In April 2023, the government of Changshu City, in Jiangsu Province, China, announced that it would officially use digital Chinese Yuan (E-CNY) as a method of wage payment to the government and state-owned enterprises staff starting in May. With the gradual improvement and application of E-CNY technologies, such as no electricity, no internet payment (offline payment), and the programmability of smart contracts, E-CNY will be officially used in China. CNN said China is on the verge of a cashless society. The advantages of E-CNY have a positive role in promoting the Chinese government’s implementation of the development goals of a low-carbon and sustainable economy. However, artificial intelligence (AI) trust concerns are the primary bottleneck in the current development based on intelligent algorithms and digital information technology. AI trust concerns are affecting the scope of use of E-CNY, and it may need to achieve effective scale-use, making it promote low-carbon and sustainable development. From the industry perspective, this article selects the housing rental enterprises, which are challenging to develop and energy-intensive, to analyze the theoretical approach and practical impact of E-CNY in promoting the low-carbon and sustainable development of China’s rental housing economy. Meanwhile, from the perspective of Chinese consumers, the impact of AI trust concerns on E-CNY in promoting low-carbon and sustainable development is analyzed in this article.

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

In this paper, we modeled and simulated two tandem solar cell structures (a) and (b), in a two-terminal configuration based on inorganic and lead-free absorber materials. The structures are composed of sub-cells already studied in our previous work, where we simulated the impact of defect density and recombination rate at the interfaces, as well as that of the thicknesses of the charge transport and absorber layers, on the photovoltaic performance. We also studied the performance resulting from the use of different materials for the electron and hole transport layers. The two structures studied include a bottom cell based on the perovskite material CsSnI₃ with a band gap energy of 1.3 eV and a thickness of 1.5 µm. The first structure has an upper sub-cell based on the CsSnGeI₃ material with an energy of 1.5 eV, while the second has an upper sub-cell made of Cs₂TiBr₆ with a band gap energy of 1.6 eV. The theoretical model used to evaluate the photocurrent density, current-voltage characteristic, and photovoltaic parameters of the constituent sub-cells and the tandem device was described. Current matching analysis was performed to find the ideal combination of absorber thicknesses that allows the same current density to be shared. An efficiency of 29.8% was obtained with a short circuit current density J_sc = 19.92 mA/cm², an open circuit potential V_oc = 1.46 V and a form factor FF = 91.5% with the first structure (a), for a top absorber thickness of CsSnGeI₃ of 190 nm, while an efficiency of 26.8% with J_sc = 16.74, V_oc = 1.50 V and FF = 91.4% was obtained with the second structure (b), for a top absorber thickness of Cs₂TiBr₆ of 300 nm. The objective of this study is to develop efficient, low-cost, stable and non-toxic tandem devices based on lead-free and inorganic perovskite.