Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 15
Describing Socioeconomic Futures for Climate Change Research and Assessment: Report of a Workshop 3 Evolving Methods and Approaches PHILOSOPHIES AND THE STATE OF SCIENCE IN PROJECTING LONG-TERM SOCIOECONOMIC CHANGE1 Robert Lempert Robert Lempert spoke on various approaches to projecting long-term socioeconomic change and their effectiveness. He noted that scenarios can have several functions: to provide consistent inputs to analysis, to inform decisions, to transform world views, and to entertain. Their purposes include predicting the future, identifying what might happen, and identifying ways to reach goals. They may be exploratory, to identify and consider many possible futures; they may be intended for decision support; they can be formal or intuitive, simple or complex. A small evaluative literature exists on scenarios for long-term decisions, including a study by RAND-Europe that looked at about 50 evaluative studies. Lempert said that the many available methods of projection derive from three schools: (1) the intuitive logics school, starting with the work of Herman Kahn, which begins with drivers and develops scenarios from them; (2) the La Prospective school (Godet, Berger), which emphasizes visioning and focuses on desired end states; and (3) a school of probabilistic modified trends, which uses expert elicitation to identify possible surprises. A variety of techniques for describing the future are 1 Lempert’s presentation is available at http://www7.nationalacademies.org/hdgc/Philosophies_and_State_ of_Science_Presentation_by_Robert_Lempert.pdf [November 2010].
OCR for page 16
Describing Socioeconomic Futures for Climate Change Research and Assessment: Report of a Workshop adopted, including expert judgment, backcasting, and various modeling methods. Scenarios can produce a number of benefits. One impact is to overcome cognitive barriers (e.g., optimism biases, strategic use of uncertainty, ambiguity aversion, status quo bias). Scenarios use various mechanisms to overcome the barriers. For example, they can focus on possibilities rather than predictions. There is some evidence that scenarios can actually reduce overconfidence and increase the coherence of beliefs, and in one study with firms, the use of scenarios was correlated with future profits. Challenges in the use of scenarios for climate analysis lie in (a) the potential for divergent views on what scenarios are, potentially leading to an illusion of communication; (b) the tension between the desire for consistency and the need to consider surprises (e.g., formal models tend to leave out the discontinuities); (c) the need to include context in scenarios (the trade-off between simplicity and utility, the tendency to ignore scenarios when they can’t deal with the projected futures); and (d) the need to emphasize process over product in decision support (National Research Council, 2009a). Scenarios for decision support can be framed as a way to analyze vulnerability under existing plans and response options. Stakeholders in a decision may disagree on much but still agree on the need to think through how and when an option may not work. A database of many model runs can help identify the key drivers of failure and the scenarios leading to failure, thus helping in the consideration of response options. For example, a group at RAND looked for climate scenarios that failed to reach a concentration target of 450 ppm and found that, in most of these cases, carbon capture and storage and transportation systems failed to meet their targets. The literature indicates that vast arrays of scenario methods are used for many different purposes. Some empirical evidence exists on the factors affecting scenario effectiveness in various applications, and these studies emphasize the importance of process (rather than products) and of close coupling with decision makers as determinants of effectiveness. Lempert concluded that, for some purposes, people may want to think less about developing standard scenarios and narratives and more about developing tools that particular decision makers can use to identify multi-stressor vulnerabilities and to consider their decision options.
