Purpose

The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.

Design/methodology/approach

From the perspective of big data, this paper uses quantitative and qualitative analysis methods to judge the correlation among green finance, environmental supervision and financial supervision. Green finance gives the entropy method to calculate the score of green finance and environmental regulation, and establishes the spatial lag model under the double fixed effects of time and space.

Findings

Spatial autocorrelation test shows that economic spatial weight matrix has obvious spatial effect on green innovation. Through the model selection test, the space lag model with fixed time and space is selected. The regression coefficients of green finance, environmental regulation and their interaction are 0.1598, 0.0541 and 0.1763, respectively, which significantly promote green innovation. The regression coefficients of openness, higher education level and per capita GDP are 0.0361, 0.0819 and 0.0686, respectively, which can significantly promote green innovation.

Originality/value

In view of the current situation of large-scale application of big data technology in green innovation of domestic energy-saving and environmental protection enterprises, this paper establishes a fixed time lag evaluation model of green innovation. M-test statistics show that the original hypothesis with no obvious spatial effect is rejected.

中文翻译：

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大数据下支持循环经济向绿色金融的绿色创新对策研究

目的

本文旨在探讨大数据背景下绿色金融的可持续发展战略。

设计/方法/方法

本文从大数据的角度，采用定量和定性的分析方法来判断绿色金融、环境监管和金融监管之间的相关性。绿色金融给出了熵法计算绿色金融和环境规制的得分，建立了时间和空间双重固定效应下的空间滞后模型。

发现

空间自相关检验表明，经济空间权重矩阵对绿色创新具有明显的空间效应。通过模型选择测试，选择了时空固定的空间滞后模型。绿色金融、环境规制及其交互作用的回归系数分别为0.1598、0.0541和0.1763，显着促进了绿色创新。开放度、高等教育水平和人均GDP的回归系数分别为0.0361、0.0819和0.0686，可以显着促进绿色创新。

原创性/价值

针对国内节能环保企业绿色创新中大数据技术大规模应用的现状，本文建立了绿色创新的固定时滞评价模型。M检验统计表明，没有明显空间效应的原假设被拒绝。