Spatiotemporal Distribution of Carbon Sink Indicators—NPP and Its Driving Analysis in Ordos City, China

Abstract

Ordos City is an important energy supply city for Chinese provinces and cities, providing a secure energy supply for China while also generating corresponding environmental pollution. Examining the spatiotemporal patterns of net primary productivity (NPP) in Ordos City and its driving factors is relevant to the realization of the carbon emission policy in Inner Mongolia. This study was undertaken to analyze NPP and its driving factors in Ordos City from 2000 to 2019 using NPP data, CO2 spatial grid data, meteorological data and statistical yearbook data accordingly. The NPP in Ordos City increased significantly from 2000 to 2019, mainly showing low values of NPP in the northwest and high values in the southeast. The usable grassland area and annual mean precipitation had a significant positive correlation with NPP, whereas the other factors had a more significant negative correlation. The usable grassland area had the largest influence on NPP, and fixed asset investment had the smallest influence on NPP. The total NPP–anthropogenic factor regression model and the mean NPP–natural factor regression model constructed allow for the prediction of NPP. Anthropogenic carbon emissions, population growth and usable grassland area were the main causes of NPP changes. Planting and protecting green plants and scientific and effective energy extraction plans are measures that enhance the degree of carbon sequestration in Ordos City.

Spatio-Temporal Variation and Future Sustainability of Net Primary Productivity from 2001 to 2021 in Hetao Irrigation District, Inner Mongolia

“…Therefore, in response to the national carbon peak policy, it is necessary to formulate relevant measures for the Hetao Irrigation District, such as returning farmland to forests and grasslands, reducing agricultural fertilizers and plastic films, and implementing reasonable irrigation policies, to strengthen ecological environmental governance in the Hetao Irrigation District and improve the environment to enhance vegetation NPP. Previous studies in Inner Mongolia (e.g., [21,28]) also suggest the necessity of taking proactive measures to improve the vegetation NPP in the study area. Table 3 and Figure 10 below illustrate the area proportions of monthly and annual NPP changes in the Hetao Irrigation District in the future.…”

“…4b). The fluctuations in annual NPP are primarily influenced by both human and natural factors [21,41]. For instance, the relatively lower NPP observed in 2017 compared to the preceding and succeeding years may be attributed to an extreme drought event occurring that year.…”

“…Compared with traditional regression models, the Biome-BGC process model employs more parameters and more sophisticated estimation methods, improving the accuracy of NPP estimation [20]. In recent years, MOD17A3 data products have been widely validated and applied in global and regional carbon cycle research [21]. Many scholars have used the Google Earth Engine (GEE) platform or the platform provided by the National Aeronautics and Space Administration (NASA) to access MODIS datasets, studying the correlations between NPP and various factors at different scales.…”

“…The formation of vegetation NPP is a complex process influenced by multiple factors, involving disciplines such as plant physiology, hydrology, meteorology, soil science, physics, and disaster research. Terrestrial ecosystems are the dynamic products of interactions between natural and human factors [21]. Current research often focuses only on single or limited factors affecting vegetation NPP.…”

“…Currently, most studies on NPP in Inner Mongolia focus on analyzing the annual spatiotemporal distribution of NPP, without delving into monthly or 8-day temporal variations. Moreover, these studies primarily emphasize the analysis of the response of NPP to individual factors such as temperature and precipitation (e.g., [21,[26][27][28]). Focusing solely on the analysis of the response of vegetation NPP to individual factors while neglecting the spatial variability analysis of NPP across multiple temporal and spatial scales is insufficient for understanding the current carbon cycling capacity of regional ecosystems and future development trends.…”