气候变化冲击下的涉农信用风险——基于2010-2019年256家农村金融机构的实证研究

气候变化可能导致的经济金融风险已经成为学术界关注的热点问题。本文首先分析了气候变化引发涉农金融风险的传导机制,以2010-2019年256家农村金融机构的经营数据为样本,将标准化后的年均气温作为刻画气候变化程度的核心指标,评估农村金融机构所在县域地理单元的气候变化程度对其信用风险的影响。研究发现,年均气温波动对农村金融机构的信用风险水平存在显著影响,且影响呈现阶段性特征;在4个季度中,冬季气温的波动对信用风险的影响最为突出;虽然城商行与农商行、村镇银行均是立足于服务地方经济发展的商业银行,但由于城商行的业务在地域上和行业上更为分散,气候变化未对其信用风险水平产生显著影响。为此,提出了开展压力测试、实施差异化监管和创新风险缓释工具三个方面的对策建议。本文为管理由气候变化导致的涉农信用风险提供了政策启示和决策参考。

Climate Change and the Credit Risk of Rural Financial Institutions

The potential economic and financial risks of climate change have become a hot topic in academia. The Global Risks Report(2020) states that the top five global risks in the next 10 years are environmental and that the financial risk associated with climate change is an important source of systemic financial risks. Avoiding these risks requires comprehensive investigation of the financial risks related to climate change. Although climate change has a systematic impact on the financial system, many financial institutions are not paying enough attention to the financial risks induced by climate change. Agricultural production is the first sector affected by climate change because the input-output efficiency of agricultural production is strongly correlated with climatic conditions. As rural financial institutions handle the finance needs of the agricultural sector and climate change increases the uncertainty of agricultural output, the potential climate risk is transmitted to rural financial institutions. This paper proposes the following transmission mechanism based on the climate change, agricultural development and climate finance literature: climate change → uncertainty in agricultural production → agricultural credit risks. This paper further proposes an empirical research scheme using the market location and primary business of rural financial institutions.In the empirical study, we exploit a panel of financial data from 2010 to 2019 that includes 249 rural commercial banks and 7 rural banks from 26 provinces, 128 prefecture cities and 251 counties. Using the average annual temperature of each county, we construct an index to quantify the degree of climate change. The average annual temperature over the past 50 years is used as a reference to standardize the average annual temperature. After standardization, the average annual temperature illustrates the fluctuations in temperature and enables comparisons of the degree of climate change between counties. The empirical study has two levels. We first investigate the influence of temperature fluctuations on credit risk using a fixed effects model. Next, we use a nonparametric model and grouped regression to analyze the heterogeneous effect of temperature fluctuations on credit risk. The results lead to the following conclusions. (1) Temperature fluctuations of the county where a rural financial institution is located significantly affects its credit risk. Using the average annual temperature over the past 50 years as a reference line, when the average annual temperature is 1 standard deviation above the reference line, the proportion of nonperforming loans increases by 0.1365%. Hence, climate change significantly increases the credit risk of rural financial institutions. (2) If the average annual temperature fluctuation is subdivided into four quarters, only the temperature fluctuation in winter significantly affects credit risk. Taking the average winter temperature over the past 50 years as a reference standard, when the average winter temperature is 1 standard deviation above the reference standard, the proportion of nonperforming loans increases by 0.0777%. (3) The effect of temperature fluctuation on credit risk level has phased characteristics. As the range of the average annual temperature fluctuation expands, the sensitivity of credit risk to climate change increases from weak to strong. (4) Although city commercial banks, rural commercial banks and rural banks all serve local economic development, climate change does not significantly affect the credit risk of city commercial banks because their business is more dispersed among regions and industries. In a robustness test, we measure climate change using the normalized difference vegetation index (1 km) and use a multi-way fixed effects model, and the results remain unchanged.Through empirical examination, this paper detects the direction and degree of the effect of climate change on agricultural credit risks. The findings provide not only empirical evidence for qualitative research but also implications for rural financial institutions and regulators to respond to climate change. The following countermeasures are suggested based on the research conclusions: carrying out stress testing, implementing differentiated supervision and innovating risk mitigation tools to prevent agricultural credit risks.