Introduction

The world’s stocks of natural assets and the flows of ecosystem services have come under increasing pressure, making the contradiction between economic growth and environmental capacity become increasingly striking. China’s extensive development model has created a miracle of economic growth over the past half-century, but it has also led to a series of problems, such as environmental degradation, resource waste, and energy shortage, revealing that the economic growth model is no longer sustainable. Hence, the need for cultivating new momentum for green development and transitioning towards sustainable models is increasingly imminent. The question of how to achieve a balance between economic growth and environmental protection through enhancing the beneficial contributions of nature to the sustainable development of the economy and human well-being will be a major challenge today and into the future (Díaz et al., 2018; Gómez-Baggethun et al., 2010; Polasky et al., 2019; Zhou et al., 2022).

In light of these concerns, gross ecosystem product (GEP) has been proposed by scholars, which refers to the gross value that the ecosystem provides to human society (Ouyang et al., 2013). There is a widespread recognition of the need to move beyond conventional economic measures, such as GDP, which has overlooked social costs and environmental impacts (Costanza et al., 2014; UN, 2015). Integrating the contribution of nature to human well-being into decision-making and realizing the value of ecosystem products are of great significance to enhancing and transforming available natural resources into sustainable economic outcomes. However, financing has become a major challenge in realizing the value of ecosystem products due to the characteristics of public goods, which results in a series of issues related to measuring, mortgages, and trading (Kemkes et al., 2010). To address these practical dilemmas, the Chinese government has issued a series of guidelines to strengthen the top-level institutional design of the mechanisms for realizing the value of ecosystem products, emphasizing the urgent need to increase the support of green financial instruments, including those on establishing and improving the value realization mechanism of ecosystem products (2021), deepening the reform of the country’s ecological compensation mechanism (2021).

The concept of green finance encompasses a variety of financial activities, including green credit, insurance, securities, and industrial funds, aiming to facilitate the balanced growth of the economy, resources, and environment (Salazar, 1998; Sachs et al., (2019); Yin and Xu, 2022). With institutional arrangements, green finance policy can further allocate the capital flows among different regions, industries, or enterprises, which can solve the issues of high cost, high risk, and long payback period in green investment, mitigate the financing constraints and the disadvantaged market position faced by the ecological industry (Xu et al., 2022). China, in particular, has witnessed a remarkable expansion in its green finance development under the dual pressure of environmental protection and economic transformation. In 2016, the People’s Bank of China and other departments issued guidelines on building a green financial system, which stressed the important role of promoting the construction of ecological civilization and achieving green economic transformation. Since 2017, China has established Green Finance Pilot Zones (GFPZ) in seven provinces, and ten prefecture-level cities, which marks a new stage in the development of green finance by strengthening the top-level design and the guidance of institutional policies “from top to bottom” while combining the innovation of green financial products and services according to local conditions “from bottom to top” (Ouyang et al., 2023). It is an important direction for the development of China’s financial institutions, as well as an inevitable choice for China’s economy to move towards a stage of green and high-quality development (Muganyi et al., 2021; Wang and Wang, 2021; Yang et al., 2021). Given this context, it is conceivable that the establishment of GFPZ may emerge as a crucial driver for the value realization of ecosystem products and the construction of ecological civilization. Against this backdrop, this paper seeks to address the critical questions: Does GFPZ significantly contribute to the value realization of ecosystem products? What are the intrinsic mechanisms and implications for policymakers, and is there any difference in the policy effects among different regions? It is of great significance to answer these questions for improving the green finance system to promote ecological civilization and enable high-quality development of green economy in China.

There is no doubt that previous literature on green finance and the value realization of ecosystem products still exhibits imperfections, leaving ample space for subsequent research. Specifically, the environmental and economic effects of GFPZ are still ambiguous, research should strive for a comprehensive analysis framework and a robust dataset to give empirical evidence and enhance the existing knowledge on the topic. One of the primary areas of contention lies in whether the implementation of GFPZ truly facilitates value realization. Some studies suggested that green policy can realize the value of ecosystem products through commercial capital investment allocation in green projects, guiding market participants to jointly promote the development of the ecological industry (Xu et al., 2022). It can also regulate the interest rate of loans to guide residents to purchase green products and gradually develop citizens’ green preferences (Becerra et al., 2023). While others argued that the green finance system has not effectively linked to industrial structure adjustment, environmental protection, and economic development, making the improvements of green finance on economic growth and ecological efficiency not significantly sound (Liu et al., 2019; Yin and Xu, 2022). In addition, the value of ecosystem products is a comprehensive index reflecting the transformation of ecological resources and economic benefits, which is consistent with the inherent goal of green finance policy. Regrettably, previous studies mostly focused on a single green finance tool or cases of ecosystem product value realization in specific regions, with a lack of systematic and comprehensive examination of green finance policy (Tang et al., 2020), making it hard to extend research results to practice.

Drawing from the considerations outlined above, this study endeavors to systematically assess the effect of green finance policy on ecosystem product value realization by taking GFPZ in China as a quasi-natural experiment. Leveraging the province-level panel GEP data from 2011 to 2020, we employ the synthetic control method (SCM), and traditional and spatial difference-in-difference (DID) methods to identify the net and spatial overflow effects of GFPZ on ecosystem product value realization. Meanwhile, we investigated its mechanism pathways, including the ecological transformation of industries and ecological industry development. In addition, we analyze the heterogeneous effects in regional finance development level, geographic location, and policy goals. Our study presents four contributions: Firstly, our study establishes a connection between green finance and ecosystem product value realization, contributing to the growing body of literature on the effects of green finance policy. Our empirical evidence can facilitate the construction of the green finance system and ecological civilization. Secondly, we develop provincial panel data on the value realization of ecosystem products based on the GEP accounting framework, which can provide more comprehensive and explanatory data to support the development of further studies. Thirdly, we probe and validate the mechanism pathways through which GFPZ policy affects ecosystem product value realization, and we also identify the spatial spillover effect and the heterogeneous policy impacts of GFPZ, highlighting the attributions of green finance policy to sustainable development and further deepen our understanding of the mechanism process. Finally, we present efficient policy implications for policymakers, emphasizing the importance of utilizing the functions of GFPZ in financial resources allocation, market pricing, and risk management, adapting to local conditions, and achieving the goal of a win-win situation between environmental protection and economic development.

The remainder of the paper is organized as follows. The “Literature review” section briefly introduces a review of the relevant literature. The “Theory and hypothesis” section provides the theoretical hypothesis. The “Research design” section outlines our estimation strategy and materials. The “Results” section presents our empirical findings and related examination. The “Conclusions and policy implications” section concludes with a summary of our findings and discusses the policy implications.

Literature review

Environmental policy is an important tool for governments to manage environmental problems and promote sustainable development, which has garnered extensive interest from scholars. A growing body of literature has examined the positive effect of various environmental policies on sustainable development, such as environmental tax reform (Zhang et al., 2023b), carbon emissions trading policy (Dong et al., 2022), green credit policy (Xu et al., 2023b), environmental pollution liability insurance (Zhu et al., 2023). However, some scholars argued that the imperfect design and the heterogeneity of environmental policies may lead to an increase in emissions, which is labeled a green paradox (Smulders et al., 2012; Guo, Yuan (2020)). Xie et al. (2017) found that there will be no further motivation for enterprises to initiate R&D efforts when concerning policies of command-and-control regulation. It has been widely suggested that market-based environmental policies consider both the positive and negative externalities of corporate environmental activities, which can provide more flexible mechanisms for enterprises to protect the environment while continuing to maintain productivity (Peng et al., 2021; Ren et al., 2020).

