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This study aims to examine how the climate affects the behaviour of the stock market. To achieve this, we have drawn on daily data from Jan 2005 to Jan 31, 2023 and several environmental factors (e.g., temperature, humidity, cloud cover and visibility) to account for extreme weather conditions using the 21-day moving average and its standard deviation. The empirical analysis has revealed three key findings regarding the impact of weather on the stock market's behaviour. First, various forms of extreme weather conditions consistently lead to influence stock behaviour. Second, results provide valuable insights into market behaviour and help investors to make more informed investment decisions. Third, the weather conditions have new information about the climate risk and investors should react to it swiftly in light of our findings. The saliency theory can help reconcile the theoretical conflicts between the real options and risk-shifting theories when it comes to investing in uncertain and extreme climate conditions. • Daily stock market data from 2005 to 2023, including weather factors. • Extreme weather conditions consistently affect stock market behavior. • Our results help investors make more informed decisions considering climate risks. • Weather conditions indicate climate risk, urging swift investor response. • Saliency theory reconciles real options and risk-shifting in climate uncertainty. [ABSTRACT FROM AUTHOR]

Motivated by the growing attention to climate change and the crucial role businesses could play in reducing greenhouse gas emissions, this study investigates entrepreneurial energy efficiency orientation in the context of carbon footprint reduction initiatives of small-and medium-sized enterprises (SMEs). We enhance understanding of the climate change action of SMEs by taking into account the mediating mechanisms (i.e., identification of green barriers and green networking) through which firm entrepreneurial energy efficiency orientation leads to superior carbon footprint reduction initiatives by overcoming barriers to green practices. A survey of 252 SME owners and top managers in the Tees Valley region, Northeast England, supported the direct impact of entrepreneurial energy efficiency orientation on overcoming barriers to green practices and the mediating role of identification of green barriers and green networking in this focal relationship. These findings reveal the importance of entrepreneurial energy efficiency orientation, identification of green barriers and green networking in helping SMEs overcome barriers to green practices and improving carbon footprint reduction initiatives. • SMEs can reduce greenhouse gas emissions, contributing to climate action. • Entrepreneurial energy efficiency aids SMEs in overcoming green practice barriers. • Two mediating roles link energy efficiency to carbon footprint reduction: barrier identification and green networking. • Insights for SMEs: Embrace energy efficiency, identify green barriers, and foster networking for a greener footprint. [ABSTRACT FROM AUTHOR]

This study investigates the impact of renewable and non-renewable energy sources on carbon emissions in the context of China's 14th Five-Year Plan (2021–2025). The plan emphasises a "Dual-control" strategy of simultaneously setting energy consumption limits and reducing energy intensity for GDP (gross domestic product) in order to meet the targets of the five-year plan. Using a comprehensive dataset of Chinese energy and macroeconomic information spanning from 1990 to 2022, we conduct a Granger causality analysis to explore the relationship between energy sources and the level of air pollution. Our findings reveal a unidirectional link, wherein renewable energy contributes to a reduction in air pollution, while non-renewable energy sources lead to an increase. Despite the government's investment in renewable energy, our results show that China's economy remains heavily reliant on traditional energy sources (e.g., fossil fuels). This research is the first systematic examination of the interplay between energy usage and carbon emissions in the Chinese context. Our findings provide valuable insights for policy and market strategies aimed at promoting carbon neutrality and driving technological advancements in both government and industries. • Impact of the interplay between energy usage and carbon emissions in the Chinese context. • Granger Causality analysis reveals that renewable energy reduces air pollution, while non-renewable energy increases it. • Despite government investment, China's economy remains reliant on traditional energy sources. • Research provides insights for promoting carbon neutrality in China's government and industries. [ABSTRACT FROM AUTHOR]

