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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]

Dematerialization at the national level occurs almost exclusively during periods of economic recession or low growth. While recession is not a sustainable strategy to curb environmental impact, such periods may offer important insights on the possibilities of reducing material use. Economic recession research has focused on the interactions between macroeconomic and financial variables with little systematic research dedicated to uncovering how resource use develops during periods of recession. Using a dynamic panel model, we investigate whether and to what extent material use in a sample of 150 economies between 1970 and 2010 was affected by recession and low growth. Recession occurred most frequently and in more than two thirds of all countries during the 1980s and the 1990s. In the 2000s, more than half of the recessions occurred during the financial crisis of 2008. Periods of recession were significantly and negatively correlated with material use - they tended to coincide with dematerialization. Material use decreased in recession years, but the significant correlations with growth in material use became weaker as growth rates increased. Construction minerals and metals, used to build stocks of manufactured capital, reacted more strongly to economic fluctuations than the throughput-dominated flows of biomass and fossil fuels. We conclude that the systematic link between recession and dematerialization can become policy-relevant if the mechanisms causing reductions in material use during these periods are identified. [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]