OCR for page 17
Describing Socioeconomic Futures for Climate Change Research and Assessment: Report of a Workshop DEMOGRAPHIC CHANGE2 Thomas Buettner Thomas Buettner spoke on projecting demographic change. He noted that even current population is uncertain: half the world’s people have no vital records, and because decennial censuses are rare, most of the information on demographic change comes from sample surveys. And uncertainty about current conditions is a problem not only for low-income countries. Germany has not had a census for 20-30 years. Buettner said that in some countries, data collections are fragmented and driven by donor demands (e.g., the U.S. Agency for International Development). Moreover, spatial resolution is a problem. Buettner said that, in the past, demographic transition theory has guided population projections successfully. Now, however, about 47 percent of the world’s population has reached the end of the demographic transition, and demographers do not have a theory for what happens after that. One possible path is equilibrium; one is a sustained path below equilibrium. Buettner also noted that the transition is stalling in some low-income countries. The low-fertility, low-mortality equilibrium predicted by transition theory is elusive. United Nations’ (UN’s) projections still assume “due progress” on the transition. They include past shocks but not possible future shocks or significant contextual changes. Buettner noted some long-term demographic trends, including population aging and the “demographic dividend” of large cohorts of young people. He noted that some low-income countries have low fertility, and that high-income countries have slowing gains in life expectancy. Expected population growth in the next 40 years will be largely urban and located in low-income countries. The UN will soon release projections to 2100. The medium variant has world population stabilizing at less than 9 billion, but the high and low scenarios are very different from that. ECONOMIC DEVELOPMENT3 Gary Yohe Gary Yohe spoke on the drivers of economic development and the ways economists look into the future. There are large unknowns, such as about when countries start to develop rapidly, how they will handle pol- 2 Buettner’s presentation is available at http://www7.nationalacademies.org/hdgc/Demographic_Change_Presentation_by_Thomas_Buettner.pdf [November 2010]. 3 Yohe’s presentation is available at http://www7.nationalacademies.org/hdgc/Economic_Development_Presentation_by_Gary_Yohe.pdf [November 2010].
OCR for page 18
Describing Socioeconomic Futures for Climate Change Research and Assessment: Report of a Workshop lution, among others. The Special Report on Emissions Scenarios of the Intergovernmental Panel on Climate Change (Intergovernmental Panel on Climate Change, 2001) looked at the important drivers: per capita gross domestic product (GDP) and emissions, demography, institutional development, development patterns over time, international trade and development, innovation and technological change, the distribution of income and opportunity, and energy intensity over time. Economic projection models presume that capital investment drives economic growth. GDP is connected to emissions through parameters, including carbon intensity of GDP, which can change over time. The devil is in the details, especially at the regional level. One can build a growth model using parameters for capital, labor, and perhaps fossil and nonfossil energy. The shares in the energy sector may change with the relative prices of energy. The models assume that these changes depend only on the price of carbon, but there is a need to look at other drivers of change, as there may not be a price for carbon. Yohe said that economics is not good at predicting inflection points. He also noted that business cycles are more moderate in higher income countries and that socioeconomic diversity implies diversity in development paths. For example, if capital-intensive technologies are placed in a low-income country, the result might be huge unemployment. He reflected on Rostow’s analysis of the prerequisites for economic takeoff (enlarged demand for a sector and the possibility of producing in the sector, which generate capital for the leading sector, the development of which can spill over across the entire economy, leading to a rapid growth rate). In the discussion of this presentation, Ottmar Edenhofer suggested that people who study endogenous growth, including technological change, should be included along with growth economists in developing economic scenarios. Granger Morgan asked whether the IPCC is effectively prohibited from considering certain unappealing scenarios (e.g., nuclear war, global pandemic, failure of development in some countries). Field replied that those prohibitions have existed in the past, and Buettner added that countries complain to the UN if the projections run counter the country’s development plans. John Weyant noted that this workshop is outside the IPCC process, in part to allow for consideration of such possibilities, so analyses of them enter the literature and can be considered by the IPCC. Edenhofer said that Working Group 3 will have a chapter on policies that will include global, national, and subnational ones. Nakičenovič noted that demographics are not independent of the other variables. For example, future migration to cities will depend on economic development paths. Anthony Janetos noted problems with distortions on data and cited measures of forest cover as an example. He said that countries with few
