Transcript Topic 3: Economic Vulnerability under Climate Change

Economic Vulnerability under Climate Change
: With an Agricultural Emphasis
Energy
Bruce A. McCarl
Distinguished Professor of Agricultural Economics
Texas A&M University [email protected]
http://agecon2.tamu.edu/people/faculty/mccarl-bruce/
Climate Change Adaptation
Climate Change Mitigation
Climate Change Effects
Ramblings from an Ongoing and Never Ending Effort
Presented at the Climate Change Class, March 2003
Economic Issues in Climate Change
• Assessment of Impact
• Externality and Market Failure
• Mitigation Policy
• Cost Benefit Analysis
Economic Issues – Assessment of Impact
• Measuring Economic Value
• Income Distribution
• Inter-generational Equity
Basic Setting
D
S
P
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e
Quantity
Basic Setting
Sc
D
S
P
r
i
c
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a
b
c
d f
e
g
CS0 =a+b+d+f
PS0 =c+e+g
TSW0 =a+b+c+d+e+g
CSc =a
PSc = b+c
TSWc =a+b+c
∆CSc =-b-d-f
PSc = b-e-g
TSWc =-d-e-f-g
Quantity
Basic Setting between regions
D
Sregion1
Sregion2
P
r
i
c
e
demand
region1
Quantity
region2
Basic Setting between regions
D
SCCregion1
P
r
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Sregion1
Sregion2
SCCregion2
demand
region1
Quantity
region2
Basic Setting between regions
No Climate change
Sregion2
Sregion1
D
P
r
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ED
region2
region1
Quantity
Basic Setting between regions
No Climate change
D
P
r
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Sregion2
Sregion1
ED
P
Qs1
Qd
Qs2
region2
region1
Quantity
Basic Setting between regions
No Climate change
SCCregion1
D
P
r
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Sregion2
Sregion1
SCCregion2
ED
Region 1 loses mkt share
and produces less
P
PCC
Region 2 gains mkt share
and produces more
QCC
QCCs1
s1
Qs1
region1
Qd QCCd
QCCs2
Qs2
region2
Quantity
Consumers gain
All producers gain (I think)
Economic Issues – Cost/Benefit Analysis
• Extent of Damages
• Uncertainties in Impact Assessment
• Assumptions on scope of impact
• Economic approach to estimating welfare
Would Climate Change Hurt/Benefit?
Assessment Methodology - Summary Steps
• Identify sectors and physical effects
• Determine spatial and time scales
• Develop scenario regarding non-climatic factors
• Obtain GCM projections
• Chose analytical framework (econ theory foundation and
models to be used) and adapt or estimate models
• Assess physical impact of GCM projections
• Make assumptions about unmodeled phenomena
• Incorporate physical impact into economic models
• Incorporate data on possible adaptations to climate change
• Do analysis including sensitivity analysis
Scope of Assessment
• Identify sectors and physical effects
– The question relates to the choice of sector of the economy for
impact assessment – agriculture, water, etc. Can this really be
treated independently?
• Economic and geographic scale
– Firm level or sector level assessment, regional or national or
international
• Time frame
– Climate change is a long-term phenomenon that requires analysts
to decide the time frame of analysis, which would determine
impact assessment results
– Dynamic Vs Static Analysis
Scenario Development
• Non-climatic Scenarios
• Climate change Scenarios
• Time frame and uncertainty
Degree of climate change - RCPs
Representative Concentration Pathway Scenarios
Representative Concentration Pathway (RCP) scenarios
specify watts of climate forcing per square meter and
reflect concentrations and corresponding emissions, but
are not directly based on socio-economic storylines.
Representative Concentration Pathways (RCPs) Scenarios
that include time series of emissions and concentrations of
the full suite of greenhouse gases and aerosols and
chemically active gases, as well as land use/land cover.
The word representative signifies that each RCP provides
only one of many possible scenarios that would lead to the
specific radiative forcing characteristics.
