This study aims to acquire a better understanding of the quantitative relationship between environmental impact factors and heating energy consumption of buildings in severe cold regions. We analyze the effects of five urban morphological parameters (building density, aspect ratio, building height, floor area ratio, and shape factor) and three climatic parameters (temperature, wind speed, and relative humidity) on the heating energy use intensity (EUI) of commercial and residential buildings in a severe cold region. We develop regression models using empirical data to quantitatively evaluate the impact of each parameter. A stepwise approach is used to ensure that all the independent variables are significant and to eliminate the effects of multicollinearity. Finally, a spatial cluster analysis is performed to identify the distribution characteristics of heating EUI. The results indicate that the building height, shape factor, temperature, and wind speed have a significant impact on heating EUI, and their effects vary with the type of building. The cluster analysis indicated that the areas in the north, east, and along the river exhibited high heating EUI. The findings obtained herein can be used to evaluate building energy efficiency for urban planners and heating companies and departments based on the surrounding environmental conditions.

The accurate estimation of the impact of urban form on CO₂ emissions is essential for the proposal of effective low-carbon spatial planning strategies. However, few studies have focused on the relationship between urban form and CO₂ emissions in small and medium-sized cities, and it is especially unclear whether the relationship varies across cities with different socioeconomic characteristics. This study took 132 small and medium-sized cities in the Yangtze River Delta in China to explore how urban form affects CO₂ emissions, considering the socioeconomic factors of industrial structure, population density, and economic development level. First, nighttime light data (DMSP-OLS and NPP-VIIRS) and provincial energy data were used to calculate CO₂ emissions. Second, four landscape metrics were used to quantify the compactness and complexity of the urban form based on Chinese urban land-use data. Finally, panel data models were established to analyze whether and how different socioeconomic factors impacted the relationship between urban form and CO₂ emissions. The results showed that the three socioeconomic factors mentioned above all had obvious influences on the relationship between urban form and per capita CO₂ emissions in small and medium-sized cities. The effect of compactness on per-capita CO₂ emissions increased with a rise in the proportion of the tertiary industry, population density, and per-capita GDP. However, compactness shows no effects on per-capita CO₂ emissions in industrial cities and low-development-level cities. The effect of complexity on per-capita CO₂ emissions only increased with the rise in population density. The results may support decision-makers in small and medium-sized cities to propose accurate, comprehensive, and differentiated plans for CO₂ emission control and reduction.

Accurate simulation and prediction of occupants’ energy use behavior are crucial in building energy consumption research. However, few studies have focused on household energy use behavior in severely cold regions that have unique energy use patterns because of the low demand of cooling in summer and the use of central heating system in winter. Thus, we developed an agent-based model to simulate the household electricity use behavior in severely cold regions, according to data for Harbin, China. The model regards apartments, residents, household appliances, and energy-management departments as agents and generates the household electricity consumption with respect to time, temperature, and energy-saving events. The simulation parameters include basic information of the residents, their energy-saving awareness, their appliance use behaviors, and the impact of energy-saving management. Electricity use patterns are described by decision-making mechanisms and probabilities obtained through a questionnaire survey. In the end, the energy-saving effects of different management strategies are evaluated. The results indicate that the model can visually present and accurately predict the dynamic energy use behavior of residents. The energy-saving potential of household electricity use in severely cold regions is mainly concentrated in lighting and standby waste, rather than cooling and heating, since the cooling demand in summer is low and the heating in winter mainly relies on central heating system of the city, not on household electricity appliances. Energy-saving promotion can significantly reduce the amount of energy waste (41.89% of lighting and 97.79% of standby energy consumption), and the best frequency of promotional events is once every four months. Residents prefer incentive policies, in which energy-saving effect is 57.7% larger than that of increasing electricity prices. This study realized the re-presentation of the changes of energy consumption in a large number of households and highlighted the particularity of household energy-saving potential in severely cold regions. The proposed model has a simple structure and high output accuracy; it can help cities in severely cold regions formulate energy-saving management policies and evaluate their effects.

Urban researchers pay more and more attention to building energy consumption from different perspectives to obtain the results of urban overall energy conservation. The research at the micro level has yielded abundant accomplishments, but the macro-level research that can support urban planning decision making is still in the exploration stage. In this study, a multi-agent-based model, including the main panel, building agent, resident agent, and household appliance agent, is established by using Anylogic software. The model integrates Harbin urban macro-level impact factors of building energy consumption by designing and linking three sub-models: an urban morphology sub-model, climate sub-model, and energy use behavior sub-model. In the end, this study explored the building energy-saving potential of different types of land under the impact of variable factors through urban morphology and climate simulation scenarios and discussed the related energy-saving strategies. Findings and suggestions include: (1) The impact of urban morphology on overall urban building energy consumption is mainly reflected in residential and commercial land. The land development intensity (building density, floor area ratio, and building height) control and the coordination of land type layout and configuration can help to reduce the building energy consumption. (2) The energy-saving potential of residential land is more evident under climate impact, and ecological means should be used to adjust the climate to reduce the building energy consumption on different lands. (3) From the methodology perspective, this model can well realize the integration of multiple impact factors at the macro-level of the city and the dynamic simulation of energy consumption. The research results are expected to provide quantitative support for creating a sustainable built environment for the city.

As a systematic, preventive, and structural adjustment method of improving building energy conservation and carbon emission reduction, urban planning has received extensive attention. However, due to the insufficient interface between energy-saving technology and urban planning systems, urban planning has not properly played a role in building energy conservation. Scientific and innovative technical methods are urgently needed to explore the role of coordinating multiple effective planning elements in overall building energy conservation through urban planning means. Due to climate conditions, there is high demand for conserving building energy in severe cold regions, but research into this has not been thoroughly carried out. Harbin, located in the northeast of China, belongs to the Dwa zone of the Köppen–Geiger Climate Classification, and is also a typical city of severe cold regions where the daily average temperature is lower than 5 °C for more than 145 days in a year. This study takes Harbin as an example and uses agent-based modeling to establish an urban-scale building energy consumption simulation model. The model contains four types of agents (a global agent, building agent, residential agent, and household equipment agent) and two types of influence factor modules (an urban form module and a climate module). Three simulation scenarios were designed, including a baseline scenario, an urban form scenario, and a climate scenario. The baseline scenario provided an overview of the urban-scale building energy consumption distribution characteristics of Harbin and served as a reference group for the simulation results of other scenarios. The urban form scenario results show that when the elements with a highly significant impact change by 1 unit, the retail building block has the most obvious change in energy consumption, up to 44.7 × 10⁶ kWh/10⁵ m²/year, while the office building block has the lowest change, with 34.5 × 10³ kWh/10⁵ m²/year. The fluctuation of electricity is the most obvious, but the total change is lower than the heating energy consumption. The climate scenario shows that the energy consumption of residential land in urban centers will consistently rise in the next 50 years, up to 5.3 × 10⁵ kWh/10⁵ m²/year. Based on these results, this study puts forward future building energy conservation planning strategies for Harbin, focusing on three aspects: the planning and control of urban form, the optimization and adjustment of the climate, and the building energy conservation planning system. These research results are expected to provide scientific support for transforming Harbin into a low-carbon city.