Can green finance promote the low-carbon transformation of the energy system? New evidence from city-level data in China.

This study explores the comprehensive effects of green finance (GF) on the low-carbon transition of the energy system (LTES) by analyzing panel data from 281 cities in China from 2006 to 2021. It is found that GF significantly reduces overall energy consumption and exhibits a U-shaped association with energy efficiency, while its relationship with the energy consumption structure is inverted U-shaped. After accounting for endogeneity in the robustness tests, these findings remain consistent and are therefore deemed reliable. A mechanistic analysis reveals that GF promotes industrial upgrading, technological progress, and economic agglomeration, collectively facilitating the LTES. The impact of GF on LTES shows considerable variation among regions, influenced by their levels of economic growth, extents of marketization, and governmental environmental preferences. Our findings provide new evidence for the relationship between GF and LTES, offering a scientific basis for formulating GF policies to accelerate this transformation.

Green horizons: Overcoming political risks with strategic environmental management and financial innovation.

The impact of political risk and financial development has been widely studied in the context of sustainable environmental practices. However, their effects on green finance and sustainable finance initiatives have not been thoroughly explored. This paper fills this gap by examining the influence of the political risk financial development index on green finance across 21 OECD economies from 1990 to 2020. Unit root and cointegration tests reveal that variables are stationary at first difference, and there is a long-run cointegration among them. For the primary analysis, we employed the novel MMQR approach, which demonstrates that the financial development index enhances green finance, while the political risk index diminishes it across all quantiles - upper, median, and lower. Robustness analysis using BSQR further confirms these findings. Policies aimed at fostering financial development and reducing political risk should acknowledge the growing significance of green finance in OECD economies.

How can green finance effectively promote low-carbon cities? Evidence from 237 cities in China.

Urban areas contribute 85% of China's CO2 emissions. Green finance is an important means to support green energy development and achieve the low-carbon transformation of high-energy-consuming industries. The motivation of this article is to investigate the impact and mechanism of green finance on urban carbon intensity. Most existing literature uses linear models to investigate urban carbon intensity, ignoring the nonlinear relationships between economic variables. The nonparametric models can fill the inherent shortcomings of linear models and effectively simulate the nonlinear nexus between economic variables. Based on the 2011-2021 panel data of 237 cities in China, this paper applies the nonparametric additive model to survey the influence of green finance on urban carbon intensity. Empirical findings exhibit that green finance exerts an inverted U-shaped effect on urban carbon intensity, indicating that the carbon reduction effect of green finance has gradually shifted from inconspicuous in the early stages to prominent in the later stages. Then, from the perspectives of region, city size, and carbon intensity, this article conducts heterogeneity analysis. The results show that the impact of green finance on various carbon intensities all exhibits obvious nonlinear feature. Furthermore, this article employs a mediation effect model to conduct mechanism analysis. The results display that technological progress and industrial structure are two important mediating variables, both of which produce an inverted U-shaped nonlinear impact on urban carbon intensity.

Do green finance and green innovation affect corporate credit rating performance? Evidence from machine learning approach.

This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability.

The impact of green finance policy on green innovation performance: Evidence from Chinese heavily polluting enterprises.

Green innovation (GI) is increasingly recognised as an effective strategy for tackling climate change, mitigating environmental issues, and promoting sustainable development. Using panel data of the Chinese listed firms from 2007 to 2019, this study adopts the difference-in-differences approach to assess the impact of the green finance policy (GFP) initiated by the Chinese government in 2012 on the green innovation performance of firms. The findings reveal that the GFP significantly boosts the green innovation performance of heavily polluting enterprises (HPEs). Notably, this effect is more pronounced in state-owned enterprises and firms with high dependence on external finance. Compared with penalty-based regulations, incentive-based and voluntary environmental regulations demonstrate more significant moderating effects on the relationship between the GFP and green innovation performance for HPEs. We also identify improved efficiency in the usage of green investments as a potential mechanism through which the GFP enhances the green innovation performance of HPEs. Further comparative analysis shows that green enterprises can achieve simultaneous improvement in both the quality and quantity of green innovation, whereas HPEs predominantly exhibit enhancements in innovation quantity. To maximise the GFP's positive effects, it is recommended to facilitate more targeted bank lending towards HPEs to support their structural transformation. Additionally, the coordinated deployment of diverse environmental policy instruments is advised to exploit their synergistic effects.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

The impact of geopolitical risk, institutional governance and green finance on attaining net-zero carbon emission.