Degree of climate change - RCPs
Four RCPs were used in AR5
RCP2.6 Radiative forcing (RF)
peaks at 3 Watts per square meter
by 2010-2020, declining
thereafter.
RCP4.5 RF is stabilized at 4.5
Wm–2 after 2100 with emissions
peak around 2040, then decline.
RCP6.0 RF stabilized at 6.0 Wm–
2 after 2100 with emissions
peaking around 2080, then
decline.
RCP8.5 RF reaches greater than
8.5 Wm–2 by 2100 with emissions
rising throughout the 21st century.
From WGI AR5
Box 1.1
Yet more could happen
What could happen
What we have
seen so far
Figure 1: Global temperature change and uncertainty. From Robustness and uncertainties in the new CMIP5 climate model projections
Reto Knutti & Jan Sedláček, Nature Climate Change 3, 369–373 (2013) doi:10.1038/nclimate1716,
Non Climatic - Socio-Economic Scenarios
• Before AR5 we had what was called the SRES
scenarios which were based on populations,
income, technology
• As of AR5 we switched to RCPS which are
purely GHG concentration based. But a group
has developed shared socioeconomic pathways
(SSPS) to accompany them
O’Neill, Brian C., et al. "A new scenario framework for climate change research: the concept of shared socioeconomic
pathways." Climatic Change 122.3 (2014): 387-400.
Riahi, Keywan, et al. "The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions
implications: an overview." Global Environmental Change (2016).
Van Vuuren, Detlef P., et al. "A new scenario framework for climate change research: scenario matrix architecture."
Climatic Change 122.3 (2014): 373-386.
Non Climatic - Socio-Economic Scenarios
Socio-economic pathways
describe the drivers of how
the future might unfold in
terms of population growth,
governance efficiency,
inequality across and within
countries, socio-economic
developments, institutional
factors, technology change,
and environmental
conditions
Fig. 1. Schematic illustration of main steps in developing the SSPs, including the narratives, socioeconomic scenario
drivers (basic SSP elements), and SSP baseline and mitigation scenarios.
Riahi, Keywan, et al. "The shared socioeconomic pathways and their energy, land use, and
greenhouse gas emissions implications: an overview." Global Environmental Change (2016).
Non Climatic SocioEconomic
Scenarios
From O’Neill, Brian C., et al. "A
new scenario framework for
climate change research: the
concept of shared socioeconomic
pathways." Climatic Change 122.3
(2014): 387-400.
Non Climatic - Socio-Economic Scenarios
Matrix from
Van Vuuren, Detlef
P., et al. "A new
scenario framework
for climate change
research: scenario
matrix
architecture." Climati
c Change 122.3
(2014): 373-386.
Not a One to one mapping
For a discussion see the special issue on this
Nakicenovic, Nebojsa, Robert J. Lempert, and Anthony C. Janetos. "A framework for the
development of new socio-economic scenarios for climate change research: introductory
essay." Climatic Change 122.3 (2014): 351-361
Non climate scenarios


Include at least two scenarios "baseline" or "reference" scenario and
"mitigation scenario"
Assumptions e.g. economic growth, technology, etc.
Figure TS.1: Qualitative directions of SRES scenarios for different indicators
Source: CC 2001 mitigation p. 24 at http://www.grida.no/climate/ipcc_tar/wg3/015.htm#24
Obtain GCMs Projections
• Data Distribution Center of IPCC maintains GCM
projections (http://ipcc-ddc.cru.uea.ac.uk/)
• Decide GCM scenarios whose projections you would use
(Ref. Guide to GCM Scenarios - DDC)
• Visualization pages /Downloadable files
• Chose GCMs that have better calibrated base climate for
the assessment country/region
• Compuate percentage changes in temp. and precipt for a
grid and apply to weather stations
• Choose more than one GCMs for sensitivity analysis
GCM - Geographic Scale
• Circa 2001
• HADCM: 3.75 x 2. 5 deg. (96X72 grids)
• CGCM: 3.75 x 3.75 deg. (96*48 grids)
• GDFL: 7.5 x 4.5 deg. (48*40 grids)
• Texas was covered by 4 grids
• Today
• GCMs depict the climate using a three
dimensional grid over the globe (see below),
typically having a horizontal resolution of
between 250 and 600 km (2.25-5.4 degrees), 10 to
20 vertical layers in the atmosphere and
sometimes as many as 30 layers in the oceans.
