The Impact of Green Finance on Export Technology Complexity: Evidence from China

Abstract

With the improvement of the technology level and the prevalence of the concept of environmental protection, green finance has been gradually applied to the field of production and economic development. As an important measure of economic development, the export sector is inevitably affected by the development of green finance. However, using the high technology of exports to analyze the relationship between green finance and exports has not drawn much attention. Based on the panel data from 30 provinces in China from 2011 to 2020, this study empirically examined the impact of green finance as well as export technology complexity, by using a combination of fixed effects and mechanism analysis. The results show that green finance has a significantly positive impact on export technology complexity, which means that an increase in the depth of green finance can improve export product quality. In terms of regional heterogeneity, the impact of green finance on the high technology of exports is greater in the eastern region than in the central and western regions of China. In terms of the manifestation of green finance, the effect of green bonds on export technology complexity is greater than that of green investment and insurance. Green finance improves the export technical complexity through three intermediaries, such as green technology innovation, capital investment strength, and product upgrading capacity. This study highlights the importance of green finance and provides new methods for governments to assist in the formulation of policies that can improve export technology complexity.

1. Introduction

Since the reform and opening up, China has mainly relied on resources, labor, and other factor inputs to achieve economic development, which has led to a series of environmental problems; exports have been lacking in core technology competitiveness, remaining mainly in labor-intensive industries, and the technology level has been at the middle and low end globally. In 2020, Xi Jinping proposed that China should achieve its carbon peak by 2030 and strive to become carbon neutral by 2060. The importance that China attaches to the ecological environment proves that on the road to building a strong trade nation in this new era, the relentless promotion of high-quality development through green development is an inherent requirement for China’s foreign trade to move to the middle and high end. To promote the process of green development, an efficient green financial service system is indispensable. Currently, China has established a green financial system and is expected to provide financial support for green technological innovation. It also provides a sustainable development direction for export technology upgrading. However, the development of export trade still suffers from a lack of green innovation capacity and excessive pollution control costs, and the traditional foreign trade model is facing a bottleneck that restricts development due to environmental factors. Can the green financial system’s enhancement of the high technology of exports be the key to break the bottleneck? Thus, this study investigates whether green finance has an enhancing effect on the high technology of China’s exports, which would explore a new development path to accelerate the building of a strong trade nation. This paper selects the panel data from 30 provinces (municipalities and autonomous regions, excluding Tibet) from 2011–2020 and digs deeper into the impact of green finance on the high technology of exports, in order to offer suggestions for upgrading the high technology of China’s exports, developing high-quality export trade, and achieving the goal of its carbon peak. The possible marginal contributions of this paper are as follows: firstly, to try to interpret the impact of green finance on the upgrading of the high technology of exports from the perspective of the high technology of exports in the context of carbon peak attainment and green development; secondly, to study the mechanism of the impact of green finance on the high technology of exports; thirdly, to conduct more precise time series and regional heterogeneity on the basis of basic regressions and robustness tests using the provincial panel data; fourthly, the impact of green finance on export technology complexity is further analyzed by using green technology innovation and green capital investment as indirect effect variables.

2. Literature Review

Green finance integrates the potential return, risk, environment, and cost into the development of enterprises, by fully considering the potential environmental factors before making decisions about the development strategy of enterprises, guides the flow of social resources to the direction of environmental protection, and promotes the sustainable development of the financial industry. The development of green finance is a new development concept put forward by the financial industry under the current situation of China’s strengthening of environmental protection, which is conducive not only to environmental protection but also to promoting the sustainable development of the financial industry, and has a very bright development prospect. Green finance is a product of the development of the financial sector in this new era, and academics have now realized the importance of green financial development for the high-quality development of various industries. For example, green finance has a positive impact on the ecological development of China’s industrial structure, and green finance could significantly promote the quality of regional export products [1]. The improvement of export product quality is also closely related to the complexity of export technology [2]. From the perspective of enterprise exports, green finance can significantly promote the development of China’s export trade, improve the margin of intensity and expansion of export trade, and optimize the structure of export trade [3]. The relevant research focuses on three areas. First, it focuses on the measurement of green finance, which is defined as green bonds, green banks, and carbon market instruments; the expansion of the scale of financing for environmentally beneficial investments, such as fiscal policy, green central banks, financial technology, community-based green funds, etc.,; and the measurement of green finance through relevant industry indicators [4,5]. Green finance also includes all forms of investment or loans that consider environmental impacts and enhance environmental sustainability; therefore, loans related to sustainable development can be used as a measure of green finance [6]. Some indicators are used, such as the equatorial principle ratio of commercial banks and the volume of disbursed green credit, to describe the level of green finance development in a country [7]. Second, the relevant research focuses on the real impact of green finance, which is, for example, the best financial strategy to reduce CO₂ emissions by investigating the development of green finance and the state of CO₂ emissions in the top 10 economies that support green finance [8]. A DEA composite indicator can also be used to build a green finance index to measure the energy, environment, and the combined impact of financial variables [9]. A quantitative analysis of export products using the maximum likelihood method concluded that green finance has a strong positive relationship on the financial performance of exporting firms and that this technique of introducing environmental issues into export products can be effective at reducing costs [10]. In addition, the uniqueness of green products has a positive impact on export entrepreneurship and green export performance, when studying the relationship between green product development capabilities and export product performance [11].

