This paper introduces a growth model that considers the indicator of economic complexity as a measure of capabilities for exporting the high value-added (sophisticated) products. Empirically, the paper analyzes the effects of the renewable and the non-renewable energy consumption on the economic growth in the panel data of 29 Organization for Economic Cooperation and Development (OECD) countries for the period from 1990 to 2013. For this purpose, the paper considers the panel autoregressive distributed lag (ARDL) and the panel quantile regression (PQR) estimations. The paper finds that not only the economic complexity, but also both the non-renewable and the renewable energy consumption are positively associated with a higher rate of economic growth.

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This paper aims to examine the effects of economic policy uncertainty (measured by the World Uncertainty Index—WUI) on the level of CO2 emissions in the United States for the period from 1960 to 2016. For this purpose, we consider the unit root test with structural breaks and the autoregressive-distributed lag (ARDL) model. We find that the per capita income promotes CO2 emissions in the long run. Similarly, the WUI measures are positively associated with CO2 emissions in the long run. Energy prices negatively affect CO2 emissions both in the short run and the long run. Possible implications of climate change are also discussed.

This paper uses the 1990-2010 natural disaster and carbon emissions data of G20 countries to examine the impact of natural disasters and climate change on the natural capital component of inclusive wealth. Our study shows that climate change and GDP have no positive impacts on the growth of natural capital. By contrast, trade openness and natural disaster frequency contribute to the accumulation of natural capital in G20 countries. There is an inverted U-shaped relationship between the growth of natural capital and the magnitude of natural disaster. Natural capital growth is not affected very much by small disasters. By contrast, large disasters tend to make the growth of natural capital fall sharply.

Nowadays, search engine use increasingly reflects investor sentiment, which affects the return on the stock market. In this article, we examine the relationship between Baidu Index sentiment and China's stock market returns. In two different GARCH models, the benchmark model and a Baidu Index extended model, the one-step forward method is used to predict the return of stock market. The study finds that Baidu Index − search volume is a valid indicator for forecasting volatility in China's stock market. The Baidu Index extended model performs better than the benchmark model, both in periods of high volatility and periods of low volatility. These results are quite robust in Shanghai Stock Exchange, Shenzhen Stock Exchange, CSI 300 Index, and CSI 100 Index. This study shows that the investor sentiment reflected in the Baidu Index can be used as a good early warning indicator of China's stock market.

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