This research investigates the impact of geopolitical risk, institutional governance and green finance on environmental outcomes, specifically focusing on carbon emissions and ecological footprint. Utilizing the dynamic CS-ARDL method and aggregated mean group analysis on a panel dataset covering 21 nations from 2000 to 2021, our findings reveal that heightened geopolitical risk leads to both short and long run increases in carbon emissions and the ecological footprint. Our study finds both a direct as well as indirect connection between governance, green finance and environmental outcomes in both the short and long run, highlighting the nuanced impact of governance on the formulation of environmental policies and regulatory frameworks. The results emphasize the need for targeted strategies, including focused investments and incentives for sustainable finance, particularly in conflict-affected regions. Furthermore, our research underscores the enduring impact of historical events, such as wars, on contemporary environmental indicators, emphasizing the importance of proactive conflict prevention measures. Our research suggests that policymakers should adopt comprehensive strategies that prioritize emission reduction during short-run spikes in geopolitical risk while maintaining a steadfast commitment to long-run sustainability.

Climate bonds: Are they invested efficiently?

Achieving a Net-Zero goal is heavily reliant on transitioning to green methods, making it a top priority. Our research, which used the Generalized Least Squares (GLS) panel method, found that each Macro-region should invest at least 2% more per capita annually in Climate Bonds, a type of green bond. Although some studies have questioned the effectiveness of Climate Bonds, our focus is on their efficient use in countries that produce more fossil fuels. Our findings show that globally, a) Climate Bonds are underutilized in areas with higher per capita use of fossil fuels, and b) High-income countries are gradually reducing their reliance on fossil fuels, while low-income countries have always used very little (with a forecast of future growth). Allocating financial resources in the form of Climate Bonds for the green transition should consider per capita use of fossil fuels, as well as the heterogeneity of population growth and different Macro-Regional economic development. Developing countries, with their large populations, will require more financial resources for an ethically acceptable green transition in the future.

Achieving synergy between carbon mitigation and pollution reduction: Does green finance matter?

The synergy between carbon mitigation and pollution reduction (CMPR) serves as the predominate catalyst for facilitating green transformation. Although the Green Finance Polit Policy (GFPP) shares a commitment to green development, its effectiveness in fostering the synergy of CMPR remains uncertain. Hence, we adopted the Difference-In-Difference (DID) model to investigate the effect of GFPP on the synergy between CMPR during 2009-2019. Initially, a composite synergy degree model is employed to assess the synergy between CMPR at the prefectural level. Our empirical findings demonstrate a notable contribution of GFPP in augmenting the synergy between CMPR. Robustness tests using Tobit model, PSM-DID, and placebo tests (temporal and regional) also confirm the stability of the findings. Furthermore, the study identifies three plausible mechanisms, including industry optimization, green innovation, and energy structure, which are likely to impact the aforementioned synergy. Empirical tests provide supportive evidence that substantiates the theoretical validity of these mechanisms, indicating that optimizing industry structure and enhancing the quality of green innovation can amplify the positive impact of green finance. Additionally, the impact of green finance policy in augmenting the synergy between CMPR exhibits greater prominence in central regions, non-resource-based cities, and non-key environmental protection cities. Our study also uncovers an additional finding that green finance has a detrimental effect on the quality of green innovation, thereby impeding the advancement of synergy between CMPR. In summary, our findings offer novel insights into promoting the synergistic relationship between CMPR by means of implementing green finance policy.

Rich forests, rich people? Sustainable finance and its links to forests.