Third, the relevant research focuses on the impact of finance on the high technology of exports. Analyzing the relationship between the two, export-oriented companies with technology property rights and intellectual property rights have much larger sales and R&D expenses than those without technology property rights, which indirectly illustrates the close relationship between technology and export product capital [12]. Specifically, through credit financing, Financing of export products can promote enterprises’ ability to develop and innovate products and promote export technological growth [13]. Good company performance needs to be achieved by producing export-technology-oriented products [14]. In addition, technology-related factors are important for SMEs’ export behavior [15]. In addition to the technology factor, other important factors affecting SMEs’ export activity are productivity spillovers through the increased availability of external financing [16]. In the case of India, financing is important for exporting goods, but firm size, total factor productivity, and investment in technology are also important drivers of export decisions and export levels for Indian service firms. The dual addition of financing capacity and product technology level is the key to improve the competitiveness of enterprises’ export products [17,18,19,20,21]. On the contrary, according to the “pollution haven” hypothesis, it can be argued that moving environmentally harmful stages of production overseas can increase a firm’s competitiveness. Firms may indeed benefit from the process of ecological dumping based on imports of polluting inputs from abroad [22,23,24], indirectly proving the importance of green development for corporate export. The different degrees of the second phase of special green development have different orientations for export products. Moreover, innovative firms can move environmentally harmful stages of production offshore are more competitive [25,26,27,28]. In summary, most of the literature at home and abroad focused on the positive impact of green finance on and the role of finance in the high technology of exports, but there is a lack of research on the impact of green finance on the high technology of exports.

3. Theoretical Analysis

The high technology of exports includes comprehensive information about the technological innovation capacity of export products and the production efficiency of products, the level of which mainly depends on technological innovation and technological progress [29]. Nevertheless, green finance has been influencing enterprises’ export product orientations and green technology innovation approaches since its emergence and its development. Green finance can influence the export technology complexity of enterprises or industries by changing the form of green technology innovation, optimizing the investment structure of capital and promoting the green upgrading of export products. Through the literature summarized above, it is easy to see that there is a certain coupling between green finance and product technological innovation, which can improve the green product export technology of enterprises.

Green finance can change the form of the technological innovation of enterprises and, thus, affect the complexity of their export technology. Green innovation is increasingly recognized as an important determinant of export product quality, which plays a key role in international business and finance. Existing research suggests that green innovation can indeed contribute to the improvement of export product quality [30]., which provides theoretical guidelines for the relationship between green finance and export products in this study. In general, green finance has to take into account external factors such as environmental issues in addition to the investment development of financial institutions [31]. Raising financing for climate investment by issuing green bonds would be key [32]. The emergence of green financing products can increase the use of renewable energy by companies, which, in turn, changes the standards of exported products and improves the technical capacity of exported products [33]. Technological innovation emphasizes that although green technological innovation requires more capital investment than other forms of innovation, there is a great boost to the vitality and productivity of enterprises, if social capital can be driven to green technological innovation and change the original high-energy-consuming innovation structure of enterprises through reasonable green financial transmission [34]. In addition, through technological innovation and green finance, enterprises with higher technological R&D investment can, to a certain extent, obtain preferential interest rates for loans from financial institutions more easily [35]. Thus, it can be seen that green finance and technological innovation enable enterprises to better achieve green technology development and transformation through a two-way influence.

Green finance can optimize the investment structure of capital to enhance the complexity of the export technology of enterprises. The adoption of environmental protection measures can directly reduce material and energy-use costs, capital assets, and labor inputs (for example, by increasing loyalty and commitment) [36]. In this way, environmental investments can lead to superior economic performance in terms of higher productivity or efficiency. Green finance optimizes the allocation of capital, which not only increases economic benefits but also reduces environmental pollution to a certain extent, but the green finance effect and its spatial spillover are still small, so it is necessary to strengthen the development of green finance. In addition, the capital aggregation function of green finance is realized through the capitalization process; through the financial market system and financial institutional arrangements, scarce resources are transformed into capital that is both valuable and profitable, which can be traded on the market [37,38]. Enterprises are bound to need a lot of financial support to upgrade the complexity of their export technology, but it is difficult for them to meet their R&D needs by themselves. Therefore, green financial support such as the green credit and green bonds provided by financial institutions to enterprises not only meets the problem of the financial demand for technological innovation but also realizes the development concept of energy conservation and environmental protection.

