**Socio-Economic Modeling\
for Large-scale Quantitative Global Change Analysis**

Draft of May 22, 2007

**Table of Contents**

[1. Motivating Questions 3](#motivating-questions)

[2. Major Challenges 4](#major-challenges)

[3. Today's State-of-the-Art 4](#todays-state-of-the-art)

[4. Accelerating Development 6](#accelerating-development)

[5. Expected Outcomes 7](#expected-outcomes)

[6. Required Investment 7](#required-investment)

[7. Major Risks 7](#major-risks)

[8. Contributors 7](#contributors)

[9. Notes from LBNL Meeting to Incorporate
7](#notes-from-lbnl-meeting-to-incorporate)

[References 8](#references)

*This document was prepared as an output from DOE's program of Town Hall
meetings on Simulation of Modeling at the Exascale for Energy,
Ecological Sustainability, and Global Security. Participants in these
workshops were asked to identify opportunities for significant advances
in scientific understanding via the application of large-scale computing
to system-level problems. This document reports the findings of
"breakout group 6," which focused on socio-economic modeling.*

**Executive Summary**

Several decades of research have greatly deepened our understanding of
the impact of greenhouse gases on climate, as documented for example in
the reports of the Intergovernmental Panel on Climate Change (IPCC).
Central to this research are earth system models incorporating detailed
representations of the atmosphere, ocean, cryosphere, and biosphere, and
their various interactions. These models are run on the world's fastest
supercomputers to enable quantitative evaluation of the earth system's
response to rising concentrations of greenhouse gases.

While climate change is a geophysical phenomenon, both its impact on
society and the ultimate effectiveness of specific policy responses
depends critically on human actors, who will determine, for example, how
supply and demand evolve over time, and how and when different
"solutions" (mitigation or adaptation) are deployed and applied. Only by
modeling these human responses can we understand the likely
effectiveness and impacts of different technical and policy solutions,
and thus how to sustain a prosperous and secure society.

The modeling of human responses is a challenging multidimensional,
multiscale, and time-dependent problem that is further complicated by
the strong economic and environmental coupling across different distance
and temporal scales and across different geographic regions. A relative
sparsity of data is another significant obstacle to model development
and validation.

Many researchers study socio-economic issues. Integrated assessment
models, which describe the environmental consequences of human
activities, incorporate treatments of certain socio-economic issues. So
do energy system models and models of relevant processes such as the tax
system, industrial competition, and technical innovation. However,
limited computing capabilities have hindered the application of modern
economic methods, increases in microeconomic fidelity, and examinations
of uncertainty and sensitivity to key parameters.

A focused DOE R&D program can achieve significant advances in the state
of the art in modeling the human elements of the climate system.
Building on DOE expertise in climate and energy system modeling, and
bringing to bear the latest methods in economics, quantitative
techniques in behavior and decision theory, and modeling and high
performance computing tools, such a program can develop a deepened
understanding of the technical, economic, political, and social issues
that underpin the climate change challenge.

Such a program should aim to create a highly integrated, time-dependent
modeling system that encompasses detailed micro-economic models and data
in a comprehensive global equilibrium framework. This system will allow
for a treatment of the marketplace and all participants, including
producers, consumers, and intermediaries, of unprecedented accuracy. It
will allow for the quantitative study of important questions such as:

-   How will different policy alternatives effect energy supply and
    demand, the overall economy, the environment, demand for individual
    products and services, public health, and the vulnerability of the
    US economy and infrastructure?

-   How are answers to these questions influenced by trends such as
    exurbanization, immigration, outsourcing, modernization in Asia,
    climate change, behavioral shifts, and increased focus on corporate
    governance?

-   For potential climate change solutions, how will their success be
    affected by technical vs. other factors such as government policy
    and obstacles to entry for new providers?

In building this system, a DOE program can build on existing climate
models (e.g., CCSM), integrated assessment models (e.g., MiniCAM),
energy system models (NEMS, AMIGA), and other relevant models (e.g., the
GREET transportation model). In a series of staged R&D steps, the
program can:

-   Enhance existing models to provide far greater geographical and
    temporal resolution of important processes, interactions, and
    feedbacks.

