Impact of Green Finance on Carbon Emissions Based on a Two-Stage LMDI Decomposition Method

Abstract

As the “double carbon” goal gains traction worldwide, achieving a balance between economic growth and environmental sustainability has become a focal point for researchers. Green finance, as a specialized financial service, offers a new approach to sustainable development. This study analyzed carbon emission sources in 25 Chinese provinces between 2007 and 2020, dividing them into energy structure, economic development, energy efficiency, and industrial structure, using a two-stage LMDI method. It then examines the linear and non-linear relationships between green finance and carbon emissions using spatial measurement methods. The findings indicate that green finance is an effective way to reduce carbon emissions, primarily through its impact on energy structure, energy efficiency, and industrial structure. There are significant non-linear relationships between green finance and the factors affecting carbon emissions, with spatial effects playing a significant role in carbon emissions influenced by energy structure, economic development, energy efficiency, and industrial structure.

1. Introduction

With the continuous development of the global economy, pollution has become an increasingly serious issue. Global warming caused by increased carbon emissions has attracted the attention of populations worldwide. Carbon emissions mainly refer to greenhouse gas emissions generated by the production, transportation, use, and recycling of a product. Low-carbon transitions are necessary to better cope with climate problems and address the contradiction between environmental and economic growth. “China will increase the intensity of its nationally determined contributions, adopt more favorable policies and measures, strive to peak carbon dioxide before 2030, and strive to achieve carbon neutrality by 2060”. Subsequently, the goals of peaking and neutralizing carbon emissions were included in China’s 14th Five-Year Plan and the 2035 Vision Goals (Zhao, 2022) [1]. This series of policies demonstrates China’s determination to encourage economic transformation and achieve green development. The setting of goals for dual carbon emissions is a sign that China wishes to address climate issues and increase carbon emission constraints on various industries in the long-term. However, the latter poses both development and challenges for enterprises. Enterprises with high energy consumption should actively maximize the efficiency of their energy structures and utilize clean energy instead of fossil fuels. Therefore, enterprises should invest more in scientific research and upgrade their technologies to enhance their energy efficiency (Li, 2022; Zhen, 2022) [2,3]. In China, it is essential that carbon dioxide emissions are balanced with economic development. At present, the use of fossil fuels is a major factor in China’s economic development. To reach the carbon peak target by 2030, it is necessary to form a reasonable arrangement between carbon dioxide emissions and economic development which may be achieved by promoting clean energy on a national scale and reducing the consumption of fossil energy. Given that the relevant green environmental protection industry has the characteristics of long cycle and high risk and low profit, accurately providing the relevant enterprises or related fields with targeted financial support has become an urgent problem for the country to solve. Green finance has become an important way of solving this problem. Green finance is an economic activity designed to support environmental improvement, respond to climate change, and economically and efficiently use resources and the environment. This is a financial service provided for project investment, operations and risk management in the fields of environmental protection, energy conservation, clean energy, green transportation, and green buildings. Green financing is a special financial service that provides a new means of environmental protection and sustainable economic development. Through the support of policies and funds, green finance helps environmental protection enterprises continue to expand, promotes the upgrading of high-pollution enterprises, promotes the optimization and upgrading of industrial structures, and serves to reduce carbon emissions.

This study considers the dual carbon target as a starting point to evaluate the impact of green finance on carbon emissions. First, using an index reflecting the development of green finance and a spatial measurement method, we investigated the relationship between green finance and carbon emissions. Carbon emission sources were then categorized into those related to energy structure, economic development, energy efficiency, and industrial structure and the linear and non-linear relationships between green finance and these four forms of carbon emissions were studied using a spatial measurement method. Finally, a test was conducted to ensure the robustness of the results. Overall, in terms of clarifying the relationship between carbon emissions and green finance, this study provides a theoretical reference for achieving carbon peak and neutrality in China in the context of green finance.

Compared with the existing literature, the marginal contributions of this study are as follows. First, this study constructs a green finance index and studies China’s carbon emissions from the perspective of green finance development, which is of great significance for the future development of green finance and carbon emissions in China. Second, this study uses a two-stage logarithmic mean Divisia index (LMDI) approach to decompose carbon emission sources into four aspects—energy structure, economic development, energy efficiency, and industrial structure—to fully study the action path through which green finance can reduce carbon emissions. Third, a semi-parametric spatial lag model was used to study the dynamic impact of green finance on carbon emissions and a significant non-linear relationship was found between green finance and carbon emissions.

2. Theoretical Analysis and Research Hypotheses

2.1. The Theoretical Analysis

Cowan (1999) formulated a comprehensive definition of green finance from an environmental finance perspective. His conception encompasses not only the financial developmental aspect but also the sustainable principles of green economics, proposing a synergy between the two realms [4]. Moreover, financing can actively contribute to sustainable and green development by bolstering environmental conservation efforts. This synthesis of ideas suggests that green finance possesses the potential to address the escalating environmental challenges. Within the existing scholarly landscape, the predominant focus has been on delineating the impact of green finance on carbon emissions, scrutinizing its influence across energy structure, economic advancement, energy efficiency, and industrial configuration. Notably, this study embarks on an examination of the green finance mechanism, delineating how it engenders a reduction in carbon emissions across these four pivotal dimensions. Foremost, through strategic policy encouragement, green finance impels enterprises to embrace clean energy alternatives—chiefly renewable sources—phasing out fossil fuels. This transformative shift in energy structure enhances the operational efficacy of enterprises, catalyzing performance upgrades while simultaneously curbing carbon emissions. Wang (2021) and Xra (2020) cogently argue that green finance’s ascendancy correlates with a decline in fossil fuel reliance, marked by a parallel surge in clean energy adoption, particularly hydropower, and culminating in a reduction in carbon dioxide emissions [5,6]. Second, the dynamic role of green finance is underscored as it fuels economic growth while serving as an exclusive financial service (Zhou, 2020) [7]. This dual facet becomes particularly pertinent in the context of China’s recent propositions concerning carbon neutrality and emission peaking. Notably, the nexus between economic advancement and carbon emissions is evident, especially when economic growth relies heavily on energy-intensive industries, invariably leading to pollution and augmented carbon dioxide emissions. Green finance steps in as a mediating factor in this interplay. Third, mirroring China’s ambitious carbon neutrality target, significant corporations must continuously curtail their carbon footprint. Achieving this mandate necessitates a sustained enhancement in energy efficiency and the curbing of fossil fuel reliance. In response, enterprises are driven to channel resources into green research and development, fostering innovation, raising technological standards, amplifying energy efficiency, and minimizing fossil fuel inputs. The research contributions of Ning (2022) and Peng (2021) substantiate this crucial evolution [8,9]. Fourth, environmental projects often confront high risk and meager returns as well as prolonged implementation timelines. This milieu is recalibrated by green finance, which not only signals government investment inclinations but also assumes a role in catalyzing market engagement. By pioneering government-led demonstrations, green finance ushers a surge of capital and corporate interest into the green sector. Parallelly, it aids environmentally-conscious enterprises in circumventing financial hurdles, fostering technology upgrades and sustained growth. As the green industry flourishes, the corresponding evolution of the industrial structure unfolds, gradually remedying the pressing concern of excessive carbon emissions. This transformation is underpinned by the research insights of Hu (2022) and Tao (2022) [10,11]. In essence, the trajectory of green finance’s impact on carbon emissions is marked by intricacies that extend across a multi-dimensional landscape. The nexus between policy intervention, economic dynamics, energy efficiency, and industrial transformation converges to underscore the transformative power of green finance in steering the course towards a greener and more sustainable future.

2.2. Literature Review and Research Hypotheses

Despite its relatively nascent stage, green finance has attracted the attention of numerous researchers delving into the intricate connection between carbon emissions and this evolving financial approach. Li (2018) undertook a nuanced dissection of carbon finance, parsing it into its constituent components, green credit, and carbon trading. Notably, Li observed conspicuous disparities in emission reductions across various regions within the ambit of carbon finance. This observation underscores the urgent need for an expanded market mechanism to trade and finance carbon emissions [12]. Chen (2021) emphasized the importance of China’s economic transformation, advocating that widespread attention be directed toward this critical endeavor. He examined the internal dynamics linking green finance and carbon emissions by deploying a spatial econometric model to unveil this intricate interplay. His findings illuminate a pathway through which the green finance sector could foster innovation in green technology and concurrently alleviate financing constraints, thereby resulting in a tangible reduction in carbon emissions [13]. Sun (2021) harnessed the power of a big data-driven machine learning approach to navigate the intersection of green finance and carbon emissions. Through meticulous analysis, Sun demonstrated a strong correlation between these two realms, thereby accentuating the pivotal role of green finance in emission reduction efforts [14]. Zhang (2022) delved into the role of green finance through the lens of the Tobit model and strategically explored its efficacy in mitigating carbon emissions. The outcome of this inquiry illuminates the paramount role of green finance in curbing carbon emissions, thus positioning it as a pivotal player in the larger emissions-reduction narrative [15]. Focusing on regional disparities, Gan (2022) injected the dimensions of mediation and threshold effects into the exploration of the relationship between carbon dioxide emissions and green finance. Gan’s intricate analysis illuminated the potential of green finance to drive emission reductions, leverage economies of scale, and spearhead innovation in tandem with green technologies. Intriguingly, the dynamics of emission reduction in response to green finance revealed a dynamic, non-linear trajectory [16]. Finally, Guo (2022) identified a unidirectional causal relationship between green finance and fertilizer application. This relationship has led to a reduction in agricultural carbon emissions. These findings add another layer of complexity to the multifaceted role that green finance plays in emissions mitigation [17]. Collectively, these studies not only highlight the evolving discourse around the interplay between green finance and carbon emissions but also underscore the critical role that green finance can play in shaping a sustainable future with reduced emission impacts.

