Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human–environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970–2050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16 W/m² in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.

Vegetation cover is crucial for the sustainability of urban ecosystems; however, this cover has been undergoing substantial changes in cities. Based on climate data, city statistical data, nighttime light data and the Normalized Difference Vegetation Index (NDVI) dataset, we investigate the spatiotemporal variations of climate factors, urban lands and vegetation cover in 71 large cities of China during 1998–2012, and explore their correlations. A regression model between growing-season NDVI (G-NDVI) and urban land proportion (PU) is built to quantify the impact of urbanization on vegetation cover change. The results indicate that the spatiotemporal variations of temperature, precipitation, PU and G-NDVI are greatly different among the 71 cities which experienced rapid urbanization. The spatial difference of G-NDVI is closely related to diverse climate conditions, while the inter-annual variations of G-NDVI are less sensitive to climate changes. In addition, there is a negative correlation between G-NDVI trend and PU change, indicating vegetation cover in cities have been negatively impacted by urbanization. For most of the inland cities, the urbanization impacts on vegetation cover in urban areas are more severe than in suburban areas. But the opposite occurs in 17 cities mainly located in the coastal areas which have been undergoing the most rapid urbanization. Overall, the impacts of urbanization on G-NDVI change are estimated to be −0.026 per decade in urban areas and −0.015 per decade in suburban areas during 1998–2012. The long-term developments of cities would persist and continue to impact on the environmental change and sustainability. We use a 15-year window here as a case study, which implies the millennia of human effects on the natural biotas and warns us to manage landscapes and preserve ecological environments properly.