As an important market-oriented measure, the policy effect of green finance has been widely considered by scholars (Zhang et al., 2021). Green finance highlights sustainable development while concurrently focusing on economic growth, which can be regarded as a combination of financial policy and environmental regulation tools (Huang et al., 2022a; Lu et al., 2022). A primary set of literature focuses on the economic effect of green finance policy. At the macro level, GFPZ can amend the limitations of current sustainable development policies in terms of efficiency, effectiveness, and fairness, catalyzing the green transition of economic structure and optimizing the efficiency of the green economy (Li and Hu, 2014; Soundarrajan and Vivek, 2016; Zhou et al., 2022). The study also suggests that the green finance policy can promote green total factor productivity, which exceeds the input-output total factor productivity (Zhang, 2021). Additionally, diversified financial services and products could also avert the boon of natural resources from transforming into a “resource curse” (Isiksal, 2023). At the micro level, extant literature mostly analyzes its effect from the perspective of enterprises. It is found that GFPZ can stimulate green innovation of polluting enterprises by increasing investment and financing costs. Banks adjust loan interest rates for non-abatement enterprises, compelling them to reconsider their abatement and production strategies, thus achieving the green transition of emerging economies (Fan et al., 2021; Hu et al., 2021). More specifically, it can improve the emerging degree of green technology and stimulate innovation activities in end-pollutant treatment and green process improvement, promoting technology diffusion and the ESG scores of industrial enterprises (Liu and Wang, 2023; Xu et al., 2023a). Furthermore, the GFPZ policy can boost corporate investment efficiency by leading to a mitigation of principal-agent problems (Yan et al., 2022); The second body of literature emphasizes the environmental effect of green finance policy. Studies have shown that green finance policy has a significant impact on industrial pollution management and environmental governance: collaborating with environmental regulation, it can reduce industrial emissions of exhaust gas, wastewater, and sulfur dioxide (Muganyi et al., 2021; Nenavath, 2022; Zhang et al., 2021), decrease haze emissions, and improve air quality (Mohsin et al., 2021; Xu et al., 2023c). Besides, GFPZ has a significant carbon emissions reduction effect (Zhang et al., 2022; Umar and Safi, 2023; Sun et al., 2023), and can lead to a win-win situation between manufacturer and supplier by determining an appropriate green investment range (An et al., 2021). The third body of literature integrates the economic and environmental effects, emphasizing the role of green finance policy in sustainable development. Hunjra et al. (2023) reveal that green finance has a significantly positive impact on sustainable development in developing countries, and green credit policy is an essential part of the development of the green economy; Chen and Bian (2023) find that green finance significantly contributes to sustainable development, which can help countries reconcile economic progress with environmental sustainability. Meanwhile, at the enterprise level, Lei and Yu (2023) shed light on the significant role of GFPZ in improving enterprises’ ESG performance and promoting sustainable development. Lyu et al. (2023) investigate that GFPZ significantly promotes enterprises’ green development by easing financing constraints and stimulating green innovation.

The value realization of ecosystem products, as a comprehensive indicator reflecting the sustainable development of the environment and economic benefits, provides a new perspective for the research on environmental policies. Since the seminal work of Costanza et al. (1998) and Daily (1997), the field of ecosystem services valuation and natural capital accounting has drawn increasing attention all over the world (Costanza et al., 2017). Building on the concept of ecosystem service and natural capital, scholars put forward the concept of ecosystem products which refers to the final services and products that ecosystems provide to human society including ecosystem provisioning services, regulating services, and cultural services (Hao et al., 2022; Islam et al., 2019). The value of ecosystem products is embodied in the benefits that humans derive from ecosystems, which can be directly utilized to generate human well-being (Boyd and Banzhaf, 2007; Hao et al., 2022). With the continuous promotion of ecological civilization in China, accounting for ecosystem products’ value and exploring the path of ecosystem product value realization have become important issues that are widely considered by scholars.

The GEP accounting framework proposed by Ouyang et al. (2013), is now commonly used to account for the ecosystem product value, which has been included as an integrated monetary index for the evaluation of final ecosystem services in the latest release version of the System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA-EA) (UN, 2021). On the one hand, research on the value accounting of ecosystem products has been conducted at different spatial and temporal scales in China, such as the GEP accounting at county, city, and province levels, as well as the application of accounting results in assessing government performance, protecting biodiversity and raising public awareness (Li et al., 2022; Ma et al., 2020; Ouyang et al., 2016; Zou et al., 2020). On the other hand, scholars have formed extensive theoretical and practical findings in the study of the path to realizing ecosystem product value. At the theoretical level, scholars categorized ecosystem products into ecosystem private products, public products, and quasi-public products based on the public goods theory, and explored their value transformation pathways, as well as the roles played by the government and the market (Li et al., 2021). At the practical level, there have been formed some typical value realization pathways from widespread practical experiences, including ecological compensation, ecological restoration, industrial development, and property rights trading (Geussens et al., 2019; Guerry et al., 2015; Johansson, 2016; Zheng et al., 2013). Successful practices have been introduced to more regions, providing more experience for the government to formulate relevant policies.

Briefly, many scholars have undertaken studies exploring the environmental and economic effect of green finance policy, laying a solid foundation for our study; however, seldom studies have given empirical evidence on the nexus between GFPZ implementation and ecosystem products value realization, and the mechanism pathways need to be further explored at a more granular level. Moreover, a large portion of the existing research on ecosystem product value realization focuses on the cases in specific regions (Tang et al., 2020; Zhang et al., 2023a), with limited studies utilizing nationwide panel data to investigate the mechanisms of value realization. To address this gap, there is an urgent need to conduct a comprehensive examination of green finance policy for the value realization of ecosystem products. Furthermore, detailed investigations into the potential mechanism pathways, the spatial effects, and the heterogeneous policy effects of GFPZ are necessary, presenting a more in-depth analysis of the topic.

Theory and hypothesis

The value realization of ecosystem products refers to the procedure of safeguarding, nurturing, and managing ecosystem products under policy intervention. From the perspective of welfare economics, the key to the process is to internalize the extrinsic value of ecosystem products and maximize economic, social, and ecological benefits, with a goal towards efficient, fair, and sustainable outcomes (Daly, 1992). However, the public goods attribute of ecosystem products has made it hard to participate in market transactions, thereby resulting in green investment projects characterized by long payback periods, high investment costs, and high operational risks. Traditional profit-seeking financial capital is more inclined to flow into innovative industries with higher returns, resulting the financing difficulty for the ecological industry. Therefore, the value realization of ecosystem products relies mostly on the “blood transfusion” modes proposed by the government, such as ecological compensation. Green finance policy can enable the optimal allocation of financial resources in environmental and economic sectors to achieve the ecological transformation of industries. Meanwhile, it can also serve as a price-seeker to perfect ecosystem products trading market. Moreover, GFPZ can improve the green financial system and expedite the innovation of products and services through institutional arrangements, taking environmental risks into the decision-making framework with a risk prevention mechanism, thereby promoting the interconnection of ecosystem products markets with financial markets (Ghys, 2013). Therefore, we analyze the effects of GFPZ from two main aspects (Fig. 1), including ecological transformation of industries and ecological industry development, trying to make up for the deficiency in the mechanism pathways of GFPZ affecting ecosystem products value realization.