OCR for page 19
Describing Socioeconomic Futures for Climate Change Research and Assessment: Report of a Workshop trees have a more expansive definition of what a forest is and that physical modeling based on countries’ reports of forest cover will be wrong. Finally, Lempert asked if there are bounding constraints on rates of economic growth, on amplitude of the business cycle, and on other major economic parameters. Yohe said there are data on this, but noted that both the rate of growth in China over a long period and its quick recovery from the recent recession have surprised economists. CONNECTING NARRATIVE STORY LINES WITH QUANTITATIVE SOCIOECONOMIC PROJECTIONS Ritu Mathur Ritu Mathur discussed issues and methodologies related to connecting narrative and quantitative projections. She noted that many socioeconomic conditions can be consistent with a single forcing pathway. Accordingly, various researchers and users may end up considering widely varying socioeconomic or even emission trajectories for a particular region for the same forcing pathway at the global scale. There can be wide variation in trajectories of emissions depending on whether assumptions regarding technological progress and consumption behavior are optimistic or pessimistic. For example, widely divergent pathways have been examined for India in various studies, and while some are due largely to differences in socioeconomic assumptions, some are related to differing perceptions about the pace of technological progress. Mathur also discussed the use of backcasting approaches to examine low-carbon pathways across regions to arrive at a warranted global emission trajectory. In such studies, there is often a disconnect between the process of allocating emission reductions across regions in alternative scenarios and the application of a backcasting approach at the regional-local level to introduce emission reduction choices that can meet the predetermined levels. Moreover, there are issues in harmonizing global assumptions defining story lines with local conditions and resultant emission trajectories, since the processes are disjointed and do not always allow for reassessing the distribution of emissions across regions. This leads to difficulty in making bottom-up analyses meet the numbers in the regional and global models. A study done by The Energy and Resources Institute jointly with the Oak Ridge National Laboratory (The Energy and Resources Institute, 2009) examined the potential impacts of relatively severe climate change on 11 states of Northern India. In this study, narratives were used to develop socioeconomic scenarios. These were based on four story lines demarcated on the basis of the relative importance attributed to environ-
OCR for page 20
Describing Socioeconomic Futures for Climate Change Research and Assessment: Report of a Workshop ment and development and government versus market solutions. These narratives were then quantified with a focus on demographic, economic, and energy-related indicators, as well as sectoral indicators for water, agriculture, and health. These variables were quantified at the state level, taking into account decadal variations in the past and the qualitative story lines for the future. Given the results of this study, Mathur concluded by asserting the need for further integrated assessment models and impact-adaptation-vulnerability analyses to generate more realistic and robust predictions on climate-related risks. This would require greater involvement of institutions at the regional and local levels to ensure that the assessed reduction potentials being considered in global studies allow for a better encapsulation of regional changes that are likely in the future. QUANTITATIVE DOWNSCALING APPROACHES4 Tom Kram Tom Kram made a presentation on behalf of Detlef Van Vuuren (who was unable to attend), based on an article in preparation by Van Vuuren and his colleagues on quantitative downscaling approaches. Kram noted that many methods of downscaling have been tried and that the chosen preferred method depends on purpose, coverage, resolution, and the availability of information. Downscaled data need to be consistent with both the larger and smaller scales, as well as internally. The article distinguishes four approaches: (1) algorithmic downscaling, which can be done (a) proportionally (assuming every unit at the smaller scale is equal), (b) by applying the change assumptions for the larger unit to the smaller units and assuming that they will converge toward the central estimate, or (c) by applying exogenous scenarios; (2) methods of intermediate complexity using simplified formulas that are calibrated differently for different subunits; (3) complex models that can be applied at a small scale; and (4) fully coupled physical-social models that use changes in the models for one year as inputs to the next year’s estimates. Some methods can lead to problems, such as when growth rate data for Asia are applied to Singapore. Kram concluded by saying that although there have been bad experiences with socioeconomic downscaling the past, better data and more advanced algorithms are now available. He said that although many methods are available, for global applications, simple methods might be adequate. 4 This presentation is available at http://www7.nationalacademies.org/hdgc/Quantitative_Downscaling_Approaches_Presentation_by_Tom_Kram.pdf [November 2010].