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Carbon lock-in has become a major obstacle to green development in all countries, and carbon unlocking (CUL) is of great significance in accelerating the process of carbon neutrality worldwide. Based on the panel data of 292 prefecture-level cities in China from 2006 to 2020, this paper constructs a PSM-DID model based on machine learning to empirically test the impact of LCCP policy on urban CUL. The regression result shows that the low carbon city pilot (LCCP) policy significantly increases the level of CUL by 1.25 % in the pilot cities, which is still valid after range of robustness tests, and the mechanism analysis shows that green innovation and green habits are important ways for the policy to affect CUL. Based on the generalized random forest (GRF) algorithm, we further find that the policy can increase the CUL level by 0.6 %, and the average treatment effect (ATE) of the sample is concentrated between 0.004 and 0.006, which exhibits obvious heterogeneity. This paper chooses population quality, openness level and industrial structure as heterogeneous factors, which are finally found to have a U-shaped or inverted U-shaped relationship with ATE. This study provides useful insights for China and other countries to break the carbon lock-in and realize green development. • Low-carbon city pilot policy can effectively promote urban carbon unlocking. • Causal forest model is used for causal inference based on generalized random forests algorithm. • The propensity score is calculated by machine learning. • Generalized random forest is used for heterogeneity analysis. • Low-carbon city pilot policy transmits policy effects through green innovation and green habits. [ABSTRACT FROM AUTHOR]

• This paper explores the effect of green finance on SDGs. • GFPZ significantly promotes SDGs. • Green finance indirectly affects SDGs through technological innovation. • The impact of green finance on SDGs exhibits heterogeneity. In pursuit of actively advancing energy transformation, ensuring the attainment of carbon peak and carbon neutrality objectives, and fostering sustainable development, China has dedicated substantial efforts. This article treats the Green Finance Reform and Innovation Pilot Zones (GFPZ) as a quasi-natural experiment, thereby constructing an analytical framework for evaluating the impact of green finance on achieving Sustainable Development Goals (SDGs). We empirically investigate this impact by analyzing data from 316 cities in China spanning the period from 2011 to 2020, employing a difference-in-differences (DID) model. The study suggests a gradual improvement in the level of sustainable development in China. Moreover, green finance contributes to SDGs, and the conclusion is robust. Further investigation reveals that green finance improves sustainable development by fostering technological innovation. Heterogeneity results indicate that the influence is notably more pronounced in western regions and locales characterized by stringent environmental regulations. This article offers fresh perspectives on achieving SDGs. [ABSTRACT FROM AUTHOR]