金融学院知识库

改革开放以来，我国经济水平显著提升，与此同时，由于长期依赖传统资源，导致环保问题日益严峻，环境污染、生产要素耗费等问题逐渐成为了全球重视的热点问题，低碳经济因而成为可持续发展的必然选择。绿色金融的发展对于赋能经济绿色低碳发展、优化经济发展模式具有重要作用，是助力“双碳”目标实现的重要抓手。依据国家环保政策，绿色金融作为推动绿色低碳发展的利器，能够有力撬动市场化资金更频繁流向节能和清洁领域，是引导经济低碳化转型和升级不可或缺的重要力量。

本文首先分析了绿色金融与低碳经济的发展现状，其次选取相关指标并对二者的发展水平进行测算，然后基于2011-2022年我国30个省份（剔除西藏、港澳台）的行政面板数据，通过应用耦合协调度模型、Dagum基尼系数及其分解方法、Moran’s I模型、Markov模型、收敛性模型及灰色关联度模型构建了新的分析框架，从全国、各省域、七大地理区域等不同视角对耦合协调性进行具体分析并进行区域差异分析、空间特征分析、演变趋势分析、收敛性分析和影响因素分析，根据实证结果提出建议。结论如下：第一，样本期内，我国绿色金融和低碳经济的发展水平均呈上升趋势，且省域之间发展水平存在不均衡现象；第二，两个系统耦合协调度表现出明显的区域差异性；第三，两系统耦合协调度总体差异逐步减小，且主要来自于组间差异；具有相似耦合协调度的对象在空间上聚集，耦合协调度的发展具有正向空间溢出效应；第四，耦合协调度指数的不同等级变化较为稳定，存在“俱乐部趋同”现象；不同时间跨度下均为相邻状态跃迁，表明两系统耦合协调发展是一个比较缓慢的过程，对前期发展水平路径依赖程度较高，需稳步推进；第五，全国与各地均表现出显著的绝对β收敛现象，在考虑了控制变量后，全国及各地区仍存在显著的条件β收敛且各个因素对耦合协调性的影响在不同地区表现不一；居民消费碳排放、工业三废处理指数、公众低碳经济知识普及程度对于耦合协调度的关联度最大，总体来看，低碳经济系统中各指标关联度高于绿色金融系统中各指标。

基于上述回归分析结果，要提高我国绿色金融与低碳经济的耦合协调度，促进两个系统协同发展，就需要针对相关影响因素靶向施策。第一、绿色产业转型，加快产业结构优化，推动高污染行业向绿色、低碳、高效方向转型。第二、绿色金融发展，各地区尤其是金融行业较弱的地区，需出台绿色金融政策，促进环保产业融资，加大绿色投资。第三、创新驱动，提高创新能力，尤其在环保和绿色技术领域，提升产业技术水平，促进绿色科技发展。第四、政府政策支持，加大政府对绿色产业的支持力度，完善环保相关法律法规，确保各地区绿色转型政策落实落细。第五、基础设施和数字经济发展，加大对中西部以及东北地区基础设施建设的投资力度，通过完善交通网络、能源供应、信息网络等关键基础设施，为地区经济社会发展奠定坚实基础。

Since the reform and opening-up, China's economic level has risen significantly. Meanwhile, due to long-term reliance on traditional resources, environmental issues have become increasingly severe. Problems such as environmental pollution and the overconsumption of production factors have gradually emerged as globally significant hot topics. Consequently, a low-carbon economy has emerged as an inevitable choice for sustainable development. The development of green finance plays a crucial role in empowering the green and low-carbon development of the economy and optimizing the economic development model, serving as an important lever for achieving the “dual carbon” goals. In line with national environmental protection policies, green finance, as a powerful tool for promoting green and low-carbon development, can effectively drive market-oriented funds to flow more frequently into energy-saving and clean sectors. It is an indispensable force for guiding the low-carbon transformation and upgrading of the economy.

This paper first analyzes the current development status of green finance and the low-carbon economy. Subsequently, it selects relevant indicators and measures the development levels of both. Then, based on administrative panel data from 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2022, it constructs a new analytical framework by applying the coupling coordination degree model, Dagum Gini coefficient and its decomposition method, Moran's I model, Markov model, convergence model, and grey relational degree model. From national, provincial, and seven geographical regional perspectives, it conducts specific analyses on coupling coordination, regional disparities, spatial characteristics, evolutionary trends, convergence, and influencing factors. Based on the empirical results, suggestions are proposed. The conclusions are as follows: First, during the sample period, both the development levels of green finance and the low-carbon economy in China have shown an upward trend, with uneven development levels among provinces. Second, the coupling coordination degree between the two systems exhibits significant regional disparities. Third, the overall difference in the coupling coordination degree between the two systems is gradually decreasing, mainly originating from inter-group differences. Objects with similar coupling coordination degrees cluster spatially, indicating a positive spatial spillover effect in the development of coupling coordination. Fourth, the changes in different grades of the coupling coordination degree index are relatively stable, with a phenomenon of “club convergence”. Transitions occur between adjacent states across different time spans, suggesting that the coupled and coordinated development of the two systems is a relatively slow process with a high degree of path dependence on the early development levels, necessitating steady progress. Fifth, both the national and regional levels exhibit significant absolute β-convergence phenomena. After considering control variables, significant conditional β-convergence still exists at the national and regional levels, with the impacts of various factors on coupling coordination varying across different regions. Residential consumption carbon emissions, the industrial “three wastes” treatment index, and the public's awareness of the low-carbon economy have the highest correlations with the coupling coordination degree. Overall, the indicators within the low-carbon economy system have higher correlations than those within the green finance system.

Based on the regression analysis results mentioned above, to enhance the coupling coordination degree between China's green finance and low-carbon economy and promote the coordinated development of the two systems, targeted measures should be implemented in response to relevant influencing factors. First, promote the transformation of green industries by accelerating industrial structure optimization and driving the transition of high-polluting industries towards green, low-carbon, and efficient directions. Second, develop green finance by introducing green finance policies, especially in regions with weaker financial industries, to facilitate financing for environmental protection industries and increase green investments. Third, drive innovation by enhancing innovation capabilities, particularly in the fields of environmental protection and green technologies, to upgrade industrial technological levels and promote the development of green technologies. Fourth, provide government policy support by increasing government support for green industries, improving environmental protection-related laws and regulations, and ensuring the meticulous implementation of green transformation policies across regions. Fifth, invest in infrastructure and the digital economy by increasing investments in infrastructure construction in central, western, and northeastern regions. By improving key infrastructures such as transportation networks, energy supplies, and information networks, a solid foundation can be laid for regional economic and social development.