# Analysis and Applications of GlobeLand30: A Review
## 4 Application Analysis
### Cause and Consequence Analysis  
Global urban areas, croplands, and plantations have enlarged dramatically in recent years, and have seriously impacted resources sustainability, food security, ecological diversity, and climate change [2]. For example, clearing of tropical forests for cultivation or grazing is responsible for 12-26% of the total emissions of carbon dioxide to the atmosphere [69; 70], and land use changes can significantly modify regional and global climate [3; 71]. Furthermore, 20-30% of the total available surface water on the planet is withdrawn for irrigation [72], and nitrogen fixation via fertilizer production and crop cultivation currently equals, or even exceeds, natural biotic fixation [73; 74]. GlobeLand30 offers a detailed portrait of such land covers, and enables researchers to understand how the current pattern forms, and the impact of GLC on the environment. Domain-specific data and methodologies need to be included in the cause and consequence analysis. One of the examples is the cause and consequence analysis of human settlement expansion at the global scale [15]. Several indicators were derived from the artificial surface layer of GlobeLand30, such as the artificial surface area per capita, population per unit area of artificial surface, gross domestic product (GDP) per unit area of artificial surface, relative population increase to artificial surface increase, and relative GDP increase to artificial surface increase. These indicators were used to reveal the artificial surface use efficiencies pattern, relationship with population and GDP, and the change during 2000-2010 at the country level. Results show that the use efficiency of artificial surfaces has distinct regional discrepancies (Figure 4), in which Canada and the United States are categorized as having abundant resources, but low land use efficiency, and South Korea, Japan, and Switzerland are characterized by limited resources but high land use efficiency. [PERSON] et al. [16] calculated the urban expansion intensity index (UEII) based on the ratio of the region's newly-appeared urban land areas to the total area within each ring from the city center. By plotting the UEII by distance to city center, a lognormal curve model was used to fit the curve. The fitting parameters were further used to calculate shape indexes of the curves, and their relationship among Chinese cities was analyzed. GlobeLand30 was also used to reveal the impact of GLC on regional biogeochemical circle and ecosystem. [PERSON], [PERSON] et al. [40] extracted land-use transitions in China from GlobeLand30 2000 and 2010 products, including transitions among forest, grassland, cropland, and artificial surface, and then evaluated the effects of land cover change on N deposition based on the estimated N deposition data from NO\({}_{2}\) column remote sensing data (Global Ozone Monitoring Experiment, GOME, and Ozone Monitoring Instrument, OMI) and the GlobeLand30 dataset. The coupling effects of N deposition and land cover change on carbon budgets in China was also assessed based on a terrestrial ecosystem process-based model IBIS (Integrated Biosphere Simulator). [PERSON] [75] used the land use regression model to simulate air pollution by using land use data from the Corine land cover dataset or the alternative GlobeLand30 in Bangkok and Mexico City. The response variable in the regression model is air pollution, and the explanatory variables are land use, traffic density, and topography. [PERSON] et al. [76] used GlobeLand30 to calculate carbon budget in North Korea, and analyzed the future carbon budget in scenarios of climate change. [PERSON] and [PERSON] [77] evaluated the ecological environment quality in Shaanxi, China, by calculating a synthetical ecological index from GlobalLand30-based biological richness index and vegetation coverage index at the 250 m level. [PERSON] et al.