Chapter 16 - Integrated Economic and Climate Modeling

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Abstract

This survey examines the history and current practice in integrated assessment models (IAMs) of the economics of climate change. It begins with a review of the emerging problem of climate change. The next section provides a brief sketch of the rise of IAMs in the 1970s and beyond. The subsequent section is an extended exposition of one IAM – the DICE/RICE family of models. The purpose of this description is to provide readers an example of how such a model is developed and what the major components are. The final section discusses major important open questions that continue to occupy IAM modelers. These involve issues such as the discount rate, uncertainty, the social cost of carbon, the potential for catastrophic climate change, algorithms and fat-tailed distributions. These issues are the ones that pose both deep intellectual challenges as well as important policy implications for climate change and climate change policy.

Introduction

Many areas of the natural and social sciences involve complex systems that link together multiple physical or intellectual sectors. This is particularly true for environmental problems, which are intrinsically ones having strong roots in the natural sciences, and require social and policy sciences to solve in an effective and efficient manner. A good example, which will be the subject of this survey, is climate change science and policy, which involve a wide variety of sciences such as atmospheric chemistry and climate sciences, ecology, economics, political science, game theory, and international law.

As understanding progresses across the different fronts, it is increasingly necessary to link together the different areas to develop effective understanding and efficient policies. In this role, integrated assessment analysis and models play a key role. Integrated assessment models (IAMs) can be defined as approaches that integrate knowledge from two or more domains into a single framework. These are sometimes theoretical, but are increasingly computerized dynamic models of varying levels of complexity.

The present survey provides a roadmap to developments in IAMs for climate change over the last quarter century. It is constructed in the following sequence. We begin in this section with a review of the emerging problem of climate change. This is necessary to lay the background and motivation for why so many social and natural scientists are spending so much of their time on this issue.

Section 16.1.6 provides a brief sketch of the rise of IAMs in the 1970s and beyond. It is relatively brief because earlier surveys have covered much of the ground in an admirable fashion.

Section 16.2 is an extended exposition of one IAM – the DICE/RICE family of models. The purpose here is to provide readers with an example of how such a model is developed and what the components are. Other IAMs will have different structures, algorithms and assumptions, but the underlying modeling philosophy of integrating modules from different disciplines is common to virtually all IAMS. The development of the modeling is followed in Section 16.3 by a set of illustrative results from the RICE-2010 model. This is used to illustrate the kind of questions that IAMs can address.

Section 16.4 discusses major important open questions that continue to occupy IAM modelers. These involve issues such as: the discount rate, uncertainty, the social cost of carbon (SCC), the potential for catastrophic climate change and fat-tailed distributions. These issues are ones that pose both deep intellectual challenges as well as important policy implications for climate change and climate change policy.

Before getting into modeling details, it will be useful to sketch the scientific basis for concerns about global warming, as reviewed by the Intergovernmental Panel on Climate Change (IPCC)’s Fourth Assessment Report (IPCC, 2007) with updates from other sources. As a result of the buildup of atmospheric greenhouse gases, it is expected that significant climate changes will occur in the coming decades and beyond. The major industrial greenhouse gases are carbon dioxide (CO2), methane, ozone, nitrous oxides and chlorofluorocarbons (CFCs). The most important greenhouse gas is CO2, whose emissions have risen rapidly in recent decades.

The atmospheric concentration of CO2 of 390 parts per million (p.p.m.) in 2011 far exceeds the range over the last 650,000 years, estimated to be between 180 and 300 p.p.m. (current estimates of CO2 concentrations at Mauna Loa are available at ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt.).

Current calculations from climate models are that doubling the amount of CO2 or the equivalent in the atmosphere compared with preindustrial levels will, in equilibrium, lead to an increase in the global surface temperature of 2–4.5°C, with a best estimate of about 3°C. The suite of models and emissions scenarios used by the IPCC produce a range of temperature change over the twenty-first century of between 1.8 and 4.0°C. Other projected effects are increases in precipitation and evaporation, an increase in extreme events such as hurricanes, and a rise in sea levels of 0.2–0.6 m over this century. Some models also predict regional shifts, such as hotter and drier climates in mid-continental regions, including the US Midwest. Climate monitoring indicates that actual global warming is occurring in line with scientific predictions.

