How large is the farm income loss due to climate change? Evidence from India
How large is the farm income loss due to climate change? Evidence from India
复制标题
气候变化导致的农业收入损失有多大？
DOI：
10.1108/caer-11-2020-0275
复制
发表时间：
2022
影响因子：
5.1
通讯作者：
P. Jena
中科院分区：
文献类型：
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作者：
R. Kalli;P. Jena
气候变化是全球经济中最受关注的问题，气候变率的增加和不确定的气候事件给农业部门带来了困扰。这项研究估计了气候变化对印度卡纳塔克邦农业收入的经济影响。该研究报告的差异，从过去的研究，估计从本研究表明更高的负面影响的上升temperature.Design/methodology/approachFixed效应面板回归方法被用来研究农业收入的变化对气候的反应。根据作物日历对气候变量进行分类，以反映气候变化造成的损害。利用1992-2012年21 a（20个地区）的区域气候数据，研究表明，平均最高气温每升高1 ℃，农业收入下降17- 21%。不同模型的预测行为进行了评估，使用样本外预测方法通过训练和测试的历史dataset.Originality/valueThe研究采用最近的数据集农业和更新的气候变量来估计气候变化对农业的影响。该研究产生了更好的结果相比，以前的传统模型在印度的文学背景。本研究进一步使用样本外预测方法评估估计模型的预测行为及稳健性。
PurposeClimate change is the most concerned issue in the global economy; increase in climate variability and uncertain climate events have caused distress in agriculture sector. The study estimates economic effect of climate change on agriculture income for the Indian state of Karnataka. The study reports the difference of result from past studies, where estimates from present study indicate higher negative impact of rise in temperature.Design/methodology/approachFixed effect panel regression method was used to examine change in agriculture revenue to climate response. Climate variables were classified based on the crop calendar to capture the damage caused by climate change. The authors use fine scale climate data set constructed at regional context for 20 districts and time period of 21 years (1992–2012).FindingsThe result showed that with 1-degree rise in average maximum temperature, the revenue declined by 17–21%. The prediction behavior of the different models was evaluated using out-of-sample forecast approach by training and testing historical data set.Originality/valueThe study adopts recent data sets on agriculture and the updated climate variables to estimate the climate change impact on agriculture. The study yields the better results when compared to previous traditional models applied in literature in Indian context. The study further evaluates the prediction behavior and robustness of the estimated models using out-of-sample forecast method.