Rural clean heating project (RCHP) in China aims to increase flexibility in the rural energy system, enhance the integration of renewable energy and distributed generation, and reduce environmental impact. While RCHP-enabling routes have been studied from a technical perspective, the economic, ecological, regulatory, and policy dimensions of RCHP are yet to be analysed in depth, especially in the underdeveloped areas in China. This paper discusses RCHP in rural Gansu in a multi-dimension approach. We firstly focus on the current issues and challenges of RCHP in rural Gansu. Then the RCHP-enabling areas are briefly zoned into six typical regions based on the resource distribution in Gansu Province, and a matching framework of RCHP is recommended. Then we focus on the economics and sustainability of RCHP-enabling technologies. Based on the medium-term assessment of RCHP in the demonstration provinces, various technical schemes and routes are analysed and compared so as to be adopted in rural Gansu. In addition to technical and economic effects of those schemes, the corresponding ecology, policy, finance, and market implications are also concerned. We briefly discuss how the national regulators incentivise the implementation of RCHP in rural Gansu. Major barriers to RCHP are identified as the sustainability of technology, economy, ecology, policy, finance, and market. Subsequently, some policy solutions to overcome these barriers are proposed.

This study explores the application of the Fully Convolutional Network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG) and thunderstorms, short-term heavy rain(STHR) in Gansu. Training data comes from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations, the performance of the proposed FCN model based on 2017 to 2021 training datasets demonstrate a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when testing against the 2022 dataset. Operational deployment in 2023 has yielded an average Critical Success Index (CSI) of 24.3%, a Probability of Detection (POD) of 62.6%, and a False Alarm Ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performance for hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model's ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future.