MIT Joint Program on theScience and Policy of Global Change

Global Economic Effects of Changes in Crops,Pasture, and Forests due to Changing Climate,

Carbon Dioxide, and OzoneJohn Reilly, Sergey Paltsev, Benjamin Felzer, Xiaodong Wang, David Kicklighter,

John Melillo, Ronald Prinn, Marcus Sarofim, Andrei Sokolov and Chien Wang

Report No. 149May 2007

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1

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.

Contents1. Introduction ................................................................................................................................ 12. Modeling Global Agricultural Economic Response to Environmental Change...................... 23. Model Descriptions .................................................................................................................... 44. Scenarios..................................................................................................................................... 65. Agriculture, Pasture and Forestry Results............................................................................... 116. Caveats and Comparison with Previous Work ....................................................................... 177. Conclusions .............................................................................................................................. 188. 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.

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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

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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 40-

50 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 re-

growth. 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

4

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 off-

line, 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), butis resolved at zonal bands of 4° matching the resolution of the atmospheric model, inadequate for capturing thespatial variation in ozone concentrations. Because of flexibility of the model, the overall behavior of Version 1and Version 2 of the IGSM is very close when key climate parameters are set to identical values as shown inSokolov et al. (2005).

5

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 3-

hour 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

6

Table 1. The Emissions Prediction and Policy Analysis (EPPA) model is a recursive-dynamic multi-regional CGE model of the world economy (Babiker et al., 2001, Paltsev et al., 2005), which is built onthe economic and energy data from the GTAP dataset (Dimaranan and McDougall, 2002) andadditional 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 etal., 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 andclimate projections for climate models Webster et al., 2002, 2003). It has been modified for this analysisto include greater disaggregation of the food and agriculture sectors, as shown in italics.

Countries/Regions, Sectors, and Factors in the EPPA-AGRI ModelCountry or Region Sectors FactorsDeveloped Non-Energy Economy-wideUnited States (USA) Services CapitalCanada (CAN) Energy Intensive products LaborJapan (JPN) Other Industries productsEuropean Union+a (EUR) Transportation EnergyAustralia/New Zealand (ANZ) Food Processing Crude Oil ResourcesFormer Soviet Unionb (FSU) Shale Oil ResourcesEastern Europec (EET) Energy Coal Resources

Coal Natural Gas ResourcesDeveloping Crude Oil Nuclear ResourcesIndia (IND) Shale Oil Hydro ResourcesChina (CHN) Refined Oil Products Wind/Solar ResourcesHigher Income East Asiad (ASI) Natural Gas, Coal GasificationIndonesia (IDZ) Electric: Fossil, Hydro, Nuclear, Land UseRest of Worlde (ROW) Solar & Wind, Biomass, Natural Gas Crop LandMexico (MEX) Combined Cycle, Integrated Coal Pasture/Grazing LandAfrica (AFR) Gasification with Sequestration Forest landCentral and South America (LAM)Middle East (MES) Agriculture

CropsLivestockForestry

a The European Union (EU-15) plus countries of the European Free Trade Area (Norway, Switzerland, Iceland).b 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, Thailande All countries not included elsewhere: Turkey, and mostly Asian countries.

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).

7

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

8

Table 2. TEM Vegetation Types and Land Use Classification.

TEMVEG Description of Vegetation Type Land Use Classification1 Ice2 Alpine Tundra and Polar Desert3 Moist and Wet Tundra4 Boreal Forest Forestry5 Forested Boreal Wetlands Forestry6 Boreal Woodlands Forestry7 Non-forested Boreal Wetlands8 Mixed Temperate Forests Forestry9 Temperate Coniferous Forests Forestry

10 Temperate Deciduous Forests Forestry11 Temperate Forested Wetlands Forestry12 Tall Grasslands Pasture13 Short Grasslands Pasture14 Tropical Savanna Pasture15 Arid Shrublands Pasture16 Tropical Evergreen Forests Forestry17 Tropical Forested Wetlands Forestry18 Tropical Deciduous Forests Forestry19 Xeromorphic Forests and Woodlands Pasture20 Tropical Forested Floodplains Forestry21 Deserts22 Tropical Non-forested Wetlands23 Tropical Non-forested Floodplains24 Temperate Non-forested Wetlands25 Temperate Forested Floodplains Forestry26 Temperate Non-forested Floodplains27 Wet Savannas28 Salt Marsh29 Mangroves30 Tidal Freshwater Marshes31 Temperate Savannas Pasture32 Cultivation Cropland33 Temperate Broadleaved Evergreen Forestry34 Reserved35 Mediterranean Shrublands Pasture

Note: Vegetation changes for ice, tundra, desert, and wetlands are excluded from any of the uses as indicated by blankspace 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.

