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As a raw material for clean energy supply for the new generation, the soybean is conducive to the realization of global energy transition and sustainable development in the context of "carbon neutrality". However, global warming has been affecting soybean yields in recent years. How to clarify the correlation between meteorological factors and soybean yields, so as to ensure the security of soybean growth and development and the stability of renewable energy development, is a key concern of the government and academia. Based on the data of temperature, precipitation, sunshine duration and active accumulated temperature during the soybean growing season in Hulunbuir, Inner Mongolia Autonomous Region from 1951 to 2019, and soybean yield data of the city from 1985 to 2019, this paper adopted statistical methods such as the Trend Analysis Method, the Rescaled Range Analysis Method and so on to analyze the trends of yield changes, characteristics of abrupt changes and periodic patterns of climate factors and soybean yields in Hulunbuir. A Pearson Correlation Analysis and a Grey Relation Analysis were used to explore the correlation between climatic factors and soybean yields, followed by a comprehensive impact model of the combined effect of temperature and precipitation on soybean yields established by the Method of Integral Regression. The results showed that temperature and active accumulated temperature are the dominant factors affecting soybean yields in Hulunbuir, while the decrease in precipitation is unfavorable to the improvement of soybean yields. Meanwhile, temperature and precipitation have different effects on the growth and development of the soybean at different stages. The conclusion of this paper is of great practical significance for Hulunbuir to promote the sustainable development of clean energy. [ABSTRACT FROM AUTHOR]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. [ABSTRACT FROM AUTHOR]

The northern edge of the East Asian monsoon region is also a transition zone of temperate continental and monsoon climate, which is a sensitive zone of global climate and ecological environment change. It is an urgent problem to find out the long-term climate change rules of this region. Using 101 tree core samples obtained from 54 trees, a 343-year tree-ring width chronology of the Platycladus orientalis (L.) Franco (1673–2016) in the Wula Mountains, Inner Mongolia, China was established. The analysis shows that the radial growth of P. orientalis is closely related to hydrothermal conditions, the average minimum temperature and relative humidity are the main factors that determine the growth of P. orientalis. Based on the strong correlation between the tree-ring indices and the annual average minimum temperature (r = −0.870, p < 0.001), we reconstructed an average annual minimum temperature series from 1673 to 2016 using a linear regression equation, and the reconstructed equation can explain 75.724% of the variance, respectively. According to the data from the past 343 years, the changes in the annual average minimum temperature series had the cycles of 58.6-, 7.7-, 3.4-, 3.1-, 2.2-, and 2.1-years; the series showed three warm periods (1824–1830, 1909–1922, and 1986–2011) and 4 cold periods (1703–1712, 1731–1738, 1754–1763, and 1937–1951). The low temperature periods recorded in the reconstructed series for the 17th and 18th centuries are consistent with the Little Ice Age that occurred in the northern hemisphere, and the heating rate significantly increased at the end of the 20th century. In addition, the cold and warm periods recorded by the reconstruction result is basically consistent with the extreme climate events recorded in the historical documents of the region and the reconstruction results in other regions. In this paper, the world's first chronology of P. orientalis tree ring width was established. A historical sequence of annual average minimum temperature of 343 years was reconstructed in a typical region of the East Asian monsoon climate. It fills in the blank of reconstruction sequence and variation characteristics of mean minimum temperature in the northern edge of East Asian monsoon and transition zone of temperate continental and monsoon climate in recent four centuries. [ABSTRACT FROM AUTHOR]

• Designing an integrated regional water environment modeling framework • Concurrent analysis of seasonal water and its driving forces across space and time • Differentiating how different blocks of driving factors interact with water scarcity • Driving factor impacts on water scarcity increase substantially over time • Local resource endowment and utilization critically impact spatial heterogeneity Increased grazing and agricultural production, industrialization, population growth, and consequent land use land cover (LULC) changes considerably increase water consumption. Global climate change exaggerates the uncertainty of water sources and supplies. Unfortunately, most current examinations are either confined within disciplinary silos or not integrated for considering wide-ranging socioenvironmental, management, and policy factors. The paper develops an integrated regional water environment modeling framework, examining how climate, LULC, socioenvironmental, and policy factors interact with the water environment. It also adopts a block-based econometric panel data analysis to quantify this framework. The paper extracts seasonal water area and LULC data through image processing from 2000 to 2014 in the Hulun-Buir watershed, Inner Mongolia of China. The paper quantitatively analyzed the interactions between seasonal water changes and major driving factors, such as climatic, land-use, socioeconomic, policy, space, and time. Many of these driving factors were interacting with the seasonal water environment and showing long-term causal relationships. The socioeconomic variables explained 71% of the variance of seasonal water change, the environmental and climatic factors about 9%, the regional disparities around 13%, and the yearly differences about 4%. The findings confirm that it is critical to carry out a time-series examination of causal relationships between seasonal water change and its manifold driving factors at the scale of regional watershed studies. This integrated watershed modeling framework is suitable for adaptation in other geographic areas or for integrated studies of other socio-environmental systems. Graphical Abstract [Display omitted]. [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]

