The Pan-Third Pole (PTP) region, one of the areas with the most intense global warming, has seen substantial changes in vegetation cover. Based on the GIMMS NDVI3g and meteorological dataset from 1982 to 2015, this study evaluated the spatio-temporal variation in fractional vegetation coverage (FVC) by using linear regression analysis, standard deviation, correlation coefficient, and multiple linear regression residuals to explore its response mechanism to climate change and human activities. The findings showed that: (1) the FVC was progressively improved, with a linear trend of 0.003•10a⁻¹. (2) The largest proportion of the contribution to FVC change was found in the unchanged area (39.29%), followed by the obvious improvement (23.83%) and the mild improvement area (13.53%). (3) The impact of both climate change and human activities is dual in FVC changes, and human activities are increasing. (4) The FVC was positively correlated with temperature and precipitation, with a stronger correlation with temperature, and the climate trend was warm and humid. The findings of the study serve to understand the impacts of climate change and human activities on the dynamic changes in the FVC and provide a scientific foundation for ecological conservation and sustainable economic development in the PTP region.

Soil erosion is serious in China—the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.

Exploring the impact of new economic forms such as the digital economy (DE) on carbon emissions is crucial for China’s “dual carbon” goals. This paper assesses the impact of the DE on carbon emission intensity (CI) from a 2011–2021 perspective on energy consumption in 30 provinces (Hong Kong, Macao, Taiwan, and Tibet are excluded) by using a double fixed-effects model for evaluating the DE. Based on the results from 2011 to 2021, (1) China’s DE and CI differ spatially and temporally. In contrast to CI, DE shows a pattern of low in the northwest and high in the southeast. The changes are similar to Hu Huanyong’s line, with considerable changes in the southeast, especially the coastal region. (2) Chinese DE’s carbon control effects (−0.027) vary by region. A significant negative effect is seen in both the eastern and western regions, with the western region having the greatest negative effect (−0.030), and a positive but insignificant impact in the central region. (3) Total energy consumption (TEC), structure (EC) and efficiency (EI) are all pathways of its influence. Path changes in China are mainly dominated by dual paths (Accounting for over 47%), with fewer single and multiple paths. Among them, the main dual path types are TEC and EC in the east and central regions, TEC and EC, and EC and EI in the western regions; meanwhile, the paths are unchanged in most of the provinces in China, and the changed provinces are mainly in the west. Based on these findings, DE development policies based on regional energy consumption differentiation are crucial to reducing carbon emissions.

【Objective】Rock fragments (>2mm diameter) are an important component of soil, and its presence has a significant impact on soil erosion and sediment yield. So it is essential to take into full account content of the rock fragments for accurate calculation of soil erodibility factor (K). 【Method】In this paper, based on the data available of the content of rock fragments and classes of soil texture with a resolution of 30 arc-second, influence of the content of rock fragments, including rock fragments in the soil profile (RFP) and gravels on the surface of the soil (SC), on K was assessed at a global scale, using the equation (Brakensiek, 1986) of the relationship between saturated hydraulic conductivity and grade of soil permeability, and the equation (Poesen) of soil erodibility attenuation under a rock fragment cover. 【Result】Results show: (1) The existence of rock fragments in the soil increased K by 4.43% and soil permeability by 5.68% on average in grade and lowering soil saturated hydraulic conductivity by 11.57% by reducing water infiltration rate of the soil and increasing surface runoff. The gravels on the surface of the mountain land and desert/gobi reduced K by 18.7% by protecting the soil from splashing of rain drops and scrubbing of runoff; so once the content of rock fragments in the soil profile and gravels on the surface of the land are taken into account in calculation, soil K may be 5.52% lower; (2)In the areas dominated with the effect of rock fragments, about 62.7% of the global land area, soil K decreased by 0.0091( t•hm2•h)•( hm-2•MJ-1•mm-1) on average, while in the area affected mainly by rock fragments in profile, about 31.1% of the global land area, soil K increased by 0.0019( t•hm2•h)•( hm-2•MJ-1•mm-1); and (3)The joint effect of rock fragments in profile and gravels on the surface reduced the soil erosion rate by 11.8% in the 6 sample areas. 【Conclusion】 The presence of RFP increases soil K while the presence of SC does reversely. The joint effect of the two leads to decrease in soil erosion. In plotting regional soil erosion maps, it is essential to take both of the two into account so as to improve accuracy of the mapping.

The soil erodibility factor (K) is the main data required for regional soil erosion investigation and mapping using soil erosion models. USLE-K, RUSLE2-K, EPIC-K and Dg-K are four widely used methods for calculating soil erodibility factor (K). However, it remains to be studied which algorithm is more suitable to calculate soil erodibility factor (K) in the global scale. While, soil erodibility factor (K) is mostly calculated based on soil physical and chemical property data, which does not involve the content of rock fragments in these algorithms. However, the amount of rock fragments and thier distribution difference have a certain influence on soil physical and chemical properties, and then affect the accuracy of the estimation of soil erodibility factor (K). In this paper, USLE-K, RUSLE2-K, EPIC-K and Dg-K algorithms were used to estimate global soil erodibility factor (K), and its spatial pattern and main controlling factors were analyzed. In this paper, the measured data of soil erodibility factor (K) were retrieved by literature search, and the measured database of K factor value was established. The rationality of the results of the above four algorithms was analyzed, and the above four algorithms for calculating K factor were modified according to the measured database of K factor. At the same time, USLE-K and RUSLE2-K algorithm are taken as an example to calculate the effect of rock fragments in the soil profile and rock fragments on the soil surface. The results showed that (1) The spatial pattern of global K factors estimated by the USLE-K, RUSLE2-K, EPIC-K and Dg-K models is similar, but the values in the K surfaces are different in some extent. (2) Comparing to 106 measured values, the mean value of estimated RUSLE2-K is the closest to the measured K factor, followed by the USLE-K algorithm and the EPIC-K algorithm, while the estimated K by Dg-K algorithm is quite different from the measured K factor. (3) The presence of rock fragment in the soil profile increased the global soil erodibility factor. The rock fragment on the soil surface reduces soil erodibility. This article made the calculation of K more complete and accurate, thereby improving the accuracy of regional soil erosion estimation. And provide the necessary scientific basis for the selection of K algorithms globally.