9

(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

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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 thecomparable cases with ozone damage (High Pollution and GHGs capped, respectively).

10

Figure 2. Geographical distribution of ozone (AOT40, ppm-h), mean monthly levels forJune–July–August of 1998.

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3500

2000 2020 2040 2060 2080 2100

Year

)rh-

mp

p( 0

4 T

OA l

ac i

por

T

GHGs capped

Pollution capped

High pollution

GHGs & pollution capped GHGs & pollution capped

Pollution capped

GHGs capped

High pollution

High pollution

GHGs capped

Pollution capped

GHGs & pollution capped

(a) (b)

(c)

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

11

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

12

(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 inyield (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

13

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 combinationwith parameters that govern substitution between land and other inputs as their prices change.

14

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

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1980 2000 2020 2040 2060 2080 2100 2120

Crop Production

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1980 2000 2020 2040 2060 2080 2100 2120

High pollution

Climate and GHGs onlyCapped pollution

GHGs cappedGHGs and pollution capped

Figure 5. Index for Crop Yield and Production

15

Forage /Pasture / Grazing Yie ld

0.95

1

1.05

1.1

1.15

1.2

1.25

1980 2000 2020 2040 2060 2080 2100 2120

Livestock Production

0.95

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

1.04

1.05

1980 2000 2020 2040 2060 2080 2100 2120

High pollution Climate and GHGs only

Capped pollution GHGs capped

GHGs and pollution capped

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

Forestry Yie ld

0.96

0.98

1

1.02

1.04

1.06

1.08

1.1

1.12

1.14

1.16

1.18

1980 2000 2020 2040 2060 2080 2100 2120

Forestry Production

0.98

0.985

0.99

0.995

1

1.005

1.01

1.015

1980 2000 2020 2040 2060 2080 2100 2120

High pollution Climate and GHGs only

Capped pollution GHGs capped

GHGs and pollution capped

Figure 7. Index for Forestry Yield and Production.

16

-0.15

-0.1

-0.05

0

0.05

0.1

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 ashare 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.

17

-5

-4

-3

-2

-1

0

1

2

3

2000 2020 2040 2060 2080 2100

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

2000 2020 2040 2060 2080 2100

USA

CAN

ANZ

EUR

CHN

ASI

AFR

LAM

global

per

cen

t

per

cen

t

(a) (b)

Figure 9. Percent Change in Macroeconomic Consumption in High Pollution Scenario (a) andClimate 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.

18

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

19

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.

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REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change

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1. Uncertainty in Climate Change Policy AnalysisJacoby & Prinn December 1994

2. Description and Validation of the MIT Version of theGISS 2D Model Sokolov & Stone June 1995

3. Responses of Primary Production and Carbon Storageto Changes in Climate and Atmospheric CO2

Concentration Xiao et al. October 19954. Application of the Probabilistic Collocation Method

for an Uncertainty Analysis Webster et al. January 19965. World Energy Consumption and CO2 Emissions:

1950-2050 Schmalensee et al. April 19966. 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 Productionand Carbon Storage Xiao et al. June 1996

9. CO2 Emissions Limits: Economic Adjustments and theDistribution of Burdens Jacoby et al. July 1997

10. Modeling the Emissions of N2O and CH4 from theTerrestrial Biosphere to the Atmosphere Liu Aug. 1996

11. Global Warming Projections: Sensitivity to Deep OceanMixing Sokolov & Stone September 1996

12. Net Primary Production of Ecosystems in China andits Equilibrium Responses to Climate ChangesXiao et al. November 1996

