MIT Joint Program on the
Science and Policy of Global Change
Global Economic Effects of Changes in Crops,
Pasture, and Forests due to Changing Climate,
Carbon Dioxide, and Ozone
John Reilly, Sergey Paltsev, Benjamin Felzer, Xiaodong Wang, David Kicklighter,
John Melillo, Ronald Prinn, Marcus Sarofim, Andrei Sokolov and Chien Wang
Report No. 149
May 2007
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Global Economic Effects of Changes in Crops, Pasture, and Forests due to Changing
Climate, Carbon Dioxide, and Ozone
John Reilly†,*, Sergey Paltsev†, Benjamin Felzer‡, Xiaodong Wang†, David Kicklighter‡,
John Melillo‡, Ronald Prinn†, Marcus Sarofim†, Andrei Sokolov† and Chien Wang†
Abstract
Multiple environmental changes will have consequences for global vegetation. To the extent that crop yields
and pasture and forest productivity are affected there can be important economic consequences. We examine
the combined effects of changes in climate, increases in carbon dioxide, and changes in tropospheric ozone
on crop, pasture, and forest lands and the consequences for the global and regional economies. We examine
scenarios where there is limited or little effort to control these substances, and policy scenarios that limit
emissions of CO2 and ozone precursors. We find the effects of climate and CO2 to be generally positive, and
the effects of ozone to be very detrimental. Unless ozone is strongly controlled damage could offset CO2 and
climate benefits. We find that resource allocation among sectors in the economy, and trade among countries,
can strongly affect the estimate of economic effect in a country.
Contents
1. Introduction ................................................................................................................................ 1
2. Modeling Global Agricultural Economic Response to Environmental Change...................... 2
3. Model Descriptions .................................................................................................................... 4
4. Scenarios..................................................................................................................................... 6
5. Agriculture, Pasture and Forestry Results............................................................................... 11
6. Caveats and Comparison with Previous Work ....................................................................... 17
7. Conclusions .............................................................................................................................. 18
8. References ................................................................................................................................ 19
1. INTRODUCTION
Multiple environmental changes will have consequences for global vegetation. To the extent
that crop yields and pasture and forest productivity are affected there can be important economic
consequences. We examine the combined effects of changes in climate, increases in carbon
dioxide, and changes in tropospheric ozone on crop, pasture, and forestland productivity and the
consequences for the global and regional economies. We consider scenarios where there is
limited or little effort to control CO2 and ozone precursors, and policy scenarios that limit
emissions of these substances. Much analysis and research on the economic impacts of climate
change and/or higher ambient levels of CO2 on agriculture has been conducted. Our study is
unique in several ways, including the focus on multiple environmental changes, use of transient
climate scenarios, comprehensive assessment of crops, pasture and forests, and evaluation of
effects in both a reference and in pollution mitigation scenarios.
We apply the MIT Integrated Global Systems Model (IGSM) (Prinn et al., 1999), updated
here to focus on the vegetation and economic effects of climate and ozone. In particular, the
Terrestrial Ecosystem Model (TEM) component is a biogeochemical model that has been
updated to include vegetation response to ozone as described in Felzer et al. (2004). We have
also altered the Emissions Prediction and Policy Analysis (EPPA) model (Paltsev et al., 2005), a
† Joint Program on Science and Policy of Global Change, Massachusetts Institute of Technology.
* Corresponding author email address: jreilly@mit.edu.
‡ Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA 02543, USA.
1
computable general equilibrium model of the world economy, to better represent crops,
livestock, and forest sectors. In Section 2 we review key previous agricultural impact studies,
identifying how our approach advances methods in this field of research. Section 3 reviews
briefly the model components used in the study. Section 4 describes the reference and pollution
mitigation scenarios. Section 5 describes the results. Section 6 offers some caveats and Section 7
summarizes key results.
2. MODELING GLOBAL AGRICULTURAL ECONOMIC RESPONSE TO
ENVIRONMENTAL CHANGE
Key previous studies of climate and CO2 effects, focusing on those that are global or pioneer
new methods, include Parry et al. (1988a, b, 1999, 2004); Adams et al. (1990); Tobey et al.
(1992); Reilly and Hohman (1993); Rosenberg (1993); Rosenzweig and Parry (1994), Mendelsohn
et al. (1994), Darwin et al. (1996), Reilly et al. (2003), Izaurralde et al. (2003), and Alig et al.
(2003). There have been no global estimates of potential economic impact of ozone damage to
crops. The most comprehensive economic study focused on current estimates of ozone damage
was for the United States (Adams et al., 1986). More recent work has examined crop production
effects in the eastern United States (Westenbarger and Frisvold, 1994, 1995) and Asia (Wang and
Mauzerall, 2004) with very limited evaluation of economic effects. There has been much
experimental work on both ozone and CO2 effects and a large number of crop site studies, and
farm or regional level studies for climate and CO2, as reviewed in Gitay et al. (2001), and Reilly
and Schimmelpfennig (1999). The methods pioneered in the literature cited above have also been
applied in other studies, and using different climate scenarios. A recent review of these major
agricultural assessment exercises is provided in Reilly (2002) and Gitay et al. (2001).
This study is unique in several ways. (1) We include the combined effects of climate, CO2,
and tropospheric ozone whereas previous work has mostly examined climate and CO2 or climate
effects only. (2) The climate and yield effects are from fully transient climate scenarios where
gradual increases in greenhouse gases (GHGs) gradually force the climate. Much previous work
is based on equilibrium-doubled CO2 climate scenarios, and so it is unclear in what year such a
climate would be observed. Some previous work has simulated economic effects through time
but have only estimated yield effects for a circa 2030, 2070, or 2100 climate scenario,
interpolating yield effects for earlier years. Most previous work has used static economic models
examining the impacts of climate change as if it occurred in the agricultural economy as it exists
today. (3) The scenarios of climate, CO2 and ozone concentrations are from consistent economic
scenarios; most previous work is based on doubled CO2 equilibrium climate scenarios, requiring
assumptions about when such a climate would be realized as well as the extent to which the
forcing was all CO2 or partly due to other greenhouse gases. (4) We consider effects in no-policy
and in policy scenarios thus making it possible to assess the “benefits” of the prescribed policy;
previous work has simply examined different climate scenarios. (5) The terrestrial
biogeochemical model we use simulates the relatively immediate response of vegetation to
climate and atmospheric change as well as the longer-term soil dynamics and its impact on
productivity. Previous work takes soil characteristics as unchanging.
There are important advances represented in previous work, and our approach follows closely
the state-of-the-art in this regard. (1) We simulate the economic effects using a global
2
computable general equilibrium (CGE) model that is recursive dynamic thus capturing the
interactions among agriculture, forestry, and livestock sectors and with the rest of the economy
as well as international trade effects as economies develop over time. Rosenzweig and Parry
(1994) and Parry et al. (1999, 2004) use a forward-looking dynamic CGE model capturing such
effects as well. Darwin et al. (1996) use a static CGE model, and so capture interactions with the
rest of the economy, but an economy of circa 1995. Other work uses partial equilibrium market
models and so fails to capture interactions with the rest of the economy, or econometric
approaches that do not consider market price effects at all. Much work considers a single country
or smaller region and is thus unable to correctly account for international trade and changes in
international prices. (2) We assess effects on a 0.5° x 0.5° latitude-longitude grid level, of which
there are about 62,000 globally, allowing for a fairly complete assessment of existing spatial
variation. Darwin et al. (1996) use 0.5° x 0.5° latitude-longitude grid but with a static CGE
model, and without a process-model representation of effects on vegetation. Izauralde et al.
(2003) approach this coverage by modeling 204 separate hydrologic unit areas, but their
application is for the U.S. only. Mendelsohn et al. (1994) use county-level data for the U.S. (of
which there are on the order of 3,000 counties) to estimate a statistical model of climate impacts
on vegetation but there are no market feedbacks and no assessment of trade effects. Most
previous work has used crop models applied at relatively sparsely located sites—as many as 4050 for the United States but sometimes just a few to represent, for example, the entire African
continent and thus one can question whether these relatively few sites are representative of
spatially varying conditions. (3) We evaluate the combined effects on crops, pasture, and forests,
activities that all compete for land use. Most studies consider only crops, and often a limited set
of crops. Reilly et al. (2003) considered impacts on crops and pasture and Alig et al. (2003)
included crops, pasture and forests but both studies were limited to the U.S.
To achieve these methodological advances we have had to simplify other aspects of the
models so that they remained computationally feasible. The biogeochemical model runs on a
monthly time step and simulates a generic crop thus making simulation for the large number of
grid cells feasible. More detailed crop models run on an hourly or quarter-day time step with
specific model parameters for each crop. We gain by representing the spatial diversity of
cropping more completely, but we cannot represent the details of the phenological development
of different crops and response to diurnal weather variability. We represent crops, livestock, and
forestry sectors in a relatively aggregate fashion, assuming that the yield effects simulated by
TEM are reflected as productivity impacts to land in the economic model. We gain by
representing all three of these large land using sectors in a single model, and by treating the
interaction of these sectors with other sectors of the economy but we are not able to represent
individual crop and livestock sectors, or the details of optimal forest rotation, harvesting and regrowth. The climate model is a zonal land-ocean resolving model, and we therefore must use a
fixed longitudinal pattern of climate that is adjusted by changes in the zonal average simulated
by the two-dimensional (2D) model. We also use a fixed spatial pattern of ozone driven by
modeled zonal mean ozone levels as projected by the 2D model. This makes simulation of
multiple climate scenarios numerically feasible, but does not adequately capture finer details of
the changing spatial pattern of climate, or possible changes in transport of ozone as climate
changes. We return to these issues in the final section where we discuss caveats and implications
3
for future research. Climate impact research remains subject to many caveats because the
accurate prediction of fine scale changes in weather patterns, even in the most highly resolved
general circulation models, remains elusive.
3. MODEL DESCRIPTIONS
We briefly describe the MIT IGSM, and then focus attention on the TEM and EPPA
components as modified for this work. The MIT IGSM includes sub-models of the relevant
aspects of the natural earth system coupled to a model of the human component as it interacts
with climate processes. A description of the system components used in Version 1, along with a
sensitivity test of key aspects of its behavior, is reported in Prinn et al. (1999).