Time scale What is projected
Hotter
Choose Analytical Framework
• Spatial Analogue/current data
• Structural Approach
Analytical Framework - Spatial Analogue
• Ricardian Land Rent Approach
• Profit Function Approach
• In both cases
EconValue = f(controls,climate)
What is Spatial Analogue?
• Spatial analogues are regions which today have a
climate analogous to that anticipated in the study
region in the future
• Predict future changes
• Used to infer how changes in an environmental
condition or input will change yields or another
measure of interest
• Need information from a range of conditions
Adams, Richard A. "On the search for the correct economic assessment method." Climatic
change 41.3 (1999): 363-370.
What is Spatial Analogue? (Example)
• What would happen if the climate warmed?
– Look at warmer regions to infer how cooler regions
would adapt if warmed
• Assumption: Farmers in the cooler region will
be able to adapt the warmer region practices
• Uses statistical/econometric methods
– Investigate change in spatial patterns of crop
production
Adams, Richard A. "On the search for the correct economic assessment
method." Climatic change 41.3 (1999): 363-370.
Limitations
• Farmers in one region may not be willing or able
to adapt in the same way that a farmer in a
different region can.
– Cool to warm climate
• Magnitude of CO2 level changes create a new
growing environment
– If changes larger than what is seen previously, we
cannot accurately predict effect
– Multiple changes simultaneously can lead to different
outcomes for adaptation practices
Limitations
• Ignores changes in input and output prices that
may result from non-climate change
– May impact adaptation decisions
• Range restriction
• Requires a data set containing multiple regions
over time
– May not be available in developing countries
Application : McCarl, Villavicencio and Wu (2008)
• Investigates how crop yield and variance effected by climate
variables
– Examine historical crop yield stationarity variability
– Allow both mean and variance to be altered
– Evaluate stationarity under climate change projection scenarios
• Uses Just-Pope production function
– Estimate how changes in climate variables influence the mean
and variance of crop yield
– 𝑦 = 𝑓 𝑥 + ℎ(𝑥)𝜀
– 𝑓 𝑥 is an average production function
– ℎ2(𝑥) accounts for variable dependent heteroskedasticity
McCarl, Bruce A., Xavier Villavicencio, and Ximing Wu. "Climate change and future analysis: is stationarity dying?.“ American Journal of Agricultural
Economics 90.5 (2008): 1241-1247.
Application: McCarl, Villavicencio and Wu
(2008)
• Used feasible generalized least squares
(FGLS)
– Years 1960-2007 for each state
• Used fixed effects model
– Includes state dummies for state specific effects
– Includes interactions between climate and region
• Changes might not be the same in all regions
Application: McCarl, Villavicencio
and Wu (2008)
• Simulate climate change impacts
– Use above estimated parameters
– Project average yields and variability using climate
projections using climate models and 10% increase
in variability
– Projections for 2030
• Yield increases vary depending on the region
Application: McCarl, Villavicencio and Wu
(2008)
• Look at stationarity and crop yields
– Examine historical crop yield stationarity variability
– Allow both mean and variance to be altered
– Evaluate stationarity under climate change projection
scenarios
• Similar to study by Chen and McCarl (2001)
– Uses Just-Pope production function
• Spatial Analogue:
– Utilize the heterogeneity between regions to estimate
changes in yields
Analytical Framework - Structural App.
• Modeling biophysical and physical
sensitivity to climate change
• Modeling demand/supply sensitivity to
unmodeled climate sensitivity
• Integrated Assessment Modeling
Appraisal Approaches
• Physical assessments that only consider
changes in physical character (e.g.
changes in yield)
• Changes in cost as estimated in Chen and
McCarl (Land rent, and Profit)
• Welfare estimates (market and nonmarket)
Selected Assessments
Fischer et al. (1996)
Mendelsohn, Morrison, and Andronova
(2000)
U.S. National Assessment (Adams et al.)
