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As hot days are getting hotter and more frequent, urban dwelling is expected to increase cooling energy use in current and future climate. The applicability of dynamic building simulation in estimating cooling loads of a city's housing stock can be limited due to lack of fine-grained on-site current and future weather inputs. For predicative modelling of residential cooling energy demand to aid a city's energy supply planning resilient to excessive heat conditions, it requires cooling energy demand projection based on a relational account of (1) the thermal-environmental interaction between housing stocks and urban microclimate conditions, (2) the city dwellers' cooling energy use behaviour, and (3) the city's climate projections. In this paper, we introduce an 'archetype-in-neighbourhood' framework to meet these requirements. Combining empirical urban data modelling and EngeryPlus model calibration, this framework was developed to obtain statistically a maximal cooling energy demand model of a city's housing stock during yearly hottest periods. We applied the framework to multiple datasets selected from Seoul's open urban data sources for the period of 2014–2017 (2014 being the earliest year of data availability, 2017 being the end of the study period), including metered electricity use data of 659 apartment buildings (51,351 households) sampled from 18 city districts. The results show that maximal month cooling energy demand (MMCD, kWh/m2) of Seoul's housing stock can be expressed as a regression function of two determinants: (1) the city's average outdoor temperature during the hottest month period (T ex , °C), and (2) estimated indoor cooling temperature set-point (T in , °C) of the city' housing stock during the same period. Through a k -fold (k = 4) validation, the current regression model (2014–17) was evaluated to have an overall coefficient of determination R2 =0.969. Assuming no housing stock renovation, we applied the model to generate scenarios of maximal month cooling demand in future years according to some of the highest summer temperatures projected for Seoul (RCP8.5 2045, RCP4.5 2047, MM5 2071–2100). We conclude this paper with a brief discussion of the implication for cooling energy supply planning and further work to extend the applicability of this new framework to housing stock adaptation planning and design. [ABSTRACT FROM AUTHOR]

Energy efficiency standards for household appliances have been adopted in many countries to mitigate negative externalities, reduce emissions, and save energy. Using Chinese provincial-level panel data from 2008 to 2018, this paper builds a difference-in-difference model to examine the mechanism by which energy-efficiency upgrades in China's air conditioners affect urban household electricity usage. The results show that the upgrades have brought about an obvious reverse effect, with urban household electricity usage increasing rather than decreasing. Reducing the unit costs of energy service has enhanced residents' sensitivity to cooling demand. Driven by the substitution and income effects, urban residents may increase the intensity and frequency of their AC use. Additionally, this reverse effect is heterogeneously dependent on the family size and urban residents' age structure rather than their educational level. When families are larger and have more children, the reverse effect is more pronounced. This paper's findings provide useful recommendations for policy-makers to formulate policies that are synergistic with energy efficiency standards for household appliances. [ABSTRACT FROM AUTHOR]

Abstract In the last two decades, a remarkable socio-economic transition has been witnessed in China. Existing studies have only shown that energy consumption of residents is influenced by economic growth and urbanization. It is unclear, however, how socio-economic transition influences the inequality of energy consumption among urban and rural residents. Focusing on the empirical investigation of two different regions (the eastern developed provinces with little energy resources and the western underdeveloped provinces with rich energy resources), this paper examined the major socioeconomic determinants of energy consumption inequality between urban and rural residents. The results of panel data analyses showed that there was a long-term relationship between socio-economic transition and changes in the inequality of energy consumption among residents. Meanwhile, the results revealed that the influence of socio-economic transition varied from regions to regions. Generally, the influence of urbanization on the inequality of both quantity and diversity was found to be the most profound and significant in all regions. The industrialization transition also exerted far-reaching effects, although its influence was determined by the growth of industry sectors. Moreover, the influence of economic transition was only significant in underdeveloped regions and such an influence fitted to a Kuznet's curve. For the transition of technology and education, its influence was significant in the developed provinces but was only effective in affecting the diversity of inequality. In summary, the findings of this research present policymakers a better understanding of the relationship between the socio-economic transition and inequality in energy consumption. [ABSTRACT FROM AUTHOR]

