3 Past Literature
There is an entire stream of the literature in this area, which devotes attention to the
development of new models, methods, and strategies to estimate the impacts of
climate change, given the uncertainties involved. Mendelsohn et al. (2000) develop
a new climate-impact model—the Global Impact Model (GIM), which predicts that
country specific results are likely to vary. Some papers (see Hallegatte et al. 2007)
develop general heuristic tools to assess the economic impacts of climate change in
urban areas. Deschênes and Greenstone (2007) estimate the economic impacts of
climate change on US agricultural land by estimating the effect of random, year–
to-year change in temperature and precipitation on agricultural profits. They find
that the hedonic approach, which is the standard in the previous literature on the
subject, to be unreliable because it produces results which are extremely sensitive to
choice of sampling, weighting, and control variables.
Some modeling studies are applied to the Indian context. Using the example of
Indian agriculture, O’ Brien et al. (2004) present a methodology for investigating
regional vulnerability to climate change in combination with other global stressors.
A state-of-the-art regional climate modeling system, known as PRECIS (Providing
Regional Climates for Impacts Studies) developed by the Hadley Centre for
Climate Prediction and Research, is applied for India by Kumar et al. (2006) to
develop high-resolution climate change scenarios. PRECIS simulations under
scenarios of increasing greenhouse gas concentrations and sulphate aerosols indicate marked increase in both rainfall and temperature toward the end of the
twenty-first century. This paper finds that warming is monotonously widespread
over the country, but there are substantial spatial differences in the projected rainfall
changes. They find that the west central part of India shows maximum expected
increase in rainfall. They predict that extremes in maximum and minimum temperatures are also expected to increase into the future, but the night temperatures are
increasing faster than the day temperatures. Another relevant finding from this
study is that extreme precipitation shows substantial increases over a large area, and
particularly over the west coast of India and west central India.
Although most models predict that higher temperatures will reduce grain yields
as the cool wheat-growing areas get warmer, they do not examine the possibility
that farmers will adapt to climate change, by making production decisions that are
in their own best interests. A recent set of models which examines cross-sectional
evidence from India and Brazil reported by Mendelsohn and Dinar (1999) find that
even though the agricultural sector is sensitive to climate change, individual farmers
do take local climatic conditions into account, and their ability to do so will help
mitigate the impacts of global warming.
Certain studies arrive at monetized estimates of the impact of climate change,
whereby impacts are expressed as functions of climate change and “vulnerability”
(Toi (2002) is an example of such a study). Gbetibouo and Hassan (2005) employed
a Ricardian model to measure the impact of climate change on South Africa’s field
crops and analyzed potential future impacts of further changes in the climate.
Economic Impacts of Climate Change in India’s Cities
283
There is an entire stream of the literature in this area, which devotes attention to the
development of new models, methods, and strategies to estimate the impacts of
climate change, given the uncertainties involved. Mendelsohn et al. (2000) develop
a new climate-impact model—the Global Impact Model (GIM), which predicts that
country specific results are likely to vary. Some papers (see Hallegatte et al. 2007)
develop general heuristic tools to assess the economic impacts of climate change in
urban areas. Deschênes and Greenstone (2007) estimate the economic impacts of
climate change on US agricultural land by estimating the effect of random, year–
to-year change in temperature and precipitation on agricultural profits. They find
that the hedonic approach, which is the standard in the previous literature on the
subject, to be unreliable because it produces results which are extremely sensitive to
choice of sampling, weighting, and control variables.
Some modeling studies are applied to the Indian context. Using the example of
Indian agriculture, O’ Brien et al. (2004) present a methodology for investigating
regional vulnerability to climate change in combination with other global stressors.
A state-of-the-art regional climate modeling system, known as PRECIS (Providing
Regional Climates for Impacts Studies) developed by the Hadley Centre for
Climate Prediction and Research, is applied for India by Kumar et al. (2006) to
develop high-resolution climate change scenarios. PRECIS simulations under
scenarios of increasing greenhouse gas concentrations and sulphate aerosols indicate marked increase in both rainfall and temperature toward the end of the
twenty-first century. This paper finds that warming is monotonously widespread
over the country, but there are substantial spatial differences in the projected rainfall
changes. They find that the west central part of India shows maximum expected
increase in rainfall. They predict that extremes in maximum and minimum temperatures are also expected to increase into the future, but the night temperatures are
increasing faster than the day temperatures. Another relevant finding from this
study is that extreme precipitation shows substantial increases over a large area, and
particularly over the west coast of India and west central India.
Although most models predict that higher temperatures will reduce grain yields
as the cool wheat-growing areas get warmer, they do not examine the possibility
that farmers will adapt to climate change, by making production decisions that are
in their own best interests. A recent set of models which examines cross-sectional
evidence from India and Brazil reported by Mendelsohn and Dinar (1999) find that
even though the agricultural sector is sensitive to climate change, individual farmers
do take local climatic conditions into account, and their ability to do so will help
mitigate the impacts of global warming.
Certain studies arrive at monetized estimates of the impact of climate change,
whereby impacts are expressed as functions of climate change and “vulnerability”
(Toi (2002) is an example of such a study). Gbetibouo and Hassan (2005) employed
a Ricardian model to measure the impact of climate change on South Africa’s field
crops and analyzed potential future impacts of further changes in the climate.
Economic Impacts of Climate Change in India’s Cities
283