From the above analysis, we derived the following hypothesis:

Hypothesis 1 (H1).

Carbon emissions can be reduced through green finance.

To delve deeper into the nuanced relationship between green finance and carbon emissions, this study comprehensively explored four distinct sources of carbon emissions. By scrutinizing the influence of green finance across these multifaceted dimensions, this study aims to contribute a holistic perspective to the existing discourse. Scholars within the existing body of literature have undertaken intricate investigations into this realm, thereby establishing a solid foundation for this study.

Turning our attention to the impact of green finance on energy structures, insights from Xiaoyan (2017) underscore the escalating significance of green development amid the intensification of environmental challenges. In this context, green finance has emerged as a pivotal avenue not only for addressing environmental concerns but also for fostering the optimization of energy structures—an imperative research trajectory [18]. Building on this premise, Chuanzhe (2019) meticulously dissected the mechanisms underlying the influence of green credit on energy consumption structures. Through empirical substantiation, Chuanzhe demonstrated the substantial impact of green credit on the optimization of energy consumption patterns [19]. Linjing’s (2021) findings echo the sentiment that green finance has the potential to recalibrate energy and industrial configurations. Exploration revealed that green finance can drive emission reduction through strategic structural adjustments, as demonstrated by a mediation effect model [20]. Sun (2022) further enriches this discourse by highlighting the pivotal role of green finance in redefining China’s energy consumption patterns. By using financial instruments, green finance has emerged as a potent catalyst for steering sustainable economic development. Empirical evidence indicates a positive correlation between the development of green finance and energy consumption structures. This symbiotic relationship has the potential to stimulate energy structure adjustments and mitigate greenhouse gas emissions [21]. The exploration continues as Wang’s (2021) multi-faceted analysis, encompassing multiple regression, panel regression, and spatial econometrics, and underscores how green finance emerges as an agent for optimizing energy structures and curtailing greenhouse gas emissions [22]. Delving further into the methodological realm, Zhu (2022) harnessed a spectrum of analytical tools, ranging from the Cobb–Douglas model to the spatial Durbin model and the dynamic panel threshold model. This intricate approach was devised to decipher the multifaceted effects of green finance across scale, technology, and structural dimensions. Zhu augmented our understanding of how green finance dynamically interacts with these dimensions [23]. Based on the above analysis, we propose the following hypothesis:

Hypothesis 2 (H2).

The development of green finance can reduce carbon emissions influenced by energy structure.

The exploration of the nexus between green finance and economic growth traces its origins to the discourse on environmental change and economic expansion. Ciegis (2007), for instance, leveraged the metric of per capita income to unveil a non-linear relationship between environmental pollution and economic progress, famously known as the “environmental Kuznets curve” [24]. Zhou (2020) further enriches the narrative by delving into the intricate links between green finance, environmental quality, and economic development. His analysis revealed that the adoption of green finance has the potential to ameliorate environmental concerns. However, a key finding is that surface variances in environmental quality and economic development manifest as direct outcomes of the interplay with green finance [7]. This discourse expanded when Shahbaz (2016) and Zhang (2011) delved into the symbiotic relationship between financing and economic expansion. Their studies reveal the dual nature of finance: it serves as a catalyst to expand financing avenues and propel economic development, thereby engendering larger production scales. However, this dynamic interplay can also inadvertently lead to an increase in carbon emissions, underscoring the need for the careful orchestration of financial mechanisms to ensure harmonious coexistence between growth and environmental stewardship [25,26]. Collectively, these studies underscore the multifaceted interplay between green finance, economic growth, and environmental preservation. This dynamic landscape necessitates a nuanced approach that optimally harnesses the potential of green finance to bolster economic progress while mitigating the unintended consequences of carbon emissions. From the above analysis, we derived the following hypothesis:

Hypothesis 3 (H3).

The development of green finance will increase the carbon emissions affected by economic development.

Several scholars have posited that the ambitious objective of curtailing carbon emissions can be realized through the instrumental role of green finance in influencing energy efficiency dynamics. Peng’s (2021) research is an example of the intricate interplay between economic advancement and environmental safeguarding. Peng quantified energy efficiency trajectories across diverse Chinese provinces and subsequently uncovered a compelling revelation. Through the conduit of financial development and strategic energy structure optimization, a profound enhancement in energy efficiency materializes, ultimately culminating in the laudable outcome of reduced carbon emissions [9]. This discourse widens as Rasoulinezhad’s (2022) inquiry enters the field, unearthing nuanced facets of the relationship between green finance and carbon emissions. Although the presence of a short-term causal link eludes detection, the potential of green financing to reduce carbon emissions remains an intriguing prospect [27]. Fang’s (2022) contribution introduces a global purview, traversing the energy-efficiency landscape of the G7 nations through the prism of DEA methodology. His findings illuminated the symbiotic nature of green finance and energy efficiency, further affirming their potential to reduce carbon emissions. The nexus between green finance and carbon emission reduction has emerged as the most potent pathway in this intricate web [28]. The intellectual tapestry broadens as Wang (2022), Zhang (2021), and Liu (2021) collectively explore the global panorama of carbon emissions. Their empirical exploration yielded consistent outcomes: the integration of green finance is indubitably linked to an enhancement in energy efficiency, catalyzing the transition toward a more optimized energy structure. The cumulative effect cascades into overarching aspiration: curbing carbon emissions while concurrently advancing environmental conservation [19,22,29]. This assemblage of scholarly insights illuminates the multifaceted potential of green financing. Green finance has emerged as a pivotal lever for steering the trajectory of carbon emission reduction and, in turn, championing environmental preservation. The following hypothesis was derived from the above analysis:

Hypothesis 4 (H4).

The development of green finance will reduce the carbon emissions affected by energy efficiency.

Green finance has been a focal point of scholarly investigations in the context of industrial structures. Liang’s pioneering work in 2014 laid the theoretical groundwork by delving into the requirements of low-carbon economies and their intersection with green finance. These findings underscore the pivotal role of industrial structural upgrades, technological strides within the energy sector, and enhanced energy utilization as facilitators for ushering in a low-carbon economy, albeit within certain bounds [30]. Further enriching this discourse, Wang’s 2021 insights cast a spotlight on the transformative potential of green finance across the industrial sectors. Utilizing the gray correlation method, Wang’s analysis illuminates the apex of green finance’s influence on tertiary industries. This revelation underscores green finance’s indispensable role as a driving force for steering economies toward a lush trajectory of green growth [5]. Venturing beyond borders, Huang’s 2022 investigation centered on Vietnam’s industrial structure and its symbiotic relationship with green finance. Employing the DEA method, Huang’s empirical study resonates with compelling findings: green finance and industrial structure have substantial influence, significantly bolstering the resurgence of Vietnam’s green economy [31]. This dialog continues as Gao (2022) and Liu (2019) conduct a rigorous analysis of the influence of green finance on industrial structures. Their dual-pronged approach—objective assessment and theoretical elucidation—revealed the intricate dynamics between green finance and industrial configurations. Their findings substantiate the pivotal role of green finance in catalyzing the evolution and enhancement of industrial frameworks [32,33]. Embarking on an analytical journey to discern the impact of green finance on low-carbon economies, Zhu (2022) created an intricate model constellation. The amalgamation of the Cobb–Douglas production function model, spatial Durbin model, and dynamic panel threshold model revealed resonance. Green finance, underpinned by technological innovation and strategic industrial structural optimization, has emerged as a potent conduit for realizing the paramount goal of low-carbon development [34]. In summary, this tapestry of scholarly endeavors converges to illuminate the multi-faceted dimensions of green finance’s entwinement with industrial structures. The amalgamation of theoretical exploration, empirical verification, and model-based analysis articulates the profound influence of green finance on choreographing the transition toward sustainable low-carbon economies.

Based on the above analysis, we propose the following hypothesis:

Hypothesis 5 (H5).

As green finance develops, industrial structure will have a positive impact on reducing carbon emissions.