The major model components of the IGSM are:
• A model of human activity and emissions (the Emission Prediction and Policy Analysis, or
EPPA model),
• An atmospheric dynamics, physics and chemistry model, which includes a sub-model of
urban chemistry,
• An ocean model with carbon cycle and sea-ice sub-models,
• A Terrestrial Ecosystem Model (TEM) that represents terrestrial ecosystem processes and
a Natural Emissions Model (NEM) that represents methane and N2O cycles.
For this work, we use Version 1 of the IGSM because we are interested in retaining the 0.5° x
0.5° resolution of the original TEM. The 0.5° x 0.5° TEM is forced off-line by the IGSM climate
scenario.1 In addition, the version of EPPA model applied here—EPPA-AGRI—is also run offline, forced by changes in crop, pasture and forest productivity as determined by TEM. The
economic changes and ozone damages that result imply changes in emissions of greenhouse
gases but we do not feed these back into the climate system. Thus, we are using the output of the
2-Dimensional Land-Ocean Resolving General Circulation Model (GCM) of the MIT IGSM as
an exogenous scenario to drive the impact models. The MIT IGSM is a flexible model in the
sense that parameters controlling climate sensitivity, response to aerosols, and ocean heat uptake
can be set so the model replicates results of other GCMs. The standard settings for the model,
and those used here, are the median values from a climate detection and attribution study, with
expert priors, of Forest et al. (2002) as applied in Webster et al. (2003).
TEM (Melillo et al., 1993; Tian et al., 1999, 2003; Felzer et al., 2004) is a process based
biogeochemistry model that simulates the cycling of carbon, nitrogen, and water among
vegetation, soils, and the atmosphere. Version 4.3 (TEM 4.3) includes modeling of the pathways
by which ozone influences the productivity and carbon storage of terrestrial ecosystems (Felzer
et al., 2004). The effects of ozone on productivity were incorporated by modifying the
calculation of Gross Primary Production (GPP) in TEM (Felzer et al., 2004). The effect of ozone
is to linearly reduce GPP above a threshold ozone level according to the Reich (1987) and
1
Version 2 of the model includes an improved land system component (more closely linking the TEM, NEM, and
Community Land Model that represents energy and water balance of the land surface with the atmosphere), but
is resolved at zonal bands of 4° matching the resolution of the atmospheric model, inadequate for capturing the
spatial variation in ozone concentrations. Because of flexibility of the model, the overall behavior of Version 1
and Version 2 of the IGSM is very close when key climate parameters are set to identical values as shown in
Sokolov et al. (2005).
4
Ollinger et al. (1997) models. Separate coefficients of linearity are calculated for hardwoods,
conifers, and crops. Although different species of trees and types of crops respond differently to
ozone, we have made this simplifying assumption based on the Reich (1987) model.
To estimate the net assimilation of CO2 into plant tissues (i.e. plant growth), we calculate net
primary production (NPP) as follows:
NPP = GPP – RA
(1)
where RA is autotrophic respiration. To estimate carbon sequestration by the ecosystem,
we calculate net carbon exchange (NCE) as follows:
NCE = NPP – RH – Ec – Ep
(2)
where RH is heterotrophic respiration, Ec is the carbon emission during the conversion of natural
ecosystems to agriculture, and Ep is the sum of carbon emission from the decomposition of
agricultural products (McGuire et al., 2001). For natural vegetation, Ec and Ep are equal to 0, so
NCE is equal to net ecosystem production (NEP). As indicated by Equations (1) and (2), the
reduction of GPP by ozone will also reduce both NPP and NCE.
The ozone effect within TEM 4.3 is based on the AOT40 index. This index is a measure of
the accumulated hourly ozone levels above a threshold of 40 ppb. Since hourly datasets of
surface ozone do not exist at the spatial extent and resolution of TEM, the Model for
Atmospheric Transport and Chemistry (MATCH) (Lawrence et al., 1999; Mahowald et al.,
1997; Rasch et al., 1997; von Kuhlmann et al., 2003) has been used, run at 2.8º x 2.8º or T42
horizontal resolution, to construct global AOT40 maps for each hour utilizing the zonal and 3hour mean surface ozone concentration provided by the IGSM. MATCH is a three-dimensional
(3D) global chemical transport model driven by reanalysis meteorological fields. The average
monthly boundary layer MATCH ozone values for 1998 are scaled by the ratio of the zonal
average ozone from the IGSM (which are 3-hourly values that have been linearly interpolated to
hourly values) to the zonal ozone from the monthly MATCH to maintain the zonal ozone values
from the IGSM. Greater detail on these procedures is provided in Felzer et al. (2005).
The Emissions Prediction and Policy Analysis (EPPA) model is a computable general
equilibrium model of the world economy that has been extensively used to examine climate and
environmental issues (Table 1). The main advantage of CGE models is their ability to capture
the influence of a sector-specific (e.g., energy, fiscal, or agricultural) policy or forces on other
industry sectors, consumption, and on international trade. A traditional limitation of CGE models
has been linkage of economic variables to physical variables such as land use, emissions,
population, and energy use. The EPPA model overcomes this limitation by developing extensive
supplementary tables on physical data as described in Paltsev et al. (2005) and is thus able to
simulate and project growth and change in the economy, its implications for pollutant emissions,
demands for natural resources, and feedback effects of environmental change on the economy.
We designate the version of EPPA used here as EPPA-AGRI because of the further
disaggregation of the agricultural sector as described in Wang (2005).
For this work, we examine the economic impacts of changes in climate, CO2, and ozone as
they affect crops, pasture, and forestry using the combined modeling system. Temporal and
spatial scales, as discussed above, are resolved at different levels requiring interpolation or
aggregation as data are passed from one modeling component to another. A complete description
5
Table 1. The Emissions Prediction and Policy Analysis (EPPA) model is a recursive-dynamic multiregional CGE model of the world economy (Babiker et al., 2001, Paltsev et al., 2005), which is built on
the economic and energy data from the GTAP dataset (Dimaranan and McDougall, 2002) and
additional data for the greenhouse gas (CO2, CH4, N2O, HFCs, PFCs and SF6) and urban gas emissions
(CO, VOC, NOX, SO2, BC, OC, NH4). It has been used extensively for the study of climate policy (Jacoby et
al., 1997; Babiker, et al., 2002, 2004; Paltsev et al., 2003; Reilly et al., 2002; McFarland et al., 2004),
climate interactions (Reilly et al., 1999; Felzer et al., 2005), and to study uncertainty in emissions and
climate projections for climate models Webster et al., 2002, 2003). It has been modified for this analysis
to include greater disaggregation of the food and agriculture sectors, as shown in italics.
Countries/Regions, Sectors, and Factors in the EPPA-AGRI Model
Country or Region
Sectors
Factors
Developed
Non-Energy
Economy-wide
United States (USA)
Services
Capital
Canada (CAN)
Energy Intensive products
Labor
Japan (JPN)
Other Industries products
European Union+a (EUR)
Transportation
Energy
Australia/New Zealand (ANZ)
Food Processing
Crude Oil Resources
Former Soviet Unionb (FSU)
Shale Oil Resources
Eastern Europec (EET)
Energy
Coal Resources
Coal
Natural Gas Resources
Developing
Crude Oil
Nuclear Resources
India (IND)
Shale Oil
Hydro Resources
China (CHN)
Refined Oil Products
Wind/Solar Resources
Higher Income East Asiad (ASI)
Natural Gas, Coal Gasification
Indonesia (IDZ)
Electric: Fossil, Hydro, Nuclear,
Land Use
Rest of Worlde (ROW)
Solar & Wind, Biomass, Natural Gas
Crop Land
Mexico (MEX)
Combined Cycle, Integrated Coal
Pasture/Grazing Land
Africa (AFR)
Gasification with Sequestration
Forest land
Central and South America (LAM)
Middle East (MES)
Agriculture
Crops
Livestock
Forestry
a
The European Union (EU-15) plus countries of the European Free Trade Area (Norway, Switzerland, Iceland).
Russia, Ukraine, Latvia, Lithuania, Estonia, Azerbaijan, Armenia, Belarus, Georgia, Kyrgyzstan, Kazakhstan, Moldova,
Tajikistan, Turkmenistan, and Uzbekistan.
c
Hungary, Poland, Bulgaria, Czech Republic, Romania, Slovakia, Slovenia.
d
South Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand
e
All countries not included elsewhere: Turkey, and mostly Asian countries.
b
of the model is provided in Prinn et al. (1999). Here we briefly describe how key linkages are
handled. The TEM model operates at a 0.5° x 0.5° latitude by longitude spatial and monthly time
scale. It includes the current monthly climatology resolved at that spatial scale. Ozone levels
were resolved at the resolution of the MATCH model (T-42, approximately 2.8° x 2.8°) and
interpolated to the TEM resolution as described in Felzer et al. (2005).
The 2D Land-Ocean resolving General Circulation Model is resolved at 20-minute time steps
and for 24 latitudinal bands. CO2 concentrations are assumed to be well mixed globally. The
changes in temperature and precipitation as predicted by the climate model for land in each
latitude zone were used to scale the 0.5° x 0.5° climatology of TEM (see Xiao et al., 1997).
6
The EPPA CGE model is resolved at 5-year time steps and for the 17-geopolitical regions
shown in Table 1. Projected emissions of greenhouse gases and other pollutants from EPPA
drive the atmosphere ocean model. Emissions are distributed to the zonal resolution of the model
and resolved for urban (high pollution) and non-urban (background pollution level) conditions.