Fig. 1: Mechanism pathways of GFPZ promoting ecosystem products value realization.
figure 1

(1) This figure reports how GFPZ promotes ecosystem product value realization. (2) Compared with traditional finance, we analyze the mechanism of action from the characteristics of GFPZ, including resource allocation, market pricing, risk management, and policy guarantee. (3) The main mechanism pathways of GFPZ affecting ecosystem product value realization are the ecological transformation of industries and ecological industry development.

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Firstly, GFPZ can promote ecological industry development and realize the ecological transformation of industries through the reallocation of financial capital, thus promoting the value realization of ecosystem products. First, GFPZ can guide the capital flow into ecological industries by developing more green investment participants like venture capital investment, and private equity, and encouraging the development of financing tools like green credit, franchise, etc. Second, GFPZ restricts the development of highly polluting industries through environmental regulation to promote the ecological transformation of polluting industries. Specifically, GFPZ can influence the financing and capital allocation behavior of enterprises through the incentive and constraint mechanism by turning the implicit environmental costs into explicit costs to be considered in production when investing. With more green investment participants and various financing tools, green enterprises will be allocated more financial support, while the financing constraints and pollution costs of polluting enterprises will substantially increase (Kong et al., 2022). According to the Porter hypothesis, environmental regulations can push firms to engage in green innovation activities through external pressure and internal incentives (Porter and van der Linde, 1995; Xu et al., 2023). The ecological transformation of the whole industry will increase the supply of ecosystem products and promote the value realization of ecosystem products.

Secondly, GFPZ can serve as a price-seeker to provide market pricing guidance for ecosystem products through diversified financial products and services, boosting the formation and development of the ecosystem products trading market. The effective allocation of financial capital positively incentivizes foundational tasks such as the confirmation and registration of natural resource ownership, which assists governments in directing more resources toward establishing a unified investigation, monitoring, and evaluation system. These efforts will lay a solid foundation for the functioning of market mechanisms and drive the development of the ecological industry, which will achieve the paid use for natural resources through environmental trading, such as rights over carbon sink, pollution discharge, water and energy use, internalizing the environmental externalities in the economic dimension, and realizing the value of ecosystem products.

Finally, GFPZ can help quantify environmental risks and promote the healthy development of industries through its risk management mechanism with institutional arrangements. GFPZ can further motivate the ecosystem products supplier to incorporate environmental protection objectives into production, developing an internal positive incentive for the operation of ecosystem products, leading to a virtuous cycle of quality ecosystem products supply. Additionally, it can mitigate operational risks and contribute to the sustainable management and operation of ecosystem products through green financial products and services innovation, such as natural disaster insurance and agricultural meteorological index insurance.

To sum up, GFPZ can facilitate the ecological transformation of industries and promote the development of ecological industry by resource allocation, market pricing, risk management, and policy guarantee, ultimately promoting the value realization of ecosystem products. The ecological transformation of industries and ecological industry development are supplementary to each other and develop in synergy, both of which are vital mechanism pathways. On the one hand, the ecological transformation of industries lays a foundation for the development of the ecological industry by conserving and cultivating ecological resources, which will foster an ecological economic chain between enterprises and industries, supplying high-tech equipment, clean energy, and transportation facilities, further promoting the development of ecological industry (Chen, 2019). On the other hand, the economic benefits generated by the ecological industry will encourage more social capital to engage in ecosystem product operations, thus accelerating the overall ecological transformation of industries. Finally, there will be ever-increasingly ecosystem products of high quality in a more mature trading market, thereby achieving ecosystem product value. Accordingly, we propose Hypothesis 1, 2, and 3 as below:

Hypothesis 1 The GFPZ policy can effectively promote the value realization of ecosystem products in the pilot zones.

Hypothesis 2 The GFPZ policy promotes the value realization of ecosystem products through the ecological transformation of industries.

Hypothesis 3 The GFPZ policy promotes the value realization of ecosystem products through the development of the ecological industry.

Research design

Empirical method

Considering that the implementations of the Gansu and Chongqing pilot zones are relatively short, this may affect the effectiveness of the estimation when including them in our study. Therefore, we take China’s first batch of GFPZ conducted in 2017 as an exogenous shock to examine the value realization effect of GFPZ using the panel data of 30 provinces (Tibet is excluded due to incomplete data) from 2011 to 2020. The main existing methods of policy evaluation include the differences-in-differences method (DID), the propensity score matching DID method (PSM-DID), and the synthetic control method (SCM). Considering that the SCM simulates the situation before the policy implementation of the treatment group by weighting multiple reference objects, and the weights are determined by data-driven, which can reduce the estimation error caused by subjective judgment, further avoiding the issues related to policy endogeneity (Athey and Imbens, 2017). This study conducts the SCM model proposed by Abadie and Gardeazabal (2003) to estimate the baseline effect of GFPZ. The SCM model uses the pre-intervention period to build a synthetic control and combines the other provinces to build a fake province that resembles very closely the trend of the treated one, then, we can compare how this synthetic control behaves after the intervention.

Assuming that the ecosystem products value realization rate of (J + 1) provinces in the t ϵ [1, T] period can be observed, where \({Y}_{it}^{I}\) denotes the ecosystem product value realization rate of the province i at time t that it has not been included in GFPZ, \({Y}_{it}^{N}\) denotes the ecosystem product value realization rate of the province i at time t that it becomes a GFPZ. Assuming that the province i becomes a GFPZ at time t = T0, then the province’s ecosystem product value realization rate in the t ϵ [1, T0] period is not affected by the GFPZ policy, that is \({Y}_{it}^{N}={Y}_{it}^{I}\); after the green finance policy is implemented, the policy effect can be identified as \(\tau ={Y}_{it}^{N}-{Y}_{it}^{I}\), where \({Y}_{it}^{N}\) of the pilot policy province can be measured, but \({Y}_{it}^{I}\) can’t be observed. The counterfactual results for this province can be estimated using a factor model based on parametric regression proposed by Abadie et al. (2010):

$${Y}_{it}^{I}=\delta _t+\theta _tZ_i+\lambda _t\mu _i+\varepsilon _{it}$$
(1)

δt represents time trend; Zi is a (K × 1) dimensional control variable unaffected by policy implementation, and θt is a vector (1 × K) of unknown parameters; μi is a (F × 1) dimensional unobservable area fixed effect, λt is an unknown common factor (1 × F); εit captures random shocks with mean zero at provincial level.