In conclusion, while the existing body of research extensively examines and deliberates upon the influence of green finance on energy structure, economic growth, energy efficiency, and industrial configuration, several research gaps persist. First, although numerous scholars have investigated the nexus between green finance and carbon emissions, the depth of understanding of how green finance precisely shapes carbon emissions, as well as the specific facets of carbon emissions that experience reduction, remains largely unexplored. Second, an apparent hiatus exists concerning the consideration of inter-regional linkages. A prevailing tendency among scholars is to compartmentalize neighboring regions within their research, inadvertently creating divergence between empirical findings and real-world outcomes. Consequently, the intricate interplay between regions is often under-examined, warranting further attention. Finally, the intricate landscape of the variable relationships often adheres to non-linear trajectories. Although a linear relationship is frequently assumed, reality is often characterized by intricate non-linear dynamics. This gap underscores the need to investigate non-linear relationships as they are integral to a comprehensive understanding of the subject matter. Building on these identified gaps, this study embarks on a journey to fill these voids. By examining the aforementioned aspects, the intent is to unravel the multifaceted and nuanced impacts of green finance on carbon emissions. This study seeks to contribute to a more holistic understanding of the interplay between green finance and carbon emissions, accounting for the intricate variables and linkages that shape this complex relationship.

3. Research Methods and Variable Selection

3.1. Two-Stage LMDI Method

With increasing research on carbon emissions, the use of methods for decomposing the influencing factors in the field of energy has gradually become a key research approach for scholars worldwide. In the existing literature, factor decomposition (SDA) and exponential factor analysis (IDA) methods have mainly been used to decompose carbon emissions into energy intensity, energy structure, output, and population factors. Among them, the LMDI method, which is an exponential factor decomposition approach that can effectively solve for zero values and residuals in the decomposition process, has been widely used. The LMDI was originally used to decompose the causes of economic growth into multiple factors and to provide the relationship(s) between two or more factors. This is an important statistical analysis method. Subsequently, Kaya Y (1989) proposed a carbon emission decomposition formula and introduced the LMDI method to study carbon emissions [35]. However, the LMDI model cannot decompose more than two structural factors simultaneously. For example, it cannot decompose energy and industrial structures simultaneously. To overcome this problem, a two-stage LMDI method was adopted in this study with reference to Guan (2022) to decompose carbon emissions with respect to energy structure and industrial structure factors. Therefore, this study uses the two-stage LMDI method to decompose the influencing factors of carbon emissions and study the relationships between green finance and carbon emissions from a deeper perspective [36].

According to the carbon emission formula proposed by Kaya Y:

$$\frac{C}{P}=\frac{C}{E}\times \frac{E}{Y}\times \frac{Y}{P}=\frac{C}{E}\times \frac{E}{Y}\times \frac{Y}{P}$$

(1)

where $C$, $E$, $Y$, and $P$ represent the carbon dioxide emissions, disposable energy consumption, GDP, and population, respectively. The per capita carbon emission is $\frac{C}{P}=\frac{C}{E}\times \frac{E}{Y}\times \frac{Y}{P}\frac{C}{P}=\frac{C}{E}\times \frac{E}{Y}\times \frac{Y}{P}$ and the energy emission intensity is $e=\frac{C}{E}={\displaystyle \sum _i\frac{E^i}{E}}\times \frac{C_i}{E_i}={\displaystyle \sum _{i}S{E}_i\times e_i}$ where $S{E}_i$ represents the emissions per unit of energy consumed and $e_i$ represents the carbon emission coefficient of the $i^{th}$ energy source. The energy efficiency is $g=\frac{E}{Y}={\sum }_j\frac{E_j}{Y_j}\times \frac{Y_j}{Y}={\sum }_j{g}_j\times S{Y}_{j}g=\frac{E}{Y}={\sum }_j\frac{E_j}{Y_j}\times \frac{Y_j}{Y}={\sum }_j{g}_j\times S{Y}_j$ which denotes energy consumption per unit of GDP where $g_j$ represents the energy intensity of the $J^{th}$ industry and $S{Y}_j$ represents the ratio of the $J^{th}$ industry in GDP. The economic development factor denotes per capita GDP and factors of economic development where $R=\frac{Y}{P}$ denotes GDP per capita.

Therefore, the per capita carbon emissions formula can be expressed as $f=e\times g\times p=({\sum }_{i}S{E}_i\times e_i)\times ({\sum }_i{g}_i\times S{Y}_j)\times R$ where $S{E}_i$ represents the change in energy structure, $e_i$ represents the change in energy emission intensity, $g_j$ represents the change in energy efficiency, $S{Y}_j$ represents the change in industrial structure, and $R$ represents the change in economic development.

According to the two-stage LMDI factor decomposition method, carbon dioxide emissions were decomposed into the following parts:

$$\Delta f=f^t-f^0=\Delta f_{SE}+\Delta f_g+\Delta f_{SY}+\Delta f_R+\Delta f_e$$

(2)

$$\Delta f_{SE}={\displaystyle \sum _i}p(f_{i,0},f_{i,t})\mathit{ln}(\frac{S{E}_i^t}{S{E}_i^0})$$

(3)

$$\Delta f_R={\displaystyle \sum _i}p(f_{i,0},f_{i,t})\mathit{ln}(\frac{R^t}{R^0})$$

(4)

$$\Delta f_g={\displaystyle \sum _i}p(f_{i,0},f_{i,t})\mathit{ln}(\frac{g_j^t}{g_j^0})$$

(5)

$$\Delta f_{SY}={\displaystyle \sum _i}p(f_{i,0},f_{i,t})\mathit{ln}(\frac{S{Y}_j^t}{S{Y}_j^0})$$

(6)

$$p(f_{i,0}-f_{i,t})=\left\{{}_{\begin{array}{c}\\ \\ f_{i,0}{f}_{i,0}=f_{i,t}\end{array}}^{\frac{f_{i,0}-f_{i,t}}{\mathit{ln}(f_{i,0}/f_{i,t})}}\right.$$

(7)

where $\Delta f_{SE}$ is the energy structure factor, $\Delta f_g$ is the energy efficiency factor, $\Delta f_{SY}$ is the industrial structure factor, and $\Delta f_R$ is the economic development factor.

3.2. Spatial Measurement

Moran’s I is a key index used to examine the green finance development-related spillover effects in space. The null hypothesis is that there is no spatial autocorrelation that exists between green finance development in the Chinese provinces. When this hypothesis is not true, green finance development is spatially correlated among Chinese provinces. Moran’s I generally assumes a value in the range of [−1, 1]. A value greater than zero indicates a positive correlation whereas a value less than zero indicates a negative correlation. When positive Moran’s I was larger the correlation was larger whereas the correlation was smaller when negative Moran’s I was smaller. Moran’s I was calculated using Equations (16) and (17):

$$I=\frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}(x_{ij}-\overline{X})(x_j-\overline{X})}{S^2{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}}$$

(8)

$$S^2=\frac{{\sum }_{i=1}^n(x_i-\overline{X}{)}^2}{n}$$

(9)

where $w_{ij}$ is the spatial weight matrix and $S^2$ is the sample variance.

To determine whether green finance development is heterogeneous or correlated between provinces, it was also necessary to calculate the local Moran’s I index and display the local Moran’s I scatter plot. The local Moran’s I was calculated using Equation (18):

$$I_i=\frac{(x_i-\overline{X}){\sum }_{i=1}^n{W}_{ij}(x_j-\overline{X})}{S^2{\sum }_{i=1}^n{\sum }_{j=1}^n{W}_{ij}}$$

(10)

If $I_i$ is positive, it means that the province and its neighboring provinces have the same trend; that is, either high development is clustered together or low development is clustered together. If $I_i$ is negative, there is no similar development trend among neighboring provinces, that is, it illustrates spatial heterogeneity where low-development provinces are clustered with high-development provinces. A positive Moran’s I value indicated a positive spatial correlation. A negative Moran’s I value indicates a negative spatial correlation. If Moran’s I is zero, then no spatial correlation exists.

3.3. Variable Selection

3.3.1. Explained Variables

Carbon emissions (CEN), for which there are no official statistics, were used as explanatory variables. As the main cause of carbon dioxide emissions is the consumption and combustion of fossil fuels, the coefficient or material balance method is generally used to measure carbon dioxide emissions. This study uses the carbon emission coefficient method to measure carbon emissions levels. The formula for the carbon emissions coefficient method is $Q={\sum }_{i=1}{E}_i\times S{C}_i\times C{F}_i$, where $Q$ is the total carbon emissions, $E_i$ is the consumption of energy source i, $S{C}_i$ is the conversion coefficient with respect to standard coal, and $C{F}_i$ is the carbon emission coefficient of the i^th energy source. The carbon emissions and standard coal conversion coefficients were derived from the China energy statistical yearbook.