The growth of crops, pasture, and forests is simulated on a monthly basis at 0.5° x 0.5°
including spatial variation in soils, current climatology, and ozone levels, although simulated
changes are only resolved at the latitudinal band level of the climate model. This follows a
widely used methodology in impact assessment where simulated changes from a more coarsely
resolved climate model are combined with actual weather/climate data that is more finely
resolved. Retaining the current climatology at the spatial scale of the more detailed impact model
retains the spatial variation in weather/climate that currently exists, but cannot capture changes
in spatial variation that are finer than the climate model. To simulate the economic effects of
these changes through the EPPA CGE model the yield and net primary productivity effects
estimated by TEM are related to each land-use type (crops, pasture, forest) based on the TEM
vegetation types (Table 2). These results are then aggregated from the 0.5° x 0.5° level to the
EPPA geopolitical regions. The productivity changes driving the EPPA model are thus an
average for each region that is based on spatial variation simulated at 0.5° x 0.5°.
4. SCENARIOS
We consider the following scenarios.
High Pollution (POLF): There are no efforts to control emissions of greenhouse gases
(GHGs). Emissions coefficients per unit of combustion for other pollutants decline in different
regions as incomes increase based on cross-section estimates of the relationship between per
capita income and these coefficients in the base year as estimated in Mayer et al. (2000). The
decline is estimated separately for each pollutant, and for different combustion sources including
large point source, small sources, and for households. In principle, this would tend to create an
environmental Kuznets’ curve relationship, but the exhibited decline in emissions per unit of fuel
combustion is insufficient to offset increases in use of fuels, and so pollution levels rise
substantially.
Climate and GHGs only (POLFCTL): The same climate and pollution scenario as the High
Pollution case but with the ozone damage mechanism in TEM turned off so that we can observe
the climate and CO2 effects alone without the effect of ozone damage.
Capped pollution (POLCAPF): Conventional pollutants (CO, VOC, NOx, SO2, NH3, black
carbon, and organic carbon) are capped at 2005 levels, but GHG emissions remain uncontrolled.
The major effect of capping these pollutants is to reduce ozone levels because many of these are
important ozone precursors and thus ozone damage to vegetation is reduced. The climate effects of
reducing these pollutants are small because of the offsetting effects from different pollutants (Prinn
et al., 2006). Sulfates are cooling substances so reducing them tends to increase the temperature but
ozone is a warming substance and so reducing ozone precursors leads to less warming.
GHGs capped (GSTABF): Greenhouse gases are controlled along a path that starts with the
Kyoto Protocol, deepening the cuts in developed countries and expanding to include developing
countries on a pathway that keeps CO2 concentrations below 550 ppm by 2100 and with
continued emissions reduction that could be consistent with stabilization of concentrations at
7
Table 2. TEM Vegetation Types and Land Use Classification.
TEMVEG
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Description of Vegetation Type
Ice
Alpine Tundra and Polar Desert
Moist and Wet Tundra
Boreal Forest
Forested Boreal Wetlands
Boreal Woodlands
Non-forested Boreal Wetlands
Mixed Temperate Forests
Temperate Coniferous Forests
Temperate Deciduous Forests
Temperate Forested Wetlands
Tall Grasslands
Short Grasslands
Tropical Savanna
Arid Shrublands
Tropical Evergreen Forests
Tropical Forested Wetlands
Tropical Deciduous Forests
Xeromorphic Forests and Woodlands
Tropical Forested Floodplains
Deserts
Tropical Non-forested Wetlands
Tropical Non-forested Floodplains
Temperate Non-forested Wetlands
Temperate Forested Floodplains
Temperate Non-forested Floodplains
Wet Savannas
Salt Marsh
Mangroves
Tidal Freshwater Marshes
Temperate Savannas
Cultivation
Temperate Broadleaved Evergreen
Reserved
Mediterranean Shrublands
Land Use Classification
Forestry
Forestry
Forestry
Forestry
Forestry
Forestry
Forestry
Pasture
Pasture
Pasture
Pasture
Forestry
Forestry
Forestry
Pasture
Forestry
Forestry
Pasture
Cropland
Forestry
Pasture
Note: Vegetation changes for ice, tundra, desert, and wetlands are excluded from any of the uses as indicated by blank
space in the use column.
550 ppm. This scenario is described in Reilly et al. (1999). Because combustion of fossil fuels is
affected, this scenario also leads to significant reduction from reference of other pollutants
including ozone precursors.
GHGs capped-no ozone (GSTABFCTL): The same climate and GHG levels as in the GHGs
capped scenario but with the ozone damage mechanism in the TEM model again turned off so
that we can observe the climate and CO2 effects alone without the effect of ozone damage.
GHGs and pollution capped (GSTABCAPF): GHGs controlled as in the GHGs capped
scenario and conventional pollutants capped as in the Capped pollution scenario.
For expositional purposes we have adopted as labels in this paper the terms above in bold
italics. For ease of comparison we include in parentheses labels that were used in Felzer et al.
8
(2005) and Prinn et al. (2006) who report carbon storage and climate impacts, respectively, of
these same emissions scenarios.
The temperature change, CO2 and ozone concentrations resulting from these emissions are
shown in Figure 1. Unrestricted GHG emissions lead to a projected increase in average global
temperature by 2.75oC over a century. The temperature is increased even in GHGs capped
scenarios by approximately 1oC. Over the century CO2 concentrations are rising from 375 ppm to
around 810 ppm in unrestricted GHGs cases, and to around 515 ppm in the GHGs capped cases.
Ozone stays at its current levels in the GHGs and pollution capped scenarios. Pollution-only
control scenario affects ozone stabilization relatively more than the GHGs-only control scenario.
As noted, the spatial pattern of ozone is constructed for present from the MATCH model and
the result is shown in Figure 2. For illustrative purposes we show a map for June-July-August
(JJA), the Northern Hemisphere summer. Ozone levels are highest in the mid-latitude temperate
areas where the largest emissions occur. However, the JJA period is the Southern Hemisphere
winter, conditions that do not favor ozone formation, and thus the very low levels of ozone in the
Southern Hemisphere partly reflect this choice of period. Figure 3 shows the yearly levels of
AOT40 for 2000 to 2100 for the 4 relevant scenarios2 for key vegetation types, chosen to
illustrate the differences between tropics, temperate, and boreal areas. Notably, the temperate
regions, dominated by Northern temperate areas including the United States, Europe, and China
have relatively high levels of AOT40. Also, note that levels of AOT40 increase much more
rapidly than the levels of ozone itself as shown in Figure 1, panel c. Whereas the global
900
3
(a)
2.5
(b)
800
Capped Pollution
700
GHGs Capped
2
ppm C O2
Temperature Change since 2000 (°C)
High Pollution
GHGs and Pollution Capped
1.5
1
600
500
400
High Pollution
300
Capped Pollution
200
0.5
GHGs Capped
100
0
2000
2020
2040
2060
2080
2100
GHGs and Pollution Capped
0
2000
2020
Year
2060
2080
2100
Year
60
ppb global mean O3
2040
(c)
50
40
30
High Pollution
20
Capped Pollution
GHGs Capped
10
0
2000
GHGs and Pollution Capped
2020
2040
2060
2080
2100
Year
Figure 1. Global Changes in (a) Temperature, (b) CO2 concentrations, (c) and Ozone levels.
2
Those without any ozone damage (Climate and GHGs only and GHGs capped, no ozone) were constructed by
leaving out the ozone damage mechanism in the TEM, and so the actual ozone levels are no different than in the
comparable cases with ozone damage (High Pollution and GHGs capped, respectively).
9
Figure 2. Geographical distribution of ozone (AOT40, ppm-h), mean monthly levels for
June–July–August of 1998.
4000
9000
(a)
3000
Temp era te AOT40 (pp m-hr)
Boreal AOT40 ppm-hr
3500
High pollution
2500
GHGs capped
2000
1500
Pollution capped
1000
500
GHGs & pollution capped
High pollution
7000
6000
GHGs capped
5000
4000
3000
Pollution capped
2000
1000
0
2000
(b)
8000
GHGs & pollution capped
0
2020
2040
2060
2080
2000
2100
2020
2040
2060
2080
2100
Ye a r
Year
3500
Tropical AOT 40 (ppm-hr)
3000
(c)
High pollution
2500
2000
GHGs capped
1500
1000
Pollution capped
500
GHGs & pollution capped
0
2000
2020
2040
2060
2080
2100
Year
Figure 3. Annual ozone levels (AOT40) by vegetation type, 2000-2100.
increase in ozone levels is less than 50% in the High Pollution case and less than 20% in the
cases where pollution and/or GHGs are controlled, AOT40 increases by as much as six times in
the High Pollution case and doubles or triples in Capped pollution and GHGs capped cases,
respectively.
The large increase in AOT40 despite much smaller increases in ozone levels themselves is
because the AOT40 is a threshold measure. Any increase in ozone in areas near or above this
threshold will add to AOT40, whereas much of current ozone contributes to levels that are below
10
this threshold and thus contribute nothing to the AOT40 index. Much of the industrial activity
leading to emissions is in the temperate regions in the Northern hemisphere and ozone levels are
highest over temperate vegetation, although ozone levels increase substantially over both boreal
and tropical vegetation.
5. AGRICULTURE, PASTURE AND FORESTRY RESULTS
Yields on croplands are taken from TEM estimates of changes in yield for a “generic” C3
crop (Felzer et al., 2004). This crop is grown on areas identified as cropland by McGuire et al.
(2001), which has been derived from the historical fractional cropland data set of Ramankutty
and Foley (1998, 1999), for the period of the early 1990’s. For pasture and forestry the change in
Net Primary Productivity (NPP) is used as a measure of yield effects. Figure 4 shows the results
for the six scenarios mapped at the 0.5° x 0.5° resolution of TEM, with absolute yield changes in
gC/m2/year between 2090-2100 and present (1995-2005). The average yield/NPP is calculated
for each of these decades. Also shown is the average percentage yield change for crops, pasture,
and forestry for each of the 16 EPPA regions over the same period.
The High pollution and Climate and GHGs only were constructed to show the separate effects
of ozone damage and climate and CO2 in a scenario where there was no explicit climate policy and
where emissions per unit of combustion of conventional pollutants fell as a function of rising per
capita income, but insufficiently to prevent pollution levels from rising significantly. Comparing
results for these scenarios in Figure 4 shows climate and CO2 effects to be beneficial almost
everywhere; however, when ozone damage is included many areas experience severely negative
effects. These negative effects are especially strong in cropland areas, in the Northern Hemisphere.