-   Incorporate numerous important elements previously treated as
    exogeneous, such as economic development, exchange rates, and
    foreign industrialization.

-   Extend large-scale modeling to new domains, such as analysis of
    market barriers to new technologies and health impacts of climate
    change.

-   Integrate existing and entirely new data sources to allow for the
    rigorous validation of existing and new models.

# Motivating Questions

*Provide here a fairly long and detailed list of the questions that we
may want to answer via modeling. Later we will indicate which questions
we think should be addressed first, and why.*

The modeling of human responses to climate change is a vast problem that
can, arguably, extend to encompass every aspect of society. A DOE
program in this area must circumscribe the set of questions to be
addressed if it is to be successful. However, we do not feel that it is
appropriate to define the scope of a potential program at this stage:
far more consultation and discussion is required. Instead, we present
some illustrative questions that may be considered.

Proposed policy responses to climate change include fleet emission
standards, carbon taxes, cap and trade. Thus, we may ask how different
policy alternatives affect energy supply and demand, the overall
economy, the environment, demand for individual products and services,
public health, and the vulnerability of the US economy and
infrastructure. In answering these questions, we may want to consider
feedbacks from human responses to the climate system via, for example,
changes in greenhouse gas emissions and surface albedo.

Answers to these questions may be sensitive to changes in population
distribution, employment patterns, demand for resources, and human
preferences. Thus, we may ask how answers to the preceding questions are
influenced by trends such as exurbanization, immigration, outsourcing,
modernization in Asia, climate change, behavioral shifts, and increased
focus on corporate governance.

Reductions in greenhouse gases may depend on the emergence and adoption
of new energy production, distribution, and consumption technologies.
Thus, we may ask what factors affect the emergence of new technologies
and, for potential climate change solutions, how their success will be
affected by technical vs. other factors such as government policy and
obstacles to entry for new providers,

The nature and pace of climate change response may depend on
international agreements. In such agreements, there may be winners and
losers. We ma want to quantify the benefits and/or costs of alternative
responses to different countries, states, industries, etc., and apply
game theory to understand the interactions that may occur.

Any study of human responses to climate change must deal with
uncertainty and risk. Some data is known with relatively high confidence
(e.g., average global warming), while others are far less certain (e.g.,
regional climate change, frequency of extreme events). We need to study
how uncertainty in key inputs propagates through economic models, both
to enable quantification of uncertainty when reporting on outcomes and
to enable evaluation of risk.

# Major Challenges

*Summarize here the reasons why answering these questions is difficult.
Later we will explain how we propose to address these challenges.*

Modeling the human response to climate change is a multidimensional,
time-dependent problem that encompasses economic relationships between
supply and demand; individual choices concerning energy use, energy
sources, and occupation; and government policies. These factors are
themselves influenced by, and ultimately influence, climate change and
its impacts on the environment, agriculture, and public health.

It is a multi-scale problem, both temporally and spatially. It is a
global problem---the strong economic and environmental coupling between
different regions means that we cannot study any single region in
isolation. Similar close coupling frequently exists between different
parts of the overall system. For example, the cost-effectiveness of new
energy sources depends in part on the distance that the resulting energy
needs to be distributed---which may be affected by both changes in
energy demands and population shifts resulting from climate change.

Finally, while vast quantities of relevant data are now available (e.g.,
from satellite imagery), much other relevant data is proprietary or of
unknown quality. Thus, both defining models and validating those models
remain challenging tasks.

# Today's State-of-the-Art

Relevant state of the art includes work on integrated assessment models,
energy system models, and XXX models. Also relevant is work on
industrial organization, development economics, agent-based modeling of
human societies, XX, and XX.