Green finance enhances the technical complexity of exports by promoting green product upgrades. Among them, product upgrading mainly includes the improvement of the productivity level and industrial structure adjustment [39]. Firstly, green finance restricts the flow of capital to polluting enterprises through the role of capital guidance, so that it can be reasonably allocated to the advantageous environmental protection industry, which optimizes the allocation of capital, while promoting industrial growth performance; this, in turn, promotes the improvement of the green productivity level and increases the technical complexity of products. Secondly, in terms of the industrial structure adjustment, green development itself advocates for cities to develop energy-saving and environmentally friendly industries and enterprises to produce environmentally friendly products. The government and financial institutions develop green finance through low-carbon policies to optimize the traditionally sloppy industrial structure and achieve industrial upgrading, which promotes the green transformation of enterprises and enhances the technical complexity of their products by increasing green technology. While improving the efficiency of resource utilization, this improves the quality of environment and realizes the upgrading of the export trade value to be green and efficient.

4. Materials and Methods

4.1. Variables

4.1.1. Dependent Variable

Export Technical Complexity (EXPY). In this paper, we use the measure of Hausmann [40] to calculate the export technology complexity. Due to the large income disparity between regions, using national-level GDP per capita to estimate provincial export technological sophistication inevitably leads to estimation bias. Therefore, in this paper, the actual regional per capita GDP is used instead of the national-level per capita GDP to construct the complexity of sectoral or product exports. The technical complexity of exports is measured in two main steps: first, the technical complexity index of exports is measured for each traded product, specifically by weighting the sum of the real per capita GDP of each province, which is weighted as the ratio of the proportion of exports of K products in each province to the sum of the proportion of K products in each province; second, the weighted average of the technical complexity of exports of products can be calculated at the provincial level of export technical complexity. The calculation method is as follows, where is the export technical complexity index of K products, is the real per capita GDP of the province, refers to the export of provincial K products, and is the total export value of province j.

Next, the technical complexity of exports of province j is calculated, i.e.,

$$EXP{Y}_j={\displaystyle \sum j\left(\frac{X_{jk}}{X_j}\right)}PROD{Y}_j$$

(1)

where is the export technical complexity of province j.

4.1.2. Independent Variable

Green Finance Indicators (GFIN). Due to the relatively short development time of green finance in China, a unified measurement method has not yet been formed in the academic community [41,42]. Most of the current studies focus on green credit as a proxy variable of green finance; some studies also analyze green finance through the quantitative synthetic control method, but the first two methods are somewhat one-sided and do not consider the qualitative aspects of green finance from the connotations included in green finance; therefore, the third method is more reasonable for constructing an index system to measure the development level of green finance. In this study, the index system of green finance development index is constructed to measure GFIN. Since 2010, green financial information disclosure began to be standardized, and the data are gradually consistent in terms of caliber; however, only data from provincial-level regions are available, so this paper selects the panel data from 30 provincial-level regions (except Tibet) in China from 2011–2020 as the sample, and the specific indicators are as follows in

Table 1. Green finance development index.

Primary Indicators	Secondary Indicators	Weights	Specific Indicators	Data Sources
Green Finance Indicators (GFIN)	Green Securities	0.1083	Total market value of six high energy-consuming industries/total market value of A-shares	Guotaian database
	Green insurance	0.1718	Agricultural insurance expenditure	Statistical yearbook of each province/WIND database
	Green investment	0.4238	Share of government fiscal expenditure on energy conservation and environmental protection	China Environmental Statistics Yearbook
	Green Credit	0.2961	Interest expenditure for high energy firm consumption	China Industrial Statistical Yearbook

. For the determination of the weights in this paper, the entropy weight method with higher accuracy and objectivity is chosen.

4.1.3. Control Variables

Foreign Direct Investment (FDI). The amount of FDI reflects the capital inflow of enterprises in cross-border trade. The amount of FDI affects China’s foreign trade performance in two directions: the expansion of trade scale and the improvement of export product mix.

Total Factor Productivity (TFPP). TFPP and technological progress are very closely related to each other, so the growth of TFPP can be a concentrated manifestation of technological progress; thus, TFPP has a certain driving effect on the technological spillover of export products.

The degree of government intervention (GOV). The government’s financial support acts as the “visible hand” of the market to macro-control the economy, especially in the technological upgrading of export products, and the government has to adopt some favorable trade policies or financial subsidies, when necessary, in order to protect the technological advantage of domestic exports.