This is evident by examining the percentage change results for cropland as compared with pasture
and forestry. The strong effects of ozone on cropland are the result of four effects as discussed in
Felzer et al. (2005): (1) inherent higher sensitivity of crops than forests/natural vegetation to ozone
as represented in response functions of Reich (1987); (2) spatial variation in ozone levels that
often lead to higher ozone concentrations over cropland; (3) spatial variation in Gross Primary
Production (GPP), with fertilized croplands tending to have higher levels, since stomatal
conductance and, thus ozone damage, is proportional to GPP; and (4) the interaction of ozone
damage with applications of nitrogen fertilizer.
Comparing the map of ozone damage in Figure 4 to the spatial pattern of high ozone
(Figure 2, and zonal increases in Figure 3) there is a general correlation between areas where
higher ozone damage occurs and higher ozone levels. The areas of high ozone damage occur
mainly in Northern mid-latitudes where industrial activity and emissions of ozone precursors are
high. With regard to (3), higher absolute effects (damage and benefit) occur where there are
higher rates of vegetation growth. The arid areas of the western US, northern and Southern
Africa, Central Asia, and much of Australia and the very cold areas of far northern Canada,
Europe and Asia all show lower absolute increases in productivity due to climate and CO2 than
relatively moist and warmer climates. Thus, some areas of high ozone levels such as southwest
U.S., the Middle East, and central Asia do not show large decreases in productivity due to
ozone exposure. However, as pointed out previously, Figure 2 represents JJA only and
significant damage can occur in other months. For a more complete comparison of these effects
see Felzer et al. (2004, 2005). With regards to (4), Felzer et al. (2004) also identify a strong
11
(a) High Pollution Scenario
(b) Climate and GHGs only Scenario
(c) Capped pollution Scenario
(d) GHGs capped Scenario
(e) GHGs capped-no ozone Scenario
(f) GHGs and pollution capped Scenario
Figure 4. Change in yield between 2000 and 2100 (gC/m2/year). Regional level percent changes in
yield (crops) and NPP (pasture, forestry): circle = crops, diamond = pasture, square = forestry.
interaction effect between ozone damage and nitrogen fertilization, beyond what one would
expect simply because N fertilizer increases productivity of plants. In these scenarios, optimum
nitrogen fertilization is applied on all cropland, and thus, the largest absolute losses of yield
occur on cropland areas exposed to high ozone. This combination—stronger response of crops,
use of N fertilizer, high productivity, and the spatial pattern of high ozone concentrations
strongly biases high ozone damage toward crops, relative to pasture or forest land. By
comparison pasture and forest land is not subject to N-fertilization in the model (reflecting
12
predominant practice) and these lands are often more remote from industrial regions where
ozone concentrations are lower and productivity is lower. All these factors contribute to less
ozone damage.
The Capped pollution scenario is intermediate between the High pollution and Climate and
GHGs only scenarios. Even capping the conventional pollutants at current levels does not
entirely prevent increases in ozone levels. CH4 is uncontrolled in these scenarios and it is an
ozone precursor. Further, there are non-linear interactions in chemistry that depend on relative
levels of these pollutants and whether they are emitted into a relatively clean or highly polluted
environment, in addition to temperature and humidity effects on various atmospheric reaction
pathways. Prinn et al. (2006) provides a more in depth evaluation of these scenarios in terms of
the implications of capping these pollutants. In general, yield of forests, pasture, and cropland are
relatively positive.
The GHGs capped, GHGs capped-no ozone, and GHGs and pollution capped scenarios
show generally less increase in yields in areas where the yield changes were dominated by the
positive effects of CO2 and climate and less ozone damage. In the GHGs capped scenario less
ozone damage occurs because the GHG policy results in less combustion of fossil fuels and
therefore a side effect is less emissions of ozone precursors as well as less CH4. Ozone damage
remains significant enough, however, to turn what would be large increase in yield in Eastern
United State, Europe, India, and Eastern China from CO2 and climate into significant negative
effects on yields. This can be seen from comparing the GHGs capped and GHGs capped-no
ozone scenarios. The GHGs and pollution capped scenario also keeps other ozone precursors
from increasing and these two factors together mean there is very little increase in ozone from
current levels as can be seen from Figure 3. The result is that the yield change map for the GHGs
and pollution capped scenario is very similar to that for the scenario where the ozone damage
mechanism was simply turned off in TEM (i.e. GHGs capped-no ozone).
The percentage yield effects at five-year intervals (the EPPA temporal resolution) were
introduced as changes in the productivity of land from the reference level in each of the sectors
(agriculture, livestock, forestry) in the EPPA model for each of the 16 regions.3 In general, land
productivity is modeled as increasing in EPPA in the reference due to improving technology, and
thus a positive effect of environmental change is a further increase in land productivity whereas a
negative environmental effect reduces the productivity increase and may cause an absolute
decline in yields (relative to current) if the environmental impact is large enough. Our principal
interest is in how changes in the environment (climate, CO2, and ozone) affect agricultural
production and the economy relative to the reference. Thus, for example, land productivity
increasing at a compounded rate of 1% per year, would imply a 64% increase by 2050, or, with
year 2000 = 1.00, an index value of 1.64 in 2050. If the TEM yield change estimate is for an
increase of 10%, the new productivity index value in 2050 for that sector/region would by
1.10*1.64=1.81, or if environmental change caused average yield to fall to 0.9%, then the new
productivity index for EPPA is 0.90*1.64=1.48.
3
We use the term “yield” to refer to estimates derived from TEM. “Land productivity” multiplier for land in the
Constant Elasticity of Substitution (CES) production functions for crops, forestry, and livestock used in EPPA.
Actual “yield” as modeled by EPPA depends on the exogenous time trend on land productivity in combination
with parameters that govern substitution between land and other inputs as their prices change.
13
We focus first on global effects on production of these yield changes. To effectively compare
the global production effects with the yield changes, we construct a measure of global yield
change for crops, pasture, and forestry derived from TEM to compare with estimated production
change once we simulate the effect of these changes in EPPA. The global yield changes are
derived by summing the total level of agroecosystem productivity (gC/year) for the globe and
calculating the difference from 2000, as we did for each of the regions. Thus, the percentage
change is weighted by the absolute productivity in different regions. The global sector
production (crop, livestock, forestry) is measured in the total value of production in real terms in
1997 US dollars and at 1997 market exchange rates as reported in EPPA. We calculate the
difference from the reference projection (without environment effects) to measure the effect on
production of each of the environmental change in terms of sector production levels. These are
plotted in Figures 5-7. For exposition, we have not plotted the GHGs capped-no ozone scenario
because it is very similar to GHGs and pollution capped scenario.
Figure 5 reports the results for crops. As expected, positive (negative) yield effects of
environmental change lead to positive (negative) production effects. However, note that the
production effects are far smaller than the yield effects. The global yield effects range from an
increase of over 60% (Climate and GHGs only) to a decline of nearly 40% (High pollution)
while the crop production effects are no larger than ± 8%. This reflects relative inelastic demand
for crops because of a relatively inelastic demand for food, the ability to substitute other inputs
for land (adapt), and the ability to shift land into or out of crops.
Figure 6 reports results for livestock. Here the livestock production results bear little
relationship to the yield effects for pasture. The pasture results are all positive whereas several of
the scenarios show reductions in livestock production. In fact, the scenarios mirror closely the
production effects on crops. This reflects the fact that feedgrains and other crops are important
inputs into livestock production, relatively more important than pasture. A reduction (increase) in
crop production is reflected in higher (lower) prices for feedgrains and other crops used in
livestock, and this tends to lead to reduced (increased) production of livestock. Again, the
percentage differences in livestock production are relatively small compared with the crop
production changes, even in cases where production increases are driven both by an increase in
crop production and an increase in pasture productivity.
Crop Yie ld
Crop Production
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1980
2000
2020
2040
High pollution
Capped pollution
2060
2080
2100
2120
1.1
1.08
1.06
1.04
1.02
1
0.98
0.96
0.94
0.92
0.9
0.88
1980
2000
2020
2040
GHGs and pollution capped
Climate and GHGs only
Figure 5. Index for Crop Yield and Production
14
2060
2080
GHGs capped
2100
2120
Live stock Production
Fora ge / P a sture / Gra zing Y ie ld
1.25
1.2
1.15
1.1
1.05
1
0.95
1980
2000
2020
2040
2060
2080
2100
1.05
1.04
1.03
1.02
1.01
1
0.99
0.98
0.97
0.96
0.95
1980
2120
2000
2020
2060
2080
2100
2120
GHGs and pollution capped
Climate and GHGs only
GHGs capped
High pollution
Capped pollution
2040
Figure 6. Index for Pasture Yield and Livestock Production.
Figure 7 reports results for forestry. The general result is that the production effects are very
small—less than 1% compared with yield effects of 3 to 15%. Notably, however, despite small
positive yield effects for forestry in all cases, the production effects are slightly negative in the
High pollution and GHGs capped cases. One result of the strong negative crop yield effect is to
use more land for crop production at the expense of forestry and pasture, and thus the negative
forestry production effect is driven by reduction in land used for forestry. The livestock
production effect is also partly driven by a reduction in pasture/grazing land but in that case the
more important effect is the increase in feedgrain prices.
An important result of the general equilibrium modeling of these impacts is that effects can be
felt beyond the agricultural sector. We can investigate the general equilibrium effects stemming
from agricultural impacts because we are simulating only the climate/CO2/ozone effects on
agriculture (including crops, pasture, and forestry) and include no other impact shocks in other
sectors. Thus, any economic effects occurring elsewhere in the economy are due to the initial
agricultural shock. We show this in Figure 8 where we have plotted macroeconomic
consumption change and the change in food consumption, both as a percent of food
consumption, in a reference case where there is no environmental feedback on the economy.