*Top down vs. bottom up? (From Science: "To explore mitigation options,
the IPCC uses two distinct strategies. Bottom-up models break the
economy down into sectors and predict how different mixes of
technologies will cut carbon emissions in each. Top-down models simulate
whole economies to compare how different global strategies, such as
carbon taxes or fixed greenhouse-gas stabilization targets, will play
out through market forces. Each approach has its drawbacks. Bottom-up
models tend to ignore economics, whereas top-down models smooth over the
differences between regions and sectors. In 2001, the two approaches
were often at odds. The good news, says Sathaye, is that "for the first
time, the range of results from bottom-up and topdown models are
starting to converge." However, enormous wiggle room remains.")*

**Integrated assessment models** describe the environmental consequences
of human activities and are used to explore the long-term dynamics of
global environmental change, taking into account many feedback
mechanisms within the society-biosphere-climate system \[7\]. Examples
are IMAGE \[7\] and MiniCAM \[5\]. IMAGE (Integrated Model to Assess the
Global Environment) \[7\] is representative. It integrates many
disciplinary models (see Figure 1). A general equilibrium economy model
(WorldScan) and population model (PHOENIX), supply the basic information
on economic and demographic developments for 17 socio-economic regions.
Details XXX.

![](media/image1.png){width="5.3125in" height="5.03125in"}

Figure 1: Models included in IMAGE 2.2

*MIT model, MiniCAM, Stanford (?). Various intercomparison efforts*.

**Energy system models** describe various aspects of energy production,
distribution, and consumption. Examples include ENPEP \[1, 3\], MARKAL
\[6\], NEMS \[2\], POLES \[8\], and PRIME \[xxx\]. These models are used
extensively to model the production, conversion, and consumption of
energy products and to study regional and global trends in energy
supply, energy demand, and greenhouse gas emissions.

However, these models have a relatively coarse geographical and temporal
resolution: for example, NEMS partitions the US into just the nine
census divisions, and performs equilibration of supply and demand only
annually. In addition, many factors are not considered in these models,
such as the impact of capital availability on energy production.

Other models focus on specific subproblems, such as decision making in
regional electricity markets (EMCAS) \[4\] and emissions and energy use
in transportation (GREET) \[9\].

All these models run on PCs.

The past decade has seen considerable advances in understanding of
micro- and macro-economics, data, modeling methods, and high-performance
computing methods required to tackle the socio-economic modeling
problems presented earlier.

Thus, the building blocks required to undertake this program are there.
However, the program will involve a massive, multiple-order-of-magnitude
scale up. Inevitably, obstacles will arise.

# Accelerating Development

A focused DOE R&D program can achieve significant advances in the state
of the art in modeling the human elements of the climate system.
Building on DOE expertise in climate and energy system modeling, and
bringing to bear the latest methods in economics, quantitative
techniques in behavior and decision theory, and modeling and high
performance computing tools, such a program can develop a deepened
understanding of the technical, economic, political, and social issues
that underpin the climate change challenge.

To this end, DOE should establish a sustained, large-scale program aimed
at applying computational science methods, with the goal of creating,
within a decade, the tools and methodologies needed to answer the
questions listed in Section 1 above.

This program should aim to create a highly integrated, time-dependent
modeling system that encompasses detailed micro-economic models and data
in a comprehensive global equilibrium framework. This system will allow
for a treatment of the marketplace and all participants, including
producers, consumers, and intermediaries, of unprecedented accuracy. It
will allow for the quantitative study of important questions such as
those listed above.

Details etc.

-   Construction of a comprehensive suite of models of unprecedented
    geospatial and temporal accuracy: some based on existing models,
    some entirely new.

-   Basic research into such foundational issues as spatial statistics,
    modeling of social processes, and relevant micro-economic and
    biosphere coupling issues.

-   Assembly and quality control of extensive data collections---much
    from existing sources, but also much from new and unconventional
    sources.

-   Comprehensive and detailed validation of both individual models and
    model systems.

-   Development of novel robust numerical techniques and
    high-performance computing approaches to deal with the expected
    orders-of-magnitude increase in model complexity.

-   A wide range of application studies aimed at both validation and
    application.

-   Education programs aimed at training the next generation of
    computational economists.

Partnerships with foreign scientists and international organizations
will be important to develop the global-scale models and obtain the data
needed for realistic assessment.

Other points made in discussion:

-   Understand interfaces.

-   Define quantitative goals.

-   Specific idea: national (distributed) center for integrated
    modeling.

-   We need to make clear where we need advances in numerical methods
    and algorithms.

