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• This paper aims at quantifying the factors affecting vegetation growth in the western of Inner Mongolia, and judging the relative contribution of natural factors and man-made factors. • It is obtained through geodetectors (GD) that Rainfall is the most important driving factor, with the q-value of 0.77 and GDP also having a huge impact on vegetation growth, q-value of 0.64. • Different from most studies, human activity also has a positive effect on vegetation growth. • The relative contribution of human activities reached 51.75%, slightly exceeding the influence of natural factors. Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest. [ABSTRACT FROM AUTHOR]

• A dynamic two-stage SBM model with non-discretionary variables is proposed. • China's CPLR efficiency under four scenarios of extreme temperatures is measured. • The influences of extreme temperatures on the CPLR efficiency are highlighted. • A comparative analysis of CPLR efficiency under four scenarios is provided. With the influence of climate changes resulting in more extremely days, we might expect to face a rise in associated coal productivity losses and land destruction. Numerous studies have focused on measuring the efficiency of coal production system. Yet scant studies have considered their internal structure, reconstructed lands, and especially the impact of extreme temperatures. Then, this paper divided such a system into stages of coal production (CP) and land reconstruction (LR), named the CPLR system. This study proposes a modified two-stage dynamic slacks-based measure model with non-discretionary variables for measuring the CPLR efficiency under four scenarios of extreme temperatures. The results are as follows. (1) The CPLR system for 24 provinces under four scenarios spanning 2012 to 2017 had relatively low efficiency except for Fujian, Inner Mongolia and Sichuan, which is mainly caused by the bad performance of the LR system. (2) Extreme temperatures must be considered in measuring CPLR efficiency and its stage efficiency, or otherwise the underestimation of CPLR efficiency and CP efficiency will become in 18 provinces. The negative impacts of extreme temperatures on the efficiency of reconstructed lands and raw coal production should be noticed, especially for Anhui. [ABSTRACT FROM AUTHOR]

The paper investigates how climate changes and socioeconomic transformations affected grassland productivity from 2000 to 2017 in counties in Inner Mongolia of China. The grassland productivity and climatic change variables included 162 readings (9 periods in the growing season × 18 years), while ten socioeconomic variables contained 18 yearly readings. The mismatch between the environmental observations and socioeconomic data compounds quantitative analysis of causal relations between them. We applied several time-series processing methods to generate five datasets. We fit them with conventional, dependent-lagged, and dynamic-lagged panel regression models to investigate which combinations augment causal relationships between grassland productivity, climate, and socioeconomic changes. The results confirmed that the conventional model with the original data failed to reveal causal relationships between these factors. The panel regression models with the original data tended to intensify the causal effects of climate factors on grassland productivity. The models with the transformed datasets disclosed more socioeconomic variables showing causal relationships with grassland productivity. The dependent-lagged model with the empirical mode decomposition transformed data produced the highest R-squared value and lowest prediction error. GDP, state fixed asset investment, highway construction, grain plantation, and livestock density were primary factors affecting grassland productivity. In brief, the integration of time-series data mining and time-lagged panel analysis effectively unveiled the causes of grassland deterioration under climate and socioeconomic changes. [Display omitted] • Augment causality hidden in env-socio-economic systems with data assimilation. • Assimilate temporal environmental and socioeconomic data into compatible time-series. • Experiment with four groups of computational data mining methods on data assimilation. • Compare combinations of data assimilation methods and lagged panel regression models. [ABSTRACT FROM AUTHOR]

• In the Mongolian Plateau, wildfires occurred frequently from 2001 to 2015. • Advancing spring phenology more obvious in the Inner Mongolian than in Mongolia. • Preseason temperature has the greatest impact on the spring phenology. • In a typical fire area, the SOS in the fire zone shifted to earlier in the year. The variation of land surface phenology (LSP) in natural ecosystems has received much attention at the regional and global scales. In this paper, we explore the relationship between variations in the start of the growing season (SOS) and their direct and indirect drivers for the Mongolian Plateau. Our results show that wildfire events mainly occurred in 2001, 2002, 2006, 2008, 2011, and 2016. The frequency of Mongolian wildfires is lower in the south and west, but higher in the east and north. The frequency of wildfires in Inner Mongolia (China) shows a spatial gradient, with a higher frequency in the northeast and a lower frequency in the southwest. Late SOS was most widely distributed in the southeast of Mongolia and the south of Inner Mongolia. The vegetation in the grassland or forest areas showed a comparatively early SOS, between the 110th and 130th day-of-year (DOY). The advancing trends of the SOS were mainly distributed around the northeast Mongolian Plateau, which is more obvious in Inner Mongolia (China) than in Mongolia. Under the control of precipitation (pre) and solar radiation, the preseason average temperature (T mean) in most areas of the study area is from 0 to 3 months. In the Mongolian Plateau, negative partial correlations between the SOS and preseason T mean was mainly concentrated in the northern and northeastern parts of the study area, while positive correlations were mainly distributed in the central and southeastern parts. We also found that in addition to climate factors, wildfires have an impact on the SOS. After wildfires, the enhanced vegetation index (EVI), T mean and SOS showed a significant difference in the greening period between the reference region and the burned area. Compared with the reference zone, the SOS in the fire zone shifted to being earlier. This study, therefore, provides a scientific basis and reference for understanding variations in the SOS in the study area and the response of the SOS to climate change and wildfires. [ABSTRACT FROM AUTHOR]

Unanticipated environmental shocks impact the livelihoods of many resource users around the world. These shocks are likely to be more unpredictable as the effects of climate change continue to mount. Yet how households adapt to these changing climatic conditions especially in the context of rapidly changing market conditions in most areas of the world, is poorly understood. These interactions have wide implications for both smallholder livelihoods and sustainable use of natural resources. In this paper, we examine the relationship between environmental shocks and stocking rates in livestock herds in the Inner Mongolian grasslands of northern China. We uniquely examine three types of shocks and how households adapt livestock production strategies in response to each. Separately and in aggregate, we look at three common shocks in Inner Mongolia: droughts, snowstorms, and locust outbreaks. We use a difference-in-differences approach to estimate changes in stocking rates among households that experience shocks versus those that do not with a panel dataset from 2009 to 2014 of 597 households. While we find no clear impact from locusts, our results suggest droughts and snowstorms have opposite effects: droughts are associated with increases in herd sizes, but snowstorms result in decreased herds. We suggest these differences are due to interactions between shocks, emerging options to borrow on credit, and livestock markets. Household adaptation to climate change will be strategic and take advantage of both available resources as well as market conditions. • Climate change is causing stronger and more unpredictable environmental shocks. • We use a difference-in-differences model to estimate the impact of shocks on stocking rate. • Drought relates to bigger herds; snowstorms relate to smaller herds. • Households adapt to shocks taking advantage of both available resources and market conditions. • Shaping well-functioning market may reduce opportunistic resource exploitation. [ABSTRACT FROM AUTHOR]