Total Trade Costs (TITC). The total trade costs are obtained by weighting the bilateral trade costs with the export value of each province to different countries, which, to a certain extent, restricts the quantity of export products.

High and New Technology Expenditures (HNTE). HNTE are measured by taking the natural logarithm of the number of domestic patent applications granted. The level of innovation of high technology represents the production efficiency of enterprises, while the product’s own technical complexity increases with the increase in technology expenditure.

4.2. Data Sources and Statistical Characteristics

In this paper, the panel data from 30 provincial regions (except Tibet) from 2011–2020 are selected as the sample, and the data are mainly obtained from the export product data classified by SITC and HS codes of the United Nations Standard International Trade Classification Statistical Database, Guotaian database, provincial statistical yearbooks, China Environmental Statistical Yearbook, WIND database, and China Industrial Statistical Yearbook. Among them, the import and export data use the three-digit code classification of SITC Rev. 2 for 239 export products in 2011–2020. The descriptive statistics of specific data are shown in

Table 2. Descriptive statistics.

Variables	Obs	Mean	Std. Dev.	Min	Max
EXPY	300	0.4471	0.2451	0	1
GFIN	300	0.4630	0.1017	0.1995	0.8297
FDI	300	0.0498	0.1116	0	1
TFPP	300	0.1906	0.1129	0.0621	0.839
GOV	300	0.4757	0.3641	0	1
TITC	300	0.2445	0.1059	0.0852	0.6268
HNTE	300	1.7687	0.6118	0.6433	5.0141

.

4.3. Model Specification

4.3.1. Benchmark Regression Model

The following econometric model is constructed to investigate the impact of green finance on export technology complexity:

$$EXP{Y}_{it}={\beta }_0+{\beta }_1+GFI{N}_{it}+\beta X+{\lambda }_t+{\mu }_{it}$$

(2)

where i represents the individual; t denotes the year; $EXP{Y}_{it}$ stands for the export technology complexity i in the year t; $GFI{N}_{it}$ represents the green finance indicators i in year t; and X is the set of control variables. The control variables at the individual level specifically include Foreign Direct Investment $(FD{I}_{it})$, Total Factor Productivity $(TFP{P}_{it})$, the degree of government intervention $(GO{V}_{it})$, Total Trade Costs $(TIT{C}_{it})$, and High and New Technology Expenditures $(HNT{E}_{it})$; ${\lambda }_t$ is the year fixed effect; and ${\mu }_{it}$ is the random error term.

4.3.2. Mechanism Analysis Models

In the previous analysis, green finance can enhance the high technology of exports by innovating technological forms, optimizing capital input structure, and upgrading products [43]. To further test the channel role of green finance and the high technology of exports, this study constructed the following models:

$$EXP{Y}_{it}={\alpha }_0+{\alpha }_{1}GFI{N}_{it}+\alpha X_{it}+{\epsilon }_{it}$$

(3)

$$ME{D}_{it}={\beta }_0+{\beta }_{1}GFI{N}_{it}+\beta X_{it}+{\epsilon }_{it}$$

(4)

$$EXP{Y}_{it}={\delta }_0+{\delta }_{1}GFI{N}_{it}+\lambda ME{D}_{it}+\delta X_{it}+{\epsilon }_{it}$$

(5)

where i and t indicate firms and years, respectively; MED are mediating variables, which are the proxy variables for technological innovation, capital investment, and product upgrading; ${\epsilon }_{it}$ are random error terms; and the meanings of the other variables are as follows.

Technological Innovation Capability

Innovation is a powerful driving force for high-quality development, especially for regional development innovation capacity, which is a decisive factor for the quality of its economic development; technological innovation is the most direct category among all kinds of innovation activities that have a role in the quality of economic development [44]. Therefore, the study selects R&D expenditure (EXP) and R&D personnel input (HUM) to portray the technological innovation capability at the regional level.

Green Capital Investment

The development of green finance inevitably has a certain impact on the capital structure of enterprises, with capital flowing from highly polluting and energy-consuming production lines to environmentally friendly ones. In addition, the level of capital investment also affects the efficiency of the technological transformation of enterprises’ export products. Green capital investment mainly includes the investment of environmental pollution control funds and green new product development investment. Therefore, in this paper, the above two variables are selected to indicate the intensity of regional capital investment.

Degree of Product Upgrading

China’s green credit has a significant effect on promoting the upgrading of industrial structure, while the adjustment and upgrading of product structure promote the economic development of the region. Green finance emphasizes the gradual withdrawal of investment in highly polluting and high-consumption industries and the strengthening of investment support in energy conservation, environmental protection, clean energy, and other fields, which are environmentally friendly industries with a development that not only will not cause further pollution to the environment but also effectively manage the existing environmental pollution by virtue of their technological development and environmental protection facilities. This paper measures the intensity of regional product upgrading through two indicators: productivity level and degree of industrial restructuring.

