Accurately assessing the impact of human activities on net primary productivity (NPP) of vegetation is of great significance to the achievement of sustainable development. However, it is difficult to disentangle the effects of climate conditions and human activities on NPP, and bridging this knowledge gap largely depends on the calculation of the NPP under natural conditions. Here, we propose a method for calculating natural vegetation NPP (NNPP) based on non-human influence grids, which are obtained according to the consistent rate of climate and actual NPP (ANPP) temporal changes. We selected Northwest China as study area, and we used a light use efficiency (LUE) model to estimate ANPP and used the random forest algorithm (RF) to estimate the NNPP. The results show that NNPP is very close to ANPP, and the human activities on NPP (HNPP) based on NNPP is close to the actual situation of human activities on NPP. From 2001 to 2017, the positive HNPP accounts for 40.28% of the total grassland area, with an average value of 28.65 gC·m−2·yr−1, while the negative HNPP accounts for 59.72% of the total area, with an average value of −31.19 gC·m−2·yr−1. The grassland NPP shows an increasing trend, which is dominated by climate factors. Human activity is the dominant factor for the grassland degradation, accounting for 42.78% of the degraded area, but promoting grassland growth in 11.4% of the restored area. This study provides a new method to estimate the impacts of human activities on vegetation, and the results can be used to evaluate the effectiveness of ecological environmental governance, providing a quantitative basis for scientifically building the harmonious relationship between human and nature.

Permafrost is a product of cold climates and is mainly distributed in cold climatic high latitudes and high elevations in the Northern Hemisphere (Zhang et al., 1999). The permafrost regions account for approximately 22% of the land area in the Northern Hemisphere (Obu et al., 2019). Permafrost degradation may lead to greenhouse gas emissions from the decomposition of organic carbon stored in the permafrost regions, further contributing to global warming and accelerating permafrost thaw (Schuur et al., 2015).The active layer is the soil layer where water and heat are exchanged between the surface and air in permafrost areas. The freeze-thaw process of the active layer can greatly affect the hydrothermal physical properties of the soil, surface evaporation, vegetation growth, and surface albedo Zhao et al., 2019).These changes regulate exchange processes such as sensible and latent heat fluxes between the soil and the atmosphere, which significantly influence regional circulation (Zhang et al., 2014). The hydrothermal conditions within the active layer are the main factors controlling the water and energy exchange between permafrost and the atmosphere (Cheng et al., 2019), which can directly affect the ecological environment, hydrological processes, and carbon cycle in permafrost regions (Jorgenson & Osterkamp, 2005).There is great spatial heterogeneity in the active layer thickness (ALT) across the Northern Hemisphere. The ALT varied from approximately 30 cm in the Arctic and circumpolar regions to greater than 10 m in the midlatitude mountainous permafrost zone during 1990(Luo et al., 2016). The regional average ALT values were 48 cm in Alaska, 93 cm in Canada, 164 cm in the Nordic countries (including Greenland and Svalbard) and Switzerland, 330 cm in Mongolia, 476 cm in Kazakhstan, and 230 cm on the Qinghai-Tibet Plateau (Luo et al., 2016).During the past decades, the rate of warming in the permafrost zones in the Northern Hemisphere has been 2-3 times the global average (Hu et al., 2021;Mu et al., 2020). Global warming has led to an increase in permafrost temperature, a reduction in the extent of permafrost, and an increase in the ALT. From 1990 to 2012,

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Most terrestrial models synchronously calculate net primary productivity (NPP) using the input climate variable, without the consideration of time-lag effects, which may increase the uncertainty of NPP simulation. Based on Normalized Difference Vegetation Index (NDVI) and climate data, we used the time lag cross-correlation method to investigate the time-lag effects of temperature, precipitation, and solar radiation in different seasons on NDVI values. Then, we selected the Carnegie-Ames-Stanford approach (CASA) model to estimate the NPP of China from 2002 to 2017. The results showed that the response of vegetation growth to climate factors had an obvious lag effect, with the longest time lag in solar radiation and the shortest time lag in temperature. The time lag of vegetation to the climate variable showed great tempo-spatial heterogeneities among vegetation types, climate types, and vegetation growth periods. Based on the validation using eddy covariance data, the results showed that the simulation accuracy of the CASA model considering the time-lag effects was effectively improved. By considering the time-lag effects, the average total amount of NPP modeled by CASA during 2001-2017 in China was 3.977 PgC a -1 , which is 11.37% higher than that of the original model. This study highlights the importance of considering the time lag for the simulation of vegetation growth, and provides a useful tool for the improvement of the vegetation productivity model.