The world is faced with significant climate change, rapid urbanization, massive energy consumption, and tremendous pressure to reduce greenhouse gases. Building heating and cooling is one primary source of energy consumption and anthropogenic carbon dioxide emissions. First, this review presents previous studies that estimate the specific amount of climate change impact on building heating and cooling energy consumption, using the statistical method, physical model method, comprehensive assessment model method, and the combination method of statistical and physical model methods. Then, because the heating and cooling degree days indices can simply and reliably indicate the effects of climate on building heating and cooling energy consumption, previous studies were reviewed from the aspects of heating and cooling degree days indices, regional spatial-temporal variations in degree days and related indices, influencing factors of the spatial distributions of degree days, and the impacts of urbanization on degree days. Finally, several potential key issues or research directions were presented according to the research gaps or fields that need to be studied further in the future, such as developing methods to simply and accurately estimate the specified amounts of climate change impact on building cooling and heating energy consumption; using more effective methods to analyze the daytime, nighttime, and all-day spatial-temporal changes in different seasons in the past and future under various environment contexts by considering not only the air temperature but also the relative humidity, solar radiation, population, etc., and further exploring the corresponding more kinds of driving forces, including the various remotely sensed indices, albedo, nighttime light intensity, etc.; estimating the daytime, nighttime, and all-day impacts of urbanization on heating degree days (HDDs), cooling degree days (CDDs), and their sum (HDDs + CDDs) for vast cities in different environmental contexts at the station site, city, regional and global scales; producing and sharing of the related datasets; and analyzing the subsequent effects induced by climate change on the energy consumption for building heating and cooling, etc.

Surface urban heat island (SUHI) in the context of urbanization has gained much attention in recent decades; however, the seasonal variations of SUHI and their drivers are still not well documented. In this study, the Beijing-Tianjin-Hebei (BTH) urban agglomeration, one of the most typical areas experiencing drastic urbanization in China, was selected to study the SUHI intensity (SUHII) based on remotely sensed land surface temperature (LST) data. Pure and unchanged urban and rural pixels from 2000 to 2010 were chosen to avoid non-concurrency between land cover data and LST data and to estimate daytime and nighttime thermal effects of urbanization. Different patterns of the seasonal variations were found in daytime and nighttime SUHIIs. Specifically, the daytime SUHII in summer (4 °C) was more evident than in other seasons while a cold island phenomenon was found in winter; the nighttime SUHII was always positive and higher than the daytime one in all the seasons except summer. Moreover, we found the highest daytime SUHII in August, which is the growing peak stage of summer maize, while nighttime SUHII showed a trough in the same month. Seasonal variations of daytime SUHII showed higher significant correlations with the seasonal variations of ∆LAI (leaf area index) (R2 = 0.81, r = −0.90) compared with ∆albedo (R2 = 0.61, r = −0.78) and background daytime LST (R2 = 0.69, r = 0.83); moreover, agricultural practices (double-cropping system) played an important role in the seasonal variations of daytime SUHII. Seasonal variations of the nighttime SUHII did not show significant correlations with either of seasonal variations of ∆LAI, ∆albedo, and background nighttime LST, which implies different mechanisms in nighttime SUHII variation needing future studies.

Based on land use and land cover (LULC) datasets in the late 1970s, the early 1990s, 2004 and 2012, we analyzed characteristics of LULC change in the headwaters of the Yangtze River and Yellow River over the past 30 years contrastively, using the transition matrix and LULC change index. The results showed that, in 2012, the LULC in the headwaters of the Yellow River were different compared to those of the headwaters of the Yangtze River, with more grassland and wet- and marshland. In the past 30 years, the grassland and wet- and marshland increasing at the expense of sand, gobi, and bare land and desert were the main LULC change types in the headwaters of the Yangtze River, with the macro-ecological situation experiencing a process of degeneration, slight melioration, and continuous melioration, in that order. In the headwaters of the Yellow River, severe reduction of grassland coverage, shrinkage of wet- and marshland and the consequential expansion of sand, gobi and bare land were noticed. The macro-ecological situation experienced a process of degeneration, obvious degeneration, and slight melioration, in that order, and the overall change in magnitude was more dramatic than that in the headwaters of the Yangtze River. These different LULC change courses were jointly driven by climate change, grassland-grazing pressure, and the implementation of ecological construction projects.

Large-scale deforestation may affect the surface energy budget and consequently climate by changing the physical properties of the land surface, namely biophysical effects. This study presents the potential energy budget change caused by deforestation in Northeastern China and its climate implications, which was evaluated by quantifying the differences in MODIS-observed surface physical properties between cropland and forest. We used the MODIS land products for the period of 2001–2010 in 112 cells of 0.75° × 0.75° each, within which only best quality satellite pixels over the pure forest and cropland pixels are selected for comparison. It is estimated that cropland has a winter (summer) mean albedo of 0.38 (0.16), which is 0.15 (0.02) higher than that of forest. Due to the higher albedo, cropland absorbs 16.84 W∙m⁻² (3.08 W∙m⁻²) less shortwave radiation than forest. Compared to forest, cropland also absorbs 8.79 W∙m⁻² more longwave radiation in winter and 8.12 W∙m⁻² less longwave radiation in summer. In total, the surface net radiation of cropland is 7.53 W∙m⁻² (11.2 W∙m⁻²) less than that of forest in winter (summer). Along with these radiation changes, the latent heat flux through evapotranspiration over cropland is less than that over forest, especially in summer (−19.12 W∙m⁻²). Average sensible heat flux increases in summer (7.92 W∙m⁻²) and decreases in winter (−8.17 W∙m⁻²), suggesting that conversion of forest to cropland may lead to warming in summer and cooling in winter in Northeastern China. However, the annual net climate effect is not notable because of the opposite sign of the energy budget change in summer and winter.