5. Results and Discussions

5.1. Benchmark Empirical

Table 3. Benchmark results.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
GFIN	0.8996 *** (0.1167)	0.7294 *** (0.1249)	0.7681 *** (0.1188)	0.7539 *** (0.1195)	0.6012 *** (0.1141)	0.5886 *** (0.1148)
FDI		0.5091 * (0.0656)	0.4986 * (0.0650)	0.4687 (0.0755)	0.2689 (0.0941)	0.2747 (0.0908)
TFPP			0.0939 ** (0.0317)	0.0952 ** (0.0317)	0.0936 ** (0.0303)	0.0790 (0.0302)
GOV				0.1233 (0.1118)	0.3791 * (0.1269)	0.3860 ** (0.1251)
TITC					−0.1778 *** (0.0444)	−0.1828 *** (0.0416)
HNTE						0.3004 *** (0.0838)
Constant	0.05754 * (0.0562)	0.0089 ** (0.5431)	0.0202 **(0.0535)	0.0645 ** (0.0701)	0.3725 * (0.1073)	0.3622 * (0.1055)
R-squared	0.2044	0.3978	0.4170	0.4189	0.4732	0.4913

Notes: standard errors in parentheses; * p < 0.10, ** p < 0.05, and *** p < 0.01.

presents the results of the benchmark regression of green finance on the export technological sophistication. The first column separately examines the relationship between green finance and the technical complexity of exports, and the results show that the regression coefficient of 0.8996 is significant at the 1% level. This indicates that green finance can indeed increase the technical complexity of exported products. By increasing the control variables, the regression coefficients of green finance on the technical complexity of exports in columns (2)–(7) have slight changes but do not affect the overall significance results. The sixth column of the table shows that the regression coefficient of green finance on the export technical complexity is 0.5886 and is significant at the 1% level, further indicating that green finance has a significant effect on the improvement of export technical complexity, i.e., green finance can contribute to the improvement of export technical complexity. This finding provides a new channel to accelerate the transformation of export products and upgrade the technical complexity of exports. Banks and other financial institutions can provide opportunities for enterprises to export green products by issuing green bonds and other funds specifically for supporting energy conservation and emission reduction and developing clean energy projects, which realizes significant and quantifiable carbon emission reduction benefits.

In terms of control variables, the regression coefficient of foreign direct investment is not significant in the interval shown in the sample, indicating that as China enters the stage of high-quality development, relying solely on foreign-owned enterprises and wholly foreign-owned enterprises or relying on the power of foreign capital for industrial transfer is not effective in improving the technological sophistication of exported products, and the opposite may become a foundry for some developed countries to claim cheap labor. As we know from the production function, in addition to capital and labor, technology is also a key factor limiting the development of productivity. Therefore, by promoting domestic export technology upgrading and combining with the guiding idea of carbon emission, green technology becomes a new trend for enterprises to improve the technical complexity of exports. In addition, it is worth noting that the regression coefficient of trade plus TITC is significantly negative, indicating that the increase in trade cost is instead detrimental to the technological upgrading of exported products. This increase in cost implies a lower level of technology, while a decrease in cost can lead to an increase in efficiency. Reduced trade costs are more likely to achieve scale of operation, and mechanized production better reflects the upgrading of Internet technology. The degree of government intervention and high-tech spending are both significantly positive, indicating that both government financial support to exporters from external forces and enterprises’ own spending on high-tech industries are important factors in upgrading the high technology of exports.

5.2. Robustness Test

To ensure the robustness of the benchmark results, this study conducted a series of robustness tests by replacing the GMM model, replacing the explanatory variables, and replacing the sample data interval.

5.2.1. Replacing the GMM Model

In order to ensure the robustness of the model of green finance on export technology complexity, GMM is used in replacing the model. As can be seen from the first column of

Table 4. Robustness test results.

Variables	(1)	(2)	(3)
GFIN	0.4156 ** (0.1806)		0.9022 *** (0.0273)
GL		0.5655 *** (0.2880)
FDI	0.2353 (0.2251)	0.2284 ** (0.0850)	0.3030 (0.0764)
TFPP	0.0384 ** (0.0185)	0.0607 ** (0.0293)	0.1893 *** (0.0262)
GOV	0.4012 (0.3221)	0.4301 *** (0.1246)	0.0590 (0.1318)
TITC	−0.2505 ** (0.1147)	−0.1343 *** (0.0401)	−0.1610 *** (0.0461)
HNTE	0.4532 *** (0.1948)	0.2989 *** (0.0819)	−0.0467 (0.6012)
Constant	0.5569 ** (0.2534)	0.3167 *** (0.0911)	0.7217 *** (0.1033)
R-squared		0.5011	0.7537
AR (1)	0.004
AR (2)	0.367
Hansen test	0.218
Observations	300	300	150

Notes: standard errors in parentheses; ** p < 0.05, and *** p < 0.01.