To further examine the impact of green finance on carbon emissions, this study decomposes carbon dioxide emissions into those related to energy structure (ESE), economic development (EDT), energy efficiency (EEN), and industrial structure (ISE). By studying the four factors that affect carbon dioxide emissions, we can assess whether green finance can reduce carbon dioxide emissions.

3.3.2. Explanatory Variables

The number of articles on the construction of green finance development indicators is low and those that exist lack comprehensive systematic evaluations, with evaluation indicators presenting “fragmentation” characteristics. As a special financial service, green finance provides services for projects in the fields of energy conservation, environmental protection, clean energy, transportation, and buildings. Presently, the development of green finance is primarily reflected in issues such as green credit, securities, investment, insurance, and carbon finance. Based on the principle of highlighting the scope of green finance services, this study combined the main components of green finance in China with reference to Zhou (2020) [25]. Thus, indicators were selected for the calculation.

Table 1. Indicator system for assessment of green finance development level in China.

Level Indicators	The Secondary Indicators	Level 3 Indicators	Index Definition	Index Attribute
Level of green finance development	Green credit	Proportion of interest from energy-intensive industries	High energy consumption industrial interest/industrial interest	-
	Green securities	Share of market capitalization of energy-intensive industries	Six high-energy consumption A stock market values/total market value of A shares	-
	Green investment	Proportion of investment in environmental pollution	Investment in pollution control/GDP	+
	Green insurance	Agricultural insurance size ratio	Agricultural insurance income/gross agricultural output	+
	Carbon finance	Carbon intensity	Carbon dioxide emissions/GDP	-

provides a list of specific indicators.

3.3.3. Control Variables

R and D investment (RDI), fiscal expenditure (LGV), economic openness (LOP), and economic development degree (LGP) were selected as the control variables. Although these factors do not directly affect carbon dioxide emissions, they affect the economic development level, technological progress, and industrial structure upgrading, thus indirectly affecting carbon dioxide emissions. Specific control variables were constructed as illustrated in

Table 2. Control variables.

Variable Name	Variable Meaning	Construction Method
RDI	R and D funding input	Log of R and D spending
LGV	Fiscal spending	Log of government fiscal expenditure
LOP	Economic openness	Log of total imports and exports by province and city
LGP	Degree of economic development	Log of GDP per capita

.

3.4. Model Setting

The development of Chinese cities is often unbalanced and green financial development tends to be clustered, presenting strong spatial clustering characteristics. Green financial development is often high in the eastern region whereas it is often low in the western and central regions. Therefore, to better understand the impact of green finance on carbon dioxide emissions, this study introduces a spatial econometric model. According to the test results, this study used the spatial Durbin model and the specific model was set as follows (11)–(15):

$$\begin{array}{cc}{\mathit{CEN}}_{it}\hfill & ={\alpha }_1{W}_{i}GF{I}_i{}_t+{\alpha }_2{W}_{i}RD{I}_{it}+{\alpha }_3{W}_{i}LG{V}_{it}\hfill \\ & +{\alpha }_4{W}_{i}LO{P}_{it}+{\alpha }_5{W}_{i}LG{P}_t+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(11)

$$\begin{array}{cc}ES{E}_{it}\hfill & ={\alpha }_1{W}_{i}GF{I}_i{}_t+{\alpha }_2{W}_{i}RD{I}_{it}+{\alpha }_3{W}_{i}LG{V}_{it}\hfill \\ & +{\alpha }_4{W}_{i}LO{P}_{it}+{\alpha }_5{W}_{i}LG{P}_t+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(12)

$$\begin{array}{cc}ED{T}_{it}\hfill & ={\alpha }_1{W}_{i}GF{I}_i{}_t+{\alpha }_2{W}_{i}RD{I}_{it}+{\alpha }_3{W}_{i}LG{V}_{it}\hfill \\ & +{\alpha }_4{W}_{i}LO{P}_{it}+{\alpha }_5{W}_{i}LG{P}_t+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(13)

$$\begin{array}{cc}EE{N}_{it}\hfill & ={\alpha }_1{W}_{i}GF{I}_i{}_t+{\alpha }_2{W}_{i}RD{I}_{it}+{\alpha }_3{W}_{i}LG{V}_{it}\hfill \\ & +{\alpha }_4{W}_{i}LO{P}_{it}+{\alpha }_5{W}_{i}LG{P}_t+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(14)

$$\begin{array}{cc}IS{E}_{it}\hfill & ={\alpha }_1{W}_{i}GF{I}_i{}_t+{\alpha }_2{W}_{i}RD{I}_{it}+{\alpha }_3{W}_{i}LG{V}_{it}\hfill \\ & +{\alpha }_4{W}_{i}LO{P}_{it}+{\alpha }_5{W}_{i}LG{P}_t+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(15)

$${\epsilon }_{it}=\lambda W_i{\epsilon }_t+v_{it}$$

where $u_i$ represents the individual effect, ${\gamma }_i$ is the time effect, $\lambda$ is the spatial autocorrelation coefficient, ${\alpha }_1\cdots {\alpha }_5$ are the regression coefficients, $W_i$ is the spatial weight matrix, and ${\epsilon }_{it}$ and $v_{it}$ are the error terms. $CEN, ESE, EDT, EEN \mathrm{and} \mathit{ISE}$ represents total carbon emissions, energy structure carbon emissions, economic development carbon emissions, energy efficiency carbon emissions, and industrial structure carbon emissions, respectively.

To better study the non-linear relationships between green finance and the energy structure, economic development, energy efficiency, and industrial structure, a semi-parametric spatial lag model was set as follows (16)–(19):

$$\begin{array}{cc}ES{E}_{it}\hfill & =\delta {\displaystyle \sum _{j\ne i}}{W}_{i}ES{E}_{it}+g(GFI)+{\alpha }_{1}RD{I}_i{}_t+{\alpha }_{2}LG{V}_{it}+{\alpha }_{3}LO{P}_{it}\hfill \\ & +{\alpha }_{4}LGP+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(16)

$$\begin{array}{cc}ED{T}_{it}\hfill & =\delta {\displaystyle \sum _{j\ne i}}{W}_{i}ED{T}_{it}+g(GFI)+{\alpha }_{1}RD{I}_i{}_t+{\alpha }_{2}LG{V}_{it}+{\alpha }_{3}LO{P}_{it}\hfill \\ & +{\alpha }_{4}LGP+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(17)

$$\begin{array}{cc}EE{N}_{it}\hfill & =\delta {\displaystyle \sum _{j\ne i}}{W}_{i}EE{N}_{it}+g(GFI)+{\alpha }_{1}RD{I}_i{}_t+{\alpha }_{2}LG{V}_{it}+{\alpha }_{3}LO{P}_{it}\hfill \\ & +{\alpha }_{4}LGP+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(18)

$$\begin{array}{cc}IS{E}_{it}\hfill & =\delta {\displaystyle \sum _{j\ne i}}{W}_{i}IS{E}_{it}+g(GFI)+{\alpha }_{1}RD{I}_i{}_t+{\alpha }_{2}LG{V}_{it}+{\alpha }_{3}LO{P}_{it}\hfill \\ & +{\alpha }_{4}LGP+u_i+{\gamma }_i+{\epsilon }_{it}\hfill \end{array}$$

(19)

where $\delta$ is the spatial lag utility coefficient and $g\left(GF{I}_{it}\right)$ is the non-parametric part, $CEN, ESE, EDT, EEN \mathrm{and} \mathit{ISE}$ represent the total carbon emissions, energy structure carbon emissions, economic development carbon emissions, energy efficiency carbon emissions, and industrial structure carbon emissions, respectively. The non-linear relationships between green finance and energy structure, economic development, energy efficiency, and industrial structure can be obtained by taking the partial derivatives of these formulas.

4. Empirical Analysis

4.1. Data Sources

The data for this study were collected from the statistical yearbooks of Chinese provinces and cities from 2007 to 2020. The data were processed as follows: (1) considering the absence of data, Hong Kong, Macao, Taiwan, Xinjiang, Ningxia, Guangxi, Hainan, Tibet, and Inner Mongolia were excluded and 25 provinces and cities in China were selected for the research; (2) in the event of partially missing data, the data were supplemented using linear interpolation; and (3) to ensure the comparability of data, energy conversion coefficients were used in the China energy statistical yearbook convert different types of energy into standard coal. The descriptive statistics of the data are provided in

Table 3. Descriptive statistics of variables.

