1. Introduction
In recent years, extreme weather events such as storms, heat waves, floods, and droughts have become increasingly frequent, seriously threatening global ecosystems and posing increasing challenges to human survival and development. The latest bulletin from the Germany-based Copernicus Climate Change Service says that 2023 was the warmest year on record globally [
1]. Climate change not only poses a direct threat to human health but also profoundly affects people’s way of life and production. Green development has become an inevitable choice for all countries, and developing a green economy is also an inevitable choice for achieving global sustainable development. In 2024, China began to promote comprehensive green development.
Green development is an economic growth model that focuses on the long term. Since industrial discharge is the main source of environmental pollution [
2], industrial production is the main area of carbon emissions; thus, surface green development needs to solve the problems of the real sector. However, the virtual sector, which mainly includes the financial and real estate sectors, is also an important part of achieving green development. Although the financial sector does not directly create economic value and needs to realize value appreciation through the real sector, the aggregation of finance can promote the optimization and upgrading of industrial structures [
3] and reduce not only the total consumption of regional resources but also the total emission of pollutants. Real estate is an important pillar of the national economy; however, cement, building materials, and other industries directly related to real estate involve high levels of energy consumption and heavy pollution. From the perspective of achieving green development, it is also necessary to balance the development of the physical sector and the virtual sector. Therefore, the purpose of this study is to analyze the impact of the allocation of financial resources between the virtual economy sector and the real economy sector on green development. The reason for choosing China as the sample is that the scale of financial resources and the degree of green development vary across different regions of China. Analyzing China as a whole and by region can provide a reference for countries with different levels of development.
The research related to this paper is divided into three aspects, namely, financial resource allocation and green development, financialization and green development, and real estate investment and green development. First, the allocation of financial resources is important for green development. Using the panel data of 30 provinces and cities in China from 1995–2016, Zongyi Hu and Yi Li built a panel threshold regression model to explore the impact of financial resource mismatch on environmental pollution, arguing that the mismatch of financial resources is not conducive to reducing pollutant emission levels; with improvements in the development level of the financial industry, the mismatch level of financial resources will decline, which will have a significant inhibitory effect on pollutant discharge [
4]. Shuyang Wen and Xiliang Liu tested the relationships between three major pollutants and the allocation of financial resources with provincial panel data from China and concluded that the severe tilt of financial resources to polluting enterprises in China has a significant adverse effect on the environment [
2]. Tianrui Sun and Xiangrong Zhang built a spatial panel data model based on China’s provincial panel data from 2000 to 2019 to study the impact of the mismatching of financial resources on environmental pollution levels and concluded that financial resource misallocation aggravates environmental pollution levels to a certain extent [
5].
Second, there is a relationship between financialization and green development. However, research in this area is very different. According to the theory of financial repression, insufficient financialization inhibits the potential of industrial green transformation [
6]. However, some scholars believe that financialization that is too fast or considered to be “shifting from real to virtual” hinders green development. Flammer showed that financialization that is too fast will lead to industry bubbles and low-quality investment; thus, enterprises tend to engage in “greenwashing” to obtain low-cost financing rather than actually investing in green technology transformation, thereby reducing the efficiency and returns of green investment [
7]. Yuchao Peng suggested that financial “shifting from real to virtual” is not conducive to the investment and scale growth of the green real economy; rather, it stimulates the overall demand and consumption of energy in the economy and society [
8]. On the basis of the data of A-share listed enterprises, Xiaoping Tian showed that with the improvement in the level of corporate financialization, enterprises are more likely to disclose more green information because of the motivation of “reputation management”, and the actual green governance performance is poor, which will distort corporate investment behavior and reduce the real productivity of enterprises [
9]. On the basis of the above discussion, scholars are increasingly inclined toward the appropriateness of financialization and sustainable development. Using the spatial Durbin model, Wei Dou et al. took the national data of 175 countries from 2003 to 2019 as samples with which to study the impact of financial development on carbon emissions and reported that financial development has a U-shaped nonlinear effect on carbon emission intensity [
10].