13. Greenhouse Policy Architectures and InstitutionsSchmalensee November 1996

14. What Does Stabilizing Greenhouse GasConcentrations Mean? Jacoby et al. November 1996

15. Economic Assessment of CO2 Capture and DisposalEckaus et al. December 1996

16. What Drives Deforestation in the Brazilian Amazon?Pfaff December 1996

17. A Flexible Climate Model For Use In IntegratedAssessments Sokolov & Stone March 1997

18. Transient Climate Change and Potential Croplands ofthe World in the 21st Century Xiao et al. May 1997

19. Joint Implementation: Lessons from Title IV’s VoluntaryCompliance Programs Atkeson June 1997

20. Parameterization of Urban Subgrid Scale Processesin Global Atm. Chemistry Models Calbo et al. July 1997

21. Needed: A Realistic Strategy for Global WarmingJacoby, Prinn & Schmalensee August 1997

22. Same Science, Differing Policies; The Saga of GlobalClimate Change Skolnikoff August 1997

23. Uncertainty in the Oceanic Heat and Carbon Uptakeand their Impact on Climate ProjectionsSokolov et al. September 1997

24. A Global Interactive Chemistry and Climate ModelWang, Prinn & Sokolov September 1997

25. Interactions Among Emissions, AtmosphericChemistry & Climate Change Wang & Prinn Sept. 1997

26. Necessary Conditions for Stabilization AgreementsYang & Jacoby October 1997

27. Annex I Differentiation Proposals: Implications forWelfare, Equity and Policy Reiner & Jacoby Oct. 1997

28. Transient Climate Change and Net EcosystemProduction of the Terrestrial BiosphereXiao 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 PreliminaryExploration Webster November 1997

31. Beyond Emissions Paths: Rethinking the Climate Impactsof Emissions Protocols Webster & Reiner November 1997

32. Kyoto’s Unfinished Business Jacoby et al. June 199833. Economic Development and the Structure of the

Demand for Commercial Energy Judson et al. April 199834. Combined Effects of Anthropogenic Emissions and

Resultant Climatic Changes on Atmospheric OHWang & Prinn April 1998

35. Impact of Emissions, Chemistry, and Climate onAtmospheric Carbon Monoxide Wang & Prinn April 1998

36. Integrated Global System Model for Climate PolicyAssessment: Feedbacks and Sensitivity StudiesPrinn et al. June 1998

37. Quantifying the Uncertainty in Climate PredictionsWebster & Sokolov July 1998

38. Sequential Climate Decisions Under Uncertainty: AnIntegrated Framework Valverde et al. September 1998

39. Uncertainty in Atmospheric CO2 (Ocean Carbon CycleModel Analysis) Holian Oct. 1998 (superseded by No. 80)

40. Analysis of Post-Kyoto CO2 Emissions Trading UsingMarginal Abatement Curves Ellerman & Decaux Oct. 1998

41. The Effects on Developing Countries of the KyotoProtocol and CO2 Emissions TradingEllerman et al. November 1998

42. Obstacles to Global CO2 Trading: A Familiar ProblemEllerman November 1998

43. The Uses and Misuses of Technology Development asa 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 ProtocolReilly et al. January 1999

46. From Science to Policy: The Science-Related Politics ofClimate Change Policy in the U.S. Skolnikoff January 1999

47. Constraining Uncertainties in Climate Models UsingClimate Change Detection TechniquesForest et al. April 1999

48. Adjusting to Policy Expectations in Climate ChangeModeling Shackley et al. May 1999

49. Toward a Useful Architecture for Climate ChangeNegotiations Jacoby et al. May 1999

50. A Study of the Effects of Natural Fertility, Weatherand Productive Inputs in Chinese AgricultureEckaus & Tso July 1999

51. Japanese Nuclear Power and the Kyoto AgreementBabiker, Reilly & Ellerman August 1999

REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change

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52. Interactive Chemistry and Climate Models in GlobalChange Studies Wang & Prinn September 1999

53. Developing Country Effects of Kyoto-Type EmissionsRestrictions Babiker & Jacoby October 1999

54. Model Estimates of the Mass Balance of theGreenland and Antarctic Ice Sheets Bugnion Oct 1999

55. Changes in Sea-Level Associated with Modificationsof Ice Sheets over 21st Century Bugnion October 1999

56. The Kyoto Protocol and Developing CountriesBabiker et al. October 1999

57. Can EPA Regulate Greenhouse Gases Before theSenate Ratifies the Kyoto Protocol?Bugnion & Reiner November 1999

58. Multiple Gas Control Under the Kyoto AgreementReilly, Mayer & Harnisch March 2000

59. Supplementarity: An Invitation for Monopsony?Ellerman & Sue Wing April 2000

60. A Coupled Atmosphere-Ocean Model of IntermediateComplexity 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 UsingOptimal Fingerprint Detection Methods Forest et al.May 2000