To find \({Y}_{it}^{I}\), defining W = (w2,⋯, wN + 1)as a (N × 1) vector of weights satisfying that Wj ≥ 0, j = 2,⋯, N + 1 and w2 +⋯+ wN + 1 = 1. Each particular value of the vector W represents a feasible synthetic control for the treated province, which is the weighted average of all provinces within the control group. Weighting the values of the variables for each control group province:

$$\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}{Y}_{jt}={\delta }_{t}+{\theta }_{t}\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}{Z}_{j}+{\lambda }_{t}\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}{\mu }_{j}+\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}{\varepsilon }_{jt}$$
(2)

Assuming that there is vector group \({W}^{\ast }=({w}_{2}^{\ast },\cdot \cdot \cdot ,{w}_{N+1}^{\ast })\), satisfying:

$$\begin{array}{ll}\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }{Y}_{j1}\,\,\,={Y}_{11},\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }{Y}_{j2}={Y}_{12},\,\cdots ,\\\,\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }{Y}_{j{T}_{0}}={Y}_{1{T}_{0}},\,\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }{Z}_{j}={Z}_{1}\end{array}$$
(3)

If \({\sum }_{t=1}^{T0}\,{\lambda }_{t}^{{\prime} }\lambda n\) is non-singular, then:

$$\begin{array}{ll}{Y}_{1t}^{I}-\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }{Y}_{jt}=&\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }\mathop{\sum }\limits_{s=1}^{{T}_{0}}{\lambda }_{t}{\left(\mathop{\sum }\limits_{n=1}^{{T}_{0}}{\lambda }_{n}^{{\prime} }{\lambda }_{n}\right)}^{-1}{\lambda }_{s}^{{\prime} }({\varepsilon }_{js}-{\varepsilon }_{1s})\\&-\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }({\varepsilon }_{jt}-{\varepsilon }_{1t})\end{array}$$
(4)

Abadie et al. (2010) have proved that the right side of Eq. (4) converges to zero in general conditions. While T0 < t ≤ T, the counterfactual results for the treated province can be represented as the results of the synthetic control group, that is \({Y}_{1t}^{I}={\sum }_{j=2}^{N+1}{w}_{j}^{\ast }Yjt\). Thus, the estimation of GFPZ policy effect \({\tau }_{1t}^{\wedge }\) can be expressed as:

$${\tau }_{1t}^{\wedge }={Y}_{1t}^{N}-\mathop{\sum }\limits_{j=2}^{N+1}{w}_{j}^{\ast }{Y}_{jt},\,{T}_{0} \,<\, t\le T$$
(5)

Variable selection

Explained variable: ecosystem products value realization rate

In 2022, China’s National Development and Reform Commission and National Bureau of Statistics issued the Guidelines for Gross Ecosystem Product (GEP) Accounting, which is the most authoritative and detailed technical guideline for GEP accounting that aligns with the academic and practical principles. Drawing on this guideline, this study categorizes ecosystem products into ecosystem provisioning services, regulating services, and cultural tourism services. We use statistical, remote sensing, and meteorological data to construct provincial-level GEP panel data from 2011 to 2020. Notably, tourism revenue shrunk significantly due to the shock of COVID-19 in 2020, which could not reflect the real trend of changes. Thus, we employ the GM(1, 1) model to conduct gray prediction through the number of tourists and total tourism revenue in each province from 2011 to 2019 to predict the value of cultural services and products in 2020. Table 1 reports the average of key indicators in our prediction models. The posterior error ratios of all prediction models are less than 0.35, and the p-values are greater than 0.8. The key indicators show that our prediction model is accurate and can simulate the tourism value of each province when it is not affected by the epidemic.

Table 1 Key indicators in GM (1, 1) model.
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Referring to the study of Wang et al. (2021a), this study measures the ecosystem products value realization rate by primary conversion rate. Firstly, the higher proportion of the primary ecosystem products, including the provisioning products and cultural products, in the composition of total ecosystem products, indicates the higher level of marketization and value realization for ecosystem products in the region. Secondly, the immature market trading mechanism and the public goods attributes make the regulating products hard to trade in the market, resulting in limited trading volume and a relatively low-value realization degree. Taking China’s first pilot city of ecosystem value realization, Lishui City, as an example, Lin et al. (2022) found that the value realization rate of regulating products was only 3.76%. Therefore, this study suggests that the primary product conversion rate is a good indicator of measuring the value realization rate of ecosystem products, which is reasonable to be applied in this study. The formula for the primary product conversion rate is shown below:

$$R=\frac{EPV+ECV}{GEP}=\frac{EPV+V}{GEP}$$
(6)

Where R denotes the primary ecosystem products conversion rate, V denotes tourism revenue. The value of provisioning products based on the GEP accounting framework has been fully converted into economic value through market transactions; while the portion of total tourism revenue in cultural products value has also been realized as economic value in market transactions, thus, they are directly included as realized value. Figure 2 reports the trend of the value realization rate in different groups. It can be seen that the value realization rate curve of pilot provinces shifts upward after 2017 and gradually exceeds the cure of non-pilot provinces, which is initially consistent with the baseline hypothesis of our study. While the tendency needs further empirical studying.

Fig. 2: Trends in value realization rate of provincial ecosystem products in China.
figure 2

(1) This figure reports the trajectory of the ecosystem product value realization rate in treated provinces, control provinces, and all provinces, respectively. (2) The value realization rate curve of pilot provinces shifts upped after 2017 and gradually exceeds the cure of non-pilot provinces. (3) The trend is initially consistent with the basic hypothesis of our study that GFPZ policy can effectively promote the value realization of ecosystem products in the pilot zones.

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Control variables

The value realization of ecosystem products is regionally heterogeneous, and its influencing factors include both natural factors such as ecological resources, and humanities factors such as economic and social development. The former group determines the supply capacity of ecosystem products, and the latter group reflects the demand potential of the ecosystem product market. Differences between supply and demand determine the differences in the value of ecosystem products and their different realization paths. Therefore, this study controls important factors from the dimensions of economic and social development, natural resources endowment, and environmental regulation that may affect the value realization of ecosystem products to obtain more accurate estimate results, including (1) economic scale: we normalize and calculate the average weights for four indicators of per capita GDP, per capita fixed asset investment completion, per capita total retail sales of consumer goods, and per capita total import and export of goods. (2) nature reserve scale: the ratio of the area of nature reserves to the total area of the region; (3) scenic quality: represented by the number of scenic spots of grade 3 A and above, of which we take the logarithm. (4) Fiscal transfer payment: the ratio of environmental protection expenditure to fiscal expenditure. (5) environmental protection investment: the ratio of environmental protection investment of A-share listed companies to the added value of the secondary industry in the region from 2011 to 2020; (6) green innovation: represented by the number of green patent applications in each province from 2011–2020, of which we take the logarithm.

