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A Structural Ricardian Valuation of Climate Change Impacts on Agriculture in Pakistan


Mirza Nomman Ahmed

This book presents the economic analysis of the impacts of climate change on agriculture in Pakistan. Particular emphasis is laid on the magnitude of implicit adaptations in overall climate impact assessment and the analysis of selected adaptation options. Using a hedonic pricing model and a revealed choice approach, this study identifies the impacts of climate change on agricultural incomes, depicts the spatial patterns and seasonality of the impacts, and models the future adaptation behavior of farmers in the crop sector. A high sensitivity of farming in Pakistan to climate change is confirmed. With a changing climate and income in mind, farmers in Pakistan are more likely to choose rice, vegetables and maize, whereas they move away from wheat, sugarcane, cotton and fruits.


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5 Ricardian Studies- Literature Review


As mentioned earlier the Ricardian technique for climate impact assessment has experienced a steady rise in popularity since its first application in 1994 for the agriculture of the USA (MENDELSOHN et al., 1994). Alongside the rise in popularity due to its proven practicality as a tool for predicting the agricultural consequences of global climate change, it has also generated an unusual amount of critical attention (TIMMINS, 2003; POLSKY, 2004). Various countries have been studied using the Ricardian Approach (Figure 5.1). Note: only peer reviewed publications have been considered following MENDELSOHN AND DINAR (2009) Figure 5.1 Global coverage of the Ricardian Approach 1994-2011 Source: own illustration (2012) The first application of the Ricardian technique for climate impact assessment on agriculture was developed by MENDELSOHN et al. (1994) and applied to a developed country, namely the USA. The intention was to measure the economic impact of climate on land prices, thus farmland values were regressed on climate, soil and socioeconomic variables in order to estimate the so called Ricardian response function (best-value function) across a wide range of US counties. In a cross-sectional setting 1982 US-Census Data averages for approximately 3000 counties in the lower 48 states were used, covering important geographical, socio-economic, geophysical and agricultural factors. The effect of climatic and other non-climatic control-variables on farm revenues and land values was examined. The effect of climate on land values is confirmed by the estimations. Moreover the analysis also revealed a significantly non-linear (hill-shaped) relationship between climate and land 118 values with...

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