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Approximately 172 million rural households in China in 2022, will still rely on traditional biofuels instead of electricity. This study explores the feasibility of achieving rural electrification in China utilization various Energy origins and assesses its potential impact on climate change mitigation. To do so, we employ the Regional Energy Model (REM) to formulate hypotheses for rural power distribution. Covering the era from 2020 until 2050. We evaluate consequences in terms of greenhouse gas discharges, principal energy consumption, associated expenses by comparing these possibilities with a Business- as -Usual (BU) scenario. Our findings reveal that diesel-based arrangements tend to produce 'the maximum Carbon Dioxide (CO2) emissions, trailed by network-based solutions. Electrification focused on renewable energy-driven applications has the potential to reduce total CO2 emissions by up to 97 % and primary energy consumption by 43 % in 2050 compared to the BU scenario. We also find that electrification through decentralized diesel systems tends to be the priciest choice. Rural electrification utilizing renewable energy emerges as a cost-effective choice when the majority of end-use applications rely on renewable energy sources. However, it becomes less economical than network expansions when electric end-user appliances are predominant. In summary, this study investigates the viability of renewable energy for rural power distribution and its role in climate change abatement in rural China, offering policy suggestions. • Analyzes six energy-based scenarios for China's rural electrification until 2050. • Evaluates GHG emissions, energy use, and costs impacts with regional energy model. • Provides policy insights and uncertainties for China's sustainability goals. • An oil price and distance-based analysis also is done with extra 6 scenarios. [ABSTRACT FROM AUTHOR]

In the era of "Internet plus," the world economy is becoming more and more globalized and informationalized. China's enterprises are facing unprecedented opportunities for their operation and development. However, it is also facing the financial uncertainties brought about by the fluctuations of the general economic environment, and the company is facing increasing financial risks. The reason why most enterprises encounter a serious financial crisis or even close down in the later stage is that they do not pay full attention to the initial financial problems and do not take effective measures to deal with the crisis in time. Financial risk warning has become an important part of modern enterprise financial management. This paper mainly puts forward the optimized BP neural system as the financial early warning model and ensures its high prediction accuracy. In the research, the operation principle and related reasoning process of the model are described, its shortcomings are analyzed, and solutions are put forward. Through the financial risk analysis of listed companies from 2017 to 2020, we find that the correct rate of the prediction results of the financial distress of normal companies in the selected companies based on the optimized BPNN has reached more than 80%, which proves the effectiveness of the optimized BPNN. [ABSTRACT FROM AUTHOR]