Assessment - Fischer et al. (1996)
• Scope: Global (112 sites from 18 countries)
• Sector: Food production
• Timeframe: 2030, 2060
• Socio-Econ Scenario: Pop. and Tech (Ag.)
• GCMs: GISS, GFDL, and HADCM models
• Assess. Meth.: Structural approach (Ag.)
• Adaptations: Two levels of adaptation
• No major global loss in global production of food
• Marked regional differences in impacts
Assessment - Mendelsohn, Morrison, and Andronova (2000)
• Global (184 countries)
• Agriculture, Forestry, and Coastal Areas
• Timeframe: 2060
• Socio-Econ Scenario: Pop. and GDP growth
• GCMs: Assumed 2 deg. temp. increase
• Assess. Meth.: Spatial Analogue
• Adaptations: Implicitly imbeded in model results
• $0.278 bil. loss, with $215 bil. from ag.
• OECD gains $69 bil., while rest of world loses $348 bil.
U.S. National Assessment
• Scope: National
• Sectors: Agri., Forest, Water, Coastal Area, & Health
• Timeframe: 2060
• Socio-Econ Scenario: Only climate changed (Ag.)
• GCMs: HADCM and CGCM
• Assess. Meth.: Structural approach
• Adaptations: Planting schedule, tech., market
• $0.5 bil. loss, with $12.5 bil. gain
• Gains from trade
Emerging or Untreated Issues in Assessment
• Probability and severity of extreme events
• Valuation of non-market impacts (loss of life and bio-diversity)
• Distributional issues (weights)
• Aggregation and extrapolation from limited geographic studies
hides heterogeneity of responses to climate change
• How can we alter policy/research innovation investment to
achieve a more desirable mix of CC effects
Plan of Presentation
Discuss weakly coupled modeling system I use
Show some results
Discuss challenges and needs that arise in use and
in biophysical/economic modeling endeavor
Broad
Targeted areas in invitation
Linking spatial scales
Dealing with temporal issues
Reliably incorporating environ. services
Challenges in modeling
new landscape types
industrial process logistics
Simulator Bundle
Crop and
Env
Simulators
Livestock
Simulators
Pest
Regressions
GREET
Runoff
Simulators
GIS
FASOMGHG “System”
(and friends/ancestors)
Ag Census
NRI
State Annual
Crop Acres
FASOMGHG
FASOM
Transport
simulation
GCMs
Non ag
Resource
demand
IPCC
Scenarios
GHG
Implications
Model
Regional
Crop Mix
input use
Env.
loads
Regionalizing
Model
County
Crop Mix
& percent
loads
Water
Quality
Model
Simulator Component Functions
Simulator Bundle
Crop and
Env
Simulators
Livestock
Simulators
Pest
Regressions
GREET
Runoff
Simulators
GIS
Crop and Environmental simulators
Why – crop yield, input use, env
emissions, grass, energy crops
What – yield, fert, water, variability,
runoff, erosion
Components – EPIC, CENTURY, CERES,
Blaney-Criddle
Example – USNA, Reilly et al
Livestock Simulators
Transport
simulation
Non ag
Resource
demand
GCMs
IPCC
Scenarios
Why – performance, feed use,
disease spread
What – yield – meat, milk, young,
land use, feed need, disease spread
Components – PHYGROW, DADS,
RIFT, AUSPREAD
Example – USNA, High Plains
Simulator Bundle
Crop and
Env
Simulators
Simulator Component Functions
Livestock
Simulators
Pest regressions
Pest
Regressions
GREET
Runoff
Simulators
GIS
Why – pest damages and climate
What – pest cost increases w climate changes
Components – regression from USDA data
Example – USNA, Reilly et al, Chen and McCarl
GREET
Transport
simulation
Non ag
Resource
demand
GCMs
IPCC
Scenarios
Why – GHG LCA data
What – GHGs from input manufacture, fuel
use, processing
Components – GREET, FASOM GHG acct