Investment products labelled as sustainable, as well as regulations on sustainable finance, such as the EU taxonomy for sustainable activities, are on the rise globally. While some of these products and regulations include forests and forestry, the forest-sustainable finance nexus is largely unexplored in academic research. This paper systematically analyses the emerging expert debate spanning across the financial and forest sectors. We conducted 51 in-depth qualitative interviews with experts from financial institutions, timberland and impact investors, international organizations, civil society organizations and academia. We chose mainly experts from Europe, as one of the regions spearheading the topic globally. Based on these, we identify five main narratives on the nexus between sustainable finance and forests. These narratives are strikingly different regarding such dimensions as emphasis on risks versus opportunities, preference for public versus private governance and investments, as well as on the sustainability of forest-related investments per se. While financial sector experts are mainly concerned about financial risks, and only partially about deforestation risks, forest sector experts with financial expertise promote investment opportunities either for the asset class, or to increase private investment in tropical forests. In contrast, some experts from both the forest and financial sectors explicitly exclude forests as investable assets for the private sector, seeing them instead as pure public goods. We conclude with underlining the importance of more cross-sectoral dialogue, but also research, to both critically assess and advance the role of sustainable finance policy and practice in supporting forest conservation, restoration, and sustainable management.

Financial reform and haze pollution: A quasi-natural experiment of the financial reform pilot zones in China.

Financial reform becomes a new tool for environmental governance because it can indirectly affect the environment by promoting economic and financial agglomeration and technological innovation. Despite China's aggressive financial reform pilot (FRP) policy since 2012, little is known about whether and how such policy affects haze pollution (HP). We exploit geographic and temporal variations in China's FRP policy and compile a dataset covering 284 cities over the period from 2003 to 2019. Employing a difference-in-differences (DID) approach, we document that China's FRP policy has a negative causal effect on HP in the pilot cities. The estimates obtained from an instrumental variable constructed by religious temples also support the haze-abatement effect of such policy. This effect is largely driven by advances in technological innovation and increases in economic agglomeration, while financial agglomeration is proven to have little effect. Finally, our estimate is particularly pronounced in cities with high levels of economic development, financial development and technological innovation, and that in large-sized and non-mineral resourced cities. Overall, our findings shed light on the importance of financial reform in environmental governance in a developing country.

How does environmental regulation influence green technological innovation? Moderating effect of green finance.

The main factor behind green economic development is green technology innovation (GTI). Environmental regulation and green finance (GF), as important ways to promote ecological civilization construction, run through the entire procedure of GTI. The purpose of this study is to investigate the influence of heterogeneous environmental regulation on GTI and the moderating effect of GF on GTI from both theoretical and empirical perspectives, to provide useful ideas for China's economic reform path selection and environmental governance system optimization. This paper uses information from 30 provinces between 2002 and 2019, and a bidirectional fixed model was constructed. The results show that: First, regulatory environmental regulation (ER1), legal environmental regulation (ER2), and economic environmental regulation (ER3) all have greatly boosted the degree of GTI in each province. Second, GF acts as a highly effective moderator between heterogeneous environmental regulation and GTI. Finally, this article investigates how GF can act as a moderator in various circumstances. The beneficial moderating effect of it is found to be more pronounced in inland areas, areas with weak spending on research and development, and areas with high energy consumption. These research results provide valuable references for accelerating the green development process in China.

The causal relationship between green finance and geopolitical risk: Implications for environmental management.

This study investigates the time-varying causal relationship between geopolitical risk and green finance during the period of 1 March 2012-February 16, 2022. By using the novel time-varying causality testing framework, our findings shed light on the nexus between geopolitical risk and green finance in informing environmental management decisions. First, we find that time heterogeneity does exist in the causal relations between geopolitical risk and green finance. Second, geopolitical risk has a more prolonged impact on the volatility of green bonds and renewable energy than the return. Yet, geopolitical risk tends to influence the return of clean energy more persistently than volatility. Third, we observe that geopolitical risk has a more sustained impact on the return and volatility of renewable energy than clean energy. This might be due to the distinct nature of the production of clean energy and renewable energy, thereby providing implications for effective environmental management. Lastly, this paper demonstrates that the impact of geopolitical risk on the return of European clean energy has diminished since the onset of 2015. The volatility of the European clean energy sector is not affected by global geopolitical risk, underscoring the necessity of promoting the development of this sector to reduce the dependence on fossil fuels and enhance energy independence.