To illustrate this effect, we present the results for two scenarios – High pollution and Climate
and GHGs only, because those are the ones that show the biggest change. This shows that, in
general, the aggregate consumption effect is bigger in absolute terms than the agricultural
Fore stry P roduction
Fore stry Y ie ld
1.18
1.16
1.14
1.12
1.1
1.08
1.06
1.04
1.02
1
0.98
0.96
1980
1.015
1.01
1.005
1
0.995
0.99
0.985
2000
2020
2040
2060
High pollution
Capped pollution
2080
2100
2120
0.98
1980
2000
Climate and GHGs only
GHGs capped
2020
2040
2080
2100
GHGs and pollution capped
Figure 7. Index for Forestry Yield and Production.
15
2060
2120
0.1
0.05
0
-0.05
-0.1
-0.15
1980
2000
2020
2040
2060
2080
2100
2120
"High pollution" food consumption
"High pollution" total consumption
"Climate and GHGs only" food consumption
"Climate and GHGs only" total consumption
Figure 8. Change in Global Food Consumption and Total Global Macroeconomic Consumption as a
share of Agriculture Production.
production effect. Thus, adaptation in the agricultural sector, which was seen most clearly in the
crop sector results, but with the general result that the production effects were much smaller than
the yield effects, is partly the result of shifting of resources into or out of these sectors, thereby
affecting the rest of the economy. Thus, we see in our results the frequently expressed view that
the adaptation potential of the agricultural sector is considerable—most yield effects are offset
leaving very little change in food consumption. But, we also see that this comes about through
resource reallocation from or to the rest of the economy and so to focus only the changes in the
agricultural sector/food consumption underestimates damages (or benefits) of the environmental
change. Thus, while yield change alone overestimates the economic effect, focusing on
agricultural production or food consumption underestimates the full economic effect. Fully
measuring the economic effect requires a general equilibrium approach that evaluates the impact
on resource reallocation beyond the agricultural sector.
Finally, we focus on the regional economic effects for the High pollution and Climate and
GHGs only scenarios for selected countries on Figure 9. These illustrate several important
results. First, the impact as a percentage of the economy differs because of the different
importance of these sectors in the economy. Food consumption is generally income inelastic, a
feature we have approximated in EPPA, and this means agriculture is generally falling as a share
of all economies over time. However, for developing country regions agriculture is currently a
relatively large share of the economy, as much as 20%, compared with as little 1 or 2% in
developed countries. Second, trade effects can be important. In the High pollution case,
tropical, Southern Hemisphere and far northern regions (AFR, LAM, ANZ, CAN) benefit
economically even though they suffer crop yield losses (or no change in the case of LAM).
Economic gains result because they export agricultural products to other regions where crop
yields are severely reduced due to ozone. The trade effects in the Climate and GHGs only
scenario are less obvious from the total economic impact, but ANZ, a major agricultural exporter
suffers economic loss from lower export prices even though crop yields are estimated to rise by
over 80%. Thus, the net economic effect due to changes in agriculture, pasture, and forestry
productivity are a complex combination of a changing pattern of trade among regions and
resource reallocation between the agriculture sectors and other sectors of the economy.
16
3
2
1.6
(a)
1.4
CAN
1.2
1
ANZ
1
EUR
0.8
CHN
0.6
ASI
0.4
AFR
-3
0.2
LAM
-4
0
0
percent
percent
USA
(b)
-1
-2
-5
2000
global
-0.2
2020
2040
2060
2080
2100
2000
2020
2040
2060
2080
2100
Figure 9. Percent Change in Macroeconomic Consumption in High Pollution Scenario (a) and
Climate and GHGs only Scenario (b), in Selected Regions.
6. CAVEATS AND COMPARISON WITH PREVIOUS WORK
There have been no similar studies of the combined effects of climate, CO2 and ozone on
global crops, pasture, and forestry. There has been considerable work on climate/CO2 effects on
crops. Our estimates (Climate and GHGs only scenario) are relatively positive compared with
previous work. The broad conclusion of past studies is that mid- and high latitude areas could see
substantial yield gains from climate or climate and CO2 effects but that yield losses are likely in
tropical regions (Gitay, et al., 2001; Reilly and Schimmelpfennig, 1999). In contrast, in this
study we see less yield gains in all regions when only climate and CO2 changes are considered,
albeit the yield gains are smaller in the tropics than in temperate or boreal regions. Several
factors likely contribute to this more positive result:
TEM grows the “generic” crop as soon as the weather is suitable and if the season is
lengthened automatically grows additional crops, or in subtropical regions may find that winter
cropping improves even if summer cropping fails. This full adaptation to changes in seasons has
generally not been considered in previous studies.
The “generic” TEM crop is a C3 crop that responds relatively strongly to CO2 fertilization. 4
In reality, agriculture includes C4 crops, which are less responsive to CO2, and so the average
response including C4 crops is likely to be lower than we estimate.
The TEM estimates assume an optimum nitrogen fertilization of crops, so that CO2
fertilization is not nitrogen-limited as it would be for natural vegetation (Kicklighter et al., 1999)
or under conditions with where fertilizer application is not optimal. Thus, neither spatial nor
temporal variations in the amount, timing and the effectiveness of fertilizer applications have
been considered, which may also contribute to the positive effect. On the other hand, the TEM
simulations also do not consider the influence of irrigation so that crop productivity may be
underestimated in arid regions.
The CO2 response modeled in TEM is similar to that used to parameterize crop models and so
does not explain a major difference with studies that have included a CO2 fertilization effect.
Some comparisons of Free Air Carbon Exchange (FACE) results have suggested much lower
4
C3 and C4 crops refer to the photosynthetic carbon pathway of the crop. The main C4 crops are maize and
sorghum. Most grains, legumes, and vegetables are C3 crops.
17
CO2 response than conventionally assumed (Long et al., 2006), however, further evaluation
shows the response of mainstream crop models to be generally consistent with the FACE results
(Tubiello et al., 2006). Nevertheless inclusion of the CO2 fertilization effect contributes a strong
positive effect on yield.
The climate scenarios are for a relatively mild increase in global temperature (2.7°C by 2100
from present, less when GHGs are controlled), reflecting work that has tried to estimate climate
sensitivity and other climate model parameters (Forest et al., 2002; Webster et al., 2003). More
negative results in some studies have resulted from climate scenarios with a mean surface
temperature increase of 4 to 5°C. There is considerable uncertainty in future temperature
projections and so an increase of 4 to 5°C cannot be ruled out if GHGs are uncontrolled (Webster
et al., 2003). An update of the Forest et al. (2002) analysis (Forest et al., 2006) likely implies
considerably higher temperatures by 2100 because they find it likely that less heat is being taken
up by the oceans and so higher temperatures will be realized sooner.
Apart from the global mean temperature change, the 2D climate scenarios used to force the
TEM model may tend to produce milder climate changes. While the IGSM is a land-ocean
resolving model it cannot project phenomena such as mid-continental drying, a result often
shown in 3D models. The zonal climate changes may thus under-represent local extremes that
are possible, particularly in precipitation. Precipitation changes remain uncertain in even highly
resolved GCMs and the 3D pattern need not create more negative crop effects but it seems likely
that it could.
TEM models vegetation on a monthly basis for a generic crop. Crop yield for specific crops
can be severely affected by short periods of heat or drought during key developmental phases.
TEM results can be seen as a case where crop breeders/changes in crop type are able to
overcome these limitations as climate changes.
7. CONCLUSIONS
Multiple environmental changes will have consequences for global vegetation. To the extent
that crop yields and pasture and forest productivity are affected there can be important economic
consequences. We examine the combined effects of changes in climate, increases in carbon
dioxide, and changes in tropospheric ozone on crop, pasture, and forests and the consequences
for the global and regional economies. We find that climate and CO2 effects are generally
positive for crop, livestock and forestry yields over most of the world. However we find
potentially highly detrimental effects of ozone damage unless ozone precursors are strongly
controlled. Because climate and CO2 effects are generally beneficial, controlling GHG emissions
tends to reduce these beneficial effects. However, controlling GHGs also limits emissions of
ozone precursors because CH4 is an ozone precursor and control of CO2 implies less combustion
of fossil fuels and lower emissions of NOx, VOCs, and other ozone precursors that are also
generated during combustion.
Simulating the effects on vegetation demonstrated some important economic results.
(1) Agriculture can successfully adapt to yield changes if adaptation is measured as change in
production relative to change in the initial yield effect of environmental change. The production
effect after adaptation is 1/5 to 1/6 the initial yield effect. (2) However, evaluating the impact
terms of agricultural consumption/production underestimates the economic effects because
18
adaptation involves shifting resources into or out of the agricultural sector. The full effect of
these changes can only be observed in looking at overall measures of economic well-being, such
as macroeconomic consumption change. (3) National and regional economic effects are strongly
influenced by trade effects such that yield effects that are positive for a region, may lead to
negative economic effects if the other countries gain more. Or, countries can gain through trade
even if yield effects are negative if other regions are more severely affected as we find for the
case with high ozone levels. Thus, analysis that purports to estimate economic effects for a
nation or region, absent a consideration of the effects on global markets or interaction with the
rest of the economy, may be in error not only in the magnitude of the effect but of its direction.
8. REFERENCES
Adams, R.M., S.A. Hamilton, and B.A. McCarl, 1986. The benefits of pollution control: the case of
ozone and U.S. agriculture, American Journal of Agricultural Economics, 68, 886-893.
Adams, R.M., C. Rosenzweig, R.M. Peart, J.T. Richie, B.A. McCarl, J.D. Glyer, R.B. Curry, J.W.
Jones, K.J. Boote and L.H, Allen. 1990. Global climate change and U.S. agriculture. Nature,
345, 219-24.
Alig, R.J., D.M. Adams, and B.A. McCarl, 2003. Projecting impacts of global climate change on
the U.S. forest and agriculture sectors and carbon budgets, Forest Ecology and Management,
169, 3-14.
Babiker, M., H. Jacoby, J. Reilly, and D. Reiner, 2002. The evolution of a climate regime: Kyoto to
Marrakech, Environmental Science and Policy, 5(3), 195-206.
Babiker, M., J. Reilly, M. Mayer, R. Eckaus, I. Sue Wing, and R. Hyman, 2001. The MIT Emissions
Prediction and Policy Analysis Model: Revisions, Sensitivities, and Comparisons of Results.