• A hydrological modeling approach was proposed to distinguish the relative contribution of climate changes and human activities on Daihai Lake's recession. • In the absence of natural runoff and human-induced water withdrawal observations, the natural state's lake water storage changes were reconstructed. • Without considering the high water years, the anthropogenic factor took the responsibility for the lake shrinkage about 58%, while the climatic factor took roughly about 42% in period 1974–2018. • This investigation serves as a useful example of applying a distributed model to the assessment of lake hydrology, especially for the lake catchments with limited observations. In recent decades, water levels in many closed inland lakes over north-western China experienced severe declines, among which the Daihai Lake in Inner Mongolia, China was particularly significant. Remotely sensed investigations indicated that the Daihai Lake had shrunk approximately 64% in an area with the dramatic decline of water level approximately 9 m from the 1980 s to 2010 s, which has caused serious concern on the sustainable development of the surrounding region. Consequently, several previous studies have concentrated on the possible causes of lake shrinkage. However, predicting climate variability and anthropogenic activities' relative contributions to lake water decline is challenging due to the lack of direct water consumption upstream and watershed-scale natural runoff observations. In this paper, a hydrological modeling approach was presented to quantitatively evaluate and separate the relative contributions of climate variations and intensifying anthropogenic activities. The simulation accuracy was evaluated through comparisons with observed data and cross-validation with previous studies. Based on a baseline period, the natural streamflow draining into the lake was reconstructed using the model. The results revealed that the water loss of the Daihai Lake was caused predominately by human activities rather than climate variability. Specifically, for wet conditions, the climate factors posed a negative effect on the water level decline of the lake and delayed the shrinking process of the lake area. The anthropogenic factors took a dominant role (roughly 58%) of lake shrinkage at the annual average scale for normal and dry conditions, while the climate factors accounted for approximately 42%. Hence, the rational policy for water resource management, ecological restoration, and dam and reservoir constructions/operations should be the essential precautions for sustainable development of the Daihai Lake and its surroundings. Additionally, the hydrological modeling approach adopted in this investigation serves as a useful example of the quantitative assessment of climate variations and human activities, especially for the ungauged catchments. [ABSTRACT FROM AUTHOR]

Abstract: Renewable energy is one of the possible solutions when addressing climate change. Today, large-scale renewable energy integration needs to include the experience to balance the discrepancy between electricity demand and supply. The electrification of transportation may have the potential to deal with this imbalance and to reduce its high dependence on oil production. For this reason, it is interesting to analyse the extent to which transport electrification can further the renewable energy integration. This paper quantifies this issue in Inner Mongolia, where the share of wind power in the electricity supply was 6.5% in 2009 and which has the plan to develop large-scale wind power. The results show that electric vehicles (EVs) have the ability to balance the electricity demand and supply and to further the wind power integration. In the best case, the energy system with EV can increase wind power integration by 8%. The application of EVs benefits from saving both energy system cost and fuel cost. However, the negative consequences of decreasing energy system efficiency and increasing the CO2 emission should be noted when applying the hydrogen fuel cell vehicle (HFCV). The results also indicate that developing renewable energy is crucial for transportation electrification. [Copyright &y& Elsevier]

In this paper, we proposed a framework for evaluating the performance of ecosystem strategies prepared for enhancing vulnerability reduction in the face of hazards due to climate change. The framework highlights the positive effects of human activities in the coupled human and natural system (CHANS) by introducing adaptive capacity as an evaluation criterion. A built-in regional vulnerability to a certain hazard was generated based upon interaction of three dimensions of vulnerability: exposure, sensitivity and adaptive capacity. We illustrated the application of this framework in the temperate farming-grazing transitional zone in the middle Inner Mongolia of the northern China, where drought hazard is the key threat to the CHANS. Specific indices were produced to translate such climate variance and social-economic differences into specific indicators. The results showed that the most exposed regions are the inner land areas, while counties located in the eastern part are potentially the most adaptive ones. Ordos City and Bayannur City are most frequently influenced by multiple climate variances, showing highest sensitivity. Analysis also indicated that differences in the ability to adapt to changes are the main causes of spatial differences. After depiction of the spatial differentiations and analysis of the reasons, climate zones were divided to depict the differences in facing to the drought threats. The climate zones were shown to be similar to vulnerability zones based on the quantitative structure of indexes drafted by a triangular map. Further analysis of the composition of the vulnerability index showed that the evaluation criteria were effective in validating the spatial differentiation but potentially ineffective because of their limited time scope. This research will be a demonstration of how to combine the three dimensions by quantitative methods and will thus provide a guide for government to vulnerability reduction management. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge about changes of regional carbon density during late Quaternary is of great significance for understanding regional and global carbon cycles. Taking the Hunshandake Sandy Land, located in eastern Inner Mongolia of China as an example, this paper aims to reconstruct Late Quaternary environmental changes, and then to discuss the fluctuations of organic carbon density and storage in the desert environment of northern China. The study area with a mean annual precipitation ca. 300 mm is characterized by the occurrence of fixed and semi-fixed sand dunes reaching a maximal height of 30 m. The research methods included field observations, examinations of grain sizes, as well as carbon and calcium carbonate contents of the sediment samples. The chronology is based on Optically Stimulated Luminescence (OSL) dating and radiocarbon analyses. This work reveals that the organic carbon density in the surface layer of the Hunshandake Sandy Land changed between at least 2500 g/m2 during the Holocene Climatic Optimum and 1500 g/m2 when aeolian processes dominate. The dune construction was restricted by periglacial processes during the Last Glacial Maximum (LGM). The Holocene Climatic Optimum, indicated by extensive formation of kastanozem soils, occurred between 5 ka and 3 ka in the study area. Although desertification might cause a 40% loss of total organic carbon storage within several dozen of years in the study area, a considerable amount of this carbon was re-deposited in the regions of downwind directions. [Copyright &y& Elsevier]

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]