	Variable	Definition	Sample	Average	Standard Deviation	Minimum	Maximum
Explained variable	CEN	Carbon emission level	350	3.625	0.261	2.715	4.153
	ESE	Energy Mix Factors	350	83.424	71.772	386.373	1.314
	EDT	Economic Development factor	350	52.007	43.340	1.284	216.780
	EEN	Energy efficiency Factor	350	568.306	474.419	30.297	2158.516
	ISE	Industrial structure factors	350	197.656	138.550	19.142	641.24.1
Explaining variable	GFI	Green Finance Development Index	350	0.415	0.078	0.201	0.662
Control variable	RDI	R and D funding input	350	0.017	0.011	0.003	0.075
	LGV	Fiscal spending	350	3.562	0.307	2.183	4.242
	LOP	Economic openness	350	2.693	0.702	0.518	4.038
	LGP	Degree of economic development	350	4.602	0.261	3.896	5.217

Data source: Chinese statistical years and China energy statistical yearbook.

:

4.2. Factor Analysis

According to the decomposition of the two-stage LMDI model, the results for energy structure, economic development, energy efficiency, and industrial structure are presented in

Table 4. Two-stage factor decomposition by region of China in 2007.

Province	Total Carbon Emissions	Energy Structure	Economic Development	Energy Efficiency	Industrial Structure
Beijing	203.476	−3.446	3.828	136.670	66.424
Tianjin	252.839	−2.496	1.914	184.392	69.029
Hebei	1495.158	−18.106	10.994	1177.366	324.904
Shanxi	892.018	−22.207	4.536	541.138	368.551
Liaoning	1103.831	−18.675	11.831	802.527	308.148
Jilin	486.365	−7.181	2.452	378.166	112.928
Heilongjiang	740.175	−8.419	4.823	572.275	171.496
Shanghai	363.436	−5.881	8.137	230.373	130.807
Jiangsu	1242.764	−18.463	10.865	966.927	283.435
Zhejiang	747.086	−12.861	4.343	564.523	191.081
Anhui	590.834	−7.382	2.921	471.467	123.828
Fujian	303.956	−5.207	2.417	223.567	83.179
Jiangxi	265.568	−4.964	2.094	200.785	67.653
Shandong	2245.685	−34.073	15.376	1794.213	470.169
Henan	1575.895	−18.998	0.746	1304.663	289.484
Hubei	575.412	−8.674	2.363	442.045	139.678
Hunan	567.048	−10.711	7.956	434.957	134.846
Guangdong	765.754	−17.136	8.920	519.986	253.984
Chongqing	227.937	−2.566	0.547	178.053	51.903
Sichuan	552.219	−8.987	3.010	437.306	120.890
Guizhou	209.635	−8.927	1.116	93.574	123.872
Yunnan	206.043	−6.913	3.008	103.332	106.616
Shaanxi	464.825	−9.401	−1.285	357.333	118.178
Gansu	305.635	−6.017	4.236	223.928	83.488
Qinghai	53.835	−1.314	0.021	35.985	19.143

and

Table 5. Two-stage factor decomposition by region of China in 2020.

Province	Total Carbon Emissions	Energy Structure	Economic Development	Energy Efficiency	Industrial Structure
Beijing	90.949	−26.161	38.523	30.298	48.289
Tianjin	245.536	−43.237	38.427	168.019	82.327
Hebei	1681.219	−220.68	162.13	1343.988	395.781
Shanxi	1495.24	−385.329	82.225	1157.103	641.241
Liaoning	1162.073	−205.367	124.576	858.52	384.344
Jilin	529.63	−76.801	63.292	422.541	120.598
Heilongjiang	868.605	−101.829	62.737	713.367	194.33
Shanghai	348.011	−69.79	76.496	201.115	140.19
Jiangsu	1465.606	−244.869	181.651	1173.035	355.789
Zhejiang	800.868	−142.844	112.442	608.546	222.724
Anhui	781.174	−145.493	84.908	663.097	178.662
Fujian	403.543	−93.444	54.269	318.752	123.966
Jiangxi	320.021	−70.677	45.788	253.871	91.039
Shandong	2625.227	−386.373	216.78	2158.516	636.304
Henan	1363.189	−188.545	187.424	1073.935	290.375
Hubei	577.337	−137.276	108.104	441.635	164.874
Hunan	575.095	−122.897	111.641	437.406	148.945
Guangdong	927.779	−171.361	146.509	645.893	306.738
Chongqing	161.984	−49.413	39.168	110.678	61.551
Sichuan	475.808	−109.486	87.071	361.674	136.549
Guizhou	218.145	−133.88	107.499	99.437	145.089
Yunnan	162.61	−89.899	74.954	62.897	114.658
Shaanxi	1023.016	−171.669	55.551	924.777	214.357
Gansu	389.315	−69.085	48.409	307.589	102.402
Qinghai	70.809	−16.458	11.145	52.087	24.035

:

(1)

Contribution analysis of energy structure

To optimize and upgrade the energy industry structure, China has formed an energy consumption system based on coal consumption, complementing various energy sources. The proportion of low-carbon and clean energy sources in the total energy consumption has been increasing and optimizing the energy structure has played a positive role in reducing emissions. Table 4 and Table 5 show that the energy structure can effectively reduce carbon emissions and that the effect of the energy structure on carbon emission reduction has improved over time. Taking Beijing as an example, the driving contribution of the energy structure to emissions reduction in 2007 was 3.446 whereas that of the energy structure to emissions reduction in 2020 was 26.161. Thus, the contribution of the energy structure to emission reduction has strengthened over time. In conclusion, the energy structure is of great significance for reducing carbon emissions and the optimization of the energy consumption structure, which is emphasized by the industrial structure, is expected to be a valuable method for reducing energy emissions in China.

(2)

Contribution share analysis of economic development

According to the decomposition results in Table 4 and Table 5, economic development is a driving force of per capita carbon emissions, the effects of which have been increasing. Economic development has increased the amount of carbon dioxide emitted per person over time, demonstrating a positive correlation. China’s economic development still depends on the extensive use of fossil fuels. However, according to the observed data, the effect of the growth rate of economic development on the increase in per capita carbon emissions is rapidly decreasing and China’s carbon emissions have appeared to reach a peak, trending toward a stable period and an inflection point.

(3)

Contribution share analysis of energy efficiency

According to the decomposition results in Table 4 and Table 5, carbon emissions per capita increased significantly as energy consumption per unit GDP increased while a decrease in energy consumption per unit GDP reduced per capita carbon emissions. The data indicate that, since 2007, technological progress and industrial structure upgrades have effectively improved energy utilization efficiency, contributing significantly to reducing emissions. However, according to the results, energy efficiency emission reductions peaked in most provinces and then started to decline. Therefore, the emissions reduction effect based on the energy-saving transformation of industrial sectors and increased efficiency are decreasing.

(4)

Contribution share analysis of industrial structure

The industrial structure in China is presently dominated by energy-intensive industries. With China’s economic transformation and industrial structure upgrading, the proportion of tertiary industries has increased and the industrial structure has changed. According to the data, the contribution of the industrial structure is positive at the regional level where the contributions of most industrial structures have reached a peak and then declined. This indicates that the change in industrial structure promoted per capita carbon emissions in the past period but the continuous optimization and upgrading of the industrial structure decreased the positive contribution share of the industrial structure, which also indicates that the upgrading and transformation of China’s industrial structure has begun to achieve certain desirable effects.

4.3. Results of the Green Finance Development Index

Based on the improved entropy weight method (Li and Wang (2022)) [12,37],

Table 6. Green finance development index for Chinese provinces from 2007 to 2020.

	2007	…	2017	2018	2019	2020
Beijing	0.287	…	0.759	0.748	0.793	0.839
Tianjin	0.148	…	0.291	0.331	0.353	0.376
Hebei	0.068	…	0.138	0.150	0.161	0.172
Shanxi	0.110	…	0.143	0.141	0.145	0.149
Liaoning	0.103	…	0.166	0.188	0.197	0.207
Jilin	0.078	…	0.144	0.142	0.147	0.152
Heilongjiang	0.081	…	0.134	0.138	0.142	0.147
Shanghai	0.165	…	0.334	0.354	0.377	0.403
Jiangsu	0.122	…	0.289	0.319	0.336	0.353
Zhejiang	0.126	…	0.301	0.322	0.339	0.356
Anhui	0.066	…	0.159	0.165	0.173	0.181
Fujian	0.100	…	0.214	0.214	0.224	0.234
Jiangxi	0.068	…	0.143	0.152	0.162	0.173
Shandong	0.115	…	0.240	0.267	0.285	0.305
Henan	0.077	…	0.166	0.174	0.186	0.199
Hubei	0.084	…	0.198	0.190	0.198	0.207
Hunan	0.075	…	0.176	0.188	0.202	0.219
Guangdong	0.176	…	0.395	0.384	0.402	0.421
Chongqing	0.097	…	0.205	0.202	0.211	0.220
Sichuan	0.096	…	0.193	0.202	0.215	0.228
Guizhou	0.075	…	0.149	0.141	0.147	0.152
Yunnan	0.070	…	0.142	0.135	0.140	0.145
Shaanxi	0.090	…	0.199	0.208	0.218	0.227
Gansu	0.080	…	0.155	0.146	0.152	0.158
Qinghai	0.075	…	0.138	0.144	0.151	0.158

and Figure 1 illustrate the indicators of green financial development for each Chinese province. In particular, the indicators of green financial development in each Chinese province show an increasing trend over time. However, according to the green finance indices, different regions in China have different levels of green finance development. The initial level of green financial development is relatively high in Shanghai, Beijing, Guangdong, Jiangsu, and Zhejiang, with a continually increasing trend over time. Meanwhile, Qinghai, Guizhou, Yunnan, Gansu, and Jiangxi have relatively low levels of green financial development and relatively slow growth rates. From a regional perspective, the initial level of green finance development in the eastern region was relatively high and a fast growth rate was observed; whereas, the initial level of the green finance development index in the western and northeastern regions was low and low growth rates were observed.