MIT Joint Program on the Science and Policy of Global Change, Report 71, Cambridge, MA.
Babiker, M., J. Reilly and L. Viguier, 2004. Is emissions trading always beneficial? Energy J.,
25(2), 33-56.
Darwin, R., M. Tsigas, J. Lewandrowski and A. Raneses, 1996. Land use and cover in ecological
economics, Ecological Economics, 17, 157-181.
Dimaranan, B., and R. McDougall, 2002. Global Trade, Assistance, and Production: The GTAP 5
Data Base. Center for Global Trade Analysis, Purdue University, West Lafayette, Indiana.
Felzer, B. S. F., D. W. Kicklighter, J.M. Melillo, C. Wang, Q. Zhuang, and R.G. Prinn, 2004. Ozone
effects on net primary production and carbon sequestration in the conterminous United States
using a biogeochemistry model, Tellus, 56B, 230–248.
Felzer, B., J. Reilly, J. Melillo, D. Kicklighter, M. Sarofim, C. Wang, R. Prinn and Q. Zhuang,
2005. Future effects of ozone on carbon sequestration and climate change policy using a global
biogeochemical model, Climatic Change, 73, 345-373.
Forest, C.E., P.H. Stone, A.P. Sokolov, M.R. Allen, and M. Webster, 2002. Quantifying
uncertainties in climate system properties with the use of recent climate observations, Science,
295, 113-117.
Forest, C.E., P.H. Stone & A.P. Sokolov, 2006. Estimated PDFs of climate system properties
including natural and anthropogenic forcings, Geophysical Research Letters, 33, L01705.
Gitay, H., S. Brown, W. Easterling, and B. Jallow, 2001. Ecosystems and their goods and services,
Chapter 5 in J. McCarthy, O. Canziani, N. Leary, D. Dokken, K. White (Eds.), Climate Change
2001: Impacts, Adaptation, and Vulnerability, Cambridge University Press, Cambridge, UK.
19
Izauralde, C., N.J. Rosenberg, R. A. Brown, and A. M. Thomson, 2003. Integrated assessment of
Hadley Center (HadCM2) climate-change impacts on agricultural productivity and irrigation
water supply in the conterminous United States. Part II. Regional agriculture production in 2030
and 2095, Agricultural and Forest Meteorology, 117, 97-122.
Jacoby, H., R. Eckaus, A.D. Ellermann, R. Prinn, D. Reiner and Z. Yang, 1997. CO2 emissions
limits: economic adjustments and the distribution of burdens, Energy Journal, 18, 31-58.
Kicklighter, D. W., M. Bruno, S. Dönges, G. Esser, M. Heimann, J. Helfrich, F. Ift, F. Joos, J.
Kaduk, G. H. Kohlmaier, A. D. McGuire, J. M. Melillo, R. Meyer, B. Moore III, A. Nadler, I.
C. Prentice, W. Sauf, A. L. Schloss, S. Sitch, U. Wittenberg and G. Würth, 1999. A first-order
analysis of the potential role of CO2 fertilization to affect the global carbon budget: A
comparison study of four terrestrial biosphere models, Tellus, 51B, 343-366.
Lawrence, M.G., P.J. Crutzen, P.J. Rasch, B.E. Eaton and N.M. Mahowald, 1999. A model for
studies of tropospheric photochemistry: Description, global distributions, and evaluation, J.
Geophys. Res., 104, 26245–26277.
Long, S.P, E.A. Ainsworth, A. D.B. Leakey, J. Nösefberger, and D.R. Ort, 2006. Food for thought:
lower-than-expected crop yield stimulation with rising CO2 concentration, Science, 312, 19181921.
Mahowald, N. M., P.J. Rasch, B.E. Eaton, S. Whittlestone and R.G. Prinn, 1997. Transport of 222
radon to the remote troposphere using the model of atmospheric transport and chemistry and
assimilated winds from ECMWF and the National Center for Environmental Prediction/NCAR,
J. Geophys. Res., 102, 28139–28152.
Mayer, M., R. Hyman, J. Harnisch, J.Reilly, 2000. Emissions inventories and time trends for
greenhouse gases and other pollutants, MIT Joint Program on the Science and Policy of Global
Change, Technical Note 1, Cambridge, MA.
McFarland, J.R., J.M. Reilly & H.J. Herzog, 2004. Representing energy technologies in top-down
economic models using bottom-up information, Energy Economics, 26(4), 685-707.
McGuire, A. D., S. Sitch, J. S. Clein, R. Dargaville, G. Esser, J. Foley, M. Heimann, F. Joos, J.
Kaplan, D. W. Kicklighter, R. A. Meier, J. M. Melillo, B. Moore III, I. C. Prentice, N.
Ramankutty, T. Reichenau, A. Schloss, H. Tian, L. J. Williams and U. Wittenberg, 2001.
Carbon balance of the terrestrial biosphere in the twentieth century: analyses of CO2, climate
and land-use effects with four process-based ecosystem models, Global Biogeochemical Cycles,
15(1), 183-206.
Melillo, J. M., A.D. McGuire, D.W. Kicklighter, B. Moore III, C.J. Vorosmarty, and A. L. Schloss,
1993. Global climate change and terrestrial net primary production, Nature, 363, 234–240.
Mendelsohn, R., W.D. Nordhaus and D. Shaw, 1994. The Impact of Global Warming on
Agriculture: A Ricardian analysis. American Economic Review, 84(4), 753-771.
Ollinger, S. V., J. Aber, and P. Reich, 1997. Simulating ozone effects on forest productivity:
Interactions among leaf-, canopy-, and stand-level processes, Ecol. Appl., 7(4), 1237–1251.
Paltsev, S., J.M. Reilly, H. D. Jacoby, R.S. Eckaus, J. McFarland, M. Sarofim, M. Asadoorian, M.
Babiker, 2005. The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4,
MIT Joint Program on the Science and Policy of Global Change, Report 125, Cambridge, MA.
Paltsev, S., J.M. Reilly, H.D. Jacoby, A.D. Ellerman, and K.H. Tay, 2003. Emissions Trading to
Reduce Greenhouse Gas Emissions in the United States: The McCain-Lieberman Proposal.
MIT Joint Program on the Science and Policy of Global Change, Report 97, Cambridge, MA.
20
Parry, M.L., Carter, T.R. and Konijn, N.T. (Eds.), 1988a. The Impact of Climate Variations on
Agriculture: Volume 1: Assessments in Cool Temperate and Cold Regions, Dordrecht, The
Netherlands, Kluwer Academic Press.
Parry, M.L., Carter, T.R. and Konijn, N.T. (Eds.), 1988b. The Impact of Climatic Variations on
Agriculture: Volume 2 Assessment in Semi-arid Regions, Dordrecht, The Netherlands, Kluwer
Academic Press.
Parry, M., C. Rosenzweig, A. Iglesis, G. Fischer, and M. Livermore, 1999. Climate change and
world food security: A new assessment. Global Environ. Change, 9, S51-S67.
Parry, M.L., C. Rosenzweig, A. Iglesias, M. Livermore, and G. Fischer, 2004. Effects of climate
change on global food production under SRES emissions and socio-economic scenarios. Global
Environ. Change, 14, 53-67.
Prinn, R., H. Jacoby, A. Sokolov, C. Wang, X. Xiao, Z. Yang, R. Eckaus, P. Stone, D. Ellerman, J.
Melillo, J. Fitzmaurice, D. Kicklighter, G. Holian and Y. Liu, 1999. Integrated global system
model for climate policy assessment: Feedbacks and sensitivity studies, Climatic Change, 41,
469-546.
Prinn, R., J. Reilly, M. Sarofim, C. Wang and B. Felzer, 2006. Effects of air pollution control on
climate, In: Integrated Assessment of Human-induced Climate Change [M. Schlesinger, ed.],
Cambridge University Press (in press), Cambridge, UK, and MIT Joint Program for the Science
and Policy of Global Change, Report No. 118, January, 2005, Cambridge, MA.
Ramankutty, N. and J. A. Foley, 1998. Characterizing patterns of global land use: an analysis of
global croplands data, Global Biogeochemical Cycles, 12, 667-685.
Ramankutty, N., and J. A. Foley. 1999. Estimating historical changes in global land cover:
croplands from 1700 to 1992, Global Biogeochemical Cycles, 13, 997-1027.
Rasch, P. J., N.M. Mahowald and B.E. Eaton, 1997. Representations of transport, convection, and
the hydrologic cycle in chemical transport models: Implications for the modeling of shortlived
and soluble species. J. Geophys. Res., 102, 28127–28138.
Reich, P. B., 1987. Quantifying plant response to ozone: A unifying theory, Tree Physiol., 3, 63–91.
Reilly (Ed.), 2002. Agriculture: The Potential Consequences of Climate Variability and Change,
Cambridge University Press, Cambridge UK.
Reilly, J. and N. Hohmann, 1993. Climate change and agriculture: the role of international trade,
American Economic Review: Papers and Proceedings, 83, 306-312.
Reilly, J., M. Mayer, and J. Harnisch, 2002. The Kyoto protocol and non-CO2 greenhouse gases and
carbon sinks, Environmental Modeling and Assessment, 7(4), 217-229.
Reilly, J., R. Prinn, J. Harnisch, J. Fitzmaurice, H.D. Jacoby, D. Kicklighter, J. Melillo, P. Stone, A.
Sokolov, and C. Wang, 1999. Multi-gas assessment of the Kyoto Protocol, Nature, 401, 549555.
Reilly, J., and D. Schimmelpfennig. 1999. Agricultural impact assessment, vulnerability, and the
scope for adaptation, Climatic Change, 43(4), 745-788.
Reilly, J., F. Tubiello, B. McCarl, D. Abler, R. Darwin, K. Fuglie, S. Hollinger, C. Izaurralde, S.
Jagtap, J. Jones,L. Mearns, D. Ojima, E. Paul, K. Paustian, S. Riha, N. Rosenberg, C.
Rosenzweig, 2003. U.S. agriculture and climate change: new results, Climatic Change, 57, 4369.
Rosenberg, N.J., 1993. Towards An Integrated Assessment Of Climate Change: The MINK Study.
