Spatial and Temporal Changes of Ecosystem Service Value in Airport Economic Zones in China

Abstract

With the rapid development of the aviation industry, many negative effects on the local environment have been reported. This study examined the land use and land cover (LULC) and ecosystem service value (ESV) of airport economic zones (AEZs) in China and assessed the changes in LULC and ESV. The results indicate that LULC changed significantly from 1990 to 2015, characterized by the increase in construction land (increase rate, 68.53%) and water bodies (increase rate, 2.32%) and the decrease in cropland (decrease rate, 4.28%), forest (decrease rate, 0.73%), grassland (decrease rate, 4.64%) and unused land (decrease rate, 6.36%). The ESV of AEZs in 1990 and 2015 was RMB 3454 and 3483 million, respectively, with an overall ESV change of RMB 29 million. The ESV of AEZs is characterized by high value in the coastal area of China. From 1990 to 2015, AEZs with a high ESV were located in Inner Mongolia, while those with a high decrease in ESV were located in the southeastern coastal area. From 1990 to 2000, the AEZs with a high increase in ESV were located in Inner Mongolia and Qinghai and the AEZs with decreased ESV were mostly located in central and south China. However, from 2000 to 2010, AEZs with high and low increases in ESV were located in central China and the south coastal area of China, respectively. From 2010 to 2015, AEZs with a high decrease in ESV were located in southeast China.

Multi-Scenario Simulation of Land Use Changes with Ecosystem Service Value in the Yellow River Basin

“…In accordance with Costanza et al [22], Xie et al [26] formulated an equivalent table of the ESV per unit area for China's terrestrial ecosystem. Subsequently, Chinese researchers have used land use data at the national [27], provincial [28], city [29], basin [30], and urban agglomeration scale [31] to determine the time scale of the ESV and land use [32], spatiotemporal distribution of the ESV [33], influence factors [34], and driving forces [35]. Several researchers have used the grey prediction (1,1) model (GM) [36], CA-Markov [37], CLUE-S [38], and FLUS [39] to study the impact on future land use change on the regional ESV and optimize the land use structure based on the ESV.…”