Due to its rapid economic development over the past few decades, China is now at the forefront of environmental issues, necessitating creative solutions that combine ICT, digital financial inclusion, environmental pressure, and free trade to encourage green investment. This study aims to investigate the linkage between ICT, digital financial inclusion, environmental pressure, free trade, and green investment in China from 1996 to 2022 by employing the Partial least squares structural equation modelling (PLS-SEM). As per our results, the statistical values of Cronbach's alpha, composite reliability, and average variance are all above the cutoff point, demonstrating the applicability of this methodology. According to the structural model's results, the path coefficients between digital financial inclusion and green investment, environmental pressure and green investment, and GDP and green investment are positively significant, implying that these three factors are crucial for boosting green investment in China. In addition, our vector autoregressive model results suggest that ICT, digital financial inclusion, environmental pressures, free trade, and GDP cause green investment to rise in China. Thus, the policymakers in China should focus on developing comprehensive policies to encourage green investment in China, which is crucial for economic and environmental sustainability.

Green growth aims to foster economic development while ensuring environmental sustainability by optimizing resource use and reducing pollution. Despite growing attention, the nexus between forest trade, bioenergy, and green growth remains underexplored. Therefore, the main aim of this study is to investigate the impact of forest trade and bioenergy on green growth. To that end, we apply cross-sectional autoregressive distributed lag (CS-ARDL) using 33 global economies. The findings of the CS-ARDL show that forest trade helps enhance green growth both in the short- and long run. However, bioenergy significantly boosts green growth only in the long run, while the short-run estimate of bioenergy is insignificant. The estimates of the regional analysis signify that forest trade and bioenergy enhance green growth in both developed and developing economies only in the long run. Policymakers in both developed and emerging economies should focus on boosting forestry trade and promoting bioenergy production to stimulate green growth.

Financial inclusion means that individuals and businesses can easily avail financial goods and services at an affordable cost. It is widely recognized that financial inclusion can help preserve the environment by increasing the consumption of renewable energy sources. Hence, our aim is to investigate whether financial inclusion has any effect on renewable energy consumption and environmental quality in China. To get the estimates of the variables, we have preferred the ARDL model. The estimates of the model confirm that a rise in the number of ATMs and total insurance has a positive impact on renewable energy consumption in China in long-run. Conversely, an increase in the number of ATMs and total insurance negatively affects CO2 emissions in China. In general, we can say that financial inclusion increases renewable energy consumption and reduces CO2 emissions in China. Therefore, by using financial inclusion, policymakers should try to divert the resources towards environmentally friendly consumption and production activities.

The demand for carbon-free or renewable energy sources is on the rise due to their role in boosting environmental performance and controlling emissions. The majority of previous research is based on the symmetry assumption while analyzing the association between tourism demand (TD), exchange rate volatility (ERV), and renewable energy consumption (REC). To plug this hole, this research scrutinizes the impact of TD and ERV on REC in China. To investigate the short and long-run implications of TD and ERV on REC, this analysis employs the autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) techniques. The study's innovative approach enhances its scientific value through a novel and insightful examination of the variables of interest. The linear model results indicate that TD has a positive and significant long-run impact on REC. Conversely, ERV has a negative and significant long-run impact on REC. The NARDL model finds that a positive TD shock significantly and positively affects REC in the long run, while a negative TD shock does not exert a noticeable impact on REC. A positive ERV shock significantly and negatively affects REC in the long term, whereas a negative ERV shock has an insignificant impact on REC. Conversely, only a positive ERV shock has a significant positive impact on REC in the short run. These findings imply that policymakers must adopt stable and reliable foreign exchange market policies and promote sustainable tourism practices at tourist hotspots.