Data sources

The statistical data used to account for GEP in this study are primarily sourced from the China Statistical Yearbook (2012–2021), China Water Statistical Yearbook (2012–2021), China Rural Statistical Yearbook (2012–2021), and Tourism Statistics Yearbook by regions from 2012–2021. The Remote sensing data includes Land Use/Cover Change Data from the Chinese Academy of Sciences (LUCC), the Chinese soil map based on the Harmonized World Soil Database (HWSD), and meteorological data sourced from the National Meteorological Scientific Data Center. More detailed data sources are elaborated in the Supplementary appendix. Among the control variables, the data on the economic scale, nature reserve scale, scenic quality, and fiscal transfer payment are collected from the China Statistical Yearbook (2012–2021) and Statistical Yearbook by regions from 2012 to 2021. Environmental protection investment data are sourced from the China Statistical Yearbook (2012–2021) and Corporate Social Responsibility Report, and the green innovation data come from the China National Intellectual Property Administration.

Results

Synthetic weights

To clearly evaluate the green finance policy effect on different provinces without concealing individual heterogeneity, a separate synthesis control is constructed for each treatment group instead of the previous practice of merging all treatment groups. Table 2 compares the characteristics of pilot provinces to those of the synthetic pilot provinces before GFPZ policy implementation, and randomly selects two years before 2017 to test the fitting results. Overall, the results in Table 2 suggest that the synthetic provinces provide good comparisons for the actual ones and the maximum Root Mean Square Percentage Error (RMSPE) of each synthetic province is only 0.021, indicating that SCM is suitable for estimating the policy effect of GFPZ.

Table 2 Ecosystem products value realization predictor means before GFPZ policy.
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Table 3 shows the weights of each province in the synthetic version of pilot provinces. Taking Guangdong as an example, the synthetic Guangdong is a weighted average of Shanxi, Jiangsu, Beijing, Tianjin, and Shandong with weights decreasing in this order. All other provinces not in the donor pool are assigned zero weights.

Table 3 Synthetic weights for pilot zones.
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Baseline results

Figure 3 displays the trajectory of ecosystem product value realization between pilot provinces and their synthetic counterpart from 2011 to 2020. The location of the vertical dashed line represents the first year of the GFPZ policy, and the solid line indicates the actual trend of change in ecosystem product value realization in different regions, the dotted line represents the trend of change in synthetic control regions. The effect of green finance policy can be indicated by the gap in value realization rate between the actual and synthetic pilot provinces. As shown in Table 2, the synthetic provinces almost exactly reproduced the ecosystem products value realization for pilot provinces before the GFPZ policy, indicating that these five provinces have better fit with the synthetic control provinces. Therefore, the evaluation results in these five provinces are highly reliable for detailed analysis. It can be seen that the GFPZ policy plays a positive role in realizing the value of ecosystem products: the solid lines are obviously above the dotted line in the first three years after GFPZ policy implementation, that is, the actual value realization rate is higher than synthetic value realization rate, hypothesis 1 is verified, which is also consistent to the view of Yang et al. (2023a). While the effects are different among regions: GFPZ policy has the greatest effect on Xinjiang and Guizhou, followed by Zhejiang, Guangdong, and Jiangxi, which may be related to the heterogeneity of regional differences and macro policy goals: Xinjiang and Guizhou, as ecologically vulnerable areas, the goals of green finance policy primarily lie in ecological restoration and protection; while Xinjiang and Guizhou have solid foundations of ecological civilization and more enriched ecological resources endowment, and green finance policy guidelines can better utilize existing resources to develop ecology-related industries and realizing the value of ecosystem products, achieving more significant policy impacts than other regions. In the “Discussion” section, we will further discuss the heterogeneity of region and policy goals of the GFPZ policy effect.

Fig. 3: Effects of GFPZ policy.
figure 3

(1) Using the SCM to identify the baseline effect of GFPZ policy. The figures display the ecosystem products value realization trajectory of pilot provinces and its synthetic counterpart; where panels a–e represent Guangdong, Guizhou, Zhejiang, Jiangxi, and Xinjiang, respectively. The location of the vertical dashed line represents the first year of the GFPZ policy, and the solid line indicates the actual trend of change in ecosystem product value realization in different regions, the dotted line represents the trend of change in treated regions. The policy effect could be indicated by the gap between the solid line and the dotted line. (2) GFPZ policy plays a positive role in realizing the value of ecosystem products. GFPZ policy has the greatest effect on Xinjiang and Guizhou, followed by Zhejiang, Guangdong, and Jiangxi. (3) Our baseline results show a positive impact of GFPZ policy on ecosystem product value realization, hypothesis 1 is verified.

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Placebo studies

To evaluate the credibility of our results, we conduct placebo studies proposed by Abadie et al. (2010), where the treatment of interest is reassigned in the data to provinces excluded from GFPZ policy. We conducted a series of placebo studies by iteratively applying the SCM used to estimate the effect of the GFPZ policy on every other province in the donor pool. If the placebo studies create gaps of a magnitude similar to the one estimated for each pilot province, then our interpretation is that our analysis does not provide significant evidence of a positive effect of GFPZ policy on ecosystem product value realization in pilot provinces. If the placebo studies show that the gap estimated for each pilot province is unusually large relative to the gaps for non-pilot provinces, it indicates that our analysis provides significant evidence of a positive value realization effect of the GFPZ policy. The RMSPE is used to measure the difference between the pilot provinces and their synthetic versions. Before running placebo studies, we excluded those poorly fitted provinces with RMSPE of more than two times the RMSPE of the real pilot provinces, which may affect our analysis on whether the change of value realization rate is due to fitting bias or GFPZ policy shock. Figure 4 displays the results of the placebo test. The gray lines show the difference in value realization rate between each province in the donor pool and its respective synthetic version. The superimposed black line denotes the gap estimated for pilot provinces. As the figure makes apparent, the estimated gap for Guangdong, Guizhou, Xinjiang, and Zhejiang during the policy implementation period is unusually large relative to the distribution of the gaps for the provinces in the donor pool, suggesting significant policy effects. However, the significance level of policy effect in Jiangxi is relatively weak, revealing that the implementation of the GFPZ policy in Jiangxi still needs to be further strengthened. The possible explanation is that the ecosystem products transaction market in China is still in its infancy with relatively low transaction volume, and the development of the ecological industry would take a long time, thereby resulting in a certain time lag in the policy outcomes such as the ecological transformation of industries, which is similar to the opinion of Yang et al. (2023b). In general, there is still no denying the effect of the GFPZ policy on ecosystem product value realization, and this study further carries out a robustness test.

Fig. 4: Results of placebo analysis on GFPZ policy.
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(1) We conduct a series of placebo studies by iteratively applying the SCM used to estimate the effect of the GFPZ policy on every other province in the donor pool. Panels a–e represents Guangdong, Guizhou, Zhejiang, Jiangxi, and Xinjiang, respectively. The gray lines show the difference in value realization rate between each province in the donor pool and its respective synthetic version. The superimposed black line denotes the gap estimated for pilot provinces. (2) As the figure shows, the estimated gap for Guangdong, Guizhou, Xinjiang, and Zhejiang during the policy implementation period is unusually large relative to the distribution of the gaps for the provinces in the donor pool. The significance level of policy effect in Jiangxi is relatively weak. (2) There is still no denying the positive effect of the GFPZ policy on ecosystem product value realization.