Do climate policy uncertainty and investor sentiment drive the dynamic spillovers among green finance markets?

Green finance is an essential instrument for improving the environment and addressing climate change. This study investigates the dynamic spillovers among green finance markets using time-varying parameter vector autoregression (TVP-VAR) spillover indices, and further investigates the impact of climate policy uncertainty and investor sentiment on spillovers based on the generalised autoregressive conditional heteroscedasticity mixed data sampling (GARCH-MIDAS) model. The results indicate that: (i) environmental, social and governance (ESG), clean energy and water markets are information transmitters in the green finance system, whereas green building, green transportation, green bond and carbon markets are mainly information receivers; (ii) green stock markets including clean energy, non-energy and ESG markets transmit and receive greater information in the green finance system, while green bond and carbon markets do less; (iii) the green bond market is more interconnected with other green finance markets after the COVID-19 outbreak; (iv) investor sentiment contributes more to the net total directional spillovers of green resource markets (water and clean energy), while climate policy uncertainty contributes more to total spillovers and the net total directional spillovers of other green finance markets. These findings offer invaluable guidance for both policymakers and environmental investors.

Green finance in circular economy: a literature review.

Developing markets are using sustainable development potential to reach zero-carbon goals. Due to the limitation of natural resources, companies need to use environmentally friendly manufacturing to develop a circular economy (CE). Green finance (GF) and the CE are linked in a systematic and complex approach; therefore, it was essential to employ the coupling coordination-level framework to explain their relationship and feedback. Any study linking green financing and CE together has been found. The objective of this research is to explore this twofold domain and determine its main characteristics. To address this objective, a comprehensive review of the literature was conducted, supplemented by a bibliometric analysis. The results confirm that GF has the potential to help society, sustainability, and the prevention to climate shifts, investing in the CE. There are many hurdles to overcome, including inadequate knowledge about CE and GF, ambiguous definitions, a lack of coherence between legal frameworks on CE and green financing, unclear laws, and a lack of financially viable motivation for investors and financial institutions that are ready to promote in sustainability. This study explores CE and GF domains. Managers may readily increase their understanding of methods, strategies, and technical solutions beneficial to assist their operations toward a green economy depending on various CE and GF elements. Finally, based on a categorization of GF types, the assessment identifies future investment potential consequences of green financing in the CE.

Spatiotemporal consistency effect of green finance on pollution emissions and its geographic attenuation process.

Improving the ecological quality is one of the essential goals of green finance, while interaction between green finance and environmental development has not reached an agreement yet. Using the panel data of 30 provinces from 2010 to 2020, based on the advantage of PTRM-SPDM (Spatial Panel Threshold Durbin Model), this study empirically analyses the relationship between green finance and pollutants emissions from the perspective of the spatiotemporal combination process. After confirming the characteristics of nonlinear constraints and spatial spillovers between green finance and pollutants emissions, the spatiotemporal consistency effects are explored, and the geographic attenuation process, including spatial spillover boundary and influencing dense areas, is also calculated. Results show that: (1) with the development of green finance in China, the environmental effects of green finance transfer gradually from local to adjacent areas, the unit elasticity of green finance decreases by 0.15, 0.19, and 0.22 on emissions of industrial sulfur dioxide, wastewater, and dust in local areas, respectively, while increases by 0.64, 0.02, and 0.82 in neighbouring regions correspondingly. (2) from the evolution trend of the peak value of Kernel density, it could be captured that the overall degree improves and the regional gap narrows for China's green finance in the past decade. (3) the effects of green finance on pollutants emissions merely exist in areas with a radius of about 1500-1750 km and reach the peak in the vicinity of around 1450-1750 km.

The role of green finance and energy innovation in neutralizing environmental pollution: Empirical evidence from the MINT economies.