, there is still a significant contribution of green finance to export technology complexity when replacing the model, which indicates that the empirical results obtained in this paper are more robust.

5.2.2. Replacing Explanatory Variables

Green finance can be understood as the combination of the trend of finance in this new era and the concept of sustainable development in the financial field. In this paper, green finance is replaced with green credit, which is similar in meaning. Green credit refers to the loan support provided by financial institutions to enterprises related to the concept of sustainable development. As can be seen from the second column of Table 4, the explanatory variable green credit still shows a significant positive effect on the technical complexity of exports, which again supports the robustness of the empirical results obtained in this study.

5.2.3. Replacing the Sample Data Interval

In the process of the high-quality development of green finance in China, the results of each stage of development are different, and the impact on the complexity of export technology varies slightly. After the introduction of green credit in 2007, China decided to establish a green financial system in September 2015. Therefore, in this paper, 2016 is taken as the critical point of green finance development for the robustness test, and the data from 2016–2020 are extracted to test the robustness of the model. As can be seen from the third column of Table 4, after replacing the sample data interval, the effect of green finance on export technology complexity is still significantly positive, further indicating that the empirical results obtained in this paper are more robust.

5.3. Endogeneity Test

The least squares (OLS) estimation of the panel fixed effects model in the baseline regression model may have endogeneity problems. On the one hand, the development of green finance may have reverse causality on the complexity of export technology. This is because the complexity of export technology may be manifested in the use of new energy sources of green technology, etc., which cannot be innovated without the support of green finance. On the other hand, omitted variables may also lead to the existence of unavoidable endogeneity problems in OLS. Therefore, the 2SLS regression was conducted based on Lee and Gordon (2005) [45], using the weighted average of the green finance obtained from other provinces, which were weighted by the inverse of the geographical distance as the instrumental variable. The specific regression results are shown in

Table 5. Endogeneity test results.

Variables	(1)	(2)	(3)	(4)
GFIN	0.2305 ** (0.0179)	0.4069 * (0.0152)	0.5158 ** (0.0156)	0.5886 *** (0.1134)
Province fixed effect	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes
Constant	0.0086 * (0.0079)	0.0182 ** (0.0067)	0.0018 * (0.0072)	0.3622 * (0.1042)
R-squared	0.2752	0.5334	0.6402	0.4913
Kleibergen–Paap rk LM	12.335 [0.000]	12.337 [0.000]	12.361 [0.000]	12.689 [0.000]
Kleibergen–Paap rk Wald F	42.624 {16.25}	42.626 {16.25}	42.631 {16.25}	42.847 {16.25}
Observations	300	300	300	300

Notes: standard errors in parentheses; * p < 0.10, ** p < 0.05, and *** p < 0.01.

. These results indicate that green finance can indeed significantly affect the technological upgrading of export products, which is generally consistent with the previous findings.

5.4. Mechanism Analysis

Columns two and three of

Table 6. Mechanism test results.

Variables	(1) EXP	(2) EXPY	(3) NPPY	(4) EXPY	(5) IND	(6) EXPY
GFIN	0.6975 *** (0.0945)	0.4863 *** (0.1094)	0.0229 * (0.0744)	0.0951 ** (0.1177)	0.3469 *** (0.6232)	0.3822 *** (0.1256)
EXP		0.8653 *** (0.0623)
NPPY				0.2234 ** (0.0903)
IND						0.0884 *** (0.0182)
Constant	0.1746 *** (0.0394)	0.0936 * (0.0477)	0.0616 (0.0354)	0.0713 (0.0574)	0.1320 (0.2708)	0.0692 (0.0541)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Province fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
R-squared	0.1599	0.5338	0.0004	0.2155	0.1176	0.2601
Observations	300	300	300	300	300	300

Notes: standard errors in parentheses; * p < 0.10, ** p < 0.05, and *** p < 0.01.

measure the regression results of R&D expenditure (EXP) as a mediating variable. The regression coefficient of green finance on R&D expenditure is 0.6975, which is significantly positive at the 1% level, and the coefficients of EXP and GFIN in the third column are also significant at the 1% level, indicating that green finance can increase the technological sophistication of product exports by increasing the expenditure on R&D.

The third and fourth columns measure the regression results of the green new product development (NPPY) inputs as a mediating variable. From an overall perspective, an increase of the development of new green products brings a significant effect, of 5%, on the technical complexity of export products. Therefore, when developing new green products, we should not only pay attention to the use of clean technology in product development but also avoid a certain degree of resource waste problems.