Example – Adams et al, Murray et al, McCarl,
Argonne
Simulator Bundle
Crop and
Env
Simulators
Livestock
Simulators
Pest
Regressions
GREET
Runoff
Simulators
GIS
Transport
simulation
Non ag
Resource
demand
GCMs
IPCC
Scenarios
Simulator Component Functions
Runoff simulators
Why – water availability under
climate/ El Nino
What – water changes as climate
changes
Components – SWAT, USGS,
regressions from water data
Example – USNA, Reilly et al, Chen,
Gillig and McCarl
GIS
Why – Producer location, crop
suitability
What – Suitable land, herd location
Components – Misc data
Example – Ward et al High plains
Simulator Bundle
Crop and
Env
Simulators
Simulator Component Functions
Livestock
Simulators
Pest
Regressions
GREET
Runoff
Simulators
GIS
Transport
simulation
Non ag
Resource
demand
GCMs
IPCC
Scenarios
Transport simulators
Why – Hauling distance and GHGs
What – Cost and hauling distance
Components – Algebraic model
Example – McCarl, Annals of OR
Non Ag Resource demand
Why – Water competition, land
conversion
What – Water demand, Land demand,
Water quality demand
Components – Regressions,
projections
Example – Chen, Gillig and McCarl,
Adams et al
Simulator Bundle
Crop and
Env
Simulators
Simulator Component Functions
Livestock
Simulators
GCMs
Pest
Regressions
GREET
Runoff
Simulators
GIS
Why – Climate conditions under GHGs
What – Temperature, Rainfall,
variability
Components – IPCC Suite
Example – IPCC 2007
IPCC Scenarios
Transport
simulation
Non ag
Resource
demand
GCMs
IPCC
Scenarios
Why – Future of society and emissions
What – Population, GHG emissions,
Income
Components – IPCC SRES
Example – IPCC 2007
Output Simulator Component Functions
Water quality simulator
Why – Water implications of land use
What – Chemistry, erosion load
Components – SWAT, NWPCAM, Regressions
Example – Pattanayak st al, Atwood et al
GHG implications simulator
Why – GHG implications, Mitigation response
What – Net GHG effects
Ag Census
Components – GHG component of FASOM StateNRI
Annual
Crop Acreage
Example – Murray et al
FASOMGHG
FASOM
GHG
Implications
Model
Regional
Crop Mix
input use
Env loads
Regionalizing
Model
County
Crop Mix
& percent
loads
Water
Quality
Model
Economic Component Functions
FASOM
Why – Land use change, market effects, Welfare
What – Acres, exports, prices, mitigation choice,
Components – Forest and Ag simulator
Example – Adams et al, Murray et al
Regionalizing Model
Why – Link to localized env models
What – Land use by county
Components Humus model, multiple objectives
Example – Pattanayak et al, Atwood et al
FASOM
GHG
Implications
Model
Regional
Crop Mix
input use
Env loads
Ag Census
NRI
State Annual
Crop Acreage
Regionalizing
Model
County
Crop Mix
and
percent loads
Water
Quality
Model
Simulator Component Functions
Others
Forest simulators
Fire simulators
Processing plant models
Regional Logistics
International economics
Economy wide models
History of McCarl Climate Change Assessments
1987 – Corn Soy, Wheat no adaptation, no irrigation, no CO2
1992 – Corn, Soy, Wheat, no adaptation, irrigation, no CO2
1995 – Corn Soy, Wheat CO2, irrigation calendar adaptation
1999 – Corn, Soy, Wheat, cotton, sorghum, tomato, potato, CO2,
irrigation, calendar adaptation, crop mix shift, livestock,
grass, input usage, water available
2001 -- Corn, Soy, Wheat, cotton, sorghum, tomato, potato, CO2,
irrigation, calendar adaptation, crop mix shift, livestock,
grass, input usage, pest, extreme event, forestry
Cost continually went down now beneficial.