4.4. Empirical Results of Green Finance on Carbon Emissions

4.4.1. Spatial Autocorrelation Test

A spatial autocorrelation test should be conducted first when performing a spatial econometric regression. If the explanatory and explained variables show spatial autocorrelation, the use of a spatial econometric model is possible; if there is no spatial autocorrelation between the explanatory and explained variables, the use of a spatial econometric model is not possible. In general, a spatial autocorrelation test is performed using Moran’s I index. A Moran’s I value greater than zero indicates a positive correlation between neighboring regions whereas a value less than zero indicates a negative correlation between neighboring regions. The Moran’s I results are presented in

Table 7. Spatial autocorrelation test between green finance and carbon emissions.

Year	Green Finance	Carbon Emissions
2007	0.115 * (0.119)	0.218 ** (0.140)
2008	0.137 * (0.119)	0.191 ** (0.140)
2009	0.153 * (0.120)	0.186 * (0.141)
2010	0.179 ** (0.120)	0.162 * (0.140)
2011	0.197 ** (0.122)	0.189 * (0.140)
2012	0.185 ** (0.121)	0.147 * (0.141)
2013	0.176 ** (0.121)	0.130 * (0.142)
2014	0.182 ** (0.118)	0.138 * (0.142)
2015	0.171 ** (0.116)	0.124 * (0.143)
2016	0.144 ** (0.111)	0.128 * (0.143)
2017	0.118 * (0.107)	0.118 * (0.143)
2018	0.185 ** 0.114)	0.142 ** (0.144)
2019	0.193 ** (0.114)	0.137 * (0.144)
2020	0.202 ** (0.115)	0.144 * (0.144)

t statistics in parentheses * p < 0.1, ** p < 0.05.

. From 2007 to 2020, Moran’s I was positive and passed the significance test, indicating significant spatial spillover effects on carbon emissions and green finance in all the provinces and cities of China. Therefore, spatial measurements can be used to study the associated relationships. As shown in Figure 2 and Figure 3, the green finance and carbon emissions of various provinces and cities are mainly distributed in the first and third quadrants, showing high and low clustering phenomena. Similar to the results of the global Moran index test, this showed a positive promoting effect. Jiangsu, Zhejiang, Shanghai, Beijing, and other provinces and cities exhibited significant characteristics of high-value agglomeration. These high-value agglomeration areas are the most active areas in China’s economic development; the financial starting point is high and the development is early, which also leads to the characteristics of high agglomeration of green finance and carbon emissions. In general, the eastern region has a higher level of green finance development and carbon emissions while the central and western regions have lower levels of green finance and carbon emissions.

To use a spatial econometric model, a spatial weight matrix must first be determined. In this study, the 0–1 matrix was selected as the spatial weight matrix which takes a value of one if two provinces are adjacent and zero otherwise. The spatial error, spatial lag, and spatial Durbin models are standard spatial econometric models. To determine which spatial metrology model should be used, this study uses the Lagrange multiplier test (LM test), likelihood ratio test (LR test), and Wald test. The test results are shown in

Table 8. Results of the spatial econometric test.

Statistic	Model (1)
	Statistic	p Values
LM (error)	374.686 ***	0.000
R-LM (error)	367.652 ***	0.000
LM (lag)	10.313 ***	0.001
R-LM (lag)	3.278 ***	0.070
Wald_lag	34.520 ***	0.000
Wald_error	35.040 ***	0.000
LR_lag	36.470 ***	0.000
LR_error	36.330 ***	0.000
Hausman	45.270 ***	0.000
Joint significance test (ind)	93.090 ***	0.000
Joint significance test (time)	933.670 ***	0.000

t statistics in parentheses *** p < 0.01.

. The LM test results are significant at the 10% level, indicating that the spatial lag and spatial correlation effects exist simultaneously; therefore, the spatial Durbin model should be chosen. The LR test results were significant at the% level, indicating that the spatial Durbin model was superior to the spatial lag and spatial error models. The Wald test results were significant at the 1% level, indicating that the spatial Durbin model cannot degenerate into a spatial error model or a spatial lag model. In summary, this study used the spatial Durbin model. Finally, using the Hausmann and joint significance tests, spatial Durbin models of individual fixed effects, time fixed effects, and double fixed effect were established, respectively. From the test results, both the Hausmann and joint significance tests are significant at the 1% level. Therefore, this study uses the individual time fixed spatial Durbin model.

4.4.2. Regression Results of Green Finance on Carbon Emissions

Table 9. Regression results of green finance on carbon emissions.

Variable	Model (1)
L.GFI	0.709 *** (0.036)
GFI	0.361 *** (0.073)
RDI	3.608 *** (1.141)
LGV	0.033 (0.021)
LOP	0.010 (0.019)
LGP	0.071 ** (0.028)
Spa-rho	0.193 *** (0.066)
R2	0.990

t statistics in parentheses ** p < 0.05, and *** p < 0.01.

presents the results of the spatial econometric regression of green finance with respect to carbon emissions. The results are summarized as follows.

(1) Green finance positively correlates with carbon emissions. When green finance increases by 1%, carbon emissions decrease by 0.361%, which passes the significance test and is consistent with the theoretical analysis above. As a special financial service, green finance provides financing for environment-friendly enterprises and guides funds from high- to low-polluting enterprises. The inflow of funds enables enterprises to make technological progress and undergo industrial transformation, ultimately, achieving their goal of reducing carbon emissions. Thus, H1 is validated;

(2) Carbon emissions and R and D investment were negatively correlated. When R and D investment increases by 1%, carbon emissions decrease by 3.608%, which is significant at the 1% level. Investment in R and D funds is closely related to advanced levels of production technology. A high level of R and D investment can promote industrial upgrading and technological optimization, thereby indirectly achieving the goals of reducing pollution and carbon emissions;

(3) Fiscal expenditure and carbon emissions were positively correlated. When fiscal expenditure increases by 1%, carbon emissions increase by 0.071%. A possible reason for this is that China is in a stage of rapid economic development and most of the government’s fiscal expenditure is used for economic construction and industrial development. Investment in these funds will inevitably affect the environment and carbon emissions will increase as a result.

In summary, the spatial econometric regression results for green finance on carbon emissions in Table 9 show a clear correlation. There is a positive correlation between green finance and carbon emissions; that is, there is a negative relationship between the growth of green finance and the reduction in carbon emissions which is consistent with the previous theoretical analysis. This result validates the role of green finance in directing capital to environmentally friendly enterprises and promoting technological progress and industrial transformation. Simultaneously, there is a negative correlation between carbon emissions and R and D investment, indicating that R and D investment can indirectly reduce carbon emissions through technological innovation. However, there is a positive correlation between fiscal expenditure and carbon emissions which may be related to China’s current rapid economic development and the main economic construction and industrial development orientation of government fiscal expenditure. Therefore, to achieve more development goals, more attention needs to be paid to environmental protection and carbon reduction factors in fiscal expenditures.

4.4.3. Spatial Econometric Regression Results Based on Two-Stage LMDI

Green financing provides financial services to environment friendly and energy-saving enterprises and sectors through financial innovation to reduce carbon dioxide emissions. To better understand the impact mechanism of green finance on carbon emissions reduction, this study divides per capita carbon emissions into energy structure, economic development, energy efficiency, and industrial structure factors. When performing spatial panel econometric analysis, the existence of spatial autocorrelation should first be considered. When energy structure, economic development, energy efficiency, and industrial structure were used as explanatory variables, Moran’s I index was significant at the 1% level and the positive spillover effects were significant. The LR, LM, WLAD, Hausman, and joint significance test statistics were all significant at the 10% level. Therefore, the spatial Durbin model with dual fixed time effects was selected for analysis and the results are presented in

Table 10. Spatial econometric regression results based on two-stage LMDI.

