Green Credit, Environmental Pollution and High-Quality Development of Green Economy

： Based on the panel data of 30 provinces (cities and districts) in China from 2003 to 45 2019, this paper uses the Green Development Index System jointly formulated and released by the 46 National Development and Reform Commission, the National Bureau of Statistics, the Ministry of 47 Environmental Protection and the Central Organization Department to construct a comprehensive index 48 system which can calculate the high-quality development index of green economy, and research the 49 impact of green credit, environmental pollution and high-quality development of green economy. The 50 results show that: (1) The improvement of green credit is conducive to promoting the high-quality 51 development of green economy. Considering the high autocorrelation of the high-quality development 52 of green economy, the impact of green credit on the high-quality development of green economy is still 53 robust and does not depend on the specific metrology. (2) With Moran Index, it is found that the 54 high-quality development of green economy has spatial characteristics. By using Spatial Dobbin Model 55 (SDM), it is found that under both (0,1) weight matrix and geographical distance weight matrix, the 56 impact of green credit on the high-quality development of green economy is positive, forming a 57 positive spatial spillover effect on the high-quality development of green economy in surrounding areas. 58 (3) By using the Intermediary Effect Model,it can be seen that environmental pollution plays a partial 59 intermediary effect between green credit and high-quality development of green economy. There is a 60 transmission channel of "green credit → environmental pollution → high -quality development of green 61 economy". (4) By using Panel Quantile Regression Model, it is found, with the improvement of 62 high-quality development of green economy, that the promotional effect brought by green credit 63 increased. clean ecological ecological energy


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Instead of concentrating on the impact of green credit on the high quality of green economy, the 145 academic circles still take green credit as an integral part of green finance, studying the impact of green 146 financial development on economic growth and believing that the green investment demand promoted 147 by green financial activities will directly contribute to economic growth while the investment increased 148 (Cowan 1998;Salazar 1998

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At present, the research has been rich enough in high-quality economic development. However, 155 there still exist three deficiencies: (a) Currently, there is scarcely theoretical basis for high-quality 156 economic development,the index evaluation system brought by scholars is not unified. Moreover, there 157 is little research on high-quality development of green economy. Only a few domestic scholars have 158 made a preliminary study on high-quality development of green economy from the characteristics of 159 regional economic development (Shen Huiyun 2020) .Therefore, the prior concern of this paper is to

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In research on high-quality economic development or high-quality development of green economy, few 163 study takes green credit as the driving variable, neither does quantitative study. Therefore, whether 164 green credit has an impact on the high-quality level of green economy? What is the degree of the

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The main contributions of this paper are as follows: (a) It builds a high-quality development index

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The rest of this paper is arranged as follows: The second part is the theoretical basis and research

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2002) and other researchers believed that the development of green credit by banks can help improve 218 the bank's reputation and risk management, it can also help banks control the loan environment and 219 social risks (Sun 2019). If a bank grants loans to a high pollution and high emission enterprise, once 220 pollution event breaks out, not only hurt the social image of the bank, but also face the risk of loan debt 221 (Zhang Hui 2021). Under the guidance of national policies, commercial banks that prefer credit funds 222 lending to green industries and green enterprises will receive more policy support, such as higher MPA 223 assessment score, lower capital support, lower reserve requirements and even more tolerant regulatory 224 environment. Therefore, commercial banks tend to increase green credit to obtain "regulatory 225 incentives", whose essence is to adjust the allocation of financial resources, using lower financing costs 226 and better financing availability "to stimulate" environment-friendly enterprises to expand their 227 production, and "to force" enterprises with higher environmental pollution to innovate their production 228 skills. The greening of the production activities of enterprises will promote the greening of the regional 229 economic development level, that is , the resource utilization rate of social production will be higher.  water flow and wind direction, the environmental problems in a certain area will inevitably be affected 257 by the adjacent areas (Ma Limei 2014). Anthropogenic factors such as industrial transfer and trade will further deepen the spatial linkage between regional environmental quality and economic development.

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Therefore, the impact of spatial factors on environmental and economic problems should not be 260 ignored (Anselin 2001). (Poon 2006) and others used spatial metrology to study the impact of energy,

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To verify the impact of green credit on the high-quality development of regional economy, this 306 paper constructs a model as follows: Among them, it gehqd represents the high-quality development level of green economy, it gc 309 represents green credit and it X is for control variables, mainly including environmental governance is the random error term of the model.

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(a) Explained variable. Green economy and high-quality development ( gehqd ). The research on 315 green economy is mainly divided into two parts. One part is to study the efficiency of green economy         The statistical results of each variable can be seen in Table 2, which shows that the average value  Table 2.      Note: (1) *, * * and * * * respectively represent they are significant at the level of 10%, 5% and 1%;

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(2) The T value in brackets is adjusted by the robust standard error. The following table is the same.

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(2) The original assumptions of AR (1) test, AR (2) test and sargan test are whether the residual term 479 has sequence correlation of order 1 and order 2 and whether the instrumental variables are set 480 reasonably.

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(4) Reexamination considering space effect 483 (a) Spatial auto-correlation test. For the setting of spatial weight, the (0, 1) weight matrix is 484 usually used, the weight of adjacent areas is 1, and the weight of non-adjacent areas is (Wrigley 1982) 485 extended the traditional (0, 1) weight matrix and introduced the total measure of the potential 486 interaction between 2 spatial units, constructed the spatial weight matrix as a function of distance.

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Geographic distance spatial weight matrix W 2 : Based on the first law of geography, "the spatial 493 correlation between units gradually decreases with the increase of distance", the inverse distance 494 spatial weight matrix is constructed as follows:

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When zone i is adjacent to zone j When zone i is non-adjacent to zone j When i=j This paper selects (0, 1) weight matrix and geographic distance space weight matrix as the weight 504 matrix of spatial econometric analysis, and tests the spatial correlation of high-quality development of 505 green economy, mainly using the method of Moran Index.The results are shown in Table 5. From the 506 results in Table 5, the high-quality development of green economy has positive spatial correlation 507 regardless of the weight matrix of (0,1) and geographical distance space weight matrix and passes the 508 significance test. That indicates that the high-quality development of green economy has spatial 509 characteristics, the high-quality development level of green economy in one region will obviously drive has a certain trend of spatial auto-correlation, which means that it is particularly important to consider 515 the space to analyze the high-quality development of green economy. Note:*, * * and * * * respectively represent they are significant at the level of 10%, 5% and 1%;     Table 7, column (1) shows the regression results of the impact of 546 green credit on the high-quality development of the green economy under the (0, 1) weight matrix. 547 Table (2) displays the regression results of the impact of green credit on the high-quality development 548 of the green economy under the geographical distance weight matrix. In all models, the spatial lag 549 coefficient of the green economic development is significant under the 1% confidence level. However, 550 the difference in weight selection will affect the sign of coefficient of the spatial lag term, which 551 confirms the correctness of introducing the spatial lag term of economic growth in this paper, and 552 ignoring this correlation will lead to errors in model estimation. Furthermore, the impact of green credit 553 on the high-quality development of green economy is significantly positive under (0, 1) weight matrix 554 and geographic distance weight matrix, which is basically consistent with the estimation results,

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The second step is to verify whether green credit affects environmental pollution. The model is 573 constructed as follows: The third step is to incorporate green credit and environmental pollution, the intermediate