Background - Climate Change Assessment
Prior Work on Agriculture

EPA several iterations (1986, 1991, 1995)

EPRI Study (1999)

National Assessment (2000-2001)
- Ag only
- Forest and Ag
At first people thought the sky was falling !
Methodology – Climate Change Assessment
Climate Scenarios –
GCMs
Crop Simulation –
regional crop yields (dry and irrigated)
regional irrigated crop water use
Hydrologic simulation – irrigation water supply,
Expert opinion –
livestock performance,
Range and hay simulation and calculation -livestock pasture usage,
animal unit month grazing supply
Other studies –
international supply and demand
Regression –
pesticide usage
Economics –
ASM sector model
Percentage Changes in Crop Yield
Canadian
2030
2090
Hadley
2030
2090
Cotton Dry
Cotton Irr
+10%
+45%
+104%
+113%
+34%
+34%
+79%
+79%
Corn Dry
Corn Irr
+19%
- 1%
+ 23%
- 2%
+17%
+ 0%
+34%
+ 7%
Soybean Dry
Soybean Irr
+16%
+16%
+ 21%
+ 27%
+26%
+17%
+60%
+34%
Wheat Dry
Wheat Irr
-16%
- 4%
+104%
- 6%
+21%
+ 5%
+55%
+13%
Tomato Irr
Oranges Irr
Hay Dry
Hay Irr
-10%
+32%
-10%
+ 3%
- 22%
+ 99%
- 1%
+ 2%
- 4%
+40%
+ 2%
+23%
- 9%
+69%
+15%
+24%
Some Technical Findings - Climate Change Assessment
Using crop simulations climate change has been found to alter dryland
and irrigated crop yields as well as irrigation water use.
Crop sensitivity varies by crop, and location as well as the magnitude of
warming, the direction and magnitude of precipitation change.
Crops are differentially sensitive. Full range of cropping possibilities
needs to be considered when assessing climate change. Early US studies
limited to corn, soybeans and wheat, in contrast to later studies which
included many more heat tolerant crops. Including cotton, sorghum etc
changed sign of total effect.
CO2 fertilization effect is important factor. Inclusion significantly raises
the estimates of climate effected yields of many crops. It is however
controversial.
Yield effects vary latitudinally across the world. Yields generally improve
in the higher latitudes,. On the other hand there are estimates that there
will be net reductions in crop yields in warmer, low latitude areas and
semi-arid areas.
Some Technical Findings - Climate Change
Assessment
Yield changes can be reduced or enhanced by adaptations made by
producers. Farmers may adapt by changing planting dates, substituting
cultivars or crops, changing irrigation practices, and changing land
allocations to crop production, pasture, and other uses.
Livestock effects can be significant. Adjustments also expected in
pasture requirements and range productivity.
Irrigation water availability is an issue. There is also a need to develop
estimates on how nonagricultural water use might change in the face of
climate change.
Some technical findings - Climate Change Assessment
How do you do it?
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U.S. Effects of Climate Change
Water Use
Irrig Land
Dry Land
Crop land
Crop Price
Crop Prod
Exports
Lvst Price
Producer income
Cons Surplus
Foreign Surp
Total Welfare
TX Mun Water Use
Canadian
Hadley
- 4%
+ 7%
-11%
- 9%
-11%
+ 9%
+25%
0%
-10%
+0.3%
+0.8%
+0.5 bill$
+2%
-16%
- 7%
- 5%
- 6%
-15%
+11%
+26%
- 4%
- 8%
+0.6%
+0.5%
+5.2 bill$
+3%
2030
Northeast
Lakestates
Cornbelt
Northplains
Applachia
Southeast
Delta
South Plains
Mountain
Pacific
Regional Effects
Canadian
Hadley
+ 3
+63
+16
- 2
-24
-60
- 6
-24
+30
+26
+ 4
+43
+14
+18
-25
-15
+25
- 7
+39
+47
Some Economic Findings - Climate Change Assessment
Climate change is not expected to greatly alter global food production or
cause a global economic disaster in food production. This occurs
because the climatic alteration is less that the range of temperatures now
experienced across agriculture.