The agreed framework for all international climate change deliberations is the UN Framework Convention on Climate Change, which took force in 1994. That document stated, “The ultimate objective … is to achieve … stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system” (United Nations, 2009). The Framework Convention was implemented in the Kyoto Protocol in 1997, in which both high-income countries and countries in transition from central planning agreed to binding emissions limits for the 2008–2012 period. The framework for implementing the Protocol is most solidly institutionalized in the EU’s Emissions Trading Scheme (ETS), which covers almost half of Europe’s CO2 emissions.

Notwithstanding its successful implementation, the Kyoto Protocol is widely seen as a troubled institution. Early problems appeared with the failure to include the major developing countries, the lack of an agreed-upon mechanism to include new countries and an agreement that is limited to a single budget period. The major blow came when the US withdrew from the Treaty in 2001. Whereas 66% of 1990 world emissions were included in the original Protocol, that number declined to about one-third in 2010 with the withdrawal of the US and strong economic growth in developing countries (Nordhaus, 2010). Strict enforcement of the Kyoto Protocol is likely to be observed primarily in those countries and industries covered by the EU ETS, but their emissions today account for only about 8% of the global total. If the current Protocol is extended at current emissions levels, models indicate that it will have little impact on global climate change (see the several studies in Weyant and Hill, 1999).

The 2009 Copenhagen Conference of the Parties was designed to negotiate a successor agreement for the post-Kyoto period. Owing to deep divisions about costs and about the distribution of emissions reductions, the meeting concluded without a binding agreement. However, it did lead to an agreement known as the “Copenhagen Accord” (United Nations, 2009). The accord adopts a target of limiting the increase in global mean temperature and states that the target is set “recognizing the scientific view that the increase … should be below 2 degrees Celsius.” Developing countries did sign on to the Accord. A close look reveals, however, that countries committed themselves to very little. They agreed to “communicate” their “nationally appropriate mitigation actions seeking international support efforts,” but no binding targets for countries were set.

The reality behind the accord is not encouraging. To begin with, even if the high-income countries fulfilled their commitments, these would probably not achieve anything close to the 2°C target, as is shown below. Meanwhile, progress on reaching a more binding agreement has stalled. At present, a global agreement is waiting for the US to take credible legislated steps. At present (2011), there are no active plans for legislation in the US and instead there are proposals to roll back current plans to regulate greenhouse gases required under the US 1970 Clean Air Act.

Climate change is a polar case of economic phenomena known as global public goods (Samuelson, 1954). Public goods are activities for which the cost of extending the service to an additional person is zero and for which it is impossible or expensive to exclude individuals from enjoying. Global public goods are ones whose influences are felt around the world rather than in one nation, town, or family. What makes global public goods different from normal economic activities is that there are at best weak economic and political mechanisms for resolving these issues efficiently and effectively.

The economic theory of public goods has been extensively discussed in many contexts (e.g. Oakland, 1987). For this reason, this review limits the discussion to the application of public-goods theory to climate change and modeling in this area.

Most economic studies of climate change, including most IAMs, integrate geophysical stocks and flows with economic stocks and flows. The major difference between IAMs and geophysical models is that economic measures include not only quantities but also valuations, which for market or near-market transactions are prices. The essence of an economic analysis is to convert or translate all economic activities into monetized values using a common unit of account and then to compare different approaches by their impact on total values or a suite of values.

There are different ways of creating a standardized unit of account. The most satisfactory is to use a common “purchasing power parity” (PPP) exchange rate across different regions. For example, I will use the unit of 2005 international US$ below. However, the values are not really money. Rather, they represent a standard bundle of goods and services (such as $1000 worth of food, $3000 of housing, $900 of medical services, and so on). Thus, we are really translating all activities into the number of such standardized bundles. Both translation of different currencies into a common currency and conversion of values over time into a present value using a discount rate are deep issues in economics and we will review the latter in Section 16.4 on open problems.

To illustrate the economic approach, suppose that an economy produces only corn. We might decide to reduce corn consumption today and store it for the future to offset the damages from climate change on future corn production. In weighing this policy, we consider the economic value of corn both today and in the future in order to decide how much corn to store and how much to consume today. In a complete economic account, “corn” would represent all economic consumption. It would include all market goods and services as well as the value of non-market and environmental goods and services. That is, economic welfare – properly measured – should include everything that is of value to people, even if those things are not included in the marketplace.

The central questions posed by economic approaches to climate change are the following: how sharply should countries reduce CO2 and other greenhouse gas emissions? What should be the time profile of emissions reductions? How should the reductions be distributed across industries and countries?

