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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 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]

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]

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]