Finally, real estate investment and sustainable development are needed. Jianshuang Fan and Lin Zhou analyzed the change in carbon emissions in Chinese provinces on the basis of the Kaya identity and LMDI decomposition methods and reported that the carbon emission coefficient of real estate investment is the most important factor in promoting carbon emission reduction and that the change in urban real estate investment intensity has a promoting effect on carbon emissions, with the effect increasing annually [
11]. In their empirical study on measuring the quality of economic growth on the basis of the “new development concept”, Hongbing Zhou et al. reported that real estate investment has a negative effect on the quality of economic growth containing “green” elements [
12]. Ze Cao and Xing Liu analyzed the spatial effect of real estate investment on total factor productivity by using the spatial Durbin model and reported that both the direct and indirect effects of real estate investment on urban green total factor productivity are significantly negative at the national level; however, the direct effect of real estate investment on green total factor productivity is the greatest in eastern China and the smallest in western China, and the indirect effect is the greatest in central China and the smallest in western China [
13].
In summary, existing studies have discussed the “virtual and real” allocation of financial resources and some aspects of green development and have reached a consensus on the following aspects: First, the misallocation of financial resources aggravates environmental pollution. Second, the “virtual and real” allocation of financial resources affects the green and sustainable development of the economy. Third, real estate investment has a negative effect on green development. However, several shortcomings remain. First, most relevant discussions focus on certain aspects of the environment, such as environmental pollution and carbon emissions, whereas green development refers to the coordinated and unified social development of nature, the economy, and society, which requires comprehensive consideration of environmental friendliness and economic growth. Second, the above-mentioned studies have used the scale of the virtual economy to represent the amount of financial resources occupied by the virtual economy rather than directly giving the amount of financial resources occupied by the virtual economy. Although the scale of the virtual economy is related to the amount of financial resources it occupies and can be indirectly represented, there are still some gaps between indirect and direct representations. Third, the extant research on the “virtual and real” allocation of financial resources and green development is mostly based on the enterprise level, while overall macrolevel research is relatively lacking. Moreover, the macrolevel studies that exist are mostly based on multicountry samples, which are difficult to use because they are not consistent with the actual situation of each country.
Further material is divided into several parts. Thus, in
Section 2, a theoretical analysis is conducted on the relationship between the “virtual real” allocation of financial resources and green development, and research hypotheses are proposed on this basis.
Section 3 presents the research design. In
Section 4, the Durbin model is used to examine the impact and spatial spillover effects of the “virtual real” allocation of financial resources on green development, and the empirical results are discussed.
Section 5 presents the conclusion and suggestions and discusses the limitations and further research on this topic.
4. Empirical Analysis and Their Discussion
4.1. Spatial Correlation Analysis and Rationality Test of Spatial Model
The premise of the spatial Durbin model is the existence of spatial autocorrelation. First, this paper tests the existence of spatial autocorrelation via Moran’s index.
Table 3 shows the Moran index of the model residuals from 2003 to 2022. The Moran index of all years is between 0 and 1, and all values are significant at the 0.05% significance level, which passes the threshold test; this indicates the existence of spatial autocorrelation. Thus, it is reasonable to use the spatial Durbin model to fit the influence of the “virtual and real” allocation of financial resources on green development.
On this basis, the research uses the LM test and Hausmann test to further test the rationality of model selection.
Table 4 reports the relevant test results. The test results indicate that the LM test statistics are significant, indicating that the choice of spatial measurement model is more reasonable. The Hausman test reveals the existence of time–region dual fixed effects in the model. On the basis of the above analysis, selecting the spatial Durbin model to analyze the time–region double fixed effect is both reasonable and effective.
4.2. Regression Analysis Based on the Full Sample
To reject hypotheses proposed in this study, the fixed effect Durbin model is used herein to estimate the parameters of the whole sample, and the regression results are reported in
Table 5. The results show that the “virtual and real” allocation index coefficient of financial resources is significantly positive at the 1% level, indicating that the greater the “virtual and real” allocation index of financial resources is, the greater the green development index value is; that is, an increase in financial resources flowing into the virtual economy will reduce the quality of green economic development. Thus, Hypothesis 1 fails to be rejected.
Figure 2 also shows the same trend. Before 2008, the inflow of financial resources into the virtual economy increased, and GG also grew larger, leading to an increase in energy consumption and pollution caused by economic development. Due to the impact of the financial crisis in 2008, the trend changes were somewhat affected. Starting from 2021, GG and K have started to increase synchronously again. Until 2016, with the gradual introduction of China’s green finance policies and the implementation of the “de real to virtual” policy for financial resources, the inflow of financial resources into the virtual economy decreased, the green development index decreased, and the energy consumption and pollution caused by economic development decreased.