This study investigates the time-varying causal relationship between geopolitical risk and green finance during the period of 1 March 2012–February 16, 2022. By using the novel time-varying causality testing framework, our findings shed light on the nexus between geopolitical risk and green finance in informing environmental management decisions. First, we find that time heterogeneity does exist in the causal relations between geopolitical risk and green finance. Second, geopolitical risk has a more prolonged impact on the volatility of green bonds and renewable energy than the return. Yet, geopolitical risk tends to influence the return of clean energy more persistently than volatility. Third, we observe that geopolitical risk has a more sustained impact on the return and volatility of renewable energy than clean energy. This might be due to the distinct nature of the production of clean energy and renewable energy, thereby providing implications for effective environmental management. Lastly, this paper demonstrates that the impact of geopolitical risk on the return of European clean energy has diminished since the onset of 2015. The volatility of the European clean energy sector is not affected by global geopolitical risk, underscoring the necessity of promoting the development of this sector to reduce the dependence on fossil fuels and enhance energy independence. • Geopolitical risk has a more prolonged impact on the volatility of green bonds and renewable energy than their return. • The return of clean energy is influenced more persistently by geopolitical risk than volatility. • Geopolitical risk poses a more sustained effect on the return and volatility of renewable energy than clean energy. • The impact of geopolitical risk on the return of European clean energy has diminished since the onset of 2015. • Promoting clean energy transition is necessary in securing energy security. [ABSTRACT FROM AUTHOR]

The foreign exchange market significantly affects international trade, making accurate exchange rate predictions essential for investors, businesses, and government organizations. In this study, we propose an AI-driven ensemble model called the multi-model dynamic prediction system (MMDPS) to enhance exchange rate forecasting accuracy, prediction direction consistency, and prediction robustness. To that end, we implemented a series of measures. First, the submodels of MMDPS apply fuzzy prediction, chaos prediction, deep learning, and linear time-series forecasting methods to enhance the information extraction capability of MMDPS. Second, we introduced an innovative hybrid wavelet denoising method to enhance the ability of MMDPS to handle noise in exchange rate data. Third, we employed a novel multi-objective metaheuristic optimization algorithm to determine the optimal combination weights, optimizing across the dimensions of predictive accuracy and directional consistency. This approach further enhances the generalization capability and predictive robustness of MMDPS. Finally, we incorporated into MMDPS multiple variables that are closely related to exchange rates and can rapidly reflect market changes, such as panic indices and commodity futures prices, to enhance the model’s predictive robustness against uncertainty shocks. The predictive performance of MMDPS was validated using 15 datasets with varying frequencies. The results of our experiments demonstrate that MMDPS significantly outperforms the benchmark models in terms of prediction accuracy, consistency in prediction direction, and predictive robustness. Furthermore, MMDPS can significantly enhance investor returns and mitigate investment risks, which underscore its critical application value. [ABSTRACT FROM AUTHOR]

Economic policies affect companies' production decisions. And the energy consumption volume is an intuitive reflection of the enterprise's production decisions. In China, coal is the main source of carbon emissions and the most important energy source. Therefore, the coal market and the uncertainty of economic policies are both directly tied to the carbon market. This study explores both the direct impact of economic policy uncertainty and coal price on carbon prices as well as the indirect impact of economic policy uncertainty on carbon prices through coal prices by utilizing the DCC-GARCH model and the NARDL model. The findings indicate that the dynamic correlations between coal prices and the CEPU are always negative and that those between the price of carbon and the CEPU vary by area. Meanwhile, the dynamic correlations between coal and carbon prices are only positive in Shenzhen and Beijing. Both coal prices and economic policy uncertainty produce asymmetrical impacts on carbon prices. Some policy implications are provided for developing the carbon markets in light of the results drawn from the study. • The linkage between coal and carbon prices are positive only in Shenzhen and Beijing. • The correlations between coal prices and EPU are negative most of the time. • The relationship between carbon prices and EPU vary from region to region. • Asymmetric effects of EPU and coal prices on carbon prices are revealed. [ABSTRACT FROM AUTHOR]