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The purpose of the paper is to assess the systemic risk in the new energy stock markets of China.

This paper first uses the VaR method to study individual stock market risks. It then introduces the DCC model to capture the dynamic conditional correlation among the new energy stock markets.

The paper shows a generally upward trend of the stock market risk over time in the recent decade. Among all the markets considered, the solar power market demonstrates the highest risk, closely followed by the wind power market, while the hydropower market exhibits the lowest risk. Furthermore, the average dynamic conditional correlations among the new energy markets stay high during the period under investigation though daily correlations vary and significantly declined in 2020.

To the best of the authors’ knowledge, this paper is the first of its kind to study the systemic risk within the new energy stock market context. In addition, it not only investigates individual new energy stock market risks but also examines the dynamic linkages among those markets, thus providing comprehensive and unprecedented evidence of systemic risk in China new energy markets, which have useful implications for both regulators and investors.

The purpose of this paper is to present a committee machine (CM) with two‐layer expert nets to overcome the lack of approximating ability of CM with single‐layer expert nets.

A frequently used structure of CM, with a fuzzy c‐means clustering algorithm as splitting and combining unit and some single‐layer linear neural nets as expert modules, was applied to short‐term climate prediction. Considering the complexity of the climate conditions, use was made of two‐layer back propagation (BP) neural nets instead of single‐layer linear nets to test the effect of the model. Experiments were performed on both synthetic and realistic climatic data.

Prediction accuracy is raised when the BP nets were used and as the number of hidden neurons increased at some stages. It implies that improving the approximating ability of individual expert module of a CM is beneficial.

The optimal learning rate, the optimal cluster numbers and the maximal number of iteration were not well treated.

The paper is a useful alternative worth consideration for the complicated prediction problems.

A CM with two‐layer expert nets are presented. Comparisons are made between CMs with simple and complex expert nets.

To offer a realistic foundation for urban cultural construction planning, we want to investigate the distribution features of Shanghai's cultural functional elements and examine the distribution patterns in urban space.

In this research, we managed to gather POI geographic data, refined and categorized them to integrate eight categories of cultural functional elements, observed the density and agglomeration, distribution direction and hot and cold spots of overall and each type of cultural functional elements using geospatial analysis methods and then investigated the factors influencing cultural functional elements using geographic detectors.

Our research shows apparent differences between regions and most cultural functional elements are found in the inner city. Second, there are hot and cold spots in the way different cultural functional elements are spread out. Its geographic structure is primarily influenced by third-party traffic service capacity and available time.

This work provides a benchmark for cultural planning in Shanghai by establishing the spatial aggregation impact of cultural functional elements.