Moreover, the spatial autocorrelation coefficient rho is positive at the significance level of 5%, indicating that the green development of a province (city) and its geographical neighbors (cities) shows spatial correlation characteristics; that is, the green development of neighboring provinces (cities) is defined as a situation in which “both prosper and lose”. The spatial lag term of the “virtual and real” allocation index of financial resources is significantly negative, indicating that the economic green development index of the neighboring province (city) decreases with the increase in the “virtual and real” allocation index of financial resources of the province (city); that is, as more financial resources of a province (city) flow into the virtual economy, the improvement in the quality of economic green development of the neighboring province is promoted (city). This outcome is related mainly to the “siphon effect” of virtual sector development on financial resources. The attraction of the virtual economy to financial resources is due mainly to profit-seeking. When the inflow of financial resources into the virtual economy of a province increases, the income of the virtual economy of the province is generally higher than that of neighboring provinces; thus, the inflow of financial resources into the virtual economy of neighboring provinces, such as the typical real estate market, decreases. The quality of green economic development in neighboring provinces is thus relatively improved. In summary, Hypothesis 3 also fails to be rejected.
On the basis of the previous analysis, this study uses the partial differential method to distinguish the different sources of the influence of explanatory variables on the explained variables. The decomposition results are shown in
Table 6. The coefficient of the direct effect of the “virtual and real” allocation index of financial resources is significantly positive, indicating that the excessive flow of financial resources into the virtual economy of a province reduces its quality of green economic development. The indirect effect coefficient of the “virtual and real” allocation index of financial resources is significantly positive, indicating that the excessive inflow of financial resources in a province into the virtual economy improves the quality of green economic development in neighboring provinces; this further indicates that Hypotheses 1 and 3 cannot be rejected.
4.3. Robustness Test
Considering the reliability of the research results, this paper further tests the robustness of the model.
- (1)
Eliminating the sample. As shown in columns 1, 2, and 3 of
Table 7, after the samples of the first year are eliminated, the Durbin model is still used to estimate the new sample data; the estimation results are shown in
Table 7. The “virtual and real” allocation index of financial resources is still significantly positive, and the spatial lag term (
W ×
k) is significantly negative. The spatial autocorrelation coefficient
roh is significantly positive, which is consistent with the estimated results of the original model, indicating that the regression model results of the impact of the “virtual and real” allocation of financial resources on green development are robust.
- (2)
Using alternative methodology. DIF-GMM is one of the main methods used for robustness testing in similar studies. This article uses DIF-GMM for regression analysis. The regression results are shown in (2) columns 4 5 6 of
Table 7, where the
p-value of AR(1) is less than 1, the
p-value of AR(2) is greater than 1, and the
p-value of Hansen’s test is greater than 0.1, demonstrating the effectiveness of the DIF-GMM method. In addition, there was no significant difference in the regression results of DIF-GMM and SDM for the core explanatory variables. This confirms the robustness of the regression and further eliminates bias caused by potential endogeneity issues.
4.4. Regional Heterogeneity Analysis
China is a vast country, with significant differences in the degree of economic development, level of financial development, and industrial structure between the central-eastern and western regions. To verify Hypothesis 2, this paper further groups the samples by region. To consider the differences in economic structure and financial development across regions and ensure that the sample size meets the empirical requirements, the samples are further analyzed by region. The 30 provinces (municipalities) and autonomous regions included in the sample are divided into two groups, namely, the central-eastern region and the western region, and spatial regression analysis is carried out for the two groups. The regression results are shown in
Table 8.
