Impacts of climate and land use change on terrestrial carbon storage: A multi-scenario case study in the Yellow River Basin (1992-2050).

Currently, socioeconomic development and climate change pose new challenges to the assessment and management of terrestrial carbon storage (CS). Accurate prediction of future changes in land use and CS under different climate scenarios is of great significance for regional land use decision-making and carbon management. Taking the Yellow River Basin (YRB) in China as the study area, this study proposed a framework integrating the land use harmonization2 (LUH2) dataset, the patch-generating land use simulation (PLUS) model, and the integrated valuation of ecosystem services and trade-offs (InVEST) model. Under this framework, we systematically analyzed the spatiotemporal evolution characteristics of land use and their impact on CS in the YRB from 1992 to 2050. The results showed that (1) CS was highest in forestland and lowest in construction land, with a spatial distribution of high in the south and low in the north. From 1992 to 2020, construction land, forestland, and grassland increased while cropland decreased, reducing the total CS by 74.04 Tg. (2) From 2020 to 2050, under SSP1-2.6 scenario, forestland increased by 158.87 %; under SSP2-4.5 scenario, unused land decreased by 65.55 %; and under SSP5-8.5 scenario, construction land increased by 13.88 %. By 2050, SSP1-2.6 scenario exhibited the highest CS (8105.25 Tg), followed by SSP2-4.5 scenario (7363.61 Tg), and SSP5-8.5 scenario was the lowest (7315.86 Tg). (3) Forestland and construction land were the most critical factors affecting the CS. Shaanxi and Shanxi had the largest CS in all scenarios, and Qinghai had a huge carbon sink potential under SSP1-2.6 scenario. Scenario modeling demonstrated that future climate and land-use changes would have significant impacts on terrestrial CS in the YRB, and green development pathways could strongly contribute to meeting the dual­carbon target. Overall, this study provides a scientific basis for promoting low-carbon development, land-use optimization, and ecological civilization construction in YRB, China.

The impacts of cropland balance policy on habitat quality in China: A multiscale administrative perspective.

Cropland protection policies are implemented to guarantee food security, especially the cropland balance policy in China. Although these policies alleviate the quantitative loss of cropland, they cause a serious decline in habitat quality and reduce biodiversity. With the reform trial of China's cropland balance policy from the within-province scale to the cross-provincial scale, the evaluation of habitat quality for obtaining cropland balance at different administrative scales is necessary to help seek sustainable strategies of cropland protection. Thus, taking Chinese mainland as the study area, this research assessed the different impacts of the cropland balance policy on habitat quality during 2000-2015 and 2015-2030 at national, provincial, municipal and county scales on the basis of the LAND System Cellular Automata model for Potential Effects (LANDSCAPE) and the Integrated Valuation of Ecosystem Services and Tradeoffs model (InVEST). The results revealed that (1) the loss of habitat quality by compensated cropland was 4.02 times that of the loss by occupied cropland in 2000-2015. Compared with occupied cropland, compensated cropland dominated the impacts of cropland change on habitat quality. (2) For both cropland quantity balance and cropland quality balance, habitat quality was slightly lost the most on the national scale in 2030. In detail, the greatest loss of habitat quality mostly occurred in areas where the ecological environment is already vulnerable at the national scale, whereas habitat quality was relatively less in these areas at the provincial, municipal and county scales. (3) Under the same administrative scales, habitat quality was lost more in cropland quality balance scenarios than in cropland quantity balance scenarios. The loss of habitat quality caused by cropland quality balance at national, provincial, municipal and county scales was 1.25, 1.05, 0.90 and 1.37 times higher than that by cropland quantity balance, respectively. The research highlights that considering the differentiated loss of habitat quality caused by cropland protection is quite important when implementing the cropland balance policy and land use planning.

Spatial-temporal evolution and responses of ecosystem service value under land use change in the Yellow River Basin:A case study of Shaanxi-Gansu-Ningxia region, Northwest China.

The Shaanxi-Gansu-Ningxia region is located in the upper and middle reaches of the Yellow River Basin in the Northwestern China, with vulnerable ecosystems. It is important to define the regional pattern of ecological security. The ecological and economic issues in this region deserved more investigation. By integrating land use data and the socio-economic data from 1995 to 2020, and using spatial analysis and geodetector, we investigated the spatial-temporal variations of land use and ecosystem service value (ESV) and the driving forces of spatial variations of ESV in the region. The results showed that the cultivated land and unused land in the study area were decreasing, whereas the construction land and forest land increased from 1995 to 2020. The overall ESV in the region showed a "decrease-increase" trend, which decreased by 1.2% from 1995 to 2000 and increased by 1.0% from 2000 to 2020. Grassland provided the largest ESV, contributing a prominent regulation on function and service. The results of geodetector indicated that NDVI was the dominant driving factor for the spatial variation, while temperature and per capita net income of farmers were the important factors. There were mainly 32 types of the index of driving forces with the spatial difference of ESV (q value) being more than 30%. The q value of NDVI and soil type was nearly 46%. The spatial variation of ESV in Shaanxi-Gansu-Ningxia region was affected by the interactive enhancement among natural, socio-economic factors, and policy factors.