The regression results when the degree of industrial restructuring (IND) is used as a mediating variable. As shown in the last two columns, green finance can promote industrial restructuring at a level of 1%. With the transfer of enterprises to the tertiary industry, it is easy to see that some enterprises have already started to invest in the future sustainable development of their products, and the emergence of green finance has enhanced the ability of enterprises to obtain green credit and has narrowed the financing constraint, which can disguise the promotion of enterprises’ product technology R&D. Thus, product upgrading drives the increasing high technology of exports by increasing productivity levels and optimizing industrial structure.

5.5. Heterogeneity Analysis

5.5.1. Area Heterogeneity

The scale of green finance development in different regions has different effects on the complexity of export technologies in that region. Given that the eastern, central, and western regions of China (the eastern region includes 12 provinces, autonomous regions, and municipalities directly under the central government, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, and Hainan; the central region includes Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, and nine other provinces and autonomous regions; the western region includes nine provinces, autonomous regions, and municipalities directly under the central government, including Sichuan, Chongqing, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia, Qinghai, and Xinjiang (excluding Tibet)) differ in terms of economic geography, resource endowment, and factor differences, this paper analyzes the heterogeneity of the three regions. The results of this regional heterogeneity are shown in the second, third, and fourth columns of

Table 7. Results based on area and green finance performance forms heterogeneity.

Variables	(1) Eastern Region	(2) Central Region	(3) Western Region	(4) Green Bonds	(5) Green Insurance	(6) Green Investment	(7) Green Loans
GFIN	0.7215 *** (0.1630)	0.1022 * (0.1974)	0.3770 (0.2357)
GS				0.8835 *** (0.6304)
GI					0.0861 *** (0.5414)
GIV						0.8706 *** (0.1533)
							0.5655 *** (0.2880)
FDI	−0.1010 (0.1092)	0.1133 *** (0.1081)	0.2250 *** (0.2889)	0.3314 (0.0920)	0.3085 * (0.0911)	0.2688 ** (0.0944)	0.2284 ** (0.0850)
TFPP	0.0931 * (0.0519)	0.0589 (0.0434)	0.0722 (0.0426)	0.0564 * (0.0305)	0.0522 * (0.0293)	0.0666 ** (0.0302)	0.0607 ** (0.0293)
GOV	0.0608 *** (0.3606)	0.9017 * (0.4783)	0.3773 ** (0.1605)	0.4126 *** (0.1239)	0.3431 ** (0.1281)	0.3947 *** (0.1337)	0.4301 *** (0.1246)
TITC	−0.4294 *** (0.0890)	0.4238 * (0.1298)	−0.1271 ** (0.0586)	−0.1780 *** (0.0433)	−0.2106 *** (0..0406)	−0.2482 *** (0.0430)	−0.1343 *** (0.0401)
HNTE	0.6149 ** (0.3013)	0.2951 (0.1631)	0.2002 ** (0.0667)	0.3463 *** (0.0795)	0.2789 *** (0.0915)	0.2824 *** (0.0920)	0.2989 *** (0.0819)
Constant	0.6083 *** (0.1707)	0.8481 *** (0.2721)	0.2592 (0.1543)	0.4351 *** (0.1193)	0.5319 *** (0.0853)	0.6464 *** (0.0843)	0.3167 *** (0.0911)
R-squared	0.4179	0.4995	0.5270	0.4585	0.4794	0.4838	0.5011
Province fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	120	90	90	300	300	300	300

Notes: standard errors in parentheses; * p < 0.10, ** p < 0.05, and *** p < 0.01.

. From the significance results of the regressions, it is easy to find that the regression coefficients of green finance in the eastern, central, and western regions are 0.7215, 0.1022, and 0.3770, respectively, with significance levels that are 1%, 10%, and insignificant, respectively. This indicates that there are more obvious differences in the influence of China’s regional green finance on the technical complexity of export products, and the influence of green finance on the technical complexity of export products is greater in the eastern region than in the central and western regions; the green finance in the western region even has no influence on the technical complexity of exports. The rapid economic development of the eastern region has led to a higher degree of green finance development, and many coastal areas in the eastern region have realized more export-oriented enterprises due to their advantageous geographical location. In addition, the eastern region has taken the lead in developing clean energy using clean technologies. As an example, 2018 saw the official launch of green asset-backed securities on the Shanghai Stock Exchange, and most of the companies that applied were from the eastern region. Therefore, green finance can be a booster for the complexity of export technologies in the eastern region. Most of the export technologies in the central region are unformed and still in their infancy, and there are fewer exporters. In addition, many enterprises in the eastern region set up branch factories in the central region and relocated high pollution and high emission enterprises to some parts of the central region, which makes it difficult for green finance to be better applied in the central region. Therefore, the significance of the central region is only 10%. In terms of control variables, unlike the eastern region, the effect of FDI on export technological sophistication is positive, indicating that FDI still has some influence in the central region and on foreign advanced technology.