Impacts on regional food supplies in low latitude regions could amount
to large changes in productive capacity and significant economic
hardship.
Climate induced productivity changes act in opposite directions for
consumers and producers. Either less is produced and consumers' pay
higher prices with producers making more money or more is produced
with opposite effect.
Climate change influences prices, acreage and market signals. Marketlevel supply increases or decreases induce behavioral responses that
mitigate impacts projected by biophysical changes alone.
Some Economic Findings - Climate Change Assessment
Welfare and productivity estimates were negative in earlier studies but
have tended to become less so or beneficial. GCMs now include aerosols,
and other improvements yielding milder temperature and precipitation
estimates and crop models have enhanced CO2 fertilization effects.
Likely shift in comparative advantage of agricultural production regions.
Yield changes will be modified by adaptations made by farmers,
consumers, government agencies, and other institutions.
Welfare effects are sensitive to assumed CO2 fertilization effect.
Pests problems may be exacerbated.
Climate change is likely to increase yield variability.
Extreme Events
Timmermann et al suggested climatic change would alter
ENSO frequency. Their results imply El Nino and La Nina
increase in probability, Neutral decreases. Strength of these
events increases
probability of event occurrence.
El Nino
La Nina
Neutral
From
Today
To under
IPPC - IS92a
0.238
0.250
0.512
0.351
0.310
0.351
with stronger events
Chen, C. C., B. A. McCarl, and R.M. Adams, "Economic Implications of Potential Climate Change Induced ENSO Frequency and
Strength Shifts", Climatic Change, 49, 147-159, 2001.
Extreme Events
No ENSO Reaction
Current Prob
1458947
New Probabilities
1458533
New Prob and Strength
[-414]
1457939
[-1008]




No Reaction
1459400
(453)
1459077
(544)
[-323]
1458495
(556)
[-905]
Agriculture loses - $323 - $414 mill under freq shift under freq
and strength $905 - $1,008 million
Variability goes down
Some value of
producer ENSO reaction becomes larger
but does not offset
Losses as big as effects of climate change on the mean
Chen, C. C., B. A. McCarl, and R.M. Adams, "Economic Implications of Potential Climate Change
Induced ENSO Frequency and Strength Shifts", Climatic Change, 49, 147-159, 2001.
Climate Effects on Yield Variability
Climate change alters yields and yield variability.
Did a statistical analysis of data from 63 regions
over 25 years on how changes in temperature and
precipitation effect mean and variance.
Elasticity of effects on mean:
Precipitation
Corn
Cotton
Sorghum
Soybeans
Wheat
-1.4461
-0.0212
0.4802
0.8194
-1.6473
Temperature
0.8923
-3.5800
-2.5633
0.0586
5.0875
McCarl, B.A., X. Villavicencio,
and X.M. Wu, "Climate Change
and Future Analysis: Is
Stationarity Dying", American
Journal of Agricultural
Economics, Volume 90, Issue 5,
1242-1247, 2008.
Climate Effects on Yield Variability
Climate Effects on Pests
Impacts of rainfall on total pesticide usage cost for corn, cotton,
soybeans and wheat are positive. mixed effect of temperature.
% Change in Pesticide Cost for a % Change in Climate
Precipitation
CORN
Temperature
-0.0292
0.2284
0.3100
0.6607
POTATOES
-0.0777
0.2998
SOYBEANS
-0.0435
-0.1042
COTTON
WHEAT
-1.1579
Chen, C.C. and B.A. McCarl, "Pesticide Usage as Influenced by Climate: A Statistical
Investigation", Climatic Change, 50, 475-487, 2001.
