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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]

With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy. [ABSTRACT FROM AUTHOR]

The Late Paleozoic is an important epoch of coal-bed accumulation in the North China, particularly in its western segment, where a world-class coal field has been found and exploited for three decades. Coal-bearing layers recorded rapid Late Carboniferous to Early Permian sea-level rise that led to the evolution of the shallow marine tidal flat basin, followed by regression that resulted in the formation of deltaic facies. However, little attention has been paid to the tectonic factors that assisted in the coal accumulation processes, which have been demonstrated worldwide to be pivotal. In this study, we evaluate the significant influence of southward subduction of the middle segment of the Paleo-Asian Ocean (PAO) during Carboniferous to Early Permian deposition of coal-bearing neritic to deltaic successions in the western North China Block (NCB). We pinpoint a direct link between basin-filling evolution of the northwestern Ordos Basin and the progressive uplift of the Inner Mongolia continental arc triggered by the south-dipping subduction of the Solonker ocean plate. Sedimentary facies variation and paleocurrent reconstruction indicate sedimentary routing from the north to northeast. The petrology and detrital zircon geochronology of sampled sandstones indicate evolving provenances from a dominated cratonic basement to a mixed orogen and continental arc. The Carboniferous samples are generally quartz arenites and contain a large number of Archean to Paleoproterozoic zircons with small amounts of Paleozoic arc pluton components, indicating sediment supply dominated by the western NCB Precambrian basement. In contrast, the youngest sample of the Shanxi Formation is sub-litharenite, the age spectrum of which is characterized by the dominance of ca. 450 and 270 Ma ages, indicative of a high influx of Paleozoic arc detritus. This shift in sandstone petrology, detrital zircon distribution, and depositional setting allows us to link the shoaling of the Ordos Basin with mountain building and the unroofing of the continental arc, which enhances our understanding of both the subduction of the PAO and coal accumulation in the NCB. [ABSTRACT FROM AUTHOR]

The heat storage and release performance of cascade phase change units are investigated numerically for users in Inner Mongolia's severe cold region. Three schemes of phase change material combinations are thoroughly tested. We obtained a better material combination scheme S3 (palmitic acid + polyethylene glycol), which has higher heat storage capacity per unit mass, higher average heat flux, and better unit synchronisation performance, so that it is more suitable for solar heating and cascade heat storage units in cold regions of Inner Mongolia. This study takes into account the irradiation variation of typical days during the winter heating season. The results show that the palmitic acid and polyethylene glycol combination scheme has the highest total heat storage per unit mass. This scheme also performs well in the synchronisation of two-stage storage units. When compared to the other two schemes, the average heat flux is increased by 25.5% for the first stage unit and 16.8% for the second stage unit. [ABSTRACT FROM AUTHOR]

To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts. [ABSTRACT FROM AUTHOR]

China hosts over half of global coal-fired power generation capacity and has the world's largest coal reserves. Its 2060 carbon neutrality goal will require coal-fired electricity generation to shrink dramatically, with or without carbon capture and storage technology. Two macroeconomic areas in which the socioeconomic impact of this decline is felt are losses in jobs and tax revenues supported by thermal coal mining, transport and power generation. At the national level, under a 'baseline' (B) scenario consistent with China's carbon neutrality goal, labour productivity growth in coal mining implies that significant job losses will occur nationally in the medium term, even if all coal plants continue operating as planned. Jobs supported by the coal power industry would decline from an estimated 2.7 million in 2021, to 1.44 million in 2035 and 94,000 in 2050, with jobs losses from mining alone expected to exceed 1.1 million by 2035. Tax revenues from thermal coal would total approximately CNY 300 billion annually from 2021–2030, peaking in 2023 at CNY 340 billion. This is significantly less than estimated subsidies of at least CNY 480 billion, suggesting coal is likely a net fiscal drain on China's public finances, even without accounting for the costs of local pollution and the social cost of carbon. As coal plant retirements accelerate, from 2034 onwards, fiscal revenues begin to fall more rapidly, with rates of decline rising from 1% in the 2020s to over 10% a year by the 2040s. More aggressive climate policy and technology scenarios bring job and tax losses forward in time, while a No Transition policy, in which all currently planned coal plants are built, delays but does not ultimately prevent these losses. At the provincial level, China's major coal-producing provinces will likely face challenges in managing the localised effects of expected job losses and finding productive alternative uses for this labour. Governments of coal-producing provinces like Inner Mongolia, with an industry highly dependent on exports to other provinces, are more exposed than others to declining tax revenues from coal, and more insulated from job losses, given their high current degree of labour efficiency. Although their provincial revenues are likely to remain stable until the early 2030s under the B scenario, the possibility of increasing policy stringency underlines the need for revenue and skill base diversification. At the firm level, China's 'Big Five' state-owned power companies were responsible for over 40% of both jobs and tax revenues in 2021. The number of jobs supported by the activities of each of the largest ten firms, with one exception, will decline by 71–84% by the early 2040s, with the tax contribution of each declining by 43–69% in the same period. [ABSTRACT FROM AUTHOR]

The Chinese government has launched a guideline for carbon emissions up to the peak (CEUP) in the 2030 target. The electric power sector has to make its own contributions to the national CO2 emissions mitigation target. In this study, a patron–client interactive optimized (PCIO) model is proposed to investigate the regional energy–water–carbon nexus optimization under the policy background of the CEUP target. Inner Mongolia, the largest energy base in China, which is also facing the prominent contradiction including the energy production and serious environmental problems, is chosen as a case study. Multiple uncertainties, including the fuel price uncertainty and output of the wind and solar power, are considered to make the optimization process more realistic. Results show that coal-fired power will gradually be substituted by the gas, wind, and solar power in Inner Mongolia to reach the CEUP target. The CO2 capture and storage technology and air-cooling systems will play important roles, especially under the strict water policy scenario. However, the achievement of the CEUP and water-saving target will be at the expense of high system costs. The PCIO model makes it possible for the decision-maker to make flexible strategies to balance the CEUP target and saving system costs. The results have demonstrated the validity of the PCIO model in addressing the hierarchical programming problems. [ABSTRACT FROM AUTHOR]