-in-differences method (PSM-DID), placebo tests, and instrumental variables. The findings of this article enrich the context of the impact of population mobility on cultural exchange and the influencing factors of diverse consumption and have theoretical and practical significance for understanding the impact of major national infrastructure construction on cultural diversity and residential consumption behaviour.

Based on panel data of urban and rural residents' consumption from 31 provinces, autonomous regions, and municipalities in China from 2011 to 2023, this study systematically explores the evolutionary characteristics, regional differences, and driving mechanisms of urban and rural residents' consumption structure upgrading by constructing a demand income elasticity model, a Gini coefficient decomposition model, and a regional consumption upgrading convergence analysis. The study finds that the consumption structure of urban and rural residents is shifting from subsistence-oriented to development-oriented and enjoyment-oriented, with the urban-rural income gap being the primary factor influencing consumption differentiation. Residential consumption exhibits high income elasticity across all dimensions, particularly in urban areas. Regional consumption upgrading differences show a dynamic pattern of eastern regions leading, central regions catching up, and western regions differentiating, with the growth rate of intra-regional differences exceeding that of inter-regional differences, reflecting the intensifying phenomenon of consumption stratification within regions. This paper proposes policy recommendations from the dimensions of urbanization effects and income distribution structure, aiming to promote the coordinated upgrading of urban and rural consumption and contribute to the realization of the goal of common prosperity.

The increasing demand for sustainable energy in residential buildings and public concerns on greenhouse gas (GHG) emissions has driven the integration of smart homes with hybrid renewable energy systems (HRESs). This research proposes an optimal scheduling strategy for home energy consumption in a grid-connected HRES that comprises a grid, wind turbines, photovoltaics and battery storage systems. The objective of the study is to reduce the net energy cost, scheduling inconvenience cost (SIC), GHG cost and battery degradation cost. An ant colony optimization algorithm is utilized in the MATLAB environment, with load profiles and meteorological data of Upington, South Africa, obtained from NASA and a residential consumption dataset to accomplish the objectives of the study. The outcomes of the study show that case study 3 is the most feasible configuration based on a net energy revenue cost of $9.8382, GHG cost of $0.0627, battery degradation cost of $0.461 and SIC of $0.66. Simulation results demonstrate that energy purchased from the grid has been reduced by 98% and 48% relative to case studies 1 and 2. The results of the study can assist households to improve the sustainability and resilience of the power system in residential environments where the grid supply is unstable and electricity costs are high.

Accurate forecasting of aggregated demand response (DR) potential is critical for load aggregators, yet remains challenging under severe data scarcity and domain shift conditions. This paper proposes a domain-adaptive transfer learning framework based on an ensemble of Random Vector Functional-Link (RVFL) neural networks for DR potential prediction without requiring any labeled target-domain data. By integrating domain adaptation layers and Maximum Mean Discrepancy (MMD) regularization, the proposed method explicitly reduces marginal feature distribution discrepancies between source and target domains, enabling effective knowledge transfer across heterogeneous operating scenarios. Compared with deep learning architectures, the RVFL-based framework offers favorable theoretical and practical properties for this application, including closed-form least-squares training, reduced risk of overfitting under limited data, and stable generalization under distribution shifts due to its direct-link structure and randomized hidden representations. These characteristics lead to significantly lower computational complexity and training cost than gradient-based deep models, while maintaining strong predictive capability. Case studies using real-world residential consumption data from the Pecan Street dataset demonstrate that the proposed approach consistently outperforms benchmark methods, including SVR, RF, and LSTM, across both intra-year and cross-year transfer scenarios. Reliable prediction accuracy is achieved even when only 10% of source-domain data are available, indicating strong data efficiency and scalability for practical aggregator deployment in day-ahead DR planning.

Residential water consumption in urban and rural areas of Ecuador: an analysis of its determinants

Abstract Background In the field of geriatric psychiatry, social isolation is considered an important risk factor for inducing depression and cognitive decline in later life. At present, research on elderly rehabilitation shows that environmental change therapy has great potential in improving negative emotions. However, previous achievements have mostly focused on traditional inpatient therapy, and there is relatively little research on the psychological intervention effects of the emerging consumption model of living and wellness. With the development of an aging society, how to repair the damaged sense of social belonging in elderly patients through remote living experiences has become an important issue in clinical psychiatric treatment. To this end, the study aims to explore the deep path of living and recuperation in improving social isolation among the elderly, and to delve into the mediating compensation mechanism between psychological integration and social connectivity. This study is of great significance for improving the quality of life of the elderly and preventing the deterioration of mental illnesses. Methods A study selected 120 elderly participants with significant social isolation tendencies at two large health and mental rehabilitation centers. Randomly divided into an intervention group and a control group, with 60 people in each group. The control group maintained their original home lifestyle and routine community spiritual support. The intervention group received a health and wellness consumption intervention during their stay, which included activities such as living in different places, community interaction, and structured psychological integration. The intervention duration is one month. The study utilized quantitative assessment tools for multi temporal measurement, including using the University of California, Los Angeles Loneliness Scale (UCLA-LS) to assess social isolation, the Psychological Integration Scale (PIS) to measure individual psychological adaptation, and the Social Connectedness Scale (SCS) to evaluate external support intensity. Data processing applies structural equation modeling to analyze the mediating effects between variables. Results The data results showed that the UCLA-LS loneliness score of the intervention group significantly decreased from 54.12 points to 38.25 points (p<.05), while the comparison of scores before and after intervention in the control group was not statistically significant (p>.05). The mediation effect analysis confirms that travel intervention first significantly enhances patients' sense of psychological integration, and then indirectly reduces the experience of loneliness by enhancing social connections. The significance of the intermediary pathway indicates that the rehabilitation effect of living abroad is mainly achieved through the synergistic effect of psychological adaptation and social resource reconstruction, with a total effect value of 0.58. Discussion The consumption of living and health care can effectively break the vicious cycle of social isolation among the elderly, and its core mechanism lies in the compensatory enhancement of psychological integration and social connection. This model provides a practical basis for the transition from closed treatment to active social participation in elderly mental rehabilitation, and has high clinical practical significance. Future research directions can further focus on the differentiated effects of different living environments on the mental health of the elderly. Funding No. 21BJY098.

