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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]

As important green energy, wind power and photovoltaic power have great development prospects. The suitability evaluation of wind and solar power plants is a popular research field, which is related to the sustainable and healthy development of wind and solar power generation. In this paper, based on multiple dimensions such as land types, climatic conditions, topographic features and policy environment, we selected 10 indicators and combined (analytical hierarchy process) AHP method to build a suitability assessment model for evaluating the suitability of solar and wind power in Inner Mongolia, China. The findings revealed that, Inner Mongolia has a great potential to generate wind and solar electricity, for wind power, the category of 'excellent' regions covers 83855 km2 and represents 7.10% of the total surface area; for solar power, 7.66% (nearly 90420 km2) are classified as 'excellent'. The suitability of both solar and wind energy in the western region is considered to have the most suitable development region, parts of Alxa League and Bayannur City have great potential for combined wind and solar power generation. The research results could provide important technical and data support for capacity evaluation and power station location decision. [ABSTRACT FROM AUTHOR]

As a clean form of energy utilization, wind power is important for alleviating climate change. Although no direct carbon emissions occur in wind power generation, there exist upstream carbon emissions from manufacturing and installation, which have indirect effects on both the locations of wind farms and areas involved in upstream production and manufacturing. In this paper, based on Input–Output based Life Cycle Analysis (IO-LCA), we explored the lifetime carbon emissions of 378 wind farms in China that were still in operation in 2015. The regional distributions of carbon emissions from wind farms during the whole lifetime were depicted. The embodied carbon emission transfers from the location of the wind farm operation to upstream turbine manufacturing regions were traced. The net emission reduction benefits among regions were also calculated. Results show that carbon emissions mainly distribute in Liaoning, Inner Mongolia, and Tianjin in the turbine manufacturing stage, with a total amount of 3.36 MT. Inner Mongolia contributes the largest carbon emissions (5.94 MT) in the farm construction stage. Inner Mongolia has transferred about 0.99 MT carbon emissions to itself and has the largest net emission reduction. Recognizing the carbon emission transfer of wind farms and dividing the carbon emission reduction responsibilities among regions may shed light on supply chain carbon emission reduction and provincial carbon quota allocation. [ABSTRACT FROM AUTHOR]