	(2)	(3)	(4)	(5)
GFI	−122.267 *** (39.804)	17.419 (2113.027)	−338.543 ** (143.381)	−147.566 *** (40.325)
RDI	12.798 (625.824)	2113.027 *** (485.997)	4528.245 ** (2264.006)	955.758 (636.034)
LGV	7.454 (36.395)	12.847 (9.036)	39.420 (41.928)	20.236 * (11.787)
LOP	36.395 *** (10.665)	5.774 (8.251)	63.713 * (38.272)	35.952 *** (10.840)
LGP	74.948 *** (15.207)	36.933 *** (11.896)	57.788 (55.050)	46.319 *** (15.494)
Spa-rho	0.566 *** (0.0057)	0.170 ** (0.0768)	0.129 (0.114)	0.177 ** (0.077)
R2	0.414	0.043	0.05	0.049

t statistics in parentheses * p < 0.1, ** p < 0.05, and *** p < 0.01.

.

According to the spatial measurement results in Table 10, in terms of energy structure factors, the development of green finance positively affected the reduction in carbon emissions with respect to the energy structure. When green finance increased by 1%, carbon emissions affected by the energy structure decreased by 122.267% which was significant at the 1% level. Thus, green financing can effectively improve the energy structure, reduce the use of fossil fuels, increase the use of clean fuels, promote the rationalization and upgrade of the energy structure, and ultimately reduce carbon emissions. In terms of the factors that contribute to economic development, green finance has a positive impact on the reduction in carbon emissions affected by economic development but the significance test does not support this claim. From the perspective of energy efficiency, the development of green finance has a positive effect on reducing carbon emissions affected by energy efficiency. When the amount of green finance increased by 1%, carbon emissions affected by energy efficiency decreased by 338.543% which was significant at the 1% level. The development of green finance has promoted the upgrading and transformation of enterprises from the perspective of capital and policies. During this period, enterprises not only optimized their energy structure but also promoted technological progress, improved energy efficiency, reduced costs, and achieved the dual goals of economic development and environmental protection. From the perspective of industrial structure factors, the development of green finance has a positive effect on the reduction in carbon emissions affected by the industrial structure and an increase in green finance by 1% reduces carbon emissions affected by the industrial structure by 147.566%. Green finance not only supports the development of environmentally friendly enterprises through financial services but also forces high-polluting enterprises to upgrade and transform, thus reducing their carbon emissions through the use of a carbon tax. Thus, green finance can reduce carbon emissions from industrial structures. In general, green finance was found to have a significant impact on reducing carbon emissions, energy efficiency, and industrial structure; however, the effect of economic development on carbon emission reduction was not significant. Therefore, Hypotheses 2, 4, and 5 are supported, whereas Hypothesis 3 is not. To further analyze this phenomenon, a semi-parametric spatial lag model was developed, as discussed in the following paragraphs.

4.4.4. Spillover Effect Analysis Based on Two-Stage LMDI

Table 11. Decomposition of spatial spillover effects.

Variable	Model (2)			Model (3)
	Direct effect	Indirect effect	Total effect	Direct effect	Indirect effect	Total effect
GFI	225.108 *** (48.370)	934.007 *** (232.747)	1159.116 *** (14,014.350)	27.804 (31.398)	288.505 *** (73.586)	256.310 *** (83.577)
RDI	−1427.698 *** (661.378)	12,586.650 *** (2667.148)	14,014.350 *** (3115.332)	2214.757 *** (425.243)	3220.527 *** (990.655)	5435.274 *** (1165.275)
LGV	5.113 (15.610)	29.513 (55.079)	24.400 (66.566)	12.677 (9.874)	19.570 (22.302)	6.893 (26.810)
LOP	8.343 (20.035)	264.758 *** (49.506)	256.415 *** (56.157)	6.152 * (8.780)	8.203 (17.898)	2.050 (19.038)
LGP	106.105 *** (20.035)	308.495 ** (91.3333)	414.601 *** (102.481)	37.075 *** (13.450)	41.776 (34.776)	4.701 (35.868)

t statistics in parentheses * p < 0.1, ** p < 0.05, and *** p < 0.01.

and

Table 12. Decomposition of spatial spillover effects.

Variable	Model (4)			Model (5)
	Direct effect	Indirect effect	Total effect	Direct effect	Indirect effect	Total effect
GFI	325.960 ** (146.474)	323.138 (262.715)	649.098 ** (273.434)	155.153 *** (40.995)	161.371 * (95.574)	316.524 *** (109.054)
RDI	5291.790 *** (1952.816)	14,706.040 *** (3370.878)	9414.236 ** (3718.686)	758.990 (556.861)	5545.053 *** (1251.862)	4786.062 *** (1486.796)
LGV	36.528 (44.643)	4.984 (81.170)	31.543 (91.229)	18.009 (12.873)	28.404 (29.309)	10.394 (35.174)
LOP	70.305 * (42.730)	277.213 *** (69.358)	206.543 (66.586)	27.240 ** (11.755)	164.550 *** (26.652)	137.309 *** (29.337)
LGP	55.997 (66.559)	313.731 ** (129.885)	369.729 *** (122.002)	58.213 *** (18.457)	193.670 *** (47.797)	251.884 *** (52.022)

t statistics in parentheses * p < 0.1, ** p < 0.05, and *** p < 0.01.

serve as illustrative tools for investigating the spatial spillover effects of the four models. Examining the outcomes of Model (1), it is evident that the influence of green finance on carbon emissions related to the energy structure is multifaceted. The direct effect is registered at −225.108, the indirect effect at −934.007, and the cumulative impact at 1159.116; each of these values demonstrates statistical significance at the 1% level. This indicates that green finance reduces carbon emissions tied to energy structure alterations driven by both its direct impact and the cascading ramifications experienced across regions.

In Model (2), the ramifications of green finance on carbon emissions linked to economic development manifest further nuances. The direct effect translates to 27.804, the indirect effect to −288.505, and the total effect to 256.310. An undeniable indirect effect stands out, boasting a statistical significance level of 1%, while the total effect also commands recognition at the same level. This underlines the fact that green finance primarily steers a reduction in carbon emissions rooted in energy structure disparities, primarily fueled by the influential interplay across regions.

In Model (3), the intersection of green finance with carbon emissions pertinent to energy efficiency provides intriguing insights. The direct effect materializes as −325.960, with a total effect of −649.098, and with both landings comfortably within the 1% significance threshold. However, the indirect utility at −323.138 falls shy of statistical significance, indicating that green finance can indeed amplify carbon emissions influenced by energy efficiency but it seemingly falls short of inciting a notable ripple effect between regions in this specific context.

Model (4) propels us to explore the interplay of green finance with carbon emissions emerging from industrial structure dynamics. The direct effect surfaces at −155.153, the indirect effect at −161.371, and the overarching outcome at −316.524; they all possess statistical significance at the 10% level. This shows that carbon emissions intertwined with industrial configuration are not only directly propelled by green finance but also exhibit an indirect upswing owing to the ripple effects unfurling across regions.

In summary, the role of green finance extends its significant influence across a quartet of factors—energy structure, economic development, energy efficiency, and industrial structure—imbuing carbon emission outcomes. The multipronged impact of green finance, encompassing direct and indirect effects, enfolded spillovers, and scalability ramifications, beckons attention. Therefore, it is imperative that China not only actively fosters the growth of green finance but also strategizes the optimization of energy structures, energy efficiency protocols, and industrial frameworks. Simultaneously, leveraging inter-regional synergies to forge collaborative partnerships is pivotal, allowing regions to leverage their respective strengths for a mutually beneficial outcome: a resounding success in the journey toward dual-carbon goals.

4.4.5. Analysis of Non-Linear Effects of Green Finance Development on Energy Consumption

According to Models (6) to (9), the following results can be obtained using the semi-parametric spatial lag model.

Figure 4 shows a partial derivative graph of carbon emissions influenced by green finance with respect to energy structure. The figure illustrates that the partial derivative of carbon emissions influenced by green finance on the energy structure exhibits a nearly linear upward trend. When green finance is between 0.08 and 0.5, its development of green finance can significantly reduce carbon emissions affected by the energy structure. When the level of green finance is between 0.5–0.83, the impact of green finance on carbon emissions through the energy structure is weakened and tends to zero. In general, the main effect of green finance is a reduction in carbon emissions affected by the energy structure; however, as green finance continuously improves, the effect of the energy structure on reducing carbon emissions gradually weakens.

Figure 5 illustrates a partial derivative graph of carbon emissions impacted by green finance with respect to economic development. As shown in the figure, there is a linear upward trend reflecting the impact of green finance on carbon emissions through economic development. When green finance is between 0.08 and 0.6, carbon emissions increase significantly with the development of green finance affected by economic development. When the level of green finance is between 0.6–0.83, the impact of green finance on carbon emissions through economic development weakens and tends to zero. In general, green finance reduces carbon emissions affected by economic development at the beginning; however, as green finance improves, its impact on carbon emissions gradually decreases.