ABSTRACT This work presents a data-efficient procedure for distinguishing between leak and no-leak days using standard hourly smart meter readings. The approach relies on a fixed Minimum Night Flow window and classifies days based on the presence or absence of continuous registered volume during night hours. Applied to more than 21,000 users over one year, it is validated through the application of a Generalised Linear Mixed Model, which confirms that leak days exhibit systematically higher hourly volumes across the usage groups. The procedure also quantifies the hourly and daily impact of leakage and characterises its temporal organisation and influence on diurnal consumption patterns. Using only routinely available hourly data, it provides utilities with a practical framework for assessing post-meter leakage and enhancing the operational value of existing smart-meter infrastructures.

This technical report documents a photovoltaic (PV) installation with self-consumption and surplus injection installed in Spain, which was designed for residential consumption and started up in an isolated single-family home with connection to the electricity distribution grid. Photovoltaic solar panels, inverter, lithium-ion batteries and a bidirectional meter have been integrated by this system. The report details the system’s technical requirements, energy flow, electrical calculations, economic analysis and compliance with Spanish regulations. Key words. Solar, battery, two-way, metering, self-consumption.

This research shows the financial and economic impact of Kolkata’s leading electricity provider CESC Limited on the development and growth of the city of Kolkata . The essay also shows the city's urban growth and development , industrial expansion and the economic stability of the metropolitan city . The essay goes into depth about the company's operational tactics , pricing technique and model , capacity growth as the demand for electricity increases and its supply reliability which have boosted residential consumption as well as allowed multiple new industrial sectors to be set up in the region . Companies around the world aim to maximise efficiency which helps them to boost their profitability thus companies like CESC have heavily upgraded their distribution network . The essay blends quantifiable data like the company's power generation capacity along with its consumer base , financial results and qualitative information to show the impact of the firm on Kolkata's evergrowing and everchanging economic landscape

ABSTRACT During the COVID-19 pandemic, the strictness of confinement measures varied widely across regions and countries, potentially influencing household water-use behaviour and leading to different consumption patterns between pre-pandemic and pandemic periods. This study investigates changes in residential water consumption in a mid-rise condominium in Goiânia, Brazil, by comparing data from the pre-pandemic and pandemic phases under conditions of a mild lockdown. Monthly water consumption records from 44 apartments were analysed from January 2018 to December 2022 using a multi-method statistical approach. The data were non-normally distributed, with most monthly consumption values ranging between 5 and 25 m3. Fewer outliers were observed during the pandemic, along with a general downward trend in water consumption over time. Comparison of means using the non-parametric Kruskal-Wallis test suggested potential differences between the two periods; however, these differences were not statistically significant under the adopted significance threshold. The results diverge from patterns commonly reported in the literature. These can be attributed to the specific local context experienced during the COVID-19 pandemic, characterised by a short period of effective isolation, resistance to restrictive measures, low adherence to social distancing, and recurrent water scarcity in the region.

ABSTRACT With pressures on water supplies increasing globally, readily accessible data on water availability and consumption is of growing importance to policymakers and water managers. In urban areas, water meters, which can provide information about water use and infrastructure resilience, as well as enable volumetric charging for water use, have become core demand-side management tools for ensuring more efficient, cost-effective, and equitable decision-making. Yet, how water meters are used in Aotearoa New Zealand and their impact on pricing structures and water supply and use has not been widely studied. For decision-makers and researchers, the lack of readily accessible quantitative data on residential consumption could be constraining how urban drinking water policy is being developed to ensure it delivers targeted wellbeing improvements for communities. Here, we explore how metering is being used in Aotearoa New Zealand and estimate the possible impact of volumetric pricing on urban drinking water consumption using case studies from Tauranga and Wellington. Our methods reveal the challenges of accessing data on drinking water availability and consumption in Aotearoa New Zealand using Official Information Act channels. We conclude with recommendations for urban drinking water supply reform.