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Research On The Impact Of Climate Change On The Development Of Financial Industry In Shanxi Province

Posted on:2020-11-29Degree:MasterType:Thesis
Country:ChinaCandidate:M LiFull Text:PDF
GTID:2370330572498649Subject:Environmental management
Abstract/Summary:PDF Full Text Request
In recent years,global temperature has been rising,and climate change has become one of the hot issues in the world.The impact of climate change has penetrated into all aspects of our life and production,seriously affecting the smooth operation of our economy.As a rising province in central China,Shanxi Province is in a critical period of economic development.As a driving force of economic development,the financial industry is bound to be affected by climate change.Therefore,a comprehensive understanding of the impact of climate change on the development of the financial industry in Shanxi Province can provide a basis for the province's financial industry to adapt to climate change and achieve green and sustainable development of the financial industry.Taking Shanxi Province as an example,the selection of the province's 2000-2016 financial sector index and climate index,using MLR model and ridge regression model,respectively on climate change to the overall financial operation in Shanxi Province and the degree of influence on the development of the financial industry specific quantitative analysis,and on this basis,through the grey prediction model,for the next five years in Shanxi Province has carried on the forecast of annual precipitation and average air temperature,according to the predict result to carry on the forecast to the development of financial industry in the future.The research results show that :(1)the development of the financial industry in Shanxi Province is not only affected by the GDP,disposable income,government expenditure and the amount of fixed asset investment in the financial industry,but also affected by climate change to some extent.(2)in the process of using the model to analyze the influence degree of climate change on the financial industry of Shanxi Province,it is found that the influence degree of temperature and precipitation on different industries is different.From the perspective of temperature,the rise of temperature has a negative impact on the insurance industry and securities industry in the province,but the increase of temperature is conducive to the steady operation of the banking industry.From theperspective of precipitation,the running results of ridge regression model show that,except for the securities industry,the precipitation coefficient values of all financial industries are lower than the temperature coefficient values of the industry,so the precipitation has a relatively small impact on the financial industry in Shanxi Province.The increase of precipitation plays a positive role in the insurance industry and the banking industry,but for the securities industry,the precipitation coefficient is negative,so the increase of precipitation is not conducive to the stable development of the securities industry.Although the degree of influence of temperature and precipitation on different financial industries is different,from the perspective of the overall operation of the financial industry,the increase of temperature and precipitation has a positive impact on the development of the financial industry.(3)through the forecast and analysis of the annual precipitation and average temperature in the next five years,it is found that the continuous increase of the average temperature and precipitation will promote the healthy and good operation of the financial industry in Shanxi Province.(4)according to the results of empirical analysis,the paper provides basis and Suggestions for shanxi's financial industry to better adapt to climate change from aspects of reinforce the supervise of climate change's information,establishing a sound green financial market system,developing green credit,green insurance and green securities.
Keywords/Search Tags:Shanxi province, Climate change, Financial industry, Multiple linear regression, Grey prediction model
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