There are also important and politically divisive issues about the instruments that should be used to impose cuts on consumers and businesses. Should there be a system of emissions limits imposed on firms, industries, and nations? Or should emissions reductions be primarily induced through taxes on greenhouse gases? Should we subsidize green industries? What should be the relative contributions of rich and poor households or nations? Are regulations an effective substitute for fiscal instruments?

In practice, an economic analysis of climate change weighs the costs of slowing climate change against the damages of more rapid climate change. On the side of the costs of slowing climate change, this means that countries must consider whether, and by how much, to reduce their greenhouse gas emissions. Reducing greenhouse gases, particularly deep reductions, will require taking costly steps to reduce CO2 emissions. Some steps involve reducing the use of fossil fuels; others involve using different production techniques or alternative fuels and energy sources. Societies have considerable experience in employing different approaches to changing energy production and use patterns. Economic history and analysis indicate that it will be most effective to use market signals, primarily higher prices on carbon fuels, to give signals and provide incentives for consumers and firms to change their energy use and reduce their carbon emissions. In the longer run, higher carbon prices will also provide incentives for firms to develop new technologies to ease the transition to a low-carbon future.

On the side of climate damages, our knowledge is very meager. For most of the time span of human civilizations, global climatic patterns have stayed within a very narrow range, varying at most a few tenths of a degree Centigrade (°C) from century to century. Human settlements, along with their ecosystems and pests, have generally adapted to the climates and geophysical features they have grown up with. Economic studies suggest that those parts of the economy that are insulated from climate, such as air-conditioned houses and most manufacturing operations, will be little affected directly by climate change over the next century or so (see by reference IPCC, 2007b).

However, those human and natural systems that are “unmanaged,” such as rain-fed agriculture, seasonal snow packs and river runoffs, and most natural ecosystems, may be significantly affected. While economic studies in this area are subject to large uncertainties, recent surveys of the literature on damages from future climate change indicate that the economic damages from climate change with no interventions will be in the order of 2–3% of world output per year by the end of the twenty-first century (for a recent review of damage estimates, see particularly Tol, 2009). The damages are likely to be most heavily concentrated in low-income and tropical regions such as tropical Africa and India. While some countries may benefit from climate change, there is likely to be significant disruption in any area that is closely tied to climate-sensitive physical systems, whether through rivers, ports, hurricanes, monsoons, permafrost, pests, diseases, frosts or droughts. Moreover, damage estimates cannot reliably include estimates of the costs of ecological impacts such as ocean acidification, species extinction, ecosystem disruption or of the dangers posed by tipping points in the earth systems.

A search on Google Scholar finds 3610 citations to “Integrated Assessment Models.” However, the number of journal publications is much smaller at 175 over the period 1995–2011. The time trend for both is shown in Figure 16.1. Clearly, there is a major growth in research in this area, although the ratio of ISI publications to Scholar publications is low. One reason is that a great deal of the work is done in the “gray literature” rather than in standard journal publications.

Although IAMs have been increasingly used for two decades, there is relatively little literature that surveys the technical aspects of models. By contrast, there is a vast literature on the results as well as on applications of models.

An exemplary survey by Weyant et al., 1996, World Bank, 2009 for the IPCC’s Second Assessment examined a range of IAMs and provided a fine survey of the state of the art at that time. Unfortunately, that survey is not currently available on the internet, but it should be the starting point for those wishing to understand the state of the art as of the mid-1990s. Weyant et al. (1996) emphasized, as we will below, the importance of multiple approaches to development of IAMs because of the difficulty of encompassing all the important elements in a single model.

A more recent pair of surveys is by Kolstad (1998) and Kelly and Kolstad (1999). These surveys examine 21 IAMS, with dates from 1992 to 1996. The authors emphasized the important distinction between policy optimization and evaluation models. This distinction remains one of the central dividing lines among different models, although it is not clearly understood. Kolstad (1998) writes that “nearly all the results have come from the so-called policy optimization models, the top-down economy-climate models. Virtually no new basic understanding appears to have emerged from the policy evaluation models….” This strong challenge appears to have been largely lost on the modeling community.

Another issue that was emphasized by Kelly and Kolstad was the importance of uncertainty. The conclusion of this survey was the following:

The integrated assessment community has done an excellent job of analyzing, comparing, and contrasting the multitude of IAMs. Because of the analysis, IAMs give a remarkably consistent message. However, despite the consistent message and the large amount of government research money which has been spent, the message is not known far outside the integrated assessment community. The integrated assessment community must still do more to bring the results to the forefront of the debate on what to do about climate change.