# Expected Outcomes

A focused DOE program in this area is expected to achieve a significant
improvement in understanding of the important questions listed in
Section 1 above. This improved understanding can improve the
effectiveness of US and international policy responses to climate
change, the ability of US industry to engage effectively in developing
and productizing climate change solutions, and the US's ability to
minimize the vulnerability of its economy and energy infrastructure.

DOE efforts in this area should also have a significant impact on the
development of tools, expertise, and understanding in economics and
social sciences as a whole.

Socio-economic models would become major users of DOE supercomputers.

DOE would become a place that other parts of the government go to for
understanding of these issues and relevant policy information.

# Required Investment

The creation of this modeling framework, the validation of models, and
applications to the understanding of human actors in the climate system
is a massive "grand challenge," requiring an interdisciplinary team of
arguably unprecedented scale and scope. A rough estimate of the required
resources quickly reaches \$100M/yr over 10 years.

*Why?*

# Major Risks

The proposed activity represents a significant departure from current
practice in the climate change and socio-economic modeling communities.
Thus, it may be that we cannot assemble sufficient expertise in
economics and related disciplines to improve existing models in the ways
that we envision; and/or that we will lack sufficient data to initialize
and validate models to the degree required. However, we believe that the
urgency of the problem and the caliber of the available intellectual
resources make these outcomes unlikely.

Perhaps the biggest challenge is that of uncertainty. The earth system
models used to quantify climate change benefit from large quantities of
data and (mostly) well understood physical laws. Yet despite enormous
complexity and large amounts of computing power, their results exhibit
considerable uncertainty, particularly when it comes to regional
effects. Human factors are in many regards less well understood.

Issues of credibility.

Complexity.

Risk of not acting.

Software engineering challenges.

Coupling challenges.

# Contributors

*List of people who attended each of the workshops and/or contributed at
other times and/or in other ways.*

# Notes from LBNL Meeting to Incorporate

Add mention of water issues.

Define interfaces to DOE science---e.g., climate, carbon cycle.

Define links to DOE integrated assessment program.

# References {#references .unnumbered}

1\. *ENergy and Power Evaluation Program (ENPEP): Brief Model Overview*.
Center for Energy, Environmental, and Economic Systems Analysis
(CEEESA), Decision and Information Sciences Division, Argonne National
Laboratory, 2002.

2\. *The National Energy Modeling System: An Overview 2003*. Energy
Information Administration, US Department of Energy, 2003.

3\. Buehring, W.A., Hamilton, B.P., Guziel, K.A., Cirillo, R.R.,
Conzelmann, G. and Koritarov, V.S. ENPEP: An Integrated Approach for
Modeling Energy Systems. *International Symposium on Electricity, Health
and the Environment*. 1995.

4\. Conzelmann, G., Boyd, G., Koritarov, V. and Veselka, T., Multi-Agent
Power Market Simulation using EMCAS. in *General Meeting of the IEEE
Power Engineering Society*, (San Francisco, CA, 2005).

5\. Edmonds, J., Wise, M., Pitcher, H., Richels, R., Wigley, T. and
MacCracken, C. An Integrated Assessment of Climate Change and the
Accelerated Introduction of Advanced Energy Technologies: An Application
of MiniCAM 1.0. *Mitigation and Adaptation Strategies for Global
Change*, *1* (4). 311-339. 1997.

6\. Fishbone, L.G. and Abilock, H. MARKAL, a linear-programming model
for energy systems analysis; technical description of the BNL version.
*Energy Research 5*. 353-375. 1981.

7\. IMAGE team *The IMAGE 2.2 implementation of the SRES scenarios. A
comprehensive analysis of emissions, climate change and impacts in the
21st century*. RIVM CD-ROM publication 481508018, National Institute for
Public Health and the Environment, Bilthoven, the Netherlands, 2001.

8\. Russ, P. and Criqui, P. Post-Kyoto CO2 emission reduction: the soft
landing scenario analysed with POLES and other world models. *Energy
Policy*, *35* (2). 786-796. 2007.

9\. Wang, M.Q. *Development and Use of GREET 1.6 Fuel-Cycle Model for
Transportation Fuels and Vehicle Technologies*. Center for
Transportation Research, Argonne National Laboratory, 2001.