The results show that the “virtual and real” allocation of financial resources has a certain effect on the impact of green development between the central-eastern region and the western region. The “virtual and real” allocation index of financial resources in the western region is significantly positive, indicating that the inflow of financial resources into the virtual economy increases, the green development index increases, and the quality of green development decreases. The “virtual and real” allocation index of financial resources in the central-eastern region is significantly negative, indicating that the inflow of financial resources into the virtual economy increases, the green development index decreases, and the quality of green development improves. There are several possible reasons for the above differences. First, with the transfer of the industrial structure in recent years, some “high-pollution and energy-consuming” industries in the eastern region have been transferred to the western region; thus, the level of green development between the eastern and western regions significantly differs. Taking the carbon emissions per unit of GDP as an example, those in the eastern region, central region and western region are approximately 100 million CNY/ton, 122 million CNY/ton and 161 million CNY/ton, respectively. The pollution that flows from the eastern region into the virtual economy, such as cement driven by the real estate sector, is credited to the western region. Second, the development level of green finance is different between the regions. In comparison, the eastern region has a more complete financial market system, better financial resources, and more active financial innovation; additionally, the financial resources flowing into the financial sector of the eastern region are better able to promote and drive the upgrading of the industrial structure, and the upgrading of the industrial structure can effectively reduce pollutant emission levels.
5. Conclusions and Suggestions
5.1. Conclusions
There are many studies that focus on the allocation of financial resources and green development; however, less attention has been given to the impact of the allocation of financial resources on the quality of economic green development in the virtual economy and real economy. Using interprovincial panel data from China from 2003 to 2022, this paper constructs a spatial Durbin model to study the impact of the “virtual and real” allocation of financial resources on green economic development. The findings are as follows: First, the “virtual and real” allocation of financial resources has a significant effect on the green development of the economy, and the excessive flow of financial resources into the virtual economy reduces the quality of the green development of the economy. Second, the “virtual and real” allocation of financial resources has an obvious spatial spillover effect on green economic development. The increase in financial resources flowing into the virtual economy in one province (city) reduces the proportion of financial resources flowing into the virtual economy in neighboring provinces, which is conducive to the green economic development of neighboring provinces. Finally, owing to the different economic structures and financial development levels in different regions, the direct and indirect effects of the “virtual and real” allocation of financial resources on the impact of economic green development are significantly different.
5.2. Suggestions
The results of the research performed present grounds for formulating the following suggestions:
First, attention should be given to the “virtual and real” allocation of financial resources, and the common development of the environmental economy should be coordinated. To explore and try a benign development model in which finance, real estate, and the real economy promote each other, financial resources should be allocated appropriately in the financial and real estate fields, and more financial resources should be guided to serve environmentally friendly and resource-saving industries on the basis of ensuring an adequate financial supply and the stable development of the real estate market. The innovation of green financial products should be promoted, financing channels for green industries should be expanded, engaging in green finance should be encouraged, and green and high-quality economic development should be promoted.
Second, inter-regional financial and green cooperation should be explored to promote overall green development in the region. Since the “virtual and real” allocation of financial resources has a spatial spillover effect on green economic development, neighboring provinces should strengthen cooperation in the fields of finance and greenness, integrate high-quality financial resources, and realize the sharing of green technologies. Through the rational allocation of financial resources in a region, the effective flow of financial resources in the region can be mobilized, and the overall green development goals can be supported.
Third, the central government designates an overall policy framework, and each region adopts differentiated measures within the policy framework based on local conditions. The empirical results show that the “virtual and real” allocation of financial resources has different effects on green development in different regions. All regions should formulate financial policies and economic green development policies on the basis of comprehensive consideration of their own economic structure and financial development level differences, and ensure that the allocation of financial resources supports the overall green development goals. For example, regions where the virtual economy occupies more financial resources should increase innovation in green financial products, while regions where the real economy occupies more financial resources should increase green technology transformation in the real economy.
5.3. Limitation and Further Research
First, the unique characteristics of China’s financial system and its rapid industrialization process could limit the applicability of these results to other regions or economies, but the methods provided in this study are universally applicable. Future research can use other economies or multiple economies as samples to apply the methods of this study.
Second, the study classifies the financial and real estate sectors of the virtual economy and the real economy but does not further classify the sectors in the real economy. Future research can classify the sectors in the real economy, such as manufacturing or agriculture.
Third, due to data availability limitations, this study did not use some variables, such as public policy initiatives, cultural attitudes toward sustainability, the share of energy from renewable sources, the amount of investment in energy-saving measures, etc. Future research can classify the sectors in the real economy, such as manufacturing or agriculture. Future research could consider incorporating these variables.
Finally, future research could investigate the long-term sustainability of the observed trends, particularly in the context of evolving global environmental challenges.