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Robustness test

Leave-one-out estimation

We run a leave-one-out estimation to test the sensitivity of our main results to changes in the control province weights. Here we iteratively re-estimate the baseline model to construct a synthetic pilot province omitting in each iteration one of the provinces that received a positive weight in Table 2. By excluding provinces that received a positive weight we sacrifice some goodness of fit, but this sensitivity check allows us to evaluate to what extent our results are driven by any particular control province. Figure 5 displays the results that the iterative synthetic estimates (gray lines) for each pilot province are consistent with the original estimates and do not show large fluctuations, indicating that the estimates of previous analysis are fairly robust to the exclusion of any particular province from our sample of comparison provinces.

Fig. 5: Leave-One-Out distribution of the synthetic control for pilot zones.
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(1) Here we iteratively re-estimate the baseline model to construct a synthetic pilot province omitting in each iteration one of the provinces that received a positive weight in Table 2; where panels a–e represent Guangdong, Guizhou, Zhejiang, Jiangxi, and Xinjiang, respectively. This sensitivity check allows us to evaluate to what extent our results are driven by any particular control province. (2) The figure displays that the iterative synthetic estimates (gray lines) for each pilot province are consistent with the original estimates and do not show large fluctuations. (3) The results indicate that the estimates of previous analysis are fairly robust to the exclusion of any particular province from our sample of comparison provinces.

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DID method

This study further conducts a DID model to examine the robustness of the GFPZ policy effect, avoiding possible identification bias caused by a single estimation model. The model is set as follows:

$$Realizationrate_{it}=\beta _0+\beta _1Treat_i\times Year_t+\beta _2X_{it}+\mu _i+\gamma _t+\varepsilon _{it}$$
(7)

Where Realizationrateit represents the ecosystem products value realization of province i in period t. Treati denotes the policy dummy variable for grouping provinces, set to 1 for policy pilot provinces and 0 for other provinces. Yeart is a time period grouping variable, set to 1 on and after 2017 and set to 0 before 2017. Xit contains a series of control variables at the province level. μi and γt represent the province-fixed effect and time-fixed effect, respectively. εit represents the residual term. β0 is a constant term; β1 represents the effect of GFPZ policy on the ecosystem products value realization of pilot provinces; and β2 is the influence coefficient of other variables on the value realization of pilot provinces.

Firstly, the DID model presupposes that the treatment group has the same trend of change as the control group before the policy implementation, so we employ the event study method with regard to the work of Alder et al. (2016) to test the change of value realization rate in the year of the policy implementation and before. We separately introduce the interaction term of the dummy variables six years before and three years after the GFPZ implementation to verify the parallel trend hypothesis. We define the gap in ecosystem products value realization rate between the treatment and control groups in 2016 as a baseline of 0 with a 95% confidence interval to examine the dynamic policy effects. Figure 6 demonstrates that the estimated coefficients in the years before GFPZ implementation failed to pass the 5% significance test, reflecting there is no pre-correlation between GFPZ policy and the ecosystem products value realization rate. After GFPZ implementation, although the estimated coefficients in the first year failed to pass the 5% significance test, an upward trend is observable, and it is clear that the value realization rate rose from the second year and the dynamic policy effect is strengthened over time. Given the potential time-lag effect of the GFPZ on ecological restoration and protection, the cumulative influence of the GFPZ on the value realization of ecosystem products can be considered significant and the parallel trend hypothesis is confirmed. Table 4 Model (1) reports the regression results, we consider the time and province-fixed effects and the coefficient of Treati × Yeart exhibits positivity and significance at the 1% level, indicating that after implementing the GFPZ policy, the ecosystem products value realization rates in pilot provinces demonstrate a significant increase, which further corroborates the conclusion of the baseline regression.

Table 4 DID estimation results.
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Replacing the explained variable

This study substitutes the explanatory variable with Gross Economic-Ecological Product (GEEP) proposed by Ma et al. (2020) and Wang et al. (2021b). Based on the theories of weak sustainability and welfare economics, GEEP aggregates two core economic and ecological accounting metrics (GGDP and GEP) and deducts the overlap, which is an integrated measure for ecological service and economic products. Besides, we also introduce the Green Gold Index proposed by Wang et al. (2021a) to measure the ecosystem products value realization. The Green Gold Index refers to the ratio of GDP and GEP, which can present the connection of the contributions from ecological resources to economic growth. Models (2) and (3) in Table 3 demonstrate the regression results of the policy effects on GEEP and Green Gold Index, respectively. Both of the coefficients are significantly positive, further bolstering the conclusion of the baseline regression.

Besides, considering the extrapolations of tourism revenue that may reduce estimation validity, here we further eliminate the data in 2020 and supplement a test for baseline results. Model (4) relays the regression results, affirming that the GFPZ significantly promotes the value realization of ecosystem products at a 1% significant level, combining with the key indicators of the GM(1,1) model reported in Table 1, both of which certify that the alternate measurements and findings are robust.

Controlling the impact of contemporaneous policies

Considering that the green finance policy effect could be potentially confounded by contemporaneous policies, leading to biased estimation results. Here we consider relevant government documents that may affect the value realization of ecosystem products, including the Program for the Construction of the Ecological Civilization Demonstration Areas (ECDA) and the Opinions on the Establishment of Unified and Standardized National Ecological Civilization Pilot Zones (NECPZ). By integrating the multiplication terms between the pilot provinces and the year of implementation for both policies into Eq. (7), respectively, we endeavor to control the possible effects induced by other policies. Models (5) to (7) of Table 4 display the estimation results, where the influence of ECDA was found to be statistically insignificant and the impact of NECPZ to be significantly positive. During the regressions, the coefficients are slightly lower than the coefficients in Model (1), the effects of GFPZ consistently emerge as significantly positive, indicating that although there could be an overestimation of the baseline results, which does not alter the overall conclusions of this study. In addition, considering that both Jiangxi and Guizhou are included in the first batch of ECDA, as well as NECPZ, we conduct a DID estimation excluding these two provinces. Model (8) shows the results that the positive effect of green finance policy is still sound. Therefore, taking into account the possible relevant policy in the same period, the results of GFPZ policy effects could be slightly overestimated, but the value realization effect remains significant, further revealing that the overall conclusion of this study is robust.

Mechanism test

According to the previous mechanism analysis, ecological transformation of industries and ecological industry development could be mechanism pathways for the effect of GFPZ; therefore, we formulate the following model to test the mechanism of action (Dinkelman, 2011):

$$Mechanisms={\beta }_{0}+{\beta }_{1}Trea{t}_{i}\times Yea{r}_{t}+{\beta }_{2}{X}_{it}+{\mu }_{i}+{\gamma }_{t}+{\varepsilon }_{it}$$
(8)

Where Mechanisms denotes the mechanism pathways of GFPZ, including the ecological transformation of industries and ecological industry development, the other variables are consistent with Eq. (7). We measure the ecological transformation of industries from the dual dimensions of energy efficiency and environmental efficiency: energy efficiency is measured by total energy consumption per unit of GDP, and environmental efficiency is composed of the amount of industrial wastewater, industrial waste solid and industrial waste gas emitted per unit of GDP. To avoid the disadvantages caused by subjective weighting, this paper uses the entropy weight method to determine the weights of each indicator to generate the value for the ecological transformation of industries. Meanwhile, in the context of ecological civilization construction in China, we adopt the levels of eco-agricultural industrialization, forestry industrialization, animal husbandry industrialization, and tourism industrialization to proxy for the level of ecological industry development (Chen, 2019). Similarly, we use the entropy weight method to determine the weights of each indicator.

