{"title":"Big Data Analysis: The Impact of Agricultural Non-point Source Pollution on Household Decision-making","authors":"Ying Wang, Chao Xue","doi":"10.52783/jes.3528","DOIUrl":null,"url":null,"abstract":"Based on intergenerational support theory, a simultaneous equation model is used in this article to analyze the impact of fertilizer loss on household medical and care decisions in rural China. Results of the big data empirical analysis indicate that one kg/ha increase in fertilizer loss alters the medical costs by CNY 35 (USD 5) per year and the opportunity cost of household caring time by CNY 5 (USD 0.7) per year. This is equivalent CNY 760 million (USD 109 million) at nation economic loss. Furthermore, fertilizer loss has a significant lag and cumulative impact on medical expenses.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52783/jes.3528","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Based on intergenerational support theory, a simultaneous equation model is used in this article to analyze the impact of fertilizer loss on household medical and care decisions in rural China. Results of the big data empirical analysis indicate that one kg/ha increase in fertilizer loss alters the medical costs by CNY 35 (USD 5) per year and the opportunity cost of household caring time by CNY 5 (USD 0.7) per year. This is equivalent CNY 760 million (USD 109 million) at nation economic loss. Furthermore, fertilizer loss has a significant lag and cumulative impact on medical expenses.