Boston, Kluwer Academic Publishers, 173 pp.
21
Rosenzweig, C., and M. L. Parry, 1994. Potential impact of climate change on world food supply.
Nature, 367, 133-138.
Sokolov, A.P., C.A. Schlosser, S. Dutkiewicz, S. Paltsev, D.W. Kicklighter, H.D. Jacoby, R.G.
Prinn, C.E. Forest, J. Reilly, C. Wang, B. Felzer, M.C. Sarofim, J. Scott, P.H. Stone, J.M.
Melillo, and J. Cohen, 2005. The MIT Integrated Global System Model (IGSM) Version 2:
Model Description and Baseline Evaluation. MIT Joint Program for the Science and Policy of
Global Change, Report 124, Cambridge, MA.
Tian, H., J.M Melillo, D.W. Kicklighter, S., Pan, J. Liu, A.D. McGuire, and B. Moore, 2003.
Regional carbon dynamics in monsoon Asia and its implications for the global carbon cycle,
Global Planet. Change, 37, 201–217.
Tian, H., J.M. Melillo, D.W. Kicklighter, A.D. McGuire, and J.V.K. Helfrich, 1999. The sensitivity
of terrestrial carbon storage to historical climate variability and atmospheric CO2 in the United
States, Tellus, 51B, 414–452.
Tobey, J., J. Reilly, S. Kane, 1992. Economic implications of global climate change for world
agriculture, Journal of Agricultural and Resource Economics, 17, 195-204.
Tubiello, F.N., J. S. Amthor, K. Boote, M. Donatelli, W. Easterling, R. Gifford, M. Howden, G.
Fischer, J. Reilly, and C. Rosenzweig, 2006. Crop response to elevated CO2 and world food
supply, European Journal of Agronomy (submitted).
Von Kuhlmann, R., M. Lawrence, P. Crutzen, and P. Rasch, 2003. A model for studies of
tropospheric ozone and nonmethane hydrocarbons: Model description and ozone results, J.
Geophys. Res., 108, doi: 10.1029/2002JD002893.
Wang, X. and D. L. Mauzerall, 2004. Characterizing distributions of surface ozone and its impact
on grain production in China, Japan and South Korea: 1990 and 2020, Atmospheric
Environment, 38, 4383-4402.
Wang, X., 2005. The Economic Impact of Global Climate and Tropospheric Ozone on World
Agricultural Production. Master of Science Thesis, Engineering Systems Division,
Massachusetts Institute of Technology, Cambridge (May).
Webster, M.D., M. Babiker, M. Mayer, J.M. Reilly, J. Harnisch, M.C. Sarofim, and C. Wang, 2002.
Uncertainty in emissions projections for climate models, Atmospheric Environment, 36(22),
3659-3670.
Webster, M., C. Forest, J. Reilly, M. Babiker, D. Kicklighter, M. Mayer, R. Prinn, M. Sarofim, A.
Sokolov, P. Stone and C. Wang, 2003. Uncertainty analysis of climate change and policy
response, Climatic Change, 62, 295-320.
Westenbarger, D. and G. Frisvold, 1994. Agricultural exposure to ozone and acid precipitation,
Atmospheric Environment, 28, 2895-2907.
Westenbarger, D. V. and G. B. Frisvold, 1995. Air pollution and farm-level crop yields: an
empirical analysis of corn and soybeans, Agricultural and Resource Economics Review, 24(2),
156-165.
Xiao, X., D. W. Kicklighter, J. M. Melillo, A. D. McGuire, P. H. Stone and A. P. Sokolov, 1997.
Linking a global terrestrial biogeochemical model and a 2-dimensional climate model:
implications for the carbon budget, Tellus, 49B, 18-37.
22
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
1. Uncertainty in Climate Change Policy Analysis
Jacoby & Prinn December 1994
2. Description and Validation of the MIT Version of the
GISS 2D Model Sokolov & Stone June 1995
3. Responses of Primary Production and Carbon Storage
to Changes in Climate and Atmospheric CO2
Concentration Xiao et al. October 1995
4. Application of the Probabilistic Collocation Method
for an Uncertainty Analysis Webster et al. January 1996
5. World Energy Consumption and CO2 Emissions:
1950-2050 Schmalensee et al. April 1996
6. The MIT Emission Prediction and Policy Analysis
(EPPA) Model Yang et al. May 1996 (superseded by No. 125)
7. Integrated Global System Model for Climate Policy
Analysis Prinn et al. June 1996 (superseded by No. 124)
8. Relative Roles of Changes in CO2 and Climate to
Equilibrium Responses of Net Primary Production
and Carbon Storage Xiao et al. June 1996
9. CO2 Emissions Limits: Economic Adjustments and the
Distribution of Burdens Jacoby et al. July 1997
10. Modeling the Emissions of N2O and CH4 from the
Terrestrial Biosphere to the Atmosphere Liu Aug. 1996
11. Global Warming Projections: Sensitivity to Deep Ocean
Mixing Sokolov & Stone September 1996
12. Net Primary Production of Ecosystems in China and
its Equilibrium Responses to Climate Changes
Xiao et al. November 1996
13. Greenhouse Policy Architectures and Institutions
Schmalensee November 1996
14. What Does Stabilizing Greenhouse Gas
Concentrations Mean? Jacoby et al. November 1996
15. Economic Assessment of CO2 Capture and Disposal
Eckaus et al. December 1996
16. What Drives Deforestation in the Brazilian Amazon?
Pfaff December 1996
17. A Flexible Climate Model For Use In Integrated
Assessments Sokolov & Stone March 1997
18. Transient Climate Change and Potential Croplands of
the World in the 21st Century Xiao et al. May 1997
19. Joint Implementation: Lessons from Title IV’s Voluntary
Compliance Programs Atkeson June 1997
20. Parameterization of Urban Subgrid Scale Processes
in Global Atm. Chemistry Models Calbo et al. July 1997
21. Needed: A Realistic Strategy for Global Warming
Jacoby, Prinn & Schmalensee August 1997
22. Same Science, Differing Policies; The Saga of Global
Climate Change Skolnikoff August 1997
23. Uncertainty in the Oceanic Heat and Carbon Uptake
and their Impact on Climate Projections
Sokolov et al. September 1997
24. A Global Interactive Chemistry and Climate Model
Wang, Prinn & Sokolov September 1997
25. Interactions Among Emissions, Atmospheric
Chemistry & Climate Change Wang & Prinn Sept. 1997
26. Necessary Conditions for Stabilization Agreements
Yang & Jacoby October 1997
27. Annex I Differentiation Proposals: Implications for
Welfare, Equity and Policy Reiner & Jacoby Oct. 1997
28. Transient Climate Change and Net Ecosystem
Production of the Terrestrial Biosphere
Xiao et al. November 1997
29. Analysis of CO2 Emissions from Fossil Fuel in Korea:
1961–1994 Choi November 1997
30. Uncertainty in Future Carbon Emissions: A Preliminary
Exploration Webster November 1997
31. Beyond Emissions Paths: Rethinking the Climate Impacts
of Emissions Protocols Webster & Reiner November 1997
32. Kyoto’s Unfinished Business Jacoby et al. June 1998
33. Economic Development and the Structure of the
Demand for Commercial Energy Judson et al. April 1998
34. Combined Effects of Anthropogenic Emissions and
Resultant Climatic Changes on Atmospheric OH
Wang & Prinn April 1998
35. Impact of Emissions, Chemistry, and Climate on
Atmospheric Carbon Monoxide Wang & Prinn April 1998
36. Integrated Global System Model for Climate Policy
Assessment: Feedbacks and Sensitivity Studies
Prinn et al. June 1998
37. Quantifying the Uncertainty in Climate Predictions
Webster & Sokolov July 1998
38. Sequential Climate Decisions Under Uncertainty: An
Integrated Framework Valverde et al. September 1998
39. Uncertainty in Atmospheric CO2 (Ocean Carbon Cycle
Model Analysis) Holian Oct. 1998 (superseded by No. 80)
40. Analysis of Post-Kyoto CO2 Emissions Trading Using
Marginal Abatement Curves Ellerman & Decaux Oct. 1998
41. The Effects on Developing Countries of the Kyoto
Protocol and CO2 Emissions Trading
Ellerman et al. November 1998
42. Obstacles to Global CO2 Trading: A Familiar Problem
Ellerman November 1998
43. The Uses and Misuses of Technology Development as
a Component of Climate Policy Jacoby November 1998
44. Primary Aluminum Production: Climate Policy,
Emissions and Costs Harnisch et al. December 1998
45. Multi-Gas Assessment of the Kyoto Protocol
Reilly et al. January 1999
46. From Science to Policy: The Science-Related Politics of
Climate Change Policy in the U.S. Skolnikoff January 1999
47. Constraining Uncertainties in Climate Models Using
Climate Change Detection Techniques
Forest et al. April 1999
48. Adjusting to Policy Expectations in Climate Change
Modeling Shackley et al. May 1999
49. Toward a Useful Architecture for Climate Change
Negotiations Jacoby et al. May 1999
50. A Study of the Effects of Natural Fertility, Weather
and Productive Inputs in Chinese Agriculture
Eckaus & Tso July 1999
51. Japanese Nuclear Power and the Kyoto Agreement
Babiker, Reilly & Ellerman August 1999
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REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
52. Interactive Chemistry and Climate Models in Global
Change Studies Wang & Prinn September 1999
53. Developing Country Effects of Kyoto-Type Emissions
Restrictions Babiker & Jacoby October 1999
54. Model Estimates of the Mass Balance of the
Greenland and Antarctic Ice Sheets Bugnion Oct 1999
55. Changes in Sea-Level Associated with Modifications
of Ice Sheets over 21st Century Bugnion October 1999
56. The Kyoto Protocol and Developing Countries
Babiker et al. October 1999
57. Can EPA Regulate Greenhouse Gases Before the
Senate Ratifies the Kyoto Protocol?