Table 5 shows the mechanism test results. Model (8) and Model (10) present the regression results of GFPZ on mechanisms according to Eq. (8), affirming that the GFPZ can significantly facilitate the ecological transformation of industries and promote the ecological industry development, which is in line with Ouyang et al. (2023) and Zhang (2023)’s findings. And we further add the mechanism variables and policy variables into the regression model of Eq. (7). Model (9) and Model (11) report the regression results that the estimated coefficients of Treati × Yeart and Mechanisms have all passed the significance test at 95% or 99% conference interval level, indicating that the implementation of GFPZ and its mechanism pathways significantly promote the ecosystem products value realization. In summary, considering the empirical results from Models (8) to (11), it is found that the implementation of GFPZ can significantly facilitate the ecological transformation of industries and the ecological industry development, further promoting the value realization of ecosystem products. Hypotheses 2 and 3 are verified.

Table 5 Mechanism test results.
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Discussion

Spillover effect analysis

There is an evident fact that regions are interrelated, whereas the traditional DID Model does not take into account the possibility of propagation of the treatment effects on both the treated regions and on the surrounding areas (control regions), which is possible to generate cross-sectional issues and produce biased estimations. Hence, we further introduce the spatial DID model (SDID) to deeply analyze the spillover effects of GFPZ implication in different regions on ecosystem product value realization. We draw on Dubé et al. (2014) and Chagas et al. (2016) to construct the SDID model based on Eq. (7).

$$\begin{array}{l}Realizationrat{e}_{it}=\rho W\times Realizationrat{e}_{it}+{\beta }_{1}Trea{t}_{i}\times Yea{r}_{t}\\\qquad\qquad\quad\qquad\quad\;\;+\,{\alpha }_{1}W\times Trea{t}_{i}\times Yea{r}_{t}+{\beta }_{2}{X}_{it}+{\beta }_{3}W\\\qquad\qquad\quad\qquad\quad\;\;\times\, {X}_{it}+{\mu }_{i}+{\gamma }_{t}+{(1-\delta W)}^{-1}{\varepsilon }_{it}\end{array}$$
(9)

Where W is a geographic distance weight matrix denoted by the spatial relation among regions; ρ is the spatial autocorrelation coefficient of the dependent variable; α1 is the GFPZ policy spillover effect; β3 is the spillover effect of the control variable; and δ is the spatial autocorrelation coefficient of the random error. Equation (9) is the general form of the SDID model, which can be classified into three models according to whether the correlation coefficient is zero or not: the spatial error DID model (SEM-DID), the spatial lagged DID model (SLM-DID) and the spatial Durbin DID model (SDM-DID), which will be further selected by correlation tests in the spatial spillover effect analysis.

The global Moran index can examine the spatial correlation and spatial spillover effects of ecosystem value realization rate, which is necessary to be verified before estimating the SDID model. Table 6 shows the global Moran index of ecosystem value realization rate from 2011 to 2020. It can be seen that the Moran indices are all positive, and most of them are significant at the 1% level, illustrating a significantly positive spatial correlation and spillover effects of ecosystem value realization rate among regions.

Table 6 Moran index of ecosystem value realization rate.
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Table 7 reports the results of the SDID model tests. Specifically, the LM-test, LR-test, and Wald-test pass the 1% significance level, demonstrating that using the SDM-DID model is superior to the SEM-DID model and the SLM-DID model. In addition, after passing the Hausman test, we perform the SDM-DID model controlling for two-way fixed effects.

Table 7 Tests of SDID models.
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Table 8 shows the regression results of the SDID model based on the geographic distance weight matrix. As represented in Model (12) and (13), the coefficients of the spatially weighted term W × Treati × Yeart are both significant at the 5% level when considering the underlying influences of control variables, indicating that GFPZ has a spatial spillover effect on ecosystem product value realization. We further decompose the coefficient of W × Treati × Yeart into direct, indirect, and total effects through partial differentiation, where the direct effect contains not only the policy impact on the pilot regions, but also the impact of the pilot regions on the surrounding non-pilot areas in turn on the pilot regions, and the indirect effect refers to the policy impact on the surrounding non-pilot regions (Lee et al., 2023). The estimated coefficients of the direct and indirect effects are both significantly positive at the 1% levels, indicating that the implementation of GFPZ can not only promote local ecosystem product value realization but also exert an obvious spatial promotion effect and radiation effect on the surrounding areas. Indeed, the dividends brought by green finance exhibit demonstration effects, which can promote green innovation and the development of energy conservation and environmental protection companies through knowledge and technology spillover, further bringing about the upgrade of the industrial structure and the improvement of the ecological environment in the surrounding areas, forming a new development pattern with mutual promotion to realize the value of ecosystem products. The results are consistent with the findings of Li and Gan (2021), Wang et al. (2021c), Huang et al. (2022b) and Lee et al. (2023), which argue that green finance has a spillover effect on surrounding areas.

Table 8 SDM-DID model regression model.
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We also introduce the spatial weight matrix of the economic distance (average of GDP per capita from 2011 to 2020) in order to enhance the robustness of the results. As is shown in Tables 7 and 8, the coefficients of the spatially weighted term W × Treati × Yeart are still significantly positive at the 1% level, indicating a positive spillover effect of GFPZ on ecosystem products value realization, confirming our estimations are robustness.

Heterogeneity analysis

As highlighted by the previous analysis, the process of ecosystem product value realization is heterogeneous due to the characteristics of ecosystem products and different policy goals. It is important to explore the heterogeneity to facilitate the green finance system construction and promote sustainable development. Therefore, this study further analyzes the heterogeneous policy effects in regional finance development levels, geographic location, and policy goals.

Regional heterogeneity

We measure regional finance development level by the ratio of the value added of the financial sector to GDP in each province, and divide the whole sample into two groups of regions with high and low financial development levels, using the median of the whole sample as the boundary. We further classify the sample into two sub-samples of the eastern region and the central, western region by geographic location. Following this, we apply the DID model to test the regional heterogeneity of green finance policy according to Eq. (7). The regression results are shown in Table 9. In the central, and western regions and the regions with higher levels of financial development, the policy effect of GFPZ on ecosystem product value realization is more significant; while it is insignificant in lower-level financial development regions. The rationale for this could be that a higher financial development level means a more complete finance system, which is conducive to the implementation of the green finance policy, otherwise may bring lower financing efficiency, leading to information asymmetry to weaken the policy effect, similar to the opinion of Lee et al., (2023), Zhou and Xu (2022); compared with east region, the central and western region, including Jiangxi, Guizhou and Xinjiang, has relatively inferior economic dynamism but superior ecological civilization construction foundations. With the implementation of the GFPZ policy, more financial support and innovative green financial products will be engaged in the environment management and the ecosystem products market transactions, resulting in a more significant improvement in the value realization rate. Besides, the eastern region has always been of economic advancement and high environmental efficiency (Zhang et al., 2023c), resulting in the improvement is not as apparent as in the central and western regions.

