اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدThe Historical Impact of Anthropogenic Climate Change on Global Agricultural Productivity
91
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that anthropogenic climate change has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 9 years of productivity growth. The effect is substantially more severe (a reduction of ~30-33%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.
قيم البحث
اقرأ أيضاً
104 -
Bakhtmina Zia
, Dr Muhammad Rafiq PhD Research Scholar
, Institute ofn Management Sciences
2020
The paper is a collection of knowledge regarding the phenomenon of climate change, competitiveness, and literature linking the two phenomena to agricultural market competitiveness. The objective is to investigate the peer reviewed and grey literature
on the subject to explore the link between climate change and agricultural market competitiveness and also explore an appropriate technique to validate the presumed relationship empirically. The paper concludes by identifying implications for developing an agricultural competitiveness index while incorporating the climate change impacts, to enhance the potential of agricultural markets for optimizing the agricultural sectors competitiveness.
Economists have predicted that damages from global warming will be as low as 2.1% of global economic production for a 3$^circ$C rise in global average surface temperature, and 7.9% for a 6$^circ$C rise. Such relatively trivial estimates of economic d
amages -- when these economists otherwise assume that human economic productivity will be an order of magnitude higher than today -- contrast strongly with predictions made by scientists of significantly reduced human habitability from climate change. Nonetheless, the coupled economic and climate models used to make such predictions have been influential in the international climate change debate and policy prescriptions. Here we review the empirical work done by economists and show that it severely underestimates damages from climate change by committing several methodological errors, including neglecting tipping points, and assuming that economic sectors not exposed to the weather are insulated from climate change. Most fundamentally, the influential Integrated Assessment Model DICE is shown to be incapable of generating an economic collapse, regardless of the level of damages. Given these flaws, economists empirical estimates of economic damages from global warming should be rejected as unscientific, and models that have been calibrated to them, such as DICE, should not be used to evaluate economic risks from climate change, or in the development of policy to attenuate damages.
This paper examines how subsistence farmers respond to extreme heat. Using micro-data from Peruvian households, we find that high temperatures reduce agricultural productivity, increase area planted, and change crop mix. These findings are consistent
with farmers using input adjustments as a short-term mechanism to attenuate the effect of extreme heat on output. This response seems to complement other coping strategies, such as selling livestock, but exacerbates the drop in yields, a standard measure of agricultural productivity. Using our estimates, we show that accounting for land adjustments is important to quantify damages associated with climate change.
We model sectoral production by cascading binary compounding processes. The sequence of processes is discovered in a self-similar hierarchical structure stylized in the economy-wide networks of production. Nested substitution elasticities and Hicks-n
eutral productivity growth are measured such that the general equilibrium feedbacks between all sectoral unit cost functions replicate the transformation of networks observed as a set of two temporally distant input-output coefficient matrices. We examine this system of unit cost functions to determine how idiosyncratic sectoral productivity shocks propagate into aggregate macroeconomic fluctuations in light of potential network transformation. Additionally, we study how sectoral productivity increments propagate into the dynamic general equilibrium, thereby allowing network transformation and ultimately producing social benefits.
This paper quantifies the significance and magnitude of the effect of measurement error in satellite weather data in the analysis of smallholder agricultural productivity. The cross-country analysis leverages multiple rounds of georeferenced, nationa
lly-representative, panel household survey data that have been collected over the last decade. These data are spatially-linked with a range of geospatial weather data sources and related metrics. The goal is to provide systematic evidence on obfuscation methods, satellite data source, and weather metrics in order to determine which of these elements have strong predictive power over a large set of crops and countries and which are only useful in highly specific settings.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