This has changed somewhat in recent years as models have been increasingly used by governments in their policy analyses.

The challenge of coping with global warming is particularly difficult because it spans many disciplines and parts of society. Ecologists may see it as a threat to ecosystems, marine biologists as a problem leading to ocean acidification, electric utilities as a debit to their balance sheets and coal miners as an existential threat to their livelihood. Businesses may view global warming as either an opportunity or a hazard, politicians as a great issue as long as they do not need to mention taxes, ski resorts may view it as a mortal danger to their already-short seasons, golfers as a boon to year-round recreation, and poor countries as a threat to their farmers as well as a potential source of financial and technological aid. This many-faceted nature also poses a challenge to natural and social scientists who must incorporate a wide variety of geophysical, economic and political disciplines into their diagnoses and prescriptions.

The task of integrated modeling is to pull together the different aspects of a problem so that a decision or analysis can consider all important endogenous variables that operate simultaneously. Figure 16.2 shows schematically the important modules in the case of climate change. A complete analysis must consider emissions, concentrations, climate change and impacts. The last arrow in the process links the impacts and policies back to emissions, thus closing the loop.

It must be emphasized that a complete integrated assessment is not necessary for all parts of the climate change challenge. Each of the different boxes in Figure 16.2 is in fact an entire discipline, with many talented scientists pursuing questions at the frontier of modern natural and social science. For example, the “climate system” box would represent the work of dozens of teams in many countries, building models, calibrating the models to data, and the like. Indeed, much of the 1000-page reports of the IPCC on science are built on scientists studying “the climate system.” Similar teams are at work in the other areas.

The point emphasized in IAMs is that we need to have at a first level of approximation models that operate all the modules simultaneously. The climate models, for example, use stylized emissions as inputs to their simulations. In the most recent round of model results (the IPCC Fourth Assessment Review), the inputs were a set of scenarios generating several years earlier in the Special Report on Emissions Scenarios study (IPCC, 2000, IPCC, 2001). There is no linkage from the climate models to the economy and then back to emissions. It is exactly this linkage that is the purpose of integrating the different parts of the climate change nexus in IAMs.

IAMs of climate change grew organically from energy models. One of the earliest careful comparisons of energy models was the Modeling Resource Group (MRG) analysis of different models (MRG, 1978). This project, chaired by economist Tjalling Koopmans, formed one of the study groups of the larger National Academy of Sciences Study of Nuclear and Alternative Energy Systems (CONAES, 1978). The MRG analyzed a number of energy models that projected energy demands and technologies over a long time horizon. The earlier work of Koopmans on the linear programming approach to production as well as the Samuelson principle of “markets as maximization” (Samuelson, 1949, Tol, 2003) formed the intellectual core of much of the energy modeling starting at that time and proceeding to the present.

It is notable that the even though the CONAES study identified climate change as a key long-term issue, none of the energy models used in the study or reviewed by the MRG explicitly included CO2 emissions or climate change in their analyses. Work of Nordhaus extending the MRG modeling approach to include a climate module was undertaken in parallel with the CONAES study and was published in Nordhaus, 1977, Nordhaus, 1979. This approach, which built on a highly disaggregated partial equilibrium model of the world energy system, was abandoned in favor of more aggregated approaches (the DICE and RICE models discussed later).

Several of the current IAMs grew out of the energy models of the 1970s and 1980s. Particularly important were the studies of Alan Manne. In a series of studies from the 1960s through the 1990s, his work on mathematical programming, integer programming, learning and integration of energy and environmental modules served as landmarks and inspiration for later models (see Manne, 1962, Manne, 1974, Manne, 1976, Manne, 1985 and well as in joint work with Richard Richels discussed later).

The first IAMs in climate change were basically energy models with an emissions model included, and later with other modules such as a carbon cycle and a small climate model. Nordhaus’s early approaches (Nordhaus, 1975, 1977, 1978) were partial equilibrium energy models with exogenous output. One of the important landmarks in development of IAMs was Manne’s ETA-Macro model, which was the first to imbed an energy system in a full economic growth model (Manne, 1977). The earliest versions of the DICE and RICE models in Nordhaus (1992, 1994a) moved to a growth-theoretic framework similar to the Manne and Manne-Richels models (Manne and Richels, 1991, Manne and Richels, 1992).

