The carbon market and the green bond market are important institutions for reducing greenhouse gas emissions and achieving economic low-carbon transformation. Accurately understanding the characteristics and correlations of the two markets is of great significance for promoting the achievement of the “dual carbon” goal. From the perspective of different time scales and market conditions, this study selected the maximal overlap discrete wavelet transform (MODWT) to decompose the price time series data of China’s carbon market and green bond market. The quantile Granger causality test was used to calculate the causal relationship between the two markets at different quantiles, and the association between the two markets was estimated based on quantile-to-quantile regression (QQR). The results show that, regardless of the time scale and market conditions, the Chinese carbon market is always the Granger cause of the green bond market. When the green bond market is in a slump state (i.e., in a “bear” market), it will have a certain negative impact on the carbon market in the short term, but in the medium and long term, the impact of the green bond market on the carbon market is positive. In addition, as the time scale increases, the synergistic effect between the green bond market and the carbon market becomes more and more significant. At medium- to long-term time scales, extreme market conditions can easily cause extreme shocks from the green bond market to the carbon market.

Renewable energy is absolutely necessary in order to stop the deterioration of the environment and to guarantee reliable access to environmentally friendly energy sources. Based on the conformity theory, logistic regression, propensity score matching, and stepwise regression are all used in the current study to investigate the mediating role of green intentions in the conformity tendencies of Chinese farmers’ adoption of renewable energy sources. This research was motivated by the notion of the dissemination of innovations as well as the technological adoption model, and it aims to shed light on how individuals are socialized and how green intentions mediate their conformity tendencies to adopt renewable energy sources. The results revealed that the average age of the farmers was 42 years, and they were primary school graduates. More than 50% were male. Almost 26% of farmers reported that they use renewable energy sources on their farms. Generally, the farmers were green intended and would like to adopt renewable energy sources. The regression results disclosed a significant impact of socially inspired peer adoption (p<0.01) on the farmers’ adoption of renewable energy sources. Renewable energy adoption is impacted more by the adoption of strong connections than by the adoption of renewable energy by weak relations, which means that farmers are more likely to adopt renewable energy if their peers also do so. Nevertheless, the extent to which farmers have green intentions regarding the use of renewable energy technology had a significant influence on the farmers’ conformity tendencies, influenced by the different intensities of their social networks.

China has experienced rapid economic growth that requires high energy consumption. This may hinder the achievement of sustainable development goals. The adoption of green energy technologies (GET), efficiency of natural resources (NR), and stringency of environmental policies (EVP) are essential to facilitate sustainable development in the country. In this regard, the current study aims to analyze the impact of GET, NR, and EVP on the green growth (GG) and energy transition (ET) of the country. For this purpose, we applied the unique econometric technique “Bootstrap Fourier quantile causality” on quarterly data from 1996Q1 to 2020Q4. The findings reveal that all the variables have unidirectional causality with GG and ET. Moreover, GET and NR had a substantial impact on GG, demonstrating that both GET and NR are crucial drivers of GG. In the case of ET, GET had a strong positive and increasing impact on ET from low to high quantiles. Similarly, NR also demonstrated a substantial impact on ET at higher quantiles only. This highlights that a higher GET and well-endowed NR may accelerate ET. EVP had a very strong synergistic impact on both GG and ET, along with GET and NR. EVP reinforced and intensified the effects of Get and NR on GG and ET. Therefore, China must focus on the development of effective EVPs and their uniform stringency across all regions to achieve a GG and sustainable ET. Moreover, investment in green technologies, the provision of financial incentives to foster the adoption of green technologies, region-based development of strategies to promote the consumption of renewable energy sources, and their integration into the national grid may significantly contribute to GG and ET.