Bugnion & Reiner November 1999
58. Multiple Gas Control Under the Kyoto Agreement
Reilly, Mayer & Harnisch March 2000
59. Supplementarity: An Invitation for Monopsony?
Ellerman & Sue Wing April 2000
60. A Coupled Atmosphere-Ocean Model of Intermediate
Complexity Kamenkovich et al. May 2000
61. Effects of Differentiating Climate Policy by Sector:
A U.S. Example Babiker et al. May 2000
62. Constraining Climate Model Properties Using
Optimal Fingerprint Detection Methods Forest et al.
May 2000
63. Linking Local Air Pollution to Global Chemistry and
Climate Mayer et al. June 2000
64. The Effects of Changing Consumption Patterns on the
Costs of Emission Restrictions Lahiri et al. Aug 2000
65. Rethinking the Kyoto Emissions Targets
Babiker & Eckaus August 2000
66. Fair Trade and Harmonization of Climate Change
Policies in Europe Viguier September 2000
67. The Curious Role of “Learning” in Climate Policy:
Should We Wait for More Data? Webster October 2000
68. How to Think About Human Influence on Climate
Forest, Stone & Jacoby October 2000
69. Tradable Permits for Greenhouse Gas Emissions:
A primer with reference to Europe Ellerman Nov 2000
70. Carbon Emissions and The Kyoto Commitment in the
European Union Viguier et al. February 2001
71. The MIT Emissions Prediction and Policy Analysis
Model: Revisions, Sensitivities and Results
Babiker et al. February 2001 (superseded by No. 125)
72. Cap and Trade Policies in the Presence of Monopoly
and Distortionary Taxation Fullerton & Metcalf March ‘01
73. Uncertainty Analysis of Global Climate Change
Projections Webster et al. Mar. ‘01 (superseded by No. 95)
74. The Welfare Costs of Hybrid Carbon Policies in the
European Union Babiker et al. June 2001
75. Feedbacks Affecting the Response of the
Thermohaline Circulation to Increasing CO2
Kamenkovich et al. July 2001
76. CO2 Abatement by Multi-fueled Electric Utilities:
An Analysis Based on Japanese Data
Ellerman & Tsukada July 2001
77. Comparing Greenhouse Gases Reilly et al. July 2001
78. Quantifying Uncertainties in Climate System
Properties using Recent Climate Observations
Forest et al. July 2001
79. Uncertainty in Emissions Projections for Climate
Models Webster et al. August 2001
80. Uncertainty in Atmospheric CO2 Predictions from a
Global Ocean Carbon Cycle Model
Holian et al. September 2001
81. A Comparison of the Behavior of AO GCMs in
Transient Climate Change Experiments
Sokolov et al. December 2001
82. The Evolution of a Climate Regime: Kyoto to
Marrakech Babiker, Jacoby & Reiner February 2002
83. The “Safety Valve” and Climate Policy
Jacoby & Ellerman February 2002
84. A Modeling Study on the Climate Impacts of Black
Carbon Aerosols Wang March 2002
85. Tax Distortions and Global Climate Policy
Babiker et al. May 2002
86. Incentive-based Approaches for Mitigating
Greenhouse Gas Emissions: Issues and Prospects for
India Gupta June 2002
87. Deep-Ocean Heat Uptake in an Ocean GCM with
Idealized Geometry Huang, Stone & Hill
September 2002
88. The Deep-Ocean Heat Uptake in Transient Climate
Change Huang et al. September 2002
89. Representing Energy Technologies in Top-down
Economic Models using Bottom-up Information
McFarland et al. October 2002
90. Ozone Effects on Net Primary Production and Carbon
Sequestration in the U.S. Using a Biogeochemistry
Model Felzer et al. November 2002
91. Exclusionary Manipulation of Carbon Permit
Markets: A Laboratory Test Carlén November 2002
92. An Issue of Permanence: Assessing the Effectiveness of
Temporary Carbon Storage Herzog et al. December 2002
93. Is International Emissions Trading Always Beneficial?
Babiker et al. December 2002
94. Modeling Non-CO2 Greenhouse Gas Abatement
Hyman et al. December 2002
95. Uncertainty Analysis of Climate Change and Policy
Response Webster et al. December 2002
96. Market Power in International Carbon Emissions
Trading: A Laboratory Test Carlén January 2003
97. Emissions Trading to Reduce Greenhouse Gas
Emissions in the United States: The McCain-Lieberman
Proposal Paltsev et al. June 2003
98. Russia’s Role in the Kyoto Protocol Bernard et al. Jun ‘03
99. Thermohaline Circulation Stability: A Box Model Study
Lucarini & Stone June 2003
100. Absolute vs. Intensity-Based Emissions Caps
Ellerman & Sue Wing July 2003
101. Technology Detail in a Multi-Sector CGE Model:
Transport Under Climate Policy Schafer & Jacoby July 2003
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REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
102. Induced Technical Change and the Cost of Climate
Policy Sue Wing September 2003
103. Past and Future Effects of Ozone on Net Primary
Production and Carbon Sequestration Using a Global
Biogeochemical Model Felzer et al. (revised) January 2004
104. A Modeling Analysis of Methane Exchanges
Between Alaskan Ecosystems and the Atmosphere
Zhuang et al. November 2003
105. Analysis of Strategies of Companies under Carbon
Constraint Hashimoto January 2004
106. Climate Prediction: The Limits of Ocean Models
Stone February 2004
107. Informing Climate Policy Given Incommensurable
Benefits Estimates Jacoby February 2004
108. Methane Fluxes Between Terrestrial Ecosystems
and the Atmosphere at High Latitudes During the
Past Century Zhuang et al. March 2004
109. Sensitivity of Climate to Diapycnal Diffusivity in the
Ocean Dalan et al. May 2004
110. Stabilization and Global Climate Policy
Sarofim et al. July 2004
111. Technology and Technical Change in the MIT EPPA
Model Jacoby et al. July 2004
112. The Cost of Kyoto Protocol Targets: The Case of
Japan Paltsev et al. July 2004
113. Economic Benefits of Air Pollution Regulation in the
USA: An Integrated Approach Yang et al. (revised) Jan. 2005
114. The Role of Non-CO2 Greenhouse Gases in Climate
Policy: Analysis Using the MIT IGSM Reilly et al. Aug. ‘04
115. Future U.S. Energy Security Concerns Deutch Sep. ‘04
116. Explaining Long-Run Changes in the Energy
Intensity of the U.S. Economy Sue Wing Sept. 2004
117. Modeling the Transport Sector: The Role of Existing
Fuel Taxes in Climate Policy Paltsev et al. November 2004
118. Effects of Air Pollution Control on Climate
Prinn et al. January 2005
119. Does Model Sensitivity to Changes in CO2 Provide a
Measure of Sensitivity to the Forcing of Different
Nature? Sokolov March 2005
120. What Should the Government Do To Encourage
Technical Change in the Energy Sector? Deutch May ‘05
121. Climate Change Taxes and Energy Efficiency in
Japan Kasahara et al. May 2005
122. A 3D Ocean-Seaice-Carbon Cycle Model and its
Coupling to a 2D Atmospheric Model: Uses in Climate
Change Studies Dutkiewicz et al. (revised) November 2005
123. Simulating the Spatial Distribution of Population
and Emissions to 2100 Asadoorian May 2005
124. MIT Integrated Global System Model (IGSM)
Version 2: Model Description and Baseline Evaluation
Sokolov et al. July 2005
125. The MIT Emissions Prediction and Policy Analysis
(EPPA) Model: Version 4 Paltsev et al. August 2005
126. Estimated PDFs of Climate System Properties
Including Natural and Anthropogenic Forcings
Forest et al. September 2005
127. An Analysis of the European Emission Trading
Scheme Reilly & Paltsev October 2005
128. Evaluating the Use of Ocean Models of Different
Complexity in Climate Change Studies
Sokolov et al. November 2005
129. Future Carbon Regulations and Current Investments
in Alternative Coal-Fired Power Plant Designs
Sekar et al. December 2005
130. Absolute vs. Intensity Limits for CO2 Emission
Control: Performance Under Uncertainty
Sue Wing et al. January 2006
131. The Economic Impacts of Climate Change: Evidence
from Agricultural Profits and Random Fluctuations in
Weather Deschenes & Greenstone January 2006
132. The Value of Emissions Trading Webster et al. Feb. 2006
133. Estimating Probability Distributions from Complex
Models with Bifurcations: The Case of Ocean
Circulation Collapse Webster et al. March 2006
134. Directed Technical Change and Climate Policy
Otto et al. April 2006
135. Modeling Climate Feedbacks to Energy Demand:
The Case of China Asadoorian et al. June 2006
136. Bringing Transportation into a Cap-and-Trade
Regime Ellerman, Jacoby & Zimmerman June 2006
137. Unemployment Effects of Climate Policy Babiker &
Eckaus July 2006
138. Energy Conservation in the United States:
Understanding its Role in Climate Policy Metcalf Aug. ‘06
139. Directed Technical Change and the Adoption of CO2
Abatement Technology: The Case of CO2 Capture and
Storage Otto & Reilly August 2006
140. The Allocation of European Union Allowances:
Lessons, Unifying Themes and General Principles
Buchner et al. October 2006
141. Over-Allocation or Abatement? A preliminary
analysis of the EU ETS based on the 2006 emissions data
Ellerman & Buchner December 2006
142. Federal Tax Policy Towards Energy Metcalf Jan. 2007
143. Technical Change, Investment and Energy Intensity
Kratena March 2007
144. Heavier Crude, Changing Demand for Petroleum
Fuels, Regional Climate Policy, and the Location of
Upgrading Capacity: A Preliminary Look Reilly, Paltsev &
Choumert April 2007
145. Biomass Energy and Competition for Land
Reilly & Paltsev April 2007
146. Assessment of U.S. Cap-and-Trade Proposals
Paltsev et al., April 2007
147. A Global Land System Framework for Integrated
Climate-Change Assessments Schlosser et al. May 2007
148. Relative Roles of Climate Sensitivity and Forcing in
Defining the Ocean Circulation Response to Climate
Change Scott et al. May 2007
149. Global Economic Effects of Changes in Crops,
Pasture, and Forests due to Changing Climate, CO2
and Ozone Reilly et al. May 2007
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