Table 9 Results of regional heterogeneity test.
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Policy goals heterogeneity

According to GFPZ policy’s objectives, we divide the pilot regions into ecologically vulnerable regions (Guizhou and Xinjiang), industrial upgrading regions (Zhejiang and Guangdong), and resources development regions (Jiangxi) to test the heterogeneity of policy goals. Considering that there are only one or two provinces in the treatment group, using the DID model may result in sample selection bias, while the PSM-DID model has more stringent selection principles for the control group, so we conduct the Synthetic Difference-in-Difference method (Synthetic DID) proposed by (Arkhangelsky et al., 2021), to test the policy effects. The control variables are consistent with the previous analysis. Compared with the traditional DID model, the Synthetic DID method improves the robustness of the estimation results and the overall accuracy of the model through the setting of weights, which can be widely introduced to the fields of program evaluation (Arkhangelsky et al., 2021; Clarke et al., 2023). Figure 7 demonstrates the impact trend of the GFPZ policy, and it can be seen that the value realization trajectory of the treatment group remains basically parallel to the control group before the policy implementation, while a significant upward shift occurs after the policy shock, which also proves that the estimation results of the main effect are robust.

Fig. 6: Results of the parallel trend test.
figure 6

(1) We employ the event study method to test the parallel trend hypothesis. We define the gap in ecosystem products value realization rate between the treatment and control groups in 2016 as a baseline of 0 with a 95% confidence interval to examine the dynamic policy effects. (2) It can be seen that the ecosystem product value realization rates between the treatment and control groups exhibited no notable difference before 2017. After the policy implementation, although the gap in the first year is not statistically significant, an upward trend is observable, and the gaps in the second and third years in the treatment group exhibit a significant rise relative to the control group. (3) Given the potential time-lag effect of the green finance policy on environmental protection and ecological restoration sectors, the cumulative influence of the green finance policy on the value realization of ecosystem products can be considered significant and the parallel trend hypothesis is confirmed.

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Fig. 7: Results of the Synthetic DID model.
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(1) Using the synthetic DID model, it demonstrates the impact of the GFPZ policy. (2) It can be seen that the ecosystem product value realization trajectory of the treatment group remains basically parallel to the control group before the policy implementation, while a significant upward shift occurs after the policy shock. (3) It shows a significant positive impact of GFPZ policy on ecosystem product value realization and further proves that the estimation results of the main effect are robust.

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Table 10 reports the average treatment effect (ATT) of GFPZ based on the Synthetic DID method. Model (1) corresponds to Fig. 6, indicating that the value realization rate of ecosystem products in pilot regions was significantly enhanced after the GFPZ policy, of which the average treatment effect is 0.032 and is significant at the 1% level. Models (2) to (4) report the effects of the GFPZ policy on pilot regions with different policy goals. The average treatment effects for ecologically vulnerable regions and industrial upgrading regions are 0.041 and 0.020, passing the significance test at 1% and 5%, respectively. Consistent with the findings obtained by SCM, Guizhou, and Xinjiang as the ecologically vulnerable regions, have the most significant increase in ecosystem product value realization after GFPZ implementation, followed by the industrial upgrading region. And the policy effect in Jiangxi, with the orientation of resource development, is not significant. The possible reasons are as follows. Firstly, resource development needs to go through the transformation of “resource-asset-capital”, which will take a long time period. Secondly, establishing the ecosystem product market and improving trading platforms are still in the initial stage, resulting in a less effective policy outcome in value realization. These suggest that the GFPZ policy should pay more attention to the field of resource development, giving full play to the role of green financial instruments in resource development projects. In addition, pilot regions need to summarize the institutional experience to support the value realization of ecosystem products according to local conditions, innovate green finance products to broaden funding resources, further activate ecological resources, and accelerate the value realization of ecosystem products.

Table 10 Results of the test in heterogeneous policy goals.
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Conclusions and policy implications

Conclusion

This study takes the GFPZ policy in China as a quasi-natural experiment and conducts a synthetic control method to verify the policy effect of GFPZ on ecosystem product value realization using the province-level panel GEP data from 2011 to 2020. The main findings are as follows.

Firstly, we find that the GFPZ policy significantly promotes the value realization of ecosystem products, this positive impact still holds after the test of spatial placebo studies, leave-one-out estimation, DID method, and controlling the effects of other policies.

Secondly, regarding the mechanism pathways, our study proves that the ecological transformation of industries and ecological industry development are important mechanisms of GFPZ to promote the value realization of ecosystem products in the pilot regions.

Finally, we note that the implementation of GFPZ exhibits certain spillover effects and heterogeneous features. For regional heterogeneity, there are more apparent policy effects in the central, and western regions and regions with higher financial development levels. For policy goals heterogeneity, GFPZ policy has greater impacts in ecologically vulnerable regions, followed by industrial upgrading regions, while the policy effect on resource development region is not significant.

Policy implications

Our findings can provide a reference for comprehensively assessing green finance policy effects and developing replicable experiences. Based on the empirical evidence, the following policy implications are put forward accordingly.

Firstly, the government should actively formulate typical models of GFPZ and further augment the scope of the pilot zones, thereby developing replicable paths for fostering a positive impact on ecosystem product value realization. On the one hand, the government should play a supporting role in the mainstream development of ecosystem products value in the economic system by enhancing the green financial system. Environmental risks should be quantified and incorporated into the government’s decision-making and performance appraisal. On the other hand, for financial institutions, it is imperative to match the demand for investment and the supply of ecosystem products in the financial market, more innovative and diversified green financial products and services should be explored, such as settlement, financing, and intermediary.

Secondly, it is necessary to accelerate the penetration of the green finance system into the whole process of ecosystem product value realization, including production, valuation, exchange, and consumption, utilize the policy effects of GFPZ in financial resources allocation, market pricing, and risk management to promote ecological industry development and facilitate the ecological transformation of industries. Government and enterprises should further expand new space for development, activate the potential for value-added ecosystem products, and accelerate the optimization and integration of the whole industrial chain to enhance the overall efficiency of GFPZ. Moreover, the government should fully realize the radiation and demonstration effects of GFPZ to strengthen innovative collaboration and sharing among regions, thereby promoting the sustainable and coordinated development of green finance, ecological industry, and ecosystem value realization.

Finally, the heterogeneous policy effect of GFPZ should be fully considered to avoid a one-size-fits-all approach. For ecologically vulnerable regions, green finance services should be more standardized and transparent, environmental costs should be incorporated into decision-making to obtain sustainable ecological projects. Industrial upgrading regions should equally focus on the important role of high-tech and ecological industry, making full use of the advantages of financial resources as well as the demand for industrial upgrading to build a coordinated relationship between environment protection and economic development. For resource development regions with relatively lagging policy outcomes, it is necessary to further strengthen the support of finance policy to realize the expansion of green enterprises and the internalization of environmental costs of high-polluting enterprises. With the ecological transition of regional industrial structure, the ecological industry will also grow significantly to turn development into a more green, efficient, and safe model, promoting the accelerated integration of ecosystem product markets and the green finance system.