• A comprehensive review of urban building energy modeling approaches and methods. • Generalized UBEM frameworks are needed to account for application-specific models. • UBEM domain requires integration with GIS, BIM and cloud-based computing platforms. • Advanced data-driven methods are promising techniques to model building stocks. The world has witnessed a significant population shift to urban areas over the past few decades. Urban areas account for about two-thirds of the world's total primary energy consumption, of which the urban building sector constitutes a significant proportion approximately 40%. Stakeholders such as urban planners and policy makers face substantial challenges when targeting sustainable energy and climate goals related to the buildings' sector, i.e. to reduce energy use and associated emissions. Urban energy modeling is one possible solution that leverages limited resources to estimate building energy use and support appropriate policy formation. Over the past few years, there have been only a few review studies on urban building energy modeling approaches. These studies lack an in-depth discussion of the challenges and future research opportunities related to data-driven, reduced-order, and simulation-based modeling methods. This paper proposes Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of approaches, methods and tools used for urban building energy modeling. Furthermore, this paper proposes a generalized framework based on existing literature for different urban energy modeling methods. The aim of this study is to assist urban planners and energy policymakers when choosing appropriate methods to develop and implement in-depth sustainable building energy planning and analysis projects based on limited available resources. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the world is experiencing many extreme climate events. The temperature fluctuation associated with climate change is often considered as a primary driving force of electricity consumption. To better explore the impact of temperature on electricity consumption, this paper aims to analyze the question from the perspective of income growth, i.e., the moderating effect of income growth on the response of urban residential electricity to temperature changes. Using the unbalanced panel data of 23 cities in the Yangtze River Delta Urban Agglomeration from 2004 to 2015, we construct the response model of residential electricity consumption to income growth, heating degree days, cooling degree days and their interactions. The results show that heating demand, cooling demand and income growth significantly increase residential energy consumption. However, urban residential disposable income growth weakens the positive effect of heating demand and cooling demand on residential electricity consumption. Because, urban residents have obvious demand for improving indoor heating comfort and cooling comfort, the moderating effect of income is shown as the crowding-out effect. Residents may adjust the structure of heating energy consumption (from electricity to natural gas) and upgrade their electrical equipment to high energy efficiency equipment, thereby reducing residential electricity consumption. This study also indicates that urban residential energy consumption is very sensitive to changes in permanent populations. The main contribution of this study is proposing the hypothesis of the negative moderating effect of income growth on temperature changes and expanding the response model of residential electricity consumption to temperature changes. Finally, this paper also proposes the adaptive strategies and policy implications for climate change. [ABSTRACT FROM AUTHOR]

An urban energy use map that illustrates the building energy consumption of a city can facilitate the formulation of energy policies. This paper used dynamic simulations of building energy consumption and building information from a geographic information system were used to develop urban energy use maps with high spatial resolutions for two urban areas in southern Taiwan. 4000 models for typical residential layouts in Taiwan were generated through the Monte Carlo method and the benchmark values of the buildings’ energy use intensity (EUI) were identified through building energy simulation and uncertainty analysis. Accordingly, the benchmark EUI values, building data, and population data were used as input parameters to derive a convenient equation for predicting energy consumption in each grid of the urban energy use maps. A comparison of the measured and predicted energy consumptions confirmed the precision of this equation. To sum up, relevant data are readily available and can be exploited to prepare urban energy use maps for other large cities in Taiwan. Moreover, the urban energy use map with high spatial resolution can present the differences in small areas, making it an important reference aid for determining energy policies [ABSTRACT FROM AUTHOR]

The world is urbanizing rapidly and under the business-as-usual scenario, energy needs in cities will also increase dramatically. Urban population is expected to double by 2030; however the global built-up area is expected to triple during the same period (Angel et al. (2005) [1] ). This building out instead of building up will dramatically increase energy requirements and costs of new infrastructure and will influence the link between cities and climate change; to this end, the timely monitoring of the built-up area features and dynamics is considered supportive for defining temporal and spatial changes in energy needs and thus facilitate changes in urban design and support adaptation and mitigation plans in cities for climate change. In this paper the capacity of indices based on Earth Observation, to support the assessment of the built-up area features and dynamics is discussed and assessed. It is found that such indices can provide timely and accurate depiction of the built-up features and dynamics, including urban sprawl. It is also found that built-up area dynamics relate to the spatial and temporal variation of the discomfort index; this is an important parameter for defining urban energy needs as the higher the discomfort index, the more demanding the energy needs for cooling. [ABSTRACT FROM AUTHOR]

Abstract: Besides technical measures, occupants’ behavior is one of the most important issues with respect to energy efficiency in households. This paper will discuss the relationship between electricity consumption and household lifestyle and evaluate the energy-saving potential by improving occupants’ behavior in domestic life through energy-saving education. After 124 households in three typical residential buildings in Hangzhou city of China being selected as research subjects, a series of surveys were conducted: (1) recording of the monthly electricity uses of all subject households from March 2007 to July 2008; (2) energy-saving education to the half of the households before July 2008; (3) a comprehensive survey about the household lifestyle of all subject households in the beginning of August 2008. By comparison analysis of the survey data, major findings are as follows: (1) residential electricity consumption will increase continually in the next years in China, because of the improvement of people''s living standard and more dependency on electric appliances; (2) improving occupants’ behavior in domestic life can save more than 10% household electricity use; and (3) some effort on residential energy savings should be shifted from technological measures to improving occupants’ behavior in ordinary domestic life. [Copyright &y& Elsevier]