Unraveling persistent urban-rural gaps: A long-term provincial analysis of residential heating and cooling loads
Abstract
With global climate change and rising demand for thermal comfort, space heating and cooling have become increasingly critical to achieving carbon neutrality in the building sector. This study presents a first attempt to develop a bottom-up regional building energy model based on prototype buildings simulated in EnergyPlus, to assess space heating and cooling loads of urban and rural residential buildings across 30 Chinese provinces from 1980 to 2024. The results indicate that: (1) Guangdong recorded the highest cooling loads in 2020, reaching 76.5 TWh/a in urban areas and 63.0 TWh/a in rural areas; Henan exhibited the highest rural heating load at 174.6 TWh/a, while urban heating loads were highest in provinces such as Liaoning and Shandong. (2) From 1980 to 2024, average cooling loads increased from 12.4 to 15.1 kWh/m2/a in urban areas but declined from 22.63 to 19.87 kWh/m2/a in rural areas. Over the same period, average heating loads decreased from 44.08 to 39.92 kWh/m2/a in urban areas and from 100.15 to 72.42 kWh/m2/a in rural areas. (3) Urban residential building stock has surpassed rural stock in 22 provinces in recent years, compared with only 4 provinces in 2000, and the presence of 12 urban energy-efficiency standards versus only one rural standard further highlights substantial envelope performance gaps. Collectively, these dynamics have led to pronounced and persistent urban-rural disparities in residential heating and cooling loads. These findings underscore the need for differentiated standards and region-specific clean heating strategies, while providing a transferable modeling framework to inform targeted energy-saving policies and support the building sector's transition toward carbon neutrality.
Summary
This paper addresses the problem of understanding and quantifying the evolving urban-rural disparities in residential heating and cooling loads across 30 Chinese provinces from 1980 to 2024. The authors hypothesize that these disparities are driven by a complex interplay of socioeconomic factors, building characteristics, climate adaptation behaviors, and the significant difference in energy efficiency standards between urban and rural areas. To investigate this, they developed a bottom-up regional building energy model based on prototype buildings simulated in EnergyPlus. They created representative urban and rural building models for different construction periods in each province, categorized by climate zones. The model considers factors like building physical form, envelope parameters (heat transfer coefficients, solar heat gain), and occupancy activity prototypes. The study then quantifies the heating and cooling loads using a bottom-up approach, aggregating the simulation results of the prototype buildings based on their distribution within each province. The key findings reveal significant spatiotemporal variations in heating and cooling loads, with Guangdong recording the highest cooling loads and Henan the highest rural heating load. Urban cooling loads increased from 12.4 to 15.1 kWh/m²/a, while rural cooling loads decreased from 22.63 to 19.87 kWh/m²/a between 1980 and 2024. Similarly, urban heating loads decreased from 44.08 to 39.92 kWh/m²/a, and rural heating loads decreased from 100.15 to 72.42 kWh/m²/a. The study also highlights the increasing dominance of urban residential building stock in many provinces and the significant gap in energy-efficiency standards (12 urban vs. 1 rural). These findings underscore the need for region-specific energy policies and contribute a transferable modeling framework to support the building sector's transition toward carbon neutrality.
Key Insights
- •The study developed a novel bottom-up regional building energy model based on EnergyPlus simulations, incorporating urban-rural differences, building construction periods, and regional climate characteristics.
- •A significant finding is the persistent gap in energy-efficiency standards, with 12 urban standards versus only one rural standard, which underpins the higher rural load intensity.
- •From 1980 to 2024, average cooling loads increased by 2.7 kWh/m²/a in urban areas but *decreased* by 2.76 kWh/m²/a in rural areas, highlighting a divergence in cooling demand trends.
- •The total heating load of rural residential buildings in Henan Province was 174.6 TWh/a in 2020, exhibiting the highest value across China, while Guangdong Province had the highest cooling loads at 76.5 TWh/a in urban areas and 63.0 TWh/a in rural areas.
- •Urban residential building stock has surpassed rural stock in 22 provinces in recent years, compared with only 4 provinces in 2000, indicating a major shift in building stock distribution.
- •Rural load intensity (kWh/m²) remains 20%-100% higher than urban load intensity, despite narrowing total load disparities, suggesting persistent inefficiencies in rural buildings.
- •The decomposition of cooling and heating loads by construction period (observed in 2024) reveals that older buildings contribute disproportionately to overall energy consumption, highlighting the need for retrofitting efforts.
Practical Implications
- •The findings provide a scientific basis for formulating differentiated energy conservation policies for urban and rural residential buildings, considering regional climate, building characteristics, and socioeconomic factors.
- •Policymakers can use the identified urban-rural disparities and the quantifiable impact of building energy efficiency standards to design targeted interventions, such as incentives for energy-efficient building materials and construction practices in rural areas.
- •The transferable modeling framework can be adapted and applied to other regions and countries to assess building energy consumption patterns and inform energy-saving policies.
- •The study highlights the need for region-specific clean heating strategies, moving away from centralized heating in certain areas and promoting decentralized and renewable energy sources.
- •Future research can focus on incorporating more detailed data on occupant behavior, building operational characteristics, and the impact of climate change on building energy demand to further refine the modeling framework and improve the accuracy of energy consumption projections.