It is not possible to make a comprehensive list of IAMs as of mid-2011. One indication of the richness of the landscape is the participation in the IAM Consortium (see http://iamconsortium.org/), which lists 42 different organizations. Table 16.1 shows the sectoral distribution of members of the Consortium (which does not map one-to-one to models, but is indicative).

IAMs are increasingly used in analyses by national governments and international assessments. Particularly important have been the intermodal comparisons undertaken by the Energy Modeling Forum (EMF) headed by John Weyant. Exemplary in this respect is the EMF-22 (Clarke, 2009), which used 17 models and compared a range of scenarios including a reference (uncontrolled) scenario along with several scenarios that constrained radiative forcings. These studies are extremely valuable because they provide a range of projections so that scientists and decision makers can understand the uncertainties of the projections.

The next section presents the DICE/RICE models as an example of an IAM. These models are discussed largely because they are small and transparent. For many scientific and policy purposes, more detailed IAMs will be necessary. Three IAMs that are widely used in the US are the NEMS (National Energy Modeling System) model (developed by the Energy Information Administration of the US government, see NEMS, 2011), the IGEM (Intertemporal General Equilibrium Model) model (developed and maintained by Dale Jorgenson and his colleagues, see IGEM, 2011, IIASA World Population Program, 2007) and the MIT EPPA (Economics, Emissions and Policy Cost) model (developed by a team of researchers currently led by John Reilly, see EPPA, 2011).

There are several other important models that have been widely used in both the scholarly and policy circles. For example, 17 models participated in the EMF-22 model comparison study. These models were PACE, IMAGE, MRN-NEEM, GTEM, MiniCAM, SGM, IGSM, WITCH, ADAGE, GEMINI, POLES, IGEM, MESSAGE, FUND, ETSAP-TIAM, MERGE and DART. Descriptions of the models are beyond the scope of this survey. For a description of the models, with references, see Clarke et al. (2009).

The larger IAMs tend to be very detailed. I will use IGEM to illustrate the complexity of large models. IGEM has about 4000 endogenous variables per period (year) and the solution works by backward induction from 2130. Policy variables include taxes on commodities, marginal and average taxes on factors, tax credits on investment, a consumption-only tax, tariffs on imports, taxes on carbon, and technology mandates. The program is written in Fortran and C codes, with a total of about 40,000 lines. According to its primary developer, IGEM is proprietary, and being too complicated to modify by outsiders, has never been transferred to another entity. Without going into the details of the larger models, it will be useful to note that such models can investigate questions at a much higher level of resolution than the smaller models. For example, such models have done important studies of the impacts of climate change policies on the distribution of income; the impacts of a specific set of policies, such as the American Clean Energy and Security Act of 2009, the impact of climate policy on US aviation, and the international leakage involved when policies are not harmonized. The larger models play a central role in policy analysis but are more difficult to use than the smaller models and, as noted above, are often difficult to transfer to other users.

Section snippets

Purpose of this section

In this section, I present an extended description of the DICE and RICE IAMs. The purpose is primarily to show the way such a model is constructed and to provide details on the components. Most IAMs have a similar analytical structure, although they vary greatly in their detail, coverage, data and algorithmics. The last part (Section 16.2.9) reviews some of the oversimplifications in IAMs.

Introduction to the models

The DICE (Dynamic Integrated model of Climate and the Economy) and RICE (Regional Integrated model of

Model outputs

IAMs have a wide variety of applications. These were comprehensively reviewed in Weyant et al. (1997). Among the most important applications are the following:

  • Making consistent projections, i.e. ones that have consistent inputs and outputs of the different components of the system (so that the GDP projections are consistent with the emissions projections).

  • Calculating the impacts of alternative assumptions on important variables such as output, emissions, temperature change and impacts.

  • Tracing

Final Thoughts

The present survey of IAMs of climate change shows the enormous progress that the field has made over the two decades since its emergence. The progress is made possible by the parallel developments in fundamental science and economics across a broad range of areas. These include development in public economics, game theory and environmental economics. However, development of the actual models has required improvement in computer hardware, software, algorithms, improved data, and the ability to

Acknowledgments

The author is grateful to Warwick McKibbin, Zhimin Li and the editors for helpful comments. Research underlying this work was supported by the National Science Foundation and the Department of Energy. The author declares no conflict of interest. Several parts of this survey draw on research published in other journals and books.

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