
AbstractClimate change affects the demand for energy consumption, especially for heating and cooling buildings. Using daily mean temperature (Tmean) data, this study analyzed the spatiotemporal changes of the starting date for heating (HS), ending date for heating (HE), length (HL) and heating degree day (HDD) of the heating season in central heating zone of China. Over China’s central heating zone, regional average HS has become later by 0.97 day per decade and HE has become earlier by 1.49 days per decade during 1960–2011, resulting in a decline of HL (−2.47 days/decade). Regional averaged HDD decreased significantly by 63.22 °C/decade, which implies a decreasing energy demand for heating over the central heating zone of China. Spatially, there are generally larger energy-saving rate in the south, due to low average HDD during the heating season. Over China’s central heating zone, Tmean had a greater effect on HL in warm localities and a greater effect on HDD in cold localities. We project that the sensitivity of HL (HDD) to temperature change will increase (decrease) in a warmer climate. These opposite sensitivities should be considered when we want to predict the effects of climate change on heating energy consumption in China in the future.
Article
Article
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 19 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