5.5.2. Heterogeneity of Green Finance Performance Forms

In the previous article, when introducing the connotation of green finance, the index system of green financial development was constructed, including green bonds, green insurance, green investment, and green loans. Green bonds are bond instruments in which the proceeds are used exclusively to finance or refinance green projects that meet the specified conditions. Green bonds can be used to support green projects for exporters and to finance these green projects. Exporters with financial support are better able to develop green technologies and produce more technologically sophisticated export products. Green insurance is a risk management tool for environmental protection, and its effective use can have a positive effect on reducing pollution accidents, by responding quickly to pollution accidents and compensating and protecting the rights of victims in a timely manner. Green investment is a direct financial support for the environmental protection industry, which is slightly different from other indirect investment tools such as bonds. Green direct investment can better enable exporters to obtain initial funds for the development of green industries and more efficiently enhance the sophistication of export technology. Thus, this study analyzes the impact of green finance on the complexity of export technology according to its above three manifestations under different ways of green finance support. The last three columns of Table 7 show that either manifestation can have a positive impact on export technology complexity. The impact of green bonds is slightly greater than that of green investment, indicating that while green investment is more direct and convenient, green bonds are safer. Secondly, green bonds have a wide range of products in the secondary market, which can provide exporters with a variety of suitable financing methods; from this perspective, green bonds are more favorable to exporters to improve the technical complexity of exports. Although the regression coefficient of green insurance is slightly lower, only because the popularity of insurance in China is not high, which results in a lower regression coefficient, it is not a bad choice. The country’s enhanced support for green insurance can still be one of the future directions of green finance.

6. Conclusions and Policy Recommendations

6.1. Conclusions

Taking green finance as the entry point, this paper empirically tested the impact of green finance on export technical complexity using the panel data from 30 provinces in China from 2011 to 2020. The following conclusions are drawn: Green finance can effectively enhance the technical complexity of manufacturing exports. In terms of regional heterogeneity, the impact of green finance on export technical complexity in the eastern region is greater than that in the central and western regions; green finance in the western region even has no effect on export technical complexity. In terms of the manifestation of green finance, the impact of green bonds is greater than that of green investment and insurance. Green finance improves the technical complexity of export products through three intermediaries, such as green technology innovation, capital investment strength, and product upgrading, and the upgrading effect of green technology innovation is greater than that of capital investment and product upgrading.

In this paper, there are some defects in the construction of the indicator system of green finance development, so some indicators may be missing. In the future research work, the variables in the index system can be added to make the qualitative change construction more accurate and three-dimensional. There are many indicators with green and sustainable development as the core, but the quantitative means need to be further explored. Based on the above findings, the following policy recommendations are made.

6.2. Policy Recommendations

Increase the development of green finance, enhance the regionalization level of green finance, guarantee the infrastructure construction of green finance development, and strengthen the implementation of green credit, green bonds, green insurance, and other products by the government and relevant financial institutions, so that enterprises can use green financial products more efficiently and conveniently to upgrade their export products’ technology. At the regional level, the eastern region continues to maintain the development momentum of green finance, while the enterprises in the central region still need to enhance the technology of their export products through external factors such as foreign direct investment; however, relying on external forces alone is not the fundamental solution, so the enterprises themselves should explore the use of green technology to innovate the use of export products. The western region has not yet fully benefited from the development of green finance. In addition to the “Western Development” plan, the western region should also pay attention to the introduction and support of environmentally friendly enterprises, strengthen the policy guidance and infrastructure construction of clean energy, and jointly help the development of green finance and promote the technological upgrading of enterprises’ products. Exporters should focus on green bonds and green investment when choosing green investment and financing methods, but green insurance and other products should also be taken into consideration. The state should increase the support for the insurance industry, so the concept of green insurance penetrates into every sustainable enterprise, and then green insurance may become a more effective green financial product in the future to enhance the technical complexity of enterprises’ exports. Enterprises themselves should favor the investment in green innovation and green capital, adopt high salaries and other means to attract R&D and technical talents, and increase the investment in R&D personnel and capital. In addition, the government should continue to strengthen green financial innovation under the strategic goal of “carbon peaking and carbon neutral”, with the main goal of supporting the development of green export products and green transformation and upgrading of pillar products, and continue to increase financial support for the construction of projects in key areas such as ecological restoration, energy conservation, environmental protection, clean energy, and green upgrading of infrastructure. Finally, we encourage enterprises to produce export products with a high complexity and aim to help improve the technical complexity of China’s exports.