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Green finance is an effective policy instrument in mitigating climate change, but to date little is known about its impact on carbon dioxide (CO 2) emissions. This study extends the literature on the relationship between green finance and CO 2 emissions while taking trade openness and economic development into consideration to comprehensively explore the conservation/feedback hypothesis using the Autoregressive-Distributed Lag–Error Correction Model (ARDL–ECM) on data collected from 2000 to 2019. By constructing a green finance index comprising green credit, green securities, green insurance, and green investment, results include the following. (1) Support was found for the conservation hypothesis running from green finance, trade openness, and economic development to emissions per capita; the conservation hypothesis also was supported for the unidirectional relationship running from trade openness and economic development to green finance. (2) Green finance is found to be significantly and negatively related to emissions per capita in both the short and long term. (3) Trade openness shows a positive effect on emissions per capita. Robustness tests are provided. This study deepens policy implications of green finance in the post-pandemic era and highlights the moderating role of renewable energy in the green finance impact on carbon mitigation. • This study constructs a green finance index for China. • The autoregressive distributed lag–error correction model is employed. • Green finance contributes to carbon mitigation in both the short and long run. • CO 2 emissions are Granger result of green finance, trade openness and economic growth. • Trade openness and economic growth are Granger causes of each other. [ABSTRACT FROM AUTHOR]

Previous studies have considered the effect of financial development on carbon emissions; however, few studies have explored the role of green finance in carbon mitigation. To bridge this gap, the current study constructs a green finance development index based on four indicators: green credit, green securities, green insurance, and green investment. A vector error correction model is used to analyze relationships between the development level of green finance, non-fossil energy consumption, and carbon intensity using data from 2000 to 2018. We find that China's green finance industry developed rapidly, and improvements in the green finance development index, as well as the increasing use of non-fossil energy, contributed to a reduction in carbon intensity. Simultaneously, an increase in carbon intensity inhibited the expansion of non-fossil energy use, impeded the investment flow to green projects, and ultimately led to a deterioration of green finance development. In addition, non-fossil energy consumption in China was primarily influenced by green finance and carbon intensity, with clear policy-driven effects. However, the impacts of green finance policies continually fell short and lacked continuity. This study proposes ways in which to improve the effect of green finance policy implementation, expand the consumption of non-fossil energy, and develop a carbon trading market. • The green finance–new energy use–carbon intensity nexus is explored for China. • A vector error correction model is employed. • A green finance development index is constructed based on four green indicators. • Green finance and new energy use are found to reduce carbon intensity. • Carbon intensity increases are found to inhibit green finance and new energy use. [ABSTRACT FROM AUTHOR]

• A weak decoupling is found between CO 2 and economic growth in China's IS industry. • Causal-chain model is used to decompose decoupling index into different factors. • Elasticities of emission reduction and value creation negatively affect total decoupling. • Elasticities of energy electricity and industrial electrification positively affect total decoupling. • Grey Verhulst model is used to predict the decoupling state from 2017 to 2025. Building a low-carbon industry is essential for the development of a sustainable economy. To study the relationship between carbon emissions and economic growth of China's iron and steel (IS) industry, this paper first measures the decoupling relationship between carbon emissions and added value of the IS industry from 2001 to 2016 based on the Tapio decoupling model. The causal-chain decomposition model is then used to decompose the decoupling index into different factors. Results show that carbon emissions and economic growth of the IS industry have mostly weak decoupling. For the influencing factors, decoupling elasticities of emission reduction and value creation exert negative impacts on overall decoupling, and the former one plays a bigger role. Decoupling elasticities of energy electricity and industrial electrification positively improve the total decoupling, but the effect is not stable yet and needs to be further enhanced. Finally, the grey Verhulst model is used to predict the decoupling state by the end of 2025. Forecasting results show that industry will still be in a weak decoupling, but the decoupling state will get better over time. Corresponding policy suggestions are posited based on the empirical findings to facilitate the green and low-carbon development of the IS industry. [ABSTRACT FROM AUTHOR]