Urban economic development is crucial to regional economy and people’s life, and enhancing the efficiency of urban economic development is of great significance to boost sustainable and healthy economic and social development. In this paper, from the perspective of sustainable development, data of 104 cities in China’s Yangtze River Economic Belt (YREB) from 2004 to 2019 are selected, and the urban resource consumption index and urban pollutant emission index are synthesized as new input-output indicators using the Time Series Global Principal Component Analysis (GPCA), combined with the Global Malmquist-Luenberger (GML) Index Model, Standard Deviation Ellipse (SDE) Model to measure the total factor productivity index of urban economic development in China’s YREB and analyze its spatial and temporal evolution. The results show that from 2004 to 2019, the total factor productivity index of urban economic development in China’s YREB showed an overall fluctuating upward trend with an average annual growth of 5.8%, and the analysis by decomposing indicators shows that the growth of total factor productivity of urban economic development in China’s YREB is mainly influenced by the growth of technological progress. Meanwhile, there are obvious regional differences in the efficiency of urban economic development in China’s YREB, with the largest difference in the middle reaches of the Yangtze River, the second largest in the upper reaches, and the smallest in the lower reaches. From 2004 to 2019, the efficiency center of gravity of urban economic development efficiency in the YREB has always been located in the middle reaches of the Yangtze River region. The spatial distribution pattern of urban economic development efficiency in the YREB is dominated by the northeast-southwest direction and tends to be concentrated in the study time period.

Identifying and forecasting macro-financial conditions is critical to stabilizing the economy. This study aims to develop a novel methodology for constructing China’s Financial Conditions Index, utilizing monthly data from six major Chinese financial markets (comprising 33 key financial indicators) along with 25 external macroeconomic variables from both China and the United States, spanning January 2002 to June 2024. Although the traditional TVP-FAVAR model can capture the linear relationship in the financial market, it cannot adequately characterize the nonlinear or asymmetric nature of the macro-financial conditions exhibited when major risk events occur at home and abroad. In this paper, we propose an innovative kernel factor-augmented time-varying parameter vector autoregressive model (TVP-KFAVAR), which can better capture the nonlinear nature of the macro-financial situation. It is shown that the TVP-KFAVAR model successfully reflects the impact of major domestic and international risk events on China’s Financial Conditions Index. Meanwhile, the ARIMA model and five machine learning techniques (GRU, LSTM, BiLSTM, TCN and Transformer) are used in this study to predict the Macro-Financial Conditions Index, and it is found that the vast majority of the machine learning techniques outperform the traditional time-series models in terms of forecasting performance. TCN has the outstanding prediction performance under different input configurations. This study can provide policymakers with a powerful tool for macro-financial regulation and risk early warning, and help improve macro-financial management in emerging markets.