63. Linking Local Air Pollution to Global Chemistry andClimate Mayer et al. June 2000

64. The Effects of Changing Consumption Patterns on theCosts of Emission Restrictions Lahiri et al. Aug 2000

65. Rethinking the Kyoto Emissions TargetsBabiker & Eckaus August 2000

66. Fair Trade and Harmonization of Climate ChangePolicies 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 ClimateForest, 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 theEuropean Union Viguier et al. February 2001

71. The MIT Emissions Prediction and Policy AnalysisModel: Revisions, Sensitivities and ResultsBabiker et al. February 2001 (superseded by No. 125)

72. Cap and Trade Policies in the Presence of Monopolyand Distortionary Taxation Fullerton & Metcalf March ‘01

73. Uncertainty Analysis of Global Climate ChangeProjections Webster et al. Mar. ‘01 (superseded by No. 95)

74. The Welfare Costs of Hybrid Carbon Policies in theEuropean Union Babiker et al. June 2001

75. Feedbacks Affecting the Response of theThermohaline Circulation to Increasing CO2

Kamenkovich et al. July 200176. CO2 Abatement by Multi-fueled Electric Utilities:

An Analysis Based on Japanese DataEllerman & Tsukada July 2001

77. Comparing Greenhouse Gases Reilly et al. July 2001

78. Quantifying Uncertainties in Climate SystemProperties using Recent Climate ObservationsForest et al. July 2001

79. Uncertainty in Emissions Projections for ClimateModels Webster et al. August 2001

80. Uncertainty in Atmospheric CO2 Predictions from aGlobal Ocean Carbon Cycle ModelHolian et al. September 2001

81. A Comparison of the Behavior of AO GCMs inTransient Climate Change ExperimentsSokolov et al. December 2001

82. The Evolution of a Climate Regime: Kyoto toMarrakech Babiker, Jacoby & Reiner February 2002

83. The “Safety Valve” and Climate PolicyJacoby & Ellerman February 2002

84. A Modeling Study on the Climate Impacts of BlackCarbon Aerosols Wang March 2002

85. Tax Distortions and Global Climate PolicyBabiker et al. May 2002

86. Incentive-based Approaches for MitigatingGreenhouse Gas Emissions: Issues and Prospects forIndia Gupta June 2002

87. Deep-Ocean Heat Uptake in an Ocean GCM withIdealized Geometry Huang, Stone & HillSeptember 2002

88. The Deep-Ocean Heat Uptake in Transient ClimateChange Huang et al. September 2002

89. Representing Energy Technologies in Top-downEconomic Models using Bottom-up InformationMcFarland et al. October 2002

90. Ozone Effects on Net Primary Production and CarbonSequestration in the U.S. Using a BiogeochemistryModel Felzer et al. November 2002

91. Exclusionary Manipulation of Carbon PermitMarkets: A Laboratory Test Carlén November 2002

92. An Issue of Permanence: Assessing the Effectiveness ofTemporary 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 AbatementHyman et al. December 2002

95. Uncertainty Analysis of Climate Change and PolicyResponse Webster et al. December 2002

96. Market Power in International Carbon EmissionsTrading: A Laboratory Test Carlén January 2003

97. Emissions Trading to Reduce Greenhouse GasEmissions in the United States: The McCain-LiebermanProposal Paltsev et al. June 2003

98. Russia’s Role in the Kyoto Protocol Bernard et al. Jun ‘0399. Thermohaline Circulation Stability: A Box Model Study

Lucarini & Stone June 2003100. Absolute vs. Intensity-Based Emissions Caps

Ellerman & Sue Wing July 2003101. Technology Detail in a Multi-Sector CGE Model:

Transport Under Climate Policy Schafer & Jacoby July 2003

REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change

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102. Induced Technical Change and the Cost of ClimatePolicy Sue Wing September 2003

103. Past and Future Effects of Ozone on Net PrimaryProduction and Carbon Sequestration Using a GlobalBiogeochemical Model Felzer et al. (revised) January 2004