Pursuing ecological sustainability while mitigating the effects of environmental pollution has become a global pursuit. Moreover, the issue of how emerging economies like Mexico, Indonesia, Turkey, and Nigeria (MINT) economies can significantly reduce environmental pollution (EVP) remains elusive. This study sought to investigate the interplay between economic growth, green finance, renewable energy use, natural resource rent, energy innovation, urbanization and environmental pollution by analyzing panel data from 1990 to 2020. This research employed the novel econometrics approach CS-ARDL to examine the short and long-term relationships among the series. The research outcome disclosed that economic growth, natural resource rent and urbanization increase environmental pollution. In contrast, the empirical findings of this study revealed that environmental pollution could be neutralized through effective mechanisms such as green finance, renewable energy consumption, and the promotion of energy innovation. This research provides a fresh insight from the MINT economies and contributes to the existing literature by examining factors contributing to environmental pollution. This research also provides a benchmark for policy-makers and governments to invest in environmentally-friendly technologies to exploit the natural resources in these countries to mitigate the effect of environmental pollution.

Promotion incentives and air pollution: From the political promotion tournament to the environment tournament.

Chinese officials play an important role in air pollution control. This paper used a sample of 282 prefecture-level cities in China to discuss the impact of promotion incentives of officials on air pollution from the perspectives of heterogeneity, mechanism and spatial effects. We found that the promotion incentives of officials reduced air pollution, and GDP per capita had positive moderating effects. The effects of promotion incentives were more significant in cities with less air pollution, in the central and western regions, for officials with higher education levels, or years after 2007. The promotion incentives could promote the development of green finance and green technology innovation, both of which were conducive to mitigating air pollution. Using the dynamic spatial Durbin model (DSDM), we found that the promotion incentives had negative spatial spillover effects. The promotion incentives in surrounding cities reduced air pollution in the local city; however, it had only short-run effects and no long-run effects.

Global low-carbon energy transition in the post-COVID-19 era.

The COVID-19 pandemic has created significant challenges for energy transition. Concerns about the overwhelming emphasis on economic recovery at the cost of energy transition progress have been raised worldwide. More voices are calling for "green" recovery scheme, which recovers the economy while not compromising on the environment. However, limited academic attention has been paid to comprehensively investigating the implications of COVID-19 for global energy transition. This study thus provides a comprehensive analysis of the dynamics between energy transition and COVID-19 around the world and proposes a low-carbon energy transition roadmap in the post-pandemic era. Using energy data from the International Energy Agency (IEA), we first summarized and reviewed the progress of energy transition prior to COVID-19. Building on prior progress, we identified the challenges for energy transition during the pandemic from the perspectives of government support, fossil fuel divestment, renewable energy production capacity, global supply chain, and energy poverty. However, the pandemic also generates opportunities for global energy transition. We hence also identified potential opportunities for energy transition presented by the pandemic from the perspectives of price competitiveness, policy implementation efficiency, and renewable energy strengths. We further provided an in-depth discussion on the impact of current worldwide economic recovery stimulus on energy transition. Based on the identified challenges and opportunities, we proposed the post-pandemic energy transition roadmap in terms of broadening green financing instruments, strengthening international cooperation, and enhancing green recovery plans. Our study sheds light on a global low-carbon energy transition framework and has practical implications for green recovery schemes in post-pandemic times.

The Spatial Heterogeneity Effect of Green Finance Development on Carbon Emissions.

This paper uses the entropy method to estimate China's green financial development from four aspects, namely, green credit, green securities, green insurance, and green investment, based on the provincial-level panel data from 2008 to 2019. The spatial Durbin model (SDM) is adopted to estimate the spatial effect of green finance on carbon emissions. We then compare the heterogeneous effect in the South and North of China. The results show that China's green financial development can significantly reduce carbon emissions, and regional heterogeneities are obvious. In the South of China, this effect from local and adjacent regions is not significant, while on the whole, green finance can significantly reduce carbon emissions; but for Northern China, this effect is not significant; nationally, the development of green finance and carbon emissions in adjacent areas showed an inverted U-shaped relationship. China's green financial development and carbon emissions also showed an inverted U-shaped relationship. These results suggest that the effect of green finance development on carbon emissions exhibits substantial regional heterogeneity in China. Our paper provides some concrete empirical evidence for policymakers to formulate green financial policies to achieve the double carbon goal in China.