Summary:
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.
刘波, 王修华, 李明贤. 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[J]. 金融研究, 2021, 498(12): 96-115.
LIU Bo, WANG Xiuhua, LI Mingxian. Climate Change and the Credit Risk of Rural Financial Institutions. Journal of Financial Research, 2021, 498(12): 96-115.
[1]陈国进、郭珺莹和赵向琴,2021,《气候金融研究进展》,《经济学动态》第8期,第131~145页。 [2]陈雨露,2020,《当前全球中央银行研究的若干重点问题》,《金融研究》第2期,第1~14页。 [3]金书秦、林煜和牛坤玉,2021,《以低碳带动农业绿色转型:中国农业碳排放特征及其减排路径》,《改革》第5期,第29~37页。 [4]连玉君、彭方平和苏治,2010,《融资约束与流动性管理行为》,《金融研究》第10期,第158~171页。 [5]马骏,2018,《金融机构环境风险》,中国金融出版社。 [6]潘根兴、高民、胡国华、魏钦平、杨晓光、张文忠、周广胜和邹建文,2011,《气候变化对中国农业生产的影响》,《农业环境科学学报》第9期,第1698~1706页。 [7]潘根兴、高民、胡国华、魏钦平、杨晓光、张文忠、周广胜和邹建文,2011,《应对气候变化对未来中国农业生产影响的问题和挑战》,《农业环境科学学报》第9期,第1707~1712页。 [8]谭林和高佳琳,2020,《气候变化风险对金融体系的作用机理及对策研究》,《金融发展研究》第3期,第13~20页。 [9]田成诗和陈雨,2021,《中国省际农业碳排放测算及低碳化水平评价——基于衍生指标与TOPSIS法的运用》,《自然资源学报》第2期,第395~410页。 [10]王信,2021,《审慎管理气候变化相关金融风险》,《中国金融》第4期,第41~42页。 [11]王修华、刘锦华和赵亚雄,2021,《绿色金融改革创新试验区的成效测度》,《数量经济技术经济研究》第10期,第107~127页。 [12]中国工商银行环境因素压力测试课题组,2016,《环境因素对商业银行信用风险的影响——基于中国工商银行的压力测试研究与应用》,《金融论坛》第2期,第3~16页。 [13]张新悦、冯禹昊、曾辉和唐志尧,2021,《1982—2014年华北及周边地区生长季NDVI变化及其与气候的关系》,《北京大学学报(自然科学版)》第1期,第153~161页。 [14]Adrian, T., D.Covitz and N.Liang, 2015, “Financial Stability Monitoring”, Annual Review of Financial Economics, 7: 357~395. [15]Boros, E., 2020, “Risks of Climate Change and Credit Institution Stress Tests”, Financial and Economic Review, 19(4): 107~131. [16]Cai, Y.and T.S.Lontzek, 2019, “The Social Cost of Carbon with Economic and Climate Risks”, Journal of Political Economy, 127(6): 2684~2734. [17]Chen, S.and B.Gong, 2021, “Response and Adaptation of Agriculture to Climate Change: Evidence from China”, Journal of Development Economics,148: 102557. [18]Clayton, J., A.Devine and R.Holtermans, 2021, “Beyond Building Certification: The Impact of Environmental Interventions on Commercial Real Estate Operations”, Energy Economics, 93: 105039. [19]Fabris, N, 2020, “Financial Stability and Climate Change”, Journal of Central Banking Theory and Practice, 3: 27~43. [20]French, A.and M.Vital, 2015, “Insurance and Financial Stability”, Bank of England Quarterly Bulletin,3. [21]Garmaise, M.J.and T.J.Moskowitz, 2009, “Catastrophic Risk and Credit Markets”, The Journal of Finance, 64(2): 657~707. [22]Hong, H., G.A.Karolyi and J.A.Scheinkman, 2020, “Climate Finance”, The Review of Financial Studies, 33(3): 1011~1023. [23]Hong, H., F.W.Li and J.Xu, 2019, “Climate Risks and Market Efficiency”, Journal of Econometrics, 208(1): 265~281. [24]Hosono, K., D.Miyakawa and T.Uchino, 2016, “Natural Disasters, Damage to Banks, and Firm Investment”, International Economic Review, 57(4): 1335~1370. [25]Hsiang, S., R.Kopp and A.Jina, 2017, “Estimating Economic Damage from Climate Change in the United States”, Science, 356(6345): 1362~1369. [26]Klomp, J., 2014, “Financial Fragility and Natural Disasters: An Empiric Alanalysis”, Journal of Financial Stability, 13:180~192. [27]Liang, X.Z., Y.Wu and R.G.Chambers, 2017, “Determining Climate Effects on US Total Agricultural Productivity”, Proceedings of the National Academy of Sciences, 114(12): E2285~E2292. [28]Lobell, D.B.and J.A.Burney, 2021, “Cleaner Air Has Contributed One-fifth of US Maize and Soybean Yield Gains Since 1999”, Environmental Research Letters, 16(7): 074049. [29]Lobell, D.B., W.Schlenker and J.Costa-Roberts, 2011, “Climate Trends and Global Crop Production since 1980”, Science, 333(6042): 616~620. [30]Möllmann, J., M.Buchholz and W.Kölle, 2020, “Do Remotely-sensed Vegetation Health Indices Explain Credit Risk in Agricultural Microfinance?”, World Development, 127: 104771. [31]Nordhaus, W.D., 1977, “Economic Growth and Climate: The Carbon Dioxide Problem”, The American Economic Review, 66(1): 341~346. [32]Noy, I., 2009, “The Macroeconomic Consequences of Disasters”, Journal of Development Economics, 88(2): 221~231. [33]Olesen, J.E.and M.Bindi, 2002, “Consequences of Climate Change for European Agricultural Productivity, Land Use and Policy”, European Journal of Agronomy, 16(4): 239~262. [34]Sampson, R.J., 2017, “Urban Sustainability in An Age of Enduring Inequalities: Advancing Theory and Ecometrics for the 21st-century City”, Proceedings of the National Academy of Sciences, 114(34): 8957~8962. [35]Scott, M., J.van Huizen and C.Jung, 2017, “The Bank of England's Response to Climate Change”, Bank of England Quarterly Bulletin, 98~109. [36]Steinfeld, H., P.Gerber and T.Wassenaart, 2006, “Livestock's Long Shadow: Environment Issues and Options”, Roman: Food and Agriculture Organization of the United Nations. [37]Guimaraes P and P Portugal, 2010, “A Simple Feasible Procedure to Fit Models with High-Dimensional Fixed Effects”.The Stata Journal, 10(4): 628~649. [38]Zhao, C., B.Liu and S.Piao, 2017, “Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates”, Proceedings of the National Academy of Sciences, 114(35): 9326~9331.