104. A Modeling Analysis of Methane ExchangesBetween Alaskan Ecosystems and the AtmosphereZhuang et al. November 2003

105. Analysis of Strategies of Companies under CarbonConstraint Hashimoto January 2004

106. Climate Prediction: The Limits of Ocean ModelsStone February 2004

107. Informing Climate Policy Given IncommensurableBenefits Estimates Jacoby February 2004

108. Methane Fluxes Between Terrestrial Ecosystemsand the Atmosphere at High Latitudes During thePast Century Zhuang et al. March 2004

109. Sensitivity of Climate to Diapycnal Diffusivity in theOcean Dalan et al. May 2004

110. Stabilization and Global Climate PolicySarofim et al. July 2004

111. Technology and Technical Change in the MIT EPPAModel Jacoby et al. July 2004

112. The Cost of Kyoto Protocol Targets: The Case ofJapan Paltsev et al. July 2004

113. Economic Benefits of Air Pollution Regulation in theUSA: An Integrated Approach Yang et al. (revised) Jan. 2005

114. The Role of Non-CO2 Greenhouse Gases in ClimatePolicy: Analysis Using the MIT IGSM Reilly et al. Aug. ‘04

115. Future U.S. Energy Security Concerns Deutch Sep. ‘04116. Explaining Long-Run Changes in the Energy

Intensity of the U.S. Economy Sue Wing Sept. 2004117. Modeling the Transport Sector: The Role of Existing

Fuel Taxes in Climate Policy Paltsev et al. November 2004118. Effects of Air Pollution Control on Climate

Prinn et al. January 2005119. Does Model Sensitivity to Changes in CO2 Provide a

Measure of Sensitivity to the Forcing of DifferentNature? Sokolov March 2005

120. What Should the Government Do To EncourageTechnical Change in the Energy Sector? Deutch May ‘05

121. Climate Change Taxes and Energy Efficiency inJapan Kasahara et al. May 2005

122. A 3D Ocean-Seaice-Carbon Cycle Model and itsCoupling to a 2D Atmospheric Model: Uses in ClimateChange Studies Dutkiewicz et al. (revised) November 2005

123. Simulating the Spatial Distribution of Populationand Emissions to 2100 Asadoorian May 2005

124. MIT Integrated Global System Model (IGSM)Version 2: Model Description and Baseline EvaluationSokolov 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 PropertiesIncluding Natural and Anthropogenic ForcingsForest et al. September 2005

127. An Analysis of the European Emission TradingScheme Reilly & Paltsev October 2005

128. Evaluating the Use of Ocean Models of DifferentComplexity in Climate Change StudiesSokolov et al. November 2005

129. Future Carbon Regulations and Current Investmentsin Alternative Coal-Fired Power Plant DesignsSekar et al. December 2005

130. Absolute vs. Intensity Limits for CO2 EmissionControl: Performance Under UncertaintySue Wing et al. January 2006

131. The Economic Impacts of Climate Change: Evidencefrom Agricultural Profits and Random Fluctuations inWeather Deschenes & Greenstone January 2006

132. The Value of Emissions Trading Webster et al. Feb. 2006133. Estimating Probability Distributions from Complex

Models with Bifurcations: The Case of OceanCirculation Collapse Webster et al. March 2006

134. Directed Technical Change and Climate PolicyOtto 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-TradeRegime 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 andStorage Otto & Reilly August 2006

140. The Allocation of European Union Allowances:Lessons, Unifying Themes and General PrinciplesBuchner et al. October 2006

141. Over-Allocation or Abatement? A preliminaryanalysis of the EU ETS based on the 2006 emissions dataEllerman & Buchner December 2006

142. Federal Tax Policy Towards Energy Metcalf Jan. 2007143. Technical Change, Investment and Energy Intensity

Kratena March 2007144. Heavier Crude, Changing Demand for Petroleum

Fuels, Regional Climate Policy, and the Location ofUpgrading Capacity: A Preliminary Look Reilly, Paltsev &Choumert April 2007

145. Biomass Energy and Competition for LandReilly & Paltsev April 2007

146. Assessment of U.S. Cap-and-Trade ProposalsPaltsev et al., April 2007

147. A Global Land System Framework for IntegratedClimate-Change Assessments Schlosser et al. May 2007

148. Relative Roles of Climate Sensitivity and Forcing inDefining the Ocean Circulation Response to ClimateChange 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