Figure 6 illustrates the partial derivative graph of carbon emissions influenced by green finance on the energy structure. The figure shows that the effect of green finance on carbon emissions and energy efficiency exhibits a linear downward trend. When green finance is between 0.08 and 0.28, developing green finance will increase the carbon emissions affected by energy efficiency. When green finance is between 0.28 and 0.6, the development of green finance reduces carbon emissions affected by energy efficiency. When the level of green finance is between 0.6–0.83, the effect of green finance on carbon emissions affected by energy efficiency becomes stable and reaches a maximum. In general, green finance initially increases carbon emissions affected by energy efficiency; however, as green finance continues to improve, carbon emissions affected by energy efficiency begin to decrease and gradually reach a stable maximum value.

Figure 7 shows a partial derivative graph of carbon emissions influenced by the impact of green finance on industrial structure. The figure illustrates that carbon emissions influenced by green finance on the industrial structure present an “inverted U-shaped” non-linear relationship. When green finance is between 0.08 and 0.35, green finance development will increase the carbon emissions influenced by the industrial structure, where the impact effect will reach the maximum value and start to decrease. When green finance is between 0.35 and 0.6, developing green finance reduces carbon emissions influenced by industrial structure. When green finance is between 0.6–0.83, the effect of green finance on carbon emissions affected by industrial structure reaches the maximum value and tends to be stable. In general, green finance initially increases carbon emissions affected by industrial structure; however, with continuous improvement in green finance, carbon emissions affected by energy efficiency will decrease and reach a maximum value.

Based on the research and analysis of the above studies, the following conclusions can be drawn. Green finance has a significant impact on several aspects of carbon emissions. In the energy structure field, the development of green finance can effectively reduce carbon emissions to a certain extent; however, with an increase in its level, the emission reduction effect gradually weakens. Similarly, in the areas of economic development and energy efficiency, green finance can significantly reduce carbon emissions initially; however, as it grows, the reduction effect diminishes. In terms of industrial structure, the development of green finance has an “inverted U-shaped” trend in carbon emissions which can initially increase carbon emissions but then gradually decrease them. In general, green finance has a significant mitigation effect on carbon emissions in the initial stages; however, with further development, the emission reduction effect gradually weakens. To achieve a more sustainable carbon reduction effect, it is necessary to comprehensively consider the impact of different fields on the development process of green finance and the diminishing impact that may occur with the improvement of green finance levels. This study provides an important reference for formulating effective carbon-reduction policies and sustainable development strategies.

4.5. Robustness Test

The regression results obtained in this study need to be further verified. Therefore, the geographical distance matrix was used to replace the proximity matrix and the above model was again regressed.

Table 13. Spatial measurement results of green finance on carbon emissions.

Variable	Model (1)
LGFI	0.710 *** (0.036)
GFI	0.355 *** (0.073)
RDI	2.872 *** (1.112)
LGV	0.041 * (0.021)
LOP	0.027 (0.021)
LGP	0.085 ***(0.028)
Spa-rho	0.324 *** (0.091)
R2	0.990

t statistics in parentheses * p < 0.1, *** p < 0.01.

and

Table 14. Spatial econometric regression results of two-stage LMDI with the replacement weight matrix.

	(1)	(2)	(3)	(4)
GFI	−166.210 *** (46.285)	29.995 (31.378)	−373.718 ** (146.047)	−184.42.876 *** (42.876)
RDI	707.633 (706.532)	2254.857 *** (478.180)	2620.741 (2237.018)	192.900 (658.405)
LGV	3.399 (13.292)	11.038 (9.020)	12.802 (42.279)	4.053 (12.440)
LOP	19.596 (13.556)	5.757 (9.178)	30.736 (42.645)	8.678 (12.602)
LGP	80.380 *** (17.945)	42.717 *** (12.248)	33.758 (57.138)	44.321 *** (16.823)
Spa-rho	0.462 *** (0.879)	0.079 (0.104)	0.190 (0.112)	0.109 (0.109)
R2	0.452	0.567	0.052	0.152

t statistics in parentheses ** p < 0.05, *** p < 0.01.

present the regression results. Based on the regression results, the following conclusions were identified: green finance can effectively reduce the level of carbon emissions and the carbon emissions affected by energy structure, energy efficiency, and industrial structure. The empirical conclusions obtained using the two types of weight matrices were the same; therefore, the conclusions drawn were considered robust.

5. Conclusions and Recommendations

This study investigates the effect of green financing on carbon emissions in the context of dual-carbon taxation. This study categorizes carbon emission sources into four aspects: energy structure, economic development, energy efficiency, and industrial structure. It then analyzes how green finance impacts carbon emissions in each of these aspects. The findings yielded several key conclusions. (1) Green finance has the potential to significantly decrease China’s carbon emissions, effectively address climate-related challenges, and facilitate a shift toward low-carbon practices. (2) Green finance can influence energy structure, energy efficiency, and industrial structure to reduce carbon emissions. However, its impact on carbon emissions relative to that of economic development was less pronounced. Historically, China’s economic growth has relied on fossil fuels. During the initial stages of green finance implementation, altering the energy mix can reduce carbon emissions. Substantial capital investments can enhance technological capabilities and energy efficiencies. Ultimately, enterprise transformation contributes to a cumulative reduction in carbon emissions. (3) The non-linear regression analysis revealed the following trends: the impact of green finance on energy efficiency led to increased carbon emissions while its effects on economic development and energy structure correlated with decreased carbon emissions. The relationship between the impact of green finance on the industrial structure and carbon emissions follows an “inverted U” pattern. As green finance continues to expand, carbon emission reductions will reach a peak and then stabilize.

The results of this study need to be situated in the context of the existing literature on green finance and carbon emission reduction. Research has also shown that green financing can effectively reduce carbon emissions. For example, Zhang and Gan found that green finance has a positive impact on China’s carbon emissions reduction [15,16]. However, it is important to note that the impact of green finance on carbon emission reduction may depend on a variety of factors, such as a country’s level of economic development, industrial structure, energy efficiency, and energy mix. The results show that green finance can reduce carbon emissions caused by energy structure, industrial structure, and energy efficiency, which is consistent with the results of previous studies. At the same time, this study further examines the non-linear relationship between them, and more precisely, the impact of green finance on carbon emissions, filling this gap. Overall, the findings of this study contribute to the growing body of evidence on the potential of green finance as a tool for achieving sustainable economic growth and reducing carbon emissions.

Based on the above analysis, the following countermeasures are proposed. First, the development of green finance should be vigorously promoted and guided, including green finance product innovation in order to boost diversified carbon emissions reduction. Financial capital is profit-driven and is typically invested in high-profit industries but it often lacks interest in green industries and projects with long cycles and low profits. Therefore, the government must promote green industry capital flow through policy and financial support and handle the relationships between economic development, structural optimization, and carbon emissions in a market-oriented way. It is also advisable for the financial sector to provide differentiated financial products to different investors, thus attracting more investors to the green industry to achieve diversified carbon emission reductions.

Second, all regions should actively summarize their development experiences and allow green financing to exert spillover effects. Given the short development time and relatively low level of green finance in China, the development of green finance in different regions is not completely consistent. The results obtained here suggest that the development of green finance in high-income regions should drive the development of green finance in surrounding regions, imparting experience to those regions and accelerating the further development of green finance. In this way, we should promote the improvement of the green finance development environment and accelerate the development of green finance in China using the linkages and complementarity of advantages among various regions to enhance emission reductions.

This study comprehensively examined the ramifications of green finance across diverse carbon emission sources. However, it is pertinent to acknowledge certain limitations in our endeavor. (1) Although this study dissected carbon emission sources—energy structure, economic development, energy efficiency, and industrial structure—and meticulously explored their empirical connections, the mechanisms through which these aspects collectively impact the total volume change in carbon emissions remain unaddressed. In addition, the potential influence of other factors on carbon emissions has not yet been investigated. (2) The construction of green finance indicators represents an area for potential refinement. Our green finance index relies on indicators spanning five dimensions: green credit, green securities, green investment, green insurance, and carbon emissions. Unfortunately, data constraints impede the exhaustive selection of indicators within each dimension. (3) Although this study incorporated spatial considerations through the use of a spatial econometric model, it is imperative to note that the subjectivity inherent in crafting a spatial weight matrix can lead to varying outcomes. This was the main constraint of the model. Future research endeavors hold the promise of optimizing and augmenting our findings in several dimensions. In terms of theoretical underpinnings, given that certain aspects have been extensively explored within the existing scholarly discourse, it is incumbent upon subsequent studies to incorporate the insights of numerous scholars. As the landscape of the green finance theory continues to evolve, these theories will be further enriched, providing a substrate for a more intricate understanding of the transmission mechanism between green finance and carbon emissions. In the field of green finance, it is imperative to acknowledge the limitations posed by nascent developments and data availability. As China’s green finance disclosure system continually matures, an abundance of more comprehensive and accurate data will emerge, paving the way for nuanced research grounded in these refined metrics. Future studies should consider introducing diverse spatial weight matrices to alleviate subjectivity, thereby generating a more robust analytical framework.