A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

Spatiotemporal variations, influence factors, and simulation of global cooling degree days

The cooling degree days (CDDs) can indicate the hot climatic impacts on energy consumption and thermal environment comfort effectively. Nevertheless, seldom studies focused on the spatiotemporal characteristics, influence factors, and simulation of global CDDs. This study analyzed the spatial-temporal characteristics of global CDDs from 1970 to 2018 and in the future, explored five determinants, and simulated CDDs and their interannual changing rates. The results showed that the global CDDs were generally higher at lower latitudes and altitudes. Many places experienced significant positive changes of CDDs (p < 0.05), and the rates became larger at lower latitudes and attitudes. In the future, most CDDs had the sustainability trends. Besides, significant negative partial correlations existed between not only CDDs but also their variation rates with latitude, altitude, and average enhanced vegetation index in the summer, while positive with the annual PM_2.5, distance to large waterbodies (p = 0.000). Moreover, the values and variation rates of CDDs can be deduced using the generalized regression neural network method. The root-mean-square errors were 231.73 °C * days and 1.71 °C * days * year^-1, respectively. These conclusions were helpful for the energy-saving for cooling with the climate change and optimization of thermal environment.

On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis

The body of literature on climate change impacts on building energy consumption is rising, driven by the urgency to implement adaptation measures. Nevertheless, the multitude of prediction methodologies, future scenarios, as well as climate zones investigated, results in a wide range of expected changes. For these reasons, the present review aims to map climate change impacts on building energy consumption from a quantitative perspective and to identify potential relationships between energy variation and a series of variables that could affect them, including heating and cooling degree-days (HDDs and CDDs), reference period, future time slices and IPCC emission scenarios, by means of statistical techniques. In addition, an overview of the main characteristics of the studies related to locations investigated, building types and methodological approaches are given. To sum up, global warming leads to: (i) decrease in heating consumptions; (ii) increase in cooling consumption; (iii) growth in total consumptions, with notable differences between climate zones. No strong correlation between the parameters was found, although a moderate linear correlation was identified between heating variation and HDDs, and total variation and HDDs. The great variability of the collected data demonstrates the importance of increasing specific impact studies, required to identify appropriate adaptation strategies.

Spatial-Temporal Changes and Associated Determinants of Global Heating Degree Days

The heating degree days (HDDs) could indicate the climate impact on energy consumption and thermal environment conditions effectively during the winter season. Nevertheless, studies on the spatial-temporal changes in global HDDs and their determinants are scarce. This study used multi-source data and several methods to explore the rules of the spatial distribution of global HDDs and their interannual changes over the past 49 years and some critical determinants. The results show that global HDDs generally became larger in regions with higher latitudes and altitudes. Most global change rates of HDDs were negative (p < 0.10) and decreased to a greater extent in areas with higher latitudes. Most global HDDs showed sustainability trends in the future. Both the HDDs and their change rates were significantly partially correlated with latitude, altitude, mean albedo, and EVI during winter, annual mean PM2.5 concentration, and nighttime light intensity (p = 0.000). The HDDs and their change rates could be simulated well by the machine learning method. Their RMSEs were 564.08 °C * days and 3.59 °C * days * year-1, respectively. Our findings could support the scientific response to climate warming, the construction of living environments, sustainable development, etc.