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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 the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

China has announced its goal of reaching carbon neutrality by 2060, which will have a profound impact on its energy and economic systems. During this process, the power sector will play a key role in helping the country on its road towards carbon neutrality. This study develops a multi-regional power dispatch and capacity expansion model to combine long-term capacity expansion with short-term power dispatch. The model is built to optimise the carbon-neutral transition pathway from the perspective of economics, focussing on power system stability and reliability under high penetrations of renewables. Using the case study of North China, the cost-effective methods for power system transitions under the targets of reaching peak emissions by 2030 and achieving carbon neutrality by 2060 are discussed under different scenarios. We find that the future power supply system will rely heavily on renewable energy which will account for more than 89% of power generation. Inner Mongolia will produce more than 84% of power in the North China region. The inter-regional power transmission capacity will be five times higher than its current level. Policy choices will lead to different transition pathways as the early-stage installation decisions will have a lock-in effect on the following carbon emission reduction strategies and determine the importance of technologies adopted in achieving carbon neutrality. Although the transition to a low-carbon economy will result in an increase in cost by 17–19%, it will bring huge climate benefits to the world. [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]