1.8678
Methodology - Climate Change Assessment
Climate change can have regional effects and water effects. Here we look at a
regional water scarce economy -- Edwards Aquifer
Edwards
Kerr
Kendall
Real
Bandera
San
Antonio
Springs
Recharge zone of the EA region
Kinney
Uvalde
Medina
Climate Scenarios –
GCMs
Crop Simulation /Blaney Criddle –
regional crop yields (dry and irrigated)
regional irrigated crop water use
Regression –
recharge water supply
M&I demand shift when hotter (Griffin and Chang)
Other studies –
national prices (USGCRP National Assessment)
Economics –
EDSIM sector model
Climate Change Edwards Aquifer
Slightly neg welfare result in San Antonio region but strong
neg on the agricultural sector. Welfare in the nonagricultural sector is only marginally reduced by the
climatic change and value of permits rises dramatically.
Agricultural water usage declines while nonagricultural
water use increases.
Environmentally, springflow decreases.
Maintaining ecology becomes substantially more expensive.
Chen,C. C., D. Gillig and B. A. McCarl, "Effects of Climatic Change on a Water
Dependent Regional Economy: A Study of the Texas Edwards Aquifer", Climatic Change,
49, 397-409, 2001.
EA Regional Results under Alternative
Climate Change Scenarios
Climate Scenario
HAD2030 HAD2090 CCC2030 CCC2090
Variable
Units
Base ----------- % change from Base Scenario
----------
Ag Water Use
1000 af
150.05
-0.89
-2.4
-1.35
-4.15
M&I Water Use
1000 af
249.72
0.63
1.54
0.9
2.59
Total Water Use
1000 af
399.77
0.06
0.06
0.06
0.06
Net AG Income
1000 $
11391
-15.85
-30.34
-29.41
-44.97
Net M&I Surplus
1000 $
337657
-0.2
-0.58
-0.36
-0.92
Authority Surplus
1000 $
6644
3.76
12.73
7.07
21.6
Net Total Welfare
1000 $
355692
-0.64
-1.3
-1.16
-1.93
Comal Flow
1000 af
379.5
-9.95
-20.15
-16.62
-24.15
San Marcos Flow
1000 af
92.8
-5.07
-10.09
-8.3
-12.06
Springflow maintenance under climate change
• Pumping level to keep springflows at the
BASE
 decreases 35,000 to 50,000 af under the 2030 scenarios
 decreases 55,000 to 80,000 af under the 2090 scenarios
» Agricultural and M&I water use reduction
» Substantial economic costs: an additional cost of $0.5 to $2
million per year
» Increase in EA authority surplus or rents to water right
holders
Effects to Consider
Temp
Rainfall CO2 SeaLevel ExtremeEvents
Plants
Crop and forage growth
Crop /forage water need
X
X
X
X
Soils
Soil moisture supply
Irrigation demand
Soil fertility
X
X
X
X
X
X
Animals
Performance
Pasture/Range Carry cap
X
X
X
X
Irrigation Water Supply
Evaporation loss
Run-off/general supply
Non-AG competition
X
X
X
X
X
X
X
X
X
X
X
X
Other
Water borne transport
Port facilities
Pest and diseases
Insurance
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
More References
http://agecon.tamu.edu/faculty/mccarl/papers.htm
Chen, C.C. and B.A. McCarl, "Pesticide Usage as Influenced by Climate:
A Statistical Investigation", Climatic Change, 50, 475-487, 2001.
Chen,C. C., D. Gillig and B. A. McCarl, "Effects of Climatic Change on a
Water Dependent Regional Economy: A Study of the Texas Edwards
Aquifer", Climatic Change, 49, 397-409, 2001.
Chen, C. C., B. A. McCarl, and R.M. Adams, "Economic Implications of
Potential Climate Change Induced ENSO Frequency and Strength
Shifts", Climatic Change, 49, 147-159, 2001.
Irland, L. C., D.M. Adams, R.J. Alig, C. J. Betz, C.C. Chen, M. Hutchins,
B.A. McCarl, K. Skog and B. L. Sohngen, "Assessing Socioeconomic
Impacts of Climate Change on U. S. Forests, Wood-Product Markets
and Forest Recreation", BioScience, 51(9) September, 753-764, 2001.