PurposeEnvironmental regulation is in a continuous state of intense change and modification amid the long-term tensions between environmental protection and economic growth. In this article, the authors creatively investigate how fluctuations of environmental regulation influence a nation's economic growth while also examining the mediating effect of technological innovation.Design/methodology/approachUsing sample data of 36 Organisation for Economic Co-operation and Development (OECD) countries from 2013 to 2018, environmental regulation is differentiated in two aspects of formal environmental regulation (FER) and informal environmental regulation (IER) and analyzed to assess the effects of regulatory fluctuations on investment and technological innovation.FindingsThe research results demonstrate that both FER fluctuation and IER fluctuation exert a significant negative impact on economic growth. These two fluctuations in environmental regulation increase uncertainty and unpredictable risks for corporations and investors, significantly stifling the willingness to contribute to innovation activities and leading to a diminished level of innovation. Technological innovation is revealed to have a mediating influence on the relationship of environmental regulation fluctuation to economic growth.Originality/valueThese findings enrich the research on the impact of environmental regulation from a dynamic, multinational perspective, contributing to the literature by exploring the relationships between environmental regulation fluctuation, technological innovation and economic growth at the OECD-country level.
Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale residual module is based on one-dimensional convolution, which is not only capable of adaptive detecting features of different time scales in multivariate time series, but also enables the fusion of these features. LSTM has the ability to learn long-term dependencies in series, which is widely used in financial time series forecasting. By mixing these two methods, the model is able to obtain highly expressive features and efficiently learn trends and interactions of multivariate time series. In the study, the impact of external factors such as macroeconomic variables and investor attention on the Bitcoin price is considered in addition to the trading information of the Bitcoin market. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures. Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRC-LSTM in short-term forecasting for multivariate time series of cryptocurrencies.
Green finance is of great significance in improving the ecological environment and achieving the purpose of energy conservation and emission reduction. In order to explore the influence of green finance on carbon intensity, four indicators of green credit, green securities, green insurance and green investment are adopted to construct the green finance development index in this paper. Based on the panel data of 30 provinces in China from 2009 to 2019, a dynamic spatial Durbin model is constructed and the method of partial differential matrix is selected to analyze the influence of green finance on carbon intensity in the short and long terms. The empirical results show that (1) the development of green finance in local area has positive effect on the reduction of carbon intensity. (2) with the significant spatial spillover effect on carbon intensity, green finance can reduce the carbon intensity of the adjacent area and promote the development of low-carbon economy. (3) dynamic test results prove that in terms of direct effect and spatial spillover effect, green finance has a greater long-term effect on carbon intensity.
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